diff --git a/.vscode/launch.json b/.vscode/launch.json
index 237a81e..9f6cc6d 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -9,7 +9,7 @@
         "console": "integratedTerminal",
         "cwd": "${workspaceFolder}",
         "args": [
-          "2025-03-01", "2025-09-22",
+          "2025-01-01", "2025-12-31",
           "--timeframes-minutes", "60", "240", "480",
           "--stop-loss", "0.02", "0.05",
           "--exit-on-bearish-flip",
diff --git a/INSTRUCTIONS_MVRV.md b/INSTRUCTIONS_MVRV.md
new file mode 100644
index 0000000..fcece2d
--- /dev/null
+++ b/INSTRUCTIONS_MVRV.md
@@ -0,0 +1,42 @@
+# 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).
diff --git a/backtest.py b/backtest.py
index d4300cd..3514fa3 100644
--- a/backtest.py
+++ b/backtest.py
@@ -66,5 +66,8 @@ def backtest(
     equity_curve = pd.Series(equity, index=df["Timestamp"])
     if log_path:
         write_trade_log(trades, log_path)
-    perf = compute_metrics(equity_curve, trades)
+    
+    # Calculate correct annualization factor based on timeframe
+    periods_per_year = int(252 * 24 * 60 / timeframe_minutes)
+    perf = compute_metrics(equity_curve, trades, periods_per_year=periods_per_year)
     return perf, equity_curve, trades
diff --git a/backtest_mvrv.py b/backtest_mvrv.py
new file mode 100644
index 0000000..713bd03
--- /dev/null
+++ b/backtest_mvrv.py
@@ -0,0 +1,270 @@
+import pandas as pd
+import numpy as np
+import pickle
+import os
+import strategy_config as config
+from trade import TradeState, enter_long, exit_long
+from logging_utils import write_trade_log
+from metrics import compute_metrics
+from pathlib import Path
+
+def backtest_mvrv(
+    df_features: pd.DataFrame,
+    df_1min: pd.DataFrame,
+    initial_cash: float = 10000.0,
+    log_path: Path | None = None,
+    test_only: bool = True  # NEW: Only backtest on test set to avoid train/test leakage
+):
+    print("--- Starting MVRV Strategy Backtest ---")
+    
+    # 1. Load Model and Generate Predictions
+    print(f"Loading model from {config.MODEL_PATH}...")
+    with open(config.MODEL_PATH, 'rb') as f:
+        model = pickle.load(f)
+    
+    # Load split info to identify test set boundary
+    split_info_path = config.MODEL_PATH.replace('.pkl', '_split.pkl')
+    if test_only and os.path.exists(split_info_path):
+        with open(split_info_path, 'rb') as f:
+            split_info = pickle.load(f)
+        test_start_idx = split_info['test_start_idx']
+        print(f"Filtering to TEST SET ONLY (starting at index {test_start_idx})")
+        print(f"  Train size was: {split_info['train_size']}, Test size: {split_info['test_size']}")
+        
+        # Filter features to test set only
+        df_features = df_features.iloc[test_start_idx:].copy()
+        
+        # Filter 1min data to match the test period
+        test_start_ts = df_features.index[0]
+        df_1min = df_1min[df_1min['Timestamp'] >= test_start_ts].copy()
+        
+        print(f"Backtest period: {df_features.index[0]} to {df_features.index[-1]}")
+    elif test_only:
+        print("WARNING: Split info not found. Running on FULL dataset (includes training data!).")
+    
+    # Prepare features for prediction
+    # Only use columns that were used in training
+    # We rely on config.FEATURE_NAMES, but we must check what's in df_features
+    # The model expects specific columns.
+    X = df_features[config.FEATURE_NAMES]
+    
+    print("Generating predictions...")
+    probs = model.predict_proba(X)[:, 1]
+    df_features['signal_prob'] = probs
+    
+    # 2. Setup Backtest Loop
+    state = TradeState(
+        cash=initial_cash,
+        fee_bps=config.FEES_PERCENT * 10000, # Convert to bps
+        slippage_bps=config.SLIPPAGE_PERCENT * 10000
+    )
+    
+    equity = []
+    trades = []
+    
+    # Track dynamic SL/TP
+    current_sl_price = 0.0
+    current_tp_price = 0.0
+    
+    # Pre-calculate entry signals to speed up loop
+    # Logic: Prob > Thresh AND Funding > Filter AND (MVRV < Thresh AND NUPL < Thresh)
+    # Note: features.py handles MVRV/NUPL Z-scores.
+    # The strategy uses raw NUPL/MVRV for regime filter, or Z-scores?
+    # Source: (mvrv_z > MVRV_Z_THRESH) | (nupl > NUPL_THRESH) -> is_overheated
+    # Check if we have 'mvrv_z' and 'nupl' columns in df_features.
+    
+    # Apply filters
+    # Defaults if cols missing (safe fallback)
+    s_prob = df_features['signal_prob']
+    
+    funding = df_features['funding_rate'] if 'funding_rate' in df_features.columns else pd.Series(0, index=df_features.index)
+    
+    # Use 'mvrv_z' if available, else 'mvrv' (but Z-score is preferred for normalization)
+    # The source strategy used 'mvrv_z' > 1.5 for overheated.
+    mvrv_z = df_features['mvrv_z'] if 'mvrv_z' in df_features.columns else pd.Series(0, index=df_features.index)
+    
+    # Source used raw 'nupl' > 0.6 for overheated
+    nupl = df_features['nupl'] if 'nupl' in df_features.columns else pd.Series(0, index=df_features.index)
+    
+    # Regime Filter: True if NOT overheated
+    is_overheated = (mvrv_z > config.MVRV_Z_THRESH) | (nupl > config.NUPL_THRESH)
+    regime_can_trade = ~is_overheated
+    
+    # Entry Signal
+    entry_signals = (
+        (s_prob > config.PROB_THRESHOLD) & 
+        (funding > config.FUNDING_FILTER) & 
+        regime_can_trade
+    )
+    
+    df_features['entry_signal'] = entry_signals
+    
+    print(f"Total Entry Signals: {entry_signals.sum()}")
+    
+    # Loop
+    # df_features is 1H. df_1min is 1m.
+    # We iterate through df_features (hourly steps).
+    # If in a trade, we check df_1min for SL/TP within that hour.
