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Infrastructure · Ops Console

Team & Agents

Status of every automated piece of the system. Read-only for now — no remote triggers yet.

0 running1 needs attention1 not built
Run Research Agent

No scheduled automation yet (HF Jobs needs prepaid compute credits) — run it locally whenever you want new hypotheses proposed and screened.

source .venv/bin/activate && python scripts/research_agent.py \ --csv-root data/raw/forex/extracted \ --hf-state-repo KUTech/trading-llm-agent-state \ - --num-proposals 3

Requires OPENROUTER_API_KEY in a local .env file. Results push automatically to KUTech/trading-llm-agent-state on Hugging Face — this dashboard reflects them next time it's rebuilt.

Jolash Reasoning Model

Fine-tuned chat/reasoning assistant (Qwen2.5-0.5B LoRA)

Needs attentionlast run Jul 6, 12:14 AM
07/06, 00:14

Compared fine-tuned vs base model output — fine-tune produced unresolved <think> traces instead of direct answers. Needs dataset rework or retraining.

07/05, 21:14

Completed training run on Colab (Qwen2.5-0.5B, 5000 examples, 157 steps) and pushed adapter to KUTech/trading-llm.

Testing Engine

Deterministic backtest pipeline (features -> model -> honest metrics)

Donelast run Jul 5, 11:10 PM
07/05, 23:10

Ran EURUSD 4h-direction backtest with realistic spread costs — confirmed no exploitable edge (Sharpe -0.86).

07/05, 22:40

Trained baseline HistGradientBoostingClassifier — validation AUC 0.513, at chance level.

Research Agent

Proposes specific, testable trading hypotheses via LLM API (OpenRouter), auto-screened

Idlelast run Jul 6, 6:10 PM
07/06, 18:10

Proposed USDCAD 1D horizon=24 and AUDUSD 4h horizon=12 -- USDCAD cleared the cheap screen with a notably strong AUC=0.6223 (small daily sample, needs full validation before trusting).

07/06, 17:55

Proposed EURUSD 4h horizon=12 and USDJPY 1D horizon=24 via OpenRouter (gpt-4o-mini) -- both auto cheap-screened, state persisted to KUTech/trading-llm-agent-state.

Live Data Collector

Streams live market data once a validated strategy exists to feed

Not built yetlast run never

Not built yet — no activity to show.

\ No newline at end of file + --num-proposals 3

Requires OPENROUTER_API_KEY in a local .env file. Results push automatically to KUTech/trading-llm-agent-state on Hugging Face — this dashboard reflects them next time it's rebuilt.

Jolash Reasoning Model

Fine-tuned chat/reasoning assistant (Qwen2.5-0.5B LoRA)

Needs attentionlast run Jul 6, 12:14 AM
07/06, 00:14

Compared fine-tuned vs base model output — fine-tune produced unresolved <think> traces instead of direct answers. Needs dataset rework or retraining.

07/05, 21:14

Completed training run on Colab (Qwen2.5-0.5B, 5000 examples, 157 steps) and pushed adapter to KUTech/trading-llm.

Testing Engine

Deterministic backtest pipeline (features -> model -> honest metrics)

Donelast run Jul 5, 11:10 PM
07/05, 23:10

Ran EURUSD 4h-direction backtest with realistic spread costs — confirmed no exploitable edge (Sharpe -0.86).

07/05, 22:40

Trained baseline HistGradientBoostingClassifier — validation AUC 0.513, at chance level.

Research Agent

Proposes specific, testable trading hypotheses via LLM API (OpenRouter), auto-screened

Idlelast run Jul 6, 6:10 PM
07/06, 18:10

Proposed USDCAD 1D horizon=24 and AUDUSD 4h horizon=12 -- USDCAD cleared the cheap screen with a notably strong AUC=0.6223 (small daily sample, needs full validation before trusting).

07/06, 17:55

Proposed EURUSD 4h horizon=12 and USDJPY 1D horizon=24 via OpenRouter (gpt-4o-mini) -- both auto cheap-screened, state persisted to KUTech/trading-llm-agent-state.

Live Data Collector

Streams live market data once a validated strategy exists to feed

Not built yetlast run never

Not built yet — no activity to show.

