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.
5,018 documents
This article describes techniques for reducing overfitting in feed-forward neural networks used to forecast market direction. It outlines L1 and L2 regularization, which penalize large model weights, and dropout, which randomly disables units during…
This article curates books, papers, and course materials that the author found useful for learning algorithmic and quantitative trading. The recommendations are grouped into practical trading, foundational statistics and time series, machine learning,…
The document outlines an experiment for studying how training-window length and predicted class-probability thresholds affect a financial prediction strategy. It constructs directional labels from returns and uses lagged returns and volatility measures as…
This essay argues that systematic traders should begin with market observation and an explanation of why a possible edge exists, rather than searching broadly across indicators and parameters for a profitable backtest. Repeated experimentation can produce…
The article argues that using large language models to generate strategies and run backtests can accelerate technical work while leaving the trader without an understanding of why an opportunity might persist. It frames sound research as a cycle of forming a…
This article demonstrates an unsupervised approach to grouping GBP/JPY candles by their shape. It represents each candle using the high, low, and close relative to the open, then applies k-means clustering with six groups. The assigned cluster labels are…
This short reflection argues that machine-learning and statistical methods are tools for analysis, not trading edges by themselves. It offers questions for assessing whether a discovered effect has a plausible structural, economic, financial, or behavioral…
This review describes Zorro as a platform for implementing, testing, optimizing, and executing systematic strategies. It argues that researchers can use an established framework to iterate on hypotheses, adapt existing strategies to new instruments, and…
This article presents a machine learning workflow for exploring candidate predictors in a simple trading system. It discusses data mining bias, feature construction, preprocessing, removing correlated inputs, and several selection methods, including maximal…
This career guide explains that quantitative and systematic trading covers many assets, holding periods, strategies, and degrees of automation. It notes overlap with related roles such as risk management, portfolio management, execution, quant development,…
The document distinguishes explicit, implicit, and tacit overfitting in trading research. Explicit overfitting comes from fitting too many parameters to historical data; suggested controls include reducing degrees of freedom, using robust fitting, and…
This brief reflection on QuantCon 2017 highlights three themes relevant to quantitative investing. It observes that machine learning was prominent, naming examples such as linear and logistic regression, decision trees, support vector machines, naive Bayes,…
The document compares the strengths of automated systems and human traders. It credits computers with speed, consistency, disciplined execution, position scaling, portfolio management, and detecting persistent or unintuitive patterns. It describes people as…
This tutorial introduces the notation and basic objects of linear algebra used in machine learning and quantitative finance. It defines scalars, vectors, matrices, and higher-order tensors, explains their dimensions and indexing, and gives examples such as…
The article introduces time series analysis as a statistical way to study sequential data modeled as outcomes of an underlying stochastic process. It highlights trends, seasonal patterns, and serial dependence, including volatility clustering, as features…
The document distinguishes four common quantitative finance roles: quantitative trader, quantitative researcher, financial engineer, and quantitative developer. Traders search for profitable signals and build trading algorithms. Researchers develop…
The article compares Windows, macOS, and Ubuntu/Linux as environments for quantitative trading research and deployment. It frames the choice around the user's research workload, preferred tools, need for automation, and comfort with command-line work.…
The article develops an object-oriented framework for generating synthetic correlation matrices as an initial component of a tool for creating correlated financial time series. An abstract base class defines a common generation interface so different models…
This conference trip report summarizes a talk about seeking trading signals in alternative data. Examples include satellite and drone imagery, purchase receipts, social media, industrial sensor data, agriculture, energy supply and demand, weather, and…
This article introduces Markov Chain Monte Carlo as a numerical way to approximate Bayesian posterior distributions when analytical calculations, including conjugate-prior shortcuts, are unavailable. It explains the Metropolis algorithm as a sequence of…
This beginner's guide explains Bayesian statistics as a framework for updating uncertainty when new evidence arrives. It contrasts Bayesian probability, interpreted as confidence in possible outcomes, with the frequentist view of probability as long-run…
The article introduces matrix inversion through systems of simultaneous linear equations. It represents the equations as A x = b, defines the identity matrix, and explains that when an inverse exists, multiplying by it gives the solution x = A⁻¹b. This…
The article argues that entering quantitative finance in one’s thirties is feasible and frames the transition around skills and preparation rather than age. It recommends an honest assessment of mathematical background, especially linear algebra, calculus,…
This article introduces statistical learning as the task of estimating a relationship between response variables and predictor features. A quantitative finance example frames index values as responses and company fundamentals as possible predictors. It…