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Algorithmic Trading for University project

Please do not post any code whatsoever on this forum post

Hey guys! This is my first semester at university. I've got an open ended software engineering project and I want to build an algorithmic trader with Python. I know very little about finance or stocks. My strategy now is to build web scrapers to pan through the web for mentions of a handful of shares alongside bearish or bullish mentions in reputable news sites, and to use the Quandl API to keep an eye on key indicators. Trouble is I have no idea what those indicators should be, or if this is even a sensible strategy.

Is my strategy sound? If so, can you guys point me in a direction of people who have done something similar? If not, can you guys suggest a more reasonable strategy?

Again, while it is ok for me to ask for direction online, it's not ok for me to get code online so do not post any code on this post.

4 responses

Context will be a problem.

"greece debt forgiven" "greece has more debt"

How can you differentiation the 2? I think that will be the most complex.
And perhaps web page age will also play a huge role. IMO it seems very difficult but i don't know much about the scrapers.

There are many sentiment data vendors that do this kind of scraping for you to generate signals. Take a look at these threads, they could give you some more ideas for the project:

https://www.quantopian.com/posts/crowd-sourced-stock-sentiment-using-stocktwits
https://www.quantopian.com/posts/accern-news-and-blog-backtest-results-using-quantopian-link-to-pdf-report-attached
https://www.quantopian.com/posts/accern-event-driven-earnings-focused-news-and-blog-backtest-results-pdf-report-attached
https://www.quantopian.com/posts/accern-market-neutral-sentiment-strategy-neutralized-risk-exposure

Good luck!

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Oh wow! Thank you so much Alisa!!

Hey Haziq, just wanted to point you to an additional resource, which is the new Quantopian Lectures Page. We have interactive lectures covering a bunch of concepts in quant finance and are adding more every month. For part of the initiative, we're working with professors at schools including MIT Sloan and Stanford, and we've based a good deal of the curriculum on what they teach.

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.