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Update btanalysis.py

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This commit is contained in:
Guitheg 2021-12-09 17:20:05 +01:00
parent 3e7ecf931b
commit 553c5e9606
1 changed files with 35 additions and 27 deletions
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58
freqtrade/data/btanalysis.py
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@ -390,19 +390,18 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
low_val = max_drawdown_df.loc[idxmin, 'cumulative']
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
def calculate_trades_mdd(data: dict, trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
) -> float:
def calculate_trades_mdd(data: dict, trades: pd.DataFrame) -> float :
"""
Calculate MDD (Max DrawDown) :
Give the max drawdown given each trades and the history candles data.
Calculate Trades MDD (Max DrawDown) :
Give the max drawdown given each trades and the history candles.
The intervals dates used to calculate the max drawdown are the trades intervals dates.
Args:
trades (pd.DataFrame): [description]
date_col (str, optional): [description]. Defaults to 'close_date'.
value_col (str, optional): [description]. Defaults to 'profit_ratio'.
:param data (dict) : dictionnary of candle dataframe per pair used to calculate the mdd.
:param trades (pd.DataFrame): trades used to find the intervals dates.
Returns:
float: [description]
:return: float: Give the maximum drawdown among each trades.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty")
@ -411,36 +410,47 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame, *, date_col: str = 'c
for pair, df in data.items():
# Gather the opening and closing trade dates into one Dates DataFrame
open_close_trades = trades.loc[trades['pair']==pair][["open_date","close_date"]]
open_close_trades = pd.concat(
[open_close_trades.rename(columns={'open_date':'date'})[['date']],
open_close_trades.rename(columns={'close_date':'date'})[['date']]]
).sort_values(by='date')
open_close_trades['open_close_mark'] = np.ones(len(open_close_trades), dtype=int)
# Mark the dates and join it to the current candle dataframe.
# This allow to determine the open and close trade dates in the current
# candle dataframe.
open_close_trades['open_close_mark'] = 1
data_join = df.set_index('date').join(open_close_trades.set_index('date'))
del open_close_trades
close_trades = trades.loc[trades['pair']==pair][["close_date"]]
close_trades = close_trades.rename(columns={'close_date':'date'})
close_trades['close_mark'] = np.ones(len(close_trades), dtype=int)
data_join = data_join.join(close_trades.set_index('date'))
del close_trades
# Gather, mark and join only the opening trade dates into the current candle
# dataframe.
# This allow to classify trades using the cumsum and split by classes
# with groupby in order to process a cummax on each trades independantly.
open_trades = trades.loc[trades['pair']==pair][["open_date"]]
open_trades = open_trades.rename(columns={'open_date':'date'})
open_trades['open_mark'] = 1
data_join = data_join.join(open_trades.set_index('date'))
del open_trades
data_join[["open_close_mark",'close_mark']] = data_join[
["open_close_mark",'close_mark']].fillna(0).astype(int)
# Set all unmarked date to 0
data_join[["open_close_mark",'open_mark']] = data_join[
["open_close_mark",'open_mark']].fillna(0).astype(int)
# Mark with one all dates between an opening date trades and a closing date trades.
data_join['is_in_trade'] = data_join.open_close_mark.cumsum()&1 # &1 <=> %2
data_join.loc[data_join['open_close_mark'] == 1, 'is_in_trade'] = 1
# Perform a cummax in each trades independtly
data_join['close_cummax'] = 0
data_join["open_close_mark"] = data_join["open_close_mark"] - data_join["close_mark"]
data_join['close_cummax'] = data_join.groupby(
data_join.open_close_mark.cumsum()
).close.cummax()
data_join['open_mark'].cumsum()
)['close'].cummax()
data_join.loc[data_join['is_in_trade'] == 0, 'close_cummax'] = 0
data_join = data_join.rename(columns={'close_mark':'drawdown'})
# Compute the drawdown at each time of each trades
data_join = data_join.rename(columns={'open_mark':'drawdown'})
data_join.loc[data_join['is_in_trade'] == 1, 'drawdown'] = \
(data_join['close_cummax'] - data_join['close']) \
/ data_join['close_cummax']
@ -449,8 +459,6 @@ def calculate_trades_mdd(data: dict, trades: pd.DataFrame, *, date_col: str = 'c
trades_mdd_pair_list.append(mdd_pair)
trades_mdd_pair_list = np.array(trades_mdd_pair_list)
return trades_mdd_pair_list.max()
def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]: