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Merge pull request #7707 from freqtrade/bt/full_detail

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backtesting - use full detail timeframe
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Matthias 2022-11-27 16:09:23 +01:00 committed by GitHub
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3 changed files with 126 additions and 71 deletions
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3
docs/backtesting.md
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@ -583,7 +583,8 @@ To utilize this, you can append `--timeframe-detail 5m` to your regular backtest
freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-detail 5m
```
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe, and Entry orders will only be placed at the main timeframe, however Order fills and exit signals will be evaluated at the 5m candle, simulating intra-candle movements.
All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.

65
freqtrade/optimize/backtesting.py
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@ -692,10 +692,11 @@ class Backtesting:
trade.orders.append(order)
return trade
def _get_exit_trade_entry(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
def _get_exit_trade_entry(
self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if self.trading_mode == TradingMode.FUTURES:
if is_first and self.trading_mode == TradingMode.FUTURES:
trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
@ -704,31 +705,6 @@ class Backtesting:
close_date=exit_candle_time,
)
if self.timeframe_detail and trade.pair in self.detail_data:
exit_candle_end = exit_candle_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
detail_data = detail_data.loc[
(detail_data['date'] >= exit_candle_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_exit_trade_entry_for_candle(trade, row)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
for det_row in detail_data[HEADERS].values.tolist():
res = self._get_exit_trade_entry_for_candle(trade, det_row)
if res:
return res
return None
else:
return self._get_exit_trade_entry_for_candle(trade, row)
def get_valid_price_and_stake(
@ -1074,7 +1050,7 @@ class Backtesting:
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
max_open_trades: int, open_trade_count_start: int) -> int:
max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
"""
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
@ -1092,9 +1068,11 @@ class Backtesting:
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
# We only open trades on the main candle, not on detail candles
trade_dir = self.check_for_trade_entry(row)
if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and is_first
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and current_time != end_date
and trade_dir is not None
@ -1120,7 +1098,7 @@ class Backtesting:
# 4. Create exit orders (if any)
if not trade.open_order_id:
self._get_exit_trade_entry(trade, row) # Place exit order if necessary
self._get_exit_trade_entry(trade, row, is_first) # Place exit order if necessary
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
@ -1171,7 +1149,6 @@ class Backtesting:
self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = LocalTrade.bt_open_open_trade_count
@ -1185,7 +1162,35 @@ class Backtesting:
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
if self.timeframe_detail and pair in self.detail_data:
exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[pair]
detail_data = detail_data.loc[
(detail_data['date'] >= current_detail_time) &
(detail_data['date'] < exit_candle_end)
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades,
open_trade_count_start)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
is_first = True
current_time_det = current_time
for det_row in detail_data[HEADERS].values.tolist():
open_trade_count_start = self.backtest_loop(
det_row, pair, current_time_det, end_date, max_open_trades,
open_trade_count_start, is_first)
current_time_det += timedelta(minutes=self.timeframe_detail_min)
is_first = False
else:
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)

123
tests/optimize/test_backtesting.py
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@ -663,30 +663,9 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
'', # Exit Signal Name
]
row_detail = pd.DataFrame(
[
[
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0, tzinfo=timezone.utc),
200, 200.1, 197, 199, 1, 0, 0, 0, '', '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=1, tzinfo=timezone.utc),
199, 199.7, 199, 199.5, 0, 0, 0, 0, '', '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=2, tzinfo=timezone.utc),
199.5, 200.8, 199, 200.9, 0, 0, 0, 0, '', '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=3, tzinfo=timezone.utc),
200.5, 210.5, 193, 210.5, 0, 0, 0, 0, '', '', '', # ROI sell (?)
