Hello, I think my Sharpe index calculation is insane? I see a strat that does 672 trades with a winrate of 14%, loses literally all the money (profit factor is 0.15, trades until down 100%), but its Sharpe Ratio is somehow 4.3, which stands of an outstandingly awesome strategy. There is my code to look at: import numpy as np
import pandas as pd
import vectorbt as vbt
import ccxt
from datetime import datetime, timedelta
from numba import jit, njit, prange
import warnings
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import time
from threading import Lock
import matplotlib.pyplot as plt
import seaborn as sns
import os
from itertools import product

warnings.filterwarnings('ignore')

class VolumeProfileStrategy:
    def __init__(self, 
                 symbol,
                 exchange_id='binance',
                 price_bucket_count=100,  # Number of price buckets for volume profile
                 profile_period='1d',     # Period for calculating volume profile
                 volume_threshold=0.0001,     # Threshold for identifying significant volume nodes
                 node_distance=0.00001,       # Minimum distance between volume nodes
                 entry_confirmation_bars=3, # Bars needed for entry confirmation
                 exit_confirmation_bars=2,  # Bars needed for exit confirmation
                 risk_per_trade=0.02,
                 interval='1m',
                 max_workers=5):
        
        self.symbol = symbol
        self.price_bucket_count = price_bucket_count
        self.profile_period = profile_period
        self.volume_threshold = volume_threshold
        self.node_distance = node_distance
        self.entry_confirmation_bars = entry_confirmation_bars
        self.exit_confirmation_bars = exit_confirmation_bars
        self.risk_per_trade = risk_per_trade
        self.interval = interval
        self.max_workers = max_workers
        
        # Create exchange connection pool
        self.exchange_pool = [
            getattr(ccxt, exchange_id)({
                'enableRateLimit': True,
                'options': {'defaultType': 'future'}
            }) for _ in range(max_workers)
        ]
        self.pool_lock = Lock()
        self.pool_index = 0
        
        # Pre-compile Numba functions by running them with dummy data
        self._compile_numba_functions()

    def _get_exchange(self):
        """Get next available exchange instance from the pool"""
        with self.pool_lock:
            exchange = self.exchange_pool[self.pool_index]
            self.pool_index = (self.pool_index + 1) % len(self.exchange_pool)
            return exchange

    @staticmethod
    @jit(nopython=True)
    def _calculate_volume_profile(prices, volumes, bucket_count):
        """Calculate volume profile metrics using Numba optimization"""
        if len(prices) == 0 or len(volumes) == 0:
            return np.zeros(bucket_count), np.zeros(bucket_count)
            
        min_price = np.min(prices)
        max_price = np.max(prices)
        
        # Prevent division by zero
        if min_price == max_price:
            price_range = 1.0
        else:
            price_range = max_price - min_price
            
        bucket_size = price_range / bucket_count
        
        volume_profile = np.zeros(bucket_count)
        price_buckets = np.zeros(bucket_count)
        
        for i in range(len(prices)):
            if bucket_size > 0:
                bucket_idx = int((prices[i] - min_price) / bucket_size)
                if 0 <= bucket_idx < bucket_count:
                    volume_profile[bucket_idx] += volumes[i]
                    price_buckets[bucket_idx] = min_price + (bucket_idx + 0.5) * bucket_size
            
        return volume_profile, price_buckets
    
    @staticmethod
    @jit(nopython=True)
    def _identify_volume_nodes(volume_profile, price_buckets, threshold, min_distance):
        """Identify high volume nodes using Numba optimization with more lenient conditions"""
        volume_profile = np.asarray(volume_profile)
        price_buckets = np.asarray(price_buckets)
        
        if len(volume_profile) == 0 or len(price_buckets) == 0:
            return np.zeros((0, 2), dtype=np.float64)
            
        total_volume = np.sum(volume_profile)
        if total_volume == 0:
            return np.zeros((0, 2), dtype=np.float64)
            
        normalized_volume = volume_profile / total_volume
        
        # Pre-allocate maximum possible nodes
        max_nodes = len(volume_profile)
        temp_nodes = np.zeros((max_nodes, 2), dtype=np.float64)
        node_count = 0
        
        # Find peaks more leniently
        for i in range(1, len(volume_profile) - 1):
            is_peak = normalized_volume[i] > threshold
            
