Well yes, I am pleased to announce that as of this week I completed final proof reading of my new book "The Art And Science of Trading"(AAST). It joins the list of my existing books with their own acronyms: Systematic Trading (ST), Smart Portfolios (SP), Leveraged Trading (LT) and Advanced Futures Trading Strategies (AFTS). Due to the inherent delays involved in global dead tree supply

The previous note showed that a backtest with a net Sharpe of 0.63 fell to 0.06 upon applying a single filter: the point-in-time membership of the index, that is, knowing with precision which companies belonged to the S&P 500 in each month of the sample. It should be noted that a paradox was then only hinted at: the most decisive input behind that result was not any price, nor any volume

Nine cohorts are cut at every anchor from the point-in-time S&P 500, three terciles of earnings predictability crossed with three of gross profitability, and each walks twenty sealed one-year windows from 2006 to 2025 at four holding periods in two book shapes: a long-short book that buys qualifying beats and shorts qualifying misses, and a long-only pair, every announcement of the cohort

The integration of artificial intelligence into algorithmic trading has ignited a race to transform generative text into systematic alpha. A new paper written by Steven Edwards empirically investigates whether constructing a synthetic consensus using large language models can simulate information aggregation dynamics or if it merely acts as a sophisticated echo chamber. By utilizing an expansive

The drift the academic literature describes does not pay in large caps: form the classic quarterly surprise book and the grid nets -0.5 percent a year across eleven sectors and twenty years. The reaction to the earnings number itself still does: enter each company the day its own number is known and the same surprise measure nets +2.2 percent a year, peaks at the ten-day hold, and fades by the

Over the past year I built an operating system for quantitative trading research: a harness that lets AI agents run the grunt work of strategy development end to end, inside rails that make self-deception structurally difficult. It recently carried two strategies from an empty folder through the full gauntlet: one crypto trend follower to a deployable, drawdown-controlled config, and one options

Trends in gold returns can be partly explained by the macroeconomic environment. Persistent monetary easing, dollar stability risks, and weak economic sentiment can each drive sustained demand for gold. This article shows how to construct simple point-in-time macro factors that capture these themes and combine them into a broad macro-support score that can serve as a trading signal for

One of the most enduring puzzles in finance is the apparent disconnect between Wall Street and Main Streetmarkets sometimes soar while the underlying economy stumbles, and vice versa. Paul Samuelson famously quipped that the stock market has predicted nine out of the last five recessions capturing the frustration economists and investors have long felt trying to extract reliable

Beta began as a way to compress a covariance matrix. The CAPM turned it into the equilibrium measure of exposure to aggregate market risk, priced by the market premium. Markowitz maps beliefs to portfolios. With common beliefs, a risk-free asset and market clearing, the CAPM adds one decisive identity: the common tangency portfolio must be the value-weighted market portfolio. The Capital Market

The literature on crypto quant trading keeps citing cross-sectional momentum as a durable edge with Sharpe around 1.1 to 1.5. I built exactly the paper spec, ran it against 303 weekly rebalances across 10 majors, no parameter tuning, no regime filters, no cheating. Annualized Sharpe came out at 0.37. It has been negative for the last 86 weeks straight. Here's what the run actually looked like

For the past months, almost every free hour I had went into one thing. Today its live. eigenscore.com In quant, everyone claims to be good, and there's no arena to settle it. Competitive programming solved that twenty years ago with rated contests. I built the same thing for us. What it is EigenScore is a competitive platform for quants: a problem archive and rated contests with a

The most liquid fund shelf a person can buy holds 3.3 independent bets. Dalio's chart needs fifteen. The Holy Grail of investing, in his words: find fifteen good, uncorrelated return streams and risk falls by roughly eighty percent while return stands still. At correlation zero, fifteen equal streams carry a quarter of one stream's volatility. Whether the shelf a person can actually buy

For those unaware, you generally make more money overnight than you do during the day when holding stocks. So much so that even if you just held the SPY overnight (buy at close, sell at market open the next day), you would net considerably more than if you held it during the day (buy at open, sell at close) Chart, line chart Description automatically generated This is known as the Overnight

