Kalshi trading bot: real-time social signals

Join the catalyst to the contract

A Kalshi trading bot needs two separate inputs: Kalshi's official interface for market state and execution, and a fast event feed for the public posts that can change a contract's probability. TweetStream supplies selected X and supported Truth Social events; your strategy decides whether the evidence is relevant and safe to trade.

Build the watchlist from the contract rules

Read the contract rules before building the watchlist. A government release may depend on an agency or official; an economic contract may depend on a scheduled report; a company event may depend on a corporate account or executive. The tighter the source map, the less noise your bot must classify under time pressure.

• Store the Kalshi event ticker, market ticker, rules, sources, evidence terms, and expiry together
• Give official and first-party accounts the highest source confidence
• Include supported Truth Social posts for political contracts when relevant
• Disable mappings as soon as the contract closes or the trading thesis expires

Turn one social event into one inspectable intent

Route each TweetStream content event by platform and author, attach later OCR or other enrichment to the same candidate, and read current Kalshi market state before producing a paper intent. The maintained local example covers both Kalshi and Polymarket mappings, duplicate rejection, stale-event checks, liquidity limits, and position limits without placing an order.

• Persist a stable event identity before any side effect
• Reject events outside the strategy's freshness window
• Read current market state instead of trusting a cached probability
• Treat image OCR and detected trading context as evidence, not an automatic order

Example: from one post to one decision

Take a contract that resolves on whether an agency announces a rule by a set date, with the agency's X account on your watchlist. When that account posts, your bot receives a tweet/content event and works through these steps before any order exists:

• Match the author and text to the stored mapping for that market ticker, and drop the event if it is outside the freshness window
• Read the current Kalshi order book and skip the trade if spread or depth breaks your limits
• Write a paper intent with the event ID, market ticker, side, size cap, and the post text as evidence
• If a later tweet/meta event adds OCR text, attach it to the same candidate and score it again before the intent goes to review

Let an AI coding agent accelerate the build

Use Claude Code, Codex, Cursor, or another AI coding agent to scaffold adapters and tests, not to improvise with production capital. Give it the public TweetStream envelope contract, the Kalshi client boundary, deterministic fixtures, and a paper-only output schema. Keep secrets and irreversible actions outside its tool access.

Measure the path that can win or lose the trade

Record source publication, local TweetStream receipt, decision completion, Kalshi order submission, exchange acknowledgement, and fill as separate timestamps. TweetStream publishes rolling server-detection percentiles for monitored X posts, but your signal-to-fill result also includes network, strategy, risk, and venue time.

Earn the right to place the first order

A production bot should fail closed on ambiguity. Replay, shadow, and paper-trade first; then start with hard exposure and daily-loss limits. No model score should bypass market-open checks, minimum liquidity, maximum spread, stale-event rejection, idempotent order handling, or a kill switch controlled outside the strategy process.

Implementation asset: prediction-market source router

Turn validated X and supported Truth Social events into typed, venue-neutral routing decisions. Adapt the source list, market mapping, and keyword rules to your strategy, then keep duplicate, stale-event, position-limit, credential, and execution controls in deterministic code around this pattern.

type SocialPlatform = "twitter" | "truth_social" | "binance_square";
 
type TweetStreamContentEvent = {
  d: {
    author: { handle?: string; platform: SocialPlatform };
    createdAt: number;
    kind: "post" | "quote" | "reply" | "retweet";
    text: string;
    tweetId: string;
  };
  id?: string;
  op: "content";
  t: "tweet";
  ts: number;
  v: 1;
};
 
const sourceRules = [
  {
    handles: ["whitehouse", "potus"],
    platform: "twitter",
    strategy: "policy-markets",
    terms: ["executive order", "tariff"],
    venue: "polymarket",
  },
  {
    handles: ["realdonaldtrump"],
    platform: "truth_social",
    strategy: "trade-policy",
    terms: ["trade policy", "announcement"],
    venue: "kalshi",
  },
] as const;
 
export function routePredictionMarketSourceEvent(event: TweetStreamContentEvent) {
  const handle = event.d.author.handle?.toLowerCase().replace(/^@/, "");
  const platform = event.d.author.platform;
  const text = event.d.text.toLowerCase();
  const rule = sourceRules.find(
    (candidate) =>
      candidate.platform === platform &&
      candidate.handles.some((candidateHandle) => candidateHandle === handle),
  );
  const matchedTerm = rule?.terms.find((term) => text.includes(term));
 
  if (!rule || !matchedTerm) {
    return { deliver: false } as const;
  }
 
  return {
    deliver: true,
    eventId: event.d.tweetId,
    matchedTerm,
    strategy: rule.strategy,
    venue: rule.venue,
  } as const;
}

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