+    # If not in a trade, we check for Entry Signal at the close of the hour (or open of next).
+    # Standard backtesting: Signals calculated on 'Close' are executable at 'Open' of next candle.
+    # But df_1min covers the interval.
+    # Let's align carefully.
+    
+    for i in range(len(df_features) - 1):
+        # Current 1H candle (completed)
+        row = df_features.iloc[i]
+        next_row = df_features.iloc[i+1]
+        
+        ts_start = row.name # Timestamp of the row (e.g. 10:00)
+        ts_end = next_row.name # Timestamp of next row (e.g. 11:00)
+        
+        # Get 1m data for this interval [ts_start, ts_end)
+        # Note: df_1min['Timestamp'] needs to be datetime
+        mask = (df_1min['Timestamp'] >= ts_start) & (df_1min['Timestamp'] < ts_end)
+        chunk_1min = df_1min.loc[mask]
+        
+        # 1. Manage Existing Position (Exit Logic)
+        # Store initial qty state to prevent re-entry in same candle if we exited
+        started_with_position = state.qty > 0
+
+        if state.qty > 0:
+            # Check for SL/TP hits in 1m data
+            for _, m_row in chunk_1min.iterrows():
+                m_high = m_row['High']
+                m_low = m_row['Low']
+                m_ts = m_row['Timestamp']
+                
+                # Check SL
+                if m_low <= current_sl_price:
+                    evt = exit_long(state, current_sl_price) # Exec at SL price
+                    if evt:
+                        prev = trades[-1]
+                        pnl = (evt["price"] - prev["price"]) * prev["qty"]
+                        evt.update({"t": m_ts.isoformat(), "reason": "stop_loss", "pnl": pnl})
+                        trades.append(evt)
+                    break # Exit loop
+                
+                # Check TP
+                if m_high >= current_tp_price:
+                    evt = exit_long(state, current_tp_price) # Exec at TP price
+                    if evt:
+                        prev = trades[-1]
+                        pnl = (evt["price"] - prev["price"]) * prev["qty"]
+                        evt.update({"t": m_ts.isoformat(), "reason": "take_profit", "pnl": pnl})
+                        trades.append(evt)
+                    break # Exit loop
+        
+        # 2. Check for New Entry (if no position)
+        # Logic: If signal was True at 'row' (completed candle), we enter at Open of 'next_row' (or first 1m candle of next hour)
+        # Actually, we can enter immediately at the start of the interval if the signal was from the *previous* completed candle.
+        # Here 'row' is the current interval processing.
+        # If 'entry_signal' is True for 'row', it means at the end of 10:00 we have a signal.
+        # We should enter at 11:00 (which is start of next interval).
+        # So we check entry_signal of 'row', and if True, we enter at first available price in 'chunk_1min'??
+        # WAIT. 'chunk_1min' is [ts_start, ts_end).
+        # If row is 10:00 (meaning data for 09:00-10:00?), standard pandas resample labels left or right?
+        # Usually 10:00 label means 10:00-11:00 or 09:00-10:00?
+        # prepare_data used resample('1h').
+        # Pandas default for 1h is usually start of bin (left).
+        # So 10:00 row contains data from 10:00 to 11:00.
+        # We can only know the signal at 11:00 (Close of the candle).
+        # So we can execute at 11:00 (start of next bin).
+        
+        # So: processing interval i (10:00-11:00).
+        # We check signal from i-1 (09:00-10:00).
+        # If i-1 had signal, we enter at start of i.
+        
+        if state.qty <= 0 and not started_with_position:
+            # Check previous row signal
+            if i > 0:
+                prev_row = df_features.iloc[i-1]
+                if prev_row['entry_signal']:
+                    # Enter Long
+                    # Price = Open of current interval (or first 1m open)
+                    entry_price = row['open'] 
+                    if not chunk_1min.empty:
+                        entry_price = chunk_1min.iloc[0]['Open']
+                    
+                    # Calculate ATR-based SL/TP
+                    atr = prev_row['atr']
+                    if pd.isna(atr) or atr == 0:
+                        atr = row['open'] * 0.01 # Fallback 1%
+                        
+                    sl_dist = atr * config.SL_ATR_MULT
+                    tp_dist = atr * config.TP_ATR_MULT
+                    
+                    current_sl_price = entry_price - sl_dist
+                    current_tp_price = entry_price + tp_dist
+                    
+                    evt = enter_long(state, entry_price)
+                    if evt:
+                        evt.update({
+                            "t": ts_start.isoformat(), 
+                            "reason": "signal_entry",
+                            "sl": current_sl_price,
+                            "tp": current_tp_price
+                        })
+                        trades.append(evt)
+
+        # Update Equity Curve (mark-to-market at close of hour)
+        current_price = row['close']
+        val = state.cash + (state.qty * current_price)
+        equity.append({'timestamp': ts_start, 'equity': val})
+
+    # Create Equity Series
+    equity_df = pd.DataFrame(equity).set_index('timestamp')
+    equity_curve = equity_df['equity']
+    
+    # Save Logs
+    if log_path:
+        write_trade_log(trades, log_path)
+    
+    # Metrics (hourly bars: 252 trading days * 24 hours = 6048 periods/year)
+    perf = compute_metrics(equity_curve, trades, periods_per_year=252 * 24)
+    
+    # Print Summary
+    print("\n--- Backtest Summary ---")
+    print(f"Total Return: {perf.total_return * 100:.2f}%")
+    print(f"Sharpe Ratio: {perf.sharpe_ratio:.2f}")
+    print(f"Max Drawdown: {perf.max_drawdown * 100:.2f}%")
+    print(f"Total Trades: {perf.num_trades}")
+    
+    return perf, equity_curve, trades
+
+import argparse
+def run():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--csv", required=True, help="Path to 1m/15m OHLCV CSV")
+    args = parser.parse_args()
+    
+    # Load 1M Data
+    print(f"Loading 1m/15m data from {args.csv}...")
+    df_1min = pd.read_csv(args.csv)
+    # Ensure Timestamp
+    if 'Timestamp' in df_1min.columns:
+        ts_max = df_1min['Timestamp'].max()
+        if ts_max < 3000000000:
+            unit = 's'
+        elif ts_max < 3000000000000:
+            unit = 'ms'
+        else:
+            unit = None
+        df_1min['Timestamp'] = pd.to_datetime(df_1min['Timestamp'], unit=unit)
+    elif 'Date' in df_1min.columns:
+        df_1min['Timestamp'] = pd.to_datetime(df_1min['Date'])
+        
+    df_1min = df_1min.sort_values('Timestamp')
+    
+    # Load Features (1H)
+    print(f"Loading features from {config.FEATURES_PATH}...")