            # Check if it's a local maximum or close to being one
            if i > 0:
                is_peak = is_peak and (normalized_volume[i] >= normalized_volume[i-1] * 0.95)  # Allow 5% tolerance
            if i < len(volume_profile) - 1:
                is_peak = is_peak and (normalized_volume[i] >= normalized_volume[i+1] * 0.95)  # Allow 5% tolerance
                
            if is_peak:
                temp_nodes[node_count, 0] = price_buckets[i]
                temp_nodes[node_count, 1] = normalized_volume[i]
                node_count += 1
        
        if node_count == 0:
            return np.zeros((0, 2), dtype=np.float64)
        
        # Create array with actual nodes
        nodes = temp_nodes[:node_count]
        
        # Sort nodes by price
        sorted_indices = np.argsort(nodes[:, 0])
        nodes = nodes[sorted_indices]
        
        # Filter nodes by minimum distance more leniently
        filtered_nodes = np.zeros((node_count, 2), dtype=np.float64)
        filtered_count = 0
        
        filtered_nodes[0] = nodes[0]
        filtered_count += 1
        
        for i in range(1, node_count):
            if abs(nodes[i, 0] - filtered_nodes[filtered_count-1, 0]) / filtered_nodes[filtered_count-1, 0] > min_distance:
                filtered_nodes[filtered_count] = nodes[i]
                filtered_count += 1
                
        return filtered_nodes[:filtered_count]

    def _compile_numba_functions(self):
        """Pre-compile Numba functions with dummy data"""
        # Use larger dummy dataset for more robust testing
        dummy_prices = np.array([100.0, 101.0, 102.0, 103.0, 104.0], dtype=np.float64)
        dummy_volumes = np.array([1.0, 2.0, 3.0, 2.0, 1.0], dtype=np.float64)
        
        # Test volume profile calculation
        profile, buckets = self._calculate_volume_profile(dummy_prices, dummy_volumes, 5)
        
        # Test volume nodes identification
        _ = self._identify_volume_nodes(profile, buckets, 0.2, 0.01)

    def fetch_raw_data(self, period):
        """Fetch raw OHLCV data and cache it."""
        cache_filename = f"raw_ohlcv_{self.symbol}_{self.interval}_{period}.pkl"

        if os.path.exists(cache_filename):
            print(f"Loading raw data from cache: {cache_filename}")
            df = pd.read_pickle(cache_filename)
            print(f"Total fetched OHLCV records: {len(df)}")
            return df

        exchange = self._get_exchange()
        end_time = int(time.time() * 1000)
        start_time = end_time - (int(period[:-1]) * 24 * 60 * 60 * 1000)

        # Fetch historical data
        all_ohlcv = []
        while start_time < end_time:
            print(f"Fetching data from {pd.to_datetime(start_time, unit='ms')}...")
            try:
                ohlcv = exchange.fetch_ohlcv(
                    symbol=self.symbol,
                    timeframe=self.interval,
                    since=start_time,
                    limit=1500
                )

                if not ohlcv:
                    break

                all_ohlcv.extend(ohlcv)
                start_time = ohlcv[-1][0] + 1

            except Exception as e:
                print(f"Error fetching data: {str(e)}")
                time.sleep(1)
                continue

        # Create DataFrame
        df = pd.DataFrame(all_ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
        df.set_index('timestamp', inplace=True)

        print(f"Saving raw data to cache: {cache_filename}")
        df.to_pickle(cache_filename)

        return df

    def process_volume_nodes(self, raw_data):
        """Calculate volume nodes based on current strategy parameters."""
        df = raw_data.copy()

        window_size = int(pd.Timedelta(self.profile_period) / pd.Timedelta(self.interval))

