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Cache dataframe before cutting the first candle

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This allows providing the "current closed" candle in all cases.
This commit is contained in:
Matthias 2021-08-10 07:09:38 +02:00
parent cf27968b97
commit 3f160c7144
2 changed files with 7 additions and 7 deletions
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6
freqtrade/optimize/backtesting.py
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@ -246,11 +246,11 @@ class Backtesting:
if has_buy_tag: if has_buy_tag:
df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1) df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Update dataprovider cache # Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed) self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons # Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.) # (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist() data[pair] = df_analyzed[headers].values.tolist()
@ -478,9 +478,9 @@ class Backtesting:
if row[DATE_IDX] > tmp: if row[DATE_IDX] > tmp:
continue continue
self.dataprovider._set_dataframe_max_index(row_index)
row_index += 1 row_index += 1
indexes[pair] = row_index indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# without positionstacking, we can only have one open trade per pair. # without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected # max_open_trades must be respected

8
tests/optimize/test_backtesting.py
View File

@ -742,9 +742,9 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
# 100 buys signals # 100 buys signals
results = result['results'] results = result['results']
assert len(results) == 100 assert len(results) == 100
# Cached data should be 199 (missing 1 candle at the start) # Cached data should be 200
analyzed_df = backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0] analyzed_df = backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0]
assert len(analyzed_df) == 199 assert len(analyzed_df) == 200
# Expect last candle to be 1 below end date (as the last candle is assumed as "incomplete" # Expect last candle to be 1 below end date (as the last candle is assumed as "incomplete"
# during backtesting) # during backtesting)
expected_last_candle_date = backtest_conf['end_date'] - timedelta(minutes=1) expected_last_candle_date = backtest_conf['end_date'] - timedelta(minutes=1)
@ -807,11 +807,11 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0 assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
# Cached data correctly removed amounts # Cached data correctly removed amounts
offset = 2 if tres == 0 else 1 offset = 1 if tres == 0 else 0
removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count
assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles
assert len(backtesting.dataprovider.get_analyzed_dataframe( assert len(backtesting.dataprovider.get_analyzed_dataframe(
'NXT/BTC', '5m')[0]) == len(data['NXT/BTC']) - 2 - backtesting.strategy.startup_candle_count 'NXT/BTC', '5m')[0]) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
backtest_conf = { backtest_conf = {
'processed': processed, 'processed': processed,