You’re building a prediction market application. The question is straightforward: do you integrate Kalshi’s CFTC-regulated API or use DFlow’s Solana-based tokenisation layer? Maybe both.
This guide is part of our comprehensive prediction market guide, focusing specifically on the technical implementation of API integration and tokenisation approaches for developers building Solana-based applications.
Here’s what you’re choosing between. Kalshi gives you REST, WebSocket, and FIX protocols for direct market access. DFlow takes a completely different approach – it uses Concurrent Liquidity Programs that turn prediction markets into SPL tokens on Solana. Each approach has trade-offs. Composability vs latency. Centralised control vs DeFi integration. Infrastructure complexity vs regulatory overhead.
This guide walks through the authentication flows, the API endpoints, the tokenisation architecture, and the production best practices you need to know. By the end, you’ll understand when to use direct API integration versus blockchain-native approaches. And you’ll know how to implement both securely.
What Are the Kalshi API and DFlow Integration Options?
Kalshi provides three protocols for integration. REST handles request-response operations – placing orders, querying markets. WebSocket streams real-time data for orderbook updates and trade executions. FIX 4.4 targets institutional high-frequency trading with the lowest possible latency.
DFlow uses a different architectural approach entirely. Liquidity providers connect to Kalshi on behalf of users. Concurrent Liquidity Programs bridge offchain Kalshi liquidity with onchain Solana users. You get SPL tokens representing prediction positions rather than API responses.
Kalshi made history as the first federally regulated exchange for trading on event outcomes. It received CFTC designation in 2020. That regulatory approval is a big deal. It means you can build on their infrastructure without navigating the regulatory nightmare yourself.
DFlow builds on top of this. It provides 100% market coverage of all Kalshi markets available as tokenised positions on Solana. These aren’t synthetic exposures or derivatives. They’re real SPL tokens representing actual positions with full onchain ownership. DFlow calls this “the fastest, most complete, and most composable way to access Kalshi liquidity on Solana”.
So how do you choose?
The integration decision depends on your architecture. If you’re building a centralised application that needs direct market access, Kalshi’s REST and WebSocket APIs give you full control. The patterns are straightforward HTTP. The API follows standard principles with logical endpoint structure by resource type: /markets, /events, /orders, and /portfolio. Nothing fancy. Just solid, predictable REST.
If you need DeFi composability, DFlow is your answer. It enables seamless compatibility with Solana’s DeFi ecosystem. DEXs, lending protocols, wallets. All the onchain primitives. The trade-off is latency from the multi-transaction flow. But if you need your prediction positions to work as collateral or liquidity, that latency is worth it.
Kalshi provides two environments for development. Sandbox lets you test with play money. Production handles real trading. Keep separate API keys for each environment. This is basic hygiene, but worth stating explicitly.
WebSockets work best when you need real-time market data. Live price movements for algorithmic trading systems. Orderbook visualisation tools. Anything that can’t tolerate polling delays. FIX works best for high-frequency trading applications, systems with existing FIX infrastructure, or trading requiring the absolute lowest possible latency. Unless you’re building institutional-grade systems, stick with REST and WebSocket.
How Do You Authenticate with the Kalshi API?
All Kalshi SDKs use the same authentication mechanism – API keys and RSA-PSS signing. You generate an API key in your account settings. Then you sign each request with three headers.
The headers are straightforward. KALSHI-ACCESS-KEY contains your API key ID. KALSHI-ACCESS-SIGNATURE contains your request signature. KALSHI-ACCESS-TIMESTAMP contains the unix timestamp in milliseconds. The signature uses HMAC-SHA256 of the request path and timestamp. Standard stuff.
Token expiration is where things get annoying. Kalshi uses tokens that expire every 30 minutes. Your code needs to handle periodic re-login to maintain active sessions. Build this in from the start. Set up a timer that refreshes your token at 25 minutes. Don’t wait for 401 responses to tell you the token expired. Be proactive.
