btc-accumulation-monitor/README.md

BTC ML Trading Strategy Optimizer

An automated optimization loop that trains ML models on BTC/USDT data, backtests trading strategies, and uses an LLM to iteratively improve the configuration.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        Optimization Loop                        │
│                                                                 │
│  ┌──────────┐    ┌───────────────┐    ┌──────────────────────┐  │
│  │   VPS    │───>│  Windows PC   │───>│     Mac Mini         │  │
│  │ (Orch.)  │<───│  (GPU/ML)     │    │     (LLM)           │  │
│  │          │<───────────────────────>│                      │  │
│  │ - Fetch  │    │ - XGBoost     │    │ - Ollama             │  │
│  │   data   │    │ - LightGBM    │    │ - qwen3.5:27b        │  │
│  │ - Coord  │    │ - CatBoost    │    │ - Analyze results    │  │
│  │ - Store  │    │ - RTX 4070 Ti │    │ - Suggest changes    │  │
│  └──────────┘    └───────────────┘    └──────────────────────┘  │
│       ▲                                        │                │
│       └────────────────────────────────────────┘                │
│                    Modified config                              │
└─────────────────────────────────────────────────────────────────┘

Machines (Tailscale)

Machine	Role	Address	Key Resources
VPS	Orchestrator	localhost	Coordination, data
Windows PC	ML Engine	100.76.218.38	RTX 4070 Ti GPU
Mac Mini	LLM	100.100.242.21	Ollama, qwen3.5:27b

Directory Structure

btc-ml-optimizer/
├── orchestrator.py              # Main loop — coordinates everything
├── ml_engine/
│   └── train_and_backtest.py    # Self-contained ML script (runs on Windows)
├── llm_client/
│   └── analyzer.py              # LLM strategy analyzer (calls Mac Mini)
├── scripts/
│   ├── fetch_data.py            # BTC/USDT data fetcher (ccxt)
│   └── setup_windows.sh         # Install deps on Windows PC
├── config/
│   └── initial_config.json      # Starting configuration
├── data/                        # OHLCV CSV files
├── results/                     # Iteration results + logs
├── requirements_vps.txt         # VPS Python dependencies
└── requirements_windows.txt     # Windows PC Python dependencies

Setup

1. VPS (this machine)

pip install -r requirements_vps.txt

2. Windows PC

# From VPS — installs all ML deps on Windows via SSH
bash scripts/setup_windows.sh

Or manually on Windows:

pip install -r requirements_windows.txt

3. Mac Mini

Ensure Ollama is running with the qwen3.5:27b model:

ollama pull qwen3.5:27b
ollama serve  # should already be running

Usage

Fetch Data

python3 scripts/fetch_data.py

Downloads 2 years of BTC/USDT 1h and 4h OHLCV data from Binance.

Run the Optimizer

python3 orchestrator.py

The optimizer will:

1. Ensure data is fetched
2. Upload ML engine + data to Windows PC
3. Train model and backtest on GPU
4. Send results to LLM for analysis
5. Apply LLM-suggested config changes
6. Repeat until convergence (or 50 iterations)

Run ML Engine Standalone (on Windows)

python train_and_backtest.py --config config.json --data btc_4h.csv --output results.json

Configuration Reference

model_type

• xgboost — XGBoost with GPU (default, generally best)
• lightgbm — LightGBM with GPU (faster training)
• catboost — CatBoost with GPU (handles interactions well)
• ensemble — Soft voting of all three

features

• technical_indicators — List of indicators to compute
• lookback_periods — Windows for return/volatility features
• use_volume_features — Include volume-derived features
• use_volatility_features — Include volatility features
• use_candle_patterns — Include candlestick pattern features
• use_lag_features — Include lagged feature values
• lag_periods — Specific lag periods to use

target

• direction — "long" or "both"
• horizon_candles — Forward-looking prediction window
• threshold_pct — Minimum % move to label as positive

hyperparameters

Standard gradient boosting params: learning_rate, max_depth, n_estimators, subsample, colsample_bytree, min_child_weight, gamma, reg_alpha, reg_lambda

strategy

• entry_threshold — Min probability to enter trade (0.5-0.8)
• stop_loss_pct — Stop loss percentage
• take_profit_pct — Take profit percentage
• trailing_stop_pct — Trailing stop distance
• position_sizing — "confidence_scaled" or "fixed"
• min_confidence_to_trade — Absolute minimum confidence

training

• walk_forward_windows — Number of walk-forward splits (3-10)
• train_pct / validation_pct / test_pct — Data split ratios

Convergence Criteria

The optimizer stops when:

• Sharpe ratio exceeds 3.0
• Sharpe improvement < 1% over 5 consecutive iterations
• Maximum 50 iterations reached

Output

• config/best_config.json — Best configuration found
• results/iterations.jsonl — Full log of every iteration
• results/results_iter_N.json — Detailed results per iteration