shanghaihongliu/stockpriceprediction

A machine learning project that predicts sotck price in the future.

Stock Price Prediction

A machine learning project that predicts sotck price in the future.
It's a course project of CSIT 600D Introduction to Big Data, HKUST, 2017.

Prerequisites

• Python 3.3+
• Tensorflow 0.12.1
• Zipline

Machine learning part

In brief, this a regression problem(supervised learning) in machine learing.

Input

Raw data is preprocessed as TP Matrix format.

Model

Convolutional Neural Networks is used to fit the labeled data.

Output

The CNNs gives a predicted stock price change ratio accroding to each input.

Backtesting part

For one single stock, we train a specific model.
On the morning of each trading day, we use the model to predict.

• If the output is large enough(positive), we buy an amount of stock.
• If the ouput is small enough(negative), we sell an amount of stock.
• Otherwise, we just take the "hold" action.

Results

Three models are trained for three different stocks. The machine learning results and backtesting results are shown below:

Apple Inc. (AAPL)

• Machine learning result for both train set and test set; Blue lines stand for ground truth and green stars for predictions.

• Backtesting results

General Electric Company (GE)

• Machine learning result for both train set and test set.

• Backtesting results

The Boeing Company (BA)

• Machine learning result for both train set and test set.

• Backtesting results

As we can see, backtesting works fine on the first two stocks but doesn't on the third one.

Future work

Till now, this project is just a demo built for course. And we've listed some needed improvements:

• Try different kinds of inputs
  • Involve more information besides raw stock price
• Use data mining to find the threshold of trading
  • Threshold for buying
  • Threshold for selling
• Keep modifying model parameters
  • The depth of networks
  • The number of neurons of each layers
  • Learning rate
  • ...
• Try more models
  • RNN and LSTM are both good at sovling time series problems.
• Try classification methods on this problem
  • Predict “rise” or “fall” of stock price

Author

HAN, Siyuan / @SiYuanHan
LI, Jianda / @jiandaLi
HU, Xiaoyu / @HuXiaoyu1994
YANG, Austin Liu / @AustinNeverPee