+    if not os.path.exists(config.FEATURES_PATH):
+        print("Error: features.csv not found. Run prepare_data.py first.")
+        return
+        
+    df_features = pd.read_csv(config.FEATURES_PATH, parse_dates=['timestamp'], index_col='timestamp')
+    
+    # Run Backtest
+    backtest_mvrv(df_features, df_1min, log_path=Path("logs/mvrv_trade_log.csv"))
+
+if __name__ == "__main__":
+    run()
diff --git a/cryptoquant_client.py b/cryptoquant_client.py
new file mode 100644
index 0000000..13e24f3
--- /dev/null
+++ b/cryptoquant_client.py
@@ -0,0 +1,263 @@
+import ssl
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+import os
+import time
+import random
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional
+import logging
+import strategy_config as config
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+class RateLimiter:
+    """Simple rate limiter for CryptoQuant's API."""
+
+    def __init__(self, max_requests: int = None, window_seconds: int = 60):
+        # Default to 20 if not specified, or read from env
+        if max_requests is None:
+            max_requests = int(os.getenv('CRYPTOQUANT_RATE_LIMIT', 20))
+        
+        self.max_requests = max_requests
+        self.window_seconds = window_seconds
+        self.requests = []  # List of timestamps
+
+    def wait_if_needed(self) -> None:
+        """Sleep if we've hit the rate limit in current window."""
+        now = time.time()
+
+        # Remove requests outside the window
+        self.requests = [ts for ts in self.requests if now -
+                         ts < self.window_seconds]
+
+        # If at limit, sleep until oldest request expires
+        if len(self.requests) >= self.max_requests:
+            sleep_time = self.window_seconds - (now - self.requests[0])
+            if sleep_time > 0:
+                logger.warning(
+                    f"Rate limit hit. Sleeping {sleep_time:.1f}s...")
+                time.sleep(sleep_time)
+
+        # Record this request
+        self.requests.append(now)
+
+    def get_remaining(self) -> int:
+        """Get requests remaining in current window."""
+        now = time.time()
+        recent_requests = [
+            ts for ts in self.requests if now - ts < self.window_seconds]
+        return self.max_requests - len(recent_requests)
+
+
+class CryptoQuantClient:
+    def __init__(self, api_key: str = None):
+        self.api_key = api_key or os.getenv('CRYPTOQUANT_API_KEY')
+        if not self.api_key:
+            raise ValueError("API key required - CRYPTOQUANT_API_KEY env var not set.")
+
+        self.base_url = 'https://api.cryptoquant.com/v1'
+        headers = {
+            'Authorization': f'Bearer {self.api_key}',
+            'User-Agent': f'CryptoQuantBot/{random.uniform(1.0, 2.0):.1f}'
+        }
+        self.logger = logger
+        self.rate_limiter = RateLimiter()
+
+        # Create a robust session with retries
+        self.session = requests.Session()
+        ssl_context = ssl.create_default_context(ssl.Purpose.SERVER_AUTH)
+        ssl_context.options |= 0x80000000
+        retry_strategy = Retry(
+            total=5,
+            status_forcelist=[429, 500, 502, 503, 504],
+            allowed_methods=["HEAD", "GET", "OPTIONS"],
+            backoff_factor=1
+        )
+        
+        class SSLAdapter(HTTPAdapter):
+            def init_poolmanager(self, *args, **kwargs):
+                kwargs['ssl_context'] = ssl_context
+                return super().init_poolmanager(*args, **kwargs)
+
+        adapter = SSLAdapter(max_retries=retry_strategy)
+        self.session.mount("https://", adapter)
+        self.session.headers.update(headers)
+
+    def _make_request(self, method: str, url: str, **kwargs) -> requests.Response:
+        """Internal method with robust error handling."""
+        try:
+            if method.upper() == 'GET':
+                response = self.session.get(url, **kwargs, timeout=30)
+            elif method.upper() == 'POST':
+                response = self.session.post(url, **kwargs, timeout=30)
+            else:
+                raise ValueError(f"Unsupported method: {method}")
+
+            response.raise_for_status()
+            return response
+        except requests.exceptions.RequestException as e:
+            self.logger.error(f"Request failed after all retries: {e}")
+            raise
+
+    def _fetch_metric_chunked(self, url_suffix: str, params: Dict, days_back: int, chunk_days: int = 90) -> List[Dict]:
+        """Fetch data in chunks to avoid API limits."""
+        all_data = []
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=days_back)
+        
+        current_start = start_date
+        while current_start < end_date:
+            current_end = min(current_start + timedelta(days=chunk_days), end_date)
+            
+            # Format dates for API
+            chunk_params = params.copy()
+            chunk_params.update({
+                'from': current_start.strftime('%Y%m%d'),
+                'to': current_end.strftime('%Y%m%d'),
+                'limit': 10000  # Request max limit just in case
+            })
+            
+            self.rate_limiter.wait_if_needed()
+            url = f"{self.base_url}/{url_suffix}"
+            
+            try:
+                self.logger.info(f"Fetching chunk {chunk_params['from']} to {chunk_params['to']}")
+                response = self._make_request('GET', url, params=chunk_params)
+                
+                # Check for specific "Out of allowed range" error to stop early if needed?
+                # Actually, if we iterate Old -> New, the early chunks fail, later succeed.
+                # If we want to optimize, we could start New -> Old and stop on failure.
+                # But current Old -> New approach ensures we get the most recent valid data eventually.
+                
+                data = response.json()
+                result = data.get('result', {}).get('data', [])
+                all_data.extend(result)
+                
+            except requests.exceptions.HTTPError as e:
+                # Handle 400 specially if it's "Out of allowed request range"
+                if e.response.status_code == 400:
+                    self.logger.warning(f"Chunk failed (likely data limit): {e}. Continuing to next chunk...")
