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Blogs by quant researchers and traders such as Robot Wealth, Alpha Architect, Quantpedia, Rob Carver and Moontower: methods, reviews and views.

46 related activitiesLast 30 days 31 itemsFeatured among them 0 items

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Activity Archive · Page 2 页

8 OctThuIssue 21–40 items
  1. Moontower17

    How I Invest: Personal Investment Chapter from 'How I Invest My Money: Volume Two' Shared

    At the invitation of Brian Portnoy, the author wrote a personal investment chapter for 'How I Invest My Money: Volume Two', to be published by Harriman House on October 20. The full text of this chapter is published behind a paywall. The book features personal investment experience sharing from 25 financial practitioners, with first volume authors Morgan Housel and Christine Benz also returning to share their own changes.

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  2. Quantpedia5

    Quantpedia Premium paid content released on October 8

    Quantpedia released Premium paid content on October 8. The content includes a risk warning for futures and foreign exchange trading, clearly stating that such trading carries the risk of total loss of principal and is only suitable for investors who can bear the corresponding risks.

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7 OctWed
  2. Moontower58

    How to Express a Specific View Using Vertical Spreads: A Practical Review of an Options Trade Around Nike’s Earnings

    In a trading lab at the Robinhood Summit, the author used Nike’s (NKE) after-hours earnings release on October 1, 2025, as a backdrop to demonstrate how to express a specific directional view using vertical spreads. At the time, NKE traded around $36, and the $36 straddle implied an expected move of about $3 (8%). The author chose to buy the 38.5/39 call vertical for a premium of $0.12, which would pay $0.50 if the stock closed above $39 at expiration, for a risk-reward ratio of roughly 3:1.

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5 OctMon
  1. Alpha Architect59

    Buying complex retail investment products may not be a good idea: Interpretation of a paper in the Review of Finance

    Alpha Architect interprets the paper "Competition, complexity, and security design: evidence from retail investment products" published in the Review of Finance in 2026. This study takes yield-enhancement products in the Swiss market as a sample and finds that issuers deliberately increase product complexity to weaken price competition when competition intensifies.

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  2. Quantpedia62

    A century of meme stock research: identifying speculative characteristics using market data, with a weekly portfolio achieving a five-factor plus momentum alpha of 11.5% annualized

    The SSRN paper "A Century of Meme Stocks and the Price of Coordination" proposes training machine learning models using market and company data to identify meme stocks, extending the traceable "memeness" indicator back to 1926.

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4 OctSun
  1. Moontower51

    Moontower Recaps Robinhood Summit Options Education: Kelly Criterion, Directional Value of Options, and Analysis of Unusual INTC Options Flow

    The author participated in an options buying education session and a roundtable for trading platform data providers at the Robinhood 2026 Summit in Houston, recapping the Kelly Criterion supporting course, the profit and loss comparison logic of buying options versus holding the underlying stock, and the analytical approach for unusual INTC options flow.

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2 OctFri
1 OctThu
28 SeptMon
  1. Quantpedia7

    Quantpedia September 27 Premium Update

    Quantpedia released a Premium version update on September 27. The platform is designed for quantitative strategy research users, and this update did not disclose specific new content. The page includes a risk warning for futures and foreign exchange trading, clearly stating that simulated backtest results have inherent limitations and do not represent actual trading performance.

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27 SeptSun
  1. Moontower62

    Moontower Derives the Optimal Kelly Bet Size from First Principles and Discusses AI-Generated Content and 'Thickness'

    Moontower author Kris Abdelmessih released this newsletter, first sharing Adam Mastroianni's article on 'thickness', distinguishing between deep works that require attention to extract value and content-free AI-generated 'slop', then step-by-step deriving the optimal Kelly bet size formula from first principles.

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4 SeptFri
  1. Rob Carver：This Blog is Systematic26

    Rob Carver Completes Final Edits on Fifth Trading Book "The Art And Science of Trading"

    Rob Carver has completed the final edits on his fifth trading book, "The Art And Science of Trading," scheduled for official publication on December 1 and now available for pre-order. Aimed at the mass market, this book serves as the unleveraged counterpart to his third book, "Leveraged Trading." It covers an introduction to trading delta-one instruments such as stocks, ETFs, and cryptocurrencies, explains trading science concepts like backtesting and the Kelly Criterion, and explores the integration of art and science within a semi-automated trading framework.

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3 JulFri
  1. Rob Carver：This Blog is Systematic66

    Testing Return Pooling Schemes by Asset Class and Portfolio Weight Distance

    In the 10th installment of his 2026 portfolio optimization series, Rob Carver conducts rolling out-of-sample backtests to compare four return pooling schemes. The test sample consists of 50 instruments randomly selected from 214 available instruments, with in-sample lengths of 5, 10, and 20 years and out-of-sample lengths of 1 and 5 years. The optimization uses shrinkage methods with an SR shrinkage of 0.5 and a correlation shrinkage of 0.75, while also incorporating the reverse rule of each trading rule, with only positive SR rules retained for optimization.

