This is a summary of links featured on Quantocracy on Tuesday, 02/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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New Book Added: Unholy Grails: A New Road to Wealth [Amazon]Whats the fastest way to lose money? Follow the herd. Nick Radge stopped following the herd many years ago. As a trader and stock broker, Nick learnt to recognise what the herd were doing and how they react to financial information. He also realised that it made no sense. Are you one of the herd? Heres a test: If a stocks price is falling do you think it represents good value, i.e. its
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Intra-day data from Quandl and a new tick database in town party time! [Mintegration]Quandl will soon be offering intra-day data (1 min bars). Rock on ! I was kindly given some data to test out (see below). I cant say much more than this but keep an eye out for an official announcement soon 🙂 With both QuantGo and Quandl offering reasonably priced intra-day data, smaller trading shops have never had it so good. Ive been involved in the integration (i.e. messaging) of
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Active strategies are an allocation, not a trade [Flirting with Models]Summary Active strategies are often defined by the factor tilts they take on. For factor tilts to continue to out-perform the market over the long-run, they must exhibit premium volatility that causes short-term under-performance. Since alpha is zero-sum, investors that fold during periods of under-performance are passing the relative performance to investors with the stomach to hold. To truly
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Trading strategies: No need for the holy grail [Predictive Alpha]We demonstrate that weak trading signals, which do not offer high risk-adjusted returns on their own, can be combined into a powerful portfolio. In other words, no need for holy grails when researching signals. We start our experiment with some key assumptions. We have 20 signals with annualized log returns of 8% and annualized Sharpe Ratios of 0.6 not exactly stellar signals. The signals make
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Top Python Libraries for Automated Trading [Quant Insti]In one of our recent articles weve talked about most popular backtesting platforms for quantitative trading. Here we are sharing most widely used Python libraries for quantitative trading. Python is a free open-source and cross-platform language which has a rich library for almost every task imaginable and specialized research environment. Python is an excellent choice for automated trading