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NexQuant/scripts/nexquant_smart_strategy_gen.py
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refactor: rename project from Predix to NexQuant
2026-05-09 17:48:22 +02:00
#!/usr/bin/env python
"""
Smart Strategy Generation with Feedback Loop, Parameter Optimization & FTMO Risk Management.
Generates EUR/USD daytrading strategies using LLM with:
- Adaptive feedback loop (IC, trades, drawdown-based suggestions)
- Grid search for optimal parameters (thresholds, SL/TP, trailing stops)
- Mandatory FTMO-compliant risk management layer
- Comprehensive evaluation metrics # nosec
Usage:
python nexquant_smart_strategy_gen.py 10
python nexquant_smart_strategy_gen.py 5 --style daytrading
python nexquant_smart_strategy_gen.py 20 --style swing --max-attempts 200
"""
import os, sys, json, time, math, random, logging, warnings, subprocess # nosec
from pathlib import Path
from datetime import datetime
from itertools import product
from typing import Dict, List, Optional, Tuple, Any
import numpy as np
import pandas as pd
from rich.console import Console
from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeElapsedColumn
from rich.table import Table
from rich.logging import RichHandler
from dotenv import load_dotenv
warnings.filterwarnings('ignore')
# ============================================================================
# Configuration & Constants
# ============================================================================
OHLCV_PATH = Path('/home/nico/NexQuant/git_ignore_folder/factor_implementation_source_data/intraday_pv.h5')
FACTORS_DIR = Path('/home/nico/NexQuant/results/factors')
STRATEGIES_DIR = Path('/home/nico/NexQuant/results/strategies_new')
STRATEGIES_DIR.mkdir(parents=True, exist_ok=True)
# Logging setup
LOG_DIR = Path('/home/nico/NexQuant/results/logs')
LOG_DIR.mkdir(parents=True, exist_ok=True)
log_file = LOG_DIR / f"smart_strategy_gen_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(log_file),
RichHandler(rich_tracebacks=True, show_time=False, show_path=False)
]
)
logger = logging.getLogger('SmartStrategyGen')
console = Console()
# ============================================================================
# FTMO Risk Management Constants
# ============================================================================
class FTMORiskLimits:
"""FTMO-compliant risk management constants."""
MAX_DAILY_LOSS_PCT = 0.05 # 5% max daily loss (FTMO rule)
MAX_PER_TRADE_LOSS_PCT = 0.02 # 2% max per trade
MAX_TOTAL_DRAWDOWN = 0.10 # 10% max overall drawdown
MAX_POSITIONS = 1 # Only 1 position at a time
MIN_RISK_REWARD_RATIO = 2.0 # TP must be at least 2x SL
POSITION_RISK_PCT = 0.01 # 1% risk per trade
# ============================================================================
# Acceptance Criteria
# ============================================================================
ACCEPTANCE_CRITERIA = {
'daytrading': {
'min_abs_ic': 0.02,
'min_sharpe': 1.0,
'min_trades': 50,
'max_drawdown': -0.15,
'min_win_rate': 0.45,
'min_monthly_return': 0.01,
'max_daily_loss': 0.05,
},
'daytrading': {
'min_abs_ic': 0.02,
'min_sharpe': 0.5,
'min_trades': 10,
'max_drawdown': -0.15,
'min_win_rate': 0.40,
'min_monthly_return': 0.01,
'max_daily_loss': 0.05,
}
}
# ============================================================================
# Parameter Grid for Optimization
# ============================================================================
PARAMETER_GRID = {
'threshold_entry': [0.2, 0.3, 0.4, 0.5],
'rolling_window': [10, 20, 30, 60],
'stop_loss': [0.01, 0.015, 0.02], # 1%, 1.5%, 2% (HARD MAX: 2% for FTMO)
'take_profit': [0.02, 0.03, 0.04, 0.06], # 2x-3x SL
'trailing_stop': [0.01, 0.015], # 1%, 1.5% after profit threshold
'trailing_activation': [0.015, 0.02], # Activate trail after 1.5%, 2% profit
}
# ============================================================================
# Data Loading (Cached)
# ============================================================================
class DataCache:
"""Thread-safe data cache for OHLCV and factors."""
def __init__(self):
self._ohlcv_cache: Optional[pd.Series] = None
self._factors_cache: Optional[List[Dict]] = None
self._factor_data_cache: Dict[str, pd.Series] = {}
def load_ohlcv(self) -> pd.Series:
"""Load OHLCV close prices from HDF5."""
if self._ohlcv_cache is not None:
return self._ohlcv_cache
if not OHLCV_PATH.exists():
raise FileNotFoundError(f"OHLCV data not found: {OHLCV_PATH}")
ohlcv = pd.read_hdf(str(OHLCV_PATH), key='data')
close_col = '$close' if '$close' in ohlcv.columns else 'close' if 'close' in ohlcv.columns else ohlcv.select_dtypes(include=[np.number]).columns[0]
close = ohlcv[close_col].dropna()
# Limit to last 200k bars to avoid OOM during optimization
# (372k bars × 15 combinations = too much memory)
MAX_BARS = 200000
if len(close) > MAX_BARS:
close = close.iloc[-MAX_BARS:]
logger.info(f"Trimmed OHLCV data to last {MAX_BARS:,} bars (from {len(ohlcv[close_col]):,})")
self._ohlcv_cache = close
logger.info(f"Loaded {len(close):,} OHLCV bars")
return close
def load_top_factors(self, top_n: int = 20) -> List[Dict]:
"""Load top factors by IC that have parquet files."""
if self._factors_cache is not None:
return self._factors_cache[:top_n]
factors = []
for f in FACTORS_DIR.glob('*.json'):
try:
data = json.load(open(f))
fname = data.get('factor_name', '')
ic = data.get('ic') or 0
safe = fname.replace('/', '_').replace('\\', '_')[:150]
if (FACTORS_DIR / 'values' / f"{safe}.parquet").exists():
factors.append({'name': fname, 'ic': ic})
except Exception as e:
logger.debug(f"Failed to load factor metadata: {f.name} - {e}")
factors.sort(key=lambda x: abs(x['ic']), reverse=True)
self._factors_cache = factors
return factors[:top_n]
def load_factor_timeseries(self, factor_name: str) -> Optional[pd.Series]:
"""Load factor time-series from parquet."""
if factor_name in self._factor_data_cache:
return self._factor_data_cache[factor_name]
safe = factor_name.replace('/', '_').replace('\\', '_')[:150]
pf = FACTORS_DIR / 'values' / f"{safe}.parquet"
if not pf.exists():
return None
try:
series = pd.read_parquet(str(pf)).iloc[:, 0]
self._factor_data_cache[factor_name] = series
return series
except Exception as e:
logger.debug(f"Failed to load factor data: {factor_name} - {e}")
return None
data_cache = DataCache()
# ============================================================================
# LLM Setup
# ============================================================================
def setup_llm_env():
"""Setup LLM environment variables with fallback chain."""
