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Extract expand_trades_over_period to it's own function

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Matthias 2020-12-24 11:18:25 +01:00
parent b990ace32d
commit b7a463539c
1 changed files with 24 additions and 6 deletions
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30
freqtrade/data/btanalysis.py
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@ -172,16 +172,19 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
return df return df
def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame: def expand_trades_over_period(results: pd.DataFrame, timeframe: str,
timeframe_min: Optional[int] = None) -> pd.DataFrame:
""" """
Find overlapping trades by expanding each trade once per period it was open Expand trades DF to have one row per candle
and then counting overlaps.
:param results: Results Dataframe - can be loaded :param results: Results Dataframe - can be loaded
:param timeframe: Timeframe used for backtest :param timeframe: Timeframe used for backtest
:return: dataframe with open-counts per time-period in timeframe :param timeframe: Timeframe in minutes. calculated from timeframe if not available.
:return: dataframe with date index (nonunique)
with trades expanded for every row from trade.open_date til trade.close_date
""" """
from freqtrade.exchange import timeframe_to_minutes if not timeframe_min:
timeframe_min = timeframe_to_minutes(timeframe) from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
# compute how long each trade was left outstanding as date indexes # compute how long each trade was left outstanding as date indexes
dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'], dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
freq=f"{timeframe_min}min")) freq=f"{timeframe_min}min"))
@ -195,6 +198,21 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
# the expanded dates list is added as a new column to the repeated trades (df2) # the expanded dates list is added as a new column to the repeated trades (df2)
df2 = pd.concat([dates, df2], axis=1) df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date') df2 = df2.set_index('date')
return df2
def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps.
:param results: Results Dataframe - can be loaded
:param timeframe: Timeframe used for backtest
:return: dataframe with open-counts per time-period in timeframe
"""
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
df2 = expand_trades_over_period(results, timeframe, timeframe_min)
# duplicate dates entries represent trades on the same candle # duplicate dates entries represent trades on the same candle
# which resampling resolves through the applied function (count) # which resampling resolves through the applied function (count)
df_final = df2.resample(f"{timeframe_min}min")[['pair']].count() df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()