lowkey_backtest/main.py

import pandas as pd
import numpy as np
from ta.volatility import AverageTrueRange
import time
import csv
import math
import os


def load_data(since, until, csv_file):
    df = pd.read_csv(csv_file)    
    df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
    df = df[(df['Timestamp'] >= pd.Timestamp(since)) & (df['Timestamp'] <= pd.Timestamp(until))]
    return df

def aggregate_data(df, timeframe):
    df = df.set_index('Timestamp')
    df = df.resample(timeframe).agg({
        'Open': 'first',
        'High': 'max',
        'Low': 'min',
        'Close': 'last',
        'Volume': 'sum'
    })
    df = df.reset_index()
    return df

def calculate_okx_taker_maker_fee(amount, is_maker=False):
        fee_rate = 0.0008 if is_maker else 0.0010
        return amount * fee_rate

def calculate_supertrend(df, period, multiplier):
    """
    Calculate the Supertrend indicator for a given period and multiplier.
    Optionally displays progress during calculation.
    Args:
        df (pd.DataFrame): DataFrame with 'High', 'Low', 'Close' columns.
        period (int): ATR period.
        multiplier (float): Multiplier for ATR.
        progress_step (int): Step interval for progress display.
        show_progress (bool): Whether to print progress updates.
    Returns:
        pd.Series: Supertrend values.
    """
    # Ensure we have enough data for ATR calculation
    if len(df) < period + 1:
        print(f"Warning: Not enough data for ATR period {period}. Need at least {period + 1} rows, got {len(df)}")
        return pd.Series([np.nan] * len(df), index=df.index)
    
    high = df['High'].values
    low = df['Low'].values
    close = df['Close'].values
    
    # Calculate True Range first
    tr = np.zeros_like(close)
    for i in range(1, len(close)):
        tr[i] = max(
            high[i] - low[i],  # Current high - current low
            abs(high[i] - close[i-1]),  # Current high - previous close
            abs(low[i] - close[i-1])    # Current low - previous close
        )
    
    # Calculate ATR using simple moving average
    atr = np.zeros_like(close)
    atr[period] = np.mean(tr[1:period+1])  # First ATR value
    for i in range(period+1, len(close)):
        atr[i] = (atr[i-1] * (period-1) + tr[i]) / period  # Exponential-like smoothing
    
    # Fill initial values with the first valid ATR
    atr[:period] = atr[period] if atr[period] > 0 else 0.001

    hl2 = (high + low) / 2
    upperband = hl2 + (multiplier * atr)
    lowerband = hl2 - (multiplier * atr)

    supertrend = np.full_like(close, np.nan)
    in_uptrend = True

    supertrend[0] = upperband[0]
    total_steps = len(close) - 1

    for i in range(1, len(close)):
        if close[i] > upperband[i-1]:
            in_uptrend = True
        elif close[i] < lowerband[i-1]:
            in_uptrend = False
        # else, keep previous trend

        if in_uptrend:
            supertrend[i] = max(lowerband[i], supertrend[i-1] if not np.isnan(supertrend[i-1]) else lowerband[i])
        else:
            supertrend[i] = min(upperband[i], supertrend[i-1] if not np.isnan(supertrend[i-1]) else upperband[i])

    return pd.Series(supertrend, index=df.index)

def add_supertrend_indicators(df):
    """
    Adds Supertrend indicators to the dataframe for the specified (period, multiplier) pairs.
    Args:
        df (pd.DataFrame): DataFrame with columns 'High', 'Low', 'Close'.
    Returns:
        pd.DataFrame: DataFrame with new Supertrend columns added.
    """
    supertrend_params = [(12, 3.0), (10, 1.0), (11, 2.0)]
    for period, multiplier in supertrend_params:
        try:
            st_col = f'supertrend_{period}_{multiplier}'
            df[st_col] = calculate_supertrend(df, period, multiplier)
        except Exception as e:
            print(f"Error calculating Supertrend {period}, {multiplier}: {e}")
            df[f'supertrend_{period}_{multiplier}'] = np.nan
    return df

