TL;DR / Key Takeaways

Kalshi is CFTC-regulated, US-legal, and has the better API for automated trading. Polymarket is unregulated offshore and runs on crypto infrastructure.
Polymarket has more liquidity on political markets. Kalshi has more liquidity on economic and weather markets.
Kalshi's fee structure is simpler and more predictable. Polymarket's fees are lower on paper but slippage on the AMM can eat that margin.
If you're building a Python bot to trade weather or inflation markets, Kalshi is the only real option. Polymarket doesn't have those contract types.

I get this question more than almost any other. Someone finds the bot, looks at the $97 price, and asks: "Why Kalshi? What about Polymarket?"

Fair question. I'll answer it without trying to sell you on either one.

The Regulatory Situation

Kalshi is a Designated Contract Market regulated by the CFTC. That's the same federal agency that oversees CME futures. It is operating legally in the United States. You can fund it with a US bank account, get a 1099, and report your gains like a normal person.

Polymarket is based offshore and is not registered with the CFTC. US users are technically blocked from using it. The block is IP-based, which means a lot of people use it anyway with a VPN. I'm not going to lecture you about that. But if you're building a production bot that you plan to run for years, the regulatory risk is real. Polymarket paid a $1.4M CFTC fine in 2022 and shut US users out as part of the settlement. That situation hasn't fundamentally changed.

If you want to algo trade prediction markets from the United States without legal exposure, Kalshi is the only regulated option right now. Full stop.

What Markets Each Platform Offers

This is where the comparison gets more interesting, because the platforms target different things.

Kalshi has:

Economic data markets (CPI, PCE, Fed rate decisions, unemployment)
Weather markets (temperature highs and lows by city)
Financial markets (S&P 500 range, earnings thresholds)
Political markets (yes, they have them after winning the lawsuit)
Macro events (debt ceiling, government shutdown)

Polymarket has:

Heavy emphasis on political markets (elections, geopolitical events)
Crypto markets
Sports markets
Some economic events, though coverage is thinner than Kalshi
Emerging tech and AI markets (model releases, benchmark results)

If your bot is built around weather data or inflation nowcasts, Polymarket doesn't have those contract types. The choice is made for you. My Weather Bot and Econ Bot trade Kalshi temperature markets and CPI/PCE markets. Polymarket simply doesn't offer those.

If you want to trade US presidential election markets or crypto price ranges, Polymarket has deeper liquidity on those specific contracts. Kalshi exists in that space too, but volume varies.

Liquidity: The Honest Numbers

Liquidity is the thing that actually matters when you're running a bot. A market with a wide spread or thin depth is expensive to trade even if the fee structure looks good.

Polymarket's top political contracts routinely clear $50M to $100M+ in volume before major events. The 2024 US presidential election saw hundreds of millions in volume on Polymarket. That's real liquidity.

Kalshi has grown substantially since the Robinhood partnership launched (Robinhood's prediction market interface runs on Kalshi infrastructure). Kalshi raised $1B at an $11B valuation in 2025. Volume on popular contracts is real, but on thinner markets like individual city temperature highs, you're typically looking at a few hundred to a few thousand dollars of open interest per contract.

What that means for a bot: you're not going to move the market with a 10-contract limit order on Kalshi weather markets. That's fine for individual traders. If you were trying to run institutional capital through these markets, you'd run into walls fast.

On economic markets like CPI, Kalshi liquidity is serviceable. The Cleveland Fed nowcast gives the Econ Bot an edge precisely because most participants aren't using it. But you're not trading against hedge fund quants in these markets. You're trading against retail, and retail is often wrong in predictable ways.

API Quality

This is the part where I have direct experience.

Kalshi has a documented REST API with RSA-PSS authentication. The docs are decent. The endpoints are predictable. There are community-maintained Python wrappers, and the authentication pattern is well understood. I built my entire auth layer from scratch and it works reliably.

The Kalshi API has its quirks. Rate limiting is real, especially on paginated position fetches. I added a 0.3-second sleep between pages after hitting 429s. The get_positions() response structure has tripped people up because the dict keys aren't what you'd expect. I fixed a bug in my own client where the wrapper was reading the wrong key and returning empty positions, which meant the bot couldn't see its own open trades. That kind of thing can kill you silently.

