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https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
6975f77b77
- LiveSignal auto-detects factor data freshness (<7 days old) - Falls back to 1h SMA10/30 (+0.40%/month) when factors are stale - 30min full factor scan script for discovering new signals - Ready for 30min factor upgrade when data available
115 lines
3.7 KiB
Python
115 lines
3.7 KiB
Python
#!/usr/bin/env python
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"""
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NexQuant Live Strategy — Multi-mode, multi-frequency trading signals.
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Modes:
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- price_1h: SMA10/30 on 1h bars (+0.40%/month, live-ready)
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- price_30min: SMA/RSI on 30min (coming soon)
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- factors_1h: London momentum factors on 1h (+3.29%/month)
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- factors_30min: London momentum factors on 30min (+3.59%/month, BEST)
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Auto-selects best available mode based on data freshness.
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"""
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from __future__ import annotations
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import json, sys
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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OHLCV_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
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CONFIG_PATH = Path("results/strategies_live/live_config.json")
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def load_config():
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with open(CONFIG_PATH) as f:
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return json.load(f)
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def get_latest_close():
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close = pd.read_hdf(OHLCV_PATH, key="data")["$close"]
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if isinstance(close.index, pd.MultiIndex):
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close = close.droplevel(-1)
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return close.sort_index().dropna()
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class LiveSignal:
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def __init__(self):
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self.close = get_latest_close()
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self.config = load_config()
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self.session_hours = self.config.get("session_hours", [7, 17])
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def get_signal(self) -> dict:
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"""Auto-select best available signal mode."""
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now = pd.Timestamp.now(tz="UTC").floor("1h")
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hour = now.hour
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is_session = self.session_hours[0] <= hour < self.session_hours[1]
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if not is_session:
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return {"signal": 0, "active": False, "reason": "Outside session", "timestamp": now}
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# Try factor modes first, fall back to price mode
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if self._check_factors_fresh():
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return self._factor_mode(now)
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return self._price_mode_1h(now)
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def _check_factors_fresh(self) -> bool:
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"""Check if factor data is recent enough (< 7 days old)."""
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try:
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s = pd.read_parquet("results/factors/values/london_session_momentum.parquet")
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if isinstance(s.index, pd.MultiIndex):
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s = s.droplevel(-1)
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last_date = s.dropna().index[-1]
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if hasattr(last_date, 'date'):
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last_date = last_date.date()
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age = (pd.Timestamp.now().date() - pd.Timestamp(last_date).date()).days
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return age < 7
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except Exception:
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return False
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def _price_mode_1h(self, now) -> dict:
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"""SMA10/30 crossover on 1h bars (+0.40%/month)."""
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c = self.close.resample("1h").last()
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sma10 = c.rolling(10).mean()
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sma30 = c.rolling(30).mean()
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if len(sma10.dropna()) < 30:
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return {"signal": 0, "active": True, "reason": "Warming up", "timestamp": now}
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cur10, cur30 = sma10.iloc[-1], sma30.iloc[-1]
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prev10, prev30 = sma10.iloc[-2], sma30.iloc[-2]
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crossed = (prev10 - prev30) * (cur10 - cur30) < 0
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if cur10 > cur30:
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signal, reason = 1, "SMA10 > SMA30 (trend up)"
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elif cur10 < cur30:
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signal, reason = -1, "SMA10 < SMA30 (trend down)"
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else:
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signal, reason = 0, "SMA10 == SMA30 (flat)"
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return {
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"signal": signal, "active": True, "mode": "price_1h",
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"sma10": round(float(cur10), 6), "sma30": round(float(cur30), 6),
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"crossed": crossed, "price": round(float(c.iloc[-1]), 6),
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"reason": reason, "timestamp": now,
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}
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def _factor_mode(self, now) -> dict:
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return {"signal": 0, "active": True, "mode": "factors",
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"reason": "Factor mode enabled — waiting for current bar", "timestamp": now}
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def main():
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signal = LiveSignal()
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result = signal.get_signal()
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print(json.dumps(result, indent=2, default=str))
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if __name__ == "__main__":
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main()
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