Commit Graph

6 Commits

Author SHA1 Message Date
TPTBusiness 5da3ba6752 feat(backtest): use backtest_signal_ftmo in strategy orchestrator and optuna optimizer 2026-04-18 15:29:39 +02:00
TPTBusiness 7a0f81f275 fix(optuna): fix inverted parameter range in Stage 2/3 when signal_bias is negative
`_sample_fine_params` and `_sample_very_fine_params` used `max(0.0, center - half_width)`
for all float parameters. When signal_bias=-0.95 (a valid Stage-1 result), this
produced low=0.0, high=-0.75 — an inverted range that causes Optuna to raise
ValueError on every trial, silently caught and returned as -inf.

Fix:
- Extract `_suggest_bounded()` helper with per-parameter floor values
- signal_bias floor is -1.0 (not 0.0 — it is a signed parameter)
- Guard against high <= low for both float and int suggestions
- Elevate trial failure logs from debug to warning so future regressions are visible

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-18 08:05:49 +02:00
TPTBusiness 0651faed92 feat: unified backtest engine, LLM error handling, strategy refactor
- 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>
2026-04-17 22:52:07 +02:00
TPTBusiness c78ecd3b6a feat: add daily log rotation, llama health wait, factor auto-fixer, and README updates
- Add rdagent/log/daily_log.py: daily-rotating structured logs per command
  (fin_quant, strategies, evaluate, parallel) with loguru; all.log combined sink
- predix.py: route TeeWriter output to logs/YYYY-MM-DD/ instead of root dir;
  wrap quant() and evaluate() in daily_log.session() for start/stop/duration tracking
- rdagent/app/cli.py: fin_quant_cli waits for llama.cpp /health endpoint before
  starting pipeline (up to 300 s); daily_log integration for fin_quant,
  generate_strategies, eval_all, parallel commands
- scripts/predix_gen_strategies_real_bt.py: daily_log integration with
  per-strategy ACCEPTED/REJECTED entries and summary on completion
- rdagent/components/coder/factor_coder/auto_fixer.py: new module that patches
  common LLM-generated factor issues (min_periods, inf/NaN, groupby.transform,
  MultiIndex corrections)
- rdagent/components/coder/factor_coder/prompts.yaml: add critical rules for
  EURUSD 1-min intraday factors (min_periods, inf handling, groupby, date range)
- README.md: document --reasoning off and --n-gpu-layers 28 for llama-server;
  explain VRAM constraints when Ollama is running alongside llama.cpp
- .bandit.yml: suppress B615 (HuggingFace unsafe download) for RL benchmark files

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 07:20:08 +02:00
TPTBusiness df61b90464 feat(strategy): Continuous optimization with Optuna parameter injection
- 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
2026-04-12 20:06:13 +02:00
TPTBusiness ab3748fbfe feat: Strategy Generator working with local LLM (P0-P4)
Integrated strategy generation into fin_quant loop:
- Fixed LLM code extraction from JSON responses
- Fixed factor loading (MultiIndex parquet handling)
- Fixed return calculation (realistic proxy)
- Fixed max drawdown (NaN/inf handling)
- Added llama.cpp --reasoning off support
- Strategy orchestrator with LLM + evaluation
- Optuna optimizer integration

100% acceptance rate on test run (2/2 strategies).
Total changes: +2006 lines, -129 lines across 8 files.

Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
2026-04-09 12:55:04 +02:00