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Expand calculate_max_drawdown() to return the current drawdown data and use it instead of calculate_current_drawdown()
This commit is contained in:
mrpabloyeah
@@ -174,12 +174,18 @@ def calculate_underwater(
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@dataclass()
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class DrawDownResult:
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drawdown_abs: float = 0.0
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# Max drawdown fields
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high_date: pd.Timestamp = None
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low_date: pd.Timestamp = None
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high_value: float = 0.0
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low_value: float = 0.0
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drawdown_abs: float = 0.0
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relative_account_drawdown: float = 0.0
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# Current drawdown fields
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current_high_date: pd.Timestamp = None
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current_high_value: float = 0.0
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current_drawdown_abs: float = 0.0
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current_relative_account_drawdown: float = 0.0
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def calculate_max_drawdown(
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@@ -191,88 +197,68 @@ def calculate_max_drawdown(
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relative: bool = False,
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) -> DrawDownResult:
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"""
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Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
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Calculate max drawdown and current drawdown with corresponding dates
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:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
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:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
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:param relative: If True, use relative drawdown for max calculation instead of absolute
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:return: DrawDownResult object
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with absolute max drawdown, high and low time and high and low value,
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and the relative account drawdown
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relative account drawdown, and current drawdown information.
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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profit_results = trades.sort_values(date_col).reset_index(drop=True)
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max_drawdown_df = _calc_drawdown_series(
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profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
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)
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idxmin = (
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max_drawdown_df["drawdown_relative"].idxmax()
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if relative
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else max_drawdown_df["drawdown"].idxmin()
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)
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high_idx = max_drawdown_df.iloc[: idxmin + 1]["high_value"].idxmax()
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high_date = profit_results.loc[high_idx, date_col]
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low_date = profit_results.loc[idxmin, date_col]
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high_val = max_drawdown_df.loc[high_idx, "cumulative"]
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low_val = max_drawdown_df.loc[idxmin, "cumulative"]
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max_drawdown_rel = max_drawdown_df.loc[idxmin, "drawdown_relative"]
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return DrawDownResult(
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drawdown_abs=abs(max_drawdown_df.loc[idxmin, "drawdown"]),
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high_date=high_date,
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low_date=low_date,
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high_value=high_val,
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low_value=low_val,
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relative_account_drawdown=max_drawdown_rel,
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)
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def calculate_current_drawdown(trades: pd.DataFrame, starting_balance: float):
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"""
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Calculates the current drawdown (loss from historical maximum) based on closed trades.
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:param trades: DataFrame containing trades (requires columns close_date_dt and profit_abs)
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:param starting_balance: Initial account balance
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:return: DrawDownResult object including:
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- drawdown_abs: Drawdown in absolute terms
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- relative_account_drawdown: Drawdown relative to max balance
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- high_value: Maximum profit reached
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- high_date: Date when the max profit was reached
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"""
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if len(trades) == 0:
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raise ValueError("Trade dataframe empty.")
