Deploy LLM-Powered Algorithmic Trading Agents for Maximum Profit

Sahaj Godhani
Coinmonks
Published in
7 min readOct 18, 2024

--

AI Engineer by SAHAJ GODHANI Published 18th OCT 2024

algorithmic trading

I began my trading journey as what I now call a Category 1 trader, also known as an uninformed investor. This group typically formulates their trading strategies based on tips from online platforms such as Reddit and TikTok. In most cases, they are gambling rather than trading, and at worst, they fall prey to scams. Their decisions are driven by speculation and luck, with little reliance on informed market analysis. This approach often leads to consistent losses. Instead of reflecting on their errors or adjusting their strategy, many Category 1 traders blame external forces like market makers, perpetuating the belief that the market is rigged against them.

Determined to profit from trading, I gradually improved my approach. I transitioned into what I call a Category 2 trader, or a systematic trader. These traders have moved past mainstream opinions and online hype, realizing that those strategies don’t work. A hallmark of a Category 2 trader is the use of trade journals to reflect on decisions and improve strategies over time. Embracing technology and trading APIs for efficiency, they focus on a more structured approach. However, unpredictable market swings can still trigger emotional decisions, leading to panic and regret when the market stabilizes.

Algorithmic Trading

Many traders quit after reaching Category 2 because they remain unprofitable. However, those who push forward evolve into the ultimate trader: the Category 3 trader, also known as the algorithmic trader. These traders don’t just use computers to execute trades; they leverage algorithmic strategies to formulate them. They master strategy optimization, understand how to prevent overfitting, and design generalizable trading strategies that can adapt to various market conditions.

The key to their success lies in their rigorous, systematic approach. They use tools like backtesting, which simulates historical returns to validate hypotheses and engage in paper trading to refine their edge before risking real capital. Unlike earlier-stage traders, they don’t panic during sudden portfolio drops because they understand that it’s mathematically impossible to win every trade. These are the traders who are consistently profitable by the end of the year.

A common misconception is that becoming an algorithmic trader requires a PhD from an institution like MIT. This couldn’t be further from the truth. With the rise of Large Language Models (LLMs), anyone with a computer and internet access can learn to develop profitable trading algorithms. In fact, with the advent of LLM-powered trading agents, it’s easier than ever to get started in algorithmic trading.

What is algorithmic trading an LLM Agent?

An LLM (Large Language Model) Agent is an advanced AI system that integrates the natural language processing and generation capabilities of LLMs with the ability to gather information, take actions, and interact with their environment to complete specific tasks. LLM Agents are capable of engaging in goal-directed behavior, assisting users, solving problems, and making decisions in real time.

My theory is that LLM Agents can make algorithmic trading more accessible to the average person. These agents can suggest a list of testable trading ideas, help evaluate strategies, and guide traders through the process by utilizing tools like paper trading — which allows users to trade with virtual money, simulating real market conditions without financial risk.

By combining AI-driven insights with user-friendly tools, LLM-powered agents can empower traders of all levels to develop and refine their algorithmic trading strategies more effectively than ever before.

The End Goal for an LLM-Powered Financial Agent

My ultimate goal is to integrate AI Agents into my algorithmic trading platform, making them accessible to retail investors who typically lack access to this level of technology.

To achieve this, I’m developing NexusTrade.io, an AI-powered algorithmic trading platform designed for both beginner and advanced traders. NexusTrade offers a comprehensive set of tools, allowing users to research trading ideas, create and optimize trading strategies, and deploy them live in the market. The gateway to many of these advanced features is Aurora, our powerful AI-driven assistant, which simplifies the entire process for users.

By democratizing access to algorithmic trading, NexusTrade aims to empower retail traders with the tools they need to succeed in today’s financial markets.

algorithmic trading

Currently, Aurora is equipped to generate trading strategies, assist with financial research, and deploy backtests — actions she can already execute as part of her existing functionality. To evolve her into a fully operational AI Agent, I am exploring two potential approaches: the semi-automated approach and the fully automated approach.

The semi-automated approach would allow users to maintain control over key decisions while leveraging AI to assist with research and strategy development. Meanwhile, the fully automated approach would enable Aurora to autonomously handle the entire trading process, from strategy generation to execution.

