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 NASDAQ limit-order lifecycles from ITCH messages, following orders from submission to their first cancellation-related event or execution. It distinguishes adds, deletes, partial cancels, replacements, and executions, then measures…
This audit record defines how several backtesting frameworks are compared with ML4T across real strategy case studies and a synthetic stress workload. It specifies the comparison rules for ordered fills, timestamps, account-money values, quantities, and…
This notebook presents correctness and runtime comparisons for VectorBT Pro and VectorBT OSS against ML4T on supported case-study strategies. It covers ETF allocation, USD-quoted foreign exchange, and a US equity panel for both editions; VectorBT Pro also…
This notebook turns stock-level predictions into validation portfolios. On each rebalance date, it sorts stocks by predicted return, buys the top group and shorts the bottom group, and assigns equal capital to every position. It applies this baseline across…
This notebook defines forward price-return labels for a fixed panel of crypto perpetual contracts and explains how label construction affects every later model and backtest. It shifts bar-open timestamps to the time completed data becomes available,…
This source module builds catalog tables from experiment registries for model predictions and backtests. It defines reserved fields for provenance, identity, completion status, artifact availability, and metrics. The catalog carries multiple measures that…
This capstone explains how to evaluate probability forecasts with Brier score, log score, expected calibration error, and sharpness, and how reliability diagrams reveal where forecasts are overconfident or underconfident. It describes fitting calibration…
This notebook demonstrates how a fixed dollar order can impose very different costs across stocks because its size relative to available trading volume matters. It applies a square-root market-impact model to a long-when-positive momentum strategy using…
This notebook evaluates LSTM and NLinear sequence configurations on CME futures products. Sequence models consume ordered windows of observations, testing whether the recent path contains information beyond engineered summary features. The LSTM learns a…
This notebook describes a shared workflow for fitting temporal convolutional networks to daily FX pair data. Instead of treating each date as an isolated feature row, the model uses a fixed lookback of consecutive observations. The runner determines eligible…
This case study evaluates daily momentum and carry signals across a small, correlated universe of G10 spot currency pairs. Its workflow moves from feasibility checks and forward-return labels through engineered and model-based features, cross-validation,…
This notebook applies token-level SHAP to a pinned FinBERT model that classifies financial text as negative, neutral, or positive. It wraps model inference to preserve the checkpoint’s label order, then explains class probabilities by measuring how masked…
This notebook adapts TSMixer to predict forward ETF returns from historical momentum features. Its architecture alternates a shared linear transformation across the time axis with a feature MLP applied separately at each date. Pre-normalization and residual…
This record documents a multi-agent pipeline that estimates whether the United States will enter a recession by the end of 2026. Agents search for economic indicators and forecasts, form individual probabilities, compare rationales through debate, and pass…
This notebook explains why a strategy’s observed Sharpe ratio needs adjustment when it was selected from many trials. It presents the Deflated Sharpe Ratio (DSR), which compares the selected result with a benchmark that rises with the number and dispersion…
This notebook turns a supply chain knowledge graph and institutional ownership filings into features for downstream financial modeling. From the company network it derives topology measures, supplier concentration, competitive exposure, and bottleneck…
This notebook explains how to fine tune a transformer for financial named entity recognition, which identifies spans such as organizations, people, amounts, dates, and percentages in text. It focuses on BIO boundary labels and the alignment problem between…
This notebook adapts a supervised autoencoder to daily US equities. The model reconstructs ranked price and volume characteristics while auxiliary and main classification heads train a shared latent representation to predict whether returns are positive…
This notebook demonstrates a data-quality workflow for daily US equity OHLCV data. Structural checks cover nulls, duplicates, chronological order, price consistency, negative values, stale prices, and extreme returns. It then distinguishes structural…
This dataset guide describes a monthly US equity panel of anonymized firms and characteristics used for machine-learning asset-pricing research. The data includes accounting and technical characteristics, returns, and pre-defined training and test periods…
This notebook presents a workflow for reducing a large set of ETF features to candidates for later modeling. It ranks features using cross-sectional Spearman information coefficients against forward returns, computed by date and then averaged. Newey–West…
This notebook explains how a stochastic discount factor (SDF) prices a cross-section of equity option signals. Unlike factor models that estimate exposures and factor returns, the SDF approach estimates one random variable that makes asset returns price…
This document introduces latent factors as common return patterns across stocks, with each stock's loading describing its exposure to a pattern. It contrasts principal component analysis, which extracts factors and stock loadings from the return panel alone,…
This document explains how to construct forward-return and direction labels for NASDAQ-100 intraday research. It defines the return using an explicit execution convention: observe a bar close, enter at the next bar, and measure the outcome over a configured…