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Quantocracy’s Daily Wrap for 05/28/2020

This is a summary of links featured on Quantocracy on Thursday, 05/28/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Performant R Programming: Chunking a Problem into Smaller Pieces [Robot Wealth]

    When data is too big to fit into memory, one approach is to break it into smaller pieces, operate on each piece, and then join the results back together. Heres how to do that to calculate rolling mean pairwise correlations of a large stock universe. Background Weve been using the problem of calculating mean rolling correlations of ETF constituents as a test case for solving in-memory
  • S&P 500 Dividend Aristocrats [Alvarez Quant Trading]

    Back in 2018, I wrote a post, Backtesting a Dividend Strategy, which was conceptually based on the S&P 500 Dividend Aristocrats. Just recently, Norgate Data started offering historical constituent data for the S&P 500 Dividend Aristocrats index. This would be a much cleaner version compared to what I was trying to do in my original post. Would using this index produces better
  • SPX Historically Bullish On Thursday After Memorial Day [Quantifiable Edges]

    Thursday after Memorial Day has been a day that has exhibited a bullish bias for many years. I last showed this on the blog a couple of years ago. The chart below shows updated results. SPX Perfromance on Thursday After Memorial Day Single-day seasonality can certainly be overrun by other forces, but the Thursday after Memorial Day has been a good one for many years. That may be something that

Quantocracy’s Daily Wrap for 05/27/2020

This is a summary of links featured on Quantocracy on Wednesday, 05/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Fill Gaps in Large Stock Data Universes Using tidyr and dplyr [Robot Wealth]

    When youre working with large universes of stock data youll come across a lot of challenges: Stocks pay dividends and other distributions that have to be accounted for. Stocks are subject to splits and other corporate actions which also have to be accounted for. New stocks are listed all the time you wont have as much history for these stocks as for other stocks. Stocks are delisted,

Quantocracy’s Daily Wrap for 05/26/2020

This is a summary of links featured on Quantocracy on Tuesday, 05/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Two Centuries of Value and Momentum [Two Centuries Investments]

    As a quant, I have been obsessed with systematic Value and Momentum since the first day I ran a backtest. Part of me knows that the future for this combo is unlikely to be as good as the past. In my R&D, I moved on to other factors more than a decade ago. But another part of me is still in love with the magical duo and wishes for them to survive. Value and Momentum have been the most beautiful
  • Tactically Adjusting Everything in a Financial Crisis? Bad Idea. [Alpha Architect]

    With the current market conditions and the wild ride weve all been on, weve pivoted our attention to focus on supplying academic research on responding to a crisis. This article investigates what the appropriate tactical adjustments investors should consider when making changes to their portfolio holdings following large losses in wealth during a crisis. What are the Academic Insights? In a
  • How to develop, test and optimize a trading strategy – complete guide [Milton FMR]

    Developing a trading strategy from start to finish is a complex process. The process follows the following steps: Formulation of the strategy Write Pseudo Code Transform into working code Start first backtests Optimize Evaluate test results Go live Monitor performance Evaluate and adjust Optimization process We will discuss each of this points separately. Here is a visualization of the design
  • Tactical ETFs: Tactfully No, Thank You? [Factor Research]

    Tactical investing aims to deliver better risk-adjusted returns and/or reduced drawdowns Tactical ETFs have not achieved either objective in recent years It is challenging to explain the consistent underperformance across different types of tactical ETFs INTRODUCTION Every investor is a tactician, whether they actively try or not. Warren Buffett and his lieutenants at Berkshire Hathaway pursue a
  • Find Cheap Options for Effective Crash Protection Using Crash Regressions [Robot Wealth]

    One way we can quantify a stocks movement relative to the market index is by calculating its beta to the market. To calculate the beta of MSFT to SPY (for example) we: calculate daily MSFT returns and daily SPY returns align the returns with one another regress MSFT returns against SPY returns. This shows the procedure, graphically:

