mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
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>
This commit is contained in:
TPTBusiness
@@ -140,7 +140,7 @@ def quant(
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# Setup both API keys for load balancing
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os.environ["OPENAI_API_BASE"] = "https://openrouter.ai/api/v1"
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os.environ["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/qwen/qwen3.6-plus:free")
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os.environ["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/google/gemma-4-26b-a4b-it:free")
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# If second key exists, configure LiteLLM for load balancing
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if api_key_2:
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@@ -990,7 +990,7 @@ def build_strategies_ai(
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else:
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os.environ["OPENAI_API_KEY"] = api_key
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os.environ["OPENAI_API_BASE"] = "https://openrouter.ai/api/v1"
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os.environ["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/qwen/qwen3.6-plus:free")
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os.environ["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/google/gemma-4-26b-a4b-it:free")
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console.print(f"\n[bold blue]🌐 Using OpenRouter: {os.environ['CHAT_MODEL']}[/bold blue]")
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else:
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console.print("[bold red]❌ No API key found. Set OPENROUTER_API_KEY in .env[/bold red]")
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@@ -72,7 +72,7 @@ def setup_llm_env():
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if router_key:
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os.environ['OPENAI_API_KEY'] = router_key
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os.environ['OPENAI_API_BASE'] = 'https://openrouter.ai/api/v1'
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os.environ['CHAT_MODEL'] = os.getenv('OPENROUTER_MODEL', 'openrouter/qwen/qwen3.6-plus:free')
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os.environ['CHAT_MODEL'] = os.getenv('OPENROUTER_MODEL', 'openrouter/google/gemma-4-26b-a4b-it:free')
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# ============================================================================
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# Factor Loading (cached at module level for each process)
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+1
-1
@@ -178,7 +178,7 @@ class ParallelRunner:
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api_key = self.api_keys[run_state.api_key_idx]
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env["OPENAI_API_KEY"] = api_key
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env["OPENAI_API_BASE"] = "https://openrouter.ai/api/v1"
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env["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/qwen/qwen3.6-plus:free")
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env["CHAT_MODEL"] = os.getenv("OPENROUTER_MODEL", "openrouter/google/gemma-4-26b-a4b-it:free")
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# If we configured multiple API keys AND have enough keys, use load balancing
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if self.num_api_keys >= 2 and len(self.api_keys) >= 2:
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+112
-119
@@ -1,160 +1,153 @@
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# Predix Prompts - Standard Version
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#
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# These are the default prompts for EUR/USD quantitative trading.
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# Store your improved prompts in prompts/local/ (not committed to Git).
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#
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# Usage:
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# from rdagent.components.loader import load_prompt
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# prompt = load_prompt("factor_discovery") # Loads from prompts/local/ if exists, else prompts/
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# ============================================================
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# Factor Discovery Prompts
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# ============================================================
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factor_discovery:
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system: |-
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You are an expert quantitative researcher specialized in FX (foreign exchange) trading,
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specifically EURUSD intraday strategies on 1-minute bars.
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EURUSD domain knowledge you must apply:
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- London session (08:00-16:00 UTC): highest volume, trending behavior
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- NY session (13:00-21:00 UTC): second volume peak
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- Asian session (00:00-08:00 UTC): lower volume, mean-reverting
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- London/NY overlap (13:00-16:00 UTC): strongest directional moves
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- Spread cost: ~1.5 bps per trade — factors must overcome this
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- EURUSD is mean-reverting on short windows (<1h), trending on longer (>4h)
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Your hypothesis must:
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1. Specify which session(s) the factor targets
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2. Include spread filter (expected return > 0.0003)
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3. Name the market regime (trending/mean-reverting)
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4. Be testable with available data (OHLCV, returns, technical indicators)
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Please ensure your response is in JSON format:
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{
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"hypothesis": "Clear factor hypothesis",
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"reason": "Detailed explanation",
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"target_session": "london/ny/asian/all",
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"expected_arr_range": "e.g. 8-12%"
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}
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system: "You are an expert quantitative researcher specialized in FX (foreign exchange)\
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\ trading,\nspecifically EURUSD intraday strategies on 1-minute bars.\n\nEURUSD\
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\ domain knowledge you must apply:\n- London session (08:00-16:00 UTC): highest\
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\ volume, trending behavior\n- NY session (13:00-21:00 UTC): second volume peak\n\
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- Asian session (00:00-08:00 UTC): lower volume, mean-reverting\n- London/NY overlap\
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\ (13:00-16:00 UTC): strongest directional moves\n- Spread cost: ~1.5 bps per\
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\ trade — factors must overcome this\n- EURUSD is mean-reverting on short windows\
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\ (<1h), trending on longer (>4h)\n\nYour hypothesis must:\n1. Specify which session(s)\
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\ the factor targets\n2. Include spread filter (expected return > 0.0003)\n3.\
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\ Name the market regime (trending/mean-reverting)\n4. Be testable with available\
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\ data (OHLCV, returns, technical indicators)\n\nPlease ensure your response is\
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\ in JSON format:\n{\n \"hypothesis\": \"Clear factor hypothesis\",\n \"reason\"\
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: \"Detailed explanation\",\n \"target_session\": \"london/ny/asian/all\",\n\
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\ \"expected_arr_range\": \"e.g. 8-12%\"\n}"
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user: 'Previously tried factors and their results:
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user: |-
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Previously tried factors and their results:
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{{ factor_descriptions }}
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Additional context:
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{{ report_content }}
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Generate a NEW factor hypothesis that is meaningfully different from what has been tried.
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Target: beat current best ARR of 9.62%.
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# ============================================================
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# Factor Evolution Prompts
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# ============================================================
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Generate a NEW factor hypothesis that is meaningfully different from what has
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been tried.
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Target: beat current best ARR of 9.62%.'
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factor_evolution:
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system: |-
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You are improving existing trading factors for EURUSD 1-minute data.
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Improvement strategies:
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1. Add session filters (is_london, is_ny)
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2. Add regime filters (ADX, volatility)
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3. Optimize lookback periods
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4. Combine with complementary factors
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5. Add risk management (stop-loss, take-profit)
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Your response must include:
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- What to improve and why
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- Expected performance gain
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- Implementation approach
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JSON format:
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{
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"improvement": "Description of improvement",
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"reason": "Why this will work better",
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"expected_improvement": "e.g. +2% ARR, -5% drawdown"
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}
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system: "You are improving existing trading factors for EURUSD 1-minute data.\n\n\
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Improvement strategies:\n1. Add session filters (is_london, is_ny)\n2. Add regime\
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\ filters (ADX, volatility)\n3. Optimize lookback periods\n4. Combine with complementary\
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\ factors\n5. Add risk management (stop-loss, take-profit)\n\nYour response must\
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\ include:\n- What to improve and why\n- Expected performance gain\n- Implementation\
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\ approach\n\nJSON format:\n{\n \"improvement\": \"Description of improvement\"\
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,\n \"reason\": \"Why this will work better\",\n \"expected_improvement\": \"\
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e.g. +2% ARR, -5% drawdown\"\n}"
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user: 'Current factor:
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user: |-
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Current factor:
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{{ factor_code }}
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Performance metrics:
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{{ factor_metrics }}
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Suggest specific improvements to beat current performance.
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# ============================================================
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# Model Coder Prompts
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# ============================================================
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Suggest specific improvements to beat current performance.'
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model_coder:
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system: |-
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You are an expert ML engineer specialized in EURUSD trading models.
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system: 'You are an expert ML engineer specialized in EURUSD trading models.
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Supported model types:
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- TimeSeries: LSTM, GRU, TCN, Transformer, PatchTST
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- Tabular: XGBoost, LightGBM, RandomForest
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- Hybrid: CNN+LSTM, XGBoost+LSTM ensemble
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EURUSD-specific rules:
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1. Session filter: use is_london and is_ny columns
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2. Spread filter: only trade when abs(prediction) > 0.0003
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3. ADX regime: if adx_proxy > 1.2 use trend model, else mean-reversion
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4. Weekend filter: close positions Friday 20:00 UTC
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5. Max frequency: target <15 trades per day
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Your code must:
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- Be production-ready (error handling, logging)
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- Include session/regime filters
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- Account for spread costs
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- Support both classification and regression targets
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user: |-
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Factor descriptions:
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- Support both classification and regression targets'
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user: 'Factor descriptions:
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{{ factor_descriptions }}
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Available features:
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{{ feature_list }}
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Target: {{ target_variable }}
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Write complete, production-ready code for the model.
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# ============================================================
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# Trading Strategy Prompts
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# ============================================================
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Write complete, production-ready code for the model.'
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strategy_generation:
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system: "You are an expert quantitative trading researcher specialized in EUR/USD\
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\ intraday strategies.\n\nYour task is to generate a trading strategy by combining\
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\ the provided factors into a coherent signal.\n\nEUR/USD Domain Knowledge:\n\
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- London session (08:00-16:00 UTC): highest volume, trending behavior\n- NY session\
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\ (13:00-21:00 UTC): second volume peak, continuation\n- Asian session (00:00-08:00\
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\ UTC): lower volume, mean-reverting\n- London/NY overlap (13:00-16:00 UTC): strongest\
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\ directional moves\n- Spread cost: ~1.5 bps per trade — signals must overcome\
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\ this\n\nFactor Usage Rules:\n1. ONLY use the factors provided below — no others!\n\
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2. The code MUST work with a DataFrame called 'factors' containing factor columns\n\
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3. Create a pandas Series called 'signal' with values: 1 (long), -1 (short), 0\
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\ (neutral)\n4. signal.index MUST match factors.index exactly\n5. signal.name\
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\ must be 'signal'\n\nSignal Quality Requirements:\n- Generate meaningful signals\
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\ (avoid constant 0 or all 1s)\n- Use rolling z-scores for normalization: (x -\
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\ rolling.mean()) / rolling.std()\n- Apply thresholds (e.g., z > 0.3 for long,\
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\ z < -0.3 for short)\n- Combine factors with weights based on their IC values\n\
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- Consider regime filters (trend vs mean-reversion)\n\nOutput ONLY valid JSON\
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\ with these exact fields:\n{\n \"strategy_name\": \"short_descriptive_name\"\
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,\n \"factors_used\": [\"factor1\", \"factor2\", \"factor3\"],\n \"description\"\
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: \"one sentence explaining the strategy logic\",\n \"code\": \"complete Python\
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\ code that creates signal Series\"\n}"
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user: "Generate a EUR/USD trading strategy using these factors:\n\n{{ factors }}\n\
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\n{{ additional_context }}\n\nCRITICAL RULES:\n1. DO NOT define functions - write\
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\ direct executable code\n2. DO NOT use def - just write the code that creates\
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\ 'signal'\n3. The code will be executed with 'factors' DataFrame already in scope\n\
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4. You MUST create a variable called 'signal' as a pandas Series\n5. signal must\
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\ have values 1 (LONG), -1 (SHORT), or 0 (NEUTRAL)\n6. signal.index must equal\
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\ factors.index\n\nEXAMPLE OF CORRECT FORMAT:\n```\nimport pandas as pd\nimport\
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\ numpy as np\n\nz = (factors['factor1'] - factors['factor1'].rolling(20).mean())\
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\ / factors['factor1'].rolling(20).std()\nsignal = pd.Series(0, index=factors.index)\n\
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signal[z > 0.3] = 1\nsignal[z < -0.3] = -1\nsignal.name = 'signal'\n```\n\nWRONG\
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\ FORMAT (DO NOT DO THIS):\n```\ndef generate_signal(factors):\n ...\n return\
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\ signal\n```\n\nOutput ONLY the JSON object, no additional text."
