- 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>
Based on production API verification showing STOP_MARKET/TAKE_PROFIT_MARKET
are handled as algo orders with different behavior than testnet.
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>
Document why ML filter was disabled (ML OFF PF 1.16 > ML ON PF 0.71),
why SOL/DOGE/TRX were removed (all PF < 1.0 in walk-forward), and
what conditions are needed to re-enable ML in the future.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds Fast Kill (8 consecutive losses) and Slow Kill (PF < 0.75 over 15 trades)
to the backtester, matching bot.py behavior. Without this, ML OFF overtrades
and self-destructs, making ML ON look artificially better.
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>
C5: Remove duplicate entry_fee deduction in backtester (balance and net_pnl)
C1: Add SL/TP retry (3x) with emergency market close on final failure
C3: Add _close_lock to prevent PnL double recording between callback and monitor
C8: Add SIGTERM/SIGINT handler with per-symbol order cancellation before exit
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
5. Add daily PnL reset loop — UTC midnight auto-reset via
_daily_reset_loop in main.py, prevents stale daily_pnl accumulation
6. Fix set_base_balance race condition — call once in main.py before
spawning bots, instead of each bot calling independently
7. Remove realized_pnl != 0 from close detection — prevents entry
orders with small rp values being misclassified as closes
8. Rename xrp_btc_rs/xrp_eth_rs → primary_btc_rs/primary_eth_rs —
generic column names for multi-symbol support (dataset_builder,
ml_features, and tests updated consistently)
9. Replace asyncio.get_event_loop() → get_running_loop() — fixes
DeprecationWarning on Python 3.10+
10. Parallelize candle preload — asyncio.gather for all symbols
instead of sequential REST calls, ~3x faster startup
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1. Margin ratio calculated on per_symbol_balance instead of total balance
— previously amplified margin reduction by num_symbols factor
2. Replace Algo Order API (algoType=CONDITIONAL) with standard
futures_create_order for SL/TP — algo API is for VP/TWAP, not
conditional orders; SL/TP may have silently failed
3. Fallback PnL (SYNC close) now sums all recent income rows instead
of using only the last entry — prevents daily_pnl corruption in
multi-fill scenarios
4. Explicit state transition in _close_and_reenter — clear local
position state after close order to prevent race with User Data
Stream callback on position count
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>
- 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.
8-task plan covering: data fetch, WF backtest, log parsing, trend tracking,
ML re-trigger check, degradation sweep, Discord formatting, CLI orchestration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added a new CLI tool `scripts/strategy_sweep.py` for executing parameter sweeps.
- Updated `get_signal()` and `_calc_signals()` methods to accept `signal_threshold`, `adx_threshold`, and `volume_multiplier` parameters for improved signal processing.
- Fixed a bug in `WalkForwardBacktester` that prevented proper propagation of signal parameters, ensuring accurate backtesting results.
- Updated documentation to reflect changes in parameter sweeps and results.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added support for multi-symbol trading (XRP, TRX, DOGE) in the dashboard.
- Updated bot log messages to include [SYMBOL] prefix for better tracking.
- Enhanced log parser for multi-symbol state tracking and updated database schema.
- Introduced new API endpoints and UI components for symbol filtering and display.
- Added new model files and backtest results for multi-symbol strategies.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added design document outlining the architecture for multi-symbol trading, including independent TradingBot instances and shared RiskManager.
- Created implementation plan detailing tasks for configuration, risk management, and execution structure for multi-symbol support.
- Updated configuration to support multiple trading symbols and correlation symbols, ensuring backward compatibility.
- Introduced shared RiskManager with constraints on position limits and direction to manage global risk effectively.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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>
Introduced CLAUDE.md to provide comprehensive guidance on the CoinTrader project, including architecture, common commands, testing, and deployment details. Added settings.json to enable the superpowers plugin for Claude. This enhances the project's documentation and configuration management.
- Introduced User Data Stream to detect TP/SL executions in real-time.
- Added a new class `UserDataStream` for managing the stream and handling events.
- Updated `bot.py` to initialize and run the User Data Stream in parallel with the candle stream.
- Enhanced `notifier.py` to send detailed Discord notifications including estimated vs actual PnL.
- Added methods in `exchange.py` for managing listenKey lifecycle (create, keepalive, delete).
- Refactored PnL recording and notification logic to streamline handling of position closures.
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
- Added `--upsert` flag to `fetch_history.py` for merging new data into existing parquet files.
- Implemented `upsert_parquet()` function to update existing rows with new values where `oi_change` and `funding_rate` are 0.0, while appending new rows.
- Created tests in `tests/test_fetch_history.py` to validate upsert behavior.
- Updated `.gitignore` to include `.cursor/` directory.
Made-with: Cursor
- Added `_close_and_reenter` method to handle immediate re-entry after closing a position when a reverse signal is detected, contingent on passing the ML filter.
- Updated `process_candle` to call `_close_and_reenter` instead of `_close_position` for reverse signals.
- Enhanced test coverage for the new functionality, ensuring correct behavior under various conditions, including ML filter checks and position limits.
- Introduced a new markdown document detailing the plan to transition the entire pipeline from a 1-minute to a 15-minute timeframe, aiming to improve model AUC from 0.49-0.50 to over 0.53.
- Updated key parameters across multiple scripts, including `LOOKAHEAD` adjustments and default data paths to reflect the new 15-minute interval.
- Modified data fetching and training scripts to ensure compatibility with the new timeframe, including changes in `fetch_history.py`, `train_model.py`, and `train_and_deploy.sh`.
- Enhanced the bot's data stream configuration to operate on a 15-minute interval, ensuring real-time data processing aligns with the new model training strategy.
- Updated training logs to capture new model performance metrics under the revised timeframe.
- Added a new markdown document outlining the plan to enhance the LightGBM model's AUC from 0.54 to 0.57+ through feature normalization, strong time weighting, and walk-forward validation.
- Implemented rolling z-score normalization for absolute value features in `src/dataset_builder.py` to improve model robustness against regime changes.
- Introduced a walk-forward validation function in `scripts/train_model.py` to accurately measure future prediction performance.
- Updated training log to include new model performance metrics and added ONNX model export functionality for compatibility.
- Adjusted model training parameters for better performance and included detailed validation results in the training log.
- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy.
- Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH.
- Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features.
- Updated various scripts and modules to support the new feature set and data handling.
- Enhanced training and deployment scripts to accommodate the new dataset structure.
This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.
- Added comprehensive plans for training a LightGBM model on M4 Mac Mini and deploying it to an LXC container.
- Created scripts for model training, deployment, and a full pipeline execution.
- Enhanced model transfer with error handling and logging for better tracking.
- Introduced profiling for training time analysis and dataset generation optimization.
Made-with: Cursor
- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.
Made-with: Cursor
