mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
50d1fb47e3
- Gold Swing Scanner: 255 daily strategies, best EMA +2.8% OOS/month - Auto-adapt timeframes for daily data (1d/1w instead of 15min/4h) - Session filter skips for daily data - XAUUSD added to instruments list
932 lines
43 KiB
Python
932 lines
43 KiB
Python
#!/usr/bin/env python3
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"""R&D Loop V2 — Multi-Instrument + Correlation Score + Session/Vola Filter + OOS.
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Changes from V1:
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1. Multi-Instrument: Evaluate on EUR/USD + GBP/USD + BTC/USD
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2. Correlation Score: Reward uncorrelated strategies (Sharpe × (1−corr))
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3. Session Filter: Only trade London session (07:00-16:00 UTC)
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4. Volatility Filter: No trades when ATR < threshold
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5. OOS Split: Report IS/OOS separately (80/20)
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"""
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import json, os, random, sys, time
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from datetime import datetime
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from pathlib import Path
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import numpy as np, pandas as pd
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from numba import jit
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PROJECT = Path(__file__).resolve().parent.parent
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OHLCV_PATH = Path(os.getenv("PREDIX_OHLCV_PATH",
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str(PROJECT / "git_ignore_folder" / "intraday_pv_all.h5")))
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RESULTS_DIR = PROJECT / "results" / "rd_loop"
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STATE_DIR = PROJECT / "git_ignore_folder" / "rd_loop_state"
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INSTRUMENTS = ["EURUSD", "GBPUSD", "BTCUSD", "XAUUSD"]
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LEADER_MAP = {"GBPUSD": "EURUSD"}
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INSTRUMENT_TIMEFRAMES = {
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"XAUUSD": ["1d", "1w"], # Daily data → daily/weekly TFs
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"default": ["5min", "15min", "30min", "1h", "4h"],
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}
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TIMEFRAMES = ["5min", "15min", "30min", "1h", "4h", "1d", "1w"]
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INDICATORS_POOL = ["MACD", "RSI", "BBands", "Donchian", "Stoch", "CCI", "WillR", "ADX", "SAR", "ROC", "MOM", "AROON", "MFI", "SMA", "EMA"]
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STRATEGY_TYPES = ["single", "multi_tf", "multi_role"]
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TREND_TFS = ["30min", "1h", "4h"]
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ENTRY_TFS = ["5min", "15min", "30min"]
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MIN_SHARPE, MIN_TRADES = 0.3, 10
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EXPLORATION_RATE = 0.40
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OOS_SPLIT = 0.2
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# ═══════════════════════════════════════════════════════════════════════════════
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# Numba-accelerated backtest
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# ═══════════════════════════════════════════════════════════════════════════════
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@jit(nopython=True)
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def _backtest_numba(prices, signals, cost=0.000264):
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n = len(prices)
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equity = np.zeros(n, dtype=np.float64)
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equity[0] = 100000.0; peak = 100000.0; max_dd = 0.0
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trade_returns = np.zeros(100000, dtype=np.float64)
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position = 0; entry_price = 0.0; trade_count = 0; wins = 0
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for i in range(1, n):
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px = prices[i]; sg = signals[i]; ps = signals[i-1]
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# Close position on signal reversal or flatten
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if position != 0 and (sg != position or sg == 0 and position != 0):
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if position == 1: ret = (px - entry_price) / entry_price - cost
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else: ret = (entry_price - px) / entry_price - cost
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equity[i] = equity[i-1] * (1.0 + ret)
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if equity[i] > peak: peak = equity[i]
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dd = (peak - equity[i]) / peak
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if dd > max_dd: max_dd = dd
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if trade_count < len(trade_returns):
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trade_returns[trade_count] = ret
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trade_count += 1
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if ret > 0: wins += 1
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position = 0
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else:
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equity[i] = equity[i-1]
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# Open new position
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if position == 0 and sg != 0:
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position = sg; entry_price = px
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# Close final position
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if position != 0:
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fp = prices[-1]
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if position == 1: ret = (fp - entry_price) / entry_price - cost
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else: ret = (entry_price - fp) / entry_price - cost
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equity[-1] = equity[-2] * (1.0 + ret)
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if trade_count < len(trade_returns):
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trade_returns[trade_count] = ret
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trade_count += 1
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if ret > 0: wins += 1
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# Ensure monotonic equity (carry forward zeros)
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for i in range(1, n):
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if equity[i] == 0: equity[i] = equity[i-1]
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total_ret = (equity[-1] - 100000.0) / 100000.0
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if trade_count > 5:
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t = trade_returns[:trade_count]
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mean_ret = np.mean(t); std_ret = np.std(t)
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sharpe = mean_ret / std_ret * np.sqrt(trade_count) if std_ret > 0 else 0.0
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else:
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sharpe = 0.0
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return equity, max_dd, trade_count, wins, total_ret, sharpe, trade_returns[:trade_count]
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# ═══════════════════════════════════════════════════════════════════════════════
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# Signal Construction
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# ═══════════════════════════════════════════════════════════════════════════════
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def build_signal(close, hypothesis):
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"""Build trading signal from hypothesis. Returns (-1,0,1) Series."""
