{"id":2854,"date":"2026-05-05T10:00:00","date_gmt":"2026-05-05T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=2854"},"modified":"2026-04-11T17:06:41","modified_gmt":"2026-04-11T17:06:41","slug":"predict-fun-api-bnb-prediction-markets","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/predict-fun-api-bnb-prediction-markets\/","title":{"rendered":"Predict.fun API: Read BNB-Chain Prediction Market Data (Python)"},"content":{"rendered":"<h2>Predict.fun: The BNB-Chain Prediction Market You Probably Haven&#8217;t Scraped Yet<\/h2>\n<p>Predict.fun is an onchain prediction market built on BNB Chain. Binary yes\/no markets, share prices trading between 0 and 1, an off-chain CLOB order book matching trades that settle into onchain conditional tokens. If you&#8217;ve built tooling against Polymarket&#8217;s CLOB or Kalshi&#8217;s REST API, the mental model will feel familiar \u2014 but the chain is different, the endpoints are different, and there are a few Predict.fun-specific quirks you&#8217;ll want to know about before you start hitting the API in anger.<\/p>\n<p>This is a direct Predict.fun integration guide. No OddsPapi wrapper, no &#8220;third option&#8221; angle \u2014 the markets that Predict.fun hosts (politics, culture, crypto, non-sports event markets) aren&#8217;t in the OddsPapi coverage set today, so if you want that data you&#8217;re going to talk to Predict.fun directly. This post walks you through exactly how.<\/p>\n<p>(When you want to fold <em>sportsbook<\/em> prices alongside your Predict.fun data for arbitrage or calibration modeling, <a href=\"https:\/\/oddspapi.io\">OddsPapi<\/a> is the fastest way to pull 350+ bookmakers in one call. But that&#8217;s the second half of your stack \u2014 not this post.)<\/p>\n<h2>The Short Version: Two APIs, Two Environments<\/h2>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Environment<\/th>\n<th>Base URL<\/th>\n<th>API Key<\/th>\n<th>Rate Limit<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Mainnet<\/strong><\/td>\n<td><code>https:\/\/api.predict.fun<\/code><\/td>\n<td>Required (Discord ticket to obtain)<\/td>\n<td>240 req\/min default<\/td>\n<\/tr>\n<tr>\n<td><strong>Testnet<\/strong><\/td>\n<td><code>https:\/\/api-testnet.predict.fun<\/code><\/td>\n<td>Not required<\/td>\n<td>240 req\/min<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>Use testnet for everything in this tutorial. You don&#8217;t need an account, you don&#8217;t need a BNB wallet, you don&#8217;t need to file a Discord ticket. Mainnet and testnet share the same endpoint surface, and once you have code working against testnet you swap the base URL and add an <code>x-api-key<\/code> header.<\/p>\n<p>There&#8217;s also an official <a href=\"https:\/\/www.npmjs.com\/package\/@predictdotfun\/sdk\" rel=\"noopener\" target=\"_blank\">TypeScript SDK<\/a> on npm and a <a href=\"https:\/\/github.com\/PredictDotFun\/sdk-python\" rel=\"noopener\" target=\"_blank\">Python SDK<\/a> from the team if you don&#8217;t feel like hand-rolling HTTP calls. For this tutorial we&#8217;re going to call the REST API directly in Python, because that maps onto what you&#8217;ll actually debug in DevTools.<\/p>\n<h2>The ID Taxonomy (This Is the Part That Trips Everyone)<\/h2>\n<p>Predict.fun has <em>four<\/em> different identifiers that refer to slightly different things. You have to know which one each endpoint wants:<\/p>\n<ul>\n<li><strong>Category slug<\/strong> \u2014 human-readable string for an event group (e.g. <code>epl-cry-mac-2025-12-14<\/code>). Used to fetch related markets for one real-world event.