Knowledge library

This notebook builds fitted-model features from foreign-exchange price histories, complementing indicators calculated directly from past prices. It describes three approaches: a state-space filter that estimates a slowly changing price level and related…

This notebook compares portfolio weighting rules for an S&P 500 options strategy that sells straddles. It holds the selected symbols and other strategy settings fixed while replacing the equal-weight baseline with allocators that use either model predictions…

This notebook measures how execution costs affect the already selected crypto perpetuals configuration. It holds the model, checkpoint, entry rule, allocator, and risk control fixed, then reruns the strategy across a declared grid of round-trip charges on…

This notebook describes a workflow for turning S&P 500 companies’ quarterly MD&A disclosures into trading features. It uses filing acceptance dates as the point-in-time anchor, collapses multiple filings for a company on the same date to the latest period,…

This chapter surveys deep learning for time series through a practical question: when does observation order contain information that cross-sectional prediction misses, and which model designs can use it? It covers recurrent networks and their sequential…

This read-only analysis brings validation predictions from several model families together to assess whether they are complete, how well they rank stocks, whether ranking skill persists across folds, and whether apparently similar models contribute distinct…

This notebook applies TabM, a tabular neural architecture that shares a two-layer feature network across ensemble members. Each member scales the shared representation with its own learned vector and uses its own output layer; averaging member predictions…

This notebook explains risk-premium PCA, which modifies the return covariance matrix by adding a weighted outer product of the training-period mean returns. Its leading eigenvectors define static factor portfolios; raising the weight tilts the factors toward…

This notebook describes producing an out-of-sample prediction set for a previously selected S&P 500 options model. It fixes the configuration using validation results, refits that configuration on pre-holdout data, and registers predictions for the holdout…

This notebook tests a two-model exit design on hourly BTC, ETH, and SOL perpetual data. An entry classifier identifies unusually strong forward returns; its predicted probability then joins technical features in a separate model that estimates whether the…

This document describes dataset utilities for preparing time-series panels for model training and rolling inference. The training dataset returns sliding sequences of features, forward returns, volatility scales, and masks, with configurable sequence length,…

This notebook builds price-derived features for ranking a broad US stock universe. It describes momentum, volatility, moving-average, and technical-indicator families, then evaluates them against a one-session forward-return label. Its central design rule is…

This chapter presents risk management as part of strategy design and live operations. It covers tail risk measurement with value at risk and conditional value at risk, drawdown depth and recovery, exposure decomposition, stress testing, adaptive controls,…

This analysis compares validation predictions from several model families for perpetual futures funding. It explains that information coefficient measures the ranking of subsequent returns, AUC measures the ordering of up and down outcomes, and log loss…

This case study assesses DeFi Llama total value locked data as a possible input to an Ether trading research pipeline. It separates signal strength, data quality, legal suitability, and commercial value, with legal issues and the lack of reconstructible…

This notebook evaluates position-level exits on crypto perpetual futures positions selected by an earlier allocation stage. It defines fixed loss stops, trailing stops tied to each position’s best price, and time exits after a set holding period. The…

This case study configures an LSTM model for forecasting FX-pair labels. It contrasts the recurrent model’s carried hidden state with fixed lookback transformations and convolutional receptive fields, while deferring comparisons with other models to a…

This notebook compares risk overlays across case-study backtests, including stop, trailing-stop, loss-limit, drawdown-breaker, and volatility-target rules. It loads each study’s overlay results, selects a baseline from an allocation-stage backtest where…

This case study applies gradient-boosted trees to NASDAQ-100 microstructure features to forecast returns and direction at several intraday horizons. It contrasts trees with linear models: trees can discover conditional relationships, such as order-flow…

This notebook compares deterministic relational retrieval with embedding search on a corpus of institutional 13F holdings. It creates a point-in-time snapshot by keeping each institution’s latest reported position before a fixed cutoff, then builds questions…

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 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 notebook shows how to tune stop-loss, take-profit, and trailing-stop rules for a momentum portfolio while preserving a chronological holdout. It compares one-dimensional parameter sweeps, joint rule grids, and stop or target priors derived from…

This notebook fits PatchTST, a transformer-style sequence model, as one member of a predeclared population of models for S&P 500 options research. It divides a lookback series into fixed-length patches, embeds them as tokens, and uses attention to relate…