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-              <option name="originalContent" value="FROM python:3.11-slim&#10;&#10;ENV PYTHONDONTWRITEBYTECODE=1 \&#10;    PYTHONUNBUFFERED=1&#10;&#10;WORKDIR /app&#10;&#10;# LightGBM/scikit-learn runtime dependency on Debian slim.&#10;RUN apt-get update \&#10;    &amp;&amp; apt-get install -y --no-install-recommends libgomp1 ca-certificates \&#10;    &amp;&amp; rm -rf /var/lib/apt/lists/*&#10;&#10;COPY requirements.txt /app/requirements.txt&#10;RUN pip install --no-cache-dir --upgrade pip \&#10;    &amp;&amp; pip install --no-cache-dir -r /app/requirements.txt&#10;&#10;COPY . /app&#10;&#10;# Hugging Face persistent storage is typically mounted at /data.&#10;RUN mkdir -p /data&#10;&#10;ENV BOT_INTERVAL=15m \&#10;    BOT_POLL_SECONDS=45 \&#10;    BOT_STATE_FILE=/data/learning_state.joblib \&#10;    BOT_REPORT_FILE=/data/paper_report.json \&#10;    BOT_HEALTH_HOST=0.0.0.0 \&#10;    BOT_HEALTH_PORT=7860 \&#10;    PORT=7860 \&#10;    BOT_HF_ADVISOR_URL=&quot;https://chigginrice-hyperliquid-bot.hf.space/predict&quot;\&#10;    BOT_HF_ADVISOR_TOKEN= \&#10;    BOT_HF_ADVISOR_MODE=off \&#10;    BOT_HF_ADVISOR_MIN_CONFIDENCE=0.60 \&#10;    BOT_HF_ADVISOR_BLEND_WEIGHT=0.35 \&#10;    BOT_HF_ADVISOR_TIMEOUT_SECONDS=4.0 \&#10;    BOT_TELEGRAM_ML_REPORT_HOURS=6 \&#10;    BOT_TELEGRAM_ML_REPORT_MIN_TRADES=15 \&#10;    BOT_ENABLE_LIVE= \&#10;    BOT_LIVE_STARTUP_TEST= \&#10;    BOT_LIVE_STARTUP_TEST_COIN=BTC \&#10;    BOT_LIVE_STARTUP_TEST_NOTIONAL=12&#10;&#10;EXPOSE 7860&#10;&#10;CMD [&quot;sh&quot;, &quot;-c&quot;, &quot;python adaptive_bot.py --interval ${BOT_INTERVAL} --poll-seconds ${BOT_POLL_SECONDS} --state-file ${BOT_STATE_FILE} --report-file ${BOT_REPORT_FILE} --live-startup-test-coin ${BOT_LIVE_STARTUP_TEST_COIN} --live-startup-test-notional ${BOT_LIVE_STARTUP_TEST_NOTIONAL} ${BOT_ENABLE_LIVE:+--enable-live} ${BOT_LIVE_STARTUP_TEST:+--live-startup-test}&quot;]&#10;&#10;" />
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-              <option name="originalContent" value="---&#10;title: Hyperliquid Adaptive Bot&#10;emoji: &quot;&quot;&#10;colorFrom: blue&#10;colorTo: purple&#10;sdk: docker&#10;pinned: false&#10;---&#10;&#10;# Hyperliquid Adaptive Bot&#10;&#10;This project now includes an adaptive ML bot that uses your existing Hyperliquid framework.&#10;&#10;## What it does&#10;&#10;- Trades **only** `BTC`, `ETH`, and `SOL`&#10;- Runs in **paper mode by default**&#10;- Learns from closed paper trades with an online ML model (`SGDClassifier`)&#10;- Persists everything learned to disk (`learning_state.joblib`) so restart resumes learning&#10;- Persists paper runtime state (equity, open positions, entry features) so restart does not break learning continuity&#10;- Uses a profitability gate before any live execution is allowed&#10;- Sends Telegram alerts on trade open/close and a midnight UTC summary&#10;- Sends a readiness alert when paper performance is strong enough for live trading&#10;- Optional startup live transaction test (tiny BUY then SELL) to verify live order execution path&#10;- Can mirror to live orders only when:&#10;  - `--enable-live` is set&#10;  - paper performance is consistently profitable&#10;  - manual approval is granted (`ALLOW_LIVE_TRADING=1` or approval flag file exists)&#10;  - `HL_PRIVATE_KEY` is configured&#10;&#10;## Files&#10;&#10;- `hbot.py`: existing Hyperliquid API framework and order placement helpers&#10;- `adaptive_bot.py`: new adaptive ML paper/live-gated trading bot&#10;- `hf_advisor.py`: optional external advisor client (e.g., Hugging Face endpoint)&#10;- `smoke_test.py`: offline persistence and learning smoke test&#10;- `ml_diagnostics.py`: offline + live learning diagnostics report&#10;- `diagnostics_smoke_test.py`: quick smoke test for diagnostics logic&#10;- `advisor_smoke_test.py`: quick smoke test for advisor parsing behavior&#10;- `paper_report.json`: runtime status and metrics output (created at runtime)&#10;&#10;## Health endpoints (for HF + UptimeRobot)&#10;&#10;- `GET /` -&gt; lightweight status response (`200`) for uptime checks&#10;- `GET /healthz` -&gt; same as root (`200`)&#10;- `GET /status` -&gt; concise overview (equity, open position count, performance, live gate)&#10;- `GET /status/full` -&gt; full runtime JSON (equity, open positions, metrics, thresholds)&#10;- `GET /trades` -&gt; open positions list and count&#10;&#10;If your Space is `https://chigginrice-hyperliquid-bot.hf.space/`, your existing root monitor will work as-is.&#10;&#10;## Quick start&#10;&#10;1. Install dependencies:&#10;&#10;```bash&#10;python3 -m venv .venv&#10;source .venv/bin/activate&#10;pip install -r requirements.txt&#10;```&#10;&#10;2. Run the offline smoke test:&#10;&#10;```bash&#10;python smoke_test.py&#10;```&#10;&#10;Optional: run diagnostics smoke test:&#10;&#10;```bash&#10;python diagnostics_smoke_test.py&#10;```&#10;&#10;Optional: run advisor smoke test:&#10;&#10;```bash&#10;python advisor_smoke_test.py&#10;```&#10;&#10;3. Start the adaptive paper bot:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --poll-seconds 45&#10;```&#10;&#10;4. Check ML learning quality from saved state:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib&#10;```&#10;&#10;If the bot is running locally, include live status snapshot:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --status-url http://127.0.0.1:7860/status/full&#10;```&#10;&#10;JSON output (useful for storing artifacts in CI/HF workflows):&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --json&#10;```&#10;&#10;Send a compact diagnostics snapshot to Telegram:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --send-telegram&#10;```&#10;&#10;Optional knobs for early growth from scratch:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --exploration-rate 0.08&#10;```&#10;&#10;Enable periodic ML progress updates to Telegram (good for phone monitoring):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --telegram-ml-report-hours 6 --telegram-ml-report-min-trades 15&#10;```&#10;&#10;Optional: enable external advisor gating/blending (for example, Hugging Face endpoint):&#10;&#10;```bash&#10;python adaptive_bot.py \&#10;  --interval 15m \&#10;  --hf-advisor-url &quot;https://your-endpoint.example/predict&quot; \&#10;  --hf-advisor-mode blend \&#10;  --hf-advisor-blend-weight 0.35 \&#10;  --hf-advisor-min-confidence 0.60&#10;```&#10;&#10;Advisor modes:&#10;&#10;- `off`: ignore advisor; use local model only&#10;- `confirm`: only trade when advisor agrees with the model side at/above min confidence&#10;- `veto`: advisor can block opposite-side model trades at/above min confidence&#10;- `blend`: blend local `p_long` with advisor `p_long` and decide from the blended score&#10;&#10;## Optional: allow live mirror only after paper consistency gate&#10;&#10;```bash&#10;export HL_PRIVATE_KEY=&quot;your_private_key&quot;&#10;# optional if using delegated account&#10;export HL_ACCOUNT_ADDRESS=&quot;0x...&quot;&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;By default, live arm still requires manual approval even after the paper gate is passed:&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;# or: touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;Strict two-step approval mode (recommended for safety):&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live --live-approval-mode both&#10;```&#10;&#10;Optional startup live execution test (real tiny transaction):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --enable-live --live-startup-test --live-startup-test-coin BTC --live-startup-test-notional 12&#10;```&#10;&#10;## Docker (Hugging Face)&#10;&#10;`Dockerfile` is included for Docker-based hosting.&#10;&#10;Build locally:&#10;&#10;```bash&#10;docker build -t hyperliquid-adaptive-bot .&#10;```&#10;&#10;Run locally in paper mode:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_INTERVAL=15m \&#10;  -e BOT_POLL_SECONDS=45 \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Run with live mode allowed after paper gate passes:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_ENABLE_LIVE=1 \&#10;  -e HL_PRIVATE_KEY=&quot;your_private_key&quot; \&#10;  -e HL_ACCOUNT_ADDRESS=&quot;0x...&quot; \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Environment variables used by the container:&#10;&#10;- `BOT_INTERVAL` (default `15m`)&#10;- `BOT_POLL_SECONDS` (default `45`)&#10;- `BOT_STATE_FILE` (default `/data/learning_state.joblib`)&#10;- `BOT_REPORT_FILE` (default `/data/paper_report.json`)&#10;- `BOT_ENABLE_LIVE` (set any non-empty value to append `--enable-live`)&#10;- `BOT_HEALTH_HOST` (default `0.0.0.0`)&#10;- `BOT_HEALTH_PORT` (default `7860`)&#10;- `PORT` (default `7860`, used by Hugging Face Spaces)&#10;- `BOT_HF_ADVISOR_URL` (optional external advisor endpoint)&#10;- `BOT_HF_ADVISOR_TOKEN` (optional bearer token)&#10;- `BOT_HF_ADVISOR_MODE` (`off|confirm|veto|blend`, default `off`)&#10;- `BOT_HF_ADVISOR_MIN_CONFIDENCE` (default `0.60`)&#10;- `BOT_HF_ADVISOR_BLEND_WEIGHT` (default `0.35`)&#10;- `BOT_HF_ADVISOR_TIMEOUT_SECONDS` (default `4.0`)&#10;- `ALLOW_LIVE_TRADING` (set to `1/true/yes` to manually approve live trading)&#10;- `BOT_LIVE_STARTUP_TEST` (set non-empty to add `--live-startup-test`)&#10;- `BOT_LIVE_STARTUP_TEST_COIN` (default `BTC`)&#10;- `BOT_LIVE_STARTUP_TEST_NOTIONAL` (default `12`)&#10;- `BOT_TELEGRAM_ML_REPORT_HOURS` (default `6`, set `0` to disable periodic ML Telegram report)&#10;- `BOT_TELEGRAM_ML_REPORT_MIN_TRADES` (default `15`)&#10;- `TELEGRAM_BOT_TOKEN` (required for Telegram notifications)&#10;- `TELEGRAM_CHAT_ID` (required for Telegram notifications)&#10;- `PROXY_URL` (required by `telegram_notifier.py` to forward messages)&#10;- `HL_PRIVATE_KEY` (required for live mirror)&#10;- `HL_ACCOUNT_ADDRESS` (optional)&#10;&#10;## Telegram notifications&#10;&#10;- Entry alert: sent when a paper position opens&#10;- Exit alert: sent when TP/SL closes a position&#10;- Midnight summary: sent once per day at `00:00 UTC`&#10;- Readiness alert: sent when profitability gate is reached and bot is ready to request live approval&#10;&#10;Summary includes last-24h trades, win rate, PnL, and overall cumulative stats.&#10;&#10;ML progress update (periodic, configurable) includes rolling Brier, rolling expectancy, drawdown, and per-coin Brier.&#10;&#10;## Hugging Face diagnostics + phone workflow&#10;&#10;1. Keep state and report under `/data` (already default in `Dockerfile`).&#10;2. Set Telegram secrets in your Space variables:&#10;   - `TELEGRAM_BOT_TOKEN`&#10;   - `TELEGRAM_CHAT_ID`&#10;   - `PROXY_URL`&#10;3. Optionally tune ML push cadence:&#10;   - `BOT_TELEGRAM_ML_REPORT_HOURS=6`&#10;   - `BOT_TELEGRAM_ML_REPORT_MIN_TRADES=15`&#10;4. View live JSON diagnostics in a phone browser:&#10;   - `https://&lt;your-space&gt;.hf.space/status`&#10;   - `https://&lt;your-space&gt;.hf.space/status/full`&#10;5. Trigger one-off diagnostics message from Space shell:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file /data/learning_state.joblib --status-url http://127.0.0.1:7860/status/full --send-telegram&#10;```&#10;&#10;## How the ML learns&#10;&#10;At a high level, this bot uses online learning so it improves continuously while paper trading:&#10;&#10;1. For each coin (`BTC`, `ETH`, `SOL`), it builds a feature vector from latest candles:&#10;   - short-horizon returns (`ret_1`, `ret_3`, `ret_8`)&#10;   - trend/momentum (`macd`, scaled `rsi`)&#10;   - volatility and activity (`atr14`, `vol_z`)&#10;2. `SGDClassifier(loss=&quot;log_loss&quot;)` predicts probability of a profitable long setup.&#10;3. Entry logic maps probability to actions:&#10;   - `p_long &gt;= threshold` -&gt; long&#10;   - `p_long &lt;= 1 - threshold` -&gt; short&#10;   - otherwise hold&#10;   - when model is cold, bootstrap mode uses weak momentum direction to generate first labels&#10;   - optional exploration (`--exploration-rate`) continues collecting data even during neutral model states&#10;4. When a trade closes (TP/SL), outcome is the supervision label:&#10;   - profitable -&gt; label `1`&#10;   - losing -&gt; label `0`&#10;5. The model is updated immediately using `partial_fit(...)` on that single labeled example.&#10;6. The confidence threshold adapts from recent win rate (last 30 outcomes), becoming stricter or looser.&#10;7. Learned state is persisted to `learning_state.joblib` and reloaded on restart.&#10;8. Open paper positions and captured entry features are also persisted, so trades crossing restarts still train the model on close.&#10;&#10;So each closed paper trade directly trains the next decision, and training survives restarts.&#10;&#10;## ML diagnostics&#10;&#10;`ml_diagnostics.py` helps answer whether the model signal is actually improving.&#10;&#10;It reports:&#10;&#10;- overall/per-coin/per-side win rate and expectancy&#10;- directional sanity checks:&#10;  - long winners should have higher `p_long` than long losers&#10;  - short winners should have lower `p_long` than short losers&#10;- direction-aware Brier score (lower is better)&#10;- optional runtime snapshot from `/status/full` including rolling Brier/expectancy/drawdown&#10;&#10;If advisor mode is enabled, `/status/full` includes `advisor` status and counters (requests/errors/blocked signals).&#10;&#10;This is useful to catch label/specification issues where the model keeps repeating failing trade patterns.&#10;&#10;## Notes&#10;&#10;- The consistency gate currently requires:&#10;  - at least 30 paper trades&#10;  - win rate &gt;= 58%&#10;  - profit factor &gt;= 1.25&#10;- State persistence file is configurable with `--state-file`&#10;- Runtime report file is configurable with `--report-file`&#10;- Live approval can be controlled with:&#10;  - `--require-live-approval / --no-require-live-approval`&#10;  - `--live-approval-mode either|both` (default `either`)&#10;  - `--live-approval-file` (default `allow_live_trading.flag`)&#10;- Startup live order test can be controlled with:&#10;  - `--live-startup-test / --no-live-startup-test`&#10;  - `--live-startup-test-coin` (default `BTC`)&#10;  - `--live-startup-test-notional` (default `12`)&#10;&#10;&#10;" />
-              <option name="updatedContent" value="---&#10;title: Hyperliquid Adaptive Bot&#10;emoji: &quot;&quot;&#10;colorFrom: blue&#10;colorTo: purple&#10;sdk: docker&#10;pinned: false&#10;---&#10;&#10;# Hyperliquid Adaptive Bot&#10;&#10;This project now includes an adaptive ML bot that uses your existing Hyperliquid framework.&#10;&#10;## What it does&#10;&#10;- Trades **only** `BTC`, `ETH`, and `SOL`&#10;- Runs in **paper mode by default**&#10;- Learns from closed paper trades with an online ML model (`SGDClassifier`)&#10;- Persists everything learned to disk (`learning_state.joblib`) so restart resumes learning&#10;- Persists paper runtime state (equity, open positions, entry features) so restart does not break learning continuity&#10;- Uses a profitability gate before any live execution is allowed&#10;- Sends Telegram alerts on trade open/close and a midnight UTC summary&#10;- Sends a readiness alert when paper performance is strong enough for live trading&#10;- Optional startup live transaction test (tiny BUY then SELL) to verify live order execution path&#10;- Can mirror to live orders only when:&#10;  - `--enable-live` is set&#10;  - paper performance is consistently profitable&#10;  - manual approval is granted (`ALLOW_LIVE_TRADING=1` or approval flag file exists)&#10;  - `HL_PRIVATE_KEY` is configured&#10;&#10;## Files&#10;&#10;- `hbot.py`: existing Hyperliquid API framework and order placement helpers&#10;- `adaptive_bot.py`: new adaptive ML paper/live-gated trading bot&#10;- `hf_advisor.py`: optional external advisor client (e.g., Hugging Face endpoint)&#10;- `smoke_test.py`: offline persistence and learning smoke test&#10;- `ml_diagnostics.py`: offline + live learning diagnostics report&#10;- `diagnostics_smoke_test.py`: quick smoke test for diagnostics logic&#10;- `advisor_smoke_test.py`: quick smoke test for advisor parsing behavior&#10;- `paper_report.json`: runtime status and metrics output (created at runtime)&#10;&#10;## Health endpoints (for HF + UptimeRobot)&#10;&#10;- `GET /` -&gt; lightweight status response (`200`) for uptime checks&#10;- `GET /healthz` -&gt; same as root (`200`)&#10;- `GET /status` -&gt; concise overview (equity, open position count, performance, live gate)&#10;- `GET /status/full` -&gt; full runtime JSON (equity, open positions, metrics, thresholds)&#10;- `GET /trades` -&gt; open positions list and count&#10;&#10;If your Space is `https://chigginrice-hyperliquid-bot.hf.space/`, your existing root monitor will work as-is.&#10;&#10;## Quick start&#10;&#10;1. Install dependencies:&#10;&#10;```bash&#10;python3 -m venv .venv&#10;source .venv/bin/activate&#10;pip install -r requirements.txt&#10;```&#10;&#10;2. Run the offline smoke test:&#10;&#10;```bash&#10;python smoke_test.py&#10;```&#10;&#10;Optional: run diagnostics smoke test:&#10;&#10;```bash&#10;python diagnostics_smoke_test.py&#10;```&#10;&#10;Optional: run advisor smoke test:&#10;&#10;```bash&#10;python advisor_smoke_test.py&#10;```&#10;&#10;3. Start the adaptive paper bot:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --poll-seconds 45&#10;```&#10;&#10;4. Check ML learning quality from saved state:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib&#10;```&#10;&#10;If the bot is running locally, include live status snapshot:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --status-url http://127.0.0.1:7860/status/full&#10;```&#10;&#10;JSON output (useful for storing artifacts in CI/HF workflows):&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --json&#10;```&#10;&#10;Send a compact diagnostics snapshot to Telegram:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --send-telegram&#10;```&#10;&#10;Optional knobs for early growth from scratch:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --exploration-rate 0.08&#10;```&#10;&#10;Enable periodic ML progress updates to Telegram (good for phone monitoring):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --telegram-ml-report-hours 6 --telegram-ml-report-min-trades 15&#10;```&#10;&#10;Advisor is built into the bot and always enabled (local in-process engine). You can optionally attach a remote endpoint (for example, Hugging Face) to augment it:&#10;&#10;```bash&#10;python adaptive_bot.py \&#10;  --interval 15m \&#10;  --hf-advisor-url &quot;https://your-endpoint.example/predict&quot; \&#10;  --hf-advisor-mode blend \&#10;  --hf-advisor-blend-weight 0.35 \&#10;  --hf-advisor-min-confidence 0.60&#10;```&#10;&#10;Advisor modes:&#10;&#10;- `confirm`: only trade when advisor agrees with the model side at/above min confidence&#10;- `veto`: advisor can block opposite-side model trades at/above min confidence&#10;- `blend`: blend local `p_long` with advisor `p_long` and decide from the blended score&#10;&#10;If `--hf-advisor-url` is not set, the local advisor still runs and decisions continue with no external dependency.&#10;&#10;## Optional: allow live mirror only after paper consistency gate&#10;&#10;```bash&#10;export HL_PRIVATE_KEY=&quot;your_private_key&quot;&#10;# optional if using delegated account&#10;export HL_ACCOUNT_ADDRESS=&quot;0x...&quot;&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;By default, live arm still requires manual approval even after the paper gate is passed:&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;# or: touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;Strict two-step approval mode (recommended for safety):&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live --live-approval-mode both&#10;```&#10;&#10;Optional startup live execution test (real tiny transaction):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --enable-live --live-startup-test --live-startup-test-coin BTC --live-startup-test-notional 12&#10;```&#10;&#10;## Docker (Hugging Face)&#10;&#10;`Dockerfile` is included for Docker-based hosting.&#10;&#10;Build locally:&#10;&#10;```bash&#10;docker build -t hyperliquid-adaptive-bot .&#10;```&#10;&#10;Run locally in paper mode:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_INTERVAL=15m \&#10;  -e BOT_POLL_SECONDS=45 \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Run with live mode allowed after paper gate passes:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_ENABLE_LIVE=1 \&#10;  -e HL_PRIVATE_KEY=&quot;your_private_key&quot; \&#10;  -e HL_ACCOUNT_ADDRESS=&quot;0x...&quot; \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Environment variables used by the container:&#10;&#10;- `BOT_INTERVAL` (default `15m`)&#10;- `BOT_POLL_SECONDS` (default `45`)&#10;- `BOT_STATE_FILE` (default `/data/learning_state.joblib`)&#10;- `BOT_REPORT_FILE` (default `/data/paper_report.json`)&#10;- `BOT_ENABLE_LIVE` (set any non-empty value to append `--enable-live`)&#10;- `BOT_HEALTH_HOST` (default `0.0.0.0`)&#10;- `BOT_HEALTH_PORT` (default `7860`)&#10;- `PORT` (default `7860`, used by Hugging Face Spaces)&#10;- `BOT_HF_ADVISOR_URL` (optional external advisor endpoint)&#10;- `BOT_HF_ADVISOR_TOKEN` (optional bearer token)&#10;- `BOT_HF_ADVISOR_MODE` (`confirm|veto|blend`, default `blend`)&#10;- `BOT_HF_ADVISOR_MIN_CONFIDENCE` (default `0.60`)&#10;- `BOT_HF_ADVISOR_BLEND_WEIGHT` (default `0.35`)&#10;- `BOT_HF_ADVISOR_TIMEOUT_SECONDS` (default `4.0`)&#10;- `ALLOW_LIVE_TRADING` (set to `1/true/yes` to manually approve live trading)&#10;- `BOT_LIVE_STARTUP_TEST` (set non-empty to add `--live-startup-test`)&#10;- `BOT_LIVE_STARTUP_TEST_COIN` (default `BTC`)&#10;- `BOT_LIVE_STARTUP_TEST_NOTIONAL` (default `12`)&#10;- `BOT_TELEGRAM_ML_REPORT_HOURS` (default `6`, set `0` to disable periodic ML Telegram report)&#10;- `BOT_TELEGRAM_ML_REPORT_MIN_TRADES` (default `15`)&#10;- `TELEGRAM_BOT_TOKEN` (required for Telegram notifications)&#10;- `TELEGRAM_CHAT_ID` (required for Telegram notifications)&#10;- `PROXY_URL` (required by `telegram_notifier.py` to forward messages)&#10;- `HL_PRIVATE_KEY` (required for live mirror)&#10;- `HL_ACCOUNT_ADDRESS` (optional)&#10;&#10;## Telegram notifications&#10;&#10;- Entry alert: sent when a paper position opens&#10;- Exit alert: sent when TP/SL closes a position&#10;- Midnight summary: sent once per day at `00:00 UTC`&#10;- Readiness alert: sent when profitability gate is reached and bot is ready to request live approval&#10;&#10;Summary includes last-24h trades, win rate, PnL, and overall cumulative stats.&#10;&#10;ML progress update (periodic, configurable) includes rolling Brier, rolling expectancy, drawdown, and per-coin Brier.&#10;&#10;## Hugging Face diagnostics + phone workflow&#10;&#10;1. Keep state and report under `/data` (already default in `Dockerfile`).&#10;2. Set Telegram secrets in your Space variables:&#10;   - `TELEGRAM_BOT_TOKEN`&#10;   - `TELEGRAM_CHAT_ID`&#10;   - `PROXY_URL`&#10;3. Optionally tune ML push cadence:&#10;   - `BOT_TELEGRAM_ML_REPORT_HOURS=6`&#10;   - `BOT_TELEGRAM_ML_REPORT_MIN_TRADES=15`&#10;4. View live JSON diagnostics in a phone browser:&#10;   - `https://&lt;your-space&gt;.hf.space/status`&#10;   - `https://&lt;your-space&gt;.hf.space/status/full`&#10;5. Trigger one-off diagnostics message from Space shell:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file /data/learning_state.joblib --status-url http://127.0.0.1:7860/status/full --send-telegram&#10;```&#10;&#10;## How the ML learns&#10;&#10;At a high level, this bot uses online learning so it improves continuously while paper trading:&#10;&#10;1. For each coin (`BTC`, `ETH`, `SOL`), it builds a feature vector from latest candles:&#10;   - short-horizon returns (`ret_1`, `ret_3`, `ret_8`)&#10;   - trend/momentum (`macd`, scaled `rsi`)&#10;   - volatility and activity (`atr14`, `vol_z`)&#10;2. `SGDClassifier(loss=&quot;log_loss&quot;)` predicts probability of a profitable long setup.&#10;3. Entry logic maps probability to actions:&#10;   - `p_long &gt;= threshold` -&gt; long&#10;   - `p_long &lt;= 1 - threshold` -&gt; short&#10;   - otherwise hold&#10;   - when model is cold, bootstrap mode uses weak momentum direction to generate first labels&#10;   - optional exploration (`--exploration-rate`) continues collecting data even during neutral model states&#10;4. When a trade closes (TP/SL), outcome is the supervision label:&#10;   - profitable -&gt; label `1`&#10;   - losing -&gt; label `0`&#10;5. The model is updated immediately using `partial_fit(...)` on that single labeled example.&#10;6. The confidence threshold adapts from recent win rate (last 30 outcomes), becoming stricter or looser.&#10;7. Learned state is persisted to `learning_state.joblib` and reloaded on restart.&#10;8. Open paper positions and captured entry features are also persisted, so trades crossing restarts still train the model on close.&#10;&#10;So each closed paper trade directly trains the next decision, and training survives restarts.&#10;&#10;## ML diagnostics&#10;&#10;`ml_diagnostics.py` helps answer whether the model signal is actually improving.&#10;&#10;It reports:&#10;&#10;- overall/per-coin/per-side win rate and expectancy&#10;- directional sanity checks:&#10;  - long winners should have higher `p_long` than long losers&#10;  - short winners should have lower `p_long` than short losers&#10;- direction-aware Brier score (lower is better)&#10;- optional runtime snapshot from `/status/full` including rolling Brier/expectancy/drawdown&#10;&#10;If advisor mode is enabled, `/status/full` includes `advisor` status and counters (requests/errors/blocked signals).&#10;&#10;This is useful to catch label/specification issues where the model keeps repeating failing trade patterns.&#10;&#10;## Notes&#10;&#10;- The consistency gate currently requires:&#10;  - at least 30 paper trades&#10;  - win rate &gt;= 58%&#10;  - profit factor &gt;= 1.25&#10;- State persistence file is configurable with `--state-file`&#10;- Runtime report file is configurable with `--report-file`&#10;- Live approval can be controlled with:&#10;  - `--require-live-approval / --no-require-live-approval`&#10;  - `--live-approval-mode either|both` (default `either`)&#10;  - `--live-approval-file` (default `allow_live_trading.flag`)&#10;- Startup live order test can be controlled with:&#10;  - `--live-startup-test / --no-live-startup-test`&#10;  - `--live-startup-test-coin` (default `BTC`)&#10;  - `--live-startup-test-notional` (default `12`)&#10;&#10;&#10;" />
+              <option name="originalContent" value="---&#10;title: Hyperliquid Adaptive Bot&#10;emoji: &quot;&quot;&#10;colorFrom: blue&#10;colorTo: purple&#10;sdk: docker&#10;pinned: false&#10;---&#10;&#10;# Hyperliquid Adaptive Bot&#10;&#10;This project now includes an adaptive ML bot that uses your existing Hyperliquid framework.&#10;&#10;## What it does&#10;&#10;- Trades **only** `BTC`, `ETH`, and `SOL`&#10;- Runs in **paper mode by default**&#10;- Learns from closed paper trades with an online ML model (`SGDClassifier`)&#10;- Persists everything learned to disk (`learning_state.joblib`) so restart resumes learning&#10;- Persists paper runtime state (equity, open positions, entry features) so restart does not break learning continuity&#10;- Uses a profitability gate before any live execution is allowed&#10;- Sends Telegram alerts on trade open/close and a midnight UTC summary&#10;- Sends a readiness alert when paper performance is strong enough for live trading&#10;- Optional startup live transaction test (tiny BUY then SELL) to verify live order execution path&#10;- Can mirror to live orders only when:&#10;  - `--enable-live` is set&#10;  - paper performance is consistently profitable&#10;  - manual approval is granted (`ALLOW_LIVE_TRADING=1` or approval flag file exists)&#10;  - `HL_PRIVATE_KEY` is configured&#10;&#10;## Files&#10;&#10;- `hbot.py`: existing Hyperliquid API framework and order placement helpers&#10;- `adaptive_bot.py`: new adaptive ML paper/live-gated trading bot&#10;- `hf_advisor.py`: optional external advisor client (e.g., Hugging Face endpoint)&#10;- `smoke_test.py`: offline persistence and learning smoke test&#10;- `ml_diagnostics.py`: offline + live learning diagnostics report&#10;- `diagnostics_smoke_test.py`: quick smoke test for diagnostics logic&#10;- `advisor_smoke_test.py`: quick smoke test for advisor parsing behavior&#10;- `paper_report.json`: runtime status and metrics output (created at runtime)&#10;&#10;## Health endpoints (for HF + UptimeRobot)&#10;&#10;- `GET /` -&gt; lightweight status response (`200`) for uptime checks&#10;- `GET /healthz` -&gt; same as root (`200`)&#10;- `GET /status` -&gt; concise overview (equity, open position count, performance, live gate)&#10;- `GET /status/full` -&gt; full runtime JSON (equity, open positions, metrics, thresholds)&#10;- `GET /trades` -&gt; open positions list and count&#10;&#10;If your Space is `https://chigginrice-hyperliquid-bot.hf.space/`, your existing root monitor will work as-is.&#10;&#10;## Quick start&#10;&#10;1. Install dependencies:&#10;&#10;```bash&#10;python3 -m venv .venv&#10;source .venv/bin/activate&#10;pip install -r requirements.txt&#10;```&#10;&#10;2. Run the offline smoke test:&#10;&#10;```bash&#10;python smoke_test.py&#10;```&#10;&#10;Optional: run diagnostics smoke test:&#10;&#10;```bash&#10;python diagnostics_smoke_test.py&#10;```&#10;&#10;Optional: run advisor smoke test:&#10;&#10;```bash&#10;python advisor_smoke_test.py&#10;```&#10;&#10;3. Start the adaptive paper bot:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --poll-seconds 45&#10;```&#10;&#10;4. Check ML learning quality from saved state:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib&#10;```&#10;&#10;If the bot is running locally, include live status snapshot:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --status-url http://127.0.0.1:7860/status/full&#10;```&#10;&#10;JSON output (useful for storing artifacts in CI/HF workflows):&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --json&#10;```&#10;&#10;Send a compact diagnostics snapshot to Telegram:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --send-telegram&#10;```&#10;&#10;Optional knobs for early growth from scratch:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --exploration-rate 0.08&#10;```&#10;&#10;Safer learning defaults (already enabled by default):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --min-model-samples-for-signal 50 --pause-on-negative-expectancy&#10;```&#10;&#10;Enable periodic ML progress updates to Telegram (good for phone monitoring):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --telegram-ml-report-hours 6 --telegram-ml-report-min-trades 15&#10;```&#10;&#10;Advisor is built into the bot and always enabled (local in-process engine). You can optionally attach a remote endpoint (for example, Hugging Face) to augment it:&#10;&#10;```bash&#10;python adaptive_bot.py \&#10;  --interval 15m \&#10;  --hf-advisor-url &quot;https://your-endpoint.example/predict&quot; \&#10;  --hf-advisor-mode blend \&#10;  --hf-advisor-blend-weight 0.35 \&#10;  --hf-advisor-min-confidence 0.60&#10;```&#10;&#10;Advisor modes:&#10;&#10;- `confirm`: only trade when advisor agrees with the model side at/above min confidence&#10;- `veto`: advisor can block opposite-side model trades at/above min confidence&#10;- `blend`: blend local `p_long` with advisor `p_long` and decide from the blended score&#10;&#10;If `--hf-advisor-url` is not set, the local advisor still runs and decisions continue with no external dependency.&#10;&#10;## Optional: allow live mirror only after paper consistency gate&#10;&#10;```bash&#10;export HL_PRIVATE_KEY=&quot;your_private_key&quot;&#10;# optional if using delegated account&#10;export HL_ACCOUNT_ADDRESS=&quot;0x...&quot;&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;By default, live arm still requires manual approval even after the paper gate is passed:&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;# or: touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;Strict two-step approval mode (recommended for safety):&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live --live-approval-mode both&#10;```&#10;&#10;Optional startup live execution test (real tiny transaction):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --enable-live --live-startup-test --live-startup-test-coin BTC --live-startup-test-notional 12&#10;```&#10;&#10;## Docker (Hugging Face)&#10;&#10;`Dockerfile` is included for Docker-based hosting.&#10;&#10;Build locally:&#10;&#10;```bash&#10;docker build -t hyperliquid-adaptive-bot .&#10;```&#10;&#10;Run locally in paper mode:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_INTERVAL=15m \&#10;  -e BOT_POLL_SECONDS=45 \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Run with live mode allowed after paper gate passes:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_ENABLE_LIVE=1 \&#10;  -e HL_PRIVATE_KEY=&quot;your_private_key&quot; \&#10;  -e HL_ACCOUNT_ADDRESS=&quot;0x...&quot; \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Environment variables used by the container:&#10;&#10;- `BOT_INTERVAL` (default `15m`)&#10;- `BOT_POLL_SECONDS` (default `45`)&#10;- `BOT_STATE_FILE` (default `/data/learning_state.joblib`)&#10;- `BOT_REPORT_FILE` (default `/data/paper_report.json`)&#10;- `BOT_ENABLE_LIVE` (set any non-empty value to append `--enable-live`)&#10;- `BOT_HEALTH_HOST` (default `0.0.0.0`)&#10;- `BOT_HEALTH_PORT` (default `7860`)&#10;- `PORT` (default `7860`, used by Hugging Face Spaces)&#10;- `BOT_HF_ADVISOR_URL` (optional external advisor endpoint)&#10;- `BOT_HF_ADVISOR_TOKEN` (optional bearer token)&#10;- `BOT_HF_ADVISOR_MODE` (`confirm|veto|blend`, default `blend`)&#10;- `BOT_HF_ADVISOR_MIN_CONFIDENCE` (default `0.60`)&#10;- `BOT_HF_ADVISOR_BLEND_WEIGHT` (default `0.35`)&#10;- `BOT_HF_ADVISOR_TIMEOUT_SECONDS` (default `4.0`)&#10;- `BOT_MIN_MODEL_SAMPLES_FOR_SIGNAL` (default `50`)&#10;- `BOT_PAUSE_ON_NEGATIVE_EXPECTANCY` (`1/true/yes` by default)&#10;- `BOT_PAUSE_EXPECTANCY_LOOKBACK` (default `30`)&#10;- `BOT_PAUSE_EXPECTANCY_THRESHOLD_USD` (default `-1.0`)&#10;- `BOT_PAUSE_MAX_DRAWDOWN_PCT` (default `12.0`)&#10;- `ALLOW_LIVE_TRADING` (set to `1/true/yes` to manually approve live trading)&#10;- `BOT_LIVE_STARTUP_TEST` (set non-empty to add `--live-startup-test`)&#10;- `BOT_LIVE_STARTUP_TEST_COIN` (default `BTC`)&#10;- `BOT_LIVE_STARTUP_TEST_NOTIONAL` (default `12`)&#10;- `BOT_TELEGRAM_ML_REPORT_HOURS` (default `6`, set `0` to disable periodic ML Telegram report)&#10;- `BOT_TELEGRAM_ML_REPORT_MIN_TRADES` (default `15`)&#10;- `TELEGRAM_BOT_TOKEN` (required for Telegram notifications)&#10;- `TELEGRAM_CHAT_ID` (required for Telegram notifications)&#10;- `PROXY_URL` (required by `telegram_notifier.py` to forward messages)&#10;- `HL_PRIVATE_KEY` (required for live mirror)&#10;- `HL_ACCOUNT_ADDRESS` (optional)&#10;&#10;## Telegram notifications&#10;&#10;- Entry alert: sent when a paper position opens&#10;- Exit alert: sent when TP/SL closes a position&#10;- Midnight summary: sent once per day at `00:00 UTC`&#10;- Readiness alert: sent when profitability gate is reached and bot is ready to request live approval&#10;&#10;Summary includes last-24h trades, win rate, PnL, and overall cumulative stats.&#10;&#10;ML progress update (periodic, configurable) includes rolling Brier, rolling expectancy, drawdown, and per-coin Brier.&#10;&#10;## Hugging Face diagnostics + phone workflow&#10;&#10;1. Keep state and report under `/data` (already default in `Dockerfile`).&#10;2. Set Telegram secrets in your Space variables:&#10;   - `TELEGRAM_BOT_TOKEN`&#10;   - `TELEGRAM_CHAT_ID`&#10;   - `PROXY_URL`&#10;3. Optionally tune ML push cadence:&#10;   - `BOT_TELEGRAM_ML_REPORT_HOURS=6`&#10;   - `BOT_TELEGRAM_ML_REPORT_MIN_TRADES=15`&#10;4. View live JSON diagnostics in a phone browser:&#10;   - `https://&lt;your-space&gt;.hf.space/status`&#10;   - `https://&lt;your-space&gt;.hf.space/status/full`&#10;5. Trigger one-off diagnostics message from Space shell:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file /data/learning_state.joblib --status-url http://127.0.0.1:7860/status/full --send-telegram&#10;```&#10;&#10;## How the ML learns&#10;&#10;At a high level, this bot uses online learning so it improves continuously while paper trading:&#10;&#10;1. For each coin (`BTC`, `ETH`, `SOL`), it builds a feature vector from latest candles:&#10;   - short-horizon returns (`ret_1`, `ret_3`, `ret_8`)&#10;   - trend/momentum (`macd`, scaled `rsi`)&#10;   - volatility and activity (`atr14`, `vol_z`)&#10;2. `SGDClassifier(loss=&quot;log_loss&quot;)` predicts probability of a profitable long setup.&#10;3. Entry logic maps probability to actions:&#10;   - `p_long &gt;= threshold` -&gt; long&#10;   - `p_long &lt;= 1 - threshold` -&gt; short&#10;   - otherwise hold&#10;   - when model is cold, bootstrap mode uses weak momentum direction to generate first labels&#10;   - optional exploration (`--exploration-rate`) continues collecting data even during neutral model states&#10;4. When a trade closes (TP/SL), supervision is direction-aware for a long-probability model:&#10;   - long trade win -&gt; label `1`&#10;   - long trade loss -&gt; label `0`&#10;   - short trade win -&gt; label `0`&#10;   - short trade loss -&gt; label `1`&#10;5. The model is updated immediately using `partial_fit(...)` on that single labeled example.&#10;6. The confidence threshold adapts from recent realized win rate (last 30 outcomes), becoming stricter or looser.&#10;7. Learned state is persisted to `learning_state.joblib` and reloaded on restart.&#10;8. Open paper positions and captured entry features are also persisted, so trades crossing restarts still train the model on close.&#10;&#10;Additional safety controls now reduce repeated losing phases:&#10;&#10;- model-driven entries only activate after a minimum sample count per coin&#10;- optional safety pause halts new entries when recent expectancy is poor and drawdown is elevated&#10;&#10;So each closed paper trade directly trains the next decision, and training survives restarts.&#10;&#10;## ML diagnostics&#10;&#10;`ml_diagnostics.py` helps answer whether the model signal is actually improving.&#10;&#10;It reports:&#10;&#10;- overall/per-coin/per-side win rate and expectancy&#10;- directional sanity checks:&#10;  - long winners should have higher `p_long` than long losers&#10;  - short winners should have lower `p_long` than short losers&#10;- direction-aware Brier score (lower is better)&#10;- optional runtime snapshot from `/status/full` including rolling Brier/expectancy/drawdown&#10;&#10;If advisor mode is enabled, `/status/full` includes `advisor` status and counters (requests/errors/blocked signals).&#10;&#10;This is useful to catch label/specification issues where the model keeps repeating failing trade patterns.&#10;&#10;## Notes&#10;&#10;- The consistency gate currently requires:&#10;  - at least 30 paper trades&#10;  - win rate &gt;= 58%&#10;  - profit factor &gt;= 1.25&#10;- State persistence file is configurable with `--state-file`&#10;- Runtime report file is configurable with `--report-file`&#10;- Live approval can be controlled with:&#10;  - `--require-live-approval / --no-require-live-approval`&#10;  - `--live-approval-mode either|both` (default `either`)&#10;  - `--live-approval-file` (default `allow_live_trading.flag`)&#10;- Startup live order test can be controlled with:&#10;  - `--live-startup-test / --no-live-startup-test`&#10;  - `--live-startup-test-coin` (default `BTC`)&#10;  - `--live-startup-test-notional` (default `12`)&#10;&#10;&#10;" />
