- Add vbt_backtest.py as single source of truth for all metric formulas
(Sharpe, drawdown, IC, transaction costs) — backtest_engine.py and
strategy_orchestrator.py now delegate to it
- Add LLMUnavailableError to exception.py; rd_loop.py catches it at the
proposal stage and raises LoopResumeError to avoid corrupting trace
history with None hypotheses
- Guard record() against None exp/hypothesis so loop resets leave
trace.hist in a consistent state
- Refactor strategy_orchestrator and optuna_optimizer to use unified
backtest path; remove duplicate metric calculation code
- Add predix_rebacktest_unified.py script for offline re-evaluation
- Update tests and README
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add _patch_strategy_code() to inject Optuna's best parameters into
LLM-generated strategy code (handles window, entry_thresh, exit_thresh,
signal_window, and .rolling(N) calls)
- Add _evaluate_with_patched_code() to re-run the patched strategy through
the full OHLCV backtest pipeline, producing comparable Sharpe metrics
- After Optuna finds best parameters, the strategy is re-evaluated with
real price data instead of Optuna's simplified factor-proxy returns
- This fixes the issue where Optuna reported Sharpe=1192 but the strategy
was still rejected because initial Sharpe was -9.82 (different calculation)
- Now strategies can be rescued if Optuna finds parameters that produce
positive Sharpe in the real backtest
- Optuna now runs for ALL strategies (accepted AND rejected)
- Fix critical bug: Optuna parameters are now injected into LLM-generated
code via regex patching (entry_thresh, exit_thresh, window, signal_window)
Previously all 30 trials executed identical code producing the same Sharpe
- Add continuous optimization loop (--max-iterations) for repeated
strategy generation and optimization cycles
- Improve prompt v5 with better IC-inversion examples and realistic
code templates
- Expand Optuna search space: zscore_window, signal_bias, max_hold_bars
- CLI: add --continuous, --max-iterations, --optuna-trials flags
- Show best strategy with optimized parameters in summary output
Step 1 - Evaluierung bekannter Strategien:
- Added 'close' to exec context for existing strategies
- Strategies can now use close.index for signal creation
- MomentumDivergenceZScore evaluates correctly: Sharpe=3.59, DD=-0.22%
Step 2 - Annualisierungsfaktor korrigiert:
- Fixed: sqrt(252*1440/96) → sqrt(252*1440) for 1-min data
- Added minimum 0.1 years to avoid extreme values for short periods
- Linear scaling for <1 year, compound for >=1 year
Test results (MomentumDivergenceZScore):
- Status: accepted
- Sharpe: 3.59 (realistic)
- Max DD: -0.22%
- Win Rate: 49.46%
- Ann Return: 543.75% (linear scaled for 259 min period)
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
Implemented realistic backtesting:
- Load real OHLCV close prices from intraday_pv.h5
- Calculate real price returns (pct_change)
- Apply signal positions to real returns with proper alignment
- Include spread costs (1.5 bps per trade)
- Fallback to factor proxy if OHLCV unavailable
Note: Sharpe values now realistic (~0 for random strategies).
Strategies need LLM to select predictive factors for positive Sharpe.
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
Implemented realistic backtesting in StrategyOrchestrator:
- Load real OHLCV close prices from intraday_pv.h5
- Calculate real price returns (pct_change)
- Apply signal positions to real returns
- Include spread costs (1.5 bps per trade)
- Fallback to factor proxy if OHLCV unavailable
Strategies now evaluated with actual market conditions.
Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
TPTBusiness