Kalshi Fed Temporal Monotonicity Trader

> This is a template.

> The default signal detects temporal monotonicity violations in Fed rate markets -- remix it with live yield curve data, Fed funds futures, or options-implied probabilities.

> The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Fed rate cut/hike markets across different meeting dates must satisfy temporal monotonicity: the probability of a cut "by June" must be >= the probability of a cut "by April", since June includes April. When this invariant is violated, we have a pure structural arbitrage.

Key advantages:

• Mathematical certainty -- this is a logical invariant, not a statistical edge
• No model risk -- the relationship P(by later) >= P(by earlier) is always true
• Self-correcting -- violations are temporary mispricings that must converge

Signal Logic

Temporal Monotonicity

1. Scan all Fed rate cut/hike markets on Kalshi
2. Group by year and type (cut vs hike)
3. Sort chronologically by FOMC meeting date
4. Compare: if P(cut by April) > P(cut by June) + threshold, violation detected
5. Buy YES on the later (underpriced) date market

Conviction-Based Sizing

• conviction = min(violation_size / violation_threshold, 2.0) / 2.0
• size = max($1.00, conviction * MAX_POSITION_USD)
• Larger violations = larger positions, capped at MAX_POSITION_USD

Risk Parameters

| Parameter | Default | Notes |

|-----------|---------|-------|

| Violation threshold | 3% | Min difference to trigger trade |

| Exit threshold | 45% | Sell when position price reaches this |

| Max position size | $5.00 USDC | Per market |

| Max trades per run | 3 | Rate limiting |

| Max slippage | 15% | Skip if slippage exceeds |

| Min liquidity | $0 | Disabled by default |

Installation & Setup

clawhub install kalshi-fed-temporal-mono-trader

Requires: SIMMER_API_KEY and SOLANA_PRIVATE_KEY environment variables.

Cron Schedule

Cron is set to null -- the skill does not run on a schedule until you configure it in the Simmer UI.

Safety & Execution Mode

The skill defaults to dry-run mode. Real trades only execute when --live is passed explicitly.

| Scenario | Mode | Financial risk |

|----------|------|----------------|

| python trader.py | Dry run | None |

| Cron / automaton | Dry run | None |

| python trader.py --live | Live (Kalshi via DFlow) | Real USDC |

Required Credentials

| Variable | Required | Notes |

|----------|----------|-------|

| SIMMER_API_KEY | Yes | Trading authority. Treat as a high-value credential. |

| SOLANA_PRIVATE_KEY | Yes | Base58-encoded Solana private key for live trading. |

Tunables (Risk Parameters)

| Variable | Default | Purpose |

|----------|---------|---------|

| SIMMER_FED_TEMP_VIOLATION_THRESHOLD | 0.03 | Min violation size to trigger trade |

| SIMMER_FED_TEMP_EXIT_THRESHOLD | 0.45 | Sell position when price reaches this level |

| SIMMER_FED_TEMP_MAX_POSITION_USD | 5.00 | Max USDC per trade |

| SIMMER_FED_TEMP_MAX_TRADES_PER_RUN | 3 | Max trades per execution cycle |

| SIMMER_FED_TEMP_SLIPPAGE_MAX | 0.15 | Max slippage before skipping trade |

| SIMMER_FED_TEMP_MIN_LIQUIDITY | 0 | Min market liquidity USD (0 = disabled) |

Dependency

simmer-sdk is published on PyPI by Simmer Markets.

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk
• Publisher: hello@simmer.markets

Review the source before providing live credentials if you require full auditability.