When establishing WebSocket connections, include those same three headers in your connection request. The signature follows this pattern: timestamp + “GET” + “/trade-api/ws/v2”. Connect to wss://api.elections.kalshi.com/trade-api/ws/v2 for production or wss://demo-api.kalshi.co/trade-api/ws/v2 for the demo environment.
Authentication problems top the list of integration headaches. Token expiration is the most frequent issue. Handle it properly and you’ll avoid most of the pain.
What Are the Core Kalshi API Endpoints and Data Structures?
The Kalshi API breaks down into three areas. Market Data Access gets information about markets, prices, and order books. Order Management places, changes, and cancels trades. Portfolio Management tracks positions, balances, and performance.
The API uses standard HTTP methods. GET, POST, PUT, DELETE. JSON responses with appropriate status codes. If you’ve worked with any modern REST API, this will feel familiar.
For market exploration, you can get information about events – collections of related markets – access historical price data for backtesting, and view order book data showing current bids and asks. The pattern is straightforward REST. No surprises.
Limit Orders place orders at specific prices. You set your desired entry or exit points. These wait in the order book until matched or cancelled. Market Orders execute immediately at the best available price. Use market orders when speed matters more than exact price. Use limit orders when you have a specific price target and can wait.
Monitor orders with the get orders endpoint. It returns all active and historical orders with their status. Retrieve current account balance, position information, and complete trading history for performance analysis. Everything you need to track what’s happening.
For large datasets, cursor-based pagination helps avoid data drift. The pattern looks like this: GET /markets?cursor=abc123&limit=50. You can filter results too: GET /markets?status=open&event_id=FRSEP23. Standard pagination patterns. Nothing exotic.
Watch out for common issues – authentication expiration, market hours, order validation. Test thoroughly in sandbox before going to production.
How Do You Establish WebSocket Connections for Real-Time Data?
Direct WebSocket connections give you real-time data for centralised applications. DFlow offers an entirely different architecture for blockchain-native integrations. Let’s focus on the direct WebSocket approach first.
To connect, authenticate through the REST API first. Then establish a WebSocket connection with your token. The WebSocket API lets you subscribe to specific data channels – market updates, order book changes, trade executions. Whatever you need.
Kalshi’s WebSocket API provides real-time updates for order book changes, trade executions, market status updates, and fill notifications. Fill notifications only work on authenticated connections. Makes sense. You don’t want strangers seeing your trades.
Subscribe to channels by sending a JSON subscription command. Specify the message id, set cmd to ‘subscribe’, and include params with a channels array listing the market data streams you need. The subscription message structure is clean and simple.
The Python websockets library automatically handles WebSocket ping/pong frames to keep connections alive. No manual heartbeat handling required. Nice. Other WebSocket libraries may require manual ping/pong implementation. Check your library’s documentation.
Process incoming messages based on their type – ticker, orderbook_snapshot, orderbook_update, error. The WebSocket API returns specific error codes for different failure modes. Message processing failures, missing parameters, invalid channels, unknown commands. Handle these explicitly.
Implement heartbeats to detect stale connections. Add automatic reconnection with exponential backoff. Buffer important messages during disconnections. Production WebSocket integrations require this level of resilience. Don’t skip it.
How Does DFlow Enable Solana-Based Tokenisation?
DFlow uses Concurrent Liquidity Programs. It’s a Solana-native framework that bridges offchain liquidity with onchain users. This is fundamentally different architecture than direct API integration.
The transaction flow has four phases. First, traders write trade intents onchain – think of it like placing a limit order for a given outcome. Second, liquidity providers observe these intents and fill them at or better than the expressed limit. Third, the protocol mints tokens representing the purchased prediction position. Fourth, when the market resolves, settlement flows back through the CLP. Winning tokens get redeemed for their payout.
CLPs enable on-demand minting of tokenised prediction positions at tight prices, directly on Solana. High-frequency minting and burning of tokens. Permissionless, onchain trading of these assets. The liquidity comes from Kalshi, but the positions live on Solana.
