AI News, BOOK REVIEW: Machine Learning for Intraday Stock Price Prediction 1: Linear Models

Machine Learning for Intraday Stock Price Prediction 1: Linear Models

This is the first of a series of posts on the task of applying machine learning for intraday stock price/return prediction.

Let’s look at the individual points in the above graph - there are more than 200,000 datapoints there, but we will just look at the first few to understand what it is that we want to predict.

Please note that it’s typically better to predict returns rather than price difference because models/techniques designed to predict returns can scale across various securities relatively better.

$0.13 price difference for a $154 stock is not much compared to $0.13 price difference for a $20 stock - $0.13 has a different meaning for a $20 stock.

In this post though, we will only use the features derived from the market data to predict the next 1 min price change.

(We will explore news/text data effects in a separate post in future.) The feature set can be broadly classified into two categories: Features describing current market snapshot and Features describing recent history

However, please note that these features try to capture the current market conditions as well as the recent past.

While the price of AAPL could range from 153 to 155 in a day, the volume over last 5 min could range from 100 to 1000000.

However, the model outputing 0 is of absolutely no value - we want opinionated models that can be useful for trading or execution.

Therefore, comparing the standard deviation of the predicted value h(x) with the standard deviation of y is necessary.

Adding a weight penalty to the error term is a simple way to regularize the model - this helps stabilize the training and the model is often better at generalization.

The following results were obtained using a 2-layer feed forward neural network with hidden_size1=100 and hidden_size2=50.

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