Knowledge library

This notebook describes a 15-minute backtest workflow for predictions on NASDAQ-100 stocks. It first runs a random-signal plumbing check: because random trading should lose after costs, a persistently profitable result can indicate problems such as…

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 builds forward-return labels for a strategy that reads listed-option signals and trades the underlying shares. It adjusts daily share prices for splits and dividends, uses a persistent security identifier, and defines returns between executable…

This notebook estimates how much an equal-weight portfolio of surviving US equities can overstate returns relative to a portfolio that includes firms leaving the dataset. It first examines when the panel records exits, finding that the usable exit history…

This notebook studies how transaction costs affect a frequently rebalanced NASDAQ-100 strategy. It first applies basis-point cost assumptions to existing pre-cost backtests to show how Sharpe changes as costs rise. It then compares full-universe and…

This notebook explains instrumented principal component analysis as a way to make latent-factor loadings depend on observable stock characteristics rather than assigning each stock a fixed loading. A shared mapping from characteristics to factor exposures…

This notebook demonstrates how to tune StopLoss, TakeProfit, and TrailingStop rules for a momentum portfolio while preserving a chronological separation between calibration and evaluation. It tests individual rule widths and joint combinations using…

This document demonstrates connecting a five-day ETF momentum strategy to Alpaca through a shared strategy interface. The strategy tracks momentum across SPY, QQQ, and IWM and generates signals when it crosses a threshold. Broker-specific adapters handle…

This document introduces a family of firm-return models that assumes a small set of unobserved common factors explains much of the cross-section. Rather than predicting returns directly from characteristics, these models infer each firm's factor exposures…

This document explains how wavelet decompositions and rolling spectral estimates describe financial returns at different time scales. Wavelets divide returns into components spanning progressively slower periods, making it possible to see which scales…

This notebook turns model prediction sets into comparable S&P 500 options backtests. On each weekly decision date, it ranks predicted returns, selects the highest-ranked symbols in the liquid universe, and sells equally weighted at-the-money straddles.…

This document explains how to compare an incumbent and a candidate model in shadow evaluation before allocating capital. It fixes their roles and promotion thresholds in advance, then assesses the candidate on five dimensions: improvement in annualized…

The notebook introduces a supervised autoencoder for equity factor modeling. Unlike unsupervised autoencoders that learn a bottleneck by reconstructing the cross-section, this model also includes forward returns in its training objective. The target…

This exploratory notebook describes the structure and interpretation of AlgoSeek NASDAQ-100 minute bars. It organizes the dataset’s precomputed fields into quote prices and sizes, trade prices, spreads, volume, trade-location buckets, tick direction,…

The notebook explains how to separate continuous price variation from jumps in intraday equity returns. It estimates daily realized variance from squared returns and uses bipower variation as a jump-robust estimate of the continuous component; their…

The document demonstrates a broker wrapper that checks orders and portfolio state before forwarding trades. Its controls include per-order share and value caps, position exposure limits, order-rate limits, asset allow and block lists, and an emergency halt.…

This chapter surveys financial text representations, from dictionaries and word counts through TF-IDF, static embeddings, recurrent networks, and Transformers. It explains the trade-offs: simpler methods are fast, interpretable, and adaptable to finance,…

This case study evaluates monthly long-short decile strategies built from US firm characteristics, comparing linear models, gradient boosting, and latent-factor approaches under point-in-time accounting lags, walk-forward validation, and era-dependent…

This analysis compares three ways to convert signed model return forecasts into binary positions: a fixed zero cutoff, a trailing percentile for each symbol, and a cross-sectional percentile across symbols. It applies the rules to registered validation…

This notebook turns 21-day forward ETF returns into an up-or-down target and evaluates L1- and L2-regularized logistic regression on chronological walk-forward folds. It scales features using each fold’s training data, then assesses predictions with…

This notebook compares gradient boosting objectives on monthly US stock returns while holding features, target, and walk-forward folds fixed. It contrasts squared error with absolute error and Huber loss, which reduces the influence of large residuals. The…

This implementation describes a conditional autoencoder for asset pricing. A beta network maps each stock’s characteristics to latent factor loadings, while a linear factor network maps managed portfolio returns to factor returns. The model predicts a stock…

This notebook assesses whether historical ETF data can support a monthly ranking strategy before any model or forecast is built. It checks point-in-time eligibility using prior-year liquidity, counts available funds on rebalance dates, converts per-share…

This notebook screens candidate features for a strategy that sells at-the-money call and put options on the same stock with the same expiry, holding the straddle to expiration. For each feature, it measures whether the feature ranks available names in the…