feat: calculate sharpe-ratio from historic balance snapshots

freqtrade/data/metrics.py

@@ -390,10 +390,55 @@ def calculate_sharpe(
         # 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_sharpe_from_balance(
+    balance_history: pd.DataFrame,
+    date_col: str = "date",
+    balance_col: str = "total_quote",
+) -> float:
+    """
+    Calculate sharpe ratio from historical balance snapshots.
+
+    :param balance_history: DataFrame containing at least date and balance columns
+    :param date_col: Column containing timestamps
+    :param balance_col: Column containing historical balance values
+    :return: sharpe
+    """
+    if (
+        len(balance_history) == 0
+        or date_col not in balance_history
+        or balance_col not in balance_history
+    ):
+        return 0.0
+
+    wallet = balance_history.loc[:, [date_col, balance_col]].copy()
+    wallet[date_col] = pd.to_datetime(wallet[date_col], utc=True, errors="coerce")
+    wallet = wallet.dropna(subset=[date_col, balance_col]).sort_values(date_col)
+
+    if len(wallet) < 2:
+        return 0.0
+
+    # Sample balance to daily end-of-day values to normalize variable snapshot frequency.
+    daily_balance = wallet.set_index(date_col)[balance_col].resample("1D").last().dropna()
+    daily_returns = daily_balance.pct_change().dropna()
+
+    if len(daily_returns) == 0:
+        return 0.0
+
+    expected_returns_mean = daily_returns.mean()
+    up_stdev = daily_returns.std(ddof=0)
+
+    if up_stdev != 0 and not np.isnan(up_stdev):
+        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
+
+    return float(sharp_ratio)
+
+
 def calculate_calmar(
     trades: pd.DataFrame,
     min_date: datetime | None,

freqtrade/optimize/optimize_reports/bt_output.py

@@ -308,6 +308,13 @@ def text_table_add_metrics(strat_results: dict) -> None:
                     ),
                 ]
             )
+            if "sharpe" in wallet_stats:
+                wallet_metrics.append(
+                    (
+                        "Sharpe ratio balance",
+                        f"{wallet_stats['sharpe']:.2f}",
+                    )
+                )
 
         # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
         # command stores these results and newer version of freqtrade must be able to handle old

freqtrade/optimize/optimize_reports/optimize_reports.py

@@ -15,6 +15,7 @@ from freqtrade.data.metrics import (
     calculate_market_change,
     calculate_max_drawdown,
     calculate_sharpe,
+    calculate_sharpe_from_balance,
     calculate_sortino,
     calculate_sqn,
 )
@@ -59,11 +60,13 @@ def generate_wallet_stats(wallet_df: DataFrame, stake_currency: str) -> dict[str
     low_balance = total_quote.loc[low_idx]
     low_date = wallet.loc[low_idx, "date"]
     high_date = wallet.loc[high_idx, "date"]
+    sharpe = calculate_sharpe_from_balance(wallet)
     return {
         "start_balance": start_balance,
         "end_balance": end_balance,
         "high_balance": high_balance,
         "low_balance": low_balance,
+        "sharpe": sharpe,
         "low_date": low_date.strftime(DATETIME_PRINT_FORMAT),
         "low_ts": int(low_date.timestamp() * 1000),
         "high_date": high_date.strftime(DATETIME_PRINT_FORMAT),