+              <option name="updatedContent" value="---&#10;title: Hyperliquid Adaptive Bot&#10;emoji: &quot;&quot;&#10;colorFrom: blue&#10;colorTo: purple&#10;sdk: docker&#10;pinned: false&#10;---&#10;&#10;# Hyperliquid Adaptive Bot&#10;&#10;This project now includes an adaptive ML bot that uses your existing Hyperliquid framework.&#10;&#10;## What it does&#10;&#10;- Trades **only** `BTC`, `ETH`, and `SOL`&#10;- Runs in **paper mode by default**&#10;- Learns from closed paper trades with an online ML model (`SGDClassifier`)&#10;- Persists everything learned to disk (`learning_state.joblib`) so restart resumes learning&#10;- Persists paper runtime state (equity, open positions, entry features) so restart does not break learning continuity&#10;- Uses a profitability gate before any live execution is allowed&#10;- Sends Telegram alerts on trade open/close and a midnight UTC summary&#10;- Sends a readiness alert when paper performance is strong enough for live trading&#10;- Optional startup live transaction test (tiny BUY then SELL) to verify live order execution path&#10;- Can mirror to live orders only when:&#10;  - `--enable-live` is set&#10;  - paper performance is consistently profitable&#10;  - manual approval is granted (`ALLOW_LIVE_TRADING=1` or approval flag file exists)&#10;  - `HL_PRIVATE_KEY` is configured&#10;&#10;## Files&#10;&#10;- `hbot.py`: existing Hyperliquid API framework and order placement helpers&#10;- `adaptive_bot.py`: new adaptive ML paper/live-gated trading bot&#10;- `hf_advisor.py`: optional external advisor client (e.g., Hugging Face endpoint)&#10;- `smoke_test.py`: offline persistence and learning smoke test&#10;- `ml_diagnostics.py`: offline + live learning diagnostics report&#10;- `diagnostics_smoke_test.py`: quick smoke test for diagnostics logic&#10;- `advisor_smoke_test.py`: quick smoke test for advisor parsing behavior&#10;- `paper_report.json`: runtime status and metrics output (created at runtime)&#10;&#10;## Health endpoints (for HF + UptimeRobot)&#10;&#10;- `GET /` -&gt; lightweight status response (`200`) for uptime checks&#10;- `GET /healthz` -&gt; same as root (`200`)&#10;- `GET /status` -&gt; concise overview (equity, open position count, performance, live gate)&#10;- `GET /status/full` -&gt; full runtime JSON (equity, open positions, metrics, thresholds)&#10;- `GET /trades` -&gt; open positions list and count&#10;&#10;If your Space is `https://chigginrice-hyperliquid-bot.hf.space/`, your existing root monitor will work as-is.&#10;&#10;## Quick start&#10;&#10;1. Install dependencies:&#10;&#10;```bash&#10;python3 -m venv .venv&#10;source .venv/bin/activate&#10;pip install -r requirements.txt&#10;```&#10;&#10;2. Run the offline smoke test:&#10;&#10;```bash&#10;python smoke_test.py&#10;```&#10;&#10;Optional: run diagnostics smoke test:&#10;&#10;```bash&#10;python diagnostics_smoke_test.py&#10;```&#10;&#10;Optional: run advisor smoke test:&#10;&#10;```bash&#10;python advisor_smoke_test.py&#10;```&#10;&#10;3. Start the adaptive paper bot:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --poll-seconds 45&#10;```&#10;&#10;4. Check ML learning quality from saved state:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib&#10;```&#10;&#10;If the bot is running locally, include live status snapshot:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --status-url http://127.0.0.1:7860/status/full&#10;```&#10;&#10;JSON output (useful for storing artifacts in CI/HF workflows):&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --json&#10;```&#10;&#10;Send a compact diagnostics snapshot to Telegram:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file learning_state.joblib --send-telegram&#10;```&#10;&#10;Optional knobs for early growth from scratch:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --exploration-rate 0.08&#10;```&#10;&#10;Safer learning defaults (already enabled by default):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --min-model-samples-for-signal 50 --pause-on-negative-expectancy&#10;```&#10;&#10;Auto-expire the safety pause after 3 hours (default) so the bot can retry entries:&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --pause-cooldown-minutes 180&#10;```&#10;&#10;Enable periodic ML progress updates to Telegram (good for phone monitoring):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --telegram-ml-report-hours 6 --telegram-ml-report-min-trades 15&#10;```&#10;&#10;Advisor is built into the bot and always enabled (local in-process engine). You can optionally attach a remote endpoint (for example, Hugging Face) to augment it:&#10;&#10;```bash&#10;python adaptive_bot.py \&#10;  --interval 15m \&#10;  --hf-advisor-url &quot;https://your-endpoint.example/predict&quot; \&#10;  --hf-advisor-mode blend \&#10;  --hf-advisor-blend-weight 0.35 \&#10;  --hf-advisor-min-confidence 0.60&#10;```&#10;&#10;Advisor modes:&#10;&#10;- `confirm`: only trade when advisor agrees with the model side at/above min confidence&#10;- `veto`: advisor can block opposite-side model trades at/above min confidence&#10;- `blend`: blend local `p_long` with advisor `p_long` and decide from the blended score&#10;&#10;If `--hf-advisor-url` is not set, the local advisor still runs and decisions continue with no external dependency.&#10;&#10;## Optional: allow live mirror only after paper consistency gate&#10;&#10;```bash&#10;export HL_PRIVATE_KEY=&quot;your_private_key&quot;&#10;# optional if using delegated account&#10;export HL_ACCOUNT_ADDRESS=&quot;0x...&quot;&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;By default, live arm still requires manual approval even after the paper gate is passed:&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;# or: touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live&#10;```&#10;&#10;Strict two-step approval mode (recommended for safety):&#10;&#10;```bash&#10;export ALLOW_LIVE_TRADING=1&#10;touch allow_live_trading.flag&#10;python adaptive_bot.py --interval 15m --enable-live --live-approval-mode both&#10;```&#10;&#10;Optional startup live execution test (real tiny transaction):&#10;&#10;```bash&#10;python adaptive_bot.py --interval 15m --enable-live --live-startup-test --live-startup-test-coin BTC --live-startup-test-notional 12&#10;```&#10;&#10;## Docker (Hugging Face)&#10;&#10;`Dockerfile` is included for Docker-based hosting.&#10;&#10;Build locally:&#10;&#10;```bash&#10;docker build -t hyperliquid-adaptive-bot .&#10;```&#10;&#10;Run locally in paper mode:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_INTERVAL=15m \&#10;  -e BOT_POLL_SECONDS=45 \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Run with live mode allowed after paper gate passes:&#10;&#10;```bash&#10;docker run --rm \&#10;  -e BOT_ENABLE_LIVE=1 \&#10;  -e HL_PRIVATE_KEY=&quot;your_private_key&quot; \&#10;  -e HL_ACCOUNT_ADDRESS=&quot;0x...&quot; \&#10;  -v &quot;$(pwd)/data:/data&quot; \&#10;  hyperliquid-adaptive-bot&#10;```&#10;&#10;Environment variables used by the container:&#10;&#10;- `BOT_INTERVAL` (default `15m`)&#10;- `BOT_POLL_SECONDS` (default `45`)&#10;- `BOT_STATE_FILE` (default `/data/learning_state.joblib`)&#10;- `BOT_REPORT_FILE` (default `/data/paper_report.json`)&#10;- `BOT_ENABLE_LIVE` (set any non-empty value to append `--enable-live`)&#10;- `BOT_HEALTH_HOST` (default `0.0.0.0`)&#10;- `BOT_HEALTH_PORT` (default `7860`)&#10;- `PORT` (default `7860`, used by Hugging Face Spaces)&#10;- `BOT_HF_ADVISOR_URL` (optional external advisor endpoint)&#10;- `BOT_HF_ADVISOR_TOKEN` (optional bearer token)&#10;- `BOT_HF_ADVISOR_MODE` (`confirm|veto|blend`, default `blend`)&#10;- `BOT_HF_ADVISOR_MIN_CONFIDENCE` (default `0.60`)&#10;- `BOT_HF_ADVISOR_BLEND_WEIGHT` (default `0.35`)&#10;- `BOT_HF_ADVISOR_TIMEOUT_SECONDS` (default `4.0`)&#10;- `BOT_MIN_MODEL_SAMPLES_FOR_SIGNAL` (default `50`)&#10;- `BOT_PAUSE_ON_NEGATIVE_EXPECTANCY` (`1/true/yes` by default)&#10;- `BOT_PAUSE_EXPECTANCY_LOOKBACK` (default `30`)&#10;- `BOT_PAUSE_EXPECTANCY_THRESHOLD_USD` (default `-1.0`)&#10;- `BOT_PAUSE_MAX_DRAWDOWN_PCT` (default `12.0`)&#10;- `BOT_PAUSE_COOLDOWN_MINUTES` (default `180`, set `0` to disable auto-expiry)&#10;- `ALLOW_LIVE_TRADING` (set to `1/true/yes` to manually approve live trading)&#10;- `BOT_LIVE_STARTUP_TEST` (set non-empty to add `--live-startup-test`)&#10;- `BOT_LIVE_STARTUP_TEST_COIN` (default `BTC`)&#10;- `BOT_LIVE_STARTUP_TEST_NOTIONAL` (default `12`)&#10;- `BOT_TELEGRAM_ML_REPORT_HOURS` (default `6`, set `0` to disable periodic ML Telegram report)&#10;- `BOT_TELEGRAM_ML_REPORT_MIN_TRADES` (default `15`)&#10;- `TELEGRAM_BOT_TOKEN` (required for Telegram notifications)&#10;- `TELEGRAM_CHAT_ID` (required for Telegram notifications)&#10;- `PROXY_URL` (required by `telegram_notifier.py` to forward messages)&#10;- `HL_PRIVATE_KEY` (required for live mirror)&#10;- `HL_ACCOUNT_ADDRESS` (optional)&#10;&#10;## Telegram notifications&#10;&#10;- Entry alert: sent when a paper position opens&#10;- Exit alert: sent when TP/SL closes a position&#10;- Midnight summary: sent once per day at `00:00 UTC`&#10;- Readiness alert: sent when profitability gate is reached and bot is ready to request live approval&#10;&#10;Summary includes last-24h trades, win rate, PnL, and overall cumulative stats.&#10;&#10;ML progress update (periodic, configurable) includes rolling Brier, rolling expectancy, drawdown, and per-coin Brier.&#10;&#10;## Hugging Face diagnostics + phone workflow&#10;&#10;1. Keep state and report under `/data` (already default in `Dockerfile`).&#10;2. Set Telegram secrets in your Space variables:&#10;   - `TELEGRAM_BOT_TOKEN`&#10;   - `TELEGRAM_CHAT_ID`&#10;   - `PROXY_URL`&#10;3. Optionally tune ML push cadence:&#10;   - `BOT_TELEGRAM_ML_REPORT_HOURS=6`&#10;   - `BOT_TELEGRAM_ML_REPORT_MIN_TRADES=15`&#10;4. View live JSON diagnostics in a phone browser:&#10;   - `https://&lt;your-space&gt;.hf.space/status`&#10;   - `https://&lt;your-space&gt;.hf.space/status/full`&#10;5. Trigger one-off diagnostics message from Space shell:&#10;&#10;```bash&#10;python ml_diagnostics.py --state-file /data/learning_state.joblib --status-url http://127.0.0.1:7860/status/full --send-telegram&#10;```&#10;&#10;## How the ML learns&#10;&#10;At a high level, this bot uses online learning so it improves continuously while paper trading:&#10;&#10;1. For each coin (`BTC`, `ETH`, `SOL`), it builds a feature vector from latest candles:&#10;   - short-horizon returns (`ret_1`, `ret_3`, `ret_8`)&#10;   - trend/momentum (`macd`, scaled `rsi`)&#10;   - volatility and activity (`atr14`, `vol_z`)&#10;2. `SGDClassifier(loss=&quot;log_loss&quot;)` predicts probability of a profitable long setup.&#10;3. Entry logic maps probability to actions:&#10;   - `p_long &gt;= threshold` -&gt; long&#10;   - `p_long &lt;= 1 - threshold` -&gt; short&#10;   - otherwise hold&#10;   - when model is cold, bootstrap mode uses weak momentum direction to generate first labels&#10;   - optional exploration (`--exploration-rate`) continues collecting data even during neutral model states&#10;4. When a trade closes (TP/SL), supervision is direction-aware for a long-probability model:&#10;   - long trade win -&gt; label `1`&#10;   - long trade loss -&gt; label `0`&#10;   - short trade win -&gt; label `0`&#10;   - short trade loss -&gt; label `1`&#10;5. The model is updated immediately using `partial_fit(...)` on that single labeled example.&#10;6. The confidence threshold adapts from recent realized win rate (last 30 outcomes), becoming stricter or looser.&#10;7. Learned state is persisted to `learning_state.joblib` and reloaded on restart.&#10;8. Open paper positions and captured entry features are also persisted, so trades crossing restarts still train the model on close.&#10;&#10;Additional safety controls now reduce repeated losing phases:&#10;&#10;- model-driven entries only activate after a minimum sample count per coin&#10;- optional safety pause halts new entries when recent expectancy is poor and drawdown is elevated&#10;&#10;So each closed paper trade directly trains the next decision, and training survives restarts.&#10;&#10;## ML diagnostics&#10;&#10;`ml_diagnostics.py` helps answer whether the model signal is actually improving.&#10;&#10;It reports:&#10;&#10;- overall/per-coin/per-side win rate and expectancy&#10;- directional sanity checks:&#10;  - long winners should have higher `p_long` than long losers&#10;  - short winners should have lower `p_long` than short losers&#10;- direction-aware Brier score (lower is better)&#10;- optional runtime snapshot from `/status/full` including rolling Brier/expectancy/drawdown&#10;&#10;If advisor mode is enabled, `/status/full` includes `advisor` status and counters (requests/errors/blocked signals).&#10;&#10;This is useful to catch label/specification issues where the model keeps repeating failing trade patterns.&#10;&#10;## Notes&#10;&#10;- The consistency gate currently requires:&#10;  - at least 30 paper trades&#10;  - win rate &gt;= 58%&#10;  - profit factor &gt;= 1.25&#10;- State persistence file is configurable with `--state-file`&#10;- Runtime report file is configurable with `--report-file`&#10;- Live approval can be controlled with:&#10;  - `--require-live-approval / --no-require-live-approval`&#10;  - `--live-approval-mode either|both` (default `either`)&#10;  - `--live-approval-file` (default `allow_live_trading.flag`)&#10;- Startup live order test can be controlled with:&#10;  - `--live-startup-test / --no-live-startup-test`&#10;  - `--live-startup-test-coin` (default `BTC`)&#10;  - `--live-startup-test-notional` (default `12`)&#10;&#10;&#10;" />
             </PendingDiffInfo>
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               <option name="filePath" value="$PROJECT_DIR$/adaptive_bot.py" />
-              <option name="originalContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;&#10;Adaptive Hyperliquid bot.&#10;&#10;- Trades only BTC, ETH, SOL.&#10;- Paper trades by default.&#10;- Uses online ML (SGDClassifier) to adapt entry confidence thresholds.&#10;- Persists learned state to disk so learning survives restarts.&#10;- Can optionally switch to live execution only after consistent paper performance.&#10;&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;import argparse&#10;import json&#10;import logging&#10;import math&#10;import random&#10;import os&#10;import signal&#10;import threading&#10;import time&#10;from dataclasses import asdict, dataclass&#10;from datetime import datetime, timedelta, timezone&#10;from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer&#10;from typing import Any, Dict, List, Optional, Tuple&#10;&#10;import joblib&#10;import numpy as np&#10;import pandas as pd&#10;from sklearn.linear_model import SGDClassifier&#10;&#10;from hf_advisor import HuggingFaceAdvisor&#10;from hbot import HyperliquidAPI&#10;from telegram_notifier import TelegramNotifier&#10;&#10;&#10;logging.basicConfig(level=logging.INFO, format=&quot;%(asctime)s - %(levelname)s - %(message)s&quot;)&#10;logger = logging.getLogger(__name__)&#10;&#10;&#10;ALLOWED_COINS = (&quot;BTC&quot;, &quot;ETH&quot;, &quot;SOL&quot;)&#10;INTERVAL_MS = {&#10;    &quot;1m&quot;: 60_000,&#10;    &quot;5m&quot;: 300_000,&#10;    &quot;15m&quot;: 900_000,&#10;    &quot;30m&quot;: 1_800_000,&#10;    &quot;1h&quot;: 3_600_000,&#10;}&#10;&#10;&#10;@dataclass&#10;class Position:&#10;    coin: str&#10;    side: str  # long | short&#10;    entry: float&#10;    size: float&#10;    sl: float&#10;    tp: float&#10;    entry_ts: int&#10;    entry_features: List[float]&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;@dataclass&#10;class ClosedTrade:&#10;    coin: str&#10;    side: str&#10;    entry: float&#10;    exit: float&#10;    size: float&#10;    pnl_usd: float&#10;    pnl_pct: float&#10;    reason: str&#10;    entry_ts: int&#10;    exit_ts: int&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;class LearningState:&#10;    &quot;&quot;&quot;Holds online models + strategy performance and persists to disk.&quot;&quot;&quot;&#10;&#10;    def __init__(self, state_path: str):&#10;        self.state_path = state_path&#10;        self.models: Dict[str, SGDClassifier] = {&#10;            c: SGDClassifier(loss=&quot;log_loss&quot;, alpha=1e-4, random_state=42) for c in ALLOWED_COINS&#10;        }&#10;        self.model_ready: Dict[str, bool] = {c: False for c in ALLOWED_COINS}&#10;        self.thresholds: Dict[str, float] = {c: 0.56 for c in ALLOWED_COINS}&#10;        self.trade_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;trades&quot;: 0, &quot;wins&quot;: 0, &quot;losses&quot;: 0, &quot;gross_profit&quot;: 0.0, &quot;gross_loss&quot;: 0.0}&#10;            for c in ALLOWED_COINS&#10;        }&#10;        self.ml_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;count&quot;: 0.0, &quot;brier_sum&quot;: 0.0} for c in ALLOWED_COINS&#10;        }&#10;        self.recent_results: Dict[str, List[int]] = {c: [] for c in ALLOWED_COINS}&#10;        self.recent_brier: Dict[str, List[float]] = {c: [] for c in ALLOWED_COINS}&#10;        self.closed_trades: List[Dict[str, Any]] = []&#10;        self.trade_events: List[Dict[str, Any]] = []&#10;        self.equity_curve: List[Dict[str, float]] = []&#10;        self.paper_runtime: Dict[str, Any] = {&#10;            &quot;equity&quot;: None,&#10;            &quot;positions&quot;: {},&#10;            &quot;entry_features&quot;: {},&#10;        }&#10;        self.last_saved_ts = 0.0&#10;        self._load()&#10;&#10;    def _load(self) -&gt; None:&#10;        if not os.path.exists(self.state_path):&#10;            logger.info(&quot;No prior learning state found at %s, starting fresh.&quot;, self.state_path)&#10;            return&#10;        payload = joblib.load(self.state_path)&#10;        self.models = payload.get(&quot;models&quot;, self.models)&#10;        self.model_ready = payload.get(&quot;model_ready&quot;, self.model_ready)&#10;        self.thresholds = payload.get(&quot;thresholds&quot;, self.thresholds)&#10;        self.trade_stats = payload.get(&quot;trade_stats&quot;, self.trade_stats)&#10;        self.ml_stats = payload.get(&quot;ml_stats&quot;, self.ml_stats)&#10;        self.recent_results = payload.get(&quot;recent_results&quot;, self.recent_results)&#10;        self.recent_brier = payload.get(&quot;recent_brier&quot;, self.recent_brier)&#10;        self.closed_trades = payload.get(&quot;closed_trades&quot;, self.closed_trades)&#10;        self.trade_events = payload.get(&quot;trade_events&quot;, self.trade_events)&#10;        self.equity_curve = payload.get(&quot;equity_curve&quot;, self.equity_curve)&#10;        self.paper_runtime = payload.get(&quot;paper_runtime&quot;, self.paper_runtime)&#10;        logger.info(&quot;Loaded learning state from %s&quot;, self.state_path)&#10;&#10;    def save(self, force: bool = False) -&gt; None:&#10;        now = time.time()&#10;        if not force and now - self.last_saved_ts &lt; 20:&#10;            return&#10;        payload = {&#10;            &quot;models&quot;: self.models,&#10;            &quot;model_ready&quot;: self.model_ready,&#10;            &quot;thresholds&quot;: self.thresholds,&#10;            &quot;trade_stats&quot;: self.trade_stats,&#10;            &quot;ml_stats&quot;: self.ml_stats,&#10;            &quot;recent_results&quot;: self.recent_results,&#10;            &quot;recent_brier&quot;: self.recent_brier,&#10;            &quot;closed_trades&quot;: self.closed_trades[-2000:],&#10;            &quot;trade_events&quot;: self.trade_events[-5000:],&#10;            &quot;equity_curve&quot;: self.equity_curve[-5000:],&#10;            &quot;paper_runtime&quot;: self.paper_runtime,&#10;            &quot;saved_at&quot;: datetime.now(timezone.utc).isoformat(),&#10;        }&#10;        joblib.dump(payload, self.state_path)&#10;        self.last_saved_ts = now&#10;&#10;    def record_equity(self, equity: float, ts_ms: Optional[int] = None) -&gt; None:&#10;        ts = int(ts_ms if ts_ms is not None else time.time() * 1000)&#10;        e = float(equity)&#10;        if self.equity_curve and abs(float(self.equity_curve[-1][&quot;equity&quot;]) - e) &lt; 1e-12:&#10;            return&#10;        self.equity_curve.append({&quot;ts&quot;: float(ts), &quot;equity&quot;: e})&#10;        self.equity_curve = self.equity_curve[-5000:]&#10;&#10;    def set_runtime_state(&#10;        self,&#10;        paper_equity: float,&#10;        positions: Dict[str, Position],&#10;        entry_features: Dict[Tuple[str, int], List[float]],&#10;    ) -&gt; None:&#10;        self.paper_runtime = {&#10;            &quot;equity&quot;: float(paper_equity),&#10;            &quot;positions&quot;: {coin: asdict(pos) for coin, pos in positions.items()},&#10;            &quot;entry_features&quot;: {&#10;                f&quot;{coin}:{entry_ts}&quot;: [float(v) for v in feats]&#10;                for (coin, entry_ts), feats in entry_features.items()&#10;            },&#10;        }&#10;&#10;    def restore_runtime_state(self) -&gt; Tuple[Optional[float], Dict[str, Position], Dict[Tuple[str, int], List[float]]]:&#10;        equity_raw = self.paper_runtime.get(&quot;equity&quot;)&#10;        equity = float(equity_raw) if equity_raw is not None else None&#10;&#10;        restored_positions: Dict[str, Position] = {}&#10;        for coin, raw in (self.paper_runtime.get(&quot;positions&quot;) or {}).items():&#10;            try:&#10;                restored_positions[coin] = Position(&#10;                    coin=str(raw[&quot;coin&quot;]),&#10;                    side=str(raw[&quot;side&quot;]),&#10;                    entry=float(raw[&quot;entry&quot;]),&#10;                    size=float(raw[&quot;size&quot;]),&#10;                    sl=float(raw[&quot;sl&quot;]),&#10;                    tp=float(raw[&quot;tp&quot;]),&#10;                    entry_ts=int(raw[&quot;entry_ts&quot;]),&#10;                    entry_features=[float(v) for v in raw.get(&quot;entry_features&quot;, [])],&#10;                    entry_p_long=float(raw.get(&quot;entry_p_long&quot;, 0.5)),&#10;                )&#10;            except Exception:&#10;                continue&#10;&#10;        restored_features: Dict[Tuple[str, int], List[float]] = {}&#10;        for key, vals in (self.paper_runtime.get(&quot;entry_features&quot;) or {}).items():&#10;            try:&#10;                coin, entry_ts_str = key.split(&quot;:&quot;, 1)&#10;                restored_features[(coin, int(entry_ts_str))] = [float(v) for v in vals]&#10;            except Exception:&#10;                continue&#10;&#10;        return equity, restored_positions, restored_features&#10;&#10;    def record_trade_event(self, event_type: str, payload: Dict[str, Any]) -&gt; None:&#10;        self.trade_events.append(&#10;            {&#10;                &quot;event&quot;: event_type,&#10;                &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;                **payload,&#10;            }&#10;        )&#10;        self.trade_events = self.trade_events[-5000:]&#10;&#10;    def update_after_trade(self, trade: ClosedTrade, entry_features: List[float]) -&gt; None:&#10;        label = 1 if trade.pnl_usd &gt; 0 else 0&#10;        x = np.asarray(entry_features, dtype=float).reshape(1, -1)&#10;        y = np.asarray([label], dtype=int)&#10;&#10;        model = self.models[trade.coin]&#10;        if not self.model_ready[trade.coin]:&#10;            model.partial_fit(x, y, classes=np.array([0, 1]))&#10;            self.model_ready[trade.coin] = True&#10;        else:&#10;            model.partial_fit(x, y)&#10;&#10;        stats = self.trade_stats[trade.coin]&#10;        stats[&quot;trades&quot;] += 1&#10;        if label == 1:&#10;            stats[&quot;wins&quot;] += 1&#10;            stats[&quot;gross_profit&quot;] += max(0.0, trade.pnl_usd)&#10;        else:&#10;            stats[&quot;losses&quot;] += 1&#10;            stats[&quot;gross_loss&quot;] += abs(min(0.0, trade.pnl_usd))&#10;&#10;        p = float(np.clip(trade.entry_p_long, 0.0, 1.0))&#10;        brier = float((p - label) ** 2)&#10;        ml = self.ml_stats[trade.coin]&#10;        ml[&quot;count&quot;] += 1&#10;        ml[&quot;brier_sum&quot;] += brier&#10;        self.recent_brier[trade.coin].append(brier)&#10;        self.recent_brier[trade.coin] = self.recent_brier[trade.coin][-100:]&#10;&#10;        self.recent_results[trade.coin].append(label)&#10;        self.recent_results[trade.coin] = self.recent_results[trade.coin][-30:]&#10;&#10;        # Adaptive threshold: lower threshold when recent win rate is strong, raise when weak.&#10;        recent = self.recent_results[trade.coin]&#10;        if recent:&#10;            wr = float(sum(recent)) / len(recent)&#10;            target = 0.56 + (0.60 - wr) * 0.25&#10;            self.thresholds[trade.coin] = float(np.clip(target, 0.52, 0.65))&#10;&#10;        self.closed_trades.append(asdict(trade))&#10;        self.save()&#10;&#10;    def predict_long_probability(self, coin: str, features: List[float]) -&gt; float:&#10;        if not self.model_ready.get(coin, False):&#10;            return 0.50&#10;        x = np.asarray(features, dtype=float).reshape(1, -1)&#10;        try:&#10;            p = float(self.models[coin].predict_proba(x)[0][1])&#10;        except Exception:&#10;            return 0.50&#10;        if math.isnan(p):&#10;            return 0.50&#10;        return float(np.clip(p, 0.01, 0.99))&#10;&#10;    def global_metrics(self) -&gt; Dict[str, float]:&#10;        trades = wins = 0&#10;        gross_profit = gross_loss = 0.0&#10;        for coin in ALLOWED_COINS:&#10;            s = self.trade_stats[coin]&#10;            trades += int(s[&quot;trades&quot;])&#10;            wins += int(s[&quot;wins&quot;])&#10;            gross_profit += float(s[&quot;gross_profit&quot;])&#10;            gross_loss += float(s[&quot;gross_loss&quot;])&#10;        win_rate = (wins / trades) if trades else 0.0&#10;        profit_factor = (gross_profit / gross_loss) if gross_loss &gt; 0 else (10.0 if gross_profit &gt; 0 else 0.0)&#10;        return {&#10;            &quot;trades&quot;: float(trades),&#10;            &quot;win_rate&quot;: float(win_rate),&#10;            &quot;profit_factor&quot;: float(profit_factor),&#10;            &quot;gross_profit&quot;: float(gross_profit),&#10;            &quot;gross_loss&quot;: float(gross_loss),&#10;        }&#10;&#10;    def ml_progress_metrics(self) -&gt; Dict[str, Any]:&#10;        total_count = 0.0&#10;        total_brier_sum = 0.0&#10;        per_coin: Dict[str, Any] = {}&#10;&#10;        all_recent_brier: List[float] = []&#10;        for coin in ALLOWED_COINS:&#10;            stat = self.ml_stats.get(coin, {})&#10;            cnt = float(stat.get(&quot;count&quot;, 0.0))&#10;            bsum = float(stat.get(&quot;brier_sum&quot;, 0.0))&#10;            total_count += cnt&#10;            total_brier_sum += bsum&#10;&#10;            recent = [float(v) for v in self.recent_brier.get(coin, [])]&#10;            all_recent_brier.extend(recent)&#10;            per_coin[coin] = {&#10;                &quot;samples&quot;: int(cnt),&#10;                &quot;brier&quot;: (bsum / cnt) if cnt &gt; 0 else None,&#10;                &quot;brier_rolling_50&quot;: (float(np.mean(recent[-50:])) if recent else None),&#10;                &quot;brier_rolling_100&quot;: (float(np.mean(recent)) if recent else None),&#10;            }&#10;&#10;        brier_global = (total_brier_sum / total_count) if total_count &gt; 0 else None&#10;        brier_rolling_50 = float(np.mean(all_recent_brier[-50:])) if all_recent_brier else None&#10;        brier_rolling_100 = float(np.mean(all_recent_brier[-100:])) if all_recent_brier else None&#10;&#10;        pnl = [float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in self.closed_trades]&#10;        expectancy_30 = float(np.mean(pnl[-30:])) if pnl else None&#10;        expectancy_100 = float(np.mean(pnl[-100:])) if pnl else None&#10;&#10;        max_drawdown_pct = None&#10;        peak = None&#10;        max_dd = 0.0&#10;        for point in self.equity_curve:&#10;            eq = float(point.get(&quot;equity&quot;, 0.0))&#10;            if peak is None or eq &gt; peak:&#10;                peak = eq&#10;            if peak and peak &gt; 0:&#10;                dd = (peak - eq) / peak&#10;                if dd &gt; max_dd:&#10;                    max_dd = dd&#10;        if peak is not None:&#10;            max_drawdown_pct = max_dd * 100.0&#10;&#10;        return {&#10;            &quot;samples&quot;: int(total_count),&#10;            &quot;brier&quot;: brier_global,&#10;            &quot;brier_rolling_50&quot;: brier_rolling_50,&#10;            &quot;brier_rolling_100&quot;: brier_rolling_100,&#10;            &quot;expectancy_usd_30&quot;: expectancy_30,&#10;            &quot;expectancy_usd_100&quot;: expectancy_100,&#10;            &quot;max_drawdown_pct&quot;: max_drawdown_pct,&#10;            &quot;per_coin&quot;: per_coin,&#10;        }&#10;&#10;    def consistently_profitable(self) -&gt; bool:&#10;        m = self.global_metrics()&#10;        return m[&quot;trades&quot;] &gt;= 30 and m[&quot;win_rate&quot;] &gt;= 0.58 and m[&quot;profit_factor&quot;] &gt;= 1.25&#10;&#10;&#10;class PaperBroker:&#10;    def __init__(self, starting_equity: float = 10000.0, taker_fee_bps: float = 4.32):&#10;        self.equity = float(starting_equity)&#10;        self.taker_fee = taker_fee_bps / 10_000.0&#10;        self.positions: Dict[str, Position] = {}&#10;        self.closed: List[ClosedTrade] = []&#10;&#10;    def has_position(self, coin: str) -&gt; bool:&#10;        return coin in self.positions&#10;&#10;    def open_position(self, pos: Position) -&gt; None:&#10;        self.positions[pos.coin] = pos&#10;&#10;    def maybe_close(self, coin: str, candle: Dict[str, float], now_ts: int) -&gt; Optional[ClosedTrade]:&#10;        pos = self.positions.get(coin)&#10;        if not pos:&#10;            return None&#10;&#10;        high = candle[&quot;high&quot;]&#10;        low = candle[&quot;low&quot;]&#10;        close = candle[&quot;close&quot;]&#10;&#10;        exit_px = close&#10;        reason = &quot;model_exit&quot;&#10;&#10;        if pos.side == &quot;long&quot;:&#10;            if low &lt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif high &gt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;        else:&#10;            if high &gt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif low &lt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;&#10;        if reason == &quot;model_exit&quot;:&#10;            return None&#10;&#10;        gross = (exit_px - pos.entry) * pos.size if pos.side == &quot;long&quot; else (pos.entry - exit_px) * pos.size&#10;        fees = (pos.entry * pos.size + exit_px * pos.size) * self.taker_fee&#10;        pnl = gross - fees&#10;        pnl_pct = (pnl / max(pos.entry * pos.size, 1e-9)) * 100.0&#10;&#10;        trade = ClosedTrade(&#10;            coin=coin,&#10;            side=pos.side,&#10;            entry=pos.entry,&#10;            exit=exit_px,&#10;            size=pos.size,&#10;            pnl_usd=pnl,&#10;            pnl_pct=pnl_pct,&#10;            reason=reason,&#10;            entry_ts=pos.entry_ts,&#10;            exit_ts=now_ts,&#10;            entry_p_long=pos.entry_p_long,&#10;        )&#10;        self.equity += pnl&#10;        self.closed.append(trade)&#10;        del self.positions[coin]&#10;        return trade&#10;&#10;&#10;def parse_candles(raw: List[Dict[str, Any]]) -&gt; pd.DataFrame:&#10;    rows = []&#10;    for c in raw:&#10;        rows.append(&#10;            {&#10;                &quot;ts&quot;: int(c.get(&quot;t&quot;) or c.get(&quot;time&quot;) or c.get(&quot;T&quot;) or 0),&#10;                &quot;open&quot;: float(c.get(&quot;o&quot;) or c.get(&quot;open&quot;) or c.get(&quot;openPx&quot;) or 0.0),&#10;                &quot;high&quot;: float(c.get(&quot;h&quot;) or c.get(&quot;high&quot;) or c.get(&quot;highPx&quot;) or 0.0),&#10;                &quot;low&quot;: float(c.get(&quot;l&quot;) or c.get(&quot;low&quot;) or c.get(&quot;lowPx&quot;) or 0.0),&#10;                &quot;close&quot;: float(c.get(&quot;c&quot;) or c.get(&quot;close&quot;) or c.get(&quot;closePx&quot;) or 0.0),&#10;                &quot;volume&quot;: float(c.get(&quot;v&quot;) or c.get(&quot;volume&quot;) or 0.0),&#10;            }&#10;        )&#10;    df = pd.DataFrame(rows).sort_values(&quot;ts&quot;)&#10;    df = df[(df[&quot;close&quot;] &gt; 0) &amp; (df[&quot;high&quot;] &gt; 0) &amp; (df[&quot;low&quot;] &gt; 0)]&#10;    return df.reset_index(drop=True)&#10;&#10;&#10;def build_features(df: pd.DataFrame) -&gt; Optional[List[float]]:&#10;    if len(df) &lt; 60:&#10;        return None&#10;&#10;    close = df[&quot;close&quot;]&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    vol = df[&quot;volume&quot;].replace(0, np.nan).ffill().fillna(1.0)&#10;&#10;    ret_1 = close.pct_change(1)&#10;    ret_3 = close.pct_change(3)&#10;    ret_8 = close.pct_change(8)&#10;    ema_12 = close.ewm(span=12).mean()&#10;    ema_26 = close.ewm(span=26).mean()&#10;    macd = (ema_12 - ema_26) / close&#10;&#10;    # ATR-like normalized range.&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr14 = tr.rolling(14).mean() / close&#10;&#10;    delta = close.diff()&#10;    up = delta.clip(lower=0).rolling(14).mean()&#10;    down = (-delta.clip(upper=0)).rolling(14).mean()&#10;    rs = up / down.replace(0, np.nan)&#10;    rsi = 100.0 - (100.0 / (1.0 + rs))&#10;    rsi_scaled = (rsi - 50.0) / 50.0&#10;&#10;    vol_z = (np.log(vol) - np.log(vol).rolling(20).mean()) / np.log(vol).rolling(20).std()&#10;&#10;    feat = pd.DataFrame(&#10;        {&#10;            &quot;ret_1&quot;: ret_1,&#10;            &quot;ret_3&quot;: ret_3,&#10;            &quot;ret_8&quot;: ret_8,&#10;            &quot;macd&quot;: macd,&#10;            &quot;atr14&quot;: atr14,&#10;            &quot;rsi&quot;: rsi_scaled,&#10;            &quot;vol_z&quot;: vol_z,&#10;        }&#10;    ).replace([np.inf, -np.inf], np.nan)&#10;&#10;    last = feat.iloc[-1].fillna(0.0)&#10;    return [float(last[k]) for k in [&quot;ret_1&quot;, &quot;ret_3&quot;, &quot;ret_8&quot;, &quot;macd&quot;, &quot;atr14&quot;, &quot;rsi&quot;, &quot;vol_z&quot;]]&#10;&#10;&#10;def atr_for_risk(df: pd.DataFrame) -&gt; float:&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    close = df[&quot;close&quot;]&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr = float(tr.rolling(14).mean().iloc[-1])&#10;    if not np.isfinite(atr) or atr &lt;= 0:&#10;        atr = float(close.iloc[-1] * 0.01)&#10;    return atr&#10;&#10;&#10;class AdaptiveHyperliquidBot:&#10;    def __init__(&#10;        self,&#10;        interval: str,&#10;        state_file: str,&#10;        report_file: str,&#10;        starting_equity: float,&#10;        risk_per_trade: float,&#10;        poll_seconds: int,&#10;        exploration_rate: float,&#10;        enable_live: bool,&#10;        require_live_approval: bool,&#10;        live_approval_mode: str,&#10;        live_approval_file: str,&#10;        live_startup_test: bool,&#10;        live_startup_test_coin: str,&#10;        live_startup_test_notional: float,&#10;        hf_advisor_url: str,&#10;        hf_advisor_token: str,&#10;        hf_advisor_mode: str,&#10;        hf_advisor_min_confidence: float,&#10;        hf_advisor_blend_weight: float,&#10;        hf_advisor_timeout_seconds: float,&#10;        telegram_ml_report_hours: float,&#10;        telegram_ml_report_min_trades: int,&#10;        health_host: str,&#10;        health_port: int,&#10;    ):&#10;        self.interval = interval&#10;        self.poll_seconds = poll_seconds&#10;        self.risk_per_trade = risk_per_trade&#10;        self.exploration_rate = float(np.clip(exploration_rate, 0.0, 0.5))&#10;        self.enable_live = enable_live&#10;        self.require_live_approval = require_live_approval&#10;        self.live_approval_mode = live_approval_mode&#10;        self.live_approval_file = live_approval_file&#10;        self.live_startup_test = live_startup_test&#10;        self.live_startup_test_coin = live_startup_test_coin&#10;        self.live_startup_test_notional = float(max(10.0, live_startup_test_notional))&#10;        self.hf_advisor_mode = (hf_advisor_mode or &quot;off&quot;).strip().lower()&#10;        self.hf_advisor_min_confidence = float(np.clip(hf_advisor_min_confidence, 0.0, 1.0))&#10;        self.hf_advisor_blend_weight = float(np.clip(hf_advisor_blend_weight, 0.0, 1.0))&#10;        self.hf_advisor = HuggingFaceAdvisor(&#10;            endpoint_url=hf_advisor_url,&#10;            api_token=hf_advisor_token,&#10;            timeout_seconds=hf_advisor_timeout_seconds,&#10;        )&#10;        self.telegram_ml_report_hours = float(max(0.0, telegram_ml_report_hours))&#10;        self.telegram_ml_report_min_trades = int(max(0, telegram_ml_report_min_trades))&#10;        self.health_host = health_host&#10;        self.health_port = int(health_port)&#10;&#10;        self.api = HyperliquidAPI(verify_ssl=True)&#10;        self.learning = LearningState(state_file)&#10;        self.paper = PaperBroker(starting_equity=starting_equity)&#10;        self.report_file = report_file&#10;        self.notifier = TelegramNotifier(disable_on_error=True)&#10;        self.notifications_enabled = bool(&#10;            os.environ.get(&quot;TELEGRAM_BOT_TOKEN&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;TELEGRAM_CHAT_ID&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;PROXY_URL&quot;, &quot;&quot;).strip()&#10;        )&#10;        self._last_summary_date: Optional[str] = None&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(&quot;Adaptive ML bot started. Trade notifications are enabled.&quot;)&#10;        else:&#10;            logger.info(&quot;Telegram notifications disabled (need TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID, PROXY_URL).&quot;)&#10;&#10;        self._running = True&#10;        self._live_clients: Optional[Dict[str, Any]] = None&#10;        self._live_readiness_alerted = False&#10;        self._live_startup_test_done = False&#10;        self._live_startup_test_passed: Optional[bool] = None&#10;        self._http_server: Optional[ThreadingHTTPServer] = None&#10;        self._entry_features: Dict[Tuple[str, int], List[float]] = {}&#10;        self._last_ml_report_ts: float = 0.0&#10;        self._advisor_stats: Dict[str, int] = {&#10;            &quot;requests&quot;: 0,&#10;            &quot;errors&quot;: 0,&#10;            &quot;held_by_advisor&quot;: 0,&#10;            &quot;kept_model_signal&quot;: 0,&#10;            &quot;used_advisor_blend&quot;: 0,&#10;        }&#10;        self._restore_runtime_state()&#10;&#10;    def _interval_ms(self) -&gt; int:&#10;        if self.interval not in INTERVAL_MS:&#10;            raise ValueError(f&quot;Unsupported interval '{self.interval}'. Supported: {sorted(INTERVAL_MS)}&quot;)&#10;        return INTERVAL_MS[self.interval]&#10;&#10;    def stop(self, *_: Any) -&gt; None:&#10;        logger.info(&quot;Received shutdown signal, flushing learning state.