Knowledge library

This notebook evaluates whether a signal’s information coefficient (IC) is distinguishable from noise when successive observations are dependent. It illustrates the issue using a momentum signal and ETF data, explaining how overlapping forward returns,…

This notebook compares ways to exit long ETF trades: fixed profit targets and stops, trailing stops, ATR-scaled barriers, machine-learning signals, and combinations. It builds indicators and trade simulations, then summarizes trade returns, win rates,…

This notebook explains how to inspect a shipped panel of economic series from FRED and interpret its calendar-day grid, metadata, and derived columns. Because the panel has been flattened onto a daily calendar, row counts conceal the source release…

This notebook treats ARIMA forecasts as input columns for later models rather than as standalone predictions. It fits models to ETF returns, uses stationarity checks and autocorrelation diagnostics to propose orders, and compares candidate orders with an…

This notebook describes how to prepare and register NLinear forecasts for FX labels. NLinear subtracts the last observed level from each fixed-length input history, focusing the model on changes within the lookback. A shared sequence eligibility process…

This shared analysis utility supports comparing trading-model predictions through registry metrics and prediction-level diagnostics. It loads precomputed information-coefficient results, optionally filters by label or model family, and by default excludes…

The document sets out how to construct forward-return labels for a cross-sectional futures strategy that ranks products by term structure. It distinguishes roll-adjusted prices, which are appropriate for returns across contract rolls, from raw settlement…

The document describes three model-based features built from futures carry: an ARIMA forecast of next-session carry, rolling Fourier measures of selected cycle lengths, and a two-state hidden Markov model that infers the market regime from average carry. Its…

This document explains a validation baseline for model predictions on CME futures. At each weekly decision, products are ranked by predicted return; the top group is held long and the bottom group short, with equal weight within each leg. The number of…

The document describes converting rolling ETF return windows into two-channel images for a conventional convolutional neural network. A Gramian angular field maps normalized observations to angles and records pairwise angular relationships; a Markov…

The document explains a temporal convolutional network for predicting crypto perpetual funding premiums from a 60-settlement window. Four causal convolution blocks use a kernel of three and dilations of 1, 2, 4, and 8. Their receptive field spans 61…

This notebook describes a GT-GAN-inspired architecture for synthesizing time series with genuinely irregular timestamps, such as tick, volume, or dollar bars. A GRU-ODE encoder maps observations into latent states, while ODE-based generator and discriminator…

This notebook compares three ways to use market regimes in a forecasting model: omit regime information, add the filtered probability of a volatile state as a feature, or fit separate experts for hard-assigned regimes. It builds regime probabilities from a…

This notebook sets out a process for evaluating a challenger model in shadow mode before it receives capital. It compares stored predictions from a fixed incumbent and candidate, constructs equal-weight ranked long-short books, applies the same…

This notebook surveys EdgarTools for exploring SEC filings and extracting structured company data. It explains how to identify filers by stable CIK, retrieve filings by form, and turn XBRL-tagged annual reports into financial statement tables. It also covers…

This case study uses linear models as a baseline for predicting forward returns across a broad US equity panel. It explains why overlapping price and volume features create collinearity, making ordinary least squares coefficients unstable, and compares Ridge…

This analysis compares registered double machine learning treatment effects from nine trading case studies. It loads current results from each study’s registry, checks registry integrity and duplicate labels, and makes coverage explicit. Effects and…

This notebook presents a framework for assessing an ETF allocation backtest using daily portfolio returns and a matched SPY benchmark. It covers risk and return measures such as Sharpe, Sortino, Calmar, and maximum drawdown; rolling metrics; drawdown…

This notebook explains how model uncertainty can add information beyond a point estimate. It fits a stochastic volatility model in which latent log volatility evolves over time and returns follow a Student-t distribution. Posterior spread, interval width,…

This notebook introduces futures backtesting mechanics through a long-short momentum example across several asset classes. It explains how contract specifications convert price movements into dollar profit and loss through multipliers, and how notional…

This notebook tests how sensitive an ETF momentum backtest is to its assumed trading fee. It holds the strategy’s weights and trading schedule fixed, reruns the simulator across a range of per-leg costs, and compares growth, Sharpe ratio, and drawdown. It…

The notebook estimates how much an equal-weight portfolio of surviving US equities can overstate returns relative to the full starting universe. It first examines when the panel records symbols leaving, finding that recorded exits begin only in the final…

This analysis reads a registered backtest pipeline for a monthly US equity strategy based on firm characteristics. It reports Sharpe uncertainty, probability and deflated Sharpe measures, paired strategy comparisons, holdout decay, and a cost analysis…

This notebook evaluates position-level stop losses, trailing stops, and time exits on the allocation lineage that ranks highest by validation Sharpe among eligible full-coverage candidates. Each overlay is compared with its own no-overlay parent, isolating…