# MVRV Strategy Port Instructions

This document explains how to run the ported MVRV/NUPL strategy.

## Prerequisites
1.  **Dependencies:** Ensure you have installed the required packages:
    ```bash
    uv add requests xgboost scikit-learn numba python-dotenv
    ```
2.  **API Key:** Your CryptoQuant API key is set in `.env` (`CRYPTOQUANT_API_KEY`).
3.  **Data:** You need a high-frequency (1m or 15m) OHLCV CSV file for BTC/USDT.

## Workflow

### 1. Prepare Data
Fetch on-chain data from CryptoQuant, merge it with your price data, and generate features.
```bash
python prepare_data.py --csv <path_to_your_ohlcv.csv> --days 730
```
*Output:* `data/features.csv`

### 2. Train Model
Train the XGBoost model using the generated features.
```bash
python train_model.py
```
*Output:* `data/model.pkl`

### 3. Run Backtest
Run the strategy backtest using the trained model and your price data.
```bash
python backtest_mvrv.py --csv <path_to_your_ohlcv.csv>
```
*Output:* Logs in `logs/mvrv_trade_log.csv` and summary printed to console.

## Configuration
You can adjust strategy parameters in `strategy_config.py`:
-   `PROB_THRESHOLD`: ML probability threshold for entry (default 0.55).
-   `SL_ATR_MULT`: Stop Loss ATR multiplier (default 0.8).
-   `TP_ATR_MULT`: Take Profit ATR multiplier (default 1.5).
-   `MVRV_Z_THRESH`: Overheated MVRV Z-score threshold (default 1.5).
-   `NUPL_THRESH`: Overheated NUPL threshold (default 0.6).