def precompute_1min_slice_indices(df_aggregated, df_1min):
    """
    Precompute start and end indices for each aggregated bar using searchsorted.
    Returns a list of (start_idx, end_idx) tuples for fast iloc slicing.
    """
    timestamps = df_aggregated['Timestamp'].values
    one_min_timestamps = df_1min['Timestamp'].values
    # Ensure both are sorted
    sorted_1min = np.argsort(one_min_timestamps)
    one_min_timestamps = one_min_timestamps[sorted_1min]
    indices = []
    prev_idx = 0
    for i in range(1, len(timestamps)):
        start, end = timestamps[i-1], timestamps[i]
        # Find indices using searchsorted (right for start, right for end)
        start_idx = np.searchsorted(one_min_timestamps, start, side='right')
        end_idx = np.searchsorted(one_min_timestamps, end, side='right')
        indices.append((start_idx, end_idx))
    return indices, sorted_1min

def estimate_slippage_rate(trade_usd_size, minute_row, base_slippage_rate=0.0003, impact_threshold_pct=0.10, impact_slope=0.0010):
    """
    Estimate total slippage rate (decimal) using a hybrid model:
    - Base slippage: fixed base_slippage_rate (e.g., 0.0003 = 3 bps)
    - Extra slippage: if trade size (USD) > impact_threshold_pct * 1-min USD volume,
      add impact_slope * (trade_size/threshold - 1)

    Args:
        trade_usd_size (float): Trade notional in USD before slippage.
        minute_row (pd.Series|None): 1-min bar with 'Volume' and a price ('Close' preferred, fallback 'Open').
        base_slippage_rate (float): Base slippage in decimal.
        impact_threshold_pct (float): Threshold as fraction of 1-min volume (e.g., 0.10 = 10%).
        impact_slope (float): Rate added per 1x over threshold (decimal).

    Returns:
        float: total slippage rate (>= base_slippage_rate).
    """
    if minute_row is None:
        return float(base_slippage_rate)
    try:
        minute_base_vol = float(minute_row.get('Volume', 0.0) or 0.0)
        minute_price = float(minute_row.get('Close', minute_row.get('Open', 0.0)) or 0.0)
        minute_quote_vol = minute_base_vol * minute_price
    except Exception:
        minute_quote_vol = 0.0

    if minute_quote_vol <= 0 or impact_threshold_pct <= 0:
        return float(base_slippage_rate)

    threshold_quote = minute_quote_vol * impact_threshold_pct
    if trade_usd_size <= threshold_quote:
        return float(base_slippage_rate)

    over_ratio = (trade_usd_size / threshold_quote) - 1.0
    extra_slippage = max(0.0, impact_slope * over_ratio)
    return float(base_slippage_rate + extra_slippage)

def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=1000,
             base_slippage_rate=0.0003, impact_threshold_pct=0.10, impact_slope=0.0010):
    """
    Backtest trading strategy based on meta supertrend logic (all three supertrends agree).
    Uses signal transitions and open prices for entry/exit to match original implementation.
    """
    start_time = time.time()
    required_st_cols = ["supertrend_12_3.0", "supertrend_10_1.0", "supertrend_11_2.0"]
    for col in required_st_cols:
        if col not in df_aggregated.columns:
            raise ValueError(f"Missing required Supertrend column: {col}")

    # Calculate trend directions for each supertrend (-1, 0, 1)
    trends = []
    for col in required_st_cols:
        # Convert supertrend values to trend direction based on close price position
        trend = np.where(df_aggregated['Close'] > df_aggregated[col], 1, -1)
        trends.append(trend)
    
    # Stack trends and calculate meta trend (all must agree)
    trends_arr = np.stack(trends, axis=1)
    meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
                         trends_arr[:,0], 0)
    
    meta_trend_signal = meta_trend #incorrect: should be lagging as it introduces lookahead bias. 
    # Next step: modify OHLCV predictor to not use supertrend as a feature or anyother feature 
    # that introduces lookahead bias and predict the next close price.
    #
    # Old code, not that efficient:
    # Add signal lagging to avoid lookahead bias
    # meta_trend_signal = np.roll(meta_trend, 1)
    # meta_trend_signal[0] = 0  # No signal for first bar

