Pssst, You! The original story is on Medium! Please clap 50 times and share the article with a friend interested in investing, finance, or AI.
|
In the early days of October, I launched an unprecedented experiment. Harnessing the capabilities of OpenAI’s advanced APIs, we crafted a series of algorithmic trading strategies. These strategies were not only generated but were also refined through sophisticated optimization processes and deployed into the real-time trading fray. The previous update painted a rather modest picture of the ChatGPT-generated strategies, which at the time were lagging behind the market. Yet, as markets often go through remarkable transformations, so have the fortunes of our AI strategies. In this article, we delve into the updated performance of these portfolios, highlighting their newfound edge over the traditional Buy and Hold strategy.
|
How did our portfolios perform?
|
Buy and Hold SPY
The stock market has been especially volatile these past few weeks. The Fed’s decision to not raise rates has spurred a market rally, bringing our Buy and Hold SPY Portfolio to a much-anticipated break-even point.
|
ChatGPT-Generated Portfolio
Consistent with our previous updates, the unoptimized ChatGPT-generated portfolio remains inactive, having not executed any trades to date.
|
One and Done Optimized Portfolio
The ‘One and Done Optimized Portfolio’, established with a single optimization at the experiment’s inception and maintained without further adjustments, stands out with the most substantial percent gain among all our portfolios. Remarkably, it surpasses the Buy and Hold SPY strategy by a significant margin.
|
In a backtest, this portfolio even outperforms Buy and Hold TQQQ, the asset the portfolio is using to make trades.
|
(Note: Backtests don't include the current day's performance)
|
While it’s too early to make any conclusions of the efficacy of optimizing portfolios, it’s promising to see at least one optimized portfolio performing so well. If one combined the speed of generating portfolios with ChatGPT, the expertise of a professional trader, and the power of genetic optimization, one can imagine an unparalleled trading experience. This is very promising.
|
Sliding Window and Expanding Window Optimized Portfolios
The sliding window optimized portfolio is re-optimized periodically, incrementing the start and end date during each optimization. During a prior re-optimization process, the strategy that was generated was a very high-risk/high-reward trading strategy that didn’t pay off. Here’s how it’s doing now.
|
Similar to the sliding window optimized portfolio, the expanding window optimized portfolio has similar performance. This portfolio is also re-optimized periodically, but the start date remains constant across all subsequent optimizations.
|
These portfolios demonstrate that the optimization process is not magic, and one needs to pay attention to the parameters of the portfolio after re-optimizing. Is the portfolio making unnecessarily high risks? Are there sane contingency plans if things don’t go your way? All important questions one should ask themselves when using an optimized portfolio.
|
When I started this experiment, my goal was never to prove that ChatGPT is secretly a Wall Street trading prodigy. My objective was transparent from the onset: to leverage ChatGPT as a catalyst for accelerating the strategy development process. Armed with this AI, traders armed with inventive strategies can now rapidly prototype, test, and refine trading tactics, a feat that previously would have demanded an extensive background in programming and statistical analysis.
|
Consider the configuration process of the past. Traders were required to grapple with programming in languages like Python and C++, tackle advanced statistical analyses, and tediously embed their trading hypotheses into lines of code. This necessity is relegated to the past, as ChatGPT ushers in a no-code era, enabling traders to pivot their focus to the essence of their craft.
|
While it’s encouraging to observe some of our optimized portfolios surpassing traditional Buy and Hold approaches, we approach these results with skepticism. Optimization is inherently retrospective, enhancing performance based on historical data. Yet, past success is no crystal ball into future market dynamics, which are ever-evolving and unpredictable.
|
A Next Generation AI Platform
|
The AI-Powered Chat by NexusTrade is nothing less than a paradigm shift. Traders are empowered to generate and backtest strategies, as well as undertake exhaustive financial analyses. This chatbot is a key competitive edge that sets NexusTrade apart in the realm of algorithmic trading platforms.
|
The implications of an AI-Powered chat extend far beyond trading. Envision educators effortlessly crafting a week’s worth of lesson plans, unencumbered by the nuances of prompt engineering and RAG pipeline complexities. Picture businesses streamlining operations through chat applications that demand no coding expertise.
|
That’s the idea behind NexusGenAI. NexusGenAI will make it possible for non-technical users to develop complex chat bots in a simple, intuitive user interface. It will borrow many ideas from NexusTrade, and prioritize ease-of-use, configurability, and delivering value for its users. The website is (obviously) not refined, but if you’re interested in integrating AI for your personal or professional use-cases, subscribe to NexusGenAI to stay up-to-date with its progress.
|
Just like in the last article, it’s too early to draw any major conclusions from these results. Nevertheless, it’s interesting to see that a very simple strategy with two rules has the potential to outperform Buy and Hold. Imagine the possible results if the strategy had 20–30 unique trading rules, all configured by a professional trader. The possibilities are endless, particularly when a trader has AI on their side.
|
Thank you for reading! Stay tuned for our next update on these portfolios. Interested in applying AI to finance? Log on to NexusTrade today!
|
|
