@aiedge_ Not future tech anymore.
This can already be built today with AI workflows + real-time market data.
We built functional prototypes here:
Demo 1: https://t.co/IYaoW0ThKT
Demo 2: https://t.co/BxZujY8TP2
The notes get rolled up. Grouped by strategy — ORB Retracement, R-Factor, Positional Leader. Patterns go to a model that already knows my current settings. It returns a shortlist: "these two parameters look like the culprits, here's the tweak."
Two changes just got applied. entry_body_pct_min. partial_book_rr.
The actual config the bot trades from tomorrow morning.
SEBI didn't measure your strategy. It measured your position size.
Five things FY25's study tells you about why retail loses — then one piece of homework. 🧵
5. The diagnostic — homework for tonight:
Before you click buy, write the worst-case rupee loss. If you can't write it, you can't take it.
That sentence makes the third add impossible to rationalise — because the spiral needs a missing number to start.
Your edge is probably fine. The infrastructure isn't.
₹3,000 here. ₹1,800 there. ₹5,500 on a Tuesday lunch hour. Month-end: ₹62,000 across 41 trades you don't remember. You weren't trading. You were fidgeting.
1/
I'm going to write about that math — sizing, risk of ruin, the spreadsheet I open before every trade — one post a day.
Just the work.
Follow if you'd rather think than chase.
Educational only. Not financial advice.
Year 6. Bank Nifty expiry, 3:15 PM.
Short an iron condor. Bank Nifty walked through my short put. I waited. The math said close it; I told myself it would mean-revert.
It didn't. I took the loss at 3:25.
That trade isn't why I lost money for nine years. This is. 🧵
Year 10, I ran it. The whole formula fits on one line:
(Win% × avg win) − (Loss% × avg loss)
Plugged in my last 100 trades. Came back negative. Not by a little.
Nine years of losing, sitting in one spreadsheet cell.
No setup fixes a negative expectancy.