“An idea is nothing more nor less than a new combination of old elements.” James Webb Young Implementing research papers can sometimes work, though a perfect replication often fails. It’s never wasted effort, though: the ideas in the paper end up feeding new ideas and good conversations with other(...)

The short version We wrote down four predictions before running a single test. Then we ran 360 measurements, tuned 320 variants, and put three complete systems through regime cross-validation. All four predictions held. What is actually inside the most famous momentum indicator in trading is not(...)

“The current extreme in margin debt offers one way to gauge the speculative exuberance of investors. – John Hussman” Thinking about this chart from John Hussman, showing margin debt relative to GDP spiking to all-time highs, with previous such instances seeming to foreshadow major market downturns:(...)

Momentum is the oldest anomaly in the book, and a new momentum paper has to justify why it exists. Landolfi's percentile-rank framework does not sell you the momentum. It sells the plumbing around it: rank each move against its own sign-consistent history instead of a raw threshold, gate(...)

The first part described the idea and put a gross Sharpe of about one and a half on it. Gross is the easy figure to produce and the least interesting one to quote, because it assumes you can trade for free. Here I put the strategy in front of a broker’s fee schedule and an integer number of shares,(...)

Two of the longest-running puzzles in accounting and asset pricing research are the accrual anomaly and the post-earnings-announcement drift, or PEAD. Both describe return patterns that standard one-period asset pricing models struggle to explain, and both have generated a huge literature. The(...)

Every few years a new architecture gets pointed at Bitcoin and a paper announces it won. This round it is Mamba, the selective state-space model that is genuinely reshaping language and vision. Sepehri, Mehradfar, Soltanolkotabi, and Avestimehr at USC built CryptoMamba, a compact Mamba network that(...)

In this episode of House of Quants, listeners will discover: How quants separate genuine edge from market noise: The episode explains why financial markets are difficult to diagnose and how researchers distinguish persistent information from randomness, temporary anomalies, and misleading patterns.(...)

Large language models are already capable of summarizing financial research, but are they ready to conduct it? In their latest paper, researchers from Google, Boston College, and Columbia introduce a framework where a large language model doesn’t just fetch data—it acts as an autonomous AI research(...)

Here is the thesis, stated before the evidence: when we re-measured seven months of work on a pipeline that reads financial headlines and asks whether each one should move a stock, the mechanisms we had found held up. The magnitudes almost never did, including, twice, the magnitudes we ourselves(...)

This is the fourth installment in our series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read parts 1, 2 and 3. In our previous studies we selected strategies based on recent return. In this study, we select “distressed” strategies, or strategies nearing or(...)

Moving averages are the first fool’s errand we make as traders. If only it was smoother, but still responsive. Predictive, not just reactive. And so many hours are lost clicking through TradingView PineScripts or MetaTrader indicators trying to find the Holy Grail amongst muck. 9 Types of Forex(...)

Key Takeaways RMP (Random-Max Percentile) is a new per-row column in our scan results and paid-plan exports: the probability that the best result pure chance could produce, across the N trials your scan actually ran, lands below this row. An RMP of 0.97 reads: even the luck record of a search this(...)

Momentum investing has been one of the most persistent and puzzling phenomena in finance for more than three decades. Traditional explanations typically focus on investor psychology, delayed information diffusion, or risk compensation. But this paper proposes something radically different. The(...)

Momentum is the one factor nobody argues about. Winners keep winning, losers keep losing, and the effect shows up in stocks, bonds, commodities, and currencies across a century of data. The old article "From Intermarket Analysis to Network Momentum" pushed a harder claim: an asset's(...)

“The value of an idea lies in the using of it.” Thomas Edison. In my experience, implementing research papers can sometimes work, though a perfect replication often fails. It’s never wasted effort, though: the ideas in the paper end up feeding new ideas and good conversations with other(...)

Ask a CTA salesperson why their fund makes money and you get a story: markets trend, we ride the trend, we cut losers and let winners run. That story is untestable. Sepp and Lucic did something the industry rarely does. They wrote down the exact profit-and-loss of a standard European trend-follower(...)

Feature selection is often presented as a simple cleanup step… remove the weak variables, keep the useful ones, and move on. In practice, it is much closer to a research decision about what information the model is allowed to trust. Every feature added to a trading system creates a cost. It may(...)

You can watch a refiner's gross margin move in real time: it trades in the futures market, tick by tick, as the crack spread — and the stocks that earn it are reliably slow to follow. That lag is small, but it is honest, market-neutral, and it rests on a mechanism rather than a curve fit. This(...)

Key Takeaways A single strategy, its trades bit-for-bit identical, grades as luck in one scan (deflated Sharpe 0.035, inside a 21.9M-trial search) and close to real in another (0.919, inside 14.8M). The strategy never changed; the crowd it was measured against did. We ran one 5.7-year BTC and ETH(...)

Key Takeaways The Deflated Sharpe Ratio (DSR) adjusts a strategy's Sharpe for how many strategies you tested. It sets the bar not at zero, but at the best Sharpe you would expect from luck alone after N tries, a bar that grows only with the logarithm of N. Of 99,878,688 BTC/USDT backtests,(...)

If your backtest joins fundamentals on the fiscal-period-end date, it is trading on numbers that did not exist yet. That's the whole bug, in one sentence. A company's fiscal year ends, and then — weeks later — a 10-K gets filed and the numbers become public. Join on the wrong date and(...)

