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Recent Quant Links from Quantocracy as of 05/26/2025

This is a summary of links recently featured on Quantocracy as of Monday, 05/26/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What are your bars hiding from you? [Trading the Breaking]

    The electronic marketplace generates vast amount of databillions of timestamped trades, quotes, and cancellationsthat demand processing to extract actionable insights. For quantitative traders, the central challenge lies not in designing strategies but in constructing a robust framework to interpret this data. Raw tick data, while granular, is computationally intensive and often contains
  • Market Timing with Macro Surveys [Quant Seeker]

    Hi there. In recent months, there has been increased chatter about the possibility of a recession triggered by President Trumps tariff war. The recent pause in tariffs appears to have eased some of those concerns. For example, JP Morgan now sees the likelihood of a U.S. recession to be below 50%, and the recession odds on Polymarket currently hover around 40%. For investors, recessions are
  • Simplicity or Complexity? Rethinking Trading Models in the Age of AI and ML [Relative Value Arbitrage]

    When it comes to trading system design, there are two schools of thought: one advocates for simpler rules, while the other favors more complex ones. Which approach is better? This newsletter explores both perspectives through the lens of machine learning. Use of Machine Learning in Pairs Trading Machine learning has become an essential tool in modern finance, transforming the way financial

Recent Quant Links from Quantocracy as of 05/25/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/25/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taming OLMAR s 1222% Backtest into a Sustainable 106% CAGR [Paper to Profit]

    Often as traders, we equate complexity with profitability. A models edge comes from it doing something that no other person on Earth has tried yet. But the data shows that simple rules based on real market factors still outperform most models. Those that continue seeking complexity are headed towards a dead end. Today Im focusing on the Online Moving Average Reversion (OLMAR) system by Bin
  • The 1 AI Prompt I Use to Generate 20 Trading Ideas in Seconds [Rogue Quant]

    My kids love bedtime stories. Like most kids. But theyre not into fairy tales or superheroes. Theyre obsessed with one thing: Dad, can you tell a witch story? A mean witch, okay? Every night. Same request. So I lie next to their bed and say, Alright, buddies. A mean witch story it is. Now, I dont know about you But coming up with original witch plots every single night (for
  • No Magic Formulas: How I Actually Decide What to Trade [Robot Wealth]

    Someone recently asked me if I have a checklist for adopting new trading strategies. You know, a neat little formula like if backtested Sharpe > 1.8, trade it or if drawdown < 15%, green light. I get the appeal. We all want clear, objective criteria to make these decisions easier. But strategy adoption just doesnt work that way. The reality is messier. More nuanced. Your
  • Applying Transformers to Financial Time Series [Gatambook]

    In the previous blog post, we gave a very simple example of how traders can use self-attention transformers as a feature selection method: in this case, to select which previous returns of a stock to use for predictions or optimizations. To be precise, the transformer assigns weights on the different transformed features for downstream applications. In this post, we will discuss how traders can
  • I Used AI for 30 Minutes and Discovered 8 New Market-Beating Systems [Paper to Profit]

    Everyone either naively thinks that an LLM will find alpha for them, or equally naively thinks LLMs cannot develop their own systems with any sort of edge. The reality is quite the opposite. When used properly, LLMs can supercharge your strategy research process by at least 10x. Those who arent using AI in their development workflows are going to be easily smoked by those who are in the next
  • Macro-aware risk parity [Macrosynergy]

    Risk parity is an investment strategy that allocates risk exposure equally across asset types through volatility-based calibration and leverage. A most profitable risk parity strategy in the past decades has been the equity-duration long-long, which harvests combined equity and long fixed-income risk premia, while containing return volatility through diversification. Alas, this position is

Recent Quant Links from Quantocracy as of 05/21/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 05/21/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cliff Smith’s BKLN Strategy [Allocate Smartly]

    Questions about this long-ago strategy from Cliff Smith land in our inbox periodically (heres another recent take). Smiths simple strategy trades senior loan (aka leveraged loan) ETFs like BKLN, and has continued to be effective at timing these ETFs in the 10+ years since it was published. Weve extended the authors original test back to 2007 using underlying index data (*). Important:
  • Comparing Affordable Intraday Data Sources: TradeStation vs. Polygon vs. Alpaca [Cracking Markets]