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import talib
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signal = None
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# Adapt timeframes to data frequency
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median_delta = (close.index[1:] - close.index[:-1]).median()
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if median_delta > pd.Timedelta("1h"):
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valid_tfs = ["1d", "1w"]
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tf_map = {"5min": "1d", "15min": "1d", "30min": "1d", "1h": "1d", "4h": "1w"}
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# Remap hypothesis timeframes
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hp = dict(hypothesis)
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if hp.get('type') in ('single', 'multi_tf') and 'timeframe' in hp:
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hp['timeframe'] = tf_map.get(hp.get('timeframe','1h'), '1d')
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if hp.get('type') == 'multi_tf' and 'timeframes' in hp:
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hp['timeframes'] = [tf_map.get(t, '1d') for t in hp['timeframes']]
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hp['timeframes'] = list(set(hp['timeframes'])) # dedup
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if hp.get('type') == 'multi_role':
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hp['trend_tf'] = tf_map.get(hp.get('trend_tf','4h'), '1w')
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hp['entry_tf'] = tf_map.get(hp.get('entry_tf','15min'), '1d')
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hypothesis = hp
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else:
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valid_tfs = ["5min", "15min", "30min", "1h", "4h"]
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if hypothesis['type'] == 'single':
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ind = hypothesis['indicator']; tf = hypothesis['timeframe']
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bars = close.resample(tf).last().dropna()
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sig = _build_indicator_signal(ind, bars, hypothesis['params'])
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signal = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)
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elif hypothesis['type'] == 'multi_tf':
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ind = hypothesis['indicator']
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sigs = {}
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for tf in hypothesis['timeframes']:
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bars = close.resample(tf).last().dropna()
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sig = _build_indicator_signal(ind, bars, hypothesis['params'])
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sigs[tf] = sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)
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port = pd.DataFrame(sigs).dropna()
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vote = port.mean(axis=1)
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signal = pd.Series(0, index=close.index)
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signal[vote > 0.25] = 1; signal[vote < -0.25] = -1
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elif hypothesis['type'] == 'multi_role':
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trend_ind = hypothesis['trend_ind']; entry_ind = hypothesis['entry_ind']
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trend_tf = hypothesis['trend_tf']; entry_tf = hypothesis['entry_tf']
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trend_bars = close.resample(trend_tf).last().dropna()
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trend_sig = _build_indicator_signal(trend_ind, trend_bars, hypothesis['trend_params'])
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trend_sig = trend_sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)
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entry_bars = close.resample(entry_tf).last().dropna()
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entry_sig = _build_indicator_signal(entry_ind, entry_bars, hypothesis['entry_params'])
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entry_sig = entry_sig.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)
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signal = pd.Series(0, index=close.index)
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signal[(trend_sig == 1) & (entry_sig == 1)] = 1
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signal[(trend_sig == -1) & (entry_sig == -1)] = -1
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return signal if signal is not None and signal.nunique() > 1 else None
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def _apply_session_filter(signal, index):
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"""Only trade London session (07:00-16:00 UTC Mon-Fri). Skip for daily data."""
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delta = (index[1:] - index[:-1]).median() if len(index) > 1 else pd.Timedelta("1min")
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if delta > pd.Timedelta("1h"):
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return signal # Skip session filter for daily/weekly data
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hours = index.hour
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days = index.dayofweek
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in_session = (days < 5) & (hours >= 7) & (hours < 16)
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if hasattr(in_session, 'values'):
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in_session = in_session.values
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return (signal * in_session.astype(int)).astype(int).clip(-1, 1)
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def _apply_vola_filter(signal, close, atr_period=14, min_atr_pct=0.0003):
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"""Don't trade when ATR is too low (flat/quiet markets)."""
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tr = pd.DataFrame({
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'hl': close.diff().abs(),
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'hc': (close - close.shift(1)).abs(),
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'lc': (close.shift(1) - close).abs(),
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}).max(axis=1)
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atr = tr.rolling(atr_period).mean()
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atr_pct = atr / close
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too_quiet = atr_pct < min_atr_pct
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return (signal * (~too_quiet).astype(int)).fillna(0).astype(int).clip(-1, 1)
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_NEWS_CACHE = None
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def _load_news_events():
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"""Load high-impact news events from YAML, return dict of currency → DatetimeIndex mask."""
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global _NEWS_CACHE
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if _NEWS_CACHE is not None:
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return _NEWS_CACHE
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import yaml
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news_file = PROJECT / "git_ignore_folder" / "economic_events_full.yaml"
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if not news_file.exists():
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_NEWS_CACHE = {}
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return _NEWS_CACHE
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with open(news_file) as f:
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data = yaml.safe_load(f)
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events = {}
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for evt in data.get('events', []):
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if evt.get('impact') != 'high':
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continue
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dt = pd.Timestamp(evt['datetime'])
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currency = evt.get('currency', 'USD')
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if currency not in events:
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events[currency] = []
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events[currency].append(dt)
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_NEWS_CACHE = events
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return _NEWS_CACHE
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def _apply_news_filter(signal, index, currency, window_min=5):
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"""Block trades during high-impact news events (+/- window_min)."""
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events = _load_news_events()
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if currency not in events and currency[:3] not in events:
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return signal
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key = currency if currency in events else currency[:3]
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timestamps = events.get(key, [])
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if not timestamps:
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return signal
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blocked = np.zeros(len(index), dtype=bool)
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for ts in timestamps:
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start = ts - pd.Timedelta(minutes=window_min)
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end = ts + pd.Timedelta(minutes=window_min)
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mask = (index >= start) & (index <= end)
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blocked |= mask
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return (signal * (~blocked).astype(int)).astype(int).clip(-1, 1)
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def _apply_cross_confirm(signal, close_follower, close_leader, lookback=5, min_pct=0.0003):
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"""Cancel follower signals when leader momentum strongly opposes (>0.03% move)."""
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leader_mom = close_leader.pct_change(lookback)
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leader_mom = leader_mom.reindex(signal.index, method='ffill')
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# Only BLOCK when leader moves strongly opposite to signal
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# Don't require confirmation — just cancel clear contrarian moves
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cancel_long = (signal == 1) & (leader_mom < -min_pct)
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cancel_short = (signal == -1) & (leader_mom > min_pct)
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cancel = cancel_long | cancel_short
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return (signal * (~cancel).astype(int)).fillna(0).astype(int).clip(-1, 1)
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def _build_indicator_signal(name, bars, params):
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"""Build indicator signal using talib + hand-rolled."""
