sowmyagowri / stock-price-prediction

A group project for CMPE272 at SJSU

Project-Team-6

Stock Price Prediction

Team Members

1. Sowmya Gowrishankar
2. Indira Priyadarshini Bobburi
3. Vijay Yadav
4. Niral Koradia

Learn about the product at: https://www.youtube.com/watch?v=jAdHvbbOOYs

Demo of our Project is at: https://www.youtube.com/watch?v=k9y4nsVdkIE&feature=youtu.be

A web based product using machine learning models to predict future stock values based on the historical values.

Machine learning models used:

FB Prophet

Technologies used:

Python for backend
Flask framework for integration of frontend and backend
JavaScript frame work with CSS and HTML for front end
Dygraphs for plotting

Steps to run the code:

1. Clone the github and goto the folder
   $git clone https://github.com/SJSU272LabF17/Project-Team-6
   $cd ./Project-Team-6/
2. create a virtual environment with python3
   $virtualenv -p /usr/local/bin/python3 Dependencies
3. Activate the virtual environment
   $source Dependencies/bin/activate
4. Install dependencies numpy, scipy, requests, pyyaml, tensorflow, keras
   $pip install -r requirements.txt
5. Run "Stock Market Prediction/prophet.py" -- this starts the http server using python flask
6. Open the port on browser and use the site.

Site available at: http://13.57.46.92:5000/

In homepage, when the company ticker symbol is given, it fetches real time data using yahoo finance api.
Once in a while, an error comes in retrieving data from yahoo finance as they check for captcha to make sure no automated system is using their data.
In that case, just go back to the homepage and try again.

The machine learning model tries to for the entire time period, predicting the data at each step using the previous data and learning from it. This helps in predicting the anamolies over the years.

In the graph below, the blue line was the prediction based on previous data at every point. And green line is the original closing stock values. In the site, the graph is interactive, drag to zoom into particular period. Double-click to zoom out.

You can also get company information and finance news in the second page.

stock-price-prediction's Issues

new

My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators).
All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction).
For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation.
And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

static/numbers.csv missing