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.
511 documents
This overview groups Python libraries by common tasks in quantitative trading workflows. For data access, it discusses tools for retrieving market prices, fundamentals, and economic datasets, along with a broker interface for working with Interactive…
The article combines company fundamentals with quantitative ranking to select equities. It describes comparing a group of large technology stocks using valuation and leverage ratios, inverting the ratios to represent value, standardizing them with Z-scores,…
A forex carry trade seeks to earn the interest-rate difference between currencies by holding a position that receives the higher rate and pays the lower one. The document explains how that return depends on the position and notional, and introduces covered…
The article presents a checklist for evaluating automated trading platforms. It recommends considering backtesting capabilities, supported programming languages, asset and market data coverage, data frequency, web or desktop access, ease of use, strategy and…
This tutorial describes a neural-network workflow for predicting the next stock price from daily OHLC data for an Indian listed company. It covers scaling inputs and targets, arranging observations into sequential training, validation, and test sets, and…
The article outlines a four-stage workflow—form a hypothesis, test it, refine it, and prepare for production—using a mean-reversion strategy on an Indian equity ETF. It describes how quantstrat represents a strategy through market instruments, indicators,…
This overview addresses common assumptions about algorithmic trading. It distinguishes algorithmic trading, which automates decisions or order execution using predefined rules, from high-frequency trading, described as a speed- and turnover-intensive subset…
The document introduces sentiment analysis, also called opinion mining, as a way to classify the tone expressed in text as positive, negative, or neutral. In a trading context, this can be applied to news and other unstructured information, which automated…
The article outlines a supervised learning pipeline for forecasting the next day’s closing price of the gold ETF GLD. It uses three-day and nine-day moving averages as predictors, shifts the closing-price series to define the next-day target, and fits a…
This webinar summary introduces the execution trade-off between market and limit orders: market orders offer faster execution with uncertain price, while limit orders specify price but may not execute if the market moves away. Execution algorithms seek a…
The article explains how Quadratic Discriminant Analysis (QDA) differs from Linear Discriminant Analysis (LDA), then applies QDA to an intraday momentum strategy using three-minute e-mini S&P 500 futures data. LDA assumes classes share a covariance matrix,…
This article presents an individual trader’s checklist for moving automated strategies from research into live trading and, potentially, managing outside capital in the United States. It recommends validating systems through backtests, walk-forward work, and…
This interview describes how an index-options trader developed a more systematic process, moving from market observation and paper trading toward strategy design, testing, and live execution. The trader emphasizes defining acceptable losses, quantifying…
This project tests an options dispersion strategy that compares BANKNIFTY implied volatility with the weight-adjusted implied volatility of its constituent stocks. The author estimates average implied volatility from first out-of-the-money calls and puts,…
The article introduces unsupervised learning as a way to find structure in data without target labels, contrasting it with supervised prediction. Its trading example uses K-means to group stocks by return on equity and market beta. The workflow standardizes…
The document explains the Black–Scholes model for pricing European call options. It introduces the main inputs—underlying price, strike, time to expiry, risk-free rate, and volatility—and describes how the formula combines the discounted strike with the…
This tutorial distinguishes syntax errors, which prevent Python from parsing a program, from exceptions, which arise when syntactically valid code encounters a problem at runtime. It explains how an interpreter may report incomplete input while a command is…
This guide introduces autoregressive moving-average models for time-series analysis, explaining why ARMA requires stationary input and how detrending or differencing can help achieve stationarity. It describes using the Augmented Dickey-Fuller test…
This introduction presents statistics as a framework for making decisions when complete information is unavailable. It distinguishes questions answerable with certainty from statistical questions, which extend beyond the observed data and carry uncertainty.…
This project uses computer vision to classify the next trading day’s direction for selected precious and industrial metals. It turns fixed windows of daily candlesticks into 224-by-224 images and trains ResNet CNNs to predict whether the following day’s…
The article describes five personal qualities it associates with algorithmic trading practitioners: careful observation, realism, balanced optimism, persistence, and continued learning. It links realistic expectations and self-awareness to avoiding…
The article presents algorithmic trading as a way to automate responses to changing prices and reduce discretionary delays. It outlines possible uses such as entering or exiting positions under predefined conditions, using stop orders during sharp declines,…
The article introduces spaCy as a Python library for processing text, with an emphasis on its production-oriented pipelines. It contrasts spaCy with NLTK and describes a workflow using a trained language model to convert text into tokens, lemmas, sentences,…
The document explains seasonality as recurring price or return patterns tied to calendar periods and surveys several proposed effects: the Santa Claus rally, sell-in-May timing, weekday patterns, month-end and turn-of-month behavior, and a December pattern…