I gave a research agent four years of prices with no predictable structure in them none, by construction and it came back with a long/short book, an in-sample Sharpe of 2.1, and a paragraph explaining the economics of an effect that does not exist. That is the measurement in this post. The more useful result is the second one: 88% of that number is accounted for by two integers how

People usually take All Weather for the fixed allocation Dalio gave Tony Robbins, which is the one every retail article prints, but that one is All Seasons and it carries no borrowing at all. All Weather proper is risk parity borrowed up until the book carries a 60/40's volatility, which means the borrowing is the strategy itself. We built that one and charged it the three-month Treasury bill

A 50/50 stock-bond portfolio that earned its target return with moderate volatility before 2000 requires an 80/20 allocation after 2000 to maintain the same profile. That 30-point shift is not a style choice or a bet on equities. It is the portfolio adjustment needed to compensate for the stock-bond correlation flipping from positive to negative, which McMillan documents across the G7 using

Twice in a single afternoon, it told me an idea was good. Twice, it was wrong, and the thing that caught the error both times was a number the headline verdict had quietly averaged away. Heres what the aggregate hid, and why I now trust the breakdown over the bottom line every time. The idea that survived The first idea was a zone-cycle pattern, price making a particular round-trip

Bitcoin's volatility does not mix randomly across time. High-vol periods follow high-vol periods, low-vol stretches extend themselves, and the transition probabilities are stable enough to build a forecast from. Borrego Roldn's 2024 study quantifies this with hourly and daily data from August 2018 to November 2024, constructing 3-state Markov chains around rolling log-volatility and

Most Tactical Asset Allocation (TAA) strategies trade once per month. Thats by design. Short-term market movement is mostly noise, and trying to time every zig and zag is a fools errand. A unique feature of our platform is the ability to follow these monthly strategies on any day of the month. Were not just executing the same signal on a different date were recalculating the signal

Turkish savers have argued about the same question for decades: keep the money in a lira time deposit and collect the interest, or convert it to dollars and hold. The dilemma is common enough to have a household name faiz mi, dolar m?, interest or dollars? Both answers have had long stretches of looking right, which is why the argument never ends. Figure 1 settles it the only way it

Financial markets process an enormous volume of corporate news every day. Earnings announcements, management changes, clinical trial results, insider transactions, partnerships, and regulatory developments can all affect a companys value. The challenge is not simply identifying whether a headline sounds positive or negative. Investors must understand its economic implications, anticipate how

Time-series momentum stands as one of the most reliable and heavily backtested anomalies in quantitative finance, serving as a foundational alpha source for modern managed futures and trend-following strategies. However, a recent academic paper by Matti Suominen and Erik Hjalmarsson, titled Boundaries of Time Series Momentum, uncovers a structural vulnerability that every practitioner must

In this episode of House of Quants, listeners will discover: Why quantitative trading is engineering: The episode challenges the myth of the lone trader searching for magical chart patterns and explains how institutional research operates as an industrial-scale system built around data, statistics, software, execution, and risk. How ideas become executable trading systems: Every strategy passes

Joel Greenblatt's Magic Formula ranks companies on two numbers and buys the ones that score well on both. One asks whether a business is cheap. The other asks whether it is any good. The claim in the book is that the pair works better than either number on its own. We tested that claim by running all three books over the same twenty one-year windows of the S&P 500, from 2006 to 2025.

The obvious rule is to buy what members of Congress buy and avoid, or short, what they sell. Across 17,859 investable common-stock disclosure events, that rule fails. One year after a disclosure became public, the typical purchase event trailed the S&P 500 by 5.36 percentage points and the typical sale event trailed by 6.39 points. Purchases did only 1.13 points better than sales on matched

Easley, O'Hara, Yang and Zhang take five textbook microstructure measures, compute them on Binance one-minute bars for BTC, ETH, XRP, SOL and ADA over January 2021 to July 2023, and ask a random forest to call the sign of tomorrow's change in the return distribution. Two numbers carry the paper. Crypto VPIN averages 0.47 against the 0.22 that the same authors found in E-mini and crude

