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.
1,124 documents
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 notebook builds a monitoring workflow for a deployed financial model using real holdout artifacts. It checks that incoming feature data is populated and structurally valid before measuring drift, then compares reference and current windows. Feature…
This notebook introduces vectorized backtesting with VectorBT through a long-only RSI mean-reversion rule on Bitcoin perpetual market data. It calculates a close-based RSI, enters when the prior day’s reading is below a lower threshold, and exits when it is…
This notebook connects position sizing with analysis of maximum adverse and favorable excursions. It demonstrates fixed-fractional sizing, where shares are determined by a per-trade risk budget and entry-to-stop distance, subject to a concentration cap. It…
This notebook explains how to interpret Ridge regression forecasts for an ETF panel with SHAP. For a linear model, each feature’s attribution is its coefficient times the feature’s deviation from its training-background mean, so the attributions can be…
This notebook compares two methods for estimating bid-ask spreads when historical data contains daily open, high, low, close, and volume but no quotes. Corwin-Schultz separates spread from volatility by comparing daily and two-day high-low ranges, relying on…
This notebook presents a workflow for turning SEC annual and quarterly filings into structured text suitable for later sentiment, topic, and embedding analysis. It maps desired sections to form-specific item numbers, emphasizing that management discussion…
This notebook explains how a conditional autoencoder extends an instrumented principal component model: a neural network maps stock characteristics to factor exposures, while the model retains the same exposure-times-factor-return structure. It fits the…
This study screens financial and GJR-GARCH volatility features against forward equity returns. For each session, it calculates the rank correlation across stocks between a feature and subsequent returns, then assesses average association, consistency across…
This reference describes two financial news corpora used for sentiment analysis, text-feature development, and experiments linking news to returns. FNSPID connects headlines with stock tickers and covers a broad historical span; a smaller sample is available…
The document derives European call and put prices under Black-Scholes, checks their relationship through put-call parity, and computes implied volatility by numerically solving for the volatility that matches an observed option price. It also defines Delta,…
This notebook compares six allocation methods on crypto perpetual strategies that have already passed a baseline selection funnel. It keeps each strategy’s predictions and entry rules fixed, then changes how capital is assigned: one allocator uses model…
This notebook demonstrates deep hedging for a short European call. It simulates underlying price paths with geometric Brownian motion, calculates Black-Scholes delta as a benchmark, and trains a semi-recurrent neural network to choose hedge positions. The…
This module defines how a holdout selection field is assembled across validation stages and strategy labels. It addresses discrepancies that can make frozen candidate sets and live reconstruction select different configurations, or cause later analysis to…
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 notebook shows how to test whether four managed-portfolio strategies add explanatory power for SPY returns after controlling for broad movements captured by ten principal components. The candidate portfolios rank ETFs by recent momentum, low volatility,…
This document describes a workflow for turning quarterly SEC 13F bulk filings into an institutional holdings panel. It selects the latest filing per manager by filing date, uses CUSIP rather than filer-entered company names to identify securities, and…
The document explains instrumented principal component analysis for modeling ETF returns. Unlike ordinary PCA, which assigns each fund fixed factor exposures, IPCA makes each fund’s exposures a shared linear function of its observable features. It estimates…
This notebook uses NASDAQ ITCH trade records to examine how volume and trading activity vary through the regular session. It resamples trades into time bars, compares price and volume for representative high-, medium-, and low-activity stocks, then…
This notebook compares predictive models for ranking NASDAQ-100 stocks by their next 15-minute return using intraday microstructure information such as spreads, depth imbalance, signed volume, and price impact. It selects one representative prediction set…
This notebook derives Kelly sizing for a binary wager by maximizing expected log wealth, then illustrates how stake size changes the distribution of simulated wealth paths. It extends the framework to continuous returns and multi-asset portfolios, where the…
This evaluation screens option-surface, price-derived, and conditional-volatility features against forward equity returns. For each session it computes a cross-sectional rank correlation between feature values and subsequent returns, then examines the time…
This notebook evaluates a previously selected ETF strategy on a reserved holdout period. It retrieves the matching predictions from their training identity and checkpoint, applies the strategy specification and trading costs established earlier, and…
This module explains how to prepare historical S&P 500 prices when ticker symbols can refer to different securities over time. It applies corporate-action adjustment factors, tracks security identifiers, and marks identity boundaries. Return calculations are…