Add underwaterplot calculation to btanalysis
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@ -361,6 +361,36 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
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return df
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def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
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) -> pd.DataFrame:
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max_drawdown_df = pd.DataFrame()
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max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
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max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
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max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
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max_drawdown_df['date'] = profit_results.loc[:, date_col]
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return max_drawdown_df
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def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_ratio'
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):
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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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: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_ratio')
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
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high and low time and high and low value.
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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(profit_results, date_col=date_col, value_col=value_col)
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return max_drawdown_df
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_ratio'
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
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@ -376,10 +406,7 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
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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 = pd.DataFrame()
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max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
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max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
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max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
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max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
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idxmin = max_drawdown_df['drawdown'].idxmin()
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if idxmin == 0:
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@ -11,10 +11,10 @@ from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
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analyze_trade_parallelism, calculate_csum,
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calculate_market_change, calculate_max_drawdown,
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combine_dataframes_with_mean, create_cum_profit,
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extract_trades_of_period, get_latest_backtest_filename,
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get_latest_hyperopt_file, load_backtest_data, load_trades,
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load_trades_from_db)
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calculate_underwater, combine_dataframes_with_mean,
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create_cum_profit, extract_trades_of_period,
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get_latest_backtest_filename, get_latest_hyperopt_file,
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load_backtest_data, load_trades, load_trades_from_db)
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from freqtrade.data.history import load_data, load_pair_history
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from tests.conftest import create_mock_trades
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from tests.conftest_trades import MOCK_TRADE_COUNT
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@ -291,9 +291,16 @@ def test_calculate_max_drawdown(testdatadir):
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assert isinstance(lval, float)
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assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
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assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
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underwater = calculate_underwater(bt_data)
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assert isinstance(underwater, DataFrame)
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
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with pytest.raises(ValueError, match='Trade dataframe empty.'):
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calculate_underwater(DataFrame())
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def test_calculate_csum(testdatadir):
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filename = testdatadir / "backtest-result_test.json"
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