- Add _remove_incomplete_candle() for timestamp-based conditional
slicing on both 15m and 1h data (replaces hardcoded [:-1])
- Add MetaFilter indicator caching to eliminate 3x duplicate calc
- Fix notifier encapsulation (_send → notify_info public API)
- Remove DataFetcher.poll_update() dead code
- Fix evaluate_oos.py symbol typo (xrpusdtusdt → xrpusdt)
- Add 20 pytest unit tests for MetaFilter, TriggerStrategy,
ExecutionManager, and _remove_incomplete_candle
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fetches MTF trade JSONL from prod server via scp, calculates
win rate / PF / cumulative PnL / avg duration by Total/LONG/SHORT,
and outputs LIVE deploy go/no-go verdict (trades >= 5 AND PF >= 1.0).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- exchange.py: cancel_all_orders() now cancels both standard and algo orders
- exchange.py: get_open_orders() merges standard + algo orders
- exchange.py: cancel_order() falls back to algo cancel on failure
- bot.py: store SL/TP prices for price-based close_reason re-determination
- bot.py: add _cancel_remaining_orders() for orphan SL/TP cleanup
- bot.py: re-classify MANUAL close_reason as SL/TP via price comparison
- bot.py: cancel orphan orders on startup when no position exists
- tests: fix env setup for testnet config and ML filter mocking
- docs: add backtest market context and algo order fix design specs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add BINANCE_TESTNET env var to switch between live/demo API keys
- Add KLINE_INTERVAL env var (default 15m) for configurable candle interval
- Pass testnet flag through to Exchange, DataStream, UDS, Notifier
- Add demo mode in bot: forced LONG entry with fixed 0.5% SL / 2% TP
- Fix UDS close_reason: use ot (original order type) field to correctly
classify STOP_MARKET/TAKE_PROFIT_MARKET triggers (was MANUAL)
- Add UDS raw event logging with ot field for debugging
- Add backtest market context (BTC/ETH regime, L/S ratio per fold)
- Separate testnet trade history to data/trade_history/testnet/
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Collect top trader account L/S ratio and global L/S ratio every 15 minutes
for XRP, BTC, ETH (6 API calls/cycle) and persist to per-symbol parquet files.
Deployed as a separate Docker service reusing the bot image.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Runs WalkForwardBacktester twice (use_ml=True/False), prints side-by-side
comparison of PF, win rate, MDD, Sharpe, and auto-judges ML filter value.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add LOOKAHEAD embargo between train/val splits in all 3 WF functions
to prevent label leakage from 6h lookahead window
- Add --ablation flag to train_model.py for signal_strength/side
dependency diagnosis (A/B/C experiment with drop analysis)
- Criteria: A→C drop ≤0.05=good, 0.05-0.10=conditional, ≥0.10=redesign
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add normalize=False parameter to MLXFilter.fit() so external callers
can skip internal normalization. Remove the external normalization +
manual _mean/_std reset hack from walk_forward_auc() in train_mlx_model.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Load trade history from data/trade_history/{symbol}.jsonl
- Show per-symbol: consecutive loss streak vs threshold, recent 15-trade PF
- 2-tier alert: clean numbers for normal, ⚠/🔴 KILLED for danger zone
- Inserted before ML retraining checklist in Discord report
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add payoff_ratio and max_consecutive_losses to backtester summary
- Add compare_symbols.py: per-symbol parameter sweep for candidate evaluation
- Add position_sizing_analysis.py: robust Monte Carlo position sizing
- Fetch historical data for SOL, LINK, AVAX candidates (365 days)
- Update existing symbol data (XRP, TRX, DOGE)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The combined summary (PF, MDD, win_rate) was indirectly reconstructed
from per-symbol averages using round(win_rate * n), which introduced
rounding errors. MDD was max() of individual symbol MDDs, ignoring
simultaneous drawdowns across the correlated crypto portfolio.
Now computes all combined metrics directly from the trade list:
- PF: sum(wins) / sum(losses) from actual trade PnLs
- MDD: portfolio equity curve from time-sorted trades
- Win rate: direct count from trade PnLs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
_fetch_oi_hist and _fetch_funding_rate had no retry logic, causing
crashes on rate limit (429) or transient errors during weekly report
data collection. Added _get_json_with_retry helper (max 3 attempts,
exponential backoff). Updated code-review docs to reflect completion.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- #1: OI division by zero — already fixed (prev_oi == 0.0 guard exists)
- #2: cumulative trade count used max() instead of sum(), breaking ML trigger
- #4: fetch_history API calls now retry 3x with exponential backoff
- #5: parquet upsert now deduplicates timestamps before sort
- #6: record_pnl() is now async with Lock for multi-symbol safety
- #8: exit_price=0.0 skips close handling with warning log
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
get_signal() now returns (signal, detail) tuple with long/short scores,
ADX value, volume surge status, and HOLD reason for easier diagnosis.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added functionality to fetch live trade data from the dashboard API.
