feat: complete BTC ML trading strategy optimizer

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>

scripts/fetch_data.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+"""Fetch BTC/USDT OHLCV data from Binance using ccxt."""
+
+import os
+import sys
+import time
+import ccxt
+import pandas as pd
+from datetime import datetime, timezone
+
+DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data")
+SYMBOL = "BTC/USDT"
+EXCHANGE_ID = "binance"
+YEARS_HISTORY = 2
+LIMIT_PER_REQUEST = 1000  # Binance max
+
+
+def fetch_ohlcv(timeframe: str) -> pd.DataFrame:
+    """Fetch OHLCV data for a given timeframe."""
+    exchange = ccxt.binance({"enableRateLimit": True})
+
+    # Calculate start time
+    now_ms = int(time.time() * 1000)
+    if timeframe == "1h":
+        ms_per_candle = 3600 * 1000
+    elif timeframe == "4h":
+        ms_per_candle = 4 * 3600 * 1000
+    else:
+        raise ValueError(f"Unsupported timeframe: {timeframe}")
+
+    since = now_ms - (YEARS_HISTORY * 365 * 24 * 3600 * 1000)
+
+    all_candles = []
+    print(f"  Fetching {SYMBOL} {timeframe} from {datetime.fromtimestamp(since / 1000, tz=timezone.utc).strftime('%Y-%m-%d')}...")
+
+    while since < now_ms:
+        try:
+            candles = exchange.fetch_ohlcv(SYMBOL, timeframe, since=since, limit=LIMIT_PER_REQUEST)
+        except Exception as e:
+            print(f"  Warning: fetch error, retrying in 5s — {e}")
+            time.sleep(5)
+            continue
+
+        if not candles:
+            break
+
+        all_candles.extend(candles)
+        since = candles[-1][0] + ms_per_candle
+        sys.stdout.write(f"\r  Downloaded {len(all_candles)} candles...")
+        sys.stdout.flush()
+        time.sleep(exchange.rateLimit / 1000)
+
+    print(f"\r  Downloaded {len(all_candles)} candles total.")
+
+    df = pd.DataFrame(all_candles, columns=["timestamp", "open", "high", "low", "close", "volume"])
+    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    df = df.drop_duplicates(subset=["timestamp"]).sort_values("timestamp").reset_index(drop=True)
+    return df
+
+
+def main():
+    os.makedirs(DATA_DIR, exist_ok=True)
+
+    for tf in ["1h", "4h"]:
+        print(f"\n[*] Fetching {tf} data...")
+        df = fetch_ohlcv(tf)
+        out_path = os.path.join(DATA_DIR, f"btc_{tf}.csv")
+        df.to_csv(out_path, index=False)
+        print(f"  Saved {len(df)} rows to {out_path}")
+        print(f"  Range: {df['timestamp'].iloc[0]} → {df['timestamp'].iloc[-1]}")
+
+    print("\nData fetch complete!")
+
+
+if __name__ == "__main__":
+    main()

scripts/setup_windows.sh

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+#!/usr/bin/env bash
+# Setup Windows PC (100.76.218.38) with ML dependencies for BTC optimizer
+set -euo pipefail
+
+WINDOWS_HOST="bizzle@100.76.218.38"
+REMOTE_DIR="~/btc-ml-optimizer"
+
+echo "=== BTC ML Optimizer — Windows PC Setup ==="
+echo "Target: $WINDOWS_HOST"
+echo ""
+
+# Create project directory on Windows
+echo "[1/3] Creating project directory..."
+ssh "$WINDOWS_HOST" "mkdir -p $REMOTE_DIR"
+
+# Install PyTorch with CUDA support + ML libraries
+echo "[2/3] Installing Python dependencies (this may take a while)..."
+ssh "$WINDOWS_HOST" "pip install --upgrade pip && \
+  pip install torch --index-url https://download.pytorch.org/whl/cu128 && \
+  pip install xgboost lightgbm catboost optuna && \
+  pip install pandas numpy scikit-learn ta"
+
+# Verify installations
+echo "[3/3] Verifying installations..."
+ssh "$WINDOWS_HOST" "python -c \"
+import torch
+print(f'PyTorch {torch.__version__}, CUDA available: {torch.cuda.is_available()}')
+if torch.cuda.is_available():
+    print(f'  GPU: {torch.cuda.get_device_name(0)}')
+import xgboost; print(f'XGBoost {xgboost.__version__}')
+import lightgbm; print(f'LightGBM {lightgbm.__version__}')
+import catboost; print(f'CatBoost {catboost.__version__}')
+import optuna; print(f'Optuna {optuna.__version__}')
+import pandas; print(f'Pandas {pandas.__version__}')
+import numpy; print(f'NumPy {numpy.__version__}')
+import ta; print('ta library OK')
+import sklearn; print(f'scikit-learn {sklearn.__version__}')
+print('All dependencies verified!')
+\""
+
+echo ""
+echo "=== Setup complete! ==="