auto populate features based on a prepended % in the strategy (remove feature assignment from config). Update doc/constants/example strategy to reflect change
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robcaulk
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@ -56,20 +56,9 @@
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],
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"train_period": 30,
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"backtest_period": 7,
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"identifier": "new_corrlist",
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"identifier": "example",
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"live_trained_timerange": "20220330-20220429",
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"live_full_backtestrange": "20220302-20220501",
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"base_features": [
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"rsi",
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"close_over_20sma",
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"relative_volume",
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"bb_width",
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"mfi",
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"roc",
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"pct-change",
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"adx",
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"macd"
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],
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"corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT",
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@ -72,11 +72,6 @@ config setup includes:
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"train_period" : 30,
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"backtest_period" : 7,
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"identifier" : "unique-id",
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"base_features": [
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"rsi",
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"mfi",
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"roc",
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],
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"corr_pairlist": [
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"ETH/USD",
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"LINK/USD",
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@ -102,11 +97,31 @@ config setup includes:
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### Building the feature set
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Most of these parameters are controlling the feature data set. The `base_features`
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indicates the basic indicators the user wishes to include in the feature set.
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The `timeframes` are the timeframes of each base_feature that the user wishes to
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include in the feature set. In the present case, the user is asking for the
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`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, etc. to be included
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Most of these parameters are controlling the feature data set. Features are added by the user
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inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`:
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```python
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def populate_any_indicators(self, pair, df, tf, informative=None, coin=""):
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informative['%-''%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14)
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informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative['%-' + coin + "adx"] = ta.ADX(informative, window=20)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
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informative[coin + "bb_lowerband"] = bollinger["lower"]
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informative[coin + "bb_middleband"] = bollinger["mid"]
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informative[coin + "bb_upperband"] = bollinger["upper"]
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informative['%-' + coin + "bb_width"] = (
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informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
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) / informative[coin + "bb_middleband"]
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```
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The user of the present example does not want to pass the `bb_lowerband` as a feature to the model,
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and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
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model for training/prediction and has therfore prepended it with `%`._
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(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
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The `timeframes` from the example config above are the timeframes of each `populate_any_indicator()`
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included metric for inclusion in the feature set. In the present case, the user is asking for the
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`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included
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in the feature set.
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In addition, the user can ask for each of these features to be included from
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@ -442,7 +442,6 @@ CONF_SCHEMA = {
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"identifier": {"type": "str", "default": "example"},
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"live_trained_timerange": {"type": "str"},
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"live_full_backtestrange": {"type": "str"},
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"base_features": {"type": "list"},
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"corr_pairlist": {"type": "list"},
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"feature_parameters": {
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"type": "object",
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@ -537,4 +536,4 @@ TradeList = List[List]
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LongShort = Literal['long', 'short']
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EntryExit = Literal['entry', 'exit']
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BuySell = Literal['buy', 'sell']
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BuySell = Literal['buy', 'sell']
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@ -483,31 +483,38 @@ class FreqaiDataKitchen:
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return
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def build_feature_list(self, config: dict, metadata: dict) -> list:
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"""
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Build the list of features that will be used to filter
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the full dataframe. Feature list is construced from the
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user configuration file.
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:params:
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:config: Canonical freqtrade config file containing all
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user defined input in config['freqai] dictionary.
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"""
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features = []
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for tf in config["freqai"]["timeframes"]:
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for ft in config["freqai"]["base_features"]:
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for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
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shift = ""
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if n > 0:
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shift = "_shift-" + str(n)
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features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
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for p in config["freqai"]["corr_pairlist"]:
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if metadata['pair'] in p:
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continue # avoid duplicate features
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features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
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# logger.info("number of features %s", len(features))
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def find_features(self, dataframe: DataFrame) -> list:
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column_names = dataframe.columns
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features = [c for c in column_names if '%' in c]
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assert features, ("Could not find any features!")
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return features
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# def build_feature_list(self, config: dict, metadata: dict) -> list:
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# """
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# SUPERCEDED BY self.find_features()
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# Build the list of features that will be used to filter
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# the full dataframe. Feature list is construced from the
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# user configuration file.
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# :params:
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# :config: Canonical freqtrade config file containing all
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# user defined input in config['freqai] dictionary.
