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.
132 documents
The document presents instrument groupings from a correlation-based clustering analysis of a broad futures universe. It compares solutions with different cluster counts, from two through ten, and interprets the resulting groups as risk-on, risk-off,…
This article considers whether a trading system should maintain a fixed expected portfolio risk or allow risk to vary with signal strength. The author's system targets a long run average volatility, while its daily expected risk varies with both aggregate…
This article evaluates a hierarchical, handcrafted method for allocating weights among trading rules and instruments. The method is intended to be transparent enough for spreadsheet implementation, grounded in theory, and responsive to uncertainty in…
The article compares trend following and mean reversion across holding periods, drawing on the author's earlier tests and a cited study spanning minutes to decades. Its broad synthesis is that mean reversion appears at horizons beyond roughly two years and…
This document describes operational checks for a systematic futures trading system. It compares monitoring displays to vehicle indicators: simple status lights, warnings, variable metrics, and interactive reports. The system logs timestamped messages by…
The article examines how geometric returns relate to compounding, diversification, and portfolio construction. It considers the claim that diversification can justify additional costs and argues that an all-equity portfolio may be inferior to one that…
The document explains why traders need detailed profit and loss records: to assess results, attribute performance by instrument or strategy, compare live trading with simulations, monitor costs and realized risk, support client reporting and taxes, and scale…
This article develops a simple breakout trading rule and evaluates different lookback speeds across a set of futures markets. It discusses forecast scaling, turnover, and how trading costs can make the fastest breakouts impractical. The author notes that…
The article tests whether trading rules should adapt as volatility changes, using historical volatility divided by its rolling ten-year average to classify market conditions. It compares momentum and carry rule performance across volatility groups, then…
The document compares the strengths of automated systems and human traders. It credits computers with speed, consistency, disciplined execution, position scaling, portfolio management, and detecting persistent or unintuitive patterns. It describes people as…
The article examines a performance measure based on the highest geometric return achievable at a strategy's optimal leverage. Under Gaussian returns and unrestricted leverage, it explains why Sharpe ratio can determine the preferred strategy, while a nonzero…
This document lists recurring trading errors, including failing to define a system, abandoning stop losses, risking too much capital, setting stops without sound money management, relying on fixed profit targets instead of trailing stops, trading…
The post investigates why strong risk-adjusted trading forecasts can have weaker subsequent outcomes than a linear relationship would imply. Since forecasts divide expected return by recent volatility, a strong signal can reflect unusually low volatility as…
The post compares two ways to estimate volatility: standard deviation of percentage returns and standard deviation of absolute price changes. For futures, it recommends forming percentage changes with back-adjusted price differences in the numerator and the…
The post explains how to examine performance by trading rule within a dynamically optimized strategy. Because positions depend on optimization, instrument selection, capital, and contract rounding, the author uses static-portfolio estimates as a proxy for…
This article uses bootstrap resampling to examine uncertainty in portfolio statistics and allocation decisions. Resampling observed returns with replacement creates alternative histories and a distribution of estimates, rather than a single point estimate.…
This article explains how bootstrap resampling can represent uncertainty in estimates used for portfolio decisions. Instead of relying on one calculated mean or on returns generated from an assumed distribution, it repeatedly samples observed returns with…
This article tests whether recent volatility levels relate to next-month risk-adjusted returns across futures markets. It builds a relative-volatility measure by dividing estimated volatility by a long-run exponential average, then compares next-month…
This article considers how much of a portfolio to allocate to trend following when historical returns may overstate future opportunities. It compares a 60:40 US equity and bond portfolio with slow and faster trend strategies trading equity and bond futures.…
The post examines whether prediction markets could support lottery-like bets with less of the expected loss imposed by a conventional lottery. It explains that a market could let participants take opposing sides on number combinations, spreading the risk…
This technical guide explains how a Python client for Interactive Brokers can resolve futures contract details, submit market and limit orders, and modify or cancel open orders. It describes tracking order identifiers and listening for broker callbacks,…
This study compares portfolio optimization methods using real trading-rule returns. Each trial samples nine rules from one instrument, varies the available in-sample history, and evaluates performance out of sample. Methods include mean-variance portfolios…
The post sketches a short-horizon futures mean-reversion scalper built around symmetric bracket limit orders. It models the strategy as a state machine: after an entry fills, the bot protects the position with a stop while retaining a profit-taking order,…
This annual review reports portfolio-wide and futures results for the UK tax year, separating mark-to-market performance, interest, fees, commissions, and slippage. It also distinguishes pure futures returns from gains and losses associated with cash-like…