    # Precompute 1-min slice indices for each aggregated bar
    slice_indices, sorted_1min = precompute_1min_slice_indices(df_aggregated, df_1min)
    df_1min_sorted = df_1min.iloc[sorted_1min].reset_index(drop=True)
    one_min_timestamps_sorted = df_1min_sorted['Timestamp'].values

    in_position = False
    init_usd = 1000
    usd = init_usd
    coin = 0
    nb_stop_loss = 0
    trade_log = []
    equity_curve = []
    trade_results = []
    entry_price = None
    entry_time = None
    total_slippage_usd = 0.0
    total_traded_usd = 0.0

    total_steps = len(df_aggregated) - 1
    for i in range(1, len(df_aggregated)):
        open_price = df_aggregated['Open'][i]  # Use open price for entry/exit
        close_price = df_aggregated['Close'][i]
        timestamp = df_aggregated['Timestamp'][i]
        
        # Get previous and current meta trend signals
        prev_mt = meta_trend_signal[i-1] if i > 0 else 0
        curr_mt = meta_trend_signal[i]

        # Track equity at each bar
        equity = usd + coin * close_price
        equity_curve.append((timestamp, equity))

        # Check stop loss if in position
        if in_position:
            start_idx, end_idx = slice_indices[i-1]
            df_1min_slice = df_1min_sorted.iloc[start_idx:end_idx]
            stop_triggered = False
            
            if not df_1min_slice.empty:
                stop_loss_threshold = entry_price * (1 - stop_loss_pct)
                below_stop = df_1min_slice['Low'] < stop_loss_threshold
                
                if below_stop.any():
                    first_idx = below_stop.idxmax()
                    stop_row = df_1min_slice.loc[first_idx]
                    stop_triggered = True
                    in_position = False
                    
                    # More realistic stop loss fill logic with slippage
                    if stop_row['Open'] < stop_loss_threshold:
                        base_exit_price = stop_row['Open']
                    else:
                        base_exit_price = stop_loss_threshold
                    trade_usd_size = float(coin * base_exit_price)
                    slip_rate = estimate_slippage_rate(trade_usd_size, stop_row, base_slippage_rate, impact_threshold_pct, impact_slope)
                    exit_price = base_exit_price * (1.0 - slip_rate)
                    
                    exit_time = stop_row['Timestamp']
                    gross_usd = coin * exit_price
                    fee = calculate_okx_taker_maker_fee(gross_usd, is_maker=False)
                    usd = gross_usd - fee
                    trade_pnl = (exit_price - entry_price) / entry_price if entry_price else 0
                    total_slippage_usd += trade_usd_size * slip_rate
                    total_traded_usd += trade_usd_size
                    trade_results.append(trade_pnl)
                    trade_log.append({
                        'type': 'stop_loss',
                        'time': exit_time,
                        'base_price': base_exit_price,
                        'effective_price': exit_price,
                        'slippage_rate': slip_rate,
                        'usd': usd,
                        'coin': 0,
                        'pnl': trade_pnl,
                        'fee': fee
                    })
                    coin = 0
                    nb_stop_loss += 1
                    entry_price = None
                    entry_time = None
            