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29 JunMon
  1. Rob Carver：This Blog is Systematic59

    Is Rolling Exponentially Weighted Estimation Better Than Full Sample: A Backtest Examination of Portfolio Optimization Parameters

    The authors use random subsample backtests to examine the effectiveness of exponentially weighted window estimates of the Sharpe ratio for portfolio optimization. They compare the out-of-sample Sharpe ratios of EWM spans of 5, 10, 20, and 30 years against full-sample estimates across various in-sample and out-of-sample periods.

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  2. Rob Carver：This Blog is Systematic64

    Estimating Profit and Loss for Cross-Instrument Combined Trading Rules: A Method Based on SR Vector Distance and Backtesting Results

    This article is the eighth installment in the Portfolio Optimization Series. The author proposes using the Euclidean distance of SR vectors across multiple trading rules for each instrument to determine whether to combine cross-instrument returns, calibrates 95% critical values for different historical lengths using random data, and then obtains pseudo-instruments through hierarchical merging. The author compares three approaches—full merging, algorithmic merging, and no merging—using in-sample backtests across 214 futures instruments. The results show inconsistent out-of-sample performance, with longer sample data favoring no merging. The author ultimately concludes that the algorithm is highly complex and the grouping results lack intuitive logic, so it will not be adopted for now, and future research will explore simpler merging rules.

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23 JunTue
  1. Rob Carver：This Blog is Systematic74

    How to Automatically Identify Structural Breakpoints in Momentum Strategy Parameter Estimation

    This article introduces a method for automatically identifying structural breakpoints in the Sharpe ratio of trading rules, using independent t-tests to compare mean differences across subsamples, recursively splitting the sample while requiring each subsample to retain at least 5 years of data. The authors provide complete Python implementation code, using the momentum4 rule for CORN futures as an example, with the algorithm automatically identifying August 26, 1982 as the breakpoint.

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18 JunThu
  1. Rob Carver：This Blog is Systematic58

    Does Portfolio Optimization Need Clustering? A Backtest Examination Across Different Asset Sizes and Sample Lengths

    This is the sixth article in a portfolio optimization series. The author conducts hundreds of random backtests to examine the impact of different numbers of clusters, correlation shrinkage, SR shrinkage, and in-sample vs. out-of-sample lengths on out-of-sample Sharpe ratios across asset sizes of 20, 30, and 40. The results show that most parameter combinations have no significant differences, with only weak evidence that moderate clustering outperforms no clustering, and that the number of clusters should not be too large. The author ultimately recommends using an empirical value of 6 clusters when clear statistical evidence is lacking, while noting that clustering significantly increases optimization time.

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  2. Rob Carver：This Blog is Systematic60

    Equal-Weighted Averaging of Portfolio Weights Generated by Different Shrinkage Methods Fails to Outperform Single Shrinkage Approaches Across Multiple In-Sample and Out-of-Sample Backtests

    The author examines the effect of equal-weighted averaging of portfolio weights generated by different shrinkage methods in the Portfolio Optimization Series, comparing 9 shrinkage parameter combinations with 6 weight averaging combinations, totaling 15 approaches. The backtests use randomly selected 9 trading rules and random instruments, with 1-, 5-, and 10-year in-sample periods and 1- and 5-year out-of-sample periods, each group containing 4,000 random portfolios.

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15 JunMon
  1. Rob Carver：This Blog is Systematic62

    Comparing Portfolio Optimization Shrinkage Methods Using Real Return Data: Backtesting Conclusions for Monte Carlo, Bootstrap, and Double Shrinkage

    In the fourth installment of the Summer 2026 Portfolio Optimization Series, the author backtests Monte Carlo, bootstrap, double shrinkage, naive mean-variance, equal weight, maximum diversification, and EPO using real trading rule and instrument return data. Each test randomly selects 9 trading rules and one instrument, comparing portfolios over 1-, 5-, and 10-year in-sample periods and 1- and 5-year out-of-sample periods. The results show that under real data, correlation estimates are less stable than Sharpe ratios, requiring more correlation shrinkage rather than more Sharpe ratio shrinkage.

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8 JunMon
  1. Robot Wealth45

    How Independent Traders Should Allocate Resources for a Triangulated Statistical Arbitrage Business

    This article is the sixth part of a series on statistical arbitrage for independent traders. It explains the three core modules of triangulated statistical arbitrage and their resource allocation priorities. High-quality pair screening is the core foundation: using 24 months of historical data, the top 10% of pairs are screened monthly and divided into 5 quality tiers, with the best tier achieving a Sharpe ratio of 1.6 and the worst only 0.42. The author discloses only the general methodology and does not reveal specific implementation details.

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