Quantocracy’s Daily Wrap for 05/25/2020

This is a summary of links featured on Quantocracy on Monday, 05/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Defensive Equity with Machine Learning [Flirting with Models]

    Defensive equity strategies are comprised of stocks that lose less than the market during bear markets while keeping up with the market during a bull market. Coarse sorts on metrics such as volatility, beta, value, and momentum lead to diversified portfolios but have mixed results in terms of their defensive characteristics, especially through different crisis periods that may favor one metric
  • Long-Short vs Long-Only Implementation of Equity Factors [Quantpedia]

    How should be equity factor strategies implemented? In a long-only (smart beta) way? As a long-short strategy, as most of the hedge funds usually do? Or in a partially-hedged fashion by going long equity factor and shorting market to offset some of the market risks? There is no one universal answer as it depends on the investment mandate and constraints of each fund manager contemplating to
  • Rolling and Expanding Windows For Dummies [Robot Wealth]

    In todays article, we are going to take a look at rolling and expanding windows. By the end of the post, you will be able to answer these questions: What is a rolling window? What is an expanding window? Why are they useful? What is a Rolling or Expanding window? Here is a normal window. We use normal windows because we want to have a glimpse of the outside, the bigger the window the more of
  • Joint predictability of FX and bond returns [SR SV]

    When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is an instance of information inefficiency. The resulting forecast errors give rise to joint predictability of currency and bond market returns. In particular, an upside shock to the rates outlook in a country

Quantocracy’s Daily Wrap for 05/22/2020

This is a summary of links featured on Quantocracy on Friday, 05/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 22 May 2020 | Tail Risk [Capital Spectator]

    The Law of Regression to the Tail: How to Mitigate COVID-19, Climate Change, and Other Catastrophic Risks Bent Flyvbjerg (University of Oxford) 13 May 2020 Regression to the mean is nice and reliable, regression to the tail is reliably scary. We live in the age of regression to the tail. It is only a matter of time until a pandemic worse than Covid-19 will hit us, and climate more extreme than any

Quantocracy’s Daily Wrap for 05/21/2020

This is a summary of links featured on Quantocracy on Thursday, 05/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Speaking to Legends: New Podcast from The Team at The Quant Conference (@TheQuantConf) [Speaking to Legends]

    Speaking to Legends is a quest for ideas, insights, and stories from the lives of the most successful hedge fund managers. We learn about their spectacular careers, we share life lessons and dissect their investment techniques. Join us for this journey.
  • Handling a Large Universe of Stock Price Data in R: Profiling with profvis [Robot Wealth]

    Recently, we wrote about calculating mean rolling pairwise correlations between the constituent stocks of an ETF. The tidyverse tools dplyr and slider solve this somewhat painful data wrangling operation about as elegantly and intuitively as possible. Why did you want to do that? Were building a statistical arbitrage strategy that relies on indexation-driven trading in the constituents. We
  • VADER Sentiment Analysis in Algorithmic Trading [Quant Insti]

    In Finance and Trading, a large amount of data is generated every day. This data comes in the form of News, Scheduled Economic releases, employment figures, etc. It is clear that the news has a great impact on the prices of stocks. Every trader takes great efforts in keeping track of the latest news and updates trade calls accordingly. Automating this task provides better trading opportunities. In
  • An Improved Volume Profile Chart with Levels [Dekalog Blog]

    Without much ado, here is the code ## Copyright (C) 2020 dekalog ## ## This program is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## ## This program is distributed in the hope that it will be useful, but ##

Quantocracy’s Daily Wrap for 05/20/2020

This is a summary of links featured on Quantocracy on Wednesday, 05/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Wrangle JSON Data in R with jsonlite, purr and dplyr [Robot Wealth]

    Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. Well use purrr::map functions to extract and transform our JSON data. And well provide intuitive examples of the cross-overs and differences between purrr and dplyr. library(tidyverse) library(here)
  • A Big Gap Up That Wipes Out A Big Loss Yesterday [Quantifiable Edges]

    After closing down more than 1% yesterday, SPY is set to open up enough to erase all of yesterdays losses. I decided to look back at other times this has happened. SPY Big Gap Up Erases Yesterdays Loss – Open to Close Results Not exactly a consistent edge, but I thought the general upside tendency might be worth noting for some intraday traders. Good luck trading today!
  • Probabilistic Sharpe Ratio [Quant Dare]

    Can a Sharpe ratio of 1.55 be better than a Sharpe ratio of 1.63 in a 1 year track-record? Not necessarily. Sharpe ratios are not comparable, unless we control the skewness and kurtosis of the returns. In this post we are going to analyze the advantages of the Probabilistic Sharpe Ratio exposed by Marcos Lpez de Prado in this paper. It will include an example coded in Python. Context The Sharpe

Quantocracy’s Daily Wrap for 05/19/2020

This is a summary of links featured on Quantocracy on Tuesday, 05/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Implied risk premia [OSM]

    In our last post, we applied machine learning to the Capital Aset Pricing Model (CAPM) to try to predict future returns for the S&P 500. This analysis was part of our overall project to analyze the various methods to set return expectations when seeking to build a satisfactory portfolio. Others include historical averages and discounted cash flow models we have discussed in prior posts. Our
  • Prospect Theory Helps Explain Return Anomalies [Alpha Architect]

    The field of behavioral finance provides us with fascinating insights into individual investor behavior, including how individuals view risk, as well as the impact of those views on asset prices. Prospect theory plays a major role in explaining investor behavior. The theory, formulated in 1979 by Amos Tversky and Daniel Kahneman, describes how individuals make choices between probabilistic
  • Using Digital Signal Processing in Quantitative Trading Strategies [Robot Wealth]

    In this post, we look at tools and functions from the field of digital signal processing. Can these tools be useful to us as quantitative traders? Whats a Digital Signal? A digital signal is a representation of physical phenomena created by sampling that phenomena at discrete time intervals. If you think about the way we typically construct a price chart, there are obvious parallels: we sample
  • Trend Following the S&P 500? Some Practical Advice [Alpha Architect]

    Now that market volatility is back, tail risk management strategies are gaining some attention. A lot of investors are dipping their toe into the water and exploring trend-following strategies on the S&P 500 arguably the most popular U.S. stock market index.This paper explores multiple trend following signals (TF) with various degrees of complexity, frequency, and trading (they also check
  • Periodically Rebalanced Static Allocation ‘Buy and Hold’ Strategies in QSTrader [Quant Start]

    For those systematic traders who are considering a long-term investment horizon one of the most common forms of generating a portfolio involves static proportional capital allocation amongst a collection of (hopefully) diversifying asset classes, which is periodically rebalanced to maintain the allocation. Such portfolios are often termed 'buy and hold' despite the fact that the
  • Profiling S&P 500 Drawdowns Since 1871 [Capital Spectator]

    Longer is better for analyzing the stock market, which is why Professor Robert Shillers data set (with an 1871 starting date) is one of the great free resources on the internet for studying the history of US equities. With that in mind, lets review how the current drawdown for the S&P 500 compares over the past century and a half. First, a few housekeeping notes. Shillers data is
  • A Volume Profile With Levels Chart [Dekalog Blog]

    Just a quick post to illustrate the latest of my ongoing chart iterations which combines a levels chart, as I have recently been posting about, but with the addition of a refined methodology of creating the horizontal histograms to more clearly represent the volumes over distinct periods. The main change is to replace the use of the Octave barh function with the fill function. A minimal working

Quantocracy’s Daily Wrap for 05/18/2020

This is a summary of links featured on Quantocracy on Monday, 05/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adding a 1-Day Lag When Executing TAA Strategies [Allocate Smartly]