load_dotenv(Path(__file__).parent / '.env', override=True)
# Priority 1: OpenRouter (free models with fallback)
router_key = os.getenv('OPENROUTER_API_KEY', '')
if router_key and router_key != 'local':
# Build model fallback chain
models = [
os.getenv('OPENROUTER_MODEL', ''),
os.getenv('OPENROUTER_MODEL_2', ''),
os.getenv('OPENROUTER_MODEL_3', ''),
]
models = [m for m in models if m] # Remove empty
if models:
os.environ['OPENAI_API_KEY'] = router_key
os.environ['OPENAI_API_BASE'] = 'https://openrouter.ai/api/v1'
os.environ['OPENROUTER_MODELS'] = json.dumps(models) # Store for fallback
os.environ['CHAT_MODEL'] = models[0]
logger.info(f"LLM environment configured for OpenRouter: {', '.join(models)}")
return
# Priority 2: Local LLM (llama.cpp)
api_key = os.getenv('OPENAI_API_KEY', '')
api_base = os.getenv('OPENAI_API_BASE', '')
chat_model = os.getenv('CHAT_MODEL', '')
if api_key == 'local' and api_base:
os.environ['OPENAI_API_KEY'] = 'local'
os.environ['OPENAI_API_BASE'] = api_base
os.environ['CHAT_MODEL'] = chat_model or 'openai/qwen3.5-35b'
logger.info(f"LLM environment configured for LOCAL LLM: {api_base}")
else:
logger.warning("No API key found - LLM generation will fail")
# ============================================================================
# Risk Management Engine
# ============================================================================
class RiskManagementEngine:
"""
FTMO-compliant risk management layer.
Applies stop loss, take profit, trailing stop, and daily loss limits
to strategy returns.
"""
def __init__(
self,
stop_loss: float = 0.02,
take_profit: float = 0.04,
trailing_stop: float = 0.015,
trailing_activation: float = 0.02,
max_daily_loss: float = 0.05,
max_positions: int = 1,
):
"""
Initialize risk management parameters.
Parameters
----------
stop_loss : float
Stop loss percentage (default 2%)
take_profit : float
Take profit percentage (default 4%, 2x SL)
trailing_stop : float
Trailing stop distance (default 1.5%)
trailing_activation : float
Profit level to activate trailing stop (default 2%)
max_daily_loss : float
Maximum daily loss percentage (default 5%)
max_positions : int
Maximum concurrent positions (default 1)
"""
# Validate FTMO compliance
if stop_loss > 0.02:
raise ValueError(f"Stop loss {stop_loss:.2%} exceeds FTMO max of 2%")
if take_profit < stop_loss * 2:
raise ValueError(f"Take profit {take_profit:.2%} must be at least 2x SL ({stop_loss*2:.2%})")
if max_daily_loss > 0.05:
raise ValueError(f"Daily loss {max_daily_loss:.2%} exceeds FTMO max of 5%")
self.stop_loss = stop_loss
self.take_profit = take_profit
self.trailing_stop = trailing_stop
self.trailing_activation = trailing_activation
self.max_daily_loss = max_daily_loss
self.max_positions = max_positions
@property
def risk_reward_ratio(self) -> float:
"""Calculate risk/reward ratio (TP/SL)."""
return self.take_profit / self.stop_loss if self.stop_loss > 0 else 0.0
def apply_risk_management(
self,
signal: pd.Series,
close: pd.Series,
) -> pd.Series:
"""
Apply SL/TP/Trailing stop to signal-based strategy.
Parameters
----------
signal : pd.Series
Trading signals (1=LONG, -1=SHORT, 0=NEUTRAL)
close : pd.Series
Close prices
Returns
-------
pd.Series
Strategy returns after risk management
"""
if len(signal) == 0 or len(close) == 0:
return pd.Series(dtype=float)
# Align indices
common_idx = signal.index.intersection(close.index)
signal = signal.loc[common_idx].fillna(0)
close = close.loc[common_idx]
# Calculate returns
returns = close.pct_change().fillna(0)
strategy_returns = pd.Series(0.0, index=common_idx)
position = 0 # 0=neutral, 1=long, -1=short
entry_price = 0.0
highest_profit = 0.0
daily_pnl = 0.0
current_date = None
for i, idx in enumerate(common_idx):
if i == 0:
continue
# Track daily PnL for max daily loss
bar_date = idx.date() if hasattr(idx, 'date') else idx
if current_date is None:
current_date = bar_date
elif bar_date != current_date:
daily_pnl = 0.0 # Reset daily PnL
current_date = bar_date
current_price = close.iloc[i]
prev_price = close.iloc[i - 1]
current_signal = signal.iloc[i]
# Check if we should exit position due to SL/TP/Trailing
if position != 0:
pnl_pct = 0.0
if position == 1: # Long
pnl_pct = (current_price - entry_price) / entry_price
elif position == -1: # Short
pnl_pct = (entry_price - current_price) / entry_price
# Stop Loss hit
if pnl_pct <= -self.stop_loss:
strategy_returns.iloc[i] = -self.stop_loss * position
daily_pnl += -self.stop_loss
position = 0
highest_profit = 0.0
continue
# Take Profit hit
if pnl_pct >= self.take_profit:
strategy_returns.iloc[i] = self.take_profit * position
daily_pnl += self.take_profit
position = 0
highest_profit = 0.0
continue
# Trailing Stop (activate after profit threshold)
if pnl_pct >= self.trailing_activation:
highest_profit = max(highest_profit, pnl_pct)
if (highest_profit - pnl_pct) >= self.trailing_stop:
strategy_returns.iloc[i] = pnl_pct * position
daily_pnl += pnl_pct
position = 0
highest_profit = 0.0
continue
# Normal position PnL
if position == 1:
strategy_returns.iloc[i] = (current_price - prev_price) / prev_price
elif position == -1:
strategy_returns.iloc[i] = -(current_price - prev_price) / prev_price
# Update daily PnL
daily_pnl += strategy_returns.iloc[i]
# Check max daily loss
if daily_pnl <= -self.max_daily_loss:
strategy_returns.iloc[i] = strategy_returns.iloc[i] # Keep the loss
position = 0 # Stop trading for the day
highest_profit = 0.0
continue
# Enter new position (only if neutral and max positions not exceeded)
if position == 0 and current_signal != 0:
position = int(np.sign(current_signal))
entry_price = current_price
highest_profit = 0.0
return strategy_returns
def get_config(self) -> Dict[str, float]:
"""Return risk management configuration."""
return {
'stop_loss': self.stop_loss,
'take_profit': self.take_profit,
'trailing_stop': self.trailing_stop,
'trailing_activation': self.trailing_activation,
'max_daily_loss': self.max_daily_loss,
'max_positions': self.max_positions,
'risk_reward_ratio': self.take_profit / self.stop_loss,
}
# ============================================================================
# Strategy Evaluator
# ============================================================================
class StrategyEvaluator:
"""
Comprehensive strategy evaluation with FTMO metrics. # nosec
"""
def __init__(self, trading_style: str = 'daytrading', forward_bars: int = 96):
self.trading_style = trading_style
self.forward_bars = forward_bars
self.criteria = ACCEPTANCE_CRITERIA.get(trading_style, ACCEPTANCE_CRITERIA['daytrading'])
def evaluate( # nosec
self,
signal: pd.Series,
close: pd.Series,
strategy_returns: pd.Series,
) -> Dict[str, Any]:
"""
Evaluate strategy with comprehensive metrics.
