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 document turns cross-sectional ETF predictions into simulated trades. It distinguishes ranking quality, measured by information coefficient, from realized strategy performance: a top-k portfolio depends on the relative score values, rebalance schedule,…
This notebook explains how to apply four market impact models in a backtest: no impact, linear impact, square-root impact, and a configurable power law. Each model estimates a signed per-share price move based on order direction, quantity, price, and volume.…
This notebook describes reconstructing a single-symbol, single-day NASDAQ limit order book from message-by-order ITCH data. It processes add, execute, cancel, delete, and replace messages while maintaining each live order reference and its remaining shares.…
This exploratory analysis explains how to interpret minute bars built from quote and trade data for NASDAQ-100 constituents. It organizes the fields into families covering bid and ask quotes, executions, spreads, volume, trade-price buckets, tick direction,…
This notebook demonstrates position-level exits and portfolio-level controls through constructed examples. Static rules include stop losses, profit targets and time exits; dynamic rules include trailing stops that follow prior highs, tightening trails, and…
This notebook presents a deterministic method for checking whether backtest and live trading pipelines behave alike. It compares successive stages: features computed from the same bars, predictions from those features, signals given the same position state,…
This notebook rehearses a deployment cycle for a crypto funding-rate direction model. It trains a LightGBM classifier on historical Binance-derived perpetual data, fetches live hourly bars and funding rates from OKX, computes the model's features, and…
This notebook explains how to parse IEX DEEP messages and maintain an aggregated limit order book at each price level. It extracts price-level updates, best bid and ask quotes, and trade reports, then uses the resulting data to examine spread and depth. The…
This notebook tests whether standard portfolio allocation can improve an every-bar NASDAQ-100 trading strategy that is already burdened by transaction costs. It selects predictions using validation performance on the declared cost-feasible universe, then…
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 deployment bridge between an offline machine-learning pipeline and QuantConnect's LEAN trading engine. It selects a model run reproducibly from a registry, exports its holdout predictions as date-indexed JSON, and uses a small…
This analysis checks whether daily options and share data can support a weekly S&P 500 strategy that ranks constituents by thirty day at the money implied volatility and buys the highest ranked shares. Options provide the signal, while the portfolio holds…
This study converts registered model predictions into comparable S&P 500 option strategies. On weekly decision dates it ranks predicted returns, filters to a liquid universe, and sells equally weighted at-the-money straddles on the highest-ranked symbols.…
This notebook synthesizes results from nine market case studies into a cumulative strategy-screening funnel. It tests, in order, whether a model has positive information coefficient, whether its selected configuration has positive validation Sharpe, whether…
This notebook explains when a strategy is clearest as precomputed arrays and when it benefits from a sequential simulation that carries positions, fills, cash, realized profit and loss, or equity forward through time. Array-based backtests are attractive…
This notebook teaches how to decode NASDAQ TotalView-ITCH binary messages and store them as structured data for later market microstructure analysis. It explains message framing, fixed-width field layouts, big-endian values, timestamps measured from…
This notebook compares pandas and Polars on operations used in financial data pipelines, including rolling calculations, grouped OHLCV summaries, window statistics, filtering, joins, lazy file scans, memory use and string processing. It generates synthetic…
This notebook builds minute-level features from NASDAQ-100 quote and trade data to study short-horizon price pressure. It treats normalized order-flow imbalance as the main signal candidate and uses spread, depth, price impact, off-exchange trading,…
This case study builds minute-level features from NASDAQ-100 quote and trade data to examine whether recent aggressive buying or selling predicts short-horizon price drift. Order-flow imbalance is the proposed signal; spread, book depth, price impact,…
This notebook constructs several sampling schemes from a single day of NASDAQ ITCH trades for an equity: calendar-time, tick, volume, dollar, imbalance, and run bars. It compares their statistical properties, including normality and autocorrelation, and…
This chapter treats transaction costs as a constraint throughout strategy research and deployment, from factor evaluation and backtesting to portfolio construction, risk oversight, and production monitoring. It distinguishes explicit fees, implicit spread…
This notebook explains when a trading strategy is clearer to simulate bar by bar with evolving state than to express as precomputed signals or weights. It contrasts array-based backtests, which can be fast and convenient for parameter sweeps, with sequential…
This notebook studies how to calibrate time, tick, volume, dollar, and imbalance bars using multiple sessions of NVDA market-by-order trade data. It filters trades to regular trading hours, uses the feed’s aggressor-side labels, and examines day-to-day…
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…