Quant Mashup Profitably Trading the SPX Opening Range. Code Included. [Quantish]This promising strategy comes from Option Alpha’s comprehensive research on trading SPX breakouts with zero-day-to-expiration (0DTE) credit spreads – selling one option while buying a further OTM option for protection, collecting premium with defined risk. If you’re not famliar with Option(...) Weekly Research Recap [Quant Seeker]The trade imbalance network and currency returns (Hou, Sarno, and Ye) While past work links a country’s trade balance to predictability of FX returns, this study shows that its position in the global network of deficits and surpluses matters too. The authors create a centrality-based measure(...) Surprisingly Profitable Pre-Holiday Drift Signal for Bitcoin [Quantpedia]Cryptocurrency markets have matured into a distinct asset class characterized by extreme volatility, deep liquidity pools, and worldwide retail participation. Traditional equity and commodity markets exhibit a well-documented pre-holiday effect, where returns on trading days immediately preceding(...) A Better Stock Rotation System [Financial Hacker]A stock rotation system is normally a safe haven, compared to other algorithmic systems. There’s no risk of losing all capital, and you can expect small but steady gains. The catch: Most of those systems, and also the ETFs derived from them, do not fare better than the stock index. Many fare even(...) PCA analysis of Futures returns for fun and profit, part deux [Investment Idiocy]In my previous post I discussed what would happen if you did the crazy thing of doing a PCA on the whole universe of futures across assets, rather than just within US equities or bonds like The Man would want you to. In this post I explore how we could do something useful with them. There is some(...) Skewness Premium in Managed Futures: A Practitioner's Guide [Invest ReSolve]Skewness-based managed futures strategies offer a unique opportunity to enhance portfolio performance by exploiting the asymmetry of return distributions across diverse asset classes. By focusing on the third moment of return distributions—skewness—these strategies seek to capitalize on the(...) Conditional Value at Risk [OS Quant]Value at Risk (VaR) is the industry’s go-to portfolio risk metric. But, it’s a cutoff completely ignoring tail risk. It tells you how often you’ll breach a threshold, not how bad losses are when you do. Conditional Value at Risk (CVaR) looks at that damage. It measures the average of your(...) Equity duration and predictability [Alpha Architect]Since 1945, a silent revolution has taken place in the way equity markets move. The classic view of stock prices responding mainly to changes in expected dividends no longer holds. Instead, expected returns now dominate. This paper digs into the reason: equity duration has increased dramatically. As(...) Tail Risk Hedging Using Option Signals and Bond ETFs [Relative Value Arbitrage]Tail risk hedging plays a critical role in portfolio management. I discussed this topic in a previous article. In this post, I continue the discussion by presenting different techniques for managing tail risks. Hedging with Puts: Do Volatility and Skew Signals Work? Portfolio hedging remains a(...) Bitcoin ETFs in Conventional Multi-Asset Portfolios [Quantpedia]Understanding how Bitcoin-related instruments can fit into traditional portfolios is increasingly relevant for investors. Some risk-averse investors do not like to hold cryptocurrencies in their portfolios strategically; however, they may be open to investing in crypto-linked assets on a tactical(...) Weekly Research Recap [Quant Seeker]Global News Networks and Return Predictability (Freire, Moin, Quaini, and Soebhag) News sentiment, extracted from a massive global article dataset, predicts daily equity index returns across 14 developed markets. Local sentiment strategies nearly double buy-and-hold Sharpe ratios (e.g., U.S. 1.34(...) Stochastic Volatility Models for Capturing ETF Dynamics and Option Term Structures [Relative Value Arbitrage]The standard Black-Scholes-Merton model is valuable in both theory and practice. However, in certain situations, more advanced models are preferable. In this post, I explore stochastic volatility models. Stock and Volatility Simulation: A Comparative Study of Stochastic Models Stochastic volatility(...) Combinatorial Purged Cross Validation for