Merge pull request #8943 from stash86/bt-metrics

merge to use  expectancy and expectancy ratio from data/metrics

docs/backtesting.md

@@ -305,7 +305,7 @@ A backtesting result will look like that:
 | Sharpe                      | 2.97                |
 | Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
-| Expectancy                  | -0.15               |
+| Expectancy (Ratio)          | -0.15 (-0.05)       |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -409,7 +409,7 @@ It contains some useful key metrics about performance of your strategy on backte
 | Sharpe                      | 2.97                |
 | Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
-| Expectancy                  | -0.15               |
+| Expectancy (Ratio)          | -0.15 (-0.05)       |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |

freqtrade/data/metrics.py

@@ -194,32 +194,35 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
     return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
 
 
-def calculate_expectancy(trades: pd.DataFrame) -> float:
+def calculate_expectancy(trades: pd.DataFrame) -> Tuple[float, float]:
     """
     Calculate expectancy
     :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
-    :return: expectancy
+    :return: expectancy, expectancy_ratio
     """
-    if len(trades) == 0:
-        return 0
 
-    expectancy = 1
+    expectancy = 0
+    expectancy_ratio = 100
 
-    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 len(trades) > 0:
+        winning_trades = trades.loc[trades['profit_abs'] > 0]
+        losing_trades = trades.loc[trades['profit_abs'] < 0]
+        profit_sum = winning_trades['profit_abs'].sum()
+        loss_sum = abs(losing_trades['profit_abs'].sum())
+        nb_win_trades = len(winning_trades)
+        nb_loss_trades = len(losing_trades)
 
-    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
+        average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
+        average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
+        winrate = (nb_win_trades / len(trades))
+        loserate = (nb_loss_trades / len(trades))
 
-    return expectancy
+        expectancy = (winrate * average_win) - (loserate * average_loss)
+        if (average_loss > 0):
+            risk_reward_ratio = average_win / average_loss
+            expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
+
+    return expectancy, expectancy_ratio
 
 
 def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,

freqtrade/optimize/optimize_reports/bt_output.py

@@ -233,8 +233,9 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('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'),
+            ('Expectancy (Ratio)', (
+                f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})" if
+                'expectancy_ratio' 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%}"),

freqtrade/optimize/optimize_reports/optimize_reports.py

@@ -389,6 +389,7 @@ def generate_strategy_stats(pairlist: List[str],
     losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
     profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
 
+    expectancy, expectancy_ratio = calculate_expectancy(results)
     backtest_days = (max_date - min_date).days or 1
     strat_stats = {
         'trades': results.to_dict(orient='records'),
@@ -414,7 +415,8 @@ 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),
+        'expectancy': expectancy,
+        'expectancy_ratio': expectancy_ratio,
         '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),

freqtrade/rpc/rpc.py

@@ -18,7 +18,7 @@ from freqtrade import __version__
 from freqtrade.configuration.timerange import TimeRange
 from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT, Config
 from freqtrade.data.history import load_data
-from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
 from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
                              State, TradingMode)
 from freqtrade.exceptions import ExchangeError, PricingError
@@ -523,20 +523,14 @@ class RPC:
 
         profit_factor = winning_profit / abs(losing_profit) if losing_profit else float('inf')
 
-        mean_winning_profit = (winning_profit / winning_trades) if winning_trades > 0 else 0
-        mean_losing_profit = (abs(losing_profit) / losing_trades) if losing_trades > 0 else 0
-
         winrate = (winning_trades / closed_trade_count) if closed_trade_count > 0 else 0
-        loserate = (1 - winrate)
-
-        expectancy, expectancy_ratio = self.__calc_expectancy(mean_winning_profit,
-                                                              mean_losing_profit,
-                                                              winrate,
-                                                              loserate)
 
         trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
                                 'profit_abs': trade.close_profit_abs}
                                for trade in trades if not trade.is_open and trade.close_date])
+
+        expectancy, expectancy_ratio = calculate_expectancy(trades_df)
+
         max_drawdown_abs = 0.0
         max_drawdown = 0.0
         if len(trades_df) > 0:
@@ -625,23 +619,6 @@ class RPC:
 
         return est_stake, est_bot_stake
 
-    def __calc_expectancy(
-            self, mean_winning_profit: float, mean_losing_profit: float,
-            winrate: float, loserate: float) -> Tuple[float, float]:
-
-        expectancy = (
-            (winrate * mean_winning_profit) -
-            (loserate * mean_losing_profit)
-        )
-
-        expectancy_ratio = float('inf')
-        if mean_losing_profit > 0:
-            expectancy_ratio = (
-                ((1 + (mean_winning_profit / mean_losing_profit)) * winrate) - 1
-            )
-
-        return expectancy, expectancy_ratio
-
     def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
         """ Returns current account balance per crypto """
         currencies: List[Dict] = []

tests/data/test_btanalysis.py

@@ -343,12 +343,24 @@ def test_calculate_expectancy(testdatadir):
     filename = testdatadir / "backtest_results/backtest-result.json"
     bt_data = load_backtest_data(filename)
 
-    expectancy = calculate_expectancy(DataFrame())
+    expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
     assert expectancy == 0.0
+    assert expectancy_ratio == 100
 
-    expectancy = calculate_expectancy(bt_data)
+    expectancy, expectancy_ratio = calculate_expectancy(bt_data)
     assert isinstance(expectancy, float)
-    assert pytest.approx(expectancy) == 0.07151374226574791
+    assert isinstance(expectancy_ratio, float)
+    assert pytest.approx(expectancy) == 5.820687070932315e-06
+    assert pytest.approx(expectancy_ratio) == 0.07151374226574791
+
+    data = {
+        'profit_abs': [100, 200, 50, -150, 300, -100, 80, -30]
+    }
+    df = DataFrame(data)
+    expectancy, expectancy_ratio = calculate_expectancy(df)
+
+    assert pytest.approx(expectancy) == 56.25
+    assert pytest.approx(expectancy_ratio) == 0.60267857
 
 
 def test_calculate_sortino(testdatadir):

tests/rpc/test_rpc.py

@@ -403,7 +403,7 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
     assert res['latest_trade_date'] == ''
     assert res['latest_trade_timestamp'] == 0
     assert res['expectancy'] == 0
-    assert res['expectancy_ratio'] == float('inf')
+    assert res['expectancy_ratio'] == 100
 
     # Create some test data
     create_mock_trades_usdt(fee)

tests/rpc/test_rpc_apiserver.py

@@ -846,7 +846,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
          'profit_closed_percent_sum': 1.5, 'profit_closed_ratio': 7.391275897987988e-07,
          'profit_closed_percent': 0.0, 'winning_trades': 2, 'losing_trades': 0,
          'profit_factor': None, 'winrate': 1.0, 'expectancy': 0.0003695635,
-         'expectancy_ratio': None, 'trading_volume': 91.074,
+         'expectancy_ratio': 100, 'trading_volume': 91.074,
          }
     ),
     (