Senior Python Trading Systems Developer — Scaling Kalshi/Polymarket RFQ and Automated Trading

# Senior Python Trading Systems Developer — Kalshi RFQ Integration ## Overview I operate a professional sports betting and prediction-market business. I have an existing Python application I have spent the past year working on that trades markets on Kalshi.  **You can find my current trading success already here on the leaderboard ** https://kalshi.com/ideas/profiles/runbadruben I am looking for an **individual senior developer** to help design and implement RFQ functionality into the existing system and potentially incorporate other exchanges apis into my daemon. I would really like someone experienced to help me continually build and scale and have this not be just a one-off thing, but the fit is going to need to be well. That is going to take some time to have confidence in. In its best case, this could be a lucrative and well-fitting scenario for both of us if the fit is precise. **Independent freelancers only. No agencies, development companies, or subcontracting.** The person applying must personally perform the work. This is not a request to build a generic trading bot from scratch. You will be working within an established codebase that already includes: * Kalshi API authentication and market data * REST and WebSocket integrations * Fair-value calculations * Order entry and cancellation functionality * Exposure and safety controls * Audit logging * Operator-facing dashboards * Automated tests ## Initial Project The first phase will be a paid technical assessment of the existing codebase and Kalshi’s RFQ workflow. The likely implementation will then proceed in stages: 1. Receive and display RFQ activity 2. Record RFQ events and state transitions 3. Calculate or surface recommended quote prices 4. Build a manual operator approval workflow 5. Add shadow-mode automated quoting 6. Potentially add live automated RFQ quoting after testing and review The system involves real money. Reliability, state management, reconnect behavior, duplicate-event prevention, and safe failure handling are more important than moving quickly. ## Responsibilities * Review the existing architecture * Design an RFQ state machine and implementation plan * Integrate the relevant Kalshi RFQ API and WebSocket functionality * Handle RFQ creation, expiration, quote submission, withdrawal, acceptance, confirmation, and failure states * Prevent duplicate or stale actions * Add comprehensive audit logging * Integrate RFQ information into the existing operator dashboard * Add automated tests for business-critical behavior * Document assumptions, risks, and unresolved API questions * Work through small, reviewable milestones * Provide clear summaries for a non-technical owner ## Required Experience * Strong production Python experience * Experience building trading, brokerage, exchange, or financial API systems * Strong REST and WebSocket experience * Experience with asynchronous Python * Experience designing reliable order or transaction state machines * Strong testing and debugging skills * Experience handling reconnects, retries, idempotency, race conditions, and partial failures * Ability to understand an existing codebase before changing it * Clear written communication ## Preferred Experience * Direct Kalshi API experience * Prediction-market development * RFQ or request-for-quote systems * Sports betting or sportsbook odds data * Market-making or automated quoting * Exchange order-management systems * Trading risk and exposure controls * Operator dashboards * Experience reviewing AI-generated code ## Engagement Structure I expect to begin with: * A paid 8–12 hour codebase and RFQ architecture assessment * A written implementation plan * A state diagram covering all RFQ and quote states * A risk assessment * A milestone and cost estimate I am open to hourly or milestone-based billing. I care more about relevant experience and careful work than choosing the lowest bid. ## To Apply Please answer each question directly: 1. Describe the most relevant live trading, exchange, brokerage, or prediction-market system you have personally built. 2. Have you worked directly with Kalshi? If so, describe exactly what you implemented. 3. Have you implemented an RFQ, quoting, or order-management state machine? 4. What weekly availability can you provide? 5. Please provide a relevant code sample, GitHub repository, technical write-up, or detailed project explanation. Applications that do not answer the questions will not be considered.