    We track 50+ public Tactical Asset Allocation (TAA) strategies in near real-time, allowing us to draw broad conclusions about TAA as a trading style. By design, most of those strategies trade just once per month, and most assume that next months asset allocation is both calculated and executed on the same day (learn more). When that day arrives each month, it can be quite stressful. The
  • How to Calculate Rolling Pairwise Correlations in the Tidyverse [Robot Wealth]

    How might we calculate rolling correlations between constituents of an ETF, given a dataframe of prices? For problems like this, the tidyverse really shines. There are a number of ways to solve this problem read on for our solution, and let us know if youd approach it differently! First, we load some packages and some data that we extracted earlier. xlfprices.RData contains a dataframe,
  • Cheap vs Expensive Factors [Factor Research]

    This research note was originally published at Alpha Architect. Here is the link. SUMMARY Factors can be valued like stocks Factor valuations have not changed structurally over the last 30 years Cheap factors outperformed expensive ones on average INTRODUCTION Tesla (TSLA) breached the $100 billion market capitalization in January 2020 and became the most valuable car manufacturer globally.
  • Thoughts on Systematic Value Investing [Two Centuries Investments]

    As a risk factor, Value is very much alive. Confusing the risk side and return side of factors creates the misconceived question of whether the value factor is dead. Something that is dead, does not move. A dead factor is a flat horizontal line with random noise. By contrast, value has been moving violently down, which is not how death looks like. It is how a crash looks like. Like other risk
  • A Comparison of Charts [Dekalog Blog]

    Earlier in May I posted about Market Profile with some charts and video. Further work on this has made me realise that my earlier post should more accurately be described as Volume Profile, so apologies to readers for that. Another, similar type of chart I have seen described as a TPO chart (TPO stands for 'That Price Occurred' or ticked) and it is a simple matter to extend the code in

Quantocracy’s Daily Wrap for 05/15/2020

This is a summary of links featured on Quantocracy on Friday, 05/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Run Python from R Studio [Robot Wealth]

    Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL; many add Java and/or Scala to their toolkit, and its not uncommon to also know ones way around JavaScript. Personally, I prefer to use R for data analysis. But, until recently, Id tend to reach for Python for anything more general, like scraping web data or
  • Is this the pullback you ve been waiting for? [Quantifiable Edges]

    It has been 47 trading days since SPX posted its last 3-day pullback. That is a long time. And it is especially long considering SPX is still below its 200ma. Should SPX fail to rally out of this early hole this morning, we will finally see the 1st 3-day pullback since March 9th. Bulls could look at it and exclaim Finally, the 3-day pullback I have been waiting for to load up!. Bears might
  • YTD Performance of Equity Factors – Update After Two Months [Quantpedia]

    Nearly two months ago, in a time of the highest turmoil during the current pandemic crisis, we performed a quick assessment of the status of performance of equity factor strategies. The world has still not been able to ward-off health-care crisis completely, but a lot of countries have made significant progress (on the other hand, there are still a lot of countries in a worse state than a few
  • Discussion: Managing the Costs of Passively Investing in Active Strategies [Alpha Architect]

    We recently covered a paper by David Blitz that highlighted the potential problems with passively investing in active strategies. The research piece is great and surfaces a lot of great concepts. Like a lot of research we publish/summarize this article appears to shoot Alpha Architect in the foot. To summarize, the piece was essentially a smack-down on implementing factors via ETFs
  • Tracking Bitcoin Gains since its 3rd Halving in May 2020 using Python [Quant at Risk]

    The Bitcoins 3rd halving was the most anticipated event this year. This a moment when a reward for all Bitcoin block miners is cut by half. It happens every 4 years or every 210,000 blocks on the Bitcoin blockchain. The previous two halving events took place in 2012 and 2016, respectively. Before the 1st halving, miners were rewarded with 50 BTC and after that only with 25 BTC per block. After