            if stop_triggered:
                continue

        # Entry condition: signal changes TO bullish (prev != 1 and curr == 1)
        if not in_position and prev_mt != 1 and curr_mt == 1:
            in_position = True
            fee = calculate_okx_taker_maker_fee(usd, is_maker=False)
            usd_after_fee = usd - fee
            # Slippage on buy increases price
            try:
                ts64 = np.datetime64(timestamp)
                idx_min = int(np.searchsorted(one_min_timestamps_sorted, ts64, side='left'))
                minute_row = df_1min_sorted.iloc[idx_min] if 0 <= idx_min < len(df_1min_sorted) else None
            except Exception:
                minute_row = None
            trade_usd_size = float(usd_after_fee)
            slip_rate = estimate_slippage_rate(trade_usd_size, minute_row, base_slippage_rate, impact_threshold_pct, impact_slope)
            effective_entry_price = open_price * (1.0 + slip_rate)
            coin = usd_after_fee / effective_entry_price
            entry_price = effective_entry_price
            entry_time = timestamp
            usd = 0
            total_slippage_usd += trade_usd_size * slip_rate
            total_traded_usd += trade_usd_size
            trade_log.append({
                'type': 'buy',
                'time': timestamp,
                'base_price': open_price,
                'effective_price': effective_entry_price,
                'slippage_rate': slip_rate,
                'usd': usd,
                'coin': coin,
                'fee': fee
            })
        
        # Exit condition: signal changes TO bearish (prev == 1 and curr == -1)
        elif in_position and prev_mt == 1 and curr_mt == -1:
            in_position = False
            # Slippage on sell reduces price
            try:
                ts64 = np.datetime64(timestamp)
                idx_min = int(np.searchsorted(one_min_timestamps_sorted, ts64, side='left'))
                minute_row = df_1min_sorted.iloc[idx_min] if 0 <= idx_min < len(df_1min_sorted) else None
            except Exception:
                minute_row = None
            base_exit_price = open_price
            trade_usd_size = float(coin * base_exit_price)
            slip_rate = estimate_slippage_rate(trade_usd_size, minute_row, base_slippage_rate, impact_threshold_pct, impact_slope)
            exit_price = base_exit_price * (1.0 - slip_rate)
            exit_time = timestamp
            gross_usd = coin * exit_price
            fee = calculate_okx_taker_maker_fee(gross_usd, is_maker=False)
            usd = gross_usd - fee
            trade_pnl = (exit_price - entry_price) / entry_price if entry_price else 0
            total_slippage_usd += trade_usd_size * slip_rate
            total_traded_usd += trade_usd_size
            trade_results.append(trade_pnl)
            trade_log.append({
                'type': 'sell',
                'time': exit_time,
                'base_price': base_exit_price,
                'effective_price': exit_price,
                'slippage_rate': slip_rate,
                'usd': usd,
                'coin': 0,
                'pnl': trade_pnl,
                'fee': fee
            })
            coin = 0
            entry_price = None
            entry_time = None

        if i % progress_step == 0 or i == total_steps:
            percent = (i / total_steps) * 100
            print(f"\rTimeframe: {timeframe},\tProgress: {percent:.1f}%\tCurrent equity: {equity:.2f}\033[K", end='', flush=True)

    # Force close any open position at the end
    if in_position:
        final_open_price = df_aggregated['Open'].iloc[-1]  # Use open price for consistency
        final_timestamp = df_aggregated['Timestamp'].iloc[-1]
        try:
            ts64 = np.datetime64(final_timestamp)
            idx_min = int(np.searchsorted(one_min_timestamps_sorted, ts64, side='left'))
            minute_row = df_1min_sorted.iloc[idx_min] if 0 <= idx_min < len(df_1min_sorted) else None
        except Exception:
            minute_row = None
        base_exit_price = final_open_price
        trade_usd_size = float(coin * base_exit_price)
        slip_rate = estimate_slippage_rate(trade_usd_size, minute_row, base_slippage_rate, impact_threshold_pct, impact_slope)
        final_effective_price = base_exit_price * (1.0 - slip_rate)
        gross_usd = coin * final_effective_price
        fee = calculate_okx_taker_maker_fee(gross_usd, is_maker=False)
        usd = gross_usd - fee
        trade_pnl = (final_effective_price - entry_price) / entry_price if entry_price else 0
        total_slippage_usd += trade_usd_size * slip_rate
        total_traded_usd += trade_usd_size
        trade_results.append(trade_pnl)
        trade_log.append({
            'type': 'forced_close',
            'time': final_timestamp,
            'base_price': base_exit_price,
            'effective_price': final_effective_price,
            'slippage_rate': slip_rate,
            'usd': usd,
            'coin': 0,
            'pnl': trade_pnl,
            'fee': fee
        })
        coin = 0
        in_position = False
        entry_price = None

    print()
    print(f"Timeframe: {timeframe},\tTotal profit: {usd - init_usd},\tNumber of stop losses: {nb_stop_loss}")

