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Hi all,
New to this community. I started diving into this world about 6 months ago. Before that, I’d made money, lost money, made it again, and lost it again with equities. I’m not a mathematical genius, just an average person working in tech with a software/coding background.
This all started with AI, and honestly probably wouldn’t have been possible without it. I suppose I’ve become something like a “vibe quant”, though I’m not sure whether that’s a good thing or not. I’m keen to hear from others who are maybe doing something similar.
I started by reading about technical trading, candlesticks, indicators, and so on, then became more interested in market microstructure and, for want of a better word, market “physics”: compression, expansion, liquidity, volume, volatility, etc.
At first I used ChatGPT to help build Pine indicators for TradingView. That began the long repetitive journey of getting excited that I’d found something, only to tear it down a day later and start again. I graduated to TradingView backtests, but eventually found them insufficient, especially for strategies spanning sub-universes of stocks.
So I signed up for market data sources like Norgate and Polygon, and started building Python projects to slice market data with NumPy, run simulations, test entries and exits, model slippage, and try to make things more realistic. I spent months iterating on small edges I thought I’d found.
I went deep into different timeframes: intraday, VWAP, daily bars, swing trading, longer-term ideas. I built some broker integrations and even ran a few real algo trades, but didn’t make any significant money.
Several times I thought I’d discovered something life-changing, only to later find a subtle lookahead bias, survivorship issue, stock split/dividend problem, or some other realism gap.
Eventually I discovered QuantConnect. I was initially hesitant to upload my “alpha” to it because I was worried about losing control of it somehow. Ironically, I later accidentally posted some code to a GitHub repo I’d forgotten to set private. In hindsight, that was probably the best thing that could have happened, because it pushed me to use QuantConnect properly, and I quickly realised I probably didn’t have much alpha at all.
Since then I’ve spent months coding strategies and running them through QC backtests. The workflow is much faster than my own tooling, and it also solved the survivorship-data problem that I previously had no good answer for.
Again, I found numerous lookahead issues, corporate action subtleties, execution assumptions, and other ways a strategy can fool you. I have eventually iterated on one earlier idea to the point where it *might* be profitable, but honestly I don’t know. It looks good in backtest, but I’ve had so many false dawns that I don’t really trust it yet.
I’m now using QC research notebooks to explore data much faster. It’s the quickest workflow I’ve had so far. I can turn around ideas in minutes instead of hours.
Truthfully though, I still don’t know whether I’ve found anything real. I’ve realised I probably need to slow down and educate myself properly, so I’ve ordered the López de Prado book and plan to work through it.
I also think I need to talk to people outside my own bubble. Right now it’s mostly me, AI, backtests, and more iterations. That has been useful, but it also feels dangerous because it is very easy to convince yourself you are being rigorous while still missing something obvious.
So that’s where I am. I don’t have anyone in real life to discuss this with, and I’m curious what others are doing. I’m very open to advice. After 6 months, I still feel like I don’t know what I don’t know. Every week it feels like I peel back another layer of the onion, only to find there are many more underneath.
This post was not written with AI, although I did use it to review and tighen up the grammar.
Thanks for reading, and good luck.