Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
1,124 documenten
This notebook describes fitting PatchTST to one-minute NASDAQ-100 data to predict returns over several forward horizons. The model groups consecutive observations into patches and applies attention across them, reducing the number of items compared while…
This notebook checks whether historical ETF data can support a monthly ranking strategy before fitting a model or making forecasts. It tests the tradable universe using prior-year liquidity, counts eligible funds on rebalance dates, converts per-share…
This notebook compares ways to allocate capital across US equity positions while holding the model, checkpoint, rebalance dates, and selected stocks fixed. It examines weights based on prediction strength, prediction-interval width, individual stock…
This notebook compares Polymarket’s crypto-settled event contracts with the regulated Kalshi venue as sources of alternative data. It explains how access rules, settlement assets, position limits, and listing policies shape which participants influence…
This notebook explains an unconditional signature-based Wasserstein GAN for generating financial time series. It transforms returns into augmented paths, computes truncated path signatures, and trains an LSTM generator driven by Brownian noise to match…
This dataset guide presents Binance perpetual futures price and volume data alongside an eight-hour premium index. Hourly OHLCV records describe market activity, while the premium measures the difference between perpetual and spot prices relative to spot. A…
This notebook evaluates stop losses, trailing stops, and fixed-duration exits on selected US equity strategies. A stop loss responds to losses from entry, a trailing stop responds to declines from a position’s peak, and a time exit closes after a set holding…
This notebook demonstrates feature-drift checks on ETF momentum and technical features across calm and stressed windows, and on crypto perpetuals with premium-index data across market regimes. It compares Population Stability Index (PSI), including its…
This notebook evaluates TSMixer as a global sequence model for an ETF panel. The model shares parameters across funds while using each fund’s own history and covariates; its mixing layers learn temporal and within-series relationships without combining one…
This notebook defines forward-return targets for a cross-sectional futures strategy that ranks products by term structure, going long those with stronger carry and short those with weaker carry. It distinguishes roll-adjusted prices, appropriate for…
This notebook explains how to interpret Kalshi’s federal funds rate contracts and prepare their prices for quantitative research. A binary contract price represents an implied event probability, but the feed contains the highest standing YES bid rather than…
This notebook describes using TSMixer to predict stock returns from ordered windows of each stock’s features. Each example contains 60 consecutive sessions; windows that cross gaps in a stock’s history are excluded, so the number of usable examples varies…
This guide organizes US equity datasets into market data, company fundamentals, investor positioning, and firm characteristics. It inventories loaders for daily and intraday bars, options, and market microstructure records, as well as SEC filing text, XBRL…
This notebook turns validation predictions from multiple model families into equal-weight, long-short portfolios. It ranks stocks by predicted return, buys the top names and shorts the bottom names, and sweeps several portfolio concentrations using a shared…
This notebook compares equity and futures commission models alongside several slippage models, emphasizing that their units and assumptions differ. Percentage fees stay constant as a share of notional, while minimums, fixed charges, per-share fees, and tier…
This document describes training a Proximal Policy Optimization agent to liquidate a fixed order over a defined horizon. It evaluates the learned pacing policy against TWAP and an Almgren-Chriss schedule using the same simulated market paths, enabling paired…
This document presents a staged process for linking company names from filings, news, and alternative data to securities. It distinguishes entity identifiers such as CIK and LEI from security identifiers such as FIGI, CUSIP, and ISIN, and explains why…
This document explains a supervised autoencoder factor model for predicting stock returns in an equity option analytics research setting. Unlike PCA, IPCA, and an unsupervised conditional autoencoder, its training objective combines reconstruction of the…
This document describes a fixed out-of-sample backtest for a selected CME futures strategy. The configuration, predictions, allocator, rebalance cadence, concentration, and transaction-cost assumptions are inherited from earlier research steps and applied…
This notebook studies linear prediction models for a cross-section of CME futures products using feature columns grouped into related families, including carry, momentum, volatility, and rolling risk measures. Because columns within a family are often…
This educational analysis compares four neural network designs on the same task: predicting the next daily return from a recent window of returns. A pooled set of eight exchange-traded funds provides varied market exposures. The models differ in how they…
This notebook audits the complete set of canonical validation predictions for an FX pairs study. It checks model identity, artifact availability, completeness, configured-label coverage, and whether the assembled configurations match the declared menu.…
This notebook audits the registered validation predictions for an FX pairs study. It checks that the catalog includes the configured model population, complete prediction sets, available artifacts, and current model identities. It then summarizes predictive…
This notebook measures how well the Lee-Ready method infers trade aggressor direction using Nasdaq order-by-order messages with venue-provided aggressor labels as ground truth. It reconstructs the limit order book from adds, modifications, cancellations,…