    # --- Performance Metrics ---
    equity_arr = np.array([e[1] for e in equity_curve])
    # Handle edge cases for empty or invalid equity data
    if len(equity_arr) == 0:
        print("Warning: No equity data available")
        return None
    returns = np.diff(equity_arr) / equity_arr[:-1]
    # Filter out infinite and NaN returns
    returns = returns[np.isfinite(returns)]
    total_return = (equity_arr[-1] - equity_arr[0]) / equity_arr[0] if equity_arr[0] != 0 else 0
    running_max = np.maximum.accumulate(equity_arr)
    if equity_arr[-1] <= 0.01:
        max_drawdown = -1.0
    else:
        drawdowns = (equity_arr - running_max) / running_max
        max_drawdown = drawdowns.min() if len(drawdowns) > 0 and np.isfinite(drawdowns).any() else 0
    if len(returns) > 1 and np.std(returns) > 1e-9:
        sharpe = np.mean(returns) / np.std(returns) * math.sqrt(252)
    else:
        sharpe = 0
    wins = [1 for r in trade_results if r > 0]
    win_rate = len(wins) / len(trade_results) if trade_results else 0
    num_trades = len(trade_results)

    print(f"Performance Metrics:")
    print(f"  Total Return: {total_return*100:.2f}%")
    print(f"  Max Drawdown: {max_drawdown*100:.2f}%")
    print(f"  Sharpe Ratio: {sharpe:.2f}")
    print(f"  Win Rate: {win_rate*100:.2f}%")
    print(f"  Number of Trades: {num_trades}")
    print(f"  Final Equity: ${equity_arr[-1]:.2f}")
    print(f"  Initial Equity: ${equity_arr[0]:.2f}")

    # --- Save Trade Log ---
    log_dir = "backtest_logs"
    os.makedirs(log_dir, exist_ok=True)
    # Format stop_loss_pct for filename (e.g., 0.05 -> 0p05)
    stop_loss_str = f"{stop_loss_pct:.2f}".replace('.', 'p')
    log_path = os.path.join(log_dir, f"trade_log_{timeframe}_sl{stop_loss_str}.csv")
    if trade_log:
        all_keys = set()
        for entry in trade_log:
            all_keys.update(entry.keys())
        all_keys = list(all_keys)

        trade_log_filled = []
        for entry in trade_log:
            filled_entry = {k: entry.get(k, None) for k in all_keys}
            trade_log_filled.append(filled_entry)

        # Calculate total fees for this backtest
        total_fees = sum(entry.get('fee', 0) for entry in trade_log)

        # Write summary header row, then trade log header and rows
        with open(log_path, 'w', newline='') as f:
            writer = csv.writer(f)
            summary_header = [
                'elapsed_time_sec', 'total_return', 'max_drawdown', 'sharpe_ratio',
                'win_rate', 'num_trades', 'final_equity', 'initial_equity', 'num_stop_losses', 'total_fees',
                'total_slippage_usd', 'avg_slippage_bps'
            ]
            summary_values = [
                f"{time.time() - start_time:.2f}",
                f"{total_return*100:.2f}%",
                f"{max_drawdown*100:.2f}%",
                f"{sharpe:.2f}",
                f"{win_rate*100:.2f}%",
                str(num_trades),
                f"${equity_arr[-1]:.2f}",
                f"${equity_arr[0]:.2f}",
                str(nb_stop_loss),
                f"${total_fees:.4f}",
                f"${total_slippage_usd:.4f}",
                f"{(total_slippage_usd / total_traded_usd * 10000.0) if total_traded_usd > 0 else 0:.2f}"
            ]
            writer.writerow(summary_header)
            writer.writerow(summary_values)
            writer.writerow([])  # Blank row for separation
            dict_writer = csv.DictWriter(f, fieldnames=all_keys)
            dict_writer.writeheader()
            dict_writer.writerows(trade_log_filled)

        print(f"Trade log saved to {log_path}")
    else:
        print("No trades to log.")

