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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

Quantocracy’s Daily Wrap for 05/14/2020

This is a summary of links featured on Quantocracy on Thursday, 05/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Financial Data Manipulation in dplyr for Quant Traders [Robot Wealth]

    In this post, were going to show how a quant trader can manipulate stock price data using the dplyr R package. Getting set up and loading data Load the dplyr package via the tidyverse package. if (!require('tidyverse')) install.packages('tidyverse') library(tidyverse) First, load some price data. energystockprices.RDS contains a data frame of daily price observations for 3
  • Academic Finance Research Galore. WFA Sessions Announced [Alpha Architect]

    Attention all finance geeks. The latest and greatest from academic researchers is available for all to review. The WFA recently released their sessions. WFA is one of the more prestigious academic conferences and papers presented at the conference often find their way into top-tier academic journals. Highly recommend you check it out! A few papers that caught my eye as we actively contemplate our

Quantocracy’s Daily Wrap for 05/13/2020

This is a summary of links featured on Quantocracy on Wednesday, 05/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning and Investing: Forecasting Fundamentals w/ Ensembles [Alpha Architect]

    Quantitative factor portfolios generally use historical company fundamental data in portfolio construction. The key assumption behind this approach is that past fundamentals proxy for elements of risk and/or systematic mispricing. However, what if we could forecast fundamentals, with a small margin of error, and compare that with market expectations? Intuitively, this seems like a more promising
  • Get Rich Quick Trading Strategies (and why they don’t work) [Robot Wealth]

    Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they dont work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once, just for fun (or so we tell ourselves). In this series, well explore three of the Get Rich Quick
  • Designing an energy arbitrage strategy (h/t @PyQuantNews) [Steve Klosterman]

    The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage

Quantocracy’s Daily Wrap for 05/12/2020

This is a summary of links featured on Quantocracy on Tuesday, 05/12/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Get Historical S&P 500 Constituents Data For Free [Robot Wealth]

    In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on the internet. Why? Well, one of the biggest challenges in looking for opportunities amongst a broad universe of stocks is choosing what stock universe to look at. One approach to dealing with this is to pick the stocks that are currently in the S&P 500 index.
  • Skulls, Financial Turbulence and Risk Management [Alpha Architect]

    When hunting for diversity, the typical investor considers only average correlations. However, when measuring an assets diversification benefits utilizing average correlations tend to mislead investors. For example, when both U.S. and non-U.S. equities produce returns greater than one standard deviation above their means (ie when times are good), their correlation equals 17 percent; but when

Quantocracy’s Daily Wrap for 05/11/2020

This is a summary of links featured on Quantocracy on Monday, 05/11/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Straddles and Trend Following [Flirting with Models]

    The convex payoff profile of trend following strategies naturally lends itself to comparative analysis with option strategies. Unlike options, however, the payout of trend following is not guaranteed. To compare and contrast the two approaches, we replicate simple trend following strategies with corresponding option straddle strategies. While trend-following has no explicit up-front cost, it also
  • How to Find Cheap Options to Buy and Expensive Options to Sell [Robot Wealth]

    If you want to make money trading, youre going to need a way to identify when an asset is likely to be cheap and when it is likely to be expensive. You want to be a net buyer of the cheap stuff and a net seller of the expensive stuff. Thanks, Capitain Obvious. Youre welcome. How does this relate to equity options? If we take the (liquid) US Equity options market as an example then there are
  • Value Investing: Even Deeper History [Two Centuries Investments]

    In last weeks post we extended the systematic value factor (or at least a pretty good proxy of it) back to 1871. The response from readers was encouraging, perhaps because of the pain that value investing has been causing lately. Long-run history gives some relief. This week we dig deeper, reconstructing another 46 years of unseen history. As a result, we now have an extra 100 years of data for
  • The Case Against Factor Investing [Factor Research]

    Factor investing is likely the best option for investors seeking to outperform the market However, the cyclicality of factors makes factor investing challenging when it underperforms Investors that do not understand this cyclicality are likely better served by plain, rather than smart beta FREE AINT EASY Free and easy are concepts that often go hand-in-hand. However, there are also many

Quantocracy’s Daily Wrap for 05/10/2020

This is a summary of links featured on Quantocracy on Sunday, 05/10/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Online Portfolio Selection: Mean Reversion [Hudson and Thames]

    Mean Reversion is an effective quantitative strategy based on the theory that prices will revert back to its historical mean. A basic example of mean reversion follows the benchmark of Constant Rebalanced Portfolio. By setting a predetermined allocation of weight to each asset, the portfolio shifts its weights from increasing to decreasing ones. This module will implement four types of mean