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.
195 documents
The document reflects on Quantopian’s closure through the strengths and constraints of its research platform. It describes benefits for systematic traders, including a team environment for exchanging ideas, training, research technology, peer feedback, and a…
The document describes a screening method for finding stocks whose behavior during sharp market declines differs from their average relationship with the broad market. It aligns daily stock and SPY returns, estimates each stock’s market beta over the full…
The document contrasts two possible trading outcomes for a strategy described as having a known, substantial edge: a favorable run and an unfavorable run. Its central lesson is that realized profit and loss can vary considerably even when the underlying…
This introductory explanation defines the expiration value of long call and put options in terms of the underlying price and strike. A call is worth zero when the underlying finishes at or below the strike, and its value rises by the amount the price exceeds…
This case study recounts a team’s experience entering crypto trading in 2021, when they viewed the market’s fragmented and developing structure as a source of inefficiencies. It describes several approaches: futures basis arbitrage, exploiting delays between…
This article demonstrates a vector autoregression (VAR) model using daily returns for a basket of U.S. homebuilding stocks. It fits the model on a rolling historical window, forecasts each asset’s next return, and converts the cross-sectional forecasts into…
This article explains how to profile an R workflow that calculates rolling pairwise correlations across S&P 500 constituents. It outlines possible ways to address memory limits, including chunking data, choosing compact data structures, using memory-focused…
This short article uses the long-run nominal growth of US stocks and bonds as a starting point for discussing risk premia. It reports that stocks rose 48,000 times in value and bonds 300 times from 1900 to the article’s present. Its explanation is that…
This article argues that traders should begin with a workable strategy and build technology in response to problems encountered in live trading. Elaborate systems designed before trading can consume time without generating market feedback, and the imagined…
This article walks through implementing a price-spread pairs trade in Zorro using GDX and GLD as an example. It defines the spread as one asset’s price minus a hedge-ratio-adjusted price of the other, then standardises the spread with a rolling z-score. The…
This article demonstrates a convex optimisation workflow for a crypto perpetual futures portfolio. It combines expected returns estimated from cross-sectional momentum and carry features with a breakout signal, then uses a covariance estimate to represent…
This article addresses whether publishing a trading edge causes it to disappear. It uses an end-of-month Treasury demand effect as an example: price-insensitive buying may temporarily move prices away from fair value, so a trader could enter ahead of the…
The article explains why VIX futures can trade at premiums or discounts to the VIX index and examines how the futures curve changes with market conditions. It introduces a cash-and-carry comparison: futures require less cash than a stock purchase, leaving…
This article demonstrates ways to speed up a portfolio backtest implemented in R. It begins with profiling a cash backtest that processes prices and target weights across dates, updates holdings using a no-trade buffer, accounts for commissions, and records…
This course overview presents a systematic trading process built around identifying an economic reason for an edge before optimizing a backtest. It recommends forming a hypothesis first, then examining data and testing the idea, and describes a framework for…
This article explains statistical arbitrage by contrasting it with cross-exchange arbitrage. Pure arbitrage seeks to buy and sell the same asset at different prices, but transfers, costs, and price changes make the apparent opportunity difficult to capture.…
This article brainstorms possible inputs for a crypto statistical arbitrage model. It covers relative price moves between similar assets, short and long horizon trends, crowded spreads that may unwind with momentum, lead-lag effects across markets, and…
This short discussion considers the role of foreign exchange in a systematic trading portfolio. Its central claim is that FX does not offer an inherent risk premium that can provide a persistent return tailwind, so traders must seek returns through active…
This installment in a deep learning for trading series explains why GPU hardware can speed up the matrix operations common in neural network workloads. It outlines a Windows setup path for using Keras with TensorFlow from R: check hardware compatibility,…
The document describes reconstructing monthly S&P 500 membership history from the current constituent list and a record of index additions and removals. Working backward month by month, the method removes stocks that were added and restores those that were…
The article demonstrates a spreadsheet workflow for exploring a claimed weekday pattern in gold-related prices. Using GLD price history, it derives log returns and calendar fields, groups returns by weekday in a pivot table, and charts the sums. It reports…
The document introduces Shannon entropy as a way to examine how random price movements appear over a chosen lookback period. It describes applying the measure to price data, selecting a period and pattern length, and plotting entropy values for several…
The article advises new trading businesses to begin trading with available skills and tools, then build operational capabilities in response to real market experience. It argues that constructing a large technology stack before trading can waste effort…
The article introduces a lag-based estimate of the Hurst exponent and applies it to simulated mean-reverting data and adjusted SPY prices. The method compares the variability of price differences across a range of lags, fits a line to the log-scaled…