Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 11/04/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 11/04/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why optimising for the left tail of outcomes is like playing chess… [Investment Idiocy]

    A short one today. Suppose I offer you N trading strategies to choose between. We can run some kind of bootstrap of their historical returns to get a final distribution of CAGR, with all the caveats implied by that. Or suppose I ask you to pick the correct level of leverage on a trading strategy. Again we can bootstrap historical returns at different leverage levels, giving us a series of N
  • AI Backtesting How good is it? [Alvarez Quant Trading]

    I was given access to Trading Breakthroughs with AI by Algo Advantage on using Claude Code as a research assistant for systematic trading. The idea is simple. You come up with the idea to test. Claude writes the code, runs the backtests and writes up the results. What caught my attention is that the course is built around the authors own instruction files, the rules that tell Claude how to
  • Optimisation with costs (oh please seriously Rob, not again…) and a sort of summary [Investment Idiocy]

    After a summer break, this is post (checks notes) #11 in my 2026 series on portfolio optimisation. Up until now I've been focusing on the optimisation of forecast weights -the weights of signals to predict a given instrument – on a pre cost basis. So now it seems that we should introduce the question of costs. But if you are a faithful follower of this blog, you may be groaning! I have
  • Momentum Mini-Portfolio Development – Part 5: Combining The Systems [TradeQuantiX]

    We spent the past four articles developing four robust momentum systems, and now its time to combine them into the momentum mini portfolio. The four systems we developed to be a part of this mini portfolio are: The TSX Dual Factor Momentum System The US Pullback Momentum System The ASX Small-Cap/Large-Cap Split Momentum System The ETF Diversifier Momentum System Each one of these systems was
  • Elder Triple Screen backtest on S&P 500 point-in-time constituents 2006-2025 [Quanter Lab]

    The Triple Screen is the trading system at the centre of Alexander Elder's "Trading for a Living". It reads a stock three ways: the long trend, a dip against that trend, and the turn that ends the dip. Elder measures the dip with his own Force Index, the day's price change times the shares traded, and he buys only when the price climbs back above the previous day's high.
  • Random Forest and XGBoost on 12 Years of FX: Signal Is Real, but It Can’t Pay the Spread [Dead Signals Lab]

    Almost every experiment in this series died of the same cause: the signal did not exist, or it existed only inside a measurement bias. The one that closes the tour of forex is different, and that is why it deserves its own note: here the signal exists, it survives nineteen placebos in each of twelve years and it also appears in the years nobody had looked at, and even so the experiment ended in a
  • Crashes Travel on a Different Network Than Returns [Aligrithm]

    An average correlation puts a quiet day and a selloff on the same edge, and so does the symmetric graph in the old article "Network Momentum as a Cross-Asset Factor." Pham, Han, Nguyen, Pham and Do estimate common volatility, the shared multiplier on residual variance they call COVOL, separately on positive returns and on negative returns. The largest negative-return shock in their
  • SPX is About to Enter an Incredibly Bullish Seasonal Period [Quantifiable Edges]

    With the calendar about to change from October to November, the market will enter its Best 6 Months. The Best 6 Months tendency was first published by Yale Hirsch, founder of the Stock Traders Almanac, in 1986. The concept behind the Best 6 Months is simple. Hirschs research suggests that over the last several decades the market has made a massive portion of its gains
  • Theory on Trial: Can Anyone Capture the Overnight Premium? [Delphic Alpha]

    According to a well-known paper, most of the US stock market's return arrives while the market is closed. That sounds like easy money, which is exactly why I wanted to check whether anyone can actually collect it. 1. The theory In "A Tug of War: Overnight Versus Intraday Expected Returns" (2019), Dong Lou, Christopher Polk and Spyros Skouras show that most of the US equity premium,

Recent Quant Links from Quantocracy as of 11/01/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 11/01/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Strategy of the Week #3: The Pre-FOMC Drift in S&P 500 Futures [Delphic Alpha]

    Buy S&P 500 futures at the 16:00 New York close on the trading day before each scheduled Fed decision, and sell at 14:00 on decision day, just before the statement comes out. That is the whole rule: one trade per scheduled meeting, eight a year, and nothing in between. The idea is not mine. Lucca and Moench documented it in "The Pre-FOMC Announcement Drift" (Journal of Finance,
  • Greenblatt value-weighted index backtest 2006-2025 [Quanter Lab]