&quot;)&#10;        self._running = False&#10;        if self._http_server is not None:&#10;            self._http_server.shutdown()&#10;            self._http_server.server_close()&#10;            self._http_server = None&#10;        self._persist_runtime_state()&#10;        self.learning.save(force=True)&#10;        self.write_report()&#10;&#10;    def status_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        approval = self._live_approval_status_details()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;            &quot;metrics&quot;: metrics,&#10;            &quot;ml_progress&quot;: self.learning.ml_progress_metrics(),&#10;            &quot;thresholds&quot;: self.learning.thresholds,&#10;            &quot;state_file&quot;: self.learning.state_path,&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_mode&quot;: self.live_approval_mode,&#10;            &quot;live_approval_granted&quot;: approval[&quot;granted&quot;],&#10;            &quot;live_approval_checks&quot;: approval,&#10;            &quot;live_startup_test_enabled&quot;: self.live_startup_test,&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;trade_events_recorded&quot;: len(self.learning.trade_events),&#10;            &quot;advisor&quot;: {&#10;                &quot;enabled&quot;: self.hf_advisor.enabled,&#10;                &quot;mode&quot;: self.hf_advisor_mode,&#10;                &quot;min_confidence&quot;: self.hf_advisor_min_confidence,&#10;                &quot;blend_weight&quot;: self.hf_advisor_blend_weight,&#10;                &quot;stats&quot;: self._advisor_stats,&#10;            },&#10;        }&#10;&#10;    def status_overview_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        ml_progress = self.learning.ml_progress_metrics()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;trades&quot;: int(metrics[&quot;trades&quot;]),&#10;            &quot;win_rate&quot;: metrics[&quot;win_rate&quot;],&#10;            &quot;profit_factor&quot;: metrics[&quot;profit_factor&quot;],&#10;            &quot;brier_rolling_50&quot;: ml_progress[&quot;brier_rolling_50&quot;],&#10;            &quot;expectancy_usd_30&quot;: ml_progress[&quot;expectancy_usd_30&quot;],&#10;            &quot;max_drawdown_pct&quot;: ml_progress[&quot;max_drawdown_pct&quot;],&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_granted&quot;: self._live_approval_granted(),&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;advisor_enabled&quot;: self.hf_advisor.enabled,&#10;            &quot;advisor_mode&quot;: self.hf_advisor_mode,&#10;        }&#10;&#10;    def _apply_hf_advisor(&#10;        self,&#10;        coin: str,&#10;        features: List[float],&#10;        candle: Dict[str, float],&#10;        now_ts: int,&#10;        p_long: float,&#10;        threshold: float,&#10;        side: Optional[str],&#10;    ) -&gt; Tuple[Optional[str], str, Optional[float], Optional[str], Optional[float]]:&#10;        mode = self.hf_advisor_mode&#10;        if mode == &quot;off&quot; or not self.hf_advisor.enabled:&#10;            return side, &quot;model&quot;, p_long, None, None&#10;&#10;        if mode not in {&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;}:&#10;            return side, &quot;model&quot;, p_long, None, None&#10;&#10;        self._advisor_stats[&quot;requests&quot;] += 1&#10;        advice = self.hf_advisor.advise(&#10;            coin=coin,&#10;            interval=self.interval,&#10;            features=features,&#10;            candle=candle,&#10;            timestamp_ms=now_ts,&#10;        )&#10;        if advice is None:&#10;            return side, &quot;model&quot;, p_long, None, None&#10;&#10;        if advice.error:&#10;            self._advisor_stats[&quot;errors&quot;] += 1&#10;            return side, &quot;model&quot;, p_long, advice.side, advice.confidence&#10;&#10;        advisor_side = advice.side&#10;        advisor_conf = float(advice.confidence)&#10;        if mode == &quot;blend&quot; and advice.p_long is not None:&#10;            blended_p = (1.0 - self.hf_advisor_blend_weight) * p_long + self.hf_advisor_blend_weight * float(advice.p_long)&#10;            blended_p = float(np.clip(blended_p, 0.01, 0.99))&#10;            blended_side: Optional[str] = None&#10;            if blended_p &gt;= threshold:&#10;                blended_side = &quot;long&quot;&#10;            elif blended_p &lt;= (1.0 - threshold):&#10;                blended_side = &quot;short&quot;&#10;            if blended_side is None:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_hold_blend&quot;, blended_p, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;used_advisor_blend&quot;] += 1&#10;            return blended_side, &quot;advisor_blend&quot;, blended_p, advisor_side, advisor_conf&#10;&#10;        if side is None:&#10;            return None, &quot;model&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;confirm&quot;:&#10;            if advisor_side == side and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;                return side, &quot;advisor_confirm&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;            return None, &quot;advisor_block_confirm&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;veto&quot;:&#10;            opposite = (side == &quot;long&quot; and advisor_side == &quot;short&quot;) or (side == &quot;short&quot; and advisor_side == &quot;long&quot;)&#10;            if opposite and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_veto&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;            return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        return side, &quot;model&quot;, p_long, advisor_side, advisor_conf&#10;&#10;    def open_trades_payload(self) -&gt; Dict[str, Any]:&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;        }&#10;&#10;    def _restore_runtime_state(self) -&gt; None:&#10;        equity, positions, entry_features = self.learning.restore_runtime_state()&#10;        if equity is not None:&#10;            self.paper.equity = float(equity)&#10;        if positions:&#10;            self.paper.positions = positions&#10;        if entry_features:&#10;            self._entry_features = entry_features&#10;        if equity is not None or positions:&#10;            logger.info(&#10;                &quot;Restored paper runtime state: equity=%.2f open_positions=%d tracked_entries=%d&quot;,&#10;                self.paper.equity,&#10;                len(self.paper.positions),&#10;                len(self._entry_features),&#10;            )&#10;&#10;    def _persist_runtime_state(self) -&gt; None:&#10;        self.learning.record_equity(self.paper.equity)&#10;        self.learning.set_runtime_state(&#10;            paper_equity=self.paper.equity,&#10;            positions=self.paper.positions,&#10;            entry_features=self._entry_features,&#10;        )&#10;&#10;    def _live_approval_granted(self) -&gt; bool:&#10;        return self._live_approval_status_details()[&quot;granted&quot;]&#10;&#10;    def _live_approval_status_details(self) -&gt; Dict[str, Any]:&#10;        env_approved = os.environ.get(&quot;ALLOW_LIVE_TRADING&quot;, &quot;&quot;).strip().lower() in {&quot;1&quot;, &quot;true&quot;, &quot;yes&quot;, &quot;y&quot;}&#10;        file_approved = bool(self.live_approval_file and os.path.exists(self.live_approval_file))&#10;        mode = (self.live_approval_mode or &quot;either&quot;).strip().lower()&#10;        missing: List[str] = []&#10;        if mode == &quot;both&quot;:&#10;            granted = env_approved and file_approved&#10;            if not env_approved:&#10;                missing.append(&quot;env:ALLOW_LIVE_TRADING&quot;)&#10;            if not file_approved:&#10;                missing.append(f&quot;file:{self.live_approval_file}&quot;)&#10;        else:&#10;            granted = env_approved or file_approved&#10;            if not granted:&#10;                missing = [&quot;env:ALLOW_LIVE_TRADING&quot;, f&quot;file:{self.live_approval_file}&quot;]&#10;        return {&#10;            &quot;mode&quot;: mode,&#10;            &quot;env_approved&quot;: env_approved,&#10;            &quot;file_approved&quot;: file_approved,&#10;            &quot;missing&quot;: missing,&#10;            &quot;granted&quot;: granted,&#10;        }&#10;&#10;    def _maybe_notify_live_readiness(self) -&gt; None:&#10;        if self._live_readiness_alerted or not self.learning.consistently_profitable():&#10;            return&#10;        m = self.learning.global_metrics()&#10;        msg = (&#10;            &quot;Paper performance gate reached.\n&quot;&#10;            f&quot;Trades: {int(m['trades'])}, win rate: {m['win_rate'] * 100.0:.1f}%, PF: {m['profit_factor']:.2f}.\n&quot;&#10;            &quot;Grant live approval to arm live mode. &quot;&#10;            f&quot;Approval mode={self.live_approval_mode}. &quot;&#10;            f&quot;Set ALLOW_LIVE_TRADING=1 and/or create {self.live_approval_file} (based on mode).&quot;&#10;        )&#10;        logger.warning(msg)&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(msg)&#10;        self._live_readiness_alerted = True&#10;&#10;    def _live_order_filled(self, result: Any) -&gt; bool:&#10;        if not isinstance(result, dict):&#10;            return False&#10;        try:&#10;            statuses = result.get(&quot;response&quot;, {}).get(&quot;data&quot;, {}).get(&quot;statuses&quot;, [])&#10;            errors = [s.get(&quot;error&quot;) for s in statuses if &quot;error&quot; in s]&#10;            if errors:&#10;                return False&#10;            filled_sz = sum(float(s[&quot;filled&quot;].get(&quot;totalSz&quot;, 0.0)) for s in statuses if &quot;filled&quot; in s)&#10;            return filled_sz &gt; 0&#10;        except Exception:&#10;            return False&#10;&#10;    def _calc_test_size(self, coin: str, px: float, min_notional: float) -&gt; float:&#10;        sz_dec = self.api.get_sz_decimals(coin)&#10;        step = 10 ** (-sz_dec)&#10;        raw = min_notional / max(px, 1e-9)&#10;        steps = max(1, int(math.ceil(raw / step)))&#10;        return float(round(steps * step, sz_dec))&#10;&#10;    def _run_live_startup_test(self) -&gt; None:&#10;        if self._live_clients is None or self._live_startup_test_done or not self.live_startup_test:&#10;            return&#10;&#10;        self._live_startup_test_done = True&#10;        coin = self.live_startup_test_coin&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;&#10;        try:&#10;            df = self._fetch_df(coin, lookback_bars=120)&#10;            if df is None:&#10;                raise RuntimeError(f&quot;No candle data available for startup live test on {coin}&quot;)&#10;&#10;            entry_px = float(df.iloc[-1][&quot;close&quot;])&#10;            test_size = self._calc_test_size(coin=coin, px=entry_px, min_notional=self.live_startup_test_notional)&#10;&#10;            logger.warning(&#10;                &quot;Running startup live order test: BUY then SELL %s size=%.8f around px=%.4f&quot;,&#10;                coin,&#10;                test_size,&#10;                entry_px,&#10;            )&#10;&#10;            buy_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=True,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;            time.sleep(1.2)&#10;            sell_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=False,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;&#10;            buy_ok = self._live_order_filled(buy_result)&#10;            sell_ok = self._live_order_filled(sell_result)&#10;            self._live_startup_test_passed = bool(buy_ok and sell_ok)&#10;&#10;            if self._live_startup_test_passed:&#10;                msg = f&quot;Startup live order test passed on {coin} (open+close filled).&quot;&#10;                logger.warning(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_info(msg)&#10;            else:&#10;                msg = f&quot;Startup live order test failed on {coin}. Review logs/order responses before trusting live mirroring.&quot;&#10;                logger.error(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;        except Exception as exc:&#10;            self._live_startup_test_passed = False&#10;            msg = f&quot;Startup live order test exception: {exc}&quot;&#10;            logger.error(msg)&#10;            if self.notifications_enabled:&#10;                self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;&#10;    def _start_health_server(self) -&gt; None:&#10;        bot = self&#10;&#10;        class HealthHandler(BaseHTTPRequestHandler):&#10;            def _write_json(self, payload: Dict[str, Any], status: int = 200) -&gt; None:&#10;                body = json.dumps(payload).encode(&quot;utf-8&quot;)&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, str(len(body)))&#10;                self.end_headers()&#10;                self.wfile.write(body)&#10;&#10;            def _write_head(self, status: int = 200) -&gt; None:&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, &quot;0&quot;)&#10;                self.end_headers()&#10;&#10;            def do_GET(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;):&#10;                    self._write_json({&quot;status&quot;: &quot;ok&quot;, &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;})&#10;                    return&#10;                if self.path == &quot;/status&quot;:&#10;                    self._write_json(bot.status_overview_payload())&#10;                    return&#10;                if self.path == &quot;/status/full&quot;:&#10;                    self._write_json(bot.status_payload())&#10;                    return&#10;                if self.path == &quot;/trades&quot;:&#10;                    self._write_json(bot.open_trades_payload())&#10;                    return&#10;                self._write_json({&quot;status&quot;: &quot;not_found&quot;}, status=404)&#10;&#10;            def do_HEAD(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;, &quot;/status&quot;, &quot;/status/full&quot;, &quot;/trades&quot;):&#10;                    self._write_head(status=200)&#10;                    return&#10;                self._write_head(status=404)&#10;&#10;            def log_message(self, format: str, *args: Any) -&gt; None:&#10;                logger.debug(&quot;health_http: &quot; + format, *args)&#10;&#10;        server = ThreadingHTTPServer((self.health_host, self.health_port), HealthHandler)&#10;        self._http_server = server&#10;        t = threading.Thread(target=server.serve_forever, daemon=True)&#10;        t.start()&#10;        logger.info(&quot;Health endpoint listening on http://%s:%s&quot;, self.health_host, self.health_port)&#10;&#10;    def _fetch_df(self, coin: str, lookback_bars: int = 200) -&gt; Optional[pd.DataFrame]:&#10;        start = int((datetime.now(timezone.utc) - timedelta(milliseconds=self._interval_ms() * lookback_bars)).timestamp() * 1000)&#10;        raw = self.api.get_candles(coin=coin, interval=self.interval, start_time=start)&#10;        if not raw:&#10;            return None&#10;        df = parse_candles(raw)&#10;        if len(df) &lt; 80:&#10;            return None&#10;        return df&#10;&#10;    def _size_for_trade(self, entry: float, stop: float) -&gt; float:&#10;        max_risk_usd = self.paper.equity * self.risk_per_trade&#10;        stop_distance = abs(entry - stop)&#10;        if stop_distance &lt;= 0:&#10;            return 0.0&#10;        size = max_risk_usd / stop_distance&#10;        max_notional = self.paper.equity * 0.25&#10;        if size * entry &gt; max_notional:&#10;            size = max_notional / entry&#10;        return float(max(size, 0.0))&#10;&#10;    def _maybe_prepare_live(self) -&gt; None:&#10;        if not self.enable_live:&#10;            return&#10;        if not self.learning.consistently_profitable():&#10;            return&#10;        if self.require_live_approval and not self._live_approval_granted():&#10;            return&#10;        if self._live_clients is not None:&#10;            return&#10;&#10;        pk = os.environ.get(&quot;HL_PRIVATE_KEY&quot;, &quot;&quot;).strip()&#10;        acct = os.environ.get(&quot;HL_ACCOUNT_ADDRESS&quot;, &quot;&quot;).strip() or None&#10;        if not pk:&#10;            logger.warning(&quot;Live mode requested and paper criteria met, but HL_PRIVATE_KEY is missing.&quot;)&#10;            return&#10;&#10;        try:&#10;            self._live_clients = self.api.create_live_execution_clients(private_key=pk, account_address=acct)&#10;            logger.warning(&quot;Paper criteria met. Live execution is now armed.&quot;)&#10;            self._run_live_startup_test()&#10;        except Exception as exc:&#10;            logger.error(&quot;Failed to initialize live execution clients: %s&quot;, exc)&#10;            self._live_clients = None&#10;&#10;    def _place_live_mirror(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if self._live_clients is None:&#10;            return&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;        is_buy = side == &quot;long&quot;&#10;        try:&#10;            self.api.place_market_order(exchange=exchange, coin=coin, is_buy=is_buy, sz=size, px=entry, sl=sl, tp=tp)&#10;        except Exception as exc:&#10;            logger.error(&quot;Live mirror order failed for %s: %s&quot;, coin, exc)&#10;&#10;    def _notify_trade_open(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        rr: Optional[float] = None&#10;        risk = abs(entry - sl)&#10;        reward = abs(tp - entry)&#10;        if risk &gt; 0:&#10;            rr = reward / risk&#10;        self.notifier.send_trade(&#10;            direction=side.upper(),&#10;            coin=coin,&#10;            entry=entry,&#10;            sl=sl,&#10;            tp=tp,&#10;            sz=size,&#10;            notional=entry * size,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;            rr=rr,&#10;            order_type=&quot;paper&quot;,&#10;        )&#10;&#10;    def _notify_trade_close(self, closed: ClosedTrade) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        self.notifier.send_exit(&#10;            coin=closed.coin,&#10;            reason=closed.reason,&#10;            pnl_pct=closed.pnl_pct,&#10;            pnl_usdc=closed.pnl_usd,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;        )&#10;&#10;    def _maybe_send_daily_summary(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        now = datetime.now(timezone.utc)&#10;        date_key = now.date().isoformat()&#10;        if now.hour != 0 or self._last_summary_date == date_key:&#10;            return&#10;&#10;        since_ms = int((now - timedelta(days=1)).timestamp() * 1000)&#10;        day_trades = [&#10;            t for t in self.learning.closed_trades&#10;            if int(t.get(&quot;exit_ts&quot;, 0)) &gt;= since_ms&#10;        ]&#10;        day_wins = sum(1 for t in day_trades if float(t.get(&quot;pnl_usd&quot;, 0.0)) &gt; 0)&#10;        day_wr = (day_wins / len(day_trades) * 100.0) if day_trades else 0.0&#10;        day_pnl = sum(float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in day_trades)&#10;&#10;        overall = self.learning.global_metrics()&#10;        ml = self.learning.ml_progress_metrics()&#10;        summary = (&#10;            &quot;Midnight summary (UTC)\n&quot;&#10;            f&quot;24h trades: {len(day_trades)}\n&quot;&#10;            f&quot;24h win rate: {day_wr:.1f}%\n&quot;&#10;            f&quot;24h pnl: {day_pnl:+.2f} USDC\n&quot;&#10;            f&quot;Total trades: {int(overall['trades'])}\n&quot;&#10;            f&quot;Total win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Paper equity: {self.paper.equity:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%&quot;&#10;        )&#10;        self.notifier.send_info(summary)&#10;        self._last_summary_date = date_key&#10;&#10;    def _maybe_send_ml_progress_report(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        if self.telegram_ml_report_hours &lt;= 0:&#10;            return&#10;&#10;        now_ts = time.time()&#10;        period_s = self.telegram_ml_report_hours * 3600.0&#10;        if self._last_ml_report_ts &gt; 0 and (now_ts - self._last_ml_report_ts) &lt; period_s:&#10;            return&#10;&#10;        overall = self.learning.global_metrics()&#10;        trades = int(overall[&quot;trades&quot;])&#10;        if trades &lt; self.telegram_ml_report_min_trades:&#10;            return&#10;&#10;        ml = self.learning.ml_progress_metrics()&#10;        per_coin = ml.get(&quot;per_coin&quot;, {})&#10;        coin_parts = []&#10;        for coin in ALLOWED_COINS:&#10;            coin_brier = per_coin.get(coin, {}).get(&quot;brier_rolling_50&quot;)&#10;            coin_parts.append(f&quot;{coin}:{coin_brier}&quot;)&#10;        coin_brier_line = &quot; | &quot;.join(coin_parts)&#10;&#10;        msg = (&#10;            &quot;ML progress update\n&quot;&#10;            f&quot;Trades: {trades}\n&quot;&#10;            f&quot;Win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%\n&quot;&#10;            f&quot;Per-coin Brier(50): {coin_brier_line}&quot;&#10;        )&#10;        self.notifier.send_info(msg)&#10;        self._last_ml_report_ts = now_ts&#10;&#10;    def _trade_coin(self, coin: str) -&gt; None:&#10;        df = self._fetch_df(coin)&#10;        if df is None:&#10;            logger.warning(&quot;Not enough candle data for %s&quot;, coin)&#10;            return&#10;&#10;        candle = {&#10;            &quot;high&quot;: float(df.iloc[-1][&quot;high&quot;]),&#10;            &quot;low&quot;: float(df.iloc[-1][&quot;low&quot;]),&#10;            &quot;close&quot;: float(df.iloc[-1][&quot;close&quot;]),&#10;        }&#10;        now_ts = int(df.iloc[-1][&quot;ts&quot;])&#10;&#10;        closed = self.paper.maybe_close(coin, candle, now_ts)&#10;        if closed:&#10;            logger.info(&quot;CLOSED %s %s pnl=$%.2f (%+.2f%%) reason=%s&quot;, closed.coin, closed.side, closed.pnl_usd, closed.pnl_pct, closed.reason)&#10;            self._notify_trade_close(closed)&#10;            self.learning.record_trade_event(&quot;close&quot;, asdict(closed))&#10;            # Entry features are captured when the position opens and used as supervised labels on close.&#10;            features_map = getattr(self, &quot;_entry_features&quot;, {})&#10;            stored = features_map.get((coin, closed.entry_ts), [])&#10;            if stored:&#10;                self.learning.update_after_trade(closed, stored)&#10;                features_map.pop((coin, closed.entry_ts), None)&#10;            self._persist_runtime_state()&#10;&#10;        if self.paper.has_position(coin):&#10;            return&#10;&#10;        features = build_features(df)&#10;        if not features:&#10;            return&#10;&#10;        p_long = self.learning.predict_long_probability(coin, features)&#10;        threshold = self.learning.thresholds[coin]&#10;&#10;        side: Optional[str] = None&#10;        signal_reason = &quot;model&quot;&#10;&#10;        if not self.learning.model_ready.get(coin, False):&#10;            # Bootstrap mode: trade weak momentum direction to generate first labels.&#10;            side = &quot;long&quot; if features[1] &gt;= 0 else &quot;short&quot;&#10;            signal_reason = &quot;bootstrap&quot;&#10;        else:&#10;            if p_long &gt;= threshold:&#10;                side = &quot;long&quot;&#10;            elif p_long &lt;= (1.0 - threshold):&#10;                side = &quot;short&quot;&#10;&#10;            if side is None and random.random() &lt; self.exploration_rate:&#10;                side = &quot;long&quot; if p_long &gt;= 0.5 else &quot;short&quot;&#10;                signal_reason = &quot;exploration&quot;&#10;&#10;        side, signal_reason, p_long, advisor_side, advisor_conf = self._apply_hf_advisor(&#10;            coin=coin,&#10;            features=features,&#10;            candle=candle,&#10;            now_ts=now_ts,&#10;            p_long=p_long,&#10;            threshold=threshold,&#10;            side=side,&#10;        )&#10;&#10;        if not side:&#10;            if advisor_side is not None:&#10;                logger.info(&#10;                    &quot;%s HOLD p_long=%.3f threshold=%.3f advisor=%s conf=%.3f reason=%s&quot;,&#10;                    coin,&#10;                    p_long,&#10;                    threshold,&#10;                    advisor_side,&#10;                    advisor_conf if advisor_conf is not None else 0.0,&#10;                    signal_reason,&#10;                )&#10;            else:&#10;                logger.info(&quot;%s HOLD p_long=%.3f threshold=%.3f&quot;, coin, p_long, threshold)&#10;            return&#10;&#10;        entry = candle[&quot;close&quot;]&#10;        atr = atr_for_risk(df)&#10;        sl_mult = 1.3&#10;        tp_mult = 2.0&#10;&#10;        if side == &quot;long&quot;:&#10;            sl = max(entry - atr * sl_mult, 1e-8)&#10;            tp = entry + atr * tp_mult&#10;        else:&#10;            sl = entry + atr * sl_mult&#10;            tp = max(entry - atr * tp_mult, 1e-8)&#10;&#10;        size = self._size_for_trade(entry, sl)&#10;        if size &lt;= 0:&#10;            return&#10;&#10;        pos = Position(&#10;            coin=coin,&#10;            side=side,&#10;            entry=entry,&#10;            size=size,&#10;            sl=sl,&#10;            tp=tp,&#10;            entry_ts=now_ts,&#10;            entry_features=features,&#10;            entry_p_long=p_long,&#10;        )&#10;        self.paper.open_position(pos)&#10;        if not hasattr(self, &quot;_entry_features&quot;):&#10;            self._entry_features = {}&#10;        self._entry_features[(coin, now_ts)] = features&#10;        self.learning.record_trade_event(&#10;            &quot;open&quot;,&#10;            {&#10;                &quot;coin&quot;: coin,&#10;                &quot;side&quot;: side,&#10;                &quot;entry&quot;: entry,&#10;                &quot;size&quot;: size,&#10;                &quot;sl&quot;: sl,&#10;                &quot;tp&quot;: tp,&#10;                &quot;entry_ts&quot;: now_ts,&#10;                &quot;signal_reason&quot;: signal_reason,&#10;                &quot;p_long&quot;: p_long,&#10;                &quot;threshold&quot;: threshold,&#10;                &quot;advisor_side&quot;: advisor_side,&#10;                &quot;advisor_confidence&quot;: advisor_conf,&#10;            },&#10;        )&#10;        self._persist_runtime_state()&#10;&#10;        logger.info(&#10;            &quot;OPEN %s %s entry=%.4f sl=%.4f tp=%.4f size=%.5f p_long=%.3f th=%.3f reason=%s equity=%.2f&quot;,&#10;            coin,&#10;            side,&#10;            entry,&#10;            sl,&#10;            tp,&#10;            size,&#10;            p_long,&#10;            threshold,&#10;            signal_reason,&#10;            self.paper.equity,&#10;        )&#10;        self._notify_trade_open(coin, side, entry, sl, tp, size)&#10;&#10;        self._maybe_prepare_live()&#10;        self._place_live_mirror(coin, side, entry, sl, tp, size)&#10;&#10;    def write_report(self) -&gt; None:&#10;        payload = self.status_payload()&#10;        with open(self.report_file, &quot;w&quot;, encoding=&quot;utf-8&quot;) as f:&#10;            json.dump(payload, f, indent=2)&#10;&#10;    def run(self) -&gt; None:&#10;        signal.signal(signal.SIGINT, self.stop)&#10;        signal.signal(signal.SIGTERM, self.stop)&#10;&#10;        self._start_health_server()&#10;&#10;        logger.info(&quot;Starting adaptive bot (paper mode=%s, live enabled=%s)&quot;, True, self.enable_live)&#10;        logger.info(&quot;Universe: %s&quot;, &quot;, &quot;.join(ALLOWED_COINS))&#10;&#10;        while self._running:&#10;            try:&#10;                for coin in ALLOWED_COINS:&#10;                    self._trade_coin(coin)&#10;                self._persist_runtime_state()&#10;                self.learning.save()&#10;                self.write_report()&#10;&#10;                m = self.learning.global_metrics()&#10;                logger.info(&#10;                    &quot;Stats trades=%d win_rate=%.2f%% pf=%.2f paper_equity=%.2f profitable_gate=%s&quot;,&#10;                    int(m[&quot;trades&quot;]),&#10;                    m[&quot;win_rate&quot;] * 100.0,&#10;                    m[&quot;profit_factor&quot;],&#10;                    self.paper.equity,&#10;                    self.learning.consistently_profitable(),&#10;                )&#10;                self._maybe_notify_live_readiness()&#10;                self._maybe_prepare_live()&#10;                self._maybe_send_daily_summary()&#10;                self._maybe_send_ml_progress_report()&#10;            except Exception as exc:&#10;                logger.exception(&quot;Loop error: %s&quot;, exc)&#10;&#10;            time.sleep(self.poll_seconds)&#10;&#10;&#10;def build_arg_parser() -&gt; argparse.ArgumentParser:&#10;    p = argparse.ArgumentParser(description=&quot;Adaptive ML Hyperliquid paper trader&quot;)&#10;    p.add_argument(&quot;--interval&quot;, default=&quot;15m&quot;, choices=sorted(INTERVAL_MS.keys()))&#10;    p.add_argument(&quot;--state-file&quot;, default=&quot;learning_state.joblib&quot;)&#10;    p.add_argument(&quot;--report-file&quot;, default=&quot;paper_report.json&quot;)&#10;    p.add_argument(&quot;--starting-equity&quot;, type=float, default=10000.0)&#10;    p.add_argument(&quot;--risk-per-trade&quot;, type=float, default=0.01, help=&quot;Fraction of equity risked per position&quot;)&#10;    p.add_argument(&quot;--poll-seconds&quot;, type=int, default=45)&#10;    p.add_argument(&quot;--exploration-rate&quot;, type=float, default=0.08, help=&quot;Chance to take an exploratory trade when model signal is neutral&quot;)&#10;    p.add_argument(&quot;--health-host&quot;, default=os.environ.get(&quot;BOT_HEALTH_HOST&quot;, &quot;0.0.0.0&quot;))&#10;    p.add_argument(&quot;--health-port&quot;, type=int, default=int(os.environ.get(&quot;PORT&quot;, os.environ.get(&quot;BOT_HEALTH_PORT&quot;, &quot;7860&quot;))))&#10;    p.add_argument(&quot;--enable-live&quot;, action=&quot;store_true&quot;, help=&quot;Allow live mirror orders after paper criteria is met&quot;)&#10;    p.add_argument(&#10;        &quot;--require-live-approval&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=True,&#10;        help=&quot;Require manual approval before arming live mirror orders&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--live-approval-mode&quot;,&#10;        choices=[&quot;either&quot;, &quot;both&quot;],&#10;        default=&quot;either&quot;,&#10;        help=&quot;Manual approval policy: 'either' (env or file) or 'both' (env and file)&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-approval-file&quot;, default=&quot;allow_live_trading.flag&quot;, help=&quot;Approval file path to authorize live trading&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=False,&#10;        help=&quot;When live is armed, place a tiny BUY then SELL to verify live order execution&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-startup-test-coin&quot;, choices=sorted(ALLOWED_COINS), default=&quot;BTC&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test-notional&quot;,&#10;        type=float,&#10;        default=12.0,&#10;        help=&quot;Approx USDC notional used for startup live order test (min effectively 10)&quot;,&#10;    )&#10;    p.add_argument(&quot;--hf-advisor-url&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_URL&quot;, &quot;&quot;), help=&quot;Optional external advisor endpoint URL&quot;)&#10;    p.add_argument(&quot;--hf-advisor-token&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_TOKEN&quot;, &quot;&quot;), help=&quot;Optional bearer token for advisor endpoint&quot;)&#10;    p.add_argument(&#10;        &quot;--hf-advisor-mode&quot;,&#10;        choices=[&quot;off&quot;, &quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;],&#10;        default=os.environ.get(&quot;BOT_HF_ADVISOR_MODE&quot;, &quot;off&quot;),&#10;        help=&quot;off=model only, confirm=must match advisor, veto=advisor can block, blend=blend p_long with advisor p_long&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-min-confidence&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_MIN_CONFIDENCE&quot;, &quot;0.60&quot;)),&#10;        help=&quot;Minimum advisor confidence used by confirm/veto modes&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-blend-weight&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_BLEND_WEIGHT&quot;, &quot;0.35&quot;)),&#10;        help=&quot;Advisor p_long blending weight for blend mode [0..1]&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-timeout-seconds&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_TIMEOUT_SECONDS&quot;, &quot;4.0&quot;)),&#10;        help=&quot;HTTP timeout for advisor requests&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-hours&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_HOURS&quot;, &quot;6&quot;)),&#10;        help=&quot;Send periodic ML progress updates to Telegram every N hours (0 disables)&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-min-trades&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_MIN_TRADES&quot;, &quot;15&quot;)),&#10;        help=&quot;Minimum number of closed trades before periodic ML Telegram reports are sent&quot;,&#10;    )&#10;    return p&#10;&#10;&#10;def main() -&gt; None:&#10;    args = build_arg_parser().parse_args()&#10;    bot = AdaptiveHyperliquidBot(&#10;        interval=args.interval,&#10;        state_file=args.state_file,&#10;        report_file=args.report_file,&#10;        starting_equity=args.starting_equity,&#10;        risk_per_trade=args.risk_per_trade,&#10;        poll_seconds=args.poll_seconds,&#10;        exploration_rate=args.exploration_rate,&#10;        enable_live=args.enable_live,&#10;        require_live_approval=args.require_live_approval,&#10;        live_approval_mode=args.live_approval_mode,&#10;        live_approval_file=args.live_approval_file,&#10;        live_startup_test=args.live_startup_test,&#10;        live_startup_test_coin=args.live_startup_test_coin,&#10;        live_startup_test_notional=args.live_startup_test_notional,&#10;        hf_advisor_url=args.hf_advisor_url,&#10;        hf_advisor_token=args.hf_advisor_token,&#10;        hf_advisor_mode=args.hf_advisor_mode,&#10;        hf_advisor_min_confidence=args.hf_advisor_min_confidence,&#10;        hf_advisor_blend_weight=args.hf_advisor_blend_weight,&#10;        hf_advisor_timeout_seconds=args.hf_advisor_timeout_seconds,&#10;        telegram_ml_report_hours=args.telegram_ml_report_hours,&#10;        telegram_ml_report_min_trades=args.telegram_ml_report_min_trades,&#10;        health_host=args.health_host,&#10;        health_port=args.health_port,&#10;    )&#10;    bot.run()&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    main()&#10;&#10;&#10;" />
-              <option name="updatedContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;&#10;Adaptive Hyperliquid bot.&#10;&#10;- Trades only BTC, ETH, SOL.&#10;- Paper trades by default.&#10;- Uses online ML (SGDClassifier) to adapt entry confidence thresholds.&#10;- Persists learned state to disk so learning survives restarts.&#10;- Can optionally switch to live execution only after consistent paper performance.&#10;&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;import argparse&#10;import json&#10;import logging&#10;import math&#10;import random&#10;import os&#10;import signal&#10;import threading&#10;import time&#10;from dataclasses import asdict, dataclass&#10;from datetime import datetime, timedelta, timezone&#10;from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer&#10;from typing import Any, Dict, List, Optional, Tuple&#10;&#10;import joblib&#10;import numpy as np&#10;import pandas as pd&#10;from sklearn.linear_model import SGDClassifier&#10;&#10;from hf_advisor import HuggingFaceAdvisor&#10;from hbot import HyperliquidAPI&#10;from telegram_notifier import TelegramNotifier&#10;&#10;&#10;logging.basicConfig(level=logging.INFO, format=&quot;%(asctime)s - %(levelname)s - %(message)s&quot;)&#10;logger = logging.getLogger(__name__)&#10;&#10;&#10;ALLOWED_COINS = (&quot;BTC&quot;, &quot;ETH&quot;, &quot;SOL&quot;)&#10;INTERVAL_MS = {&#10;    &quot;1m&quot;: 60_000,&#10;    &quot;5m&quot;: 300_000,&#10;    &quot;15m&quot;: 900_000,&#10;    &quot;30m&quot;: 1_800_000,&#10;    &quot;1h&quot;: 3_600_000,&#10;}&#10;&#10;&#10;@dataclass&#10;class Position:&#10;    coin: str&#10;    side: str  # long | short&#10;    entry: float&#10;    size: float&#10;    sl: float&#10;    tp: float&#10;    entry_ts: int&#10;    entry_features: List[float]&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;@dataclass&#10;class ClosedTrade:&#10;    coin: str&#10;    side: str&#10;    entry: float&#10;    exit: float&#10;    size: float&#10;    pnl_usd: float&#10;    pnl_pct: float&#10;    reason: str&#10;    entry_ts: int&#10;    exit_ts: int&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;class LearningState:&#10;    &quot;&quot;&quot;Holds online models + strategy performance and persists to disk.&quot;&quot;&quot;&#10;&#10;    def __init__(self, state_path: str):&#10;        self.state_path = state_path&#10;        self.models: Dict[str, SGDClassifier] = {&#10;            c: SGDClassifier(loss=&quot;log_loss&quot;, alpha=1e-4, random_state=42) for c in ALLOWED_COINS&#10;        }&#10;        self.model_ready: Dict[str, bool] = {c: False for c in ALLOWED_COINS}&#10;        self.thresholds: Dict[str, float] = {c: 0.56 for c in ALLOWED_COINS}&#10;        self.trade_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;trades&quot;: 0, &quot;wins&quot;: 0, &quot;losses&quot;: 0, &quot;gross_profit&quot;: 0.0, &quot;gross_loss&quot;: 0.0}&#10;            for c in ALLOWED_COINS&#10;        }&#10;        self.ml_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;count&quot;: 0.0, &quot;brier_sum&quot;: 0.0} for c in ALLOWED_COINS&#10;        }&#10;        self.recent_results: Dict[str, List[int]] = {c: [] for c in ALLOWED_COINS}&#10;        self.recent_brier: Dict[str, List[float]] = {c: [] for c in ALLOWED_COINS}&#10;        self.closed_trades: List[Dict[str, Any]] = []&#10;        self.trade_events: List[Dict[str, Any]] = []&#10;        self.equity_curve: List[Dict[str, float]] = []&#10;        self.paper_runtime: Dict[str, Any] = {&#10;            &quot;equity&quot;: None,&#10;            &quot;positions&quot;: {},&#10;            &quot;entry_features&quot;: {},&#10;        }&#10;        self.last_saved_ts = 0.0&#10;        self._load()&#10;&#10;    def _load(self) -&gt; None:&#10;        if not os.path.exists(self.state_path):&#10;            logger.info(&quot;No prior learning state found at %s, starting fresh.&quot;, self.state_path)&#10;            return&#10;        payload = joblib.load(self.state_path)&#10;        self.models = payload.get(&quot;models&quot;, self.models)&#10;        self.model_ready = payload.get(&quot;model_ready&quot;, self.model_ready)&#10;        self.thresholds = payload.get(&quot;thresholds&quot;, self.thresholds)&#10;        self.trade_stats = payload.get(&quot;trade_stats&quot;, self.trade_stats)&#10;        self.ml_stats = payload.get(&quot;ml_stats&quot;, self.ml_stats)&#10;        self.recent_results = payload.get(&quot;recent_results&quot;, self.recent_results)&#10;        self.recent_brier = payload.get(&quot;recent_brier&quot;, self.recent_brier)&#10;        self.closed_trades = payload.get(&quot;closed_trades&quot;, self.closed_trades)&#10;        self.trade_events = payload.get(&quot;trade_events&quot;, self.trade_events)&#10;        self.equity_curve = payload.get(&quot;equity_curve&quot;, self.equity_curve)&#10;        self.paper_runtime = payload.get(&quot;paper_runtime&quot;, self.paper_runtime)&#10;        logger.info(&quot;Loaded learning state from %s&quot;, self.state_path)&#10;&#10;    def save(self, force: bool = False) -&gt; None:&#10;        now = time.time()&#10;        if not force and now - self.last_saved_ts &lt; 20:&#10;            return&#10;        payload = {&#10;            &quot;models&quot;: self.models,&#10;            &quot;model_ready&quot;: self.model_ready,&#10;            &quot;thresholds&quot;: self.thresholds,&#10;            &quot;trade_stats&quot;: self.trade_stats,&#10;            &quot;ml_stats&quot;: self.ml_stats,&#10;            &quot;recent_results&quot;: self.recent_results,&#10;            &quot;recent_brier&quot;: self.recent_brier,&#10;            &quot;closed_trades&quot;: self.closed_trades[-2000:],&#10;            &quot;trade_events&quot;: self.trade_events[-5000:],&#10;            &quot;equity_curve&quot;: self.equity_curve[-5000:],&#10;            &quot;paper_runtime&quot;: self.paper_runtime,&#10;            &quot;saved_at&quot;: datetime.now(timezone.utc).isoformat(),&#10;        }&#10;        joblib.dump(payload, self.state_path)&#10;        self.last_saved_ts = now&#10;&#10;    def record_equity(self, equity: float, ts_ms: Optional[int] = None) -&gt; None:&#10;        ts = int(ts_ms if ts_ms is not None else time.time() * 1000)&#10;        e = float(equity)&#10;        if self.equity_curve and abs(float(self.equity_curve[-1][&quot;equity&quot;]) - e) &lt; 1e-12:&#10;            return&#10;        self.equity_curve.append({&quot;ts&quot;: float(ts), &quot;equity&quot;: e})&#10;        self.equity_curve = self.equity_curve[-5000:]&#10;&#10;    def set_runtime_state(&#10;        self,&#10;        paper_equity: float,&#10;        positions: Dict[str, Position],&#10;        entry_features: Dict[Tuple[str, int], List[float]],&#10;    ) -&gt; None:&#10;        self.paper_runtime = {&#10;            &quot;equity&quot;: float(paper_equity),&#10;            &quot;positions&quot;: {coin: asdict(pos) for coin, pos in positions.items()},&#10;            &quot;entry_features&quot;: {&#10;                f&quot;{coin}:{entry_ts}&quot;: [float(v) for v in feats]&#10;                for (coin, entry_ts), feats in entry_features.items()&#10;            },&#10;        }&#10;&#10;    def restore_runtime_state(self) -&gt; Tuple[Optional[float], Dict[str, Position], Dict[Tuple[str, int], List[float]]]:&#10;        equity_raw = self.paper_runtime.get(&quot;equity&quot;)&#10;        equity = float(equity_raw) if equity_raw is not None else None&#10;&#10;        restored_positions: Dict[str, Position] = {}&#10;        for coin, raw in (self.paper_runtime.get(&quot;positions&quot;) or {}).items():&#10;            try:&#10;                restored_positions[coin] = Position(&#10;                    coin=str(raw[&quot;coin&quot;]),&#10;                    side=str(raw[&quot;side&quot;]),&#10;                    entry=float(raw[&quot;entry&quot;]),&#10;                    size=float(raw[&quot;size&quot;]),&#10;                    sl=float(raw[&quot;sl&quot;]),&#10;                    tp=float(raw[&quot;tp&quot;]),&#10;                    entry_ts=int(raw[&quot;entry_ts&quot;]),&#10;                    entry_features=[float(v) for v in raw.get(&quot;entry_features&quot;, [])],&#10;                    entry_p_long=float(raw.get(&quot;entry_p_long&quot;, 0.5)),&#10;                )&#10;            except Exception:&#10;                continue&#10;&#10;        restored_features: Dict[Tuple[str, int], List[float]] = {}&#10;        for key, vals in (self.paper_runtime.get(&quot;entry_features&quot;) or {}).items():&#10;            try:&#10;                coin, entry_ts_str = key.split(&quot;:&quot;, 1)&#10;                restored_features[(coin, int(entry_ts_str))] = [float(v) for v in vals]&#10;            except Exception:&#10;                continue&#10;&#10;        return equity, restored_positions, restored_features&#10;&#10;    def record_trade_event(self, event_type: str, payload: Dict[str, Any]) -&gt; None:&#10;        self.trade_events.append(&#10;            {&#10;                &quot;event&quot;: event_type,&#10;                &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;                **payload,&#10;            }&#10;        )&#10;        self.trade_events = self.trade_events[-5000:]&#10;&#10;    def update_after_trade(self, trade: ClosedTrade, entry_features: List[float]) -&gt; None:&#10;        label = 1 if trade.pnl_usd &gt; 0 else 0&#10;        x = np.asarray(entry_features, dtype=float).reshape(1, -1)&#10;        y = np.asarray([label], dtype=int)&#10;&#10;        model = self.models[trade.coin]&#10;        if not self.model_ready[trade.coin]:&#10;            model.partial_fit(x, y, classes=np.array([0, 1]))&#10;            self.model_ready[trade.coin] = True&#10;        else:&#10;            model.partial_fit(x, y)&#10;&#10;        stats = self.trade_stats[trade.coin]&#10;        stats[&quot;trades&quot;] += 1&#10;        if label == 1:&#10;            stats[&quot;wins&quot;] += 1&#10;            stats[&quot;gross_profit&quot;] += max(0.0, trade.pnl_usd)&#10;        else:&#10;            stats[&quot;losses&quot;] += 1&#10;            stats[&quot;gross_loss&quot;] += abs(min(0.0, trade.pnl_usd))&#10;&#10;        p = float(np.clip(trade.entry_p_long, 0.0, 1.0))&#10;        brier = float((p - label) ** 2)&#10;        ml = self.ml_stats[trade.coin]&#10;        ml[&quot;count&quot;] += 1&#10;        ml[&quot;brier_sum&quot;] += brier&#10;        self.recent_brier[trade.coin].append(brier)&#10;        self.recent_brier[trade.coin] = self.recent_brier[trade.coin][-100:]&#10;&#10;        self.recent_results[trade.coin].append(label)&#10;        self.recent_results[trade.coin] = self.recent_results[trade.coin][-30:]&#10;&#10;        # Adaptive threshold: lower threshold when recent win rate is strong, raise when weak.