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,

In managing our book, we run trend strategies across multiple asset classes and at different speeds, with exposure ranging from slower multi-day systems to faster intraday signals. Regardless of model specifications, we keep observing the same pattern: small implementation details can produce surprisingly large differences in realized performance. As we like to say dispersion is in the details.

For U.S. equities, fixed income, and commodities, reconstructing long-term historical datasets is relatively straightforward, and we have already explored these challenges in several previous studies, including 100 Years of Multi-Asset Trend Following, Extending Historical Daily Bond Data to 100 Years, and Extending Historical Daily Commodities Data to 100 Years. Moreover, the broader methodology

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. Introduction: This is the

Last night the pipeline pulled 1,199 financial news articles tagged across nine GICS sectors. It started at 9 PM Mountain Time and finished around 1 AM. By morning we had sorted the catch. One hundred and six articles carried direction. The other one thousand ninety-three were ore. Thanks for reading! Subscribe for free to receive new posts and support my work. If you mine gold in the western

In the high-stakes arena of prediction markets, a counterintuitive pattern emerges: retail traders who correctly pick winners more than half the time still lose money, while automated traders with coin-flip accuracy pocket nine-figure profits. Using 222 million prediction market trades with directly observable terminal payoffs, the paper Who Profits from Prediction? Execution, Not

Hedging is a fundamental risk management tool. The most common hedging instruments are futures and options associated with a given underlying asset, when available. For equity exposure, index options are also widely used for hedging. However, hedging can be done not only through equity index options, but also through volatility derivatives, although the latter are considerably more complex and

There is by now a small mountain of vendor material claiming that AI agents will run hedge funds. The reality on the ground for those of us who actually do the work is more interesting and more useful. Agentic workflows, properly constructed, materially accelerate the parts of quant research that consume the most time. They also fail in specific, predictable ways that you can defend

Static currency hedging is a coin toss with predictable losers. The investor either pays the interest-rate differential as carry every month for years, or accepts the full drawdown when the dollar reverses. A trend-following forecast on EUR/USD, combined with carry treated honestly as a cost rather than a feature, sizes the hedge dynamically and finishes ahead of the US-domiciled investor over

Commodity markets are back in investors focus. After years in which equities and growth assets dominated portfolios, the recent rise in geopolitical tensions, inflation uncertainty, supply-chain fragmentation, and renewed resource nationalism has reminded allocators that commodities remain a critical macro asset class. That is why a newly released research paper, An Index of Commodity Futures

A few days ago on X, Charlie Bilello pointed out SPX had gained more than 16% over the previous six weeks and thats one of the biggest six-week rallies of all time. I looked back at the top 20 non-overlapping 30-trading-day rallies since 1950. They can all be found in the table below. Shaded rows are those instances where there was less than a 20% drawdown at the start of the rally. The current

In one of my recent articles, we looked at a paper that proposed a measure of how many strategies you effectively tested in-sample. I found the idea of such a measure really interesting and useful, so I went deeper into it, uncovered problems with existing measures, and ultimately came up with my own measure that has all the properties I desire from such a measure! What is the point of such a

Are the markets like they were in March 2000, right at the top of tech-bubble? I have been seeing lots of facts about how expensive or extended this market is. The Schiller P/E ratio near the 2000 top. The Buffett Indicator at over 2 standard deviations, similar to the 2000 top. US Total Stock Market Value/GDP at all-time highs, far exceeding the 2000 top. The weight of the top 10 stocks in the

This is the third installment in a series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read Part 1 and Part 2. We advocate combining multiple TAA strategies together into Model Portfolios to limit the risk of any single strategy underperforming. In our previous studies we selected strategies for our Model Portfolio based on recent return. In this

Even if you have skill, you can look wrong for a very long time. Cliff Asness A backtest (or even many of them) can tell you whether a strategy survived a historical test. It cannot tell you whether you were testing the right idea, in the right way, for the right purpose. That gap matters. There are plenty of methodologies for minimising over-fitting and increasing confidence that an

Why does sentiment predict returns at all? The textbook answer is that markets are slow. A positive headline drops at 4:01 PM. By 4:30, sell-side analysts at maybe a dozen banks are scrambling to update their models. By 6 PM, three of them have published preliminary notes. By 9 AM the next day, the buy-side has read those notes, decided, and placed orders. By close on day one, the price reflects