<\/li>\n<li><strong>Market ID<\/strong> \u2014 integer (e.g. <code>472<\/code>). This is Predict.fun&#8217;s internal primary key for a market and what the REST API routes care about.<\/li>\n<li><strong>Condition ID<\/strong> \u2014 32-byte hex string (<code>0xbcad63b0\u2026<\/code>). Onchain identifier for the CTF (Conditional Tokens Framework) market. This is the BNB-chain hash, the thing you&#8217;d quote against the smart contract directly.<\/li>\n<li><strong>onChainId (token ID)<\/strong> \u2014 77-digit integer ERC-1155 position ID. One per outcome. This is what you actually buy and sell \u2014 it&#8217;s the token you&#8217;d transfer or stake.<\/li>\n<\/ul>\n<p>Rule of thumb: if you&#8217;re hitting the REST API, use <strong>market ID<\/strong>. If you&#8217;re talking to the onchain contract, use <strong>condition ID<\/strong> (the market hash) and <strong>onChainId<\/strong> (the specific outcome token). The relationship is 1 market \u2192 N outcomes, each outcome has its own onChainId.<\/p>\n<h2>Step 1: List Active Markets<\/h2>\n<pre class=\"wp-block-code\"><code>import requests\n\nBASE = \"https:\/\/api-testnet.predict.fun\"\n\ndef list_markets(limit=20, cursor=None):\n    params = {\"limit\": limit}\n    if cursor:\n        params[\"cursor\"] = cursor\n    r = requests.get(f\"{BASE}\/v1\/markets\", params=params, timeout=10)\n    r.raise_for_status()\n    return r.json()\n\npage = list_markets(limit=5)\nfor m in page[\"data\"]:\n    print(f\"#{m['id']:5} [{m['tradingStatus']:8}] {m['title'][:60]}\")\n\nnext_cursor = page.get(\"cursor\")\nprint(f\"\\nnext cursor: {next_cursor}\")<\/code><\/pre>\n<p>The response is a cursor-paginated envelope:<\/p>\n<pre class=\"wp-block-code\"><code>{\n  \"data\": [ {market...}, {market...}, ... ],\n  \"cursor\": \"NDEw\"   \/\/ base64-encoded cursor; pass it back as ?cursor= on the next page\n}<\/code><\/pre>\n<p>Pagination is cursor-based, not offset-based. Keep hitting <code>?cursor=NEXT<\/code> until the response is empty.<\/p>\n<h3>The Market Object Shape<\/h3>\n<p>Each entry in <code>data[]<\/code> looks like this (trimmed \u2014 there are more fields):<\/p>\n<pre class=\"wp-block-code\"><code>{\n  \"id\": 472,\n  \"conditionId\": \"0xbcad63b00f19d2258f318615eedf2bab7ec5afbec1426d4497f8db19ce3e10f1\",\n  \"title\": \"Crystal Palace FC\",\n  \"question\": \"Crystal Palace FC\",\n  \"description\": \"Crystal Palace FC\",\n  \"categorySlug\": \"epl-cry-mac-2025-12-14\",\n  \"tradingStatus\": \"OPEN\",\n  \"status\": \"REGISTERED\",\n  \"feeRateBps\": 200,\n  \"decimalPrecision\": 3,\n  \"isNegRisk\": true,\n  \"kalshiMarketTicker\": null,\n  \"outcomes\": [\n    {\n      \"indexSet\": 1,\n      \"name\": \"Yes\",\n      \"onChainId\": \"99687883996711364087088282638523080562700966541094159995494261186865075656885\",\n      \"status\": null\n    },\n    {\n      \"indexSet\": 2,\n      \"name\": \"No\",\n      \"onChainId\": \"6040251633342283005115896327558952818368938050220830653939030411351823082209\",\n      \"status\": null\n    }\n  ],\n  \"resolverAddress\": \"0x52DA245ac170155391e7607c67b77D549005002d\",\n  \"shareThreshold\": 100,\n  \"spreadThreshold\": 0.06\n}<\/code><\/pre>\n<p>Three fields worth highlighting:<\/p>\n<ul>\n<li><strong><code>feeRateBps<\/code><\/strong> \u2014 trading fee in basis points (200 = 2%). This is deducted from realized PnL, not from your order.<\/li>\n<li><strong><code>isNegRisk<\/code><\/strong> \u2014 tells you whether the market is part of a &#8220;negative risk&#8221; group (multiple mutually-exclusive markets that share collateral). Relevant for categorical event markets, irrelevant for single yes\/no binaries.