Stock Prediction

Predict future stock prices based on historical data using simplified linear regression model. Historical stock data are shared at Google Finance.

Installation

####Download the data

1. Clone this repo to your computer.
2. Get into the folder using cd stock-prediction-lr.
3. Run mkdir data.
4. Switch into the data directory using cd data.
5. Download the data files from Google Finance into the data directory.

• It's recommended to download all the data from first date to last.
• Change DATA_FILENAME variable to your data file name in settings.py.

6. Switch back into the stock-prediction-lr directory using cd ...

####Install the requirements

Install the requirements using pip install -r requirements.txt.

• Make sure you use Python 3.
• You may want to use a virtual environment for this.

Usage

1. Run mkdir processed to create a directory for our processed datasets.
2. Run python trend.py, which will:

• include prepare.py, which clean and prepare data,
• create trainX.csv and trainY.csv in the processed folder,
• run linear regression across the training set,
• print the accuracy score,
• show charts with test set and all data.

Extending this

If you want to extend this work, here are a few places to start:

• Modify cleaning data, for example remove '-' values or replace it with some values.
• Generate more features in prepare.py.
• Modify features in prepare.py.
• Switch algorithms in trend.py.