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.
715 documents
This document describes building a cross-sectional feature matrix for S&P 500 stocks by combining adjusted share-price histories with summarized option implied-volatility surfaces. It organizes features by role, input, lookback, and observability delay.…
This notebook runs or replays a panel of identical research agents answering a shared forecasting question, then inspects their forecasts, search activity, and conversation traces. Separate per-agent tracing preserves attribution during parallel calls. The…
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 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 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 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 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 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…
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 describes loading ETF market data with optional symbol and date filters, plus a deterministic limit on the symbols returned. Its main analytical point is to match the price series to the quantity being measured: adjusted prices are appropriate…
This notebook builds model-based conditional volatility features for S&P 500 shares with a GJR-GARCH(1,1) model, then compares an option-implied volatility spread measured against realized volatility with one measured against a model forecast. Since…
This CME futures study varies gradient boosting tree capacity and loss function while recording predictions at multiple training checkpoints. Tree leaf count controls how finely a model partitions the feature space, while squared, absolute, and Huber losses…
This notebook studies how Ridge regularization choices affect information coefficient on an ETF panel and how hyperparameter search can inflate reported performance. It first evaluates a wide alpha grid across walk-forward folds, scaling features within each…
This notebook explains how to construct one-, five-, and 21-session forward spot returns for a fixed FX-pair universe. It first maps four-hour bars into trading sessions using a New York rollover calendar, then builds returns without removing rows before…
This notebook explains instrumented principal component analysis (IPCA), which models each ETF’s factor exposures as a shared linear function of that fund’s observed features. Alternating least squares estimates the feature-to-exposure map and the factor…
The notebook presents an autonomous research workflow in which a language model uses general-purpose tools to inspect files, run commands, query a backtest registry, and examine data. Rather than hard-coding research actions, the operator discovers…
This notebook fits a PCA latent-factor model to a panel of stock returns, without using option-surface features, characteristics, or target information. PCA extracts leading directions of historical cross-sectional co-movement, estimates each stock's…
This shared feature-engineering module defines construction rules and audits for financial predictors used across multiple case studies. It records each feature family with its hypothesis, inputs, rolling lookback, information lag, role, and potential…