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
This tutorial explains how Python functions organize reusable operations, covering built-in, user-defined, and anonymous functions. It describes parameters and arguments, returning one or more values, default parameter values, and functions that accept…
The document introduces RAPIDS, a set of NVIDIA GPU libraries for data science, and explains the roles of cuDF for dataframe operations, cuML for machine learning, and cuPy for numerical arrays. It outlines system and installation requirements, then…
The article explains mutually exclusive events as outcomes that cannot occur together and contrasts them with independent events, which may occur simultaneously without being related. It uses coin flips to introduce the probability of alternative outcomes,…
The article surveys market microstructure as the study of how trading rules and market design shape price formation, liquidity, and execution. It distinguishes quote-driven markets, where dealers or market makers provide quoted prices, from order-driven…
This article demonstrates simulated AR, MA, and ARMA processes and uses their autocorrelation and partial autocorrelation patterns to explain how model structure appears in data. It describes the Box–Jenkins diagnostic approach: AR processes tend to show a…
The article introduces time series as observations arranged over time and outlines how traders can analyze and forecast such data. It distinguishes univariate from multivariate series and stationary from nonstationary series. Autocorrelation and partial…
This article explains a long butterfly spread as a defined-risk options position for an underlying expected to remain near a target price at expiration. The call version combines one lower-strike long call, two short calls at a middle strike, and one…
This article describes a simple way to test whether website sentiment indicators can inform stock trades. The author collects buy, sell, and hold readings for a stock list by extracting values from a financial portal, then stores them in a data frame or…
This article proposes a conservative money-management approach that scales position size according to the observed win rate of long and short entries. It first describes Fixed Ratio sizing, which adds contracts only after profits reach a specified amount per…
This tutorial builds a long-short moving average crossover backtest in a spreadsheet using a stock’s historical prices. It calculates short and long simple moving averages, generates buy and sell signals when they cross, records transaction prices, and…
The article contrasts technical analysis with quantitative research in algorithmic trading. It describes technical analysts as using historical market behavior and indicators to form directional signals, while quants build and assess systematic strategies…
This project tests whether adjusting percentage-based take-profit (TP) and stop-loss (SL) levels can improve strategies using simple or exponential moving-average crossovers and RSI signals. For each strategy and market series, it searches TP and SL…
The document introduces the Bag of Words (BoW) method for representing text as counts of vocabulary terms. It outlines basic preprocessing and explains how a document collection becomes a matrix in which rows represent documents, columns represent unique…
The document explains how to size positions so instruments with different price volatility make more balanced contributions to portfolio risk. It first distinguishes account capital from notional exposure, including how margin and contract multipliers can…
This tutorial introduces polynomial regression as a way to let a linear regression model represent nonlinear relationships. Using Punjab National Bank price data, it creates lagged closing-price features and binary indicators based on higher highs and lower…
This article presents coding habits intended to make R projects easier to read, maintain, and repeat. It recommends documenting a script and its sections, loading and checking packages, organizing source files, naming data structures consistently, indenting…
This interview follows a quant developer’s move from commodity trading in his family’s business to automated trading and research. It describes building execution and research platforms, and explains how programming helped him explore rule-based trading. The…
The article outlines common hurdles for people learning algorithmic trading: reluctance to program, weak mathematical foundations, confusion between technical and quantitative analysis, limited data skills, conflating algorithmic trading with high-frequency…
The Parabolic Stop and Reverse indicator places a trailing value above or below price to indicate a possible trend direction and reversal. The article gives its update formula, which moves the prior SAR toward the trend’s extreme price using an acceleration…
The article surveys ways artificial intelligence is being used in quantitative finance, including automated trading, risk analysis, portfolio management, market prediction, fraud detection, and high-frequency trading. It describes how quant work has shifted…
This overview curates Python resources for quantitative and algorithmic trading rather than developing one strategy. The listed topics include market-data retrieval and visualization, technical indicators, VaR estimation, machine-learning classification,…
The guide describes Dijkstra’s greedy method for finding shortest paths from a starting node to other nodes in a weighted graph. It initializes tentative distances from the source, repeatedly selects the unprocessed node with the lowest current distance, and…
The article explains momentum trading as taking a position in the direction of recent price movement and holding it until signs of reversal. Its worked example applies this idea to five-minute BTCUSDT candles: a close above the prior three-candle high…
The article introduces LightGBM, a tree-based gradient boosting method, and explains its leaf-wise tree growth, Gradient-based One-Side Sampling, and Exclusive Feature Bundling. It also describes histogram binning, categorical feature handling, and selected…