        # Initialize fixed-size arrays for volume nodes
        max_nodes = self.price_bucket_count  # Assuming maximum nodes equal to bucket count
        n = len(df)
        volume_nodes_prices = np.zeros((n, max_nodes), dtype=np.float64)
        volume_nodes_volumes = np.zeros((n, max_nodes), dtype=np.float64)
        num_volume_nodes = np.zeros(n, dtype=np.int32)

        print(f"Calculating volume profiles for {n} rows with current parameters...")
        for i in range(window_size, n):
            if i % 1000 == 0:  # Progress update
                print(f"Processing row {i}/{n}")

            try:
                window = df.iloc[i - window_size:i]

                if window.empty or window['close'].isna().any() or window['volume'].isna().any():
                    continue

                profile, buckets = self._calculate_volume_profile(
                    window['close'].values.astype(np.float64),
                    window['volume'].values.astype(np.float64),
                    self.price_bucket_count
                )

                nodes = self._identify_volume_nodes(
                    profile,
                    buckets,
                    self.volume_threshold,
                    self.node_distance
                )

                num_nodes = len(nodes)
                if num_nodes > max_nodes:
                    num_nodes = max_nodes  # Cap to maximum nodes

                num_volume_nodes[i] = num_nodes

                for j in range(num_nodes):
                    volume_nodes_prices[i, j] = nodes[j, 0]
                    volume_nodes_volumes[i, j] = nodes[j, 1]

            except Exception as e:
                print(f"Error calculating volume profile for window ending at {df.index[i]}: {str(e)}")
                continue

        # Assign to DataFrame
        df['volume_nodes_prices'] = list(volume_nodes_prices)
        df['volume_nodes_volumes'] = list(volume_nodes_volumes)
        df['num_volume_nodes'] = num_volume_nodes

        print(f"Volume nodes calculated and assigned to data.")

        return df

    def plot_data(self, df):
        """Plots the OHLCV and volume profile data."""
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
        
        # Plot OHLCV
        ax1.plot(df.index, df['close'], label='Close Price')
        ax1.set_title(f"{self.symbol} Price")
        ax1.set_ylabel("Price")
        ax1.legend()
        
        # Plot Volume Profile
        if 'volume_nodes_prices' in df.columns and 'volume_nodes_volumes' in df.columns:
            latest_prices = df['volume_nodes_prices'].iloc[-1]
            latest_volumes = df['volume_nodes_volumes'].iloc[-1]
            valid = latest_volumes > 0
            ax2.barh(latest_prices[valid], latest_volumes[valid], alpha=0.3)
            ax2.set_title("Volume Profile")
            ax2.set_xlabel("Volume")
            ax2.set_ylabel("Price")
        
        plt.tight_layout()
        plt.show()

    @staticmethod
    @njit(parallel=True)
    def generate_signals_numba(
        close_prices,
        volume_nodes_prices,
        num_volume_nodes,
        entry_conf_bars,
        exit_conf_bars,
        node_distance
    ):
        n = len(close_prices)
        long_entries = np.zeros(n, dtype=np.bool_)
        short_entries = np.zeros(n, dtype=np.bool_)
        exits = np.zeros(n, dtype=np.bool_)

        for i in prange(n):
            if num_volume_nodes[i] == 0:
                continue  # No volume nodes to process

            current_price = close_prices[i]
            nodes = volume_nodes_prices[i, :num_volume_nodes[i]]

            # Initialize nearest nodes
            nearest_above = np.inf
            nearest_below = -np.inf

            # Find nearest above and below nodes
            for j in range(num_volume_nodes[i]):
                price = nodes[j]
                if price > current_price and price < nearest_above:
                    nearest_above = price
                elif price < current_price and price > nearest_below:
                    nearest_below = price

            # Long Entry Condition
            if nearest_below != -np.inf:
                price_diff = np.abs(current_price - nearest_below) / current_price
                if price_diff < node_distance:
                    # Check confirmation bars
                    if i >= entry_conf_bars:
                        positive_bars = 0
                        for k in range(i - entry_conf_bars, i):
                            if close_prices[k] > close_prices[k - 1]:
                                positive_bars += 1
                        if positive_bars >= entry_conf_bars * 0.5:
                            long_entries[i] = True

            # Short Entry Condition
            if nearest_above != np.inf:
                price_diff = np.abs(current_price - nearest_above) / current_price
                if price_diff < node_distance:
                    # Check confirmation bars
                    if i >= entry_conf_bars:
                        negative_bars = 0
                        for k in range(i - entry_conf_bars, i):
                            if close_prices[k] < close_prices[k - 1]:
                                negative_bars += 1
                        if negative_bars >= entry_conf_bars * 0.5:
                            short_entries[i] = True