+                else:
+                    self.logger.error(f"HTTP Error fetching chunk: {e}")
+            except Exception as e:
+                self.logger.error(f"Error fetching chunk: {e}")
+                
+            current_start = current_end + timedelta(days=1) # Move to next day
+            time.sleep(0.5) # Gentle pace
+
+        # Remove duplicates based on date if any? usually API is clean.
+        return all_data
+
+    def get_nupl(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 364) -> List[Dict]:
+        logger.info(f"Fetching {asset} NUPL ({days_back} days)")
+        params = {'window': window}
+        return self._fetch_metric_chunked(f"{asset.lower()}/network-indicator/nupl", params, days_back)
+
+    def get_mvrv(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 364) -> List[Dict]:
+        logger.info(f"Fetching {asset} MVRV ({days_back} days)")
+        params = {'window': window}
+        return self._fetch_metric_chunked(f"{asset.lower()}/market-indicator/mvrv", params, days_back)
+
+    def get_lth_sopr(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 364) -> List[Dict]:
+        logger.info(f"Fetching {asset} LTH-SOPR ({days_back} days)")
+        params = {'window': window}
+        return self._fetch_metric_chunked(f"{asset.lower()}/market-indicator/sopr", params, days_back)
+
+    def get_puell_multiple(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 364) -> List[Dict]:
+        logger.info(f"Fetching {asset} Puell Multiple ({days_back} days)")
+        params = {'window': window}
+        return self._fetch_metric_chunked(f"{asset.lower()}/network-indicator/puell-multiple", params, days_back)
+
+    def get_fund_flow_ratio(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 364) -> List[Dict]:
+        logger.info(f"Fetching {asset} Fund Flow Ratio ({days_back} days)")
+        params = {'window': window, 'exchange': 'all_exchange'}
+        return self._fetch_metric_chunked(f"{asset.lower()}/flow-indicator/fund-flow-ratio", params, days_back)
+
+    def get_funding_rates(self, asset: str = config.ASSET, exchange: str = 'all_exchange', window: str = 'day', days_back: int = 90) -> List[Dict]:
+        self.logger.info(f"Fetching {asset} funding rates ({days_back} days)")
+        params = {'window': window, 'exchange': exchange}
+        return self._fetch_metric_chunked(f"{asset.lower()}/market-data/funding-rates", params, days_back)
+
+    def get_exchange_net_flow(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 90) -> List[Dict]:
+        self.logger.info(f"Fetching {asset} exchange netflow ({days_back} days)")
+        params = {'window': window, 'exchange': 'all_exchange'}
+        return self._fetch_metric_chunked(f"{asset.lower()}/exchange-flows/netflow", params, days_back)
+
+    def get_sopr_ratio(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 90) -> List[Dict]:
+        self.logger.info(f"Fetching {asset} SOPR Ratio ({days_back} days)")
+        params = {'window': window}
+        return self._fetch_metric_chunked(f"{asset.lower()}/market-indicator/sopr-ratio", params, days_back)
+
+    def get_active_addresses(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 90) -> List[Dict]:
+        self.logger.info(f"Fetching {asset} active addresses ({days_back} days)")
+        params = {'window': window}
+        if asset.lower() == 'eth':
+            suffix = f"{asset.lower()}/network-data/addresses-count-all"
+        else:
+            suffix = f"{asset.lower()}/network-data/addresses-count"
+        return self._fetch_metric_chunked(suffix, params, days_back)
+
+    def get_leverage_ratio(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 90) -> List[Dict]:
+        """Get Estimated Leverage Ratio."""
+        self.logger.info(f"Fetching {asset} Estimated Leverage Ratio ({days_back} days)")
+        params = {'window': window, 'exchange': 'all_exchange'}
+        return self._fetch_metric_chunked(f"{asset.lower()}/market-indicator/estimated-leverage-ratio", params, days_back)
+
+    def get_exchange_whale_ratio(self, asset: str = config.ASSET, window: str = 'day', days_back: int = 90) -> List[Dict]:
+        """Get the ratio of whale-sized deposits to total exchange deposits."""
+        self.logger.info(f"Fetching {asset} Exchange Whale Ratio ({days_back} days)")
+        params = {'window': window, 'exchange': 'all_exchange'}
+        return self._fetch_metric_chunked(f"{asset.lower()}/flow-indicator/exchange-whale-ratio", params, days_back)
+
+    def fetch_all_onchain(self, asset: str = config.ASSET, days_back: int = 364) -> Dict[str, List[Dict]]:
+        """Batch all features from config.ONCHAIN_FEATURE_NAMES."""
+        features = {}
+        for feat in config.ONCHAIN_FEATURE_NAMES:
+            self.rate_limiter.wait_if_needed()
+            if feat == 'funding_rate':
+                features[feat] = self.get_funding_rates(
+                    asset, 'all_exchange', 'day', days_back)
+            elif feat == 'net_exchange_flow':
+                features[feat] = self.get_exchange_net_flow(
+                    asset, 'day', days_back)
+            elif feat == 'sopr_ratio':
+                features[feat] = self.get_sopr_ratio(asset, 'day', days_back)
+            elif feat == 'active_addresses':
+                features[feat] = self.get_active_addresses(
+                    asset, 'day', days_back)
+            elif feat == 'leverage_ratio':
+                features[feat] = self.get_leverage_ratio(
+                    asset, 'day', days_back)
+            elif feat == 'exchange_whale_ratio':
+                features[feat] = self.get_exchange_whale_ratio(
+                    asset, 'day', days_back)
+            elif feat == 'nupl':
+                features[feat] = self.get_nupl(asset, 'day', days_back)
+            elif feat == 'mvrv':
+                features[feat] = self.get_mvrv(asset, 'day', days_back)
+            elif feat == 'lth_sopr':
+                features[feat] = self.get_lth_sopr(asset, 'day', days_back)
+            elif feat == 'puell_multiple':
+                features[feat] = self.get_puell_multiple(
+                    asset, 'day', days_back)
+            elif feat == 'fund_flow_ratio':
+                features[feat] = self.get_fund_flow_ratio(
+                    asset, 'day', days_back)
+
+            time.sleep(1)  # Pace for limits
+        return features
+
+if __name__ == "__main__":
+    # Test
+    try:
+        client = CryptoQuantClient()
+        print("Fetching funding rates as test...")