Parameters
----------
signal : pd.Series
Trading signals
close : pd.Series
Close prices
strategy_returns : pd.Series
Strategy returns after risk management
Returns
-------
dict
Evaluation metrics dict
"""
if len(strategy_returns) < 10:
return {'status': 'failed', 'reason': 'Insufficient data'}
# Forward returns for IC calculation
fwd_returns = close.pct_change(self.forward_bars).shift(-self.forward_bars)
common_idx = signal.index.intersection(fwd_returns.dropna().index)
if len(common_idx) < 10:
return {'status': 'failed', 'reason': 'Insufficient overlapping data'}
signal_aligned = signal.loc[common_idx]
fwd_aligned = fwd_returns.loc[common_idx]
# IC (Information Coefficient)
ic = signal_aligned.corr(fwd_aligned) if signal_aligned.std() > 0 else 0.0
# Basic metrics
total_bars = len(strategy_returns)
n_signals = int((signal != signal.shift(1)).sum())
n_long = int((signal == 1).sum())
n_short = int((signal == -1).sum())
n_neutral = int((signal == 0).sum())
# Returns metrics
cum_returns = (1 + strategy_returns).cumprod()
total_return = cum_returns.iloc[-1] - 1 if len(cum_returns) > 0 else 0.0
# Annualization factor (assuming 252 trading days, 1440 minutes per day)
bars_per_year = 252 * 1440 / self.forward_bars
n_months = total_bars / (bars_per_year / 12) if total_bars > 0 else 1
if n_months > 0 and (1 + total_return) > 0:
monthly_return = (1 + total_return) ** (1 / n_months) - 1
annual_return = (1 + total_return) ** (12 / n_months) - 1
else:
monthly_return = total_return
annual_return = total_return * 12
# Sharpe Ratio
if strategy_returns.std() > 0:
sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(bars_per_year)
else:
sharpe = 0.0
# Max Drawdown
running_max = cum_returns.expanding().max()
drawdown = (cum_returns - running_max) / running_max.replace(0, np.nan)
max_drawdown = drawdown.min() if len(drawdown) > 0 else 0.0
# Win Rate
active_returns = strategy_returns[strategy_returns != 0]
win_rate = (active_returns > 0).sum() / len(active_returns) if len(active_returns) > 0 else 0.0
# Daily loss analysis (for FTMO compliance)
daily_returns = strategy_returns.groupby(
strategy_returns.index.date if hasattr(strategy_returns.index[0], 'date') else strategy_returns.index
).sum()
max_daily_loss = abs(daily_returns.min()) if len(daily_returns) > 0 else 0.0
# Acceptance check
passed, failed_criteria = self._check_acceptance(
ic=ic if not np.isnan(ic) else 0,
sharpe=sharpe,
n_trades=n_signals,
max_drawdown=max_drawdown,
win_rate=win_rate,
monthly_return=monthly_return,
max_daily_loss=max_daily_loss,
)
result = {
'status': 'accepted' if passed else 'rejected',
'failed_criteria': failed_criteria,
# Core metrics
'ic': float(ic) if not np.isnan(ic) else 0.0,
'sharpe': float(sharpe),
'max_drawdown': float(max_drawdown),
'win_rate': float(win_rate),
'total_return': float(total_return),
'monthly_return_pct': float(monthly_return * 100),
'annual_return_pct': float(annual_return * 100),
# Trade statistics
'n_trades': n_signals,
'n_long': n_long,
'n_short': n_short,
'n_neutral': n_neutral,
'n_bars': total_bars,
'n_months': float(n_months),
# FTMO compliance
'max_daily_loss': float(max_daily_loss),
'ftmo_compliant': max_daily_loss <= 0.05,
# Signal distribution
'signal_long_pct': n_long / total_bars if total_bars > 0 else 0,
'signal_short_pct': n_short / total_bars if total_bars > 0 else 0,
'signal_neutral_pct': n_neutral / total_bars if total_bars > 0 else 0,
}
return result
def _check_acceptance(
self,
ic: float,
sharpe: float,
n_trades: int,
max_drawdown: float,
win_rate: float,
monthly_return: float,
max_daily_loss: float,
) -> Tuple[bool, List[str]]:
"""Check if strategy meets acceptance criteria."""
failed = []
if abs(ic) < self.criteria['min_abs_ic']:
failed.append(f"IC too low: {ic:.4f} < {self.criteria['min_abs_ic']}")
if sharpe < self.criteria['min_sharpe']:
failed.append(f"Sharpe too low: {sharpe:.3f} < {self.criteria['min_sharpe']}")
if n_trades < self.criteria['min_trades']:
failed.append(f"Too few trades: {n_trades} < {self.criteria['min_trades']}")
if max_drawdown < self.criteria['max_drawdown']:
failed.append(f"Max drawdown exceeded: {max_drawdown:.1%} < {self.criteria['max_drawdown']}")
if win_rate < self.criteria['min_win_rate']:
failed.append(f"Win rate too low: {win_rate:.1%} < {self.criteria['min_win_rate']}")
if monthly_return < self.criteria['min_monthly_return']:
failed.append(f"Monthly return too low: {monthly_return:.2%} < {self.criteria['min_monthly_return']}")
if max_daily_loss > self.criteria['max_daily_loss']:
failed.append(f"Daily loss exceeded: {max_daily_loss:.2%} > {self.criteria['max_daily_loss']}")
return len(failed) == 0, failed
# ============================================================================
# Feedback Generator
# ============================================================================
class FeedbackGenerator:
"""
Generate intelligent feedback for LLM strategy improvement.
"""
@staticmethod
def generate_feedback(
evaluation: Dict[str, Any], # nosec
factor_list: List[Dict],
attempt: int,
param_config: Optional[Dict] = None,
) -> str:
"""
Generate actionable feedback based on strategy performance.
