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Quantocracy’s Daily Wrap for 09/03/2021

This is a summary of links featured on Quantocracy on Friday, 09/03/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Handling Big Data [Jonathan Kinlay]

    One of the major challenges that users face when trying to do data science is how to handle big data. Leaving aside the important topic of database connectivity/functionality and the handling of data too large to fit in memory, my concern here is with the issue of how to handle large data files, which are often in csv format, but which are not too large to fit into available memory. It is well
  • A Streamlit Dashboard for the @AlpacaHQ API (h/t @PyQuantNews)

    The Alpaca brokerage service is very useful for algorithmic traders that comes with an API to retrieve data and execute trades in a paper or live environment. While you can also check the status and returns of your positions through the API, Alpaca has spent some time creating a frontend where users can visually check their live and paper accounts. Seeing that Alpaca is more focused on building
  • Factor Timing Is Tempting [Alpha Architect]

    Academic research has found that factor premiums are both time-varying and dependent on the economic cycle. For example, Arnav Sheth, and Tee Lim, authors of the December 2017 study Fama-French Factors and Business Cycles, examined the behavior of six Fama-French factorsmarket beta (MKT), size (SMB), value (HML), momentum (MOM), investment (CMA) and profitability (RMW)across business

Quantocracy’s Daily Wrap for 09/02/2021

This is a summary of links featured on Quantocracy on Thursday, 09/02/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The three kinds of (over) fitting [Investment Idiocy]

    This post is something that I've banged on about in many presentations at several conferences* (most complete slides are here), and in various interviews, but never actually formally described in a blog post. In fact this post has existed in draft form since 2015 (!). * you know, when you leave your house and listen to someone else speaking. Something that in late 2021 is a distant memory,

Quantocracy’s Daily Wrap for 09/01/2021

This is a summary of links featured on Quantocracy on Wednesday, 09/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Purchasing Power Parity [Quant Dare]

    Purchasing Power Parity (PPP) is a well-known measure used to compare the currencies of different countries in terms of price levels. So, in this post, we are going to explain PPP and study, through an example, its relation with the currency pairs. PPP is based on the law of one price (LOOP). For that reason, in order to understand PPP, first, we are going to explain the LOOP. This law states that
  • VVIX/VIX as a Return Indicator? [CXO Advisory]

    Is the ratio of implied volatility of implied volatility (CBOE VVIX Index), interpretable as a measure of changes in investor fear level, to CBOE VIX Index itself a useful indicator of future stock market returns? To investigate, we relate monthly VVIX/VIX and monthly change in VVIX/VIX to monthly SPDR S&P 500 (SPY) total returns. Using end-of-month levels of both VVIX and VIX and
  • Mutual Funds: Negative $125B in Value-Add? [Alpha Architect]

    Elton, Gruber, and Busse (2004) as well as Hortacsu and Syverson (2004) suggest that mutual fund markets are not perfectly competitive and that fees do matter to investors. In contrast, the neoclassical view of mutual funds (see for example Berk and Green, 2004; Pastor, Stambaugh and Taylor, 2019 and others) implies that fees do not matter because with competitive markets and rational investors,

Quantocracy’s Daily Wrap for 08/30/2021

This is a summary of links featured on Quantocracy on Monday, 08/30/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Caveats in Calibrating the OU Process [Hudson and Thames]

    This is a series where we aim to cover in detail various aspects of the classic Ornstein-Uhlenbeck (OU) model and the Ornstein-Uhlenbeck Jump (OUJ) model, with applications focusing on mean-reverting spread modeling under the context of pairs trading or statistical arbitrage. Given the universality and popularity of those models, the techniques discussed can easily be applied to other areas where
  • Training Neural Networks: Why, As With Humans, Teaching Methods Matter [Enjine]

