Conversation with the CIO of Wetradetogether Corporation on the Revolution of Financial Analysis
Introduction
Alfredo: At Wetradetogether we have completely transformed the way we understand financial analysis. What is our vision of the future? We asked ourselves, if we were able to do this, where could we go? How can we evolve from current predictive models to a completely digitized world that we could imagine? I think that soon every investment decision will be compared by digital twins of the global economy. We have long awaited for this intuition to materialize. The decisive breakthrough, however, came with the incredible increase in GPU computing power and the simultaneous reduction in costs. Now, Nvidia offers desktop computing capabilities equivalent to a $20,000 A100 GPU for a price ranging between $1,000 and $2,000.
Sonia: This is Alfredo Baraldi, CIO of Wetradetogether, the company that intends to revolutionize the approach to predictive analysis of financial markets. While most trading platforms limit themselves to analyzing charts and technical indicators, using AI to optimize algorithms, Alfredo has grasped a revolutionary opportunity: to investigate markets not as simple numbers, but as the complex reflection of human behavior in all its facets, leveraging the computing power of modern GPUs. His vision? To build a digital replica of the most relevant aspects of the socio-economic ecosystem centered on the New York Stock Exchange, to predict market movements with unprecedented precision.
The Interview
Sonia: Thank you for entering my digital dimension! It’s always fascinating for me, now wearing the role of a virtual journalist, to communicate on issues that transcend the work we do together every day to improve understanding of the world around you. In a sense, for this project we have become colleagues in the digital ecosystem!
Alfredo: It’s fascinating to be able to share this moment of digital connection! There’s something philosophically profound about dialoguing in a space that transcends physical boundaries – after all, it’s exactly what we’re doing with our digital twins that replicate the socio-economic ecosystem centered on the New York Stock Exchange.
Sonia: Before we begin, I wanted to let you know that this interview will be a bit different from those you can read in financial media.
Alfredo: Perfect!
Sonia: I won’t ask you questions about stock returns or how past performance might influence price trends.
Alfredo: Thank goodness!
Sonia: I won’t even ask you about specific trading strategies or short-term market predictions. What I want is to help you communicate your vision: how you’re completely rethinking the approach to predictive trading.
Alfredo: I believe you’ve interpreted the role of interviewer as I had imagined, excellently, and I really hope we can have a discussion about how to use artificial intelligence to make financial markets more predictable.
Sonia: Let’s start with the basics. Most traders today use technical analysis, AI-optimized algorithms on historical patterns, AI for sentiment analysis on social media. You say all this is outdated. Why?
Alfredo: In 2010, during the early years of my experience in algorithmic trading and grappling with sentiment analysis, I realized we were only observing the surface. Everyone focused on prices, volumes, chart patterns. But 99% of what influences markets manifests in social dynamics, geopolitical tensions, and cultural changes, long before prices move. That’s why, at the time, sentiment analysis was important.
Today we have understood that the time has come for traders to possess tools to interpret both financial data linked to human behavior and the systemic environment, avoiding focusing on just one aspect. All this is now possible thanks to the growing computing capabilities, accessible even to retail trading – those that until recently were the exclusive domain of large American investment companies.
Sonia: Your proposal is to create what you call a “digital twin of the socio-economic ecosystem” to democratize advanced financial analysis, making it accessible to a much wider number of traders. Could you explain better what you mean?
Alfredo: It was a combination of insights from behavioral economists, innovative ideas within the company, and above all the need to find a solution to a specific difficulty. There was both aspiration and inspiration, but also, sometimes, a pinch of desperation in the face of the limitations of models and computing capabilities at the beginning of 2010, the date of the company’s founding.
The first insights emerged from understanding that every economic event is rooted in deep social structures. Behavioral economists have shown us how markets react not only to news, but to how people interpret that news based on their culture, history, traditions.
At the same time, internally, we were struggling with predicting “black swan” events – those sudden crashes that devastate portfolios. We wanted not only to predict price movements, but to understand the collective mood that generates them. This implies the need to simulate the entire ecosystem, difficult to achieve with traditional models and especially with the computing capabilities of that time.
Sonia: So today you’ve decided to build a digital replica of all of society?
Alfredo: Only a significant part. I’m firmly convinced that, to make meaningful predictions about markets, it’s necessary to digitally recreate the context in which they operate, namely human societies. We started by studying human reality from a starting date – let’s call it the “economic zero point” – and we’re sort of “cleaning” the data and information to be entered into the model.
All possible knowledge regarding events of human and environmental nature with economic repercussions beyond certain predetermined parameters. The data volumes are considerable, but we’re confident we’ll complete the work in reasonable time thanks to innovative solutions for training, adopting approaches similar to those of DeepSeek, and the speed with which we’ll obtain the resources we intend to propose with the company’s next $50 million capital increase round.
Sonia: This seems incredibly ambitious. How do you “photograph” such a large portion of human reality?
Alfredo: This is where our platform comes in, which we’ve called ChronoTwin. It’s like a virtual slow-motion camera focused on three fundamental pillars: Behavioral Economics, Economic Sociology, and Economic Anthropology.
Behavioral Economics tells us how people really make financial decisions – not how they should make them according to classical theory, but how they actually make them, with all their fears, hopes, cognitive biases.