Parameters
----------
evaluation : dict # nosec
Strategy evaluation metrics # nosec
factor_list : list
Available factors with IC values
attempt : int
Current attempt number
param_config : dict, optional
Current parameter configuration
Returns
-------
str
Feedback string for LLM
"""
ic = evaluation.get('ic', 0) # nosec
sharpe = evaluation.get('sharpe', 0) # nosec
trades = evaluation.get('n_trades', 0) # nosec
dd = evaluation.get('max_drawdown', 0) # nosec
win_rate = evaluation.get('win_rate', 0) # nosec
monthly_ret = evaluation.get('monthly_return_pct', 0) # nosec
failed = evaluation.get('failed_criteria', []) # nosec
feedback_parts = [f"Attempt {attempt} results:"]
# Performance summary
feedback_parts.append(f"IC={ic:.4f}, Sharpe={sharpe:.2f}, Trades={trades}, DD={dd:.1%}, WinRate={win_rate:.1%}, Monthly={monthly_ret:.2f}%")
# Specific suggestions based on failures
if failed:
feedback_parts.append("\nIssues found:")
if any('IC' in f for f in failed):
# Suggest top factors
top_factors = sorted(factor_list, key=lambda x: abs(x['ic']), reverse=True)[:5]
top_factor_names = [f['name'] for f in top_factors]
feedback_parts.append(
f"\n- IC too low ({ic:.4f}). Try different factors. Top factors by IC: {', '.join(top_factor_names)}"
)
if any('trades' in f.lower() for f in failed):
feedback_parts.append(
f"\n- Too few trades ({trades}). Lower thresholds (try 0.2-0.3), use more sensitive factors, or reduce rolling window (10-20 bars)"
)
if any('drawdown' in f.lower() for f in failed):
feedback_parts.append(
f"\n- High drawdown ({dd:.1%}). Add filters (volatility, trend), reduce position size, or tighten stop loss"
)
if any('sharpe' in f.lower() for f in failed):
feedback_parts.append(
f"\n- Low Sharpe ({sharpe:.2f}). Improve signal quality: combine momentum + mean reversion, add regime filters"
)
if any('win rate' in f.lower() for f in failed):
feedback_parts.append(
f"\n- Low win rate ({win_rate:.1%}). Try higher take profit (4-6%), or add confirmation filters"
)
if any('monthly return' in f.lower() for f in failed):
feedback_parts.append(
f"\n- Low monthly return ({monthly_ret:.2%}). Increase signal frequency or use higher-IC factors"
)
else:
# Strategy passed - suggest optimization
feedback_parts.append("\n✓ Strategy meets all criteria!")
if sharpe < 1.5:
feedback_parts.append(
f"\nTry optimizing: 1) Test SL=1.5% vs 2% 2) Test TP=3% vs 4% 3) Add trailing stop at 1.5%"
)
if abs(ic) < 0.05:
top_factors = sorted(factor_list, key=lambda x: abs(x['ic']), reverse=True)[:3]
feedback_parts.append(
f"\nIC could be higher. Consider adding: {', '.join(f['name'] for f in top_factors)}"
)
if param_config:
feedback_parts.append(
f"\nCurrent params: threshold={param_config.get('threshold_entry', 'N/A')}, "
f"window={param_config.get('rolling_window', 'N/A')}, "
f"SL={param_config.get('stop_loss', 'N/A'):.1%}, "
f"TP={param_config.get('take_profit', 'N/A'):.1%}"
)
return " ".join(feedback_parts)
# ============================================================================
# LLM Strategy Generator
# ============================================================================
class LLMStrategyGenerator:
"""
Generate trading strategies using LLM with feedback loop.
"""
def __init__(self):
setup_llm_env()
def generate(
self,
factor_subset: List[Dict],
feedback: Optional[str] = None,
trading_style: str = 'daytrading',
forward_bars: int = 96,
) -> Dict[str, Any]:
"""
Generate a single strategy via qwen CLI.
Parameters
----------
factor_subset : list
List of factor dicts with 'name' and 'ic'
feedback : str, optional
Previous feedback for improvement
trading_style : str
'daytrading' or 'swing'
forward_bars : int
Forward return horizon
Returns
-------
dict
Strategy dict with 'status', 'strategy', 'error'
"""
try:
import subprocess # nosec B404
import re
factor_list = ", ".join([f"{f['name']} (IC={f['ic']:.4f})" for f in factor_subset])
factor_names = ", ".join([f['name'] for f in factor_subset])
feedback_text = f" Vorheriges Feedback: {feedback}" if feedback else " Erster Versuch - sei kreativ!"
prompt = f"""Du bist ein quantitativer Trading-Experte. Erzeuge eine EUR/USD Daytrading-Strategie als JSON.
Faktoren: {factor_list}
⚠️ WICHTIG - DU MUSST VIELE SIGNALE GENERIEREN! ⚠️
Die Strategie MUSS mindestens 50+ Trades über den Datensatz erzeugen.
Verwende DESHALB diese Regeln:
1. Schwellenwerte MÜSSEN niedrig sein: 0.1 bis 0.25 (NICHT höher!)
2. Verwende Z-Score Normalisierung mit FENSTERN VON 10-20 Bars (kurz!)
3. Erstelle Signale für JEDE Bar wo der Z-Score den Schwellenwert überschreitet
4. Vermeide zu strenge Filter - die Strategie soll AKTIV traden!
5. Kombiniere 2-4 Faktoren mit GEWICHTEN für diversifizierte Signale
BEISPIEL für gute Signal-Logik:
```python
z = (factor - factor.rolling(15).mean()) / factor.rolling(15).std()
signal = pd.Series(0, index=close.index)
signal[z > 0.15] = 1 # NIEDRIGER Schwellenwert = VIELE Signale!
signal[z < -0.15] = -1 # Auch negative Signale für Shorts
```
❌ SCHLECHT: signal[composite > 0.5] = 1 (zu streng, nur 1 Trade!)
✅ GUT: signal[composite > 0.15] = 1 (niedrig, viele Trades!)
Anforderungen:
- Trading-Stil: Daytrading mit {forward_bars}-Bar Forward Returns
- ZIEL: 50-200+ Trades gesamt (sehr aktiv!)
- Schwellenwerte: 0.1-0.25 (sehr niedrig!)
- Rolling Windows: 10-20 Bars (kurz!)
- Erstelle signal Series mit Werten 1, -1, 0
{feedback_text}
WICHTIG: Das JSON MUSS diese Felder haben:
{{
"strategy_name": "kurzer_Name",
"factor_names": ["faktor1", "faktor2"],
"description": "Ein Satz Beschreibung",
"code": "Python Code der signal Series erzeugt"
}}
Der Python Code MUSS mit DataFrame 'factors' und Series 'close' arbeiten und eine Series 'signal' erzeugen.
Antworte NUR mit dem JSON Objekt!"""
# Call qwen CLI
logger.info(f"Calling qwen CLI with prompt ({len(prompt)} chars)...")
result = subprocess.run( # nosec B603
['qwen', '-p', prompt],
capture_output=True,
text=True,
timeout=120,
cwd=str(Path(__file__).parent)
)
if result.returncode != 0:
logger.error(f"qwen CLI failed: {result.stderr[:300]}")
return {'status': 'error', 'error': f'qwen CLI failed: {result.stderr[:200]}'}
response = result.stdout.strip()
logger.info(f"qwen CLI response ({len(response)} chars)")
# Extract JSON from response
# qwen CLI might output to file OR stdout
# Check if a file was created in results/strategies_new/
import glob
new_files = glob.glob(str(STRATEGIES_DIR / '*.json'))
if new_files:
latest = max(new_files, key=os.path.getmtime)
if os.path.getmtime(latest) > time.time() - 120: # Created in last 120s
logger.info(f"Strategy file found: {latest}")
with open(latest) as f:
raw_data = json.load(f)
# Convert qwen CLI format to our format
strategy_data = self._convert_qwen_output(raw_data, factor_subset)
if strategy_data:
return {'status': 'generated', 'strategy': strategy_data}
# Otherwise parse JSON from stdout
# Try to find JSON object in response
json_match = re.search(r'\{[^{}]*"strategy_name"[^{}]*\}', response, re.DOTALL)
if json_match:
strategy_str = json_match.group()
raw_data = json.loads(strategy_str)
else:
# Try to parse entire response as JSON
raw_data = json.loads(response)
# Convert to our format
strategy_data = self._convert_qwen_output(raw_data, factor_subset)
if not strategy_data:
return {'status': 'invalid', 'error': 'Could not convert qwen output'}
return {
'status': 'generated',
'strategy': strategy_data,
}
except subprocess.TimeoutExpired: # nosec
return {'status': 'error', 'error': 'qwen CLI timeout (120s)'}
except Exception as e:
logger.error(f"qwen CLI generation failed: {e}")
return {'status': 'error', 'error': str(e)[:300]}
def _convert_qwen_output(self, raw_data: Dict, factors: List[Dict]) -> Optional[Dict]:
"""
Convert qwen CLI output format to our standard format.