By expanding Aurora’s capabilities, I aim to further enhance her role within NexusTrade.io, making her an indispensable tool for retail traders seeking to leverage AI-driven algorithmic trading.

Aurora: Your AI-Powered Trading Assistant

Aurora is an advanced AI-driven trading assistant designed to help traders make informed decisions before the market opens. By analyzing stocks on your watchlist, Aurora fetches relevant financial news, reviews recent earnings reports, and tracks significant price changes. With this data, she provides optimized portfolios that have proven successful in similar market environments.

Like GitHub Copilot for software engineers, Aurora enhances a trader’s abilities without replacing them. Whether you’re a seasoned professional or a developing trader, Aurora allows you to research more ideas, evaluate them critically, and expand your portfolio strategies. The more experienced you are, the more value Aurora brings — empowering you to manage multiple agents and streamline your trading process.

Aurora’s Evolution: Fully Autonomous Trading Agents

While semi-automated trading has its advantages, the future lies in fully autonomous trading agents. Imagine going beyond an AI assistant like GitHub Copilot to something more advanced — similar to Devin, the world’s first AI agent for software development. In this approach, Aurora becomes an autonomous AI-driven agent, capable of iteratively finding the best trading strategies based on given goals and hypotheses.

To achieve this, a framework like ReAct can be employed, enabling Aurora to autonomously use available tools to reach your trading objectives. The potential for advanced techniques such as retrieval-augmented generation, model-based reinforcement learning, and LLM-based algorithms like the Decision Transformer offers even greater opportunities for enhanced decision-making and strategy optimization.

Evaluating the Risks of AI-Powered Trading Agents

While AI-powered trading agents hold great promise, it’s crucial to address the risks and challenges associated with deploying these systems. Key concerns include high costs, the risk of AI agents getting stuck in repetitive loops, failure to account for external market factors, and broader deployment risks.

If the semi-automated approach resembles GitHub Copilot, then fully autonomous agents are akin to Devin, an AI for software development. However, fully autonomous agents come with greater risks. While semi-automated agents could be ready for user deployment before summer, thanks to their lower likelihood of going off-track and incurring significant costs, fully autonomous agents will likely remain in paper-trading mode through the year’s end. Nevertheless, testing their real-time performance in paper-trading environments presents an intriguing opportunity for evaluating future potential.

The Future of Trading: AI-Powered Agents and the Financial Industry

The financial sector is on the brink of a major transformation as artificial intelligence and AI-powered trading agents begin to reshape the landscape. While theoretical advancements like FinGPT have gained attention, practical implementations remain limited. NexusTrade.io is leading the charge to close this gap by developing and deploying both semi-automated and fully autonomous trading agents.

However, deploying AI-powered trading agents comes with significant risks. These agents can encounter feedback loops, leading to costly mistakes and inefficiencies. Additionally, their inability to account for factors beyond their programmed environment, such as breaking news or influential tweets, can limit their effectiveness in real-world trading. Addressing these risks is essential for the responsible adoption of AI in the financial market.

Despite these challenges, AI trading agents present immense value for experienced traders looking to optimize their strategies and decision-making. NexusTrade.io aims to democratize access to these advanced tools, enabling everyday investors to elevate their trading skills and compete at a higher level in the complex financial landscape.

As AI adoption in trading accelerates, expect a wave of innovative applications and use cases to emerge. The potential for AI to revolutionize the financial industry is tremendous, and NexusTrade.io is at the forefront of this exciting evolution. By staying informed, implementing AI responsibly, and continually improving these cutting-edge technologies, we can unlock new opportunities and redefine the future of trading.

Nicholas Renotte

Visit us at sahajgodhani.in

Featured Article:

Follow us on Medium, Linkedin

--

--

Coinmonks
Coinmonks

Published in Coinmonks

Coinmonks is a non-profit Crypto Educational Publication. Other Project — https://coincodecap.com/ & Email — gaurav@coincodecap.com

Sahaj Godhani
Sahaj Godhani

Written by Sahaj Godhani

AI Engineer || LLM || Gen AI || Data Scientist ||