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The Python ta library has 200+ "technical analysis" indicators. Taking these as inputs (along with their interactions, eg -, /, *) could be very useful. (from #1406)
* Add ta_features parameter to PredictTrainFeedset constructor
* Add ta_features parameter to PredictTrainFeedset constructor
* Add TechnicalIndicator class for calculating technical indicators
* formatting
* Add MACD technical indicator class
* Add RSI technical indicator class
* Add get_ta_indicator function for retrieving technical indicator class
* Add ta_features parameter to SimEngine constructor
* Formatting
* Add ta_features parameter to ppss.yaml
* Format
* Add technical indicator features to AimodelDataFactory
The code changes in `aimodel_data_factory.py` introduce the `ta_features` parameter to the `AimodelDataFactory` class. This parameter allows for the calculation of technical indicator features based on the provided feeds. The technical indicators are retrieved using the `get_ta_indicator` function, which has been added in a recent commit.
* add ta
* assert correct
* Typo fix
* Refactor TechnicalIndicator constructor parameter names for clarity
* linter
* linter
* Formatting
* Add mypy configuration for ta package
* Better handling
* remove unused import
* Readability
* formatting
* Add MockTechnicalIndicator for testing purposes
* Add conftest.py for technical indicators tests
* test get_ta_indicator
* Add unit test for MACD indicator
* test RSI calculation against ta library
* test TechnicalIndicator
* Linter fixes
* Update tests
* linter fixes
Background
The Python ta library has 200+ "technical analysis" indicators. Taking these as inputs (along with their interactions, eg -, /, *) could be very useful. (from #1406)
TODOs
Step1: #1578
predict_train_feedsets
to accept an array of technical indicatorsStep2:
Step3:
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