Markowitz's 1952 paper deliberately begins after investors have formed beliefs about future returns. Portfolio choice is the second stage; estimating its inputs is the first. Rebuilding Robert Shiller's three-asset classroom example with two defensible US equity series moves the recommended equity weight from 38% to 26%, while estimated portfolio risk and return barely move. Repeating

A currency futures book built from other currency futures scores a Sharpe of 0.16. The same contracts, wired into a graph that also contains equities, bonds, and commodities, jump the FX sleeve to 0.66. That is Pu, Roberts, Dong, and Zohren, 64 futures, 2000 to 2022, volatility-targeted to 15%. Phylaktis and Yamani ran the pairwise version of the same instinct and got a harsher split: a

Form 4 looks unusually convenient for quantitative research. It is public, structured and tied to a regulatory deadline: in general, a reporting person must file by the end of the second business day after a reportable transaction. The SEC's Form 4 instructions state the rule and also show why a filing is more complicated than one insider, one trade. A filing can contain many

In July 2022 Wouter Keller published Bold Asset Allocation. This paper walks the aggressive variant, BAA-G4: hold the single strongest of QQQ, VWO, VEA and BND by relative momentum, or retreat into bonds and bills when a canary basket says so. We ran the published rules on the real tradable funds through seventeen registered one-year windows, 2009 through 2025. The crash switch is real: in 2022 it

The Sharpe Ratio1, one of the most commonly used measure of risk-adjusted performance2, is usually reported as a point estimate (Morningstar, Quantalys, etc.). Thanks to the work of Lo3, Opdyke4 and more recently5 de Prado et al.6, it is nevertheless well understood that such a point estimate [] does not convey information about statistical significance6, so that a more meaningful way to

A reader asked whether the birthplace lottery that decided equity outcomes also decided bond and gold outcomes. It did, and the shape of the answer is different from what the equity article found. The same measurement applied to bonds The construction matches the equity article. One unit of currency, invested at home in long government bonds, deflated by the home consumer price index, held from

Kelly, Malamud and Zhou opened their Journal of Finance paper by reporting out-of-sample market timing Sharpe ratio improvements "relative to market buy-and-hold" of roughly 0.47 per year, earned by a model with 12,000 predictors trained on 12 monthly observations. Daniel Buncic re-ran it on the same data with the same code and found the first problem in the first sentence. Buy-and-hold

Regime calls are a steering wheel, not a brake. We walked two strategies through the same registered one-year windows on the nine original Select Sector SPDRs. One holds all nine, equal weight, quarterly rebalance, always fully invested. The other holds three at a time, routed between a risk-on book of technology, discretionary and financials, a cyclical book of industrials, materials and energy,

A critical decision in wealth management is the allocation of capital across asset classes and cash. Theory and empirical evidence suggest that out- and underperformance of broad asset classes depends on the macroeconomic environment. Consequently, the systematic, low-frequency adjustment of portfolio weights in response to macro factors can produce material excess returns. It is a practical and

Recently we kicked off the momentum mini-portfolio series. The first article was a TSX market dual-factor momentum system with a hybrid trend following type exit. The system turned out to be pretty robust after being run through a vast set of robustness tests. We also took a deep dive into minimum system allocation, as I felt that topic is not as well explored online. If you missed that one you

The other I threw away, and the only thing that told them apart was a control group that most people building trading systems never bother to build. Heres what the control saw that my eyes couldnt. Thanks for reading! Subscribe for free to receive new posts and support my work. What a control group is, and why trading skips it In a drug trial you dont just give people the drug and check

Hernndez-Romo and co-authors ran four population-based optimizers 31 times each, 1000 generations per run, population of 50, tuning an eight-parameter moving-average strategy on five-minute BTC/USDC bars from January 2020 to March 2025. Differential Evolution won on average annualized return with 107.36%, against 92.05% for the Whale Optimization Algorithm, 73.47% for Particle Swarm and 61.80%

