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

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

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 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

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

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

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

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, we’ll continue looking at identifying market regimes

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

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

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

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

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

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’

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

Regime-Based Strategic Asset Allocation Eric Bouyé 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

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

We’ve 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

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

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

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

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

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?

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

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

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

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

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,

Here are some things we know about Tactical Asset Allocation: (Learn more: What is TAA?) We shouldn’t go 100% “all in” on just one TAA strategy. That introduces “specification risk”, or the risk that we’ve bet on an underperforming horse. Instead, we should combine multiple strategies

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

My first test of trading forex news announcements is to test the efficacy of breakouts immediately following a news announcement related to the US dollar, specifically, only the high impact news as shown on the forexfactory calendar in red. The intention would be to capture some of the profit

Embeddings are used in neural networks to transform large, sparse data into manageable, dense formats. In other words, they simplify complex data, making it easier to analyze. We can use embeddings to capture dense information about drivers of stock returns. This approach is a great way to select

Market-neutral multi-factor products have reached all-time highs However, long-only multi-factor products have consistently underperformed Portfolio construction and implementation matters INTRODUCTION Investors have been style investing since the inception of stock markets. Some chase trends, while

Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models

The equal risk contribution (ERC) portfolio, introduced in Maillard et al.1, is a portfolio aiming to equalize the risk contributions from [its] different components1. Empirically, the ERC portfolio has been found to be a middle-ground alternative1 to an equally weighted portfolio and a minimum

Last week, we looked at simple data analysis techniques to test for persistence. But we only looked at a feature that is measured over a single day – the absolute range. Such a feature makes it easy to test persistence because you don’t have the problem of overlapping data. Each data point is

Diversification is the cornerstone of investing. This principle, fundamentally understood as not concentrating all resources into a single investment, inherently seeks to minimise risk while potentially smoothing out returns over the long term. By spreading investments across uncorrelated assets,

It’s been almost 11 years now since I posted my introduction to quantitative investing. It’s kind of hard to believe as I’m typing these words. A lot has happened since that summer day in 2013. And a lot has stayed the same. Learning about, implementing, and allocating to this type of

While economic information undeniably wields a significant and widespread influence on financial markets, the systematic incorporation of macroeconomic data into trading strategies has thus far been limited. This reflects skepticism towards economic theory and serious data problems, such as

Quality and low volatility stocks provide substantially different exposures Neither has outperformed the stock market, but both were less volatile Low volatility stocks have done poorly in recent years INTRODUCTION If you believe that the economy is entering a recession and can only invest in

This blog is more a note to self for experimenting further with DSPy (arXiv, GitHub) than a pedagogical or original intro to the framework. It essentially follows this weaviate tutorial with small adaptations, notably removing the weaviate part of it, and replacing their retrieval module by a very

In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge

The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers haven’t had an empirical model to mimic the impact of removing sector “effects” on the measurement and performance of factor strategies. The authors develop and test a

It’s rumored that Renaissance Technologies uses hidden Markov models in their trading. In fact, the Baum-Welch algorithm used with Markov models was partially invented by Leonard Baum who would help found RenTec. In today’s newsletter, we’ll look an example of using a Markov model to detect

Macroeconomic Announcement Premium Hengjie Ai (University of Wisconsin-Madison), et al. November 2023 The paper reviews the evidence on the macroeconomic announcement premium and its implications on equilibrium asset pricing models. Empirically, a large fraction of the equity market risk premium is

While empirical research on cross-sectional (long-short) momentum has shown high returns, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926, there have been several momentum crashes that featured short but persistent periods of

After my last post on using trend following in Crypto markets I thought I’d continue on that thread and look at applying trend following on a newish concept (similar concepts have existed at the institutional level for a long time) in creating diversified portfolios, called return stacking. First,

Some factor trends continued from 2023, but there were also two significant rotations Momentum performed the best, size the worst Most long-short multi-factor products have generated positive excess returns INTRODUCTION We present the performance of five well-known factors on an annual basis for the

It has been a while since I’ve updated the blog. These days I spend most of my time writing for subscribers of my newsletter but I’d like to get back to writing publicly again. So, to launch myself back into public blogging I thought I would write about Crypto and how classic trend following

Regression-based statistical learning helps build trading signals from multiple candidate constituents. The method optimizes models and hyperparameters sequentially and produces point-in-time signals for backtesting and live trading. This post applies regression-based learning to macro trading