pass metadata dictionary to feature_engineering_* and set_freqai_targets functions. Add doc
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@ -28,6 +28,10 @@ It is advisable to start from the template `feature_engineering_*` functions in
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All features must be prepended with `%` to be recognized by FreqAI internals.
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Access metadata such as the current pair/timeframe/period with:
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`metadata["pair"]` `metadata["tf"]` `metadata["period"]`
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:param df: strategy dataframe which will receive the features
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:param period: period of the indicator - usage example:
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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@ -75,6 +79,10 @@ It is advisable to start from the template `feature_engineering_*` functions in
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Features defined here will *not* be automatically duplicated on user defined
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`indicator_periods_candles`
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Access metadata such as the current pair/timeframe with:
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`metadata["pair"]` `metadata["tf"]`
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All features must be prepended with `%` to be recognized by FreqAI internals.
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:param df: strategy dataframe which will receive the features
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@ -98,6 +106,10 @@ It is advisable to start from the template `feature_engineering_*` functions in
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This function is a good place for any feature that should not be auto-expanded upon
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(e.g. day of the week).
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Access metadata such as the current pair with:
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`metadata["pair"]`
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All features must be prepended with `%` to be recognized by FreqAI internals.
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:param df: strategy dataframe which will receive the features
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@ -113,6 +125,10 @@ It is advisable to start from the template `feature_engineering_*` functions in
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Required function to set the targets for the model.
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All targets must be prepended with `&` to be recognized by the FreqAI internals.
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Access metadata such as the current pair with:
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`metadata["pair"]`
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:param df: strategy dataframe which will receive the targets
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usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
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"""
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@ -161,6 +177,19 @@ You can ask for each of the defined features to be included also for informative
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In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
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$= 3 * 3 * 3 * 2 * 2 = 108$.
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### Gain finer control over `feature_engineering_*` functions with `metadata`
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All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc.
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```py
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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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if metadata["tf"] == "1h":
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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```
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This will block `ta.ROC()` from being added to any timeframes other than `"1h"`.
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### Returning additional info from training
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Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class.
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@ -1247,17 +1247,21 @@ class FreqaiDataKitchen:
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tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
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for tf in tfs:
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metadata = {"pair": pair, "tf": tf}
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informative_df = self.get_pair_data_for_features(
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pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
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informative_copy = informative_df.copy()
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for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
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metadata["period"] = t
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df_features = strategy.feature_engineering_expand_all(
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informative_copy.copy(), t)
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informative_copy.copy(), t, metadata=metadata)
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suffix = f"{t}"
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informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
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generic_df = strategy.feature_engineering_expand_basic(informative_copy.copy())
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metadata.pop("period")
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generic_df = strategy.feature_engineering_expand_basic(
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informative_copy.copy(), metadata=metadata)
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suffix = "gen"
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informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
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@ -1326,8 +1330,8 @@ class FreqaiDataKitchen:
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"include_corr_pairlist", [])
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dataframe = self.populate_features(dataframe.copy(), pair, strategy,
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corr_dataframes, base_dataframes)
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dataframe = strategy.feature_engineering_standard(dataframe.copy())
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metadata = {"pair": pair}
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dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
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# ensure corr pairs are always last
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for corr_pair in corr_pairs:
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if pair == corr_pair:
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@ -1336,7 +1340,7 @@ class FreqaiDataKitchen:
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dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
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corr_dataframes, base_dataframes, True)
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dataframe = strategy.set_freqai_targets(dataframe.copy())
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dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
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self.get_unique_classes_from_labels(dataframe)
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@ -58,6 +58,10 @@ class FreqaiExampleStrategy(IStrategy):
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All features must be prepended with `%` to be recognized by FreqAI internals.
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Access metadata such as the current pair/timeframe/period with:
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`metadata["pair"]` `metadata["tf"]` `metadata["period"]`
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More details on how these config defined parameters accelerate feature engineering
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in the documentation at:
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@ -114,6 +118,10 @@ class FreqaiExampleStrategy(IStrategy):
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All features must be prepended with `%` to be recognized by FreqAI internals.
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Access metadata such as the current pair/timeframe with:
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`metadata["pair"]` `metadata["tf"]`
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More details on how these config defined parameters accelerate feature engineering
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in the documentation at:
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@ -144,6 +152,10 @@ class FreqaiExampleStrategy(IStrategy):
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All features must be prepended with `%` to be recognized by FreqAI internals.
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Access metadata such as the current pair with:
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`metadata["pair"]`
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More details about feature engineering available:
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https://www.freqtrade.io/en/latest/freqai-feature-engineering
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@ -161,6 +173,10 @@ class FreqaiExampleStrategy(IStrategy):
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Required function to set the targets for the model.
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All targets must be prepended with `&` to be recognized by the FreqAI internals.
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Access metadata such as the current pair with:
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`metadata["pair"]`
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More details about feature engineering available:
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https://www.freqtrade.io/en/latest/freqai-feature-engineering
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