Convert winrate to ratio instead of % in calculations

freqtrade/rpc/rpc.py

@@ -526,8 +526,8 @@ def _rpc_trade_statistics(
         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) * 100 if closed_trade_count > 0 else 0
-        loserate = (100 - winrate)
+        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,
@@ -630,14 +630,14 @@ def __calc_expectancy(
             winrate: float, loserate: float) -> Tuple[float, float]:
 
         expectancy = (
-            ((winrate / 100) * mean_winning_profit) -
-            ((loserate / 100) * mean_losing_profit)
+            (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 / 100)) - 1
+                ((1 + (mean_winning_profit / mean_losing_profit)) * winrate) - 1
             )
 
         return expectancy, expectancy_ratio

freqtrade/rpc/telegram.py

@@ -878,7 +878,7 @@ async def _profit(self, update: Update, context: CallbackContext) -> None:
                 f"`{first_trade_date}`\n"
                 f"*Latest Trade opened:* `{latest_trade_date}`\n"
                 f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`\n"
-                f"*Winrate:* `{winrate:.2f}%`\n"
+                f"*Winrate:* `{winrate:.2%}%`\n"
                 f"*Expectancy (Ratio):* `{expectancy:.2f} ({expectancy_ratio:.2f})`"
             )
             if stats['closed_trade_count'] > 0:

tests/rpc/test_rpc.py

@@ -415,15 +415,15 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
     assert pytest.approx(stats['profit_all_coin']) == -77.45964918
     assert pytest.approx(stats['profit_all_percent_mean']) == -57.86
     assert pytest.approx(stats['profit_all_fiat']) == -85.205614098
-    assert pytest.approx(stats['winrate']) == 66.666666667
-    assert pytest.approx(stats['expectancy']) == 0.9133333333333327
+    assert pytest.approx(stats['winrate']) == 0.666666667
+    assert pytest.approx(stats['expectancy']) == 0.913333333
+    assert pytest.approx(stats['expectancy_ratio']) == 0.223308883
     assert stats['trade_count'] == 7
     assert stats['first_trade_humanized'] == '2 days ago'
     assert stats['latest_trade_humanized'] == '17 minutes ago'
     assert stats['avg_duration'] in ('0:17:40')
     assert stats['best_pair'] == 'XRP/USDT'
     assert stats['best_rate'] == 10.0
-    assert stats['expectancy_ratio'] == 0.22330888345558253
 
     # Test non-available pair
     mocker.patch(f'{EXMS}.get_rate',

tests/rpc/test_rpc_apiserver.py

@@ -845,7 +845,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
          'profit_closed_percent_mean': 0.75, 'profit_closed_ratio_sum': 0.015,
          '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': 100.0, 'expectancy': 0.0003695635,
+         'profit_factor': None, 'winrate': 1.0, 'expectancy': 0.0003695635,
          'expectancy_ratio': None, 'trading_volume': 91.074,
          }
     ),
@@ -861,7 +861,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
          'profit_closed_percent_mean': 0.25, 'profit_closed_ratio_sum': 0.005,
          'profit_closed_percent_sum': 0.5, 'profit_closed_ratio': -5.429078808526421e-06,
          'profit_closed_percent': -0.0, 'winning_trades': 1, 'losing_trades': 1,
-         'profit_factor': 0.02775724835771106, 'winrate': 50.0,
+         'profit_factor': 0.02775724835771106, 'winrate': 0.5,
          'expectancy': -0.0027145635000000003, 'expectancy_ratio': -0.48612137582114445,
          'trading_volume': 91.074,
          }