Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
1,124 documents
This analysis reconstructs individual NASDAQ limit orders from ITCH add, delete, partial-cancel, replace, and execution messages. It measures whether orders are cancelled or filled and how long they take to reach those events, using precise timestamps and…
This notebook fits ridge, lasso, and elastic net models to published firm characteristics for individual US stocks. The features span value, profitability, investment, size, momentum, reversal, risk, and liquidity. Walk-forward folds keep configurations…
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 explains how to extract narrative sections from 10-K and 10-Q filings for later text analysis. It maps form-specific item numbers, converts filing HTML while preserving paragraph boundaries, cleans page furniture, and identifies section starts…
This notebook turns institutional 13F disclosures into a bipartite graph linking managers to stocks, then derives stock-level ownership features. It aggregates duplicate manager-stock positions, normalizes issuer labels, excludes option rows, and uses…
This document describes how to reduce large S&P 500 option chains into daily per-symbol datasets for research. Its surface summary selects options nearest target absolute deltas within maturity buckets, then derives at-the-money implied volatility at…
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…
This module defines a monthly ETF allocation baseline using a risk-adjusted momentum score: trailing cumulative return divided by annualized realized volatility. It selects the highest-ranked assets when the 10-year minus 2-year yield-curve slope exceeds a…
This document explains a run-log system for recording model training, predictions, causal estimates, and backtests across trading case studies. It describes how configurations flow from case-study settings through training menus and model presets, then…
This configuration specifies a long-short strategy research workflow for crypto perpetual futures. It defines a 19-asset volume-selected universe, decisions aligned to eight-hour funding settlements, and execution at the funding timestamp. The primary target…
This notebook turns institutional 13F holdings into a graph connecting managers and stocks, with sector relationships and holdings edges that carry shares, value, reporting period, and public filing date. It demonstrates graph queries for shared ownership…
This notebook clusters monthly returns from nine equity, bond, currency, and commodity factor and market series using Gaussian mixture models. After dropping months with incomplete data, it standardizes the series so differences in scale do not dominate the…