Remove transform_space requirement from FeatureTransformer and `F…

…eaturePipeline` and move the functionality into `Exchange`.

docs/source/components/features/FeaturePipeline.md

@@ -1,4 +1,5 @@
 # FeaturePipeline
+
 Feature pipelines are meant for transforming observations from the environment into meaningful features for an agent to learn from. If a pipeline has been added to a particular exchange, then observations will be passed through the `FeaturePipeline` before being output to the environment.
 
 For example, a feature pipeline could normalize all price values, make a time series stationary, add a moving average column, and remove an unnecessary column, all before the observation is returned to the agent.
@@ -23,47 +24,48 @@ model = Sequential([
 ])
 ```
 
-
 ## Class Parameters
-* `steps` 
-  * A list of feature transformations to apply to observations.
-* `dtype`
-  * The `dtype` elements in the pipeline should be cast to.
 
-## Properties and Setters
+- `steps`
+  - A list of feature transformations to apply to observations.
+- `dtype`
+  - The `dtype` elements in the pipeline should be cast to.
 
-* `steps`
-  * A list of feature transformations to apply to observations.
-* `dtype`
-  * The `dtype` that elements in the pipeline should be input and output as.
-* `reset`
-  * Reset all transformers within the feature pipeline.
+## Properties and Setters
 
+- `steps`
+  - A list of feature transformations to apply to observations.
+- `dtype`
+  - The `dtype` that elements in the pipeline should be input and output as.
+- `reset`
+  - Reset all transformers within the feature pipeline.
 
 ## Functions
 
-Below are the functions that the `FeaturePipeline` uses to effectively operate. 
+Below are the functions that the `FeaturePipeline` uses to effectively operate.
 
 ### Private
-* `_transform`
-  * Utility method for transforming observations via a list of *make changes here* `FeatureTransformer` objects.
-  * In other words, it runs through all of the `steps` in a for loop, and casts the response. 
+
+- `_transform`
+  - Utility method for transforming observations via a list of _make changes here_ `FeatureTransformer` objects.
+  - In other words, it runs through all of the `steps` in a for loop, and casts the response.
 
 **The code from the transform function:**
 As you see, it iterates through every step and adds the observation to the dataframe.
+
 ```py
 for transformer in self._steps:
-    observations = transformer.transform(observations, input_space)
+    observations = transformer.transform(observations)
 ```
 
 At the end the observations are converted into a ndarray so that they can be interpreted by the agent.
 
 ### Public
 
-* `reset`
-  * Reset all transformers within the feature pipeline.
-* `transform_space`
-  * Apply the pipeline of feature transformations to an observation frame.
+- `reset`
+  - Reset all transformers within the feature pipeline.
+- `transform`
+  - Apply the pipeline of feature transformations to an observation frame.
 
 ## Use Cases
 
@@ -84,4 +86,4 @@ feature_pipeline = FeaturePipeline(steps=[normalize_price,
                                           difference_all])
 
 exchange.feature_pipeline = feature_pipeline
-```
+```

docs/source/components/features/FeatureTransformer.md

@@ -1,26 +1,23 @@
 # FeatureTransformer
 
-As stated before in the [overview](../overview.md),  We use an `ABCMeta` abstract hierarchy to handle the transformation calls of each asset. The `FeatureTransformer` is an abstract of all other price transformers available inside of the `tensortrade` library. As such, it has a set of common functions that are called on almost every transformer.
+As stated before in the [overview](../overview.md), We use an `ABCMeta` abstract hierarchy to handle the transformation calls of each asset. The `FeatureTransformer` is an abstract of all other price transformers available inside of the `tensortrade` library. As such, it has a set of common functions that are called on almost every transformer.
 
 ## Properties and Setters
 
-* `columns`
-  * A list of column names to normalize
-
+- `columns`
+  - A list of column names to normalize
 
 ## Functions
 
-Below are the functions that the `FeatureTransformer` uses to effectively operate. 
+Below are the functions that the `FeatureTransformer` uses to effectively operate.
 
 ### Private
 
 `None`
 
 ### Public
 
-* `reset`
-  * Optionally implementable method for resetting stateful transformers.
-* `transform_space` 
-  * Get the transformed output space for a given input space.
-* `transform`
-  * Transform the data set and return a new data frame.
+- `reset`
+  - Optionally implementable method for resetting stateful transformers.
+- `transform`
+  - Transform the data set and return a new data frame.

docs/source/components/features/indicators/TAlibIndicator.md

@@ -1,44 +1,37 @@
 # TAlibIndicator
-Adds one or more TAlib indicators to a data frame, based on existing open, high, low, and close column values.
 
+Adds one or more TAlib indicators to a data frame, based on existing open, high, low, and close column values.
 
 ![TalibIndicator](../../../_static/images/talib_transform.png)
 
-
 ## Class Parameters
-* `indicators`
-  * A list of indicators you want to transform the price information to.
-* `lows` 
-  * The lower end of the observation space. See `spaces.Box` to best understand.
-* `highs`
-  * The lower end of the observation space. See `spaces.Box` to best understand.
 
-## Properties and Setters
-
-* **NONE**
+- `indicators`
+  - A list of indicators you want to transform the price information to.
+- `lows`
+  - The lower end of the observation space. See `spaces.Box` to best understand.
+- `highs`
+  - The lower end of the observation space. See `spaces.Box` to best understand.
 
+## Properties and Setters
 
+- **NONE**
 
 ## Functions
 
-Below are the functions that the `TAlibIndicator` uses to effectively operate. 
+Below are the functions that the `TAlibIndicator` uses to effectively operate.
 
 ### Private
-* `_str_to_indicator` - Converts the name of an indicator to an actual instance of the indicator. For a list of indicators see list [here](http://mrjbq7.github.io/ta-lib/).
 
-### Public
-
-* `transform_space`
-  * Get the transformed output space for a given input space.
-* `transform`
-  * Transform the data set and return a new data frame.
+- `_str_to_indicator` - Converts the name of an indicator to an actual instance of the indicator. For a list of indicators see list [here](http://mrjbq7.github.io/ta-lib/).
 
+### Public
 
+- `transform`
+  - Transform the data set and return a new data frame.
 
 ## Use Cases:
 
-
-
 ## Use Cases
 
 **Use Case #1: Selecting Indicators**
@@ -51,7 +44,6 @@ talib_indicator = TAlibIndicator(["rsi", "ema"])
 
 This runs through the indicators in the list, at runtime and matches them to what is seen inside of TA-Lib. The features are then flattened into the `output_space`, both into the `high` and `low` segment of `space.Box`.
 