    I achieved my lifes biggest accomplishment in 2004, when I defeated dozens of other contestants to clinch the Canadian Settlers of Catan championship. Settlers, as its called by its enthusiasts, is a strategy board game where players collect resources, build settlements, trade with and rob from each other to reach 10 points first. It remains a favourite among many board game
  • The Best Systematic Trading Strategies in 2021: Part 3 [Quantpedia]

    Finally, what are the five top-performing quantitative trading strategies in 2021? In part 1 of our article, we analyzed tendencies and trends among the Top 10 quantitative strategies of 2021. Thanks to Quantpedia Pros screener, we published several interesting insights about them. In part 2 of our article, we got deeper into the first five specific strategies, which are significantly
  • Building an Inflation Portfolio Using Asset Classes [Factor Research]

    We recently explored using stocks to create an inflation-proxy portfolio that resulted in a collection of stocks with strong sector and factor biases. Specifically, the portfolio exhibited overweights in energy and financial stocks, perhaps as expected, as well as a long position in the value and short positions in the momentum and quality factors. However, despite selecting stocks based on their
  • Dijkstra algorithm [Quant Insti]

    Start learning all about the Dijkstra algorithm for finding the shortest path. We briefly review the Kruskal algorithm, Prim algorithm, Johnson algorithm and Bellman algorithm as well. We'll cover: What is the Dijkstra algorithm? How does the Dijkstra algorithm work? Pseudo code of Dijkstra algorithm Dijkstra algorithm table Dijkstra algorithm time complexity When to use the Dijkstra

Quantocracy’s Daily Wrap for 08/27/2021

This is a summary of links featured on Quantocracy on Friday, 08/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Countercyclical Trend Following [Allocate Smartly]

    This is a test of a tactical strategy based on contrarian timing of the business cycle, increasing risk during periods of stress and decreasing risk during periods of calm. The strategy adds trend-following to this countercyclical approach to manage short-term market shocks. Backtested results from 1980 follow. Results are net of transaction costs see backtest assumptions. Learn about what we
  • International Tests of Factor Anomalies: Most Don t Survive [Alpha Architect]

    Since the development of the capital asset pricing model (CAPM) about 50 years ago, academic researchers have documented hundreds of anomalies that generate significant positive alpha. There are now so many that economist John Cochrane, in his 2011 presidential address to the American Finance Association, referred to them as the factor zoo, which I hit on in my post Is there a

Quantocracy’s Daily Wrap for 08/26/2021

This is a summary of links featured on Quantocracy on Thursday, 08/26/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Use Exotic Assets to Improve Your Trading Strategy [Quantpedia]

    As we have mentioned several times, the best course of action for a quant analyst who wants to develop a new trading strategy is to understand a well-known investment anomaly/factor fundamentally and then improve it. Quantpedia is a big fan of transferring ideas derived from academic research from one asset class to another. But thats not the only possibility of improvement we can try to
  • 4 Ways to Trade the Trend Intensity Indicator [Raposa Trade]

    Determining the strength of a trend can provide a valuable edge to your trading strategy and help you determine when to go long and let it ride, or not. This is what the Trend Intensity Indicator (TII) was designed to do. This indicator is as simple to interpret as more familiar values like the RSI. Its scaled from 0-100 where higher numbers indicate a stronger upward trend, lower values a
  • Linear regression on market data using Python and R [Quant Insti]

    This is the second installment of my series on regression analysis used in finance. In the first installment, we touched upon the most important technique in financial econometrics: regression analysis, specifically linear regression and two of its most popular flavours: univariate linear regression, and multivariate linear regression. In this post, we apply our knowledge of regression to actual

Quantocracy’s Daily Wrap for 08/25/2021

This is a summary of links featured on Quantocracy on Wednesday, 08/25/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Entry: At Open vs. Intraday Pullback vs Confirmation [Alvarez Quant Trading]

    For the mean reversion strategies that I have created in the past and are trading now, they typically enter at the next days open or wait for a further pullback intraday before entering. My current mean reversion strategy, which enters on a limit down, was doing great until a few months ago when the performance started to slip. Looking at the missed trades and the trades taken, it seemed like
  • The Value Premium Might be Smaller Than We Originally Thought [Alpha Architect]

