Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 10/22/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 10/22/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Momentum timed with the rate of change (ROC) on the S&P 500’s ten steepest trends [Quanter Lab]

    Momentum is one of the oldest ideas in trading: buy what has been rising, and hold it while it keeps rising. Its known weakness is the fall. When a trend breaks, yesterday's strongest stocks can fall the hardest. A common fix is a second check before holding a stock: its rate of change, how far its price has moved over the last few weeks, in percent. We tested that fix. Every six months we
  • “Only EURUSD Strategy You Need” Backtest [YuRa Trading]

    A popular TradingView script trades EURUSD at the New York stock market open: a buy stop at the last swing high, a sell stop at the last swing low, a 2:1 target. We measured it on 2019 to 2021 one-minute data, 664 trades. It made +0.524 pip per trade before costs. Spread and commission cost 1.21 pip. In 2021 it lost money even before costs. Short answer Does "The Only EURUSD Trading Strategy
  • EMA9 Crossing the VWAP: Real Direction Before Costs, No Edge After Costs [Krueger Algorithms]

    The VWAP (volume-weighted average price since the session start) is seen as the fair price of the day, the EMA(9) as a fast trend line. When the fast line crosses the VWAP upwards, you buy, downwards you sell. The rule appears in many day-trading courses and was missing from our earlier indicator scan: that scan included EMA crossovers with each other (one of 640 cells with net t 2) and price
  • Stock Price Bubble Dissection Requires a look at Fundamentals and Speculation [Alpha Architect]

    Stock market bubbles are easy to identify in hindsight. The dot-com boom, the run-up to the global financial crisis, and the surge in technology stocks during the pandemic are all familiar examples. But identifying a bubble while it is forming is much more difficult. The challenge is that rising stock prices do not necessarily indicate speculation. Prices may increase because investors expect
  • Commodity Currencies: Predictable Only Where They’re Thin [Aligrithm]

    A one-standard-deviation rise in a country's commodity export prices predicts a 0.39% appreciation of its currency against the dollar over the next month. Jeanneret and Sokolovski find that slope in eight commodity-exposed currencies from January 1985 to April 2020, and the Australian dollar and the Canadian dollar contribute almost none of it. The forecast sits in the thinner names. In the
  • RSI(2) and Other Pullback Rules on 15 Markets [Krueger Algorithms]

    Buying after a short, sharp sell-off provides liquidity to those who have to sell, and it pays when the price comes back. On the S&P 500, Nasdaq 100 and Dow exactly this rule passed our holdout: RSI(2) below 10 above the 200-day average, +48 bps per trade after costs. The obvious question: is this a general mechanism that carries on other markets just as well, or a peculiarity of the US
  • Bitcoin Runs on a Clock: Why Every Price Indicator Dies and the Halving Clock Doesn t [Quantpedia]

    For anyone trading or investing in Bitcoin, a familiar indicator can become a dangerous comfort blanket. Josh Molnars Bitcoin Runs on a Clock argues that classic cycle signalssuch as MVRV, the Mayer Multiple and Pi Cycle Tophave weakened as Bitcoins price swings become less extreme. Thresholds calibrated to earlier cycles may consequently stop triggering, leaving investors waiting for
  • Stairway to Heaven Gold Breakout Backtest [YuRa Trading]

    A gold breakout system on r/algotrading showed +188% in 18 months, with a profit factor of 1.74. Its author asked if it holds up outside that window. We ran it on 2019 to 2021 real-tick data with full costs. At its own speed of about three trades a day, every matching setting lost 76% to 97% of the account. Short answer Does the Stairway to Heaven gold breakout strategy work outside 2025? Not on

Recent Quant Links from Quantocracy as of 10/18/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 10/18/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Less Wrong [Quantitativo]

    The biggest risk is that you have a losing strategy when you think you have a winning one. Jeff Yass Last month, while listening to Annie Dukes TED Talk about risk, I learned the quote above from Jeff Yass, founder of Susquehanna International Group. Annie is a former professional poker player turned author and decision-strategy consultant. Her famous book, Thinking in Bets (2018), is a
  • Hedgefundie’s Excellent Adventure (HFEA) tested 2010-2025 [Quanter Lab]

    Hedgefundie's Excellent Adventure is a well-known strategy built from leveraged funds. A leveraged fund borrows so that it moves more than the market each day. The strategy borrows its name from the film Bill & Ted's Excellent Adventure. Like the film, it travels through time very well, as long as it travels backwards. It holds two funds. UPRO moves three times as much as the S&P
  • The Messy Business of Building a Futures Security Master [Handelsmeisterei]