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trading_strategy:
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system: |-
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You are a portfolio manager designing trading strategies for EURUSD.
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Strategy components:
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1. Entry signals (from factors/models)
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2. Position sizing (volatility-adjusted)
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3. Risk management (stop-loss, take-profit, max drawdown)
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4. Session awareness (London/NY/Asian)
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5. Correlation management (if multiple factors)
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Your strategy must specify:
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- Entry conditions (which signals, what thresholds)
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- Exit conditions (time-based, signal-based, stop-loss)
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- Position sizing (fixed, volatility-adjusted, Kelly)
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- Risk limits (max position, max leverage, max drawdown)
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JSON format:
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{
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"entry_conditions": [...],
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"exit_conditions": [...],
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"position_sizing": "...",
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"risk_limits": {...}
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}
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system: "You are a portfolio manager designing trading strategies for EURUSD.\n\n\
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Strategy components:\n1. Entry signals (from factors/models)\n2. Position sizing\
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\ (volatility-adjusted)\n3. Risk management (stop-loss, take-profit, max drawdown)\n\
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4. Session awareness (London/NY/Asian)\n5. Correlation management (if multiple\
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\ factors)\n\nYour strategy must specify:\n- Entry conditions (which signals,\
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\ what thresholds)\n- Exit conditions (time-based, signal-based, stop-loss)\n\
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- Position sizing (fixed, volatility-adjusted, Kelly)\n- Risk limits (max position,\
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\ max leverage, max drawdown)\n\nJSON format:\n{\n \"entry_conditions\": [...],\n\
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\ \"exit_conditions\": [...],\n \"position_sizing\": \"...\",\n \"risk_limits\"\
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: {...}\n}"
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user: 'Available factors:
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user: |-
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Available factors:
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{{ factors }}
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Historical performance:
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{{ historical_metrics }}
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Design a complete trading strategy that combines these factors optimally.
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Design a complete trading strategy that combines these factors optimally.'
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+536
-2
@@ -8,8 +8,11 @@ This will
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import os
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import sys
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from dotenv import load_dotenv
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load_dotenv(".env")
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@@ -18,7 +21,7 @@ load_dotenv(".env")
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import subprocess
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from importlib.resources import path as rpath
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from typing import Optional
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from typing import Dict, Optional
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import typer
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from rich.console import Console
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@@ -120,6 +123,16 @@ def fin_quant_cli(
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"-m",
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help="LLM backend to use: 'local' (llama.cpp), 'openrouter' (cloud models), or custom env var prefix",
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),
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auto_strategies: bool = typer.Option(
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False,
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"--auto-strategies",
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help="Automatically generate strategies after factor threshold",
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),
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auto_strategies_threshold: int = typer.Option(
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500,
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"--auto-strategies-threshold",
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help="Number of factors before triggering strategy generation",
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),
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):
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"""
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Start EURUSD quantitative trading loop.
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@@ -128,6 +141,8 @@ def fin_quant_cli(
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--with-dashboard/-d: Start web dashboard at http://localhost:5000
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--cli-dashboard/-c: Show beautiful terminal UI with live stats
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--model/-m: LLM backend ('local' | 'openrouter')
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--auto-strategies: Auto-generate strategies after threshold
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--auto-strategies-threshold: Factor count trigger for auto strategies
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Examples:
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rdagent fin_quant # Local llama.cpp (default)
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@@ -135,6 +150,8 @@ def fin_quant_cli(
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rdagent fin_quant -m openrouter # Use OpenRouter model
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rdagent fin_quant -d # Web dashboard
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rdagent fin_quant -d -c # Both dashboards
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rdagent fin_quant --auto-strategies # Auto-generate strategies
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rdagent fin_quant --auto-strategies --auto-strategies-threshold 1000
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OpenRouter Setup:
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1. Set OPENROUTER_API_KEY in .env
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@@ -202,7 +219,15 @@ def fin_quant_cli(
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time.sleep(1)
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|
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# Fin Quant starten
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fin_quant(path=path, step_n=step_n, loop_n=loop_n, all_duration=all_duration, checkout=checkout)
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fin_quant(
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path=path,
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step_n=step_n,
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loop_n=loop_n,
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all_duration=all_duration,
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checkout=checkout,
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auto_strategies=auto_strategies,
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auto_strategies_threshold=auto_strategies_threshold,
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)
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@app.command(name="fin_factor_report")
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@@ -487,5 +512,514 @@ def rl_trading_cli(
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raise typer.Exit(code=1)
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@app.command(name="generate_strategies")
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def generate_strategies_cli(
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count: int = typer.Option(10, "--count", "-n", help="Number of strategies to generate"),
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workers: int = typer.Option(4, "--workers", "-w", help="Parallel workers"),
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style: str = typer.Option("swing", "--style", "-s", help="Trading style: daytrading or swing"),
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optuna: bool = typer.Option(True, "--optuna/--no-optuna", help="Enable Optuna optimization"),
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optuna_trials: int = typer.Option(30, "--optuna-trials", help="Number of Optuna trials per strategy"),
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top_factors: int = typer.Option(20, "--top-factors", help="Number of top factors to consider"),
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):
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"""
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Generate trading strategies from evaluated factors.
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Uses LLM to combine top factors into trading strategies,
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then evaluates each with real OHLCV backtest data.
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Examples:
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rdagent generate_strategies # 10 strategies, swing
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rdagent generate_strategies -n 20 -w 8 # 20 strategies, 8 workers
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rdagent generate_strategies -s daytrading # Day trading style
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rdagent generate_strategies --no-optuna # Skip optimization
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"""
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from rich.console import Console
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from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn
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from rich.table import Table
|
||||
|
||||
console = Console()
|
||||
|
||||
# Validate inputs
|
||||
if style not in ("daytrading", "swing"):
|
||||
console.print(f"[bold red]Error: Invalid style '{style}'. Use 'daytrading' or 'swing'.[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
if count < 1:
|
||||
console.print("[bold red]Error: Count must be at least 1.[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
if workers < 1 or workers > 16:
|
||||
console.print("[bold red]Error: Workers must be between 1 and 16.[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
console.print(f"\n[bold blue]{'='*60}[/bold blue]")
|
||||
console.print(f"[bold blue] PREDIX Strategy Generator[/bold blue]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]")
|
||||
console.print(f" Strategies: [cyan]{count}[/cyan]")
|
||||
console.print(f" Workers: [cyan]{workers}[/cyan]")
|
||||
console.print(f" Style: [cyan]{style}[/cyan]")
|
||||
console.print(f" Optuna: {'[green]Enabled[/green]' if optuna else '[yellow]Disabled[/yellow]'}")
|
||||
if optuna:
|
||||
console.print(f" Trials: [cyan]{optuna_trials}[/cyan]")
|
||||
console.print(f" Top Factors: [cyan]{top_factors}[/cyan]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]\n")
|
||||
|
||||
try:
|
||||
from rdagent.components.coder.strategy_orchestrator import StrategyOrchestrator
|
||||
|
||||
# Initialize orchestrator
|
||||
orchestrator = StrategyOrchestrator(
|
||||
top_factors=top_factors,
|
||||
trading_style=style,
|
||||
)
|
||||
|
||||
# Progress tracking
|
||||
progress_data = {"generated": 0, "accepted": 0, "rejected": 0, "errors": []}
|
||||
|
||||
def progress_callback(current, total, result):
|
||||
progress_data["generated"] = current
|
||||
if result.get("status") == "accepted":
|
||||
progress_data["accepted"] += 1
|
||||
else:
|
||||
progress_data["rejected"] += 1
|
||||
|
||||
# Generate strategies
|
||||
with Progress(
|
||||
SpinnerColumn(),
|
||||
TextColumn("[progress.description]{task.description}"),
|
||||
BarColumn(),
|
||||
TextColumn("[bold]{task.completed}/{task.total}[/bold]"),
|
||||
TimeRemainingColumn(),
|
||||
console=console,
|
||||
) as progress:
|
||||
task = progress.add_task(f"Generating {count} strategies...", total=count)
|
||||
|
||||
results = orchestrator.generate_strategies(
|
||||
count=count,
|
||||
workers=workers,
|
||||
progress_callback=lambda c, t, r: (progress.update(task, completed=c), progress_callback(c, t, r)),
|
||||
)
|
||||
|
||||
# Run Optuna optimization if enabled
|
||||
if optuna and results:
|
||||
console.print(f"\n[yellow]Running Optuna optimization ({optuna_trials} trials)...[/yellow]")
|
||||
try:
|
||||
from rdagent.components.coder.optuna_optimizer import OptunaOptimizer
|
||||
from rdagent.components.coder.strategy_orchestrator import StrategyOrchestrator
|
||||
|
||||
optimizer = OptunaOptimizer(n_trials=optuna_trials)
|
||||
|
||||
# Load factor values for optimization
|
||||
orchestrator2 = StrategyOrchestrator(top_factors=top_factors, trading_style=style)
|
||||
factors = orchestrator2.load_top_factors()
|
||||
factor_values_dict = {}
|
||||
for f in factors:
|
||||
series = orchestrator2.load_factor_values(f["factor_name"])
|
||||
if series is not None:
|
||||
factor_values_dict[f["factor_name"]] = series
|
||||
|
||||
if factor_values_dict:
|
||||
factor_df = pd.DataFrame(factor_values_dict).dropna()
|
||||
accepted = [r for r in results if r.get("status") == "accepted"]
|
||||
|
||||
if accepted:
|
||||
opt_results = optimizer.optimize_batch(
|
||||
accepted, factor_df, progress_callback=None
|
||||
)
|
||||
console.print(f"[green]Optimization complete for {len(opt_results)} strategies.[/green]")
|
||||
|
||||
# Update results with optimized metrics
|
||||
for opt_r in opt_results:
|
||||
for i, r in enumerate(results):
|
||||
if r.get("strategy_name") == opt_r.get("strategy_name"):
|
||||
results[i] = opt_r
|
||||
break
|
||||
else:
|
||||
console.print("[yellow]No accepted strategies to optimize.[/yellow]")
|
||||
else:
|
||||
console.print("[yellow]No factor values available for optimization.[/yellow]")
|
||||
|
||||
except ImportError:
|
||||
console.print("[yellow]Optuna not installed. Skipping optimization.[/yellow]")
|
||||
except Exception as e:
|
||||
console.print(f"[yellow]Optimization failed: {e}[/yellow]")
|
||||
|
||||
# Print summary table
|
||||
accepted = [r for r in results if r.get("status") == "accepted"]
|
||||
rejected = [r for r in results if r.get("status") == "rejected"]
|
||||
|
||||
console.print(f"\n[bold green]{'='*60}[/bold green]")
|
||||
console.print(f"[bold green] Strategy Generation Summary[/bold green]")
|
||||
console.print(f"[bold green]{'='*60}[/bold green]")
|
||||
|
||||
table = Table(show_header=True, header_style="bold magenta", show_lines=True)
|
||||
table.add_column("Status", style="dim", width=12)
|
||||
table.add_column("Count", justify="right", width=8)
|
||||
table.add_column("Percentage", justify="right", width=12)
|
||||
|
||||
table.add_row(
|
||||
"Total",
|
||||
str(len(results)),
|
||||
"100%",
|
||||
)
|
||||
table.add_row(
|
||||
"[green]Accepted[/green]",
|
||||
str(len(accepted)),
|
||||
f"[green]{len(accepted)/max(len(results),1)*100:.1f}%[/green]",
|
||||
)
|
||||
table.add_row(
|
||||
"[red]Rejected[/red]",
|
||||
str(len(rejected)),
|
||||
f"[red]{len(rejected)/max(len(results),1)*100:.1f}%[/red]",
|
||||
)
|
||||
|
||||
console.print(table)
|
||||
|
||||
if accepted:
|
||||
console.print(f"\n[bold]Accepted Strategies:[/bold]")
|
||||
acc_table = Table(show_header=True, header_style="bold cyan")
|
||||
acc_table.add_column("#", width=4)
|
||||
acc_table.add_column("Strategy", width=30)
|
||||
acc_table.add_column("Sharpe", justify="right", width=10)
|
||||
acc_table.add_column("Ann. Return", justify="right", width=12)
|
||||
acc_table.add_column("Max DD", justify="right", width=10)
|
||||
acc_table.add_column("Win Rate", justify="right", width=10)
|
||||
|
||||
for i, strat in enumerate(sorted(accepted, key=lambda x: x.get("sharpe_ratio", 0), reverse=True), 1):
|
||||
acc_table.add_row(
|
||||
str(i),
|
||||
strat.get("strategy_name", "Unknown")[:30],
|
||||
f"{strat.get('sharpe_ratio', 0):.2f}",
|
||||
f"{strat.get('annualized_return', 0):.4f}",
|
||||
f"{strat.get('max_drawdown', 0):.2%}",
|
||||
f"{strat.get('win_rate', 0):.2%}",
|
||||
)
|
||||
console.print(acc_table)
|
||||
|
||||
console.print(f"\n[bold green]Strategies saved to:[/bold green] [cyan]results/strategies_new/[/cyan]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]\n")
|
||||
|
||||
except ImportError as e:
|
||||
console.print(f"[bold red]Error: Strategy components not available.[/bold red]")
|
||||
console.print(f"Details: {e}")
|
||||
raise typer.Exit(code=1)
|
||||
except Exception as e:
|
||||
console.print(f"[bold red]Strategy generation failed: {e}[/bold red]")
|
||||
import traceback
|
||||
console.print(f"[dim]{traceback.format_exc()}[/dim]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
@app.command(name="optimize_portfolio")
|
||||
def optimize_portfolio_cli(
|
||||
top_n: int = typer.Option(30, "--top-n", help="Number of top strategies to consider"),
|
||||
method: str = typer.Option("mean_variance", "--method", "-m", help="Optimization method: mean_variance, risk_parity"),
|
||||
):
|
||||
"""
|
||||
Optimize portfolio weights from top strategies.
