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.
566 documents
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 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 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 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 evaluates 57 accounting and price-based characteristics as individual predictors of next-month US stock returns. It calculates monthly cross-sectional rank correlations, averages them over time, and adjusts uncertainty for serial dependence and…
This notebook uses NASDAQ ITCH-derived trade and order data to illustrate microstructure patterns. It examines negative first-order autocorrelation in tick-level trade returns, comparing the effect with mid-price returns, and contrasts trading activity and…
This demonstration extends a single-order workflow into a daily rebalance for a fixed universe of large-cap US stocks. It explains how to compare current broker positions with model targets, construct orders for the differences, and optionally submit the…
This educational notebook surveys price- and volume-derived features used in quantitative research. It covers simple and logarithmic returns across horizons, skip-one momentum, overnight and intraday returns, moving-average distance, trend and reversal…
This notebook presents a workflow for asking whether six-month skip-recent momentum affects 21-day forward returns and whether the effect changes with market volatility. It estimates effects on training data using double machine learning, with volatility and…
This notebook builds a reusable diagnostic survey for financial datasets before preprocessing or feature engineering. It checks time-index dtype, null timestamps, ordering and uniqueness; finds exact and key-based duplicates; measures missing values by…
This notebook compares two pretrained time-series foundation models, Chronos and TinyTimeMixer, with an LSTM and a penalized linear baseline on an ETF panel. Each model receives a univariate historical context; the pretrained models forecast that input…
This chapter presents a framework for evaluating causal claims in quantitative trading research. It starts with clear definitions of treatment, outcome, estimand, counterfactual, and adjustment set, then matches research questions to methods: Double Machine…
This notebook fits linear models to NASDAQ-100 microstructure features observed at one-minute intervals to predict returns at several forward horizons. Its purpose is to establish a simple baseline before more expressive models. Because spread and imbalance…
This document describes a daily US equity dataset covering roughly 3,200 companies from 1962 through March 2018. It includes delisted issuers, making it useful for historical research where excluding failed or delisted firms could distort results. The data…
This notebook evaluates how transaction-cost assumptions affect the validation Sharpe of one selected S&P 500 equity and options allocation. It carries forward the configuration chosen through a frozen candidate set when available, then reruns the same…
This notebook compares implementations of a univariate SPY price forecast across raw PyTorch, sktime, and Darts. Its main purpose is to assess practical effort: code length, interface requirements, installation success, and combined fit-and-predict time. A…
This NASDAQ-100 case study investigates whether 15-minute microstructure signals can survive trading costs. The workflow evaluates order flow, quote staleness, and relative spreads, then compares a full-universe strategy with one restricted to…
This notebook checks whether a daily US equity ranking strategy has enough usable data for a fair evaluation before fitting any model. It explains how to form a point-in-time universe with historical constituents, using printed prices and prior-session…
This notebook runs a previously selected US equities configuration on holdout predictions. The selected model, predictions, allocator, concentration, risk overlay, rebalance cadence, and transaction cost assumptions are inherited from earlier stages. Its…
This document presents a cost sensitivity analysis for a fixed ETF momentum strategy. It holds the universe, weights, schedule, and simulator constant while rerunning the backtest over a range of per-leg fees. The resulting curve shows how Sharpe ratio and…
This notebook models institutional equity holdings from SEC 13F filings as a graph linking managers to securities, with sector relationships for aggregation. Its schema retains both the quarter-end report date and the later filing availability date: the…