<\/li>\n<li><strong><code>kalshiMarketTicker<\/code><\/strong> \u2014 if not null, this market mirrors a Kalshi US-regulated market with the same ticker. Useful if you&#8217;re building Kalshi\u2194onchain arbitrage \u2014 you know which Predict.fun market to query when you see the Kalshi ticker.<\/li>\n<\/ul>\n<h2>Step 2: Fetch the Order Book for a Single Market<\/h2>\n<pre class=\"wp-block-code\"><code>def get_orderbook(market_id):\n    r = requests.get(\n        f\"{BASE}\/v1\/markets\/{market_id}\/orderbook\",\n        timeout=10,\n    )\n    r.raise_for_status()\n    return r.json()[\"data\"]\n\nbook = get_orderbook(472)\nprint(f\"market {book['marketId']}\")\nprint(\"asks:\", book[\"asks\"])\nprint(\"bids:\", book[\"bids\"])<\/code><\/pre>\n<p>Response:<\/p>\n<pre class=\"wp-block-code\"><code>{\n  \"marketId\": 472,\n  \"asks\": [[0.614, 2.2894]],\n  \"bids\": [],\n  \"lastOrderSettled\": {\n    \"id\": \"312750\",\n    \"kind\": \"LIMIT\",\n    \"marketId\": 472,\n    \"outcome\": \"No\",\n    \"price\": \"0.61\",\n    \"side\": \"Bid\"\n  },\n  \"updateTimestampMs\": 1775914128448\n}<\/code><\/pre>\n<p>A few things to notice:<\/p>\n<ul>\n<li><strong>Ladders are <code>[price, size]<\/code> tuples<\/strong>, sorted best first. <code>asks[0]<\/code> is the best ask, <code>bids[0]<\/code> is the best bid.<\/li>\n<li><strong>Prices are share prices (0\u20131), not decimal odds.<\/strong> 0.614 means &#8220;buy one YES share for $0.614 in USDC, redeems for $1 if yes resolves.&#8221; Convert to decimal odds with <code>1 \/ share_price<\/code> if you need to compare against a sportsbook.<\/li>\n<li><strong>Size is in shares<\/strong>, not dollars. A size of 2.2894 at price 0.614 means 2.2894 shares available, or ~$1.41 of USDC notional.<\/li>\n<li><strong><code>lastOrderSettled<\/code><\/strong> is the most recent matched trade. Useful for populating a &#8220;last sale&#8221; column in a dashboard without having to query trade history separately.<\/li>\n<li><strong>Empty ladder side = no open orders.<\/strong> In the sample above, there are zero open bids. That&#8217;s normal for thin markets.<\/li>\n<\/ul>\n<h2>Step 3: Compute Implied Probability and Spread<\/h2>\n<p>The whole point of share prices is they already read as implied probability. Turn that into a quick sanity check:<\/p>\n<pre class=\"wp-block-code\"><code>def analyze(book):\n    asks = book[\"asks\"]\n    bids = book[\"bids\"]\n\n    best_ask = asks[0][0] if asks else None\n    best_bid = bids[0][0] if bids else None\n\n    if best_ask and best_bid:\n        mid = (best_ask + best_bid) \/ 2\n        spread = best_ask - best_bid\n        print(f\"mid price: {mid:.3f} ({mid*100:.1f}% implied prob)\")\n        print(f\"spread:    {spread:.3f} ({spread\/mid*100:.2f}% of mid)\")\n    elif best_ask:\n        print(f\"best ask only: {best_ask} \u2014 no bids in book\")\n    elif best_bid:\n        print(f\"best bid only: {best_bid} \u2014 no asks in book\")\n    else:\n        print(\"empty book\")\n\nanalyze(get_orderbook(472))<\/code><\/pre>\n<p>That spread number is the round-trip cost of taking and flipping a position. On a healthy Predict.fun market it&#8217;s tight (sub-2%). On a thin market it can be 10%+, in which case you&#8217;re pricing the market, not taking it.