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extract Find parallel trades per interval

This commit is contained in:
Matthias 2019-10-30 13:35:55 +01:00
parent 669a6cf119
commit dac88c6aed
2 changed files with 27 additions and 11 deletions
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32
freqtrade/data/btanalysis.py
View File

@ -52,16 +52,17 @@ def load_backtest_data(filename) -> pd.DataFrame:
return df
def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
def parallel_trade_analysis(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
and then counting overlaps.
:param results: Results Dataframe - can be loaded
:param freq: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
:param timeframe: Timeframe used for backtest
:return: dataframe with open-counts per time-period in timeframe
"""
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
@ -69,8 +70,23 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
return df_final
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
max_open_trades: int) -> 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: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
"""
df_final = parallel_trade_analysis(results, timeframe)
return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:

6
tests/optimize/test_backtesting.py
View File

@ -714,9 +714,9 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
results = backtesting.backtest(backtest_conf)
# Make sure we have parallel trades
assert len(evaluate_result_multi(results, '5min', 2)) > 0
assert len(evaluate_result_multi(results, '5m', 2)) > 0
# make sure we don't have trades with more than configured max_open_trades
assert len(evaluate_result_multi(results, '5min', 3)) == 0
assert len(evaluate_result_multi(results, '5m', 3)) == 0
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
@ -727,7 +727,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
assert len(evaluate_result_multi(results, '5min', 1)) == 0
assert len(evaluate_result_multi(results, '5m', 1)) == 0
def test_backtest_record(default_conf, fee, mocker):