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.
9,797 documents
The commentary explains how bullish sentiment after news about spot Ethereum exchange-traded funds widened front-month BTC and ETH futures basis. It describes a delta-neutral trade: buy spot and short futures to capture the spread as contract prices…
The document outlines a market-structure proposal for managing financial exposure to computing capacity. It argues that GPU-hours are difficult to price consistently because capacity differs by chip architecture, workload, location, and contract terms.…
The article maps AI inference services onto trading-market roles. Users buy tokens, inference providers quote prices while committing GPU capacity, and aggregators route demand and charge fees. It argues that growing use of open models and software-driven…
This market update explains how optimism around a July 2024 event coincided with a sharp increase in Bitcoin's front-month futures basis. The basis compares futures prices with spot and can support a delta-neutral cash-and-carry position: buy BTC in the spot…
The report analyzes an August 2023 Bitcoin selloff through futures positioning, trading activity, price levels, and on-chain holder data. It describes a rapid decline that liquidated about $2.75 billion in futures open interest, with low exchange volumes…
The commentary explains how October's rise in Bitcoin coincided with a sharp increase and subsequent easing in front-month futures basis. A higher basis reflects stronger demand for leverage and can support a cash-and-carry trade: buy spot BTC and sell…
This analysis reviews exchange proposals to create a faster route for listing crypto exchange-traded funds. It explains three proposed eligibility paths: trading on an Intermarket Surveillance Group member market, having a regulated futures contract with at…
This report reviews crypto-collateralized lending and futures activity in Q4 2025, emphasizing the different responses of on-chain and centralized finance (CeFi) lending to a major liquidation event. The analysis finds that on-chain borrowing contracted as…
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…