Fix annualised return percent

tests/backtest/test_backtest_profit_calculation.py

@@ -297,11 +297,22 @@ def test_daily_returns(backtest_result_hourly: State):
     """
     cum_profit = calculate_cumulative_daily_returns(backtest_result_hourly)
     assert isinstance(cum_profit, pd.Series)
+
+    # remove last entry, since it was added to fill the gap
+    cum_profit = cum_profit[:-1]
 
     non_cum_profit = calculate_non_cumulative_daily_returns(backtest_result_hourly)
     assert isinstance(non_cum_profit, pd.Series)
 
-    non_cum_profit.add(1).cumprod().sub(1).iloc[-1] == pytest.approx(cum_profit.iloc[-1], abs=1e-10)
+    cum_profit_2 = non_cum_profit.add(1).cumprod().sub(1)
+
+    assert cum_profit.index.equals(cum_profit_2.index)
+
+    assert all(cum_profit - cum_profit_2 < 1e-10)
+
+    assert cum_profit_2.iloc[-3] == pytest.approx(cum_profit.iloc[-3], abs=1e-10)
+    assert cum_profit_2.iloc[-2] == pytest.approx(cum_profit.iloc[-2], abs=1e-10)
+    assert cum_profit_2.iloc[-1] == pytest.approx(cum_profit.iloc[-1], abs=1e-10)
 
 
 def test_profitabilities_are_same(backtest_result_hourly: State):

tests/test_state.py

@@ -503,6 +503,7 @@ def test_statistics(usdc, weth_usdc, aave_usdc, start_ts):
     assert stats.long_short_metrics_latest["live_stats"].rows['won_positions'].value['Long'] == '1'
     assert stats.long_short_metrics_latest["live_stats"].rows['won_positions'].value['Short'] == '0'
     assert stats.long_short_metrics_latest["live_stats"].rows['average_position'].value['Long'] == '25.00%'
+    assert stats.long_short_metrics_latest["live_stats"].rows['return_percent'].value['All'] == '4.91%'
     assert stats.long_short_metrics_latest["backtested_stats"].rows == {}
 
     assert len(stats.positions) == 2

tradeexecutor/statistics/statistics_table.py

@@ -141,7 +141,7 @@ def _serialise_long_short_stats_as_json_table(
     if compounding_returns is not None and len(compounding_returns) > 0:
         daily_returns = calculate_non_cumulative_daily_returns(source_state)
         portfolio_return = compounding_returns.iloc[-1]
-        annualised_return_percent = portfolio_return * 365 * 24 * 60 * 60 / (calculation_window_end_at - calculation_window_start_at).seconds
+        annualised_return_percent = portfolio_return * 365 * 24 * 60 * 60 / (calculation_window_end_at - calculation_window_start_at).total_seconds()
         summary.loc['Return %']['All'] = format_value(as_percent(portfolio_return))
         summary.loc['Annualised return %']['All'] = format_value(as_percent(annualised_return_percent))
 

tradeexecutor/visual/equity_curve.py

@@ -561,5 +561,5 @@ def calculate_cumulative_daily_returns(state: State, freq_base: pd.offsets.DateO
     """
     returns = calculate_compounding_realised_trading_profitability(state)
     _returns = returns.copy()
-    cumulative_daily_returns = _returns.add(1).resample(freq_base).prod(min_count=1).sub(1).ffill()
+    cumulative_daily_returns = _returns.resample(freq_base).last().ffill()
     return cumulative_daily_returns