Recorded Kalshi yes/no ladders, up to 100 levels per side. Test fees, slippage, partial fills, and depth-aware execution on stored book observations.
Backtests are only honest on real depth. A mid-price hides the spread, the size resting at each level, and the slippage your order would actually pay. DepthFeed keeps the whole book.
You should not need a data pipeline and a research notebook to find out whether an idea has an edge. The Backtest Lab runs the whole test in the browser, against the real recorded book.
A backtest is a claim about the past. Paper trading is where that claim meets markets that haven't happened yet — with virtual cash, real prices, and a track record you can't fake.
A single number — the last trade or the mid. It tells you nothing about the size waiting to fill, or how far the price moves when you take it.
The full bid/ask ladder with the size resting at each price — best quote through the deep book, asks above the spread and bids below it.
Order-book depth is forward-only — miss it live and it's gone. We store every frame, so a backtest fills against the liquidity that was really there.
The Backtest Lab exposes the fill model, fee, slippage, stake, partial fills, and unfillable entries. A result is a simulation against recorded book states—not a promise of live queue position.
Open Backtest LabWalks the price/size arrays in a recorded ladder and reports partial or unfillable entries when displayed depth is insufficient.
Choose optimistic midpoint, midpoint plus slippage, or recorded-book execution, then enter an explicit fee per share.
Results use resolved market outcomes and show net P&L, ROI, drawdown, win rate, profit factor, and per-trade fills.
Strategies can move into paper trading, where displayed live-book prices are used without claiming that paper orders consume real liquidity.
Every price level with its size, on both sides, at every change. Measure real slippage and liquidity, not a single mid-price.
Kalshi's public REST orderbook, polled continuously at full depth — far finer than any hourly archive.
Latest and historical order-book snapshots over REST — JSON, epoch-millis timestamps, keyset pagination.
A high-frequency reference price series — Binance spot/futures plus Chainlink settlement marks — that joins to any Kalshi snapshot by epoch-millis timestamp, so you can line up book state with the spot move that drove it.
Depth this fine is expensive to record and impossible to backfill, so almost nobody keeps it. We do — full order-book and price data across Polymarket, Kalshi, and Limitless, every level on both sides, captured tick by tick and served clean over a metered API.
Not the last trade or the top of book — the full bid/ask ladder with the size resting at each level, captured at every change. The depth a real order actually fills against.
Polymarket, Kalshi, and Limitless in a single, stable JSON shape — event-driven capture on Polymarket and Limitless, continuous full-depth polling on Kalshi, each joined to a high-frequency underlying price.
Order-book depth is forward-only — miss it live and it's gone for good. We've recorded continuously since early 2026, so the window your plan buys is backed by stored data, not a promise.
DepthFeed is an independent project (not affiliated with the venues) that exists to record the Kalshi order book almost nobody else keeps. Every figure below is measured directly from our own live capture, so you can backtest on real liquidity and trade on the same data.
since January 2026
Kalshi crypto markets
the full REST orderbook
BTC · ETH · SOL · XRP · DOGE · BNB · HYPE
Measured directly from DepthFeed's live capture, June 21, 2026.
We collect Kalshi's crypto series with the complete yes/no book — the depth that short-dated, strike-based contracts trade on.
Hit the REST API to discover live markets and pull the full historical book. Clean JSON, epoch-millis timestamps, keyset pagination — no scraping.
# Full-depth Kalshi book — REST API, Bearer key
import requests
H = {"Authorization": f"Bearer {KEY}"}
b = requests.get(
"https://api.depthfeed.com/v3/kalshi/KXBTCD-26JUN05/orderbook/latest",
headers=H,
).json()["data"]
best_yes = b["yes_prices"][0] # full ladder in yes_prices[]
best_no = b["no_prices"][0]
print(b["recv_ts_ms"], best_yes, best_no, b["yes_sizes"][0])
# Tickers (KXBTCD / KXETHD / KXSOLD) come from /v3/kalshi/markets.Public Kalshi data gives you trades and a last price. To backtest a strategy you need the yes/no order book it would have hit — every resting level on both sides — not a single number sampled once an hour.
A last price tells you nothing about the size waiting at each level. DepthFeed captures the complete Kalshi order book — up to 100 levels per side, yes and no — so you can measure spreads, slippage, and the real liquidity behind every contract instead of guessing.
We poll Kalshi's public REST order book under one shared, upstream-aware request budget. Each response requests up to 100 levels per side; realized cadence varies with the number of tracked markets, network latency, discovery work, and Kalshi's quota. The API exposes the observations that were actually stored rather than promising a universal refresh interval.
Pull Kalshi historical book observations through the API and model the fill against the displayed yes/no ladder. The Backtest Lab can report depth-aware VWAP, partial fills, unfillable entries, user-supplied fees, and slippage; those are simulation assumptions, not a guarantee of queue position or live execution.
A limited sample of the complete DepthFeed API.
Every venue, with room to build.
3× the history, 2× the throughput of Quant.
Dedicated capacity for systematic desks.
Kalshi crypto series for BTC, ETH, SOL, XRP, DOGE, BNB, and HYPE across supported 15-minute, threshold, and directional windows. The collector requests the full public REST order book up to depth 100; stored cadence and date floors vary by series and are documented rather than represented as one universal interval.
Free to start, no card. Upgrade when your strategy is ready for the full book.
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