Skip to content

Latest commit

 

History

History
75 lines (49 loc) · 2.59 KB

README.md

File metadata and controls

75 lines (49 loc) · 2.59 KB
Raw

PrOFIT: Predictive Operative For Intelligent Trading

An Algorithmic Trading Framework for Investing in Stocks in the S&P 500

A capstone project developed and submitted for the fullfillment of the requirements for the M.S. degree in Information at the University of Arizona.

Getting Started

Prerequisites

pip install -r requirements.txt

You will also need to install MongoDB

Setting up the database

The two important collections that I have included in the repository are the fundamentals and the technicals. This data from the database was exported into two json files: Database/Fundamentals.json and database/Technicals.json. To import this data into collections in a new database on a local machine, run:

mongo use AlgoTradingDB

mongo import --db AlgoTradingDB --collection 10y_Fundamentals --file Database/Fundamentals.json --jsonArray

mongo import --db AlgoTradingDB --collection 5Y_technicals --file Database/Technicals.json --jsonArray

This will create a mongo database called AlgoTradingDB and two collections within that database called 10y_Fundamentals and 5Y_technicals. The trading system will get data from these two collections and create new collections within this database from the trading results.

Using the PrOFIT system

Simulating trading of a single stock

To run the trading system, you run tradingSystem.py with the virtual environment. The arguments are

  • Starting Date YYYY-MM-DD (default = 2017-09-05)
  • Starting Capital (default = 15000)
  • Predictive Model (Arima, SVM, or LSTM) (default = SVM)
  • Amount of loss you are willing to lose (default = 0.3)
  • Stock Ticker
python environment.py --model SVM --startDate 2017-09-05 --startingCapital 15000 --ticker googl  --loss 0.3

Running the Trading System

To run the trading system, you run tradingSystem.py with the virtual environment. The arguments are

  • Starting Date YYYY-MM-DD (default = 2017-09-05)
  • Starting Capital (default = 15000)
  • Predictive Model (Arima, SVM, or LSTM) (default = SVM)
  • Amount of loss you are willing to lose (default = 0.3)
python tradingSystem.py 2017-09-05 15000 SVM 0.3

Built With

Author

Adele Kapellusch

License

This project is licensed under the MIT License - see the LICENSE.md file for details