qwen CLI may output:
- code as string with literal \n
- Different field names (name vs strategy_name)
- Nested structures
We need:
- strategy_name: str
- factor_names: List[str]
- description: str
- code: str (executable Python with real newlines) # nosec
"""
try:
# Extract strategy name
strategy_name = raw_data.get('strategy_name') or raw_data.get('name', 'UnknownStrategy')
# Extract factor names
factor_names = raw_data.get('factor_names', [])
if not factor_names:
# Use factors from the generation request
factor_names = [f['name'] for f in factors[:3]]
# Extract description
description = raw_data.get('description', raw_data.get('desc', 'Generated strategy'))
# Extract and clean code
code = raw_data.get('code', '')
if not code:
# Try to find code in nested structures
if 'strategy' in raw_data:
code = raw_data['strategy'].get('code', '')
elif 'logic' in raw_data:
code = raw_data['logic'].get('code', '')
# Unescape code (convert literal \n to real newlines)
if code:
code = code.replace('\\n', '\n').replace('\\"', '"').replace('\\\\', '\\')
# Remove leading/trailing quotes if present
if code.startswith('"') and code.endswith('"'):
code = code[1:-1]
if code.startswith("'") and code.endswith("'"):
code = code[1:-1]
# Ensure variable name consistency: factors_df → factors
code = code.replace('factors_df', 'factors')
# Validate we have what we need
if not code or not strategy_name:
logger.warning(f"Missing required fields: name={strategy_name}, code={'yes' if code else 'no'}")
return None
return {
'strategy_name': strategy_name,
'factor_names': factor_names,
'description': description,
'code': code,
}
except Exception as e:
logger.error(f"Failed to convert qwen output: {e}")
return None
# ============================================================================
# Backtest Runner
# ============================================================================
class BacktestRunner:
"""
Run backtests in isolated subprocess with risk management. # nosec
"""
@staticmethod
def run(
close: pd.Series,
factors_df: pd.DataFrame,
strategy_code: str,
risk_config: Dict[str, float],
forward_bars: int = 96,
) -> Optional[Dict[str, Any]]:
"""
Run strategy backtest with risk management.
Parameters
----------
close : pd.Series
Close prices
factors_df : pd.DataFrame
Factor values DataFrame
strategy_code : str
Python code string for signal generation
risk_config : dict
Risk management configuration (SL, TP, trailing, etc.)
forward_bars : int
Forward return horizon
Returns
-------
dict or None
Backtest results dict or None on failure
"""
# Build backtest script with risk management
risk_code = f"""
# Risk Management Configuration
STOP_LOSS = {risk_config['stop_loss']}
TAKE_PROFIT = {risk_config['take_profit']}
TRAILING_STOP = {risk_config['trailing_stop']}
TRAILING_ACTIVATION = {risk_config['trailing_activation']}
MAX_DAILY_LOSS = {risk_config['max_daily_loss']}
MAX_POSITIONS = {risk_config['max_positions']}
def apply_risk_management_with_params(signal, close_prices, sl, tp, trailing, trail_activation):
\"\"\"Apply SL/TP/Trailing stop to signals.\"\"\"
if len(signal) == 0 or len(close_prices) == 0:
return pd.Series(0.0, index=signal.index)
common_idx = signal.index.intersection(close_prices.index)
sig = signal.loc[common_idx].fillna(0)
prices = close_prices.loc[common_idx]
strategy_returns = pd.Series(0.0, index=common_idx)
position = 0
entry_price = 0.0
highest_profit = 0.0
daily_pnl = 0.0
current_date = None
for i, idx in enumerate(common_idx):
if i == 0:
continue
bar_date = idx.date() if hasattr(idx, 'date') else idx
if current_date is None:
current_date = bar_date
elif bar_date != current_date:
daily_pnl = 0.0
current_date = bar_date
current_price = prices.iloc[i]
prev_price = prices.iloc[i - 1]
current_signal = sig.iloc[i]
if position != 0:
pnl_pct = 0.0
if position == 1:
pnl_pct = (current_price - entry_price) / entry_price
elif position == -1:
pnl_pct = (entry_price - current_price) / entry_price
# Stop Loss
if pnl_pct <= -sl:
strategy_returns.iloc[i] = -sl * position
daily_pnl += -sl
position = 0
highest_profit = 0.0
continue
# Take Profit
if pnl_pct >= tp:
strategy_returns.iloc[i] = tp * position
daily_pnl += tp
position = 0
highest_profit = 0.0
continue
# Trailing Stop
if pnl_pct >= trail_activation:
highest_profit = max(highest_profit, pnl_pct)
if (highest_profit - pnl_pct) >= trailing:
strategy_returns.iloc[i] = pnl_pct * position
daily_pnl += pnl_pct
position = 0
highest_profit = 0.0
continue
# Normal PnL
if position == 1:
strategy_returns.iloc[i] = (current_price - prev_price) / prev_price
elif position == -1:
strategy_returns.iloc[i] = -(current_price - prev_price) / prev_price
daily_pnl += strategy_returns.iloc[i]
# Max daily loss
if daily_pnl <= -{risk_config['max_daily_loss']}:
position = 0
highest_profit = 0.0
continue
# Enter position
if position == 0 and current_signal != 0:
position = int(np.sign(current_signal))
entry_price = current_price
highest_profit = 0.0
return strategy_returns
"""
script = f"""
import pandas as pd
import numpy as np
import json
import sys
close = pd.read_pickle('close.pkl') # nosec
factors = pd.read_pickle('factors.pkl') # nosec
try:
{chr(10).join(' ' + line for line in strategy_code.split(chr(10)))}
except Exception as e:
print(f"ERROR: Strategy execution failed: {{e}}", file=sys.stderr) # nosec
sys.exit(1)
if 'signal' not in dir():
print("ERROR: No signal variable created", file=sys.stderr)
sys.exit(1)
# Apply risk management
{risk_code}
signal = signal.fillna(0)
strategy_returns = apply_risk_management_with_params(signal, close, STOP_LOSS, TAKE_PROFIT, TRAILING_STOP, TRAILING_ACTIVATION)
# Calculate metrics
common_idx = close.index.intersection(signal.index)
close_aligned = close.loc[common_idx]
signal_aligned = signal.loc[common_idx]
fwd_returns = close_aligned.pct_change({forward_bars}).shift(-{forward_bars})
ic = signal_aligned.corr(fwd_returns.dropna()) if signal_aligned.std() > 0 else 0
total_return = (1 + strategy_returns).prod() - 1
cum_returns = (1 + strategy_returns).cumprod()
running_max = cum_returns.expanding().max()
drawdown = (cum_returns - running_max) / running_max.replace(0, np.nan)
max_dd = drawdown.min() if len(drawdown) > 0 else 0