    The figures below are the interactive versions of the figures in the article, in the articles order and under the articles numbers. Hover a series for its values, drag across a chart to zoom and double-click to reset, and click a legend entry to hide or isolate a series. The security master connects broker instruments to signal and cost data. Figure 1. The security master connects
  • Chasing Skew in Trend Following [Concretum Group]

    Readers familiar with the trend following space (whether for professional reasons or out of personal interest) probably know that there are certain evergreen themes that cyclically resurface in online discussions. The Anatomy of a Successful Trend Program The Anatomy of a Successful Trend Program Concretum Research May 9 Read full story You Can Trade (Almost) Like Mulvaney You Can Trade
  • Hidden Markov model vs VIX regime detection [Quanter Lab]

    Many investors change their mix of stocks with the market's mood. When the market is calm, they lean on the stocks that have been rising. When it turns choppy or stressed, they move toward profitable, cheaper companies, which they expect to hold up better. Investors call these moods regimes. The hard part is telling which regime the market is in, and two ways are popular. The first is the
  • The Real IS OOS Sharpe Haircut (Closed Form, Not “50%”) [Aligrithm]

    Twelve commodity futures, three momentum windows and an intercept, ten years of daily closes: 444 coefficients on 2,520 rows. Jacquier, Muhle-Karbe, and Mulligan compute the fraction of the in-sample Sharpe ratio this linear book is expected to keep on new data. Their commodity case study puts that fraction at 30 percent for a ten-year backtest. They attribute the rule of thumb that keeps 50
  • The Role of Liquidity in Stock Returns and Option Pricing [Relative Value Arbitrage]

    Liquidity is an important factor in financial markets, but it is not always the first consideration in asset pricing, trading, or risk management. In this post, we examine the importance of liquidity from two different perspectives: its predictive relationship with stock returns and its role in option pricing and hedging. How Liquidity of Stock Affects Its Future Expected Return The liquidity of a
  • A Trend Factor for the Crypto Cross-Section (and Its Overfit Tell) [Aligrithm]

    Fieberg, Liedtke, Poddig, Walker, and Zaremba build a weekly score from 28 technical indicators, buy the value-weighted top fifth of coins, and short the bottom fifth. From April 2015 to May 2022 that hedge earns 3.87% a week, t-statistic 5.19, annualized Sharpe ratio 1.94. They then rebuild the same trade under 55,296 research designs. The published Sharpe sits at the upper edge of that pile. The

Recent Quant Links from Quantocracy as of 10/15/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 10/15/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Financial Mentor’s OptimumV [Allocate Smartly]

    This is a test of OptimumV from Todd Tresidder of Financial Mentor. Backtested results from 1987 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 real-time. Logarithmically-scaled. Click for linearly-scaled results. Tresidder has requested that we not disclose the OptimumV ruleset. To be
  • Clenow’s Stocks on the Move tested on the S&P 500 [Quanter Lab]

    Stocks on the Move is a 2015 book by Andreas Clenow. It gives simple rules for picking stocks from the S&P 500. In short: buy the stocks that rose fastest and most smoothly over the last three months, and stop buying new stocks while the whole market is falling. The book's promise is to beat the market. We ran these rules on the S&P 500 from 2006 to 2025. On each day we used the
  • PEAD Died in 2016, and the Death Can Be Dated [Dead Signals Lab]

    The first note of this series told how a post-earnings drift sleeve with a 0.63 Sharpe died upon applying point-in-time membership: that number was an inclusion look-ahead, not an edge. A question was left pending, promised as an epilogue: with the bias corrected, what remains of the underlying phenomenon? Post-earnings announcement drift (PEAD) is the dean of the anomaly literature, documented

Recent Quant Links from Quantocracy as of 10/14/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 10/14/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does More History Help? A Market-Regime HMM and the Trouble with Out-of-Sample Evaluation [Quantpedia]

    This paper investigates whether longer historical training periods improve the out-of-sample performance of a Hidden Markov Model (HMM) used for identifying financial market regimes. Four four-state Gaussian HMMs are compared, differing only in the starting point of their training data (1970, 1980, 1990, and 2000), while being evaluated on the same test period. The model uses five market-based
  • I Built A Recursive Self-Improving AI, It Learned How to Outperform Bitcoin [Paper to Profit]

