Quantocracy

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Quantocracy’s Daily Wrap for 05/30/2024

This is a summary of links featured on Quantocracy on Thursday, 05/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hidden miners [OSM]

    We conclude our discussion of market regime detection by examining Hidden Markov Models (HMMs). Recall this series was inspired by a post from PyQuant News that highlighted a longer article from the London Stock Exchange Group (LSEG). Those who took the CFA exams probably forgot using HMMs in the quant section. Whatever the case, the intuition behind them is clever. HMMs use observable data to
  • New Volatility Based Trading Techniques with Rob Hanna (@QuantifiablEdgs) [Better System Trader]

    Could traders be using the VIX wrong? Is there an even better way to time the markets and reduce risk? In this episode, discover the new secrets of volatility-based trading with Rob Hanna. Rob shares his award-winning insights into using the VIX and SPX to time the market, challenging conventional wisdom and uncovering new strategies for volatility trading. Whether youre an advanced trader or
  • Crypto Perpetual Contract Pair Trading [Quant Insti]

    Statistical arbitrage is a classic quantitative trading strategy, and pairs trading is one of them. Digital currency perpetual contracts are non-delivery perpetual futures. This project describes using data from the Binance exchange to find perpetual contract pairs whose pairing spreads conform to the mean reversion trend. Based on this backtest, find the relatively optimal trading parameters.
  • Unlock the Secrets of Seasonal Trading [Milton FMR]

    Seasonal trading strategies are grounded in the belief that certain patterns repeat over specific periods due to predictable events and behaviors. These strategies can be a powerful tool for traders, helping them to capitalize on regular market trends. This article will delve deeper into the world of seasonal trading, providing 15 more examples of seasonal patterns, including the presidential

Quantocracy’s Daily Wrap for 05/28/2024

This is a summary of links featured on Quantocracy on Tuesday, 05/28/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantpedia Awards 2024 – Winners Announcement [Quantpedia]

    Hello all, Welcome to the Quantpedia Awards 2024 winners announcement. This is the moment we all have been waiting for, and today, we would like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive? 5th Place Soroush Ghazi, Mark Schneider, Jack Strauss:
  • Carry versus Trend Following [Finominal]

    The carry strategy has become more attractive given higher yields However, the strategy is highly correlated to equities in periods of market stress CTAs are better diversifiers INTRODUCTION Carry strategies were widely popular before the global financial crisis in 2009, but less thereafter given a substantial drawdown during the crisis when investors reversed their positions and fled to safe but

Quantocracy’s Daily Wrap for 05/25/2024

This is a summary of links featured on Quantocracy on Saturday, 05/25/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Oanda’s API to Place Entry Orders [Dekalog Blog]

    Since my last post about end of initial testing I have been working on Oanda API functions in Octave to programmatically place entry orders and associated take profit and stop orders for a future possible forex news trading system. The reason for this is simple – it would be next to impossible to manually place a series of entry orders in the last few moments before a news release, so this would
  • Momentum Top N with Docker, Jupyter and QSTrader [Quant Start]

    In the previous tutorial we set up a backtesting environment using the QSTrader backtesting framework inside a Jupyter Notebook. We isolated this research environment and its dependencies using Docker, with Docker Compose. In this article we will show you how to implement one of the example strategies for QSTrader, the Momentum Top N tactical asset allocation strategy. In order to follow along
  • Momentum Everywhere, Even Cross-Country Factor Momentum [Alpha Architect]

    Among the many factors cited in academic research, only a handful have been sufficiently reliable for use in asset pricing models. One of those is momentum. The evidence has been robust for not only cross-sectional (relative) and time-series (absolute or trend) momentum, but also for factor momentum, which has received much attention from researchers. The empirical research on factor momentum,

Quantocracy’s Daily Wrap for 05/21/2024

This is a summary of links featured on Quantocracy on Tuesday, 05/21/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Gaussian gold [OSM]

    Our previous post, used hierarchical clustering to identify market regimes in the gold miners ETF, GDX. This was inspired by a post from PyQuant News that highlighted a longer article from the London Stock Exchange Group (LSEG). In this post, well continue looking at identifying market regimes and using those predictions as signals for a simple trading strategy. As noted, the LSEG article
  • How to easily improve your Sharpe ratio (in no time) [PyQuant News]

