Knowledge library

This utility module builds tables and figures for evaluating predictive models across validation and holdout data. Its diagnostics include confidence intervals around information coefficients, validation-to-holdout decay, selection-adjusted backtest…

This notebook presents a workflow for acquiring and maintaining financial time series with a unified manager, predefined or custom symbol universes, and partitioned Parquet storage. It covers single and parallel multi-symbol fetching, loading data into…

The chapter explains classical approaches to generating synthetic financial data, covering continuous-time price processes, GARCH volatility, bootstrap resampling and model comparison. It describes how models such as geometric Brownian motion,…

This exploratory analysis tests whether a twelve-month momentum signal, omitting the latest month, is associated with next-month returns across a 100-ETF universe. It builds a monthly panel from adjusted closing prices, ranks eligible ETFs into quintiles…

This notebook compares portfolio sizing methods for leading S&P 500 equity signal configurations. It first selects each configuration’s strongest full-coverage checkpoint using validation Sharpe, then applies alternative allocators—including score weighting,…

This notebook runs the NLinear member of a declared three-model sequence-learning population for S&P 500 options. It resolves the requests and checkpoints for the full population before fitting this member, so later notebooks can execute the LSTM and…

This notebook describes the holdout prediction stage of a US firm-characteristics study. It takes the model configuration selected using validation results, reconstructs its training specification and checkpoint, then fits it again using data that ends…

This notebook compares Optuna hyperparameter searches for XGBoost, LightGBM, and CatBoost on a firm-characteristics dataset. Each library receives its own search study, using a tree-structured Parzen estimator, median pruning, early stopping, and persistent…

This demonstration checks whether one dual moving-average crossover implementation produces matching signals in backtest and live engines. Both engines receive the same prepared ETF bars and use the same strategy parameters and fill convention, so the…

This training utility describes an optimization loop for a DeePM-style model. It uses AdamW to update model parameters from a robust Sharpe-oriented loss, passing forward returns, volatility scaling, masks, transaction costs, and configured burn-in and…

This case study fits gradient-boosted trees to ETF return labels and compares them with a linear baseline that performed well under strong ridge regularization. It explains why trees can discover interactions, such as momentum behaving differently across…

This exploratory analysis profiles a historical US equities panel, explains its raw and adjusted price fields, and examines symbol coverage over time. It compares yearly counts of active symbols with first and last observations, showing why both entry and…

This notebook builds conditional-volatility features for an S&P 500 equity and options study using a GJR-GARCH model. Unlike fixed rules based on past prices, fitted features depend on their estimation window. The method therefore declares a refit schedule,…

This document demonstrates feature-store concepts directly with case-study Parquet data. A feature view specifies the entity key, event timestamp, usable lifetime, and feature columns. For model training, historical values are joined to the decision time at…

This analysis notebook explains how to assess a US equity strategy built from firm characteristics using registry results rather than new backtests. It reads uncertainty-aware performance statistics, ranks configurations on shared support, and uses paired…

This document describes a benchmark for database engines used with financial time-series data. It compares bulk writes, full scans, time-range queries, OHLCV aggregation, and as-of joins for trade and quote data. Its methodology treats client-side conversion…

This document presents data-cleaning methods for financial datasets and explains how to keep preprocessing aligned with train and test splits. Its central lesson is to estimate transformations such as scaling and winsorization from training data only; using…

The document explains three rules for closing positions before a portfolio’s scheduled rebalance: a fixed loss stop, a trailing stop measured from the position’s best price, and a time exit that closes after a set holding period. Because overlays only remove…

This document introduces public Fama-French and AQR factor return datasets as inputs for benchmarking, risk attribution, and factor research. It describes market, size, value, profitability, investment, and momentum factors from the Fama-French library,…

This notebook demonstrates a deployment boundary between an offline machine-learning pipeline and QuantConnect's LEAN engine. It selects a prediction set through the ETF case study registry, verifies training and prediction provenance, and exports…

This notebook assesses whether Binance perpetual-futures data can support a cross-sectional strategy that ranks contracts by premium and takes long and short positions. It does not fit a model or make forecasts. Instead, it checks the declared trading…

This notebook compares the effect of assumed trading costs across strategies drawn from multiple case studies. It reads cost-sensitivity backtests for each deployed strategy configuration, compares gross Sharpe with net Sharpe at the case study's own assumed…

This notebook explains path signatures as fixed-length features that preserve the order and joint shape of observations in a time series. It describes how the first signature level records total changes, while second-level terms capture signed areas between…

This notebook reports parity comparisons between Backtrader, Zipline Reloaded, and an internal trading framework on selected real case-study strategies. It includes ETF and US equity-panel comparisons for both external engines, plus CME futures and…