Below I have shared my story of an idea that passed four checks Id set in advance and died on the fifth, and why the fifth one is now the first thing I run. The setup I like to day-trade MNQ, and Id built a framework around specific price levels that I have created. The core claim was simple: price reacts at certain levels within each hundred-point block. Falls into one, bounces. Thanks for

Every mean-reversion trade starts with a lie you tell yourself about where price "should" be. You call it fair value, you subtract it from the spot price, and you bet the gap closes. Most traders reach for a moving average and stop thinking. Xu, Firoozye, Koukorinis, Treleaven, and Zhu at UCL take the question seriously and reframe fair value as the output of a tunable low-pass filter,

Move like the wind, be still as the mountain, the old strategists taught. The moving-average ribbon promises to tell you which moment you're in. The catch: it's built from the past, so the wind it reads has already blown. Stack a fan of moving averages on a chart and assign colours to them. Wait for the bands to fan out and align, and price is "in a trend." When the lines

Split one Bitcoin day into two pieces and only one of them predicts anything. The piece that runs while US stock exchanges are closed, from yesterday's 4pm close to today's 9:30am open, carries a signal for tomorrow's VIX. The piece that runs while those exchanges are open carries nothing. Gu, Lin, and Liu ran five-minute Bitcoin data from 2018 to 2023 through that split and found

We spend our trading lives looking at charts, plugging in different indicators, not to realize that we are painting on top of the same old picture. Instead of trying a different color of paint, we need to change the canvas. And in doing just that, you may have won yourself a spot in the lead. Heres the thing: Everyone is looking at the same charts. Everyone has access to the same information

Every indicator you use on a rolling window, momentum, RSI, Bollinger bands, discards the order in which events occurred. Two 2-hour windows with identical total return and identical range expansion score identically, even if one saw price rally first and volatility respond, while the other saw volatility spike first and price catch up later. These are different market events (a breakout versus a

Trend following was one of the most persistent anomalies in finance for nearly two centuries, yet its performance deteriorated sharply after the 2008 financial crisis. An analysis of approximately 100 liquid futures contracts from 1995 to 2025 shows that this decline is highly selective. The decisive factor is not asset class, liquidity, market electronification, or strategy crowding, but

Most quantitative backtests start with prices: a clean historical database, ideally free of survivorship bias and the usual data traps. We have written a lot about that already, and shared practical ways for independent researchers to build more reliable datasets. At some point, though, research usually asks for more than prices. How does a strategy behave around major macro announcements? Does

Key Takeaways An OHLC bar can show that take-profit and stop-loss prices were both touched. It cannot show which came first. Run the same search under both TP-first and SL-first before promoting a candidate. We evaluated 48,825,000 identical configurations twice, forming 97,650,000 backtest runs. Total return changed in 37,168,124 pairs, or 76.13%. Among the changed pairs, the median SL-first

Twenty years of retail research points the arrow one way. You read the VIX, and the VIX tells you where the S&P 500 is going. Oversold VIX means complacency, sell stocks; spiked VIX means panic, buy the dip. Connors built a cottage industry on it, and every trading forum still runs some version of the "VIX says buy" screenshot. Rob Hanna ran the tests both directions and found the

Modern portfolio theory is built on a remarkably intuitive idea: investors should earn higher expected returns for bearing greater risk. This principle lies at the heart of the Capital Asset Pricing Model (CAPM), one of the most influential models in financial economics. According to the theory, stocks with higher systematic risk should compensate investors with higher long-term returns. Yet among

In this episode of House of Quants, listeners will discover: My personal perspective: The episode traces the journey from engineering, statistics, data science, and algorithmic trading toward quantitative research, explaining how each field contributed to a deeper understanding of uncertainty, validation, execution, and risk. Why long-term market forecasting often is useless: It examines the

When inflation shifts from low to high, a traditional 60/40 equity-and-bond allocation breaks down because both fall together. You need real assets to act as the ballast. Ray Dalio Inflation is one of the most powerful forces in asset allocationand one of the hardest to measure in real time.Everyone understands why it matters. The harder question is how to capture it before markets

Alpha rarely arrives as one heroic discovery. It is more like an ant colony carrying a leaf many times its own size: dozens of small contributions, each unimpressive on its own, somehow producing an impressive result. Unfortunately, research also resembles an ant colony in another respect. Much of the work involves running in circles. The first version

In a previous blog post, I described the NAAIM Exposure Index, which represents the average exposure to U.S. equity markets as reported by members of the National Association of Active Investment Managers (NAAIM) in a weekly survey. In this second post of this series on sentiment indicators, I will show how that survey of professional money managers can be turned into a relative sentiment

I wrote my newest paper mainly to challenge the conventional way of judging an investment or trading strategy through a single observational point of its performance metric an approach that discards precious information about a strategy's effectiveness and can lead to false conclusions. Rather than routinely collapsing a strategy's performance metric to one observational scalar, I

Networks are everywhere. All you need is an eye for them. Albert-Lszl Barabsi. Albert-Lszl Barabsi is a Romanian-born Hungarian-American physicist, renowned for his pioneering discoveries in network science. In his seminal 1999 paper with Rka Albert, Emergence of Scaling in Random Networks, he reshaped how we understand connected systems. His 2002 book Linked carried the

