This is a summary of links featured on Quantocracy on Thursday, 12/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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New Book Added (Machine Learning): Probabilistic Graphical ModelsMost tasks require a person or an automated system to reason — to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned
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Placing your first Forex trade with Python [Jon.IO]Update: I updated the code so it works with Oanda's new API. Get it here Time to talk about brokers, how to place a trade programmatically and most importantly how not to get scammed. This is the third part of the series: How to build your own algotrading platform. A broker is nothing more than a company that lets you trade (buy or sell) assets on a market through their platform. What is very
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Conditional Value-at-Risk in the Normal and Student t Linear VaR Model [Quant at Risk]Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a magical threshold, the CVaR provides us with more intuitive expectation of how much we will