This episode focuses on building a momentum-based stock selection model, expanding on the idea that momentum is a sustainable investment approach. It details how to calculate generic momentum using past stock returns and explores three types: short-term, long-term, and intermediate-term momentum, noting that intermediate-term momentum (6-12 month look-back) exhibits continuation of returns, unlike the reversals seen in short-term and long-term. The source also discusses maximizing momentum by considering "path dependency" (smooth vs. jumpy price movements) and incorporating seasonality (quarter-ending and year-end effects) into rebalancing strategies. Finally, it presents a "Quantitative Momentum" process that integrates these elements, analyzes its historical performance and risks, and suggests combining it with value investing for a more diversified and robust portfolio

This episode explores the concept of momentum investing as a stock-selection tool, contrasting it with traditional technical and fundamental analysis. It argues that both fundamental (value) and technical (momentum) strategies can be effective, driven by investor behavioral biases and limits to arbitrage for "smart money." The text highlights that momentum investing is distinct from growth investing, demonstrating that historically, momentum has yielded superior returns with comparable or even lower risk. Ultimately, the source advocates for a combined value and momentum strategy, suggesting that this blend offers enhanced risk-adjusted returns and a smoother investment journey across various global markets

This episode outlines crucial strategies for managing futures trading, moving beyond market prediction to focus on efficient and safe execution. It first addresses contract selection and rolling, detailing the complexities of choosing expiry dates, assessing liquidity and risk, and the methods for transitioning between contracts. Next, execution tactics are discussed, emphasizing the importance of minimizing trading costs through advanced measurement, optimal timing, and the use of execution algorithms over simple market orders. The text then covers cash and compounding, explaining proper account capitalization, managing free cash, and different compounding approaches like full and half compounding, highlighting their impact on risk and returns. Finally, the source thoroughly details risk management, including strategies for handling margin issues, implementing an exogenous risk overlay to control portfolio, jump, correlation, and leverage risks, and setting comprehensive position and trade limits to safeguard against unforeseen problems

This episode introduces relative value (RV) strategies, which involve forecasting the price movement of one asset relative to another, rather than predicting individual asset prices. It categorizes these strategies by the type of assets (across instruments or different expiries of the same instrument) and the number of markets included (spread trades with two markets or triplet trades with three). The text then explores spread instruments, explaining how they are created by taking a long position in one instrument and a short position in a related one, and detailing calculations for their price, risk, position sizing, and costs. It also discusses cross instrument triplets, an extension of spread trading involving three instruments, and calendar trading strategies, which focus on relative value within different expiries of the same instrument. Throughout, the text highlights the increased leverage, costs, and tail risk associated with RV strategies compared to directional trading, while also noting their potential for diversification within a portfolio

This episode introduces fast directional strategies, focusing on mean reversion using hourly data, a significant departure from the slower, daily-data-driven strategies discussed earlier in the book. It highlights that markets are not fractal, meaning different trading strategies are effective at varying time scales, with mean reversion often performing well over short durations (days to less than a second) and trend following over intermediate periods (weeks to months). A key challenge with these fast strategies, particularly mean reversion, is managing trading costs, which are mitigated through smarter execution tactics involving limit orders. The text then presents Strategy Twenty-Six: Fast Mean Reversion, detailing its methodology and performance, which, while profitable, suffers from negative skew and poor performance during high volatility. To address these issues, Strategy Twenty-Seven: Safer Fast Mean Reversion is introduced, incorporating trend-following overlays and volatility-based position size reduction to improve profitability and reduce risk, resulting in a more robust and safer strategy despite increased complexity

This episode introduces advanced directional strategies for futures trading, building upon a position management framework that quantifies predictions of future risk-adjusted returns. It outlines four distinct strategies: Breakout, Value, Acceleration, and Skew, each designed to exploit different sources of returns beyond basic trend and carry. The text provides detailed methodologies for constructing forecasts for each strategy, including scientific methods for determining price ranges, measuring underperformance, and assessing the rate of change in trend forecasts, and converting quantifiable data into tradeable forecasts. Furthermore, it discusses the evaluation and combination of these strategies, highlighting their individual performance, correlations, and the benefits of diversification within a portfolio. Finally, it presents Strategy twenty-five: Dynamic Optimisation, a sophisticated method for traders with limited capital to approximate the diversification benefits of a large portfolio by dynamically selecting optimal integer positions, complete with a detailed explanation of its underlying algorithms and cost considerations

This comprehensive episode explores advanced trend following and carry strategies for financial trading, building upon foundational concepts from an earlier part. The document details specific strategies (twelve through twenty) that either modify existing trend and carry approaches or introduce new standalone methodologies. Key modifications include adjusting trend forecasts based on reversal probabilities, adapting both trend and carry strategies to different volatility regimes, and utilizing synthetic spot prices for trend analysis to separate carry's influence. New standalone strategies presented focus on normalised trend, asset-class specific trend following, cross-sectional momentum, and cross-sectional carry, each evaluated for its standalone performance and its integration into a diversified portfolio. The author also offers insights into the predictability of strategy performance and the relative benefits of diversifying across instruments versus adding more trading rules

This comprehensive text explores systematic trading strategies for futures contracts, offering in-depth reviews of Robert Carver's books on the subject. It meticulously breaks down trading costs, profit calculations, and risk measurement metrics like standard deviation and Sharpe ratio, applying these to various assets such as S&P 500 micro futures and US 10-year bond futures. The source then progresses through four main trading strategies: a basic buy-and-hold, a fixed risk targeting approach, a variable risk targeting method, and a diversified portfolio strategy utilizing "risk parity" and "All Weather" allocations. Finally, it elaborates on trend-following strategies, including moving average crossovers and exponentially weighted moving averages across different speeds, before introducing a "basic carry" strategy and its combination with trend following for enhanced portfolio performance and diversification benefits

this episode describe several options arbitrage strategies, which exploit temporary mispricings in financial markets to achieve risk-free profits. These strategies include boundary arbitrage, focusing on single option price violations; vertical spread arbitrage, addressing inconsistencies between two options of the same type and expiration; convexity arbitrage, targeting non-convex pricing across three options of the same type and expiration; parity arbitrage, which leverages deviations from the put-call parity relationship using a call, a put, and the underlying asset; and box arbitrage, combining two call spreads and two put spreads to effectively create a risk-free loan. Each strategy details its mechanics, data requirements, opportunity detection logic, and crucial risk considerations for real-world implementation

This paper focuses on volatility trading strategies for individual investors using VIX-linked Exchange Traded Notes (ETNs). It begins with a historical overview of volatility as a tradable asset, from early option pricing models to the introduction of VIX futures and ETNs, acknowledging significant market events like "Volmageddon." The core of the paper introduces four progressively sophisticated rule-based strategies designed to capture the volatility risk premium (VRP) by trading VIX ETNs. These strategies evolve from a simple passive short-volatility approach to a dynamic model that integrates expected VRP, the VIX term structure, and adaptive position sizing, significantly improving performance metrics such as CAGR and Sharpe ratio while reducing correlation to equity markets. The authors also provide a Python workflow for automating the most advanced strategy via Interactive Brokers, demonstrating the accessibility of such quantitative approaches to retail traders. The paper concludes by emphasizing the importance of education and discipline in navigating the risks and opportunities presented by modern volatility trading instruments