&#10;        recent = self.recent_results[trade.coin]&#10;        if recent:&#10;            wr = float(sum(recent)) / len(recent)&#10;            target = 0.56 + (0.60 - wr) * 0.25&#10;            self.thresholds[trade.coin] = float(np.clip(target, 0.52, 0.65))&#10;&#10;        self.closed_trades.append(asdict(trade))&#10;        self.save()&#10;&#10;    def predict_long_probability(self, coin: str, features: List[float]) -&gt; float:&#10;        if not self.model_ready.get(coin, False):&#10;            return 0.50&#10;        x = np.asarray(features, dtype=float).reshape(1, -1)&#10;        try:&#10;            p = float(self.models[coin].predict_proba(x)[0][1])&#10;        except Exception:&#10;            return 0.50&#10;        if math.isnan(p):&#10;            return 0.50&#10;        return float(np.clip(p, 0.01, 0.99))&#10;&#10;    def global_metrics(self) -&gt; Dict[str, float]:&#10;        trades = wins = 0&#10;        gross_profit = gross_loss = 0.0&#10;        for coin in ALLOWED_COINS:&#10;            s = self.trade_stats[coin]&#10;            trades += int(s[&quot;trades&quot;])&#10;            wins += int(s[&quot;wins&quot;])&#10;            gross_profit += float(s[&quot;gross_profit&quot;])&#10;            gross_loss += float(s[&quot;gross_loss&quot;])&#10;        win_rate = (wins / trades) if trades else 0.0&#10;        profit_factor = (gross_profit / gross_loss) if gross_loss &gt; 0 else (10.0 if gross_profit &gt; 0 else 0.0)&#10;        return {&#10;            &quot;trades&quot;: float(trades),&#10;            &quot;win_rate&quot;: float(win_rate),&#10;            &quot;profit_factor&quot;: float(profit_factor),&#10;            &quot;gross_profit&quot;: float(gross_profit),&#10;            &quot;gross_loss&quot;: float(gross_loss),&#10;        }&#10;&#10;    def ml_progress_metrics(self) -&gt; Dict[str, Any]:&#10;        total_count = 0.0&#10;        total_brier_sum = 0.0&#10;        per_coin: Dict[str, Any] = {}&#10;&#10;        all_recent_brier: List[float] = []&#10;        for coin in ALLOWED_COINS:&#10;            stat = self.ml_stats.get(coin, {})&#10;            cnt = float(stat.get(&quot;count&quot;, 0.0))&#10;            bsum = float(stat.get(&quot;brier_sum&quot;, 0.0))&#10;            total_count += cnt&#10;            total_brier_sum += bsum&#10;&#10;            recent = [float(v) for v in self.recent_brier.get(coin, [])]&#10;            all_recent_brier.extend(recent)&#10;            per_coin[coin] = {&#10;                &quot;samples&quot;: int(cnt),&#10;                &quot;brier&quot;: (bsum / cnt) if cnt &gt; 0 else None,&#10;                &quot;brier_rolling_50&quot;: (float(np.mean(recent[-50:])) if recent else None),&#10;                &quot;brier_rolling_100&quot;: (float(np.mean(recent)) if recent else None),&#10;            }&#10;&#10;        brier_global = (total_brier_sum / total_count) if total_count &gt; 0 else None&#10;        brier_rolling_50 = float(np.mean(all_recent_brier[-50:])) if all_recent_brier else None&#10;        brier_rolling_100 = float(np.mean(all_recent_brier[-100:])) if all_recent_brier else None&#10;&#10;        pnl = [float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in self.closed_trades]&#10;        expectancy_30 = float(np.mean(pnl[-30:])) if pnl else None&#10;        expectancy_100 = float(np.mean(pnl[-100:])) if pnl else None&#10;&#10;        max_drawdown_pct = None&#10;        peak = None&#10;        max_dd = 0.0&#10;        for point in self.equity_curve:&#10;            eq = float(point.get(&quot;equity&quot;, 0.0))&#10;            if peak is None or eq &gt; peak:&#10;                peak = eq&#10;            if peak and peak &gt; 0:&#10;                dd = (peak - eq) / peak&#10;                if dd &gt; max_dd:&#10;                    max_dd = dd&#10;        if peak is not None:&#10;            max_drawdown_pct = max_dd * 100.0&#10;&#10;        return {&#10;            &quot;samples&quot;: int(total_count),&#10;            &quot;brier&quot;: brier_global,&#10;            &quot;brier_rolling_50&quot;: brier_rolling_50,&#10;            &quot;brier_rolling_100&quot;: brier_rolling_100,&#10;            &quot;expectancy_usd_30&quot;: expectancy_30,&#10;            &quot;expectancy_usd_100&quot;: expectancy_100,&#10;            &quot;max_drawdown_pct&quot;: max_drawdown_pct,&#10;            &quot;per_coin&quot;: per_coin,&#10;        }&#10;&#10;    def consistently_profitable(self) -&gt; bool:&#10;        m = self.global_metrics()&#10;        return m[&quot;trades&quot;] &gt;= 30 and m[&quot;win_rate&quot;] &gt;= 0.58 and m[&quot;profit_factor&quot;] &gt;= 1.25&#10;&#10;&#10;class PaperBroker:&#10;    def __init__(self, starting_equity: float = 10000.0, taker_fee_bps: float = 4.32):&#10;        self.equity = float(starting_equity)&#10;        self.taker_fee = taker_fee_bps / 10_000.0&#10;        self.positions: Dict[str, Position] = {}&#10;        self.closed: List[ClosedTrade] = []&#10;&#10;    def has_position(self, coin: str) -&gt; bool:&#10;        return coin in self.positions&#10;&#10;    def open_position(self, pos: Position) -&gt; None:&#10;        self.positions[pos.coin] = pos&#10;&#10;    def maybe_close(self, coin: str, candle: Dict[str, float], now_ts: int) -&gt; Optional[ClosedTrade]:&#10;        pos = self.positions.get(coin)&#10;        if not pos:&#10;            return None&#10;&#10;        high = candle[&quot;high&quot;]&#10;        low = candle[&quot;low&quot;]&#10;        close = candle[&quot;close&quot;]&#10;&#10;        exit_px = close&#10;        reason = &quot;model_exit&quot;&#10;&#10;        if pos.side == &quot;long&quot;:&#10;            if low &lt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif high &gt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;        else:&#10;            if high &gt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif low &lt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;&#10;        if reason == &quot;model_exit&quot;:&#10;            return None&#10;&#10;        gross = (exit_px - pos.entry) * pos.size if pos.side == &quot;long&quot; else (pos.entry - exit_px) * pos.size&#10;        fees = (pos.entry * pos.size + exit_px * pos.size) * self.taker_fee&#10;        pnl = gross - fees&#10;        pnl_pct = (pnl / max(pos.entry * pos.size, 1e-9)) * 100.0&#10;&#10;        trade = ClosedTrade(&#10;            coin=coin,&#10;            side=pos.side,&#10;            entry=pos.entry,&#10;            exit=exit_px,&#10;            size=pos.size,&#10;            pnl_usd=pnl,&#10;            pnl_pct=pnl_pct,&#10;            reason=reason,&#10;            entry_ts=pos.entry_ts,&#10;            exit_ts=now_ts,&#10;            entry_p_long=pos.entry_p_long,&#10;        )&#10;        self.equity += pnl&#10;        self.closed.append(trade)&#10;        del self.positions[coin]&#10;        return trade&#10;&#10;&#10;def parse_candles(raw: List[Dict[str, Any]]) -&gt; pd.DataFrame:&#10;    rows = []&#10;    for c in raw:&#10;        rows.append(&#10;            {&#10;                &quot;ts&quot;: int(c.get(&quot;t&quot;) or c.get(&quot;time&quot;) or c.get(&quot;T&quot;) or 0),&#10;                &quot;open&quot;: float(c.get(&quot;o&quot;) or c.get(&quot;open&quot;) or c.get(&quot;openPx&quot;) or 0.0),&#10;                &quot;high&quot;: float(c.get(&quot;h&quot;) or c.get(&quot;high&quot;) or c.get(&quot;highPx&quot;) or 0.0),&#10;                &quot;low&quot;: float(c.get(&quot;l&quot;) or c.get(&quot;low&quot;) or c.get(&quot;lowPx&quot;) or 0.0),&#10;                &quot;close&quot;: float(c.get(&quot;c&quot;) or c.get(&quot;close&quot;) or c.get(&quot;closePx&quot;) or 0.0),&#10;                &quot;volume&quot;: float(c.get(&quot;v&quot;) or c.get(&quot;volume&quot;) or 0.0),&#10;            }&#10;        )&#10;    df = pd.DataFrame(rows).sort_values(&quot;ts&quot;)&#10;    df = df[(df[&quot;close&quot;] &gt; 0) &amp; (df[&quot;high&quot;] &gt; 0) &amp; (df[&quot;low&quot;] &gt; 0)]&#10;    return df.reset_index(drop=True)&#10;&#10;&#10;def build_features(df: pd.DataFrame) -&gt; Optional[List[float]]:&#10;    if len(df) &lt; 60:&#10;        return None&#10;&#10;    close = df[&quot;close&quot;]&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    vol = df[&quot;volume&quot;].replace(0, np.nan).ffill().fillna(1.0)&#10;&#10;    ret_1 = close.pct_change(1)&#10;    ret_3 = close.pct_change(3)&#10;    ret_8 = close.pct_change(8)&#10;    ema_12 = close.ewm(span=12).mean()&#10;    ema_26 = close.ewm(span=26).mean()&#10;    macd = (ema_12 - ema_26) / close&#10;&#10;    # ATR-like normalized range.&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr14 = tr.rolling(14).mean() / close&#10;&#10;    delta = close.diff()&#10;    up = delta.clip(lower=0).rolling(14).mean()&#10;    down = (-delta.clip(upper=0)).rolling(14).mean()&#10;    rs = up / down.replace(0, np.nan)&#10;    rsi = 100.0 - (100.0 / (1.0 + rs))&#10;    rsi_scaled = (rsi - 50.0) / 50.0&#10;&#10;    vol_z = (np.log(vol) - np.log(vol).rolling(20).mean()) / np.log(vol).rolling(20).std()&#10;&#10;    feat = pd.DataFrame(&#10;        {&#10;            &quot;ret_1&quot;: ret_1,&#10;            &quot;ret_3&quot;: ret_3,&#10;            &quot;ret_8&quot;: ret_8,&#10;            &quot;macd&quot;: macd,&#10;            &quot;atr14&quot;: atr14,&#10;            &quot;rsi&quot;: rsi_scaled,&#10;            &quot;vol_z&quot;: vol_z,&#10;        }&#10;    ).replace([np.inf, -np.inf], np.nan)&#10;&#10;    last = feat.iloc[-1].fillna(0.0)&#10;    return [float(last[k]) for k in [&quot;ret_1&quot;, &quot;ret_3&quot;, &quot;ret_8&quot;, &quot;macd&quot;, &quot;atr14&quot;, &quot;rsi&quot;, &quot;vol_z&quot;]]&#10;&#10;&#10;def atr_for_risk(df: pd.DataFrame) -&gt; float:&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    close = df[&quot;close&quot;]&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr = float(tr.rolling(14).mean().iloc[-1])&#10;    if not np.isfinite(atr) or atr &lt;= 0:&#10;        atr = float(close.iloc[-1] * 0.01)&#10;    return atr&#10;&#10;&#10;class AdaptiveHyperliquidBot:&#10;    def __init__(&#10;        self,&#10;        interval: str,&#10;        state_file: str,&#10;        report_file: str,&#10;        starting_equity: float,&#10;        risk_per_trade: float,&#10;        poll_seconds: int,&#10;        exploration_rate: float,&#10;        enable_live: bool,&#10;        require_live_approval: bool,&#10;        live_approval_mode: str,&#10;        live_approval_file: str,&#10;        live_startup_test: bool,&#10;        live_startup_test_coin: str,&#10;        live_startup_test_notional: float,&#10;        hf_advisor_url: str,&#10;        hf_advisor_token: str,&#10;        hf_advisor_mode: str,&#10;        hf_advisor_min_confidence: float,&#10;        hf_advisor_blend_weight: float,&#10;        hf_advisor_timeout_seconds: float,&#10;        telegram_ml_report_hours: float,&#10;        telegram_ml_report_min_trades: int,&#10;        health_host: str,&#10;        health_port: int,&#10;    ):&#10;        self.interval = interval&#10;        self.poll_seconds = poll_seconds&#10;        self.risk_per_trade = risk_per_trade&#10;        self.exploration_rate = float(np.clip(exploration_rate, 0.0, 0.5))&#10;        self.enable_live = enable_live&#10;        self.require_live_approval = require_live_approval&#10;        self.live_approval_mode = live_approval_mode&#10;        self.live_approval_file = live_approval_file&#10;        self.live_startup_test = live_startup_test&#10;        self.live_startup_test_coin = live_startup_test_coin&#10;        self.live_startup_test_notional = float(max(10.0, live_startup_test_notional))&#10;        self.hf_advisor_mode = (hf_advisor_mode or &quot;blend&quot;).strip().lower()&#10;        if self.hf_advisor_mode not in {&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;}:&#10;            logger.warning(&quot;Unknown advisor mode '%s'; defaulting to 'blend'.&quot;, self.hf_advisor_mode)&#10;            self.hf_advisor_mode = &quot;blend&quot;&#10;        self.hf_advisor_min_confidence = float(np.clip(hf_advisor_min_confidence, 0.0, 1.0))&#10;        self.hf_advisor_blend_weight = float(np.clip(hf_advisor_blend_weight, 0.0, 1.0))&#10;        self.hf_advisor = HuggingFaceAdvisor(&#10;            endpoint_url=hf_advisor_url,&#10;            api_token=hf_advisor_token,&#10;            timeout_seconds=hf_advisor_timeout_seconds,&#10;        )&#10;        self.telegram_ml_report_hours = float(max(0.0, telegram_ml_report_hours))&#10;        self.telegram_ml_report_min_trades = int(max(0, telegram_ml_report_min_trades))&#10;        self.health_host = health_host&#10;        self.health_port = int(health_port)&#10;&#10;        self.api = HyperliquidAPI(verify_ssl=True)&#10;        self.learning = LearningState(state_file)&#10;        self.paper = PaperBroker(starting_equity=starting_equity)&#10;        self.report_file = report_file&#10;        self.notifier = TelegramNotifier(disable_on_error=True)&#10;        self.notifications_enabled = bool(&#10;            os.environ.get(&quot;TELEGRAM_BOT_TOKEN&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;TELEGRAM_CHAT_ID&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;PROXY_URL&quot;, &quot;&quot;).strip()&#10;        )&#10;        self._last_summary_date: Optional[str] = None&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(&quot;Adaptive ML bot started. Trade notifications are enabled.&quot;)&#10;        else:&#10;            logger.info(&quot;Telegram notifications disabled (need TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID, PROXY_URL).&quot;)&#10;&#10;        self._running = True&#10;        self._live_clients: Optional[Dict[str, Any]] = None&#10;        self._live_readiness_alerted = False&#10;        self._live_startup_test_done = False&#10;        self._live_startup_test_passed: Optional[bool] = None&#10;        self._http_server: Optional[ThreadingHTTPServer] = None&#10;        self._entry_features: Dict[Tuple[str, int], List[float]] = {}&#10;        self._last_ml_report_ts: float = 0.0&#10;        self._advisor_stats: Dict[str, int] = {&#10;            &quot;requests&quot;: 0,&#10;            &quot;errors&quot;: 0,&#10;            &quot;remote_used&quot;: 0,&#10;            &quot;local_used&quot;: 0,&#10;            &quot;held_by_advisor&quot;: 0,&#10;            &quot;kept_model_signal&quot;: 0,&#10;            &quot;used_advisor_blend&quot;: 0,&#10;        }&#10;        self._restore_runtime_state()&#10;&#10;    def _interval_ms(self) -&gt; int:&#10;        if self.interval not in INTERVAL_MS:&#10;            raise ValueError(f&quot;Unsupported interval '{self.interval}'. Supported: {sorted(INTERVAL_MS)}&quot;)&#10;        return INTERVAL_MS[self.interval]&#10;&#10;    def stop(self, *_: Any) -&gt; None:&#10;        logger.info(&quot;Received shutdown signal, flushing learning state.&quot;)&#10;        self._running = False&#10;        if self._http_server is not None:&#10;            self._http_server.shutdown()&#10;            self._http_server.server_close()&#10;            self._http_server = None&#10;        self._persist_runtime_state()&#10;        self.learning.save(force=True)&#10;        self.write_report()&#10;&#10;    def status_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        approval = self._live_approval_status_details()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;            &quot;metrics&quot;: metrics,&#10;            &quot;ml_progress&quot;: self.learning.ml_progress_metrics(),&#10;            &quot;thresholds&quot;: self.learning.thresholds,&#10;            &quot;state_file&quot;: self.learning.state_path,&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_mode&quot;: self.live_approval_mode,&#10;            &quot;live_approval_granted&quot;: approval[&quot;granted&quot;],&#10;            &quot;live_approval_checks&quot;: approval,&#10;            &quot;live_startup_test_enabled&quot;: self.live_startup_test,&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;trade_events_recorded&quot;: len(self.learning.trade_events),&#10;            &quot;advisor&quot;: {&#10;                &quot;enabled&quot;: self.hf_advisor.enabled,&#10;                &quot;remote_enabled&quot;: self.hf_advisor.remote_enabled,&#10;                &quot;mode&quot;: self.hf_advisor_mode,&#10;                &quot;min_confidence&quot;: self.hf_advisor_min_confidence,&#10;                &quot;blend_weight&quot;: self.hf_advisor_blend_weight,&#10;                &quot;stats&quot;: self._advisor_stats,&#10;            },&#10;        }&#10;&#10;    def status_overview_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        ml_progress = self.learning.ml_progress_metrics()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;trades&quot;: int(metrics[&quot;trades&quot;]),&#10;            &quot;win_rate&quot;: metrics[&quot;win_rate&quot;],&#10;            &quot;profit_factor&quot;: metrics[&quot;profit_factor&quot;],&#10;            &quot;brier_rolling_50&quot;: ml_progress[&quot;brier_rolling_50&quot;],&#10;            &quot;expectancy_usd_30&quot;: ml_progress[&quot;expectancy_usd_30&quot;],&#10;            &quot;max_drawdown_pct&quot;: ml_progress[&quot;max_drawdown_pct&quot;],&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_granted&quot;: self._live_approval_granted(),&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;advisor_enabled&quot;: self.hf_advisor.enabled,&#10;            &quot;advisor_remote_enabled&quot;: self.hf_advisor.remote_enabled,&#10;            &quot;advisor_mode&quot;: self.hf_advisor_mode,&#10;        }&#10;&#10;    def _apply_hf_advisor(&#10;        self,&#10;        coin: str,&#10;        features: List[float],&#10;        candle: Dict[str, float],&#10;        now_ts: int,&#10;        p_long: float,&#10;        threshold: float,&#10;        side: Optional[str],&#10;    ) -&gt; Tuple[Optional[str], str, Optional[float], Optional[str], Optional[float]]:&#10;        mode = self.hf_advisor_mode&#10;&#10;        self._advisor_stats[&quot;requests&quot;] += 1&#10;        advice = self.hf_advisor.advise(&#10;            coin=coin,&#10;            interval=self.interval,&#10;            features=features,&#10;            candle=candle,&#10;            timestamp_ms=now_ts,&#10;        )&#10;        if advice is None:&#10;            return side, &quot;advisor_unavailable&quot;, p_long, None, None&#10;&#10;        if advice.error:&#10;            self._advisor_stats[&quot;errors&quot;] += 1&#10;        if advice.source == &quot;remote&quot;:&#10;            self._advisor_stats[&quot;remote_used&quot;] += 1&#10;        else:&#10;            self._advisor_stats[&quot;local_used&quot;] += 1&#10;&#10;        advisor_side = advice.side&#10;        advisor_conf = float(advice.confidence)&#10;        if mode == &quot;blend&quot; and advice.p_long is not None:&#10;            blended_p = (1.0 - self.hf_advisor_blend_weight) * p_long + self.hf_advisor_blend_weight * float(advice.p_long)&#10;            blended_p = float(np.clip(blended_p, 0.01, 0.99))&#10;            blended_side: Optional[str] = None&#10;            if blended_p &gt;= threshold:&#10;                blended_side = &quot;long&quot;&#10;            elif blended_p &lt;= (1.0 - threshold):&#10;                blended_side = &quot;short&quot;&#10;            if blended_side is None:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_hold_blend&quot;, blended_p, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;used_advisor_blend&quot;] += 1&#10;            return blended_side, &quot;advisor_blend&quot;, blended_p, advisor_side, advisor_conf&#10;&#10;        if side is None:&#10;            return None, &quot;advisor_no_model_side&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;confirm&quot;:&#10;            if advisor_side == side and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;                return side, &quot;advisor_confirm&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;            return None, &quot;advisor_block_confirm&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;veto&quot;:&#10;            opposite = (side == &quot;long&quot; and advisor_side == &quot;short&quot;) or (side == &quot;short&quot; and advisor_side == &quot;long&quot;)&#10;            if opposite and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_veto&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;            return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;    def open_trades_payload(self) -&gt; Dict[str, Any]:&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;        }&#10;&#10;    def _restore_runtime_state(self) -&gt; None:&#10;        equity, positions, entry_features = self.learning.restore_runtime_state()&#10;        if equity is not None:&#10;            self.paper.equity = float(equity)&#10;        if positions:&#10;            self.paper.positions = positions&#10;        if entry_features:&#10;            self._entry_features = entry_features&#10;        if equity is not None or positions:&#10;            logger.info(&#10;                &quot;Restored paper runtime state: equity=%.2f open_positions=%d tracked_entries=%d&quot;,&#10;                self.paper.equity,&#10;                len(self.paper.positions),&#10;                len(self._entry_features),&#10;            )&#10;&#10;    def _persist_runtime_state(self) -&gt; None:&#10;        self.learning.record_equity(self.paper.equity)&#10;        self.learning.set_runtime_state(&#10;            paper_equity=self.paper.equity,&#10;            positions=self.paper.positions,&#10;            entry_features=self._entry_features,&#10;        )&#10;&#10;    def _live_approval_granted(self) -&gt; bool:&#10;        return self._live_approval_status_details()[&quot;granted&quot;]&#10;&#10;    def _live_approval_status_details(self) -&gt; Dict[str, Any]:&#10;        env_approved = os.environ.get(&quot;ALLOW_LIVE_TRADING&quot;, &quot;&quot;).strip().lower() in {&quot;1&quot;, &quot;true&quot;, &quot;yes&quot;, &quot;y&quot;}&#10;        file_approved = bool(self.live_approval_file and os.path.exists(self.live_approval_file))&#10;        mode = (self.live_approval_mode or &quot;either&quot;).strip().lower()&#10;        missing: List[str] = []&#10;        if mode == &quot;both&quot;:&#10;            granted = env_approved and file_approved&#10;            if not env_approved:&#10;                missing.append(&quot;env:ALLOW_LIVE_TRADING&quot;)&#10;            if not file_approved:&#10;                missing.append(f&quot;file:{self.live_approval_file}&quot;)&#10;        else:&#10;            granted = env_approved or file_approved&#10;            if not granted:&#10;                missing = [&quot;env:ALLOW_LIVE_TRADING&quot;, f&quot;file:{self.live_approval_file}&quot;]&#10;        return {&#10;            &quot;mode&quot;: mode,&#10;            &quot;env_approved&quot;: env_approved,&#10;            &quot;file_approved&quot;: file_approved,&#10;            &quot;missing&quot;: missing,&#10;            &quot;granted&quot;: granted,&#10;        }&#10;&#10;    def _maybe_notify_live_readiness(self) -&gt; None:&#10;        if self._live_readiness_alerted or not self.learning.consistently_profitable():&#10;            return&#10;        m = self.learning.global_metrics()&#10;        msg = (&#10;            &quot;Paper performance gate reached.\n&quot;&#10;            f&quot;Trades: {int(m['trades'])}, win rate: {m['win_rate'] * 100.0:.1f}%, PF: {m['profit_factor']:.2f}.\n&quot;&#10;            &quot;Grant live approval to arm live mode. &quot;&#10;            f&quot;Approval mode={self.live_approval_mode}. &quot;&#10;            f&quot;Set ALLOW_LIVE_TRADING=1 and/or create {self.live_approval_file} (based on mode).&quot;&#10;        )&#10;        logger.warning(msg)&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(msg)&#10;        self._live_readiness_alerted = True&#10;&#10;    def _live_order_filled(self, result: Any) -&gt; bool:&#10;        if not isinstance(result, dict):&#10;            return False&#10;        try:&#10;            statuses = result.get(&quot;response&quot;, {}).get(&quot;data&quot;, {}).get(&quot;statuses&quot;, [])&#10;            errors = [s.get(&quot;error&quot;) for s in statuses if &quot;error&quot; in s]&#10;            if errors:&#10;                return False&#10;            filled_sz = sum(float(s[&quot;filled&quot;].get(&quot;totalSz&quot;, 0.0)) for s in statuses if &quot;filled&quot; in s)&#10;            return filled_sz &gt; 0&#10;        except Exception:&#10;            return False&#10;&#10;    def _calc_test_size(self, coin: str, px: float, min_notional: float) -&gt; float:&#10;        sz_dec = self.api.get_sz_decimals(coin)&#10;        step = 10 ** (-sz_dec)&#10;        raw = min_notional / max(px, 1e-9)&#10;        steps = max(1, int(math.ceil(raw / step)))&#10;        return float(round(steps * step, sz_dec))&#10;&#10;    def _run_live_startup_test(self) -&gt; None:&#10;        if self._live_clients is None or self._live_startup_test_done or not self.live_startup_test:&#10;            return&#10;&#10;        self._live_startup_test_done = True&#10;        coin = self.live_startup_test_coin&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;&#10;        try:&#10;            df = self._fetch_df(coin, lookback_bars=120)&#10;            if df is None:&#10;                raise RuntimeError(f&quot;No candle data available for startup live test on {coin}&quot;)&#10;&#10;            entry_px = float(df.iloc[-1][&quot;close&quot;])&#10;            test_size = self._calc_test_size(coin=coin, px=entry_px, min_notional=self.live_startup_test_notional)&#10;&#10;            logger.warning(&#10;                &quot;Running startup live order test: BUY then SELL %s size=%.8f around px=%.4f&quot;,&#10;                coin,&#10;                test_size,&#10;                entry_px,&#10;            )&#10;&#10;            buy_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=True,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;            time.sleep(1.2)&#10;            sell_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=False,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;&#10;            buy_ok = self._live_order_filled(buy_result)&#10;            sell_ok = self._live_order_filled(sell_result)&#10;            self._live_startup_test_passed = bool(buy_ok and sell_ok)&#10;&#10;            if self._live_startup_test_passed:&#10;                msg = f&quot;Startup live order test passed on {coin} (open+close filled).&quot;&#10;                logger.warning(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_info(msg)&#10;            else:&#10;                msg = f&quot;Startup live order test failed on {coin}. Review logs/order responses before trusting live mirroring.&quot;&#10;                logger.error(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;        except Exception as exc:&#10;            self._live_startup_test_passed = False&#10;            msg = f&quot;Startup live order test exception: {exc}&quot;&#10;            logger.error(msg)&#10;            if self.notifications_enabled:&#10;                self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;&#10;    def _start_health_server(self) -&gt; None:&#10;        bot = self&#10;&#10;        class HealthHandler(BaseHTTPRequestHandler):&#10;            def _write_json(self, payload: Dict[str, Any], status: int = 200) -&gt; None:&#10;                body = json.dumps(payload).encode(&quot;utf-8&quot;)&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, str(len(body)))&#10;                self.end_headers()&#10;                self.wfile.write(body)&#10;&#10;            def _write_head(self, status: int = 200) -&gt; None:&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, &quot;0&quot;)&#10;                self.end_headers()&#10;&#10;            def do_GET(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;):&#10;                    self._write_json({&quot;status&quot;: &quot;ok&quot;, &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;})&#10;                    return&#10;                if self.path == &quot;/status&quot;:&#10;                    self._write_json(bot.status_overview_payload())&#10;                    return&#10;                if self.path == &quot;/status/full&quot;:&#10;                    self._write_json(bot.status_payload())&#10;                    return&#10;                if self.path == &quot;/trades&quot;:&#10;                    self._write_json(bot.open_trades_payload())&#10;                    return&#10;                self._write_json({&quot;status&quot;: &quot;not_found&quot;}, status=404)&#10;&#10;            def do_HEAD(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;, &quot;/status&quot;, &quot;/status/full&quot;, &quot;/trades&quot;):&#10;                    self._write_head(status=200)&#10;                    return&#10;                self._write_head(status=404)&#10;&#10;            def log_message(self, format: str, *args: Any) -&gt; None:&#10;                logger.debug(&quot;health_http: &quot; + format, *args)&#10;&#10;        server = ThreadingHTTPServer((self.health_host, self.health_port), HealthHandler)&#10;        self._http_server = server&#10;        t = threading.Thread(target=server.serve_forever, daemon=True)&#10;        t.start()&#10;        logger.info(&quot;Health endpoint listening on http://%s:%s&quot;, self.health_host, self.health_port)&#10;&#10;    def _fetch_df(self, coin: str, lookback_bars: int = 200) -&gt; Optional[pd.DataFrame]:&#10;        start = int((datetime.now(timezone.utc) - timedelta(milliseconds=self._interval_ms() * lookback_bars)).timestamp() * 1000)&#10;        raw = self.api.get_candles(coin=coin, interval=self.interval, start_time=start)&#10;        if not raw:&#10;            return None&#10;        df = parse_candles(raw)&#10;        if len(df) &lt; 80:&#10;            return None&#10;        return df&#10;&#10;    def _size_for_trade(self, entry: float, stop: float) -&gt; float:&#10;        max_risk_usd = self.paper.equity * self.risk_per_trade&#10;        stop_distance = abs(entry - stop)&#10;        if stop_distance &lt;= 0:&#10;            return 0.0&#10;        size = max_risk_usd / stop_distance&#10;        max_notional = self.paper.equity * 0.25&#10;        if size * entry &gt; max_notional:&#10;            size = max_notional / entry&#10;        return float(max(size, 0.0))&#10;&#10;    def _maybe_prepare_live(self) -&gt; None:&#10;        if not self.enable_live:&#10;            return&#10;        if not self.learning.consistently_profitable():&#10;            return&#10;        if self.require_live_approval and not self._live_approval_granted():&#10;            return&#10;        if self._live_clients is not None:&#10;            return&#10;&#10;        pk = os.environ.get(&quot;HL_PRIVATE_KEY&quot;, &quot;&quot;).strip()&#10;        acct = os.environ.get(&quot;HL_ACCOUNT_ADDRESS&quot;, &quot;&quot;).strip() or None&#10;        if not pk:&#10;            logger.warning(&quot;Live mode requested and paper criteria met, but HL_PRIVATE_KEY is missing.&quot;)&#10;            return&#10;&#10;        try:&#10;            self._live_clients = self.api.create_live_execution_clients(private_key=pk, account_address=acct)&#10;            logger.warning(&quot;Paper criteria met. Live execution is now armed.&quot;)&#10;            self._run_live_startup_test()&#10;        except Exception as exc:&#10;            logger.error(&quot;Failed to initialize live execution clients: %s&quot;, exc)&#10;            self._live_clients = None&#10;&#10;    def _place_live_mirror(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if self._live_clients is None:&#10;            return&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;        is_buy = side == &quot;long&quot;&#10;        try:&#10;            self.api.place_market_order(exchange=exchange, coin=coin, is_buy=is_buy, sz=size, px=entry, sl=sl, tp=tp)&#10;        except Exception as exc:&#10;            logger.error(&quot;Live mirror order failed for %s: %s&quot;, coin, exc)&#10;&#10;    def _notify_trade_open(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        rr: Optional[float] = None&#10;        risk = abs(entry - sl)&#10;        reward = abs(tp - entry)&#10;        if risk &gt; 0:&#10;            rr = reward / risk&#10;        self.notifier.send_trade(&#10;            direction=side.upper(),&#10;            coin=coin,&#10;            entry=entry,&#10;            sl=sl,&#10;            tp=tp,&#10;            sz=size,&#10;            notional=entry * size,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;            rr=rr,&#10;            order_type=&quot;paper&quot;,&#10;        )&#10;&#10;    def _notify_trade_close(self, closed: ClosedTrade) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        self.notifier.send_exit(&#10;            coin=closed.coin,&#10;            reason=closed.reason,&#10;            pnl_pct=closed.pnl_pct,&#10;            pnl_usdc=closed.pnl_usd,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;        )&#10;&#10;    def _maybe_send_daily_summary(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        now = datetime.now(timezone.utc)&#10;        date_key = now.date().isoformat()&#10;        if now.hour != 0 or self._last_summary_date == date_key:&#10;            return&#10;&#10;        since_ms = int((now - timedelta(days=1)).timestamp() * 1000)&#10;        day_trades = [&#10;            t for t in self.learning.closed_trades&#10;            if int(t.get(&quot;exit_ts&quot;, 0)) &gt;= since_ms&#10;        ]&#10;        day_wins = sum(1 for t in day_trades if float(t.get(&quot;pnl_usd&quot;, 0.0)) &gt; 0)&#10;        day_wr = (day_wins / len(day_trades) * 100.0) if day_trades else 0.0&#10;        day_pnl = sum(float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in day_trades)&#10;&#10;        overall = self.learning.global_metrics()&#10;        ml = self.learning.ml_progress_metrics()&#10;        summary = (&#10;            &quot;Midnight summary (UTC)\n&quot;&#10;            f&quot;24h trades: {len(day_trades)}\n&quot;&#10;            f&quot;24h win rate: {day_wr:.1f}%\n&quot;&#10;            f&quot;24h pnl: {day_pnl:+.2f} USDC\n&quot;&#10;            f&quot;Total trades: {int(overall['trades'])}\n&quot;&#10;            f&quot;Total win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Paper equity: {self.paper.equity:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%&quot;&#10;        )&#10;        self.notifier.send_info(summary)&#10;        self._last_summary_date = date_key&#10;&#10;    def _maybe_send_ml_progress_report(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        if self.telegram_ml_report_hours &lt;= 0:&#10;            return&#10;&#10;        now_ts = time.time()&#10;        period_s = self.telegram_ml_report_hours * 3600.0&#10;        if self._last_ml_report_ts &gt; 0 and (now_ts - self._last_ml_report_ts) &lt; period_s:&#10;            return&#10;&#10;        overall = self.learning.global_metrics()&#10;        trades = int(overall[&quot;trades&quot;])&#10;        if trades &lt; self.telegram_ml_report_min_trades:&#10;            return&#10;&#10;        ml = self.learning.ml_progress_metrics()&#10;        per_coin = ml.get(&quot;per_coin&quot;, {})&#10;        coin_parts = []&#10;        for coin in ALLOWED_COINS:&#10;            coin_brier = per_coin.get(coin, {}).get(&quot;brier_rolling_50&quot;)&#10;            coin_parts.append(f&quot;{coin}:{coin_brier}&quot;)&#10;        coin_brier_line = &quot; | &quot;.join(coin_parts)&#10;&#10;        msg = (&#10;            &quot;ML progress update\n&quot;&#10;            f&quot;Trades: {trades}\n&quot;&#10;            f&quot;Win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%\n&quot;&#10;            f&quot;Per-coin Brier(50): {coin_brier_line}&quot;&#10;        )&#10;        self.notifier.send_info(msg)&#10;        self._last_ml_report_ts = now_ts&#10;&#10;    def _trade_coin(self, coin: str) -&gt; None:&#10;        df = self._fetch_df(coin)&#10;        if df is None:&#10;            logger.warning(&quot;Not enough candle data for %s&quot;, coin)&#10;            return&#10;&#10;        candle = {&#10;            &quot;high&quot;: float(df.iloc[-1][&quot;high&quot;]),&#10;            &quot;low&quot;: float(df.iloc[-1][&quot;low&quot;]),&#10;            &quot;close&quot;: float(df.iloc[-1][&quot;close&quot;]),&#10;        }&#10;        now_ts = int(df.iloc[-1][&quot;ts&quot;])&#10;&#10;        closed = self.paper.maybe_close(coin, candle, now_ts)&#10;        if closed:&#10;            logger.info(&quot;CLOSED %s %s pnl=$%.2f (%+.2f%%) reason=%s&quot;, closed.coin, closed.side, closed.pnl_usd, closed.pnl_pct, closed.reason)&#10;            self._notify_trade_close(closed)&#10;            self.learning.record_trade_event(&quot;close&quot;, asdict(closed))&#10;            # Entry features are captured when the position opens and used as supervised labels on close.&#10;            features_map = getattr(self, &quot;_entry_features&quot;, {})&#10;            stored = features_map.get((coin, closed.entry_ts), [])&#10;            if stored:&#10;                self.learning.update_after_trade(closed, stored)&#10;                features_map.pop((coin, closed.entry_ts), None)&#10;            self._persist_runtime_state()&#10;&#10;        if self.paper.has_position(coin):&#10;            return&#10;&#10;        features = build_features(df)&#10;        if not features:&#10;            return&#10;&#10;        p_long = self.learning.predict_long_probability(coin, features)&#10;        threshold = self.learning.thresholds[coin]&#10;&#10;        side: Optional[str] = None&#10;        signal_reason = &quot;model&quot;&#10;&#10;        if not self.learning.model_ready.get(coin, False):&#10;            # Bootstrap mode: trade weak momentum direction to generate first labels.