            # Exit Conditions
            if i >= exit_conf_bars:
                # Check if there's an open position to exit
                if long_entries[i]:
                    if (i + exit_conf_bars) < n:
                        exits[i + exit_conf_bars] = True
                elif short_entries[i]:
                    if (i + exit_conf_bars) < n:
                        exits[i + exit_conf_bars] = True

        return long_entries, short_entries, exits

    def generate_signals(self, data):
        close_prices = data['close'].values.astype(np.float64)
        volume_nodes_prices = np.vstack(data['volume_nodes_prices'].values).astype(np.float64)
        # No need for volume_nodes_volumes in signal generation
        num_volume_nodes = data['num_volume_nodes'].values.astype(np.int32)

        long_entries, short_entries, exits = self.generate_signals_numba(
            close_prices,
            volume_nodes_prices,
            num_volume_nodes,
            self.entry_confirmation_bars,
            self.exit_confirmation_bars,
            self.node_distance
        )

        signals = pd.DataFrame({
            'long_entries': long_entries,
            'short_entries': short_entries,
            'exits': exits
        }, index=data.index)

        return signals

    def backtest(self, data, initial_capital, fee_rate):
        """Run backtest using vectorbt with additional debugging"""
        # Define the numeric columns used in backtesting
        numeric_cols = ['close', 'volume']  # Add other numeric columns if necessary

        # Check for NaN values in numeric columns only
        if data[numeric_cols].isna().any().any():
            print("Data contains NaN values in numeric columns. Please clean the data before backtesting.")
            raise ValueError("Data contains NaN values.")

        # Check for infinite values in numeric columns only
        if np.isinf(data[numeric_cols].values).any():
            print("Data contains infinite values in numeric columns. Please clean the data before backtesting.")
            raise ValueError("Data contains infinite values.")

        signals = self.generate_signals(data)

        # Calculate position sizes and stop-losses
        entry_prices = pd.Series(index=data.index, dtype=float)
        entry_prices[signals['long_entries']] = data.loc[signals['long_entries'], 'close']
        entry_prices[signals['short_entries']] = data.loc[signals['short_entries'], 'close']

        stop_prices = pd.Series(index=data.index, dtype=float)
        stop_prices[signals['long_entries']] = entry_prices[signals['long_entries']] * (1 - self.risk_per_trade)
        stop_prices[signals['short_entries']] = entry_prices[signals['short_entries']] * (1 + self.risk_per_trade)

        # Calculate position size based on initial capital and first close price
        size = initial_capital / data['close'].iloc[0]

        try:
            portfolio = vbt.Portfolio.from_signals(
                close=data['close'],
                entries=signals['long_entries'] | signals['short_entries'],
                exits=signals['exits'],
                short_entries=signals['short_entries'],
                sl_stop=stop_prices,
                init_cash=initial_capital,
                fees=fee_rate,
                size=size,
                freq=self.interval
            )

            trades = portfolio.trades.records

            metrics = {
                'Total Return (%)': portfolio.total_return() * 100,
                'Sharpe Ratio': portfolio.sharpe_ratio(),
                'Max Drawdown (%)': portfolio.max_drawdown() * 100,
                'Number of Trades': len(trades) if trades is not None else 0,
                'Win Rate (%)': (np.sum(trades['pnl'] > 0) / len(trades)) * 100 if trades is not None and len(trades) > 0 else 0,
                'Profit Factor': np.sum(trades[trades['pnl'] > 0]['pnl']) / abs(np.sum(trades[trades['pnl'] < 0]['pnl'])) if trades is not None and len(trades) > 0 and abs(np.sum(trades[trades['pnl'] < 0]['pnl'])) > 0 else 0
            }

            return metrics, portfolio, data

        except Exception as e:
            print(f"\nError in vectorbt processing: {str(e)}")
            raise

    def verify_volume_nodes(self, data):
        """Verify that volume nodes are being calculated correctly"""
        print("\nVerifying volume nodes calculation...")
        
