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Refactor backtesting logic to include slippage estimation, enhancing trade execution realism. Update load_data function to accept a CSV file parameter. Improve summary output with slippage metrics and adjust main script for new slippage configuration. Correct typos in project documentation.

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Simon Moisy 2025-08-18 09:57:01 +08:00
parent 21b14d4fe4
commit a25499e016
2 changed files with 139 additions and 28 deletions
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4
.cursor/rules/project.mdc
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@ -10,10 +10,10 @@ Unify the project structure and interraction with tools and console
### System tools
- **ALWAYS** use UV for package management
- **ALWAYS** use windows PowerShell command for terminal
- **ALWAYS** use Arch linux compatible command for terminal
### Coding patterns
- **ALWYAS** check the arguments and methods before use to avoid errors with whron parameters or names
- **ALWYAS** check the arguments and methods before use to avoid errors with wrong parameters or names
- If in doubt, check [CONTEXT.md](mdc:CONTEXT.md) file and [architecture.md](mdc:docs/architecture.md)
- **PREFER** ORM pattern for databases with SQLAclhemy.
- **DO NOT USE** emoji in code and comments

163
main.py
View File

@ -7,8 +7,8 @@ import math
import os
def load_data(since, until):
df = pd.read_csv('../data/btcusd_1-min_data.csv')
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
@ -131,7 +131,45 @@ def precompute_1min_slice_indices(df_aggregated, df_1min):
indices.append((start_idx, end_idx))
return indices, sorted_1min
def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=1000):
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.
@ -166,6 +204,7 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
# 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
@ -177,6 +216,8 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
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)):
@ -208,22 +249,29 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
stop_triggered = True
in_position = False
# More realistic stop loss fill logic
# More realistic stop loss fill logic with slippage
if stop_row['Open'] < stop_loss_threshold:
exit_price = stop_row['Open']
base_exit_price = stop_row['Open']
else:
exit_price = stop_loss_threshold
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,
'price': exit_price,
'base_price': base_exit_price,
'effective_price': exit_price,
'slippage_rate': slip_rate,
'usd': usd,
'coin': 0,
'pnl': trade_pnl,
@ -242,14 +290,28 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
in_position = True
fee = calculate_okx_taker_maker_fee(usd, is_maker=False)
usd_after_fee = usd - fee
coin = usd_after_fee / open_price # Use open price
entry_price = open_price
# 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,
'price': open_price,
'base_price': open_price,
'effective_price': effective_entry_price,
'slippage_rate': slip_rate,
'usd': usd,
'coin': coin,
'fee': fee
@ -258,17 +320,31 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
# 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
exit_price = open_price # Use open price
# 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 * open_price
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,
'price': exit_price,
'base_price': base_exit_price,
'effective_price': exit_price,
'slippage_rate': slip_rate,
'usd': usd,
'coin': 0,
'pnl': trade_pnl,
@ -286,15 +362,29 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
if in_position:
final_open_price = df_aggregated['Open'].iloc[-1] # Use open price for consistency
final_timestamp = df_aggregated['Timestamp'].iloc[-1]
gross_usd = coin * final_open_price
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_open_price - entry_price) / entry_price if entry_price else 0
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,
'price': final_open_price,
'base_price': base_exit_price,
'effective_price': final_effective_price,
'slippage_rate': slip_rate,
'usd': usd,
'coin': 0,
'pnl': trade_pnl,
@ -365,7 +455,8 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
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'
'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}",
@ -377,7 +468,9 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
f"${equity_arr[-1]:.2f}",
f"${equity_arr[0]:.2f}",
str(nb_stop_loss),
f"${total_fees:.4f}"
f"${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)
@ -402,22 +495,30 @@ def backtest(timeframe, df_aggregated, df_1min, stop_loss_pct, progress_step=100
'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_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"]
timeframes = ["5min", "15min", "30min", "1h", "4h", "1d", "2d"]
# timeframes = ["5min", "15min", "1h", "4h", "1d"]
# timeframes = ["30min"]
stoplosses = [0.03, 0.05, 0.1]
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')
# 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'
'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)
@ -426,7 +527,15 @@ if __name__ == "__main__":
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)
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 = {
@ -440,6 +549,8 @@ if __name__ == "__main__":
'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_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)