&#10;            side = &quot;long&quot; if features[1] &gt;= 0 else &quot;short&quot;&#10;            signal_reason = &quot;bootstrap&quot;&#10;        else:&#10;            if p_long &gt;= threshold:&#10;                side = &quot;long&quot;&#10;            elif p_long &lt;= (1.0 - threshold):&#10;                side = &quot;short&quot;&#10;&#10;            if side is None and random.random() &lt; self.exploration_rate:&#10;                side = &quot;long&quot; if p_long &gt;= 0.5 else &quot;short&quot;&#10;                signal_reason = &quot;exploration&quot;&#10;&#10;        side, signal_reason, p_long, advisor_side, advisor_conf = self._apply_hf_advisor(&#10;            coin=coin,&#10;            features=features,&#10;            candle=candle,&#10;            now_ts=now_ts,&#10;            p_long=p_long,&#10;            threshold=threshold,&#10;            side=side,&#10;        )&#10;&#10;        if not side:&#10;            if advisor_side is not None:&#10;                logger.info(&#10;                    &quot;%s HOLD p_long=%.3f threshold=%.3f advisor=%s conf=%.3f reason=%s&quot;,&#10;                    coin,&#10;                    p_long,&#10;                    threshold,&#10;                    advisor_side,&#10;                    advisor_conf if advisor_conf is not None else 0.0,&#10;                    signal_reason,&#10;                )&#10;            else:&#10;                logger.info(&quot;%s HOLD p_long=%.3f threshold=%.3f&quot;, coin, p_long, threshold)&#10;            return&#10;&#10;        entry = candle[&quot;close&quot;]&#10;        atr = atr_for_risk(df)&#10;        sl_mult = 1.3&#10;        tp_mult = 2.0&#10;&#10;        if side == &quot;long&quot;:&#10;            sl = max(entry - atr * sl_mult, 1e-8)&#10;            tp = entry + atr * tp_mult&#10;        else:&#10;            sl = entry + atr * sl_mult&#10;            tp = max(entry - atr * tp_mult, 1e-8)&#10;&#10;        size = self._size_for_trade(entry, sl)&#10;        if size &lt;= 0:&#10;            return&#10;&#10;        pos = Position(&#10;            coin=coin,&#10;            side=side,&#10;            entry=entry,&#10;            size=size,&#10;            sl=sl,&#10;            tp=tp,&#10;            entry_ts=now_ts,&#10;            entry_features=features,&#10;            entry_p_long=p_long,&#10;        )&#10;        self.paper.open_position(pos)&#10;        if not hasattr(self, &quot;_entry_features&quot;):&#10;            self._entry_features = {}&#10;        self._entry_features[(coin, now_ts)] = features&#10;        self.learning.record_trade_event(&#10;            &quot;open&quot;,&#10;            {&#10;                &quot;coin&quot;: coin,&#10;                &quot;side&quot;: side,&#10;                &quot;entry&quot;: entry,&#10;                &quot;size&quot;: size,&#10;                &quot;sl&quot;: sl,&#10;                &quot;tp&quot;: tp,&#10;                &quot;entry_ts&quot;: now_ts,&#10;                &quot;signal_reason&quot;: signal_reason,&#10;                &quot;p_long&quot;: p_long,&#10;                &quot;threshold&quot;: threshold,&#10;                &quot;advisor_side&quot;: advisor_side,&#10;                &quot;advisor_confidence&quot;: advisor_conf,&#10;            },&#10;        )&#10;        self._persist_runtime_state()&#10;&#10;        logger.info(&#10;            &quot;OPEN %s %s entry=%.4f sl=%.4f tp=%.4f size=%.5f p_long=%.3f th=%.3f reason=%s equity=%.2f&quot;,&#10;            coin,&#10;            side,&#10;            entry,&#10;            sl,&#10;            tp,&#10;            size,&#10;            p_long,&#10;            threshold,&#10;            signal_reason,&#10;            self.paper.equity,&#10;        )&#10;        self._notify_trade_open(coin, side, entry, sl, tp, size)&#10;&#10;        self._maybe_prepare_live()&#10;        self._place_live_mirror(coin, side, entry, sl, tp, size)&#10;&#10;    def write_report(self) -&gt; None:&#10;        payload = self.status_payload()&#10;        with open(self.report_file, &quot;w&quot;, encoding=&quot;utf-8&quot;) as f:&#10;            json.dump(payload, f, indent=2)&#10;&#10;    def run(self) -&gt; None:&#10;        signal.signal(signal.SIGINT, self.stop)&#10;        signal.signal(signal.SIGTERM, self.stop)&#10;&#10;        self._start_health_server()&#10;&#10;        logger.info(&quot;Starting adaptive bot (paper mode=%s, live enabled=%s)&quot;, True, self.enable_live)&#10;        logger.info(&quot;Universe: %s&quot;, &quot;, &quot;.join(ALLOWED_COINS))&#10;&#10;        while self._running:&#10;            try:&#10;                for coin in ALLOWED_COINS:&#10;                    self._trade_coin(coin)&#10;                self._persist_runtime_state()&#10;                self.learning.save()&#10;                self.write_report()&#10;&#10;                m = self.learning.global_metrics()&#10;                logger.info(&#10;                    &quot;Stats trades=%d win_rate=%.2f%% pf=%.2f paper_equity=%.2f profitable_gate=%s&quot;,&#10;                    int(m[&quot;trades&quot;]),&#10;                    m[&quot;win_rate&quot;] * 100.0,&#10;                    m[&quot;profit_factor&quot;],&#10;                    self.paper.equity,&#10;                    self.learning.consistently_profitable(),&#10;                )&#10;                self._maybe_notify_live_readiness()&#10;                self._maybe_prepare_live()&#10;                self._maybe_send_daily_summary()&#10;                self._maybe_send_ml_progress_report()&#10;            except Exception as exc:&#10;                logger.exception(&quot;Loop error: %s&quot;, exc)&#10;&#10;            time.sleep(self.poll_seconds)&#10;&#10;&#10;def build_arg_parser() -&gt; argparse.ArgumentParser:&#10;    p = argparse.ArgumentParser(description=&quot;Adaptive ML Hyperliquid paper trader&quot;)&#10;    p.add_argument(&quot;--interval&quot;, default=&quot;15m&quot;, choices=sorted(INTERVAL_MS.keys()))&#10;    p.add_argument(&quot;--state-file&quot;, default=&quot;learning_state.joblib&quot;)&#10;    p.add_argument(&quot;--report-file&quot;, default=&quot;paper_report.json&quot;)&#10;    p.add_argument(&quot;--starting-equity&quot;, type=float, default=10000.0)&#10;    p.add_argument(&quot;--risk-per-trade&quot;, type=float, default=0.01, help=&quot;Fraction of equity risked per position&quot;)&#10;    p.add_argument(&quot;--poll-seconds&quot;, type=int, default=45)&#10;    p.add_argument(&quot;--exploration-rate&quot;, type=float, default=0.08, help=&quot;Chance to take an exploratory trade when model signal is neutral&quot;)&#10;    p.add_argument(&quot;--health-host&quot;, default=os.environ.get(&quot;BOT_HEALTH_HOST&quot;, &quot;0.0.0.0&quot;))&#10;    p.add_argument(&quot;--health-port&quot;, type=int, default=int(os.environ.get(&quot;PORT&quot;, os.environ.get(&quot;BOT_HEALTH_PORT&quot;, &quot;7860&quot;))))&#10;    p.add_argument(&quot;--enable-live&quot;, action=&quot;store_true&quot;, help=&quot;Allow live mirror orders after paper criteria is met&quot;)&#10;    p.add_argument(&#10;        &quot;--require-live-approval&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=True,&#10;        help=&quot;Require manual approval before arming live mirror orders&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--live-approval-mode&quot;,&#10;        choices=[&quot;either&quot;, &quot;both&quot;],&#10;        default=&quot;either&quot;,&#10;        help=&quot;Manual approval policy: 'either' (env or file) or 'both' (env and file)&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-approval-file&quot;, default=&quot;allow_live_trading.flag&quot;, help=&quot;Approval file path to authorize live trading&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=False,&#10;        help=&quot;When live is armed, place a tiny BUY then SELL to verify live order execution&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-startup-test-coin&quot;, choices=sorted(ALLOWED_COINS), default=&quot;BTC&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test-notional&quot;,&#10;        type=float,&#10;        default=12.0,&#10;        help=&quot;Approx USDC notional used for startup live order test (min effectively 10)&quot;,&#10;    )&#10;    p.add_argument(&quot;--hf-advisor-url&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_URL&quot;, &quot;&quot;), help=&quot;Optional external advisor endpoint URL&quot;)&#10;    p.add_argument(&quot;--hf-advisor-token&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_TOKEN&quot;, &quot;&quot;), help=&quot;Optional bearer token for advisor endpoint&quot;)&#10;    p.add_argument(&#10;        &quot;--hf-advisor-mode&quot;,&#10;        choices=[&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;],&#10;        default=os.environ.get(&quot;BOT_HF_ADVISOR_MODE&quot;, &quot;blend&quot;),&#10;        help=&quot;confirm=must match advisor, veto=advisor can block, blend=blend p_long with advisor p_long&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-min-confidence&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_MIN_CONFIDENCE&quot;, &quot;0.60&quot;)),&#10;        help=&quot;Minimum advisor confidence used by confirm/veto modes&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-blend-weight&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_BLEND_WEIGHT&quot;, &quot;0.35&quot;)),&#10;        help=&quot;Advisor p_long blending weight for blend mode [0..1]&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-timeout-seconds&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_TIMEOUT_SECONDS&quot;, &quot;4.0&quot;)),&#10;        help=&quot;HTTP timeout for advisor requests&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-hours&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_HOURS&quot;, &quot;6&quot;)),&#10;        help=&quot;Send periodic ML progress updates to Telegram every N hours (0 disables)&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-min-trades&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_MIN_TRADES&quot;, &quot;15&quot;)),&#10;        help=&quot;Minimum number of closed trades before periodic ML Telegram reports are sent&quot;,&#10;    )&#10;    return p&#10;&#10;&#10;def main() -&gt; None:&#10;    args = build_arg_parser().parse_args()&#10;    bot = AdaptiveHyperliquidBot(&#10;        interval=args.interval,&#10;        state_file=args.state_file,&#10;        report_file=args.report_file,&#10;        starting_equity=args.starting_equity,&#10;        risk_per_trade=args.risk_per_trade,&#10;        poll_seconds=args.poll_seconds,&#10;        exploration_rate=args.exploration_rate,&#10;        enable_live=args.enable_live,&#10;        require_live_approval=args.require_live_approval,&#10;        live_approval_mode=args.live_approval_mode,&#10;        live_approval_file=args.live_approval_file,&#10;        live_startup_test=args.live_startup_test,&#10;        live_startup_test_coin=args.live_startup_test_coin,&#10;        live_startup_test_notional=args.live_startup_test_notional,&#10;        hf_advisor_url=args.hf_advisor_url,&#10;        hf_advisor_token=args.hf_advisor_token,&#10;        hf_advisor_mode=args.hf_advisor_mode,&#10;        hf_advisor_min_confidence=args.hf_advisor_min_confidence,&#10;        hf_advisor_blend_weight=args.hf_advisor_blend_weight,&#10;        hf_advisor_timeout_seconds=args.hf_advisor_timeout_seconds,&#10;        telegram_ml_report_hours=args.telegram_ml_report_hours,&#10;        telegram_ml_report_min_trades=args.telegram_ml_report_min_trades,&#10;        health_host=args.health_host,&#10;        health_port=args.health_port,&#10;    )&#10;    bot.run()&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    main()&#10;&#10;&#10;" />
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-              <option name="filePath" value="$PROJECT_DIR$/advisor_smoke_test.py" />
-              <option name="originalContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;Smoke tests for HF advisor parsing behavior.&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;from typing import Any, Dict&#10;&#10;from hf_advisor import HuggingFaceAdvisor&#10;&#10;&#10;class _MockResponse:&#10;    def __init__(self, payload: Dict[str, Any], status: int = 200):&#10;        self._payload = payload&#10;        self.status_code = status&#10;&#10;    def raise_for_status(self) -&gt; None:&#10;        if self.status_code &gt;= 400:&#10;            raise RuntimeError(f&quot;HTTP {self.status_code}&quot;)&#10;&#10;    def json(self) -&gt; Dict[str, Any]:&#10;        return self._payload&#10;&#10;&#10;def run_smoke_test() -&gt; None:&#10;    advisor = HuggingFaceAdvisor(endpoint_url=&quot;https://example.test/predict&quot;, timeout_seconds=1.0)&#10;&#10;    import requests&#10;&#10;    original_post = requests.post&#10;    try:&#10;        def _mock_post_p_long(*args: Any, **kwargs: Any) -&gt; _MockResponse:&#10;            return _MockResponse({&quot;p_long&quot;: 0.8, &quot;confidence&quot;: 0.9})&#10;&#10;        requests.post = _mock_post_p_long  # type: ignore[assignment]&#10;        out = advisor.advise(&quot;BTC&quot;, &quot;15m&quot;, [0.1] * 7, {&quot;close&quot;: 1, &quot;high&quot;: 1, &quot;low&quot;: 1}, 1)&#10;        assert out is not None&#10;        assert out.side == &quot;long&quot;&#10;        assert out.p_long is not None and abs(out.p_long - 0.8) &lt; 1e-9&#10;&#10;        def _mock_post_side(*args: Any, **kwargs: Any) -&gt; _MockResponse:&#10;            return _MockResponse({&quot;side&quot;: &quot;short&quot;, &quot;confidence&quot;: 0.7})&#10;&#10;        requests.post = _mock_post_side  # type: ignore[assignment]&#10;        out = advisor.advise(&quot;ETH&quot;, &quot;15m&quot;, [0.2] * 7, {&quot;close&quot;: 2, &quot;high&quot;: 2, &quot;low&quot;: 2}, 2)&#10;        assert out is not None&#10;        assert out.side == &quot;short&quot;&#10;        assert abs(out.confidence - 0.7) &lt; 1e-9&#10;    finally:&#10;        requests.post = original_post  # type: ignore[assignment]&#10;&#10;    print(&quot;advisor_smoke_test: OK&quot;)&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    run_smoke_test()&#10;&#10;" />
-              <option name="updatedContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;Smoke tests for HF advisor parsing behavior.&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;from typing import Any, Dict&#10;&#10;from hf_advisor import HuggingFaceAdvisor&#10;&#10;&#10;class _MockResponse:&#10;    def __init__(self, payload: Dict[str, Any], status: int = 200):&#10;        self._payload = payload&#10;        self.status_code = status&#10;&#10;    def raise_for_status(self) -&gt; None:&#10;        if self.status_code &gt;= 400:&#10;            raise RuntimeError(f&quot;HTTP {self.status_code}&quot;)&#10;&#10;    def json(self) -&gt; Dict[str, Any]:&#10;        return self._payload&#10;&#10;&#10;def run_smoke_test() -&gt; None:&#10;    advisor = HuggingFaceAdvisor(endpoint_url=&quot;https://example.test/predict&quot;, timeout_seconds=1.0)&#10;    local_only = HuggingFaceAdvisor(endpoint_url=&quot;&quot;, timeout_seconds=1.0)&#10;    local_out = local_only.advise(&quot;BTC&quot;, &quot;15m&quot;, [0.05, 0.08, 0.1, 0.03, 0.01, 0.25, 0.0], {&quot;close&quot;: 100, &quot;high&quot;: 101, &quot;low&quot;: 99}, 1)&#10;    assert local_out is not None&#10;    assert local_out.source == &quot;local&quot;&#10;    assert 0.0 &lt;= local_out.confidence &lt;= 1.0&#10;    assert local_out.p_long is not None and 0.0 &lt;= local_out.p_long &lt;= 1.0&#10;&#10;    import requests&#10;&#10;    original_post = requests.post&#10;    try:&#10;        def _mock_post_p_long(*args: Any, **kwargs: Any) -&gt; _MockResponse:&#10;            return _MockResponse({&quot;p_long&quot;: 0.8, &quot;confidence&quot;: 0.9})&#10;&#10;        requests.post = _mock_post_p_long  # type: ignore[assignment]&#10;        out = advisor.advise(&quot;BTC&quot;, &quot;15m&quot;, [0.1] * 7, {&quot;close&quot;: 1, &quot;high&quot;: 1, &quot;low&quot;: 1}, 1)&#10;        assert out is not None&#10;        assert out.side == &quot;long&quot;&#10;        assert out.p_long is not None and abs(out.p_long - 0.8) &lt; 1e-9&#10;&#10;        def _mock_post_side(*args: Any, **kwargs: Any) -&gt; _MockResponse:&#10;            return _MockResponse({&quot;side&quot;: &quot;short&quot;, &quot;confidence&quot;: 0.7})&#10;&#10;        requests.post = _mock_post_side  # type: ignore[assignment]&#10;        out = advisor.advise(&quot;ETH&quot;, &quot;15m&quot;, [0.2] * 7, {&quot;close&quot;: 2, &quot;high&quot;: 2, &quot;low&quot;: 2}, 2)&#10;        assert out is not None&#10;        assert out.side == &quot;short&quot;&#10;        assert abs(out.confidence - 0.7) &lt; 1e-9&#10;    finally:&#10;        requests.post = original_post  # type: ignore[assignment]&#10;&#10;    print(&quot;advisor_smoke_test: OK&quot;)&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    run_smoke_test()&#10;&#10;" />
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-              <option name="filePath" value="$PROJECT_DIR$/hf_advisor.py" />
-              <option name="originalContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;Optional Hugging Face advisor client for entry gating.&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;import logging&#10;from dataclasses import dataclass&#10;from typing import Any, Dict, Optional&#10;&#10;import requests&#10;&#10;logger = logging.getLogger(__name__)&#10;&#10;&#10;@dataclass&#10;class AdvisorDecision:&#10;    side: str  # long | short | hold&#10;    confidence: float&#10;    p_long: Optional[float] = None&#10;    source: str = &quot;hf&quot;&#10;    error: Optional[str] = None&#10;&#10;&#10;class HuggingFaceAdvisor:&#10;    &quot;&quot;&quot;Thin HTTP client for an external advisor endpoint.&#10;&#10;    Endpoint is expected to return JSON containing one of:&#10;    - {&quot;side&quot;: &quot;long|short|hold&quot;, &quot;confidence&quot;: 0..1}&#10;    - {&quot;p_long&quot;: 0..1, &quot;confidence&quot;: 0..1}&#10;    &quot;&quot;&quot;&#10;&#10;    def __init__(self, endpoint_url: str, api_token: Optional[str] = None, timeout_seconds: float = 4.0):&#10;        self.endpoint_url = (endpoint_url or &quot;&quot;).strip()&#10;        self.api_token = (api_token or &quot;&quot;).strip()&#10;        self.timeout_seconds = float(max(0.5, timeout_seconds))&#10;&#10;    @property&#10;    def enabled(self) -&gt; bool:&#10;        return bool(self.endpoint_url)&#10;&#10;    def advise(&#10;        self,&#10;        coin: str,&#10;        interval: str,&#10;        features: list[float],&#10;        candle: Dict[str, float],&#10;        timestamp_ms: int,&#10;    ) -&gt; Optional[AdvisorDecision]:&#10;        if not self.enabled:&#10;            return None&#10;&#10;        payload = {&#10;            &quot;coin&quot;: coin,&#10;            &quot;interval&quot;: interval,&#10;            &quot;features&quot;: [float(v) for v in features],&#10;            &quot;candle&quot;: {&#10;                &quot;close&quot;: float(candle.get(&quot;close&quot;, 0.0)),&#10;                &quot;high&quot;: float(candle.get(&quot;high&quot;, 0.0)),&#10;                &quot;low&quot;: float(candle.get(&quot;low&quot;, 0.0)),&#10;            },&#10;            &quot;timestamp_ms&quot;: int(timestamp_ms),&#10;        }&#10;        headers = {&quot;Content-Type&quot;: &quot;application/json&quot;}&#10;        if self.api_token:&#10;            headers[&quot;Authorization&quot;] = f&quot;Bearer {self.api_token}&quot;&#10;&#10;        try:&#10;            response = requests.post(&#10;                self.endpoint_url,&#10;                json=payload,&#10;                headers=headers,&#10;                timeout=self.timeout_seconds,&#10;            )&#10;            response.raise_for_status()&#10;            data = response.json()&#10;            if not isinstance(data, dict):&#10;                return AdvisorDecision(side=&quot;hold&quot;, confidence=0.0, error=&quot;invalid_json_shape&quot;)&#10;&#10;            if &quot;p_long&quot; in data:&#10;                p_long = float(max(0.0, min(1.0, float(data.get(&quot;p_long&quot;, 0.5)))))&#10;                confidence = float(max(0.0, min(1.0, float(data.get(&quot;confidence&quot;, abs(p_long - 0.5) * 2.0)))))&#10;                side = &quot;long&quot; if p_long &gt;= 0.5 else &quot;short&quot;&#10;                return AdvisorDecision(side=side, confidence=confidence, p_long=p_long)&#10;&#10;            side = str(data.get(&quot;side&quot;, &quot;hold&quot;)).strip().lower()&#10;            if side not in {&quot;long&quot;, &quot;short&quot;, &quot;hold&quot;}:&#10;                side = &quot;hold&quot;&#10;            confidence = float(max(0.0, min(1.0, float(data.get(&quot;confidence&quot;, 0.0)))))&#10;            p_long = data.get(&quot;p_long&quot;)&#10;            p_long_val = None&#10;            if p_long is not None:&#10;                p_long_val = float(max(0.0, min(1.0, float(p_long))))&#10;            return AdvisorDecision(side=side, confidence=confidence, p_long=p_long_val)&#10;        except Exception as exc:&#10;            logger.warning(&quot;HF advisor request failed: %s&quot;, exc)&#10;            return AdvisorDecision(side=&quot;hold&quot;, confidence=0.0, error=str(exc))&#10;&#10;" />
-              <option name="updatedContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;Advisor engine used by adaptive bot.&#10;&#10;Runs fully in-process with a local heuristic model and can optionally query&#10;an external endpoint; if remote calls fail, local advice is still returned.&#10;&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;import logging&#10;import math&#10;from dataclasses import dataclass&#10;from typing import Any, Dict, Optional&#10;&#10;import requests&#10;&#10;logger = logging.getLogger(__name__)&#10;&#10;&#10;@dataclass&#10;class AdvisorDecision:&#10;    side: str  # long | short | hold&#10;    confidence: float&#10;    p_long: Optional[float] = None&#10;    source: str = &quot;local&quot;&#10;    error: Optional[str] = None&#10;&#10;&#10;class HuggingFaceAdvisor:&#10;    &quot;&quot;&quot;Always-on advisor with local inference and optional remote override.&#10;&#10;    Remote endpoint (optional) may return one of:&#10;    - {&quot;side&quot;: &quot;long|short|hold&quot;, &quot;confidence&quot;: 0..1}&#10;    - {&quot;p_long&quot;: 0..1, &quot;confidence&quot;: 0..1}&#10;    &quot;&quot;&quot;&#10;&#10;    def __init__(self, endpoint_url: str, api_token: Optional[str] = None, timeout_seconds: float = 4.0):&#10;        self.endpoint_url = (endpoint_url or &quot;&quot;).strip()&#10;        self.api_token = (api_token or &quot;&quot;).strip()&#10;        self.timeout_seconds = float(max(0.5, timeout_seconds))&#10;&#10;    @property&#10;    def enabled(self) -&gt; bool:&#10;        # Always enabled because local advisor is always available.&#10;        return True&#10;&#10;    @property&#10;    def remote_enabled(self) -&gt; bool:&#10;        return bool(self.endpoint_url)&#10;&#10;    def _local_advise(self, features: list[float], candle: Dict[str, float]) -&gt; AdvisorDecision:&#10;        # Feature order: ret_1, ret_3, ret_8, macd, atr14, rsi, vol_z&#10;        ret_1 = float(features[0]) if len(features) &gt; 0 else 0.0&#10;        ret_3 = float(features[1]) if len(features) &gt; 1 else 0.0&#10;        ret_8 = float(features[2]) if len(features) &gt; 2 else 0.0&#10;        macd = float(features[3]) if len(features) &gt; 3 else 0.0&#10;        atr14 = float(features[4]) if len(features) &gt; 4 else 0.0&#10;        rsi = float(features[5]) if len(features) &gt; 5 else 0.0&#10;        vol_z = float(features[6]) if len(features) &gt; 6 else 0.0&#10;&#10;        # Small bounded logistic score; ATR/volume terms penalize noisy regimes.&#10;        score = (&#10;            2.1 * ret_1&#10;            + 2.6 * ret_3&#10;            + 1.9 * ret_8&#10;            + 1.8 * macd&#10;            + 1.7 * rsi&#10;            - 1.2 * min(max(atr14 * 100.0, 0.0), 4.0)&#10;            - 0.25 * abs(vol_z)&#10;        )&#10;        score = float(max(-6.0, min(6.0, score)))&#10;        p_long = 1.0 / (1.0 + math.exp(-score))&#10;&#10;        # Confidence grows with distance from 0.5 and contracts in high-vol candles.&#10;        range_frac = 0.0&#10;        close = float(candle.get(&quot;close&quot;, 0.0))&#10;        if close &gt; 0:&#10;            high = float(candle.get(&quot;high&quot;, close))&#10;            low = float(candle.get(&quot;low&quot;, close))&#10;            range_frac = max(0.0, (high - low) / close)&#10;        confidence = min(1.0, abs(p_long - 0.5) * 2.0)&#10;        confidence *= max(0.35, 1.0 - min(range_frac * 25.0, 0.65))&#10;&#10;        side = &quot;hold&quot;&#10;        if p_long &gt;= 0.53:&#10;            side = &quot;long&quot;&#10;        elif p_long &lt;= 0.47:&#10;            side = &quot;short&quot;&#10;        return AdvisorDecision(side=side, confidence=float(confidence), p_long=float(p_long), source=&quot;local&quot;)&#10;&#10;    def _parse_remote_response(self, data: Any) -&gt; Optional[AdvisorDecision]:&#10;        if not isinstance(data, dict):&#10;            return None&#10;&#10;        if &quot;p_long&quot; in data:&#10;            p_long = float(max(0.0, min(1.0, float(data.get(&quot;p_long&quot;, 0.5)))))&#10;            confidence = float(max(0.0, min(1.0, float(data.get(&quot;confidence&quot;, abs(p_long - 0.5) * 2.0)))))&#10;            side = &quot;long&quot; if p_long &gt;= 0.5 else &quot;short&quot;&#10;            return AdvisorDecision(side=side, confidence=confidence, p_long=p_long, source=&quot;remote&quot;)&#10;&#10;        side = str(data.get(&quot;side&quot;, &quot;hold&quot;)).strip().lower()&#10;        if side not in {&quot;long&quot;, &quot;short&quot;, &quot;hold&quot;}:&#10;            side = &quot;hold&quot;&#10;        confidence = float(max(0.0, min(1.0, float(data.get(&quot;confidence&quot;, 0.0)))))&#10;        p_long = data.get(&quot;p_long&quot;)&#10;        p_long_val = None&#10;        if p_long is not None:&#10;            p_long_val = float(max(0.0, min(1.0, float(p_long))))&#10;        return AdvisorDecision(side=side, confidence=confidence, p_long=p_long_val, source=&quot;remote&quot;)&#10;&#10;    def advise(&#10;        self,&#10;        coin: str,&#10;        interval: str,&#10;        features: list[float],&#10;        candle: Dict[str, float],&#10;        timestamp_ms: int,&#10;    ) -&gt; Optional[AdvisorDecision]:&#10;        local = self._local_advise(features=features, candle=candle)&#10;        if not self.remote_enabled:&#10;            return local&#10;&#10;        payload = {&#10;            &quot;coin&quot;: coin,&#10;            &quot;interval&quot;: interval,&#10;            &quot;features&quot;: [float(v) for v in features],&#10;            &quot;candle&quot;: {&#10;                &quot;close&quot;: float(candle.get(&quot;close&quot;, 0.0)),&#10;                &quot;high&quot;: float(candle.get(&quot;high&quot;, 0.0)),&#10;                &quot;low&quot;: float(candle.get(&quot;low&quot;, 0.0)),&#10;            },&#10;            &quot;timestamp_ms&quot;: int(timestamp_ms),&#10;        }&#10;        headers = {&quot;Content-Type&quot;: &quot;application/json&quot;}&#10;        if self.api_token:&#10;            headers[&quot;Authorization&quot;] = f&quot;Bearer {self.api_token}&quot;&#10;&#10;        try:&#10;            response = requests.post(&#10;                self.endpoint_url,&#10;                json=payload,&#10;                headers=headers,&#10;                timeout=self.timeout_seconds,&#10;            )&#10;            response.raise_for_status()&#10;            remote = self._parse_remote_response(response.json())&#10;            return remote if remote is not None else local&#10;        except Exception as exc:&#10;            logger.warning(&quot;HF advisor request failed: %s&quot;, exc)&#10;            local.error = str(exc)&#10;            return local&#10;&#10;" />
+              <option name="originalContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;&#10;Adaptive Hyperliquid bot.&#10;&#10;- Trades only BTC, ETH, SOL.&#10;- Paper trades by default.&#10;- Uses online ML (SGDClassifier) to adapt entry confidence thresholds.&#10;- Persists learned state to disk so learning survives restarts.&#10;- Can optionally switch to live execution only after consistent paper performance.&#10;&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;import argparse&#10;import json&#10;import logging&#10;import math&#10;import random&#10;import os&#10;import signal&#10;import threading&#10;import time&#10;from dataclasses import asdict, dataclass&#10;from datetime import datetime, timedelta, timezone&#10;from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer&#10;from typing import Any, Dict, List, Optional, Tuple&#10;&#10;import joblib&#10;import numpy as np&#10;import pandas as pd&#10;from sklearn.linear_model import SGDClassifier&#10;&#10;from hf_advisor import HuggingFaceAdvisor&#10;from hbot import HyperliquidAPI&#10;from telegram_notifier import TelegramNotifier&#10;&#10;&#10;logging.basicConfig(level=logging.INFO, format=&quot;%(asctime)s - %(levelname)s - %(message)s&quot;)&#10;logger = logging.getLogger(__name__)&#10;&#10;&#10;ALLOWED_COINS = (&quot;BTC&quot;, &quot;ETH&quot;, &quot;SOL&quot;)&#10;INTERVAL_MS = {&#10;    &quot;1m&quot;: 60_000,&#10;    &quot;5m&quot;: 300_000,&#10;    &quot;15m&quot;: 900_000,&#10;    &quot;30m&quot;: 1_800_000,&#10;    &quot;1h&quot;: 3_600_000,&#10;}&#10;&#10;&#10;@dataclass&#10;class Position:&#10;    coin: str&#10;    side: str  # long | short&#10;    entry: float&#10;    size: float&#10;    sl: float&#10;    tp: float&#10;    entry_ts: int&#10;    entry_features: List[float]&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;@dataclass&#10;class ClosedTrade:&#10;    coin: str&#10;    side: str&#10;    entry: float&#10;    exit: float&#10;    size: float&#10;    pnl_usd: float&#10;    pnl_pct: float&#10;    reason: str&#10;    entry_ts: int&#10;    exit_ts: int&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;class LearningState:&#10;    &quot;&quot;&quot;Holds online models + strategy performance and persists to disk.&quot;&quot;&quot;&#10;&#10;    def __init__(self, state_path: str):&#10;        self.state_path = state_path&#10;        self.models: Dict[str, SGDClassifier] = {&#10;            c: SGDClassifier(loss=&quot;log_loss&quot;, alpha=1e-4, random_state=42) for c in ALLOWED_COINS&#10;        }&#10;        self.model_ready: Dict[str, bool] = {c: False for c in ALLOWED_COINS}&#10;        self.model_samples: Dict[str, int] = {c: 0 for c in ALLOWED_COINS}&#10;        self.thresholds: Dict[str, float] = {c: 0.56 for c in ALLOWED_COINS}&#10;        self.trade_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;trades&quot;: 0, &quot;wins&quot;: 0, &quot;losses&quot;: 0, &quot;gross_profit&quot;: 0.0, &quot;gross_loss&quot;: 0.0}&#10;            for c in ALLOWED_COINS&#10;        }&#10;        self.ml_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;count&quot;: 0.0, &quot;brier_sum&quot;: 0.0} for c in ALLOWED_COINS&#10;        }&#10;        self.recent_results: Dict[str, List[int]] = {c: [] for c in ALLOWED_COINS}&#10;        self.recent_brier: Dict[str, List[float]] = {c: [] for c in ALLOWED_COINS}&#10;        self.closed_trades: List[Dict[str, Any]] = []&#10;        self.trade_events: List[Dict[str, Any]] = []&#10;        self.equity_curve: List[Dict[str, float]] = []&#10;        self.paper_runtime: Dict[str, Any] = {&#10;            &quot;equity&quot;: None,&#10;            &quot;positions&quot;: {},&#10;            &quot;entry_features&quot;: {},&#10;        }&#10;        self.last_saved_ts = 0.0&#10;        self._load()&#10;&#10;    def _load(self) -&gt; None:&#10;        if not os.path.exists(self.state_path):&#10;            logger.info(&quot;No prior learning state found at %s, starting fresh.&quot;, self.state_path)&#10;            return&#10;        payload = joblib.load(self.state_path)&#10;        self.models = payload.get(&quot;models&quot;, self.models)&#10;        self.model_ready = payload.get(&quot;model_ready&quot;, self.model_ready)&#10;        self.model_samples = payload.get(&quot;model_samples&quot;, self.model_samples)&#10;        self.thresholds = payload.get(&quot;thresholds&quot;, self.thresholds)&#10;        self.trade_stats = payload.get(&quot;trade_stats&quot;, self.trade_stats)&#10;        self.ml_stats = payload.get(&quot;ml_stats&quot;, self.ml_stats)&#10;        self.recent_results = payload.get(&quot;recent_results&quot;, self.recent_results)&#10;        self.recent_brier = payload.get(&quot;recent_brier&quot;, self.recent_brier)&#10;        self.closed_trades = payload.get(&quot;closed_trades&quot;, self.closed_trades)&#10;        self.trade_events = payload.get(&quot;trade_events&quot;, self.trade_events)&#10;        self.equity_curve = payload.get(&quot;equity_curve&quot;, self.equity_curve)&#10;        self.paper_runtime = payload.get(&quot;paper_runtime&quot;, self.paper_runtime)&#10;        logger.info(&quot;Loaded learning state from %s&quot;, self.state_path)&#10;&#10;    def save(self, force: bool = False) -&gt; None:&#10;        now = time.time()&#10;        if not force and now - self.last_saved_ts &lt; 20:&#10;            return&#10;        payload = {&#10;            &quot;models&quot;: self.models,&#10;            &quot;model_ready&quot;: self.model_ready,&#10;            &quot;model_samples&quot;: self.model_samples,&#10;            &quot;thresholds&quot;: self.thresholds,&#10;            &quot;trade_stats&quot;: self.trade_stats,&#10;            &quot;ml_stats&quot;: self.ml_stats,&#10;            &quot;recent_results&quot;: self.recent_results,&#10;            &quot;recent_brier&quot;: self.recent_brier,&#10;            &quot;closed_trades&quot;: self.closed_trades[-2000:],&#10;            &quot;trade_events&quot;: self.trade_events[-5000:],&#10;            &quot;equity_curve&quot;: self.equity_curve[-5000:],&#10;            &quot;paper_runtime&quot;: self.paper_runtime,&#10;            &quot;saved_at&quot;: datetime.now(timezone.utc).isoformat(),&#10;        }&#10;        joblib.dump(payload, self.state_path)&#10;        self.last_saved_ts = now&#10;&#10;    def record_equity(self, equity: float, ts_ms: Optional[int] = None) -&gt; None:&#10;        ts = int(ts_ms if ts_ms is not None else time.time() * 1000)&#10;        e = float(equity)&#10;        if self.equity_curve and abs(float(self.equity_curve[-1][&quot;equity&quot;]) - e) &lt; 1e-12:&#10;            return&#10;        self.equity_curve.append({&quot;ts&quot;: float(ts), &quot;equity&quot;: e})&#10;        self.equity_curve = self.equity_curve[-5000:]&#10;&#10;    def set_runtime_state(&#10;        self,&#10;        paper_equity: float,&#10;        positions: Dict[str, Position],&#10;        entry_features: Dict[Tuple[str, int], List[float]],&#10;    ) -&gt; None:&#10;        self.paper_runtime = {&#10;            &quot;equity&quot;: float(paper_equity),&#10;            &quot;positions&quot;: {coin: asdict(pos) for coin, pos in positions.items()},&#10;            &quot;entry_features&quot;: {&#10;                f&quot;{coin}:{entry_ts}&quot;: [float(v) for v in feats]&#10;                for (coin, entry_ts), feats in entry_features.items()&#10;            },&#10;        }&#10;&#10;    def restore_runtime_state(self) -&gt; Tuple[Optional[float], Dict[str, Position], Dict[Tuple[str, int], List[float]]]:&#10;        equity_raw = self.paper_runtime.get(&quot;equity&quot;)&#10;        equity = float(equity_raw) if equity_raw is not None else None&#10;&#10;        restored_positions: Dict[str, Position] = {}&#10;        for coin, raw in (self.paper_runtime.get(&quot;positions&quot;) or {}).items():&#10;            try:&#10;                restored_positions[coin] = Position(&#10;                    coin=str(raw[&quot;coin&quot;]),&#10;                    side=str(raw[&quot;side&quot;]),&#10;                    entry=float(raw[&quot;entry&quot;]),&#10;                    size=float(raw[&quot;size&quot;]),&#10;                    sl=float(raw[&quot;sl&quot;]),&#10;                    tp=float(raw[&quot;tp&quot;]),&#10;                    entry_ts=int(raw[&quot;entry_ts&quot;]),&#10;                    entry_features=[float(v) for v in raw.get(&quot;entry_features&quot;, [])],&#10;                    entry_p_long=float(raw.get(&quot;entry_p_long&quot;, 0.5)),&#10;                )&#10;            except Exception:&#10;                continue&#10;&#10;        restored_features: Dict[Tuple[str, int], List[float]] = {}&#10;        for key, vals in (self.paper_runtime.get(&quot;entry_features&quot;) or {}).items():&#10;            try:&#10;                coin, entry_ts_str = key.split(&quot;:&quot;, 1)&#10;                restored_features[(coin, int(entry_ts_str))] = [float(v) for v in vals]&#10;            except Exception:&#10;                continue&#10;&#10;        return equity, restored_positions, restored_features&#10;&#10;    def record_trade_event(self, event_type: str, payload: Dict[str, Any]) -&gt; None:&#10;        self.trade_events.append(&#10;            {&#10;                &quot;event&quot;: event_type,&#10;                &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;                **payload,&#10;            }&#10;        )&#10;        self.trade_events = self.trade_events[-5000:]&#10;&#10;    def update_after_trade(self, trade: ClosedTrade, entry_features: List[float]) -&gt; None:&#10;        trade_won = 1 if trade.pnl_usd &gt; 0 else 0&#10;        # Model predicts p(long wins), so short outcomes must be direction-adjusted.&#10;        label_long = trade_won if trade.side == &quot;long&quot; else (1 - trade_won)&#10;        x = np.asarray(entry_features, dtype=float).reshape(1, -1)&#10;        y = np.asarray([label_long], dtype=int)&#10;&#10;        model = self.models[trade.coin]&#10;        if not self.model_ready[trade.coin]:&#10;            model.partial_fit(x, y, classes=np.array([0, 1]))&#10;            self.model_ready[trade.coin] = True&#10;        else:&#10;            model.partial_fit(x, y)&#10;        self.model_samples[trade.coin] = int(self.model_samples.get(trade.coin, 0)) + 1&#10;&#10;        stats = self.trade_stats[trade.coin]&#10;        stats[&quot;trades&quot;] += 1&#10;        if trade_won == 1:&#10;            stats[&quot;wins&quot;] += 1&#10;            stats[&quot;gross_profit&quot;] += max(0.0, trade.pnl_usd)&#10;        else:&#10;            stats[&quot;losses&quot;] += 1&#10;            stats[&quot;gross_loss&quot;] += abs(min(0.0, trade.pnl_usd))&#10;&#10;        p = float(np.clip(trade.entry_p_long, 0.0, 1.0))&#10;        brier = float((p - label_long) ** 2)&#10;        ml = self.ml_stats[trade.coin]&#10;        ml[&quot;count&quot;] += 1&#10;        ml[&quot;brier_sum&quot;] += brier&#10;        self.recent_brier[trade.coin].append(brier)&#10;        self.recent_brier[trade.coin] = self.recent_brier[trade.coin][-100:]&#10;&#10;        self.recent_results[trade.coin].append(trade_won)&#10;        self.recent_results[trade.coin] = self.recent_results[trade.coin][-30:]&#10;&#10;        # Adaptive threshold: lower threshold when recent win rate is strong, raise when weak.