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.
1,124 documents
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 study uses ten trading days of NVDA market-by-order data to examine how daily trading activity affects bar sampling. It filters trades to regular US market hours, classifies aggressor sides from venue labels, and reports day-to-day variation in trade…
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 published factor returns as potential portfolio building blocks. It separates three questions: whether a factor’s average return clears a demanding statistical bar, whether factors diversify one another for reasons that may persist,…
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 assembles a forecasting system with four stages: a panel of research agents, probability aggregation, an adversarial debate, and a supervisor that identifies disagreements, searches for clarifying evidence, and issues a final probability. The…
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 demonstrates training a generative adversarial network on engineered ETF features with differential privacy. It introduces the privacy parameters epsilon and delta, explains why DP-SGD clips each record’s gradient before aggregation and adds…
This notebook applies classical ordinary least squares inference to ETF features and forward returns. It reviews coefficient estimates, standard errors, t statistics, p values, and tests for heteroscedasticity, residual autocorrelation, non-normality, and…
This document describes utilities for loading parsed NASDAQ ITCH messages and rebuilding a limit order book from order additions, deletions, cancellations, executions, and replacements. It tracks remaining shares per order so later book updates use the…
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 compares time bars with tick, volume, dollar, imbalance, and run bars built from a single day of AAPL exchange trades. The first four sampling approaches group observations by elapsed time, trade count, shares traded, or notional traded.…
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 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…
This overview organizes a set of trading case studies by asset class, universe size, data frequency, decision cadence, cost regime, and evaluation protocol. It reads each study’s declared setup and presents comparative tables, including training and…
This document defines two diagnostics for labels whose forward return windows overlap: pooled panel autocorrelation and effective sample size based on label uniqueness. Autocorrelation pairs observations only within the same entity and at the requested…
This notebook shows how to use an estimation-window correlation network to describe an equity universe and build a network-diversified portfolio. It selects liquid symbols using only data available before evaluation, converts return correlations to…
This notebook builds an LSTM allocator that maps sequences of ETF returns and rolling volatility features directly to long-only portfolio weights. A softmax output keeps weights nonnegative and fully invested. Instead of forecasting returns and feeding those…