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