    Systematic risk affects the entire market and impacts the Sharpe ratio. Any trading strategy must consider the impact of systematic risk. While a strategy must involve some risk to make money, systematic risk cannot be diversified away. So, we need to build a hedge to get rid of it. By hedging systematic risk, we can better protect our strategies and ultimately outperform the market. After having
  • Skewness of Funds – Friend or Foe? [Finominal]

    Some funds exhibit strong skewness profiles Skewness is highly time-varying and not necessarily a negative criteria Should be measured but unlikely managed INTRODUCTION The trouble with investing in emerging markets is that they are quite different, which requires extensive due diligence on each of them, and they also can change quickly. For example, take Argentina versus China. The former has

Quantocracy’s Daily Wrap for 05/17/2024

This is a summary of links featured on Quantocracy on Friday, 05/17/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation and Taxes: FIFO vs LIFO Deep Dive [Allocate Smartly]

    This is a deep dive into which share disposal method FIFO or LIFO would have been more tax advantageous for the 80+ asset allocation strategies we track. When selling shares FIFO (first in, first out), the oldest shares held are sold first. When selling LIFO (last in, first out), the most recently purchased shares are sold first. We find that the investors share disposal method would
  • Is There Alpha in Borrow Fees? [Quant Rocket]

    Borrow fees reflect how likely short sellers think a stock is to decline. Can this information be incorporated into trading strategies as an alpha factor? This article uses Alphalens to explore the relationship between borrow fees and forward returns and uses Moonshot and Zipline to demonstrate ways to incorporate borrow fees into long or short strategies. Summary High borrow fees are an
  • Golden clusters [OSM]

    We recently saw a post from PyQuant News that piqued our interest, compelling us to dust off the old blog files and get back into the saddle. The post highlights a longer article from the London Stock Exchange Group (LSEG) on how to use different machine learning models to identify and forecast market regimes. That article uses Refinitiv, a market data service like Bloomberg, which we dont have
  • Ehlers Ultimate Smoother [Financial Hacker]

    In TASC 3/24, John Ehlers presented several functions for smoothing a price curve without lag, smoothing it even more, and applying a highpass and bandpass filter. No-lag smoothing, highpass, and bandpass filters are already available in the indicator library of the Zorro platform, but not Ehlers latest invention, the Ultimate Smoother. It achieves its tremendous smoothing power by subtracting
  • Social Media: The Value of Seeking Alpha s Recommendations [Alpha Architect]

    The increased popularity of social media as a forum for market participants to post and exchange opinions has been accompanied by heightened interest from academic researchers who have sought to determine if there is valuable information in the postings. For example, the June 2020 study Do Individual Investors Trade on Investment-related Internet Postings? investigated whether social media
  • Research Review | 17 May 2024 | Market Analytics [Capital Spectator]

    Regime-Based Strategic Asset Allocation Eric Bouy and Jerome Teiletche (World Bank) April 2024 What should investors do in the presence of economic regimes? Researchers and practitioners usually address this topic from a tactical asset allocation point of view. In this article, we depart from the literature by tackling the issue strategically and analytically. Modeling economic regimes as a

Quantocracy’s Daily Wrap for 05/13/2024

This is a summary of links featured on Quantocracy on Monday, 05/13/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Message Arrival Rates and Latency [Mark Best]

    There is a common debate when people are discussing code optimisation that relates to how fast code needs to be. A recent Twitter post about parsing binance BBA messages stated processing times of around 200ns. This is, in my admission, very fast. To put it into perspective, Serde is a common rust deserialization library and is incredibly easy to use. It is however, a lot slower than the optimised
  • Maximum Ulcer Performance Index (UPI) Portfolios [Allocate Smartly]

    Weve added a new objective to the Portfolio Optimizer. Members can now find the combination of TAA strategies that would have maximized the Ulcer Performance Index (UPI), aka the Martin Ratio. Members: begin exploring the Max UPI portfolios now. UPI is a measure of return relative to drawdowns (i.e. losses). It captures both the length and severity of all drawdowns, not just the single
  • Rob Hanna Wins the 2024 NAAIM Founders Award [Quantifiable Edges]

    It was an exciting week here at Quantifiable Edges as it was officially announced that Rob Hanna won the National Association of Active Investment Managers (NAAIM) Founders Award, which is its annual white paper competition. The paper: Chicken & Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX? challenges prevailing market wisdom by suggesting that S&P 500 Index
  • Options Trading with Cross-Sectional Volatility Factors [Robot Wealth]

