The source material offers an extensive discussion of quantitative investment strategies, particularly focusing on trend following and managed futures. Several sections are dedicated to demonstrating the robust historical performance of trend following over centuries across diverse asset classes, highlighting its strong Sharpe ratio and diversification benefits compared to traditional buy-and-hold strategies. Other key themes include the critical need for multiple testing correction in statistical analysis of trading strategies to avoid false discoveries and the importance of implementing rigorous risk management techniques, such as optimal betting using the Kelly Formula and understanding the implications of skewness and kurtosis in portfolio construction

This episode provides a collection of interviews and commentary from prominent figures in the world of systematic and quantitative finance, primarily focusing on trend following and managed futures strategies. Key figures like Ed Seykota, Martin Lueck, Jean-Philippe Bouchaud, Ewan Kirk, Alex Greyserman, Campbell Harvey, and Lasse Heje Pedersen discuss the philosophical and technical underpinnings of their systematic approaches, often drawing connections between mathematics, physics, and behavioral economics. A major theme explored is the importance of removing human emotion and discretion from trading through rigorous back-testing and systematized risk management, in contrast to traditional discretionary trading. The experts also address the benefits of diversification across numerous markets and the persistent challenge of distinguishing genuine skill from luck in investment performance

This episode offer an extensive exploration of trend following, a systematic trading strategy focused on price action rather than fundamental analysis or prediction. The text covers core trend following principles, emphasizing the importance of cutting losses quickly, letting profits run, and accepting small, frequent losses as the cost of finding large trends. It also features interviews and insights from prominent, successful trend followers like Ed Seykota, John W. Henry, and Bill Dunn, highlighting their reliance on mechanical, quantitative systems and risk management. Finally, the sources contrast trend following with conventional finance theories like the efficient market hypothesis and buy-and-hold strategies, asserting that trend following thrives during unpredictable market crises and "black swan" events

The academic paper investigates stock price prediction in Korean markets by comparing deep learning models that use only raw OHLCV (open-high-low-close-volume) data against traditional machine learning models utilizing extensive technical indicators. The authors employ triple barrier labeling to generate classification targets for the prediction task, optimizing the parameters to a 29-day window and 9% barriers for a balanced label distribution. A key finding is that a simple Long Short-Term Memory (LSTM) network trained on raw OHLCV data achieves performance comparable to highly optimized traditional models like XGBoost that rely on engineered features. Furthermore, the study identifies that the optimal model performance results from a specific joint optimization of the input window size and the LSTM hidden size, specifically a window of 100 days and a hidden size of eight. This research challenges the conventional emphasis on complex feature engineering in financial forecasting by demonstrating the sufficiency of raw data with appropriate deep learning architectures

This episode offer an extensive exploration of statistical distributions characterized by fat tails, by NASSIM NICHOLAS TALEB, focusing on their implications for statistical inference, risk management, and decision making. The core argument criticizes the reliance on traditional statistical methods, such as those based on the Gaussian distribution or the Central Limit Theorem, arguing these are inadequate for phenomena featuring extreme events or Black Swans. The text introduces alternative concepts like Preasymptotics, Stable (Lévy stable) Distributions, and the use of the Mean Absolute Deviation over the unstable standard deviation for fat-tailed variables. Furthermore, the material applies these principles to real-world issues, including the pricing of financial options, the analysis of war casualties and pandemics, and critiques of professional practices like rent seeking in academia and naive empirical methodologies

This episode offers an extensive exploration of the complex world of trading, modeling, and organizational alignment, drawing upon principles from economics, philosophy, sociology, and technology. It begins with a philosophical discussion on models as useful simplifications of reality, distinguishing between generative and phenomenological models, and examining the dangers of mistaking a model for the actual truth, particularly in the context of financial crises like the one in 2007–2008. The source then transitions into analyzing costs and capacity in trading, covering different types of costs and the phenomenon of trade depreciation, before exploring the concept of possibility in markets, cautioning against assuming the non-occurrence of improbable, but not impossible, events, exemplified by the 1987 market crash and the Swiss franc unpegging. Finally, the text addresses the importance of alignment and incentive structures within high-performance organizations, especially trading firms, to mitigate agency problems, and concludes with an examination of technology's transformative role and the necessity of human-machine symbiosis and organizational adaptation in a rapidly evolving, competitive environment

This episode discusses foundational concepts in financial markets and decision-making, arguing that everyone is inherently a trader by nature of daily risk and reward assessments. Key themes explored include the problem of adverse selection due to information asymmetry, the necessity of risk management and hedging for traders, the critical importance of understanding liquidity in both financial instruments and everyday life, and the nature of "edge"—the unique knowledge or capacity that enables profitable trading in competitive environments. The text emphasizes that cognitive biases, such as overconfidence and confirmation bias, must be mitigated through rational decision-making processes like precommitment and rigorous review, which apply universally to decisions from buying a car to choosing a job

The provided document details how OpenAI utilizes its Codex system across various engineering teams, including Security, Frontend, and Infrastructure, to expedite numerous development tasks. Engineers use Codex daily to accelerate processes such as understanding complex codebases, executing large-scale refactoring and migration efforts, and improving performance by identifying and optimizing slow code paths. The system is also employed to enhance test coverage, increase overall development velocity by scaffolding new features, and help engineers maintain focus amidst fragmented schedules. Additionally, the document outlines several best practices for prompting Codex, such as starting with an implementation plan using "Ask Mode" and structuring prompts like a Github issue, to maximize its effectiveness

This episode offers an in-depth guide to portfolio rebalancing and repair, emphasizing the importance of minimizing trading costs against the uncertain benefits of adjustment. It details techniques for managing portfolio drift, which occurs due to price changes or dividends, by employing a No-Trade-Zone and a Minimum-Trade-Size to reduce turnover and transaction fees. The document also addresses complex rebalancing scenarios, such as asset substitution due to factors like model selection changes or corporate takeovers, outlining a cost-benefit analysis that incorporates tax considerations and the Rule of 20 for calculating the present value of future benefits. Finally, it presents a prioritized six-step plan for portfolio repair, aimed at correcting severely misaligned portfolios by first addressing major issues like under-diversification and high costs

This episode provides an extensive analysis of various investment strategies, moving from the initial necessity of establishing a strategic asset allocation without forecasting to exploring methods for predicting risk-adjusted returns. It introduces simple systematic forecasting models, specifically momentum and dividend yield models, as superior to human intuition for adjusting portfolio weights and selecting assets, offering detailed procedures and examples for their implementation across different portfolio levels. Additionally, the text critically examines the value of active fund managers and smart beta funds, concluding that their often-higher costs are rarely justified by statistically significant outperformance compared to cheaper, passive alternatives. Finally, it cautions against the use of robo advisors due to their costs and potential lack of flexibility or flawed optimization