This study investigates short-term price reversalstemporary retracements following adverse daily returnsand develops a systematic trading framework to capture this effect across multiple asset classes. Using daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities over the period 20062025, the strategy applies a long-term trend filter based on a

TLDR: This is a post about something that doesn't work. So don't read if you only care about cherry picked delightful backtests. This is my fifth post in a rapid fire intense series on portfolio optimisation. In my last post I looked at the optimal amount of shrinkage to use with real data, when running a bayesian methodology for mean variance optimisation. I found two things. Firstly,

In a previous blog post of this series, the main univariate Value-at-Risk (VaR) estimation methods were described. Among these, and for scenario-based VaR estimation like historical VaR or Monte Carlo VaR, the most widely used [non-parametric] estimator is the corresponding order statistic of the empirical quantile of the portfolio return distribution, or a linear combination of two subsequent

This week we backtested 2,000,000 trading strategies. Every one of them was pure noise. We generated the returns ourselves with a random number generator, so we know, with complete certainty, that the true Sharpe ratio of every single strategy is exactly zero. The best ones still looked brilliant. Run 1,000 of these noise strategies over ten years of daily data and pick the best, and you get a

Commodities represent a vital but highly volatile asset class, characterized by pronounced cyclicality, lack of yield, and susceptibility to severe macroeconomic drawdowns. While cross-sectional (relative) momentum is a well-documented anomaly, its application in commodities often forces portfolios to hold the least declining assets during broad-based bear markets, resulting in unacceptable

It won 5th place at the Quantpedia Awards 2026. The strategy compounds at 16.3% per year over a 17-year backtest, delivers a Sharpe ratio of 1, and keeps equity market correlation near 15%. This article shows how to build an automated VIX volatility trading strategy using Python and Interactive Brokers. Based on our award-winning research, the strategy seeks to capture the volatility risk premium

This is my fourth post in my summer 2026 mini series on portfolio optimisation. It will very much follow the format of (also with a sports alluding title) blog post number two, so it might be worth rereading that. A reminder if you can't be bothered, I used random data to compare some optimisation methods: monte carlo (random, parameteric) bootstrapping (random, non parametric) double

Financial markets produce mountains of data, spanning simple price movements to limit order book dynamics. A common misconception assumes a larger dataset guarantees superior predictions. Reality proves the opposite. Excessive variables introduce noise, and invite to overfitting. Every input added to a model represents a specific market hypothesis. Including a volatility metric implies price

Its a Tuesday morning, you pull up a name youve been watching, and its 30-day implied vol is printing 48, sitting right near the top of where its traded all year. Vol is mean-reverting, everyone knows that, so you do the obvious thing. You sell the straddle, perhaps even an iron condor. Two days later, the print is 61, the short is deep underwater, and the reversion you were promised is

This is the fourth article in the small market effect research series. The first looked at the holiday effect on SPY. The second looked at the turn of the month effect, also on SPY. The third looked at the holiday effect on gas and energy assets All three sets of research resulted in tradable systems that I have now implemented as overlays on my personal systematic trading portfolio. If you missed

Today, AI and machine learning techniques are evolving at a rapid pace, making the development of trading systems increasingly accessible. Generating signals, building models, and testing ideas is easier than ever. As a result, the challenge is no longer simply developing a trading strategy, but determining whether it is genuinely robust or merely the product of overfitting and data mining. In

Asset pricing research often focuses on risk, valuation, and macroeconomic forces. But this paper highlights another surprisingly powerful driver of returns: the timing of dividend payments. Across 44 international equity markets, the authors uncover a large and persistent dividend premium. Dividend-paying stocks outperform non-payers by a meaningful margin, even after controlling for

The piece we present today stems from some internal exchange within the Concretum team ahead of the highly anticipated SpaceX IPO, an offering that has dominated headlines for a string of firsts in recent market history, from its record valuation (~$1.75 trillion) to the one that interests us most: the prospect of a fast-track inclusion into the Nasdaq 100. A few numbers can frame why this

Academics have identified hundreds of factors that supposedly explain stock returns. New research shows most of them are telling the same story in different words and only a few truly distinct forces actually drive the market. The problem: too many factors, too little meaning. Over the past few decades, academic researchers have proposed more than 400 factors characteristics or variables,

Yes, this is my first article without a podcast attached. The first of many, I hope. In time, Id also like to produce more YouTube content that isnt strictly interview-based. Ill admit it: Ive been biting off more than I can chew. Courses, podcasts, trading, farming (yep!). As you know, Im working on courses with Market Wizards, but Im also building my own courses, which I plan

Every retail trader eventually asks the same question: do candlestick patterns predict where price goes next? I decided to measure instead of believe. I built a small pipeline on SPY that discretizes each bar into one of twelve candle states, tests whether the next bars direction depends on that state, and then asks what survives once you account for sample size, transaction costs, and the

Monte-Carlo Simulation is the most straightforward way to price an option, and if you dont care about speed, its a solid choice. The moment you care about speed, like when quoting live, or when calibrating a pricing model where you need to reprice options thousands of times, Monte Carlo quickly becomes unusable. This article presents three Fourier-based methods that solve this problem,

