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.
105 documents
This document describes a daily ETF candidate universe covering equities, fixed income, commodities, and currencies. It outlines a workflow for downloading market data, loading it for analysis, inspecting coverage by symbol and category, and filtering by…
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 notebook assesses whether an LSTM can use the ordering of ETF feature histories to improve on flat-feature linear and gradient-boosting models. It resolves the declared sequence population against current data, checks eligible funds and fund-date rows,…
This case study compares predictive models for monthly cross-asset rotation across ETFs spanning equities, fixed income, commodities, currencies, and real estate. Its central lesson is that information coefficient (IC) and trading performance can rank models…
This notebook explains how double machine learning (DML) estimates whether FX momentum affects future returns after adjusting for configured confounders. It distinguishes this intervention question from prediction: predictive models are compared by…
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 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 explains how to build a cross-sectional futures feature matrix from three contract tenors per product. It derives carry and curve curvature from exchange-settled prices, while using roll-adjusted prices for return, momentum, and volatility…
This notebook develops a financial feature matrix for a cross-asset ETF momentum hypothesis: assets with stronger relative performance may continue to outperform over the following month. It combines trailing returns at several horizons, risk-adjusted…
This document explains how to build and inspect a feature matrix for a cross-sectional ETF momentum and rotation hypothesis. It defines each feature’s lookback and information lag, then constructs trailing returns, risk-adjusted returns, volatility, trend…
This document reframes a five-session equity return prediction by sampling daily data on Fridays. The label remains a five-session return, but on the weekly grid it spans about one model step. The notebook compares direct regression, using a fixed lookback…
This notebook constructs price-derived features for a broad US equities panel, including momentum, moving averages, and volatility measures. It is designed to rank stocks against one another, using a tradability screen, per-symbol rolling calculations, and…
This notebook checks whether four-hour spot FX data can support a daily cross-sectional strategy that ranks currency pairs using momentum and carry. It tests whether the declared instruments have prices at each decision point, whether the universe represents…
This notebook describes a double machine learning analysis of the effect associated with an FX momentum treatment after adjustment for configured confounders. Flexible nuisance models estimate the outcome and treatment from those confounders; cross-fitting…
This notebook tests whether an LSTM can extract temporal structure from ETF feature histories that flat-feature models may miss. It resolves the declared sequence population against available features, labels, entities, and walk-forward folds before fitting.…
This notebook builds rule-based features for a cross-section of CME futures, centered on carry from the spread between nearby delivery contracts. It also constructs momentum, volatility, curve-shape, and calendar features. The design distinguishes raw…
This notebook defines close-to-close forward returns over two trading horizons for a cross-section of ETFs, with each horizon measured from an adjusted close to the close a fixed number of sessions later. It explains why labels are built on complete symbol…
This case study builds minute-level features from NASDAQ-100 quote and trade data to examine whether recent aggressive buying or selling predicts short-horizon price drift. Order-flow imbalance is the proposed signal; spread, book depth, price impact,…
This notebook applies double machine learning (DML) to estimate the effect of skip-recent momentum on ETF forward returns, a causal question distinct from forecasting returns. It models the outcome and the momentum treatment using declared confounders, then…
This notebook defines forward-return labels for a US equities panel and explains why their construction affects every downstream model and backtest. It specifies adjusted-price return windows in trading sessions, checks that each stock has the required…
This notebook demonstrates an operational workflow for connecting a shared backtest and live strategy to an Interactive Brokers paper-trading session. It checks account identity and state, requests historical bars to initialize indicators, subscribes to…
This chapter review describes a hypothesis-driven process for defining trading strategies before backtesting. It connects documented data assumptions and immutable configuration to exploratory analysis, event studies, and a structured strategy term sheet.…
This notebook explains how futures contract specifications affect backtest accounting. It shows how the contract multiplier converts price moves into dollar P&L, how price times multiplier determines notional value for sizing, and why per-contract…
This notebook defines forward price-return labels for a fixed panel of crypto perpetual contracts and explains how label construction affects every later model and backtest. It shifts bar-open timestamps to the time completed data becomes available,…