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Step-by-step strategy breakdowns, code explanations, and backtesting procedures that can be followed directly.
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This article analyzes the combination of risk premiums for TIPS and crude oil. Based on data as of October 9, 2026, the 10-year TIPS real yield is 2.91%, up 116 basis points from the same period last year, and the 10-year breakeven inflation rate is 2.33%. This dual-asset portfolio can enhance expected returns with virtually no additional risk, while also offering educational value for dynamic position management.
Read originalAmp leverages OpenAI's GPT-6 Astra, Ultrafast, and GPT-Live-1 models, along with Sign in with ChatGPT, to expand AI agent use cases beyond coding into other domains. This practice aims to explore the capability boundaries of AI agents.
Read originalWhen evaluating AI heat risk models used in company valuations, investors should focus on whether they fully cover the entire transmission chain from heat hazards to asset exposure, operational sensitivity, financial impact, and ultimately valuation. Existing heat risk estimates from different institutions show significant discrepancies and often omit key impact dimensions such as employee productivity. Investors can reference the case of Électricité de France (EDF) and require companies to disclose quantifiable operational thresholds, financial sensitivities, and adaptation costs to determine whether heat-related losses are already reflected in company value.
Read originalQuantocracy has released its latest roundup of quantitative research links as of October 10, 2026, featuring four related research pieces. It notes that the 30-year U.S. Treasury yield closed at 5.60% in the first week of October, the highest level since 2002; the remaining content covers research discussions on momentum portfolio development, pre-market patterns, and market stop-loss hunting.
Read originalIn 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.
Read originalThe 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.
Read originalMoontower 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.
Read originalIn 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.
Read originalThis 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.
Read originalThis 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.
Read originalThe 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.
Read originalThis 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.
Read originalThis article is the fifth in a statistical arbitrage series for independent traders, focusing on the triangulated statistical arbitrage method. Using the energy sector spread network as an example, the author explains how to cross-validate multiple overlapping spreads to identify genuinely mispriced individual stocks, rather than treating deviations in a single spread as a direct trading signal.
Read originalThis article introduces a triangulated statistical arbitrage approach. Using 8 spread groups constructed from 6 energy stocks as an example, the z-score signal of each spread is decomposed into opposite votes for the two individual stocks, which are then aggregated into long-short signals at the individual stock level. This allows direct trading of undervalued or overvalued stocks, replacing the traditional pairs-trading model of hedging both legs simultaneously. The approach can improve capital efficiency, reduce transaction costs, and increase signal utilization, while achieving variance control similar to pairs trading through a long-short basket portfolio. The article will further explain how to evaluate the quality of aggregated signals.
Read originalThe author argues that mean reversion, trend, and momentum are merely labels for price movements rather than trading edges. A true edge requires first identifying the underlying structural cause and then validating it with data. The article uses three types of mean-reversion scenarios as examples: forced liquidation by leveraged funds, scheduled rebalancing of crypto leveraged tokens, and month-end stock-bond rebalancing by wealth management institutions. All three scenarios involve identifiable trading flows that are insensitive to price.
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