    When building an intraday systematic strategy, the quality and consistency of historical data can make or break your trading results. Cost, however, is also a critical factor for many traders. We conducted a comprehensive analysis comparing three popular data providers offering REST APIs for intraday minute-level data: TradeStation, Polygon.io, and Alpaca. These providers differ significantly in
  • Could data drift be silently sabotaging your PnL? [Trading the Breaking]

    In the day-to-day grind of systematic trading, volatility isnt just a market featureits the atmosphere we operate in. It drives the edge, defines the risk, and sets the tempo. But while volatility creates the conditions for profit, it also contains the seeds of our destruction. That tensionbetween opportunity and ruinsits at the heart of every execution cycle. And thats precisely
  • Is Machine Learning Better in Prediction of Direction or Value? [Quantpedia]

    Building machine learning models for trading is full of nuances, and one important but often overlooked question is: what exactly should we try to predictthe direction of the next market move or the actual value of the assets return? A recent paper by Cheng, Shang, and Zhao, titled Direction is More Important than Speed offers a clear and practical answer. Their research shows that

Recent Quant Links from Quantocracy as of 05/20/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 05/20/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is your strategy built on distributional lies? [Trading the Breaking]

    During the previous optimization cycle, I was tasked with enhancing inventory management protocols for a legacy trading system operating under low-latency constraintsorder cycle times 500ms. While the academic corpus fixates on high-frequency trading paradigmsmicrosecond latency optimization, toxic flow mitigation, and continuous limit order book dynamicsthese constructs proved
  • Weekly Research Recap [Quant Seeker]

    Asset Allocation How Much Should You Pay for Alpha? Measuring the Value of Active Management with Utility Calculations (Ang and Basu) Many investors chase high-performing funds expecting them to beat the market, but rarely ask how much that outperformance is actually worth to them. Even when a fund delivers strong returns, the benefit to investors shrinks once risk, fees, and overlap with a
  • The Cybernetic Oscillator [Financial Hacker]

    Oscillator-type indicators swing around the zero line. They are often used for opening positions when oscillator exceeds a positive or negative threshold. In his article series about no-lag indicators, John Ehlers presents in the TASC June issue the Cybernetic Oscillator. It is built by applying a highpass and afterwards a lowpass filter to the price curve, then normalizing the result. We already
  • Low-Volatility Stocks: Reducing Risk Without Sacrificing Returns [Relative Value Arbitrage]

    The recent market turbulence highlights the need for improved risk management and strategies to reduce portfolio volatility. In this post, Ill explore how to enhance portfolio diversification using low-volatility stocks. Gold and Low-Volatility Stocks as Diversifiers Gold has long been regarded as a valuable diversification tool in investment portfolios due to its unique characteristics. As an

Recent Quant Links from Quantocracy as of 05/18/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/18/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Poor Person’s Transformer: Transformer as a sample-specific feature selection method [EP Chan]

    For those of us who grew up before GenAI became a thing (e.g. Ernie), we often use tree-based algorithms for supervised learning. Trees work very well with heterogeneous and tabular feature sets, and by limiting the number of nodes or the depth of a branch, there is feature selection by default. With neural networks (NN), before deep learning comes around, it is quite common to perform feature
  • I Found a One-Hour Edge in the S&P, Then Three LLMs Made It Better [Rogue Quant]

    A friend of mine owns a Neapolitan-style pizzeria this is the real pizzeria When he first opened, he had one recurring headache: He could never guess how many pizzas hed sell each night. Some days he ran out of dough by 9pm. Other days he overprepared and ended up tossing dozens of unused bases. But a few months in, something clicked. He told me he started watching the playground. The
  • Research Review | 16 May 2025 | Asset Allocation [Capital Spectator]

    Rethinking the Stock-Bond Correlation Thierry Roncalli (Amundi Asset Management & University of Evry) February 2025 The stock-bond correlation is a basics of finance and is related to some of the fundamentals of asset management. However, understanding the stock-bond correlation is not easy. In this presentation, we answer the following questions What is the natural sign of the stock-bond
  • What Can We Expect from Long-Run Asset Returns? [Quantpedia]