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import talib
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c = bars.values.astype(np.float64)
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if name == 'MACD':
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mc, sc, _ = talib.MACD(c, fastperiod=params.get('fast', 3),
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slowperiod=params.get('slow', 15),
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signalperiod=params.get('sig', 3))
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s = pd.Series(0, index=bars.index); s[mc > sc] = 1; s[mc < sc] = -1
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elif name == 'RSI':
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v = talib.RSI(c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < params.get('oversold', 30)] = 1; s[v > params.get('overbought', 70)] = -1
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elif name == 'BBands':
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up, mi, lo = talib.BBANDS(c, timeperiod=params.get('period', 20),
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nbdevup=params.get('std', 2), nbdevdn=params.get('std', 2))
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s = pd.Series(0, index=bars.index); s[c < lo] = 1; s[c > up] = -1
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elif name == 'Donchian':
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hi = bars.rolling(params.get('period', 20)).max()
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lo = bars.rolling(params.get('period', 20)).min()
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s = pd.Series(0, index=bars.index); s[bars > hi.shift(1)] = 1; s[bars < lo.shift(1)] = -1
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s = s.replace(0, np.nan).ffill(limit=params.get('hold', 1)).fillna(0).astype(int)
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elif name == 'Stoch':
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k, d = talib.STOCH(c, c, c, fastk_period=params.get('fastk', 9),
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slowk_period=params.get('slowk', 3), slowd_period=params.get('slowd', 3))
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s = pd.Series(0, index=bars.index); s[(k > d) & (k < 30)] = 1; s[(k < d) & (k > 70)] = -1
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elif name == 'CCI':
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v = talib.CCI(c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < -100] = 1; s[v > 100] = -1
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elif name == 'WillR':
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v = talib.WILLR(c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < -80] = 1; s[v > -20] = -1
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elif name == 'ADX':
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pdi = talib.PLUS_DI(c, c, c, timeperiod=params.get('period', 14))
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ndi = talib.MINUS_DI(c, c, c, timeperiod=params.get('period', 14))
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adx = talib.ADX(c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index)
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s[(pdi > ndi) & (adx > params.get('threshold', 20))] = 1
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s[(ndi > pdi) & (adx > params.get('threshold', 20))] = -1
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elif name == 'SAR':
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v = talib.SAR(c, c, acceleration=params.get('accel', 0.02), maximum=params.get('max_accel', 0.2))
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s = pd.Series(0, index=bars.index); s[c > v] = 1; s[c < v] = -1
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elif name == 'ROC':
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v = talib.ROC(c, timeperiod=params.get('period', 10))
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s = pd.Series(0, index=bars.index); s[v > params.get('threshold', 0.2)] = 1; s[v < -params.get('threshold', 0.2)] = -1
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elif name == 'MOM':
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v = talib.MOM(c, timeperiod=params.get('period', 10))
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s = pd.Series(0, index=bars.index); s[v > 0] = 1; s[v < 0] = -1
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elif name == 'AROON':
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up, dn = talib.AROON(c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[up > dn] = 1; s[up < dn] = -1
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elif name == 'MFI':
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v = talib.MFI(c, c, c, c, timeperiod=params.get('period', 14))
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s = pd.Series(0, index=bars.index); s[v < 20] = 1; s[v > 80] = -1
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elif name == 'SMA':
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s = pd.Series(0, index=bars.index)
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s[bars.rolling(params.get('fast', 10)).mean() > bars.rolling(params.get('slow', 50)).mean()] = 1
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s[bars.rolling(params.get('fast', 10)).mean() < bars.rolling(params.get('slow', 50)).mean()] = -1
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elif name == 'EMA':
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ef = bars.ewm(span=params.get('fast', 5), adjust=False).mean()
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es = bars.ewm(span=params.get('slow', 26), adjust=False).mean()
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s = pd.Series(0, index=bars.index); s[ef > es] = 1; s[ef < es] = -1
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else:
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s = pd.Series(0, index=bars.index)
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return s.fillna(0).astype(int).clip(-1, 1)
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# ═══════════════════════════════════════════════════════════════════════════════
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# Multi-Instrument Evaluation
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# ═══════════════════════════════════════════════════════════════════════════════
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def evaluate_multi(closes, hypothesis, use_session=True, use_vola=False):
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"""Evaluate strategy on all instruments, return combined metrics + per-instrument."""
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results = {}
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equity_curves = {}
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for inst, close in closes.items():
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signal = build_signal(close, hypothesis)
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if signal is None:
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results[inst] = {"sharpe": 0, "monthly_pct": 0, "n_trades": 0}
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continue
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# Apply filters
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if use_session:
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signal = _apply_session_filter(signal, close.index)
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if use_vola:
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signal = _apply_vola_filter(signal, close)
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# News filter: block trades during high-impact events for this currency
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signal = _apply_news_filter(signal, close.index, inst.replace("USD", "").replace("BTC", "BTC"))
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# Cross-pair confirmation: validate follower with leader momentum
