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feat: Strategy Generator working with local LLM (P0-P4)

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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
2026-04-09 12:55:04 +02:00
parent 41d9a9e3b3
commit ab3748fbfe
8 changed files with 2003 additions and 126 deletions
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rdagent/components/coder/optuna_optimizer.py
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"""
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}")
rdagent/components/coder/strategy_orchestrator.py
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"""
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