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Guitheg
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@ -390,8 +390,9 @@ 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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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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Give the max drawdown given each trades and the history candles.
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@ -399,7 +400,6 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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Args:
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:param data: (dict) dictionnary of candle dataframe per pair used to calculate the mdd.
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:param trades: (pd.DataFrame) trades used to find the intervals dates.
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Returns:
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:return: (float) Give the maximum drawdown among each trades.
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:raise: (ValueError) if trade-dataframe was found empty.
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@ -407,16 +407,14 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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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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trades_mdd_pair_list = []
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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 = 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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[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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@ -430,18 +428,18 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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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 = 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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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['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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@ -452,10 +450,10 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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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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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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@ -466,6 +464,7 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
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trades_mdd_pair_list = np.array(trades_mdd_pair_list)
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return trades_mdd_pair_list.max()
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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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Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
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@ -468,7 +468,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
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drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
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results, value_col='profit_abs')
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strat_stats.update({
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'trades_mdd' : trades_mdd,
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'trades_mdd': trades_mdd,
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'max_drawdown': max_drawdown,
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'max_drawdown_abs': drawdown_abs,
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'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
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@ -718,11 +718,11 @@ def text_table_add_metrics(strat_results: Dict) -> str:
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strat_results['stake_currency'])),
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('Max balance', round_coin_value(strat_results['csum_max'],
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strat_results['stake_currency'])),
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('', ''), # Empty line to improve readability
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('', ''), # Empty line to improve readability
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('Max Drawdown (in trades)', f"{strat_results['trades_mdd']:.2%}"),
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('Max Drawdown Total', f"{strat_results['max_drawdown']:.2%}"),
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('Max Drawdown Total', round_coin_value(strat_results['max_drawdown_abs'],
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strat_results['stake_currency'])),
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strat_results['stake_currency'])),
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('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
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strat_results['stake_currency'])),
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('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
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@ -10,7 +10,8 @@ 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, calculate_trades_mdd,
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calculate_market_change, calculate_max_drawdown,
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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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@ -333,13 +334,14 @@ def test_calculate_max_drawdown2():
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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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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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