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leader_inst = LEADER_MAP.get(inst)
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if leader_inst and leader_inst in closes:
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signal = _apply_cross_confirm(signal, close, closes[leader_inst])
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if signal.nunique() <= 1:
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results[inst] = {"sharpe": 0, "monthly_pct": 0, "n_trades": 0}
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continue
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# OOS split
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n = len(close)
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is_n = int(n * (1 - OOS_SPLIT))
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close_is = close.iloc[:is_n]; signal_is = signal.iloc[:is_n]
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close_oos = close.iloc[is_n:]; signal_oos = signal.iloc[is_n:]
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# IS backtest
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prices_is = close_is.values.astype(np.float64); sigs_is = signal_is.values.astype(np.int32)
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eq_is, dd_is, tr_is, wins_is, ret_is, sh_is, _ = _backtest_numba(prices_is, sigs_is)
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# OOS backtest
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prices_oos = close_oos.values.astype(np.float64); sigs_oos = signal_oos.values.astype(np.int32)
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eq_oos, dd_oos, tr_oos, wins_oos, ret_oos, sh_oos, _ = _backtest_numba(prices_oos, sigs_oos)
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# Full backtest (for equity curve)
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prices_full = close.values.astype(np.float64); sigs_full = signal.values.astype(np.int32)
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eq_full, dd_full, tr_full, wins_full, ret_full, sh_full, trades_full = _backtest_numba(prices_full, sigs_full)
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n_days = (close.index[-1] - close.index[0]).days
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mon = ((1+ret_full)**(1/(n_days/30.44))-1)*100 if ret_full > -1 else 0
|
||
mon_oos = ((1+ret_oos)**(1/((close_oos.index[-1] - close_oos.index[0]).days/30.44))-1)*100 if ret_oos > -1 else 0
|
||
|
||
results[inst] = {
|
||
"sharpe": float(sh_full), "sharpe_is": float(sh_is), "sharpe_oos": float(sh_oos),
|
||
"monthly_pct": float(mon), "monthly_oos": float(mon_oos),
|
||
"n_trades": int(tr_full), "n_trades_oos": int(tr_oos),
|
||
"win_rate": float(wins_full/tr_full) if tr_full>0 else 0,
|
||
"max_dd": float(-dd_full), "total_return": float(ret_full),
|
||
}
|
||
equity_curves[inst] = eq_full.copy()
|
||
|
||
# Combined metrics (harmonic mean — only good if ALL instruments good)
|
||
valid = [r for r in results.values() if r['sharpe'] > 0]
|
||
if not valid:
|
||
combined = {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0, "n_trades_oos": 0}
|
||
else:
|
||
combined = {
|
||
"sharpe": float(np.mean([r['sharpe'] for r in valid])),
|
||
"monthly_pct": float(np.mean([r['monthly_pct'] for r in valid])),
|
||
"monthly_oos": float(np.mean([r['monthly_oos'] for r in valid])),
|
||
"n_trades": int(np.sum([r['n_trades'] for r in valid])),
|
||
"n_trades_oos": int(np.sum([r['n_trades_oos'] for r in valid])),
|
||
}
|
||
combined['per_instrument'] = results
|
||
combined['equity_curves'] = equity_curves
|
||
|
||
return combined
|
||
|
||
|
||
def correlation_penalty(result, sota_equity_curves):
|
||
"""Compute avg correlation of this strategy's returns with SOTA returns."""
|
||
if not sota_equity_curves:
|
||
return 0.0
|
||
my_returns = []
|
||
for eq in result.get('equity_curves', {}).values():
|
||
if len(eq) > 1:
|
||
my_returns.append(np.diff(eq) / eq[:-1])
|
||
if not my_returns:
|
||
return 0.5
|
||
# Use longest equity curve for this strategy
|
||
my_ret = max(my_returns, key=len)
|
||
|
||
correlations = []
|
||
for sota_eq_dict in sota_equity_curves:
|
||
for eq in sota_eq_dict.values():
|
||
if len(eq) > 1:
|
||
sota_ret = np.diff(eq) / eq[:-1]
|
||
# Align to shorter length
|
||
min_len = min(len(my_ret), len(sota_ret))
|
||
if min_len > 10:
|
||
corr = np.corrcoef(my_ret[:min_len], sota_ret[:min_len])[0, 1]
|
||
if not np.isnan(corr):
|
||
correlations.append(corr)
|
||
|
||
return np.mean(correlations) if correlations else 0.0
|
||
|
||
|
||
def composite_score(result, sota_equity_curves):
|
||
"""Composite score = sharpe × (1 - correlation) → rewards uncorrelated profit."""
|
||
sh = result.get('sharpe', 0)
|
||
if sh <= 0:
|
||
return 0
|
||
corr = abs(correlation_penalty(result, sota_equity_curves))
|
||
# Bonus for OOS consistency
|
||
oos_ratio = min(result.get('monthly_oos', 0) / max(result.get('monthly_pct', 1), 0.01), 1.0)
|
||
oos_ratio = max(oos_ratio, 0)
|
||
return sh * (1 - 0.5 * corr) * (0.3 + 0.7 * oos_ratio)
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
# Hypothesis Generation
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
|
||
class ResearchLoop:
|
||
"""Multi-instrument R&D loop with correlation-aware feedback."""
|
||
|
||
def __init__(self, closes):
|
||
self.closes = closes # {instrument: close_series}
|
||
self.sota = [] # State-of-the-art strategies (sorted by composite score)
|
||
self.sota_equity = [] # Equity curves for correlation calc
|
||
self.history = []
|
||
self.iteration = 0
|
||
self.best_score = 0
|
||
self.best_sharpe = 0
|
||
self.exploration_rate = EXPLORATION_RATE
|
||
|
||
def hypothesize(self):
|
||
self.iteration += 1
|
||
|
||
# Every 2000: ML (higher priority, runs before Optuna)
|
||
if self.iteration % 2000 == 0 and len(self.sota) >= 5:
|
||
return {'type': 'ml', 'generation': 'ml',
|
||
'description': f"ML: LightGBM on {len(self.sota)} strategies",
|
||
'sota': self.sota[:5]}
|
||
|
||
# Every 500: Optuna optimize best strategy
|
||
if self.iteration % 500 == 0 and self.sota:
|
||
hp = dict(self.sota[0]['hypothesis'])
|
||
hp['generation'] = 'optuna'
|
||
hp['description'] = f"Optuna: {hp.get('description','?')}"
|
||
return hp
|
||
|
||
# Every 100: force non-dominant indicator
|
||
if self.iteration % 100 == 0 and len(self.sota) >= 5:
|
||
top = self._top_indicator()
|
||
hp = self._random_hypothesis()
|
||
hp = self._force_different_indicator(hp, top)
|
||
hp['generation'] = 'explore'
|
||
return hp
|
||
|
||
# Adaptive exploration rate
|
||
effective_rate = self.exploration_rate
|
||
if len(self.sota) >= 10:
|
||
top = self._top_indicator()
|
||
dominated = sum(1 for r in self.sota if
|
||
r['hypothesis'].get('trend_ind', r['hypothesis'].get('indicator')) == top)
|
||
if dominated > len(self.sota) * 0.8:
|
||
effective_rate += 0.25
|
||
|
||
if random.random() < effective_rate or not self.sota:
|
||
return self._random_hypothesis()
|
||
else:
|
||
base = random.choice(self.sota[:5])
|
||
return self._mutate_hypothesis(base['hypothesis'])
|
||
|
||
def _top_indicator(self):
|
||
if not self.sota:
|
||
return 'MACD'
|
||
return self.sota[0]['hypothesis'].get('trend_ind',
|
||
self.sota[0]['hypothesis'].get('indicator', 'MACD'))
|
||
|
||
def _force_different_indicator(self, hp, top_ind):
|
||
if hp.get('type') == 'multi_role':
|
||
if hp['trend_ind'] == top_ind and hp['entry_ind'] == top_ind:
|
||
if random.random() < 0.5:
|
||
hp['trend_ind'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
|
||
hp['trend_params'] = self._random_params(hp['trend_ind'])
|
||
else:
|
||
hp['entry_ind'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
|
||
hp['entry_params'] = self._random_params(hp['entry_ind'])
|
||
elif hp.get('indicator') == top_ind:
|
||
hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != top_ind])
|
||
hp['params'] = self._random_params(hp['indicator'])
|
||
hp['description'] = self._make_desc(hp)
|
||
return hp
|
||
|
||
def _make_desc(self, hp):
|
||
t = hp.get('type', '?')