-
 ```py
 for i in range(len(self._indicators)):
     output_space.low = np.append(output_space.low, self._lows[i])

docs/source/components/features/scalers/MinMaxNormalizer.md

@@ -4,34 +4,31 @@ A transformer for normalizing values within a feature pipeline by the column-wis
 
 ## Class Parameters
 
-
-* `columns` 
-  * A list of column names to normalize.
-* `feature_min`
-  * The minimum value in the range to scale to.
-* `feature_max`
-  * The maximum value in the range to scale to.
-* `inplace`
-  * If `False`, a new column will be added to the output for each input column.
+- `columns`
+  - A list of column names to normalize.
+- `feature_min`
+  - The minimum value in the range to scale to.
+- `feature_max`
+  - The maximum value in the range to scale to.
+- `inplace`
+  - If `False`, a new column will be added to the output for each input column.
 
 ## Properties and Setters
 
 None
 
 ## Functions
 
-Below are the functions that the `MinMaxNormalizer` uses to effectively operate. 
+Below are the functions that the `MinMaxNormalizer` uses to effectively operate.
 
 ### Private
-*None*
 
-### Public
+_None_
 
-* `transform_space`
-  * Get the transformed output space for a given input space.
-* `transform`
-  * Apply the pipeline of feature transformations to an observation frame.
+### Public
 
+- `transform`
+  - Apply the pipeline of feature transformations to an observation frame.
 
