43 lines
1.4 KiB
Markdown
43 lines
1.4 KiB
Markdown
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# MVRV Strategy Port Instructions
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This document explains how to run the ported MVRV/NUPL strategy.
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## Prerequisites
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1. **Dependencies:** Ensure you have installed the required packages:
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```bash
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uv add requests xgboost scikit-learn numba python-dotenv
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```
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2. **API Key:** Your CryptoQuant API key is set in `.env` (`CRYPTOQUANT_API_KEY`).
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3. **Data:** You need a high-frequency (1m or 15m) OHLCV CSV file for BTC/USDT.
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## Workflow
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### 1. Prepare Data
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Fetch on-chain data from CryptoQuant, merge it with your price data, and generate features.
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```bash
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python prepare_data.py --csv <path_to_your_ohlcv.csv> --days 730
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```
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*Output:* `data/features.csv`
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### 2. Train Model
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Train the XGBoost model using the generated features.
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```bash
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python train_model.py
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```
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*Output:* `data/model.pkl`
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### 3. Run Backtest
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Run the strategy backtest using the trained model and your price data.
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```bash
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python backtest_mvrv.py --csv <path_to_your_ohlcv.csv>
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```
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*Output:* Logs in `logs/mvrv_trade_log.csv` and summary printed to console.
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## Configuration
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You can adjust strategy parameters in `strategy_config.py`:
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- `PROB_THRESHOLD`: ML probability threshold for entry (default 0.55).
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- `SL_ATR_MULT`: Stop Loss ATR multiplier (default 0.8).
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- `TP_ATR_MULT`: Take Profit ATR multiplier (default 1.5).
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- `MVRV_Z_THRESH`: Overheated MVRV Z-score threshold (default 1.5).
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- `NUPL_THRESH`: Overheated NUPL threshold (default 0.6).
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