From dac88c6aedf8582ea608df7471720cc83e015727 Mon Sep 17 00:00:00 2001 From: Matthias Date: Wed, 30 Oct 2019 13:35:55 +0100 Subject: [PATCH] extract Find parallel trades per interval --- freqtrade/data/btanalysis.py | 32 ++++++++++++++++++++++-------- tests/optimize/test_backtesting.py | 6 +++--- 2 files changed, 27 insertions(+), 11 deletions(-) diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py index 0f5d395ff..9dbd69e3e 100644 --- a/freqtrade/data/btanalysis.py +++ b/freqtrade/data/btanalysis.py @@ -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: diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py index ba87848ec..5912c5489 100644 --- a/tests/optimize/test_backtesting.py +++ b/tests/optimize/test_backtesting.py @@ -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):