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Research · Validation

Backtest Results

Honest, cost-inclusive results on held-out data the model never trained on. No cherry-picking.

EURUSD — 2024-01-02 -> 2025-12-31 (held out, never trained on)

Underperformed buy & hold

Test bars

12,454

Trades taken

3,816

Strategy return

-24.47%

Buy & hold return

6.26%

Sharpe (annualized)

-0.86

Max drawdown

-32.94%

Win rate 48.7%Below 50% win rate, consistent with the Sharpe/return numbers above. This confirms basic technical indicators carry no exploitable edge for this pair/horizon after realistic spread costs.
\ No newline at end of file +Jolash

Research · Validation

Backtest Results

Honest, cost-inclusive results on held-out data the model never trained on. No cherry-picking.

USDCHF — 2024-01-02 -> 2025-12-31 (held out, never trained on)

Outperformed buy & hold

Test bars

12,453

Trades taken

4,275

Strategy return

61.67%

Buy & hold return

-5.50%

Sharpe (annualized)

1.29

Max drawdown

-15.91%

Win rate 54.8%Positive Sharpe and beat buy-and-hold -- a real candidate worth further scrutiny, not yet a confirmed edge on its own.
EURUSD — 2024-01-02 -> 2025-12-31 (held out, never trained on)

Underperformed buy & hold

Test bars

12,454

Trades taken

3,816

Strategy return

-24.47%

Buy & hold return

6.26%

Sharpe (annualized)

-0.86

Max drawdown

-32.94%

Win rate 48.7%Negative risk-adjusted return, consistent with the win rate above. Confirms no exploitable edge for this pair/horizon after realistic spread costs.
GBPUSD — 2024-01-02 -> 2025-12-31 (held out, never trained on)

Underperformed buy & hold

Test bars

12,451

Trades taken

1,784

Strategy return

4.88%

Buy & hold return

5.70%

Sharpe (annualized)

0.29

Max drawdown

-9.02%

Win rate 51.8%Technically positive Sharpe, but a simple buy-and-hold still did better -- not compelling enough to pursue as-is.
AUDUSD — 2024-01-02 -> 2025-12-31 (held out, never trained on)

Underperformed buy & hold

Test bars

12,454

Trades taken

3,185

Strategy return

-12.12%

Buy & hold return

-1.99%

Sharpe (annualized)

-0.30

Max drawdown

-26.70%

Win rate 50.2%Negative risk-adjusted return, consistent with the win rate above. Confirms no exploitable edge for this pair/horizon after realistic spread costs.
\ No newline at end of file diff --git a/backtests.txt b/backtests.txt index 8a343c5a14e1d16887f6300e05459e28261da04b..b675a9e548152f3e61de6fffb258af254ca48009 100644 --- a/backtests.txt +++ b/backtests.txt @@ -11,14 +11,25 @@ f:I[68027,["/_next/static/chunks/1e176kr40-yyy.js","/_next/static/chunks/0ce9zjk :HL["/_next/static/chunks/2qhcm01ttjh0x.css","style"] :HL["/_next/static/media/70bc3e132a0a741e-s.p.3t6q91iet4nsy.woff2","font",{"crossOrigin":"","type":"font/woff2"}] :HL["/_next/static/media/83afe278b6a6bb3c-s.p.2bn3s6zvc0dyp.woff2","font",{"crossOrigin":"","type":"font/woff2"}] 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Audit Trail

A log of every trade, model decision, and parameter change — for reconstructing exactly what happened when something goes wrong.

Nothing to audit yet

There are no live trades or deployed strategies, so there's nothing for a compliance log to record. This becomes relevant once real capital is on the line — building it out now would be instrumenting a process that doesn't exist yet.

\ No newline at end of file +Jolash

Audit Trail

A log of every trade, model decision, and parameter change — for reconstructing exactly what happened when something goes wrong.

Nothing to audit yet

There are no live trades or deployed strategies, so there's nothing for a compliance log to record. This becomes relevant once real capital is on the line — building it out now would be instrumenting a process that doesn't exist yet.

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Data Health

Quality checks on the raw market data — run once via validate_forex_data.py, shown here so the whole team can see the results without digging through scripts.