Johann Christian Lotter opens his workshop deck with four lines that do more work than most methodology chapters. A model is not the reality. The reality is unknown. The same reality can be described with many different models. The best model must be selected by experiment. Every argument in Pillar 1 reduces to those four lines, and the deck states them in under thirty words before showing a

We document a persistent intramonth momentum cycle in U.S. sector ETFs that yields meaningful risk-adjusted returns when properly sequenced. Using the nine original Select Sector SPDR ETFs and SPY as the market benchmark from December 1998 through June 2026, we show that trailing 252-day sector momentum generates a positive spread on the first trading day of the monthand then sharply reverses

In May I published a case study on running alpha research through a team of LLM agents: four roles, instrumented handoffs, and roughly a 2 lift in hypotheses tested per week. The post was careful about what it measured. It was silent about a risk I had not thought to price. Here is the risk. If your research agent and your competitors research agent are the same base model, then whatever that

Take two stocks that both end the month flat. The first bled lower for two weeks, then clawed all the way back. The second ran up for two weeks, then gave it all back. Same start, same finish, same zero return. Sort every stock in the market by past return and these two land in the identical bucket, because return only sees the endpoints. Gulen and Woeppel show that the shape between the endpoints

An 8-week dual-momentum switch between GLD and IBIT returns 79.91% a year at a Sharpe of 1.64. Vojtko and Dujava report that number on a Quantpedia note covering 31 December 2018 through April 2026, weekly rebalance at Wednesday's close, cash when both lookbacks print negative. Hold the other column: maximum drawdown is still -43.94%. They tested ten lookbacks and published the peak. The

In September 2017, Alpha Architect published VIX and Trend-Following, the Killer Combo?, an empirical examination of whether volatility information could improve a traditional trend-following allocation model. The central idea was intuitive: market volatility may contain useful information about how quickly an investor should measure momentum. A shorter momentum window may react more effectively

Factor timing is the seductive idea that you can do better than holding a basket of factors: lean into value when value looks cheap, load momentum when momentum is running, cut a factor when its volatility spikes. Every one of those moves has a tidy formula and a plausible story. And when you run them out of sample on a clean universe, almost all of them lose to the dumbest thing you could have

I recently came across Lpez de Prado et al.'s (2026) treatment of the Sharpe ratio estimator: Normal, with the true as its mean, and a variance (see the Normal section of the image below) conditioned on sample size, the return series' autocorrelation, its skewness, its kurtosis, and the level of itself. It's an elegant correction but stubbornly a bell curve underneath.

Markets have been behaving unusually lately. In May, equity indices rose while volatility and skew also increased, a relatively rare occurrence historically. Since last week, the same phenomenon has emerged again, with the spot/volatility correlation turning positive. Is this still a rare occurrence? We dont know. But one thing is clear: regime detection is becoming increasingly important in

Three papers crossed my desk this month. One detects market manipulation with graph neural networks and reports 98.7% accuracy at 8.3 milliseconds per decision. A second, same author cluster, detects anomalies in high-frequency trading and reports a 15% accuracy improvement with an F1 of 0.915. A third uses XGBoost on Bitcoin technical indicators and reports 92.40% accuracy with a ROC AUC of

You lock in gains, you stop giving winners back, and, the part everyone repeats, your win rate goes up. I tested it on 3,966 of my own trades. Two of those three claims are false, and the third one cost me a fortune. The setup I run an automated futures system with a fixed exit: each strategy takes its full target or its full stop. No partials, no breakeven moves; the trade rides to one of two

Where this came from. A reader replied to Autopsy 001 with a Morningstar page showing NANC ahead of the index and a fair question: what am I missing? They were not missing anything. The fund really has outperformed. This is the answer to what that outperformance is made of. Unlike our other case files this is not a pre-registered test of our own hypothesis. It is a descriptive analysis of

Over the past few years, the liquid alternatives space has moved decisively into a new ETF era. What was once a narrow category now stretches from CTA to hedge-fund replication strategies, sometimes pairing those return streams directly with core stocks and bonds exposures. And thanks to their low-cost, daily liquidity and ease of access, these vehicles are attracting progressively more inflows.