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.
100 documents
This notebook describes an out-of-sample backtest for a selected crypto perpetual funding strategy. It reuses predictions generated from training history that ends before the holdout period, then applies the chosen strategy configuration, including its…
This document outlines a shared data system for quantitative trading research, cataloging datasets across equities, options, futures, crypto, foreign exchange, factors, macroeconomics, filings, positioning, news, and prediction markets. It describes the…
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 studies how ridge, lasso, and elastic net behave when a crypto perpetuals feature matrix measures one economic quantity—the premium—many different ways. Premium levels, changes, volatility, standardized positions, ranks, and related funding…
This document describes data access and alignment conventions for crypto perpetual futures and related on-chain series. It explains that premium-index bars are timestamped at their opening time: an eight-hour bar records the premium leading into the funding…
This notebook develops a two-model exit policy for hourly crypto perpetuals. An entry classifier identifies unusually strong forward returns, while an exit classifier predicts whether the next forward return will be negative. The exit model receives…
This notebook assesses whether total value locked can serve as an alternative-data signal for ether returns. TVL aggregates the dollar value of crypto assets deposited in decentralized finance protocols. Because it is a price-valued stock rather than a…
This data exploration examines eight-hour premium-index observations for USDT-margined crypto perpetual contracts and explains how the premium relates to funding payments. The index uses executable impact bid and ask prices relative to the price index,…
This notebook compares Polymarket’s crypto-settled event contracts with the regulated Kalshi venue as sources of alternative data. It explains how access rules, settlement assets, position limits, and listing policies shape which participants influence…
This dataset guide presents Binance perpetual futures price and volume data alongside an eight-hour premium index. Hourly OHLCV records describe market activity, while the premium measures the difference between perpetual and spot prices relative to spot. A…
This notebook demonstrates feature-drift checks on ETF momentum and technical features across calm and stressed windows, and on crypto perpetuals with premium-index data across market regimes. It compares Population Stability Index (PSI), including its…
This document describes training a Proximal Policy Optimization agent to liquidate a fixed order over a defined horizon. It evaluates the learned pacing policy against TWAP and an Almgren-Chriss schedule using the same simulated market paths, enabling paired…
This case study fits linear models to predict returns and direction in crypto perpetual futures. Its feature matrix is dominated by variations of the perpetual premium, the gap between the contract and spot price that drives funding payments. It contrasts…
This notebook screens financial and model-based features for their relationship with the next eight-hour return of crypto perpetual contracts. For each settlement, it computes cross-sectional Spearman correlations across eligible contracts, then summarizes…
This notebook treats total value locked as alternative data and evaluates the hypothesis that DeFi capital conditions can help predict ether returns. It defines TVL as the dollar value of crypto assets deposited in protocols, then emphasizes that it is a…
This notebook trains a vanilla autoencoder on hourly returns for a basket of crypto perpetual markets. Returns are standardized using the training period, and a neural network compresses the multi-asset input into a two-dimensional latent representation…
This notebook presents correctness and runtime comparisons for VectorBT Pro and VectorBT OSS against ML4T on supported case-study strategies. It covers ETF allocation, USD-quoted foreign exchange, and a US equity panel for both editions; VectorBT Pro also…
This notebook defines forward price-return labels for a fixed panel of crypto perpetual contracts and explains how label construction affects every later model and backtest. It shifts bar-open timestamps to the time completed data becomes available,…
This notebook asks whether sequence models can improve on hand-built summaries of perpetual funding premium history. It compares NLinear, a simple linear model that reads an ordered window, with a two-layer LSTM. Both use the same feature order, walk-forward…
This notebook builds a simulated market-making task in which a proximal policy optimization agent chooses quote skew and spread width. The environment uses a GARCH volatility process calibrated to hourly crypto perpetual-futures returns, a reservation price…
This notebook studies six allocation methods applied to selected crypto perpetual strategy configurations. The rankings and entry rules remain fixed while the capital assigned to each position changes. The methods use model scores, individual contract…
The notebook presents a reporting method for a long-only RSI mean-reversion strategy on BTC. It compares gross and net performance, then benchmarks the strategy against buy-and-hold using the same trading dates, exposure, execution engine, fill timing, and…
This analysis explains how Binance’s perpetual-futures premium index relates to spot prices and how the exchange transforms that premium into periodic funding. The index uses executable impact bid and ask prices relative to an underlying price index,…
This notebook describes how to evaluate a selected crypto perpetual funding strategy on a later holdout period. It reuses predictions generated from training data ending before the holdout, carries the chosen signal, allocation method, and exit overlay…