PairRowsDate rangeDuplicate timestampsUnexplained gapsStatus
EURUSD4,084,8822015-01-02 -> 2025-12-3129 (negligible, out of 4,084,882)0Clean
GBPUSD4,084,1502015-01-02 -> 2025-12-3132 (negligible, out of 4,084,150)0Clean
AUDUSD4,080,4132015-01-02 -> 2025-12-3127 (negligible, out of 4,080,413)0Clean
NZDUSD4,070,3532015-01-02 -> 2025-12-3121 (negligible, out of 4,070,353)0Clean
USDCAD4,074,2422015-01-02 -> 2025-12-3125 (negligible, out of 4,074,242)0Clean
USDCHF4,057,8702015-01-02 -> 2025-12-3118 (negligible, out of 4,057,870)0Clean
USDJPY4,085,9962015-01-02 -> 2025-12-3125 (negligible, out of 4,085,996)0Clean

All flagged gaps (~20 per pair, >3hrs on weekdays) were traced to Christmas/New Year market closures, not missing data — see scripts/validate_forex_data.py in the training repo for the check itself.

\ No newline at end of file +Jolash

Data Health

Quality checks on the raw market data — run once via validate_forex_data.py, shown here so the whole team can see the results without digging through scripts.

PairRowsDate rangeDuplicate timestampsUnexplained gapsStatus
EURUSD4,084,8822015-01-02 -> 2025-12-3129 (negligible, out of 4,084,882)0Clean
GBPUSD4,084,1502015-01-02 -> 2025-12-3132 (negligible, out of 4,084,150)0Clean
AUDUSD4,080,4132015-01-02 -> 2025-12-3127 (negligible, out of 4,080,413)0Clean
NZDUSD4,070,3532015-01-02 -> 2025-12-3121 (negligible, out of 4,070,353)0Clean
USDCAD4,074,2422015-01-02 -> 2025-12-3125 (negligible, out of 4,074,242)0Clean
USDCHF4,057,8702015-01-02 -> 2025-12-3118 (negligible, out of 4,057,870)0Clean
USDJPY4,085,9962015-01-02 -> 2025-12-3125 (negligible, out of 4,085,996)0Clean

All flagged gaps (~20 per pair, >3hrs on weekdays) were traced to Christmas/New Year market closures, not missing data — see scripts/validate_forex_data.py in the training repo for the check itself.

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Datasets & Models

Everything pushed to the KUTech Hugging Face org — the shared source of truth for the team.

dataset

KUTech/trading-llm-data

20k finance-reasoning SFT conversations (ODA-Fin-SFT-318k derived, English-filtered).

~20k examplesupdated 2026-07-04
dataset

KUTech/trading-llm-market-data

Raw 1-minute OHLCV forex data, 7 pairs, 2015-2025, validated for gaps/duplicates.

~4M rows/pair, 553MBupdated 2026-07-04
dataset

KUTech/trading-llm-market-reasoning

Market-grounded reasoning conversations generated from real price action, no look-ahead leakage.

23,835 examplesupdated 2026-07-04
space

KUTech/trading-llm-training

Training, dataset-build, and validation scripts for the fine-tuning pipeline.

scriptsupdated 2026-07-04
model

KUTech/trading-llm

LoRA adapter checkpoints, including the Colab Qwen2.5-0.5B fine-tune run.

PEFT adaptersupdated 2026-07-06
model

KUTech/JOLASH

Project roadmap model card: reasoning model -> forecasting -> probability modeling -> risk management -> API deployment.

roadmapupdated 2026-07-01
\ No newline at end of file +Jolash

Datasets & Models

Everything pushed to the KUTech Hugging Face org — the shared source of truth for the team.

dataset

KUTech/trading-llm-data

20k finance-reasoning SFT conversations (ODA-Fin-SFT-318k derived, English-filtered).

~20k examplesupdated 2026-07-04
dataset

KUTech/trading-llm-market-data

Raw 1-minute OHLCV forex data, 7 pairs, 2015-2025, validated for gaps/duplicates.

~4M rows/pair, 553MBupdated 2026-07-04
dataset

KUTech/trading-llm-market-reasoning

Market-grounded reasoning conversations generated from real price action, no look-ahead leakage.