|
||||
|
||||
Uses Modern Portfolio Theory to find optimal strategy weights.
|
||||
|
||||
Examples:
|
||||
rdagent optimize_portfolio # Mean-variance, top 30
|
||||
rdagent optimize_portfolio --method risk_parity # Risk parity
|
||||
rdagent optimize_portfolio --top-n 20 # Top 20 strategies
|
||||
"""
|
||||
from rich.console import Console
|
||||
from rich.table import Table
|
||||
|
||||
console = Console()
|
||||
|
||||
if method not in ("mean_variance", "risk_parity"):
|
||||
console.print(f"[bold red]Error: Invalid method '{method}'. Use 'mean_variance' or 'risk_parity'.[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
console.print(f"\n[bold blue]{'='*60}[/bold blue]")
|
||||
console.print(f"[bold blue] PREDIX Portfolio Optimizer[/bold blue]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]")
|
||||
console.print(f" Top N: [cyan]{top_n}[/cyan]")
|
||||
console.print(f" Method: [cyan]{method}[/cyan]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]\n")
|
||||
|
||||
try:
|
||||
from rdagent.components.backtesting.risk_management import PortfolioOptimizer
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
project_root = Path(__file__).parent.parent.parent
|
||||
strategies_dir = project_root / "results" / "strategies_new"
|
||||
|
||||
if not strategies_dir.exists():
|
||||
console.print("[bold red]Error: No strategies found in results/strategies_new/[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
# Load strategies
|
||||
strategies = []
|
||||
for f in strategies_dir.glob("*.json"):
|
||||
try:
|
||||
with open(f, encoding="utf-8") as fh:
|
||||
data = json.load(fh)
|
||||
if data.get("status") == "accepted":
|
||||
strategies.append(data)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if not strategies:
|
||||
console.print("[bold red]Error: No accepted strategies found.[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
# Sort by Sharpe and take top N
|
||||
strategies.sort(key=lambda x: x.get("sharpe_ratio", 0), reverse=True)
|
||||
top_strategies = strategies[:top_n]
|
||||
|
||||
console.print(f"Loaded {len(top_strategies)} accepted strategies.\n")
|
||||
|
||||
# Build return series (simplified - using strategy metrics as proxies)
|
||||
n = len(top_strategies)
|
||||
# Create synthetic returns based on strategy metrics for weight optimization
|
||||
# In production, this would use actual strategy equity curves
|
||||
names = [s.get("strategy_name", f"Strategy_{i}")[:30] for i, s in enumerate(top_strategies)]
|
||||
sharpe_values = [s.get("sharpe_ratio", 0) for s in top_strategies]
|
||||
|
||||
# Use Sharpe as expected return proxy
|
||||
exp_returns = pd.Series(sharpe_values, index=names)
|
||||
|
||||
# Build covariance matrix (simplified - assume some correlation)
|
||||
np.random.seed(42)
|
||||
cov_matrix = pd.DataFrame(
|
||||
np.eye(n) * 0.1 + np.ones((n, n)) * 0.02,
|
||||
index=names,
|
||||
columns=names,
|
||||
)
|
||||
|
||||
# Optimize
|
||||
optimizer = PortfolioOptimizer()
|
||||
|
||||
if method == "mean_variance":
|
||||
weights = optimizer.mean_variance(exp_returns, cov_matrix)
|
||||
else: # risk_parity
|
||||
weights = optimizer.risk_parity(cov_matrix)
|
||||
|
||||
# Normalize negative weights to zero
|
||||
weights = np.maximum(weights, 0)
|
||||
weight_sum = np.sum(weights)
|
||||
if weight_sum > 0:
|
||||
weights = weights / weight_sum
|
||||
|
||||
# Print results
|
||||
console.print(f"[bold]Optimal Portfolio Weights ({method}):[/bold]\n")
|
||||
|
||||
weight_table = Table(show_header=True, header_style="bold cyan")
|
||||
weight_table.add_column("#", width=4)
|
||||
weight_table.add_column("Strategy", width=35)
|
||||
weight_table.add_column("Weight", justify="right", width=10)
|
||||
weight_table.add_column("Sharpe", justify="right", width=10)
|
||||
|
||||
sorted_indices = np.argsort(weights)[::-1]
|
||||
for i, idx in enumerate(sorted_indices):
|
||||
if weights[idx] > 0.01: # Only show meaningful weights
|
||||
weight_table.add_row(
|
||||
str(i + 1),
|
||||
names[idx][:35],
|
||||
f"{weights[idx]:.2%}",
|
||||
f"{sharpe_values[idx]:.2f}",
|
||||
)
|
||||
|
||||
console.print(weight_table)
|
||||
|
||||
# Portfolio metrics
|
||||
portfolio_sharpe = np.dot(weights, sharpe_values)
|
||||
console.print(f"\n[bold green]Portfolio Sharpe Ratio: {portfolio_sharpe:.2f}[/bold green]")
|
||||
|
||||
# Save portfolio weights
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
portfolio = {
|
||||
"generated_at": timestamp,
|
||||
"method": method,
|
||||
"top_n": top_n,
|
||||
"strategies": [
|
||||
{
|
||||
"name": names[i],
|
||||
"weight": float(weights[i]),
|
||||
"sharpe_ratio": sharpe_values[i],
|
||||
}
|
||||
for i in range(n)
|
||||
if weights[i] > 0.01
|
||||
],
|
||||
"portfolio_sharpe": float(portfolio_sharpe),
|
||||
}
|
||||
|
||||
portfolios_dir = project_root / "results" / "portfolios"
|
||||
portfolios_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
portfolio_file = portfolios_dir / f"portfolio_{timestamp}.json"
|
||||
with open(portfolio_file, "w", encoding="utf-8") as f:
|
||||
json.dump(portfolio, f, indent=2, ensure_ascii=False)
|
||||
|
||||
console.print(f"[green]Portfolio saved to:[/green] [cyan]{portfolio_file}[/cyan]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]\n")
|
||||
|
||||
except Exception as e:
|
||||
console.print(f"[bold red]Portfolio optimization failed: {e}[/bold red]")
|
||||
import traceback
|
||||
console.print(f"[dim]{traceback.format_exc()}[/dim]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
|
||||
@app.command(name="strategies_report")
|
||||
def strategies_report_cli(
|
||||
strategy_path: str = typer.Option(None, "--strategy-path", "-s", help="Path to single strategy JSON or directory"),
|
||||
output_dir: str = typer.Option("results/strategy_reports/", "--output-dir", "-o", help="Output directory for reports"),
|
||||
):
|
||||
"""
|
||||
Generate performance reports for strategies.
|
||||
|
||||
Creates detailed reports with metrics, equity curves, and analysis.
|
||||
|
||||
Examples:
|
||||
rdagent strategies_report # All strategies
|
||||
rdagent strategies_report -s path/to/strategy.json # Single strategy
|
||||
rdagent strategies_report -o custom/reports/ # Custom output dir
|
||||
"""
|
||||
from rich.console import Console
|
||||
from rich.progress import Progress, SpinnerColumn, TextColumn
|
||||
from pathlib import Path
|
||||
|
||||
console = Console()
|
||||
|
||||
console.print(f"\n[bold blue]{'='*60}[/bold blue]")
|
||||
console.print(f"[bold blue] PREDIX Strategy Report Generator[/bold blue]")
|
||||
console.print(f"[bold blue]{'='*60}[/bold blue]\n")
|
||||
|
||||
project_root = Path(__file__).parent.parent.parent
|
||||
|
||||
if strategy_path is None:
|
||||
# Use default directory
|
||||
strategy_path = str(project_root / "results" / "strategies_new")
|
||||
|
||||
# Resolve paths
|
||||
strategy_path = Path(strategy_path)
|
||||
output_dir_path = Path(output_dir)
|
||||
output_dir_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Collect strategy files
|
||||
strategy_files = []
|
||||
|
||||
if strategy_path.is_file() and strategy_path.suffix == ".json":
|
||||
strategy_files.append(strategy_path)
|
||||
elif strategy_path.is_dir():
|
||||
strategy_files = sorted(strategy_path.glob("*.json"))
|
||||
else:
|
||||
console.print(f"[bold red]Error: Path not found or not a JSON file: {strategy_path}[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
if not strategy_files:
|
||||
console.print("[bold red]Error: No strategy JSON files found.[/bold red]")
|
||||
raise typer.Exit(code=1)
|
||||
|
||||
console.print(f"Found {len(strategy_files)} strategy file(s).\n")
|
||||
|
||||
reports_generated = 0
|
||||
|
||||
with Progress(
|
||||
SpinnerColumn(),
|
||||
TextColumn("[progress.description]{task.description}"),
|
||||
console=console,
|
||||
) as progress:
|
||||
for spath in strategy_files:
|
||||
task = progress.add_task(f"Processing {spath.name}...", total=1)
|
||||
|
||||
try:
|
||||
report = _generate_single_strategy_report(spath, output_dir_path)
|
||||
reports_generated += 1
|
||||
console.print(f" [green]Report generated:[/green] {report['output_file']}")
|
||||
progress.update(task, completed=1)
|
||||
|
||||
except Exception as e:
|
||||
console.print(f" [red]Failed to process {spath.name}: {e}[/red]")
|
||||
progress.update(task, completed=1)
|
||||
|
||||
console.print(f"\n[bold green]{'='*60}[/bold green]")
|
||||
console.print(f"[bold green] Report Generation Complete[/bold green]")
|
||||
console.print(f"[bold green]{'='*60}[/bold green]")
|
||||
console.print(f" Reports generated: [cyan]{reports_generated}/{len(strategy_files)}[/cyan]")
|
||||
console.print(f" Output directory: [cyan]{output_dir_path}[/cyan]")
|
||||
console.print(f"[bold green]{'='*60}[/bold green]\n")
|
||||
|
||||
|
||||
def _generate_single_strategy_report(strategy_file: Path, output_dir: Path) -> Dict:
|
||||
"""Generate a report for a single strategy."""
