test done
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Guitheg
parent
91e57f64d4
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@ -390,6 +390,7 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
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low_val = max_drawdown_df.loc[idxmin, 'cumulative']
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return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
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# TODO : is supposed to work only with long positions
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def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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"""
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Calculate Trades MDD (Max DrawDown) :
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@ -409,57 +410,55 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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trades_mdd_pair_list = []
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for pair, df in data.items():
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if df is None:
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break
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for pair, df in data.items():
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if isinstance(df, pd.DataFrame):
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# Gather the opening and closing trade dates into one Dates DataFrame
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open_close_trades = trades.loc[trades['pair']==pair][["open_date","close_date"]]
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open_close_trades = pd.concat(
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[open_close_trades.rename(columns={'open_date':'date'})[['date']],
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open_close_trades.rename(columns={'close_date':'date'})[['date']]]
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).sort_values(by='date')
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# Mark the dates and join it to the current candle dataframe.
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# This allow to determine the open and close trade dates in the current
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# candle dataframe.
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open_close_trades['open_close_mark'] = 1
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data_join = df.set_index('date').join(open_close_trades.set_index('date'))
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del open_close_trades
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# Gather the opening and closing trade dates into one Dates DataFrame
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open_close_trades = trades.loc[trades['pair']==pair][["open_date","close_date"]]
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open_close_trades = pd.concat(
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[open_close_trades.rename(columns={'open_date':'date'})[['date']],
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open_close_trades.rename(columns={'close_date':'date'})[['date']]]
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).sort_values(by='date')
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# Mark the dates and join it to the current candle dataframe.
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# This allow to determine the open and close trade dates in the current
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# candle dataframe.
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open_close_trades['open_close_mark'] = 1
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data_join = df.set_index('date').join(open_close_trades.set_index('date'))
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del open_close_trades
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# Gather, mark and join only the opening trade dates into the current candle
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# dataframe.
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# This allow to classify trades using the cumsum and split by classes
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# with groupby in order to process a cummax on each trades independantly.
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open_trades = trades.loc[trades['pair']==pair][["open_date"]]
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open_trades = open_trades.rename(columns={'open_date':'date'})
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open_trades['open_mark'] = 1
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data_join = data_join.join(open_trades.set_index('date'))
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del open_trades
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# Gather, mark and join only the opening trade dates into the current candle
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# dataframe.
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# This allow to classify trades using the cumsum and split by classes
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# with groupby in order to process a cummax on each trades independantly.
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open_trades = trades.loc[trades['pair']==pair][["open_date"]]
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open_trades = open_trades.rename(columns={'open_date':'date'})
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open_trades['open_mark'] = 1
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data_join = data_join.join(open_trades.set_index('date'))
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del open_trades
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# Set all unmarked date to 0
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data_join[["open_close_mark",'open_mark']] = data_join[
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["open_close_mark",'open_mark']].fillna(0).astype(int)
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# Set all unmarked date to 0
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data_join[["open_close_mark",'open_mark']] = data_join[
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["open_close_mark",'open_mark']].fillna(0).astype(int)
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# Mark with one all dates between an opening date trades and a closing date trades.
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data_join['is_in_trade'] = data_join.open_close_mark.cumsum()&1 # &1 <=> %2
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data_join.loc[data_join['open_close_mark'] == 1, 'is_in_trade'] = 1
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# Perform a cummax in each trades independtly
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data_join['close_cummax'] = 0
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data_join['close_cummax'] = data_join.groupby(
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data_join['open_mark'].cumsum()
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)['close'].cummax()
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data_join.loc[data_join['is_in_trade'] == 0, 'close_cummax'] = 0
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# Mark with one all dates between an opening date trades and a closing date trades.
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data_join['is_in_trade'] = data_join.open_close_mark.cumsum()&1 # &1 <=> %2
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data_join.loc[data_join['open_close_mark'] == 1, 'is_in_trade'] = 1
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# Perform a cummax in each trades independtly
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data_join['close_cummax'] = 0
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data_join['close_cummax'] = data_join.groupby(
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data_join['open_mark'].cumsum()
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)['close'].cummax()
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data_join.loc[data_join['is_in_trade'] == 0, 'close_cummax'] = 0
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# Compute the drawdown at each time of each trades
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data_join = data_join.rename(columns={'open_mark':'drawdown'})
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data_join.loc[data_join['is_in_trade'] == 1, 'drawdown'] = \
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(data_join['close_cummax'] - data_join['close']) \
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/ data_join['close_cummax']
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# Compute the drawdown at each time of each trades
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data_join = data_join.rename(columns={'open_mark':'drawdown'})
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data_join.loc[data_join['is_in_trade'] == 1, 'drawdown'] = \
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(data_join['close_cummax'] - data_join['close']) \
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/ data_join['close_cummax']
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mdd_pair = data_join['drawdown'].max()
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trades_mdd_pair_list.append(mdd_pair)
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mdd_pair = data_join['drawdown'].max()
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trades_mdd_pair_list.append(mdd_pair)
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if trades_mdd_pair_list == []:
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raise ValueError("All dataframe in candle data are None")
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@ -1,7 +1,7 @@
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from math import isclose
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from pathlib import Path
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from unittest.mock import MagicMock
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import numpy as np
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import pytest
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from arrow import Arrow
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from pandas import DataFrame, DateOffset, Timestamp, to_datetime
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@ -10,7 +10,7 @@ from freqtrade.configuration import TimeRange
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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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calculate_market_change, calculate_max_drawdown, calculate_trades_mdd,
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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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@ -332,3 +332,15 @@ def test_calculate_max_drawdown2():
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df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
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with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
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calculate_max_drawdown(df, date_col='open_date', value_col='profit')
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def test_calculate_trades_mdd(testdatadir):
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backtest_file = testdatadir / "backtest-result_test.json"
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trades = load_backtest_data(backtest_file)
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pairlist = set(trades["pair"])
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with pytest.raises(ValueError, match='All dataframe in candle data are None'):
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calculate_trades_mdd({"BTC/BUSD" : None}, trades)
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data = load_data(datadir=testdatadir, pairs=pairlist, timeframe='5m')
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trades_mdd = calculate_trades_mdd(data, trades)
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assert np.round(trades_mdd, 6) == 0.138943
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