This is a summary of links featured on Quantocracy on Thursday, 11/14/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
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Podcast: Jim Simons – The pinnacle of trading greatness w/ author @GZuckerman [Chat With Traders]Gregory Zuckerman is a writer at the Wall Street Journal and author of The Man Who Solved the Market: How Jim Simons Launched The Quant Revolution. For anyone unfamiliar, Jim Simons is the brilliant-minded mathematician who founded hedge fund Renaissance Technologies. Using quantitative models and with billions in AUM, Renaissance has averaged annualized returns of net 39% since 1988. And these
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How to avoid unwanted curve fitting during backtest [Philipp Kahler]Whenever you develop an algorithmic trading strategy, curve fitting is one of the most dangerous hazards. It will lead to severe losses in real time trading. This article will show you some ways to detect if the performance of your algorithmic trading strategy is based on curve fitting. Curve fitting what is it? Every algorithmic trading strategy will have some parameters. There is no way
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The Investment Factor and Expected Returns [Alpha Architect]It is well documented in the literature that over the long term, low-investment firms have outperformed high-investment firms.(1) This finding has led to the investment factor (CMA, or conservative minus aggressive) being incorporated into the leading asset pricing modelsthe four-factor Q model (market beta, size, investment and profitability), the Fama-French five-factor model that adds value,
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Hiring a Software Developer to Code Up a Trading Strategy [Quant Start]At QuantStart we place an emphasis on fully automated systematic trading and the processes that surround it. However we should be careful to distinguish between the separate concepts of systemisation and automation. The former involves a trading strategy that can be codified into a set of rules, which canand often iscalculated and traded in a manual fashion. The latter encompasses the case