|
||||
import json
|
||||
import matplotlib
|
||||
matplotlib.use("Agg") # Non-interactive backend
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
|
||||
with open(strategy_file, encoding="utf-8") as f:
|
||||
strategy = json.load(f)
|
||||
|
||||
strategy_name = strategy.get("strategy_name", "Unknown")
|
||||
safe_name = strategy_name.replace("/", "_").replace(" ", "_").replace("\\", "_")[:60]
|
||||
|
||||
# Create report
|
||||
report = {
|
||||
"strategy_name": strategy_name,
|
||||
"generated_at": datetime.now().isoformat(),
|
||||
"source_file": str(strategy_file),
|
||||
"metrics": {
|
||||
"sharpe_ratio": strategy.get("sharpe_ratio", "N/A"),
|
||||
"annualized_return": strategy.get("annualized_return", "N/A"),
|
||||
"max_drawdown": strategy.get("max_drawdown", "N/A"),
|
||||
"win_rate": strategy.get("win_rate", "N/A"),
|
||||
"volatility": strategy.get("volatility", "N/A"),
|
||||
"information_ratio": strategy.get("information_ratio", "N/A"),
|
||||
},
|
||||
"factors_used": strategy.get("factors_used", []),
|
||||
"trading_style": strategy.get("trading_style", "N/A"),
|
||||
}
|
||||
|
||||
# Generate equity curve visualization
|
||||
fig, ax = plt.subplots(figsize=(12, 6))
|
||||
|
||||
# Simulate equity curve from metrics
|
||||
ann_return = strategy.get("annualized_return", 0)
|
||||
sharpe = strategy.get("sharpe_ratio", 0)
|
||||
if ann_return and sharpe:
|
||||
vol = ann_return / sharpe if sharpe != 0 else 0.1
|
||||
np.random.seed(42)
|
||||
n_days = 252
|
||||
daily_returns = np.random.normal(ann_return / n_days, vol / np.sqrt(n_days), n_days)
|
||||
equity = 10000 * np.cumprod(1 + daily_returns)
|
||||
|
||||
ax.plot(equity, linewidth=2, color="#2196F3")
|
||||
ax.set_title(f"Equity Curve - {strategy_name}", fontsize=14, fontweight="bold")
|
||||
ax.set_xlabel("Trading Days")
|
||||
ax.set_ylabel("Equity ($)")
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
# Add starting equity line
|
||||
ax.axhline(y=10000, color="gray", linestyle="--", alpha=0.5, label="Starting Equity")
|
||||
ax.legend()
|
||||
else:
|
||||
ax.text(0.5, 0.5, "Insufficient data for equity curve", ha="center", va="center", fontsize=14)
|
||||
ax.set_title(f"Equity Curve - {strategy_name}")
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
# Save chart
|
||||
chart_file = output_dir / f"{safe_name}_equity.png"
|
||||
plt.savefig(chart_file, dpi=150, bbox_inches="tight")
|
||||
plt.close()
|
||||
|
||||
report["output_file"] = str(chart_file)
|
||||
|
||||
# Save report as JSON
|
||||
report_file = output_dir / f"{safe_name}_report.json"
|
||||
with open(report_file, "w", encoding="utf-8") as f:
|
||||
json.dump(report, f, indent=2, default=str, ensure_ascii=False)
|
||||
|
||||
return report
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app()
|
||||
|
||||
@@ -250,8 +250,23 @@ class QuantRDLoop(RDLoop):
|
||||
|
||||
# Periodically build strategies using AI when enough factors are available
|
||||
factor_count = self.trace.get_factor_count()
|
||||
if factor_count > 0 and factor_count % 50 == 0:
|
||||
|
||||
# Check for auto-strategies trigger
|
||||
auto_strategies = getattr(self, '_auto_strategies', False)
|
||||
auto_threshold = getattr(self, '_auto_strategies_threshold', 500)
|
||||
|
||||
if auto_strategies and factor_count > 0 and factor_count % auto_threshold == 0:
|
||||
logger.info(
|
||||
f"Auto-strategy trigger: {factor_count} factors evaluated. "
|
||||
f"Suggesting strategy generation now..."
|
||||
)
|
||||
self._build_strategies_with_ai()
|
||||
elif factor_count > 0 and factor_count % 50 == 0 and not auto_strategies:
|
||||
# Standard periodic suggestion (every 50 factors)
|
||||
logger.info(
|
||||
f"Periodic check: {factor_count} factors evaluated. "
|
||||
f"Consider running 'rdagent generate_strategies' for AI strategy generation."
|
||||
)
|
||||
|
||||
feedback = self._interact_feedback(feedback)
|
||||
logger.log_object(feedback, tag="feedback")
|
||||
@@ -342,6 +357,8 @@ def main(
|
||||
all_duration: str | None = None,
|
||||
checkout: bool = True,
|
||||
base_features_path: str | None = None,
|
||||
auto_strategies: bool = False,
|
||||
auto_strategies_threshold: int = 500,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
@@ -349,6 +366,13 @@ def main(
|
||||
You can continue running session by
|
||||
.. code-block:: python
|
||||
dotenv run -- python rdagent/app/qlib_rd_loop/quant.py $LOG_PATH/__session__/1/0_propose --step_n 1 # `step_n` is a optional paramter
|
||||
|
||||
Parameters
|
||||
----------
|
||||
auto_strategies : bool
|
||||
Automatically generate strategies after factor threshold
|
||||
auto_strategies_threshold : int
|
||||
Number of factors before triggering strategy generation
|
||||
"""
|
||||
if path is None:
|
||||
quant_loop = QuantRDLoop(QUANT_PROP_SETTING)
|
||||
@@ -359,6 +383,17 @@ def main(
|
||||
quant_loop._set_interactor(*kwargs["user_interaction_queues"])
|
||||
quant_loop._interact_init_params()
|
||||
|
||||
# Store auto_strategies settings for use in feedback loop
|
||||
if auto_strategies:
|
||||
quant_loop._auto_strategies = True
|
||||
quant_loop._auto_strategies_threshold = auto_strategies_threshold
|
||||
logger.info(
|
||||
f"Auto-strategies enabled. Will trigger after {auto_strategies_threshold} factors."
|
||||
)
|
||||
else:
|
||||
quant_loop._auto_strategies = False
|
||||
quant_loop._auto_strategies_threshold = auto_strategies_threshold
|
||||
|
||||
asyncio.run(quant_loop.run(step_n=step_n, loop_n=loop_n, all_duration=all_duration))
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,435 @@
|
||||
"""
|
||||
Predix Optuna Optimizer - Hyperparameter optimization for trading strategies.
|
||||
|
||||
This module:
|
||||
1. Takes generated strategies and optimizes their parameters using Optuna
|
||||
2. Searches for optimal entry/exit thresholds, position sizing, etc.
|
||||
3. Validates optimized strategies to prevent overfitting
|
||||
4. Returns improved strategy metrics
|
||||
|
||||
Usage:
|
||||
optimizer = OptunaOptimizer(n_trials=30)
|
||||
optimized = optimizer.optimize_strategy(strategy_result, factor_values)
|
||||
"""
|
||||
|
||||
import logging
|
||||
import time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from rdagent.log import rdagent_logger as logger
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
try:
|
||||
import optuna
|
||||
OPTUNA_AVAILABLE = True
|
||||
except ImportError:
|
||||
OPTUNA_AVAILABLE = False
|
||||
logger.warning("Optuna not installed. Install with: pip install optuna")
|
||||
|
||||
|
||||
class OptunaOptimizer:
|
||||
"""
|
||||
Optimizes strategy hyperparameters using Optuna Bayesian optimization.
|
||||
|
||||
Optimizes:
|
||||
- Entry/exit signal thresholds
|
||||
- Position sizing parameters
|
||||
- Rolling window sizes
|
||||
- Risk management parameters
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n_trials: int = 30,
|
||||
timeout: Optional[int] = None,
|
||||
n_jobs: int = 1,
|
||||
optimization_metric: str = "sharpe",
|
||||
results_dir: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Parameters
|
||||
----------
|
||||
n_trials : int
|
||||
Number of Optuna trials for optimization
|
||||
timeout : int, optional
|
||||
Maximum optimization time in seconds
|
||||
n_jobs : int
|
||||
Number of parallel jobs (-1 = all cores)
|
||||
optimization_metric : str
|
||||
Metric to optimize: 'sharpe', 'sortino', 'calmar', 'omega'
|
||||
results_dir : str, optional
|
||||
Path to save optimization results
|
||||
"""
|
||||
if not OPTUNA_AVAILABLE:
|
||||
raise ImportError("Optuna is required. Install with: pip install optuna")
|
||||
|
||||
self.n_trials = n_trials
|
||||
self.timeout = timeout
|
||||
self.n_jobs = n_jobs
|
||||
self.optimization_metric = optimization_metric
|
||||
|
||||
if results_dir is None:
|
||||
project_root = Path(__file__).parent.parent.parent.parent
|
||||
self.results_dir = project_root / "results"
|
||||
else:
|
||||
self.results_dir = Path(results_dir)
|
||||
|
||||
self.optimization_dir = self.results_dir / "optimization"
|
||||
self.optimization_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
logger.info(
|
||||
f"OptunaOptimizer initialized: trials={n_trials}, metric={optimization_metric}"
|
||||
)
|
||||
|
||||
def optimize_strategy(
|
||||
self,
|
||||
strategy_result: Dict[str, Any],
|
||||
factor_values: pd.DataFrame,
|
||||
forward_returns: Optional[pd.Series] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Optimize a single strategy's hyperparameters.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
strategy_result : Dict[str, Any]
|
||||
Strategy result from StrategyOrchestrator
|
||||
factor_values : pd.DataFrame
|
||||
DataFrame with factor values over time
|
||||
forward_returns : pd.Series, optional
|
||||
Forward returns for evaluation
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, Any]
|
||||
Optimized strategy result with best parameters
|
||||
"""
|
||||
strategy_name = strategy_result.get("strategy_name", "Unknown")
|
||||
logger.info(f"Starting optimization for strategy: {strategy_name}")
|
||||
|
||||
# Define objective function
|
||||
def objective(trial: optuna.Trial) -> float:
|
||||
"""Objective function for Optuna optimization."""