    Inside the cheap end of the market Greenblatt's weighting did what he said it would. More money in the cheaper companies earned more than the same money spread evenly, with financial companies in or out, with a limit or without, and the even split earned more than the split by size. Against the index, the thirty cheapest in equal weights never came out ahead, and weighting them by how cheap
  • Hedge Fund Performance, Market Regimes, and Drawdown Risk [Relative Value Arbitrage]

    Market dynamics are changing rapidly, and managing a hedge fund is becoming increasingly competitive and challenging. The same is true for investors seeking to allocate capital to hedge funds. In this post, we explore whether investing in hedge funds is worthwhile and how to evaluate their performance, risks, and potential for generating future returns. Do Hedge Funds Add Value? Despite the
  • Quantitative Methods of Statistical Arbitrage: The Purdue Thesis Tour [Aligrithm]

    Ning's equal-weight book of six sector pairs earns a daily Sharpe of 0.09 in 2022. Daily Sharpe here is the mean daily percent return divided by the daily percent standard deviation, with no square root of 252 attached. You enter long when a pair's spread falls half a standard deviation through its 63-day average, and short when the spread rises the same distance above that average; you

Recent Quant Links from Quantocracy as of 10/31/2026

This is a summary of links recently featured on Quantocracy as of Saturday, 10/31/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Outperforming the S&P500 with macro factors [Macrosynergy]

    We present a cost-efficient method for outperforming the broad U.S. equity market. It uses deep learning and macroeconomic factors to optimize individual stock weights, thus creating a macro-aware portfolio. Rather than building a separate macro factor model for each company, we select a small set of macro divergence factors and map them to individual stocks using standard feed-forward
  • What “Congrats on the Quarter” Tells You About a Stock [Equibles]

    On the morning of October 30, 2025, International Paper (IP $36.37 +0.94%) held its third-quarter call. The first analyst to speak opened with "Congratulations on your progress in a challenging environment." Two of the next five said much the same. By the following day's close the stock was down 11.9% against the S&P 500. Analysts open their questions with "congrats on the
  • Dimensionality Collapse: The Warning Signal Before Diversification Dies [Aligrithm]

    McMillan finds that from 2009 a US stock-bond book needs an 80% stock weight to stay on the return path a 50/50 book had traced before 2000. The correlation between stock and bond returns flipped from positive to negative around 2000 in six of the G7 markets, and it moves back toward positive in the last two years of a sample that ends in December 2023. A negative correlation hedges. A positive
  • Gold vs TIPS: Competing Safe Assets and the Post-2022 Decoupling [Aligrithm]

    Batten, Loncarski, Szilagyi, and Zhou put a long gold book next to the 10-year real yield and the dollar. On monthly data from February 2003 to February 2026, the log change in the London gold fix falls by 0.0639 for each percentage point the 10-year TIPS yield rises, holding the ICE dollar index fixed. A 100 basis point backup, dollar unchanged, is a 6.39% drop in the log gold price. That slope
  • The 10m Moving Average Is the Most Famous Rule in Tactical Investing. I Tested It for 40 Years [Strategy Lab]

    If you have read anything about tactical asset allocation, you have met this rule. Each month, compare the S&P 500's price to its 10-month moving average. Above it, hold stocks. Below it, hold bonds or cash. Mebane Faber's 2007 paper made it the default starting point for a generation of retail systematic investors, and it is the engine inside a large share of the "trend
  • Do Companies Really Bury Bad News on Friday Night? [Equibles]

    At 4:07 p.m. on Friday, September 25, DraftKings (DKNG $20.78 -2.07%) filed an 8-K saying its audit committee had dismissed BDO as its auditor and appointed Deloitte. Nothing in it was alarming. The filing reports no disagreements with BDO, the switch takes effect after the 2026 audit, and the committee had made the decision two days earlier. But it went public at a time with a reputation: Friday,

Recent Quant Links from Quantocracy as of 10/27/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 10/27/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Part Time Trader Part 5: Half a Million Backtests & a Coin Flip [Algorithmic Advantage]

    Half a million tests can map an opportunity or manufacture a winner; the difference is how the outputs are used. Then the coin toss every ranking has to beat. Welcome back to the series on building rotational momentum strategies. Earlier I called broad reconnaissance the opposite of overfitting. That shorthand needs a qualification: not taking the best row does not remove selection bias. Choosing
  • O’Shaughnessy Trending Value backtest on S&P 500 point-in-time constituents [Quanter Lab]