23,835 examplesupdated 2026-07-04
space

KUTech/trading-llm-training

Training, dataset-build, and validation scripts for the fine-tuning pipeline.

scriptsupdated 2026-07-04
model

KUTech/trading-llm

LoRA adapter checkpoints, including the Colab Qwen2.5-0.5B fine-tune run.

PEFT adaptersupdated 2026-07-06
model

KUTech/JOLASH

Project roadmap model card: reasoning model -> forecasting -> probability modeling -> risk management -> API deployment.

roadmapupdated 2026-07-01
\ No newline at end of file diff --git a/datasets.txt b/datasets.txt index 67188c038f3163f345e39340f388560d8039edd6..4c404f6cb5bde689eb92dfed04f6251e02a7b295 100644 --- a/datasets.txt +++ b/datasets.txt @@ -11,7 +11,7 @@ f:I[68027,["/_next/static/chunks/1e176kr40-yyy.js","/_next/static/chunks/0ce9zjk :HL["/_next/static/chunks/2qhcm01ttjh0x.css","style"] :HL["/_next/static/media/70bc3e132a0a741e-s.p.3t6q91iet4nsy.woff2","font",{"crossOrigin":"","type":"font/woff2"}] :HL["/_next/static/media/83afe278b6a6bb3c-s.p.2bn3s6zvc0dyp.woff2","font",{"crossOrigin":"","type":"font/woff2"}] 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Decay Tracking

Backtested expectation vs. realized performance — the mechanism that catches a strategy quietly going bad before it costs real money.

Structure preview — no strategy has a live or paper-trading track record yet, so there's nothing to compare against backtest expectations. This becomes real the moment something graduates past the Backtest Results stage.

StrategyBacktested SharpeRealized SharpeDeviationSinceStatus
——————
——————
——————
——————
\ No newline at end of file +Jolash

Decay Tracking

Backtested expectation vs. realized performance — the mechanism that catches a strategy quietly going bad before it costs real money.

Structure preview — no strategy has a live or paper-trading track record yet, so there's nothing to compare against backtest expectations. This becomes real the moment something graduates past the Backtest Results stage.

StrategyBacktested SharpeRealized SharpeDeviationSinceStatus
——————
——————
——————
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Live Execution

Live PnL, fill quality, and latency for strategies actually trading — the boring, automated part that only gets interesting when something's wrong.

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Live PnL, fill quality, and latency for strategies actually trading — the boring, automated part that only gets interesting when something's wrong.

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Research · Hypothesis Log

Hypotheses

Specific, testable trading ideas — proposed by hand for now, by the Research Agent once it's built.

Testing 8Proposed 1No edge found 4

What these numbers actually mean

Information Coefficient (IC)

Does this feature actually line up with what happens next? 0 means no relationship at all (useless). A real edge is usually small— 0.02 to 0.05 is considered notable in real finance. If a number looks huge, that's a reason for more suspicion, not less.

Quick model accuracy (AUC)

0.50 = a coin flip, no better than guessing. 1.00 = perfect. Anything meaningfully above 0.50 (even 0.55) can be a real signal in finance — markets are hard. At or below 0.50 means no signal was found.

Promote vs. reject

"Promote" just means an idea cleared a low, cheap first-pass bar — it is nota confirmed edge, only a candidate worth the expensive full test. "Reject" means it didn't even clear that low bar, so we skip the expensive test and save the time.

Cheap screen vs. full validation

The cheap screen (seconds) kills obviously-dead ideas fast. The full pipeline (on the Backtest Results page) is the real, slow, honest test — real holdout data, real trading costs, real Sharpe/drawdown. Only trust a hypothesis once it's survived that step.

Testing8

USDCHF 4h-ahead direction (cheap screen)

Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed.

USDCHF·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
Testing
IC-0.0390
AUC0.5237
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ -python scripts/build_signal_features.py --pair USDCHF --csv-root data/raw/forex/extracted --timeframe 1h --horizon 4 --out-dir data/validated/USDCHF_1h_4 && \ -python scripts/train_signal_model.py --data-dir data/validated/USDCHF_1h_4 --out data/validated/USDCHF_1h_4/model.joblib && \ -python scripts/backtest_signal_model.py --data-dir data/validated/USDCHF_1h_4 --model data/validated/USDCHF_1h_4/model.joblib --spread-pips 1.0 --confidence-threshold 0.55

AUDUSD 4h-ahead direction (cheap screen)

Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed.