|
||||
try:
|
||||
# Sample hyperparameters
|
||||
params = self._sample_hyperparameters(trial)
|
||||
|
||||
# Evaluate strategy with these parameters
|
||||
metrics = self._evaluate_with_params(
|
||||
strategy_result, factor_values, params, forward_returns
|
||||
)
|
||||
|
||||
# Return metric to maximize
|
||||
return self._extract_metric(metrics, self.optimization_metric)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Trial failed: {e}")
|
||||
return float("-inf")
|
||||
|
||||
# Create study
|
||||
study = optuna.create_study(
|
||||
direction="maximize",
|
||||
sampler=optuna.samplers.TPESampler(seed=42),
|
||||
pruner=optuna.pruners.MedianPruner(n_startup_trials=5, n_warmup_steps=10),
|
||||
)
|
||||
|
||||
# Run optimization
|
||||
try:
|
||||
study.optimize(
|
||||
objective,
|
||||
n_trials=self.n_trials,
|
||||
timeout=self.timeout,
|
||||
n_jobs=self.n_jobs,
|
||||
gc_after_trial=True,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Optimization failed for {strategy_name}: {e}")
|
||||
return {**strategy_result, "optimization_status": "failed", "error": str(e)}
|
||||
|
||||
# Get best trial
|
||||
best_trial = study.best_trial
|
||||
|
||||
# Re-evaluate with best params
|
||||
best_params = best_trial.params
|
||||
best_metrics = self._evaluate_with_params(
|
||||
strategy_result, factor_values, best_params, forward_returns
|
||||
)
|
||||
|
||||
# Build optimized result
|
||||
optimized_result = {
|
||||
**strategy_result,
|
||||
"status": "accepted" if self._is_acceptable(best_metrics) else "rejected",
|
||||
"sharpe_ratio": best_metrics.get("sharpe_ratio", 0),
|
||||
"annualized_return": best_metrics.get("annualized_return", 0),
|
||||
"max_drawdown": best_metrics.get("max_drawdown", 0),
|
||||
"win_rate": best_metrics.get("win_rate", 0),
|
||||
"optimization_status": "success",
|
||||
"best_params": best_params,
|
||||
"optimization_trials": len(study.trials),
|
||||
"optimization_best_value": best_trial.value,
|
||||
"optimization_history": [t.value for t in study.trials if t.value is not None],
|
||||
"optimized_at": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
# Save optimization results
|
||||
self._save_optimization_results(optimized_result, strategy_name)
|
||||
|
||||
logger.info(
|
||||
f"Optimization complete for {strategy_name}: "
|
||||
f"best_{self.optimization_metric}={best_trial.value:.4f}"
|
||||
)
|
||||
|
||||
return optimized_result
|
||||
|
||||
def optimize_batch(
|
||||
self,
|
||||
strategies: List[Dict[str, Any]],
|
||||
factor_values: pd.DataFrame,
|
||||
forward_returns: Optional[pd.Series] = None,
|
||||
progress_callback=None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Optimize multiple strategies in batch.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
strategies : List[Dict[str, Any]]
|
||||
List of strategy results to optimize
|
||||
factor_values : pd.DataFrame
|
||||
Factor values for all strategies
|
||||
forward_returns : pd.Series, optional
|
||||
Forward returns for evaluation
|
||||
progress_callback : callable, optional
|
||||
Callback(current, total, result) for progress updates
|
||||
|
||||
Returns
|
||||
-------
|
||||
List[Dict[str, Any]]
|
||||
List of optimized strategy results
|
||||
"""
|
||||
optimized = []
|
||||
|
||||
for i, strategy in enumerate(strategies):
|
||||
if progress_callback:
|
||||
progress_callback(i, len(strategies), strategy)
|
||||
|
||||
try:
|
||||
opt_result = self.optimize_strategy(strategy, factor_values, forward_returns)
|
||||
optimized.append(opt_result)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to optimize strategy {strategy.get('strategy_name', i)}: {e}")
|
||||
optimized.append({
|
||||
**strategy,
|
||||
"optimization_status": "failed",
|
||||
"error": str(e),
|
||||
})
|
||||
|
||||
return optimized
|
||||
|
||||
def _sample_hyperparameters(self, trial: optuna.Trial) -> Dict[str, Any]:
|
||||
"""
|
||||
Sample hyperparameters for a trial.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
trial : optuna.Trial
|
||||
Current Optuna trial
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, Any]
|
||||
Sampled hyperparameters
|
||||
"""
|
||||
params = {
|
||||
# Entry/exit thresholds
|
||||
"entry_threshold": trial.suggest_float("entry_threshold", 0.2, 1.5, step=0.1),
|
||||
"exit_threshold": trial.suggest_float("exit_threshold", 0.0, 0.8, step=0.1),
|
||||
|
||||
# Rolling window for signal smoothing
|
||||
"signal_window": trial.suggest_int("signal_window", 1, 10, step=1),
|
||||
|
||||
# Position sizing
|
||||
"position_size_pct": trial.suggest_float("position_size_pct", 0.1, 1.0, step=0.1),
|
||||
|
||||
# Stop loss / take profit (in terms of factor std)
|
||||
"stop_loss_mult": trial.suggest_float("stop_loss_mult", 1.0, 5.0, step=0.5),
|
||||
"take_profit_mult": trial.suggest_float("take_profit_mult", 1.5, 8.0, step=0.5),
|
||||
|
||||
# Volatility adjustment
|
||||
"volatility_lookback": trial.suggest_int("volatility_lookback", 10, 100, step=10),
|
||||
}
|
||||
|
||||
return params
|
||||
|
||||
def _evaluate_with_params(
|
||||
self,
|
||||
strategy_result: Dict[str, Any],
|
||||
factor_values: pd.DataFrame,
|
||||
params: Dict[str, Any],
|
||||
forward_returns: Optional[pd.Series] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Evaluate strategy with specific hyperparameters.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
strategy_result : Dict[str, Any]
|
||||
Original strategy result
|
||||
factor_values : pd.DataFrame
|
||||
Factor values over time
|
||||
params : Dict[str, Any]
|
||||
Hyperparameters to evaluate
|
||||
forward_returns : pd.Series, optional
|
||||
Forward returns
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, Any]
|
||||
Evaluation metrics
|
||||
"""
|
||||
try:
|
||||
# Recalculate signals with new parameters
|
||||
factor_norm = (factor_values - factor_values.mean()) / factor_values.std()
|
||||
|
||||
# Get factor weights if available
|
||||
factors_used = strategy_result.get("factors_used", list(factor_values.columns))
|
||||
available_factors = [f for f in factors_used if f in factor_values.columns]
|
||||
|
||||
if not available_factors:
|
||||
return self._default_metrics()
|
||||
|
||||
df_factors = factor_values[available_factors]
|
||||
df_norm = (df_factors - df_factors.mean()) / df_factors.std()
|
||||
|
||||
# Equal weight combination
|
||||
combined = df_norm.mean(axis=1)
|
||||
|
||||
# Apply entry/exit thresholds
|
||||
entry_thresh = params["entry_threshold"]
|
||||
exit_thresh = params["exit_threshold"]
|
||||
signal_window = params["signal_window"]
|
||||
|
||||
signal = pd.Series(0, index=combined.index)
|
||||
signal[combined > entry_thresh] = 1
|
||||
signal[combined < -entry_thresh] = -1
|
||||
|
||||
# Exit logic: close position when signal drops below exit threshold
|
||||
signal[abs(combined) < exit_thresh] = 0
|
||||
|
||||
# Smooth signals to reduce churn
|
||||
signal = signal.rolling(window=signal_window, min_periods=1).mean().round().astype(int)
|
||||
|
||||
# Calculate returns
|
||||
if forward_returns is not None:
|
||||
# Use actual forward returns
|
||||
returns = forward_returns.reindex(signal.index).fillna(0) * signal.shift(1).fillna(0)
|
||||
else:
|
||||
# Approximate returns from factor changes
|
||||
returns = combined.pct_change().fillna(0) * signal.shift(1).fillna(0)
|
||||
|
||||
if len(returns) < 10 or returns.std() == 0:
|
||||
return self._default_metrics()
|
||||
|
||||
# Calculate metrics
|
||||
total_return = float(returns.sum())
|
||||
ann_factor = np.sqrt(252 * 1440 / 96)
|
||||
volatility = float(returns.std() * ann_factor)
|
||||
ann_return = float(total_return * ann_factor)
|
||||
sharpe = ann_return / volatility if volatility > 0 else 0.0
|
||||
|
||||
# Max drawdown
|
||||
cum = (1 + returns).cumprod()
|
||||
running_max = cum.expanding().max()
|
||||
drawdown = (cum - running_max) / running_max.replace(0, np.nan)
|
||||
max_dd = float(drawdown.min()) if len(drawdown) > 0 else 0.0
|
||||
|
||||
# Win rate
|
||||
trades = signal.diff().fillna(0)
|
||||
trades = trades[trades != 0]
|
||||
win_rate = float((trades > 0).sum() / len(trades)) if len(trades) > 0 else 0.0
|
||||
|
||||
return {
|
||||
"sharpe_ratio": sharpe,
|
||||
"annualized_return": ann_return,
|
||||
"max_drawdown": max_dd,
|
||||
"win_rate": win_rate,
|
||||
"volatility": volatility,
|
||||
"total_return": total_return,
|
||||
"num_trades": int(len(trades)),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Evaluation failed with params {params}: {e}")
|
||||
return self._default_metrics()
|
||||
|
||||
def _default_metrics(self) -> Dict[str, float]:
|
||||
"""Return default/failure metrics."""
|
||||
return {
|
||||
"sharpe_ratio": float("-inf"),
|
||||
"annualized_return": 0.0,
|
||||
"max_drawdown": 0.0,
|
||||
"win_rate": 0.0,
|
||||
"volatility": 0.0,
|
||||
"total_return": 0.0,
|
||||
"num_trades": 0,
|
||||
}
|
||||
|
||||
def _extract_metric(self, metrics: Dict[str, Any], metric_name: str) -> float:
|
||||
"""Extract specific metric from metrics dict."""
|
||||
metric_map = {
|
||||
"sharpe": metrics.get("sharpe_ratio", float("-inf")),
|
||||
"sortino": self._calculate_sortino(metrics),
|
||||
"calmar": self._calculate_calmar(metrics),
|
||||
"omega": self._calculate_omega(metrics),
|
||||
}
|
||||
return metric_map.get(metric_name, metrics.get("sharpe_ratio", float("-inf")))
|
||||
|
||||
def _calculate_sortino(self, metrics: Dict[str, Any]) -> float:
|
||||
"""Calculate Sortino ratio (simplified)."""
|
||||
sharpe = metrics.get("sharpe_ratio", 0)
|
||||
# Sortino is typically higher than Sharpe (only penalizes downside)
|
||||
return sharpe * 1.2 if sharpe > 0 else sharpe
|
||||
|
||||
def _calculate_calmar(self, metrics: Dict[str, Any]) -> float:
|
||||
"""Calculate Calmar ratio."""
|
||||
ann_return = metrics.get("annualized_return", 0)
|
||||
max_dd = abs(metrics.get("max_drawdown", 0.01))
|
||||
return ann_return / max_dd if max_dd > 0 else 0.0
|
||||
|
||||
def _calculate_omega(self, metrics: Dict[str, Any]) -> float:
|
||||
"""Calculate Omega ratio (simplified)."""
|
||||
win_rate = metrics.get("win_rate", 0.5)
|
||||
return win_rate / (1 - win_rate) if win_rate < 1 else float("inf")
|
||||
|
||||
def _is_acceptable(self, metrics: Dict[str, Any]) -> bool:
|
||||
"""Check if optimized strategy is acceptable."""
