Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket from @quantpedia.bsky.social

Inflation as a trading signal [Macrosynergy]

The AutoTune filter [Financial Hacker]

Recent Quant Links from Quantocracy as of 04/16/2026

Looking Inside The Black Box [Vertox Quant] People often criticise how ML models are just black boxes that take in some features and spit out a prediction. While some models (like linear regression) are naturally a lot more interpretable than others (like neural networks), its wrong that you cant figure out why a model made a certain prediction and how the different features affect the prediction. In this article, we will look at some

Looking Inside The Black Box [Vertox Quant]

We Trusted FinBERT to Filter the Noise. It Was Also Filtering the Signal from @tommijohnsen.bsky.social It starts with a basic problem every quantitative researcher faces: you have a universe of stocks, a universe of news, and a question. Which of todays headlines actually matter for tomorrows price? Step One: Finding the News Before you can classify sentiment, you have to collect articles. The naive approach search for a company name and take everything produces a lot of noise. An

We Trusted FinBERT to Filter the Noise. It Was Also Filtering the Signal from @tommijohnsen.bsky.social

Time Series Database Review: RayforceDB from @antonvorobets.bsky.social RayforceDB is a recently open sourced time series database that offers blazingly fast performance. It is built with inspiration from kdb+, which is also known for its fast performance and minimal application size. RayforceDB offers similar benefits, being written in pure C and having a binary size of less than 1MB. Another benefit of RayforceDB is that it offers Python bindings with minimal

Time Series Database Review: RayforceDB from @antonvorobets.bsky.social

Annual performance update- year 12 [Investment Idiocy] This is how I started last years update: "Mad out there isn't it? Tarrifs on/off/on/partially off/on... USD/SP500/Gold/US10/Bitcoin all yoyoing like crazy." Well the orange peril is still at it, and as I write this the global supply of oil has been severly curtailed for several weeks now; with a certain amount of reaction in oil futures (which some of it perhaps supressed since

Annual performance update- year 12 [Investment Idiocy]

Recent Quant Links from Quantocracy as of 04/15/2026

What's the Optimal Stack? [Return Stacked] The most common question we hear from advisors is whats the optimal stack? So we ran the optimizer bootstrapping 10,000 simulated 25-year histories across five asset classes to find the portfolio that would have maximized return at 60/40 volatility. The answer is mathematically elegant and practically unusable. In this piece, we walk through why the optimal portfolio would have been

What's the Optimal Stack? [Return Stacked]

A Historical Look At $SPX on Tax Day [Quantifiable Edges]

The Many Facets of Stock Momentum: Distinguishing Factor and Stock Components [Alpha Architect]

Recent Quant Links from Quantocracy as of 04/13/2026

Meb Faber's "Tactical Yield", Simple and Intuitive from @allocatesmartly.bsky.social This is a test of Meb Fabers Tactical Yield from T-Bills and ChillMost of the Time. Backtested results from 1930 follow compared to a benchmark of 50% int-term US Treasuries (IEF) and 50% US corporate bonds (LQD). Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time.

Meb Faber's "Tactical Yield", Simple and Intuitive from @allocatesmartly.bsky.social

Data transformations: Data shape and predictive features [Trading the Breaking] Imagine that a team downloads a price series, defines a target, applies a transformation, and moves on to signal design, model fitting, validation, and execution. That sequence looks efficient. However, the transformation of the data is is the first act of model construction. That is why data-shape transformation sits at the true front line of feature engineering. The problem is whether the

Data transformations: Data shape and predictive features [Trading the Breaking]

When Correlations Fail: A Bayesian Approach to Sizing Sparse Overlays [Beyond Passive] A portfolio of seasonal strategies presents a problem that modern portfolio theory was not designed for. Most of these strategies are active fewer than sixty days per year. Many pairs share zero overlapping observations. The covariance matrix the standard tool for combining return streams produces nothing but noise. You need a different approach. The Foundation The IVOL three-asset core

When Correlations Fail: A Bayesian Approach to Sizing Sparse Overlays [Beyond Passive]

To Trend or Not To Trend? (Wrong question) [Robot Wealth] Someone asked me recently whether strategies based on mean reversion, trend following, and momentum are good or just data mining. Its a reasonable question, but it reveals some confusion that arises from mixing up two things that sound similar but are very different. Mean reversion, trend, momentum: these arent edges. Theyre labels for how prices move. They describe patterns, not

To Trend or Not To Trend? (Wrong question) [Robot Wealth]

Factor MAX: A New Signal for Predicting Factor Returns [Alpha Architect]

Recent Quant Links from Quantocracy as of 04/09/2026

Systematic Tactical Allocation in Emerging Markets vs. U.S.: A Momentum-Based Approach from @quantpedia.bsky.social

David Varadi's "Growth and Inflation Sector Timing", a Wildcard Strategy from @allocatesmartly.bsky.social This is a test of a novel strategy from David Varadi: Growth and Inflation Sector Timing. Backtested results from 1991 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time. Logarithmically-scaled. Click for linearly-scaled results. Members know that we are especially interested

David Varadi's "Growth and Inflation Sector Timing", a Wildcard Strategy from @allocatesmartly.bsky.social

Large Language Models in Trading: Models and Market Dynamics from @harbourfrontquant.substack.com

Recent Quant Links from Quantocracy as of 04/05/2026

A Junior Quant's Guide to Event-Driven Trading [Quant Galore] You have to know that it didnt get that way overnight. And more often than not, it didnt get that way quietly. On every step of the way down, companies like this are forced by regulators to publicly share every detail on exactly how business is going and what theyve got planned. All you have to do is look for it. So, thats exactly what we did. A Primer on Advanced Event-Driven Trading

A Junior Quant's Guide to Event-Driven Trading [Quant Galore]

Two Calendar Effects at the Month Boundary [Beyond Passive] This article examines two distinct effects that share the same calendar window and the same tickers. The first is a pure bond seasonality: TLT tends to weaken in the first week of each month and rally in the last few days, regardless of what equities do. The second is a conditioned reversal trade: when stocks outperform bonds during the first half of the month, the underperformer tends to recover

Two Calendar Effects at the Month Boundary [Beyond Passive]

Uncertainty [Quantitativo] Doubt is not a pleasant condition, but certainty is a ridiculous one. Voltaire Voltaire was arguably the most influential intellectual of 18th-century France. More than that, he was a provocateur. He spent his life as a one-man war against dogma, against anyone who claimed to know the truth with absolute certainty. The Age of Enlightenment didnt begin with answers: it began with

Uncertainty [Quantitativo]

When Elon Musk Cant Sleep, Your Portfolio Feels It from @tommijohnsen.bsky.social It is 3:47 AM Pacific Time. Elon Musk, reportedly on his fourth espresso and second viewing of a documentary about Roman emperors, picks up his phone. He types something. He posts it. Within eleven minutes, Teslas stock has moved. Within forty, three semiconductor companies have been dragged along for the ride. By morning, a fund manager in Oslo is explaining to her clients why their portfolio

When Elon Musk Cant Sleep, Your Portfolio Feels It from @tommijohnsen.bsky.social

How imputation helps statistical learning for macro trading signals [Macrosynergy]

One Year Later: Is ChatGPT Finally Worth Using for Quantitative Analysis? from @quantpedia.bsky.social

Recent Quant Links from Quantocracy as of 03/31/2026