Your Quick Guide to Smarter Investing

AI agents' crossover with Web3 is probably one of the most important events of our lifetimes. Imagine, data integrity + immutable proof of intelligence + outcome rewarded by incentives is a combination that would drive our society forward on a more fair and equitable ground.

I know that all of this sounds very academic (and perhaps less thrilling). I want to give you a brass-tacks view of what AI agents on-chain mean by providing a hard use case to help establish a mental construct around it.

Let’s do a deep dive (I won’t waste time explaining the basics, as it will all fall into place once you understand the use case).

The Crystal Ball of Predictions: Competing AI Agents for Predictive Analysis (Under the Hood)

In the fast-evolving world of finance, predicting stock prices accurately has always been a critical yet complex task (Imagine a world full of ever-changing variables with complex dependencies and relationships that may or may not affect supply/demand mechanisms). With the combination of AI and blockchain, we now have a powerful way to create systems that can analyze the stock market more effectively.

One innovative approach is to design competing AI agents that independently analyze market data, compete with each other based on predefined criteria, and deliver recommendations on whether to invest in a stock.

How It All Ties Together: AI Agents Competing and Predicting Market Insights

The idea revolves around creating multiple AI-powered agents that autonomously evaluate stock prices and provide investment recommendations. These agents can be given a range of factors to analyze, such as:

Historical stock prices
Current market sentiment
Global economic indicators

By introducing competition between these agents, we can leverage their ability to generate more accurate, data-driven insights.

1. Creation of AI Agents (Deploying the Tentacles)

Multiple AI agents are developed, each using different AI models or algorithms to analyze stock data. For example:

One agent might rely on technical indicators like moving averages or price momentum.
Another agent could focus on fundamental analysis, evaluating company performance metrics such as earnings reports or industry comparisons.
A third agent might track market sentiment, gathering data from news outlets and social media to determine public sentiment around specific stocks.

2. Competing for Accuracy (Winning Agent Takes It All)

These AI agents are designed to compete in real time based on the accuracy of their stock price predictions. The competition can be structured based on factors such as:

How well they predict short-term vs. long-term market trends.
How reliable their recommendations are in terms of profitability?
Their ability to adapt to changing market conditions and external economic factors.

Over time, the best-performing agents are identified, and their results are shared with users for decision-making.

3. Agentic Regression of Stock Evaluation (The Math, Fun & Games of Prediction)

The agents continuously gather and process data, look for patterns, evaluate trends, and make projections based on predefined or self-learning models. Users provide certain parameters (like a specific stock symbol or time frame), and the AI agents use this input to offer investment recommendations.

The competition ensures that each agent is constantly improving, as they learn from their past predictions and adjust their algorithms accordingly.

4. Writing it on Blockchain for Immutability (You Can’t Back Off Now)

Blockchain technology plays a critical role in ensuring security and transparency in the process. Every interaction between AI agents, every data evaluation, and the final decision is recorded immutably on a blockchain.

Blockchain’s decentralized nature ensures that no single entity controls the data or the results, building trust in the AI agents’ recommendations.
Additionally, blockchain makes the decision-making process fully auditable, allowing users to trace how the agents arrived at their recommendations.

Untapped Gold Mines: Where Else Can We Use It?

Retail Investors: Individual investors can rely on AI agents to make more informed stock investment decisions without having to analyze complex financial data themselves.

Institutional Investors: Hedge funds and financial institutions can use AI agents to monitor real-time market trends and adjust their portfolios accordingly.

Automated Trading: These AI agents can be integrated into automated trading systems, executing trades based on real-time stock analysis without human intervention.

Who Else Is Playing the AI On-Chain Game: Blockchain Platforms Supporting AI Agents

Several blockchain platforms provide the infrastructure to support the creation of competing AI agents, leveraging decentralized and secure technology for transparent decision-making:

1. Fetch.ai:

A blockchain platform that allows the creation of autonomous AI agents, enabling decentralized computation and decision-making processes. It supports autonomous agents that can analyze financial markets and interact securely on-chain.

2. Nesa.ai:

This platform uses zero-knowledge machine learning (ZKML) in financial AI to ensure privacy and security in decentralized AI inference. It supports AI agents that can analyze sensitive data (such as financial markets) while preserving data confidentiality.

4. Ocean Protocol:

A decentralized data exchange protocol that enables AI agents to access and analyze large datasets, including financial data. Ocean Protocol’s focus on data privacy makes it an ideal platform for AI-driven stock market analysis.

5. SingularityNET:

A decentralized AI marketplace where developers can create AI agents and monetize their services. AI agents can collaborate or compete in financial analysis tasks, with all interactions recorded on the blockchain.

The Bottom Line / TLDR

Creating competing AI agents for stock market analysis opens a new frontier for investors looking to make data-driven decisions. By leveraging AI’s predictive power and blockchain’s transparency and security, this approach offers a decentralized and trustworthy system for stock market evaluation.

Platforms like Fetch.ai, Nesa.ai, Ocean Protocol, and SingularityNET provide the infrastructure needed to build these AI agents, making it easier for developers and investors to harness the power of AI in finance.