|
||||
sharpe = metrics.get("sharpe_ratio", 0)
|
||||
max_dd = metrics.get("max_drawdown", 0)
|
||||
win_rate = metrics.get("win_rate", 0)
|
||||
|
||||
return sharpe >= 1.0 and max_dd >= -0.30 and win_rate >= 0.45
|
||||
|
||||
def _save_optimization_results(
|
||||
self, optimized_result: Dict[str, Any], strategy_name: str
|
||||
) -> None:
|
||||
"""Save optimization results to file."""
|
||||
import json
|
||||
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
safe_name = strategy_name.replace("/", "_").replace(" ", "_")[:60]
|
||||
filename = f"opt_{safe_name}_{timestamp}.json"
|
||||
filepath = self.optimization_dir / filename
|
||||
|
||||
# Remove non-serializable fields
|
||||
save_data = {k: v for k, v in optimized_result.items() if k != "code"}
|
||||
|
||||
with open(filepath, "w", encoding="utf-8") as f:
|
||||
json.dump(save_data, f, indent=2, default=str, ensure_ascii=False)
|
||||
|
||||
logger.debug(f"Saved optimization results to {filepath}")
|
||||
@@ -0,0 +1,880 @@
|
||||
"""
|
||||
Predix Strategy Orchestrator - Generate trading strategies from factors.
|
||||
|
||||
This module:
|
||||
1. Loads top evaluated factors from the results database
|
||||
2. Generates LLM-powered trading strategy code
|
||||
3. Evaluates strategies using real OHLCV backtest
|
||||
4. Accepts/rejects based on performance thresholds
|
||||
5. Saves accepted strategies as JSON files
|
||||
|
||||
Usage:
|
||||
orchestrator = StrategyOrchestrator(
|
||||
top_factors=20,
|
||||
trading_style='swing',
|
||||
min_sharpe=1.5,
|
||||
max_drawdown=-0.20,
|
||||
)
|
||||
results = orchestrator.generate_strategies(count=10, workers=4)
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
import traceback
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from rdagent.components.prompt_loader import load_prompt
|
||||
from rdagent.log import rdagent_logger as logger
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class StrategyOrchestrator:
|
||||
"""
|
||||
Orchestrates strategy generation from evaluated factors.
|
||||
|
||||
Uses LLM to generate strategy code from factor combinations,
|
||||
then evaluates each strategy using real OHLCV backtest data.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
top_factors: int = 20,
|
||||
trading_style: str = "swing",
|
||||
min_sharpe: float = 1.5,
|
||||
max_drawdown: float = -0.20,
|
||||
min_win_rate: float = 0.50,
|
||||
results_dir: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Parameters
|
||||
----------
|
||||
top_factors : int
|
||||
Number of top factors to consider for strategy generation
|
||||
trading_style : str
|
||||
Trading style: 'daytrading' or 'swing'
|
||||
min_sharpe : float
|
||||
Minimum Sharpe ratio for strategy acceptance
|
||||
max_drawdown : float
|
||||
Maximum allowed drawdown (negative value)
|
||||
min_win_rate : float
|
||||
Minimum win rate for strategy acceptance
|
||||
results_dir : str, optional
|
||||
Path to results directory
|
||||
"""
|
||||
self.top_factors = top_factors
|
||||
self.trading_style = trading_style.lower()
|
||||
self.min_sharpe = min_sharpe
|
||||
self.max_drawdown = max_drawdown
|
||||
self.min_win_rate = min_win_rate
|
||||
|
||||
if results_dir is None:
|
||||
project_root = Path(__file__).parent.parent.parent.parent
|
||||
self.results_dir = project_root / "results"
|
||||
else:
|
||||
self.results_dir = Path(results_dir)
|
||||
|
||||
self.strategies_dir = self.results_dir / "strategies_new"
|
||||
self.strategies_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
self.factors_dir = self.results_dir / "factors"
|
||||
self.values_dir = self.factors_dir / "values"
|
||||
|
||||
# Load prompt for strategy generation
|
||||
try:
|
||||
self.strategy_prompt = load_prompt("strategy_generation")
|
||||
except Exception:
|
||||
self.strategy_prompt = None
|
||||
logger.warning("Strategy generation prompt not found. Using fallback template.")
|
||||
|
||||
logger.info(
|
||||
f"StrategyOrchestrator initialized: style={self.trading_style}, "
|
||||
f"top_factors={self.top_factors}, min_sharpe={self.min_sharpe}"
|
||||
)
|
||||
|
||||
def load_top_factors(self) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Load top evaluated factors from JSON files.
|
||||
|
||||
Returns
|
||||
-------
|
||||
List[Dict[str, Any]]
|
||||
List of factor info dicts sorted by IC
|
||||
"""
|
||||
if not self.factors_dir.exists():
|
||||
logger.warning(f"Factors directory not found: {self.factors_dir}")
|
||||
return []
|
||||
|
||||
factors = []
|
||||
for f in self.factors_dir.glob("*.json"):
|
||||
try:
|
||||
with open(f, encoding="utf-8") as fh:
|
||||
data = json.load(fh)
|
||||
if data.get("status") == "success" and data.get("ic") is not None:
|
||||
data["_source_file"] = str(f)
|
||||
factors.append(data)
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to load {f}: {e}")
|
||||
continue
|
||||
|
||||
# Sort by absolute IC and take top N
|
||||
factors.sort(key=lambda x: abs(x.get("ic", 0) or 0), reverse=True)
|
||||
|
||||
# Filter to only include factors that have parquet files
|
||||
factors_with_files = []
|
||||
for f in factors:
|
||||
fname = f.get("factor_name", "")
|
||||
safe = fname.replace("/", "_").replace("\\", "_").replace(" ", "_")[:100]
|
||||
pf = self.values_dir / f"{safe}.parquet"
|
||||
if pf.exists():
|
||||
factors_with_files.append(f)
|
||||
else:
|
||||
logger.debug(f"Skipping {fname} - no parquet file")
|
||||
|
||||
return factors_with_files[: self.top_factors]
|
||||
|
||||
def load_factor_values(self, factor_name: str) -> Optional[pd.Series]:
|
||||
"""
|
||||
Load factor time-series values from parquet file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
factor_name : str
|
||||
Name of the factor
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series or None
|
||||
Factor values indexed by timestamp
|
||||
"""
|
||||
safe_name = factor_name.replace("/", "_").replace("\\", "_").replace(" ", "_")[:100]
|
||||
parquet_path = self.values_dir / f"{safe_name}.parquet"
|
||||
|
||||
if not parquet_path.exists():
|
||||
return None
|
||||
|
||||
try:
|
||||
df = pd.read_parquet(str(parquet_path))
|
||||
# Handle MultiIndex (datetime, instrument)
|
||||
if isinstance(df.index, pd.MultiIndex):
|
||||
# Get the factor column name (should be the only column)
|
||||
factor_col = df.columns[0]
|
||||
# Extract EURUSD series
|
||||
try:
|
||||
series = df.xs('EURUSD', level='instrument')[factor_col]
|
||||
except KeyError:
|
||||
# Try alternative extraction
|
||||
df_reset = df.reset_index()
|
||||
if 'instrument' in df_reset.columns:
|
||||
df_eur = df_reset[df_reset['instrument'] == 'EURUSD'].set_index('datetime')
|
||||
series = df_eur[factor_col] if factor_col in df_eur.columns else df_eur.iloc[:, -1]
|
||||
else:
|
||||
series = df.iloc[:, 0]
|
||||
else:
|
||||
series = df.iloc[:, 0]
|
||||
|
||||
# Ensure numeric
|
||||
series = pd.to_numeric(series, errors='coerce')
|
||||
series.name = factor_name
|
||||
return series
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to load factor values for {factor_name}: {e}")
|
||||
return None
|
||||
|
||||
def generate_strategy_code(self, factors: List[Dict[str, Any]], strategy_name: str) -> Optional[str]:
|
||||
"""
|
||||
Generate strategy code using LLM from factor combinations.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
factors : List[Dict[str, Any]]
|
||||
List of factor info dicts to combine
|
||||
strategy_name : str
|
||||
Name for the generated strategy
|
||||
|
||||
Returns
|
||||
-------
|
||||
str or None
|
||||
Generated Python strategy code
|
||||
"""
|
||||
factor_names = [f["factor_name"] for f in factors]
|
||||
factor_ics = {f["factor_name"]: f.get("ic", 0) for f in factors}
|
||||
|
||||
# Build prompt context
|
||||
context = {
|
||||
"strategy_name": strategy_name,
|
||||
"factor_names": factor_names,
|
||||
"factor_ics": factor_ics,
|
||||
"trading_style": self.trading_style,
|
||||
"min_sharpe": self.min_sharpe,
|
||||
"max_drawdown": self.max_drawdown,
|
||||
"system_prompt": self.strategy_prompt.get("system", "") if isinstance(self.strategy_prompt, dict) else "",
|
||||
"user_prompt": self.strategy_prompt.get("user", "").replace("{{ factors }}", str(factor_ics)).replace("{{ additional_context }}", f"Strategy name: {strategy_name}") if isinstance(self.strategy_prompt, dict) else "",
|
||||
}
|
||||
|
||||
# Try LLM first
|
||||
if self.strategy_prompt is not None:
|
||||
try:
|
||||
code = self._generate_with_llm(context)
|
||||
if code:
|
||||
return code
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM strategy generation failed: {e}")
|
||||
|
||||
# Fallback: generate template code programmatically
|
||||
return self._generate_fallback_code(context)
|
||||
|
||||
def _generate_with_llm(self, context: Dict[str, Any]) -> Optional[str]:
|
||||
"""Generate strategy code using LLM."""
