b49e39dcb4
- Enhanced the `UserIndicator` class to include an optional `timeframe` attribute for custom indicator timeframes. - Updated the `get_indicator_data` method in `MarketDataIntegrator` to fetch and calculate indicators based on the specified timeframe, ensuring proper data alignment and handling. - Modified the `ChartBuilder` to pass the correct DataFrame for plotting indicators with different timeframes. - Added UI elements in the indicator modal for selecting timeframes, improving user experience. - Updated relevant JSON templates to include the new `timeframe` field for all indicators. - Refactored the `prepare_chart_data` function to ensure it returns a DataFrame with a `DatetimeIndex` for consistent calculations. This commit enhances the flexibility and usability of the indicator system, allowing users to analyze data across various timeframes.
36 lines
3.1 KiB
Markdown
36 lines
3.1 KiB
Markdown
## Relevant Files
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- `data/common/indicators.py` - This is the primary file to be refactored. The `TechnicalIndicators` class will be modified to be DataFrame-centric.
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- `components/charts/utils.py` - The `prepare_chart_data` function in this file needs to be corrected to ensure it properly creates and returns a DataFrame with a `DatetimeIndex`.
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- `components/charts/data_integration.py` - This file's `get_indicator_data` method will be simplified to pass the correctly prepared DataFrame to the calculation engine.
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- `app_new.py` - The main application file, which will be used to run the dashboard and perform end-to-end testing.
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### Notes
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- The goal of this refactoring is to create a more robust and maintainable data pipeline for indicator calculations, preventing recurring data type and index errors.
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- Pay close attention to ensuring that DataFrames have a consistent `DatetimeIndex` with proper timezone information throughout the pipeline.
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## Tasks
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- [x] 1.0 Refactor `TechnicalIndicators` Class in `data/common/indicators.py` to be DataFrame-centric.
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- [x] 1.1 Modify `sma`, `ema`, `rsi`, `macd`, and `bollinger_bands` methods to accept a pre-formatted DataFrame as their primary input, not a list of candles.
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- [x] 1.2 Remove the redundant `prepare_dataframe` call from within each individual indicator method.
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- [x] 1.3 Rename `prepare_dataframe` to `_prepare_dataframe_from_list` to signify its new role as a private helper for converting list-based data.
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- [x] 1.4 Update the main `calculate` method to be the single point of data preparation, handling both DataFrame and list inputs.
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- [x] 2.0 Correct DataFrame Preparation in `components/charts/utils.py`.
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- [x] 2.1 Review the `prepare_chart_data` function to identify why the `DatetimeIndex` is being dropped.
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- [x] 2.2 Modify the function to ensure it returns a DataFrame with the `timestamp` column correctly set as the index, without a `reset_index()` call at the end.
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- [x] 3.0 Simplify Data Flow in `components/charts/data_integration.py`.
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- [x] 3.1 In the `get_indicator_data` function, remove the workaround that converts the DataFrame to a list of dictionaries (`to_dict('records')`).
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- [x] 3.2 Ensure the function correctly handles both main and custom timeframes, passing the appropriate DataFrame to the calculation engine.
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- [x] 3.3 Verify that the final `reindex` operation works correctly with the consistent DataFrame structure.
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- [x] 4.0 End-to-End Testing and Validation.
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- [x] 4.1 Run the dashboard and test the indicator plotting functionality with both matching and custom timeframes.
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- [x] 4.2 Verify that no new errors appear in the console during chart interaction.
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- [x] 5.0 Update Indicators documentation to reflect the new DataFrame-centric approach.
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- [x] 5.1 Review the documentation in the `/docs` directory related to indicators.
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- [x] 5.2 Update the documentation to explain that the calculation engine now uses DataFrames.
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- [x] 5.3 Provide clear examples of how to use the refactored `TechnicalIndicators` class. |