    # Return summary metrics (excluding elapsed time)
    return {
        'timeframe': timeframe,
        'stop_loss': stop_loss_pct,
        'total_return': total_return,
        'max_drawdown': max_drawdown,
        'sharpe_ratio': sharpe,
        'win_rate': win_rate,
        'num_trades': num_trades,
        'final_equity': equity_arr[-1],
        'initial_equity': equity_arr[0],
        'num_stop_losses': nb_stop_loss,
        'total_fees': total_fees if trade_log else 0,
        'total_slippage_usd': total_slippage_usd,
        'avg_slippage_bps': (total_slippage_usd / total_traded_usd * 10000.0) if total_traded_usd > 0 else 0.0
    }

if __name__ == "__main__":
    timeframes = ["5min", "15min", "30min", "1h", "4h", "1d", "2d"]
    # timeframes = ["5min", "15min", "1h", "4h", "1d"]
    # timeframes = ["30min"]
    stoplosses = [0.1, 0.2, 0.3, 0.4, 0.5]
    # Slippage configuration (OKX Spot): base in bps, plus volume-impact model
    slippage_base_bps = 10  # 10 bps base slippage (realistic, conservative)
    impact_threshold_pct = 0.10  # e.g., start impact beyond 10% of 1-min volume
    impact_slope = 0.0010  # incremental slippage per 1x over threshold
    
    # df_1min = load_data('2021-11-01', '2024-10-16', '../data/btcusd_1-min_data.csv')
    df_1min = load_data('2021-11-01', '2025-08-19', '../data/btcusd_okx_1-min_data.csv')

    # Prepare summary CSV
    summary_csv_path = "backtest_summary.csv"
    summary_header = [
        'timeframe', 'stop_loss', 'total_return', 'max_drawdown', 'sharpe_ratio',
        'win_rate', 'num_trades', 'final_equity', 'initial_equity', 'num_stop_losses', 'total_fees',
        'total_slippage_usd', 'avg_slippage_bps'
    ]
    with open(summary_csv_path, 'w', newline='') as summary_file:
        writer = csv.DictWriter(summary_file, fieldnames=summary_header)
        writer.writeheader()
        for timeframe in timeframes:
            df_aggregated = aggregate_data(df_1min, timeframe)
            df_aggregated = add_supertrend_indicators(df_aggregated)
            for stop_loss_pct in stoplosses:
                summary = backtest(
                    timeframe,
                    df_aggregated,
                    df_1min,
                    stop_loss_pct=stop_loss_pct,
                    base_slippage_rate=slippage_base_bps / 10000.0,
                    impact_threshold_pct=impact_threshold_pct,
                    impact_slope=impact_slope
                )
                if summary is not None:
                    # Format values for CSV (e.g., floats as rounded strings)
                    summary_row = {
                        'timeframe': summary['timeframe'],
                        'stop_loss': summary['stop_loss'],
                        'total_return': f"{summary['total_return']*100:.2f}%",
                        'max_drawdown': f"{summary['max_drawdown']*100:.2f}%",
                        'sharpe_ratio': f"{summary['sharpe_ratio']:.2f}",
                        'win_rate': f"{summary['win_rate']*100:.2f}%",
                        'num_trades': summary['num_trades'],
                        'final_equity': f"${summary['final_equity']:.2f}",
                        'initial_equity': f"${summary['initial_equity']:.2f}",
                        'num_stop_losses': summary['num_stop_losses'],
                        'total_fees': f"${summary['total_fees']:.4f}",
                        'total_slippage_usd': f"${summary['total_slippage_usd']:.4f}",
                        'avg_slippage_bps': f"{summary['avg_slippage_bps']:.2f}"
                    }
                    writer.writerow(summary_row)