Use "combined" enter_tag column
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@ -15,8 +15,7 @@ class SignalTagType(Enum):
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"""
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Enum for signal columns
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"""
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LONG_TAG = "long_tag"
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SHORT_TAG = "short_tag"
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ENTER_TAG = "enter_tag"
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class SignalDirection(Enum):
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@ -46,7 +46,6 @@ ELONG_IDX = 6 # Exit long
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SHORT_IDX = 7
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ESHORT_IDX = 8 # Exit short
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ENTER_TAG_IDX = 9
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SHORT_TAG_IDX = 10
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class Backtesting:
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@ -253,7 +252,7 @@ class Backtesting:
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# Every change to this headers list must evaluate further usages of the resulting tuple
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# and eventually change the constants for indexes at the top
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headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
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'enter_short', 'exit_short', 'long_tag', 'short_tag']
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'enter_short', 'exit_short', 'enter_tag']
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data: Dict = {}
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self.progress.init_step(BacktestState.CONVERT, len(processed))
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@ -271,8 +270,7 @@ class Backtesting:
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if 'exit_long' in pair_data.columns:
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pair_data.loc[:, 'exit_long'] = 0
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pair_data.loc[:, 'exit_short'] = 0
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pair_data.loc[:, 'long_tag'] = None
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pair_data.loc[:, 'short_tag'] = None
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pair_data.loc[:, 'enter_tag'] = None
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df_analyzed = self.strategy.advise_exit(
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self.strategy.advise_entry(pair_data, {'pair': pair}),
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@ -287,7 +285,7 @@ class Backtesting:
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df_analyzed.loc[:, 'enter_short'] = df_analyzed.loc[:, 'enter_short'].shift(1)
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df_analyzed.loc[:, 'exit_long'] = df_analyzed.loc[:, 'exit_long'].shift(1)
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df_analyzed.loc[:, 'exit_short'] = df_analyzed.loc[:, 'exit_short'].shift(1)
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df_analyzed.loc[:, 'long_tag'] = df_analyzed.loc[:, 'long_tag'].shift(1)
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df_analyzed.loc[:, 'enter_tag'] = df_analyzed.loc[:, 'enter_tag'].shift(1)
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# Update dataprovider cache
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self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
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@ -454,8 +452,7 @@ class Backtesting:
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if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
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# Enter trade
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# TODO-lev: SHORT_TAG ...
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has_buy_tag = len(row) >= ENTER_TAG_IDX + 1
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has_enter_tag = len(row) >= ENTER_TAG_IDX + 1
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trade = LocalTrade(
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pair=pair,
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open_rate=row[OPEN_IDX],
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@ -465,7 +462,7 @@ class Backtesting:
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fee_open=self.fee,
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fee_close=self.fee,
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is_open=True,
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buy_tag=row[ENTER_TAG_IDX] if has_buy_tag else None,
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buy_tag=row[ENTER_TAG_IDX] if has_enter_tag else None,
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exchange=self._exchange_name,
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is_short=(direction == 'short'),
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)
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@ -519,8 +519,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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dataframe[SignalType.EXIT_LONG.value] = 0
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dataframe[SignalType.ENTER_SHORT.value] = 0
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dataframe[SignalType.EXIT_SHORT.value] = 0
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dataframe[SignalTagType.LONG_TAG.value] = None
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dataframe[SignalTagType.SHORT_TAG.value] = None
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dataframe[SignalTagType.ENTER_TAG.value] = None
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# Other Defs in strategy that want to be called every loop here
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# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
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@ -690,10 +689,10 @@ class IStrategy(ABC, HyperStrategyMixin):
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enter_tag_value: Optional[str] = None
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if enter_long == 1 and not any([exit_long, enter_short]):
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enter_signal = SignalDirection.LONG
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enter_tag_value = latest.get(SignalTagType.LONG_TAG.value, None)
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enter_tag_value = latest.get(SignalTagType.ENTER_TAG.value, None)
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if enter_short == 1 and not any([exit_short, enter_long]):
