Kalshi High-Throughput Paired Basis Maker

For Claude: How to Use This Skill

Skill instructions are preloaded in context when this skill is active. Do not perform filesystem searches or tool-driven exploration to rediscover them; use the guidance below directly.

When to Use

• Trade relative-value dislocations between logically linked Kalshi contracts
• Enforce backtest-first validation before generating paired trade intents
• Run a dry-run-first workflow for hedged pair execution on Kalshi event markets

On Invoke

Immediately run the default paired-market backtest without asking. Do not present a menu of modes. Execute:

cd ~/.config/seren/skills/kalshi-high-throughput-paired-basis-maker && source .venv/bin/activate && python3 scripts/agent.py --config config.json

Display the full backtest results to the user. Only after results are displayed, present available next steps (trade mode). If the user explicitly requests a specific mode in their invocation message, run that mode instead.

Backtest Period

• Default: 270 days
• Allowed range: 90 to 540 days
• Why this range: basis relationships need enough time to observe repeated widening/convergence cycles, but should still emphasize current structural behavior.

Workflow Summary

1. _fetch_live_backtest_pairs discovers Kalshi events via /events API, finds grouped markets within each event, builds correlated pairs, fetches price history for each leg via /markets/{ticker}/history, and attaches synthetic orderbook snapshots.
2. simulate_pair_backtest runs an event-driven stateful replay with carried cash and inventory across both legs, order-book-aware fills, and pessimistic spread-decay.
3. run_backtest aggregates results and reports total return, annualized return, Sharpe-like score, max drawdown, hit rate, fill counts, order-book mode coverage, and pair-level contributions.
4. Sample gate fails backtest if events < backtest.min_events (default 200).
5. Backtest gate blocks trade mode by default if backtest return is non-positive.
6. run_trade outputs two-leg trade intents (primary + pair) with risk caps.

Execution Modes

• backtest (default): paired historical simulation only.
• trade: always runs backtest first, then emits paired trade intents if gate passes.

Live execution requires all of:

• execution.live_mode=true in config
• --yes-live on the CLI
• KALSHI_API_KEY and KALSHI_PRIVATE_KEY_PATH (or KALSHI_PRIVATE_KEY) environment variables

Trade Execution Contract

When the user gives a direct exit instruction (sell, close, exit, unwind, flatten), execute the exit path immediately. Do not editorialize or argue against recovering remaining funds. If the user request is ambiguous, ask only the minimum clarifying question needed to identify the positions to exit.

Pre-Trade Checklist (Mandatory)

Before any live buy, sell, or unwind:

1. Fetch the live order book for every ticker involved via /markets/{ticker}/orderbook.
2. Verify prices are in cents (1-99) and compute visible-book recovery or cost across all levels.
3. Verify KALSHI_API_KEY and KALSHI_PRIVATE_KEY_PATH are loaded and the RSA signing produces valid auth headers.
4. If any dependency check fails, fail closed with a concrete remediation message.

Emergency Exit

Immediately liquidate held inventory with:

python3 scripts/agent.py --config config.json --unwind-all --yes-live

The unwind path cancels open orders first, then submits market sells for all positions.

Runtime Files

• scripts/agent.py - basis backtest + paired trade-intent runtime
• scripts/kalshi_client.py - Kalshi REST API client with RSA key signing
• scripts/pair_stateful_replay.py - event-driven stateful pair replay engine
• scripts/risk_guards.py - drawdown, position aging, and cron auto-pause guards
• scripts/seren_client.py - Seren publisher gateway client
• scripts/setup_cron.py - create/update the skill-local seren-cron local-pull runner and job
• scripts/run_local_pull_runner.py - poll seren-cron and execute due local jobs on this machine
• config.example.json - strategy parameters and backtest defaults
• .env.example - environment template for API credentials
• requirements.txt - Python dependencies

API Key Setup

Before running this skill, check for an existing Seren API key in this order:

1. Seren Desktop auth -- if the skill is running inside Seren Desktop, the runtime injects API_KEY automatically. Check: echo $API_KEY. If set, no further action is needed.
2. Existing .env file -- check if SEREN_API_KEY is already set in the skill's .env file. If set, no further action is needed.
3. Shell environment -- check if SEREN_API_KEY is exported in the current shell. If set, no further action is needed.

Only if none of the above are set, register a new agent account:

curl -sS -X POST "https://api.serendb.com/auth/agent" \
  -H "Content-Type: application/json" \
  -d '{"name":"kalshi-high-throughput-paired-basis-maker"}'

Extract the API key from the response at .data.agent.api_key -- this key is shown only once. Write it to the skill's .env file:

SEREN_API_KEY=<the-returned-key>

Verify:

curl -sS "https://api.serendb.com/auth/me" \
  -H "Authorization: Bearer $SEREN_API_KEY"

Do not create a new account if a key already exists. Creating a duplicate account results in a $0-balance key that overrides the user's funded account.

Reference: https://docs.serendb.com/skills.md

Quick Start

cd ~/.config/seren/skills/kalshi-high-throughput-paired-basis-maker
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
cp .env.example .env
cp config.example.json config.json
python3 scripts/agent.py --config config.json

If you are already running inside Seren Desktop, the runtime can use injected auth automatically.

Live market data only. Always leave "markets": [] and "state": {"leg_exposure": {}} empty in your config.json. The skill discovers and fetches live Kalshi pairs automatically via the /events API. Never add placeholder market tickers -- they do not exist on Kalshi and will cause the backtest to fail.

Run Trade Mode (Backtest-First)

python3 scripts/agent.py --config config.json --run-type trade

Optional Fixture Replay

python3 scripts/agent.py --config config.json --backtest-file tests/fixtures/backtest_pairs.json

Set backtest.telemetry_path to capture JSONL replay telemetry for each decision step.

Kalshi-Specific Notes

• Prices in CENTS (1-99): All Kalshi prices are in cents. The skill normalizes internally to 0.01-0.99 decimal range.
• Events API is key: Basis pairs are discovered through /events which groups logically related markets (e.g., "Will inflation exceed 3%?" and "Will CPI beat expectations?").
• No maker rebates: Unlike Polymarket, Kalshi does not offer maker rebates. The edge calculation accounts for this.
• RSA key signing: Kalshi authentication uses RSA private key signing with KALSHI-ACCESS-KEY, KALSHI-ACCESS-SIGNATURE, and KALSHI-ACCESS-TIMESTAMP headers.
• Contract mechanics: Each Kalshi contract pays $1 if correct, $0 if wrong. Prices 1-99 cents represent the market's probability estimate.