Merge pull request #7810 from stash86/bt-metrics
Add more calculations for backtest metrics
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Matthias
GitHub
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973cfd0182
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@ -300,7 +300,11 @@ A backtesting result will look like that:
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| Absolute profit | 0.00762792 BTC |
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| Total profit % | 76.2% |
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| CAGR % | 460.87% |
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| Sortino | 1.88 |
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| Sharpe | 2.97 |
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| Calmar | 6.29 |
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| Profit factor | 1.11 |
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| Expectancy | -0.15 |
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| Avg. stake amount | 0.001 BTC |
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| Total trade volume | 0.429 BTC |
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@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
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| Absolute profit | 0.00762792 BTC |
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| Total profit % | 76.2% |
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| CAGR % | 460.87% |
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| Sortino | 1.88 |
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| Sharpe | 2.97 |
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| Calmar | 6.29 |
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| Profit factor | 1.11 |
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| Expectancy | -0.15 |
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| Avg. stake amount | 0.001 BTC |
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| Total trade volume | 0.429 BTC |
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@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
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- `Absolute profit`: Profit made in stake currency.
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- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
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- `CAGR %`: Compound annual growth rate.
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- `Sortino`: Annualized Sortino ratio.
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- `Sharpe`: Annualized Sharpe ratio.
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- `Calmar`: Annualized Calmar ratio.
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- `Profit factor`: profit / loss.
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- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
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- `Total trade volume`: Volume generated on the exchange to reach the above profit.
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@ -1,4 +1,6 @@
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import logging
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import math
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from datetime import datetime
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from typing import Dict, Tuple
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import numpy as np
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@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
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:return: CAGR
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"""
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return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
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def calculate_expectancy(trades: pd.DataFrame) -> float:
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"""
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Calculate expectancy
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:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
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:return: expectancy
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"""
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if len(trades) == 0:
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return 0
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expectancy = 1
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profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
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loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
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nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
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nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
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if (nb_win_trades > 0) and (nb_loss_trades > 0):
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average_win = profit_sum / nb_win_trades
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average_loss = loss_sum / nb_loss_trades
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risk_reward_ratio = average_win / average_loss
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winrate = nb_win_trades / len(trades)
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expectancy = ((1 + risk_reward_ratio) * winrate) - 1
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elif nb_win_trades == 0:
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expectancy = 0
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return expectancy
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def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
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starting_balance: float) -> float:
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"""
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Calculate sortino
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:param trades: DataFrame containing trades (requires columns profit_abs)
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:return: sortino
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"""
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if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
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return 0
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total_profit = trades['profit_abs'] / starting_balance
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days_period = max(1, (max_date - min_date).days)
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expected_returns_mean = total_profit.sum() / days_period
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down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
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if down_stdev != 0:
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sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
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else:
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# Define high (negative) sortino ratio to be clear that this is NOT optimal.
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sortino_ratio = -100
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# print(expected_returns_mean, down_stdev, sortino_ratio)
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return sortino_ratio
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def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
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starting_balance: float) -> float:
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"""
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Calculate sharpe
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:param trades: DataFrame containing trades (requires column profit_abs)
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:return: sharpe
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"""
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if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
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return 0
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total_profit = trades['profit_abs'] / starting_balance
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days_period = max(1, (max_date - min_date).days)
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expected_returns_mean = total_profit.sum() / days_period
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up_stdev = np.std(total_profit)
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if up_stdev != 0:
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sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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sharp_ratio = -100
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# print(expected_returns_mean, up_stdev, sharp_ratio)
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return sharp_ratio
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def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
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starting_balance: float) -> float:
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"""
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Calculate calmar
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:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
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:return: calmar
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"""
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if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
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return 0
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total_profit = trades['profit_abs'].sum() / starting_balance
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days_period = max(1, (max_date - min_date).days)
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# adding slippage of 0.1% per trade
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# total_profit = total_profit - 0.0005
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expected_returns_mean = total_profit / days_period * 100
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# calculate max drawdown
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try:
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_, _, _, _, _, max_drawdown = calculate_max_drawdown(
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trades, value_col="profit_abs", starting_balance=starting_balance
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)
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except ValueError:
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max_drawdown = 0
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if max_drawdown != 0:
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calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
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else:
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# Define high (negative) calmar ratio to be clear that this is NOT optimal.