|
||
if t == 'multi_role':
|
||
return f"{hp['trend_ind']}({hp['trend_tf']})→{hp['entry_ind']}({hp['entry_tf']})"
|
||
elif t == 'multi_tf':
|
||
return f"{hp.get('indicator','?')} on {','.join(hp.get('timeframes',[])[:2])}"
|
||
else:
|
||
return f"{hp.get('indicator','?')} on {hp.get('timeframe','?')}"
|
||
|
||
def _random_hypothesis(self):
|
||
stype = random.choice(STRATEGY_TYPES)
|
||
if stype == 'single':
|
||
ind = random.choice(INDICATORS_POOL); tf = random.choice(TIMEFRAMES)
|
||
return {'type': 'single', 'indicator': ind, 'timeframe': tf,
|
||
'params': self._random_params(ind),
|
||
'description': f"{ind} on {tf}", 'generation': 'explore'}
|
||
elif stype == 'multi_tf':
|
||
ind = random.choice(INDICATORS_POOL)
|
||
tfs = random.sample(TIMEFRAMES, k=random.randint(2, 4))
|
||
return {'type': 'multi_tf', 'indicator': ind, 'timeframes': tfs,
|
||
'params': self._random_params(ind),
|
||
'description': f"{ind} on {','.join(tfs)}", 'generation': 'explore'}
|
||
else: # multi_role
|
||
trend_ind = random.choice(INDICATORS_POOL)
|
||
entry_ind = random.choice(INDICATORS_POOL)
|
||
trend_tf = random.choice(TREND_TFS)
|
||
entry_tf = random.choice([t for t in ENTRY_TFS if t < trend_tf])
|
||
return {'type': 'multi_role',
|
||
'trend_ind': trend_ind, 'trend_params': self._random_params(trend_ind),
|
||
'trend_tf': trend_tf,
|
||
'entry_ind': entry_ind, 'entry_params': self._random_params(entry_ind),
|
||
'entry_tf': entry_tf,
|
||
'description': f"{trend_ind}({trend_tf})→{entry_ind}({entry_tf})",
|
||
'generation': 'explore'}
|
||
|
||
def _mutate_hypothesis(self, base):
|
||
hp = dict(base); hp['generation'] = 'exploit'
|
||
|
||
if hp.get('type') == 'multi_role':
|
||
mut = random.choice(['trend_ind', 'entry_ind', 'trend_tf', 'entry_tf',
|
||
'trend_params', 'entry_params'])
|
||
if mut == 'trend_ind':
|
||
hp['trend_ind'] = random.choice([i for i in INDICATORS_POOL if i != hp['trend_ind']])
|
||
hp['trend_params'] = self._random_params(hp['trend_ind'])
|
||
elif mut == 'entry_ind':
|
||
hp['entry_ind'] = random.choice([i for i in INDICATORS_POOL if i != hp['entry_ind']])
|
||
hp['entry_params'] = self._random_params(hp['entry_ind'])
|
||
elif mut == 'trend_tf':
|
||
hp['trend_tf'] = random.choice(TREND_TFS)
|
||
if hp['trend_tf'] <= hp['entry_tf']:
|
||
hp['entry_tf'] = random.choice([t for t in ENTRY_TFS if t < hp['trend_tf']])
|
||
elif mut == 'entry_tf':
|
||
hp['entry_tf'] = random.choice([t for t in ENTRY_TFS if t < hp['trend_tf']])
|
||
elif mut == 'trend_params':
|
||
p = dict(hp['trend_params']); k = random.choice(list(p.keys()))
|
||
if isinstance(p[k], (int, float)): p[k] = p[k] * random.uniform(0.5, 1.5)
|
||
hp['trend_params'] = p
|
||
elif mut == 'entry_params':
|
||
p = dict(hp['entry_params']); k = random.choice(list(p.keys()))
|
||
if isinstance(p[k], (int, float)): p[k] = p[k] * random.uniform(0.5, 1.5)
|
||
hp['entry_params'] = p
|
||
hp['description'] = f"{hp['trend_ind']}({hp['trend_tf']})→{hp['entry_ind']}({hp['entry_tf']})"
|
||
return hp
|
||
|
||
mutations = ['params', 'indicator', 'timeframe']
|
||
mutation = random.choice(mutations)
|
||
|
||
if mutation == 'params' and 'params' in hp:
|
||
params = dict(hp['params']); key = random.choice(list(params.keys()))
|
||
if isinstance(params[key], (int, float)):
|
||
params[key] = params[key] * random.uniform(0.5, 1.5)
|
||
if isinstance(params[key], float): params[key] = round(params[key], 1)
|
||
hp['params'] = params
|
||
hp['description'] = f"{hp.get('indicator','?')} (mutated {key})"
|
||
elif mutation == 'indicator' and 'indicator' in hp:
|
||
hp['indicator'] = random.choice([i for i in INDICATORS_POOL if i != hp.get('indicator')])
|
||
hp['params'] = self._random_params(hp['indicator'])
|
||
hp['description'] = f"{hp['indicator']} (replaced)"
|
||
elif mutation == 'timeframe':
|
||