Optimization [Trading the Breaking]Traditional grid or Bayesian searches conducted on a single path reward parameters that overfit to this specific historical path. This inflates performance metrics through selection bias and temporal leakage. Combinatorial Purged Cross-Validation (CPCV) addresses this flaw by generating a multitude(...) New open-source library: Conditional Gaussian Mixture Models (CGMM) [Sitmo]I’ve released a small, lightweight Python library that learns conditional distributions and turns them e.g. into scenarios, fan charts, and risk bands with just a few lines of code. It’s built on top of scikit-learn (fits naturally into sklearn-style workflows and tooling). Example usage: In the(...) The Reversal Tendency of Labor Day Week [Quantifiable Edges]In the subscriber letter over the last several years I have demonstrated that the performance during the week of Labor Day has been impacted by the performance in the month leading up to it. Interestingly, is has been somewhat of a momentum reversal week. When SPX has rallied up to Labor Day, then(...) Volume Shocks and Overnight Returns [Quantitativo]Albert Einstein had a way of capturing deep truths in simple words. His quote is a reminder, especially relevant to us when building models. Stripping away unnecessary complexity is vital, but going too far risks oversimplification: a model that looks neat but fails to capture reality. This week, we(...) The 5 Point Trade Quality Scoring System [Paper to Profit]Often we have a trading system with a countless number of trades (in my case 70,000,000) with little to no way to understand actually what is going on. Sure, we get massive printouts and tear sheets with a ton of figures that quantify our strategy. But, what about on a trade-by-trade basis? What we(...) DataFrame Rec Tests with Recx [OS Quant]Code changes. Data changes. Outputs change. Somewhere between the first analysis and an odd position in production, little mismatches creep in: a misstated value, off-by-one date ranges, rounding shifts, subtle drift in calculations, missing IDs. The most reliable way to catch them is to compare a(...) The (hidden) trading value of central bank liquidity information [Macrosynergy]Central banks regularly adjust the economy’s monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term(...) Finding Edges [Robot Wealth]How do we find edges? First, we must be clear about what constitutes a good idea. It isn’t as simple as it having to make money. The risk profile must also be tolerable. This is a personal preference. Next, we need to be able to trade it. Robinhood won’t let you sell naked options. You can’t(...) Neural Nets and Factor Models [Falkenblog]Gu, Kelly, and Xiu (2020) - "Empirical Asset Pricing via Machine Learning" and Chen, Pelger, and Zhu (2019) - "Deep Learning in Asset Pricing" examine various machine learning and neural net algorithms. Both find significant improvements to standard factor models. Several hidden(...) How Can We Explain the Low-Risk Anomaly? [Quantpedia]The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various(...) Cross-Sectional Momentum: Results from Commodities and Equities [Relative Value Arbitrage]Momentum strategies can be divided into two categories: time series and cross-sectional. In a previous newsletter, I discussed time series momentum. In this post, I focus on cross-sectional momentum strategies. Cross-Sectional Momentum in the Commodity Market Momentum trading is often divided into 2(...) Weekly Research Recap [Quant Seeker]Asymmetry and Crude Oil Returns (Liu, Zhang, and Bouri) This paper introduces a new distribution-based asymmetry factor (OIS) for crude oil that strongly predicts WTI futures returns. A one-standard-deviation rise in OIS, signaling right-tail clustering, forecasts a 3.15% drop in next-month returns(...) Walk-Forward optimization [Trading the Breaking]I want to start by saying that the key is in the data, not in the model or its parameters. Therefore, if your data is garbage, no matter how much you parameterize it, the results will still be garbage. If you parameterize a model, it's to fine-tune something that already works. Period. Knowing(...) Laurens Bensdorp - Building Strategies with Purpose [Algorithmic Advantage]There’s a special place in trading graveyards reserved for the back-test that looked gorgeous on paper and then detonated in production. I’ve been there. If