    The media has been doom and gloom about AI killing us all (with a 10% chance). The biggest sticking point to this argument is around the threat of recursive self-improvement in which the current models train the next generation and somehow get out of control as if we as people are not the ones tuning and aligning them to our social needs. But Heres the Thing Beyond just being a
  • Does Complexity Actually Help? (flagship) [Aligrithm]

    Buy-and-hold on the volatility-scaled US market earns an out-of-sample Sharpe ratio of 0.5494 from January 1940 to December 2020. The 12,000-feature timing model that Kelly, Malamud, and Zhou offer as evidence that performance rises with complexity earns 0.4851 on that same window. A ridge regression on the 15 Goyal-Welch predictors, with an intercept and a 120-month expanding window, earns
  • Learning to Rank, the Original: Pairwise and Listwise Cross-Sectional Momentum [Aligrithm]

    You rebalance a long-short of NYSE names once a month, and you keep the top 100 and the bottom 100. The score's level never enters the book. A loss that matches next month's return, one name at a time, can land on the same average error and still hand you the opposite two names. Pairwise and listwise losses train the order the book actually trades. Poh, Lim, Zohren, and Roberts run that
  • The Questions That Change Everything [Robot Wealth]

    Simon from The Algorithmic Advantage had me on the show, and the episode went up last week. We hadnt prepared much, which turned out to be the right call, because what came out is about the most complete walk-through of how I think about trading that exists in one place. Seventy-two minutes, from the meetup where a prop trader asked me what my edge was (I didnt have one) to what my book
  • How Market Feedback Loops Affect Alpha [Delphic Alpha]

    Momentum is usually profitable on strong trends. The literature on feedback trading and liquidity predicts one exception: the week after a trend moved further while liquidity was low. I combine four strands of that literature into one model and test it on daily data for ETFs, futures, S&P 500 stocks and crypto, 2006 to 2026. Used as a filter on a multi-lookback trend strategy in S&P 500

Recent Quant Links from Quantocracy as of 10/12/2026

This is a summary of links recently featured on Quantocracy as of Monday, 10/12/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant Observer: A curated directory of quant tools, data, research, etc.

    quant.observer collects useful quant trading tools and frontier research in one place. The goal is to save time finding reliable infrastructure and reading material. We do not run a trading platform or offer investment advice. Everything here is reference material for your own work.
  • The insider-buying edge was mostly 2008 [Donttrade]

    When several company insiders buy their own stock in the same week, the stock tends to outperform. It is one of the better-documented anomalies in finance, it has a mechanism you can state in a sentence the people who know most about a business are putting their own money into it and it has been measured repeatedly over forty years. So I built the thing that reads those filings: all 81
  • TTM Squeeze and Bollinger Squeeze: breakout or volatility clustering? [Quanter Lab]

    Chart books call the squeeze the calm before the storm: when a stock's Bollinger Bands slip inside its Keltner Channel, a big move is supposed to be near. Econometricians say calm is followed by more calm, and volatility by more volatility. We measured both on S&P 500 stocks from 2006 to 2025, against every trading day of every member with prices on file. Volatility clustering held. The
  • From Signal to Bet: A Blueprint for Multi-Asset, Multi-Horizon Trading Pipelines [Delphic Alpha]

    A system that runs several models across many assets, bar sizes and forecast horizons has to answer the same questions for every prediction. How long is the call held? How big is the bet? Which calls are traded at all? How do positions from models on different clocks become one position per symbol? Where is risk controlled? What is sent to the market? This guide follows that path stage by stage,
  • Does Empirical Finance Replicate? AI answers the question. [Alpha Architect]

    Published research often becomes part of the permanent investment toolkit. Findings are cited, models are calibrated, and strategies are built. Empirical relationships estimated decades ago can continue to influence decisions long after the original sample ends, but there are really two different questions. Can a published result be reproduced using the original sample? And does the same result

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

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

  • Why Long Bonds Still Belong in the Portfolio [Beyond Passive]

    In the first week of October the thirty-year Treasury yield closed at 5.60%, its highest since 2002. TLT, the long-bond fund in the three-asset, risk-balanced portfolio this publication uses as its baseline for a long-term macro strategy, trades at a price it last saw twenty-two years ago. Ray Dalio is short debt and expects a crisis within two years, and traders reloaded their shorts the day
  • Momentum Mini-Portfolio Development – Part 4: ETF Diversifier [TradeQuantiX]

