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.
213 documents
This overview compares free and paid sources for historical market data accessed through Python APIs. It describes retrieving single and multiple instruments, using daily or intraday frequencies, and handling several asset classes, with examples involving…
The article explains short selling as borrowing an asset, selling it, then buying it back to return to the lender. Its gold illustration and a stock example show how a falling price can create a gain after borrowing costs and transaction charges. It also…
The article explains why systematic research depends on reliable, structured inputs and outlines a Python workflow that retrieves end-of-day prices and fundamental growth data through financial data APIs. Its illustrative research question is whether…
The document introduces LangChain as a way to connect large language models with external data and compose repeatable analysis workflows. It explains basic components including model calls, prompt templates, chains, batching, and agents. Its equity-analysis…
The article surveys stock market simulators for practicing trades with virtual funds. It describes services for manual trading, historical chart exercises, and, in some cases, automated strategies or broker connections. The listed features include market…
The document explains how the risk-constrained Kelly criterion modifies standard Kelly position sizing. Standard Kelly sizing seeks to maximize long-run log growth using estimated win probability and win/loss payoff, but can lead to prolonged, deep…
The article explains random forests as ensembles of decision trees that reduce reliance on any single tree’s prediction. Trees are built from randomly selected data features, and their classifications are combined by majority vote; for continuous outputs,…
The article presents reinforcement learning (RL) as a trial-and-error approach in which an agent learns actions from rewards, with an emphasis on maximizing longer-term outcomes. It maps the framework to trading through states, such as price and indicators;…
This article describes an introductory online course on momentum trading offered through B3’s education platform in partnership with QuantInsti. It presents the course as suitable for learners with basic Python knowledge and says the material covers…
This project tests a mean-reversion pairs strategy on Mexican stocks. It screens an initial equity universe for complete price histories and minimum average trading volume, then tests within-industry pairs for cointegration with an augmented Dickey-Fuller…
This project describes an automated strategy that uses live EURUSD prices to generate signals for EURUSD, USDCHF, and XOM. A long signal occurs when EURUSD rises above the highest close of the prior five days; a short signal occurs below the lowest close.…
The document introduces LEAPS as options with expirations more than a year away, allowing investors to take long-horizon directional positions or hedge stock holdings without buying or shorting shares outright. It explains that long-dated contracts can…
The document explains how moving averages summarize a rolling window of prices and how traders compare a faster average with a slower one. A cross above the slower average is commonly treated as a potential bullish signal, while a cross below is treated as…
The article describes a workflow for using generative language models to assemble a thematic universe of healthcare companies involved in artificial intelligence. It starts with S&P 500 constituents, filters for healthcare firms, gathers company news, and…
This strategy uses a large language model to set long-only exposure for AAPL according to market states, rather than asking it to predict price direction. Historical price features are discretized into readable states, and monthly statistics for each state…
This study tests whether public filings reporting C-suite purchases of common shares are followed by abnormal stock returns. It builds a research sample from SEC Form 4 data, carefully distinguishing transaction rows, aggregated purchase components, and…
The document explains how to explore portfolio allocations by repeatedly assigning random weights to four U.S. financial-sector stocks, calculating each portfolio’s annualized return and standard deviation, and comparing the results. It defines three…
This project describes two classifiers intended to predict whether Bank Nifty and its leading constituents would open higher or lower on the following trading day. The stock models use daily OHLCV history and technical indicators for five constituents; the…
The article considers how increasingly capable artificial intelligence could change trading and financial markets. It distinguishes current rule-based automated trading from systems that learn and adapt, then speculates that AI could assess technical,…
This webinar description explains how high-frequency prices can extend portfolio risk analysis beyond the low-frequency data commonly used in portfolio metrics. The proposed approach uses intraday observations to estimate risk and support portfolio…
The article introduces multithreading as a way to handle several stock data downloads concurrently. Since network requests spend time waiting for external responses, separate threads can work on different tickers while other requests are pending. It outlines…
This interview with trader Priyanka S. includes practical advice for developing and testing equity signals. She cautions that familiar technical indicators such as moving average crossovers may contain little information about future prices, and encourages…
The article explains how a time-series generative adversarial network can produce synthetic financial observations when historical data is limited. It describes the generator and discriminator conceptually, then focuses on the conditional probabilistic…
This project tests a simple ETF pairs strategy in oil, technology, and financial sectors: USO with XLE, XLK with IYW, and XLF with PSCF. It estimates a hedge ratio by regression, evaluates spread stationarity with an Augmented Dickey-Fuller test, then enters…