u/Fynca

We are seeking swing traders who wish to put their research and execution to the test.

All participants are given an identical demo capital and compete with the aim of achieving the highest return. There is no personal money on the line and you are under no obligation to share your strategy.

The first Fynca Trading Challenge has already started and traders are currently competing for $1,000 in real cash prizes. The leaderboard is becoming more competitive each day.

Following the response to the first challenge, we’ve opened registration for the next one with a $2,500 cash prize pool:

🥇 1st place: $1,200
🥈 2nd place: $600
🥉 3rd place: $400
4th place: $200
5th place: $100

Entry is free, but places are limited. If you'd like to join, you can do it directly on the website or leave a comment below.

Quick update: the current Fynca Trading Challenge is officially underway.

A huge thank you to everyone who signed up and is now competing for a share of $1,000 in real cash prizes. The leaderboard is already getting competitive.

The response exceeded our expectations, so registration is now open for the next challenge.

This time, $2,500 in real cash prizes will be split among the top five finishers.

All trades are simulated using demo capital, so none of your own money is at risk.

Spots will be limited again, and registration will close once they’re filled. If you’re interested in joining, leave a comment below, and I’ll share the details.

Hey everyone, our first post got more interest than we expected. We’ve confirmed 22 traders, so only 3 spots remain.

The challenge starts this Monday. Everyone trades with demo capital for seven days. The trader with the highest return wins $1,000 in real cash.

No real money is at risk, and no one has to share their strategy.

Interested? Leave a comment, and I’ll send over the details.

The deployment of robotaxis in 28 cities is remarkable.

I began by researching Uber and soon found myself examining NVIDIA.

Initially, the connection seemed unlikely, but further research revealed its significance. Uber is investing in autonomous mobility, while NVIDIA develops the AI and computing infrastructure that enables it.

Notably, these two companies, typically viewed as distinct, are both increasingly influenced by the same long-term industry trends.

This process highlights what makes research engaging for me: beginning with one company, exploring its connections, and ultimately developing a new investment thesis.

At first, I thought the main value would be faster answers.

But crypto moves on more than just price and charts. Sentiment, news, narratives and momentum can shift very quickly.

So instead of asking AI “what happens next?”, we started thinking more about how it can help connect those signals and explain what’s actually changing.

For me, that’s a much more useful role for AI in crypto.

50+ analysts changed how we think about AI for market research

When we first started working on Fynca, I believed AI’s main benefit would be getting better answers more quickly.

But after talking with over 50 analysts, I don’t think that’s true anymore.

Most of them weren’t looking for AI to tell them what to buy or sell. Instead, they wanted help moving through research faster while keeping all the context.

This feedback changed the way we built our product.

Social sentiment is a good example. The real value isn’t just knowing if Reddit or X is bullish or bearish. It’s about understanding why sentiment shifts and whether the data backs it up.

That’s why we included social data alongside filings, fundamentals, charts, and news in the same research flow.

The more we build, the more I see AI as a valuable research partner, not just a tool for picking stocks.

After speaking with 50 analysts worldwide, we built Fynca, an AI-powered visual workspace for researching market ideas.

Now we’re inviting 25 traders to test it in a simulated trading challenge.

Each trader gets $200 in demo capital. After seven days, the trader with the highest return wins real $1,000.

No real money is traded. Just your research, strategy, and execution.

As we built Fynca and talked with analysts, we kept running into the same issue: research for a trade often gets scattered across charts, news, filings, notes, and AI chats.

Fynca brings that process into one visual canvas. Before a trade, you can place the chart, recent news, company filings, fundamentals, sentiment, and your own notes together, then document the thesis, catalyst, and invalidation.

You can link the sources you choose and ask AI questions that use only that research.

Fynca doesn’t execute trades or give trading signals. Its purpose is to help you organize the research behind your decisions.

The image shows a real research canvas in action. Fynca works best on desktop and you can start for free to use:

https://fynca.io/

I’m on the team that built Fynca, and I’m here to answer any questions. What features would help make it more useful for your workflow?