    At the end of Part 3 I said I wasnt exactly sure where this series would go next… But after some brainstorming, the answer was pretty clear. We already built a TSX momentum system, a US momentum system and an ASX momentum system. Three different markets and three different ways of trading momentum… but only on equities. All three of the systems thus far are long only equities systems, so
  • After a Strong Pre-Open Hour the Move After the Open Is Bigger, Not Smaller [Krueger Algorithms]

    A common expectation: if the market has run hard in the hour before the cash open (start of the regular session), part of the move is done and less comes at the open. We ask about the size of the move at the open, not its direction. The push is the close of the last minute before the open minus the open of the first pre-open minute, divided by the ATR, the typical daily range (mean of the daily
  • Do markets hunt the stops beyond yesterday’s high and low? [Daru Finance]

    Trading forums, indicator libraries and paid courses repeat a claim often enough that most traders take it as given: stop orders pile up just beyond obvious levels such as yesterday's high or low, larger players push price through to fill against them, after which price reverses. Traders act on it in two ways, placing their own stops some distance past the obvious level and buying the

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

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

  • Podcast with Kris Longmore of Robot Wealth [Algorithmic Advantage]

    Ex-prop trader on why solo trading is a different game, and how to win with real edges & portfolios of noisy strategies Rather than writing an article along with this pod, I think the relevant piece to refer you to is the series Im currently writing called The Part Time Trader because I think it tackles one of the main issues I discussed with Kris in this interview: specifically, how
  • Trading the Multi-Asset Drift Around U.S. Elections [Quantpedia]

    We analyze a multi-asset calendar anomaly around U.S. federal elections that is consistent across assets and simple to trade. While our previous study documents a robust pre-election drift, the cross-asset pricing of its D+1 resolution remains unmapped beyond SPY. Using a diversified ETF basket spanning technology, emerging markets, real estate, high-yield credit, gold, oil, and foreign exchange,
  • The Market Reads the Announcement. By the Time It s News, It s Old. [Tommi Johnsen]

    For three months we thought we had found that good news pushes a stock up the following day. Here is what was really going on. When a company reports its quarterly earnings, it often does so just after the stock market closes at 4 pm. Traders read the announcement within minutes and buy or sell in after-hours trading, so the stock price moves right away. About an hour or so later, reporters
  • Frog-in-the-pan momentum tested on the S&P 500 [Quanter Lab]

    Momentum investors buy last year's winners. A well-known refinement, the frog in the pan of Da, Gurun and Warachka (2014), also looks at how a winner went up. A stock that climbed a little on most days is said to be a better buy than one that rose in a few big jumps, because news that arrives slowly is noticed late and keeps pushing the price. Alpha Architect's momentum method is built

Recent Quant Links from Quantocracy as of 10/05/2026

This is a summary of links recently featured on Quantocracy as of Monday, 10/05/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Century of Meme Stocks and the Price of Coordination [Quantpedia]

    Meme stocks are commonly studied through social-media activity, but this approach limits both the historical scope of research and the signals available to practitioners. In A Century of Meme Stocks and the Price of Coordination, Chad Schmerling develops an alternative: a machine-learning model trained to identify the holdings of the Roundhill MEME ETF using market and firm-level data rather than
  • Building a Statistically Valid Backtest: The Checklist Nobody Runs [Aligrithm]

    McLean and Pontiff record stock-portfolio predictors whose performance is 26% lower out of sample than in sample, and 58% lower after publication. Suhonen, Lennkh, and Perez look at 215 smart-beta strategies banks offered and find a median deterioration of 73% in the Sharpe ratio from the backtest to the live period. Arakelian, Bolesta, Liu, Osterrieder, Poti, Schwendner, Sutiene, Vlah Jeri, and
  • What Happens After an Analyst Downgrade? [Talval]

    In August we scored 22,000 analyst upgrades and downgrades against the market and found that the median one is a coin flip. That was the average call. This study asks what the average hides: does the same rating change mean something different depending on what the stock had already done? It does, in one place, and in the direction nobody acts on. A downgrade that lands on a stock that has already
  • The 200-day moving average vs volatility targeting [Quanter Lab]

    Anyone who held stocks through 2008 or the Covid crash has asked the same question: is there a simple rule that gets you out before the worst of it? Two answers are famous. The 200-day line sells when the price ends the day below its average of the last 200 trading days. Volatility targeting holds less of the fund when its daily moves grow larger than usual, and more when they calm down. We tested
  • Orthogonal Cluster Risk Parity (OCRP) [CSS Analytics]

    luster Risk Parity (CRP)was originally introduced in 2013 and conceived in 2012 by David Varadi and Michael Kapler (Systematic Investor). It was designed as an attempt to make ERC by Maillard, and Roncalli aware of the portfolios total asset allocation and allocate risk contributions equally both within and across the portfolio instead of across the individual assets which requires careful