&#10;        recent = self.recent_results[trade.coin]&#10;        if recent:&#10;            wr = float(sum(recent)) / len(recent)&#10;            target = 0.56 + (0.60 - wr) * 0.25&#10;            self.thresholds[trade.coin] = float(np.clip(target, 0.52, 0.65))&#10;&#10;        self.closed_trades.append(asdict(trade))&#10;        self.save()&#10;&#10;    def enough_samples(self, coin: str, min_samples: int) -&gt; bool:&#10;        return int(self.model_samples.get(coin, 0)) &gt;= int(max(1, min_samples))&#10;&#10;    def predict_long_probability(self, coin: str, features: List[float]) -&gt; float:&#10;        if not self.model_ready.get(coin, False):&#10;            return 0.50&#10;        x = np.asarray(features, dtype=float).reshape(1, -1)&#10;        try:&#10;            p = float(self.models[coin].predict_proba(x)[0][1])&#10;        except Exception:&#10;            return 0.50&#10;        if math.isnan(p):&#10;            return 0.50&#10;        return float(np.clip(p, 0.01, 0.99))&#10;&#10;    def global_metrics(self) -&gt; Dict[str, float]:&#10;        trades = wins = 0&#10;        gross_profit = gross_loss = 0.0&#10;        for coin in ALLOWED_COINS:&#10;            s = self.trade_stats[coin]&#10;            trades += int(s[&quot;trades&quot;])&#10;            wins += int(s[&quot;wins&quot;])&#10;            gross_profit += float(s[&quot;gross_profit&quot;])&#10;            gross_loss += float(s[&quot;gross_loss&quot;])&#10;        win_rate = (wins / trades) if trades else 0.0&#10;        profit_factor = (gross_profit / gross_loss) if gross_loss &gt; 0 else (10.0 if gross_profit &gt; 0 else 0.0)&#10;        return {&#10;            &quot;trades&quot;: float(trades),&#10;            &quot;win_rate&quot;: float(win_rate),&#10;            &quot;profit_factor&quot;: float(profit_factor),&#10;            &quot;gross_profit&quot;: float(gross_profit),&#10;            &quot;gross_loss&quot;: float(gross_loss),&#10;        }&#10;&#10;    def ml_progress_metrics(self) -&gt; Dict[str, Any]:&#10;        total_count = 0.0&#10;        total_brier_sum = 0.0&#10;        per_coin: Dict[str, Any] = {}&#10;&#10;        all_recent_brier: List[float] = []&#10;        for coin in ALLOWED_COINS:&#10;            stat = self.ml_stats.get(coin, {})&#10;            cnt = float(stat.get(&quot;count&quot;, 0.0))&#10;            bsum = float(stat.get(&quot;brier_sum&quot;, 0.0))&#10;            total_count += cnt&#10;            total_brier_sum += bsum&#10;&#10;            recent = [float(v) for v in self.recent_brier.get(coin, [])]&#10;            all_recent_brier.extend(recent)&#10;            per_coin[coin] = {&#10;                &quot;samples&quot;: int(cnt),&#10;                &quot;brier&quot;: (bsum / cnt) if cnt &gt; 0 else None,&#10;                &quot;brier_rolling_50&quot;: (float(np.mean(recent[-50:])) if recent else None),&#10;                &quot;brier_rolling_100&quot;: (float(np.mean(recent)) if recent else None),&#10;            }&#10;&#10;        brier_global = (total_brier_sum / total_count) if total_count &gt; 0 else None&#10;        brier_rolling_50 = float(np.mean(all_recent_brier[-50:])) if all_recent_brier else None&#10;        brier_rolling_100 = float(np.mean(all_recent_brier[-100:])) if all_recent_brier else None&#10;&#10;        pnl = [float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in self.closed_trades]&#10;        expectancy_30 = float(np.mean(pnl[-30:])) if pnl else None&#10;        expectancy_100 = float(np.mean(pnl[-100:])) if pnl else None&#10;&#10;        max_drawdown_pct = None&#10;        peak = None&#10;        max_dd = 0.0&#10;        for point in self.equity_curve:&#10;            eq = float(point.get(&quot;equity&quot;, 0.0))&#10;            if peak is None or eq &gt; peak:&#10;                peak = eq&#10;            if peak and peak &gt; 0:&#10;                dd = (peak - eq) / peak&#10;                if dd &gt; max_dd:&#10;                    max_dd = dd&#10;        if peak is not None:&#10;            max_drawdown_pct = max_dd * 100.0&#10;&#10;        return {&#10;            &quot;samples&quot;: int(total_count),&#10;            &quot;brier&quot;: brier_global,&#10;            &quot;brier_rolling_50&quot;: brier_rolling_50,&#10;            &quot;brier_rolling_100&quot;: brier_rolling_100,&#10;            &quot;expectancy_usd_30&quot;: expectancy_30,&#10;            &quot;expectancy_usd_100&quot;: expectancy_100,&#10;            &quot;max_drawdown_pct&quot;: max_drawdown_pct,&#10;            &quot;per_coin&quot;: per_coin,&#10;        }&#10;&#10;    def consistently_profitable(self) -&gt; bool:&#10;        m = self.global_metrics()&#10;        return m[&quot;trades&quot;] &gt;= 30 and m[&quot;win_rate&quot;] &gt;= 0.58 and m[&quot;profit_factor&quot;] &gt;= 1.25&#10;&#10;&#10;class PaperBroker:&#10;    def __init__(self, starting_equity: float = 10000.0, taker_fee_bps: float = 4.32):&#10;        self.equity = float(starting_equity)&#10;        self.taker_fee = taker_fee_bps / 10_000.0&#10;        self.positions: Dict[str, Position] = {}&#10;        self.closed: List[ClosedTrade] = []&#10;&#10;    def has_position(self, coin: str) -&gt; bool:&#10;        return coin in self.positions&#10;&#10;    def open_position(self, pos: Position) -&gt; None:&#10;        self.positions[pos.coin] = pos&#10;&#10;    def maybe_close(self, coin: str, candle: Dict[str, float], now_ts: int) -&gt; Optional[ClosedTrade]:&#10;        pos = self.positions.get(coin)&#10;        if not pos:&#10;            return None&#10;&#10;        high = candle[&quot;high&quot;]&#10;        low = candle[&quot;low&quot;]&#10;        close = candle[&quot;close&quot;]&#10;&#10;        exit_px = close&#10;        reason = &quot;model_exit&quot;&#10;&#10;        if pos.side == &quot;long&quot;:&#10;            if low &lt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif high &gt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;        else:&#10;            if high &gt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif low &lt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;&#10;        if reason == &quot;model_exit&quot;:&#10;            return None&#10;&#10;        gross = (exit_px - pos.entry) * pos.size if pos.side == &quot;long&quot; else (pos.entry - exit_px) * pos.size&#10;        fees = (pos.entry * pos.size + exit_px * pos.size) * self.taker_fee&#10;        pnl = gross - fees&#10;        pnl_pct = (pnl / max(pos.entry * pos.size, 1e-9)) * 100.0&#10;&#10;        trade = ClosedTrade(&#10;            coin=coin,&#10;            side=pos.side,&#10;            entry=pos.entry,&#10;            exit=exit_px,&#10;            size=pos.size,&#10;            pnl_usd=pnl,&#10;            pnl_pct=pnl_pct,&#10;            reason=reason,&#10;            entry_ts=pos.entry_ts,&#10;            exit_ts=now_ts,&#10;            entry_p_long=pos.entry_p_long,&#10;        )&#10;        self.equity += pnl&#10;        self.closed.append(trade)&#10;        del self.positions[coin]&#10;        return trade&#10;&#10;&#10;def parse_candles(raw: List[Dict[str, Any]]) -&gt; pd.DataFrame:&#10;    rows = []&#10;    for c in raw:&#10;        rows.append(&#10;            {&#10;                &quot;ts&quot;: int(c.get(&quot;t&quot;) or c.get(&quot;time&quot;) or c.get(&quot;T&quot;) or 0),&#10;                &quot;open&quot;: float(c.get(&quot;o&quot;) or c.get(&quot;open&quot;) or c.get(&quot;openPx&quot;) or 0.0),&#10;                &quot;high&quot;: float(c.get(&quot;h&quot;) or c.get(&quot;high&quot;) or c.get(&quot;highPx&quot;) or 0.0),&#10;                &quot;low&quot;: float(c.get(&quot;l&quot;) or c.get(&quot;low&quot;) or c.get(&quot;lowPx&quot;) or 0.0),&#10;                &quot;close&quot;: float(c.get(&quot;c&quot;) or c.get(&quot;close&quot;) or c.get(&quot;closePx&quot;) or 0.0),&#10;                &quot;volume&quot;: float(c.get(&quot;v&quot;) or c.get(&quot;volume&quot;) or 0.0),&#10;            }&#10;        )&#10;    df = pd.DataFrame(rows).sort_values(&quot;ts&quot;)&#10;    df = df[(df[&quot;close&quot;] &gt; 0) &amp; (df[&quot;high&quot;] &gt; 0) &amp; (df[&quot;low&quot;] &gt; 0)]&#10;    return df.reset_index(drop=True)&#10;&#10;&#10;def build_features(df: pd.DataFrame) -&gt; Optional[List[float]]:&#10;    if len(df) &lt; 60:&#10;        return None&#10;&#10;    close = df[&quot;close&quot;]&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    vol = df[&quot;volume&quot;].replace(0, np.nan).ffill().fillna(1.0)&#10;&#10;    ret_1 = close.pct_change(1)&#10;    ret_3 = close.pct_change(3)&#10;    ret_8 = close.pct_change(8)&#10;    ema_12 = close.ewm(span=12).mean()&#10;    ema_26 = close.ewm(span=26).mean()&#10;    macd = (ema_12 - ema_26) / close&#10;&#10;    # ATR-like normalized range.&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr14 = tr.rolling(14).mean() / close&#10;&#10;    delta = close.diff()&#10;    up = delta.clip(lower=0).rolling(14).mean()&#10;    down = (-delta.clip(upper=0)).rolling(14).mean()&#10;    rs = up / down.replace(0, np.nan)&#10;    rsi = 100.0 - (100.0 / (1.0 + rs))&#10;    rsi_scaled = (rsi - 50.0) / 50.0&#10;&#10;    vol_z = (np.log(vol) - np.log(vol).rolling(20).mean()) / np.log(vol).rolling(20).std()&#10;&#10;    feat = pd.DataFrame(&#10;        {&#10;            &quot;ret_1&quot;: ret_1,&#10;            &quot;ret_3&quot;: ret_3,&#10;            &quot;ret_8&quot;: ret_8,&#10;            &quot;macd&quot;: macd,&#10;            &quot;atr14&quot;: atr14,&#10;            &quot;rsi&quot;: rsi_scaled,&#10;            &quot;vol_z&quot;: vol_z,&#10;        }&#10;    ).replace([np.inf, -np.inf], np.nan)&#10;&#10;    last = feat.iloc[-1].fillna(0.0)&#10;    return [float(last[k]) for k in [&quot;ret_1&quot;, &quot;ret_3&quot;, &quot;ret_8&quot;, &quot;macd&quot;, &quot;atr14&quot;, &quot;rsi&quot;, &quot;vol_z&quot;]]&#10;&#10;&#10;def atr_for_risk(df: pd.DataFrame) -&gt; float:&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    close = df[&quot;close&quot;]&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr = float(tr.rolling(14).mean().iloc[-1])&#10;    if not np.isfinite(atr) or atr &lt;= 0:&#10;        atr = float(close.iloc[-1] * 0.01)&#10;    return atr&#10;&#10;&#10;class AdaptiveHyperliquidBot:&#10;    def __init__(&#10;        self,&#10;        interval: str,&#10;        state_file: str,&#10;        report_file: str,&#10;        starting_equity: float,&#10;        risk_per_trade: float,&#10;        poll_seconds: int,&#10;        exploration_rate: float,&#10;        enable_live: bool,&#10;        require_live_approval: bool,&#10;        live_approval_mode: str,&#10;        live_approval_file: str,&#10;        live_startup_test: bool,&#10;        live_startup_test_coin: str,&#10;        live_startup_test_notional: float,&#10;        hf_advisor_url: str,&#10;        hf_advisor_token: str,&#10;        hf_advisor_mode: str,&#10;        hf_advisor_min_confidence: float,&#10;        hf_advisor_blend_weight: float,&#10;        hf_advisor_timeout_seconds: float,&#10;        min_model_samples_for_signal: int,&#10;        pause_on_negative_expectancy: bool,&#10;        pause_expectancy_lookback: int,&#10;        pause_expectancy_threshold_usd: float,&#10;        pause_max_drawdown_pct: float,&#10;        telegram_ml_report_hours: float,&#10;        telegram_ml_report_min_trades: int,&#10;        health_host: str,&#10;        health_port: int,&#10;    ):&#10;        self.interval = interval&#10;        self.poll_seconds = poll_seconds&#10;        self.risk_per_trade = risk_per_trade&#10;        self.exploration_rate = float(np.clip(exploration_rate, 0.0, 0.5))&#10;        self.enable_live = enable_live&#10;        self.require_live_approval = require_live_approval&#10;        self.live_approval_mode = live_approval_mode&#10;        self.live_approval_file = live_approval_file&#10;        self.live_startup_test = live_startup_test&#10;        self.live_startup_test_coin = live_startup_test_coin&#10;        self.live_startup_test_notional = float(max(10.0, live_startup_test_notional))&#10;        self.hf_advisor_mode = (hf_advisor_mode or &quot;blend&quot;).strip().lower()&#10;        if self.hf_advisor_mode not in {&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;}:&#10;            logger.warning(&quot;Unknown advisor mode '%s'; defaulting to 'blend'.&quot;, self.hf_advisor_mode)&#10;            self.hf_advisor_mode = &quot;blend&quot;&#10;        self.hf_advisor_min_confidence = float(np.clip(hf_advisor_min_confidence, 0.0, 1.0))&#10;        self.hf_advisor_blend_weight = float(np.clip(hf_advisor_blend_weight, 0.0, 1.0))&#10;        self.hf_advisor = HuggingFaceAdvisor(&#10;            endpoint_url=hf_advisor_url,&#10;            api_token=hf_advisor_token,&#10;            timeout_seconds=hf_advisor_timeout_seconds,&#10;        )&#10;        self.min_model_samples_for_signal = int(max(10, min_model_samples_for_signal))&#10;        self.pause_on_negative_expectancy = bool(pause_on_negative_expectancy)&#10;        self.pause_expectancy_lookback = int(max(10, pause_expectancy_lookback))&#10;        self.pause_expectancy_threshold_usd = float(pause_expectancy_threshold_usd)&#10;        self.pause_max_drawdown_pct = float(max(0.0, pause_max_drawdown_pct))&#10;        self.telegram_ml_report_hours = float(max(0.0, telegram_ml_report_hours))&#10;        self.telegram_ml_report_min_trades = int(max(0, telegram_ml_report_min_trades))&#10;        self.health_host = health_host&#10;        self.health_port = int(health_port)&#10;&#10;        self.api = HyperliquidAPI(verify_ssl=True)&#10;        self.learning = LearningState(state_file)&#10;        self.paper = PaperBroker(starting_equity=starting_equity)&#10;        self.report_file = report_file&#10;        self.notifier = TelegramNotifier(disable_on_error=True)&#10;        self.notifications_enabled = bool(&#10;            os.environ.get(&quot;TELEGRAM_BOT_TOKEN&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;TELEGRAM_CHAT_ID&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;PROXY_URL&quot;, &quot;&quot;).strip()&#10;        )&#10;        self._last_summary_date: Optional[str] = None&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(&quot;Adaptive ML bot started. Trade notifications are enabled.&quot;)&#10;        else:&#10;            logger.info(&quot;Telegram notifications disabled (need TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID, PROXY_URL).&quot;)&#10;&#10;        self._running = True&#10;        self._live_clients: Optional[Dict[str, Any]] = None&#10;        self._live_readiness_alerted = False&#10;        self._live_startup_test_done = False&#10;        self._live_startup_test_passed: Optional[bool] = None&#10;        self._http_server: Optional[ThreadingHTTPServer] = None&#10;        self._entry_features: Dict[Tuple[str, int], List[float]] = {}&#10;        self._last_ml_report_ts: float = 0.0&#10;        self._advisor_stats: Dict[str, int] = {&#10;            &quot;requests&quot;: 0,&#10;            &quot;errors&quot;: 0,&#10;            &quot;remote_used&quot;: 0,&#10;            &quot;local_used&quot;: 0,&#10;            &quot;held_by_advisor&quot;: 0,&#10;            &quot;kept_model_signal&quot;: 0,&#10;            &quot;used_advisor_blend&quot;: 0,&#10;        }&#10;        self._restore_runtime_state()&#10;&#10;    def _interval_ms(self) -&gt; int:&#10;        if self.interval not in INTERVAL_MS:&#10;            raise ValueError(f&quot;Unsupported interval '{self.interval}'. Supported: {sorted(INTERVAL_MS)}&quot;)&#10;        return INTERVAL_MS[self.interval]&#10;&#10;    def stop(self, *_: Any) -&gt; None:&#10;        logger.info(&quot;Received shutdown signal, flushing learning state.&quot;)&#10;        self._running = False&#10;        if self._http_server is not None:&#10;            self._http_server.shutdown()&#10;            self._http_server.server_close()&#10;            self._http_server = None&#10;        self._persist_runtime_state()&#10;        self.learning.save(force=True)&#10;        self.write_report()&#10;&#10;    def status_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        approval = self._live_approval_status_details()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;            &quot;metrics&quot;: metrics,&#10;            &quot;ml_progress&quot;: self.learning.ml_progress_metrics(),&#10;            &quot;thresholds&quot;: self.learning.thresholds,&#10;            &quot;state_file&quot;: self.learning.state_path,&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_mode&quot;: self.live_approval_mode,&#10;            &quot;live_approval_granted&quot;: approval[&quot;granted&quot;],&#10;            &quot;live_approval_checks&quot;: approval,&#10;            &quot;live_startup_test_enabled&quot;: self.live_startup_test,&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;trade_events_recorded&quot;: len(self.learning.trade_events),&#10;            &quot;advisor&quot;: {&#10;                &quot;enabled&quot;: self.hf_advisor.enabled,&#10;                &quot;remote_enabled&quot;: self.hf_advisor.remote_enabled,&#10;                &quot;mode&quot;: self.hf_advisor_mode,&#10;                &quot;min_confidence&quot;: self.hf_advisor_min_confidence,&#10;                &quot;blend_weight&quot;: self.hf_advisor_blend_weight,&#10;                &quot;stats&quot;: self._advisor_stats,&#10;            },&#10;            &quot;model_samples&quot;: self.learning.model_samples,&#10;            &quot;min_model_samples_for_signal&quot;: self.min_model_samples_for_signal,&#10;        }&#10;&#10;    def status_overview_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        ml_progress = self.learning.ml_progress_metrics()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;trades&quot;: int(metrics[&quot;trades&quot;]),&#10;            &quot;win_rate&quot;: metrics[&quot;win_rate&quot;],&#10;            &quot;profit_factor&quot;: metrics[&quot;profit_factor&quot;],&#10;            &quot;brier_rolling_50&quot;: ml_progress[&quot;brier_rolling_50&quot;],&#10;            &quot;expectancy_usd_30&quot;: ml_progress[&quot;expectancy_usd_30&quot;],&#10;            &quot;max_drawdown_pct&quot;: ml_progress[&quot;max_drawdown_pct&quot;],&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_granted&quot;: self._live_approval_granted(),&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;advisor_enabled&quot;: self.hf_advisor.enabled,&#10;            &quot;advisor_remote_enabled&quot;: self.hf_advisor.remote_enabled,&#10;            &quot;advisor_mode&quot;: self.hf_advisor_mode,&#10;        }&#10;&#10;    def _apply_hf_advisor(&#10;        self,&#10;        coin: str,&#10;        features: List[float],&#10;        candle: Dict[str, float],&#10;        now_ts: int,&#10;        p_long: float,&#10;        threshold: float,&#10;        side: Optional[str],&#10;    ) -&gt; Tuple[Optional[str], str, Optional[float], Optional[str], Optional[float]]:&#10;        mode = self.hf_advisor_mode&#10;&#10;        self._advisor_stats[&quot;requests&quot;] += 1&#10;        advice = self.hf_advisor.advise(&#10;            coin=coin,&#10;            interval=self.interval,&#10;            features=features,&#10;            candle=candle,&#10;            timestamp_ms=now_ts,&#10;        )&#10;        if advice is None:&#10;            return side, &quot;advisor_unavailable&quot;, p_long, None, None&#10;&#10;        if advice.error:&#10;            self._advisor_stats[&quot;errors&quot;] += 1&#10;        if advice.source == &quot;remote&quot;:&#10;            self._advisor_stats[&quot;remote_used&quot;] += 1&#10;        else:&#10;            self._advisor_stats[&quot;local_used&quot;] += 1&#10;&#10;        advisor_side = advice.side&#10;        advisor_conf = float(advice.confidence)&#10;        if mode == &quot;blend&quot; and advice.p_long is not None:&#10;            blended_p = (1.0 - self.hf_advisor_blend_weight) * p_long + self.hf_advisor_blend_weight * float(advice.p_long)&#10;            blended_p = float(np.clip(blended_p, 0.01, 0.99))&#10;            blended_side: Optional[str] = None&#10;            if blended_p &gt;= threshold:&#10;                blended_side = &quot;long&quot;&#10;            elif blended_p &lt;= (1.0 - threshold):&#10;                blended_side = &quot;short&quot;&#10;            if blended_side is None:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_hold_blend&quot;, blended_p, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;used_advisor_blend&quot;] += 1&#10;            return blended_side, &quot;advisor_blend&quot;, blended_p, advisor_side, advisor_conf&#10;&#10;        if side is None:&#10;            return None, &quot;advisor_no_model_side&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;confirm&quot;:&#10;            if advisor_side == side and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;                return side, &quot;advisor_confirm&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;            return None, &quot;advisor_block_confirm&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;veto&quot;:&#10;            opposite = (side == &quot;long&quot; and advisor_side == &quot;short&quot;) or (side == &quot;short&quot; and advisor_side == &quot;long&quot;)&#10;            if opposite and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_veto&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;            return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;    def open_trades_payload(self) -&gt; Dict[str, Any]:&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;        }&#10;&#10;    def _restore_runtime_state(self) -&gt; None:&#10;        equity, positions, entry_features = self.learning.restore_runtime_state()&#10;        if equity is not None:&#10;            self.paper.equity = float(equity)&#10;        if positions:&#10;            self.paper.positions = positions&#10;        if entry_features:&#10;            self._entry_features = entry_features&#10;        if equity is not None or positions:&#10;            logger.info(&#10;                &quot;Restored paper runtime state: equity=%.2f open_positions=%d tracked_entries=%d&quot;,&#10;                self.paper.equity,&#10;                len(self.paper.positions),&#10;                len(self._entry_features),&#10;            )&#10;&#10;    def _persist_runtime_state(self) -&gt; None:&#10;        self.learning.record_equity(self.paper.equity)&#10;        self.learning.set_runtime_state(&#10;            paper_equity=self.paper.equity,&#10;            positions=self.paper.positions,&#10;            entry_features=self._entry_features,&#10;        )&#10;&#10;    def _entry_pause_reason(self) -&gt; Optional[str]:&#10;        if not self.pause_on_negative_expectancy:&#10;            return None&#10;        pnl = [float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in self.learning.closed_trades]&#10;        if len(pnl) &lt; self.pause_expectancy_lookback:&#10;            return None&#10;        exp_recent = float(np.mean(pnl[-self.pause_expectancy_lookback:]))&#10;        if exp_recent &gt;= self.pause_expectancy_threshold_usd:&#10;            return None&#10;&#10;        max_dd = self.learning.ml_progress_metrics().get(&quot;max_drawdown_pct&quot;)&#10;        max_dd_val = float(max_dd) if max_dd is not None else 0.0&#10;        if max_dd_val &lt; self.pause_max_drawdown_pct:&#10;            return None&#10;        return (&#10;            f&quot;safety_pause expectancy_{self.pause_expectancy_lookback}={exp_recent:+.3f} &quot;&#10;            f&quot;&lt; {self.pause_expectancy_threshold_usd:+.3f} and max_dd={max_dd_val:.2f}%&quot;&#10;        )&#10;&#10;    def _live_approval_granted(self) -&gt; bool:&#10;        return self._live_approval_status_details()[&quot;granted&quot;]&#10;&#10;    def _live_approval_status_details(self) -&gt; Dict[str, Any]:&#10;        env_approved = os.environ.get(&quot;ALLOW_LIVE_TRADING&quot;, &quot;&quot;).strip().lower() in {&quot;1&quot;, &quot;true&quot;, &quot;yes&quot;, &quot;y&quot;}&#10;        file_approved = bool(self.live_approval_file and os.path.exists(self.live_approval_file))&#10;        mode = (self.live_approval_mode or &quot;either&quot;).strip().lower()&#10;        missing: List[str] = []&#10;        if mode == &quot;both&quot;:&#10;            granted = env_approved and file_approved&#10;            if not env_approved:&#10;                missing.append(&quot;env:ALLOW_LIVE_TRADING&quot;)&#10;            if not file_approved:&#10;                missing.append(f&quot;file:{self.live_approval_file}&quot;)&#10;        else:&#10;            granted = env_approved or file_approved&#10;            if not granted:&#10;                missing = [&quot;env:ALLOW_LIVE_TRADING&quot;, f&quot;file:{self.live_approval_file}&quot;]&#10;        return {&#10;            &quot;mode&quot;: mode,&#10;            &quot;env_approved&quot;: env_approved,&#10;            &quot;file_approved&quot;: file_approved,&#10;            &quot;missing&quot;: missing,&#10;            &quot;granted&quot;: granted,&#10;        }&#10;&#10;    def _maybe_notify_live_readiness(self) -&gt; None:&#10;        if self._live_readiness_alerted or not self.learning.consistently_profitable():&#10;            return&#10;        m = self.learning.global_metrics()&#10;        msg = (&#10;            &quot;Paper performance gate reached.\n&quot;&#10;            f&quot;Trades: {int(m['trades'])}, win rate: {m['win_rate'] * 100.0:.1f}%, PF: {m['profit_factor']:.2f}.\n&quot;&#10;            &quot;Grant live approval to arm live mode. &quot;&#10;            f&quot;Approval mode={self.live_approval_mode}. &quot;&#10;            f&quot;Set ALLOW_LIVE_TRADING=1 and/or create {self.live_approval_file} (based on mode).&quot;&#10;        )&#10;        logger.warning(msg)&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(msg)&#10;        self._live_readiness_alerted = True&#10;&#10;    def _live_order_filled(self, result: Any) -&gt; bool:&#10;        if not isinstance(result, dict):&#10;            return False&#10;        try:&#10;            statuses = result.get(&quot;response&quot;, {}).get(&quot;data&quot;, {}).get(&quot;statuses&quot;, [])&#10;            errors = [s.get(&quot;error&quot;) for s in statuses if &quot;error&quot; in s]&#10;            if errors:&#10;                return False&#10;            filled_sz = sum(float(s[&quot;filled&quot;].get(&quot;totalSz&quot;, 0.0)) for s in statuses if &quot;filled&quot; in s)&#10;            return filled_sz &gt; 0&#10;        except Exception:&#10;            return False&#10;&#10;    def _calc_test_size(self, coin: str, px: float, min_notional: float) -&gt; float:&#10;        sz_dec = self.api.get_sz_decimals(coin)&#10;        step = 10 ** (-sz_dec)&#10;        raw = min_notional / max(px, 1e-9)&#10;        steps = max(1, int(math.ceil(raw / step)))&#10;        return float(round(steps * step, sz_dec))&#10;&#10;    def _run_live_startup_test(self) -&gt; None:&#10;        if self._live_clients is None or self._live_startup_test_done or not self.live_startup_test:&#10;            return&#10;&#10;        self._live_startup_test_done = True&#10;        coin = self.live_startup_test_coin&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;&#10;        try:&#10;            df = self._fetch_df(coin, lookback_bars=120)&#10;            if df is None:&#10;                raise RuntimeError(f&quot;No candle data available for startup live test on {coin}&quot;)&#10;&#10;            entry_px = float(df.iloc[-1][&quot;close&quot;])&#10;            test_size = self._calc_test_size(coin=coin, px=entry_px, min_notional=self.live_startup_test_notional)&#10;&#10;            logger.warning(&#10;                &quot;Running startup live order test: BUY then SELL %s size=%.8f around px=%.4f&quot;,&#10;                coin,&#10;                test_size,&#10;                entry_px,&#10;            )&#10;&#10;            buy_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=True,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;            time.sleep(1.2)&#10;            sell_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=False,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;&#10;            buy_ok = self._live_order_filled(buy_result)&#10;            sell_ok = self._live_order_filled(sell_result)&#10;            self._live_startup_test_passed = bool(buy_ok and sell_ok)&#10;&#10;            if self._live_startup_test_passed:&#10;                msg = f&quot;Startup live order test passed on {coin} (open+close filled).&quot;&#10;                logger.warning(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_info(msg)&#10;            else:&#10;                msg = f&quot;Startup live order test failed on {coin}. Review logs/order responses before trusting live mirroring.&quot;&#10;                logger.error(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;        except Exception as exc:&#10;            self._live_startup_test_passed = False&#10;            msg = f&quot;Startup live order test exception: {exc}&quot;&#10;            logger.error(msg)&#10;            if self.notifications_enabled:&#10;                self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;&#10;    def _start_health_server(self) -&gt; None:&#10;        bot = self&#10;&#10;        class HealthHandler(BaseHTTPRequestHandler):&#10;            def _write_json(self, payload: Dict[str, Any], status: int = 200) -&gt; None:&#10;                body = json.dumps(payload).encode(&quot;utf-8&quot;)&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, str(len(body)))&#10;                self.end_headers()&#10;                self.wfile.write(body)&#10;&#10;            def _write_head(self, status: int = 200) -&gt; None:&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, &quot;0&quot;)&#10;                self.end_headers()&#10;&#10;            def do_GET(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;):&#10;                    self._write_json({&quot;status&quot;: &quot;ok&quot;, &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;})&#10;                    return&#10;                if self.path == &quot;/status&quot;:&#10;                    self._write_json(bot.status_overview_payload())&#10;                    return&#10;                if self.path == &quot;/status/full&quot;:&#10;                    self._write_json(bot.status_payload())&#10;                    return&#10;                if self.path == &quot;/trades&quot;:&#10;                    self._write_json(bot.open_trades_payload())&#10;                    return&#10;                self._write_json({&quot;status&quot;: &quot;not_found&quot;}, status=404)&#10;&#10;            def do_HEAD(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;, &quot;/status&quot;, &quot;/status/full&quot;, &quot;/trades&quot;):&#10;                    self._write_head(status=200)&#10;                    return&#10;                self._write_head(status=404)&#10;&#10;            def log_message(self, format: str, *args: Any) -&gt; None:&#10;                logger.debug(&quot;health_http: &quot; + format, *args)&#10;&#10;        server = ThreadingHTTPServer((self.health_host, self.health_port), HealthHandler)&#10;        self._http_server = server&#10;        t = threading.Thread(target=server.serve_forever, daemon=True)&#10;        t.start()&#10;        logger.info(&quot;Health endpoint listening on http://%s:%s&quot;, self.health_host, self.health_port)&#10;&#10;    def _fetch_df(self, coin: str, lookback_bars: int = 200) -&gt; Optional[pd.DataFrame]:&#10;        start = int((datetime.now(timezone.utc) - timedelta(milliseconds=self._interval_ms() * lookback_bars)).timestamp() * 1000)&#10;        raw = self.api.get_candles(coin=coin, interval=self.interval, start_time=start)&#10;        if not raw:&#10;            return None&#10;        df = parse_candles(raw)&#10;        if len(df) &lt; 80:&#10;            return None&#10;        return df&#10;&#10;    def _size_for_trade(self, entry: float, stop: float) -&gt; float:&#10;        max_risk_usd = self.paper.equity * self.risk_per_trade&#10;        stop_distance = abs(entry - stop)&#10;        if stop_distance &lt;= 0:&#10;            return 0.0&#10;        size = max_risk_usd / stop_distance&#10;        max_notional = self.paper.equity * 0.25&#10;        if size * entry &gt; max_notional:&#10;            size = max_notional / entry&#10;        return float(max(size, 0.0))&#10;&#10;    def _maybe_prepare_live(self) -&gt; None:&#10;        if not self.enable_live:&#10;            return&#10;        if not self.learning.consistently_profitable():&#10;            return&#10;        if self.require_live_approval and not self._live_approval_granted():&#10;            return&#10;        if self._live_clients is not None:&#10;            return&#10;&#10;        pk = os.environ.get(&quot;HL_PRIVATE_KEY&quot;, &quot;&quot;).strip()&#10;        acct = os.environ.get(&quot;HL_ACCOUNT_ADDRESS&quot;, &quot;&quot;).strip() or None&#10;        if not pk:&#10;            logger.warning(&quot;Live mode requested and paper criteria met, but HL_PRIVATE_KEY is missing.&quot;)&#10;            return&#10;&#10;        try:&#10;            self._live_clients = self.api.create_live_execution_clients(private_key=pk, account_address=acct)&#10;            logger.warning(&quot;Paper criteria met. Live execution is now armed.&quot;)&#10;            self._run_live_startup_test()&#10;        except Exception as exc:&#10;            logger.error(&quot;Failed to initialize live execution clients: %s&quot;, exc)&#10;            self._live_clients = None&#10;&#10;    def _place_live_mirror(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if self._live_clients is None:&#10;            return&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;        is_buy = side == &quot;long&quot;&#10;        try:&#10;            self.api.place_market_order(exchange=exchange, coin=coin, is_buy=is_buy, sz=size, px=entry, sl=sl, tp=tp)&#10;        except Exception as exc:&#10;            logger.error(&quot;Live mirror order failed for %s: %s&quot;, coin, exc)&#10;&#10;    def _notify_trade_open(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        rr: Optional[float] = None&#10;        risk = abs(entry - sl)&#10;        reward = abs(tp - entry)&#10;        if risk &gt; 0:&#10;            rr = reward / risk&#10;        self.notifier.send_trade(&#10;            direction=side.upper(),&#10;            coin=coin,&#10;            entry=entry,&#10;            sl=sl,&#10;            tp=tp,&#10;            sz=size,&#10;            notional=entry * size,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;            rr=rr,&#10;            order_type=&quot;paper&quot;,&#10;        )&#10;&#10;    def _notify_trade_close(self, closed: ClosedTrade) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        self.notifier.send_exit(&#10;            coin=closed.coin,&#10;            reason=closed.reason,&#10;            pnl_pct=closed.pnl_pct,&#10;            pnl_usdc=closed.pnl_usd,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;        )&#10;&#10;    def _maybe_send_daily_summary(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        now = datetime.now(timezone.utc)&#10;        date_key = now.date().isoformat()&#10;        if now.hour != 0 or self._last_summary_date == date_key:&#10;            return&#10;&#10;        since_ms = int((now - timedelta(days=1)).timestamp() * 1000)&#10;        day_trades = [&#10;            t for t in self.learning.closed_trades&#10;            if int(t.get(&quot;exit_ts&quot;, 0)) &gt;= since_ms&#10;        ]&#10;        day_wins = sum(1 for t in day_trades if float(t.get(&quot;pnl_usd&quot;, 0.0)) &gt; 0)&#10;        day_wr = (day_wins / len(day_trades) * 100.0) if day_trades else 0.0&#10;        day_pnl = sum(float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in day_trades)&#10;&#10;        overall = self.learning.global_metrics()&#10;        ml = self.learning.ml_progress_metrics()&#10;        summary = (&#10;            &quot;Midnight summary (UTC)\n&quot;&#10;            f&quot;24h trades: {len(day_trades)}\n&quot;&#10;            f&quot;24h win rate: {day_wr:.1f}%\n&quot;&#10;            f&quot;24h pnl: {day_pnl:+.2f} USDC\n&quot;&#10;            f&quot;Total trades: {int(overall['trades'])}\n&quot;&#10;            f&quot;Total win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Paper equity: {self.paper.equity:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%&quot;&#10;        )&#10;        self.notifier.send_info(summary)&#10;        self._last_summary_date = date_key&#10;&#10;    def _maybe_send_ml_progress_report(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        if self.telegram_ml_report_hours &lt;= 0:&#10;            return&#10;&#10;        now_ts = time.time()&#10;        period_s = self.telegram_ml_report_hours * 3600.0&#10;        if self._last_ml_report_ts &gt; 0 and (now_ts - self._last_ml_report_ts) &lt; period_s:&#10;            return&#10;&#10;        overall = self.learning.global_metrics()&#10;        trades = int(overall[&quot;trades&quot;])&#10;        if trades &lt; self.telegram_ml_report_min_trades:&#10;            return&#10;&#10;        ml = self.learning.ml_progress_metrics()&#10;        per_coin = ml.get(&quot;per_coin&quot;, {})&#10;        coin_parts = []&#10;        for coin in ALLOWED_COINS:&#10;            coin_brier = per_coin.get(coin, {}).get(&quot;brier_rolling_50&quot;)&#10;            coin_parts.append(f&quot;{coin}:{coin_brier}&quot;)&#10;        coin_brier_line = &quot; | &quot;.join(coin_parts)&#10;&#10;        msg = (&#10;            &quot;ML progress update\n&quot;&#10;            f&quot;Trades: {trades}\n&quot;&#10;            f&quot;Win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%\n&quot;&#10;            f&quot;Per-coin Brier(50): {coin_brier_line}&quot;&#10;        )&#10;        self.notifier.send_info(msg)&#10;        self._last_ml_report_ts = now_ts&#10;&#10;    def _trade_coin(self, coin: str) -&gt; None:&#10;        df = self._fetch_df(coin)&#10;        if df is None:&#10;            logger.warning(&quot;Not enough candle data for %s&quot;, coin)&#10;            return&#10;&#10;        candle = {&#10;            &quot;high&quot;: float(df.iloc[-1][&quot;high&quot;]),&#10;            &quot;low&quot;: float(df.iloc[-1][&quot;low&quot;]),&#10;            &quot;close&quot;: float(df.iloc[-1][&quot;close&quot;]),&#10;        }&#10;        now_ts = int(df.iloc[-1][&quot;ts&quot;])&#10;&#10;        closed = self.paper.maybe_close(coin, candle, now_ts)&#10;        if closed:&#10;            logger.info(&quot;CLOSED %s %s pnl=$%.2f (%+.2f%%) reason=%s&quot;, closed.coin, closed.side, closed.pnl_usd, closed.pnl_pct, closed.reason)&#10;            self._notify_trade_close(closed)&#10;            self.learning.record_trade_event(&quot;close&quot;, asdict(closed))&#10;            # Entry features are captured when the position opens and used as supervised labels on close.&#10;            features_map = getattr(self, &quot;_entry_features&quot;, {})&#10;            stored = features_map.get((coin, closed.entry_ts), [])&#10;            if stored:&#10;                self.learning.update_after_trade(closed, stored)&#10;                features_map.pop((coin, closed.entry_ts), None)&#10;            self._persist_runtime_state()&#10;&#10;        if self.paper.has_position(coin):&#10;            return&#10;&#10;        features = build_features(df)&#10;        if not features:&#10;            return&#10;&#10;        pause_reason = self._entry_pause_reason()&#10;        if pause_reason:&#10;            logger.warning(&quot;%s HOLD %s&quot;, coin, pause_reason)&#10;            return&#10;&#10;        p_long = self.learning.predict_long_probability(coin, features)&#10;        threshold = self.learning.thresholds[coin]&#10;&#10;        side: Optional[str] = None&#10;        signal_reason = &quot;model&quot;&#10;&#10;        if not self.learning.enough_samples(coin, self.min_model_samples_for_signal):&#10;            # Bootstrap mode: trade weak momentum direction to generate first labels.