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Designed, optimized, tested, and deployed a machine learning stock price prediction model as a Flask application

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kaigg96/The-Advantage-of-Being-Small-A-Model-for-Individual-Investors

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Equity-market-analysis

Equity markets consist of some of the most studied data in the world. With millions invested into research, and computational tools beyond what's accessible to the average individual, it's not hard to see that an individual investor doing their own analysis will have a tough time outperforming. Currently, I see two paths to success:

  1. Find something the institutions haven't: while possible, this is unlikely to happen considering the disparity in resources spent on the problem. You'd have to be incredibly lucky, and even still it's probably only a matter of time before an institution discovers your strategy, at which point any alpha is lost.
  2. Find a strategy that institutions can't implement: while the large amount of resources gives institutions ample opportunity, they also impose limitations. In particular, investing large amounts of capital has an impact on the market by moving equity prices and as a result signalling the rest of the market. This effect is especially pronounced in smaller caps. Moving large blocks of stock can be difficult and imprecise.

The second point presents an opportunity, and is the basis for this project.

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Designed, optimized, tested, and deployed a machine learning stock price prediction model as a Flask application

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