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
The document outlines a session on managing option portfolios that span multiple instruments. Its stated focus is on Delta and Gamma, including how market characteristics affect their behavior, and on making several higher-order Greeks easier to understand:…
This interview follows Derek’s path from futures trading and professional card games into quantitative trading and the creation of Golden Compass with collaborator Maxime. Their partnership developed through continued remote research, a shared derivatives…
This first-person account describes experiments with a volatility-triggered scalping strategy and a moving-average momentum strategy. The initial scalping rule used Average True Range to set a trading threshold and compared recent price action, but its…
The article introduces logistic regression as a supervised learning method for estimating the probability of a categorical outcome. For trading, it presents a binary classification setup: predict whether a stock will rise or fall using historical inputs such…
The document explains leverage as the use of borrowed capital to increase investment exposure, with the aim of earning more than the financing cost. Hedge funds may use it to hold long and short positions larger than their capital base. The article frames…
The article compares programming languages used in algorithmic trading and frames language choice as dependent on the task. It characterizes Python as a readable, open-source option for research, strategy development, and backtesting, with libraries that…
The guide explains how to estimate return variance for a single asset, covariance between asset returns, and total variance for a portfolio. It begins with historical returns, using their average as an expected-return estimate and variance or standard…
The article surveys five skills it presents as useful for modern trading: quantitative analysis, Python, backtesting, machine learning, and risk management. It links quantitative research to statistical testing of market data and technical signals, and…
The project builds a long-only daily strategy across nine SPDR sector ETFs using short- and long-term exponential moving average crossovers. It reallocates capital quarterly based on each ETF’s relative quarterly return, aiming to direct more capital toward…
The document defines the Arms Index, or TRIN, as the ratio of advancing stocks to declining stocks divided by advancing volume to declining volume. It interprets readings below 1 as generally bullish and readings above 1 as generally bearish, while unusually…
The document explains the role of a securities master: a shared system for collecting, storing, cleaning, validating, and distributing instrument, pricing, fundamental, and transaction data. It describes a range of instruments and downstream uses including…
The document introduces price action trading as analysis of raw price movements and chart patterns, with limited reliance on indicators. It surveys support and resistance, trend following, candlestick setups, breakouts, range trading, and analysis across…
This article explains how exchange-traded funds pool securities into units traded on exchanges, then surveys market, sector, dividend, style, commodity, currency, and bond ETFs. It describes their potential uses, including broad diversification, targeted…
This article contrasts algorithmic trading, where coded rules generate and execute orders, with discretionary trading, where a person interprets market information and decides when to trade. It illustrates rule-based execution with a moving-average example…
This guide collects free educational materials for learning machine learning and applying it to trading. It groups courses, books, blogs, research papers, and videos, ranging from introductory machine learning and Python tools to interpretable models, neural…
This project outlines a process for predicting trade exits with machine learning. It samples market data into dollar bars, estimates volatility from each bar’s high and low using Parkinson’s method, and generates initial buy or sell signals from a…
The article introduces blockchain as a distributed ledger for recording transactions across networked computers. It explains how blocks hold records and the previous block’s hash, linking the history so that changes become detectable. Copies of the ledger…
This interview describes how options trader Vishnu Bharath moved from an engineering and telecommunications background into quantitative trading by combining market familiarity with programming skills. His initial interest in markets came through discussions…
This article introduces technical indicators as mathematical transformations of price, volume, or related market data, and distinguishes technical analysis from fundamental valuation. It groups indicators into trend, momentum, volume, and volatility…
This overview summarizes a SEBI discussion paper on algorithmic trading and co-location in Indian markets. It reports that algorithms generate a large share of exchange orders and a substantial portion of trades, and reviews research findings that…
This project outlines an intraday strategy for trading the spread between a security’s spot price and its futures price. It classifies futures trading above spot as contango and below spot as backwardation, then calculates the spread from one-minute prices.…
This article presents a basic text sentiment workflow for financial commentary using R. It applies a bag-of-words approach: prepare positive and negative term lists, clean an earnings-call transcript, tokenize it into words and n-grams, and compare the…
This guide lays out a sequence for building an algorithmic strategy: define the trading idea and market, specify instrument filters, check the logic visually, backtest, tune parameters, forward test in simulation, and deploy. Its running example is a daily…
This project describes a dispersion strategy using Bank Nifty options and options on its constituent bank stocks. It compares implied volatility across the index and its components, using implied correlation as a signal: high correlation points toward…