Diving into the world of quantitative investing, recommendation engine design, and poker gameplay has become a fascinating journey for me recently. My quest involves not only comprehending these seemingly disparate subjects but also finding ways to leverage them profitably. It was during a recent poker event in Cyprus, as I relished the games that a particular thought began to occupy my mind. I pondered the connections that bind these seemingly disparate topics, the common threads that unite them, and the distinctions that set them apart. What emerged from my contemplation was a realization that the underlying commonality among them is the art of strategy crafting. In this article, I’ll explore how strategy design serves as the linchpin that unites quantitative investing, recommendation engine development, and poker gameplay.
Making the Connection
In the realm of quantitative investing, every decision to buy or sell a financial instrument resembles a hand in a poker game. Each trade comes with potential rewards. Quantitative trading deals with a multitude of decisions happening concurrently across various markets within a specific time frame, creating a wealth of observations and data points. Just like in poker, each bet carries its own set of odds, risks, and potential rewards. In this context, trading becomes a continuous series of poker hands, each trade representing a new hand. The key distinction between quantitative and traditional trading here is that in quantitative trading, we often deal with a high volume of decisions that can occur simultaneously across multiple markets within a specific time frame. This abundance of decisions results in better probabilistic modeling on a wealth of observations and data points, unlike traditional trading, which may involve fewer samples and hands.
Recommendation engines, on the other hand, can be compared to a poker game with a unique twist. Let’s Consider each user’s visit to an e-commerce site represents a poker hand, and the goal is to recommend items persuasively enough to encourage purchases. The ‘winning hand’ in this context signifies a successful recommendation that leads to a purchase. What’s intriguing is that recommendation engines deal with thousands of ‘hands’ daily, and their success is influenced by recommendation items, similar to how poker strategy plays a crucial role.
This way, we’ve drawn parallels between quantitative investment, recommendation engines, and poker, emphasizing that continuous decision-making is key.
Evaluating Decision Quality
In the context of the poker analogy, it becomes clear that evaluating a player’s strategy cannot be based solely on the results of one or two individual hands. Just as Annie Duke emphasizes in her book “Thinking in Bets,” winning a poker hand doesn’t necessarily mean that the decisions made were optimal, and conversely, losing a hand doesn’t always indicate a poor decision. The outcome of a single hand can be influenced by a myriad of factors, including luck. Individual hand outcomes can be influenced by many factors, including a mysterious factor named luck!
Therefore, when it comes to assessing the quality of decisions, a different perspective is needed. To assess decision quality effectively, the focus must shift from individual observations to strategy evaluations. To truly understand the effectiveness of a strategy, you need to consider a more comprehensive view of decision-making, one that encompasses a large number of observations and the overarching goals of the strategy.
It’s worth noting that distinguishing between evaluating individual observations and strategy evaluations is indeed a challenging aspect of decision-making in these domains. In future blog posts, I’ll delve deeper into this topic. For now, we can simplify the understanding that strategy evaluation is distinct from assessing individual observations and is integral to achieving better decision quality. Distinguishing between assessing observations and strategies can be hard to understand intuitively, but it’s essential in these domains.
The Role of Expected Value (EV)
Expected value (EV) is a central concept in quantitative poker strategies, quantitative trading, and recommendation engines. It involves considering the potential outcomes of actions and assessing whether they yield a profit over time.
Quantitative Poker Strategies:
In the realm of quantitative poker strategies, the concept of expected value (EV) plays a pivotal role. When making decisions in poker, you often have to consider the potential outcomes of your actions. For instance, deciding whether to bet, raise, or fold is akin to making a financial investment. Each action carries its own set of odds and potential returns. By calculating the expected value of these actions, you can make informed decisions. A positive EV implies that, over time and many hands, your strategy will likely yield a profit. This notion of EV provides a framework for designing and optimizing poker strategies.
Quantitative Trading:
Just as in poker, quantitative trading strategies rely on the concept of expected value. When you decide to buy or sell a financial instrument, you essentially place a bet on the future price movement. For your trading strategy to be successful in the long term, the EV of your trades should be positive. While EV alone may not dictate the fate of your entire portfolio, it is a controllable parameter that significantly influences the direction your portfolio takes. By consistently aiming for positive EV trades, you increase the likelihood of your portfolio heading to more favorable destinations.
Recommendation Engines:
Recommendation engines operate based on pre-defined policies that are often influenced by expected values. These policies are shaped by your objectives and the potential rewards. For example, you might have a policy aimed at increasing your income through recommendations. This policy could involve deciding whether to recommend a large number of cheaper items or a smaller number of higher-priced items. To make this choice, you can estimate the expected value based on factors such as the chance of a user accepting your offer (conversion rate, driven by a probability distribution) and the expected price of the items sold (average selling price). Calculating the EV of your recommendation strategy helps you determine whether your approach aligns with your income objectives and if it’s likely to be successful.
In all these domains, the focus on expected value as a key parameter for decision-making and strategy design underscores the importance of aligning your actions with the potential for positive long-term outcomes, whether in poker, quantitative trading, or recommendation engine policies. By doing so, you increase the odds of achieving your goals and making profitable decisions. Consistently focusing on expected value is crucial for aligning your actions with the potential for positive long-term outcomes in poker, trading, or recommendation engines.