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# """
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# features = []
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# for tf in config["freqai"]["timeframes"]:
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# for ft in config["freqai"]["base_features"]:
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# for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
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# shift = ""
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# if n > 0:
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# shift = "_shift-" + str(n)
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# features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
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# for p in config["freqai"]["corr_pairlist"]:
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# if metadata['pair'] in p:
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# continue # avoid duplicate features
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# features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
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# # logger.info("number of features %s", len(features))
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# return features
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def check_if_pred_in_training_spaces(self) -> None:
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"""
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Compares the distance from each prediction point to each training data
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@ -53,9 +53,8 @@ class CatboostPredictionModel(IFreqaiModel):
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logger.info("--------------------Starting training--------------------")
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# create the full feature list based on user config info
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self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata)
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self.dh.training_features_list = self.dh.find_features(unfiltered_dataframe)
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unfiltered_labels = self.make_labels(unfiltered_dataframe)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = self.dh.filter_features(
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unfiltered_dataframe,
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@ -127,7 +126,7 @@ class CatboostPredictionModel(IFreqaiModel):
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# logger.info("--------------------Starting prediction--------------------")
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original_feature_list = self.dh.build_feature_list(self.config, metadata)
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original_feature_list = self.dh.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = self.dh.filter_features(
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unfiltered_dataframe, original_feature_list, training_filter=False
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)
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@ -62,8 +62,11 @@ class FreqaiExampleStrategy(IStrategy):
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def populate_any_indicators(self, pair, df, tf, informative=None, coin=""):
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"""
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Function designed to automatically generate, name and merge features
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from user indicated timeframes in the configuration file. User can add
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additional features here, but must follow the naming convention.
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from user indicated timeframes in the configuration file. User controls the indicators
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passed to the training/prediction by prepending indicators with `'%-' + coin `
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(see convention below). I.e. user should not prepend any supporting metrics
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(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
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model.
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:params:
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:pair: pair to be used as informative
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:df: strategy dataframe which will receive merges from informatives
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@ -74,49 +77,50 @@ class FreqaiExampleStrategy(IStrategy):
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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informative[coin + "rsi"] = ta.RSI(informative, timeperiod=14)
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informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative[coin + "adx"] = ta.ADX(informative, window=20)
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informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14)
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informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative['%-' + coin + "adx"] = ta.ADX(informative, window=20)
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informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20)
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informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21)
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informative[coin + "bmsb"] = np.where(
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informative['%-' + coin + "bmsb"] = np.where(
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informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0
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)
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informative[coin + "close_over_20sma"] = informative["close"] / informative[coin + "20sma"]
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informative['%-' + coin + "close_over_20sma"] = informative["close"] / informative[
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coin + "20sma"]
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informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21)
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informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20)
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stoch = ta.STOCHRSI(informative, 15, 20, 2, 2)
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informative[coin + "srsi-fk"] = stoch["fastk"]
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informative[coin + "srsi-fd"] = stoch["fastd"]
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informative['%-' + coin + "srsi-fk"] = stoch["fastk"]
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informative['%-' + coin + "srsi-fd"] = stoch["fastd"]
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
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informative[coin + "bb_lowerband"] = bollinger["lower"]
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informative[coin + "bb_middleband"] = bollinger["mid"]
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informative[coin + "bb_upperband"] = bollinger["upper"]
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informative[coin + "bb_width"] = (
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informative['%-' + coin + "bb_width"] = (
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informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
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) / informative[coin + "bb_middleband"]
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informative[coin + "close-bb_lower"] = (
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informative['%-' + coin + "close-bb_lower"] = (
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informative["close"] / informative[coin + "bb_lowerband"]
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)
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informative[coin + "roc"] = ta.ROC(informative, timeperiod=3)
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informative[coin + "adx"] = ta.ADX(informative, window=14)
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informative['%-' + coin + "roc"] = ta.ROC(informative, timeperiod=3)
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informative['%-' + coin + "adx"] = ta.ADX(informative, window=14)
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macd = ta.MACD(informative)
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informative[coin + "macd"] = macd["macd"]
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informative['%-' + coin + "macd"] = macd["macd"]
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informative[coin + "pct-change"] = informative["close"].pct_change()
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informative[coin + "relative_volume"] = (
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informative['%-' + coin + "relative_volume"] = (
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informative["volume"] / informative["volume"].rolling(10).mean()
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)
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informative[coin + "pct-change"] = informative["close"].pct_change()
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indicators = [col for col in informative if col.startswith(coin)]
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indicators = [col for col in informative if col.startswith('%')]
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for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
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if n == 0:
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@ -154,7 +158,6 @@ class FreqaiExampleStrategy(IStrategy):
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pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
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)
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print('dataframe_built')
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# the model will return 4 values, its prediction, an indication of whether or not the
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# prediction should be accepted, the target mean/std values from the labels used during
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# each training period.
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@ -181,7 +184,6 @@ class FreqaiExampleStrategy(IStrategy):
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value')
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sell_conditions = [
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(dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1)
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]
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