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 notebook defines monthly total-return labels for a cross-sectional US firm-characteristics study and checks how those outcomes align with the provider’s rows. The panel pairs characteristics from the prior month with the return earned in the month…
This notebook shows how explicit execution costs can erode a hypothetical intraday strategy’s gross returns. It anchors the crossing spread to the median volume-weighted quoted spread across NASDAQ-100 constituents, then builds crossing, worked-order, and…
This notebook explains how a temporal convolutional network (TCN) can forecast returns from ordered financial data. Causal convolutions ensure that an output at a given time depends only on current and earlier inputs. Dilations expand the receptive field…
This document presents an event-driven method for holding a limited number of intraday positions. Predictions are aligned to price bars using the latest available score, subject to an optional freshness limit. Entry signals use a rolling quantile computed…
This case study turns stored model predictions for US stocks into long-short, equal-weight portfolios. It sweeps multiple entry schemes, each selecting the highest-ranked names for long positions and the lowest-ranked names for short positions, to examine…
This document describes several ways to convert prediction scores into trading signals: fixed cutoffs, rolling per-asset percentiles, and cross-sectional percentiles. Fixed thresholds use a chosen score level; rolling thresholds adapt to an asset’s recent…
This notebook reviews a selected foreign-exchange strategy and its holdout lineage using registered research artifacts. It reproduces the choice from a frozen validation candidate set, ranking eligible solvent backtests by validation Sharpe with a…
This live-trading demonstration describes a daily rebalance workflow for a fixed universe of US large-cap stocks. It compares broker-held positions with model targets, converts the differences into an order basket, and routes orders through a risk-control…
This event study uses Bayesian structural time-series models to estimate how Federal Reserve announcements affect a bond ETF. It learns the target’s relationship with selected international equity and commodity ETF returns during a pre-event window, then…
This notebook builds descriptive market regimes from monthly Federal Reserve economic data: unemployment, the federal funds rate, the Treasury yield curve, and inflation. It explains how to align series with different release frequencies, transform trending…
This document describes utilities for comparing registered trading strategies and assembling structured case-study assessments. A central ranking method restricts return series to their exact shared timestamps before calculating portfolio metrics, so each…
This notebook builds a simulated market-making task in which a proximal policy optimization agent chooses quote skew and spread width. The environment uses a GARCH volatility process calibrated to hourly crypto perpetual-futures returns, a reservation price…
This chapter surveys methods for recovering common structure from return and characteristic panels, from PCA and eigenportfolios to IPCA, risk-premium PCA, conditional autoencoders, stochastic discount factor estimation, and supervised autoencoders. Its…
This notebook compares sklearn HistGradientBoosting, XGBoost, LightGBM, and CatBoost on an ETF return-prediction task. It measures cross-sectional information coefficient, training time, and process memory across CPU and, where supported, GPU runs, using…
The notebook diagnoses how a fixed monthly ETF momentum strategy performed across volatility, trend, and yield-curve conditions. Regime labels are designed to be known before the return they describe: volatility and index trend use prior closes and are…
This notebook turns cross-sectional stock predictions into long-short portfolios by ranking stocks at each rebalance, buying the top k and shorting the bottom k with equal capital per position. Equal weighting provides a baseline that isolates differences in…
The notebook previews S&P 100 10-K and 8-K filing data intended for financial knowledge graph construction. It checks schema completeness, unique company and accession keys, form consistency, filing year, and recorded text length. Annual reports supply…
This notebook presents a feature-selection workflow for ETF research, intended to narrow many candidate transforms and lookbacks to a smaller set for later modeling. It computes cross-sectional Spearman information coefficients between features and forward…
This notebook evaluates model predictions as NASDAQ-100 trading strategies using a shared backtest engine. It first runs a plumbing check with random signals: persistent profits after costs would point to issues such as lookahead, misaligned data, or…
This notebook checks whether a fifteen-minute cross-sectional stock-ranking strategy is feasible before fitting a model or making forecasts. It uses NASDAQ-100 quote data, midpoint returns, and a pre-holdout development window to examine the strategy’s…
This code describes how backtest settings are resolved from case study configuration and how run identity accounts for changes to the traded universe and prediction age. Its central lesson is that any filter that changes the portfolio or prediction set must…
The document outlines ways to compare strategy returns with two external benchmarks: daily SPY total returns and Fama-French market returns formed by adding the market risk premium and risk-free rate. It provides date filtering and alignment helpers,…
The document describes fitting a simple linear sequence model to NASDAQ-100 one-minute features to forecast forward returns at several horizons. Each input contains the preceding hour for one stock, expressed as changes from the latest observation, and the…
The document presents a machine-learning trading research workflow that carries ideas from data and feature construction through model training, backtesting, transaction costs, portfolio and risk decisions, deployment, and monitoring. It emphasizes an…