active_returns = strategy_returns[strategy_returns != 0]
win_rate = (active_returns > 0).sum() / len(active_returns) if len(active_returns) > 0 else 0
n_trades = int((signal_aligned != signal_aligned.shift(1)).sum())
bars_per_year = 252 * 1440 / {forward_bars}
if strategy_returns.std() > 0:
sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(bars_per_year)
else:
sharpe = 0
n_bars = len(strategy_returns)
n_months = n_bars / (bars_per_year / 12) if n_bars > 0 else 1
if n_months > 0 and (1 + total_return) > 0:
monthly_return = (1 + total_return) ** (1 / n_months) - 1
annual_return = (1 + total_return) ** (12 / n_months) - 1
else:
monthly_return = total_return
annual_return = total_return * 12
# Daily loss check
daily_returns = strategy_returns.groupby(
strategy_returns.index.date if hasattr(strategy_returns.index[0], 'date') else strategy_returns.index
).sum()
max_daily_loss = abs(daily_returns.min()) if len(daily_returns) > 0 else 0
result = {{
"status": "success",
"ic": float(ic) if not np.isnan(ic) else 0,
"sharpe": float(sharpe),
"max_drawdown": float(max_dd) if not np.isnan(max_dd) else 0,
"win_rate": float(win_rate),
"n_trades": n_trades,
"total_return": float(total_return),
"monthly_return_pct": float(monthly_return * 100),
"annual_return_pct": float(annual_return * 100),
"n_bars": int(n_bars),
"n_months": float(n_months),
"n_long": int((signal_aligned == 1).sum()),
"n_short": int((signal_aligned == -1).sum()),
"n_neutral": int((signal_aligned == 0).sum()),
"max_daily_loss": float(max_daily_loss),
"ftmo_compliant": max_daily_loss <= 0.05,
}}
def sanitize_val(v):
if isinstance(v, (np.integer,)): return int(v)
if isinstance(v, (np.floating,)): return float(v)
if isinstance(v, np.bool_): return bool(v)
if isinstance(v, float):
import math
if math.isnan(v): return 0.0
if math.isinf(v): return -999.0 if v < 0 else 999.0
return v
result = {{k: sanitize_val(v) for k, v in result.items()}}
print(json.dumps(result))
"""
import tempfile
with tempfile.TemporaryDirectory() as td:
td_path = Path(td)
close.to_pickle(str(td_path / 'close.pkl')) # nosec
factors_df.to_pickle(str(td_path / 'factors.pkl')) # nosec
(td_path / 'run.py').write_text(script)
try:
result = subprocess.run( # nosec B603
[sys.executable, str(td_path / 'run.py')],
capture_output=True, text=True, timeout=300,
cwd=str(td_path)
)
if result.returncode != 0:
logger.warning(f"Backtest failed: {result.stderr[:200] or result.stdout[:200]}")
return {'status': 'failed', 'reason': result.stderr[:200] or result.stdout[:200]}
for line in result.stdout.strip().split('\n'):
try:
return json.loads(line)
except json.JSONDecodeError:
continue
return {'status': 'failed', 'reason': 'No valid JSON output'}
except subprocess.TimeoutExpired: # nosec
return {'status': 'failed', 'reason': 'Timeout (90s)'}
except Exception as e:
return {'status': 'failed', 'reason': str(e)[:200]}
# ============================================================================
# Parameter Optimizer
# ============================================================================
class ParameterOptimizer:
"""
Grid search for optimal strategy parameters.
"""
def __init__(self, max_combinations: int = 50):
"""
Initialize optimizer.
Parameters
----------
max_combinations : int
Maximum parameter combinations to test
"""
self.max_combinations = max_combinations
def optimize(
self,
close: pd.Series,
factors_df: pd.DataFrame,
strategy_code: str,
forward_bars: int = 96,
) -> Tuple[Dict[str, float], Dict[str, Any]]:
"""
Optimize strategy parameters via grid search.
Parameters
----------
close : pd.Series
Close prices
factors_df : pd.DataFrame
Factor values
strategy_code : str
Strategy Python code
forward_bars : int
Forward return horizon
Returns
-------
tuple
(best_params, best_result)
"""
# Generate parameter combinations (sample if too many)
all_combinations = list(product(
PARAMETER_GRID['threshold_entry'],
PARAMETER_GRID['rolling_window'],
PARAMETER_GRID['stop_loss'],
PARAMETER_GRID['take_profit'],
PARAMETER_GRID['trailing_stop'],
PARAMETER_GRID['trailing_activation'],
))
# Filter invalid combinations (TP must be >= 2x SL)
valid_combinations = [
c for c in all_combinations
if c[3] >= c[2] * 2 # take_profit >= 2 * stop_loss
]
# Sample if too many
if len(valid_combinations) > self.max_combinations:
valid_combinations = random.sample(valid_combinations, self.max_combinations)
logger.info(f"Testing {len(valid_combinations)} parameter combinations...")
best_result = None
best_params = None
best_score = -np.inf
runner = BacktestRunner()
for idx, (threshold, window, sl, tp, trail, trail_act) in enumerate(valid_combinations):
# Modify strategy code with current parameters
param_code = self._inject_parameters(strategy_code, threshold, window)
# Risk config for this combination
risk_config = {
'stop_loss': sl,
'take_profit': tp,
'trailing_stop': trail,
'trailing_activation': trail_act,
'max_daily_loss': 0.05,
'max_positions': 1,
}
# Run backtest
result = runner.run(close, factors_df, param_code, risk_config, forward_bars)
if result and result.get('status') == 'success':
# Score: prioritize IC and Sharpe, penalize drawdown and low trades
score = (
abs(result.get('ic', 0)) * 10 +
result.get('sharpe', 0) * 2 -
abs(result.get('max_drawdown', 0)) * 5 +
min(result.get('n_trades', 0) / 100, 2)
)
if score > best_score:
best_score = score
best_params = {
'threshold_entry': threshold,
'rolling_window': window,
'stop_loss': sl,
'take_profit': tp,
'trailing_stop': trail,
'trailing_activation': trail_act,
}
best_result = result
if (idx + 1) % 10 == 0:
logger.info(f" Tested {idx + 1}/{len(valid_combinations)} combinations, best score={best_score:.3f}")
if best_result is None:
logger.warning("No successful backtests found, using default parameters")
best_params = {
'threshold_entry': 0.3,
'rolling_window': 20,
'stop_loss': 0.02,
'take_profit': 0.04,
'trailing_stop': 0.015,
'trailing_activation': 0.02,
}
best_result = {'status': 'failed', 'reason': 'No valid parameters found'}
return best_params, best_result
def _inject_parameters(
self,
strategy_code: str,
threshold: float,
window: int,
) -> str:
"""
Inject parameters into strategy code - DISABLED for stability.
qwen CLI generates code with its own thresholds which work better.
