add explicit metadata argument to example strat, include it with backtesting
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		| @@ -16,7 +16,7 @@ Meanwhile, high level feature engineering is handled within `"feature_parameters | ||||
| It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: | ||||
|  | ||||
| ```python | ||||
|     def feature_engineering_expand_all(self, dataframe, period, **kwargs): | ||||
|     def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         This function will automatically expand the defined features on the config defined | ||||
| @@ -66,7 +66,7 @@ It is advisable to start from the template `feature_engineering_*` functions in | ||||
|  | ||||
|         return dataframe | ||||
|  | ||||
|     def feature_engineering_expand_basic(self, dataframe, **kwargs): | ||||
|     def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         This function will automatically expand the defined features on the config defined | ||||
| @@ -94,7 +94,7 @@ It is advisable to start from the template `feature_engineering_*` functions in | ||||
|         dataframe["%-raw_price"] = dataframe["close"] | ||||
|         return dataframe | ||||
|  | ||||
|     def feature_engineering_standard(self, dataframe, **kwargs): | ||||
|     def feature_engineering_standard(self, dataframe, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         This optional function will be called once with the dataframe of the base timeframe. | ||||
| @@ -119,7 +119,7 @@ It is advisable to start from the template `feature_engineering_*` functions in | ||||
|         dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 | ||||
|         return dataframe | ||||
|  | ||||
|     def set_freqai_targets(self, dataframe, **kwargs): | ||||
|     def set_freqai_targets(self, dataframe, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         Required function to set the targets for the model. | ||||
| @@ -183,7 +183,7 @@ In total, the number of features the user of the presented example strat has cre | ||||
|  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. | ||||
|  | ||||
|  ```py | ||||
| def feature_engineering_expand_all(self, dataframe, period, **kwargs): | ||||
| def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs): | ||||
|  if metadata["tf"] == "1h": | ||||
|     dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) | ||||
| ``` | ||||
|   | ||||
| @@ -1253,13 +1253,11 @@ class FreqaiDataKitchen: | ||||
|             informative_copy = informative_df.copy() | ||||
|  | ||||
|             for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]: | ||||
|                 metadata["period"] = t | ||||
|                 df_features = strategy.feature_engineering_expand_all( | ||||
|                     informative_copy.copy(), t, metadata=metadata) | ||||
|                 suffix = f"{t}" | ||||
|                 informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix) | ||||
|  | ||||
|             metadata.pop("period") | ||||
|             generic_df = strategy.feature_engineering_expand_basic( | ||||
|                 informative_copy.copy(), metadata=metadata) | ||||
|             suffix = "gen" | ||||
|   | ||||
| @@ -324,9 +324,11 @@ class IFreqaiModel(ABC): | ||||
|                     populate_indicators = False | ||||
|  | ||||
|                 dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :] | ||||
|                 dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train) | ||||
|                 dataframe_base_train = strategy.set_freqai_targets( | ||||
|                     dataframe_base_train, metadata=metadata) | ||||
|                 dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :] | ||||
|                 dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest) | ||||
|                 dataframe_base_backtest = strategy.set_freqai_targets( | ||||
|                     dataframe_base_backtest, metadata=metadata) | ||||
|  | ||||
|                 dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train) | ||||
|                 dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest) | ||||
|   | ||||
| @@ -46,7 +46,7 @@ class FreqaiExampleStrategy(IStrategy): | ||||
|     std_dev_multiplier_sell = CategoricalParameter( | ||||
|         [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True) | ||||
|  | ||||
|     def feature_engineering_expand_all(self, dataframe, period, **kwargs): | ||||
|     def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         This function will automatically expand the defined features on the config defined | ||||
| @@ -58,9 +58,9 @@ class FreqaiExampleStrategy(IStrategy): | ||||
|  | ||||
|         All features must be prepended with `%` to be recognized by FreqAI internals. | ||||
|  | ||||
|         Access metadata such as the current pair/timeframe/period with: | ||||
|         Access metadata such as the current pair/timeframe with: | ||||
|  | ||||
|         `metadata["pair"]` `metadata["tf"]` `metadata["period"]` | ||||
|         `metadata["pair"]` `metadata["tf"]` | ||||
|  | ||||
|         More details on how these config defined parameters accelerate feature engineering | ||||
|         in the documentation at: | ||||
| @@ -103,7 +103,7 @@ class FreqaiExampleStrategy(IStrategy): | ||||
|  | ||||
|         return dataframe | ||||
|  | ||||
|     def feature_engineering_expand_basic(self, dataframe, **kwargs): | ||||
|     def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         This function will automatically expand the defined features on the config defined | ||||
| @@ -138,7 +138,7 @@ class FreqaiExampleStrategy(IStrategy): | ||||
|         dataframe["%-raw_price"] = dataframe["close"] | ||||
|         return dataframe | ||||
|  | ||||
|     def feature_engineering_standard(self, dataframe, **kwargs): | ||||
|     def feature_engineering_standard(self, dataframe, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         This optional function will be called once with the dataframe of the base timeframe. | ||||
| @@ -167,7 +167,7 @@ class FreqaiExampleStrategy(IStrategy): | ||||
|         dataframe["%-hour_of_day"] = dataframe["date"].dt.hour | ||||
|         return dataframe | ||||
|  | ||||
|     def set_freqai_targets(self, dataframe, **kwargs): | ||||
|     def set_freqai_targets(self, dataframe, metadata, **kwargs): | ||||
|         """ | ||||
|         *Only functional with FreqAI enabled strategies* | ||||
|         Required function to set the targets for the model. | ||||
|   | ||||
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