if 'timeframe' in hp:
|
||
hp['timeframe'] = random.choice(TIMEFRAMES)
|
||
elif 'timeframes' in hp:
|
||
hp['timeframes'] = random.sample(TIMEFRAMES, k=len(hp['timeframes']))
|
||
hp['description'] = f"{hp.get('indicator','?')} (timeframe change)"
|
||
return hp
|
||
|
||
def _random_params(self, indicator):
|
||
param_sets = {
|
||
'MACD': {'fast': random.choice([3,5,8,12]), 'slow': random.choice([10,15,20,26]), 'sig': random.choice([3,5,9])},
|
||
'RSI': {'period': random.choice([7,14,21]), 'oversold': random.choice([20,25,30]), 'overbought': random.choice([70,75,80])},
|
||
'BBands': {'period': random.choice([10,20,40]), 'std': random.choice([1.5,2.0,2.5])},
|
||
'Donchian': {'period': random.choice([5,10,20,30,50]), 'hold': random.choice([1,2,3,5])},
|
||
'Stoch': {'fastk': random.choice([5,9,14]), 'slowk': 3, 'slowd': random.choice([3,5])},
|
||
'CCI': {'period': random.choice([14,20,50])},
|
||
'WillR': {'period': random.choice([7,14,21])},
|
||
'ADX': {'period': random.choice([7,14,21]), 'threshold': random.choice([15,20,25])},
|
||
'SAR': {'accel': random.choice([0.02,0.05,0.08]), 'max_accel': random.choice([0.2,0.3,0.5])},
|
||
'ROC': {'period': random.choice([5,10,20]), 'threshold': random.choice([0.1,0.2,0.5])},
|
||
'MOM': {'period': random.choice([5,10,20,50])},
|
||
'AROON': {'period': random.choice([7,14,21])},
|
||
'MFI': {'period': random.choice([7,14,21])},
|
||
'SMA': {'fast': random.choice([5,10,20,50]), 'slow': random.choice([20,50,100,200])},
|
||
'EMA': {'fast': random.choice([3,5,8,12]), 'slow': random.choice([15,26,50,100])},
|
||
}
|
||
return param_sets.get(indicator, {'period': 14})
|
||
|
||
def feedback(self, result):
|
||
"""Update SOTA sorted by COMPOSITE score (not just Sharpe)."""
|
||
if result['sharpe'] <= MIN_SHARPE or result['n_trades'] < MIN_TRADES:
|
||
return False
|
||
|
||
score = composite_score(result, self.sota_equity)
|
||
result['composite_score'] = float(score)
|
||
|
||
# Check if this strategy is diverse enough to add
|
||
is_diverse = True
|
||
if self.sota:
|
||
# Skip if very similar to existing (same indicators, TF, type)
|
||
for existing in self.sota[:3]:
|
||
if self._similar(result, existing):
|
||
is_diverse = False
|
||
break
|
||
|
||
if is_diverse:
|
||
self.sota.append(result)
|
||
self.sota.sort(key=lambda r: r.get('composite_score', 0), reverse=True)
|
||
self.sota = self.sota[:30] # Keep top 30
|
||
self.sota_equity = [s['equity_curves'] for s in self.sota]
|
||
if score > self.best_score:
|
||
self.best_score = score
|
||
return True # NEW BEST
|
||
|
||
if result['sharpe'] > self.best_sharpe:
|
||
self.best_sharpe = result['sharpe']
|
||
|
||
return False
|
||
|
||
def _similar(self, a, b):
|
||
"""Check if two strategies are too similar (same indicator combo, type, TFs)."""
|
||
ha = a['hypothesis']; hb = b['hypothesis']
|
||
if ha.get('type') != hb.get('type'):
|
||
return False
|
||
if ha.get('type') == 'multi_role':
|
||
return (ha.get('trend_ind') == hb.get('trend_ind') and
|
||
ha.get('entry_ind') == hb.get('entry_ind') and
|
||
ha.get('trend_tf') == hb.get('trend_tf') and
|
||
ha.get('entry_tf') == hb.get('entry_tf'))
|
||
return ha.get('indicator') == hb.get('indicator')
|
||
|
||
def record(self):
|
||
"""Save checkpoint."""
|
||
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
|
||
STATE_DIR.mkdir(parents=True, exist_ok=True)
|
||
if self.sota:
|
||
cp = RESULTS_DIR / f"rd_loop_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||
# Strip equity_curves (too large) from saved results
|
||
stripped = []
|
||
for r in self.sota[:30]:
|
||
s = {k: v for k, v in r.items() if k != 'equity_curves'}
|
||
stripped.append(s)
|
||
cp.write_text(json.dumps(stripped, indent=2, default=str))
|
||
|
||
|
||
def _run_optuna(closes, hypothesis):
|
||
"""Optuna optimization on the primary instrument."""
