Add day of week to backtest breakdowns

freqtrade/commands/cli_options.py

@@ -245,7 +245,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "backtest_breakdown": Arg(
         "--breakdown",
-        help="Show backtesting breakdown per [day, week, month, year].",
+        help="Show backtesting breakdown per [day, week, month, year, weekday].",
         nargs="+",
         choices=constants.BACKTEST_BREAKDOWNS,
     ),

freqtrade/constants.py

@@ -61,7 +61,7 @@ AVAILABLE_PAIRLISTS = [
     "VolatilityFilter",
 ]
 AVAILABLE_DATAHANDLERS = ["json", "jsongz", "feather", "parquet"]
-BACKTEST_BREAKDOWNS = ["day", "week", "month", "year"]
+BACKTEST_BREAKDOWNS = ["day", "week", "month", "year", "weekday"]
 BACKTEST_CACHE_AGE = ["none", "day", "week", "month"]
 BACKTEST_CACHE_DEFAULT = "day"
 DRY_RUN_WALLET = 1000

freqtrade/optimize/optimize_reports/bt_output.py

@@ -132,8 +132,13 @@ def text_table_periodic_breakdown(
     :param days_breakdown_stats: Days breakdown metrics
     :param stake_currency: Stakecurrency used
     """
+    if period == "weekday":
+        first_column = "Week day"
+    else:
+        first_column = period.capitalize()
+
     headers = [
-        period.capitalize(),
+        first_column,
         "Trades",
         f"Tot Profit {stake_currency}",
         "Profit Factor",

freqtrade/optimize/optimize_reports/optimize_reports.py

@@ -256,40 +256,66 @@ def _get_resample_from_period(period: str) -> str:
         return "1ME"
     if period == "year":
         return "1YE"
+    if period == "weekday":
+        # Required to pass the test
+        return "weekday"
     raise ValueError(f"Period {period} is not supported.")
 
 
+def _calculate_stats_for_period(data: DataFrame) -> dict[str, Any]:
+    profit_abs = data["profit_abs"].sum().round(10)
+    wins = sum(data["profit_abs"] > 0)
+    draws = sum(data["profit_abs"] == 0)
+    losses = sum(data["profit_abs"] < 0)
+    trades = wins + draws + losses
+    winning_profit = data.loc[data["profit_abs"] > 0, "profit_abs"].sum()
+    losing_profit = data.loc[data["profit_abs"] < 0, "profit_abs"].sum()
+    profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
+
+    return {
+        "profit_abs": profit_abs,
+        "wins": wins,
+        "draws": draws,
+        "losses": losses,
+        "trades": trades,
+        "profit_factor": round(profit_factor, 8),
+    }
+
+
 def generate_periodic_breakdown_stats(
     trade_list: list | DataFrame, period: str
 ) -> list[dict[str, Any]]:
     results = trade_list if not isinstance(trade_list, list) else DataFrame.from_records(trade_list)
     if len(results) == 0:
         return []
+
     results["close_date"] = to_datetime(results["close_date"], utc=True)
-    resample_period = _get_resample_from_period(period)
-    resampled = results.resample(resample_period, on="close_date")
-    stats = []
-    for name, day in resampled:
-        profit_abs = day["profit_abs"].sum().round(10)
-        wins = sum(day["profit_abs"] > 0)
-        draws = sum(day["profit_abs"] == 0)
-        losses = sum(day["profit_abs"] < 0)
-        trades = wins + draws + losses
-        winning_profit = day.loc[day["profit_abs"] > 0, "profit_abs"].sum()
-        losing_profit = day.loc[day["profit_abs"] < 0, "profit_abs"].sum()
-        profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
-        stats.append(
-            {
-                "date": name.strftime("%d/%m/%Y"),
-                "date_ts": int(name.to_pydatetime().timestamp() * 1000),
-                "profit_abs": profit_abs,
-                "wins": wins,
-                "draws": draws,
-                "losses": losses,
-                "trades": trades,
-                "profit_factor": round(profit_factor, 8),
-            }
-        )
+
+    if period == "weekday":
+        day_names = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
+        results["weekday"] = results["close_date"].dt.dayofweek
+
+        stats = []
+        for day_num in range(7):
+            day_data = results[results["weekday"] == day_num]
+            if len(day_data) > 0:
+                period_stats = _calculate_stats_for_period(day_data)
+                stats.append({"date": day_names[day_num], "date_ts": day_num, **period_stats})
+    else:
+        resample_period = _get_resample_from_period(period)
+        resampled = results.resample(resample_period, on="close_date")
+
+        stats = []
+        for name, period_data in resampled:
+            period_stats = _calculate_stats_for_period(period_data)
+            stats.append(
+                {
+                    "date": name.strftime("%d/%m/%Y"),
+                    "date_ts": int(name.to_pydatetime().timestamp() * 1000),
+                    **period_stats,
+                }
+            )
+
     return stats