+        rates = client.get_funding_rates(days_back=7)
+        print(f"Got {len(rates)} records")
+        if rates:
+            print(rates[-1])
+    except Exception as e:
+        print(f"Error: {e}")
diff --git a/features.py b/features.py
new file mode 100644
index 0000000..7d3b6bb
--- /dev/null
+++ b/features.py
@@ -0,0 +1,164 @@
+import pandas as pd
+import numpy as np
+import strategy_config as config
+from numba import njit
+
+# ==============================================================================
+# --- LOOKBACK WINDOW FOR ROLLING Z-SCORES (PREVENTS LOOK-AHEAD BIAS) ---
+# ==============================================================================
+ZSCORE_LOOKBACK = 168  # 1 week in hourly candles
+MIN_PERIODS_ZSCORE = 24
+
+@njit
+def ema_nb(arr, window):
+    alpha = 2.0 / (window + 1.0)
+    ema_arr = np.full_like(arr, np.nan)
+    ema_arr[0] = arr[0]
+    for i in range(1, len(arr)):
+        ema_arr[i] = alpha * arr[i] + (1.0 - alpha) * ema_arr[i-1]
+    return ema_arr
+
+@njit
+def atr_nb(high, low, close, window):
+    n = len(close)
+    tr = np.zeros(n)
+    atr = np.full_like(close, np.nan)
+    for i in range(1, n):
+        tr[i] = max(high[i] - low[i], abs(high[i] - close[i-1]),
+                    abs(low[i] - close[i-1]))
+    tr_series = tr[1:window+1]
+    atr[window] = np.mean(tr_series)
+    for i in range(window + 1, n):
+        atr[i] = (atr[i-1] * (window - 1) + tr[i]) / window
+    return atr
+
+def _add_price_action_features(df: pd.DataFrame) -> pd.DataFrame:
+    """Adds price action based features like returns, momentum, SMAs."""
+    # Ensure column names match what we expect (lowercase usually)
+    # If user has 'Close', 'Open' etc. we might need to adjust or rename before calling this.
+    # Assuming the input df has lowercase 'close', 'high', 'low' etc. or we handle it.
+    
+    # Map typical Capitalized names to lowercase if needed
+    mapper = {c: c.lower() for c in df.columns if c in ['Open', 'High', 'Low', 'Close', 'Volume']}
+    if mapper:
+        df = df.rename(columns=mapper)
+
+    df['returns'] = df['close'].pct_change()
+    df['log_returns'] = np.log(df['close'] / df['close'].shift(1))
+    
+    df[f'momentum_{config.MOMENTUM_1_PERIODS}'] = df['close'].pct_change(
+        periods=config.MOMENTUM_1_PERIODS)
+    df[f'momentum_{config.MOMENTUM_2_PERIODS}'] = df['close'].pct_change(
+        periods=config.MOMENTUM_2_PERIODS)
+        
+    df[f'SMA_{config.SMA_FAST_PERIODS}'] = df['close'].rolling(
+        window=config.SMA_FAST_PERIODS).mean()
+    df[f'SMA_{config.SMA_SLOW_PERIODS}'] = df['close'].rolling(
+        window=config.SMA_SLOW_PERIODS).mean()
+        
+    df[f'volatility_{config.VOLATILITY_PERIODS}'] = df['log_returns'].rolling(
+        window=config.VOLATILITY_PERIODS).std()
+    return df
+
+def _add_mean_reversion_features(df: pd.DataFrame) -> pd.DataFrame:
+    """Adds Bollinger Band features."""
+    window = config.BBAND_PERIODS
+    df['bb_middle'] = df['close'].rolling(window=window).mean()
+    bb_std = df['close'].rolling(window=window).std()
+    df['bb_upper'] = df['bb_middle'] + (bb_std * 2)
+    df['bb_lower'] = df['bb_middle'] - (bb_std * 2)
+    
+    df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / df['bb_middle']
+    
+    denom = (df['bb_upper'] - df['bb_lower'])
+    denom = denom.replace(0, np.nan)
+    df['bb_percent'] = (df['close'] - df['bb_lower']) / denom
+    return df
+
+def _add_volatility_features(df: pd.DataFrame) -> pd.DataFrame:
+    """Adds ATR features."""
+    atr_values = atr_nb(
+        df['high'].to_numpy(),
+        df['low'].to_numpy(),
+        df['close'].to_numpy(),
+        config.ATR_PERIOD
+    )
+    df['atr'] = atr_values
+    return df
+
+def _add_onchain_features(df: pd.DataFrame) -> pd.DataFrame:
+    """Adds on-chain features with z-score normalization and cycle MAs."""
+    for col_name in config.ONCHAIN_FEATURE_NAMES:
+        if col_name in df.columns:
+            col_data = df[col_name]
+            # Ensure numeric
+            df[col_name] = pd.to_numeric(col_data, errors='coerce')
+
+            # Custom transforms
+            if col_name == 'net_exchange_flow':
+                df[col_name] = -df[col_name]  # Outflows bullish
+            elif col_name == 'funding_rate':
+                df[col_name] *= 100  # To %
+
+            # Rolling Z-Score (prevents look-ahead bias)
+            if df[col_name].notna().sum() > MIN_PERIODS_ZSCORE:
+                rolling_mean = df[col_name].rolling(
+                    window=ZSCORE_LOOKBACK, min_periods=MIN_PERIODS_ZSCORE).mean()
+                rolling_std = df[col_name].rolling(
+                    window=ZSCORE_LOOKBACK, min_periods=MIN_PERIODS_ZSCORE).std()
+                
+                rolling_std = rolling_std.replace(0, np.nan)
+                df[f'{col_name}_z'] = (df[col_name] - rolling_mean) / rolling_std
+
+            # Cycle MAs for key metrics
+            if col_name == 'nupl':
+                df[f'{col_name}_ma_{config.NUPL_MA_PERIODS}'] = df[col_name].rolling(
+                    config.NUPL_MA_PERIODS).mean()
+            elif col_name == 'mvrv':
+                df[f'{col_name}_ma_{config.MVRV_MA_PERIODS}'] = df[col_name].rolling(
+                    config.MVRV_MA_PERIODS).mean()
+        else:
+            # Create NaN columns if missing so model doesn't crash (or handle later)
+            df[f'{col_name}_z'] = np.nan
+            
+    return df
+
+def _add_target_variables(df: pd.DataFrame) -> pd.DataFrame:
+    """Adds target variables for ML training."""
