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.
45 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 introduces derivatives as contracts whose value depends on an underlying asset, index, or rate. It describes forwards, futures, options, and swaps, explaining basic contract features such as long and short positions, strike prices, option…
This overview explains how European Union financial regulation applies to algorithmic trading. It describes ESMA’s role in setting standards and the role of national regulators in implementing and supervising them. It introduces MiFID II as a framework…
This profile follows a California data analyst’s move toward quantitative and algorithmic trading. His engineering, econometrics, and data work led him to explore Python, futures, automated analysis, and discretionary trading based on macro news sentiment.…
This interview describes David U. Ordiz’s progression from discretionary Bund futures trading to systematic research and portfolio management. His approach focuses on intraday algorithms seeking short-term trend or counter-trend moves across index futures,…
The document explains how to stitch successive futures contracts into a longer time series for analysis when each individual contract has limited history. Simply joining contract prices can create artificial jumps because adjacent expiries may trade at…
The article introduces spread trading as a hedged position that buys and sells related contracts, such as options on the same security with different strikes or expiries, or futures with different delivery months, commodities, or locations. It recommends…
The article walks through a simple rule-based strategy implemented with the Quantiacs Python toolbox. It describes configuring a backtest, loading stock or futures data, setting parameters such as the lookback, capital, and slippage, and examining results…
This project describes a statistical arbitrage strategy for Chinese futures. It screens contract pairs with an Augmented Dickey-Fuller test for stationary spreads, estimates a dynamic hedge ratio with a Kalman filter, and uses the spread’s half-life to set a…
This guide introduces index futures as standardized contracts linked to stock indexes, generally settled in cash rather than through delivery of constituent shares. It illustrates settlement by multiplying the change between the agreed index level and the…
Angela Zhao’s career profile includes several practical observations about quantitative trading. She describes moving from finance and discretionary investing into data analytics and machine learning, with algorithmic trading appealing as a way to make…
This project describes an intraday Nifty strategy using five-minute data, a 200-period simple moving average, and a 50-period exponential moving average. It takes long or short positions when the index closes beyond both averages, with no position when the…
The article argues that a backtest should approximate live trading conditions rather than maximize the appearance of historical returns. It recommends including commissions and slippage, with estimates adjusted to the instrument and checked against actual…
This project describes a cloud based automated system for WTI futures that uses machine learning to classify market conditions as trending or ranging. Several models vote within separate trend and range groups; when the groups disagree, their confidence…
The document discusses SEBI’s approval for Indian exchanges to set equity derivatives trading hours between 9 a.m. and 11:55 p.m., subject to suitable risk systems and infrastructure. Approval alone does not ensure the exchanges will extend their sessions.…
The document demonstrates how a simple 20-day moving average crossover strategy on natural gas futures can look compelling in a vectorized backtest, then lose credibility as realism is added. It recommends inspecting intermediate data and plots, checking…
This overview explains how standardized futures contracts differ from private forward agreements, and describes contract expiry, delivery months, tickers, margin, and profit and loss. It also introduces futures continuation series, which join successive…
The article describes three sentiment measures and proposes contrarian trades based on them. VIX is presented as an options-derived estimate of expected S&P 500 volatility; high readings are associated with fear and falling prices, while low readings are…
This document explains how the IBrokers R package connects a strategy to Interactive Brokers through Trader Workstation (TWS). It outlines functions for requesting contract details, live quotes, market depth, real-time bars, and historical data, along with…
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 guide describes a trend-following method that calculates short- and long-window moving averages on the Nifty index, then uses their crossovers to signal long call option trades. It outlines a Python workflow: obtain index and option data, combine the…
This tutorial explains how to prepare market data for classification and regression decision trees. It uses daily E-mini S&P 500 futures data and constructs predictors from exponential moving averages, average true range, average directional index, relative…
The document introduces covered calls as a way to earn option premium while holding shares, explaining that the seller keeps the premium if the stock stays below the strike but may have to sell shares at the strike if the price rises. It then outlines a…