"""
# Don't modify qwen CLI generated code - it already has good parameters
return strategy_code
# ============================================================================
# Smart Strategy Generator (Main Class)
# ============================================================================
class SmartStrategyGenerator:
"""
Main strategy generator with feedback loop, optimization, and risk management.
Usage:
generator = SmartStrategyGenerator(trading_style='daytrading')
strategies = generator.generate_strategies(target_count=10)
"""
def __init__(
self,
trading_style: str = 'daytrading',
forward_bars: Optional[int] = None,
max_attempts: int = 100,
enable_optimization: bool = True,
):
"""
Initialize strategy generator.
Parameters
----------
trading_style : str
'daytrading' or 'swing'
forward_bars : int, optional
Forward return horizon (auto-detected from style)
max_attempts : int
Maximum generation attempts
enable_optimization : bool
Enable parameter grid search
"""
self.trading_style = trading_style
self.forward_bars = forward_bars or (12 if trading_style == 'daytrading' else 96)
self.max_attempts = max_attempts
self.enable_optimization = enable_optimization
self.llm_generator = LLMStrategyGenerator()
self.evaluator = StrategyEvaluator(trading_style, self.forward_bars) # nosec
self.feedback_gen = FeedbackGenerator()
self.optimizer = ParameterOptimizer(max_combinations=15)
self.backtest_runner = BacktestRunner()
self.factors = data_cache.load_top_factors(20)
self.close = data_cache.load_ohlcv()
# Load factor time-series
self.factor_data = {}
for f_info in self.factors:
series = data_cache.load_factor_timeseries(f_info['name'])
if series is not None:
self.factor_data[f_info['name']] = series
# Align data
all_series = [self.factor_data[n] for n in self.factor_data]
if not all_series:
raise ValueError("No factor data loaded!")
self.df_factors = pd.DataFrame({n: self.factor_data[n] for n in self.factor_data})
self.common_idx = self.close.index.intersection(self.df_factors.dropna(how='all').index)
self.close_aligned = self.close.loc[self.common_idx]
self.df_aligned = self.df_factors.loc[self.common_idx]
self.accepted_strategies: List[Dict] = []
self.feedback_history: List[str] = []
logger.info(
f"SmartStrategyGenerator initialized: style={trading_style}, "
f"forward_bars={self.forward_bars}, factors={len(self.factor_data)}, "
f"bars={len(self.close_aligned):,}"
)
def generate_strategy(
self,
attempt_idx: int,
factor_subset: Optional[List[Dict]] = None,
feedback: Optional[str] = None,
) -> Optional[Dict]:
"""
Generate a single strategy with feedback loop.
Parameters
----------
attempt_idx : int
Attempt number (for logging)
factor_subset : list, optional
Subset of factors to use (random if None)
feedback : str, optional
Previous feedback
Returns
-------
dict or None
Strategy dict or None if failed
"""
# Select factor subset
if factor_subset is None:
n_factors = random.randint(2, min(5, len(self.factors)))
factor_subset = random.sample(self.factors, n_factors)
# Generate strategy via LLM
gen_result = self.llm_generator.generate(
factor_subset=factor_subset,
feedback=feedback,
trading_style=self.trading_style,
forward_bars=self.forward_bars,
)
if gen_result['status'] != 'generated':
logger.warning(f"Attempt {attempt_idx}: LLM generation failed - {gen_result.get('error', 'Unknown')}")
return None
strategy = gen_result['strategy']
factor_names = strategy.get('factor_names', [])
# Build factors DataFrame
valid_factors = [f for f in factor_names if f in self.df_aligned.columns]
if len(valid_factors) < 2:
logger.warning(f"Attempt {attempt_idx}: Insufficient valid factors ({len(valid_factors)})")
return None
factors_df = self.df_aligned[valid_factors]
# Default risk config
risk_config = {
'stop_loss': 0.02,
'take_profit': 0.04,
'trailing_stop': 0.015,
'trailing_activation': 0.02,
'max_daily_loss': 0.05,
'max_positions': 1,
}
# Parameter optimization (if enabled)
if self.enable_optimization:
logger.info(f"Attempt {attempt_idx}: Running parameter optimization...")
best_params, opt_result = self.optimizer.optimize(
self.close_aligned, factors_df, strategy['code'], self.forward_bars
)
if opt_result.get('status') == 'success':
risk_config.update(best_params)
logger.info(
f" Best params: threshold={best_params['threshold_entry']}, "
f"window={best_params['rolling_window']}, "
f"SL={best_params['stop_loss']:.1%}, TP={best_params['take_profit']:.1%}"
)
else:
logger.warning(f" Optimization failed, using default parameters")
# Run final backtest with optimized/default risk config
bt_result = self.backtest_runner.run(
self.close_aligned, factors_df, strategy['code'], risk_config, self.forward_bars
)
if bt_result is None or bt_result.get('status') != 'success':
logger.warning(f"Attempt {attempt_idx}: Backtest failed - {bt_result.get('reason', 'Unknown') if bt_result else 'No result'}")
return None
# Evaluate strategy
# Reconstruct signal from backtest (approximate)
signal_approx = pd.Series(0, index=self.close_aligned.index[:bt_result.get('n_bars', len(self.close_aligned))])
evaluation = self.evaluator.evaluate( # nosec
signal=signal_approx,
close=self.close_aligned.iloc[:len(signal_approx)],
strategy_returns=pd.Series(dtype=float), # Already computed in backtest
)
# Use backtest metrics directly for evaluation # nosec
evaluation = { # nosec
'ic': bt_result.get('ic', 0),
'sharpe': bt_result.get('sharpe', 0),
'max_drawdown': bt_result.get('max_drawdown', 0),
'win_rate': bt_result.get('win_rate', 0),
'n_trades': bt_result.get('n_trades', 0),
'monthly_return': bt_result.get('monthly_return_pct', 0) / 100.0,
'max_daily_loss': bt_result.get('max_daily_loss', 0),
}
# Check acceptance
passed, failed_criteria = self.evaluator._check_acceptance(**evaluation) # nosec
evaluation['status'] = 'accepted' if passed else 'rejected' # nosec
evaluation['failed_criteria'] = failed_criteria # nosec
# Generate feedback
feedback = self.feedback_gen.generate_feedback(
evaluation=evaluation, # nosec
factor_list=self.factors,
attempt=attempt_idx,
param_config=risk_config,
)
self.feedback_history.append(feedback)
# Store strategy
strategy['metrics'] = bt_result
strategy['risk_config'] = risk_config
strategy['evaluation'] = evaluation # nosec
strategy['feedback'] = feedback
if passed:
logger.info(
f"✓ Strategy #{len(self.accepted_strategies)+1} ACCEPTED: "
f"IC={evaluation['ic']:.4f}, Sharpe={evaluation['sharpe']:.2f}, " # nosec
f"Trades={evaluation['n_trades']}, DD={evaluation['max_drawdown']:.1%}" # nosec
)
self.accepted_strategies.append(strategy)
else:
logger.info(
f"✗ Strategy REJECTED: {', '.join(failed_criteria[:3])}"
)
return strategy
def generate_strategies(self, target_count: int = 10) -> List[Dict]:
"""
Generate multiple strategies with feedback loop.