This is my third post in a series about optimisation and fitting. In my previous post I used random data to calibrate and evaluate many portfolio optimisation techniques. It's worth quoting in full from that post: Random data is not real data: Well duh. But why is this important? Because random data is drawn from a fixed and well behaved distribution. This means the optimiser only has to

A Tale of Two Prices (the core idea of stat arb) Moneyball (finding undervalued pairs using unconventional metrics) The Winter of our Pairs Trading Discontent (problems, limitations, frustrations) The Metamorphosis (from pairs to portfolio) When is a Mispricing Not a Mispricing? (something looks mispriced why?) At its simplest, stat arb is a bet on convergence: two related stocks drift

Combine a three-asset risk-premia portfolio with a trend-following program and the Sharpe ratio jumps from 1.1 to nearly 1.5. It looks like free diversification. But a trend program is long equities, bonds and metals and a risk-premia core is equities, bonds and metals. So before we accept the free lunch, we should ask what part of it we are paying for twice, and what part is genuinely new.

As I said in my last post I'm currently in the process of a mega-sized research project on fitting. In the first post I examined the correct way to cluster combinations of trading rules and instruments. This next post is rather meatier, and is about evaluating and calibrating some portfolio optimisation techniques. We might call this 'meta optimisation', since we want to find the

Two different sources each tell you something about where a stock is going. The first is insider trading filings: SEC Form 4, which executives, directors, and large shareholders are required to submit within two business days of buying or selling shares in their own company. The second is the news cycle: analyst reports, earnings coverage, breaking stories. Each source has its own academic

How much do we really know about corporate bond returns before the modern data era? Until recently, the answer was: not enough. Most empirical work in corporate bond pricing has relied on relatively short samples, especially the post-2002 TRACE period, leaving open the question of whether observed risk premia are robust over longer horizons. Ghaderi, Plante, Roussanov, and Seo (2026) Ghaderi,

This will be the first in a series of posts about portfolio optimisation. Main reason being I'm planning to write a book about backtesting, and that will include a big chunk of material on optimisation. Yes, I know, my latest book isn't out yet (it's out in December – in time for Christmas). But this backtesting book is going to be quite deep (and probably long!) so I need to start

At Concretum Group, a relevant part of our research effort goes into developing strategies for external clients, each arriving with different requirements about what market behavior to model and, just as importantly, about how much capital a given strategy is meant to run on. This brings us to a very delicate part of our work, which might not seem exciting at first, but becomes crucial before

If youve ever scrolled through Polymarket or Kalshi wondering whether the wisdom of crowds is actually wisdomor just organized noiseyoure not alone. A new paper, How Wise is the Crowd? Bias and Edge in Prediction Markets, tears into the microstructure of modern prediction markets to ask a practical question: Whos actually making money, and whos just paying for the

Measuring Bubbles via Put-Call Disparity: A Model-Free Approach Robert A. Jarrow (Cornell U.) and Simon Kwok (U. of Sydney) May 2026 This paper introduces simple, model-free lower and upper bounds for measuring the size of asset price bubbles. Assuming only that the market satisfies no-free-lunch-with-vanishing-risk and that all trading strategies are admissible, our framework avoids restrictive

Hello again! Todays post was originally published in the November 2025 issue of Wilmott Magazine. What if you could make it a lot easier for readers to replicate your paper? That was the idea I followed when Wilmott called for articles to be published in a special issue on the replication crisis. Subscribe to my Substack to receive my posts in your inbox, or follow me on Twitter or LinkedIn if

Regression is one of the oldest and widely used statistical techniques. It has found applications across the social sciences, engineering, natural sciences, and finance. Despite the rapid rise of machine learning and AI, regression remains a useful tool for modeling relationships, making forecasts, and extracting signals from data. In this post, we revisit regression-based trading systems and

Every strategy and Model Portfolio now includes a Return Contribution analysis, showing each assets contribution to overall annual return. We further aggregate results by asset category and risk on/off, as well as estimate the drag from trading friction (transaction costs + slippage). Lets walk through a sample return contribution analysis using the most popular strategy on our

Part 1 left a gap. Regressing the synthetic backtrack against the whole universe at once recovered the program in ten contracts at a Sharpe of 0.84, against the programs 1.03 a fifth of a Sharpe unaccounted for. I argued there that the gap lived in the regressions blindness to the structure inside the program: equities and bonds, energy and grains, all blended into one optimisation, so

Last time, I showed you a pattern in energy spreads and asked what it meant. The answer seemed obvious: XOM is the outlier. Every spread involving XOM is stretched. The spreads not involving XOM are near zero. But on this seemingly obvious map of mispricings, XOM may not mark the spot The name Triangulated Stat Arb comes from triangulation, the navigation technique. One bearing on a landmark

In-sample looks great. Live trading is where the truth lives. A few weeks ago we wrote about how we use AI alongside Trading Blox what it does well, what it doesnt, and the workflow we run. The single biggest risk we flagged was overfitting specifically, the way AI overfitting in trading systems quietly destroys live performance after pretty backtests. That deserves its own post,