The NAAIM Exposure Index 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. That index, like any other sentiment indicator, is a useful gauge of the possible future direction of a market1 that can be incorporated into ones asset allocation process. In this blog post, I will analyze

Run the same three-asset strategy out of New York and out of Frankfurt. The American gets Sharpe 0.97 and a 22% drawdown. The European, holding identical positions but spending in euros, gets Sharpe 0.65 and a 45% drawdown. The trades are the same. The difference is a currency position the European never chose to take, sized by the strategys gross exposure rather than by any view on EUR/USD.

TL;DR: This post builds a forex backtest where a DeepSeek LLM labels market regimes from compact numeric summaries. We compare it to a KMeans baseline, apply monthly walk-forward optimization, and report out-of-sample results from 2023 onward. Prerequisites To fully grasp the regime-labeling approach in this blog, it helps to have a basic familiarity with clustering methods and market regimes. For

Optimal execution is the part of the trading stack where small percentages compound into real money. A long-only equity manager turning over 80% a year on a USD 5bn book pays roughly 4 bps 1.6m for every basis point of slippage. The textbook approach AlmgrenChriss (AC) or its risk-neutral cousin TWAP has been the operating standard for two decades, and for good reason: it is

Hello friends~ This post, we will discuss the introduction of a state of the art performance Python logging subsystem in quantpylib, and discuss some of the key design principles that allow us to achieve this. To my knowledge, among all Python logging frameworks, it is the lowest latency implementation out there. As an aside, I am focused on making quantpylib into a more mature platform for

A look at recent academic research connecting market volatility spikes to the underperformance of momentum strategies (especially for long/short versions of the strategy) The Big Picture If youve used momentum as part of your investment strategy over the past decade and found it disappointing, youre not imagining things. Haim Mozes, author of the study Volatility Spikes and Momentum,

This is the second installment in a multipart series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read Part 1. We advocate combining multiple TAA strategies together into Model Portfolios to limit the risk of any single strategy underperforming. In our previous study we selected strategies for our portfolio with the highest recent return. In this

From the trading desk to the portfolio committee, investors face a familiar question: how should alternative stores of value fit into a diversified portfolio? This research explores that question through a systematic dual-momentum framework comparing Bitcoin and physical gold in a rules-based tactical allocation model. Rather than debating ideology, we focus on practical portfolio construction and

A strategy you have been trading for years has just had a terrible month. Looking at the market environment, you think these trades should not have been taken. You make some small changes to your strategy and now the backtest shows that the terrible month is OK and the overall strategy statistics improve. Should you keep that change in your strategy? For the longest time, I would keep that

For much of last week I was at the National Association of Active Investment Managers (NAAIM) Uncommon Knowledge conference. NAAIM is a terrific organization that I have become more involved with over the years. NAAIM has published its NAAIM Exposure Index since 2006. I did some research a few years ago on the index to determine whether the numbers might be valuable as part of a model. I

Most sentiment research treats the question as one thing. Take a corpus of news articles. Classify each as positive, negative, or neutral. Aggregate to the daily level. Correlate with next-day returns. Report a coefficient. Argue about which classifier is best. This is tidy. It is also wrong, in a specific way that took us a while to see clearly. The articles being classified are not the same kind

In an era of increasingly fragmented market microstructure, the emergence of cross-asset connectedness between Crypto and public equity markets presents a critical challenge for modern portfolio construction. This blog post examines the recent working paper by Harin de Silva, The Attention Factor: The Speculative Risk You May Already Own, which identifies a previously underappreciated

A self-contained synthetic benchmark of a small mask-conditional CNN against calendar-projected linear interpolation and a per-slice SVI fit. A volatility surface marker is rarely a clean rectangle of quotes. Strikes go unobserved during illiquid hours, wings get crossed and then erased, broker stripes drop out across an entire maturity, and weeklies arrive at the desk with random missingness on