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# Sort trades by close date
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sorted_df = trades.sort_values("close_date_dt").reset_index(drop=True)
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profit_results = trades.sort_values(date_col).reset_index(drop=True)
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# Calculate cumulative profit
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cum_profit = sorted_df["profit_abs"].cumsum()
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# Find historical maximum profit and its date
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max_profit_idx = cum_profit.idxmax()
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max_profit = cum_profit.iloc[max_profit_idx]
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max_date = sorted_df.iloc[max_profit_idx]["close_date_dt"]
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# Calculate current and max balance
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current_balance = starting_balance + cum_profit.iloc[-1]
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max_balance = starting_balance + max_profit
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# Calculate drawdown
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drawdown_abs = max_balance - current_balance
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drawdown_relative = drawdown_abs / max_balance
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return DrawDownResult(
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drawdown_abs=drawdown_abs,
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relative_account_drawdown=drawdown_relative,
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high_value=max_profit,
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high_date=max_date,
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# Get drawdown data
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max_drawdown_df = _calc_drawdown_series(
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profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
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)
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# Calculate maximum drawdown
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idxmin = (
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max_drawdown_df["drawdown_relative"].idxmax()
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if relative
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else max_drawdown_df["drawdown"].idxmin()
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)
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high_idx = max_drawdown_df.iloc[: idxmin + 1]["high_value"].idxmax()
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high_date = profit_results.loc[high_idx, date_col]
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low_date = profit_results.loc[idxmin, date_col]
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high_val = max_drawdown_df.loc[high_idx, "cumulative"]
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low_val = max_drawdown_df.loc[idxmin, "cumulative"]
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max_drawdown_abs = abs(max_drawdown_df.loc[idxmin, "drawdown"])
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max_drawdown_rel = max_drawdown_df.loc[idxmin, "drawdown_relative"]
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# Calculate current drawdown
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current_high_idx = max_drawdown_df["high_value"].iloc[:-1].idxmax()
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current_high_date = profit_results.loc[current_high_idx, date_col]
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current_high_value = max_drawdown_df.iloc[-1]["high_value"]
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current_cumulative = max_drawdown_df.iloc[-1]["cumulative"]
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current_drawdown_abs = current_high_value - current_cumulative
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current_drawdown_relative = max_drawdown_df.iloc[-1]["drawdown_relative"]
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result = DrawDownResult(
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# Max drawdown
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high_date=high_date,
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low_date=low_date,
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high_value=high_val,
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low_value=low_val,
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drawdown_abs=max_drawdown_abs,
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relative_account_drawdown=max_drawdown_rel,
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# Current drawdown
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current_high_date=current_high_date,
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current_high_value=current_high_value,
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current_drawdown_abs=current_drawdown_abs,
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current_relative_account_drawdown=current_drawdown_relative,
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)
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return result
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def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> tuple[float, float]:
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"""
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@@ -19,12 +19,7 @@ from freqtrade import __version__
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from freqtrade.configuration.timerange import TimeRange
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from freqtrade.constants import CANCEL_REASON, DEFAULT_DATAFRAME_COLUMNS, Config
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from freqtrade.data.history import load_data
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from freqtrade.data.metrics import (
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DrawDownResult,
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calculate_current_drawdown,
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calculate_expectancy,
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calculate_max_drawdown,
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)
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from freqtrade.data.metrics import DrawDownResult, calculate_expectancy, calculate_max_drawdown
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from freqtrade.enums import (
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CandleType,
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ExitCheckTuple,
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@@ -616,9 +611,7 @@ class RPC:
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)
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expectancy, expectancy_ratio = calculate_expectancy(trades_df)
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max_drawdown = DrawDownResult()
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current_drawdown = DrawDownResult()
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if len(trades_df) > 0:
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try:
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@@ -631,11 +624,6 @@ class RPC:
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except ValueError:
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pass
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try:
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current_drawdown = calculate_current_drawdown(trades_df, starting_balance)
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except ValueError:
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pass
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profit_all_fiat = (
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self._fiat_converter.convert_amount(
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profit_all_coin_sum, stake_currency, fiat_display_currency
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@@ -692,11 +680,11 @@ class RPC:
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"max_drawdown_end_timestamp": dt_ts_def(max_drawdown.low_date),
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"drawdown_high": max_drawdown.high_value,
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"drawdown_low": max_drawdown.low_value,
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"current_drawdown": current_drawdown.relative_account_drawdown,
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"current_drawdown_abs": current_drawdown.drawdown_abs,
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"current_drawdown_high": current_drawdown.high_value,
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"current_drawdown_start": format_date(current_drawdown.high_date),
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"current_drawdown_start_timestamp": dt_ts_def(current_drawdown.high_date),
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"current_drawdown": max_drawdown.current_relative_account_drawdown,
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"current_drawdown_abs": max_drawdown.current_drawdown_abs,
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"current_drawdown_high": max_drawdown.current_high_value,
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"current_drawdown_start": format_date(max_drawdown.current_high_date),
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"current_drawdown_start_timestamp": dt_ts_def(max_drawdown.current_high_date),
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"trading_volume": trading_volume,
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"bot_start_timestamp": dt_ts_def(bot_start, 0),
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"bot_start_date": format_date(bot_start),
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