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.
2,013 documents
This document presents a reusable diagnostic survey for financial datasets, covering index integrity, duplicates, missingness, outliers, calendar gaps, and domain-specific anomalies. It checks time types, ordering and uniqueness, including per-symbol…
This notebook presents a cross-case-study inventory of engineered financial features across nine asset classes. It counts features and name-based families from each available feature panel, compares feature-space sizes, and summarizes the strongest…
This notebook compares equal weight, inverse volatility, minimum variance, and hierarchical risk parity using the same ETF universe, forecasts, rebalance schedule, and backtest protocol. A rolling Ridge model ranks ETFs from momentum, moving-average…
The notebook explains Value at Risk (VaR), which marks a loss quantile, and Conditional Value at Risk (CVaR), which averages losses beyond that threshold. Using daily ETF returns, it compares historical, Gaussian, Cornish–Fisher-adjusted, and Student-t Monte…
This notebook expands an ETF feature evaluation from a single return horizon to a scan across ten features and three horizons, correcting for multiple tests. It then applies mechanism-based diagnostics to long-lookback 12-1 momentum and short-term reversal:…
This notebook explains Hierarchical Risk Parity (HRP), a portfolio construction method designed to limit sensitivity to noisy covariance estimates. It describes its three stages: cluster assets by correlation distance, reorder them according to the resulting…
This notebook assesses registered ETF backtests across the strategy pipeline, from signals and allocation through costs and risk overlays. It reads existing predictions and backtests rather than fitting models or adding new backtests, while deriving cohort…
This notebook adapts Diffusion-TS to generate synthetic daily ETF return sequences. Its denoiser predicts the original series and decomposes that prediction into a polynomial trend, selected Fourier components, and a residual. A combined time-domain and…
This feature-engineering module prepares daily price data for systematic macro portfolio models. It creates horizon returns scaled by estimated volatility, several multi-scale MACD signals, and rolling z-scores of log prices. The return horizons can use a…
This chapter surveys ways to turn fitted statistical procedures into features for trading models. It covers diagnostics and stationarity, structural breaks, fractional differencing, Kalman filtering, spectral and path-signature methods, ARIMA and GARCH…
This configuration specifies a historical macroeconomic dataset from FRED for regime filtering and cross-asset analysis. It organizes series by daily, weekly, monthly, and quarterly frequency, including Treasury yields, the federal funds rate, the VIX, labor…
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 document describes a workflow for collecting FRED Treasury yields and economic indicators, aligning series with different reporting frequencies to a daily calendar, and loading or filtering the resulting dataset. The indicators include Treasury rates…
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 explains how to inspect a shipped panel of economic series from FRED and interpret its calendar-day grid, metadata, and derived columns. Because the panel has been flattened onto a daily calendar, row counts conceal the source release…
The document explains two ways to estimate bid-ask spreads when only daily OHLCV data is available. Corwin-Schultz compares high-low ranges over adjacent one-day and two-day intervals, using the different behavior of volatility and spread to separate them;…
This analysis compares registered model families on a cross-asset ETF panel using walk-forward validation results. Models predict 21-trading-day returns and are assessed by cross-sectional information coefficients, which measure how well forecasts rank funds…
This overview organizes a set of trading case studies by asset class, universe size, data frequency, decision cadence, cost regime, and evaluation protocol. It reads each study’s declared setup and presents comparative tables, including training and…
This dataset guide describes a workflow for obtaining FRED series, aligning observations to a daily calendar, and loading selected indicators for analysis. The examples include Treasury yields, the 10-year minus 2-year spread, VIX, employment measures,…
This notebook presents a workflow for acquiring and maintaining financial time series with a unified manager, predefined or custom symbol universes, and partitioned Parquet storage. It covers single and parallel multi-symbol fetching, loading data into…
This exploratory analysis tests whether a twelve-month momentum signal, omitting the latest month, is associated with next-month returns across a 100-ETF universe. It builds a monthly panel from adjusted closing prices, ranks eligible ETFs into quintiles…
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 notebook clusters monthly returns from nine equity, bond, currency, and commodity factor and market series using Gaussian mixture models. After dropping months with incomplete data, it standardizes the series so differences in scale do not dominate the…
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…