analysis/EXECUTION-SCRIPT-FOR-SONNET.md

EXECUTION SCRIPT — Signal Analysis Run

For: Claude Sonnet (with computer control) · Written by: Fable 5 planning session · 2026-07-11

Everything you need is in THIS folder: ~/Documents/trade-copilot-web/analysis/

CONTEXT (30 seconds)

Trade Copilot (utkarsh's crypto scanner, live at utkarshpathak48-trade-copilot.hf.space) sends Telegram alerts at confidence ≥8.8. We must measure how the last ~50 alerts ACTUALLY performed under Utkarsh's real style (market entry at alert moment → exit 100% at TP2, SL as given, no time limit) vs as-alerted (entry price → TP1). The analysis toolkit is BUILT AND TESTED (self-test + end-to-end verified). Only the input data is missing: the alert history, which exists ONLY in Utkarsh's Telegram chat with the bot. Full methodology: signal-analysis-plan.md (this folder). Your job: get the data, run the pipeline, interpret.

STEP 1 — GET THE ALERT HISTORY (choose path A, fall back to B, then C)

Path A (preferred): Telegram Desktop export

The App Store "Telegram" for macOS has NO export feature. The official "Telegram Desktop" (tdesktop) does.

1. Have Utkarsh download Telegram Desktop from https://desktop.telegram.org (or brew install --cask telegram-desktop).
2. UTKARSH logs in himself (QR scan from phone). ⚠ You must NEVER type his phone number, password, or login code — he does all authentication personally.
3. In Telegram Desktop: open the Trade Copilot bot chat → ⋮ (top right) → Export chat history → UNTICK all media → Format: Machine-readable JSON → Export. Result: a folder containing result.json.
4. Copy result.json into this analysis/ folder.

Path B (if A fails): copy-paste transcript

1. In any Telegram client, Utkarsh selects the alert messages (or scrolls + selects all text) and copies them into a plain text file alerts_paste.txt in this folder. Timestamps matter: if the paste lacks dates, have him paste in chunks per day and note dates, or hover each message — TG copy usually includes "[DD.MM.YY HH:MM]" prefixes when copying multiple messages.
2. Adapt parse_alerts.py: add a txt mode that splits the paste into messages (each alert starts with 🟢 or 🔴 followed by "SYMBOL-USDT LONG/SHORT · X.X/10") and reads the nearest preceding "[date time]" stamp. The regexes in the script already match the alert body — you only add message-splitting + date capture. Keep output CSV columns identical.

Path C (last resort): MTProto user-session script

Use Telethon with Utkarsh's own api_id/api_hash from my.telegram.org (HE creates them and HE enters the login code). Script: iterate messages from the bot chat, dump text+date to JSON matching Telegram-export shape, feed to parse_alerts.py unchanged. Do not store the session file afterwards; delete it.

STEP 2 — RUN THE PIPELINE (2 commands, this folder)

cd ~/Documents/trade-copilot-web/analysis
python3 parse_alerts.py result.json          # → alerts.csv (prints count + how many ≥8.8)
python3 replay_signals.py alerts.csv         # → replay_results.csv + signal_report.md

• Scripts are stdlib-only Python 3. replay fetches public BingX 1m candles (no keys), caches in ./cache/, ~1–3 min for 50 alerts. Rerun-safe.
• Sanity first: python3 replay_signals.py --selftest must print SELFTEST OK.
• If some alerts print NO_DATA: BingX 1m history doesn't reach that far back for that symbol — acceptable, note the count. If MANY fail, retry those with interval 5m (edit fetch interval fallback or note it).
• If alerts.csv has <20 rows, tell Utkarsh how many were found before proceeding — maybe the export missed older history.

STEP 3 — INTERPRET (this is the real deliverable)

Read signal_report.md + replay_results.csv and write signal_verdict.md answering EXACTLY these:

1. HEADLINE: S1 vs S2 win rate, total R, and net $ outcome on a $10 all-in account at 4× and 6× leverage (loss per losing trade ≈ leverage × SL-distance% × $10; compound sequentially in alert order; include the −0.1%×lev fee drag per trade).
2. LATENESS VERDICT: median Lateness Index; % of alerts with LI > 0.7; win rate per LI bucket. → Recommend the LI cutoff above which trades should be SKIPPED (this becomes automation config LI_LIMIT).
3. TP VERDICT: median MFE vs median TP2 distance (tp2_dist_r column); heartbreak rate (came ≥75% of way to TP2 then lost). → Recommend: keep TP2 / dynamic TP at k×median-MFE / TP1.5 midpoint — whichever maximizes total R recomputed from the MFE data.
4. SL VERDICT: MAE distribution of WINNING trades (s2_mae on wins). If many winners saw MAE > 0.8R, SL is too tight. → Recommend keep/widen (+ leverage compensation note).
5. THRESHOLD: compare 8.8–9.0 vs 9.0+ cohorts (conf column) on S2 expectancy → recommend CONF_MIN.
6. SKIP-LIST: any symbol or direction with ≥3 trades and 0 wins.
7. CAVEATS: n, confidence interval (95% CI ≈ ±1.96×sqrt(wr×(1−wr)/n)), regime concentration (all alerts from ~1 month). Every recommendation must cite the numbers. If data contradicts Utkarsh's beliefs, say so plainly — he wants truth, not comfort.

RULES

• Do NOT commit or push anything in the git repo without Utkarsh's explicit approval. Do NOT touch static/index.html, main.py, or any production file — this task lives entirely in analysis/.
• If .git/index.lock errors appear during any git command Utkarsh runs: rm -f ~/Documents/trade-copilot-web/.git/index.lock (known stale-lock issue).
• Never handle credentials/tokens/login codes — Utkarsh types those himself, always.
• You MAY spawn up to 3 sub-agents (per Utkarsh): sensible split = (1) data acquisition+parsing, (2) replay run+data QA, (3) interpretation+verdict writing. Only if genuinely useful — this is a linear task; agents optional.
• When finished: present signal_verdict.md + replay_results.csv + signal_report.md to Utkarsh, and tell him to give signal_verdict.md to the next planning session so the automation config (LI_LIMIT, TP policy, SL policy, CONF_MIN, skip-list = rules R1–R5) gets locked.

WHAT'S ALREADY DONE (do not redo)

• Toolkit written + verified: parser (handles TG JSON mixed entities, skips heartbeats, 4h dedup), replay engine (self-test green: TP/SL/same-candle-loss/expiry/fees/MFE/MAE/lateness), e2e pipeline proven with injected candles. BingX kline API shape confirmed live.
• Methodology doc: signal-analysis-plan.md (metric definitions §4-5, decision rules §6).
• Automation blueprint waiting on your verdict: the planning session's evolution-plan/automation-architecture-v2.md.