I am doing a university project on index/stock price prediction. I plan to use a combined cnn-lstm model, and I have several different types of data: Open High Low Close Volume, values, fundamental data such as unemployment and various rates, technical indicators like RSI, MACD and others, and moving averages like SMA, EMA, WMA and etc. What is the best way to prepare data for the network?

At this moment I am using the following transformations
for OHLC – simple differentiation
for fundamental data – logarithmization
for moving averages – subtract the candle opening value from the value of this moving average
indicator values unchanged
Then I use StandardizeNormalizer for all dataset. I also tried normalizing (robust scaling, standardization, minmax scaling too) each sequence separately, and differentiating all the data, but it was not effective