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.
2,013 documents
This notebook brings an ETF case study’s registered backtests together into an uncertainty-aware strategy assessment. It reads performance measures with block-bootstrap confidence intervals and uses paired bootstraps to compare successive pipeline stages,…
This notebook explains how a macro panel stamped with the period it measures can leak future information into a trading backtest. It estimates publication dates by adding the period length to the stamped date and then applying a conservative release lag…
This notebook implements DeePM, an end-to-end portfolio policy for a diversified ETF universe. Its model combines temporal features, asset metadata, cross-sectional attention, and a macro graph prior that permits attention within asset classes and across…
This feature-engineering notebook constructs variables that require information beyond one asset’s price history. For futures, it computes annualized roll yield from contemporaneous near and deferred contract levels, plus term-structure slope and curvature…
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…
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…
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 presents a current audit comparing VectorBT Pro and VectorBT OSS with ML4T on supported real-data strategy workloads. Both VectorBT editions participate in ETF allocation, USD-quoted foreign-exchange allocation, and a US equity panel. Pro also…
This notebook compares four ways to discover relationships in a panel of ETF returns: NOTEARS for contemporaneous linear DAGs, VAR-LiNGAM for lagged and instantaneous structure, PCMCI for conditional-independence tests, and Granger tests as pairwise…
This tutorial explains how to organize market data beyond one-time downloads. It presents a unified manager for fetching single symbols or batches, predefined and custom symbol universes, and Hive-partitioned Parquet storage. It also covers querying date…
This utility constructs a Boolean asset-to-asset adjacency matrix from asset group labels and optional links between groups. Assets assigned to the same macro group are connected to one another, and each supplied cross-group edge connects all members of both…
The document describes end-to-end pipelines for combining historical and recent equity data and preparing hourly crypto perpetual data. Its central example stitches WikiPrices and Yahoo observations at their coverage boundary, validates OHLC data and…
This audit compares multiple backtesting engines on shared historical inputs for ETF allocation, CME futures, crypto perpetuals with funding, foreign exchange, and US equities. Each supported pair receives the same content-addressed market data and frozen…
This dataset note describes a diversified collection of exchange traded funds used in a momentum strategy and a broader sequence of financial research examples. It provides daily open, high, low, close, and volume observations beginning in 2006, grouped…
This notebook describes a supervised autoencoder that compresses ETF feature rows into a low-dimensional representation and predicts forward returns directly from that representation. Unlike a conditional autoencoder, it does not assume that returns arise…
This analysis compares registered ETF prediction models across several families on a shared cross-asset panel. It evaluates how well each model ranks funds by subsequent returns using information coefficients, examines confidence intervals and fold-level…
This analysis explains how CME futures data is organized into products, expiring contracts, and volume-rolled continuous series. It uses E-mini S&P 500 data to show how individual contracts have finite trading windows that overlap around a roll, while a…
This experiment combines validation predictions from a gradient-boosted tree model, a tabular neural network, and a latent-factor model for monthly ETF ranking. It first standardizes predictions cross-sectionally with z-scores, then averages them. The…
This notebook presents PCMCI as a way to discover lagged relationships in multivariate financial time series without specifying a causal graph in advance. It applies the method to ETF and volatility-index returns, compares its conditional analysis with…
This notebook isolates results for the LEAN engine from an audit comparing retained real-strategy runs with matching ML4T Backtest profiles. It identifies four supported workloads: ETF allocation, crypto perpetual funding, USD-quoted FX allocation, and a US…
This notebook presents a cross-case-study inventory of engineered financial features and the strongest registry information coefficient for each market study. It counts features and feature-name families, compares feature-space sizes, and summarizes…
This notebook compares four reference ETF allocations using historical crisis windows, hand-authored simultaneous asset shocks, Student-t Monte Carlo simulations, and market-regime statistics. It explains a maximum drawdown measure and uses daily…
This audit compares trading engines replaying the same frozen model targets and content-addressed historical inputs across ETF allocation, futures, crypto perpetual funding, foreign exchange, and US equities. It evaluates execution parity through matching…
This notebook builds an LSTM allocator that maps recent ETF return and volatility features directly to long-only portfolio weights. A softmax output keeps weights nonnegative and fully invested, while a differentiable Sharpe-ratio objective trains the…