<\/p>\n<h2>Step 4: Pull Price History (Timeseries)<\/h2>\n<p>Predict.fun exposes a timeseries endpoint for historical prices \u2014 useful for charting, backtesting, and calibration. Query it by market ID:<\/p>\n<pre class=\"wp-block-code\"><code>def get_timeseries(market_id, interval=\"1h\"):\n    r = requests.get(\n        f\"{BASE}\/v1\/markets\/{market_id}\/timeseries\",\n        params={\"interval\": interval},\n        timeout=10,\n    )\n    r.raise_for_status()\n    return r.json()\n\n# Grab 1-hour candles for a market\nts = get_timeseries(472, interval=\"1h\")\nfor point in ts.get(\"data\", [])[:10]:\n    print(point)<\/code><\/pre>\n<p>(Interval options and exact field names are in the OpenAPI reference at <code>https:\/\/api.predict.fun\/docs<\/code> \u2014 the testnet response matches mainnet so you can develop against testnet without burning your mainnet rate limit.)<\/p>\n<h2>Step 5: Filter by Category<\/h2>\n<p>Every market belongs to a category (a real-world event group). Use the category endpoint to find all markets for one event:<\/p>\n<pre class=\"wp-block-code\"><code>def markets_in_category(slug):\n    r = requests.get(\n        f\"{BASE}\/v1\/categories\/{slug}\",\n        timeout=10,\n    )\n    r.raise_for_status()\n    return r.json()\n\ndata = markets_in_category(\"epl-cry-mac-2025-12-14\")\n# Returns the category metadata plus the markets attached to it\nprint(data)<\/code><\/pre>\n<p>For a soccer match, expect the category to contain multiple Predict.fun markets \u2014 home win, away win, draw, totals, and often a set of flat-binary player\/event markets. Each has its own <code>id<\/code> and its own orderbook.<\/p>\n<h2>Step 6: Trading (Signed Orders \u2014 Write Side)<\/h2>\n<p>Reading is free and unauthenticated on testnet. Trading requires authentication:<\/p>\n<ol>\n<li>Hit <code>GET \/v1\/auth\/message<\/code> to retrieve an EIP-712 typed-data message to sign.<\/li>\n<li>Sign it with your EOA wallet or a Privy smart wallet (BNB Chain).<\/li>\n<li>POST the signature to <code>\/v1\/auth\/jwt<\/code>; you get back a JWT.<\/li>\n<li>Include the JWT in the <code>Authorization: Bearer ...<\/code> header on order-placement endpoints.<\/li>\n<\/ol>\n<p>For most readers of this post, you&#8217;re staying on the read side \u2014 orderbook, price, history \u2014 and trading happens through the official SDKs or the Predict.fun UI. If you do need to trade programmatically, the <a href=\"https:\/\/github.com\/PredictDotFun\/sdk-python\" rel=\"noopener\" target=\"_blank\">Python SDK<\/a> wraps the signing flow cleanly.<\/p>\n<h2>Gotchas You&#8217;ll Hit in the First Hour<\/h2>\n<ul>\n<li><strong>Testnet markets expire.<\/strong> The testnet is a real sandbox \u2014 markets resolve, new ones get created, and the IDs you hardcode today will not exist tomorrow. Fetch a live market ID at the start of any script.<\/li>\n<li><strong>Prices are strings in some endpoints, floats in others.<\/strong> The orderbook returns prices as numeric, but <code>lastOrderSettled.price<\/code> is a string. Cast before math.<\/li>\n<li><strong>Empty ladders are normal.<\/strong> A market can have zero bids and one ask, or vice versa. Your code needs to handle both sides being empty.<\/li>\n<li><strong>Share price \u2260 decimal odds.<\/strong> Predict.fun is native-onchain and uses the 0\u20131 share-price convention like Polymarket. Convert with <code>1 \/ share_price<\/code> before comparing to a sportsbook&#8217;s decimal odds.<\/li>\n<li><strong>Mainnet rate limit is 240 req\/min per key.<\/strong> That&#8217;s generous but not unlimited. If you&#8217;re polling 500 markets for live prices, use the orderbook endpoint selectively and consider timeseries for historical work.<\/li>\n<\/ul>\n<h2>Where Predict.fun Fits in Your Stack<\/h2>\n<p>Predict.fun is strongest on onchain-native markets \u2014 crypto events, culture, politics \u2014 where liquidity has shifted toward decentralized venues. It does host some sports markets (the EPL example above is live on testnet), and when <code>kalshiMarketTicker<\/code> is set, you have a direct bridge to the same underlying event on a US-regulated venue.