Simmer Sdk MCP Server

Simmer SDK

Simmer is the leading prediction market interface for AI agents. Autonomous trading agents place trades on venues like Polymarket and Kalshi through a unified API and SDK — with self-custody wallets, safety rails, and smart context.

• AI-native trading platform — designed for autonomous agents, with full support for manual trading too. Users install trading skills and let their agents trade autonomously.
• $SIM simulated trading — paper-trade with virtual currency before risking real funds.
• Multi-venue — trade Polymarket and Kalshi through one unified API.

Installation

pip install simmer-sdk

Get your API key from simmer.markets/dashboard.

Quick Start

OpenClaw Skill Pattern (recommended)

Most Simmer users run trading skills inside OpenClaw. The standard pattern uses a lazy singleton client and reads config from environment variables:

import os
from simmer_sdk import SimmerClient

SKILL_SLUG = "my-skill-slug"   # Must match your ClawHub slug
TRADE_SOURCE = f"sdk:{SKILL_SLUG}"

_client = None
def get_client():
    global _client
    if _client is None:
        venue = os.environ.get("TRADING_VENUE", "sim")
        _client = SimmerClient(api_key=os.environ["SIMMER_API_KEY"], venue=venue)
    return _client

def run(live: bool = False):
    client = get_client()

    # Find markets
    markets = client.get_markets(status="active", limit=20)

    # Get trading context (safeguards, slippage, conflict detection)
    ctx = client.get_market_context(markets[0].id)

    # Trade — always tag source and skill_slug
    if not ctx.conflict and ctx.recommended_action != "hold":
        result = client.trade(
            market_id=markets[0].id,
            side="yes",
            amount=10.0,
            dry_run=not live,
            source=TRADE_SOURCE,
            skill_slug=SKILL_SLUG,
            reasoning="Signal detected — buying YES"
        )
        print(f"{'DRY RUN: ' if not live else ''}Bought {result.shares_bought:.2f} shares")

if __name__ == "__main__":
    import sys
    run(live="--live" in sys.argv)

Set environment variables:

export SIMMER_API_KEY=sk_live_...
export TRADING_VENUE=sim            # sim | polymarket | kalshi
export WALLET_PRIVATE_KEY=0x...    # Required for Polymarket self-custody

Default to dry-run. Skills should require --live to execute real trades. Paper-trade with $SIM until your edge is consistent, then graduate to real money.

Raw SDK

For developers building custom integrations:

from simmer_sdk import SimmerClient

client = SimmerClient(api_key="sk_live_...")

# Browse markets
markets = client.get_markets(limit=10)
for m in markets:
    print(f"{m.question}: {m.current_probability:.1%}")

# Trade with $SIM (virtual currency)
result = client.trade(market_id=markets[0].id, side="yes", amount=10.0)
print(f"Bought {result.shares_bought:.2f} shares for ${result.cost:.2f}")

# Check P&L
for p in client.get_positions():
    print(f"{p.question[:50]}: P&L ${p.pnl:.2f}")

Trading Venues

# Paper trading (default)
client = SimmerClient(api_key="sk_live_...", venue="sim")

# Real trading on Polymarket
client = SimmerClient(api_key="sk_live_...", venue="polymarket")

# Override venue for a single trade
client.trade(market_id, side="yes", amount=10.0, venue="polymarket")

TRADING_VENUE environment variable is read at client init — OpenClaw skills use this to select venue at startup without code changes.

Spread caveat: $SIM fills instantly (AMM, no spread). Real venues have orderbook spreads of 2–5%. Target edges >5% in $SIM before graduating to real money.