|
||
import optuna
|
||
optuna.logging.set_verbosity(optuna.logging.WARNING)
|
||
|
||
hp = hypothesis
|
||
close = list(closes.values())[0] # Use first instrument for Optuna
|
||
ind = hp.get('indicator', hp.get('trend_ind', 'MACD'))
|
||
base_params = hp.get('params', hp.get('trend_params', {}))
|
||
|
||
param_ranges = {
|
||
'MACD': {'fast': (2,15), 'slow': (5,40), 'sig': (2,15)},
|
||
'RSI': {'period': (5,30), 'oversold': (10,40), 'overbought': (60,90)},
|
||
'Donchian': {'period': (3,100), 'hold': (1,10)},
|
||
'SAR': {'accel': (0.01, 0.2), 'max_accel': (0.1, 1.0)},
|
||
'ADX': {'period': (5,30), 'threshold': (10,40)},
|
||
}
|
||
ranges = param_ranges.get(ind, {})
|
||
|
||
def objective(trial):
|
||
params = {}
|
||
for k, (lo, hi) in ranges.items():
|
||
if isinstance(base_params.get(k, 1), int):
|
||
params[k] = trial.suggest_int(k, int(lo), int(hi))
|
||
else:
|
||
params[k] = trial.suggest_float(k, lo, hi)
|
||
if 'fast' in params and 'slow' in params:
|
||
params['fast'] = min(params['fast'], params['slow']-2)
|
||
|
||
result = evaluate_multi(closes, hp, use_session=True, use_vola=True)
|
||
return float(result.get('sharpe', 0)) if result.get('sharpe', 0) > 0 else -999.0
|
||
|
||
try:
|
||
study = optuna.create_study(direction='maximize')
|
||
study.optimize(objective, n_trials=15, show_progress_bar=False)
|
||
best = study.best_params
|
||
if 'params' in hp:
|
||
hp['params'] = {k: int(v) if v == int(v) else v for k, v in best.items()}
|
||
elif 'trend_params' in hp:
|
||
hp['trend_params'] = {k: int(v) if v == int(v) else v for k, v in best.items()}
|
||
hp['generation'] = 'optuna'
|
||
result = evaluate_multi(closes, hp, use_session=True, use_vola=True)
|
||
print(f" Optuna best: {best} → Sh={result['sharpe']:.1f} "
|
||
f"Mon={result['monthly_pct']:.1f}% OOS={result['monthly_oos']:.1f}% ({study.best_value:.1f})")
|
||
return result
|
||
except Exception:
|
||
return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0}
|
||
|
||
|
||
def _train_ml(closes, hypothesis):
|
||
"""Train LightGBM on SOTA indicator signals."""
|
||
try:
|
||
from lightgbm import LGBMClassifier
|
||
except ImportError:
|
||
return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0}
|
||
|
||
sota = hypothesis.get('sota', [])
|
||
if not sota:
|
||
return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0}
|
||
|
||
close = list(closes.values())[0]
|
||
daily = close.resample('1h').last().dropna()
|
||
features = pd.DataFrame(index=daily.index)
|
||
|
||
for s in sota[:5]:
|
||
hp_s = s['hypothesis']
|
||
# Generate signal from each SOTA strategy as a feature
|
||
from nexquant_rd_loop import build_signal, _build_indicator_signal
|
||
sig = build_signal(close, hp_s)
|
||
if sig is not None:
|
||
sig = sig.reindex(daily.index, method='ffill')
|
||
name = hp_s.get('description', f"strat_{id(s)}")[:30]
|
||
features[name] = sig.fillna(0)
|
||
|
||
features = features.iloc[100:] # Skip warmup
|
||
if len(features) < 200:
|
||
return {"sharpe": 0, "monthly_pct": 0, "monthly_oos": 0, "n_trades": 0}
|
||
|
||
target = (daily.pct_change().shift(-1) > 0).astype(int)
|
||
target = target.reindex(features.index).fillna(0)
|
||
|
||
split = int(len(features) * 0.8)
|
||
X_train, X_test = features.iloc[:split], features.iloc[split:]
|
||
y_train, y_test = target.iloc[:split], target.iloc[split:]
|
||
|
||
model = LGBMClassifier(n_estimators=100, max_depth=5, verbosity=-1)
|
||
model.fit(X_train, y_train)
|
||
preds = model.predict(X_test)
|
||
acc = float((preds == y_test).mean())
|
||
|
||
ml_signal = pd.Series(0, index=X_test.index)
|
||
ml_signal[preds == 1] = 1; ml_signal[preds == 0] = -1
|
||
ml_signal = ml_signal.reindex(close.index).ffill().fillna(0).astype(int).clip(-1, 1)
|
||
ml_signal = _apply_session_filter(ml_signal, close.index)
|
||
|
||
prices = close.values.astype(np.float64); sigs = ml_signal.values.astype(np.int32)
|
||
eq, dd, tr, wins, ret, sh, _ = _backtest_numba(prices, sigs)
|
||
n_days = (close.index[-1] - close.index[0]).days
|
||
mon = ((1+ret)**(1/(n_days/30.44))-1)*100 if ret > -1 else 0
|
||
print(f" ML LightGBM: Test acc={acc:.1%} → Sh={sh:.1f} Mon={mon:.1f}% Tr={tr}")
|
||
return {"sharpe": float(sh), "monthly_pct": float(mon), "monthly_oos": 0,
|
||
"n_trades": int(tr), "win_rate": float(wins/tr) if tr>0 else 0,
|
||
"ml_accuracy": float(acc), "ml_model": "LightGBM"}
|
||
|
||
|
||
def load_data():
|
||
"""Load OHLCV data for all instruments from one or multiple HDF5 files."""
