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.
59 documents
The document describes a feature pipeline that combines equity prices with summaries of listed options implied-volatility surfaces. Its central hypothesis is that disagreement between option-implied volatility and realized share volatility can help rank…
The document shows how to turn weekly Commitment of Traders reports into futures positioning features. It explains the trader categories in the financial futures and disaggregated commodity formats, and why participant groups matter when aggregate net…
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 US equities feature study explains how to generate features from estimated models without allowing future data into earlier observations. Its estimation schedule uses a history burn-in, fits parameters only on data before each output block, then…
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 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 builds minute-level features from NASDAQ-100 quote and trade data to study short-horizon price pressure. It treats normalized order-flow imbalance as the main signal candidate and uses spread, depth, price impact, off-exchange trading,…
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 constructs several sampling schemes from a single day of NASDAQ ITCH trades for an equity: calendar-time, tick, volume, dollar, imbalance, and run bars. It compares their statistical properties, including normality and autocorrelation, and…
This shared feature-engineering module defines construction rules and audits for financial predictors used across multiple case studies. It records each feature family with its hypothesis, inputs, rolling lookback, information lag, role, and potential…
The notebook presents a reporting method for a long-only RSI mean-reversion strategy on BTC. It compares gross and net performance, then benchmarks the strategy against buy-and-hold using the same trading dates, exposure, execution engine, fill timing, and…
This document explains fractional differencing as a way to make a price series more stationary while retaining some information about its level. It derives the lag weights from the binomial expansion of a fractional difference and shows how truncating small…
This demonstration runs one dual moving-average crossover strategy through both a historical backtest engine and a live engine replaying the same daily ETF bars. It holds the strategy, inputs, parameters, and fill convention fixed, then compares the…
This notebook introduces path signatures as fixed-length features that preserve the order and joint geometry of observations in a time-series window. The first level records each coordinate's total change; the second captures signed area between coordinate…
The document demonstrates a VectorBT workflow for a long-only Bitcoin RSI mean-reversion rule. It calculates RSI from daily close prices, enters when the prior day’s reading falls below a lower threshold, and exits when it exceeds an upper threshold.…
This notebook implements a long-only RSI mean-reversion rule for BTC/USDT perpetuals using an event-driven backtesting engine. It aggregates intraday bars into UTC daily OHLCV data, computes a rolling gain-and-loss RSI, enters when the indicator falls below…
This notebook compares three ways to convert signed return predictions into binary positions: a fixed zero cutoff, a trailing percentile of each symbol’s own scores, and a cross-sectional percentile across symbols. It measures signal activation and state…
This notebook explains how GARCH turns volatility clustering into a per-session feature. It first uses return plots and an ARCH-LM test to check whether squared returns depend on their own lags. In a GARCH(1,1) model, the response to a new shock and the…
This document develops a daily feature panel for ranking twenty currency pairs at the New York 5 PM close. It distinguishes rankable signals, such as standardized multi-horizon returns and channel position, from market-state measures such as volatility,…
This notebook introduces a workflow that connects standardized ETF data loading, a feature registry, and signal diagnostics. It shows how to discover indicator metadata, compute features using defaults or explicit parameters, and store configurations for…
This notebook compares ways to measure volatility and develops heterogeneous autoregressive (HAR) volatility models alongside roughness analysis. It uses intraday returns to estimate session realized variance, distinguishes intraday movement from overnight…
This notebook examines information-driven sampling by constructing tick and volume imbalance bars from trade data. It verifies a manual tick-imbalance implementation against a library sampler, then sweeps expected bar sizes and adaptation settings to compare…
This notebook demonstrates SHAP explanations for a LightGBM model predicting ETF forward returns. TreeSHAP supplies feature contributions for individual predictions and aggregate importance summaries. Beeswarm and dependence plots show how feature values…