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.
427 documents
This notebook describes an out-of-sample backtest for a selected crypto perpetual funding strategy. It reuses predictions generated from training history that ends before the holdout period, then applies the chosen strategy configuration, including its…
This document turns cross-sectional ETF predictions into simulated trades. It distinguishes ranking quality, measured by information coefficient, from realized strategy performance: a top-k portfolio depends on the relative score values, rebalance schedule,…
This notebook explains how to apply four market impact models in a backtest: no impact, linear impact, square-root impact, and a configurable power law. Each model estimates a signed per-share price move based on order direction, quantity, price, and volume.…
This notebook builds a daily feature panel for a long-short ranking strategy across twenty FX pairs. It aggregates four-hour spot bars into sessions ending at the New York 5 PM rollover, then constructs trailing return, channel, volatility, drawdown, range,…
This read-only assessment reconstructs the selected strategy from configured, full-coverage registry results. It follows the progression from an equal-weight baseline through allocation, risk controls, and transaction-cost sensitivity, then reads the holdout…
This case study describes producing an out-of-sample prediction set for an already selected S&P 500 options model. The holdout configuration is fixed using validation results, then fitted again on data ending before the holdout window. A label buffer…
This notebook presents lightweight falsification diagnostics for feature triage, explicitly distinguishing mechanism consistency from causal identification. It first scans ETF features across forward-return horizons with multiple-testing correction, then…
This notebook surveys supervised-learning labels using ETF price data. It covers fixed-horizon forward returns for regression or direction classification, time-series rolling percentiles, cross-sectional percentile labels, triple-barrier labels with fixed or…
This notebook compares learned and analytical hedges for a short European call when rebalancing is discrete and trading incurs proportional costs. It defines the self-financing terminal P&L from hedge gains, turnover costs, and the option payoff, then trains…
This notebook demonstrates position-level exits and portfolio-level controls through constructed examples. Static rules include stop losses, profit targets and time exits; dynamic rules include trailing stops that follow prior highs, tightening trails, and…
This notebook shows how to align macroeconomic observations with the dates traders could actually have known them. It distinguishes the period a value measures from its publication date, estimates release dates from period length and agency lag schedules,…
This notebook estimates the adjusted effect of a continuous ETF momentum measure on forward returns using double machine learning. It contrasts an unadjusted regression with DML estimates that control for recent and longer-term volatility, market regime, and…
This notebook presents a deterministic method for checking whether backtest and live trading pipelines behave alike. It compares successive stages: features computed from the same bars, predictions from those features, signals given the same position state,…
This utility builds label artifacts for S&P 500 option straddles using the same symbol, strike, and expiration at entry and exit. It aligns feature dates to subsequent market sessions, constructs five- and ten-session exit dates, and joins call and put…
This notebook applies position-level risk controls to leading ETF allocation combinations while keeping each underlying prediction, concentration, and allocator fixed. It compares stop-losses, trailing stops, and time exits with the original strategy,…
The document describes how a trading research pipeline assesses whether latent-factor model fits completed in a usable state. For models trained by gradient descent, it checks that the final recorded training objective is finite; for the stochastic discount…
This notebook queries backtest registries across nine case studies and assembles comparable tables for downstream analysis. It organizes results by asset class and data frequency, then records performance across signal, allocation, cost, and risk stages,…
This document describes a one year out of sample backtest of S&P 500 option straddles. It applies predictions from a model refit on pre holdout history, together with the previously selected strategy, allocation, concentration, weekly entry schedule, hedge…
This notebook specifies and executes a double machine learning analysis of the effect of the variance risk premium on short-option returns through expiry. Before execution, it resolves the treatment, outcome, confounders, timing, nuisance model, temporal…
The document explains how an experiment registry can track a model from its training configuration through predictions to backtest results. Each stage receives an identifier derived from a canonicalized specification, allowing repeated identical runs to…
This chapter presents portfolio construction as the process of converting return forecasts, risk estimates, and constraints into weights, leverage, and rebalancing decisions. It lays out a research workflow for documenting allocator choices, avoiding…
This notebook explains how to evaluate position-level exits and combine them with portfolio-wide controls. Fixed stop-loss, take-profit, and time exits are contrasted with trailing and tightening stops; a scaled exit reduces a position at successive profit…
This notebook studies how ridge, lasso, and elastic net behave when a crypto perpetuals feature matrix measures one economic quantity—the premium—many different ways. Premium levels, changes, volatility, standardized positions, ranks, and related funding…
This study converts registered model predictions into comparable S&P 500 option strategies. On weekly decision dates it ranks predicted returns, filters to a liquid universe, and sells equally weighted at-the-money straddles on the highest-ranked symbols.…