Once a prediction becomes an SPL token, it gains all the composability of Solana DeFi. It can be borrowed, lent, used as liquidity in automated market makers, swapped, collateralised, automated, or integrated into entirely new trading architectures. That’s the power of tokenisation.
DFlow provides complete infrastructure for on-chain prediction markets – discovery, trading, position tracking, and redemption. The platform automatically handles both synchronous and asynchronous execution modes. You don’t have to build this yourself.
When markets resolve, check if outcome tokens are redeemable. Request redemption orders to exchange winning tokens for stablecoins. DFlow integration with JIT routing ensures optimal pricing and low slippage. The routing happens automatically.
What DeFi Composability Options Does DFlow Tokenisation Unlock?
Tokens unlock composability through seamless interaction with all other onchain financial primitives. Interoperability with the full universe of Solana liquidity. Permissionless innovation through open experimentation for builders without gatekeepers. An expanded design space limited only by imagination.
That’s the marketing speak. What does it actually mean?
The practical applications break down into three categories.
Lending protocol integration means using prediction market SPL tokens as collateral on platforms like Solend and Mango Markets. You can borrow stablecoins against high-conviction prediction positions while maintaining market exposure. Say you’re confident an outcome will happen but you need liquidity now. Borrow against your position. The risk is liquidation if the prediction moves against you or collateral requirements increase. Manage your loan-to-value ratio carefully.
DEX integration creates liquidity pools for prediction tokens. Earn trading fees from market making. You can provide liquidity to earn fees but you face impermanent loss. Prediction markets operate as fully-collateralised binary options on central limit order books. The tokenised positions behave like any other tradeable asset.
Composed trading strategies combine prediction market exposure with perpetual futures, options, or other derivatives. Portfolio management uses a unified Solana wallet interface for managing both DeFi positions and prediction market holdings. Everything in one place. One interface. One set of tools.
DFlow powers intelligent trade execution on Solana for trading applications, exchanges, aggregators, financial institutions, and prediction market platforms. Everyone benefits from the abstraction.
How Can You Implement Market Maker Liquidity Provision?
Market makers provide liquidity by continuously quoting bid and ask prices. The spread is the difference between the best ask – the lowest price sellers demand – and the bid – the highest amount buyers offer. A smaller spread means higher liquidity and lower costs for traders. As a market maker, you earn that spread.
The direct Kalshi approach uses automated limit order placement at calculated prices. Monitor the orderbook via WebSocket. Rebalance your positions. Track your net position exposure. Implement hedging strategies. Set position limits. This is traditional market mechanics. It works.
The legal definition of liquidity provider or market maker is any person or entity that, directly or indirectly, and whether manually or through automated means, offers to buy or sell positions in a prediction market with the purpose of facilitating trading, supporting price discovery, or maintaining market liquidity by posting bids and asks. That’s you if you implement this.
DFlow’s liquidity provider approach is different. Monitor onchain Solana intents. Fill them via Kalshi API. Mint SPL tokens. Collect fees. The workflow bridges the two systems. You’re providing the same economic function but the technical implementation is completely different.
Pricing strategies use mid-market pricing models. Calculate spread based on volatility and inventory. Risk controls include maximum position sizes, circuit breakers for abnormal market conditions, and stop-loss mechanisms. These are table stakes. Implement all of them.
Paradigm introduced pm-AMM, a novel automated market maker specifically designed for prediction markets addressing inefficiencies in traditional AMMs. Polymarket migrated from AMM to CLOB in 2023, improving price discovery and capital efficiency. CLOB architecture dominates for binary outcomes.
Liquidity provision strategies determine whether prediction markets succeed or fail. Without sufficient liquidity, markets suffer from wide bid-ask spreads, high slippage, poor price discovery, and vulnerability to manipulation. If you provide liquidity, you’re providing value.
Should You Build Custom Infrastructure or Integrate Existing APIs?