Here's the basic structure for polling your open positions on Kalshi:

import time
import requests
from auth import build_headers  # your RSA-PSS signing layer

def get_all_positions(base_url: str, session: requests.Session) -> list:
    positions = []
    cursor = None
    limit = 100

    while True:
        params = {"limit": limit, "count_filter": "position"}
        if cursor:
            params["cursor"] = cursor

        headers = build_headers("GET", "/trade-api/v2/portfolio/positions", params)
        response = session.get(
            f"{base_url}/trade-api/v2/portfolio/positions",
            headers=headers,
            params=params,
            timeout=10
        )
        response.raise_for_status()
        data = response.json()

        # The key is 'market_positions', not 'positions'
        # This is the bug that bit me in production
        batch = data.get("market_positions", [])
        positions.extend(batch)

        cursor = data.get("cursor")
        if not cursor or len(batch) < limit:
            break

        time.sleep(0.3)  # respect rate limits on paginated calls

    return [p for p in positions if p.get("position_fp", 0) != 0]

Polymarket's API situation is more complicated. Polymarket uses an AMM (automated market maker) model built on Polygon, a Layer 2 Ethereum chain. To interact with it programmatically, you're working with the CLOB (Central Limit Order Book) API they added later, plus on-chain transactions. You need a crypto wallet, USDC, and an understanding of how their conditional token framework works.

That is not impossible. There are Python clients for it. But it is substantially more infrastructure than a REST API with RSA-PSS headers. If you're a data engineer who knows Python but doesn't want to deal with Web3 plumbing, Kalshi is the path of least resistance by a large margin.

Fee Structures

Kalshi charges a percentage of winnings, not of volume. The current fee is 7% of profits on winning contracts. There are no fees on losing trades.

On a contract you buy at 0.60 and it settles at 1.00, you made 0.40 per contract. Kalshi takes 7% of that. You net 0.372 per contract.

That math changes how you think about edge. You don't just need to be right more often than the market implies. You need to be right by enough that 7% of your winnings doesn't erase your expected value. For small edges, that's a real constraint. I filter out trades where the absolute dollar edge is under $0.50. Below that threshold you're trading noise after fees and slippage.

Polymarket charges 2% on winnings in their AMM model. Lower fee on paper. But the AMM model means you're not always getting the price you want. Slippage on thin markets can exceed that fee difference. On the CLOB side, fees vary. The on-chain gas costs on Polygon are low but not zero, and they add up if you're running frequent automated cycles.

Net-net: Kalshi's fees are higher but the pricing is transparent and predictable. Polymarket's fees look lower but the total cost of execution is harder to model in advance.

Settlement and Counterparty Risk

Kalshi is a clearinghouse. Your funds sit with Kalshi and are subject to CFTC rules. Counterparty risk is real but regulated.

Polymarket settles via UMA Protocol's optimistic oracle. The oracle relies on human voters to resolve disputes. In most cases this works fine. In edge cases involving contested market resolutions, there's a dispute mechanism that can delay settlement. I've read about resolution disputes on politically charged markets that took weeks to settle. That's not something you want when your bot has capital locked in a position.

For systematic trading, Kalshi's settlement process is cleaner. Markets resolve based on official data sources (BLS CPI release, NOAA temperature records, Fed announcements). The resolution criteria are specific and documented before you trade.

Which One Should You Build On

If you're building a bot that trades US economic data, weather, or financial events, build on Kalshi. It's regulated, the API is buildable in pure Python, the contract types match what systematic data-driven trading is good at, and settlement is clean.

If you're specifically interested in political event markets and you have a crypto wallet setup and are comfortable with Web3 tooling, Polymarket has genuine liquidity advantages on those contract types. Build there.

If you want to do both, the architectures are different enough that you're essentially building two separate systems. I chose to go deep on Kalshi because the weather and economic data angles are where I have actual informational edge. Polymarket's political markets are harder to model from public data alone.

The Predict & Profit bot trades Kalshi specifically because that's where the systematic data advantage is clearest. ECMWF ensemble agreement on a temperature market, or a Cleveland Fed nowcast deviation from market pricing, are exploitable edges on Kalshi. Those same edges don't exist on Polymarket because Polymarket doesn't offer those contracts.

The Honest Summary

Polymarket has more political liquidity and lower headline fees. Kalshi has better regulatory standing, a cleaner API, more relevant contract types for data-driven trading, and transparent settlement.

Neither is perfect. Both have quirks that will bite you in production. But for the specific use case of running a Python bot that trades markets based on quantitative data signals, Kalshi is where I'd put my time. I did, and the system runs.

If that changes as the industry evolves, I'll update this post. The prediction market space is moving fast enough that anything written in 2026 might look different by 2027.

Polymarket vs Kalshi: An Honest Comparison for Algo Traders in 2026