8ff35c1a86
Multi-machine optimization loop: - VPS orchestrator coordinates training and LLM analysis - Windows PC (RTX 4070 Ti) runs XGBoost/LightGBM/CatBoost with GPU - Mac Mini runs qwen3.5:27b via Ollama for strategy analysis Includes 60+ technical features, walk-forward validation, confidence-scaled position sizing, and automated convergence detection. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
77 lines
2.4 KiB
Python
Executable File
77 lines
2.4 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Fetch BTC/USDT OHLCV data from Binance using ccxt."""
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import os
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import sys
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import time
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import ccxt
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import pandas as pd
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from datetime import datetime, timezone
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DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data")
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SYMBOL = "BTC/USDT"
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EXCHANGE_ID = "binance"
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YEARS_HISTORY = 2
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LIMIT_PER_REQUEST = 1000 # Binance max
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def fetch_ohlcv(timeframe: str) -> pd.DataFrame:
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"""Fetch OHLCV data for a given timeframe."""
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exchange = ccxt.binance({"enableRateLimit": True})
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# Calculate start time
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now_ms = int(time.time() * 1000)
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if timeframe == "1h":
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ms_per_candle = 3600 * 1000
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elif timeframe == "4h":
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ms_per_candle = 4 * 3600 * 1000
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else:
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raise ValueError(f"Unsupported timeframe: {timeframe}")
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since = now_ms - (YEARS_HISTORY * 365 * 24 * 3600 * 1000)
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all_candles = []
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print(f" Fetching {SYMBOL} {timeframe} from {datetime.fromtimestamp(since / 1000, tz=timezone.utc).strftime('%Y-%m-%d')}...")
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while since < now_ms:
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try:
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candles = exchange.fetch_ohlcv(SYMBOL, timeframe, since=since, limit=LIMIT_PER_REQUEST)
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except Exception as e:
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print(f" Warning: fetch error, retrying in 5s — {e}")
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time.sleep(5)
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continue
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if not candles:
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break
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all_candles.extend(candles)
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since = candles[-1][0] + ms_per_candle
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sys.stdout.write(f"\r Downloaded {len(all_candles)} candles...")
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sys.stdout.flush()
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time.sleep(exchange.rateLimit / 1000)
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print(f"\r Downloaded {len(all_candles)} candles total.")
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df = pd.DataFrame(all_candles, columns=["timestamp", "open", "high", "low", "close", "volume"])
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df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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df = df.drop_duplicates(subset=["timestamp"]).sort_values("timestamp").reset_index(drop=True)
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return df
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def main():
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os.makedirs(DATA_DIR, exist_ok=True)
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for tf in ["1h", "4h"]:
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print(f"\n[*] Fetching {tf} data...")
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df = fetch_ohlcv(tf)
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out_path = os.path.join(DATA_DIR, f"btc_{tf}.csv")
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df.to_csv(out_path, index=False)
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print(f" Saved {len(df)} rows to {out_path}")
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print(f" Range: {df['timestamp'].iloc[0]} → {df['timestamp'].iloc[-1]}")
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print("\nData fetch complete!")
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if __name__ == "__main__":
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main()
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