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calmar_ratio = -100
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# print(expected_returns_mean, max_drawdown, calmar_ratio)
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return calmar_ratio
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@ -9,8 +9,9 @@ from tabulate import tabulate
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from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
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Config)
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from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
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calculate_max_drawdown)
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from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
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calculate_expectancy, calculate_market_change,
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calculate_max_drawdown, calculate_sharpe, calculate_sortino)
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from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
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from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
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@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
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'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
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'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
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'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
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'expectancy': calculate_expectancy(results),
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'sortino': calculate_sortino(results, min_date, max_date, start_balance),
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'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
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'calmar': calculate_calmar(results, min_date, max_date, start_balance),
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'profit_factor': profit_factor,
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'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
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'backtest_start_ts': int(min_date.timestamp() * 1000),
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@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
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strat_results['stake_currency'])),
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('Total profit %', f"{strat_results['profit_total']:.2%}"),
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('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
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('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
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('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
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('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
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('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
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in strat_results else 'N/A'),
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('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
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in strat_results else 'N/A'),
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('Trades per day', strat_results['trades_per_day']),
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('Avg. daily profit %',
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f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
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@ -12,9 +12,11 @@ from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelis
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get_latest_hyperopt_file, load_backtest_data,
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load_backtest_metadata, load_trades, load_trades_from_db)
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from freqtrade.data.history import load_data, load_pair_history
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from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
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calculate_max_drawdown, calculate_underwater,
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combine_dataframes_with_mean, create_cum_profit)
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from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
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calculate_expectancy, calculate_market_change,
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calculate_max_drawdown, calculate_sharpe, calculate_sortino,
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calculate_underwater, combine_dataframes_with_mean,
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create_cum_profit)
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from freqtrade.exceptions import OperationalException
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from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
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from tests.conftest_trades import MOCK_TRADE_COUNT
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@ -336,6 +338,69 @@ def test_calculate_csum(testdatadir):
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csum_min, csum_max = calculate_csum(DataFrame())
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def test_calculate_expectancy(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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expectancy = calculate_expectancy(DataFrame())
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assert expectancy == 0.0
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expectancy = calculate_expectancy(bt_data)
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assert isinstance(expectancy, float)
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assert pytest.approx(expectancy) == 0.07151374226574791
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def test_calculate_sortino(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sortino = calculate_sortino(DataFrame(), None, None, 0)
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assert sortino == 0.0
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sortino = calculate_sortino(
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bt_data,
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bt_data['open_date'].min(),
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bt_data['close_date'].max(),
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0.01,
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)
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assert isinstance(sortino, float)
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assert pytest.approx(sortino) == 35.17722
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def test_calculate_sharpe(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sharpe = calculate_sharpe(DataFrame(), None, None, 0)
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assert sharpe == 0.0
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sharpe = calculate_sharpe(
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bt_data,
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bt_data['open_date'].min(),
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bt_data['close_date'].max(),
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0.01,
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)
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assert isinstance(sharpe, float)
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assert pytest.approx(sharpe) == 44.5078669
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def test_calculate_calmar(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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calmar = calculate_calmar(DataFrame(), None, None, 0)
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assert calmar == 0.0
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calmar = calculate_calmar(
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bt_data,
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bt_data['open_date'].min(),
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bt_data['close_date'].max(),
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0.01,
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)
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assert isinstance(calmar, float)
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assert pytest.approx(calmar) == 559.040508
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@pytest.mark.parametrize('start,end,days, expected', [
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(64900, 176000, 3 * 365, 0.3945),
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(64900, 176000, 365, 1.7119),
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