Blog Posts

Tariffs are government-imposed taxes on imported goods that serve as both a revenue source and a strategic policy tool to influence trade balances, protect domestic industries, and respond to geopolitical tensions. In our current times, tariffs, debt cycles, monetary imbalances, geopolitical distrust, domestic political polarization, and deglobalization are converging to reshape capital flows, inflation dynamics, […]

When evaluating an income-producing investment like a bond fund, ETF, or mutual fund, you’ll often encounter three common yield metrics.  At first glance, they might look similar, but each tells a different story about the income potential and performance of the fund.  Let’s break down the 30-Day SEC Yield, Distribution Yield, and 12-Month Yield – […]

A situation where the S&P 500 and USD are down simultaneously is rare and potentially profitable if you know how to trade it.  This environment upends the usual risk-off playbook (long USD, long Treasuries, short risk), and that opens the door to some creative derivatives trades that capitalize on correlation shifts, macro shifts, and mispriced […]

All the methods discussed in our original article on reinforcement learning (value-based methods, policy-based methods, and model-based methods) are model-free: they directly learn from interaction with the environment without trying to understand how the environment works internally. In contrast, model-based RL involves the agent building or using a model of the environment’s dynamics. In trading, […]

The entire purpose of trading and investing is to earn more money back over time than is lost. At worst, it should preserve your purchasing power over time. Drawdowns are the one thing that should be avoided at all costs. Warren Buffett is known for his quotes on investing, one of which pertains to his […]

We look at the current level of participation by individual, or “retail,” traders and investors in the United States stock market.  We look at historical trends of retail investor involvement, current estimates of their market share, the impact of online trading platforms, and considers the activity of retail investors across major stock exchanges.  We’ll also […]

A variety of reinforcement learning algorithms have been explored for trading applications. Here, we look at key RL algorithms and how they apply to markets ranging from stocks and currencies to commodities and bonds.   Key Takeaways – Reinforcement Learning Algorithms for Trading RL algorithms help trading agents learn strategies that maximize returns by interacting […]

Reinforcement learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment through trial and error, receiving feedback in the form of rewards. In a trading context, the environment is the financial market, the agent’s actions might be buying, selling, or holding assets, and the reward […]

Trading the financial markets is a complex undertaking that involves managing a variety of trade-offs. These trade-offs are not merely binary choices but often exist on a spectrum between two or more interests or wants and across a variety of dimensions. Accordingly, traders have to balance competing interests, resources, and strategies. Understanding these trade-offs is […]

Imagine standing in a casino with rows of slot machines, each promising different payouts. Your goal?  Maximize your winnings without knowing which machine is “hot.”  This captures the essence of Multi-Armed Bandit (MAB) problems – a framework for balancing exploration (testing uncertain options) and exploitation (leveraging known winners).  In trading, MAB methods are a capital […]