SQL
SeqLL Inc. Common stock
stock NASDAQ

Two years ago, I teamed up with two friends to try and beat the market. They started the with a VM, a SQL server downloading daily data since 2016, and deterministic algorithms (hard-coded rules).
They had some success, but deterministic algos need constant update. As soon as the market regime changes, the rules break.
So, I took over the architecture with the idea of applying Machine Learning and make it more efficient and automated. I built a dataset with and trained a series of ML models.
Since July 2024, I’ve had 500+ models in production on our VM.
Every day, they scan the market and output "Buy" signals for the next day, targeting a 3-day exit window.
Here's the numbers.
* Win Rate: ~70%
* Theoretical Cumulative Return: around +400% since July 2024.
However, that +400% is a theoretical metric assuming you take every single signal. In reality, the models spit out many opportunities every day and you would need massive capital to execute all of them properly.
Realistically, the system can target about something around 0.5% per day (on average), but with max drawdowns up to 10/15%.
I am currently trading these signals myself, but I’m investing small sums ($2k - $3k max).
I live in the EU. Between high broker commissions and a 26% capital gains tax, the friction is eating me alive.
To make a meaningful gains (e.g., $8,000/month) purely from trading this system, I would need a starting capital of $100k+ and I’d have to carry 100% of the risk.
This brings me to the conclusion I'd rather be a "shovel seller" than a gold digger. I ran the numbers on a community model:
* Trading: Needs $100k capital + Risk + Stress = ~$8k/mo profit.
* Community: 1,000 users @ a cheap $8/mo = $8k/mo profit (Risk-Free).
I’ve automated a pipeline to post these signals to Telegram and X. I'm building a discord channel and a website with my associates.
I've been struggling to promote my service as I honestly look like another trading signal scam.
I have legitimate math, years of SQL/Python and ML architectures, and rigorous validation behind this.
If you have experience in systematic trading or fintech:
How do I bridge the trust gap? How do I prove the math is real without giving away the IP? How do I certify the system? And grow my community?
Please if considered spam just delete this.

To get into quant risk (specifically market risk), it's far easier than getting into a trading seat but the skills are generally transferable. In general terms, good risk quant ideally someone who's very analytical, got a good sense of probability and statistics, coding skills (Python, R, SQL), and it helps to be commercially inclined. More specifically be across options and generally derivatives pricing, risk models (at-risk models, FRTB, Monte Carlo Sims, stress tests etc), liquidity risk, and an understanding of markets and trading businesses.
You'll likely be seeking roles at either banks or commodities trading houses. Although roles at quant trading firms, super funds, etc also exist (caveating this Aussie centric advice)
As a uni student I'd suggest getting good data handling skills, nailing prob and stats classes, and building good social skills (in the role -- especially at a high level -- it's a lot communicating and negotiating with the trading desk). it always helps to take courses related to numerical methods, derivatives, and stochastic calc.

45% of their entire cloud revenue is based on OpenAI future contracts that they may or may not be able to pay. And that money is being loaned to them by Microsoft and others.
I am not confident that Microsoft can recover when their core services now are degrading in favor of supporting OpenAI as a business entirely, at the expense of other customers. Windows and the rest of their product offerings are also heavily degraded, and SQL server and other enterprise products keep dipping downward due to licensing etc.
I don't think there's any surefire benefit to anything right now, and the future for a lot of tech is very murky.