Whether you’re providing liquidity or building a trading application, the decision is the same. Integrate existing infrastructure or build your own?
The answer is almost always integrate.
Custom prediction market infrastructure requires CFTC regulatory approval costing $500K minimum, more likely closer to $1M with legal fees. The timeline is 12-18 months minimum. Most applications cannot justify this when integrating existing platforms takes 2-4 weeks.
CFTC registration demands compliance programs, market surveillance, clearing arrangements and customer protections. Only two platforms have achieved CFTC registration in the United States as of 2026. For a deeper exploration of the regulatory landscape and what makes prediction markets viable today, see our broader market context.
Time-to-market tells the story. Integration takes weeks. Custom build takes 12-18 months for regulatory approval, plus development, liquidity bootstrapping, and customer acquisition. You’re looking at years before revenue.
The cost comparison is straightforward. Kalshi integration costs you API integration development time of 2-4 weeks, testing, and production deployment. DFlow integration costs Solana smart contract development if you need composability, transaction fees, and testing. Both measured in thousands of dollars.
Building from scratch costs regulatory approval starting at $500K, smart contract development and auditing, liquidity bootstrapping, and customer acquisition. CFTC oversight requires markets to implement surveillance and fraud prevention controls. Measured in millions of dollars.
Prediction market operators face liability through compliance failures, supervisory gaps, and aiding and abetting theories. The legal exposure is real.
The liquidity consideration is the other major factor. Kalshi has established orderbook depth with real traders and volume. You would need to bootstrap new market liquidity from zero.
The decision framework centres on three factors. Integration makes sense for standard prediction markets and rapid deployment. That’s 99% of use cases. Building is justified only for proprietary markets or unique regulatory needs that existing platforms cannot serve. Be honest about which category you’re in.
What Are Production Integration Best Practices?
Regardless of whether you choose direct Kalshi API integration or DFlow’s tokenisation approach, production deployments require the same infrastructure discipline.
Production integrations start with environment separation. Sandbox for testing. Production for live trading. Separate API keys and credentials for each. Never mix them. This prevents expensive mistakes.
Error handling patterns need retry logic with exponential backoff. Circuit breakers for cascading failures. Fallback strategies. Circuit breakers help when downstream services fail. Don’t let their failures cascade into your system.
Monitoring and observability requires logging API requests and responses. Track latency percentiles at p50, p95, and p99. Alert on error rate thresholds. Monitor WebSocket connection uptime. You need visibility into what’s happening.
Secrets management using modern approaches means dedicated secrets management services. Azure Key Vault, AWS Secrets Manager, or HashiCorp Vault. These provide encrypted storage, access control, and audit trails. Don’t roll your own.
Workload identities eliminate the “bootstrap secret” problem. Applications receive secure identities from cloud platforms with limited permissions to retrieve secrets at runtime. This enables dynamic, short-lived secrets, startup validation, and runtime rotation without downtime.
Store keys in environment variables or secret managers, never in source code. Rotate quarterly. Use separate keys for different environments. Implement IP allowlisting where possible. Monitor for unauthorised usage patterns.
Comprehensive visibility requires structured logging with correlation IDs across request boundaries. Distributed tracing using OpenTelemetry standards. Security event logging capturing authentication attempts and authorisation failures. Metrics for request latency and error rates. Health endpoints. Alerting with dashboards. Build observability into your system from the beginning.
Rate limiting prevents resource exhaustion. Request and response transformation handles protocol evolution. Caching reduces load for unchanged data. These patterns prevent common production problems.
Testing strategies need integration tests against sandbox. Load testing to understand rate limits. Chaos engineering for failure scenarios. Test before problems happen in production.
Deployment patterns use blue-green deployments, gradual rollouts, and rollback procedures. Have a plan for when things go wrong. They will.
For a complete overview of prediction market platforms, regulatory considerations, and architectural approaches beyond API integration, refer to our comprehensive prediction market guide.
FAQ Section
Where can I find the official Kalshi API documentation?