&#10;            side = &quot;long&quot; if features[1] &gt;= 0 else &quot;short&quot;&#10;            signal_reason = &quot;bootstrap&quot;&#10;        else:&#10;            if p_long &gt;= threshold:&#10;                side = &quot;long&quot;&#10;            elif p_long &lt;= (1.0 - threshold):&#10;                side = &quot;short&quot;&#10;&#10;            if side is None and random.random() &lt; self.exploration_rate:&#10;                side = &quot;long&quot; if p_long &gt;= 0.5 else &quot;short&quot;&#10;                signal_reason = &quot;exploration&quot;&#10;&#10;        side, signal_reason, p_long, advisor_side, advisor_conf = self._apply_hf_advisor(&#10;            coin=coin,&#10;            features=features,&#10;            candle=candle,&#10;            now_ts=now_ts,&#10;            p_long=p_long,&#10;            threshold=threshold,&#10;            side=side,&#10;        )&#10;&#10;        if not side:&#10;            if advisor_side is not None:&#10;                logger.info(&#10;                    &quot;%s HOLD p_long=%.3f threshold=%.3f advisor=%s conf=%.3f reason=%s&quot;,&#10;                    coin,&#10;                    p_long,&#10;                    threshold,&#10;                    advisor_side,&#10;                    advisor_conf if advisor_conf is not None else 0.0,&#10;                    signal_reason,&#10;                )&#10;            else:&#10;                logger.info(&quot;%s HOLD p_long=%.3f threshold=%.3f&quot;, coin, p_long, threshold)&#10;            return&#10;&#10;        entry = candle[&quot;close&quot;]&#10;        atr = atr_for_risk(df)&#10;        sl_mult = 1.3&#10;        tp_mult = 2.0&#10;&#10;        if side == &quot;long&quot;:&#10;            sl = max(entry - atr * sl_mult, 1e-8)&#10;            tp = entry + atr * tp_mult&#10;        else:&#10;            sl = entry + atr * sl_mult&#10;            tp = max(entry - atr * tp_mult, 1e-8)&#10;&#10;        size = self._size_for_trade(entry, sl)&#10;        if size &lt;= 0:&#10;            return&#10;&#10;        pos = Position(&#10;            coin=coin,&#10;            side=side,&#10;            entry=entry,&#10;            size=size,&#10;            sl=sl,&#10;            tp=tp,&#10;            entry_ts=now_ts,&#10;            entry_features=features,&#10;            entry_p_long=p_long,&#10;        )&#10;        self.paper.open_position(pos)&#10;        if not hasattr(self, &quot;_entry_features&quot;):&#10;            self._entry_features = {}&#10;        self._entry_features[(coin, now_ts)] = features&#10;        self.learning.record_trade_event(&#10;            &quot;open&quot;,&#10;            {&#10;                &quot;coin&quot;: coin,&#10;                &quot;side&quot;: side,&#10;                &quot;entry&quot;: entry,&#10;                &quot;size&quot;: size,&#10;                &quot;sl&quot;: sl,&#10;                &quot;tp&quot;: tp,&#10;                &quot;entry_ts&quot;: now_ts,&#10;                &quot;signal_reason&quot;: signal_reason,&#10;                &quot;p_long&quot;: p_long,&#10;                &quot;threshold&quot;: threshold,&#10;                &quot;advisor_side&quot;: advisor_side,&#10;                &quot;advisor_confidence&quot;: advisor_conf,&#10;            },&#10;        )&#10;        self._persist_runtime_state()&#10;&#10;        logger.info(&#10;            &quot;OPEN %s %s entry=%.4f sl=%.4f tp=%.4f size=%.5f p_long=%.3f th=%.3f reason=%s equity=%.2f&quot;,&#10;            coin,&#10;            side,&#10;            entry,&#10;            sl,&#10;            tp,&#10;            size,&#10;            p_long,&#10;            threshold,&#10;            signal_reason,&#10;            self.paper.equity,&#10;        )&#10;        self._notify_trade_open(coin, side, entry, sl, tp, size)&#10;&#10;        self._maybe_prepare_live()&#10;        self._place_live_mirror(coin, side, entry, sl, tp, size)&#10;&#10;    def write_report(self) -&gt; None:&#10;        payload = self.status_payload()&#10;        with open(self.report_file, &quot;w&quot;, encoding=&quot;utf-8&quot;) as f:&#10;            json.dump(payload, f, indent=2)&#10;&#10;    def run(self) -&gt; None:&#10;        signal.signal(signal.SIGINT, self.stop)&#10;        signal.signal(signal.SIGTERM, self.stop)&#10;&#10;        self._start_health_server()&#10;&#10;        logger.info(&quot;Starting adaptive bot (paper mode=%s, live enabled=%s)&quot;, True, self.enable_live)&#10;        logger.info(&quot;Universe: %s&quot;, &quot;, &quot;.join(ALLOWED_COINS))&#10;&#10;        while self._running:&#10;            try:&#10;                for coin in ALLOWED_COINS:&#10;                    self._trade_coin(coin)&#10;                self._persist_runtime_state()&#10;                self.learning.save()&#10;                self.write_report()&#10;&#10;                m = self.learning.global_metrics()&#10;                logger.info(&#10;                    &quot;Stats trades=%d win_rate=%.2f%% pf=%.2f paper_equity=%.2f profitable_gate=%s&quot;,&#10;                    int(m[&quot;trades&quot;]),&#10;                    m[&quot;win_rate&quot;] * 100.0,&#10;                    m[&quot;profit_factor&quot;],&#10;                    self.paper.equity,&#10;                    self.learning.consistently_profitable(),&#10;                )&#10;                self._maybe_notify_live_readiness()&#10;                self._maybe_prepare_live()&#10;                self._maybe_send_daily_summary()&#10;                self._maybe_send_ml_progress_report()&#10;            except Exception as exc:&#10;                logger.exception(&quot;Loop error: %s&quot;, exc)&#10;&#10;            time.sleep(self.poll_seconds)&#10;&#10;&#10;def build_arg_parser() -&gt; argparse.ArgumentParser:&#10;    p = argparse.ArgumentParser(description=&quot;Adaptive ML Hyperliquid paper trader&quot;)&#10;    p.add_argument(&quot;--interval&quot;, default=&quot;15m&quot;, choices=sorted(INTERVAL_MS.keys()))&#10;    p.add_argument(&quot;--state-file&quot;, default=&quot;learning_state.joblib&quot;)&#10;    p.add_argument(&quot;--report-file&quot;, default=&quot;paper_report.json&quot;)&#10;    p.add_argument(&quot;--starting-equity&quot;, type=float, default=10000.0)&#10;    p.add_argument(&quot;--risk-per-trade&quot;, type=float, default=0.01, help=&quot;Fraction of equity risked per position&quot;)&#10;    p.add_argument(&quot;--poll-seconds&quot;, type=int, default=45)&#10;    p.add_argument(&quot;--exploration-rate&quot;, type=float, default=0.08, help=&quot;Chance to take an exploratory trade when model signal is neutral&quot;)&#10;    p.add_argument(&quot;--health-host&quot;, default=os.environ.get(&quot;BOT_HEALTH_HOST&quot;, &quot;0.0.0.0&quot;))&#10;    p.add_argument(&quot;--health-port&quot;, type=int, default=int(os.environ.get(&quot;PORT&quot;, os.environ.get(&quot;BOT_HEALTH_PORT&quot;, &quot;7860&quot;))))&#10;    p.add_argument(&quot;--enable-live&quot;, action=&quot;store_true&quot;, help=&quot;Allow live mirror orders after paper criteria is met&quot;)&#10;    p.add_argument(&#10;        &quot;--require-live-approval&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=True,&#10;        help=&quot;Require manual approval before arming live mirror orders&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--live-approval-mode&quot;,&#10;        choices=[&quot;either&quot;, &quot;both&quot;],&#10;        default=&quot;either&quot;,&#10;        help=&quot;Manual approval policy: 'either' (env or file) or 'both' (env and file)&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-approval-file&quot;, default=&quot;allow_live_trading.flag&quot;, help=&quot;Approval file path to authorize live trading&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=False,&#10;        help=&quot;When live is armed, place a tiny BUY then SELL to verify live order execution&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-startup-test-coin&quot;, choices=sorted(ALLOWED_COINS), default=&quot;BTC&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test-notional&quot;,&#10;        type=float,&#10;        default=12.0,&#10;        help=&quot;Approx USDC notional used for startup live order test (min effectively 10)&quot;,&#10;    )&#10;    p.add_argument(&quot;--hf-advisor-url&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_URL&quot;, &quot;&quot;), help=&quot;Optional external advisor endpoint URL&quot;)&#10;    p.add_argument(&quot;--hf-advisor-token&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_TOKEN&quot;, &quot;&quot;), help=&quot;Optional bearer token for advisor endpoint&quot;)&#10;    p.add_argument(&#10;        &quot;--hf-advisor-mode&quot;,&#10;        choices=[&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;],&#10;        default=os.environ.get(&quot;BOT_HF_ADVISOR_MODE&quot;, &quot;blend&quot;),&#10;        help=&quot;confirm=must match advisor, veto=advisor can block, blend=blend p_long with advisor p_long&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-min-confidence&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_MIN_CONFIDENCE&quot;, &quot;0.60&quot;)),&#10;        help=&quot;Minimum advisor confidence used by confirm/veto modes&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-blend-weight&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_BLEND_WEIGHT&quot;, &quot;0.35&quot;)),&#10;        help=&quot;Advisor p_long blending weight for blend mode [0..1]&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-timeout-seconds&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_TIMEOUT_SECONDS&quot;, &quot;4.0&quot;)),&#10;        help=&quot;HTTP timeout for advisor requests&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--min-model-samples-for-signal&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_MIN_MODEL_SAMPLES_FOR_SIGNAL&quot;, &quot;50&quot;)),&#10;        help=&quot;Minimum labeled samples per coin before model-driven entries are trusted&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-on-negative-expectancy&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=(os.environ.get(&quot;BOT_PAUSE_ON_NEGATIVE_EXPECTANCY&quot;, &quot;1&quot;).strip().lower() in {&quot;1&quot;, &quot;true&quot;, &quot;yes&quot;, &quot;y&quot;}),&#10;        help=&quot;Pause new entries when recent expectancy is poor and drawdown is elevated&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-expectancy-lookback&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_PAUSE_EXPECTANCY_LOOKBACK&quot;, &quot;30&quot;)),&#10;        help=&quot;Number of latest closed trades used for safety pause expectancy&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-expectancy-threshold-usd&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_PAUSE_EXPECTANCY_THRESHOLD_USD&quot;, &quot;-1.0&quot;)),&#10;        help=&quot;Pause if recent expectancy is below this USD per-trade threshold&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-max-drawdown-pct&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_PAUSE_MAX_DRAWDOWN_PCT&quot;, &quot;12.0&quot;)),&#10;        help=&quot;Pause only when max drawdown is also at/above this percentage&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-hours&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_HOURS&quot;, &quot;6&quot;)),&#10;        help=&quot;Send periodic ML progress updates to Telegram every N hours (0 disables)&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-min-trades&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_MIN_TRADES&quot;, &quot;15&quot;)),&#10;        help=&quot;Minimum number of closed trades before periodic ML Telegram reports are sent&quot;,&#10;    )&#10;    return p&#10;&#10;&#10;def main() -&gt; None:&#10;    args = build_arg_parser().parse_args()&#10;    bot = AdaptiveHyperliquidBot(&#10;        interval=args.interval,&#10;        state_file=args.state_file,&#10;        report_file=args.report_file,&#10;        starting_equity=args.starting_equity,&#10;        risk_per_trade=args.risk_per_trade,&#10;        poll_seconds=args.poll_seconds,&#10;        exploration_rate=args.exploration_rate,&#10;        enable_live=args.enable_live,&#10;        require_live_approval=args.require_live_approval,&#10;        live_approval_mode=args.live_approval_mode,&#10;        live_approval_file=args.live_approval_file,&#10;        live_startup_test=args.live_startup_test,&#10;        live_startup_test_coin=args.live_startup_test_coin,&#10;        live_startup_test_notional=args.live_startup_test_notional,&#10;        hf_advisor_url=args.hf_advisor_url,&#10;        hf_advisor_token=args.hf_advisor_token,&#10;        hf_advisor_mode=args.hf_advisor_mode,&#10;        hf_advisor_min_confidence=args.hf_advisor_min_confidence,&#10;        hf_advisor_blend_weight=args.hf_advisor_blend_weight,&#10;        hf_advisor_timeout_seconds=args.hf_advisor_timeout_seconds,&#10;        min_model_samples_for_signal=args.min_model_samples_for_signal,&#10;        pause_on_negative_expectancy=args.pause_on_negative_expectancy,&#10;        pause_expectancy_lookback=args.pause_expectancy_lookback,&#10;        pause_expectancy_threshold_usd=args.pause_expectancy_threshold_usd,&#10;        pause_max_drawdown_pct=args.pause_max_drawdown_pct,&#10;        telegram_ml_report_hours=args.telegram_ml_report_hours,&#10;        telegram_ml_report_min_trades=args.telegram_ml_report_min_trades,&#10;        health_host=args.health_host,&#10;        health_port=args.health_port,&#10;    )&#10;    bot.run()&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    main()&#10;&#10;&#10;" />
+              <option name="updatedContent" value="#!/usr/bin/env python3&#10;&quot;&quot;&quot;&#10;Adaptive Hyperliquid bot.&#10;&#10;- Trades only BTC, ETH, SOL.&#10;- Paper trades by default.&#10;- Uses online ML (SGDClassifier) to adapt entry confidence thresholds.&#10;- Persists learned state to disk so learning survives restarts.&#10;- Can optionally switch to live execution only after consistent paper performance.&#10;&quot;&quot;&quot;&#10;&#10;from __future__ import annotations&#10;&#10;import argparse&#10;import json&#10;import logging&#10;import math&#10;import random&#10;import os&#10;import signal&#10;import threading&#10;import time&#10;from dataclasses import asdict, dataclass&#10;from datetime import datetime, timedelta, timezone&#10;from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer&#10;from typing import Any, Dict, List, Optional, Tuple&#10;&#10;import joblib&#10;import numpy as np&#10;import pandas as pd&#10;from sklearn.linear_model import SGDClassifier&#10;&#10;from hf_advisor import HuggingFaceAdvisor&#10;from hbot import HyperliquidAPI&#10;from telegram_notifier import TelegramNotifier&#10;&#10;&#10;logging.basicConfig(level=logging.INFO, format=&quot;%(asctime)s - %(levelname)s - %(message)s&quot;)&#10;logger = logging.getLogger(__name__)&#10;&#10;&#10;ALLOWED_COINS = (&quot;BTC&quot;, &quot;ETH&quot;, &quot;SOL&quot;)&#10;INTERVAL_MS = {&#10;    &quot;1m&quot;: 60_000,&#10;    &quot;5m&quot;: 300_000,&#10;    &quot;15m&quot;: 900_000,&#10;    &quot;30m&quot;: 1_800_000,&#10;    &quot;1h&quot;: 3_600_000,&#10;}&#10;&#10;&#10;@dataclass&#10;class Position:&#10;    coin: str&#10;    side: str  # long | short&#10;    entry: float&#10;    size: float&#10;    sl: float&#10;    tp: float&#10;    entry_ts: int&#10;    entry_features: List[float]&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;@dataclass&#10;class ClosedTrade:&#10;    coin: str&#10;    side: str&#10;    entry: float&#10;    exit: float&#10;    size: float&#10;    pnl_usd: float&#10;    pnl_pct: float&#10;    reason: str&#10;    entry_ts: int&#10;    exit_ts: int&#10;    entry_p_long: float = 0.5&#10;&#10;&#10;class LearningState:&#10;    &quot;&quot;&quot;Holds online models + strategy performance and persists to disk.&quot;&quot;&quot;&#10;&#10;    def __init__(self, state_path: str):&#10;        self.state_path = state_path&#10;        self.models: Dict[str, SGDClassifier] = {&#10;            c: SGDClassifier(loss=&quot;log_loss&quot;, alpha=1e-4, random_state=42) for c in ALLOWED_COINS&#10;        }&#10;        self.model_ready: Dict[str, bool] = {c: False for c in ALLOWED_COINS}&#10;        self.model_samples: Dict[str, int] = {c: 0 for c in ALLOWED_COINS}&#10;        self.thresholds: Dict[str, float] = {c: 0.56 for c in ALLOWED_COINS}&#10;        self.trade_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;trades&quot;: 0, &quot;wins&quot;: 0, &quot;losses&quot;: 0, &quot;gross_profit&quot;: 0.0, &quot;gross_loss&quot;: 0.0}&#10;            for c in ALLOWED_COINS&#10;        }&#10;        self.ml_stats: Dict[str, Dict[str, float]] = {&#10;            c: {&quot;count&quot;: 0.0, &quot;brier_sum&quot;: 0.0} for c in ALLOWED_COINS&#10;        }&#10;        self.recent_results: Dict[str, List[int]] = {c: [] for c in ALLOWED_COINS}&#10;        self.recent_brier: Dict[str, List[float]] = {c: [] for c in ALLOWED_COINS}&#10;        self.closed_trades: List[Dict[str, Any]] = []&#10;        self.trade_events: List[Dict[str, Any]] = []&#10;        self.equity_curve: List[Dict[str, float]] = []&#10;        self.paper_runtime: Dict[str, Any] = {&#10;            &quot;equity&quot;: None,&#10;            &quot;positions&quot;: {},&#10;            &quot;entry_features&quot;: {},&#10;        }&#10;        self.last_saved_ts = 0.0&#10;        self._load()&#10;&#10;    def _load(self) -&gt; None:&#10;        if not os.path.exists(self.state_path):&#10;            logger.info(&quot;No prior learning state found at %s, starting fresh.&quot;, self.state_path)&#10;            return&#10;        payload = joblib.load(self.state_path)&#10;        self.models = payload.get(&quot;models&quot;, self.models)&#10;        self.model_ready = payload.get(&quot;model_ready&quot;, self.model_ready)&#10;        self.model_samples = payload.get(&quot;model_samples&quot;, self.model_samples)&#10;        self.thresholds = payload.get(&quot;thresholds&quot;, self.thresholds)&#10;        self.trade_stats = payload.get(&quot;trade_stats&quot;, self.trade_stats)&#10;        self.ml_stats = payload.get(&quot;ml_stats&quot;, self.ml_stats)&#10;        self.recent_results = payload.get(&quot;recent_results&quot;, self.recent_results)&#10;        self.recent_brier = payload.get(&quot;recent_brier&quot;, self.recent_brier)&#10;        self.closed_trades = payload.get(&quot;closed_trades&quot;, self.closed_trades)&#10;        self.trade_events = payload.get(&quot;trade_events&quot;, self.trade_events)&#10;        self.equity_curve = payload.get(&quot;equity_curve&quot;, self.equity_curve)&#10;        self.paper_runtime = payload.get(&quot;paper_runtime&quot;, self.paper_runtime)&#10;        logger.info(&quot;Loaded learning state from %s&quot;, self.state_path)&#10;&#10;    def save(self, force: bool = False) -&gt; None:&#10;        now = time.time()&#10;        if not force and now - self.last_saved_ts &lt; 20:&#10;            return&#10;        payload = {&#10;            &quot;models&quot;: self.models,&#10;            &quot;model_ready&quot;: self.model_ready,&#10;            &quot;model_samples&quot;: self.model_samples,&#10;            &quot;thresholds&quot;: self.thresholds,&#10;            &quot;trade_stats&quot;: self.trade_stats,&#10;            &quot;ml_stats&quot;: self.ml_stats,&#10;            &quot;recent_results&quot;: self.recent_results,&#10;            &quot;recent_brier&quot;: self.recent_brier,&#10;            &quot;closed_trades&quot;: self.closed_trades[-2000:],&#10;            &quot;trade_events&quot;: self.trade_events[-5000:],&#10;            &quot;equity_curve&quot;: self.equity_curve[-5000:],&#10;            &quot;paper_runtime&quot;: self.paper_runtime,&#10;            &quot;saved_at&quot;: datetime.now(timezone.utc).isoformat(),&#10;        }&#10;        joblib.dump(payload, self.state_path)&#10;        self.last_saved_ts = now&#10;&#10;    def record_equity(self, equity: float, ts_ms: Optional[int] = None) -&gt; None:&#10;        ts = int(ts_ms if ts_ms is not None else time.time() * 1000)&#10;        e = float(equity)&#10;        if self.equity_curve and abs(float(self.equity_curve[-1][&quot;equity&quot;]) - e) &lt; 1e-12:&#10;            return&#10;        self.equity_curve.append({&quot;ts&quot;: float(ts), &quot;equity&quot;: e})&#10;        self.equity_curve = self.equity_curve[-5000:]&#10;&#10;    def set_runtime_state(&#10;        self,&#10;        paper_equity: float,&#10;        positions: Dict[str, Position],&#10;        entry_features: Dict[Tuple[str, int], List[float]],&#10;    ) -&gt; None:&#10;        self.paper_runtime = {&#10;            &quot;equity&quot;: float(paper_equity),&#10;            &quot;positions&quot;: {coin: asdict(pos) for coin, pos in positions.items()},&#10;            &quot;entry_features&quot;: {&#10;                f&quot;{coin}:{entry_ts}&quot;: [float(v) for v in feats]&#10;                for (coin, entry_ts), feats in entry_features.items()&#10;            },&#10;        }&#10;&#10;    def restore_runtime_state(self) -&gt; Tuple[Optional[float], Dict[str, Position], Dict[Tuple[str, int], List[float]]]:&#10;        equity_raw = self.paper_runtime.get(&quot;equity&quot;)&#10;        equity = float(equity_raw) if equity_raw is not None else None&#10;&#10;        restored_positions: Dict[str, Position] = {}&#10;        for coin, raw in (self.paper_runtime.get(&quot;positions&quot;) or {}).items():&#10;            try:&#10;                restored_positions[coin] = Position(&#10;                    coin=str(raw[&quot;coin&quot;]),&#10;                    side=str(raw[&quot;side&quot;]),&#10;                    entry=float(raw[&quot;entry&quot;]),&#10;                    size=float(raw[&quot;size&quot;]),&#10;                    sl=float(raw[&quot;sl&quot;]),&#10;                    tp=float(raw[&quot;tp&quot;]),&#10;                    entry_ts=int(raw[&quot;entry_ts&quot;]),&#10;                    entry_features=[float(v) for v in raw.get(&quot;entry_features&quot;, [])],&#10;                    entry_p_long=float(raw.get(&quot;entry_p_long&quot;, 0.5)),&#10;                )&#10;            except Exception:&#10;                continue&#10;&#10;        restored_features: Dict[Tuple[str, int], List[float]] = {}&#10;        for key, vals in (self.paper_runtime.get(&quot;entry_features&quot;) or {}).items():&#10;            try:&#10;                coin, entry_ts_str = key.split(&quot;:&quot;, 1)&#10;                restored_features[(coin, int(entry_ts_str))] = [float(v) for v in vals]&#10;            except Exception:&#10;                continue&#10;&#10;        return equity, restored_positions, restored_features&#10;&#10;    def record_trade_event(self, event_type: str, payload: Dict[str, Any]) -&gt; None:&#10;        self.trade_events.append(&#10;            {&#10;                &quot;event&quot;: event_type,&#10;                &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;                **payload,&#10;            }&#10;        )&#10;        self.trade_events = self.trade_events[-5000:]&#10;&#10;    def update_after_trade(self, trade: ClosedTrade, entry_features: List[float]) -&gt; None:&#10;        trade_won = 1 if trade.pnl_usd &gt; 0 else 0&#10;        # Model predicts p(long wins), so short outcomes must be direction-adjusted.&#10;        label_long = trade_won if trade.side == &quot;long&quot; else (1 - trade_won)&#10;        x = np.asarray(entry_features, dtype=float).reshape(1, -1)&#10;        y = np.asarray([label_long], dtype=int)&#10;&#10;        model = self.models[trade.coin]&#10;        if not self.model_ready[trade.coin]:&#10;            model.partial_fit(x, y, classes=np.array([0, 1]))&#10;            self.model_ready[trade.coin] = True&#10;        else:&#10;            model.partial_fit(x, y)&#10;        self.model_samples[trade.coin] = int(self.model_samples.get(trade.coin, 0)) + 1&#10;&#10;        stats = self.trade_stats[trade.coin]&#10;        stats[&quot;trades&quot;] += 1&#10;        if trade_won == 1:&#10;            stats[&quot;wins&quot;] += 1&#10;            stats[&quot;gross_profit&quot;] += max(0.0, trade.pnl_usd)&#10;        else:&#10;            stats[&quot;losses&quot;] += 1&#10;            stats[&quot;gross_loss&quot;] += abs(min(0.0, trade.pnl_usd))&#10;&#10;        p = float(np.clip(trade.entry_p_long, 0.0, 1.0))&#10;        brier = float((p - label_long) ** 2)&#10;        ml = self.ml_stats[trade.coin]&#10;        ml[&quot;count&quot;] += 1&#10;        ml[&quot;brier_sum&quot;] += brier&#10;        self.recent_brier[trade.coin].append(brier)&#10;        self.recent_brier[trade.coin] = self.recent_brier[trade.coin][-100:]&#10;&#10;        self.recent_results[trade.coin].append(trade_won)&#10;        self.recent_results[trade.coin] = self.recent_results[trade.coin][-30:]&#10;&#10;        # Adaptive threshold: lower threshold when recent win rate is strong, raise when weak.&#10;        recent = self.recent_results[trade.coin]&#10;        if recent:&#10;            wr = float(sum(recent)) / len(recent)&#10;            target = 0.56 + (0.60 - wr) * 0.25&#10;            self.thresholds[trade.coin] = float(np.clip(target, 0.52, 0.65))&#10;&#10;        self.closed_trades.append(asdict(trade))&#10;        self.save()&#10;&#10;    def enough_samples(self, coin: str, min_samples: int) -&gt; bool:&#10;        return int(self.model_samples.get(coin, 0)) &gt;= int(max(1, min_samples))&#10;&#10;    def predict_long_probability(self, coin: str, features: List[float]) -&gt; float:&#10;        if not self.model_ready.get(coin, False):&#10;            return 0.50&#10;        x = np.asarray(features, dtype=float).reshape(1, -1)&#10;        try:&#10;            p = float(self.models[coin].predict_proba(x)[0][1])&#10;        except Exception:&#10;            return 0.50&#10;        if math.isnan(p):&#10;            return 0.50&#10;        return float(np.clip(p, 0.01, 0.99))&#10;&#10;    def global_metrics(self) -&gt; Dict[str, float]:&#10;        trades = wins = 0&#10;        gross_profit = gross_loss = 0.0&#10;        for coin in ALLOWED_COINS:&#10;            s = self.trade_stats[coin]&#10;            trades += int(s[&quot;trades&quot;])&#10;            wins += int(s[&quot;wins&quot;])&#10;            gross_profit += float(s[&quot;gross_profit&quot;])&#10;            gross_loss += float(s[&quot;gross_loss&quot;])&#10;        win_rate = (wins / trades) if trades else 0.0&#10;        profit_factor = (gross_profit / gross_loss) if gross_loss &gt; 0 else (10.0 if gross_profit &gt; 0 else 0.0)&#10;        return {&#10;            &quot;trades&quot;: float(trades),&#10;            &quot;win_rate&quot;: float(win_rate),&#10;            &quot;profit_factor&quot;: float(profit_factor),&#10;            &quot;gross_profit&quot;: float(gross_profit),&#10;            &quot;gross_loss&quot;: float(gross_loss),&#10;        }&#10;&#10;    def ml_progress_metrics(self) -&gt; Dict[str, Any]:&#10;        total_count = 0.0&#10;        total_brier_sum = 0.0&#10;        per_coin: Dict[str, Any] = {}&#10;&#10;        all_recent_brier: List[float] = []&#10;        for coin in ALLOWED_COINS:&#10;            stat = self.ml_stats.get(coin, {})&#10;            cnt = float(stat.get(&quot;count&quot;, 0.0))&#10;            bsum = float(stat.get(&quot;brier_sum&quot;, 0.0))&#10;            total_count += cnt&#10;            total_brier_sum += bsum&#10;&#10;            recent = [float(v) for v in self.recent_brier.get(coin, [])]&#10;            all_recent_brier.extend(recent)&#10;            per_coin[coin] = {&#10;                &quot;samples&quot;: int(cnt),&#10;                &quot;brier&quot;: (bsum / cnt) if cnt &gt; 0 else None,&#10;                &quot;brier_rolling_50&quot;: (float(np.mean(recent[-50:])) if recent else None),&#10;                &quot;brier_rolling_100&quot;: (float(np.mean(recent)) if recent else None),&#10;            }&#10;&#10;        brier_global = (total_brier_sum / total_count) if total_count &gt; 0 else None&#10;        brier_rolling_50 = float(np.mean(all_recent_brier[-50:])) if all_recent_brier else None&#10;        brier_rolling_100 = float(np.mean(all_recent_brier[-100:])) if all_recent_brier else None&#10;&#10;        pnl = [float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in self.closed_trades]&#10;        expectancy_30 = float(np.mean(pnl[-30:])) if pnl else None&#10;        expectancy_100 = float(np.mean(pnl[-100:])) if pnl else None&#10;&#10;        max_drawdown_pct = None&#10;        peak = None&#10;        max_dd = 0.0&#10;        for point in self.equity_curve:&#10;            eq = float(point.get(&quot;equity&quot;, 0.0))&#10;            if peak is None or eq &gt; peak:&#10;                peak = eq&#10;            if peak and peak &gt; 0:&#10;                dd = (peak - eq) / peak&#10;                if dd &gt; max_dd:&#10;                    max_dd = dd&#10;        if peak is not None:&#10;            max_drawdown_pct = max_dd * 100.0&#10;&#10;        return {&#10;            &quot;samples&quot;: int(total_count),&#10;            &quot;brier&quot;: brier_global,&#10;            &quot;brier_rolling_50&quot;: brier_rolling_50,&#10;            &quot;brier_rolling_100&quot;: brier_rolling_100,&#10;            &quot;expectancy_usd_30&quot;: expectancy_30,&#10;            &quot;expectancy_usd_100&quot;: expectancy_100,&#10;            &quot;max_drawdown_pct&quot;: max_drawdown_pct,&#10;            &quot;per_coin&quot;: per_coin,&#10;        }&#10;&#10;    def consistently_profitable(self) -&gt; bool:&#10;        m = self.global_metrics()&#10;        return m[&quot;trades&quot;] &gt;= 30 and m[&quot;win_rate&quot;] &gt;= 0.58 and m[&quot;profit_factor&quot;] &gt;= 1.25&#10;&#10;&#10;class PaperBroker:&#10;    def __init__(self, starting_equity: float = 10000.0, taker_fee_bps: float = 4.32):&#10;        self.equity = float(starting_equity)&#10;        self.taker_fee = taker_fee_bps / 10_000.0&#10;        self.positions: Dict[str, Position] = {}&#10;        self.closed: List[ClosedTrade] = []&#10;&#10;    def has_position(self, coin: str) -&gt; bool:&#10;        return coin in self.positions&#10;&#10;    def open_position(self, pos: Position) -&gt; None:&#10;        self.positions[pos.coin] = pos&#10;&#10;    def maybe_close(self, coin: str, candle: Dict[str, float], now_ts: int) -&gt; Optional[ClosedTrade]:&#10;        pos = self.positions.get(coin)&#10;        if not pos:&#10;            return None&#10;&#10;        high = candle[&quot;high&quot;]&#10;        low = candle[&quot;low&quot;]&#10;        close = candle[&quot;close&quot;]&#10;&#10;        exit_px = close&#10;        reason = &quot;model_exit&quot;&#10;&#10;        if pos.side == &quot;long&quot;:&#10;            if low &lt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif high &gt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;        else:&#10;            if high &gt;= pos.sl:&#10;                exit_px = pos.sl&#10;                reason = &quot;stop_loss&quot;&#10;            elif low &lt;= pos.tp:&#10;                exit_px = pos.tp&#10;                reason = &quot;take_profit&quot;&#10;&#10;        if reason == &quot;model_exit&quot;:&#10;            return None&#10;&#10;        gross = (exit_px - pos.entry) * pos.size if pos.side == &quot;long&quot; else (pos.entry - exit_px) * pos.size&#10;        fees = (pos.entry * pos.size + exit_px * pos.size) * self.taker_fee&#10;        pnl = gross - fees&#10;        pnl_pct = (pnl / max(pos.entry * pos.size, 1e-9)) * 100.0&#10;&#10;        trade = ClosedTrade(&#10;            coin=coin,&#10;            side=pos.side,&#10;            entry=pos.entry,&#10;            exit=exit_px,&#10;            size=pos.size,&#10;            pnl_usd=pnl,&#10;            pnl_pct=pnl_pct,&#10;            reason=reason,&#10;            entry_ts=pos.entry_ts,&#10;            exit_ts=now_ts,&#10;            entry_p_long=pos.entry_p_long,&#10;        )&#10;        self.equity += pnl&#10;        self.closed.append(trade)&#10;        del self.positions[coin]&#10;        return trade&#10;&#10;&#10;def parse_candles(raw: List[Dict[str, Any]]) -&gt; pd.DataFrame:&#10;    rows = []&#10;    for c in raw:&#10;        rows.append(&#10;            {&#10;                &quot;ts&quot;: int(c.get(&quot;t&quot;) or c.get(&quot;time&quot;) or c.get(&quot;T&quot;) or 0),&#10;                &quot;open&quot;: float(c.get(&quot;o&quot;) or c.get(&quot;open&quot;) or c.get(&quot;openPx&quot;) or 0.0),&#10;                &quot;high&quot;: float(c.get(&quot;h&quot;) or c.get(&quot;high&quot;) or c.get(&quot;highPx&quot;) or 0.0),&#10;                &quot;low&quot;: float(c.get(&quot;l&quot;) or c.get(&quot;low&quot;) or c.get(&quot;lowPx&quot;) or 0.0),&#10;                &quot;close&quot;: float(c.get(&quot;c&quot;) or c.get(&quot;close&quot;) or c.get(&quot;closePx&quot;) or 0.0),&#10;                &quot;volume&quot;: float(c.get(&quot;v&quot;) or c.get(&quot;volume&quot;) or 0.0),&#10;            }&#10;        )&#10;    df = pd.DataFrame(rows).sort_values(&quot;ts&quot;)&#10;    df = df[(df[&quot;close&quot;] &gt; 0) &amp; (df[&quot;high&quot;] &gt; 0) &amp; (df[&quot;low&quot;] &gt; 0)]&#10;    return df.reset_index(drop=True)&#10;&#10;&#10;def build_features(df: pd.DataFrame) -&gt; Optional[List[float]]:&#10;    if len(df) &lt; 60:&#10;        return None&#10;&#10;    close = df[&quot;close&quot;]&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    vol = df[&quot;volume&quot;].replace(0, np.nan).ffill().fillna(1.0)&#10;&#10;    ret_1 = close.pct_change(1)&#10;    ret_3 = close.pct_change(3)&#10;    ret_8 = close.pct_change(8)&#10;    ema_12 = close.ewm(span=12).mean()&#10;    ema_26 = close.ewm(span=26).mean()&#10;    macd = (ema_12 - ema_26) / close&#10;&#10;    # ATR-like normalized range.&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr14 = tr.rolling(14).mean() / close&#10;&#10;    delta = close.diff()&#10;    up = delta.clip(lower=0).rolling(14).mean()&#10;    down = (-delta.clip(upper=0)).rolling(14).mean()&#10;    rs = up / down.replace(0, np.nan)&#10;    rsi = 100.0 - (100.0 / (1.0 + rs))&#10;    rsi_scaled = (rsi - 50.0) / 50.0&#10;&#10;    vol_z = (np.log(vol) - np.log(vol).rolling(20).mean()) / np.log(vol).rolling(20).std()&#10;&#10;    feat = pd.DataFrame(&#10;        {&#10;            &quot;ret_1&quot;: ret_1,&#10;            &quot;ret_3&quot;: ret_3,&#10;            &quot;ret_8&quot;: ret_8,&#10;            &quot;macd&quot;: macd,&#10;            &quot;atr14&quot;: atr14,&#10;            &quot;rsi&quot;: rsi_scaled,&#10;            &quot;vol_z&quot;: vol_z,&#10;        }&#10;    ).replace([np.inf, -np.inf], np.nan)&#10;&#10;    last = feat.iloc[-1].fillna(0.0)&#10;    return [float(last[k]) for k in [&quot;ret_1&quot;, &quot;ret_3&quot;, &quot;ret_8&quot;, &quot;macd&quot;, &quot;atr14&quot;, &quot;rsi&quot;, &quot;vol_z&quot;]]&#10;&#10;&#10;def atr_for_risk(df: pd.DataFrame) -&gt; float:&#10;    high = df[&quot;high&quot;]&#10;    low = df[&quot;low&quot;]&#10;    close = df[&quot;close&quot;]&#10;    tr = pd.concat(&#10;        [&#10;            (high - low).abs(),&#10;            (high - close.shift(1)).abs(),&#10;            (low - close.shift(1)).abs(),&#10;        ],&#10;        axis=1,&#10;    ).max(axis=1)&#10;    atr = float(tr.rolling(14).mean().iloc[-1])&#10;    if not np.isfinite(atr) or atr &lt;= 0:&#10;        atr = float(close.iloc[-1] * 0.01)&#10;    return atr&#10;&#10;&#10;class AdaptiveHyperliquidBot:&#10;    def __init__(&#10;        self,&#10;        interval: str,&#10;        state_file: str,&#10;        report_file: str,&#10;        starting_equity: float,&#10;        risk_per_trade: float,&#10;        poll_seconds: int,&#10;        exploration_rate: float,&#10;        enable_live: bool,&#10;        require_live_approval: bool,&#10;        live_approval_mode: str,&#10;        live_approval_file: str,&#10;        live_startup_test: bool,&#10;        live_startup_test_coin: str,&#10;        live_startup_test_notional: float,&#10;        hf_advisor_url: str,&#10;        hf_advisor_token: str,&#10;        hf_advisor_mode: str,&#10;        hf_advisor_min_confidence: float,&#10;        hf_advisor_blend_weight: float,&#10;        hf_advisor_timeout_seconds: float,&#10;        min_model_samples_for_signal: int,&#10;        pause_on_negative_expectancy: bool,&#10;        pause_expectancy_lookback: int,&#10;        pause_expectancy_threshold_usd: float,&#10;        pause_max_drawdown_pct: float,&#10;        pause_cooldown_minutes: float,&#10;        telegram_ml_report_hours: float,&#10;        telegram_ml_report_min_trades: int,&#10;        health_host: str,&#10;        health_port: int,&#10;    ):&#10;        self.interval = interval&#10;        self.poll_seconds = poll_seconds&#10;        self.risk_per_trade = risk_per_trade&#10;        self.exploration_rate = float(np.clip(exploration_rate, 0.0, 0.5))&#10;        self.enable_live = enable_live&#10;        self.require_live_approval = require_live_approval&#10;        self.live_approval_mode = live_approval_mode&#10;        self.live_approval_file = live_approval_file&#10;        self.live_startup_test = live_startup_test&#10;        self.live_startup_test_coin = live_startup_test_coin&#10;        self.live_startup_test_notional = float(max(10.0, live_startup_test_notional))&#10;        self.hf_advisor_mode = (hf_advisor_mode or &quot;blend&quot;).strip().lower()&#10;        if self.hf_advisor_mode not in {&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;}:&#10;            logger.warning(&quot;Unknown advisor mode '%s'; defaulting to 'blend'.&quot;, self.hf_advisor_mode)&#10;            self.hf_advisor_mode = &quot;blend&quot;&#10;        self.hf_advisor_min_confidence = float(np.clip(hf_advisor_min_confidence, 0.0, 1.0))&#10;        self.hf_advisor_blend_weight = float(np.clip(hf_advisor_blend_weight, 0.0, 1.0))&#10;        self.hf_advisor = HuggingFaceAdvisor(&#10;            endpoint_url=hf_advisor_url,&#10;            api_token=hf_advisor_token,&#10;            timeout_seconds=hf_advisor_timeout_seconds,&#10;        )&#10;        self.min_model_samples_for_signal = int(max(10, min_model_samples_for_signal))&#10;        self.pause_on_negative_expectancy = bool(pause_on_negative_expectancy)&#10;        self.pause_expectancy_lookback = int(max(10, pause_expectancy_lookback))&#10;        self.pause_expectancy_threshold_usd = float(pause_expectancy_threshold_usd)&#10;        self.pause_max_drawdown_pct = float(max(0.0, pause_max_drawdown_pct))&#10;        self.pause_cooldown_minutes = float(max(0.0, pause_cooldown_minutes))&#10;        self._safety_pause_started_ts: Optional[float] = None&#10;        self._safety_pause_wait_recovery: bool = False&#10;        self.telegram_ml_report_hours = float(max(0.0, telegram_ml_report_hours))&#10;        self.telegram_ml_report_min_trades = int(max(0, telegram_ml_report_min_trades))&#10;        self.health_host = health_host&#10;        self.health_port = int(health_port)&#10;&#10;        self.api = HyperliquidAPI(verify_ssl=True)&#10;        self.learning = LearningState(state_file)&#10;        self.paper = PaperBroker(starting_equity=starting_equity)&#10;        self.report_file = report_file&#10;        self.notifier = TelegramNotifier(disable_on_error=True)&#10;        self.notifications_enabled = bool(&#10;            os.environ.get(&quot;TELEGRAM_BOT_TOKEN&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;TELEGRAM_CHAT_ID&quot;, &quot;&quot;).strip()&#10;            and os.environ.get(&quot;PROXY_URL&quot;, &quot;&quot;).strip()&#10;        )&#10;        self._last_summary_date: Optional[str] = None&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(&quot;Adaptive ML bot started. Trade notifications are enabled.&quot;)&#10;        else:&#10;            logger.info(&quot;Telegram notifications disabled (need TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID, PROXY_URL).&quot;)&#10;&#10;        self._running = True&#10;        self._live_clients: Optional[Dict[str, Any]] = None&#10;        self._live_readiness_alerted = False&#10;        self._live_startup_test_done = False&#10;        self._live_startup_test_passed: Optional[bool] = None&#10;        self._http_server: Optional[ThreadingHTTPServer] = None&#10;        self._entry_features: Dict[Tuple[str, int], List[float]] = {}&#10;        self._last_ml_report_ts: float = 0.0&#10;        self._advisor_stats: Dict[str, int] = {&#10;            &quot;requests&quot;: 0,&#10;            &quot;errors&quot;: 0,&#10;            &quot;remote_used&quot;: 0,&#10;            &quot;local_used&quot;: 0,&#10;            &quot;held_by_advisor&quot;: 0,&#10;            &quot;kept_model_signal&quot;: 0,&#10;            &quot;used_advisor_blend&quot;: 0,&#10;        }&#10;        self._restore_runtime_state()&#10;&#10;    def _interval_ms(self) -&gt; int:&#10;        if self.interval not in INTERVAL_MS:&#10;            raise ValueError(f&quot;Unsupported interval '{self.interval}'. Supported: {sorted(INTERVAL_MS)}&quot;)&#10;        return INTERVAL_MS[self.interval]&#10;&#10;    def stop(self, *_: Any) -&gt; None:&#10;        logger.info(&quot;Received shutdown signal, flushing learning state.