mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 15:37:44 +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
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||||
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||||
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))
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user