you trade long enough, you will too. We all know the over-fittings issues, and I’ll get into that, but there’s another reason why(...) The Best Strategies for FX Hedging [Quantpedia]Foreign exchange (FX) markets are a cornerstone of global finance, offering investors and corporations opportunities to manage currency risk, enhance returns, and optimize portfolio performance. Among the most critical challenges in FX is the design of robust hedging strategies to mitigate exposure(...) Unlocking REIT Returns: Real Estate Investment Factors [Alpha Architect]As of 2024, real estate investment trusts (REITs) have cemented their role as a $1.5 trillion segment within global capital markets, offering investors a liquid and regulated gateway to commercial real estate. With robust dividend mandates, leverage restrictions, and transparent operations, REITs(...) Cesar Alvarez - A Novel Way to Combine Trend, Reversion, ETFs, Volatility & More [Algorithmic Advantage]When I sat down recently with Cesar Alvarez of Alvarez Quant Trading, I knew I'd be tapping into a deep reservoir of quantitative trading wisdom. Cesar’s journey into systematic trading began similarly to many of us—starting with discretionary trades, dabbling in mutual funds, and(...) Quantifying Global Real Estate Returns Over Centuries [Quantpedia]In the realm of quantitative finance, understanding the dynamics of real estate returns over extended periods is often overlooked, which is not good, as real estate constitutes a significant portion of investors’ portfolios. The article titled Global Housing Returns, Discount Rates, and the(...) Correlation Matrix Generation using Object Oriented Python [Quant Start]In the last article Generating Synthetic Equity Data with Realistic Correlation Structure we discussed how to generate synthetic structured correlation matrices for the purposes of generating synthetic correlated equities data. This has a number of uses within systematic trading backtesting(...) Weekly Research Recap [Quant Seeker]Is Gold an Inflation Hedge? (Baur) Gold is not a consistent hedge against average inflation. Between 1971 and 2025, realized inflation explains less than 3% of gold’s price variation, and the hedge effect evident in the 1970s–80s largely disappears thereafter. Gold does, however, respond(...) Predictive Information of Options Volume in Equity Markets [Relative Value Arbitrage]A lot of research in options literature has been devoted to the volatility risk premia and developing advanced pricing models. Much less attention has been given to volume. In this post, I’ll discuss some aspects of options volume. Can Options Volume Predict Market Returns? Most of the research in(...) Cross-Sectional Alpha Factors in Crypto: 2+ Sharpe Ratio Without Overfitting [Unexpected Correlations]In the early ’90s, the quant forefathers (Fama and French) introduced their now-canonical factor models: first three, then five, and eventually seven, explaining much of the variation in US equity returns. Today, these models are used to understand what easy-to-replicate risk factors managers are(...) Trading Signals in High Definition [Robot Wealth]We’ve all used on/off type trading signals at some point. But you can nearly always extract more insight with a simple adjustment that focuses on using data efficiently. Let me show you how using a crypto trend example. The problem with binary signals You’ve seen them everywhere. “If price is(...) Python Tooling in 2025 [OS Quant]Today, Python’s ecosystem offers an abundance of tooling to support every aspect of the development workflow. From dependency management to static analysis, from linting to environment setup, there are more options than ever. This article presents a modern, opinionated toolchain for Python(...) Research Review | 15 August 2025 | Forecasting [Capital Spectator]Partisan Bias in Professional Macroeconomic Forecasts Benjamin S. Kay (Federal Reserve), et al. June 2025 Using a novel dataset linking professional forecasters in the Wall Street Journal Economic Forecasting Survey to their political affiliations, we document a partisan bias in GDP growth(...) Systematic equity allocation across countries for dollar-based investors [Macrosynergy]This post demonstrates that country allocation with macroeconomic factors can materially enhance the returns on international equity portfolios in dollar terms. We identify a range of economic