Cross-sectional momentum has one job: at each rebalance, order a universe of assets from worst to best, buy the top, sell the bottom. Everyone agrees on that. Where strategies quietly disagree is on how they produce the order. Classic momentum sorts on the past twelve-month return. A neural net predicts each asset's next return and sorts on the prediction. Both treat the ranking as a

The first part described the idea and put a gross Sharpe of about one and a half on it. The second part set it in front of a brokers fees and an integer number of shares, and watched most of the edge go to the cost of trading. This part adds a second signal, drawn from the same spread. It does not raise the return by much. What it does is trade far less, and on a small account that is worth

This article shows a practical method for optimizing equity portfolios with point-in-time macroeconomic information and sequential statistical learning. The learning process relies on neural networks, as they learn portfolio weights directly from a full historical panel of macroeconomic divergence factors and return data. They do not require stock-by-stock theoretical priors for model

The CAPE that Cried Wolf Dino Palazzo (Board of Governors of the Federal Reserve System) May 2026 The Capital Spectators Takeaway The paper reports that traditional CAPE ratios false warnings of market overvaluation since the 1990s are an accounting illusion caused by mandatory R&D expensing and volatile special-item write-downs. By stripping out these regulatory distortions, CAPE-H

Key takeaways Neither entries nor exits controlled every result. Win rate was more sensitive to exits in all ten market-direction jobs, while maximum drawdown was more sensitive to entries in all ten. Changing an entry indicator was a much larger move than nudging that indicator's period. Treating both as "entry tuning" hides the useful distinction. For the study's

Recent interesting research from Cakici and Zaremba, highlights an often-overlooked aspect of machine learning for equity return prediction: the choice of prediction target. Rather than focusing on increasingly sophisticated model architectures or feature engineering, the authors show that how returns are represented during training has a much larger impact on predictive performance. In

Advances in computing power, declining hardware costs, and the rapid rise of machine learning and algorithmic trading have fundamentally transformed modern financial markets. While these technologies have improved market efficiency and execution, they have also introduced new challenges and risks. In this post, we examine research on the impact of algorithmic trading, from its influence on

Chaos theory is a fancy topic that alludes to a world almost as mystic and mysterious as quantum physics. Everyone has heard of it, but not many people really know what it means or how to use it. A Mandelbrot Set, or infinitely complex shape described by a simple formula, a classic example of chaos theory where simple rules create complex results. Fortunately for us, you cant judge a book by

Feed 920 firm characteristics into a Lasso to predict next month's stock returns and it will do something that should stop you cold: it loses to a model that predicts zero for every stock. Not "underperforms a good benchmark." Loses to the number 0. Shen and Xiu prove this is not bad luck or a coding bug. In the regime where economics and finance actually live, where signals are

I ran the same trend-following strategy across futures, stocks, FX, and crypto. On futures it produced a net Sharpe of 0.73. Crypto came in at 0.56. US stocks barely broke even at 0.42. FX was marginal at 0.24. Same signal, same methodology, four very different outcomes. All four have near-zero correlation to the S&P 500. The difference isn't the signal. It's the market. This is a

Are 0DTE straddles systematically overpriced? It is probably the most argued question in options trading since daily expirations took over SPY volume – and it has a testable answer. We replayed 193 Wednesday sessions from July 2022 through April 2026 on the FlashAlpha Historical 0DTE endpoint, snapshotting the same-day straddle at 10:00 ET and comparing its implied move to what SPY actually did

The short version The RSI 70/30 rule (above 70 = overbought, sell; below 30 = oversold, buy) is the most repeated pattern in trading. We measured what price actually does after the signal, across six timeframes and four assets, and ran the traded version through six market regimes. It has no edge: forward travel is ~0.00% where the sample is real, it points the wrong way on the 4-hour chart, and

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. Its never wasted effort, though: the ideas in the paper end up feeding new ideas and good conversations with other researchers. Heres the 3rd edition of the Quantitativo weekly, featuring papers that caught

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 what its users think. The claim on trial MACD is on every platform, in every course, behind every

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: Its very easy to look at a chart like this with hindsight and identify the top of each

Man AHL has recently published a research piece titled A Trend Following Deep Dive: Cash (Equities) Is King (Panjabi, Bordigoni, and Buchanan, 2026) which has resonated not only with researchers in the trend-following space but also with those specializing in equity markets. The authors show that cross-sectional momentum techniques can be successfully applied across equity-style factors,

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 entries and exits with a hysteresis band so the signal stops flip-flopping, and validate with a grid of

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 brokers fee schedule and an integer number of shares, and the single number becomes a curve one that depends almost entirely on the size of the

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 recurring question has been the same: is the market mispricing the information, or is it rationally

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 reads 14 days of Bitcoin OHLCV and predicts the next day's close. The numbers are real and they

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. How data problems create false strategies: It examines survivorship bias, timestamp errors,

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 doesnt just fetch datait acts as an autonomous AI research agent capable of navigating the hypothesis discovery loop. By placing an LLM within a

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 had computed and believed. The pipelines job is narrow. For roughly 850 tickers a night, it reads

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 exceeding their previous max drawdown. We looked at this subject way back in 2020 and concluded

Moving averages are the first fools 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 Trading Strategies Just one more indicator, bro. I swear. Were almost there. But maybe its not

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 size sits below this row with 97% probability. It exists because deflated Sharpe has a blind zone.

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 authors argue that momentum profits are largely driven by institutional cash-management mechanics.