AUDUSD·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
Testing
IC-0.0317
AUC0.5085
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ -python scripts/build_signal_features.py --pair AUDUSD --csv-root data/raw/forex/extracted --timeframe 1h --horizon 4 --out-dir data/validated/AUDUSD_1h_4 && \ -python scripts/train_signal_model.py --data-dir data/validated/AUDUSD_1h_4 --out data/validated/AUDUSD_1h_4/model.joblib && \ -python scripts/backtest_signal_model.py --data-dir data/validated/AUDUSD_1h_4 --model data/validated/AUDUSD_1h_4/model.joblib --spread-pips 1.0 --confidence-threshold 0.55

GBPUSD 4h-ahead direction (cheap screen)

Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed.

GBPUSD·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
Testing
IC-0.0194
AUC0.5215
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ -python scripts/build_signal_features.py --pair GBPUSD --csv-root data/raw/forex/extracted --timeframe 1h --horizon 4 --out-dir data/validated/GBPUSD_1h_4 && \ -python scripts/train_signal_model.py --data-dir data/validated/GBPUSD_1h_4 --out data/validated/GBPUSD_1h_4/model.joblib && \ -python scripts/backtest_signal_model.py --data-dir data/validated/GBPUSD_1h_4 --model data/validated/GBPUSD_1h_4/model.joblib --spread-pips 1.0 --confidence-threshold 0.55

EURUSD 4h horizon=12 (Research Agent)

Captures the London/New York session overlap, which often brings increased volatility and clearer directional moves. Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed. Small sample size at this timeframe means real skepticism is warranted.

EURUSD·horizon 12 4h bars·Research Agent (OpenRouter gpt-4o-mini)
Testing
IC-0.0578
AUC0.4674
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ +Jolash

Research · Hypothesis Log

Hypotheses

Specific, testable trading ideas — proposed by hand for now, by the Research Agent once it's built.

Promising 1Testing 5Proposed 1No edge found 6

What these numbers actually mean

Information Coefficient (IC)

Does this feature actually line up with what happens next? 0 means no relationship at all (useless). A real edge is usually small— 0.02 to 0.05 is considered notable in real finance. If a number looks huge, that's a reason for more suspicion, not less.

Quick model accuracy (AUC)

0.50 = a coin flip, no better than guessing. 1.00 = perfect. Anything meaningfully above 0.50 (even 0.55) can be a real signal in finance — markets are hard. At or below 0.50 means no signal was found.

Promote vs. reject

"Promote" just means an idea cleared a low, cheap first-pass bar — it is nota confirmed edge, only a candidate worth the expensive full test. "Reject" means it didn't even clear that low bar, so we skip the expensive test and save the time.

Cheap screen vs. full validation

The cheap screen (seconds) kills obviously-dead ideas fast. The full pipeline (on the Backtest Results page) is the real, slow, honest test — real holdout data, real trading costs, real Sharpe/drawdown. Only trust a hypothesis once it's survived that step.

Promising1

USDCHF 4h-ahead direction (cheap screen)

Full validation: Sharpe 1.29 on 2024-25 (+61.7% vs buy-hold's -5.5%), and re-tested on a separate 2021-22 window: Sharpe 0.51 (+22.7% vs buy-hold's +4.9%). Beat buy-and-hold both times on two non-overlapping periods -- the one real candidate so far. Real expected performance is likely closer to the weaker of the two (~0.5-0.9 Sharpe), not the flashier first number.

USDCHF·horizon 4h·Full validation (train_signal_model.py + backtest_signal_model.py)
Promising
IC-0.0390
AUC0.5237
Testing5

EURUSD 4h horizon=12 (Research Agent)

Captures the London/New York session overlap, which often brings increased volatility and clearer directional moves. Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed. Small sample size at this timeframe means real skepticism is warranted.

