Knowledge library

This notebook measures how transaction costs affect a fixed CME futures strategy configuration. It selects the reported configuration through a shared validation-stage process, carries its risk overlay into the cost runs, and applies a declared grid of…

This notebook compares ways to hedge a short European call when rebalancing is discrete and trades incur proportional costs. It defines self-financing terminal P&L, then trains a neural hedger to minimize expected shortfall under Heston simulated prices. The…

This notebook compares single-objective hyperparameter tuning with a multiobjective search for LightGBM prediction models. The baseline maximizes validation information coefficient (IC). The NSGA-II search instead maximizes IC while minimizing normalized…

This chapter synthesizes nine case studies that take machine-learning signals through portfolio construction, trading costs, risk overlays, and frozen holdout evaluation. It treats each case study’s progression as the unit of analysis instead of ranking…

This assessment traces a single US equities strategy from a frozen validation backtest set to its holdout evaluation. It applies a deterministic rule: choose the candidate with the highest validation Sharpe, breaking ties by backtest hash. Registry records…

This notebook runs a selected NASDAQ-100 microstructure configuration on its registered holdout predictions. The model, allocator, concentration, rebalance schedule, risk overlay, and cost assumptions are inherited from earlier work and applied unchanged;…

This notebook compares ways to size positions in a US equities panel while holding the model, checkpoint, rebalance dates, and selected stocks fixed. Prediction-based methods scale capital by forecast magnitude or interval uncertainty; inverse volatility and…

This notebook explains how to select one strategy from a fixed set of US equity backtests and assess the resulting strategy on a separate holdout period. It validates that candidates share the required data and protocol identities, then ranks them by…

This notebook compares three linear time-series models, a Transformer encoder, and parameter-free forecasts on daily SPY returns. The linear approaches map a historical window to a multi-day forecast, with variants that separate a smooth component from its…

This notebook examines whether gradient-boosted trees can find nonlinear relationships in NASDAQ-100 microstructure features that a linear model may miss. It focuses on the possibility that order-flow imbalance predicts returns differently depending on the…

This notebook explains an operator-style research agent for iterating on quantitative case studies. Instead of limiting the model to predefined forecasting actions, it gives it general tools for files, shell commands, registry queries, and data inspection.…

This feasibility analysis checks whether a daily long-short equity ranking strategy can be researched with the available US stock panel. It examines point-in-time universe construction using price and trailing turnover thresholds, compares proportional…

This notebook refits the configuration selected by earlier validation stages using pre-2021 history, then publishes predictions for a 2021 holdout. It retrieves the chosen configuration from a recorded candidate set or applies the same ranking rule when that…

This notebook audits an annual-report corpus before it is indexed for financial research. It measures the delay between a fiscal period end and the public filing date, showing why point-in-time applications must use the publication date. It also identifies…

This notebook screens financial and model-based features for their relationship with the next eight-hour return of crypto perpetual contracts. For each settlement, it computes cross-sectional Spearman correlations across eligible contracts, then summarizes…

This notebook aggregates existing walk-forward model results across financial case studies to compare LSTM, NLinear, TSMixer, TCN, and PatchTST with linear, gradient-boosted, and tabular neural-network baselines. Its main measure is average daily Spearman…

This shared analysis module describes ways to estimate trading frictions across asset classes. It includes high-low and return-autocovariance estimators for bid-ask spreads, rolling average volume measures, and regression approaches for calibrating…

This notebook examines why a model’s information coefficient does not determine the Sharpe ratio of a strategy built from its predictions. Across a set of case studies, it compares signal rankings with backtest outcomes and considers how entry rules,…

This notebook implements a daily educational adaptation of an adversarial stochastic discount factor model. A portfolio-weight network constructs a factor from next-day excess returns using characteristics and market state available at the prior close. An…

This analysis assesses whether selected trading signals appear reliable by comparing information coefficient (IC) evidence with the Sharpe ratios produced after predictions are converted into positions. It aggregates model-family results across asset…

This notebook aggregates previously run walk-forward results to compare LSTM, NLinear, TSMixer, TCN, and PatchTST across case studies, alongside linear, gradient-boosted, and TabM baselines. It reports average daily Spearman information coefficients with HAC…

This feasibility analysis asks whether the data and assumptions for a weekly, delta-hedged short-straddle strategy on S&P 500 constituents are plausible before fitting a model. It explains how calls and puts at the same strike and expiration form a straddle,…

This exploratory notebook profiles hourly CME futures data across products and asset classes, using the E-mini S&P 500 as an example. It explains the hierarchy from product to expiring contract to continuous series, summarizes coverage windows, and checks…

This notebook compares portfolio weighting rules for selling straddles on selected S&P 500 symbols. It holds the chosen symbols constant and varies allocation, using equal weight as the baseline. The methods include weighting by predicted score, inverse…