Knowledge library

This notebook compares two simulations of the same monthly ETF momentum strategy. Both use identical target weights, universe, dates, and total trading cost. One computes returns from lagged weights and asset returns; the other processes orders sequentially…

This notebook fits PatchTST as one member of a declared sequence-model population for S&P 500 options. PatchTST divides a lookback window into fixed-size patches, embeds them as tokens, and uses attention to learn relationships among portions of the history.…

This notebook aggregates LightGBM results across multiple trading case studies and compares them with linear and TabM baselines. It examines the highest-IC configuration for each case study, reports heteroskedasticity-and-autocorrelation-consistent…

This utility describes a diagnostic workflow for data artifacts produced by quantitative research pipelines. It profiles numeric columns for nulls, non-finite values, zeros, distinct values, quantiles, and tail behavior, then flags columns that cross…

This notebook tests whether learned graph embeddings add predictive information to a tabular model for US stocks. It builds a network from absolute return correlations measured before the target period, constructs momentum, volatility, and trend features…

This document describes an LSTM sequence model for an S&P 500 options study. Unlike cross-sectional models that rely on engineered features to summarize a symbol’s history, the LSTM receives chronological windows and learns which parts of the sequence…

This notebook describes how to create holdout predictions for an FX strategy after its configuration has already been selected using validation results. It resolves the highest-Sharpe validation backtest among admitted candidates that remained solvent, then…

This notebook explains a market data lifecycle for daily OHLCV data: load an initial history, fetch a small overlapping delta, detect missing sessions, and summarize each symbol’s freshness and validation issues. It describes why the update window should…

This notebook compares DQN, PPO, and A2C in a simulated cryptocurrency trading environment. It calibrates a GARCH(1,1) volatility process on hourly Bitcoin perpetual-futures returns, then simulates paths that preserve volatility clustering while leaving…

This notebook diagnoses how a fixed, long-only ETF momentum baseline performed across market conditions from 2010 to 2024. It labels each daily return using volatility and trend measures known before that return began, with expanding historical medians…

This notebook applies previously selected firm-characteristic predictions and a fixed portfolio configuration to a one-year holdout period. It makes no new choices about predictions, allocation, concentration, rebalancing, or transaction costs, preserving…

This audit compares trading engines replaying the same frozen model targets and content-addressed historical inputs across ETF allocation, futures, crypto perpetual funding, foreign exchange, and US equities. It evaluates execution parity through matching…

This analysis compares risk overlays across case studies by loading their overlay backtests from a registry and measuring Sharpe and maximum drawdown against an allocation-stage baseline, with a signal-stage fallback. It classifies configurations into rule…

This notebook evaluates position-level stop-losses, trailing stops, and time exits on top allocation-stage configurations in a NASDAQ-100 intraday strategy. Decisions occur every fifteen minutes while the backtest engine monitors positions every minute, so…

This notebook describes a patched transformer for sequence prediction in an S&P 500 equity and options study. It divides a time window into contiguous blocks and represents each block as a token, allowing attention to compare distant parts of the window…

This ETF feature notebook explains how fitted statistical models can leak future information through their estimated parameters, even when their formulas use past observations. Its remedy is a walk-forward refit schedule: allow an initial burn-in, fit using…

This notebook reconstructs the selected FX pairs configuration from a frozen validation candidate set, then assesses its validation and holdout evidence. Selection uses the highest eligible validation Sharpe, with a deterministic backtest identity…

This notebook studies gradient boosting for a US equities panel, varying tree capacity, objective function, and training checkpoint across three forward-return horizons. It explains how shallow trees can learn feature interactions, while increasing capacity…

This notebook describes how to generate holdout predictions for a crypto perpetual funding strategy after its configuration has been selected using validation results. It resolves the highest-Sharpe eligible validation backtest, carries over the model…

This shared utility module supports causal analysis of trading data with time-series double machine learning (DML). Its documented tools include walk-forward estimation with an embargo, nuisance models, and refutation checks. Block permutation preserves…

This notebook explains TSMixer, a time-series neural network that alternates two operations: a shared linear map across lookback days for each feature, and a shared feature MLP applied independently at each day. Residual connections and pre-normalization…

This case study constructs features for comparing variance risk premiums across S&P 500 stocks using 30-day at-the-money straddles and underlying price data. It measures implied volatility against realized volatility at several horizons, adds option-market…

This case study explains how to generate predictions for a holdout period after model and strategy choices have been made on validation data. It resolves the previously selected configuration, reconstructs its training specification, and fits it again using…

This notebook builds an LSTM allocator that maps recent ETF return and volatility features directly to long-only portfolio weights. A softmax output keeps weights nonnegative and fully invested, while a differentiable Sharpe-ratio objective trains the…