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 notebook compares six allocation methods on crypto perpetual strategies that have already passed a baseline selection funnel. It keeps each strategy’s predictions and entry rules fixed, then changes how capital is assigned: one allocator uses model…
This module defines how a holdout selection field is assembled across validation stages and strategy labels. It addresses discrepancies that can make frozen candidate sets and live reconstruction select different configurations, or cause later analysis to…
This notebook derives Kelly sizing for a binary wager by maximizing expected log wealth, then illustrates how stake size changes the distribution of simulated wealth paths. It extends the framework to continuous returns and multi-asset portfolios, where the…
This notebook evaluates a previously selected ETF strategy on a reserved holdout period. It retrieves the matching predictions from their training identity and checkpoint, applies the strategy specification and trading costs established earlier, and…
This module builds a mean forecast ensemble from registered prediction sets, with particular attention to reproducibility and avoiding selection bias. It resolves qualifying model configurations from the registry rather than maintaining a hand-written member…
This notebook uses principal component analysis on a liquid subset of US equities to extract latent factors and form long-short eigenportfolios. It explains how standardized PCA vectors are converted to raw-return weights and normalized by gross exposure,…
This notebook examines linear-model regularization for a universe of currency pairs whose returns are linked because each pair is a quote between shared currencies. It distinguishes feature collinearity, which ridge, lasso, and elastic net can address, from…
This notebook uses historical US equity returns to build a correlation network and explore portfolio construction. It selects a liquid universe using estimation-window data, converts pairwise correlations into distances, and extracts a minimum spanning tree…
This case study examines whether information from listed options can help select S&P 500 stocks. It combines daily equity prices with option-implied volatility levels, skew, term structure, and variance risk premium measures across 633 stocks from 2017 to…
The document describes an FX portfolio allocation sweep designed to isolate position sizing from signal selection. It starts from frozen equal-weight baseline candidates ranked by validation backtest performance, then advances configurations while preserving…
This cross-case analysis examines how predictive information, measured by information coefficient (IC), translates into portfolio Sharpe across nine trading case studies. Its central point is that implementation mediates the relationship: entry rules,…
This case study compares alternative position sizing methods for CME futures against an equal weight baseline. It explains how inverse volatility sizing uses each contract’s own volatility, while covariance based methods also account for relationships…
This notebook turns currency-pair prediction rankings into a traded baseline. At each decision time, it forms equal-sized long and short sleeves from the highest- and lowest-ranked pairs, then evaluates them with an FX backtest engine. Equal weighting is…
This module defines a differentiable objective for end-to-end portfolio learning. It turns bounded asset-level risk weights into portfolio exposures using volatility scaling, computes average gross returns over available assets, and optionally subtracts…
This notebook establishes a signal-stage baseline for evaluating model predictions on CME futures. At each weekly decision, it ranks products by predicted return, buys the top group, shorts the bottom group, and gives each position equal weight. It varies…
This configuration specifies a long-only ETF ranking and rebalancing experiment. It defines a broad 100-ETF universe spanning equity, bond, commodity, and regional exposures, with monthly decisions and next-open execution. The main label forecasts a 21-day…
This notebook adapts Word2Vec skip-gram to institutional equity holdings. It treats each institution’s portfolio as a sentence, stock identifiers as tokens, and positions ordered by reported holding value as word order. Stocks that repeatedly appear in…
This notebook tests whether model-based position sizing improves on equal weighting in a US firm characteristics strategy. It compares score weighting, which allocates more to names with higher predicted scores, with conformal weighting, which reduces…
This shared analysis utility supports comparing trading-model predictions through registry metrics and prediction-level diagnostics. It loads precomputed information-coefficient results, optionally filters by label or model family, and by default excludes…
This document explains a validation baseline for model predictions on CME futures. At each weekly decision, products are ranked by predicted return; the top group is held long and the bottom group short, with equal weight within each leg. The number of…
This notebook sets out a process for evaluating a challenger model in shadow mode before it receives capital. It compares stored predictions from a fixed incumbent and candidate, constructs equal-weight ranked long-short books, applies the same…
This notebook presents a framework for assessing an ETF allocation backtest using daily portfolio returns and a matched SPY benchmark. It covers risk and return measures such as Sharpe, Sortino, Calmar, and maximum drawdown; rolling metrics; drawdown…
This notebook compares portfolio weighting rules for an S&P 500 options strategy that sells straddles. It holds the selected symbols and other strategy settings fixed while replacing the equal-weight baseline with allocators that use either model predictions…
This notebook explains risk-premium PCA, which modifies the return covariance matrix by adding a weighted outer product of the training-period mean returns. Its leading eigenvectors define static factor portfolios; raising the weight tilts the factors toward…