+    # 1% gain threshold in prediction horizon
+    df['future_price'] = df['close'].shift(-config.PREDICTION_PERIOD)
+    future_ret = (df['future_price'] - df['close']) / df['close']
+    
+    df['target'] = (future_ret > 0.01).astype(int)
+    return df
+
+def create_features(df: pd.DataFrame) -> pd.DataFrame:
+    """Engineers all features using modular sub-functions."""
+    df_feat = df.copy()
+
+    # Pass through raw data needed for ATR/exits
+    # Ensure lowercase columns first
+    mapper = {c: c.lower() for c in df_feat.columns if c in ['Open', 'High', 'Low', 'Close', 'Volume']}
+    if mapper:
+        df_feat = df_feat.rename(columns=mapper)
+
+    df_feat['high_raw'] = df_feat['high']
+    df_feat['low_raw'] = df_feat['low']
+    df_feat['close_raw'] = df_feat['close']
+    
+    # 1. Price-Action
+    df_feat = _add_price_action_features(df_feat)
+
+    # 2. Mean-Reversion
+    df_feat = _add_mean_reversion_features(df_feat)
+
+    # 3. Volatility
+    df_feat = _add_volatility_features(df_feat)
+
+    # 4. On-Chain
+    df_feat = _add_onchain_features(df_feat)
+
+    # 5. Target
+    df_feat = _add_target_variables(df_feat)
+    
+    return df_feat
diff --git a/metrics.py b/metrics.py
index 82d80be..6e8e599 100644
--- a/metrics.py
+++ b/metrics.py
@@ -19,7 +19,21 @@ class Perf:
     avg_slippage_bps: float
 
 
-def compute_metrics(equity_curve: pd.Series, trades: list[dict]) -> Perf:
+def compute_metrics(equity_curve: pd.Series, trades: list[dict], periods_per_year: int = None) -> Perf:
+    """
+    Compute backtest performance metrics.
+    
+    Args:
+        equity_curve: Series of portfolio values over time
+        trades: List of trade event dictionaries
+        periods_per_year: Number of periods in a year for Sharpe annualization.
+                          Defaults to hourly (252 * 24 = 6048).
+                          For minute bars: 252 * 24 * 60 = 525600
+                          For daily bars: 252
+    """
+    if periods_per_year is None:
+        periods_per_year = 252 * 24  # Default to hourly bars
+    
     ret = equity_curve.pct_change().fillna(0.0)
     total_return = equity_curve.iat[-1] / equity_curve.iat[0] - 1.0
     cummax = equity_curve.cummax()
@@ -27,7 +41,7 @@ def compute_metrics(equity_curve: pd.Series, trades: list[dict]) -> Perf:
     max_drawdown = dd
 
     if ret.std(ddof=0) > 0:
-        sharpe = (ret.mean() / ret.std(ddof=0)) * np.sqrt(252 * 24 * 60)  # minute bars -> annualized
+        sharpe = (ret.mean() / ret.std(ddof=0)) * np.sqrt(periods_per_year)
     else:
         sharpe = 0.0
 
diff --git a/prepare_data.py b/prepare_data.py
new file mode 100644
index 0000000..3ef343a
--- /dev/null
+++ b/prepare_data.py
@@ -0,0 +1,140 @@
+import pandas as pd
+import strategy_config as config
+from cryptoquant_client import CryptoQuantClient
+from features import create_features
+import argparse
+import os
+import time
+
+def fetch_onchain_data(client, asset='BTC', days_back=365*2):
+    """Fetches and aligns on-chain data."""
+    print(f"Fetching on-chain data for {asset}...")
+    
+    # 1. Fetch all metrics
+    # Note: This might take a while due to rate limits
+    raw_data = client.fetch_all_onchain(asset, days_back=days_back)
+    
+    dfs = []
+    for metric_name, records in raw_data.items():
+        if not records:
+            print(f"⚠️  No data for {metric_name}")
+            continue
+            
+        df = pd.DataFrame(records)
+        # Standardize date column
+        if 'date' in df.columns:
+            df['timestamp'] = pd.to_datetime(df['date'])
+        else:
+            # Try to find date-like column
+            cols = [c for c in df.columns if 'date' in c or 'time' in c]
+            if cols:
+                df['timestamp'] = pd.to_datetime(df[cols[0]])
+            else:
+                print(f"❌ Could not find date column for {metric_name}")
+                continue
+        
+        df = df.set_index('timestamp').sort_index()
+        
+        # Keep only the value column
+        # Value column name varies by endpoint. 
+        # usually same as metric name or 'value' or specific name.
+        # Simple heuristic: take the first numeric column that isn't 'timestamp'
+        numeric_cols = df.select_dtypes(include=['number']).columns
+        if len(numeric_cols) > 0:
+            val_col = numeric_cols[0]
+            df = df[[val_col]].rename(columns={val_col: metric_name})
+            
+            # Resample to hourly and forward fill (since on-chain is daily/block)
+            # CAUTION: Daily data from CryptoQuant (e.g. Total Active Addresses) is usually 
+            # timestamped at 00:00 but represents the FULL day's activity.
+            # If we use it at 10:00 AM on the same day, that is Lookahead Bias.
+            # We must SHIFT it by 1 day to ensure we only use it AFTER it's available (next day).
+            # Funding rates might be 8h, but 'window=day' implies daily aggregation.
+            # Safer to lag by 24h.
+            
+            df = df.shift(1, freq='D') # Shift index by 1 Day
+            
+            df = df.resample('1h').ffill()
+            dfs.append(df)
+        else:
+            print(f"❌ No numeric data for {metric_name}")
+
+    if not dfs:
+        return pd.DataFrame()
+
+    # Concatenate all on-chain metrics
+    onchain_df = pd.concat(dfs, axis=1)
+    return onchain_df
+
+def main():
+    parser = argparse.ArgumentParser(description="Prepare data for MVRV Strategy")
+    parser.add_argument("--csv", required=True, help="Path to OHLCV CSV file")
+    parser.add_argument("--days", type=int, default=365, help="Days of on-chain data to fetch")
+    args = parser.parse_args()
+
+    # 1. Load OHLCV
+    print(f"Loading OHLCV from {args.csv}...")
