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CS297 Proposal

Algorithmic Trading

Sonal Kabra (sonal.kabra@sjsu.edu)

Advisor: Dr. Chris Pollett

Description:

Algorithmic trading is the process of using a computer program to follow a defined set of instructions for placing a trades at speeds, complexity, or frequency beyond what a human trader can do. Algorithmic trading can be based on timing, price, quantity or any mathematical model. For this project I will use St Louis Fed for economic indicators and Qandl stock data sets. I will build multi-layer neural networks to predict buy, sell or hold classification for a single or a portfolio of stocks for time scales of one day, one month, and one year. I will also work on neural networks to try to improve the estimates of stocks or economic volatility measures.

Schedule:

Week 1: Sep 7 - Sep 13 Talk about the project in detail with the advisor. Prepare and deliver CS 297 Proposal.
Week 2: Sep 14 - Sep 20Presentation on Algorithmic trading and techniques
Week 3: Sep 21 - Sep 27Understand and manipulate previous years stock data set in python
Week 4: Sep 28 - Oct 4 Deliverable 1:Measure volatility of stock by inputing stock ticker in program
Week 5: Oct 5 - Oct 11Understanding and implementing Neural Net with respect to algorithmic trading
Week 6: Oct 12 - Oct 18Deliverable 2:Implement the simple neural net for estimating starting month of give dataset
Week 7: Oct 19 - Oct 25Understanding and building deep learning algorithms using python library
Week 8: Oct 26 - Nov 1Understand the K- nearest neighbor and Decision tree algorithm for stock prediction
Week 9: Nov 2 - Nov 8Deliverable 3:Implement the K- nearest neighbor and Decision tree algorithm for stock prediction
Week 10: Nov 9 - Nov 15Build program to work with portfolio of stock
Week 11: Nov 16 - Nov 22Continue work from week 11
Week 12: Nov 23 - Nov 29Deliverable 4:Implement program to work with the portfolio of stock
Week 13: Nov 30 - Dec 6Start compiling CS 297 Report
Week 14: Dec 7 - Dec 13Deliverable 5:CS297 Report

Deliverables:

The full project will be done when CS298 is completed. The following will be done by the end of CS297:

1. Measure volatility of stock by inputing stock ticker

2. Implement the simple neural net for estimating starting month of give dataset

3. Implement the K- nearest neighbor and Decision tree algorithm for stock prediction

4. Get the previous program to work with the portfolio of stock

5. CS297 Report

References:

[2015] Basics of Algorithmic Trading: Concepts and Examples. Shobhit Seth. Investopedia. 2015.

[2013] ALGORITHMIC TRADING USING MACHINE LEARNING TECH-NIQUES: FINAL REPORT. Shao, Chenxu, and Zheming Zheng. Learning 5. 2013.