知识库

This notebook explains how to rebuild a single-symbol NASDAQ limit order book from message-by-order data. It processes adds, executions, cancellations, deletes, and replacements while maintaining the remaining size of each live order. Replacements require…

This source module provides loaders for U.S. equity prices, index data, options, market microstructure, fundamentals, and institutional holdings. Its most substantive methodological detail concerns resampling intraday trade data: open, high, low, close, and…

This notebook explains how to reconstruct a model experiment catalog from a case-study registry and compare its contents with MLflow. It follows experiments through training runs, prediction sets, and backtest runs, linking each stage to the preceding one.…

This exploratory analysis profiles hourly OHLCV for crypto perpetual contracts alongside an eight-hour premium index. It examines symbol and date coverage, changing contract membership, premium units and distribution, OHLC integrity, missing values, and…

This notebook demonstrates a shared OHLCV interface across Yahoo Finance, a long-history U.S. equity archive, and FRED macroeconomic series. It builds an ETF universe for a later rotation case study, shows how to combine historical and current sources, and…

This case study builds features for researching whether at-the-money implied variance exceeds subsequent realized variance for S&P 500 names, and whether the difference varies across securities. It combines straddle quotes with underlying prices to measure…

This notebook compares ordinary least squares with Ridge, LASSO, and Elastic Net for predicting forward returns across an ETF universe. All models use the same walk-forward folds, with a purge gap matched to the label horizon, so differences can be…

This notebook develops a unit-aware framework for estimating trading costs across equities, ETFs, futures, foreign exchange, and crypto perpetuals. It first distinguishes traded volume from activity measures that cannot be converted into dollar turnover,…

This chapter outlines how to build time-consistent fundamentals and alternative-data research across equities, macroeconomic series, futures, crypto, and prediction markets. Its central methods are bitemporal storage and as-of queries, source-aware…

This notebook builds the long-only, fully invested mean-variance framework for a diversified ETF universe. It constructs feasible portfolios and an efficient frontier, then solves for maximum-Sharpe and minimum-variance weights using estimated returns and…

This notebook reprices leading ETF allocation configurations across two cost grids while holding predictions, concentration, and allocation rules fixed. It compares the case study's per-share commission and half-spread model, which reflects how ETF trading…

This notebook measures how execution costs affect the already selected crypto perpetual futures configuration. It holds the model, checkpoint, entry rule, allocation, and risk control fixed, then reruns backtests across a grid of round-trip charges on traded…

This notebook uses principal component analysis on a liquid subset of US stocks to build gross-normalized eigenportfolios and study their role as latent risk factors. It examines explained variance, interprets components through sector ETF correlations,…

This notebook compares linear models for predicting ETF returns from overlapping price-derived features. Since multiple return horizons and indicator families can encode similar information, ordinary least squares may assign unstable opposing coefficients to…

This configuration describes monthly long-short US equity portfolios ranked on firm characteristics. It specifies a complete-case universe with 46 characteristics, month-end decisions, next-open execution, equal weighting within the long and short legs, and…

This guide maps causal questions to Python libraries and clarifies the distinction between estimating effects under an assumed causal structure and discovering possible structures from data. It introduces double machine learning for adjusted treatment…

This framework demonstrates data-quality checks on daily US equity OHLCV data. Structural validation tests price relationships, positive prices, nonnegative volume, missing values, duplicate dates, chronological order, stale prices, and extreme returns. The…

This notebook introduces a stochastic discount factor model for pricing a cross-section of ETFs. Instead of separately estimating fund exposures and factor returns, it trains networks to find a common discount-factor series whose covariation with fund…

This notebook builds a monthly ETF rotation backtest from portfolio returns through to simulated trades. It ranks ten funds using trailing risk-adjusted returns, applies a Treasury yield-curve regime to choose between the leaders and a defensive bond…

This notebook applies TabM, a weight-sharing neural ensemble, to a panel of cryptocurrency perpetual-futures features. Its members share a neural backbone but have separate scaling vectors and output layers, combining efficient ensembling with nonlinear…

This notebook reconstructs the prevailing best bid and offer for each AAPL trade during a single March 2020 session, then applies the Lee–Ready method to infer whether buyers or sellers initiated trades. It interleaves quote and trade events in timestamp…

This notebook screens financial and model-derived features for their ability to rank ETFs by subsequent returns. It calculates a date-by-date information coefficient by comparing each feature’s cross-sectional ranks with forward-return ranks, then examines…

This notebook builds an auditable report for a long-only Bitcoin strategy that enters and exits using RSI thresholds. It compares gross and net results, then measures performance against a buy-and-hold benchmark run with the same data, warmup, exposure,…

This notebook frames a focused test: whether a learned sequence representation of perpetual-funding features improves on hand-built summaries of the premium, the gap between perpetual and spot prices. It compares NLinear, which applies a linear mapping to…