+    df_ohlcv = pd.read_csv(args.csv)
+    
+    # Standardize OHLCV columns/index
+    # Expecting Timestamp/Date column
+    if 'Timestamp' in df_ohlcv.columns:
+        # Smart detection of unit
+        ts_max = df_ohlcv['Timestamp'].max()
+        if ts_max < 3000000000: # < 3B, likely seconds (valid until ~2065)
+            unit = 's'
+        elif ts_max < 3000000000000: # < 3T, likely milliseconds
+            unit = 'ms'
+        else:
+            unit = None # Let pandas guess (ns?)
+            
+        df_ohlcv['timestamp'] = pd.to_datetime(df_ohlcv['Timestamp'], unit=unit)
+    elif 'Date' in df_ohlcv.columns:
+        df_ohlcv['timestamp'] = pd.to_datetime(df_ohlcv['Date'])
+    
+    df_ohlcv = df_ohlcv.set_index('timestamp').sort_index()
+    
+    # Resample to 1H for feature engineering
+    df_1h = df_ohlcv.resample('1h').agg({
+        'Open': 'first',
+        'High': 'max',
+        'Low': 'min',
+        'Close': 'last',
+        'Volume': 'sum'
+    }).dropna()
+    
+    # Rename to lowercase for features.py
+    df_1h = df_1h.rename(columns={
+        'Open': 'open', 'High': 'high', 'Low': 'low', 'Close': 'close', 'Volume': 'volume'
+    })
+    
+    print(f"OHLCV 1H shape: {df_1h.shape}")
+
+    # 2. Fetch On-Chain
+    client = CryptoQuantClient()
+    df_onchain = fetch_onchain_data(client, asset=config.ASSET, days_back=args.days)
+    print(f"On-Chain shape: {df_onchain.shape}")
+
+    # 3. Merge
+    # Left join on OHLCV index
+    df_merged = df_1h.join(df_onchain, how='left')
+    
+    # Forward fill on-chain data (it's slower than price)
+    df_merged = df_merged.ffill()
+    
+    # Drop rows where we still have NaNs (start of data)
+    df_merged = df_merged.dropna()
+    print(f"Merged shape: {df_merged.shape}")
+
+    # 4. Create Features
+    print("Engineering features...")
+    df_features = create_features(df_merged)
+    df_features = df_features.dropna()
+    
+    # 5. Save
+    print(f"Saving features to {config.FEATURES_PATH}...")
+    df_features.to_csv(config.FEATURES_PATH)
+    print("Done.")
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
index 12727f2..f096102 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,5 +5,10 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
+    "numba>=0.63.1",
+    "python-dotenv>=1.2.1",
+    "requests>=2.32.5",
+    "scikit-learn>=1.8.0",
     "ta>=0.11.0",
+    "xgboost>=3.1.2",
 ]
diff --git a/strategy_config.py b/strategy_config.py
new file mode 100644
index 0000000..c364e60
--- /dev/null
+++ b/strategy_config.py
@@ -0,0 +1,95 @@
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# ==============================================================================
+# --- 1. MASTER STRATEGY CONFIG ---
+# ==============================================================================
+ASSET = 'BTC'
+TIMEFRAME_HOURS = 1.0  # We work with 1h candles for the ML model
+
+# --- Define Strategy Logic (in HOURS) ---
+PREDICT_HOURS = 5.0
+SMA_FAST_HOURS = 12.0
+SMA_SLOW_HOURS = 50.0
+VOLATILITY_HOURS = 12.0
+BBAND_HOURS = 20.0
+MOMENTUM_1_HOURS = 5.0
+MOMENTUM_2_HOURS = 10.0
+ATR_PERIOD_HOURS = 10.0
+
+# --- Backtest / Trade Config ---
+PROB_THRESHOLD = 0.55
+SL_ATR_MULT = 1.5  # Widened from 0.8
+TP_ATR_MULT = 3.0  # Widened from 1.5
+FEES_PERCENT = 0.001
+SLIPPAGE_PERCENT = 0.001
+
+# --- MVRV/NUPL Strategy-Specific Config ---
+NUPL_MA_HOURS = 200.0  # ~200-day MA (normalized to hours if needed, source used hours directly)
+MVRV_MA_HOURS = 111.0  # ~111-day MA
+
+# Thresholds for "Overheated" Regime
+MVRV_Z_THRESH = 1.5
+NUPL_THRESH = 0.6
+FUNDING_FILTER = -0.05  # Filter out if funding rate is below this (but source used > -0.05, wait check source)
+
+# Source: (fund_grid_bool_base > funding_rate_filters_b) where filter is -0.05.
+# So we want Funding Rate > -0.05.