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=4, tzinfo=timezone.utc),
200, 200.1, 193, 199, 0, 0, 0, 0, '', '', '',
],
], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag']
)
# No data available.
res = backtesting._get_exit_trade_entry(trade, row_sell)
res = backtesting._get_exit_trade_entry(trade, row_sell, True)
assert res is not None
assert res.exit_reason == ExitType.ROI.value
assert res.close_date_utc == datetime(2020, 1, 1, 5, 0, tzinfo=timezone.utc)
@ -699,20 +678,9 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
[], columns=['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'long_tag', 'short_tag', 'exit_tag'])
res = backtesting._get_exit_trade_entry(trade, row)
res = backtesting._get_exit_trade_entry(trade, row, True)
assert res is None
# Assign backtest-detail data
backtesting.detail_data[pair] = row_detail
res = backtesting._get_exit_trade_entry(trade, row_sell)
assert res is not None
assert res.exit_reason == ExitType.ROI.value
# Sell at minute 3 (not available above!)
assert res.close_date_utc == datetime(2020, 1, 1, 5, 3, tzinfo=timezone.utc)
sell_order = res.select_order('sell', True)
assert sell_order is not None
def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
default_conf['use_exit_signal'] = False
@ -788,17 +756,98 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
for _, t in results.iterrows():
assert len(t['orders']) == 2
ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate
# Check open trade rate aligns to open rate
assert not ln.empty
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low
# check close trade rate aligns to close rate or is between high and low
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
assert not ln1.empty
assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln1.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6))
@pytest.mark.parametrize('use_detail', [True, False])
def test_backtest_one_detail(default_conf_usdt, fee, mocker, testdatadir, use_detail) -> None:
default_conf_usdt['use_exit_signal'] = False
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
if use_detail:
default_conf_usdt['timeframe_detail'] = '1m'
patch_exchange(mocker)
def advise_entry(df, *args, **kwargs):
# Mock function to force several entries
df.loc[(df['rsi'] < 40), 'enter_long'] = 1
return df
def custom_entry_price(proposed_rate, **kwargs):
return proposed_rate * 0.997
backtesting = Backtesting(default_conf_usdt)
backtesting._set_strategy(backtesting.strategylist[0])
backtesting.strategy.populate_entry_trend = advise_entry
backtesting.strategy.custom_entry_price = custom_entry_price
pair = 'XRP/ETH'
# Pick a timerange adapted to the pair we use to test
timerange = TimeRange.parse_timerange('20191010-20191013')
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['XRP/ETH'],
timerange=timerange)
if use_detail:
data_1m = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['XRP/ETH'],
timerange=timerange)
backtesting.detail_data = data_1m
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
result = backtesting.backtest(
processed=deepcopy(processed),
start_date=min_date,
end_date=max_date,
max_open_trades=10,
)
results = result['results']
assert not results.empty
# Timeout settings from default_conf = entry: 10, exit: 30
assert len(results) == (2 if use_detail else 3)
assert 'orders' in results.columns
data_pair = processed[pair]
data_1m_pair = data_1m[pair] if use_detail else pd.DataFrame()
late_entry = 0
for _, t in results.iterrows():
assert len(t['orders']) == 2
entryo = t['orders'][0]
entry_ts = datetime.fromtimestamp(entryo['order_filled_timestamp'] // 1000, tz=timezone.utc)
if entry_ts > t['open_date']:
late_entry += 1
# Get "entry fill" candle
ln = (data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
if use_detail else data_pair.loc[data_pair["date"] == entry_ts])
# Check open trade rate aligns to open rate
assert not ln.empty
# assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
assert round(ln.iloc[0]["low"], 6) <= round(
t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
# check close trade rate aligns to close rate or is between high and low
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
if use_detail:
ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
assert not ln1.empty or not ln1_1m.empty
else:
assert not ln1.empty
ln2 = ln1_1m if ln1.empty else ln1
assert (round(ln2.iloc[0]["low"], 6) <= round(
t["close_rate"], 6) <= round(ln2.iloc[0]["high"], 6))
assert late_entry > 0
def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
# This strategy intentionally places unfillable orders.
default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice'