Can I connect my own trading bot?

Yes — that's the primary mode. Each strategy has a secret webhook URL; your bot POSTs JSON signals ({"action":"buy","venue":"polymarket","market":" ","usd":100}) and DepthFeed handles execution, position accounting, settlement, and the equity chart. Rate limit is 120 signals per minute per strategy, and an optional client_id makes each signal idempotent.

How is this different from the Backtest Lab?

The Backtest Lab replays strategies against recorded order books on resolved markets — instant results over history. Paper trading runs strategies forward on live markets in real time. They're built to chain: backtest a rule on the archive, then deploy the survivor to paper trading and watch it trade markets that hadn't happened yet.

Can I backtest Kalshi strategies with this data?

Yes — that's what it's for. You get the full historical order book to replay a market and size fills against the liquidity that was actually there, then run the backtest in your own stack. Because history and the live stream share one JSON format, the code you backtest with also trades live.

Can I cancel anytime?

Yes. Paid plans are month to month and the free Explorer tier needs no card to start. Cancel anytime from the dashboard.

Kalshi paper trading

Kalshi Paper Trading

Forward-test Kalshi strategies with virtual cash before risking real money. Your fills are priced from DepthFeed's live order books — buys at the ask, sells at the bid, positions settled automatically at market resolution — and every strategy gets its own equity curve, open-position view, and full trade log in the dashboard.

DepthFeed Paper Trading is a forward-testing environment in the DepthFeed dashboard: strategies hold virtual cash and trade live Kalshi markets at real order-book prices. Signals arrive from your own system via a per-strategy webhook, or from a deployed Backtest Lab rule, and results are tracked as an equity curve with realized and unrealized P&L.

Kalshi paper trading at a glance

Fills: Live book — buys at ask, sells at bid
Settlement: Automatic at resolution ($0 / $1)
Signal webhook: POST /paper/hook/{token} · 120/min
Strategies per plan: 1 free · 5 Quant · 15 Desk Lite · 40 Desk
Starting balance: $100–$1,000,000 virtual (default $10,000)
Mark cadence: ~90 seconds

How paper trading works

Bring your own bot — signals over webhook

Each strategy gets a secret webhook URL. Your system — a bot, a cron job, a notebook — POSTs buy/sell/close signals with a market id and a dollar size, and DepthFeed fills them at the live book at signal time. A payload can never set its own price, so the track record is honest by construction. Idempotency keys make retries safe, and the token rotates on demand.

Or deploy a rule from the Backtest Lab

The Backtest Lab backtests entry rules on resolved up/down markets — time-window entries, level crosses, dip reversion — with depth-aware VWAP fills against the recorded ladder. When a rule survives the backtest, deploy it to paper trading with a stake, take-profit/stop-loss, and a max-open cap; it then runs server-side against live quotes, no infrastructure on your end.

A real track record, not a spreadsheet

Positions are marked from the live book on a ~90-second cadence and settle to $0/$1 automatically when the market resolves — the same settlement data the API serves. The equity curve, realized vs unrealized P&L, and per-trade log give a strategy the forward evidence a backtest alone can't: how it behaves on markets that hadn't happened yet.

Start pulling kalshi paper trading

Free Explorer tier, no card. Full bid/ask depth and the underlying price on every snapshot, over a REST API and a live WebSocket stream.

Start free — no card Read the API docs

Questions, answered.

Every fill is priced from DepthFeed's live order book at the moment the signal arrives: buys pay the best ask, sells hit the best bid, and positions settle to $0 or $1 automatically when the market resolves. Signals can never supply their own price, so a paper track record can't be gamed. One honest caveat: paper fills use real displayed liquidity for pricing but don't consume it, so a large live order could see more slippage than the top-of-book paper fill.