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danielgafni t1_j7wsnlw wrote

The approach you are describing isn’t the best.

  1. There is no sense in rendering these images as OHLCV data is timeseries, not 2D images. Most of the data would just be white pixels. Which is not really wrong but is greatly inefficient. Instead of using 2D convolutions 1D convolutions can be used on the timeseries directly (which is called a Wavenet) which would remove rendering from your pipeline and greatly speedup training and inference.

  2. OHLCV data won’t give you enough information to neither predict the future or backtest your trading algorithm accurately due to loss of data after aggregations.

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