developments that, according to standard theory and in conjunction with market inattention, should predict(...) Retrospective Simulation in Trading: Testing Strategies Beyond Realized Price Paths [Quant Insti]This blog introduces retrospective simulation, inspired by Taleb’s "Fooled by Randomness," to simulate 1,000 alternate historical price paths using a non-parametric Brownian bridge method. Using SENSEX data (2000–2020) as in-sample data, the author optimises an EMA crossover strategy(...) Robeco's One-Legged Vol Factor [Falkenblog]Two months ago, Robeco’s Amar Soehbag, Guido Baltussen, and Pim van Vliet posted a new empirical paper, Factoring in the Low-Volatility Factor. I consider Pim a good friend, and he is one of the initial low-vol portfolio managers, as he started his conservative fund at Robeco around 2006 (the(...) Understanding "why" beats statistical significance [Robot Wealth]Do you find yourself obsessing over p-values and t-stats when evaluating trading ideas? I get it. If you come from an academic or scientific background, statistical significance feels like the gold standard for determining whether something is “real” or just random noise. And in many fields,(...) Weekly Research Recap [Quant Seeker]Is Social Media Information Noise or Fundamentals? Evidence from the Crude Oil Market (Ma, Tourani-Rad, Xu, and Zhou) Social media sentiment from Thomson Reuters MarketPsych Indices predicts crude oil returns, with a one-standard-deviation rise implying a next-day gain of roughly 21 bps. Positive(...) The Impact of Market Regimes on Stop Loss Performance [Relative Value Arbitrage]Stop loss is a risk management technique. It has been advocated as a way to control portfolio risk, but how effective is it? In this post, I will discuss certain aspects of stop loss. When Are Stop Losses Effective? A stop loss serves as a risk management tool, helping investors limit potential(...) New Contributor: GLD Put-Write Strategy [Deltaray]Exploring alternative assets like GLD ETF options enhances portfolio diversification by tapping into distinct volatility profiles and correlation patterns, especially beneficial during volatile market environments. In this post, we examine a simple, yet effective Put-Write strategy applied to GLD(...) Options: Iron Butterfly [Trading the Breaking]In the previous article, we deconstructed the Iron Condor, a robust strategy for harvesting the variance risk premium in markets characterized by range-bound behavior. The Condor, with its constituent out-of-the-money credit spreads, offers a wide plateau of profitability, making it a forgiving(...) Overnight Returns: Risk or Conspiracy? [Falkenblog]TL;DR Virtually all of crypto returns come outside of NYSE trading hours, more so for coins pulled from the top 100, more so than for ETH & BTC Overnight returns dominate the WallStreetBets meme stock pumps of 2021 This pattern could be a signature of a conspiratorial pump or the nature of risky(...) Step-by-Step Python Guide for Regime-Specific Trading Using HMM and Random Forest [Quant Insti]Most trading strategies fail because they assume the market behaves the same all the time. But real markets shift between calm and chaotic, and strategies must adapt accordingly. This project builds a Python-based adaptive trading strategy that: Detects current market regime using a Hidden Markov(...) Quantamental Catch-Up [Anton Vorobets]Many of you have undoubtedly enjoyed the summer holidays, so you might have missed out on the first five lectures of the Applied Quantitative Investment Management course. So far, we have been through the first four chapters of the Portfolio Construction and Risk Management book, reaching a point(...) Cultural Calendars and the Gold Drift: Are Holidays Moving GLD ETF? [Quantpedia]Financial markets exhibit persistent calendar anomalies, which often defy the efficient‐market hypothesis by generating predictable return patterns tied to institutional or cultural events. In this paper, we document a novel, globally pervasive drift in gold prices surrounding major(...) Weekly Research Recap [Quant Seeker]Commodities and Conundrums: Decoding Behavioural Finance in Market Dynamics (Till) Investors often underestimate the influence of psychological biases in trading, particularly in commodity markets. This paper examines real-world cases, such as the collapse of MF Global, where overconfidence, loss(...)