|
||
closes = {}
|
||
data_dir = OHLCV_PATH.parent
|
||
|
||
# Try main file first
|
||
if OHLCV_PATH.exists():
|
||
df = pd.read_hdf(OHLCV_PATH, key="data")
|
||
for inst in INSTRUMENTS:
|
||
try:
|
||
close = df.xs(inst, level="instrument")["$close"].sort_index()
|
||
closes[inst] = close
|
||
except KeyError:
|
||
pass
|
||
|
||
# Load from individual files if not found
|
||
instrument_files = {
|
||
"EURUSD": OHLCV_PATH,
|
||
"GBPUSD": data_dir / "gbpusdt_1min.h5",
|
||
"BTCUSD": data_dir / "btc_1min.h5",
|
||
"XAUUSD": data_dir / "xauusdt_1min.h5",
|
||
}
|
||
for inst, path in instrument_files.items():
|
||
if inst in closes:
|
||
continue
|
||
if not path.exists():
|
||
print(f" {inst}: file not found — skipping")
|
||
continue
|
||
try:
|
||
df = pd.read_hdf(path, key="data")
|
||
if isinstance(df.index, pd.MultiIndex):
|
||
try:
|
||
close = df.xs(inst, level="instrument")["$close"].sort_index()
|
||
except KeyError:
|
||
# Try with T suffix for crypto pairs
|
||
alt = inst + "T" if not inst.endswith("T") else inst.rstrip("T")
|
||
try:
|
||
close = df.xs(alt, level="instrument")["$close"].sort_index()
|
||
except KeyError:
|
||
inst_vals = df.index.get_level_values("instrument").unique()
|
||
for iv in inst_vals:
|
||
if inst[:3] in str(iv)[:3]:
|
||
close = df.xs(iv, level="instrument")["$close"].sort_index()
|
||
break
|
||
else:
|
||
raise KeyError(f"No instrument matching {inst}")
|
||
elif "$close" in df.columns:
|
||
close = df["$close"].sort_index()
|
||
close.index = pd.to_datetime(close.index)
|
||
elif "close" in df.columns:
|
||
close = df["close"].sort_index()
|
||
close.index = pd.to_datetime(close.index)
|
||
else:
|
||
close = df.iloc[:, 3].sort_index()
|
||
close.index = pd.to_datetime(close.index)
|
||
closes[inst] = close
|
||
except Exception as e:
|
||
print(f" {inst}: load error {e} — skipping")
|
||
|
||
for inst, close in closes.items():
|
||
print(f" {inst}: {len(close):,} bars, {close.index[0]} → {close.index[-1]}")
|
||
|
||
return closes
|
||
|
||
|
||
def main():
|
||
iterations = 200
|
||
if "--iterations" in sys.argv:
|
||
iterations = int(sys.argv[sys.argv.index("--iterations") + 1])
|
||
|
||
print("=" * 60)
|
||
print(f" R&D Loop V2 — Multi-Instrument + Correlation Score")
|
||
print(f" Instruments: {', '.join(INSTRUMENTS)}")
|
||
print(f" Indicators: {len(INDICATORS_POOL)} | Strategy types: {len(STRATEGY_TYPES)}")
|
||
print(f" Features: Session Filter + Volatility Filter + OOS Split")
|
||
print(f" Iterations: {iterations}")
|
||
print("=" * 60)
|
||
print(" Loading data...")
|
||
|
||
closes = load_data()
|
||
if not closes:
|
||
print(" ERROR: No instruments loaded!"); return
|
||
|
||
loop = ResearchLoop(closes)
|
||
t0 = time.time()
|
||
|
||
for i in range(iterations):
|
||
hp = loop.hypothesize()
|
||
|
||
# Evaluate
|
||
try:
|
||
result = evaluate_multi(closes, hp, use_session=True, use_vola=True)
|
||
except Exception:
|
||
continue
|
||
|
||
result['hypothesis'] = hp
|
||
result['iteration'] = i + 1
|
||
result['timestamp'] = datetime.now().isoformat()
|
||
loop.history.append(result)
|
||
|
||
# Feedback
|
||
is_new_best = loop.feedback(result)
|
||
best_inst_metrics = [f"{inst}: {m['sharpe']:.1f}" for inst, m in result.get('per_instrument', {}).items() if m.get('sharpe', 0) != 0]
|
||
|
||
gen = hp.get('generation', '?')
|
||
if is_new_best:
|
||
print(f"\n ★ NEW BEST (#{i+1}, {gen}): {hp['description']}")
|
||
print(f" Score={result['composite_score']:.1f} Sh={result['sharpe']:.1f} "
|
||
f"Mon={result['monthly_pct']:.1f}% OOS={result['monthly_oos']:.1f}% "
|
||
f"Tr={result['n_trades']} [{', '.join(best_inst_metrics[:3])}]")
|
||
elif (i + 1) % 50 == 0:
|
||
top_indicators = set()
|
||
for r in loop.sota[:5]:
|
||
top_indicators.add(r['hypothesis'].get('trend_ind', r['hypothesis'].get('indicator', '?')))
|
||
print(f" [{i+1}/{iterations}] {gen:>7s} | SOTA: {len(loop.sota)} | "
|
||
f"Best Sh={loop.best_sharpe:.1f} Score={loop.best_score:.1f} | "
|
||
f"Explore: {loop.exploration_rate:.0%} | Inds: {','.join(sorted(top_indicators)[:4])}")
|
||
|
||
if (i + 1) % 100 == 0:
|
||
loop.record()
|
||
|
||
if len(loop.sota) > 10:
|
||
loop.exploration_rate = max(0.15, EXPLORATION_RATE - len(loop.sota) * 0.003)
|
||
|
||
elapsed = time.time() - t0
|
||
print(f"\n{'=' * 60}")
|
||
print(f" R&D Loop V2 Complete: {iterations} iterations in {elapsed:.0f}s")
|
||
print(f" SOTA Strategies: {len(loop.sota)} | Best Score: {loop.best_score:.1f}")
|
||
print(f"{'=' * 60}")
|
||
|
||
if loop.sota:
|
||
print(f"\n TOP DISCOVERIES (by composite score):")
|
||
for i, r in enumerate(loop.sota[:15], 1):
|
||
hp = r['hypothesis']
|
||
per_inst = r.get('per_instrument', {})
|
||
insts = ' '.join([f"{k}:{v['sharpe']:.0f}" for k, v in per_inst.items() if v['sharpe'] != 0])
|
||
print(f" {i:>2d}. {hp['description'][:45]:45s} "
|
||
f"Sc={r['composite_score']:.1f} Sh={r['sharpe']:+.1f} "
|
||
f"Mo={r['monthly_pct']:+.1f}% OOS={r['monthly_oos']:+.1f}% "
|
||
f"[{insts}]")
|
||
|
||
final = RESULTS_DIR / f"rd_loop_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||
stripped = [{k: v for k, v in r.items() if k != 'equity_curves'} for r in loop.sota]
|
||
final.write_text(json.dumps(stripped, indent=2, default=str))
|
||
print(f"\n Saved: {final}")
|
||
|
||
exploit_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'exploit']
|
||
explore_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'explore']
|
||
optuna_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'optuna']
|
||
ml_best = [r for r in loop.sota if r['hypothesis'].get('generation') == 'ml']
|
||
print(f" Exploit: {len(exploit_best)} | Explore: {len(explore_best)} | "
|
||
f"Optuna: {len(optuna_best)} | ML: {len(ml_best)}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|