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.
5,922 documents
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…
This notebook describes how to evaluate a selected crypto perpetual funding strategy on a later holdout period. It reuses predictions generated from training data ending before the holdout, carries the chosen signal, allocation method, and exit overlay…
This notebook uses correlation-based principal component analysis on sector ETF returns to describe common risk factors and sector rotation. Standardizing returns gives each sector unit variance before decomposition, reducing the chance that a more volatile…
This exploratory analysis profiles a daily panel of exchange-traded funds across nine groups. It checks symbol coverage through time, compares the panel against the classification dictionary, reviews nulls and zero-volume observations, and evaluates…
This analysis applies correlation-based principal component analysis to returns from sector exchange-traded funds. Standardizing returns before decomposition gives each sector unit variance, so the leading components describe co-movement rather than being…
This document describes implementation principles for a shared backtest runner and focuses on interpreting return grids correctly when annualizing performance. It estimates the observed sampling frequency from typical gaps between return observations,…
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 tutorial turns ETF cross-sectional signals into long-only, equal-weight top-ten portfolios and compares Ridge regression and logistic classification with momentum and equal-weight baselines. Models are fitted on walk-forward training windows, with a…
This reference describes two public SEC filing sources for studying equity positions and insider activity. Quarterly 13F reports provide institutional holdings, either for a curated set of managers or through bulk data covering a full filing window. Form 4…
This notebook turns a real supply-chain knowledge graph and institutional 13F holdings into machine-learning features. It derives network measures such as centrality and dependency indicators, estimates supplier concentration and competitive exposure, and…
This tutorial builds a monthly ETF rotation simulator using a trailing risk-adjusted momentum ranking and a Treasury yield-curve regime filter. At each month-end, the rule ranks ten funds using their recent price history; risk-on months hold the top three…
The notebook explains value at risk as a loss quantile and conditional value at risk, or expected shortfall, as the average loss beyond that threshold. It estimates one-day tail risk for a broad equity ETF using four approaches: empirical historical…
This notebook explains Hierarchical Risk Parity as an alternative to mean-variance allocation when covariance estimates are noisy. HRP clusters assets using correlation distances, orders them according to the hierarchy, then recursively bisects the ordered…
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 builds a monthly model matrix for a US firm-characteristics study from a released panel whose characteristics have already been ranked across firms. It adds composites and interactions using values in the same firm-month row, without fitting…
This notebook converts FX pair predictions into baseline trading results. At each decision time it ranks pairs, takes equal-sized long and short sleeves, and runs the selected prediction configurations and checkpoints through an existing backtest engine.…
This notebook compares two simulations of the same monthly ETF momentum strategy. Both use identical target weights, universe, dates, and total trading cost. One computes returns from lagged weights and asset returns; the other processes orders sequentially…
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 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…
This notebook applies principal component analysis to changes in eight Treasury constant-maturity yields, spanning one to thirty years. It removes forward-filled calendar rows with no yield changes and standardizes the remaining observations before PCA, so…
This script gathers institutional holdings from SEC 13F filings in two ways: it can retrieve filings for a curated set of managers or download the SEC’s quarterly bulk files. Both paths normalize holdings into a shared schema, with the bulk path designed for…
This notebook describes DeePM, an end-to-end ETF portfolio policy designed to improve performance across varying market conditions. Its components include volatility-normalized return and trend features, an LSTM-based model, variable selection, conditioning…
This algorithm example demonstrates a two-stage method for narrowing a stock universe. Its coarse filter ranks available stocks by daily dollar volume and passes the five highest-volume names onward. A fine filter then ranks those candidates by…
This workflow proposes using holders of a successful token as a starting group for discovering other assets they own. It filters holders by token ownership share and portfolio value, removes addresses labeled as exchanges, project treasuries, or protocol…