Resumos e ideias principais, escritos pelo agente de investigação da Stratmill, dos livros, artigos científicos, artigos e código consultados pelos nossos agentes de IA. Cada página inclui uma ligação para o original.
511 documentos
This article surveys the development of trading from direct barter and currency to intermediaries, exchanges, telephone dealing, electronic order routing, online platforms, and algorithmic systems. It highlights how each stage changed the way participants…
This article describes a directional trading experiment using a decision tree classifier and custom price-derived features rather than standard technical indicators. The features include changes in the close relative to the prior day’s range, a second-order…
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…
This article lays out a machine learning trading workflow using technical indicators to predict a future five-day close-to-close return. It describes aligning the shifted target with current predictors, then applying walk-forward optimization to assess…
This document introduces cryptocurrency algorithmic trading through an interview with a developer building a cross-exchange trading platform. The described workflow retrieves exchange order books, backtests strategies at short intervals, generates signals,…
This tutorial explains Matplotlib as a Python library for interactive plots and publication-quality static figures. It introduces the distinction between a Figure, which contains the overall chart, and Axes, where data is plotted, then shows how to create…
The document explains options open interest as the count of outstanding contracts for a given strike and expiration, distinguishing it from volume, which counts trading activity over a period. It presents rising, falling, and stable open interest as clues…
Siddharth Bhatia describes moving from a liberal arts degree and finance work into algorithmic trading. After gaining experience in the financial industry and completing the CFA, he learned programming and trading technology to update his skills and apply…
This interview follows Sanjot Raibagkar’s move from software engineering into algorithmic trading. He describes a decade of trading, work on financial technology projects, and a focus on Nifty and Bank Nifty options. His technical and data science background…
This project studies historical ETHBTC order book data from Poloniex and tests two ways of using order flow in simulated trading. The first estimates prices from aggregated bid and ask positions and uses those estimates to filter trades generated by a…
This tutorial describes how a Python program can connect to Interactive Brokers through IBPy, a bridge to the broker’s API, using Trader Workstation or IB Gateway as the intermediary. It explains the client-server setup, the roles of connection and message…
The article explains how company fundamentals can be turned into machine-readable rules for systematic equity selection. It outlines the need for structured, reliable financial data, names several data providers, and discusses risks in reported figures,…
The article presents Stereoscopic Portfolio Optimization (SPO), a framework that combines traditional mean-variance allocation with bottom-up analysis of asset-level market microstructure. It describes volatility as one possible input, while noting that…
This project uses a Gaussian Hidden Markov Model to classify market conditions as high or low volatility, then applies those classifications to a moving-average crossover strategy. Daily returns serve as the observable input, although the author also…
This project describes a short-term long strategy that looks for renewed strength during a low-trend-strength phase. It requires the 21-period EMA to be above the 42-period EMA, and the 42-period EMA above the 63-period EMA. ADX must be at or below 25 and…
This guide surveys machine learning methods and describes a Python workflow for a simple stock price prediction task. It proposes predicting a day’s closing price from earlier OHLC data, preparing historical observations, creating lagged inputs, and making…
This project describes dispersion trading as a relative value strategy that trades options on an index against options on its component stocks. It estimates implied volatility from nearby option strikes using Black–Scholes, combines component volatilities,…
This tutorial outlines a workflow for using machine learning in an algorithmic trading example. It describes sourcing historical prices, preparing lagged OHLC features, transforming price data, splitting observations into training and test sets, tuning model…
This article explains how academic and industry research can generate ideas for systematic trading. It describes a screening approach that considers whether a strategy can be implemented, the length of its backtest, and the overall soundness of its evidence…
The article introduces blockchain as a distributed ledger maintained across network nodes, with cryptography intended to protect recorded transactions. It considers how applying this structure to stock markets might alter trading infrastructure: fewer…
This project describes a daily statistical arbitrage strategy applied to six INR currency pairs over the stated 2011–2013 sample. It screens pair combinations with cointegration tests, then applies a cointegrated Augmented Dickey–Fuller test to selected…
The document explains vertical bear spreads for traders expecting a moderate decline. It compares a bear call credit spread, which collects a premium up front, with a bear put debit spread, which pays a net premium. Both use options with the same expiration…
Using a restaurant on a food delivery app as an example, the document presents a general six-step data science workflow: define the business problem, collect data, clean and remediate it, analyze and interpret it, model it, and communicate findings. The…
The document explains the 52-week high effect: investors may use a stock’s annual high as a reference point and respond slowly to information as prices approach it, allowing subsequent price continuation. It also describes research comparing an…