Master of My Own Market: Building an Autonomous Sentiment-Aware Portfolio Agent

How I built a self-balancing investment PoC that breathes market sentiment, for my own personal finance experiments.

TL;DR
I built an autonomous AI agent, FinAgent, as a personal PoC to explore sentiment-driven portfolio rebalancing. Using LLMs for NLP-based market signal extraction and a dynamic weighting strategy, the agent out performed a static 60/40 benchmark in my 30-day simulation. This article details my experiments, the design decisions I made, and the complete technical implementation.

Introduction
In my opinion, the most exciting part of the current AI wave isn't just "chatting" with models, but giving them the autonomy to act on real-world data. From my experience, personal finance is one of the most practical and high-stakes playgrounds for this. I’ve often wondered: what if my portfolio could "read" the news the way I do, but with the discipline of a machine?

I began this experimental article as a way to prove that sense-making—converting noisy news streams into actionable rebalancing signals—is finally accessible to the solo developer. I think we’re moving away from static spreadsheets and toward living, breathing financial systems. This project is my attempt to capture that shift.

What's This Article About?
This article is a deep dive into my experiments with "FinAgent," an autonomous agent that monitors market sentiment and dynamically rebalances asset holdings. I observed that traditional rebalancing is often reactive; I wanted to see if I could make it proactive.

I put it this way because I want to emphasize that this is a Proof of Concept (PoC). I decided to use real-time sentiment analysis as the "brain" for a portfolio manager, allowing the system to tilt weights toward assets with positive institutional and social momentum while trimming exposure to those shrouded in controversy or poor earnings forecasts.

Tech Stack
To build this, I chose a stack that balances performance with rapid prototyping:

1. Python 3.12: The backbone of my implementation.
2. OpenAI GPT-4o: For extracting nuanced sentiment from financial news.
3. Matplotlib & Pandas: To visualize performance and sentiment trends.
4. ImageIO: To generate the animated status graphics I used throughout this PoC.
5. Standard Financial Libraries: For basic portfolio math.

Why Read It?
I think if you're interested in the intersection of LLMs and autonomous systems, this PoC provides a clear template. I wrote this because I felt most tutorials stop at "summarizing news." I wanted to go the final mile: actually letting the agent make a decision.

You'll see how I designed the agent's logic to be resilient, how I visualized the data to make it understandable, and how I structured the entire flow to be repeatable for any asset class.

Let's Design
I observed that the biggest challenge in autonomous finance isn't the execution—it's the signal processing. I spent a lot of time thinking about the architecture. From my perspective, a sentiment-aware agent needs three distinct layers working in harmony.

1. The Observer: This layer continuously polls news streams. I decided to simulate this for the PoC, but the logic is ready for real API integration.
2. The Analyser: This where the LLM shines. I decided to have the LLM output a structured sentiment score between -1.0 and 1.0. I found this more effective than simple "positive/negative" labels.
3. The Portfolio Engine: This layer takes the scores and re-weights the assets. I chose a momentum-based tilt strategy where the agent pushes more capital into high-sentiment assets while maintaining a baseline level of diversification.

Let’s Get Cooking

I started by building the core agent loop. I thought a lot about how to make the code modular so I could swap out components later.

Step 1: The Core Agent Logic

class RebalancingAgent:
    """Core Agent coordinating sentiment analysis and portfolio management."""
    def __init__(self):
        self.portfolio = PortfolioManager()
        self.analyzer = SentimentAnalyzer()

    def step(self, news_stream):
        # I decided to use a step function to represent a single rebalancing window
        sentiment_scores = self.analyzer.analyze_news(news_stream)

        current_weights = self.portfolio.holdings
        # I thought it was important to use adjustment factors based on sentiment
        adjustment_factors = {ticker: 1 + score for ticker, score in sentiment_scores.items()}

        raw_weights = {ticker: current_weights[ticker] * adjustment_factors[ticker] for ticker in self.tickers}
        total_raw = sum(raw_weights.values())
        new_weights = {ticker: weight / total_raw for ticker, weight in raw_weights.items()}

        result = self.portfolio.rebalance(new_weights)
        return result, new_weights, sentiment_scores

What This Does: This is the orchestrator. It takes a "news stream," passes it to the analyzer, and then computes new portfolio weights.

Why I Chose This: I put it this way because it separates the intent (the sentiment) from the execution (the portfolio math). I think this makes the system much easier to debug. If the rebalancing is weird, I can check if it's the sentiment score or the math.

Step 2: Visualization and Data Storytelling

I wanted to ensure my experiments were data-backed. I wrote a script to generate charts that showed me exactly what was happening under the hood.

In my opinion, data is useless if you can't see the trends. I designed the visualization module to compare my autonomous agent against a static index-like benchmark.

Let's Setup
Step by step details can be found at: GitHub Repository

1. Clone my repository.
2. Create your virtual environment.
3. Install the requirements.
4. Set your OPENAI_API_KEY.

Let's Run
Based on my testing, running the agent shows a clear divergence in performance as sentiment signals build up over time. I observed the agent successfully identifying "fading" assets and rotating capital before the market fully corrected.

Closing Thoughts
As per my experience, the era of "automated" finance is just starting. I learned that while the AI is powerful, it still needs clear guardrails and a robust architectural foundation. I think the next version of my PoCs will involve multi-agent systems where one agent cross-validates the sentiment of another.

This was a fascinating experiment for me, and I hope it sparks some ideas for your own personal projects.

Code Repository: The full implementation is available at: https://github.com/aniket-work/fin-agent-sentiment-portfolio

Disclaimer
The views and opinions expressed here are solely my own and do not represent the views, positions, or opinions of my employer or any organization I am affiliated with. The content is based on my personal experience and experimentation and may be incomplete or incorrect. Any errors or misinterpretations are unintentional, and I apologize in advance if any statements are misunderstood or misrepresented.