    What can we realistically expect from investing across different asset classes over the long run? Thats the kind of big-picture question the Long-Run Asset Returns paper tacklesoffering a sweeping look at how stocks, bonds, real estate, and commodities have performed over the past 200 years. The paper goes beyond just listing historical returnsit explains how reliable (or not) those
  • Profitability Retrospective: Key Takeaways for Investors [Alpha Architect]

    In his 2013 paper The Other Side of Value: The Gross Profitability Premium, Robert Novy-Marx documented that profitability, broadly measured, has as much power as relative price in predicting cross-sectional differences in expected returns. With the publication of that paper, profitability quickly become a prominent theme in asset pricing research. For example, Dimensional began

Recent Quant Links from Quantocracy as of 05/15/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 05/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are you blind to the tail risks lurking in calm markets? [Trading the Breaking]

    Algorithmic trading systems can give you this sleek, high-tech confidencelike the robots have everything under control. Theyre fast, precise, and backtested to death, right? But thats where the trap snaps shut. When your risk metrics are built on things like standard deviation or recent drawdowns, youre basically judging a hurricane by the breeze in your backyard. Sure, those stats
  • Are Sector-Specific Machine Learning Models Better Than Generalists? [Quantpedia]

    Can machine learning models better predict stock returns if they are tailored to specific industries, or is a one-size-fits-all (generalist) approach sufficient? This question lies at the heart of a recent research paper by Matthias Hanauer, Amar Soebhag, Marc Stam, and Tobias Hoogteijling. Their findings suggest that the optimal solution lies somewhere in between: a Hybrid machine learning
  • The Virtue of Complexity in Return Prediction [Alpha Architect]

    In the realm of investment strategies, simplicity has long been favored. Traditional models with a limited number of parameters are prized for their interpretability and ease of use. However, recent research challenges this convention, suggesting that embracing complexity can lead to more accurate return predictions. The study The Virtue of Complexity in Return Prediction by Bryan T. Kelly,

Recent Quant Links from Quantocracy as of 05/13/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 05/13/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How I Fused Momentum and Mean-Reversion to Achieve 20% CAGR on ETFs Since 2000 [Paper to Profit]

    We think of momentum and mean reversion as opposing forcespick one or the other. Yet, data from 2000 shows that blending both via a local adaptive learning filter produces 20% CAGR on liquid equities versus 8% buy-and-hold. Traders ignoring this hybrid edge are leaving significant extra returns on the table. In todays article, Ill walk you through how I built the LOAD strategya clean,
  • Bias-Variance Decomposition for Trading: ML Pipeline with PCA, VIF & Evaluation [Quant Insti]

    Welcome to the second part of this two-part blog series on the bias-variance tradeoff and its application to trading in financial markets. In the first part, we attempted to develop an intuition for bias-variance decomposition. In this part, well extend what we learned and develop a trading strategy. Prerequisites A reader with some basic knowledge of Python and ML should be able to read and
  • Weekly Research Recap [Quant Seeker]

    Time for another round of the latest investing research. Below is a curated list of last weeks highlights, each linked to the original source for easy access. Appreciate your continued support! If youre finding value in these posts, feel free to like and subscribe if you havent already. Bonds Cross-Bond Momentum Spillovers (Wang, Wu, and Yang) A large body of research has studied the

Recent Quant Links from Quantocracy as of 05/11/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/11/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beta hedging [Quantitativo]

    "If you're not thinking about risk, then you're not thinking." William Sharpe. William Sharpe is a Nobel Prize-winning economist renowned for his work on the Capital Asset Pricing Model (CAPM) and the Sharpe Ratio, both of which highlight the central role of risk in pricing and evaluating assets. While the quote If you're not thinking about risk, then you're not
  • Equity trend-following with market and macro data [Macrosynergy]

    The popularity of trend-following bears the risk of market excesses. Medium-term market price trends often fuel economic trends that eventually oppose them (macro headwinds). Fortunately, relevant point-in-time economic indicators can provide critical information on the sustainability of medium-term market movements and are a natural complement to standard trend signals. This post
  • The Calendar Effects in Volatility Risk Premium [Relative Value Arbitrage]

    I recently covered calendar anomalies in the stock markets. Interestingly, patterns over time also appear in the volatility space. In this post, Ill discuss the seasonality of volatility risk premium (VRP) in more detail. Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns The decomposition of the volatility risk premium (VRP) into overnight and intraday components is an
  • Weekly Research Recap [Quant Seeker]