Recent Quant Links from Quantocracy as of 10/03/2026

This is a summary of links recently featured on Quantocracy as of Saturday, 10/03/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Buffett’s be greedy when others are fearful, tested [Quanter Lab]

    "Be greedy when others are fearful" is Warren Buffett's best-known advice, and CNN's Fear & Greed Index is how many people check what the others feel. We rebuilt CNN's index from the seven market measures it combines, which takes it back to 2007, and followed the advice to the letter: buy the S&P 500 at Extreme Fear, sell it at Extreme Greed. From 2009 to 2026 the
  • Polynomial Regression Bands With a Profit Factor Below One [Aligrithm]

    Gil Cohen fits second-, third-, and fourth-degree polynomial moving regression bands to Nasdaq-100 names from 2017 through March 2024, charges 0.3% on each fill, and names the four-degree model best. Table 3 puts the average net profit at 162.75 dollars per name. The abstract, the conclusion, and Table 4 print 162.73. The risk case next to that crown is a profit factor of 0.55 and a minimum win
  • What Really Drives the Asset Growth Anomaly? New Evidence Points to Mispricing, Not Risk [Alpha Architect]

    One of the most well-documented patterns in the cross-section of stock returns is that firms with high asset growth subsequently underperform firms with low asset growth. This asset growth anomaly is so well established that it now sits at the core of two of the most widely used benchmark factor models: the Fama-French five-factor model (via the conservative-minus-aggressive, or CMA, factor)
  • The turn of the month effect tested: where the stock market’s payday is now [Quanter Lab]

    For most of the last century US stocks made their money on four days a month, the last trading day of the month and the first three of the next, which economists call the turn of the month. The usual explanation is a payday, because salaries and pensions arrive at the end of the month and part of that money goes into stocks. A payday everyone knows about invites buyers to get ahead of it, and US
  • Practical transaction cost checks for macro trading strategies [Macrosynergy]

    This article demonstrates practical transaction-cost checks for systematic macro trading strategies, using a Python class from the Macrosynergy package. The method requires only basic estimates of transaction sizes and costs, which can often be obtained from trading desks or through LLM queries. These approximate cost checks help assess a strategys economic value, scalability, suitable

Recent Quant Links from Quantocracy as of 10/02/2026

This is a summary of links recently featured on Quantocracy as of Friday, 10/02/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 897,000 Tests, About 150 Pattern Families, One Confirmed Finding [Krueger Algorithms]

    The question was simple: what do you find if you forget everything you think you know and search the data from scratch? No own setups, no favourite ideas, no weighting by gut feeling. Instead, every pattern family that books, forums and papers call profitable, on the same data and under the same rules. The rules were strict, because a pattern search always finds something otherwise. Test 900,000
  • How Commodity Prices Affect Stocks: 13 Oil Spikes Since 1970 [Portfolio Terminal]

    US crude oil (WTI) rose 50% or more within a year 13 times from 1970 to August 2026, and US CPI inflation was higher at every one of the 13 oil peaks than a year earlier, by a median 2.0 percentage points. In the 8 oil spikes since 1970 that took WTI crude to a 5-year high, the S&P 500 fell in 5 over the 12 months to the oil peak (median 11.3%); in the 5 spikes that stayed under the
  • What Held Up When Bond Yields Rose? 63 Years of Data [Portfolio Terminal]

    In the 7 episodes from 1963 to 2026 when the 10-year Treasury yield rose 1 point or more in a year and the S&P 500 fell, 10-year Treasuries lost money all 7 times (median 5.5%) and 1-month Treasury bills gained all 7 times (median +6.1%). In those 7 episodes a 60/40 portfolio of US stocks and 10-year Treasuries lost a median 6.6%, barely less than the 7.4% lost by US stocks alone; the same
  • Is Attention a Factor? WSB Herding, BERT Sentiment, and the Meme Confound [Aligrithm]

    Huang and Shum Nolan buy three names. Each month they take the tickers WallStreetBets mentioned most, keep a name only when the posts were majority bullish, and hold the equal-weight book for the next month. Regress that book's daily percent return on the market, size, value, and momentum portfolios (the Fama-French-Carhart regression) and the intercept is 0.433 percent a day. Multiply by