Knjižnica znanja

This notebook explains how a conditional autoencoder extends instrumented principal component analysis (IPCA): it retains the two-stage structure in which fund features map to latent factor exposures and those exposures combine with factor returns, but uses…

This analysis compares predictive models for ranking NASDAQ-100 stocks by their next 15-minute return using intraday microstructure features such as spreads, depth imbalance, signed volume, and volatility. It emphasizes selecting comparable prediction sets…

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 compares sklearn HistGradientBoosting, XGBoost, LightGBM, and CatBoost for predicting forward ETF returns. It measures cross-sectional information coefficient, training time, and memory use across model-complexity presets, with GPU runs…

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 maps a family of ETF return models that infer common latent directions in a panel, with features used to estimate fund exposures. It distinguishes five approaches: unconditional principal components; instrumented PCA with a linear…

This notebook explains how to design a search tool for a forecasting agent so evidence has a consistent structure, a traceable origin, and an auditable path into the model. A shared client protocol returns typed results across providers and includes a cutoff…

This notebook studies how position sizing affects FX backtests after the model has already selected which currency pairs to trade. It preserves the winning baseline’s predictions, signal mapping, costs, and execution settings, then varies allocation rules.…

This utility builds label artifacts for S&P 500 option straddles using the same symbol, strike, and expiration at entry and exit. It aligns feature dates to subsequent market sessions, constructs five- and ten-session exit dates, and joins call and put…

This notebook applies position-level risk controls to leading ETF allocation combinations while keeping each underlying prediction, concentration, and allocator fixed. It compares stop-losses, trailing stops, and time exits with the original strategy,…

This notebook explains how to build a cross-sectional futures feature matrix from three contract tenors per product. It derives carry and curve curvature from exchange-settled prices, while using roll-adjusted prices for return, momentum, and volatility…

This notebook shows why searching across many signals or strategies makes the top observed result look stronger than its underlying predictive value. A simulation uses factors with no true information to illustrate how selecting the largest information…

This notebook explains how to turn a released, cross-sectionally ranked US firm characteristic panel into a keyed feature matrix for a factor study. It groups inputs into declared families, combines characteristics and interactions without fitting…

The document describes how a trading research pipeline assesses whether latent-factor model fits completed in a usable state. For models trained by gradient descent, it checks that the final recorded training objective is finite; for the stochastic discount…

This US equities feature study explains how to generate features from estimated models without allowing future data into earlier observations. Its estimation schedule uses a history burn-in, fits parameters only on data before each output block, then…

This notebook applies TSMixer to ETF sequences using one globally shared function across funds. Each example contains an individual fund's history and covariates; temporal and feature interactions are learned across the panel, but one fund's observations are…

This notebook describes a stochastic discount factor (SDF) model for pricing the cross-section of US firm returns. Instead of first estimating factors and then applying them, it directly learns firm-month weights under a no-arbitrage condition: discounted…

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 queries backtest registries across nine case studies and assembles comparable tables for downstream analysis. It organizes results by asset class and data frequency, then records performance across signal, allocation, cost, and risk stages,…

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 notebook evaluates four news-derived signals—weighted surprise, average sentiment, sentiment change, and article coverage—against forward stock returns. It computes a daily cross-sectional Spearman information coefficient, summarizes its mean,…

This document describes fitting temporal convolutional networks to NASDAQ 100 minute level microstructure features. Causal convolutions prevent a prediction from using later observations, while dilation lets successive layers capture patterns over multiple…

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…