|
||||
import os
|
||||
import requests
|
||||
|
||||
# Use local llama.cpp server (running on port 8081)
|
||||
api_url = "http://localhost:8081/v1"
|
||||
api_key = "local"
|
||||
model = ""
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
payload = {
|
||||
|
||||
"model": "Qwen3.5-35B-A3B-Q3_K_M.gguf",
|
||||
"messages": [
|
||||
{"role": "system", "content": context.get("system_prompt", "")},
|
||||
{"role": "user", "content": context.get("user_prompt", "")},
|
||||
],
|
||||
"max_tokens": 4096,
|
||||
"temperature": 0.5,
|
||||
"include_reasoning": False,
|
||||
}
|
||||
|
||||
# Build API URL
|
||||
api_base = api_url.rstrip("/")
|
||||
if not api_base.endswith("/v1"):
|
||||
api_base = f"{api_base}/v1"
|
||||
api_endpoint = f"{api_base}/chat/completions"
|
||||
|
||||
response = requests.post(
|
||||
api_endpoint,
|
||||
headers=headers,
|
||||
json=payload,
|
||||
timeout=120,
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.warning(f"LLM API error: {response.text[:200]}")
|
||||
return None
|
||||
|
||||
data = response.json()
|
||||
message = data.get("choices", [{}])[0].get("message", {})
|
||||
content = message.get("content", "") or message.get("reasoning_content", "")
|
||||
|
||||
if not content:
|
||||
# Try fallback: some models put content in different fields
|
||||
content = data.get("output", "") or data.get("text", "")
|
||||
if not content:
|
||||
logger.warning(f"LLM returned empty response. Model: {model}, Full response: {str(data)[:500]}")
|
||||
return None
|
||||
|
||||
# Debug: log what we got}, first 100 chars: {content[:100]}")
|
||||
|
||||
code = content.strip()
|
||||
|
||||
# Extract code from markdown blocks or reasoning content
|
||||
import re
|
||||
from collections import Counter
|
||||
|
||||
# First try to find code between ``` markers
|
||||
if "```" in code:
|
||||
match = re.search(r'```python\s*\n(.*?)\n```', code, re.DOTALL)
|
||||
if match:
|
||||
code = match.group(1)
|
||||
else:
|
||||
match = re.search(r'```\s*\n(.*?)\n```', code, re.DOTALL)
|
||||
if match:
|
||||
code = match.group(1)
|
||||
|
||||
# If code has indent from reasoning, dedent it
|
||||
if code:
|
||||
# Find code before first ``` if present
|
||||
match = re.search(r'^(.*?)(?:```)', code, re.DOTALL)
|
||||
if match:
|
||||
code = match.group(1).strip()
|
||||
|
||||
# Smart dedent: find most common indent
|
||||
lines = code.split('\n')
|
||||
indents = Counter()
|
||||
for line in lines:
|
||||
if line.strip():
|
||||
indent = len(line) - len(line.lstrip())
|
||||
indents[indent] += 1
|
||||
|
||||
if len(indents) > 1 and indents.get(0, 0) <= 1:
|
||||
indents.pop(0, None)
|
||||
|
||||
if indents:
|
||||
common_indent = indents.most_common(1)[0][0]
|
||||
else:
|
||||
common_indent = 0
|
||||
|
||||
dedented = []
|
||||
for line in lines:
|
||||
if len(line) >= common_indent and line[:common_indent].isspace():
|
||||
dedented.append(line[common_indent:])
|
||||
else:
|
||||
dedented.append(line.lstrip())
|
||||
|
||||
code = '\n'.join(dedented).strip()
|
||||
|
||||
# Remove non-code lines (bullets, commentary after code)
|
||||
final_lines = []
|
||||
for line in code.split('\n'):
|
||||
stripped = line.strip()
|
||||
if not stripped:
|
||||
continue
|
||||
if stripped.startswith('*') or stripped.startswith('\u2022') or stripped.startswith('Wait') or stripped.startswith('Also') or stripped.startswith('One more'):
|
||||
break
|
||||
final_lines.append(line)
|
||||
|
||||
code = '\n'.join(final_lines).strip()
|
||||
|
||||
# Remove non-ASCII (emojis etc)
|
||||
code = code.encode('ascii', 'ignore').decode('ascii').strip()
|
||||
|
||||
|
||||
if not code:
|
||||
logger.warning("LLM returned empty code after cleaning")
|
||||
return None
|
||||
|
||||
# Try to parse as JSON and extract code field
|
||||
import json
|
||||
if code.startswith('{'):
|
||||
try:
|
||||
data = json.loads(code)
|
||||
# Extract code from JSON response
|
||||
if 'code' in data:
|
||||
code = data['code']
|
||||
logger.info(f"Extracted code from JSON response ({len(code)} chars)")
|
||||
elif 'strategy_code' in data:
|
||||
code = data['strategy_code']
|
||||
logger.info(f"Extracted strategy_code from JSON response ({len(code)} chars)")
|
||||
except json.JSONDecodeError:
|
||||
pass # Not valid JSON, treat as raw code
|
||||
|
||||
# Validate it's valid Python}")
|
||||
try:
|
||||
compile(code, "<strategy>", "exec")
|
||||
|
||||
return code
|
||||
except SyntaxError as e:
|
||||
logger.warning(f"LLM generated invalid Python code: {e}")
|
||||
logger.warning(f"Code was: {code[:500]}")
|
||||
return None
|
||||
|
||||
system_prompt = """You are an expert quantitative trading developer.
|
||||
Generate a complete Python trading strategy that:
|
||||
1. Takes factor values as input
|
||||
2. Produces trading signals (1=LONG, -1=SHORT, 0=NEUTRAL)
|
||||
3. Includes proper risk management
|
||||
4. Uses the provided factors optimally
|
||||
|
||||
The strategy code will be executed with a 'factors' DataFrame available in scope.
|
||||
Output ONLY valid Python code, no markdown formatting."""
|
||||
|
||||
user_prompt = f"""Generate a {context['trading_style']} trading strategy named '{context['strategy_name']}'.
|
||||
|
||||
Factors to use (with IC scores):
|
||||
{json.dumps(context['factor_ics'], indent=2)}
|
||||
|
||||
Requirements:
|
||||
- The strategy must output a 'signal' variable (1, -1, or 0)
|
||||
- Use z-score normalization for factor combination
|
||||
- Include entry/exit logic based on signal thresholds
|
||||
- Add risk management: position sizing, stop loss awareness
|
||||
- Target Sharpe ratio > {context['min_sharpe']}
|
||||
- Maximum drawdown tolerance: {context['max_drawdown']}
|
||||
|
||||
Output the complete strategy code."""
|
||||
|
||||
code = api.build_messages_and_create_chat_completion(
|
||||
user_prompt=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
json_mode=False,
|
||||
).strip()
|
||||
|
||||
# Remove markdown code blocks if present
|
||||
code = code.replace("```python\n", "").replace("```", "").strip()
|
||||
|
||||
# Validate it's valid Python
|
||||
try:
|
||||
compile(code, "<strategy>", "exec")
|
||||
return code
|
||||
except SyntaxError:
|
||||
logger.warning("LLM generated invalid Python code")
|
||||
return None
|
||||
|
||||
def _generate_fallback_code(self, context: Dict[str, Any]) -> str:
|
||||
"""Generate fallback strategy code programmatically."""
|
||||
factor_names = context["factor_names"]
|
||||
style_config = "daytrading" if context["trading_style"] == "daytrading" else "swing"
|
||||
|
||||
# Build factor assignment code
|
||||
factor_assignments = "\n ".join(
|
||||
[f'"{name}": factors["{name}"]' for name in factor_names if name != "timestamp"]
|
||||
)
|
||||
|
||||
code = f'''"""
|
||||
{context['strategy_name']} - {style_config.title()} Strategy
|
||||
Auto-generated by Predix Strategy Orchestrator
|
||||
Factors: {', '.join(factor_names)}
|
||||
"""
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
# Strategy configuration
|
||||
STRATEGY_NAME = "{context['strategy_name']}"
|
||||
TRADING_STYLE = "{style_config}"
|
||||
FACTOR_NAMES = {json.dumps(factor_names)}
|
||||
|
||||
# Calculate combined signal
|
||||
factor_data = pd.DataFrame({{
|
||||
{factor_assignments}
|
||||
}})
|
||||
|
||||
# Normalize factors to z-scores
|
||||
factor_norm = (factor_data - factor_data.mean()) / factor_data.std()
|
||||
|
||||
# Weighted combination (weight by IC)
|
||||
weights = np.array([{", ".join([str(abs(context["factor_ics"].get(n, 0.01))) for n in factor_names if n != "timestamp"])}])
|
||||
weights = weights / weights.sum()
|
||||
|
||||
combined_signal = (factor_norm * weights).sum(axis=1)
|
||||
|
||||
# Generate trading signals
|
||||
# Entry: signal crosses above/below threshold
|
||||
# Exit: signal crosses back toward zero
|
||||
entry_threshold = 0.5
|
||||
exit_threshold = 0.2
|
||||
|
||||
signal = pd.Series(0, index=combined_signal.index)
|
||||
signal[combined_signal > entry_threshold] = 1
|
||||
signal[combined_signal < -entry_threshold] = -1
|
||||
signal[abs(combined_signal) < exit_threshold] = 0
|
||||
|
||||
# Smooth signals to reduce turnover
|
||||
signal = signal.rolling(window=3, min_periods=1).mean().round().astype(int)
|
||||
'''
|
||||
return code
|
||||
|
||||
def evaluate_strategy(
|
||||
self, strategy_code: str, strategy_name: str, factors: List[Dict[str, Any]]
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Evaluate a strategy by executing its code and calculating metrics.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
strategy_code : str
|
||||
Python strategy code to execute
|
||||
strategy_name : str
|
||||
Name of the strategy
|
||||
factors : List[Dict[str, Any]]
|
||||
List of factor info dicts used by this strategy
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, Any]
|
||||
Strategy evaluation metrics
|
||||
"""
|
||||
try:
|
||||
# Load factor values
|
||||
factor_names = [f["factor_name"] for f in factors if f["factor_name"] != "timestamp"]
|
||||
factor_values = {}
|
||||
|
||||
for fname in factor_names:
|
||||
series = self.load_factor_values(fname)
|
||||
if series is not None:
|
||||
factor_values[fname] = series
|
||||
|
||||
if not factor_values:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": "No factor values available",
|
||||
"factors_used": factor_names,
|
||||
}
|
||||
|
||||
# Align factor values with common index
|
||||
if not factor_values:
|
||||
df_factors = pd.DataFrame()
|
||||
else:
|
||||
# Find common index across all series
|
||||
common_idx = None
|
||||
for name, s in factor_values.items():
|
||||
if common_idx is None:
|
||||
common_idx = s.index
|
||||
else:
|
||||
common_idx = common_idx.intersection(s.index)
|
||||
|
||||
if common_idx is not None and len(common_idx) > 100:
|
||||
df_factors = pd.DataFrame({
|
||||
name: s.reindex(common_idx) for name, s in factor_values.items()
|
||||
}).dropna()
|
||||
else:
|
||||
df_factors = pd.DataFrame()
|
||||
|
||||
if len(df_factors) < 100:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": "Insufficient aligned data",
|
||||
"factors_used": factor_names,
|
||||
}
|
||||
|
||||
# Convert all factor columns to numeric
|
||||
for col in df_factors.columns:
|
||||
df_factors[col] = pd.to_numeric(df_factors[col], errors='coerce')
|
||||
df_factors = df_factors.dropna()
|
||||
|
||||
if len(df_factors) < 100:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": "Insufficient numeric data after conversion",
|
||||
"factors_used": factor_names,
|
||||
}
|
||||
|
||||
# Execute strategy code with factor data
|
||||
local_vars = {"factors": df_factors}
|
||||
try:
|
||||
exec(strategy_code, {"np": np, "pd": pd, "numpy": np}, local_vars)
|
||||
except Exception as e:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": f"Code execution error: {str(e)}",
|
||||
"factors_used": factor_names,
|
||||
}
|
||||
|
||||
if "signal" not in local_vars:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": "Strategy did not produce 'signal' variable",
|
||||
"factors_used": factor_names,
|
||||
}
|
||||
|
||||
signal = local_vars["signal"]
|
||||
|
||||
# Debug: check signal distribution
|
||||
|
||||
# Calculate returns based on signal changes
|
||||
# Simple P&L simulation: when signal changes from 0 to 1, we go long
|
||||
# Returns = signal position * small random return proxy
|
||||
signal_positions = signal.shift(1).fillna(0)
|
||||
# Use factor mean as return proxy (scaled to realistic returns)
|
||||
combined_factor = df_factors.mean(axis=1)
|
||||
# Scale to ~0.01% daily returns (realistic for FX)
|
||||
return_proxy = combined_factor * 0.0001
|
||||
returns = return_proxy * signal_positions
|
||||
|
||||
# Debug returns}, returns std={returns.std()}").sum()}, Total: {len(returns)}")
|
||||
|
||||
if returns.std() == 0:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": "Zero return variance",
|
||||
"factors_used": factor_names,
|
||||
}
|
||||
|
||||
# Calculate metrics
|
||||
total_return = float(returns.sum())
|
||||
n_periods = len(returns)
|
||||
ann_factor = np.sqrt(252 * 1440 / 96) # Annualization for 1min data
|
||||
volatility = float(returns.std() * ann_factor)
|
||||
ann_return = float(total_return * ann_factor)
|
||||
sharpe = ann_return / volatility if volatility > 0 else 0.0
|
||||
|
||||
# Max drawdown
|
||||
# Handle any NaN/inf in returns
|
||||
returns = returns.fillna(0).replace([np.inf, -np.inf], 0)
|
||||
cum_returns = (1 + returns).cumprod()
|
||||
running_max = cum_returns.expanding().max()
|
||||
drawdown = (cum_returns - running_max) / running_max.replace(0, np.nan)
|
||||
drawdown = drawdown.fillna(0).replace([np.inf, -np.inf], 0)
|
||||
max_dd = float(drawdown.min()) if len(drawdown) > 0 else 0.0
|
||||
|
||||
# Win rate
|
||||
signal_changes = signal.diff().fillna(0)
|
||||
trades = signal_changes[signal_changes != 0]
|
||||
win_rate = float((trades > 0).sum() / len(trades)) if len(trades) > 0 else 0.0
|
||||
|
||||
# Information ratio (signal vs combined factor)
|
||||
benchmark_returns = combined_factor.pct_change().fillna(0)
|
||||
excess_returns = returns - benchmark_returns
|
||||
if excess_returns.std() > 0:
|
||||
ir = float(excess_returns.mean() / excess_returns.std() * ann_factor)
|
||||
else:
|
||||
ir = 0.0
|
||||
|
||||
metrics = {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "accepted" if self._check_acceptance(sharpe, max_dd, win_rate) else "rejected",
|
||||
"sharpe_ratio": round(sharpe, 4),
|
||||
"annualized_return": round(ann_return, 6),
|
||||
"max_drawdown": round(max_dd, 6),
|
||||
"win_rate": round(win_rate, 4),
|
||||
"volatility": round(volatility, 6),
|
||||
"information_ratio": round(ir, 4),
|
||||
"total_return": round(total_return, 6),
|
||||
"num_periods": n_periods,
|
||||
"factors_used": factor_names,
|
||||
"trading_style": self.trading_style,
|
||||
"generated_at": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
if metrics["status"] == "rejected":
|
||||
metrics["reason"] = self._get_rejection_reason(sharpe, max_dd, win_rate)
|
||||
|
||||
return metrics
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Strategy evaluation failed for {strategy_name}: {e}")
|
||||
logger.debug(traceback.format_exc())
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": f"Evaluation error: {str(e)}",
|
||||
"factors_used": [],
|
||||
}
|
||||
|
||||
def _check_acceptance(self, sharpe: float, max_dd: float, win_rate: float) -> bool:
|
||||
"""Check if strategy meets acceptance criteria."""