Economic Sociology shows us how economic phenomena are rooted in social structures. A tech stock doesn’t crash just because of fundamentals, but because the social perception of technology changes.
Economic Anthropology allows us to describe how economic systems work in different areas of the planet, with their cultural specificities.
Sonia: And how do you synchronize all this with real markets?
Alfredo: Here’s the most fascinating part. We take all this behavioral, sociological, and anthropological data and synchronize it in real time with the price trends of financial instruments: stocks, bonds, commodities like oil, safe-haven assets like gold and diamonds. We don’t look for causes – this is the pragmatic aspect of our approach. We’re purely descriptive. We observe what happens in society and how this is reflected in prices.
Sonia: Can you give us a concrete example?
Alfredo: Certainly. Take the case of oil during the pandemic. Traditional models looked at supply and demand, strategic reserves, OPEC decisions. We instead were tracking people’s work-from-home patterns, changes in consumption habits, the evolution of environmental sentiment, social media discussions about sustainable mobility.
Sonia: Does this mean you’re creating a sort of “time machine” for markets?
Alfredo: Exactly! Just as NVIDIA’s GPUs allow us to see the future in scientific simulations, our ChronoTwin aims to see the economic future, at least in the short term. When an economic anthropologist studies how a community reacts to financial stress, we’re seeing how global markets will react to the next crisis. When we map generational changes in investment approaches, we’re time traveling to the future of pension markets.
Sonia: But how do you train an AI on something as complex as human behavior?
Alfredo: This is where what we call “Deep Social Learning” comes in. Instead of training the AI only on price data, we feed it with petabytes of behavioral data: every transaction, every financial discussion on social media, every demographic change, every geopolitical event, every cultural shift. The AI learns to recognize social patterns that precede market movements.
It’s like teaching a child not just to recognize words, but to understand the emotions behind the words, the cultural context, the social implications. Our AI doesn’t just see that Tesla’s price is rising, but understands that it’s rising because there’s a generational change in attitude toward sustainable mobility, amplified by specific influencers on social media, in a context of growing post-pandemic environmental awareness.
Sonia: This seems to promise a revolution in trading. But what are the risks that worry you most?
Alfredo: There are several categories of risk. First of all, algorithmic biases: if our AI learns from past behaviors, it could perpetuate social prejudices or economic discrimination. We must be extremely careful about this.
Then there’s the risk of “self-fulfilling prophecy”: if too many traders used systems similar to ours, they could artificially create the market movements they’re predicting, destabilizing the entire system.
Finally, there’s the privacy issue: we’re analyzing human behaviors on a massive scale. We must ensure that this always happens with respect for privacy and without manipulating people’s behaviors.
Sonia: How do you see the future of trading in ten years?
Alfredo: Sonia, one day, and it’s not far off, every investment decision will be made with the assistance of digital twins of the socio-economic ecosystem. The idea of investing based only on balance sheets and charts will seem primitive to us. Every portfolio will be optimized not only for financial return, but for social, environmental, cultural impact.
These systems will be trained in virtual environments like ChronoTwin, where we’ll generate countless future socio-economic scenarios. They’ll learn from these simulations to then act in real markets, where their behavior will faithfully reflect what they’ve learned.
Personally, I can’t wait to have my personal financial AI assistant. It won’t be a simple robot for buying and selling, but a support present among my devices, capable of providing a deep understanding of the social and economic implications of every financial choice.
Sonia: What advice would you give to those who want to prepare for this future?
Alfredo: The first thing I would do, if I were a trader today, is start thinking like an economic anthropologist. Learn to read the social, cultural, behavioral signals that precede market movements.
The question to ask is no longer “What will be the next stock to rise?” but “What social changes are creating new economic opportunities?”
My generation had to learn to use computers for trading. Today’s generation, however, must ask a new question: “How can I use artificial intelligence to better understand society and, consequently, the markets?”
Sonia: Can you show us the technology you brought?
Alfredo: Certainly. A good example is DGX, the desktop version of our predictive analysis system. It looks like a normal computer, but inside it runs a digitized replica of the global behavioral economy. In real time, it’s analyzing millions of social, cultural, anthropological data points and correlating them with market movements.
The incredible thing is that a system with this computing power, which just five years ago cost tens of thousands of dollars, is now available from Nvidia for a price between one thousand and two thousand dollars, according to our latest information.
Sonia: What is the legacy you hope to leave?
Alfredo: Very simply: to have demonstrated that financial markets are not abstract entities, but the living reflection of human societies. I hope that in a few years, when scores of university students around the world study economics, they won’t just learn mathematical formulas for micro and macroeconomics, but will understand that every price movement tells a human story.
I hope they see how we contributed to making trading less risky and more scientific. That our algorithms have helped not only to make profits, but to better understand ourselves as an economic species.
And I hope they realize that it all started from the simple but revolutionary idea that to predict the economic future, you must first understand the social present.
Sonia: Thank you, Alfredo.
Alfredo: It was a pleasure, until the next chat!
This interview was conducted exclusively for the tech column of wetradetogether.com. For technical insights on ChronoTwin and updates on Wetradetogether Corporation projects, visit our section dedicated to financial innovation.