Merge pull request #7810 from stash86/bt-metrics

Add more calculations for backtest metrics

docs/backtesting.md

@@ -300,7 +300,11 @@ A backtesting result will look like that:
 | Absolute profit             | 0.00762792 BTC      |
 | Total profit %              | 76.2%               |
 | CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
+| Expectancy                  | -0.15               |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
 | Absolute profit             | 0.00762792 BTC      |
 | Total profit %              | 76.2%               |
 | CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
+| Expectancy                  | -0.15               |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Absolute profit`: Profit made in stake currency.
 - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
 - `CAGR %`: Compound annual growth rate.
+- `Sortino`: Annualized Sortino ratio.
+- `Sharpe`: Annualized Sharpe ratio.
+- `Calmar`: Annualized Calmar ratio.
 - `Profit factor`: profit / loss.
 - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
 - `Total trade volume`: Volume generated on the exchange to reach the above profit.

freqtrade/data/metrics.py

@@ -1,4 +1,6 @@
 import logging
+import math
+from datetime import datetime
 from typing import Dict, Tuple
 
 import numpy as np
@@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
     :return: CAGR
     """
     return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
+
+
+def calculate_expectancy(trades: pd.DataFrame) -> float:
+    """
+    Calculate expectancy
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :return: expectancy
+    """
+    if len(trades) == 0:
+        return 0
+
+    expectancy = 1
+
+    profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
+    loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
+    nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
+    nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
+
+    if (nb_win_trades > 0) and (nb_loss_trades > 0):
+        average_win = profit_sum / nb_win_trades
+        average_loss = loss_sum / nb_loss_trades
+        risk_reward_ratio = average_win / average_loss
+        winrate = nb_win_trades / len(trades)
+        expectancy = ((1 + risk_reward_ratio) * winrate) - 1
+    elif nb_win_trades == 0:
+        expectancy = 0
+
+    return expectancy
+
+
+def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                      starting_balance: float) -> float:
+    """
+    Calculate sortino
+    :param trades: DataFrame containing trades (requires columns profit_abs)
+    :return: sortino
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+
+    down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
+
+    if down_stdev != 0:
+        sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+        sortino_ratio = -100
+
+    # print(expected_returns_mean, down_stdev, sortino_ratio)
+    return sortino_ratio
+
+
+def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate sharpe
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :return: sharpe
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+    up_stdev = np.std(total_profit)
+
+    if up_stdev != 0:
+        sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+        sharp_ratio = -100
+
+    # print(expected_returns_mean, up_stdev, sharp_ratio)
+    return sharp_ratio
+
+
+def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate calmar
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: calmar
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'].sum() / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    # adding slippage of 0.1% per trade
+    # total_profit = total_profit - 0.0005
+    expected_returns_mean = total_profit / days_period * 100
+
+    # calculate max drawdown
+    try:
+        _, _, _, _, _, max_drawdown = calculate_max_drawdown(
+            trades, value_col="profit_abs", starting_balance=starting_balance
+        )
+    except ValueError:
+        max_drawdown = 0
+
+    if max_drawdown != 0:
+        calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
+    else:
+        # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+        calmar_ratio = -100
+
+    # print(expected_returns_mean, max_drawdown, calmar_ratio)
+    return calmar_ratio

freqtrade/optimize/optimize_reports.py

@@ -9,8 +9,9 @@ from tabulate import tabulate
 
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
                                  Config)
-from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
+from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
-                                    calculate_max_drawdown)
+                                    calculate_expectancy, calculate_market_change,
+                                    calculate_max_drawdown, calculate_sharpe, calculate_sortino)
 from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
 
@@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
         'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
         'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
         'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
+        'expectancy': calculate_expectancy(results),
+        'sortino': calculate_sortino(results, min_date, max_date, start_balance),
+        'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
+        'calmar': calculate_calmar(results, min_date, max_date, start_balance),
         'profit_factor': profit_factor,
         'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
         'backtest_start_ts': int(min_date.timestamp() * 1000),
@@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                   strat_results['stake_currency'])),
             ('Total profit %', f"{strat_results['profit_total']:.2%}"),
             ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
+            ('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
+            ('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
+            ('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
             ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
                               in strat_results else 'N/A'),
+            ('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
+                           in strat_results else 'N/A'),
             ('Trades per day', strat_results['trades_per_day']),
             ('Avg. daily profit %',
              f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

tests/data/test_btanalysis.py

@@ -12,9 +12,11 @@ from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelis
                                        get_latest_hyperopt_file, load_backtest_data,
                                        load_backtest_metadata, load_trades, load_trades_from_db)
 from freqtrade.data.history import load_data, load_pair_history
-from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
+from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
-                                    calculate_max_drawdown, calculate_underwater,
+                                    calculate_expectancy, calculate_market_change,
-                                    combine_dataframes_with_mean, create_cum_profit)
+                                    calculate_max_drawdown, calculate_sharpe, calculate_sortino,
+                                    calculate_underwater, combine_dataframes_with_mean,
+                                    create_cum_profit)
 from freqtrade.exceptions import OperationalException
 from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
 from tests.conftest_trades import MOCK_TRADE_COUNT
@@ -336,6 +338,69 @@ def test_calculate_csum(testdatadir):
         csum_min, csum_max = calculate_csum(DataFrame())
 
 
+def test_calculate_expectancy(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    expectancy = calculate_expectancy(DataFrame())
+    assert expectancy == 0.0
+
+    expectancy = calculate_expectancy(bt_data)
+    assert isinstance(expectancy, float)
+    assert pytest.approx(expectancy) == 0.07151374226574791
+
+
+def test_calculate_sortino(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    sortino = calculate_sortino(DataFrame(), None, None, 0)
+    assert sortino == 0.0
+
+    sortino = calculate_sortino(
+        bt_data,
+        bt_data['open_date'].min(),
+        bt_data['close_date'].max(),
+        0.01,
+        )
+    assert isinstance(sortino, float)
+    assert pytest.approx(sortino) == 35.17722
+
+
+def test_calculate_sharpe(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    sharpe = calculate_sharpe(DataFrame(), None, None, 0)
+    assert sharpe == 0.0
+
+    sharpe = calculate_sharpe(
+        bt_data,
+        bt_data['open_date'].min(),
+        bt_data['close_date'].max(),
+        0.01,
+        )
+    assert isinstance(sharpe, float)
+    assert pytest.approx(sharpe) == 44.5078669
+
+
+def test_calculate_calmar(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    calmar = calculate_calmar(DataFrame(), None, None, 0)
+    assert calmar == 0.0
+
+    calmar = calculate_calmar(
+        bt_data,
+        bt_data['open_date'].min(),
+        bt_data['close_date'].max(),
+        0.01,
+        )
+    assert isinstance(calmar, float)
+    assert pytest.approx(calmar) == 559.040508
+
+
 @pytest.mark.parametrize('start,end,days, expected', [
     (64900, 176000, 3 * 365, 0.3945),
     (64900, 176000, 365, 1.7119),