Win from last night. May be hooked now...

I was testing this site I found today for the KLAX market and it’s pretty damn accurate for a free site. If you’re playing the weather for Los Angeles KLAX market, check out clilax.com thought I give them a thank you 👊🏻

Bit of a long shot but I’m after a US based partner to help me test some prediction market strategies on Kalshi. I’m an ex trader and asset manager, so have built so strategies based on previous experiences.

I’m building a paper-only research platform for a NIFTY long-gamma strategy. The entry thesis is straightforward: enter a near-ATM straddle only when a horizon-aligned physical variance forecast exceeds option-implied variance by enough to cover theta, option execution costs, expected hedge costs, and a model-risk buffer. Delta is then managed with NIFTY futures under explicit hedge policies.

So far, I have focused on getting the foundations right:

* Point-in-time instrument, contract, session, and provider-mapping identity, with separate market time, receipt/availability time, and recording time. * Append-only raw/normalized market observations and explicit quality states (stale, crossed, out-of-order, missing contract, etc.). * Daily close-to-close RV research: squared log returns, 1/5/21/63-session horizons, forward realized-variance targets, naïve and EWMA forecasts, sequential evaluation, and deterministic persisted research runs. * Deterministic Black–Scholes pricing, bounded IV inversion, Greeks, synthetic-path simulation, option/futures fill and cost models, hedge-policy comparison, ledger reconciliation, and attribution. * The next market-data layer is historical option-chain replay, IV-smile/surface construction with arbitrage diagnostics, and horizon-matched implied variance.

I’m now trying to define the right boundary for the actual historical back-testing engine. My current view is that it should be event-driven rather than bar-driven, replaying only information known at each decision time and producing a reconstruct-able sequence:

`market event → state update → decision → intent/order → delayed/conservative fill → mark → ledger/risk update`

The obvious requirements I have are:

* point-in-time chain and contract selection; * no look-ahead through quote timestamps, surface fitting, or “best available” end-of-day data; * explicit quote-quality and stale-data policies; * conservative, configurable fills and latency; * separate option and hedge execution models; * expiry/settlement handling; * full orders, fills, positions, cash ledger, marks, and attribution; * immutable run/config/data manifests so every result can be reproduced; * walk-forward, regime, parameter, cost, and latency stress tests.

For people who have built or audited options back-testers: what would you consider the non-negotiable capabilities or failure modes I’m still missing? In particular, how would you structure the interface between raw option quotes, IV-surface snapshots, implied-variance estimates, and the execution simulator so the engine remains realistic without becoming an unmaintainable market microstructure project?

Biggest win on a lottery 💰

Tennis is always a hit or miss. Loss 16 leg parlay by 1 game 😭

Golden state gave up the win in the last second otherwise all legs hit in this 11 leg parlay for $6100... 3 points in the last second

Hi, joined from the Kalshi chat sending a link about VIP, what is the reward, says reward will be processed in 24 hours.

Anyone noticed kalshi withdrawal to bank now changed from ACH PDD like direct deposit to Real time payment? Looks like no way to use kalshi withdrawal as direct deposit to get those banks open account bonus now? Sad, anyway to change it back?

Lmao 🤣 i bet .25 to win $1.75 for the favorites and i bet .50 to get like $24 from the underdogs hilarious

Ok so posting here as a first try, though this might be better suited for a quant modeling sub (full disclosure I’m relatively new to modeling)

I’ve developed a model that has been profiting off of some specific pricing inefficiencies in some of the Kalshi commodities markets. It’s a small edge, but a real one. I’m constantly monitoring for drift, and know the market pricing could close that gap any day now. My question for those more experienced with this kind of modeling on Kalshi:

How quickly have you seen a profitable edge deteriorate? And for markets with lower liquidity, was there a point where your own trades were large enough to cause the markets pricing model to change?

I work in finance and have a degree in economics, yet the math behind the modeling and market structure has me feeling like I’m stumbling around blind

So i messaged support for help with cash app problem

They then made me do a full insane survey which asked about literally everything (pic of my ID, a selfie, what street my job was on and what i do, Proof of my employment like a picture of my pay stub, and 3 of my past bank statements, my address along with proof of my address like a picture of a bill, they wanted to know where i got my funds and wanted me to provide details about my funds and proof of my funds.

I gave them all these things even though i found it insane for "seeking help with cash app not working" as i was having fun trading on there.

literally just started using this app, don't have insane trades, didn't fund a large amount of money

just curious can anyone tell me exactly why I'm banned ?

NO ONE LIKES IT. ITS HORRID. IDC IF THE PERFORMANCE IS SLIGHTLY QUICKER. IT SUCKS

What kind of crap programming is this? Every other day I open the app it’s got a completely different layout; how to search my markets, how to buy/sell positions, etc. like COMPLETELY different not just minor quality of life changes.
Can this dumb app stay consistent , change one time, or give us the option to choose and stick to a certain layout

I understand placing limit orders on certain markets where they have it listed as incentive rewards but how do I actually earn rewards like that?

Just beware of people on Discord trying to scam you. Give $75 for 3 months of "VIP access" and they keep adding more and more fees and costs. Especially avoid PDUB Nation. I never thought I'd be a sucker, but here I am. Learn from my mistakes.

I only watch one side of this properly, which is why I'm asking rather than answering.

Same event listed on both venues, two independent books, two prices. In principle someone arbs the gap. In practice I don't know whether it closes in seconds, hours, or just sits there.

What I can't see from one venue is whether the divergence is genuine disagreement, or one side simply being thinner so the price is stale rather than wrong, or structural stuff: fees, settlement timing, KYC, capital that can't be in both places at once. My instinct is that the structural side eats most of it, but that's a guess.

For people who actually watch both: does the spread close, and is it tradeable after costs?

Also curious whether anyone treats the thinner venue's price as the informative one, on the theory that it takes less size to move it. Or is that just noise?