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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 3 页

1 JunMonIssue 41–46 items
  1. Robot Wealth54

    Triangulated statistical arbitrage: how to identify genuine pricing deviations rather than mispricing

    This 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.

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22 MayFri
  1. Robot Wealth53

    From Pairs to Portfolios, Part 4: A Triangulated Statistical Arbitrage Approach for Independent Traders

    This 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.

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29 AprWed
  1. Robot Wealth36

    Data mining and AI-driven 'quant by feel' are essentially the same in quantitative trading; the core should focus on the structural source of trading edge.

    The article points out that data mining and AI-driven 'quant by feel' are identical in essence and philosophy, both attempting to skip the core step of understanding how markets work and profit directly from the market. A genuine trading edge requires identifying the counterparty being profited from, the reason they continue to pay, and the rationale for one's own ability to participate in the trade; such structural edge cannot be identified through backtesting optimization alone.

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10 AprFri
  1. Robot Wealth53

    Trend or No Trend? That's the Wrong Question

    The 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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29 MarSun
  1. Robot Wealth64

    Brave New Backtest: Why LLMs Have Three Structural Limitations in Trading Research

    Drawing on two academic papers, the author identifies three structural limitations of using LLMs in trading research: First, training data is saturated with trading common sense and lacks mechanism-driven edge thinking. Second, across 35+ models, accuracy in time-series value tracking declines logarithmically as the number of historical updates increases, and neither prompt engineering nor expanding the context window can fix this. Third, outputs from 25+ models on open-ended questions show 71%–82% similarity, and models with different architectures converge to the same common answers.

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23 MarMon
  1. Robot Wealth54

    AI Will Create Millions of Quantitative Traders, Most of Whom Will Lose Money

    The author notes that AI has dramatically lowered the barrier to developing quantitative strategies, enabling fully coded and backtested strategies to be generated within minutes. However, cheap experimentation amplifies the multiple-testing problem, producing a flood of false backtest results that appear effective but are actually overfit to noise.

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