    A few years ago, I got deep into the idea of constructing a long/short equity options portfolio based on the kind of simple factor sorts that had been so successful in quant equity. My original intention was to set up an index and license it to fund managers. Of course, there are many reasons why this is a very hard business problem so I never really got off the ground with it. But I do keep
  • How Volatility and Turnover Affect Return Reversals [Alpha Architect]

    In the research reviewed here, the authors analyze the relationship of aggregate market liquidity to the time-series performance of reversal strategies. The strength and persistence of reversals and reversal driven strategies appear to be different depending on specific risk features of those providing market liquidity to the stock. Reversals and the Returns to Liquidity Provision Wei Dai, Mamdouh
  • Using Machine Learning Programs to Forecast the Equity Risk Premium [Alpha Architect]

    The ability to predict stock returns and the equity risk premium (ERP) is of great interest to academics, financial practitioners, and investors, as future estimated returns have implications for asset allocations. To date, the best metric we have for forecasting future equity returns and the ERP is current valuations (whether using current P/E ratios or some cyclically-adjusted average such as

Quantocracy’s Daily Wrap for 05/10/2024

This is a summary of links featured on Quantocracy on Friday, 05/10/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Design Patterns for Order Latency – Why You May Need to Implement Your Own Gateway for Trading [Hanguk Quant]

    Most recently, I announced some coming, exciting upcoming things for quantpylib: Exciting Additions to Quantpylib Exciting Additions to Quantpylib HangukQuant May 6 Read full story as well as a demontration of its powers: Dual Momentum in Cryptocurrencies? Dual Momentum in Cryptocurrencies? HangukQuant May 4 Read full story Today, I want to talk about design principles when order latency is
  • Building Better High Yield Portfolios – II [Finominal]

    The higher the yield, the lower the total return on average Combining the highest yielding strategies leads to risky portfolios Combining high yielding but uncorrelated strategies is more sensible INTRODUCTION We previously highlighted an almost linear inverse relationship between an investment strategys yield and its total return (read Building Better High Yield Portfolios). Investors,

Quantocracy’s Daily Wrap for 05/05/2024

This is a summary of links featured on Quantocracy on Sunday, 05/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix [Portfolio Optimizer]

    Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statistic, with plenty of possible variations depending on the exact context (non-dependent data, dependent data). Because bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well
  • Dual Momentum in Cryptocurrencies? [Hanguk Quant]

    Over the last few weeks, we talked about funding arbitrage, Crypto Arbitrage (1 Week Setup) Crypto Arbitrage (1 Week Setup) HangukQuant Apr 10 Read full story extended the quant library to incorporate crypto backtesting Announcing Crypto and Other Backtesting Logic ; Quantpylib Announcing Crypto and Other Backtesting Logic ; Quantpylib HangukQuant Apr 27 Read full story and introduced new
  • Inventory scores and metal futures returns [SR SV]

    Inventory scores are quantamental (point-in-time) indicators of the inventory states and dynamics of economies or commodity sectors. Inventory scores plausibly predict base metal futures returns due to two effects. First, they influence the convenience yield of a metal and the discount at which futures are trading relative to physical stock. Second, they predict demand changes for restocking by
  • Momentum and the Clarity of the Trend [Alpha Architect]

    Momentum continues to receive much attention from researchers because of the strong empirical evidence. Out of the hundreds of exhibits in the factor zoo, momentum (both cross-sectional [long-short] and absolute [trend]) was one of just five equity factors that met all the criteria (persistent, pervasive, robust, implementable, and intuitive) Andrew Berkin and I established in our book Your

Quantocracy’s Daily Wrap for 05/03/2024

This is a summary of links featured on Quantocracy on Friday, 05/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Make Things Easy on Yourself: Roll up Small Asset Positions [Allocate Smartly]

    Here are some things we know about Tactical Asset Allocation: (Learn more: What is TAA?) We shouldnt go 100% all in on just one TAA strategy. That introduces specification risk, or the risk that weve bet on an underperforming horse. Instead, we should combine multiple strategies together into what we call Model Portfolios. When trading a diversified combination of TAA strategies,

Quantocracy’s Daily Wrap for 04/30/2024

This is a summary of links featured on Quantocracy on Tuesday, 04/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicate Fama French 5-Factor Model from publicly available data sources [DileQuante]

    As an equity quantitative analyst, you have recurring positioning analysis tasks. Your most effective approach is to model your object of study (usually stocks, portfolios or indexes) and decompose its behavior into common risk factors. You can create your own factor model or use existing models. There are robust models generated by data providers like MSCI or Qontigo, which are quite