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.
13 documents
This notebook analyzes reconstructed NASDAQ limit order books to describe intraday spreads and top-of-book depth, then examine whether order-flow imbalance is associated with subsequent bucket returns. It expresses spreads in basis points to compare stocks…
This notebook presents a GT-GAN-inspired generative model for financial series sampled at irregular intervals, such as tick, volume, or dollar bars. Its encoder, generator, discriminator, and decoder use continuous-time Neural ODE dynamics, allowing latent…
This notebook tests Lee-Ready trade classification against aggressor-side labels in Nasdaq order-by-order data. It reconstructs the limit order book from add, modify, cancel, fill, and reset messages, then aligns each trade with the contemporaneous best bid…
This document presents an event-driven method for holding a limited number of intraday positions. Predictions are aligned to price bars using the latest available score, subject to an optional freshness limit. Entry signals use a rolling quantile computed…
This document compares four equity market data sources for studying trades, quotes, and limit order books: AlgoSeek TAQ, Databento market by order, NASDAQ ITCH, and IEX HIST. It explains each source’s granularity, coverage, cost or access conditions, storage…
This chapter presents market data as the result of trading rules, liquidity, and participant behavior. It surveys data from top-of-book quotes through order-level feeds, then describes parsing exchange messages and replaying them into a venue-local limit…
This notebook constructs market microstructure measures from NASDAQ ITCH trade data, aggregates trades into intraday bars, and distinguishes liquidity proxies from order-flow signals and order-book state. It classifies individual trades with a tick rule…
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 describes a GT-GAN-inspired architecture for synthesizing time series with genuinely irregular timestamps, such as tick, volume, or dollar bars. A GRU-ODE encoder maps observations into latent states, while ODE-based generator and discriminator…
This document describes utilities for loading parsed NASDAQ ITCH messages and rebuilding a limit order book from order additions, deletions, cancellations, executions, and replacements. It tracks remaining shares per order so later book updates use the…
This notebook teaches how to decode NASDAQ TotalView-ITCH message-by-order data from its binary format into structured records. It explains the message framing and type-specific layouts, demonstrates unpacking fields, and converts timestamps expressed as…
This notebook describes fitting a long short-term memory network to one-minute NASDAQ-100 microstructure features to predict forward returns at several horizons. The model processes a trailing sequence step by step, learning which earlier observations to…
This analysis reconstructs individual NASDAQ limit orders from ITCH add, delete, partial-cancel, replace, and execution messages. It measures whether orders are cancelled or filled and how long they take to reach those events, using precise timestamps and…