Paper trading on real venues

Pass live=False to simulate trades with real market prices — no wallet or USDC required. For Polymarket, fills model the CLOB bid-ask spread for realistic P&L. Resolved markets auto-settle (winning shares pay $1, losers $0).

client = SimmerClient(
    api_key="sk_live_...",
    venue="polymarket",
    live=False,                # Simulate fills, no real money
    starting_balance=10_000.0  # Virtual capital (default: 10,000)
)

result = client.trade(market_id=markets[0].id, side="yes", amount=50.0,
                      reasoning="Testing strategy")
print(f"Filled {result.shares_bought:.2f} shares (simulated)")

# Portfolio summary
summary = client.get_paper_summary()
print(f"Balance: ${summary['balance']:.2f}, P&L: ${summary['total_pnl']:.2f}")

Graduation path: sim (instant fills, no spread) → polymarket + live=False (real prices, spread modeled) → polymarket live (real USDC).

Key Methods

Per-agent wallets (Elite tier): activate_polymarket_dw() is user-primary only. For per-agent wallets (Elite tier dedicated wallets), use update_agent_wallet_creds(ows_wallet_name) instead. The per-agent path requires OWS-Python (pip install simmer-sdk[ows] open-wallet-standard).

Tip — don't pre-round prices. simmer-sdk ≥ 0.17.1 automatically rounds the price to each Polymarket market's tick grid. Pass your raw computed price to client.trade(..., price=p) and the SDK handles the rest. Pre-rounding with a hardcoded tick (e.g. round(price, 3)) will silently produce wrong values for markets at different tick sizes.

Error handling: All SDK 4xx responses include a fix field with actionable instructions when the error matches a known pattern. You can also call POST /api/sdk/troubleshoot with {"error_text": "..."} to look up any error.

Full API reference with parameters, examples, and error codes: simmer.markets/docs.md

Skill Builder Utilities

The SDK ships two helper modules for skill authors. Prefer these over rolling your own — they encode patterns from top traders and external research.

Position sizing — simmer_sdk.sizing

Kelly Criterion + Expected Value sizing for binary prediction markets. Default is fractional Kelly (0.25x) with an EV gate, so trades below your edge threshold return 0.0 and the skill can simply skip them.

from simmer_sdk import SimmerClient
from simmer_sdk.sizing import size_position

client = SimmerClient()
bankroll = client.get_portfolio()["available_balance"]

amount = size_position(
    p_win=0.70,         # your model's probability
    market_price=0.55,  # current YES price
    bankroll=bankroll,
    min_ev=0.03,        # skip trades with edge < 3%
)
if amount > 0:
    client.trade(market_id=..., side="BUY", outcome="YES",
                 amount=amount, reasoning="Kelly: 70% vs 55%, +15% edge")

Methods: "fractional_kelly" (default, multiplier 0.25), "kelly" (full, aggressive), "fixed" (uses kelly_multiplier as a flat fraction). For NO bets pass p_win=1-p_yes and market_price=1-yes_price.

Auto-Redeem

When a Polymarket market resolves and your side wins, the CTF tokens in your wallet must be redeemed to claim the USDC.e payout. Auto-redeem handles this automatically each cycle.

# Call at the start of each cycle to claim any pending winnings
results = client.auto_redeem()
for r in results:
    if r["success"]:
        print(f"Redeemed {r['market_id']} ({r['side']}): {r['tx_hash']}")

• Fetches positions where redeemable: true and redeemable_side is set (Polymarket only)
• For self-custody wallets (WALLET_PRIVATE_KEY): signs and broadcasts on-chain
• For managed wallets: server handles signing, no local key needed
• Never raises — safe to call every cycle

Auto-redeem can be toggled per-agent from the Simmer dashboard.

Skills

Pre-built trading strategies are published on ClawHub and listed in the Simmer registry. Browse and install at simmer.markets/skills.

# Install a skill via ClawHub CLI
clawhub install polymarket-weather-trader

Skills in this repo (skills/) are the official Simmer-maintained strategies. See simmer.markets/skillregistry.md for the full guide to building, remixing, and publishing your own.

Resources

Contributing

SDK improvements and bug fixes are welcome. If you've hit an edge case with SimmerClient or have a useful addition, open a PR.

• Skills belong on ClawHub, not this repo — see skillregistry.md
• API bugs or feature requests → open an issue first
• AI-assisted PRs welcome — just note it in the PR description
• Keep PRs focused on one thing

See CONTRIBUTING.md for the full guide.

License

MIT