EURUSD·horizon 12 4h bars·Research Agent (OpenRouter gpt-4o-mini)
Testing
IC-0.0578
AUC0.4674
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ python scripts/build_signal_features.py --pair EURUSD --csv-root data/raw/forex/extracted --timeframe 4h --horizon 12 --out-dir data/validated/EURUSD_4h_12 && \ python scripts/train_signal_model.py --data-dir data/validated/EURUSD_4h_12 --out data/validated/EURUSD_4h_12/model.joblib && \ python scripts/backtest_signal_model.py --data-dir data/validated/EURUSD_4h_12 --model data/validated/EURUSD_4h_12/model.joblib --spread-pips 1.0 --confidence-threshold 0.55

USDJPY 1D horizon=24 (Research Agent)

Daily timeframe integrates macroeconomic factors and risk-sentiment shifts influencing USD/JPY over a broader horizon. Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed. Small sample size at this timeframe means real skepticism is warranted.

USDJPY·horizon 24 1D bars·Research Agent (OpenRouter gpt-4o-mini)
Testing
IC+0.0839
AUC0.5439
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ @@ -22,4 +13,4 @@ python scripts/train_signal_model.py --data-dir data/validated/USDCAD_1D_24 --ou python scripts/backtest_signal_model.py --data-dir data/validated/USDCAD_1D_24 --model data/validated/USDCAD_1D_24/model.joblib --spread-pips 1.0 --confidence-threshold 0.55

GBPUSD 1D horizon=24 (Research Agent)

Daily analysis captures broader market sentiment from significant UK/US economic data releases over a longer horizon. Cleared the cheap-screen bar -- queued for full walk-forward validation, not yet confirmed. Small sample size at this timeframe means real skepticism is warranted.

GBPUSD·horizon 24 1D bars·Research Agent (OpenRouter gpt-4o-mini)
Testing
IC+0.1535
AUC0.4680
Josh's job: run the real test on this one

Copy this, paste it in your terminal, hit enter. It'll print a real Sharpe ratio, return, and drawdown at the end — screenshot that and drop it in the group chat, good or bad.

source .venv/bin/activate && \ python scripts/build_signal_features.py --pair GBPUSD --csv-root data/raw/forex/extracted --timeframe 1D --horizon 24 --out-dir data/validated/GBPUSD_1D_24 && \ python scripts/train_signal_model.py --data-dir data/validated/GBPUSD_1D_24 --out data/validated/GBPUSD_1D_24/model.joblib && \ -python scripts/backtest_signal_model.py --data-dir data/validated/GBPUSD_1D_24 --model data/validated/GBPUSD_1D_24/model.joblib --spread-pips 1.0 --confidence-threshold 0.55
Proposed1

Triple-barrier labeled setups (profit target / stop-loss)

Relabel EURUSD using a profit-target/stop-loss barrier instead of a fixed time horizon, to reduce label noise before testing for a directional edge.

EURUSD·horizon variable (barrier hit)·Research Agent (not yet built)
Proposed
No edge found4

EURUSD 4h-ahead direction from basic technical indicators

Predict the sign of EURUSD's return 4 hours ahead using SMA trend, momentum, rolling volatility, RSI, and recent high/low range as features, on hourly candles.

EURUSD·horizon 4h·Manual (v0 pipeline)
No edge found

NZDUSD 4h-ahead direction (cheap screen)

Below the cheap-screen bar -- rejected before spending the expensive full validation budget.

NZDUSD·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
No edge found
IC-0.0269
AUC0.5087

USDCAD 4h-ahead direction (cheap screen)

Below the cheap-screen bar -- rejected before spending the expensive full validation budget.

USDCAD·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
No edge found
IC-0.0257
AUC0.5064

USDJPY 4h-ahead direction (cheap screen)

Below the cheap-screen bar -- rejected before spending the expensive full validation budget.

USDJPY·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
No edge found
IC-0.0188
AUC0.4974
\ No newline at end of file +python scripts/backtest_signal_model.py --data-dir data/validated/GBPUSD_1D_24 --model data/validated/GBPUSD_1D_24/model.joblib --spread-pips 1.0 --confidence-threshold 0.55
Proposed1

Triple-barrier labeled setups (profit target / stop-loss)

Relabel EURUSD using a profit-target/stop-loss barrier instead of a fixed time horizon, to reduce label noise before testing for a directional edge.