Expressing Strategies
As we’ve observed in our previous examples and across various contexts, we’re not simply implementing decision-rule systems when crafting strategies. Rather, we engage in an intricate process of strategy execution. These problems can be effectively represented with the following framework:
‘We aim to OPTIMIZE a specific OBJECTIVE over a defined TIME PERIOD.’
This formulation guides strategy development by emphasizing the importance of defining precise objective functions and timeframes. In essence, we define our strategies by filling in this sentence. Here are a few illustrative examples:
- Maximizing the Number of Winning Poker Hands in a Year
- Minimizing Losses in the Upcoming Month (Quantitative Trading)
- Maximizing User Engagement in the Next Quarter (Recommendation Engines)
The common thread is that, in strategy design, we are working to ‘Optimize’—whether it’s maximizing or minimizing—’Something’ that aligns with a pre-defined objective. The timeframe, denoting when we seek to achieve these objectives, is equally critical.
This leads us to a pivotal realization: Before tackling our problems, during the stages of problem definition and modeling, we need to craft precise objective functions and establish the designated timeframes. This sets the foundation for effective strategy development and ensures we’re on the right track to meeting our goals.
Objective Functions in Strategies
The design of objective functions is crucial in strategy development. For instance, strategies aiming to maximize user engagement and user average order value are distinct, emphasizing the need for a tailored objective function. In other words:
Decisions From (Maximizing User Engagement in the Next Quarter ) ≠ Decisions From (Maximizing User Average Order Value (AOV) in the Next Quarter ) (Recommendation Engines)
This implies that you cannot expect a strategy designed to maximize user engagement to maximize user AOV automatically, and vice versa. In other words, strategies tailored to achieve specific objectives may lead to vastly different results. Similarly, in the context of poker, a strategy that maximizes the number of winning hands may not necessarily align with a strategy designed to maximize your income.
Therefore, the crux of the matter lies in formulating an appropriate objective function tailored to the problem. Designing the right objective function is the pivotal initial step in defining and modeling the problem effectively. It acts as a compass, guiding your strategy development process and ensuring that your efforts are aligned with the desired outcomes. By carefully crafting your objective function, you set the stage for strategy success in achieving your predefined goals.
Time Frame in Strategies
The timeframe is equally critical, as it influences the number of opportunities or hands you will have to play and, subsequently, your strategy’s feasibility.
the determination of the specific period of time is as crucial as the design of the objective function. It’s a concept that should not be underestimated. This aspect of time plays a significant role in strategy development.
It’s essential to recognize that a strategy aimed at maximizing an objective over a given period cannot simply be broken down into smaller pieces and then combined to achieve the same result. For example, a strategy focused on maximizing earnings in poker, trading, or recommendation engines over the course of a year cannot be equated to merely repeating a strategy designed for a single day 365 times.
The mathematical rationale behind this lies in the fact that when you define a specific time frame for your strategy, you are implicitly determining the number of opportunities or hands you will have to play. By specifying the number of hands, you also influence the confidence level in reaching your estimated expected values. This time-based constraint has a profound impact on the feasibility and effectiveness of your strategy. For a more detailed exploration of this concept, I encourage readers to visit your provided link at https://rezafahmi.com/2023/06/27/feasibility-of-arbitrary-return. It delves into the feasibility and implications of arbitrary returns in greater depth, shedding light on the intricate relationship between time, strategy, and expected values.
In conclusion, crafting the right objective function and defining a suitable timeframe are paramount for strategy success.
The Power of Diversification
Diversification is a powerful concept in investment, spreading investments across assets to reduce risk and optimize returns. This principle applies to poker and recommendation strategies as well. Diversifying strategies across poker tables or recommendation algorithms can mitigate risk and improve overall performance.
Drawing parallels to other contexts, such as poker and recommendation strategies, we can infer that diversification can play a valuable role there as well. In poker, diversifying your strategies across multiple tables can help you spread risk and potentially improve your overall performance. Each table represents a unique set of players, dynamics, and opportunities. Diversifying your approach can reduce the impact of a single unfavorable table or hand on your overall results.
In the realm of recommendation engines, diversification can involve employing multiple strategies or algorithms to target different user segments or preferences. This approach can increase the chances of successfully persuading users to engage with your platform and make purchases, ultimately aligning with your predefined objectives.
Diversification is a fundamental principle in various domains, enabling you to achieve your goals more effectively while reducing exposure to undesirable outcomes.By recognizing the common threads of strategy, expected value, and diversification in these diverse fields, you’ll be better equipped to navigate the challenges and make profitable decisions.
Conclusion
In conclusion, the intricate web of strategy, the ever-present expected value, and the powerful tool of diversification weave together these seemingly distinct domains of quantitative investing, recommendation engines, and poker. Through the lens of strategy, we’ve unraveled the continuous nature of decision-making and the imperative need to evaluate strategies rather than individual observations.
Expected value, our guiding star, shines brightly across these fields, shaping decisions and nudging us toward long-term success. Whether it’s in the trading world, the poker table, or the realm of recommendation engines, a focus on expected value empowers us to make informed choices and increase our chances of achieving our objectives.
As we’ve explored, expressing strategies mathematically and defining the right objectives and timeframes are pivotal steps in the strategy development process. It’s akin to charting a course—precise objectives serve as our compass, ensuring we’re on track to meet our goals effectively.
