mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
760961d5e7
NEW ARCHITECTURE:
┌─────────────────────────────────────────────────┐
│ Phase 1: Factor Generation (Open Source) │
│ - Generate factors with LLM v3 prompt │
│ - Backtest each factor in Qlib Docker │
│ - Save to results/factors/ with code + desc │
│ - Continue until 5000+ valid factors │
└─────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────┐
│ Phase 2: ML Training (Closed Source - Local) │
│ - Load top 50 factors │
│ - Train LightGBM model │
│ - Validate (IC, Sharpe) │
│ - Save to results/models/ │
└─────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────┐
│ Phase 3: Portfolio Optimization (Closed Source) │
│ - Select uncorrelated factors (max corr 0.3) │
│ - Optimize weights by IC │
│ - Backtest portfolio │
│ - Save to results/portfolios/ │
└─────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────┐
│ Phase 4: Strategy Generation (Closed Source) │
│ - Generate trading rules │
│ - Add risk management │
│ - Save to results/strategies/ │
└─────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────┐
│ Phase 5: Iterative Improvement (Closed Source) │
│ - Use ML results as feedback │
│ - Generate better factors │
│ - Loop back to Phase 1 │
└─────────────────────────────────────────────────┘
FILES CREATED (Closed Source - NOT in Git):
- rdagent/scenarios/qlib/local/ml_trainer.py
- rdagent/scenarios/qlib/local/portfolio_optimizer.py
- rdagent/scenarios/qlib/local/quant_loop_advanced.py
- rdagent/scenarios/qlib/local/__init__.py
FILES MODIFIED (Open Source - in Git):
- rdagent/scenarios/qlib/quant_loop_factory.py
- .gitignore (added local/ exclusion)
GRACEFUL DEGRADATION:
- If local/ components don't exist → Standard loop
- If < 5000 factors → Standard loop
- If LightGBM not installed → Falls back
- Open source users get FULLY FUNCTIONAL system
USAGE:
# Standard (always works):
rdagent fin_quant
# Advanced (automatic if local components exist + 5000+ factors):
# Same command - factory auto-selects appropriate loop
105 lines
1.1 KiB
Plaintext
105 lines
1.1 KiB
Plaintext
# Environment
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.env
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.env.*
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!.env.example
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual environments
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venv/
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ENV/
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env/
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.venv/
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# IDE
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.idea/
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.vscode/
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*.swp
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*.swo
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*~
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# Testing
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.pytest_cache/
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.coverage
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htmlcov/
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.tox/
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.nox/
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# Logs
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*.log
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log/
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# Cache
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pickle_cache/
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prompt_cache.db
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.cache/
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# Generated/processed data
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git_ignore_folder/
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data_raw/
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# Build artifacts
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*.manifest
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*.spec
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# Local scripts (generated)
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convert_1min.py
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import_1min_qlib.py
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# Results (Backtesting, Factors, Runs)
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results/
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*.db
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*.csv
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*_export.json
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*.h5
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# Documentation (generated)
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QWEN.md
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# AI Agent Files (generated by Qwen Code)
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.qwen/
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# Parallel run workspaces (isolated per run)
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RD-Agent_workspace_run*/
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# Internal documentation (not for public)
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TODO.md
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# Private prompts (your improved versions)
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prompts/local/
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*.local.yaml
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*_private.yaml
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# Private models (your improved versions)
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models/local/
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*.local.py
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*_private.py
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# Test credentials
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.env.test
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*.test.env
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test_credentials.py
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# Closed source local components
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rdagent/scenarios/qlib/local/
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