메인 봇 PF 0.89, ML/멀티심볼/공개API 피처 전수 테스트 실패 이력을 정리하고,
피처 추가가 아닌 접근 방식 전환(MTF 풀백)으로의 의사결정 맥락을 기술.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
전략 핵심 아이디어, Module 1~4 작동 원리 상세, 메인 루프 흐름도,
메인 봇과의 차이점 비교표를 추가. 테스트 수치도 최신화(183→191).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- README: add MTF bot to feature list and project structure
- ARCHITECTURE: add section 5-1 with MTF bot architecture, data flow,
and design principles (4-module structure, 250 candle buffer, etc.)
- CLAUDE.md: add mtf-pullback-bot to plan history table
- mtf_bot.py: fix stale comment (maxlen=200 → 250)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- SOL/DOGE/TRX removed: all showed PF < 1.0 across all parameter combinations
in strategy sweep (2026-03-17) and live trading (2026-03-21)
- ML filter disabled: Walk-Forward showed ML ON PF < ML OFF PF;
ablation confirmed signal_strength/side dependency (A→C drop 0.08-0.09)
- XRP ADX threshold: 30 → 25 (ADX=30 blocked all trades in current market)
- Current production: XRPUSDT only, SL=1.5x TP=4.0x ADX≥25, NO_ML_FILTER=true
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Revised the architecture document to enhance clarity on system overview, trading decision process, and technical stack.
- Updated the README to emphasize the bot's operational guidelines and risk management features.
- Added new sections in the architecture document detailing the trading decision gates and data pipeline flow.
- Improved the table of contents for better navigation and understanding of the bot's architecture.
- 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.
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>
Add negative_ratio parameter to generate_dataset_vectorized() that
samples HOLD candles as label=0 negatives alongside signal candles.
This increases training data from ~535 to ~3,200 samples when enabled.
- Split valid_rows into base_valid (shared) and sig_valid (signal-only)
- Add 'source' column ("signal" vs "hold_negative") for traceability
- HOLD samples get label=0 and random 50/50 side assignment
- Default negative_ratio=0 preserves backward compatibility
- Fix incorrect column count assertion in existing test
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Introduced a comprehensive architecture document detailing the CoinTrader system, including an overview, core layer architecture, MLOps pipeline, and key operational scenarios.
- Updated README to reference the new architecture document and added a configuration option to disable the ML filter.
- Enhanced the ML filter to allow for complete signal acceptance when the NO_ML_FILTER environment variable is set.
