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Weekly Roundup 29 May – Crash Protection, Sloppy Regressions and Data Munging Skillz
Here’s a round-up of our new articles this week. They cover crash protection, sloppy, noisy regressions, and data-munging skills. Finding
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RTools of the tradePerformant R Programming: Chunking a Problem into Smaller Pieces
When data is too big to fit into memory, one approach is to break it into smaller pieces, operate on
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Quant tradingRTools of the tradeHow to Fill Gaps in Large Stock Data Universes Using tidyr and dplyr
When you’re working with large universes of stock data you’ll come across a lot of challenges: Stocks pay dividends and
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OptionsQuant tradingRFind Cheap Options for Effective Crash Protection Using Crash Regressions
One way we can quantify a stock’s movement relative to the market index is by calculating its “beta” to the
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PythonTools of the tradeRolling and Expanding Windows For Dummies
In today’s article, we are going to take a look at rolling and expanding windows. By the end of the
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OptionsQuant tradingRTools of the tradeTrading strategies
Weekly Roundup 22 May – Doubling Down in Losing Trades Like a Drunken Hedge Fund Manager
Here’s a round-up of our new articles this week. They cover options trading, digital signal processing, data munging and Kris’s luxurious
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RTools of the tradeHandling a Large Universe of Stock Price Data in R: Profiling with profvis
Recently, we wrote about calculating mean rolling pairwise correlations between the constituent stocks of an ETF. The tidyverse tools dplyr
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RTools of the tradeHow to Wrangle JSON Data in R with jsonlite, purr and dplyr
Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools
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Quant tradingRTrading strategiesZorro
Using Digital Signal Processing in Quantitative Trading Strategies
In this post, we look at tools and functions from the field of digital signal processing. Can these tools be
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RTools of the tradeHow to Calculate Rolling Pairwise Correlations in the Tidyverse
How might we calculate rolling correlations between constituents of an ETF, given a dataframe of prices? For problems like this,
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PythonRTools of the trade
How to Run Python from R Studio
Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL;
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Financial Data Manipulation in dplyr for Quant Traders
In this post, we’re going to show how a quant trader can manipulate stock price data using the dplyr R
