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 presents hypothesis testing as an early step in quantitative strategy research. It uses a claim about whether the average return of Nifty 50 stocks exceeds a specified benchmark to explain how to define null and alternative hypotheses, choose a…
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…
This article introduces five technical indicators for assessing price trends, momentum, and volatility: moving averages, the Average Directional Index, Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. It distinguishes…
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 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 article surveys a collection of blog posts for readers learning about algorithmic trading. The topics range from mathematical and statistical foundations to strategy families such as momentum, arbitrage, market making, and machine learning. It also…
The article introduces delta as option price sensitivity and gamma as the rate at which delta changes with the underlying price. It describes gamma scalping as repeatedly adjusting an options portfolio to manage its Greek exposures while seeking to benefit…
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…
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…
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,…
Sourabh Sisodiya describes moving from discretionary trading based on technical analysis and candlestick patterns toward rule-based strategies after questioning whether his approach had a reliable edge. He presents backtesting as a way to assess a system and…
This study proposes distinguishing human-originated orders from high-frequency algorithmic orders using the time taken to modify an order before execution. Orders with a minimum or average replacement time below a selected threshold are labeled algorithmic;…
This overview explains the academic and practical skills that can support work in algorithmic trading. It maps computer science to programming, mathematics and statistics to probability and quantitative methods, finance and economics to markets and risk, and…
This tutorial walks through setting up Zipline for backtesting on Windows. It covers creating a Conda environment, installing Jupyter and Zipline, configuring a Quandl data key, and ingesting historical data. It also describes using Pyfolio to produce a…
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 event announcement outlines a talk on risk oversight for automated trading. It emphasizes that algorithmic systems add operational and technology concerns to familiar market, financial, credit, and liquidity risks. The proposed discussion uses failures…
The article describes trading ideas as hypotheses about how an asset may behave in particular circumstances, then suggests developing them through experience, research papers, forums, books, and learning from practitioners. It gives momentum research as an…
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;…
The article introduces Bitcoin’s transaction ledger, UTXO accounting, public nodes, and Proof of Work consensus. It explains how miners compete to find a valid nonce, how difficulty targets regulate block production, and how block rewards and transaction…
The article distinguishes algorithmic trading, high-frequency trading (HFT), and news-based trading by their aims, time horizons, speeds, and data sources. It describes algorithmic systems as rule-based automation across varied horizons, HFT as speed-focused…
This article introduces Bayesian inference by estimating the unknown probability of heads for a coin. It contrasts the frequentist view, where the parameter is fixed but unknown, with the Bayesian view, where uncertainty about the parameter is represented by…