kslee9572/stock-prediction

tries to predict stock direction

Stock-Prediction

A machine learning approach to prdicting stock price movement.

Introduction

Different price prediction methods have been proposed using various machine learning methods such as support vector machines, random trees, neural networks have been proposed. In this project we focus on recurrent neural networks(RNN) by creaing a model based on three layers of long short term memory (LSTM) cells.

By first constructing a crude model and experimenting with raw data, we find that the odds are no different from that of guessing.

For better approximation we convert raw data in to technical indicators and feed them into the our network under given paramters. The end result is a contour plot between our given paramters and the corresponding accuracy at each point.

Requirements

python 3.8.6 (developed used pyenv) AlphaVantage for raw data

Parameters

We investiage the relationship of two parameters per point to the up/down of price movement.

timesteps : the number of data points fed into the network forecast_days : the number of days forward the program aims to predict

Usage

Once running the program, it will first prompt

Type symbol of stock to be analyzed:

The run.py is executed, it will return a contour plot

Results

We can notice that for each set of data, there is a peak - an optimal paramter that yields highest accuracy. Perhaps these paramters can be used as pointers when testing in the real world.