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.
742 documents
This module defines a monthly ETF allocation baseline using a risk-adjusted momentum score: trailing cumulative return divided by annualized realized volatility. It selects the highest-ranked assets when the 10-year minus 2-year yield-curve slope exceeds a…
This document explains a run-log system for recording model training, predictions, causal estimates, and backtests across trading case studies. It describes how configurations flow from case-study settings through training menus and model presets, then…
This configuration specifies a long-short strategy research workflow for crypto perpetual futures. It defines a 19-asset volume-selected universe, decisions aligned to eight-hour funding settlements, and execution at the funding timestamp. The primary target…
This notebook evaluates 57 accounting and price-based characteristics as individual predictors of next-month US stock returns. It calculates monthly cross-sectional rank correlations, averages them over time, and adjusts uncertainty for serial dependence and…
This chapter presents a framework for turning a trading idea into a documented feature specification. It asks researchers to align feature horizons with the intended decision, state a driver hypothesis, and distinguish predictive signals from contextual…
This guide distinguishes causal effect estimation from causal structure discovery and maps common research questions to suitable Python libraries. EconML and DoWhy estimate effects when the adjustment structure is known or explicitly specified; Tigramite and…
This overview consolidates configuration data from nine trading case studies spanning equities, crypto, foreign exchange, futures, options, and multi-asset portfolios. It compares each study’s universe, data frequency, decision cadence, cost model, and…
This notebook presents a deterministic framework for checking whether backtest and live trading pipelines behave consistently. It compares the stages that turn price bars into decisions: feature calculations, predictions, signals, and order sizes. A fixed…
This notebook trains a reinforcement learning agent to schedule a fixed sell order across hourly bars from a crypto perpetual futures market. Its state includes lagged returns, volatility, prior-hour volume, the perpetual premium to spot, and time until…
This notebook aggregates linear-model results, including OLS, Ridge, Lasso, and ElasticNet, across nine case studies. It compares each study’s strongest complete configuration on its primary label, then examines regularization, fold-level information…
This case study evaluates TabM, a parameter-efficient neural ensemble, on the same point-in-time CME futures feature rows and walk-forward folds used by other model families. It motivates TabM as a way to average across model variation while sharing most…
This notebook presents a workflow for asking whether six-month skip-recent momentum affects 21-day forward returns and whether the effect changes with market volatility. It estimates effects on training data using double machine learning, with volatility and…
This case study evaluates short at-the-money straddles on S&P 500 constituents, entered weekly and held until expiry with a daily delta hedge. Its main methodological choice is to model hold-to-maturity returns: the option position incurs an entry spread but…
This notebook compares two pretrained time-series foundation models, Chronos and TinyTimeMixer, with an LSTM and a penalized linear baseline on an ETF panel. Each model receives a univariate historical context; the pretrained models forecast that input…
This chapter presents a framework for evaluating causal claims in quantitative trading research. It starts with clear definitions of treatment, outcome, estimand, counterfactual, and adjustment set, then matches research questions to methods: Double Machine…
This notebook fits linear models to NASDAQ-100 microstructure features observed at one-minute intervals to predict returns at several forward horizons. Its purpose is to establish a simple baseline before more expressive models. Because spread and imbalance…
This notebook describes how to evaluate a previously selected FX strategy on holdout predictions. It reuses the selected configuration’s registered backtest specification, keeping the signal, allocation, risk controls, rebalance rule, costs, and account…
This document describes a daily US equity dataset covering roughly 3,200 companies from 1962 through March 2018. It includes delisted issuers, making it useful for historical research where excluding failed or delisted firms could distort results. The data…
This feature evaluation screens financial and model-derived candidates against forward returns for CME futures. For each feature, it checks data coverage and staleness, measures daily cross-sectional rank correlation between feature values and forward…
This notebook evaluates how transaction-cost assumptions affect the validation Sharpe of one selected S&P 500 equity and options allocation. It carries forward the configuration chosen through a frozen candidate set when available, then reruns the same…
This notebook compares implementations of a univariate SPY price forecast across raw PyTorch, sktime, and Darts. Its main purpose is to assess practical effort: code length, interface requirements, installation success, and combined fit-and-predict time. A…
This chapter argues that durable trading research depends on a disciplined process that can adapt to changing markets, noisy evidence, and implementation costs. It introduces distinctions among structural breaks, regimes, data drift, and concept drift, then…
This evaluation notebook screens financial and model-based features for their relationship to the next eight-hour return of crypto perpetual futures. It builds the evaluation sample from walk-forward validation windows, keeps the final holdout outside the…
This notebook checks whether a daily US equity ranking strategy has enough usable data for a fair evaluation before fitting any model. It explains how to form a point-in-time universe with historical constituents, using printed prices and prior-session…