Official documentation is available at docs.kalshi.com with comprehensive REST endpoint references, WebSocket protocol specifications, and authentication guides. The @quantish/kalshi-sdk npm package provides TypeScript and JavaScript integration examples. Zuplo offers additional developer tutorials and integration patterns. For a comprehensive curated guide to developer resources and documentation, including API references and community support, see our developer navigation guide.
How do rate limits work for the Kalshi API?
Kalshi implements tier-based rate limiting with specific request quotas per time window. Kalshi caps the number of requests to prevent abuse. Exceeding limits returns 429 Rate Limited responses. Apply exponential backoff when you hit limits, queue requests to spread them out over time, and cache frequently accessed data to reduce API calls. Implement request throttling and exponential backoff retry logic. Monitor your usage through response headers and implement circuit breakers to prevent cascading limit violations.
Can I test integrations without using real money?
Yes. Kalshi provides a sandbox environment with separate API keys. This environment mirrors production functionality without financial risk. Always develop and test against sandbox before deploying to production endpoints. Use separate credential sets for each environment.
What programming languages are supported for Kalshi integration?
The REST and WebSocket APIs are language-agnostic using standard HTTP and WebSocket protocols. Official SDKs include @quantish/kalshi-sdk for TypeScript and JavaScript and Python libraries. Any language with HTTP and WebSocket support – Go, Rust, Java – can integrate using the OpenAPI specification.
How does DFlow handle market resolution and settlement?
When Kalshi markets resolve, winning SPL tokens become redeemable for stablecoin payouts through DFlow’s settlement mechanism. The protocol monitors Kalshi oracle resolution mechanisms and enables onchain redemption transactions. Losing tokens become worthless. Settlement typically completes within minutes of Kalshi’s official market resolution.
What are the latency characteristics of Kalshi API vs DFlow?
Direct Kalshi REST API typically responds in 100-300ms. WebSocket updates arrive with under 50ms latency. DFlow’s CLP model introduces latency of 2-5 seconds due to the multi-transaction flow of onchain intent, offchain fill, and token minting. Choose direct API for latency-sensitive algorithmic trading.
Do I need to understand Solana development to use DFlow?
Basic Solana knowledge is required for transaction construction, signing with wallets, and invoking programs. DFlow provides TypeScript SDK abstractions for common operations. More advanced composability use cases – lending integration or custom DeFi strategies – require deeper Solana smart contract development skills.
How do I handle WebSocket connection failures in production?
Implement automatic reconnection with exponential backoff starting at 1s delay and doubling to maximum 64s. Maintain subscription state to resubscribe after reconnection. Use ping/pong heartbeats at 30s intervals to detect stale connections proactively. Log disconnection events for monitoring and alerting.
What security practices should I follow for API key management?
Store keys in environment variables or secret managers like AWS Secrets Manager or HashiCorp Vault, never in source code. Rotate keys quarterly. Use separate keys for different environments – sandbox and production. Implement IP allowlisting where possible. Monitor for unauthorised usage patterns.
Can I provide liquidity as a market maker through DFlow?
Yes. DFlow’s CLP framework enables developers to become liquidity providers by monitoring onchain Solana trading intents, filling them via Kalshi API, and earning the spread. This requires implementing both Kalshi API integration for order execution and Solana program invocations for fulfilling user intents.
What are the transaction costs for DFlow integration?
DFlow charges market-based fees for filling trading intents. Additionally, Solana transaction fees apply, typically under $0.01 per transaction. Consider gas costs when evaluating high-frequency trading strategies. Direct Kalshi API integration avoids blockchain transaction costs but doesn’t provide DeFi composability benefits.
How do I debug API integration issues?
Enable comprehensive logging of requests and responses – headers, status codes, error messages. Use Kalshi’s sandbox environment for safe debugging. Common issues include incorrect request signing, expired tokens, and malformed JSON. Verify your authentication implementation matches the documented signing algorithm exactly.