        # Check a specific time window
        sample_window = data.iloc[-100:]  # Last 100 rows
        
        # Calculate volume profile for this window
        prices = sample_window['close'].values.astype(np.float64)
        volumes = sample_window['volume'].values.astype(np.float64)
        
        profile, buckets = self._calculate_volume_profile(
            prices,
            volumes,
            self.price_bucket_count
        )
        
        nodes = self._identify_volume_nodes(
            profile,
            buckets,
            self.volume_threshold,
            self.node_distance
        )
        
        print(f"\nSample window statistics:")
        print(f"Price range: {prices.min():.2f} - {prices.max():.2f}")
        print(f"Volume range: {volumes.min():.2f} - {volumes.max():.2f}")
        print(f"Number of volume nodes found: {len(nodes)}")
        
        if len(nodes) > 0:
            print("\nVolume nodes found:")
            for node in nodes:
                print(f"Price level: {node[0]:.2f}, Relative volume: {node[1]:.4f}")
        else:
            print("No volume nodes found - this may indicate an issue with the parameters")

def progress_bar(current, total):
    """Prints a simple progress bar."""
    percent_complete = int(100 * current / total)
    if percent_complete % 1 == 0: # Only update for whole percentage changes
        bar_length = 20
        filled_length = int(bar_length * current / total)
        bar = '=' * filled_length + '-' * (bar_length - filled_length)
        print(f'\r[{bar}] {percent_complete}%', end='', flush=True)

def optimize_parameters(strategy, param_grid, initial_capital, fee_rate, period):
    results = []
    print("Fetching raw data...")
    raw_data = strategy.fetch_raw_data(period)  # Fetch raw OHLCV data here
    print("Raw data fetched successfully.")

    param_combinations = [dict(zip(param_grid.keys(), values)) 
                         for values in product(*param_grid.values())]
    
    total_combinations = len(param_combinations)
    print(f"Testing {total_combinations} parameter combinations...")
    
    for i, params in enumerate(param_combinations, 1):
        progress_bar(i, total_combinations)
        print(f"\nTesting combination {i}/{total_combinations}: {params}")
        
        # Update strategy parameters
        for param, value in params.items():
            setattr(strategy, param, value)
        
        # Recalculate volume nodes with updated parameters
        data = strategy.process_volume_nodes(raw_data)
        strategy.verify_volume_nodes(data)  # Optional: for debugging
        
        # Run backtest
        try:
            metrics, _, _ = strategy.backtest(data, initial_capital, fee_rate)
            print(f"Metrics for combination {i}: {metrics}")
            results.append({**params, **metrics})
        except Exception as e:
            print(f"Error testing combination {params}: {str(e)}")
            results.append({**params, 'Error': str(e)})

    results_df = pd.DataFrame(results)
    print("\nAvailable columns in results:")
    print(results_df.columns.tolist())  # Print list of columns
    
    if results_df.empty:
        print("No successful backtests. `results` DataFrame is empty.")
    else:
        print("Backtests completed successfully.")
    
    return results_df

if __name__ == "__main__":
    # Initialize strategy
    strategy = VolumeProfileStrategy(
        symbol='SOL/USDT',
        exchange_id='binance'
    )
    
    # Define parameter grid
    param_grid = {
        'price_bucket_count': np.linspace(10, 25, 5).astype(int).tolist(),
        'volume_threshold': [0.1],
        'node_distance': np.linspace(0.0001, 0.015, 5).tolist(),
        'entry_confirmation_bars': [2],
        'exit_confirmation_bars': [1],
        'risk_per_trade': [0.02]
    }
        
    # Optimize parameters
    results = optimize_parameters(
        strategy=strategy,
        param_grid=param_grid,
        initial_capital=10000,
        fee_rate=0.001,
        period='10d'
    )
    
    print("\nTop 5 parameter combinations by Sharpe ratio:")
    print(results.sort_values('Sharpe Ratio', ascending=False).head())
    
    # Create the heatmap with corrected scaling
    if 'Sharpe Ratio' in results.columns:
        heatmap_data = results.pivot_table(values='Sharpe Ratio', index='price_bucket_count', columns='node_distance')
        plt.figure(figsize=(10, 8))
        sns.heatmap(heatmap_data, annot=False, cmap='viridis', fmt=".2f", 
                    vmin=heatmap_data.min().min(), vmax=heatmap_data.max().max())
        plt.title('Sharpe Ratio Heatmap')
        plt.ylabel('price_bucket_count')
        plt.xlabel('node_distance')
        plt.show()
    else:
        print("No Sharpe Ratio data available for heatmap.")