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I consider myself to be a pretty smart guy. In high school, my GPA exceeded 4.0. I hold degrees from esteemed institutions such as Cornell and Carnegie Mellon, and I excelled in challenging subjects like organic chemistry, genetics, and calculus.
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Yet, even for me, algorithmic trading was very hard. It demanded proficiency in software architecture, data science, and finance, all before one could even begin to craft their first algorithm. However, recently, the landscape has undergone a transformative shift, thanks to the emergence of Large Language Models (LLMs). Platforms like NexusTrade, Pluto, and Composor have effectively torn down the barriers to algorithmic trading, making it accessible to the average person.
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This article will delve into this transformative journey, shedding light on the evolution of algorithmic trading into a realm that is now within reach for the everyday person.
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The Unattainable Thresholds of Traditional Algorithmic Trading
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Option 1: Self-Programming
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Originally, the primary route to algorithmic trading was self-programming. This required proficiency in programming languages such as Python, along with libraries like Pandas, Numpy, and Matplotlib. Starting from scratch, one had to not only master these tools but also understand financial markets, strategy development using fundamental or technical data, and complex concepts like lookahead bias. Additionally, gaining access to and integrating API keys for backtesting and executing trades was a necessity.
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Option 2: Using a Framework
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The second option involved using pre-built frameworks. These frameworks, though somewhat more accessible than starting from scratch, still demanded significant coding skills. For example, Quantopian was a powerful framework, but it still required you to code in their proprietary Python-like language.
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In both cases, you needed technical expertise. The average investor simply didn’t have the resources to learn algorithmic trading. Most investors simply didn’t care; they don’t understand why technology is useful when aiding in trading, even though the use of it is ubiquitous among institutional investors, prop shops, and hedge funds. There was a large gap between institutions and retail.
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The Unattainable Thresholds of Traditional Algorithmic Trading
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Large Language Models (LLMs) inadvertently changed the game for algorithmic trading. The requirement that you had to be a technical expert to start algorithmic trading is now gone. Platforms like NexusTrade use LLMs to translate plain English into actionable trading strategies, allow you to backtest those strategies, and perform financial research.
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Now, a user who wants to perform financial analysis can simply ask an AI Agent to retrieve the relevant information and display it on an easy-to-read UI.
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This process streamlines the investment decision-making process. After performing your financial analysis, a user can create a trading strategy directly in the chat.
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They can then backtest and deploy their strategy live to the market with the click of a button.
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Take a moment to appreciate how easy this process is. The investor doesn’t have to concern themselves with how to code up a trading strategy or connect to their portfolio using an API. They can simply express their ideas in plain English, test them in the UI, and then click a button to make their portfolios execute trades.
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Large Language Models weren’t built so that traders and investors could have an easier time. Yet, this technology has invaded the world of finance and brought about a transformational shift. For the first time ever, retail traders have access to powerful financial analysis tools that were traditionally only available to hedge funds and prop shops. Whether or not retail takes advantage of this technology — that remains to be seen.
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