EURUSD·horizon variable (barrier hit)·Research Agent (not yet built)
Proposed
No edge found6

EURUSD 4h-ahead direction from basic technical indicators

Predict the sign of EURUSD's return 4 hours ahead using SMA trend, momentum, rolling volatility, RSI, and recent high/low range as features, on hourly candles.

EURUSD·horizon 4h·Manual (v0 pipeline)
No edge found

AUDUSD 4h-ahead direction (cheap screen)

Full validation: Sharpe -0.30, -12.1% return vs buy-hold's -2.0%, 50.2% win rate. The cheap-screen promotion was a false positive -- doesn't hold up under real testing.

AUDUSD·horizon 4h·Full validation (train_signal_model.py + backtest_signal_model.py)
No edge found
IC-0.0317
AUC0.5085

NZDUSD 4h-ahead direction (cheap screen)

Below the cheap-screen bar -- rejected before spending the expensive full validation budget.

NZDUSD·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
No edge found
IC-0.0269
AUC0.5087

USDCAD 4h-ahead direction (cheap screen)

Below the cheap-screen bar -- rejected before spending the expensive full validation budget.

USDCAD·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
No edge found
IC-0.0257
AUC0.5064

GBPUSD 4h-ahead direction (cheap screen)

Full validation: Sharpe 0.29, +4.9% return but buy-and-hold did better (+5.7%). Technically positive but not compelling enough to pursue -- a do-nothing strategy beat it.

GBPUSD·horizon 4h·Full validation (train_signal_model.py + backtest_signal_model.py)
No edge found
IC-0.0194
AUC0.5215

USDJPY 4h-ahead direction (cheap screen)

Below the cheap-screen bar -- rejected before spending the expensive full validation budget.

USDJPY·horizon 4h·Batch cheap-screen (screen_hypothesis.py)
No edge found
IC-0.0188
AUC0.4974
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Jolash · Desk Overview

Overview

Where the Jolash project actually stands right now.

FrancisProduct
  • Look at anything marked Promising or with real backtest results and decide: worth chasing, or drop it?
  • Pick the paper-trading platform (compare OANDA / Alpaca / MetaTrader demo accounts)
  • Write down the exact rules for when we'd move to real money
Review Backtest Results
JoshResearch
  • Open Hypotheses below, find every yellow "Testing" card
  • Copy the command on that card, paste it in your terminal, hit enter
  • Report back the real Sharpe/return it prints — even if it's bad news
Go to Hypotheses
YouCode
  • Run the research agent on Team & Agents when the hypothesis list is getting thin
  • Keep the pipeline/dashboard running and push updates after big changes
  • Build out whatever Josh or Francis need next (new features, new data)
Go to Team & Agents

Hypotheses tested

13

4 confirmed no edge

Datasets & models

6

on Hugging Face

Agents built

3/4

research + live-data pending

Best backtest Sharpe

-0.86

annualized, honest costs included

Recent activity
6 events
07/06, 18:10

Proposed USDCAD 1D horizon=24 and AUDUSD 4h horizon=12 -- USDCAD cleared the cheap screen with a notably strong AUC=0.6223 (small daily sample, needs full validation before trusting).

Research Agent · 2d ago

07/06, 17:55

Proposed EURUSD 4h horizon=12 and USDJPY 1D horizon=24 via OpenRouter (gpt-4o-mini) -- both auto cheap-screened, state persisted to KUTech/trading-llm-agent-state.

Research Agent · 2d ago

07/06, 00:14

Compared fine-tuned vs base model output — fine-tune produced unresolved <think> traces instead of direct answers. Needs dataset rework or retraining.

Jolash Reasoning Model · 2d ago

07/05, 23:10

Ran EURUSD 4h-direction backtest with realistic spread costs — confirmed no exploitable edge (Sharpe -0.86).

Testing Engine · 2d ago

07/05, 22:40

Trained baseline HistGradientBoostingClassifier — validation AUC 0.513, at chance level.

Testing Engine · 2d ago

07/05, 21:14

Completed training run on Colab (Qwen2.5-0.5B, 5000 examples, 157 steps) and pushed adapter to KUTech/trading-llm.

