Quant Mashup 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(...) The Limits of Out-of-Sample Testing [Relative Value Arbitrage]In trading system design, out-of-sample (OOS) testing is a critical step to assess robustness. It is a necessary step, but not sufficient. In this post, I’ll explore some issues with OOS testing. How Well Overfitted Trading Systems Perform Out-of-Sample? In-sample overfitting is a serious problem(...) We interrupt this service for an important message [Klement on Investing]Hi everyone Usually, I don’t comment too much on current affairs on this substack. Still, Trump firing the Head of the BLS, Erika McEntarfer, because he didn’t like the labour market data, is extremely dangerous for investors everywhere. If you have investments in the US, you should be highly(...) A Quant's Guide to Covariance Matrix Estimation [OS Quant]In this article, we explore three techniques to improve covariance matrix estimation: evaluating estimates independently of backtests, decoupling variance and correlation, and applying shrinkage for more robust outputs. Author Adrian Letchford Published 2 August 2025 Length 12 minutes Like what you(...) Overnight Crypto Returns [Falkenblog]On Monday, I examined the flaw in capturing the overnight equity return anomaly. The basic issue was that the anomaly shrank considerably after the 2008 bear market, and given that one has to turn over the entire portfolio twice a day, the minuscule transaction costs eliminate any alpha. The guys(...) A Different Way of Looking at Returns [Mark Best]It would be nice if it were possible to trade a moving average cross. The problem with this is always that the data lags. It’s not possible to trade the current value of a moving average since it requires trading prices in the past. The advantage to doing so is that, due to the smoothing,(...) 100 Papers an Hour: 10x'ing Your Strategy Research Speed With AI [Paper to Profit]As much as LLMs and AI seem to be writing our code, creating our art, and potentially replacing (or at least supplementing) our own artistic souls, they also still excel at pretty mundane tasks. When applied correctly, they can chew through hundreds of research papers at a time and give you deeper(...) Weekly Research Recap [Quant Seeker]The “Actual Retail Price” of Equity Trades (Schwarz, Barber, Huang, Jorion, and Odean) Contrary to conventional wisdom, payment for order flow (PFOF) isn’t systematically linked to worse execution. This paper finds large cost differences across brokers for identical orders, not explained by(...) First trading day of the month has generally been strong…except August [Quantifiable Edges]I’ve shown the chart below several times over the years. It breaks down by month the performance of the first trading day of the month. July has long had the strongest Day 1. But August is also notable for it’s lack of Day 1 performance. As you can see it is the only month with a negative Day 1(...) Options: Iron Condor Strategy [Trading the Breaking]The iron condor’s appeal is statistically seductive: a high-probability, defined-risk structure promising steady income from time decay and volatility erosion. Yet beneath its deceptively flat payoff profile lies a quantitatively intricate reality—one where theoretical win rates often mask a(...) The Equity Overnight Anomaly ETFs [Falkenblog]TL;DR The overnight return anomaly became much less anomalous around 2009 The failed ETFs designed to capture it suffered from horrible timing, but also transaction costs Transaction costs are much greater than fees, and also greater than fees + (ask-bid)/2 The overnight equity anomaly is that most(...) From Defense to Offense: A Tactical Model for All Seasons [Quantitativo]“Basketball is a game of adjustments.” Bob Knight. Bob Knight was the last coach to lead an NCAA team to a perfect season: 32 wins, zero losses. That record still stands nearly half a century later. His secret? He was a masterful tactician. Obsessed with preparation, relentless on fundamentals,(...) How to Identify Ponzi Funds? [Quantpedia]Can we spot a Ponzi scheme before it collapses? That question haunts regulators, investors, and journalists alike. But what if some modern investment funds operate on dynamics that, while not technically illegal, closely resemble Ponzi-like behavior? A new paper by Philippe van der Beck,(...) The Risks of Passive Investing Dominance [Alpha Architect]Fueled by the persistent failure of active management (as evidenced, for example, by the annual SPIVA scorecards), passive investing now commands the majority of assets under management. This structural shift is not without consequence. Chris Brightman and Campbell Harvey’s May 2025 paper(...) Sentiment as Signal: Forecasting with Alternative Data and Generative AI [Relative Value Arbitrage]Quantitative trading based on market sentiment is a less developed area compared to traditional approaches. With the explosion of social media, advances in computing resources, and AI technology, sentiment-based trading is making progress. In this post, I will explore some aspects of sentiment(...) Why the Last Few Minutes of Trading Might Matter More Than You Think [Alpha Architect]This paper reveals a striking pattern in U.S. stock markets: the prices of individual stocks often reverse direction at the very end of the trading day. Using high-frequency data, the authors find that the last few minutes—particularly the closing auction—are dominated by large institutional(...) Weekly Research Recap [Quant Seeker]News Sentiment and Commodity Futures Investing (Yeguang, El-Jahel, and Vu) While momentum and carry strategies are well-known in commodities, this paper shows that weekly news sentiment from financial media also predicts returns. Using Refinitiv’s MarketPsych indices, the authors construct(...) Carlson's "Defense First" [Allocate Smartly]This is a test of Thomas Carlson’s “Defense First” strategy from his paper Defense First: A Multi-Asset Tactical Model for Adaptive Downside Protection. Strategy results from 1971 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and follow 90+(...) When your strategy works, is it just dumb luck? How to stack the odds in your favour [Robot Wealth]Recently, we had an excellent question on the Trade Like a Quant Discord server: “How do you know if your strategy is working out of coincidence rather than actual edge? The strategy might work over a long period just because of blind luck.” Damn good question. It hits right in the insecurity(...) The Memorization Problem: Can We Trust LLMs’ Forecasts? [Quantpedia]Everyone is excited about the potential of large language models (LLMs) to assist with forecasting, research, and countless day-to-day tasks. However, as their use expands into sensitive areas like financial prediction, serious concerns are emerging—particularly around memory leaks. In the recent(...) Behavioral Biases and Retail Options Trading [Relative Value Arbitrage]Why Do Investors Lose Money? Behavioral finance is the study of how financial behavior affects economic decisions and market outcomes, and how those decisions and outcomes are affected by psychological, social, and cultural factors. Behavioral finance research has shown that people do not always(...) Do Smart Machines Make Smarter Trades? [Alpha Architect]Can machine learning models help us exploit stock market anomalies more effectively? This paper says yes—but with a few important caveats. By applying gradient boosting algorithms to a wide array of established anomalies (like value, momentum, and quality), the authors show that machine learning(...) The Unintended Consequences of Rebalancing [Quantitativo]“I picked up one or two pieces and examined them attentively... I then collected four or five pieces and went to Mr. Scott... I said, ‘I believe this is gold.’” James W. Marshall. He found gold… and died broke. James W. Marshall unintentionally sparked one of the greatest migrations in(...) Weekly Research Recap [Quant Seeker]In-Sample and Out-of-Sample Sharpe Ratios for Linear Predictive Models (Jacquier, Muhle-Karbe, and Mulligan) Combining many weak signals can raise a model’s in-sample Sharpe ratio, but this paper shows it often backfires out of sample due to overfitting. Even if the combined model looks better in(...) How Fragile is Liquidity Across Asset Classes? [Quantpedia]The paper “Through Stormy Seas: How Fragile is Liquidity Across Asset Classes?” is a very interesting examination of how liquidity properties have evolved over the past decade. Although the average bid–ask spread has declined, the kurtosis and skewness of the spread distribution have(...)