    Bitcoin Arbitrage: The Role of a Single Exchange (Flowerday, Gandal, Halaburda, Olson, and Ardel) Cross-exchange arbitrage has historically been common in crypto markets. This paper analyzes Bitcoin price differences across major exchanges from 2017 to 2020 and finds that Bitfinex was responsible for most discrepancies, driven by withdrawal restrictions, reliance on Tether, and regulatory
  • Andrea Unger – 672% Returns? Sure! Would You Like Some Risk with That? [Algorithmic Advantage]

    Finishing our little mini-series on shorter-term futures trading we talk to Andrea Unger and happily inject some click-bait in the form of gloating about his 672% return in a single year when he won the World Trading Competition. Naturally, we know that this kind of return is generated by specifically trying to win the comp, and taking on the associated risks! If you've been asleep the first

Recent Quant Links from Quantocracy as of 05/05/2025

This is a summary of links recently featured on Quantocracy as of Monday, 05/05/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can I build a scalping bot? A blogpost with numerous double digit SR [Investment Idiocy]

    Two minute to 30 minute horizon: Mean reversion works, and is most effective at the 4-8 minute horizon from a predictive perspective; although from a Sharpe Ratio angle it's likely the benefits of speeding up to a two minute trade window would overcome the slight loss in predictability. There is no possibility that you would be able to overcome trading costs unless you were passively filled,
  • The Aggregated Equity Risk Premium [Alpha Architect]

    This article explores how researchers forecast market returns by aggregating expected returns from individual stocks. Using machine learning, they improve accuracy over traditional methods. The approach helps identify when to increase or reduce market exposure. This can lead to better-informed investment decisions and improved performance. To explore how aggregate signals can aid in market timing,
  • Stock-Bond Correlation: What Drives It and How to Predict It [Relative Value Arbitrage]

    The correlation between stocks and bonds plays a crucial role in portfolio allocation and diversification strategies. In this issue, I discuss stock-bond relationships, the factors that influence their correlation, and techniques for forecasting it. What Influences Stock-Bond Correlation? Correlation between stocks and bonds is crucial for portfolio allocation and diversification, but this

Recent Quant Links from Quantocracy as of 05/04/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/04/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation-Based Clustering: Spectral Clustering Methods [Portfolio Optimizer]

    Clustering consists in trying to identify groups of similar behavior1 – called clusters – from a dataset, according to some chosen characteristics. An example of such a characteristic in finance is the correlation coefficient between two time series of asset returns, whose usage to partition a universe of assets into groups of close and distant assets thanks to a hierarchical
  • A New Approach to Regime Detection and Factor Timing [Alpha Architect]

    The financial research literature has found that the performance of assets (and factors) can vary substantially across regimes (for example, see here and here)factor premiums can be regime dependent. Unfortunately, the real-time identification of the current economic regime is one of the biggest challenges in finance. Amara Mulliner, Campbell Harvey, Chao Xia, and Ed Fang, authors of the March
  • Why data mining risks your trading career [Robot Wealth]

    I was recently talking to someone about data mining as an approach to finding edges to trade. I get the appeal. Feed enough data into a computer, run enough tests, and surely something profitable will emerge, right? Maybe. But almost certainly not. But the worst thing about this approach is that it risks your entire trading career. Sound extreme? Ill explain. Two types of traders Let me tell
  • Revisiting Pragmatic Asset Allocation: Simple Rules for Complex Times [Quantpedia]

    Pragmatic Asset Allocation (PAA) represents a portfolio construction approach that seeks to balance the benefits of systematic trend-following with the realities faced by semi-active investors (mainly taxes and lack of time to manage positions). Building upon the insights presented in Quantpedias initial overview and later in the follow-up, we see clear potential in this frameworkespecially
  • Weekly Research Recap [Quant Seeker]

    Time for another batch of top-tier investing research. Below is a carefully curated list of great papers from last week, each linked to the original source for easy access. If youre enjoying these posts, a like or subscribe is always appreciated, thank you for your support! Asset Allocation Global Tactical Asset Allocation: Updated Results and Real-Market Implementation Using Python and IBKR