I've been spending the last month or two making my AI stock predictor, how should I improve it?
I won't be sharing the code for privacy reasons, but essentially it is an LSTM model trained using data of over 200 stocks that can predict, backtest against a buy and hold strategy, and rank stocks over various time periods (1d, 5d, 7d).
It is a 2-layer LSTM with a 512-unit hidden state, and a fully connected regression head
It takes in a input of:
- Close and open prices
- Log return
- Overnight gap
- Moving averages (10d, 20d, 30d)
- Exponential moving averages (10d, 30d)
- Volatility (10d, 20d, 30d)
- RSI
- MACD
- DayOfWeek
- DayOfMonth
- Month
- News article count
- News sentiment mean
- News sentiment standard deviation
- Ratio of positive news articles
- Ratio of negative news articles
Overall when I'm backtesting I get about a 98% accuracy for predictions, but only a 54% directional accuracy.
And I was just wondering if there was anything that i should add, or any more features that I should engineer that come to mind? I was thinking of possibly analyzing twitter posts next, but I just wanted a bit more of a general direction in where to go next to improve my model's accuracy and directional accuracy, thanks in advance!