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.
243 documents
This notebook estimates how execution costs can erode hypothetical intraday strategy returns. It first measures volume-weighted quoted spreads across NASDAQ-100 constituents using minute-bar quotes, then uses the median constituent spread as an empirical…
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 traces ETF ranking signals through portfolio formation and a simplified backtest. Ridge regression and logistic classification are trained with walk-forward splits, then compared with momentum and equal-weight portfolios. At monthly rebalances,…
This overview organizes a set of trading case studies by asset class, universe size, data frequency, decision cadence, cost regime, and evaluation protocol. It reads each study’s declared setup and presents comparative tables, including training and…
This notebook assesses how transaction costs affect a fixed CME futures configuration selected elsewhere in the research process. It applies an all-in cost grid, splitting each level evenly between commission and slippage, and carries the selected risk…
This notebook compares PyPortfolioOpt, Riskfolio-Lib, and skfolio by fitting portfolio allocators on a shared ETF return panel and evaluating their frozen weights on later observations. It covers mean-risk, tail-risk, hierarchical risk parity, and penalized…
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 document models broker and exchange order handling as a finite state machine. It defines explicit states for submission, acknowledgment, acceptance, partial fills, cancellation, rejection, expiry, and suspension, then maps events to valid transitions. A…
This notebook develops the Almgren–Chriss solution for liquidating a position while balancing market impact against the risk of remaining exposed to price moves. It uses implementation shortfall relative to the arrival price, models permanent and temporary…
The document explains how to construct trade-based microstructure features from NASDAQ trade data aggregated into intraday bars. It covers Kyle lambda, Amihud illiquidity, the Roll spread estimator, trade intensity, and order flow imbalance (OFI). It…
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 measures how a validation-selected FX strategy responds to changes in proportional transaction costs. For each prediction label, it selects a parent strategy from the signal, allocation, and risk-overlay results, then reruns that strategy…
This notebook applies one monthly ETF momentum and yield-curve regime strategy in two ways: vectorized return arithmetic and a sequential account simulation. Both use the same target weights, assets, dates, and total trading cost, allowing the comparison to…
This notebook walks through a deployment rehearsal for a crypto funding-rate direction model. It trains a three-class LightGBM model on historical Binance-derived perpetual data, then fetches live hourly bars and funding rates from OKX, aggregates data to…
This notebook presents a workflow for acquiring and maintaining financial time series with a unified manager, predefined or custom symbol universes, and partitioned Parquet storage. It covers single and parallel multi-symbol fetching, loading data into…
This exploratory analysis tests whether a twelve-month momentum signal, omitting the latest month, is associated with next-month returns across a 100-ETF universe. It builds a monthly panel from adjusted closing prices, ranks eligible ETFs into quintiles…
This notebook compares behavior cloning and inverse reinforcement learning (IRL) using demonstrations from a time-weighted average price (TWAP) execution rule. Behavior cloning maps observed market states directly to actions, while IRL fits a linear reward…
This demonstration checks whether one dual moving-average crossover implementation produces matching signals in backtest and live engines. Both engines receive the same prepared ETF bars and use the same strategy parameters and fill convention, so the…
This notebook examines tick-level AlgoSeek trade and quote data for AAPL during the March 16, 2020 market crash. It filters to regular trading hours, separates trade prints from NBBO updates, and studies intraday activity, spreads, trading sizes, venue…
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…
This analysis notebook explains how to assess a US equity strategy built from firm characteristics using registry results rather than new backtests. It reads uncertainty-aware performance statistics, ranks configurations on shared support, and uses paired…
This document describes a benchmark for database engines used with financial time-series data. It compares bulk writes, full scans, time-range queries, OHLCV aggregation, and as-of joins for trade and quote data. Its methodology treats client-side conversion…
This notebook demonstrates a deployment boundary between an offline machine-learning pipeline and QuantConnect's LEAN engine. It selects a prediction set through the ETF case study registry, verifies training and prediction provenance, and exports…
This notebook compares the effect of assumed trading costs across strategies drawn from multiple case studies. It reads cost-sensitivity backtests for each deployed strategy configuration, compares gross Sharpe with net Sharpe at the case study's own assumed…