+
+# ==============================================================================
+# --- 2. CRYPTOQUANT METRICS ---
+# ==============================================================================
+ONCHAIN_FEATURE_NAMES = [
+    'funding_rate', 'sopr_ratio', 'leverage_ratio',
+    'net_exchange_flow', 'fund_flow_ratio',
+    'exchange_whale_ratio',
+    'nupl', 'mvrv', 'lth_sopr', 'puell_multiple',
+    'active_addresses'
+]
+
+# ==============================================================================
+# --- 3. AUTO-CALCULATED PARAMETERS ---
+# ==============================================================================
+import math
+
+def hours_to_candles(hours):
+    return math.ceil(hours / TIMEFRAME_HOURS)
+
+PREDICTION_PERIOD = hours_to_candles(PREDICT_HOURS)
+SMA_FAST_PERIODS = hours_to_candles(SMA_FAST_HOURS)
+SMA_SLOW_PERIODS = hours_to_candles(SMA_SLOW_HOURS)
+VOLATILITY_PERIODS = hours_to_candles(VOLATILITY_HOURS)
+BBAND_PERIODS = hours_to_candles(BBAND_HOURS)
+MOMENTUM_1_PERIODS = hours_to_candles(MOMENTUM_1_HOURS)
+MOMENTUM_2_PERIODS = hours_to_candles(MOMENTUM_2_HOURS)
+ATR_PERIOD = hours_to_candles(ATR_PERIOD_HOURS)
+
+NUPL_MA_PERIODS = hours_to_candles(NUPL_MA_HOURS)
+MVRV_MA_PERIODS = hours_to_candles(MVRV_MA_HOURS)
+
+# --- FEATURE NAMES ---
+BASE_FEATURE_NAMES = [
+    'returns', 'log_returns', f'momentum_{MOMENTUM_1_PERIODS}',
+    f'momentum_{MOMENTUM_2_PERIODS}', f'SMA_{SMA_FAST_PERIODS}',
+    f'SMA_{SMA_SLOW_PERIODS}', f'volatility_{VOLATILITY_PERIODS}',
+    'bb_lower', 'bb_middle', 'bb_upper', 'bb_width', 'bb_percent', 'atr'
+]
+
+# Append cycle MAs
+BASE_FEATURE_NAMES += [
+    f'nupl_ma_{NUPL_MA_PERIODS}', f'mvrv_ma_{MVRV_MA_PERIODS}'
+]
+
+ONCHAIN_Z_SCORES = [f'{feat}_z' for feat in ONCHAIN_FEATURE_NAMES]
+FEATURE_NAMES = BASE_FEATURE_NAMES + ONCHAIN_Z_SCORES
+
+# --- PATHS ---
+DATA_DIR = 'data'
+if not os.path.exists(DATA_DIR):
+    os.makedirs(DATA_DIR)
+
+FEATURES_PATH = os.path.join(DATA_DIR, 'features.csv')
+MODEL_PATH = os.path.join(DATA_DIR, 'model.pkl')
diff --git a/train_model.py b/train_model.py
new file mode 100644
index 0000000..7c8ba28
--- /dev/null
+++ b/train_model.py
@@ -0,0 +1,129 @@
+import pandas as pd
+import numpy as np
+import xgboost as xgb
+import pickle
+import sys
+from pathlib import Path
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report, precision_recall_fscore_support
+import strategy_config as config
+
+def train_model():
+    print(f"--- Starting Model Training Pipeline ---")
+
+    try:
+        if not os.path.exists(config.FEATURES_PATH):
+            print(f"Error: {config.FEATURES_PATH} not found. Run prepare_data.py first.")
+            return
+
+        df = pd.read_csv(config.FEATURES_PATH)
+        # Ensure index if needed, but read_csv usually reads generic index unless specified
+        # prepare_data saved with index (timestamp)
+        if 'timestamp' in df.columns:
+            df = df.set_index('timestamp')
+        
+        print(f"Loaded {len(df)} data points from {config.FEATURES_PATH}")
+
+        y = df['target']
+        print(f"Buy signals rate: {y.mean():.1%}")
+        
+        # Use the dynamic feature list directly from config.py
+        # Check if all features exist
+        available_feats = [f for f in config.FEATURE_NAMES if f in df.columns]
+        missing_feats = [f for f in config.FEATURE_NAMES if f not in df.columns]
+        
+        if missing_feats:
+            print(f"⚠️ Missing features: {missing_feats}")
+            print(f"Proceeding with {len(available_feats)} features.")
+        
+        X = df[available_feats]
+
+        X_train, X_test, y_train, y_test = train_test_split(
+            X, y, test_size=0.3, shuffle=False
+        )
+        
+        # Save the test set start index for the backtester to use
+        # This prevents train/test leakage during backtesting
+        test_start_idx = len(X_train)
+        test_start_timestamp = df.index[test_start_idx] if hasattr(df.index, '__getitem__') else test_start_idx
+        
+        # Save split info
+        split_info = {
+            'test_start_idx': test_start_idx,
+            'test_start_timestamp': str(test_start_timestamp),
+            'train_size': len(X_train),
+            'test_size': len(X_test)
+        }
+        split_info_path = config.MODEL_PATH.replace('.pkl', '_split.pkl')
+        with open(split_info_path, 'wb') as f:
+            pickle.dump(split_info, f)
+        print(f"Split info saved: Test starts at index {test_start_idx} ({test_start_timestamp})")
+        
+        print(f"Training set size: {len(X_train)}")
+        print(f"Test set size: {len(X_test)}")
+
+        print("\nTraining XGBoost model...")
+        model = xgb.XGBClassifier(
+            objective='binary:logistic',
+            eval_metric='logloss',
+            n_estimators=200,
+            learning_rate=0.05,
+            scale_pos_weight=8.0,  
+            max_depth=5,
+            subsample=0.8,
+            random_state=42,
+            early_stopping_rounds=10
+        )
+        
+        model.fit(
+            X_train, y_train, 
+            eval_set=[(X_test, y_test)], 
+            verbose=False
+        )
+        print("Model training complete.")
+
+        y_pred = model.predict(X_test)
+        y_proba = model.predict_proba(X_test)[:, 1]
+        
+        accuracy = accuracy_score(y_test, y_pred)
+        print(f"\n--- Model Evaluation ---")
+        print(f"Accuracy on Test Set: {accuracy * 100:.2f}%")
+        print("\nClassification Report:")
+        print(classification_report(y_test, y_pred, target_names=['Hold/Sell (0)', 'Buy (1)']))
+        
+        print("\n--- Probability Threshold Analysis ---")
+        thresholds = [0.35, 0.40, 0.45, 0.50, 0.55, 0.60]
+        for thresh in thresholds:
+            pred_at_thresh = (y_proba >= thresh).astype(int)
+            if pred_at_thresh.sum() > 0:
+                precision, recall, f1, _ = precision_recall_fscore_support(
+                    y_test, pred_at_thresh, average='binary', zero_division=0
+                )
+                signal_rate = pred_at_thresh.mean() * 100
+                print(f"  Thresh {thresh:.2f}: Precision={precision:.2f}, Recall={recall:.2f}, "
+                      f"F1={f1:.2f}, Signals={signal_rate:.1f}%")
+            else:
+                print(f"  Thresh {thresh:.2f}: No signals generated")
+
+        with open(config.MODEL_PATH, 'wb') as f:
+            pickle.dump(model, f)
+            
+        print(f"\nSUCCESS: Model saved to {config.MODEL_PATH}")
+
+        # Feature Importance
+        importance = pd.DataFrame({
+            'feature': X.columns,
+            'importance': model.feature_importances_
+        }).sort_values('importance', ascending=False)
+
+        print("\nTop 10 Features:")
+        print(importance.head(10))
+
+    except Exception as e:
+        print(f"AN ERROR OCCURRED: {e}")
+        import traceback
+        traceback.print_exc()
+
+import os
+if __name__ == "__main__":
+    train_model()
diff --git a/uv.lock b/uv.lock
index d7fed2f..59fc1d2 100644
--- a/uv.lock
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