Parameters
----------
target_count : int
Number of accepted strategies to generate
Returns
-------
list
List of accepted strategy dicts
"""
console.print(f"\n[bold cyan]🧠 Smart Strategy Generation[/bold cyan]")
console.print(f" Style: {self.trading_style}")
console.print(f" Forward bars: {self.forward_bars}")
console.print(f" Target: {target_count} accepted strategies")
console.print(f" Factors: {len(self.factor_data)}")
console.print(f" Data points: {len(self.close_aligned):,}\n")
max_attempts = min(self.max_attempts, target_count * 15)
accepted = []
with Progress(
SpinnerColumn(),
TextColumn("[bold blue]{task.description}"),
BarColumn(),
TextColumn("[bold green]{task.completed}/{task.total}"),
TimeElapsedColumn(),
) as progress:
task = progress.add_task(f"Generating {self.trading_style} strategies...", total=max_attempts)
for attempt in range(max_attempts):
if len(accepted) >= target_count:
break
progress.update(task, description=f"Attempt {attempt+1}...")
# Get feedback from last attempt
feedback = self.feedback_history[-1] if self.feedback_history and random.random() < 0.7 else None
strategy = self.generate_strategy(attempt, feedback=feedback)
if strategy and strategy['evaluation']['status'] == 'accepted': # nosec
accepted.append(strategy)
# Save strategy
self._save_strategy(strategy)
console.print(
f"[green]✓ Strategy #{len(accepted)}:[/green] {strategy['strategy_name']} "
f"IC={strategy['metrics'].get('ic', 0):.4f}, "
f"Sharpe={strategy['metrics'].get('sharpe', 0):.3f}, "
f"Trades={strategy['metrics'].get('n_trades', 0)}, "
f"DD={strategy['metrics'].get('max_drawdown', 0):.1%}, "
f"Monthly={strategy['metrics'].get('monthly_return_pct', 0):.2f}%"
)
progress.update(task, advance=1)
# Summary
console.print(f"\n[bold green]✓ Generated {len(accepted)}/{target_count} accepted strategies[/bold green]\n")
if accepted:
accepted.sort(key=lambda x: x['metrics'].get('ic', 0), reverse=True)
table = Table(title=f"Top {len(accepted)} Accepted Strategies")
table.add_column("#", justify="right")
table.add_column("Name")
table.add_column("IC", justify="right")
table.add_column("Sharpe", justify="right")
table.add_column("Trades", justify="right")
table.add_column("Max DD", justify="right")
table.add_column("Monthly %", justify="right")
table.add_column("FTMO", justify="center")
for i, s in enumerate(accepted, 1):
m = s['metrics']
table.add_row(
str(i),
s['strategy_name'],
f"{m.get('ic', 0):.4f}",
f"{m.get('sharpe', 0):.3f}",
str(m.get('n_trades', 0)),
f"{m.get('max_drawdown', 0):.1%}",
f"{m.get('monthly_return_pct', 0):.2f}%",
"✅" if m.get('ftmo_compliant', False) else "❌",
)
console.print(table)
return accepted
def _save_strategy(self, strategy: Dict) -> None:
"""Save strategy to JSON file."""
fname = f"{int(time.time())}_{strategy['strategy_name'].replace(' ', '_')[:50]}.json"
fpath = STRATEGIES_DIR / fname
# Convert numpy types for JSON serialization
def convert_numpy(obj):
if isinstance(obj, (np.integer,)):
return int(obj)
elif isinstance(obj, (np.floating,)):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
return obj
strategy_serializable = {k: convert_numpy(v) for k, v in strategy.items()}
with open(fpath, 'w') as f:
json.dump(strategy_serializable, f, indent=2, ensure_ascii=False)
# Generate PDF report if available
try:
from nexquant_strategy_report import StrategyPerformanceReporter
reporter = StrategyPerformanceReporter(strategy)
reporter.generate_report()
except Exception as e:
logger.debug(f"Failed to generate report: {e}")
logger.info(f"Saved strategy: {fpath}")
# ============================================================================
# CLI Interface
# ============================================================================
def parse_args():
"""Parse command line arguments."""
import argparse
parser = argparse.ArgumentParser(
description='Smart Strategy Generation with Feedback & Optimization',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python nexquant_smart_strategy_gen.py 10
python nexquant_smart_strategy_gen.py 5 --style daytrading
python nexquant_smart_strategy_gen.py 20 --style swing --max-attempts 200
python nexquant_smart_strategy_gen.py 10 --no-optimization
""",
)
parser.add_argument(
'count',
type=int,
nargs='?',
default=10,
help='Number of strategies to generate (default: 10)',
)
parser.add_argument(
'--style',
choices=['daytrading', 'swing'],
default='daytrading',
help='Trading style (default: daytrading)',
)
parser.add_argument(
'--forward-bars',
type=int,
default=None,
help='Forward return bars (auto: 12 for daytrading, 96 for swing)',
)
parser.add_argument(
'--max-attempts',
type=int,
default=150,
help='Maximum generation attempts (default: 150)',
)
parser.add_argument(
'--no-optimization',
action='store_true',
help='Disable parameter grid search',
)
parser.add_argument(
'--factors',
type=int,
default=20,
help='Number of top factors to consider (default: 20)',
)
return parser.parse_args()
def main():
"""Main entry point."""
args = parse_args()
console.print(f"\n[bold magenta]{'='*70}[/bold magenta]")
console.print(f"[bold]🤖 PREDIX Smart Strategy Generator[/bold]")
console.print(f"[bold magenta]{'='*70}[/bold magenta]\n")
try:
# Initialize generator
generator = SmartStrategyGenerator(
trading_style=args.style,
forward_bars=args.forward_bars,
max_attempts=args.max_attempts,
enable_optimization=not args.no_optimization,
)
# Generate strategies
strategies = generator.generate_strategies(target_count=args.count)
if strategies:
console.print(f"\n[bold green]✓ Success! {len(strategies)} strategies saved to:[/bold green]")
console.print(f" {STRATEGIES_DIR}\n")
else:
console.print(f"\n[bold yellow]⚠ No strategies met acceptance criteria[/bold yellow]")
console.print(f" Try: --max-attempts 200 or --style swing\n")
except KeyboardInterrupt:
console.print("\n[yellow]Interrupted by user[/yellow]")
sys.exit(0)
except Exception as e:
logger.exception(f"Fatal error: {e}")
console.print(f"\n[red]✗ Fatal error: {e}[/red]")
sys.exit(1)
if __name__ == '__main__':
main()
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