Switch from pair(str) to metadata(dict)
This commit is contained in:
Matthias
parent
941879dc19
commit
787d6042de
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@ -39,7 +39,6 @@ A strategy file contains all the information needed to build a good strategy:
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- Sell strategy rules
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- Minimal ROI recommended
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- Stoploss recommended
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- Hyperopt parameter
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The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
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You can test it with the parameter: `--strategy TestStrategy`
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@ -61,17 +60,16 @@ file as reference.**
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### Buy strategy
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Edit the method `populate_buy_trend()` into your strategy file to
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update your buy strategy.
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Edit the method `populate_buy_trend()` into your strategy file to update your buy strategy.
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Sample from `user_data/strategies/test_strategy.py`:
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```python
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def populate_buy_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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@ -93,11 +91,11 @@ Please note that the sell-signal is only used if `use_sell_signal` is set to tru
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Sample from `user_data/strategies/test_strategy.py`:
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```python
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def populate_sell_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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@ -110,7 +108,7 @@ def populate_sell_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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return dataframe
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```
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## Add more Indicator
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## Add more Indicators
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As you have seen, buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
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@ -119,9 +117,16 @@ You should only add the indicators used in either `populate_buy_trend()`, `popul
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Sample:
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```python
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def populate_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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dataframe['sar'] = ta.SAR(dataframe)
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dataframe['adx'] = ta.ADX(dataframe)
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@ -152,6 +157,11 @@ def populate_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
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return dataframe
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```
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### Metadata dict
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The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
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Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
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### Want more indicator examples
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Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
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@ -230,7 +230,7 @@ class Backtesting(object):
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.advise_sell(
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self.advise_buy(pair_data, pair), pair)[headers].copy()
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self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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@ -75,7 +75,7 @@ class Hyperopt(Backtesting):
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return arg_dict
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@staticmethod
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def populate_indicators(dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['adx'] = ta.ADX(dataframe)
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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@ -228,7 +228,7 @@ class Hyperopt(Backtesting):
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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def populate_buy_trend(dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use
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"""
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@ -28,7 +28,7 @@ class DefaultStrategy(IStrategy):
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# Optimal ticker interval for the strategy
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ticker_interval = '5m'
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def populate_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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@ -36,7 +36,7 @@ class DefaultStrategy(IStrategy):
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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@ -199,11 +199,11 @@ class DefaultStrategy(IStrategy):
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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@ -221,11 +221,11 @@ class DefaultStrategy(IStrategy):
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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@ -74,29 +74,29 @@ class IStrategy(ABC):
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self.config = config
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@abstractmethod
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def populate_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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@abstractmethod
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def populate_buy_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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@abstractmethod
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def populate_sell_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with sell column
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"""
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@ -106,16 +106,16 @@ class IStrategy(ABC):
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"""
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return self.__class__.__name__
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def analyze_ticker(self, ticker_history: List[Dict], pair: str) -> DataFrame:
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def analyze_ticker(self, ticker_history: List[Dict], metadata: dict) -> DataFrame:
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"""
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Parses the given ticker history and returns a populated DataFrame
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add several TA indicators and buy signal to it
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:return DataFrame with ticker data and indicator data
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"""
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dataframe = parse_ticker_dataframe(ticker_history)
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dataframe = self.advise_indicators(dataframe, pair)
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dataframe = self.advise_buy(dataframe, pair)
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dataframe = self.advise_sell(dataframe, pair)
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dataframe = self.advise_indicators(dataframe, metadata)
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dataframe = self.advise_buy(dataframe, metadata)
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dataframe = self.advise_sell(dataframe, metadata)
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return dataframe
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def get_signal(self, pair: str, interval: str, ticker_hist: List[Dict]) -> Tuple[bool, bool]:
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@ -130,7 +130,7 @@ class IStrategy(ABC):
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return False, False
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try:
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dataframe = self.analyze_ticker(ticker_hist, pair)
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dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
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except ValueError as error:
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logger.warning(
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'Unable to analyze ticker for pair %s: %s',
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@ -275,15 +275,15 @@ class IStrategy(ABC):
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"""
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Creates a dataframe and populates indicators for given ticker data
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"""
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return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), pair)
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return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
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for pair, pair_data in tickerdata.items()}
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def advise_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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This method should not be overridden.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param pair: The currently traded pair
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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if self._populate_fun_len == 2:
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@ -291,14 +291,14 @@ class IStrategy(ABC):
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"the current function headers!", DeprecationWarning)
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return self.populate_indicators(dataframe) # type: ignore
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else:
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return self.populate_indicators(dataframe, pair)
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return self.populate_indicators(dataframe, metadata)
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def advise_buy(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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This method should not be overridden.
