Knowledge library

The document describes a validation-stage comparison of fixed and trailing stop rules for FX strategies. For each return label, it selects a parent strategy from a sealed cohort of signal and allocation results, then varies the declared position-level risk…

This notebook compares ordinary least squares with Ridge, LASSO, and Elastic Net for predicting 21-day forward returns across ETFs. The models use the same declared walk-forward folds, with a purge gap matched to the label horizon. Each fold clips feature…

The document examines the ADIA Lab causal discovery challenge as supervised inference within a known synthetic data-generating family. The task assigns variables to eight roles around a treatment and outcome, using many labeled dataset and graph pairs. It…

This module builds a mean forecast ensemble from registered prediction sets, with particular attention to reproducibility and avoiding selection bias. It resolves qualifying model configurations from the registry rather than maintaining a hand-written member…

This notebook expresses a four-phase forecasting workflow using plain Python orchestration, CrewAI, and LangGraph. It compares where state is held, how failures are inspected or recovered, what dependencies each approach adds, and how tracing works. The…

This notebook examines linear-model regularization for a universe of currency pairs whose returns are linked because each pair is a quote between shared currencies. It distinguishes feature collinearity, which ridge, lasso, and elastic net can address, from…

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 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 case study examines whether information from listed options can help select S&P 500 stocks. It combines daily equity prices with option-implied volatility levels, skew, term structure, and variance risk premium measures across 633 stocks from 2017 to…

This module defines a reader-facing workflow for CME futures research, covering model requests, prediction and backtest execution, candidate pools, and final selection. It reads the configured label sweep from a shared setup declaration, preserves its order,…

This notebook uses maximum favorable excursion (MFE) and maximum adverse excursion (MAE) to ground triple-barrier label widths in observed price paths. For a position entered at a bar’s close, it measures the best favorable and worst adverse movement over a…

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…

This notebook demonstrates an operational cycle for an ETF equity strategy: refresh market data, compute financial features, retrain a Ridge model, generate current cross-sectional predictions, replay them through a backtest engine, and stage a top-ranked…

The document describes an FX portfolio allocation sweep designed to isolate position sizing from signal selection. It starts from frozen equal-weight baseline candidates ranked by validation backtest performance, then advances configurations while preserving…

The document describes a specialized backtest for a weekly S&P 500 options strategy. Each cohort selects highly ranked constituents, sells near-the-money call and put options with roughly a month to expiration, and delta-hedges with shares when net delta…

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…

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 refits the FX configuration already selected by validation and registers its predictions for a later holdout backtest. It resolves the selection from solvent validation backtests, constrained to a frozen candidate set, then rebuilds the…

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 case study compares alternative position sizing methods for CME futures against an equal weight baseline. It explains how inverse volatility sizing uses each contract’s own volatility, while covariance based methods also account for relationships…