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.
5,922 documents
The article argues that systematic traders should expect short-term randomness to obscure an edge, and avoid changing a strategy in response to every losing trade or market fluctuation. It frames the trader’s task as following a researched plan over time,…
The article compares systematic trading with discretionary order flow and chart analysis. It argues that these approaches seek the same underlying opportunity: a pricing inefficiency created when buying or selling pressure pushes a market away from a…
The article explains equal risk contribution (ERC) portfolio construction, which chooses asset weights so each holding contributes equally to portfolio risk. Because ERC depends on estimated covariances rather than expected returns, the quality of the…
The article explains how exponentially weighted moving averages (EWMAs) give more influence to recent observations while retaining a diminishing contribution from older data. It motivates the method with changing correlations between SPY and TLT:…
This short note lists ways traders can lose money: excessive trading increases fees and market impact, oversized positions can impair compounding or cause ruin, and shorting positive drift or risk premia can create persistent losses. It also cautions against…
The article frames long-term investing as earning compensation for bearing uncertainty. Stocks and bonds have historically risen over long periods, but their shorter-term losses and volatility help explain why investors expect a premium for holding them. It…
The article presents a framework for judging whether an observed market feature is likely to persist: consider its economic rationale, inspect historical evidence, check consistency across time, and compare across markets. It illustrates the process with…
This installment proposes converting signals from overlapping pairs into security-level signals. For each spread, its z-score becomes two opposing votes: the relatively rich ticker receives a positive signal and the relatively cheap ticker a negative one.…
The article treats trading as an operating business that must allocate limited capital, time, and skills across strategy research, infrastructure, reporting, accounting, and ongoing learning. Its guiding question is how to improve the trading setup in ways…
This article uses hypothetical investment paths to illustrate how compounding and randomness could shape an investor’s experience in Renaissance Technologies’ Medallion Fund. It describes a return and volatility scenario, then contrasts outcomes associated…
This article frames alpha as an opportunity created when traders transact at disadvantageous prices, and emphasizes understanding why they do so. Reasons include limited information or behavioral biases, binding risk or mandate constraints, and non-profit…
The article introduces rsims, an R package for fast portfolio backtests that emphasizes translating target weights into trades while accounting for costs and constraints. It describes a threshold rule: trade toward a target only when the current weight moves…
A no-trade region places a buffer around a strategy’s target position. The portfolio is left alone while its current holding remains inside the buffer, and a trade is made only after it moves beyond the boundary. With minimum commissions, the example rule…
The document presents statistical arbitrage as a broader portfolio problem than trading matched pairs. It ranks assets by expected cheapness or expensiveness, then builds long and short positions intended to capture relative value convergence while…
This article advises traders with small accounts to begin with comparatively simple, forgiving strategies that support consistent process-building and skill development. It cautions that niche, high-capacity-constrained opportunities may offer attractive…
This page catalogs a broad set of trading strategy case studies, including risk-premia portfolios, volatility strategies, equity and FX effects, crypto trades, and bond ideas. It points readers to related research, implementation examples, and tools, while…
This brief article outlines a portfolio philosophy built around collecting risk premia while allowing for the possibility that active signals or discretionary views will be wrong. It proposes selecting assets with positive carry and varied exposures to…
The article compares the equity risk premium (ERP), the expected compensation for holding risky equities, with the volatility risk premium (VRP), the tendency for implied volatility to exceed realised volatility. It frames the ERP as a long-term return…
The document presents a judgment-based framework for deciding whether to adopt a trading strategy, emphasizing that there is no universal performance threshold or checklist. The first question is whether the effect has a plausible explanation and a reason to…
The document explains the fallen angel effect: bonds downgraded from investment grade to high yield may face forced selling from institutions whose mandates restrict junk bond holdings. That selling can push prices below their reduced fundamental value,…
This article develops intuition for using convex optimisation to turn forecasts into portfolio positions under practical constraints. It begins with a long-only, unlevered return-maximisation example, then adds existing holdings and transaction costs to show…
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 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 essay argues that systematic traders should investigate a market effect before building elaborate backtests, optimized rules, or machine learning systems. It recommends forming small, falsifiable hypotheses and using direct, data-efficient analysis such…