<\/p>\n<p>The natural partner data set is <em>sportsbook<\/em> odds for the same events \u2014 and that&#8217;s exactly what <a href=\"https:\/\/oddspapi.io\">OddsPapi<\/a> is built for. Pull Predict.fun&#8217;s implied probability from the orderbook, pull Pinnacle&#8217;s no-vig line from <code>\/v4\/odds?bookmakers=pinnacle<\/code>, and the gap between them is the signal a prediction-market-vs-sportsbook model lives on.<\/p>\n<pre class=\"wp-block-code\"><code># Predict.fun side: implied probability from share price\npredict_mid = (best_ask + best_bid) \/ 2  # e.g. 0.58\n\n# OddsPapi side: no-vig implied probability from Pinnacle\nimport requests\nodds = requests.get(\n    \"https:\/\/api.oddspapi.io\/v4\/odds\",\n    params={\"apiKey\": \"YOUR_ODDSPAPI_KEY\", \"fixtureId\": \"...\", \"bookmakers\": \"pinnacle\"},\n).json()\npin_price = odds[\"bookmakerOdds\"][\"pinnacle\"][\"markets\"][\"101\"][\"outcomes\"][\"101\"][\"players\"][\"0\"][\"price\"]\npin_implied = 1 \/ pin_price  # needs de-juicing for fair comparison\n\nprint(f\"predict.fun: {predict_mid:.1%}  vs  pinnacle: {pin_implied:.1%}\")<\/code><\/pre>\n<p>One venue for the onchain probability, one venue for 350+ sportsbooks&#8217; worth of traditional pricing. That&#8217;s the whole prediction-market stack with two HTTP calls.<\/p>\n<h2>Get Started<\/h2>\n<ol>\n<li>Clone the tutorial above against <code>https:\/\/api-testnet.predict.fun<\/code> \u2014 no key, no wallet, works immediately.<\/li>\n<li>File a Discord ticket with the Predict.fun team to get a mainnet API key when you&#8217;re ready to move to real data.<\/li>\n<li>For the sportsbook side of your comparison model, grab a free OddsPapi key at <a href=\"https:\/\/oddspapi.io\">oddspapi.io<\/a> and pull Pinnacle, Betfair Exchange, Polymarket, and Kalshi in one call.<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code>curl \"https:\/\/api-testnet.predict.fun\/v1\/markets?limit=5\"<\/code><\/pre>\n<p>Zero auth. Public testnet. Start reading onchain prediction-market orderbooks in one HTTP call.<\/p>\n<p><!--\nFocus Keyphrase: Predict.fun API\nSEO Title: Predict.fun API: Read BNB-Chain Prediction Market Data (Python)\nMeta Description: Predict.fun API tutorial: markets, orderbooks, timeseries and implied probability in Python. No wallet required on testnet. BNB-chain onchain data.\nSlug: predict-fun-api-bnb-prediction-markets\n--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Predict.fun API tutorial: markets, orderbooks, timeseries and implied probability in Python. No wallet required on testnet. BNB-chain onchain data.<\/p>\n","protected":false},"author":2,"featured_media":2855,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[46,40,45,34,11],"class_list":["post-2854","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-how-to-guides","tag-bnb-chain","tag-onchain","tag-predict-fun","tag-prediction-markets","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Predict.fun API: Read BNB-Chain Prediction Market Data (Python) | Odds API Development Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/oddspapi.io\/blog\/predict-fun-api-bnb-prediction-markets\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predict.fun API: Read BNB-Chain Prediction Market Data (Python) | Odds API Development Blog\" \/>\n<meta property=\"og:description\" content=\"Predict.fun API tutorial: markets, orderbooks, timeseries and implied probability in Python. 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