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You have general and specific questions, so I'll my best here. I have a forex robot that does 30% p.a. 8 years running. It's technical indicators. It's also using one set of rules that is aware of peoples-patterns. (Target prices that traders would commonly sell at). It must be people-aware because even an HFT (and some have failed in big ways) has human ...


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Another example might be, if companies that exhibit certain revenue growth metrics, or margin improvement, would that signal a potential buy opportunity? Or perhaps if certain words in their annual report, quarterly filings, press releases indicate this company is likely to do well? The keys to quantitative investment are research and data analysis. ...


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This may not directly answer your questions. There's a class offered by Georgia Tech called Machine Learning for Trading, you might find it useful. https://www.udacity.com/course/machine-learning-for-trading--ud501


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Read Max Dama on Automated trading (PDF) - This is the best introduction to algorithmic trading out there: http://www.decal.org/download/2582


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HFT firms are liquidity providers. They post bids and offers at prices around what they believe the fair price of the stock is at the current moment. The distance between those bids and offers can be thought of as a confidence interval. So, to put it quite simply, they can use machine learning to better estimate the fair price of the asset or better estimate ...


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I was looking for someone else's answer to compare with mine. Vectorization is clearly the way to go. Question is, how do we vectorize this problem. Below is a commented function that accomplishes this in python using numpy and pandas. import numpy as np import pandas as pd def max_dd(returns, rolling=None): # make into a DataFrame so that it is a ...



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