Jolash Reasoning Model · 2d ago

Agent status
Jolash Reasoning ModelNeeds attentionTesting EngineDoneResearch AgentIdleLive Data CollectorNot built yet
\ No newline at end of file +Jolash

Jolash · Desk Overview

Overview

Where the Jolash project actually stands right now.

FrancisProduct
  • Look at anything marked Promising or with real backtest results and decide: worth chasing, or drop it?
  • Pick the paper-trading platform (compare OANDA / Alpaca / MetaTrader demo accounts)
  • Write down the exact rules for when we'd move to real money
Review Backtest Results
JoshResearch
  • Open Hypotheses below, find every yellow "Testing" card
  • Copy the command on that card, paste it in your terminal, hit enter
  • Report back the real Sharpe/return it prints — even if it's bad news
Go to Hypotheses
YouCode
  • Run the research agent on Team & Agents when the hypothesis list is getting thin
  • Keep the pipeline/dashboard running and push updates after big changes
  • Build out whatever Josh or Francis need next (new features, new data)
Go to Team & Agents

Hypotheses tested

13

6 confirmed no edge

Datasets & models

6

on Hugging Face

Agents built

3/4

research + live-data pending

Best backtest Sharpe

1.29

annualized, honest costs included

Recent activity
6 events
07/06, 18:10

Proposed USDCAD 1D horizon=24 and AUDUSD 4h horizon=12 -- USDCAD cleared the cheap screen with a notably strong AUC=0.6223 (small daily sample, needs full validation before trusting).

Research Agent · 2d ago

07/06, 17:55

Proposed EURUSD 4h horizon=12 and USDJPY 1D horizon=24 via OpenRouter (gpt-4o-mini) -- both auto cheap-screened, state persisted to KUTech/trading-llm-agent-state.

Research Agent · 2d ago

07/06, 00:14

Compared fine-tuned vs base model output — fine-tune produced unresolved <think> traces instead of direct answers. Needs dataset rework or retraining.

Jolash Reasoning Model · 2d ago

07/05, 23:10

Ran EURUSD 4h-direction backtest with realistic spread costs — confirmed no exploitable edge (Sharpe -0.86).

Testing Engine · 2d ago

07/05, 22:40

Trained baseline HistGradientBoostingClassifier — validation AUC 0.513, at chance level.

Testing Engine · 2d ago

07/05, 21:14

Completed training run on Colab (Qwen2.5-0.5B, 5000 examples, 157 steps) and pushed adapter to KUTech/trading-llm.

Jolash Reasoning Model · 2d ago

Agent status
Jolash Reasoning ModelNeeds attentionTesting EngineDoneResearch AgentIdleLive Data CollectorNot built yet
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Live Market Data

Streaming data from whichever market we choose, once a validated strategy exists to feed it.

Not built yet — by design

We're holding off on live data collection until there's an actual validated hypothesis worth feeding it. Right now that would be solving a problem we don't have — check the Backtest Results page for what's actually been tested so far.

\ No newline at end of file +Jolash

Live Market Data

Streaming data from whichever market we choose, once a validated strategy exists to feed it.

Not built yet — by design

We're holding off on live data collection until there's an actual validated hypothesis worth feeding it. Right now that would be solving a problem we don't have — check the Backtest Results page for what's actually been tested so far.

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Risk Dashboard

Real-time position exposure, VaR, and drawdown limits — the most-watched screen at a real quant shop, once there's real capital to watch.

Structure preview — this is the layout risk monitoring will use once a strategy has real capital allocated. No positions exist yet, so every row below is a placeholder, not a hidden or zeroed-out number.

Asset / PairPosition SizeValue at Risk (VaR)Max Drawdown LimitCurrent DrawdownStatus
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——————
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Risk Dashboard

Real-time position exposure, VaR, and drawdown limits — the most-watched screen at a real quant shop, once there's real capital to watch.

Structure preview — this is the layout risk monitoring will use once a strategy has real capital allocated. No positions exist yet, so every row below is a placeholder, not a hidden or zeroed-out number.

Asset / PairPosition SizeValue at Risk (VaR)Max Drawdown LimitCurrent DrawdownStatus
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