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enter_signal = SignalDirection.SHORT
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enter_tag_value = latest.get(SignalTagType.SHORT_TAG.value, None)
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enter_tag_value = latest.get(SignalTagType.ENTER_TAG.value, None)
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timeframe_seconds = timeframe_to_seconds(timeframe)
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@ -963,7 +962,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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else:
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df = self.populate_buy_trend(dataframe, metadata)
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if 'enter_long' not in df.columns:
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df = df.rename({'buy': 'enter_long', 'buy_tag': 'long_tag'}, axis='columns')
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df = df.rename({'buy': 'enter_long', 'buy_tag': 'enter_tag'}, axis='columns')
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return df
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@ -18,7 +18,7 @@ class BTrade(NamedTuple):
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sell_reason: SellType
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open_tick: int
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close_tick: int
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buy_tag: Optional[str] = None
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enter_tag: Optional[str] = None
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class BTContainer(NamedTuple):
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@ -49,15 +49,13 @@ def _build_backtest_dataframe(data):
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if len(data[0]) == 8:
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# No short columns
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data = [d + [0, 0] for d in data]
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columns = columns + ['long_tag'] if len(data[0]) == 11 else columns
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columns = columns + ['enter_tag'] if len(data[0]) == 11 else columns
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frame = DataFrame.from_records(data, columns=columns)
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frame['date'] = frame['date'].apply(_get_frame_time_from_offset)
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# Ensure floats are in place
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for column in ['open', 'high', 'low', 'close', 'volume']:
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frame[column] = frame[column].astype('float64')
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if 'long_tag' not in columns:
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frame['long_tag'] = None
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if 'short_tag' not in columns:
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frame['short_tag'] = None
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if 'enter_tag' not in columns:
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frame['enter_tag'] = None
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return frame
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@ -532,7 +532,7 @@ tc33 = BTContainer(data=[
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sell_reason=SellType.TRAILING_STOP_LOSS,
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open_tick=1,
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close_tick=1,
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buy_tag='buy_signal_01'
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enter_tag='buy_signal_01'
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)]
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)
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@ -621,6 +621,6 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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for c, trade in enumerate(data.trades):
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res = results.iloc[c]
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assert res.sell_reason == trade.sell_reason.value
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assert res.buy_tag == trade.buy_tag
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assert res.buy_tag == trade.enter_tag
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assert res.open_date == _get_frame_time_from_offset(trade.open_tick)
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assert res.close_date == _get_frame_time_from_offset(trade.close_tick)
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@ -59,7 +59,7 @@ def test_returns_latest_signal(ohlcv_history):
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assert _STRATEGY.get_exit_signal('ETH/BTC', '5m', mocked_history, True) == (False, False)
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mocked_history.loc[1, 'exit_long'] = 0
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mocked_history.loc[1, 'enter_long'] = 1
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mocked_history.loc[1, 'long_tag'] = 'buy_signal_01'
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mocked_history.loc[1, 'enter_tag'] = 'buy_signal_01'
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assert _STRATEGY.get_entry_signal(
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'ETH/BTC', '5m', mocked_history) == (SignalDirection.LONG, 'buy_signal_01')
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@ -70,8 +70,10 @@ def test_returns_latest_signal(ohlcv_history):
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mocked_history.loc[1, 'enter_long'] = 0
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mocked_history.loc[1, 'enter_short'] = 1
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mocked_history.loc[1, 'exit_short'] = 0
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mocked_history.loc[1, 'enter_tag'] = 'sell_signal_01'
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assert _STRATEGY.get_entry_signal(
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'ETH/BTC', '5m', mocked_history) == (SignalDirection.SHORT, None)
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'ETH/BTC', '5m', mocked_history) == (SignalDirection.SHORT, 'sell_signal_01')
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assert _STRATEGY.get_exit_signal('ETH/BTC', '5m', mocked_history) == (False, False)
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assert _STRATEGY.get_exit_signal('ETH/BTC', '5m', mocked_history, True) == (True, False)
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