    Remember HML? It was the original formulation for estimating the value premium published by Fama & French in 1992. In that seminal article, FF argued based on the results they obtained, that the risk of owning equity is multidimensional. One of those dimensions of risk they used was financial distress proxied by the BE/ME ratio 1, which itself was originally based on the distress factor

Quantocracy’s Daily Wrap for 08/23/2021

This is a summary of links featured on Quantocracy on Monday, 08/23/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation Matrix Stress Testing: Shrinkage Toward an Equicorrelation Matrix [Portfolio Optimizer]

    Financial research has consistently shown that correlations between assets tend to increase during crises and tend to decrease during recoveries1. The recent COVID-19 market crash was no exception, as illustrated on Alvarez Quant Trading blog post Correlations go to One for both the individual constituents of the S&P500 and several broad ETFs commonly used in tactical asset allocation
  • The Best Systematic Trading Strategies in 2021: Part 2 [Quantpedia]

    The year 2021 has been an incredible year for passive equity investors so far. However, in the first part of our article, we talked about quantitative strategies which achieved even better results in 2021 than passive US equity investors. Indeed, there do exist such strategies, at least definitely in Quantpedias database of 650+ trading strategies. We focused the first part of the article more
  • Building a Long Volatility Strategy without Using Options [Factor Research]

    Long volatility strategies can be built without using options Securities can be selected on different risk metrics like the VIX or high yield spread Although portfolios differ, the strategies exhibited similar trends INTRODUCTION We started our exploration of long volatility strategies by analyzing the Eurekahedge Long Volatility Hedge Fund Index and highlighted that this would have provided
  • Macro trends for trading models [SR SV]

    Unlike market price trends, macroeconomic trends are hard to track in real-time. Conventional econometric models are immutable and not backtestable for algorithmic trading. That is because they are built with hindsight and do not aim to replicate perceived economic trends of the past (even if their parameters are sequentially updated). Fortunately, the rise of machine learning breathes new life

Quantocracy’s Daily Wrap for 08/20/2021

This is a summary of links featured on Quantocracy on Friday, 08/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Crypto Trading Depth [Tr8dr]

    I have a collection of crypto stat/arb strategies I plan to trade as a portfolio of strategies. Each strategy trades a small mean-reversion portfolio of loosely cointegrated coins, based on a bayesian state-based model. The returns in cryptos for this sort of strategy are phenomenal, however, finding enough size can be difficult for some coin portfolios. In my universe of roughly 220 coins, there
  • Optimising the rsims package for fast backtesting in R [Robot Wealth]

    rsims is a new package for fast, quasi event-driven backtesting in R. You can find the source on GitHub, docs here, and an introductory blog post here. Our use case for rsims was accurate but fast simulation of trading strategies. Ive had a few questions about how I made the backtester as fast as it is after all, it uses a giant for loop, and R is notoriously slow for such operations so
  • The Impact of Goodwill on Stock Returns [Alpha Architect]

    A firms stock price should reflect the value of both its tangible and intangible capital. While tangible capital has been widely studied, intangible capital has been receiving more attention due to its increasing importance in economic values. According to a December 29, 2020, Forbes article, In 1975, less than 20% of the S&P 500s market value was derived from intangible assets such

Quantocracy’s Daily Wrap for 08/18/2021

This is a summary of links featured on Quantocracy on Wednesday, 08/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing Turtle Trading: The System that Made Newbie Traders Millions [Raposa Trade]

    In 1982, a group of inexperienced traders were recruited to be a part of an experiment that would make many of them multi-millionaires. Richard Dennis bet his partner William Eckhardt that anyone could be a successful trader given they had training and a system to follow. It was a re-hash of the nature vs nurture debate, but now with millions of dollars on the line. Dennis and Eckhardt trained