Awesome

Can Machine Reads Like Analysts Do?

• Train a CNN to read candlestick graphs, predicting future trend.

• Training & testing Dataset from Huge Stock Market Dataset-Full Historical Daily Price + Volume Data For All U.S. Stocks & ETFs.

• Pytorch implementation

update:

Related paper: Trading via Image Classification (by J.P. Morgan)

Two Approaches

Approach 1

• cnn4matrix.py

• Apply convolution on data matrices directly.

Input: (5*n) matrices -> (Open,High,Low,Close,Volume)*(d1,d2,...,dn)

Output: classification result

Approach 2

• cnn.py

• Generate candlestick graphs first.

Input: candlestick graphs

Output: classification result

Current results

11 layers + residual block