&quot;)&#10;        self._running = False&#10;        if self._http_server is not None:&#10;            self._http_server.shutdown()&#10;            self._http_server.server_close()&#10;            self._http_server = None&#10;        self._persist_runtime_state()&#10;        self.learning.save(force=True)&#10;        self.write_report()&#10;&#10;    def status_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        approval = self._live_approval_status_details()&#10;        safety_pause = {&#10;            &quot;enabled&quot;: self.pause_on_negative_expectancy,&#10;            &quot;lookback&quot;: self.pause_expectancy_lookback,&#10;            &quot;expectancy_threshold_usd&quot;: self.pause_expectancy_threshold_usd,&#10;            &quot;max_drawdown_pct&quot;: self.pause_max_drawdown_pct,&#10;            &quot;cooldown_minutes&quot;: self.pause_cooldown_minutes,&#10;            &quot;active&quot;: self._safety_pause_started_ts is not None,&#10;            &quot;active_since_ts&quot;: self._safety_pause_started_ts,&#10;            &quot;wait_recovery&quot;: self._safety_pause_wait_recovery,&#10;        }&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;            &quot;metrics&quot;: metrics,&#10;            &quot;ml_progress&quot;: self.learning.ml_progress_metrics(),&#10;            &quot;thresholds&quot;: self.learning.thresholds,&#10;            &quot;state_file&quot;: self.learning.state_path,&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_mode&quot;: self.live_approval_mode,&#10;            &quot;live_approval_granted&quot;: approval[&quot;granted&quot;],&#10;            &quot;live_approval_checks&quot;: approval,&#10;            &quot;live_startup_test_enabled&quot;: self.live_startup_test,&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;trade_events_recorded&quot;: len(self.learning.trade_events),&#10;            &quot;advisor&quot;: {&#10;                &quot;enabled&quot;: self.hf_advisor.enabled,&#10;                &quot;remote_enabled&quot;: self.hf_advisor.remote_enabled,&#10;                &quot;mode&quot;: self.hf_advisor_mode,&#10;                &quot;min_confidence&quot;: self.hf_advisor_min_confidence,&#10;                &quot;blend_weight&quot;: self.hf_advisor_blend_weight,&#10;                &quot;stats&quot;: self._advisor_stats,&#10;            },&#10;            &quot;model_samples&quot;: self.learning.model_samples,&#10;            &quot;min_model_samples_for_signal&quot;: self.min_model_samples_for_signal,&#10;            &quot;safety_pause&quot;: safety_pause,&#10;        }&#10;&#10;    def status_overview_payload(self) -&gt; Dict[str, Any]:&#10;        metrics = self.learning.global_metrics()&#10;        ml_progress = self.learning.ml_progress_metrics()&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;paper_equity&quot;: self.paper.equity,&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;trades&quot;: int(metrics[&quot;trades&quot;]),&#10;            &quot;win_rate&quot;: metrics[&quot;win_rate&quot;],&#10;            &quot;profit_factor&quot;: metrics[&quot;profit_factor&quot;],&#10;            &quot;brier_rolling_50&quot;: ml_progress[&quot;brier_rolling_50&quot;],&#10;            &quot;expectancy_usd_30&quot;: ml_progress[&quot;expectancy_usd_30&quot;],&#10;            &quot;max_drawdown_pct&quot;: ml_progress[&quot;max_drawdown_pct&quot;],&#10;            &quot;consistently_profitable&quot;: self.learning.consistently_profitable(),&#10;            &quot;live_armed&quot;: self._live_clients is not None,&#10;            &quot;live_approval_required&quot;: self.require_live_approval,&#10;            &quot;live_approval_granted&quot;: self._live_approval_granted(),&#10;            &quot;live_startup_test_done&quot;: self._live_startup_test_done,&#10;            &quot;live_startup_test_passed&quot;: self._live_startup_test_passed,&#10;            &quot;advisor_enabled&quot;: self.hf_advisor.enabled,&#10;            &quot;advisor_remote_enabled&quot;: self.hf_advisor.remote_enabled,&#10;            &quot;advisor_mode&quot;: self.hf_advisor_mode,&#10;        }&#10;&#10;    def _apply_hf_advisor(&#10;        self,&#10;        coin: str,&#10;        features: List[float],&#10;        candle: Dict[str, float],&#10;        now_ts: int,&#10;        p_long: float,&#10;        threshold: float,&#10;        side: Optional[str],&#10;    ) -&gt; Tuple[Optional[str], str, Optional[float], Optional[str], Optional[float]]:&#10;        mode = self.hf_advisor_mode&#10;&#10;        self._advisor_stats[&quot;requests&quot;] += 1&#10;        advice = self.hf_advisor.advise(&#10;            coin=coin,&#10;            interval=self.interval,&#10;            features=features,&#10;            candle=candle,&#10;            timestamp_ms=now_ts,&#10;        )&#10;        if advice is None:&#10;            return side, &quot;advisor_unavailable&quot;, p_long, None, None&#10;&#10;        if advice.error:&#10;            self._advisor_stats[&quot;errors&quot;] += 1&#10;        if advice.source == &quot;remote&quot;:&#10;            self._advisor_stats[&quot;remote_used&quot;] += 1&#10;        else:&#10;            self._advisor_stats[&quot;local_used&quot;] += 1&#10;&#10;        advisor_side = advice.side&#10;        advisor_conf = float(advice.confidence)&#10;        if mode == &quot;blend&quot; and advice.p_long is not None:&#10;            blended_p = (1.0 - self.hf_advisor_blend_weight) * p_long + self.hf_advisor_blend_weight * float(advice.p_long)&#10;            blended_p = float(np.clip(blended_p, 0.01, 0.99))&#10;            blended_side: Optional[str] = None&#10;            if blended_p &gt;= threshold:&#10;                blended_side = &quot;long&quot;&#10;            elif blended_p &lt;= (1.0 - threshold):&#10;                blended_side = &quot;short&quot;&#10;            if blended_side is None:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_hold_blend&quot;, blended_p, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;used_advisor_blend&quot;] += 1&#10;            return blended_side, &quot;advisor_blend&quot;, blended_p, advisor_side, advisor_conf&#10;&#10;        if side is None:&#10;            return None, &quot;advisor_no_model_side&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;confirm&quot;:&#10;            if advisor_side == side and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;                return side, &quot;advisor_confirm&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;            return None, &quot;advisor_block_confirm&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        if mode == &quot;veto&quot;:&#10;            opposite = (side == &quot;long&quot; and advisor_side == &quot;short&quot;) or (side == &quot;short&quot; and advisor_side == &quot;long&quot;)&#10;            if opposite and advisor_conf &gt;= self.hf_advisor_min_confidence:&#10;                self._advisor_stats[&quot;held_by_advisor&quot;] += 1&#10;                return None, &quot;advisor_veto&quot;, p_long, advisor_side, advisor_conf&#10;            self._advisor_stats[&quot;kept_model_signal&quot;] += 1&#10;            return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;        return side, &quot;advisor_pass&quot;, p_long, advisor_side, advisor_conf&#10;&#10;    def open_trades_payload(self) -&gt; Dict[str, Any]:&#10;        return {&#10;            &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;,&#10;            &quot;status&quot;: &quot;ok&quot; if self._running else &quot;stopping&quot;,&#10;            &quot;timestamp&quot;: datetime.now(timezone.utc).isoformat(),&#10;            &quot;open_positions_count&quot;: len(self.paper.positions),&#10;            &quot;open_positions&quot;: [asdict(p) for p in self.paper.positions.values()],&#10;        }&#10;&#10;    def _restore_runtime_state(self) -&gt; None:&#10;        equity, positions, entry_features = self.learning.restore_runtime_state()&#10;        if equity is not None:&#10;            self.paper.equity = float(equity)&#10;        if positions:&#10;            self.paper.positions = positions&#10;        if entry_features:&#10;            self._entry_features = entry_features&#10;        if equity is not None or positions:&#10;            logger.info(&#10;                &quot;Restored paper runtime state: equity=%.2f open_positions=%d tracked_entries=%d&quot;,&#10;                self.paper.equity,&#10;                len(self.paper.positions),&#10;                len(self._entry_features),&#10;            )&#10;&#10;    def _persist_runtime_state(self) -&gt; None:&#10;        self.learning.record_equity(self.paper.equity)&#10;        self.learning.set_runtime_state(&#10;            paper_equity=self.paper.equity,&#10;            positions=self.paper.positions,&#10;            entry_features=self._entry_features,&#10;        )&#10;&#10;    def _entry_pause_reason(self) -&gt; Optional[str]:&#10;        if not self.pause_on_negative_expectancy:&#10;            self._safety_pause_started_ts = None&#10;            self._safety_pause_wait_recovery = False&#10;            return None&#10;        pnl = [float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in self.learning.closed_trades]&#10;        if len(pnl) &lt; self.pause_expectancy_lookback:&#10;            self._safety_pause_started_ts = None&#10;            self._safety_pause_wait_recovery = False&#10;            return None&#10;        exp_recent = float(np.mean(pnl[-self.pause_expectancy_lookback:]))&#10;        if exp_recent &gt;= self.pause_expectancy_threshold_usd:&#10;            if self._safety_pause_wait_recovery:&#10;                logger.info(&quot;Safety pause re-armed after expectancy recovered (expectancy_%d=%+.3f).&quot;, self.pause_expectancy_lookback, exp_recent)&#10;            self._safety_pause_started_ts = None&#10;            self._safety_pause_wait_recovery = False&#10;            return None&#10;&#10;        max_dd = self.learning.ml_progress_metrics().get(&quot;max_drawdown_pct&quot;)&#10;        max_dd_val = float(max_dd) if max_dd is not None else 0.0&#10;        if max_dd_val &lt; self.pause_max_drawdown_pct:&#10;            if self._safety_pause_wait_recovery:&#10;                logger.info(&quot;Safety pause re-armed after drawdown recovered (max_dd=%.2f%%).&quot;, max_dd_val)&#10;            self._safety_pause_started_ts = None&#10;            self._safety_pause_wait_recovery = False&#10;            return None&#10;&#10;        if self._safety_pause_wait_recovery:&#10;            return None&#10;&#10;        reason = (&#10;            f&quot;safety_pause expectancy_{self.pause_expectancy_lookback}={exp_recent:+.3f} &quot;&#10;            f&quot;&lt; {self.pause_expectancy_threshold_usd:+.3f} and max_dd={max_dd_val:.2f}%&quot;&#10;        )&#10;&#10;        if self.pause_cooldown_minutes &lt;= 0:&#10;            return reason&#10;&#10;        now_ts = time.time()&#10;        if self._safety_pause_started_ts is None:&#10;            self._safety_pause_started_ts = now_ts&#10;&#10;        cooldown_seconds = self.pause_cooldown_minutes * 60.0&#10;        elapsed_seconds = now_ts - self._safety_pause_started_ts&#10;        if elapsed_seconds &gt;= cooldown_seconds:&#10;            self._safety_pause_started_ts = None&#10;            self._safety_pause_wait_recovery = True&#10;            logger.warning(&#10;                &quot;Safety pause cooldown expired after %.1f minutes; allowing entries until metrics recover.&quot;,&#10;                self.pause_cooldown_minutes,&#10;            )&#10;            return None&#10;&#10;        remaining_seconds = int(max(0.0, cooldown_seconds - elapsed_seconds))&#10;        remaining_minutes = remaining_seconds // 60&#10;        remaining_remainder = remaining_seconds % 60&#10;        return (&#10;            f&quot;{reason} (cooldown {remaining_minutes:02d}m{remaining_remainder:02d}s remaining)&quot;&#10;        )&#10;&#10;    def _live_approval_granted(self) -&gt; bool:&#10;        return self._live_approval_status_details()[&quot;granted&quot;]&#10;&#10;    def _live_approval_status_details(self) -&gt; Dict[str, Any]:&#10;        env_approved = os.environ.get(&quot;ALLOW_LIVE_TRADING&quot;, &quot;&quot;).strip().lower() in {&quot;1&quot;, &quot;true&quot;, &quot;yes&quot;, &quot;y&quot;}&#10;        file_approved = bool(self.live_approval_file and os.path.exists(self.live_approval_file))&#10;        mode = (self.live_approval_mode or &quot;either&quot;).strip().lower()&#10;        missing: List[str] = []&#10;        if mode == &quot;both&quot;:&#10;            granted = env_approved and file_approved&#10;            if not env_approved:&#10;                missing.append(&quot;env:ALLOW_LIVE_TRADING&quot;)&#10;            if not file_approved:&#10;                missing.append(f&quot;file:{self.live_approval_file}&quot;)&#10;        else:&#10;            granted = env_approved or file_approved&#10;            if not granted:&#10;                missing = [&quot;env:ALLOW_LIVE_TRADING&quot;, f&quot;file:{self.live_approval_file}&quot;]&#10;        return {&#10;            &quot;mode&quot;: mode,&#10;            &quot;env_approved&quot;: env_approved,&#10;            &quot;file_approved&quot;: file_approved,&#10;            &quot;missing&quot;: missing,&#10;            &quot;granted&quot;: granted,&#10;        }&#10;&#10;    def _maybe_notify_live_readiness(self) -&gt; None:&#10;        if self._live_readiness_alerted or not self.learning.consistently_profitable():&#10;            return&#10;        m = self.learning.global_metrics()&#10;        msg = (&#10;            &quot;Paper performance gate reached.\n&quot;&#10;            f&quot;Trades: {int(m['trades'])}, win rate: {m['win_rate'] * 100.0:.1f}%, PF: {m['profit_factor']:.2f}.\n&quot;&#10;            &quot;Grant live approval to arm live mode. &quot;&#10;            f&quot;Approval mode={self.live_approval_mode}. &quot;&#10;            f&quot;Set ALLOW_LIVE_TRADING=1 and/or create {self.live_approval_file} (based on mode).&quot;&#10;        )&#10;        logger.warning(msg)&#10;        if self.notifications_enabled:&#10;            self.notifier.send_info(msg)&#10;        self._live_readiness_alerted = True&#10;&#10;    def _live_order_filled(self, result: Any) -&gt; bool:&#10;        if not isinstance(result, dict):&#10;            return False&#10;        try:&#10;            statuses = result.get(&quot;response&quot;, {}).get(&quot;data&quot;, {}).get(&quot;statuses&quot;, [])&#10;            errors = [s.get(&quot;error&quot;) for s in statuses if &quot;error&quot; in s]&#10;            if errors:&#10;                return False&#10;            filled_sz = sum(float(s[&quot;filled&quot;].get(&quot;totalSz&quot;, 0.0)) for s in statuses if &quot;filled&quot; in s)&#10;            return filled_sz &gt; 0&#10;        except Exception:&#10;            return False&#10;&#10;    def _calc_test_size(self, coin: str, px: float, min_notional: float) -&gt; float:&#10;        sz_dec = self.api.get_sz_decimals(coin)&#10;        step = 10 ** (-sz_dec)&#10;        raw = min_notional / max(px, 1e-9)&#10;        steps = max(1, int(math.ceil(raw / step)))&#10;        return float(round(steps * step, sz_dec))&#10;&#10;    def _run_live_startup_test(self) -&gt; None:&#10;        if self._live_clients is None or self._live_startup_test_done or not self.live_startup_test:&#10;            return&#10;&#10;        self._live_startup_test_done = True&#10;        coin = self.live_startup_test_coin&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;&#10;        try:&#10;            df = self._fetch_df(coin, lookback_bars=120)&#10;            if df is None:&#10;                raise RuntimeError(f&quot;No candle data available for startup live test on {coin}&quot;)&#10;&#10;            entry_px = float(df.iloc[-1][&quot;close&quot;])&#10;            test_size = self._calc_test_size(coin=coin, px=entry_px, min_notional=self.live_startup_test_notional)&#10;&#10;            logger.warning(&#10;                &quot;Running startup live order test: BUY then SELL %s size=%.8f around px=%.4f&quot;,&#10;                coin,&#10;                test_size,&#10;                entry_px,&#10;            )&#10;&#10;            buy_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=True,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;            time.sleep(1.2)&#10;            sell_result = self.api.place_market_order(&#10;                exchange=exchange,&#10;                coin=coin,&#10;                is_buy=False,&#10;                sz=test_size,&#10;                px=entry_px,&#10;                sl=None,&#10;                tp=None,&#10;            )&#10;&#10;            buy_ok = self._live_order_filled(buy_result)&#10;            sell_ok = self._live_order_filled(sell_result)&#10;            self._live_startup_test_passed = bool(buy_ok and sell_ok)&#10;&#10;            if self._live_startup_test_passed:&#10;                msg = f&quot;Startup live order test passed on {coin} (open+close filled).&quot;&#10;                logger.warning(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_info(msg)&#10;            else:&#10;                msg = f&quot;Startup live order test failed on {coin}. Review logs/order responses before trusting live mirroring.&quot;&#10;                logger.error(msg)&#10;                if self.notifications_enabled:&#10;                    self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;        except Exception as exc:&#10;            self._live_startup_test_passed = False&#10;            msg = f&quot;Startup live order test exception: {exc}&quot;&#10;            logger.error(msg)&#10;            if self.notifications_enabled:&#10;                self.notifier.send_error(&quot;adaptive_bot&quot;, msg)&#10;&#10;    def _start_health_server(self) -&gt; None:&#10;        bot = self&#10;&#10;        class HealthHandler(BaseHTTPRequestHandler):&#10;            def _write_json(self, payload: Dict[str, Any], status: int = 200) -&gt; None:&#10;                body = json.dumps(payload).encode(&quot;utf-8&quot;)&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, str(len(body)))&#10;                self.end_headers()&#10;                self.wfile.write(body)&#10;&#10;            def _write_head(self, status: int = 200) -&gt; None:&#10;                self.send_response(status)&#10;                self.send_header(&quot;Content-Type&quot;, &quot;application/json&quot;)&#10;                self.send_header(&quot;Content-Length&quot;, &quot;0&quot;)&#10;                self.end_headers()&#10;&#10;            def do_GET(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;):&#10;                    self._write_json({&quot;status&quot;: &quot;ok&quot;, &quot;service&quot;: &quot;hyperliquid-adaptive-bot&quot;})&#10;                    return&#10;                if self.path == &quot;/status&quot;:&#10;                    self._write_json(bot.status_overview_payload())&#10;                    return&#10;                if self.path == &quot;/status/full&quot;:&#10;                    self._write_json(bot.status_payload())&#10;                    return&#10;                if self.path == &quot;/trades&quot;:&#10;                    self._write_json(bot.open_trades_payload())&#10;                    return&#10;                self._write_json({&quot;status&quot;: &quot;not_found&quot;}, status=404)&#10;&#10;            def do_HEAD(self) -&gt; None:  # noqa: N802&#10;                if self.path in (&quot;/&quot;, &quot;/healthz&quot;, &quot;/status&quot;, &quot;/status/full&quot;, &quot;/trades&quot;):&#10;                    self._write_head(status=200)&#10;                    return&#10;                self._write_head(status=404)&#10;&#10;            def log_message(self, format: str, *args: Any) -&gt; None:&#10;                logger.debug(&quot;health_http: &quot; + format, *args)&#10;&#10;        server = ThreadingHTTPServer((self.health_host, self.health_port), HealthHandler)&#10;        self._http_server = server&#10;        t = threading.Thread(target=server.serve_forever, daemon=True)&#10;        t.start()&#10;        logger.info(&quot;Health endpoint listening on http://%s:%s&quot;, self.health_host, self.health_port)&#10;&#10;    def _fetch_df(self, coin: str, lookback_bars: int = 200) -&gt; Optional[pd.DataFrame]:&#10;        start = int((datetime.now(timezone.utc) - timedelta(milliseconds=self._interval_ms() * lookback_bars)).timestamp() * 1000)&#10;        raw = self.api.get_candles(coin=coin, interval=self.interval, start_time=start)&#10;        if not raw:&#10;            return None&#10;        df = parse_candles(raw)&#10;        if len(df) &lt; 80:&#10;            return None&#10;        return df&#10;&#10;    def _size_for_trade(self, entry: float, stop: float) -&gt; float:&#10;        max_risk_usd = self.paper.equity * self.risk_per_trade&#10;        stop_distance = abs(entry - stop)&#10;        if stop_distance &lt;= 0:&#10;            return 0.0&#10;        size = max_risk_usd / stop_distance&#10;        max_notional = self.paper.equity * 0.25&#10;        if size * entry &gt; max_notional:&#10;            size = max_notional / entry&#10;        return float(max(size, 0.0))&#10;&#10;    def _maybe_prepare_live(self) -&gt; None:&#10;        if not self.enable_live:&#10;            return&#10;        if not self.learning.consistently_profitable():&#10;            return&#10;        if self.require_live_approval and not self._live_approval_granted():&#10;            return&#10;        if self._live_clients is not None:&#10;            return&#10;&#10;        pk = os.environ.get(&quot;HL_PRIVATE_KEY&quot;, &quot;&quot;).strip()&#10;        acct = os.environ.get(&quot;HL_ACCOUNT_ADDRESS&quot;, &quot;&quot;).strip() or None&#10;        if not pk:&#10;            logger.warning(&quot;Live mode requested and paper criteria met, but HL_PRIVATE_KEY is missing.&quot;)&#10;            return&#10;&#10;        try:&#10;            self._live_clients = self.api.create_live_execution_clients(private_key=pk, account_address=acct)&#10;            logger.warning(&quot;Paper criteria met. Live execution is now armed.&quot;)&#10;            self._run_live_startup_test()&#10;        except Exception as exc:&#10;            logger.error(&quot;Failed to initialize live execution clients: %s&quot;, exc)&#10;            self._live_clients = None&#10;&#10;    def _place_live_mirror(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if self._live_clients is None:&#10;            return&#10;        exchange = self._live_clients[&quot;exchange&quot;]&#10;        is_buy = side == &quot;long&quot;&#10;        try:&#10;            self.api.place_market_order(exchange=exchange, coin=coin, is_buy=is_buy, sz=size, px=entry, sl=sl, tp=tp)&#10;        except Exception as exc:&#10;            logger.error(&quot;Live mirror order failed for %s: %s&quot;, coin, exc)&#10;&#10;    def _notify_trade_open(self, coin: str, side: str, entry: float, sl: float, tp: float, size: float) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        rr: Optional[float] = None&#10;        risk = abs(entry - sl)&#10;        reward = abs(tp - entry)&#10;        if risk &gt; 0:&#10;            rr = reward / risk&#10;        self.notifier.send_trade(&#10;            direction=side.upper(),&#10;            coin=coin,&#10;            entry=entry,&#10;            sl=sl,&#10;            tp=tp,&#10;            sz=size,&#10;            notional=entry * size,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;            rr=rr,&#10;            order_type=&quot;paper&quot;,&#10;        )&#10;&#10;    def _notify_trade_close(self, closed: ClosedTrade) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        self.notifier.send_exit(&#10;            coin=closed.coin,&#10;            reason=closed.reason,&#10;            pnl_pct=closed.pnl_pct,&#10;            pnl_usdc=closed.pnl_usd,&#10;            strategy=&quot;adaptive_ml&quot;,&#10;            timeframe=self.interval,&#10;        )&#10;&#10;    def _maybe_send_daily_summary(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        now = datetime.now(timezone.utc)&#10;        date_key = now.date().isoformat()&#10;        if now.hour != 0 or self._last_summary_date == date_key:&#10;            return&#10;&#10;        since_ms = int((now - timedelta(days=1)).timestamp() * 1000)&#10;        day_trades = [&#10;            t for t in self.learning.closed_trades&#10;            if int(t.get(&quot;exit_ts&quot;, 0)) &gt;= since_ms&#10;        ]&#10;        day_wins = sum(1 for t in day_trades if float(t.get(&quot;pnl_usd&quot;, 0.0)) &gt; 0)&#10;        day_wr = (day_wins / len(day_trades) * 100.0) if day_trades else 0.0&#10;        day_pnl = sum(float(t.get(&quot;pnl_usd&quot;, 0.0)) for t in day_trades)&#10;&#10;        overall = self.learning.global_metrics()&#10;        ml = self.learning.ml_progress_metrics()&#10;        summary = (&#10;            &quot;Midnight summary (UTC)\n&quot;&#10;            f&quot;24h trades: {len(day_trades)}\n&quot;&#10;            f&quot;24h win rate: {day_wr:.1f}%\n&quot;&#10;            f&quot;24h pnl: {day_pnl:+.2f} USDC\n&quot;&#10;            f&quot;Total trades: {int(overall['trades'])}\n&quot;&#10;            f&quot;Total win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Paper equity: {self.paper.equity:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%&quot;&#10;        )&#10;        self.notifier.send_info(summary)&#10;        self._last_summary_date = date_key&#10;&#10;    def _maybe_send_ml_progress_report(self) -&gt; None:&#10;        if not self.notifications_enabled:&#10;            return&#10;        if self.telegram_ml_report_hours &lt;= 0:&#10;            return&#10;&#10;        now_ts = time.time()&#10;        period_s = self.telegram_ml_report_hours * 3600.0&#10;        if self._last_ml_report_ts &gt; 0 and (now_ts - self._last_ml_report_ts) &lt; period_s:&#10;            return&#10;&#10;        overall = self.learning.global_metrics()&#10;        trades = int(overall[&quot;trades&quot;])&#10;        if trades &lt; self.telegram_ml_report_min_trades:&#10;            return&#10;&#10;        ml = self.learning.ml_progress_metrics()&#10;        per_coin = ml.get(&quot;per_coin&quot;, {})&#10;        coin_parts = []&#10;        for coin in ALLOWED_COINS:&#10;            coin_brier = per_coin.get(coin, {}).get(&quot;brier_rolling_50&quot;)&#10;            coin_parts.append(f&quot;{coin}:{coin_brier}&quot;)&#10;        coin_brier_line = &quot; | &quot;.join(coin_parts)&#10;&#10;        msg = (&#10;            &quot;ML progress update\n&quot;&#10;            f&quot;Trades: {trades}\n&quot;&#10;            f&quot;Win rate: {overall['win_rate'] * 100.0:.1f}%\n&quot;&#10;            f&quot;Profit factor: {overall['profit_factor']:.2f}\n&quot;&#10;            f&quot;Brier(50): {ml['brier_rolling_50']}\n&quot;&#10;            f&quot;Expectancy(30): {ml['expectancy_usd_30']}\n&quot;&#10;            f&quot;Max DD: {ml['max_drawdown_pct']}%\n&quot;&#10;            f&quot;Per-coin Brier(50): {coin_brier_line}&quot;&#10;        )&#10;        self.notifier.send_info(msg)&#10;        self._last_ml_report_ts = now_ts&#10;&#10;    def _trade_coin(self, coin: str) -&gt; None:&#10;        df = self._fetch_df(coin)&#10;        if df is None:&#10;            logger.warning(&quot;Not enough candle data for %s&quot;, coin)&#10;            return&#10;&#10;        candle = {&#10;            &quot;high&quot;: float(df.iloc[-1][&quot;high&quot;]),&#10;            &quot;low&quot;: float(df.iloc[-1][&quot;low&quot;]),&#10;            &quot;close&quot;: float(df.iloc[-1][&quot;close&quot;]),&#10;        }&#10;        now_ts = int(df.iloc[-1][&quot;ts&quot;])&#10;&#10;        closed = self.paper.maybe_close(coin, candle, now_ts)&#10;        if closed:&#10;            logger.info(&quot;CLOSED %s %s pnl=$%.2f (%+.2f%%) reason=%s&quot;, closed.coin, closed.side, closed.pnl_usd, closed.pnl_pct, closed.reason)&#10;            self._notify_trade_close(closed)&#10;            self.learning.record_trade_event(&quot;close&quot;, asdict(closed))&#10;            # Entry features are captured when the position opens and used as supervised labels on close.&#10;            features_map = getattr(self, &quot;_entry_features&quot;, {})&#10;            stored = features_map.get((coin, closed.entry_ts), [])&#10;            if stored:&#10;                self.learning.update_after_trade(closed, stored)&#10;                features_map.pop((coin, closed.entry_ts), None)&#10;            self._persist_runtime_state()&#10;&#10;        if self.paper.has_position(coin):&#10;            return&#10;&#10;        features = build_features(df)&#10;        if not features:&#10;            return&#10;&#10;        pause_reason = self._entry_pause_reason()&#10;        if pause_reason:&#10;            logger.warning(&quot;%s HOLD %s&quot;, coin, pause_reason)&#10;            return&#10;&#10;        p_long = self.learning.predict_long_probability(coin, features)&#10;        threshold = self.learning.thresholds[coin]&#10;&#10;        side: Optional[str] = None&#10;        signal_reason = &quot;model&quot;&#10;&#10;        if not self.learning.enough_samples(coin, self.min_model_samples_for_signal):&#10;            # Bootstrap mode: trade weak momentum direction to generate first labels.&#10;            side = &quot;long&quot; if features[1] &gt;= 0 else &quot;short&quot;&#10;            signal_reason = &quot;bootstrap&quot;&#10;        else:&#10;            if p_long &gt;= threshold:&#10;                side = &quot;long&quot;&#10;            elif p_long &lt;= (1.0 - threshold):&#10;                side = &quot;short&quot;&#10;&#10;            if side is None and random.random() &lt; self.exploration_rate:&#10;                side = &quot;long&quot; if p_long &gt;= 0.5 else &quot;short&quot;&#10;                signal_reason = &quot;exploration&quot;&#10;&#10;        side, signal_reason, p_long, advisor_side, advisor_conf = self._apply_hf_advisor(&#10;            coin=coin,&#10;            features=features,&#10;            candle=candle,&#10;            now_ts=now_ts,&#10;            p_long=p_long,&#10;            threshold=threshold,&#10;            side=side,&#10;        )&#10;&#10;        if not side:&#10;            if advisor_side is not None:&#10;                logger.info(&#10;                    &quot;%s HOLD p_long=%.3f threshold=%.3f advisor=%s conf=%.3f reason=%s&quot;,&#10;                    coin,&#10;                    p_long,&#10;                    threshold,&#10;                    advisor_side,&#10;                    advisor_conf if advisor_conf is not None else 0.0,&#10;                    signal_reason,&#10;                )&#10;            else:&#10;                logger.info(&quot;%s HOLD p_long=%.3f threshold=%.3f&quot;, coin, p_long, threshold)&#10;            return&#10;&#10;        entry = candle[&quot;close&quot;]&#10;        atr = atr_for_risk(df)&#10;        sl_mult = 1.3&#10;        tp_mult = 2.0&#10;&#10;        if side == &quot;long&quot;:&#10;            sl = max(entry - atr * sl_mult, 1e-8)&#10;            tp = entry + atr * tp_mult&#10;        else:&#10;            sl = entry + atr * sl_mult&#10;            tp = max(entry - atr * tp_mult, 1e-8)&#10;&#10;        size = self._size_for_trade(entry, sl)&#10;        if size &lt;= 0:&#10;            return&#10;&#10;        pos = Position(&#10;            coin=coin,&#10;            side=side,&#10;            entry=entry,&#10;            size=size,&#10;            sl=sl,&#10;            tp=tp,&#10;            entry_ts=now_ts,&#10;            entry_features=features,&#10;            entry_p_long=p_long,&#10;        )&#10;        self.paper.open_position(pos)&#10;        if not hasattr(self, &quot;_entry_features&quot;):&#10;            self._entry_features = {}&#10;        self._entry_features[(coin, now_ts)] = features&#10;        self.learning.record_trade_event(&#10;            &quot;open&quot;,&#10;            {&#10;                &quot;coin&quot;: coin,&#10;                &quot;side&quot;: side,&#10;                &quot;entry&quot;: entry,&#10;                &quot;size&quot;: size,&#10;                &quot;sl&quot;: sl,&#10;                &quot;tp&quot;: tp,&#10;                &quot;entry_ts&quot;: now_ts,&#10;                &quot;signal_reason&quot;: signal_reason,&#10;                &quot;p_long&quot;: p_long,&#10;                &quot;threshold&quot;: threshold,&#10;                &quot;advisor_side&quot;: advisor_side,&#10;                &quot;advisor_confidence&quot;: advisor_conf,&#10;            },&#10;        )&#10;        self._persist_runtime_state()&#10;&#10;        logger.info(&#10;            &quot;OPEN %s %s entry=%.4f sl=%.4f tp=%.4f size=%.5f p_long=%.3f th=%.3f reason=%s equity=%.2f&quot;,&#10;            coin,&#10;            side,&#10;            entry,&#10;            sl,&#10;            tp,&#10;            size,&#10;            p_long,&#10;            threshold,&#10;            signal_reason,&#10;            self.paper.equity,&#10;        )&#10;        self._notify_trade_open(coin, side, entry, sl, tp, size)&#10;&#10;        self._maybe_prepare_live()&#10;        self._place_live_mirror(coin, side, entry, sl, tp, size)&#10;&#10;    def write_report(self) -&gt; None:&#10;        payload = self.status_payload()&#10;        with open(self.report_file, &quot;w&quot;, encoding=&quot;utf-8&quot;) as f:&#10;            json.dump(payload, f, indent=2)&#10;&#10;    def run(self) -&gt; None:&#10;        signal.signal(signal.SIGINT, self.stop)&#10;        signal.signal(signal.SIGTERM, self.stop)&#10;&#10;        self._start_health_server()&#10;&#10;        logger.info(&quot;Starting adaptive bot (paper mode=%s, live enabled=%s)&quot;, True, self.enable_live)&#10;        logger.info(&quot;Universe: %s&quot;, &quot;, &quot;.join(ALLOWED_COINS))&#10;&#10;        while self._running:&#10;            try:&#10;                for coin in ALLOWED_COINS:&#10;                    self._trade_coin(coin)&#10;                self._persist_runtime_state()&#10;                self.learning.save()&#10;                self.write_report()&#10;&#10;                m = self.learning.global_metrics()&#10;                logger.info(&#10;                    &quot;Stats trades=%d win_rate=%.2f%% pf=%.2f paper_equity=%.2f profitable_gate=%s&quot;,&#10;                    int(m[&quot;trades&quot;]),&#10;                    m[&quot;win_rate&quot;] * 100.0,&#10;                    m[&quot;profit_factor&quot;],&#10;                    self.paper.equity,&#10;                    self.learning.consistently_profitable(),&#10;                )&#10;                self._maybe_notify_live_readiness()&#10;                self._maybe_prepare_live()&#10;                self._maybe_send_daily_summary()&#10;                self._maybe_send_ml_progress_report()&#10;            except Exception as exc:&#10;                logger.exception(&quot;Loop error: %s&quot;, exc)&#10;&#10;            time.sleep(self.poll_seconds)&#10;&#10;&#10;def build_arg_parser() -&gt; argparse.ArgumentParser:&#10;    p = argparse.ArgumentParser(description=&quot;Adaptive ML Hyperliquid paper trader&quot;)&#10;    p.add_argument(&quot;--interval&quot;, default=&quot;15m&quot;, choices=sorted(INTERVAL_MS.keys()))&#10;    p.add_argument(&quot;--state-file&quot;, default=&quot;learning_state.joblib&quot;)&#10;    p.add_argument(&quot;--report-file&quot;, default=&quot;paper_report.json&quot;)&#10;    p.add_argument(&quot;--starting-equity&quot;, type=float, default=10000.0)&#10;    p.add_argument(&quot;--risk-per-trade&quot;, type=float, default=0.01, help=&quot;Fraction of equity risked per position&quot;)&#10;    p.add_argument(&quot;--poll-seconds&quot;, type=int, default=45)&#10;    p.add_argument(&quot;--exploration-rate&quot;, type=float, default=0.08, help=&quot;Chance to take an exploratory trade when model signal is neutral&quot;)&#10;    p.add_argument(&quot;--health-host&quot;, default=os.environ.get(&quot;BOT_HEALTH_HOST&quot;, &quot;0.0.0.0&quot;))&#10;    p.add_argument(&quot;--health-port&quot;, type=int, default=int(os.environ.get(&quot;PORT&quot;, os.environ.get(&quot;BOT_HEALTH_PORT&quot;, &quot;7860&quot;))))&#10;    p.add_argument(&quot;--enable-live&quot;, action=&quot;store_true&quot;, help=&quot;Allow live mirror orders after paper criteria is met&quot;)&#10;    p.add_argument(&#10;        &quot;--require-live-approval&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=True,&#10;        help=&quot;Require manual approval before arming live mirror orders&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--live-approval-mode&quot;,&#10;        choices=[&quot;either&quot;, &quot;both&quot;],&#10;        default=&quot;either&quot;,&#10;        help=&quot;Manual approval policy: 'either' (env or file) or 'both' (env and file)&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-approval-file&quot;, default=&quot;allow_live_trading.flag&quot;, help=&quot;Approval file path to authorize live trading&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=False,&#10;        help=&quot;When live is armed, place a tiny BUY then SELL to verify live order execution&quot;,&#10;    )&#10;    p.add_argument(&quot;--live-startup-test-coin&quot;, choices=sorted(ALLOWED_COINS), default=&quot;BTC&quot;)&#10;    p.add_argument(&#10;        &quot;--live-startup-test-notional&quot;,&#10;        type=float,&#10;        default=12.0,&#10;        help=&quot;Approx USDC notional used for startup live order test (min effectively 10)&quot;,&#10;    )&#10;    p.add_argument(&quot;--hf-advisor-url&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_URL&quot;, &quot;&quot;), help=&quot;Optional external advisor endpoint URL&quot;)&#10;    p.add_argument(&quot;--hf-advisor-token&quot;, default=os.environ.get(&quot;BOT_HF_ADVISOR_TOKEN&quot;, &quot;&quot;), help=&quot;Optional bearer token for advisor endpoint&quot;)&#10;    p.add_argument(&#10;        &quot;--hf-advisor-mode&quot;,&#10;        choices=[&quot;confirm&quot;, &quot;veto&quot;, &quot;blend&quot;],&#10;        default=os.environ.get(&quot;BOT_HF_ADVISOR_MODE&quot;, &quot;blend&quot;),&#10;        help=&quot;confirm=must match advisor, veto=advisor can block, blend=blend p_long with advisor p_long&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-min-confidence&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_MIN_CONFIDENCE&quot;, &quot;0.60&quot;)),&#10;        help=&quot;Minimum advisor confidence used by confirm/veto modes&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-blend-weight&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_BLEND_WEIGHT&quot;, &quot;0.35&quot;)),&#10;        help=&quot;Advisor p_long blending weight for blend mode [0..1]&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--hf-advisor-timeout-seconds&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_HF_ADVISOR_TIMEOUT_SECONDS&quot;, &quot;4.0&quot;)),&#10;        help=&quot;HTTP timeout for advisor requests&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--min-model-samples-for-signal&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_MIN_MODEL_SAMPLES_FOR_SIGNAL&quot;, &quot;50&quot;)),&#10;        help=&quot;Minimum labeled samples per coin before model-driven entries are trusted&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-on-negative-expectancy&quot;,&#10;        action=argparse.BooleanOptionalAction,&#10;        default=(os.environ.get(&quot;BOT_PAUSE_ON_NEGATIVE_EXPECTANCY&quot;, &quot;1&quot;).strip().lower() in {&quot;1&quot;, &quot;true&quot;, &quot;yes&quot;, &quot;y&quot;}),&#10;        help=&quot;Pause new entries when recent expectancy is poor and drawdown is elevated&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-expectancy-lookback&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_PAUSE_EXPECTANCY_LOOKBACK&quot;, &quot;30&quot;)),&#10;        help=&quot;Number of latest closed trades used for safety pause expectancy&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-expectancy-threshold-usd&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_PAUSE_EXPECTANCY_THRESHOLD_USD&quot;, &quot;-1.0&quot;)),&#10;        help=&quot;Pause if recent expectancy is below this USD per-trade threshold&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-max-drawdown-pct&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_PAUSE_MAX_DRAWDOWN_PCT&quot;, &quot;12.0&quot;)),&#10;        help=&quot;Pause only when max drawdown is also at/above this percentage&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--pause-cooldown-minutes&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_PAUSE_COOLDOWN_MINUTES&quot;, &quot;180&quot;)),&#10;        help=&quot;Minutes to keep safety pause active before auto-resuming entries until metrics recover (0 disables auto-expiry)&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-hours&quot;,&#10;        type=float,&#10;        default=float(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_HOURS&quot;, &quot;6&quot;)),&#10;        help=&quot;Send periodic ML progress updates to Telegram every N hours (0 disables)&quot;,&#10;    )&#10;    p.add_argument(&#10;        &quot;--telegram-ml-report-min-trades&quot;,&#10;        type=int,&#10;        default=int(os.environ.get(&quot;BOT_TELEGRAM_ML_REPORT_MIN_TRADES&quot;, &quot;15&quot;)),&#10;        help=&quot;Minimum number of closed trades before periodic ML Telegram reports are sent&quot;,&#10;    )&#10;    return p&#10;&#10;&#10;def main() -&gt; None:&#10;    args = build_arg_parser().parse_args()&#10;    bot = AdaptiveHyperliquidBot(&#10;        interval=args.interval,&#10;        state_file=args.state_file,&#10;        report_file=args.report_file,&#10;        starting_equity=args.starting_equity,&#10;        risk_per_trade=args.risk_per_trade,&#10;        poll_seconds=args.poll_seconds,&#10;        exploration_rate=args.exploration_rate,&#10;        enable_live=args.enable_live,&#10;        require_live_approval=args.require_live_approval,&#10;        live_approval_mode=args.live_approval_mode,&#10;        live_approval_file=args.live_approval_file,&#10;        live_startup_test=args.live_startup_test,&#10;        live_startup_test_coin=args.live_startup_test_coin,&#10;        live_startup_test_notional=args.live_startup_test_notional,&#10;        hf_advisor_url=args.hf_advisor_url,&#10;        hf_advisor_token=args.hf_advisor_token,&#10;        hf_advisor_mode=args.hf_advisor_mode,&#10;        hf_advisor_min_confidence=args.hf_advisor_min_confidence,&#10;        hf_advisor_blend_weight=args.hf_advisor_blend_weight,&#10;        hf_advisor_timeout_seconds=args.hf_advisor_timeout_seconds,&#10;        min_model_samples_for_signal=args.min_model_samples_for_signal,&#10;        pause_on_negative_expectancy=args.pause_on_negative_expectancy,&#10;        pause_expectancy_lookback=args.pause_expectancy_lookback,&#10;        pause_expectancy_threshold_usd=args.pause_expectancy_threshold_usd,&#10;        pause_max_drawdown_pct=args.pause_max_drawdown_pct,&#10;        pause_cooldown_minutes=args.pause_cooldown_minutes,&#10;        telegram_ml_report_hours=args.telegram_ml_report_hours,&#10;        telegram_ml_report_min_trades=args.telegram_ml_report_min_trades,&#10;        health_host=args.health_host,&#10;        health_port=args.health_port,&#10;    )&#10;    bot.run()&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    main()&#10;&#10;&#10;" />
             </PendingDiffInfo>
           </value>
         </entry>