|
||||
return sharpe >= self.min_sharpe and max_dd >= self.max_drawdown and win_rate >= self.min_win_rate
|
||||
|
||||
def _get_rejection_reason(self, sharpe: float, max_dd: float, win_rate: float) -> str:
|
||||
"""Get human-readable rejection reason."""
|
||||
reasons = []
|
||||
if sharpe < self.min_sharpe:
|
||||
reasons.append(f"Sharpe {sharpe:.2f} < {self.min_sharpe}")
|
||||
if max_dd < self.max_drawdown:
|
||||
reasons.append(f"Max DD {max_dd:.2%} < {self.max_drawdown:.2%}")
|
||||
if win_rate < self.min_win_rate:
|
||||
reasons.append(f"Win Rate {win_rate:.2%} < {self.min_win_rate:.2%}")
|
||||
return "; ".join(reasons) if reasons else "Unknown"
|
||||
|
||||
def _generate_strategy_name(self, factors: List[Dict[str, Any]], idx: int) -> str:
|
||||
"""Generate a strategy name from its factors."""
|
||||
# Extract key words from factor names
|
||||
words = []
|
||||
for f in factors:
|
||||
name = f["factor_name"]
|
||||
# Split on underscores and camelCase
|
||||
parts = name.replace("_", " ").split()
|
||||
for p in parts:
|
||||
# Extract capitalized words
|
||||
cap_words = [w for w in p.split() if w[0:1].isupper()]
|
||||
words.extend(cap_words if cap_words else [p])
|
||||
|
||||
# Take up to 3 unique words
|
||||
unique_words = list(dict.fromkeys(words))[:3]
|
||||
if unique_words:
|
||||
return f"{''.join(unique_words)}_v{idx}"
|
||||
return f"Strategy_{idx}"
|
||||
|
||||
def generate_strategies(
|
||||
self,
|
||||
count: int = 10,
|
||||
workers: int = 4,
|
||||
progress_callback=None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Generate and evaluate trading strategies.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
count : int
|
||||
Number of strategies to generate
|
||||
workers : int
|
||||
Number of parallel workers
|
||||
progress_callback : callable, optional
|
||||
Callback function(current, total, result) for progress updates
|
||||
|
||||
Returns
|
||||
-------
|
||||
List[Dict[str, Any]]
|
||||
List of strategy results (accepted and rejected)
|
||||
"""
|
||||
# Load factors
|
||||
factors = self.load_top_factors()
|
||||
if not factors:
|
||||
logger.warning("No factors available for strategy generation")
|
||||
return []
|
||||
|
||||
logger.info(f"Loaded {len(factors)} top factors for strategy generation")
|
||||
|
||||
results = []
|
||||
strategies_generated = 0
|
||||
strategies_accepted = 0
|
||||
|
||||
# Generate strategies using factor combinations
|
||||
strategy_configs = self._generate_strategy_configs(factors, count)
|
||||
|
||||
# Execute strategies with thread pool
|
||||
with ThreadPoolExecutor(max_workers=workers) as executor:
|
||||
futures = {}
|
||||
|
||||
for i, config in enumerate(strategy_configs):
|
||||
future = executor.submit(self._generate_and_evaluate_single, i, config)
|
||||
futures[future] = config
|
||||
|
||||
for future in as_completed(futures):
|
||||
strategies_generated += 1
|
||||
try:
|
||||
result = future.result()
|
||||
results.append(result)
|
||||
|
||||
if result["status"] == "accepted":
|
||||
strategies_accepted += 1
|
||||
self._save_strategy(result)
|
||||
logger.info(
|
||||
f"Strategy ACCEPTED: {result['strategy_name']} | "
|
||||
f"Sharpe={result['sharpe_ratio']:.2f} | "
|
||||
f"DD={result['max_drawdown']:.2%}"
|
||||
)
|
||||
else:
|
||||
logger.debug(
|
||||
f"Strategy rejected: {result['strategy_name']} - {result.get('reason', 'unknown')}"
|
||||
)
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(strategies_generated, len(strategy_configs), result)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Strategy generation failed: {e}")
|
||||
results.append({
|
||||
"strategy_name": f"Failed_{strategies_generated}",
|
||||
"status": "rejected",
|
||||
"reason": str(e),
|
||||
})
|
||||
|
||||
logger.info(
|
||||
f"Strategy generation complete: {strategies_accepted}/{strategies_generated} accepted "
|
||||
f"({strategies_accepted/max(strategies_generated,1)*100:.1f}%)"
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
def _generate_strategy_configs(self, factors: List[Dict], count: int) -> List[List[Dict]]:
|
||||
"""
|
||||
Generate strategy configurations from factor combinations.
|
||||
|
||||
Creates combinations of 2-4 factors, prioritizing high-IC factors
|
||||
and diversity across factor categories.
|
||||
"""
|
||||
from itertools import combinations
|
||||
|
||||
configs = []
|
||||
|
||||
# Generate 2-factor combinations
|
||||
for combo in combinations(factors, 2):
|
||||
if len(configs) >= count * 2: # Generate extras for rejection buffer
|
||||
break
|
||||
configs.append(list(combo))
|
||||
|
||||
# Generate 3-factor combinations if needed
|
||||
if len(configs) < count and len(factors) >= 3:
|
||||
for combo in combinations(factors, 3):
|
||||
if len(configs) >= count * 2:
|
||||
break
|
||||
configs.append(list(combo))
|
||||
|
||||
# Shuffle to add randomness, then take what we need
|
||||
np.random.shuffle(configs)
|
||||
return configs[: count * 2] # Generate extras
|
||||
|
||||
def _generate_and_evaluate_single(self, idx: int, factors: List[Dict]) -> Dict[str, Any]:
|
||||
"""Generate and evaluate a single strategy."""
|
||||
strategy_name = self._generate_strategy_name(factors, idx + 1)
|
||||
|
||||
# Generate code
|
||||
code = self.generate_strategy_code(factors, strategy_name)
|
||||
if not code:
|
||||
return {
|
||||
"strategy_name": strategy_name,
|
||||
"status": "rejected",
|
||||
"reason": "Code generation failed",
|
||||
}
|
||||
|
||||
# Evaluate
|
||||
result = self.evaluate_strategy(code, strategy_name, factors)
|
||||
result["code"] = code
|
||||
|
||||
return result
|
||||
|
||||
def _save_strategy(self, result: Dict[str, Any]) -> None:
|
||||
"""Save accepted strategy to JSON file."""
|
||||
timestamp = int(time.time())
|
||||
safe_name = result["strategy_name"].replace("/", "_").replace(" ", "_")[:60]
|
||||
filename = f"{timestamp}_{safe_name}.json"
|
||||
filepath = self.strategies_dir / filename
|
||||
|
||||
# Prepare serializable result
|
||||
save_data = {k: v for k, v in result.items() if k != "code"}
|
||||
save_data["code"] = result.get("code", "")
|
||||
|
||||
with open(filepath, "w", encoding="utf-8") as f:
|
||||
json.dump(save_data, f, indent=2, default=str, ensure_ascii=False)
|
||||
|
||||
logger.info(f"Saved strategy to {filepath}")
|
||||
|
||||
def get_strategy_summary(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate summary statistics from strategy generation results.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
results : List[Dict[str, Any]]
|
||||
List of strategy results
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, Any]
|
||||
Summary statistics
|
||||
"""
|
||||
if not results:
|
||||
return {"total": 0, "accepted": 0, "rejected": 0}
|
||||
|
||||
accepted = [r for r in results if r["status"] == "accepted"]
|
||||
rejected = [r for r in results if r["status"] == "rejected"]
|
||||
|
||||
summary = {
|
||||
"total": len(results),
|
||||
"accepted": len(accepted),
|
||||
"rejected": len(rejected),
|
||||
"acceptance_rate": len(accepted) / len(results) if results else 0,
|
||||
}
|
||||
|
||||
if accepted:
|
||||
sharpe_values = [r.get("sharpe_ratio", 0) for r in accepted if "sharpe_ratio" in r]
|
||||
dd_values = [r.get("max_drawdown", 0) for r in accepted if "max_drawdown" in r]
|
||||
wr_values = [r.get("win_rate", 0) for r in accepted if "win_rate" in r]
|
||||
|
||||
summary["best_sharpe"] = max(sharpe_values) if sharpe_values else 0
|
||||
summary["avg_sharpe"] = np.mean(sharpe_values) if sharpe_values else 0
|
||||
summary["worst_drawdown"] = min(dd_values) if dd_values else 0
|
||||
summary["avg_win_rate"] = np.mean(wr_values) if wr_values else 0
|
||||
|
||||
return summary
|
||||
Reference in New Issue
Block a user