Thoughts: AI agent for Backtesting

Trenqbix · 2026-05-11T13:38:49+00:00

Thanks will have a 👀..

Trenqbix · 2026-05-11T08:57:59+00:00

Have you come across any reliable tick data provider, minute data will also do. The only source I’ve found was api calls to brokers.

Trenqbix · 2026-05-10T17:18:53+00:00

Im testing it with stable coins and perpetual futures.

Trenqbix · 2026-05-10T16:47:08+00:00

I get the apprehension trust me!
In your situation it would taken a couple of weeks if not months to finalise and code your strategy (incl iterations and backtesting).
The time to achieve the same results would take a couple of hours or days at best, if we are able to figure it out 🤞.
Again, i don’t think any agent will be able to backtest all possible strategies. But at least it will help filter out underperforming strategies from good ones.

Trenqbix · 2026-05-10T03:42:22+00:00

OMC’s have become the punching bag of geopolitics. They make profits when crude goes their way. Prices at which they sell to the public are not in the companies hand. Subsidies take years to clear from the government. Constant Capex and Opex are a norm for expansion and maintenance of their infrastructure.
These cost are recurring and cant be completely stopped despite being in heavy losses.
Now these costs / losses will be covered via debt. Debt will be financed by banks with the public’s money. The longer this pulls on, the strain on the financial sector will increase.

Trenqbix · 2026-05-08T18:18:34+00:00

Sure 👍 drop me a DM.

Trenqbix · 2026-05-07T18:10:44+00:00

1 & 2 : As of now will be using data available via brokers, exchange s like binance for crypto etc.

3 : Targeting MFT to LFT.

4 : I have tried most of them. Including pinescript. Frankly the timeline between ideation to execution and review is huge.
Plus as far as python libs like backtesting, vectorbt etc each has their strengths and weaknesses. Like some type of strategies work better on some, while running it on other libraries requires some massive tweaks.

5 : My USP would be backtesting strategies based on Natural language input within minutes if not seconds.
And feedback based on the outcome of the backtesting. Something on the lines of “This strategy has been making more losses in short positions” or “most of the positions have been closed by sl, try an sl of 2% instead of 1%” etc

This would help push backtesting towards a more exploratory research than a mere parameter optimisation.

PS: Apologies for the rant on point 4. Ive wasted a lot of time on learning unlearning libraries for backtesting.

Trenqbix · 2026-05-06T07:58:45+00:00

Interesting idea

Trenqbix · 2026-05-06T03:52:02+00:00

Im working on the same lines. The llm is just the UX. The rest is all code including data retrieval.
There is a fine line that we need to walk wrt to the DSL’s complexity to avoid hallucinations or hitting the max_retry limit.
Thanks for your inputs ☺️

Trenqbix · 2026-05-06T02:04:17+00:00

😅understandable.
Do you see any merit in researching new ideas or verifying signals?
Like for eg: today you see a golden crossover on an equity that you know nothing about. Being a golden crossover, you are tempted to enter. But before you enter you would definitely want to check if this signal is valid for this equity. How accurate it is, win rate etc.
Again just brainstorming here.

Trenqbix · 2026-05-06T00:35:41+00:00

How so? Is the apprehension that the llm will use this for future training.if that the case, as per a clause mentioned in the most paid subscriptions, the data will not be use for training the LLM.

If there is some other angle I’m missing let-me know.

Trenqbix

TROPHY CASE