This is a summary of links featured on Quantocracy on Wednesday, 09/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
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Time of Day effects in FX [Quant Journey]Time of day is critical for trading, it is even possible building trading strategies solely depending on time of day (I will keep this for another post) I will be using the concept of quality and define a high quality market, from an intraday timing perspective, as a market when trading range and volume are high and spread is low. I assume this as a good time to trade as trading cost (spread) is
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A Random Forest Test For Jumps in Stock Markets Using R [Top of The Bell Curve]In the previous article we looked at how one can use Neural Networks to detect jumps present in returns of a particular stock. In this blog post, we build on the thinking established in the previous article and use a Random Forest to detect jumps present in stock market returns. I have build an interactive web application which allows the user to select the share they want to test for jumps, and
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R vs MATLAB – round 4 [Eran Raviv]This is another comparison between R and MATLAB (Python also in the mix this time). In previous rounds we discussed the differences in 3d visualization, differences in syntax and input-output differences. Today is about computational speed. Spoiler alert: MATLAB wins by a knockout. A genuinely fair speed comparison across different software can be tricky. Almost all operations can be coded in more
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Foreseeing the future: a user s guide [Quant Dare]Everybody would like to see the future. If youre a portfolio manager, youd definitely love to see the future. Many posts here on QuantDare deal with the challenge of predicting the future (with Prophet, Random Forests, Lasso, etc). This time, we talk about something different: imagine we are able to predict the future exactly. Now what? How could we exploit this priceless information? As we
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Modeling Expected Drawdown Risk [Capital Spectator]There are no silver bullets for profiling risk, but drawdowns properties arguably give this metric a leg up over most of the competition. The combination of an intuitive framework, simplicity, and sharp focus on how markets actually behave is a tough act to beat. Perhaps the strongest argument in favor of drawdown can be summed up by recognizing that peak-to-trough declines always resonate with
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Broken Strategy or Market Change: Investigating Underperformance [Alvarez Quant Trading]I recently had someone email me about the performance of a strategy I created back in late 2005/early 2006 and traded for a few years. I remember the strategy being a daily mean reversion set up with an intraday pullback entry. I figured it probably had not done well over the last decade. I stopped trading in the middle of 2008 because I did not like how it was behaving. In the backtest it did