Knowledge library

This notebook studies whether an extreme perpetual futures premium affects subsequent BTC returns or premium reversion. It defines treatment as a high-premium state and compares a broad market outcome, which may be influenced by many forces, with premium…

This chapter surveys ways to turn fitted statistical procedures into features for trading models. It covers diagnostics and stationarity, structural breaks, fractional differencing, Kalman filtering, spectral and path-signature methods, ARIMA and GARCH…

This notebook builds forward share-return labels for a strategy that reads listed options data to rank equities and then holds the shares. It creates an adjusted price series that accounts for corporate actions and identifies companies by persistent security…

This notebook describes a one-feature-at-a-time screen for candidate signals across 20 currency pairs. It computes each feature’s daily cross-sectional rank agreement with the next-session return using only walk-forward validation windows. The evaluation…

This notebook presents principal component analysis as an unconditioned latent-factor baseline for forecasting stock returns in an option analytics study. PCA uses the historical return panel to estimate common movements and each stock’s exposure to them,…

This notebook compares exhaustive grid search with Optuna’s TPE sampler for tuning a LightGBM return-prediction model. It first gives both methods the same trial budget over the same discrete parameter grid, where enumeration covers every combination and…

This notebook evaluates a cross-sectional momentum factor using information coefficients, quantile returns, top-to-bottom spreads, and classification diagnostics. It introduces Spearman IC as the date-by-date rank correlation between a signal and subsequent…

This notebook explains pairwise and cross-sectional features using ETF panels. It compares Engle–Granger and Johansen cointegration tests, distinguishing a stationary spread from simple co-movement. Its energy fund and crude oil fund example fails both…

This configuration specifies a historical macroeconomic dataset from FRED for regime filtering and cross-asset analysis. It organizes series by daily, weekly, monthly, and quarterly frequency, including Treasury yields, the federal funds rate, the VIX, labor…

This analysis asks whether the data can support a cross-sectional crypto perpetual strategy before fitting a model or making forecasts. The proposed approach ranks contracts by premium at each funding settlement, then buys one side of the ranking and sells…

The document presents GReaT, a framework that serializes rows of mixed numerical and categorical financial data as text, fine-tunes a language model on those records, and parses generated text back into tables. Its example builds ETF features from historical…

The document explains how uncertainty from a model can provide information beyond its point estimate. It contrasts GARCH, which defines volatility from past returns, with a stochastic volatility model that treats volatility as a latent autoregressive process…

This cross-case analysis examines how predictive information, measured by information coefficient (IC), translates into portfolio Sharpe across nine trading case studies. Its central point is that implementation mediates the relationship: entry rules,…

This notebook uses principal component analysis to represent the common movements in a broad US equities panel. PCA extracts directions that explain shared variation in returns; each stock’s loading measures its exposure to each direction. A prediction…

This notebook evaluates security rules for a document-grounded financial assistant using six hand-built cases. The cases cover prompt injection, fabricated figures in untrusted material, attempted actions, and citations to content that was not retrieved. Two…

This notebook presents a temporal convolutional network (TCN) for predicting ETF forward returns from sequences of trailing return features. Causal convolutions ensure an output uses only observations up to its date, while exponentially increasing dilation…

This notebook explains tick and volume imbalance bars, then compares adaptive, fixed threshold, and rolling window approaches on NVDA trade data. Tick imbalance accumulates signed trade directions until a threshold is reached; the expected threshold depends…

This notebook explains how to turn fitted GJR-GARCH and hidden Markov models into trading features while controlling look-ahead bias. GJR-GARCH estimates conditional volatility and captures the greater impact of negative return shocks; a two-state Gaussian…

This notebook surveys methods for finding changes in the process that generates a financial series, using daily SPY data as its example. It contrasts the Zivot–Andrews test, which estimates one break while testing for a unit root, with multiple-break…

This utility prepares prediction artifacts for live-demo notebooks by normalizing differing column names and date formats into a common timestamp, asset, and prediction structure. It groups duplicate timestamp-asset observations and averages their scores.…

This notebook trains a Skip-gram Word2Vec model on a small, sentiment-labeled financial news corpus and examines what its nearest neighbors reveal. The method represents words based on the contexts in which they appear. In the example, profit and loss become…

This document describes a workflow for collecting FRED Treasury yields and economic indicators, aligning series with different reporting frequencies to a daily calendar, and loading or filtering the resulting dataset. The indicators include Treasury rates…

This notebook analyzes reconstructed NASDAQ order books to describe intraday spreads and depth at the best bid and ask, then examines whether order flow imbalance is associated with the next bucket’s return. It expresses spreads in basis points to make costs…

This notebook uses linear regression as a baseline for forecasting NASDAQ-100 returns from minute-level microstructure features. Because spread and order-imbalance measures overlap across lookbacks, the feature matrix is highly collinear: ordinary least…