Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
23 documents
This 2018 report reviews managed futures, including how CTA strategies trade futures and options and how they differ by analysis method, trading style, holding period, and markets covered. It describes systematic and discretionary approaches alongside trend…
This podcast summary discusses crypto market structure, decentralized finance, governance, and emerging chain ecosystems. Its trading content centers on automated arbitrage between centralized exchanges: bots use exchange APIs to act on price differences,…
This article explains a proposed arbitrage mechanism involving Chinese A-shares, stock purchases, securities borrowing, and short sales. Its example describes buying shares to push the price higher, borrowing shares to sell at that higher price, and later…
This 2018 research summary introduces China’s two-year government bond futures contract, covering its notional size, eligible delivery bonds, price limits, and minimum margin. It explains that the delivery basket’s remaining-maturity range is narrow, helping…
This overview explains statistical arbitrage as a family of strategies that trade relative mispricing across related instruments. It distinguishes cross-market, cross-asset, ETF, and market-neutral approaches, and gives pairs trading as a central example:…
This overview describes several ways quantitative trading attempts to identify and capture market opportunities. It presents historical data analysis and backtesting as tools for assessing a strategy, then discusses automated execution, momentum examples,…
This Chinese-language document summarizes the development and adoption of algorithmic trading, describing early automated portfolio trading in the United States, later growth alongside computing, and broader access through commercial trading platforms. It…
This overview explains how high-frequency trading can profit from price movements and fragmented U.S. securities markets. Because a listed security may trade on multiple exchanges, differences in liquidity, participants, or information timing can create…
This Chinese-language report reviews the development of China’s quantitative fund industry across three periods: before 2010, 2010–2015, and 2016 onward. It outlines a strategy landscape spanning market-neutral equity, index enhancement, quantitative stock…
This overview surveys a broad range of investment approaches and the decisions they emphasize. It covers value and growth selection, quantitative modeling, momentum, income generation, index investing, and diversification across asset classes. It also…
The document summarizes research on how high-frequency trading in equities affects liquidity in options on those stocks. The study combines Nasdaq HFT records with options transaction data and other market data for 103 stocks, then uses instrumental-variable…
This overview surveys a broad set of quantitative approaches, describing their basic mechanisms and the market settings or investor capabilities they may suit. It covers trend following, machine learning, arbitrage and market neutral methods, factor…
The article argues that Rust can suit quantitative trading infrastructure where large data workloads, dense computation, concurrency, and low latency matter. It attributes this fit to Rust’s performance, memory and thread safety guarantees, and lack of…
This report examines whether stocks expected to enter or leave the CSI 300 earn abnormal returns around index reconstitutions. It attributes potential price effects to index-tracking funds adjusting holdings. For the 20 trading days before an adjustment…
The document summarizes research on whether informed investors use industry ETFs to hedge long positions in stocks with favorable company-specific information. It describes a paired measure combining hedge funds’ unusually large stock holdings with an…
The research note examines two convertible bond approaches: buying bonds trading below conversion value and positioning in deeply out-of-the-money bonds. For discount arbitrage, it proposes buying the bond and shorting the underlying stock where feasible,…
The article discusses how artificial intelligence and machine learning may affect trading, investment advice, and market structure. It describes machine learning as a way to identify economically useful predictive features and combine them with classifiers.…
The transcript surveys four ways quantitative methods enter trading: high frequency strategies, statistical arbitrage, execution algorithms, and quantitative tools used alongside discretionary investing. It describes market making as earning bid–ask spreads,…
The article gives a high-level taxonomy of nine quantitative approaches and the market effects each seeks to exploit. Trend following aims to capture persistent moves; mean reversion trades price dislocations around an average. Value and growth models screen…
This overview contrasts efficient markets, where prices are assumed to incorporate available information, with inefficient markets, where prices may diverge from estimated value. It outlines the weak, semi-strong, and strong forms of the efficient market…
This introductory guide describes quantitative trading as building rules from data and statistical analysis to generate systematic entry and exit signals. It explains positive expectancy as a favorable average outcome across repeated trades, while…
This overview explains how traders can translate technical rules into algorithms that monitor markets and place or manage orders. It describes automation’s practical uses: tracking multiple assets and indicators, high-frequency trading, cross-market…
This paper summary describes an online learning method based on adversarial experts for selecting parameters in a zero-cost portfolio of technical trading strategies. The method combines a collection of historically tested strategies and studies their…