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.
12 documents
This article brainstorms possible inputs for a crypto statistical arbitrage model. It covers relative price moves between similar assets, short and long horizon trends, crowded spreads that may unwind with momentum, lead-lag effects across markets, and…
The article introduces a lag-based estimate of the Hurst exponent and applies it to simulated mean-reverting data and adjusted SPY prices. The method compares the variability of price differences across a range of lags, fits a line to the log-scaled…
The article groups systematic strategies into three broad types, ordered by increasing turnover. Risk-premia harvesting seeks compensation for bearing risks that investors tend to avoid, using diversified exposure and sensible risk control; examples include…
The article argues that AI makes it easy to generate and test trading rules, but that speed also encourages data mining. Repeatedly changing parameters, filters, timeframes, or asset universes amounts to many hypothesis tests; a strong historical result can…
This article demonstrates a practical way to reduce trading costs in a crypto statistical-arbitrage portfolio: keep existing positions until they drift sufficiently far from their target weights. The example uses perpetual futures, excludes stablecoins, and…
This article curates books, papers, and course materials that the author found useful for learning algorithmic and quantitative trading. The recommendations are grouped into practical trading, foundational statistics and time series, machine learning,…
The document argues that mean reversion, momentum, and trend describe observed price behavior but do not by themselves establish a tradable edge. A credible hypothesis should pair supportive data with a plausible mechanism explaining who trades, why the flow…
This tutorial lays out a Zorro workflow for rotating among ETFs. It describes maintaining an instrument universe in an asset list, setting a calendar-based rebalance date, loading price histories, calculating each ETF’s lookback return, ranking the results,…
The article presents a workflow for studying and combining signals on Binance crypto perpetual futures. It examines carry from funding rates and cross-sectional momentum alongside a breakout measure based on closeness to recent highs. The author first…
The article explains how to express trading signals as expected returns, giving a common scale for comparing features and combining them with risk estimates and trading costs. Its example uses Binance perpetual futures and considers carry, short-term…
The article examines momentum as a way to time exposure to a diversified risk-premia strategy. It describes measuring each asset’s trailing six-month return, ranking assets, and rotating into the top four with weights inversely related to their volatility…
This brief excerpt raises the question of how a trader can tell whether a strategy has an edge. It points first to setting reasonable expectations for the profit and loss distribution, then to evaluating results after trading begins. It also suggests that…