This is a summary of links featured on Quantocracy on Wednesday, 07/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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Machine Learning in Algorithmic Trading Systems Presentation [Robot Wealth]Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using machine learning in algorithmic trading systems. Here you can download the presentation Many thanks to all who attended and particularly for the engaging questions. I thoroughly enjoyed myself! In particular, thanks to Andrien Juric for oraganising the event and Sharon Lu from Tyro for making available
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Unbalanced Classes in Machine Learning and the Stock Market [MKTSTK]Many assets exhibit bull or bear trends which persist for long periods of time. This presents an interesting problem for anyone trying to predict the future return of an asset: a lack of diversity in your training set. This problem is known as unbalanced classes in the machine learning field. The basic issue is that many classification methods work best when your training data is roughly uniform
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Hull Moving Average Filter | Trading Strategy (Entry & Exit) [Oxford Capital]Developer: Alan Hull. Source: Kaufman, P. J. (2013). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Trend following trading strategy based on low lag moving averages. Research Goal: To verify performance of the Hull Moving Average (HMA). Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Two Hull Moving Averages turn upwards. Short Trades: Two
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Impact of 1987 Black Monday on Trading Behavior of Stock Investors [Quantpedia]Using a simple sign test, we report new empirical evidence, taken from both the US and the German stock markets, showing that trading behavior substantially changed around Black Monday in 1987. It turned out that before Black Monday investors behaved more as in the momentum strategy; and after Black Monday more as in the contrarian strategy. We argue that crashes, in general, themselves are merely