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:param dataframe: DataFrame
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:param pair: The currently traded pair
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:param pair: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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if self._buy_fun_len == 2:
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@ -306,14 +306,14 @@ class IStrategy(ABC):
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"the current function headers!", DeprecationWarning)
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return self.populate_buy_trend(dataframe) # type: ignore
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else:
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return self.populate_buy_trend(dataframe, pair)
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return self.populate_buy_trend(dataframe, metadata)
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def advise_sell(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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This method should not be overridden.
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:param dataframe: DataFrame
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:param pair: The currently traded pair
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:param pair: Additional information, like the currently traded pair
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:return: DataFrame with sell column
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"""
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if self._sell_fun_len == 2:
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@ -321,4 +321,4 @@ class IStrategy(ABC):
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"the current function headers!", DeprecationWarning)
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return self.populate_sell_trend(dataframe) # type: ignore
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else:
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return self.populate_sell_trend(dataframe, pair)
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return self.populate_sell_trend(dataframe, metadata)
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@ -60,10 +60,7 @@ def test_search_strategy():
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def test_load_strategy(result):
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resolver = StrategyResolver({'strategy': 'TestStrategy'})
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pair = 'ETH/BTC'
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assert len(resolver.strategy.populate_indicators.__annotations__) == 3
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assert 'dataframe' in resolver.strategy.populate_indicators.__annotations__
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assert 'pair' in resolver.strategy.populate_indicators.__annotations__
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assert 'adx' in resolver.strategy.advise_indicators(result, pair=pair)
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assert 'adx' in resolver.strategy.advise_indicators(result, metadata=pair)
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def test_load_strategy_invalid_directory(result, caplog):
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@ -92,7 +89,7 @@ def test_strategy(result):
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config = {'strategy': 'DefaultStrategy'}
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resolver = StrategyResolver(config)
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pair = 'ETH/BTC'
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metadata = {'pair': 'ETH/BTC'}
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assert resolver.strategy.minimal_roi[0] == 0.04
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assert config["minimal_roi"]['0'] == 0.04
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@ -102,13 +99,13 @@ def test_strategy(result):
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assert resolver.strategy.ticker_interval == '5m'
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assert config['ticker_interval'] == '5m'
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df_indicators = resolver.strategy.advise_indicators(result, pair=pair)
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df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
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assert 'adx' in df_indicators
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dataframe = resolver.strategy.advise_buy(df_indicators, pair=pair)
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dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
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assert 'buy' in dataframe.columns
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dataframe = resolver.strategy.advise_sell(df_indicators, pair='ETH/BTC')
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dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
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assert 'sell' in dataframe.columns
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@ -196,21 +193,21 @@ def test_call_deprecated_function(result, monkeypatch):
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default_location = path.join(path.dirname(path.realpath(__file__)))
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resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
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'strategy_path': default_location})
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pair = 'ETH/BTC'
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metadata = {'pair': 'ETH/BTC'}
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# Make sure we are using a legacy function
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assert resolver.strategy._populate_fun_len == 2
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assert resolver.strategy._buy_fun_len == 2
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assert resolver.strategy._sell_fun_len == 2
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indicator_df = resolver.strategy.advise_indicators(result, pair=pair)
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indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
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assert type(indicator_df) is DataFrame
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assert 'adx' in indicator_df.columns
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buydf = resolver.strategy.advise_buy(result, pair=pair)
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buydf = resolver.strategy.advise_buy(result, metadata=metadata)
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assert type(buydf) is DataFrame
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assert 'buy' in buydf.columns
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selldf = resolver.strategy.advise_sell(result, pair=pair)
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selldf = resolver.strategy.advise_sell(result, metadata=metadata)
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assert type(selldf) is DataFrame
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assert 'sell' in selldf
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@ -45,7 +45,7 @@ class TestStrategy(IStrategy):
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# Optimal ticker interval for the strategy
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ticker_interval = '5m'
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def populate_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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@ -53,7 +53,7 @@ class TestStrategy(IStrategy):
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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@ -215,11 +215,11 @@ class TestStrategy(IStrategy):
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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@ -232,11 +232,11 @@ class TestStrategy(IStrategy):
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param pair: Pair currently analyzed
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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