feat: add wallet to dataframe conversion

freqtrade/ft_types/backtest_result_type.py

@@ -55,6 +55,7 @@ class BacktestContentTypeIcomplete(TypedDict, total=False):
     backtest_start_time: int
     backtest_end_time: int
     run_id: str
+    wallet_summary: DataFrame
 
 
 class BacktestContentType(BacktestContentTypeIcomplete, total=True):

freqtrade/optimize/backtesting.py

@@ -51,6 +51,7 @@ from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.backtest_caching import get_strategy_run_id
 from freqtrade.optimize.bt_progress import BTProgress
 from freqtrade.optimize.optimize_reports import (
+    convert_bt_wallet_collection,
     generate_backtest_stats,
     generate_rejected_signals,
     generate_trade_signal_candles,
@@ -1752,6 +1753,7 @@ class Backtesting:
             "canceled_entry_orders": self.canceled_entry_orders,
             "replaced_entry_orders": self.replaced_entry_orders,
             "final_balance": self.wallets.get_total(self.strategy.config["stake_currency"]),
+            "wallet_summary": convert_bt_wallet_collection(self.wallet_captures),
         }
 
     def backtest_one_strategy(

freqtrade/optimize/optimize_reports/__init__.py

@@ -12,6 +12,7 @@ from freqtrade.optimize.optimize_reports.bt_output import (
 )
 from freqtrade.optimize.optimize_reports.bt_storage import store_backtest_results
 from freqtrade.optimize.optimize_reports.optimize_reports import (
+    convert_bt_wallet_collection,
     generate_all_periodic_breakdown_stats,
     generate_backtest_stats,
     generate_daily_stats,

freqtrade/optimize/optimize_reports/optimize_reports.py

@@ -29,6 +29,20 @@ from freqtrade.util import decimals_per_coin, fmt_coin, format_duration, get_dry
 logger = logging.getLogger(__name__)
 
 
+def convert_bt_wallet_collection(wallet_captures: list[tuple]) -> DataFrame:
+    """
+    Convert the wallet capture list to a DataFrame.
+    Assumes the wallet_captures list contains tuples with the following structure:
+    (date, currency, price, balance).
+    """
+    if len(wallet_captures) == 0:
+        return []
+    return DataFrame(
+        wallet_captures,
+        columns=["date", "currency", "price", "balance"],
+    )
+
+
 def generate_trade_signal_candles(
     preprocessed_df: dict[str, DataFrame], bt_results: BacktestContentType, date_col: str
 ) -> dict[str, DataFrame]: