u/guitly_spark_echo

I want to start with trading and I am starting to read the books recommend by most on the Sub. And I see a lot of posts that we need to constantly learn the market , understand the market etc etc.

I am a software engineer, I am trying to objectively identify strategies , Backtest them and trying to find out if they are profitable.

I do not understand what exactly I have to learn ?

Do I stare at charts all day ? Nobody shares a working strategy because they will lose the edge , fair enough.

But can anyone who's retired or wants to help , tell exactly how to find strategies? Do I use mathematical formulas ? Stare at charts ? Analyze market sentiment?

Can someone please give me objective ways to learn to build strategies.

I've spent a lot of time building infrastructure around Kalshi, and I keep noticing people here get stuck on the same part - not the modeling, the plumbing around it.

Stuff I've built and could help with:

- WebSocket capture that actually survives (reconnects, sequence gap detection, resync). Getting this right is more annoying than it sounds

- Orderbook reconstruction from delta streams, replay to any timestamp

- Storage that doesn't cost anything - Parquet, compression, cheap object storage

- Backtest harnesses with realistic fills instead of last-trade nonsense

- Kalshi's fee formula (it's non-linear and peaks at 50c, easy to get wrong)

- RSA auth, rate limit handling, all the API stuff nobody wants to deal with

If you're a few hours from working and stuck on something specific, ask here or DM. I'll usually just answer.

Bigger builds we can talk about terms.

Not a data vendor, not selling a product, no link. Just built this stuff already and would rather it be useful.

I've been running automated strategies on Kalshi for a while, mostly sports, and I keep hitting the same wall.

The historical API is fine for what it covers — settled markets, trades, candlesticks, all there. But there's no historical depth. So I can see a contract traded at 43c, and I have no idea whether I could have actually gotten filled at 43c, what the spread was, or how much size was sitting behind it. On thin markets and anything short-dated, that's basically the whole question. A backtest that assumes you fill at last trade is telling you a story.

What I've been doing is recording depth myself off the WebSocket, but that means I only have history going forward from whenever I happened to start, and I lose everything if the box goes down for a day.

So, curious what everyone else does:

- Are you recording depth yourself? How long have you been running it and how do you handle gaps?

- Paying for one of the datasets that's floating around? Was it usable?

- Modelling slippage with some assumption instead — fixed haircut, half-spread, something else? What did you land on and how'd you calibrate it?

- Or just sizing small and treating the backtest as directional only?

Also curious where people actually need this. I assume sports since that's most of the volume, but the crypto up/down markets seem to be where more of the systematic folks are.

Not selling anything, no link, just tired of rebuilding the same capture setup and wondering whether everyone else solved this years ago and I missed it.

(English isn't my first language, so I ran this through AI for grammar. Words and question are mine.)

Hi I am working for one of the big compnies in a regulated sector. we are tasked with building a mcp that interacts with a few api's and a retrieval system.

one of the challenges has been that we dont control the LLM application ( owned by a diff team ) our tool gets called and they sont send us the conversation or session id. this this is creating challenges , as the llm keeps forgetting previously fetchd data and repeatedly asks the users to enter info they have already provided us.

Can anyone share insights on how they tackled this ?

Hi I am working for one of the big compnies in a regulated sector. we are tasked with building a mcp that interacts with a few api's and a retrieval system.

one of the challenges has been that we dont control the LLM application ( owned by a diff team ) our tool gets called and they sont send us the conversation or session id. this this is creating challenges , as the llm keeps forgetting previously fetchd data and repeatedly asks the users to enter info they have already provided us.

Can anyone share insights on how they tackled this ?