This is a summary of links featured on Quantocracy on Wednesday, 09/25/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
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Intraday Futures Calendar Spreads and the Impact of Transaction Costs [Quant Rocket]Intraday trading strategies offer great promise as well as great peril. This post explores an intraday trading strategy for crude oil calendar spreads and highlights the impact of transaction costs on its profitability. Background In a previous post, I explored an end-of-day pairs trading strategy in which the chief difficulty was to find suitable pairs. Pairs that cointegrate in-sample often
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The Simplest Momentum Indicator [Alvarez Quant Trading]We all have our favorite momentum indicators. One of mine is percent off 1 year high. This requires 252 data points and comparisons, plus a division. Another one is the 200-day moving average. This requires 200 closing prices, 199 additions and a division. A simple momentum indicator is Rate of Change which is the return of the asset of the last N days. This requires two prices and a division to
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Volatility Clustering: Are large price moves followed by large price moves? [Oxford Capital]Concept: Volatility clustering: Large price moves tend to be followed by large price moves, and small price moves tend to be followed by small price moves. Research Question: Is there a tendency of large price moves in one direction to be followed by large price moves in the opposite direction? Specification: Table 1. Results: Figure 1-4. Trade Setup: We identify large price moves via Wide Range
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Pairs Trading in Zorro [Robot Wealth]In our previous post, we looked into implementing a Kalman filter in R for calculating the hedge ratio in a pairs trading strategy. You know, light reading We saw that while R makes it easy to implement a relatively advanced algorithm like the Kalman filter, there are drawbacks to using it as a backtesting tool. Setting up anything more advanced than the simplest possible vectorised backtesting