Weve added Safe and Perpetual Withdrawal Rates to your custom Model Portfolios. New here? Learn more: What is a Model Portfolio? What are Withdrawal Rates? The Safe Withdrawal Rate (SWR) measures the max amount that could have been withdrawn each year in retirement (with an annual adjustment for inflation) without running out of money over the worst retirement period. Its the source of the

Welcome to the Systematic Trading with TradeQuantiX newsletter, your go-to resource for all things systematic trading. This publication will equip you with a complete toolkit to support your systematic trading journey, sent straight to your inbox. Remember, its more than just another newsletter; its everything you need to be a successful systematic trader. I recently launched a

Asset prices are often viewed through a simple lens. Investors form expectations, discount future cash flows, and determine prices accordingly. But in reality, expectations themselves are complex. They vary across institutions, across asset classes, and over time. This paper introduces a new perspective. Institutional expectations are not random or purely behavioral. They are structured,

More and more traders are using Claude Code, ChatGPT, Cursor, and other LLMs to build and automate their trading systems. It works. You can go from strategy idea to a working bot in a day. The code compiles, the backtest looks good, orders fire on paper trading, and you move to production. Then stuff breaks. Not the strategy logic – the infrastructure around it. Over the years, weve repeatedly

The published literature on trend-following replication treats the program being copied as a black box. When the program is your own, this is the wrong way around and fixing it changes the result more than I expected. The story of trend following as a systematic strategy reaches back to the 1970s, when a handful of futures traders observed that prices in commodity markets tended to persist in

Last week, we shared some findings of an intraday short-selling signal taken from our internal research archives. Today, picking up on the same theme, we present some evidence behind an effect we believe stems from the very presence of short sellers in stocks with the same characteristics highlighted in our previous piece. We recommend you first read our original analysis here. Identifying Stocks

Two previous articles, Trend-Following Filters Part 7 [1] and Trend-Following Filters Part 9 [2], examined, from a digital signal processing (DSP) time domain perspective, digital filters commonly used by technical analysts to aid in making trading decisions. The filters examined in Part 7 include moving average (MA), linear weighted moving average (LWMA), and exponential

The Sharpe stability ratio measures the consistency of risk-adjusted PnL value generation. It divides the mean Sharpe ratio over sequential overlapping lookback periods by its estimated standard error. Thereby, it quantifies significance and intertemporal stability. Both are critical for selecting factors and for assessing the commercial viability of a strategy. If two strategies produced the same

Welcome to the Quantpedia Awards 2026 winners announcement. For the third time, we are proud to celebrate excellence in quantitative research and recognize the researchers behind innovative studies in quantitative trading. We are also pleased to see that the Quantpedia Awards have become an established and recognized brand within the quant community. This is the moment we have all been waiting

That line, usually pinned to Einstein, fits this article rather well. In trading strategy research, we can spend a long time counting the wrong thing: like, as Martyn Tinsley says – whether the single best in-sample parameter set survives out-of-sample testing. Martyn Tinsleys novel new approach, Walk Forward Correlation, argues that this is often a comforting illusion. Conversely, the

The academic literature on legal insider trading is unusually mature. Sixty years of work, replicated across multiple samples and methodologies, has converged on a few consistent claims: insider purchases carry information, insider sales mostly do not, cluster buying by multiple insiders is stronger than individual transactions, and the alpha has been compressing for decades as the market got

Pairs trading remains a feasible approach for the indie trader. But, as we saw last time, there are inherent limitations. Trading both legs eats a lot of buying power and limits the number of pairs you can trade. Trading only the mispriced leg helps, but introduces a ton of variance. Essentially, the trade-off is accepting a wilder ride in exchange for higher expected returns and better capital

Our study explores a weekly-rebalanced dual-momentum-based Global Tactical Asset Allocation (GTAA) strategy applied to a diversified set of ETFs. The strategy selects assets based on relative momentum and applies an absolute momentum filter to avoid declining investments. Ultimately, a single combined strategy was created by merging two sub-strategies, incorporating both shorter- and longer-term

Without a shade of doubt, Market Wizards books have been a staple in the upbringing of whole generations of traders and investors, and rightfully so we ourselves have been inspired by the exceptional stories within them. The series, authored by Jack Schwager, began in 1989: what has made it so enduring is not the trading insights alone, but the human stories behind them, of rigor, discipline,

Choi, Huh and Su have a very good paper entitled Tighter uniform bounds for BlackScholes implied volatility and the applications to root-finding. Whats particularly great is that it gives both a decent lower bound and a proof a monotone convergence using Newtons method starting from this lower bound. The industry standard for solving the Black-Scholes implied volatility is Peter Jckel

For decades, academic researchers have catalogued hundreds of patterns in the stock market statistical regularities linking firm characteristics to future returns. These persistent return patterns, unexplained by standard risk models, are known as anomalies. They now form the intellectual backbone of a multi-trillion-dollar industry called factor investing, implemented through mutual funds,