mirror of
https://github.com/BrentNeale1/fx-quant.git
synced 2026-08-04 14:17:44 +00:00
Brent Neale
dce54845c2
- Built event-driven backtesting engine with spread/slippage modeling, 3-TP partial closes, trailing stops, and rich trade logging (20+ features) - Implemented 5 strategy signal generators (MA Breakout, VWAP Reversal, Key Level Breakout, EMA Ribbon Scalp, Momentum Exhaustion) - Full indicator library (EMA, SMA, RSI, ATR, MACD, ADX, Stochastic, Session VWAP bands, swing points, key levels, RSI divergence) - Data pipeline: Dukascopy download, validation, 70/30 train/test split - Baseline results: all 5 strategies generate 200+ trades on training data (Jan 2021 - Aug 2023), best profit factors 0.82-0.96 on select pairs - Trade logs and reports saved for Phase 3 ML feature engineering Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
92 lines
2.9 KiB
Python
92 lines
2.9 KiB
Python
# src/load_strategy_data.py
|
|
"""
|
|
Bulk historical data loader for all 5 trading strategies.
|
|
Fetches candles for all required pairs and timeframes, computes features,
|
|
and uploads to Supabase.
|
|
|
|
Usage:
|
|
python src/load_strategy_data.py
|
|
"""
|
|
|
|
import sys
|
|
from datetime import datetime, timezone
|
|
|
|
from config_loader import load_config
|
|
from historical_loader import fetch_all_candles
|
|
from data_engine import build_all_features
|
|
from supabase_upload import upload_dataframe
|
|
|
|
# Pairs required per strategy
|
|
STRATEGY_PAIRS = {
|
|
"S1_MA_Breakout_Retest": ["GBP_AUD", "EUR_AUD", "EUR_CAD", "EUR_NZD"],
|
|
"S2_Session_VWAP_Reversal": ["GBP_USD", "EUR_USD", "GBP_JPY", "USD_JPY"],
|
|
"S3_Key_Level_Breakout": ["GBP_JPY", "USD_JPY", "GBP_USD", "EUR_GBP"],
|
|
"S4_EMA_Ribbon_Scalp": ["GBP_AUD", "EUR_AUD", "EUR_GBP"],
|
|
"S5_Momentum_Exhaustion": ["GBP_AUD", "EUR_AUD", "EUR_GBP", "GBP_CAD", "EUR_CAD"],
|
|
}
|
|
|
|
# All unique pairs
|
|
ALL_PAIRS = sorted(set(p for pairs in STRATEGY_PAIRS.values() for p in pairs))
|
|
|
|
# Timeframes and their required lookback in days
|
|
TIMEFRAME_DAYS = {
|
|
"M15": 750, # ~70,000 candles (market hours)
|
|
"H1": 850, # ~20,000 candles
|
|
"H4": 1000, # ~6,000 candles
|
|
}
|
|
|
|
|
|
def main():
|
|
cfg = load_config()
|
|
feature_cfg = cfg.get("features", {})
|
|
|
|
print("=" * 70)
|
|
print("Strategy Data Loader")
|
|
print(f"Pairs: {len(ALL_PAIRS)} - {ALL_PAIRS}")
|
|
print(f"Timeframes: {list(TIMEFRAME_DAYS.keys())}")
|
|
print(f"Started: {datetime.now(timezone.utc).isoformat()}")
|
|
print("=" * 70)
|
|
|
|
total = len(ALL_PAIRS) * len(TIMEFRAME_DAYS)
|
|
done = 0
|
|
errors = []
|
|
|
|
for instrument in ALL_PAIRS:
|
|
for granularity, days_back in TIMEFRAME_DAYS.items():
|
|
done += 1
|
|
print(f"\n[{done}/{total}] {instrument} / {granularity} - {days_back} days back")
|
|
print("-" * 60)
|
|
|
|
try:
|
|
df = fetch_all_candles(instrument, granularity, days_back=days_back)
|
|
if df.empty:
|
|
print(f" No data returned. Skipping.")
|
|
errors.append((instrument, granularity, "no data"))
|
|
continue
|
|
|
|
print(f" Candles: {len(df)} | Range: {df.index[0]} -> {df.index[-1]}")
|
|
|
|
print(f" Computing features...")
|
|
df = build_all_features(df, config=feature_cfg)
|
|
|
|
print(f" Uploading to Supabase ({len(df)} rows)...")
|
|
upload_dataframe(df, instrument=instrument, granularity=granularity, chunk_size=500)
|
|
print(f" Done.")
|
|
|
|
except Exception as e:
|
|
print(f" ERROR: {e}")
|
|
errors.append((instrument, granularity, str(e)))
|
|
|
|
print("\n" + "=" * 70)
|
|
print("Load complete.")
|
|
print(f" Successful: {done - len(errors)} / {total}")
|
|
if errors:
|
|
print(f" Errors ({len(errors)}):")
|
|
for inst, gran, err in errors:
|
|
print(f" {inst} / {gran}: {err}")
|
|
print("=" * 70)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|