Knowledge library

This document presents data-cleaning methods for financial datasets and explains how to keep preprocessing aligned with train and test splits. Its central lesson is to estimate transformations such as scaling and winsorization from training data only; using…

This document introduces public Fama-French and AQR factor return datasets as inputs for benchmarking, risk attribution, and factor research. It describes market, size, value, profitability, investment, and momentum factors from the Fama-French library,…

This notebook turns institutional 13F disclosures into a bipartite graph linking managers to stocks, then derives stock-level ownership features. It aggregates duplicate manager-stock positions, normalizes issuer labels, excludes option rows, and uses…

This notebook assesses whether Binance perpetual-futures data can support a cross-sectional strategy that ranks contracts by premium and takes long and short positions. It does not fit a model or make forecasts. Instead, it checks the declared trading…

This notebook compares the effect of assumed trading costs across strategies drawn from multiple case studies. It reads cost-sensitivity backtests for each deployed strategy configuration, compares gross Sharpe with net Sharpe at the case study's own assumed…

This notebook explains path signatures as fixed-length features that preserve the order and joint shape of observations in a time series. It describes how the first signature level records total changes, while second-level terms capture signed areas between…

This document explains a run-log system for recording model training, predictions, causal estimates, and backtests across trading case studies. It describes how configurations flow from case-study settings through training menus and model presets, then…

This notebook turns institutional 13F holdings into a graph connecting managers and stocks, with sector relationships and holdings edges that carry shares, value, reporting period, and public filing date. It demonstrates graph queries for shared ownership…

This notebook clusters monthly returns from nine equity, bond, currency, and commodity factor and market series using Gaussian mixture models. After dropping months with incomplete data, it standardizes the series so differences in scale do not dominate the…

This notebook evaluates 57 accounting and price-based characteristics as individual predictors of next-month US stock returns. It calculates monthly cross-sectional rank correlations, averages them over time, and adjusts uncertainty for serial dependence and…

This chapter presents a framework for turning a trading idea into a documented feature specification. It asks researchers to align feature horizons with the intended decision, state a driver hypothesis, and distinguish predictive signals from contextual…

This guide distinguishes causal effect estimation from causal structure discovery and maps common research questions to suitable Python libraries. EconML and DoWhy estimate effects when the adjustment structure is known or explicitly specified; Tigramite and…

This overview consolidates configuration data from nine trading case studies spanning equities, crypto, foreign exchange, futures, options, and multi-asset portfolios. It compares each study’s universe, data frequency, decision cadence, cost model, and…

This notebook presents a deterministic framework for checking whether backtest and live trading pipelines behave consistently. It compares the stages that turn price bars into decisions: feature calculations, predictions, signals, and order sizes. A fixed…

This notebook uses NASDAQ ITCH-derived trade and order data to illustrate microstructure patterns. It examines negative first-order autocorrelation in tick-level trade returns, comparing the effect with mid-price returns, and contrasts trading activity and…

This notebook aggregates linear-model results, including OLS, Ridge, Lasso, and ElasticNet, across nine case studies. It compares each study’s strongest complete configuration on its primary label, then examines regularization, fold-level information…

This educational notebook surveys price- and volume-derived features used in quantitative research. It covers simple and logarithmic returns across horizons, skip-one momentum, overnight and intraday returns, moving-average distance, trend and reversal…

This notebook presents a workflow for asking whether six-month skip-recent momentum affects 21-day forward returns and whether the effect changes with market volatility. It estimates effects on training data using double machine learning, with volatility and…

This notebook builds a reusable diagnostic survey for financial datasets before preprocessing or feature engineering. It checks time-index dtype, null timestamps, ordering and uniqueness; finds exact and key-based duplicates; measures missing values by…

This notebook compares two pretrained time-series foundation models, Chronos and TinyTimeMixer, with an LSTM and a penalized linear baseline on an ETF panel. Each model receives a univariate historical context; the pretrained models forecast that input…

This chapter presents a framework for evaluating causal claims in quantitative trading research. It starts with clear definitions of treatment, outcome, estimand, counterfactual, and adjustment set, then matches research questions to methods: Double Machine…

This notebook fits linear models to NASDAQ-100 microstructure features observed at one-minute intervals to predict returns at several forward horizons. Its purpose is to establish a simple baseline before more expressive models. Because spread and imbalance…

This feature evaluation screens financial and model-derived candidates against forward returns for CME futures. For each feature, it checks data coverage and staleness, measures daily cross-sectional rank correlation between feature values and forward…

This notebook compares implementations of a univariate SPY price forecast across raw PyTorch, sktime, and Darts. Its main purpose is to assess practical effort: code length, interface requirements, installation success, and combined fit-and-predict time. A…