- Implemented weekly report generation that includes backtest results, live trade statistics, and performance trends.
- Enhanced error handling for API requests and improved logging for better traceability.
- Updated tests to cover new features and ensure reliability of the report generation process.
- Updated .gitignore to include .venv and .worktrees.
- Removed symlink for the virtual environment.
- Added new parquet files for dogeusdt, trxusdt, and xrpusdt datasets.
- Introduced model files and training logs for dogeusdt, trxusdt, and xrpusdt.
- Enhanced fetch_history.py to support caching of correlation symbols.
- Updated train_and_deploy.sh to manage correlation cache directory.
- Added support for demo trading on Binance Futures with a new configuration for 1-minute candles and 125x leverage.
- Updated various components including Config, Exchange, DataStream, UserDataStream, and Bot to handle demo and testnet flags.
- Enhanced the training pipeline to collect 1-minute data and adjusted the lookahead for model training.
- Updated environment variables in .env and .env.example to include demo settings.
This commit lays the groundwork for testing ML-based automated trading strategies in a controlled environment.
- Introduced a new plan to modify the Optuna objective function to prioritize precision under a recall constraint of 0.35, improving model performance in scenarios where false positives are costly.
- Updated training scripts to implement precision-based metrics and adjusted the walk-forward cross-validation process to incorporate precision and recall calculations.
- Enhanced the active LGBM parameters and training log to reflect the new metrics and model configurations.
- Added a new design document outlining the implementation steps for the precision-focused optimization.
This update aims to refine the model's decision-making process by emphasizing precision, thereby reducing potential losses from false positives.
Added HOLD candles as negative samples to increase training data from ~535 to ~3,200 samples. Introduced a negative_ratio parameter in generate_dataset_vectorized() for sampling HOLD candles alongside signal candles. Implemented stratified undersampling to ensure signal samples are preserved during training. Updated relevant tests to validate new functionality and maintain compatibility with existing tests.
- Modified dataset_builder.py to include HOLD negative sampling logic
- Updated train_model.py to apply stratified undersampling
- Added tests for new sampling methods
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- tune_hyperparams.py: 탐색 완료 후 Best AUC > Baseline AUC 이면
models/active_lgbm_params.json 자동 갱신
- tune_hyperparams.py: 베이스라인을 active 파일 기준으로 측정
(active 없으면 코드 내 기본값 사용)
- train_model.py: _load_lgbm_params()에 active 파일 자동 탐색 추가
우선순위: --tuned-params > active_lgbm_params.json > 하드코딩 기본값
- models/active_lgbm_params.json: 현재 best 파라미터로 초기화
- .gitignore: tune_results_*.json 제외, active 파일은 git 추적 유지
Made-with: Cursor
- scripts/tune_hyperparams.py: Optuna + Walk-Forward 5폴드 AUC 목적 함수
- 데이터셋 1회 캐싱으로 모든 trial 공유 (속도 최적화)
- num_leaves <= 2^max_depth - 1 제약 강제 (소규모 데이터 과적합 방지)
- MedianPruner로 저성능 trial 조기 종료
- 결과: 콘솔 리포트 + models/tune_results_YYYYMMDD_HHMMSS.json
- requirements.txt: optuna>=3.6.0 추가
- README.md: 하이퍼파라미터 자동 튜닝 사용법 섹션 추가
- docs/plans/: 설계 문서 및 구현 플랜 추가
Made-with: Cursor
바이낸스 OI 히스토리 API가 최근 30일치만 제공하는 제약을 우회하기 위해
upsert_parquet() 함수를 추가. 매일 실행 시 기존 parquet의 oi_change/funding_rate가
0.0인 구간만 신규 값으로 덮어써 점진적으로 과거 데이터를 채워나감.
--no-upsert 플래그로 기존 덮어쓰기 동작 유지 가능.
Made-with: Cursor
- Updated README.md to reflect new features including dynamic margin ratio, model hot-reload, and multi-symbol streaming.
- Modified bot logic to ensure raw signals are passed to the `_close_and_reenter` method, even when the ML filter is loaded.
- Introduced a new script `run_tests.sh` for streamlined test execution.
- Improved test coverage for signal processing and re-entry logic, ensuring correct behavior under various conditions.