A simple intraday volatility breakout can work surprisingly well. But in live trading, the edge is often shaped by small practical details: how we exit, how much slippage we pay, and whether we can express the same idea through ETFs, futures, or 0DTE options. In this article I share several(...)

Here is a thing that turns out to be true: two thoughtful humans can look at the same piece of financial news, both decide it is good news, agree with each other, and both be wrong in exactly the same way. Not wrong about the news. Wrong about what the stock was going to do. And the machine we had(...)

Up until now we have explored four small market effects, and developed five systems from those explorations. Each of the effects were researched to characterize what makes them a better or worse trade, and what features are stable over time. None of these systems we developed as part of this series(...)

“I constantly see people rise in life who are not the smartest, sometimes not even the most diligent, but they are learning machines.” Charlie Munger. As I mentioned in the piece “Two years of Quantitativo,” the newsletter went paid. In order to continue contributing to the broader community, I(...)

Retail trading, especially in the options market, which has traditionally been the domain of institutional traders, has received relatively little attention. However, with the rapid growth of educational content, AI, social media, and commission-free trading platforms, this is no longer the case.(...)

Last year I built a stock scanner in Python. The first backtest said my signals returned +40% over two years. I was, briefly, a genius. Then I fixed three bugs and the same signals returned roughly nothing. None of the bugs were in the strategy. All of them were in the measurement. The strategy(...)

This is post #10 in my 2026 series on portfolio optimisation. Time for a quick recap. I'm not going to revisit every post but instead summarise what I now think one should be doing when optimising forecast weights before costs (I haven't yet incorporated costs, nor thought about(...)

The market changes all the time. Sometimes it trends, sometimes it oscillates, sometimes it goes sidewards. Trading systems that do not react on market regime change will bring uncomfortable times for their traders (and their wallets). In TASC 9/2026, Gaetano Di Prima and Fabio Baruffa provide a(...)

We investigate a Donchian breakout rotation strategy between QQQ (NASDAQ-100) and Bitcoin (BTC), with a cash fallback during consolidation, and test it across eight lookback horizons (5–50 trading days) and two priority variants over a seven-year sample spanning 2019–2026. The strategy consistently(...)

An autonomous research agent replicated nine published US-equity anomalies on clean, survivorship-free data. The question is not only what it found (out-of-sample decay is the rule, and on a faithful build none survive — the lone apparent survivor turned out to be a construction error the(...)

This is the eighth post in a series I'm writing on portfolio optimisation. I haven't done one of these for a few posts, so here is the story so far: In the first post I showed that if you are optimising across forecasts from different trading rules and instruments, then you should first(...)

The mega blog post series on portfolio optimisation continues! A couple of posts ago, here, I looked at using the idea of formal testing for structural breaks in parameter estimates. Important parameters like Sharpe Ratio (SR). Because stuff like this happens: This is the pre-cost performance of(...)

The first three parts of this series built a trend-following program and took it apart: sixty-two futures markets replicated, distilled to one contract per sector, then measured for what trend actually adds to a risk-premia core. All of it sized to a volatility target, indifferent to the account(...)

Regression-based statistical learning is a convenient and transparent method for combining trading factors into composite signals. Sequential statistical learning considers only the data available at each time point to choose and parameterize the “best” model and to generate signals without(...)

Evidence-based investors have long debated the efficient market hypothesis (EMH), popularized by Gene Fama. In the new era of social media echo chambers, meme stocks, and information overload, it has become fashionable to argue that markets are growing less rational. BlackRock’s William Ezratty,(...)

Mean-Variance Optimization remains the intellectual cornerstone of modern portfolio theory, yet its real-world deployment via plug-in MVO often delivers unstable, over-leveraged portfolios that collapse out-of-sample. The core insight from VertoxQuant’s analysis is profound: raw plug-in MVO does(...)

TL;DR This project tests whether trend-following, a strategy family with decades of documented success in futures markets, transfers to spot FX. Three approaches (time-series momentum, moving-average crossover, and channel breakout) were backtested across the seven major currency pairs from 2003 to(...)

Here's a nice picture from a lovely book written by a top bloke: It shows the cumulative p&l from different speeds of momentum over time (for portfolios containing 102 instruments) over 50 years of data. Notice how the two fastest speeds (2&4) get worse in the second half of the(...)

This is a very preliminary result (snapshot June 15, 2026) It rests on 21 earnings events from a single three-week window, and every number below should be read as a first sighting, not a finding. We are publishing it now to describe a pattern and to set a baseline we can check against as the(...)

My work on covariance estimation has recently been published as an Advanced Review in WIREs Computational Statistics, a highly regarded, peer-reviewed journal in the field. It feels remarkably rewarding to see a decade of my curiosity finally bound together in one place. The writing process started(...)

Diversification is a key principle in portfolio construction, yet equal-weight portfolios often fail to deliver true risk diversification. This study shows that capital-based allocation can mask strong concentration in a small number of underlying risk factors. We analyze a simple multi-asset(...)

This is the sixth (!) post in a series I'm writing on portfolio optimisation. A quick reminder of the story so far: In the first post I showed that if you are optimising across forecasts from different trading rules and instruments, that the rules within an instrument cluster naturally(...)

Maintaining the status quo and searching for information that confirms its sufficiency are fairly well established human biases. Hence, producing research that satisfies these biases is an easy way to make it popular among many, although it does not contribute anything new scientifically and is(...)

This study investigates short-term price reversals—temporary retracements following adverse daily returns—and 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(...)

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(...)

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(...)

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.(...)

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(...)