    Trending Value is a stock-picking formula from James O'Shaughnessy's book "What Works on Wall Street", and stock screeners have copied it for years. It has two components. The value component finds the cheapest stocks in the market, judged on six price measures at once. The trend component then buys, from those cheap stocks, the ones whose prices did best over the last six
  • Optimal Rebalancing under Fixed and Variable Trading Costs [Yannick Kalber]

    Whenever current portfolio weights differ from their target weights, either due to market movements, signal changes or other reasons, a portfolio manager in a frictionless world would immediately trade to the target portfolio. In reality, however, every adjustment incurs trading costs. Therefore a portfolio manager faces an optimization problem, where he needs to balance portfolio suboptimality
  • AI Models Don’t Predict Markets. They Remember Them [Delphic Alpha]

    If you trade macro themes, you have seen the pitch: let a language model read the news and position you across futures. If you backtest rules, you know the question to ask first: what is the edge, and does it hold on years the model never saw? I built that AI macro fund and backtested it on 12 futures over 17 years, then ran the control most people skip. Everyone building one assumes the model is
  • EIA Inventory Report: The First Reaction of WTI Crude Is Neither Continued Nor Reversed [Krueger Algorithms]

    Every Wednesday at 10:30 New York time (Thursday at 11:00 in holiday weeks) the US Energy Information Administration reports how crude and gasoline inventories changed. The oil price reacts within seconds. The question behind this: after the first five minutes does the price keep running in the same direction because the market processes the figures with a delay? Or was the reaction exaggerated
  • Calm Uptrends That Accelerate: No Edge Beyond the Trend Itself [Krueger Algorithms]

    Every trader knows the idea: a market rises for weeks, quietly, with small pullbacks and low volatility. At some point the rise gets steeper and momentum picks up. The mechanism usually named is dealer hedging: whoever sold call options has to buy more as the price rises, and when the VIX rises at the same time the effect feeds on itself. If you spot the acceleration early, the hope goes, you ride

Recent Quant Links from Quantocracy as of 10/24/2026

This is a summary of links recently featured on Quantocracy as of Saturday, 10/24/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Shorting Leveraged ETFs: Role of Borrowing Costs and Rebalancing in Volatility-Decay Strategies [Quantpedia]

    Leveraged ETFs provide amplified exposure to an underlying index, but their daily reset mechanism creates volatility drag, which can substantially reduce their long-term value. This raises the possibility of exploiting the effect by simultaneously shorting leveraged ETFs with opposite exposures. We examine a 50/50 short portfolio of TQQQ and SQQQ, which provides approximately neutral exposure to
  • The Log-Linear Regression: Usage for Long-Term Financial Asset Analysis [Portfolio Optimizer]

    In finance, various studies have explored the use of regression models in forecasting stock prices [] based on historical data and other relevant variables1. Among these models, the log-linear regression model is of particular importance due to its relationship with the geometric Brownian motion (GBM) asset pricing model introduced by Paul Samuelson2 and at the heart of the BlackScholes
  • Long Volatility Is the Wrong Hedge for a Risk-Parity Portfolio [Beyond Passive]

    A common piece of advice for any leveraged portfolio is to hold some long volatility on the side, a small position in VIX futures that pays when the stock market crashes. This article tests that advice on a risk-parity portfolio of stocks, bonds and gold and finds it wrong: VIX insures stocks, and this portfolios worst days are mostly not stock-market days. What protects it is a limit on how
  • Do round numbers matter for gold? [Quantex]

    Do round prices such as US$2,000 act as barriers for gold? Using 5,138 daily XAUUSD bars from 2005 to 2024, we test whether gold closes back on its side after touching a US$100 level and compare the round grid with 80 placebo grids of the same spacing, using rank-based tests, a Holm correction and block bootstraps. On 232 first touches gold held 45.0% of the time against 49.2% for ordinary levels;
  • The Part Time Trader Part 4: Building the Lab [Algorithmic Advantage]

    Most back-tests are wrong before the first trade is placed; heres everything I settled before I let a single result count. Apologies for some of this content being dry, I just want to make sure that if you take this series in its entirety, you basically have a mini-course in building momentum strategies, and these foundations count. The payload in the article is the full factor bank of 34
  • Ornstein-Uhlenbeck (OU) mean reversion on S&P 500 point-in-time constituents [Quanter Lab]

    The Ornstein-Uhlenbeck process is the textbook model of mean reversion: a price that wanders away from its usual level is pulled back toward it, like a ball on a spring. Traders use it to buy a stock when its price is unusually far below its estimated level, and to sell when the price has come back. One number in the model matters most for a trader: the half-life, how many days a gap between the

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