A repository of ML and NN methods and libraries along with sample stock data for training and testing. These are meant to be simple, easy to understand and highlight the essential components of each method. Examples also show how to run the models on current data in order to get stock predictions.

All models were trained and tested on the example_data from D.AT to rank which performed best.

Precision p-value is the method for comparing performance. Why precision? When investing, you care much more about the performance of the stocks you have purchased than those that you decided not to buy. Put in real terms, the 10,000 no-buy decisions (negatives) are not nearly as important as the 50 buy decisions (positives) if all 50 have profitable exits (true positives). P-values are chosen as the measure because a low p-value rewards for both high precision and a large amount of true positives, connoting a more robust model.

Methods with a stochastic component have been run 5 times; deterministic methods only once.
The median precision p-value of the runs is selected as the performance.

Note that these are the most basic examples. Efforts were made for the neural nets to be apples-to-apples, but there may be variations in the implementations. More sophisticated versions of these methods or hyperparameter tuning could produce better results.

Abbreviations:

• FFNN: Feedforward Neural Network
• RNN: Recurrent Neural Network
• LSTM: Long Short-Term Memory

1. Clone this repository.
2. Navigate to the project directory.
3. Install the necessary libraries:

pip install -r requirements.txt

The data provided in example_data is an example of what is provided by the D.AT platform.

This dataset encapsulates 5 years of stock data from the S&P 500, segmented into intervals of 30 trading days each. The data in each segment has been normalized using a method where values are divided by the most recent data point within the segment. Each row in the dataset represents a specific segment, providing a snapshot of the stock data available on a particular trading day. Rows are labeled to indicate when the stock had a minimum gain of 5% within the subsequent 10 trading days.

• train.csv: Of the 5 years, it contains the first 4 years of data.
• test.csv: Of the 5 years, it contains the final year of data.
• latest.csv: This file contains data from the most recent trading day for all stocks listed. While it lacks labels (since these pertain to future events), each row maintains the same feature vector structure as those in the train and test files. The rows commence with the stock ticker symbol, serving as a key tool to pinpoint stocks with promising prospects for good performance.

Recent data customizable with different trading strategies and feature engineering options can be downloaded for free at D.AT.