Quantocracy

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Recent Quant Links from Quantocracy as of 07/12/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 07/12/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Network Momentum as a Cross-Asset Factor [Aligrithm]

    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 momentum can leak into the assets it is linked to, so the return of one contract carries information
  • Quantitativo weekly [Quantitativo]

    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. Its never wasted effort, though: the ideas in the paper end up feeding new ideas and good conversations with other researchers. Quant Trading Rules is a reader-supported publication. To receive new posts and support my
  • Trend-Following P&L Is a Function of Autocorrelation (Closed Form) [Aligrithm]

    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 and factored it into two things you can measure directly: the autocorrelation of the traded returns
  • Feature selection: Wrapper-based feature selection methods [Trading the Breaking]

    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 require another data source, increase latency, make the model harder to interpret, or create another

Recent Quant Links from Quantocracy as of 07/10/2026

This is a summary of links recently featured on Quantocracy as of Friday, 07/10/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Cross-Asset Lead-Lag Trade in US Refiners [Beyond Passive]

    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 is the first of three parts: the idea and the evidence here, the execution and its costs in the
  • How Many Backtests Is Too Many? We Ran the Same Search Twice to Find Out [Rulyfi]

    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 perpetuals search twice, changing one thing: run B removed the swept configurations of two
  • Skill or Luck? We Ran 100 Million Bitcoin Backtests to Show You How to Tell [Rulyfi]

    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, 336,818 cleared a plain significance test (PSR > 0.95), but only 3,130 (0.003%) cleared a full

Recent Quant Links from Quantocracy as of 07/09/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 07/09/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Lookahead Bias in Fundamental Backtests: 66 Days, Measured [Tradevo Data]

    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 every fundamental data point in your backtest arrives early. We measured how early. Across the
  • Quantitative Value [Concretum Group]

    Value investing has been one of the most enduring investment philosophies, pioneered by Benjamin Graham and later popularized by Warren Buffett. At its core, value investing is based on the idea that markets are not always efficient, and mispricings occur, allowing investors to buy stocks at a discount to their intrinsic value. Graham, in Security Analysis (1934) and The Intelligent Investor
  • Intraday Breakout Details That Matter: Exits, Slippage and 0DTE Options [Cracking Markets]

    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 practical tests that are helping me move the strategy toward a more robust and more tradeable
  • How We Tried to Teach Claude to Read Like an Analyst, and the Market Said No [Tommi Johnsen]

    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 spent six months half-trusting and half-arguing with was the one that got it right, precisely because

Recent Quant Links from Quantocracy as of 07/06/2026

This is a summary of links recently featured on Quantocracy as of Monday, 07/06/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building the Market Effect Mini-Portfolio [TradeQuantiX]

    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 are amazing on their own. And nobody should go out of their way to allocate hard-earned portfolio
  • Quantitativo weekly [Quantitativo]

    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 decided to share quick summaries of the recent papers Ive read over the past few weeks. These
  • The Market Impact of Retail Options Trading [Relative Value Arbitrage]

    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. Today, retail investors account for a significant share of options market volume and are changing

Recent Quant Links from Quantocracy as of 07/04/2026

This is a summary of links recently featured on Quantocracy as of Saturday, 07/04/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Your Backtest Is Lying to You: Building a Walk-Forward Validation Harness in Python [Jdiv930]

    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 hadnt changed; my ruler had. That experience turned into a validation harness that now gates every
  • Collinearity in Parameter Sweeps: Plateaus, Not Peaks [Aligrithm]

    You vary your parameters, watch performance hold up across the range, and conclude the system is robust. The old article "Parameter Stability Beats Best Parameter" told you to prefer the stable region over the lucky peak, and you did. The trap is that you can run a parameter sweep that holds up beautifully and proves nothing, because the sweep never tested the parameter space at all. It
  • Jumping back in the pool(ing): pooling by asset class and portfolio weight distance [Investment Idiocy]

    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 instrument weights). Pool all instrument returns together At a minimum use a 40 year EWM for SR estimates (and
  • The Market Regime Filter [Financial Hacker]

    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 solution. Their market regime filter consists of three components for detecting trend, volatility, and
  • Silicon vs. Satoshi: Tactical Asset Rotation Between NASDAQ-100 and Bitcoin [Quantpedia]

    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 (550 trading days) and two priority variants over a seven-year sample spanning 20192026. The strategy consistently outperforms passive benchmarks on a risk-adjusted basis, achieving Sharpe ratios up to 1.69,

Recent Quant Links from Quantocracy as of 06/30/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 06/30/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Guardrails Make the Researcher: What an AI Agent Got Right (And Wrong) [Quantpedia]

