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.
715 documents
This analysis compares validation predictions from several model families for perpetual futures funding. It explains that information coefficient measures the ranking of subsequent returns, AUC measures the ordering of up and down outcomes, and log loss…
This case study assesses DeFi Llama total value locked data as a possible input to an Ether trading research pipeline. It separates signal strength, data quality, legal suitability, and commercial value, with legal issues and the lack of reconstructible…
This notebook evaluates published factor returns as potential portfolio building blocks. It separates three questions: whether a factor’s average return clears a demanding statistical bar, whether factors diversify one another for reasons that may persist,…
This notebook compares risk overlays across case-study backtests, including stop, trailing-stop, loss-limit, drawdown-breaker, and volatility-target rules. It loads each study’s overlay results, selects a baseline from an allocation-stage backtest where…
This case study applies gradient-boosted trees to NASDAQ-100 microstructure features to forecast returns and direction at several intraday horizons. It contrasts trees with linear models: trees can discover conditional relationships, such as order-flow…
This notebook assembles a forecasting system with four stages: a panel of research agents, probability aggregation, an adversarial debate, and a supervisor that identifies disagreements, searches for clarifying evidence, and issues a final probability. The…
This notebook compares deterministic relational retrieval with embedding search on a corpus of institutional 13F holdings. It creates a point-in-time snapshot by keeping each institution’s latest reported position before a fixed cutoff, then builds questions…
This notebook demonstrates training a generative adversarial network on engineered ETF features with differential privacy. It introduces the privacy parameters epsilon and delta, explains why DP-SGD clips each record’s gradient before aggregation and adds…
This notebook applies classical ordinary least squares inference to ETF features and forward returns. It reviews coefficient estimates, standard errors, t statistics, p values, and tests for heteroscedasticity, residual autocorrelation, non-normality, and…
This notebook compares time bars with tick, volume, dollar, imbalance, and run bars built from a single day of AAPL exchange trades. The first four sampling approaches group observations by elapsed time, trade count, shares traded, or notional traded.…
This notebook fits PatchTST, a transformer-style sequence model, as one member of a predeclared population of models for S&P 500 options research. It divides a lookback series into fixed-length patches, embeds them as tokens, and uses attention to relate…
This document defines two diagnostics for labels whose forward return windows overlap: pooled panel autocorrelation and effective sample size based on label uniqueness. Autocorrelation pairs observations only within the same entity and at the requested…
This notebook shows how to use an estimation-window correlation network to describe an equity universe and build a network-diversified portfolio. It selects liquid symbols using only data available before evaluation, converts return correlations to…
This exploratory analysis examines the legacy Quandl WIKI US equities panel, focusing on price conventions, data coverage, and what its history can support in a backtest. It distinguishes raw prices, which reflect traded price levels, from adjusted prices…
This notebook treats a Sharpe ratio as an estimate with sampling uncertainty. It develops confidence intervals and probabilistic Sharpe calculations that account for sample length, skewness, and kurtosis, then uses minimum track record and power planning to…
This notebook compares three ways to classify financial news sentences as positive, neutral, or negative: TF-IDF word and word-pair features with logistic regression, averaged pretrained GloVe vectors with logistic regression, and a pretrained FinBERT…
This shared allocation module turns cross-sectional predictions into portfolio weights. It ranks assets by score to select long positions or top and bottom baskets for long-short portfolios. Position weights can reflect conformal prediction widths, where…
This notebook constructs features from minute-level NASDAQ-100 data using three procedures: a rolling heterogeneous autoregressive regression for near-term variance forecasts, a Fourier transform to describe periodic structure in recent activity, and…
This analysis uses NASDAQ TotalView-ITCH messages to describe venue activity and build a trade table linked to ticker, side, and order attributes. It counts message types, compares trading with the much more numerous quoting and removal events, and ranks…
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 assesses whether a monthly long–short strategy based on firm characteristics can be studied with a released US firm panel. It checks the data encoding, how the available universe changes over time, whether each rebalance has enough firms to…
This notebook shows how to analyze a financial event graph without confusing when an event happened, when it became public, and when the graph pipeline extracted it. It loads timestamped 8-K relationships, measures disclosure and extraction delays, and…
The document describes how to compare validation predictions from linear, gradient-boosting, tabular deep-learning, and sequence models for S&P 500 options. Before reporting diagnostics, it checks that each registered prediction population is complete…
The document explains how double machine learning (DML) estimates whether buyer-initiated volume share affects future returns after accounting for spread, short-term volatility, and trailing returns. It residualizes both treatment and outcome against those…