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.
1,747 documents
This notebook adds a temporal convolutional network (TCN) to a comparison of sequence models for forecasting cryptocurrency perpetual funding premiums. Causal padding ensures each position uses only current and earlier observations, while dilated…
This module estimates parameters for simulated crypto markets used in reinforcement-learning environments. It loads hourly perpetual-market data for a selected symbol, computes close-to-close log returns, and fits a GARCH(1,1) volatility model. If the…
This notebook studies whether an extreme perpetual futures premium affects subsequent BTC returns or premium reversion. It defines treatment as a high-premium state and compares a broad market outcome, which may be influenced by many forces, with premium…
This analysis asks whether the data can support a cross-sectional crypto perpetual strategy before fitting a model or making forecasts. The proposed approach ranks contracts by premium at each funding settlement, then buys one side of the ranking and sells…
This notebook explains how to turn fitted GJR-GARCH and hidden Markov models into trading features while controlling look-ahead bias. GJR-GARCH estimates conditional volatility and captures the greater impact of negative return shocks; a two-state Gaussian…
This notebook compares two causal questions about extreme premiums in Bitcoin perpetual futures: whether a premium state predicts forward returns, and whether it predicts subsequent premium reversion. The treatment is a binary indicator for a premium z-score…
This notebook compares DQN, PPO, and A2C in a shared simulated trading environment calibrated to hourly Bitcoin perpetual-futures returns. A GARCH(1,1) model supplies volatility clustering, while the environment charges for position changes and applies an…
The document explains a temporal convolutional network for predicting crypto perpetual funding premiums from a 60-settlement window. Four causal convolution blocks use a kernel of three and dilations of 1, 2, 4, and 8. Their receptive field spans 61…
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 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 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 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 document describes a dataset and workflow for studying cryptocurrency perpetual futures alongside their premium index. It outlines hourly OHLCV observations and eight-hour premium readings across a configured universe, with download, loading, filtering,…
This notebook walks through a deployment rehearsal for a crypto funding-rate direction model. It trains a three-class LightGBM model on historical Binance-derived perpetual data, then fetches live hourly bars and funding rates from OKX, aggregates data to…
This notebook assesses whether Binance perpetual-futures data can support a cross-sectional strategy that ranks contracts by premium and takes long and short positions. It does not fit a model or make forecasts. Instead, it checks the declared trading…
This configuration specifies a long-short strategy research workflow for crypto perpetual futures. It defines a 19-asset volume-selected universe, decisions aligned to eight-hour funding settlements, and execution at the funding timestamp. The primary target…
This demonstration describes the operational shape of a continuously running crypto strategy connected to a USD spot broker. It maps a larger perpetual-futures case-study universe to the smaller set of available spot pairs, then routes a momentum z-score…
This notebook trains a reinforcement learning agent to schedule a fixed sell order across hourly bars from a crypto perpetual futures market. Its state includes lagged returns, volatility, prior-hour volume, the perpetual premium to spot, and time until…
This evaluation notebook screens financial and model-based features for their relationship to the next eight-hour return of crypto perpetual futures. It builds the evaluation sample from walk-forward validation windows, keeps the final holdout outside the…
The document explains why TabM is used to model perpetual funding panel data: its shared neural backbone combines all input features, while nonlinear layers can represent interactions. Unlike a tree that chooses one feature at each split, this design can use…
This notebook studies gradient-boosted models for forecasting returns and direction in crypto perpetuals. Its grid varies tree capacity and loss function, including squared-error, absolute-error, and Huber objectives, while scoring models at multiple points…
This notebook estimates whether the premium z-score of a crypto perpetual affects subsequent eight-hour returns when three declared drivers—price volatility, funding rate, and average premium deviation—are held fixed. Because treatment is continuous, the…
This exploratory analysis profiles hourly OHLCV for crypto perpetual contracts alongside an eight-hour premium index. It examines symbol and date coverage, changing contract membership, premium units and distribution, OHLC integrity, missing values, and…
This notebook measures how execution costs affect the already selected crypto perpetual futures configuration. It holds the model, checkpoint, entry rule, allocation, and risk control fixed, then reruns backtests across a grid of round-trip charges on traded…