    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 discipline caught) but whether you can trust an agent to find it, and the checks that decide the answer. Can
  • One of These Things Is Not Like the Others. Or is it? Pooling rule p&l estimates [Investment Idiocy]

    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 fit within; and then across, instruments. As I do anyway. In my second post I ran some experiments
  • Rolling, rolling, rolling…. updating statistical estimates yes or no [Investment Idiocy]

    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 momentum4 rule on CORN. The formal test found a structural break in 1989. It's fair to say the

Recent Quant Links from Quantocracy as of 06/28/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 06/28/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend Following (4/4): The Poor Man s Trend Program [Beyond Passive]

    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 behind it. This closing part asks the question that indifference skips what can a private investor
  • Estimating the Capacity of a Trading Strategy [Concretum Group]

    Recently, we shared a deep-dive on the importance of modeling transaction costs correctly, an exercise that inevitably forces us to confront the non-linear nature of market frictions. The Non-Linear Costs of Trading The Non-Linear Costs of Trading Concretum Research Jun 6 Read full story If you have ever worked on quant-trading desks, or been involved in advisory work, you already know that
  • Systematic FX trading with regression learning and transaction cost analysis [Macrosynergy]

    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 hindsight bias. Yet assessing PnL potential in backtests also requires estimates of transaction costs as
  • The Impressive Markets Hypothesis: Prices Still Know the Future [Alpha Architect]

    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. BlackRocks William Ezratty, Gerald Garvey, Timothy McDade, and Andrew Robinson, authors of the study The Impressive Markets
  • Why Mean-Variance Optimization Breaks Down [Quantpedia]

    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 VertoxQuants analysis is profound: raw plug-in MVO does not merely propagate estimation errorit systematically amplifies it. This error-maximization
  • FX Trend-Following: A Walk-Forward Validation Study [Quant Insti]

    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 2025, using 23 rolling walk-forward windows (3-year train, 1-year test), with parameters chosen for

Recent Quant Links from Quantocracy as of 06/24/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 06/24/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Breaking Badly: finding the structural breaks in parameter estimates [Investment Idiocy]

    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 sample. I've called the line #2 here the 'second most famous hockey stick graph in history'.
  • Global Tactical Asset Allocation, Automated With Python and IBKR [Concretum Group]

    Meb Faber published A Quantitative Approach to Tactical Asset Allocation in 2007. It became one of the most influential investment research papers of the past two decades. The rules are simple: five asset classes, one trend signal per asset, monthly rebalancing. The original backtest ran from 1972 to 2005 and produced a Sharpe ratio of 0.81, a CAGR of 11.7%, and a maximum drawdown of 9.5%. We
  • News and earnings sentiment agree, mostly at the extremes [Tommi Johnsen]

    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 sample grows. Thanks for reading! Subscribe for free to receive new posts and support my work. What this
  • Covariance Estimation for Wide Data [Eran Raviv]

    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 about 4.5 years ago on evenings, weekends, and holidays as a side-project. But I actually wrote my

Recent Quant Links from Quantocracy as of 06/21/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 06/21/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Most Portfolios Are Under Diversified [Quantpedia]

    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 portfolio of ten ETFs spanning equities, bonds, commodities, credit, private equity, and Bitcoin. Despite
  • Should You Trade Thin Stocks? [Concretum Group]

    Last month, we published two articles touching on a topic that over the past year has been, and continues to be, quite central to our research efforts: short-term trading opportunities in single-name equities. You can access the first two articles by clicking on the banners below. Identifying Stocks to Fade Identifying Stocks to Fade Concretum Research May 23 Read full story When Short Sellers
  • To Cluster Or Not To Cluster That is the Question… [Investment Idiocy]

    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 together, suggesting you should first fit within; and then across, instruments. Luckily, this is what
  • Academic Confirmation Bias [Anton Vorobets]

    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 often directly anti-scientific. I have seen many examples of this in finance and economics academia. In

Recent Quant Links from Quantocracy as of 06/17/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 06/17/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy Discovery [Quantpedia]

    This study investigates short-term price reversalstemporary retracements following adverse daily returnsand 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 commodities over the period 20062025, the strategy applies a long-term trend filter based on a
  • Honey I shrunk the weights (instead of the inputs!) [Investment Idiocy]

    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 data, when running a bayesian methodology for mean variance optimisation. I found two things. Firstly,
  • Value-at-Risk Estimation: Improved Estimates with the Harrell-Davis Quantile Estimator [Portfolio Optimizer]

    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 empirical quantile of the portfolio return distribution, or a linear combination of two subsequent