This post presents an accessible introduction to the Exponential GARCH (EGARCH) modela widely used tool in financial econometrics for modeling time-varying volatility in asset returns. Unlike standard GARCH models, EGARCH captures both volatility clustering and the leverage effect, whereby negative shocks tend to increase future volatility more than positive shocks of equal magnitude. The post

May got off to a positive start. But a strong start to May has typically been followed by a dip in the next few days. This can be seen in the study below, which was featured in this weekends subscriber letter. Of the 25 instances that rose on the first day in May since 1987, 17 of them closed lower 4 days later. Below is an equity curve that shows how it has played out over time. Ill note

The risk of overfitting is serious and can lead to significant losses. It has been discussed in previous posts. In this edition, we revisit the topic, given its continued relevance to quantitative strategy development. Formal Study of Overfitting in Trading System Design A serious problem when designing a trading system is the overfitting phenomenon, wherein the system is excessively tuned to

Why I'm publishing this I wanted to build a trading bot like a lot of people did once Claude integrated with TradingView. Took the leap, my strategies kept failing, and the backtests kept being way too optimistic compared to what happened when I actually ran them. Started digging into why. This post is what 10 days of running 22 popular strategies on real Binance fees with real L2 spread

The previous article extended the inverse-volatility allocation across SPY, TLT, and GLD back to 1968 using a synthetic price construction. Over fifty-eight years the strategy delivered a CAGR of 7.1%, volatility of 7.5%, a Sharpe of 0.97, and a maximum drawdown of 22%. The volatility-targeting overlay, justified by the persistence of volatility across the same window, kept realised vol close to

Several years ago, I had explored accuracy and performance of different ways to imply the Black-Scholes volatility. Jherek Healy proposed some improvements over my naive algorithm on his blog. Recently, a Linkedin post mentioned a new paper from Wolfgang Schadner which presents an almost explicit formula for the implied volatility. Almost because it actually relies on some implementation of the

Most trend-following research focuses on signal construction: how to detect trends better, faster, or earlier. The paper asks a different question, and arguably a more important one for investors: once a market regime has been identified, what is the optimal portfolio exposure in that regime? That is the central novelty of the paper which is available here. Traditional time-series momentum

The shape of yield curves in developed swap markets reflects the state of growth, inflation, and credit supply. This is primarily because central banks adjust short-term policy rates in response to evolving economic conditions, while their credibility helps anchor longer-term forward rates. In monetary policy regimes committed to price stability, and when short rates are above the zero lower

This is the first of a multipart series examining the selection of Tactical Asset Allocation (TAA) strategies based on recent performance. We are proponents of combining multiple TAA strategies together into what we call Model Portfolios to limit the risk of any single strategy going of the rails. In this study we ask, what if, each month, we selected strategies for our portfolio that had

Why the path to making money in trading runs through work youd better find interesting Data mining and vibe quanting are essentially the same thing. Both fundamentally and philosophically. Fundamentally, data mining says: Ill try enough rules until something sticks. Vibe quanting says: Ill get AI to try enough rules until something sticks. Same thing, different packaging.

The growing dominance of passive investing has fundamentally altered the dynamics of equity markets. A substantial share of trading volume is now driven by index-tracking strategies, which mechanically allocate capital based on index membership rather than company-specific fundamentals. This raises an important question: can predictable flows associated with index rebalancing be systematically

A research team in Zurich has shown that the financial sentiment classifiers running inside many automated trading and risk pipelines can be flipped quietly, undetectably, and for pennies by anyone with access to GPT-4o. Thanks for reading! Subscribe for free to receive new posts and support my work. The paper has been out a few weeks. It deserves more attention than its getting. The

Cohen, Malloy and Nguyen's Lazy Prices paper found that small year-on-year changes in 10-K filings predict large negative returns. Here is what the paper actually says, and how Snowflake Cortex AI and Semantic Views collapse the original eight-year engineering pipeline into an afternoon's work. On 23 February 2010, Baxter International filed its annual report with the SEC. The stock did

In Beyond 60/40 I argued that the classic balanced portfolio rests on an assumption that stocks and bonds will hedge each other and that the assumption fails when the macroeconomic regime changes. The argument was built on the post-2005 ETF era, the only window where clean real-price data exists for the three assets needed to test it. Twenty years made the case. Fifty-eight years sharpens