 ## Use Cases:
 
@@ -48,4 +45,4 @@ feature_pipeline = FeaturePipeline(steps=[normalize_price,
                                           moving_averages,
                                           difference_all])
 exchange.feature_pipeline = feature_pipeline
-```
+```

docs/source/components/features/scalers/StandardNormalizer.md

@@ -4,37 +4,33 @@ A transformer for normalizing values within a feature pipeline by removing the m
 
 ## Class Parameters
 
-
-* `columns` 
-  * A list of column names to normalize.
-* `feature_min`
-  * The minimum value in the range to scale to.
-* `feature_max`
-  * The maximum value in the range to scale to.
-* `inplace`
-  * If `False`, a new column will be added to the output for each input column.
+- `columns`
+  - A list of column names to normalize.
+- `feature_min`
+  - The minimum value in the range to scale to.
+- `feature_max`
+  - The maximum value in the range to scale to.
+- `inplace`
+  - If `False`, a new column will be added to the output for each input column.
 
 ## Properties and Setters
 
-* None
+- None
 
 ## Functions
 
 Below are the functions that the `StandardNormalizer` uses to effectively operate.
 
 ### Private
 
-*None*
+_None_
 
 ### Public
 
-* `transform_space`
-  * Get the transformed output space for a given input space.
-* `transform`
-  * Apply the pipeline of feature transformations to an observation frame.
-* `reset`
-  * Resets the history of the standard scaler.
-
+- `transform`
+  - Apply the pipeline of feature transformations to an observation frame.
+- `reset`
+  - Resets the history of the standard scaler.
 
 ## Use Cases:
 
@@ -56,5 +52,3 @@ feature_pipeline = FeaturePipeline(steps=[normalize_price,
                                           difference_all])
 exchange.feature_pipeline = feature_pipeline
 ```
-
-

docs/source/components/features/stationarity/FractionalDifference.md

@@ -2,53 +2,46 @@
 
 A transformer for differencing values within a feature pipeline by a fractional order. It removes the stationarity of the dataset available in realtime. To learn more about why non-stationarity should be converted to stationary information, please look at the blog [here](https://towardsdatascience.com/preserving-memory-in-stationary-time-series-6842f7581800).
 
-
 ## Class Parameters
-* `columns`
-  *  A list of column names to difference.
-* `difference_order`
-  * The fractional difference order. Defaults to 0.5.
-* `difference_threshold`
-  * A type or str corresponding to the dtype of the `observation_space`.
-* `inplace`
-  * If `False`, a new column will be added to the output for each input column.
+
+- `columns`
+  - A list of column names to difference.
+- `difference_order`
+  - The fractional difference order. Defaults to 0.5.
+- `difference_threshold`
+  - A type or str corresponding to the dtype of the `observation_space`.
+- `inplace`
+  - If `False`, a new column will be added to the output for each input column.
 
 ## Functions
 
-Below are the functions that the `FractionalDifference` uses to effectively operate. 
+Below are the functions that the `FractionalDifference` uses to effectively operate.
 
 ### Private
 
-* `_difference_weights`
-  * Gets the weights for ...
-* `_fractional_difference`
-  * Computes fractionally differenced series, with an increasing window width.
+- `_difference_weights`
+  - Gets the weights for ...
+- `_fractional_difference`
+  - Computes fractionally differenced series, with an increasing window width.
 
 ### Public
 
-* `transform_space`
-  * Get the transformed output space for a given input space.
-* `transform`
-  * Apply the pipeline of feature transformations to an observation frame.
-* `reset`
-  * Resets the history of the standard scaler.
-
+- `transform`
+  - Apply the pipeline of feature transformations to an observation frame.
+- `reset`
+  - Resets the history of the standard scaler.
 
 ## Use Cases:
 
 **Use Case #1: Different Input Spaces**
 
 This `FeatureTransformer` operates differently depending on if we pretransform the observation to an ndarray or keep it as a pandas dataframe.
 
-
 ```py
 from tensortrade.features import FeaturePipeline
 from tensortrade.features.stationarity import FractionalDifference
 price_columns = ["open", "high", "low", "close"]
 difference_all = FractionalDifference(difference_order=0.6) # fractional difference is seen here
-feature_pipeline = FeaturePipeline(steps=[difference_all]) 
+feature_pipeline = FeaturePipeline(steps=[difference_all])
 exchange.feature_pipeline = feature_pipeline
 ```
-
-
-