Fetch and Filter Arbitrage Opportunities

The arbitrage opportunities endpoint is the core data feed for API key customers. It returns a scored and classified list of price discrepancies detected across Arbitrage integrated platforms — Polymarket, Limitless, Predict.fun, Manifold, PredictIt, Azuro, and Drift — so your bot can evaluate and act on them programmatically. Every opportunity includes fee-adjusted profit estimates, lifecycle status, and a multi-factor quality score to help you separate signal from noise.

Endpoint

GET https://api.predexy.com/api/v1/external/arbitrage/opportunities

Authentication: X-API-Key header with your pdx_ key. This endpoint requires the read:arbitrage permission.

Query parameters

Parameter	Type	Default	Description
`min_score`	integer (0–100)	40	Minimum arbitrage score to include.
`classification`	string	—	Filter by tier: `actionable`, `informational`, or `noise`.
`category`	string	—	Market category, e.g. `crypto`, `politics`.
`status`	string	—	Lifecycle state: `active`, `stale`, or `resolved`.
`guarantee`	string	—	Policy guarantee class: `STRICT` or `QUASI`.
`risk_gate`	string	—	Runtime risk gate: `allowed`, `degraded`, or `blocked`.
`limit`	integer (1–200)	50	Maximum number of opportunities to return.

Understanding classification

Arbitrage scoring engine assigns each opportunity a score from 0 to 100 based on three factors:

Execution quality — spread magnitude, net profit after fees, fee efficiency
Market quality — liquidity depth, 24-hour trading volume
Temporal quality — time remaining before market expiry (longer is better)

That score maps to a classification tier:

Classification	Score range	Additional criteria
`actionable`	≥ 70	Positive net spread, liquidity > $500
`informational`	40–69	Or low liquidity at any score
`noise`	< 40	—

Understanding risk gate

The risk_gate_state field reflects the current runtime governance state:

allowed — no restrictions; normal execution
degraded — execution may be affected by platform or global controls
blocked — execution is blocked by policy

Combine classification=actionable and risk_gate=allowed to get the best opportunities with no execution restrictions. Add status=active to exclude stale or resolved entries.

Example request

The following fetches up to 25 actionable, active opportunities with a minimum score of 70, filtered to risk gate allowed:

curl "https://api.predexy.com/api/v1/external/arbitrage/opportunities\
?classification=actionable\
&status=active\
&min_score=70\
&risk_gate=allowed\
&limit=25" \
  -H "X-API-Key: pdx_a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"

import httpx

API_KEY = "pdx_a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
BASE_URL = "https://api.predexy.com"

params = {
    "classification": "actionable",
    "status": "active",
    "min_score": 70,
    "risk_gate": "allowed",
    "limit": 25,
}

response = httpx.get(
    f"{BASE_URL}/api/v1/external/arbitrage/opportunities",
    headers={"X-API-Key": API_KEY},
    params=params,
)
response.raise_for_status()
payload = response.json()

opportunities = payload["data"]["opportunities"]
stats = payload["data"]["stats"]

print(f"Actionable opportunities: {stats['actionable_count']}")
for opp in opportunities:
    print(
        f"{opp['question_title']} | "
        f"Score: {opp['arbitrage_score']} | "
        f"Net spread: {opp['net_spread']:.3f} | "
        f"Profit/100: ${opp['estimated_profit']:.2f}"
    )

Sample response

{
  "data": {
    "opportunities": [
      {
        "id": "arb_poly_limit_abc123",
        "canonical_question_id": "550e8400-e29b-41d4-a716-446655440000",
        "question_title": "Will BTC reach $100k by 2026?",
        "question_slug": "will-btc-reach-100k-by-2026",
        "category": "crypto",
        "type": "direct",
        "buy_platform": {
          "id": "a1b2c3d4-...",
          "slug": "limitless",
          "name": "Limitless"
        },
        "sell_platform": {
          "id": "e5f6g7h8-...",
          "slug": "polymarket",
          "name": "Polymarket"
        },
        "buy_price": 0.42,
        "sell_price": 0.48,
        "spread": 0.06,
        "spread_bps": 600,
        "net_spread": 0.02,
        "estimated_profit": 2.00,
        "arbitrage_score": 78,
        "classification": "actionable",
        "status": "active",
        "liquidity": 15000.00,
        "volume24h": 52000.00,
        "risk_gate_state": "allowed",
        "guarantee": "STRICT",
        "detected_at": "2026-04-26T10:15:00Z",
        "fees": {
          "buy_fee": 0.02,
          "sell_fee": 0.02,
          "total_fees": 0.04
        }
      }
    ],
    "stats": {
      "total_opportunities": 47,
      "actionable_count": 12,
      "informational_count": 23,
      "avg_spread": 0.034,
      "max_profit": 8.50,
      "platform_pairs": {
        "polymarket↔limitless": 15,
        "polymarket↔predictfun": 8
      },
      "scanned_questions": 312,
      "duration_ms": 1250,
      "scanned_at": "2026-04-26T10:15:05Z"
    }
  },
  "meta": {
    "count": 1,
    "offset": 0,
    "limit": 25,
    "source": "database"
  }
}

Reading the response

Opportunity fields

Each object in opportunities represents a detected price discrepancy. Key fields:

Field	Description
`id`	Unique opportunity identifier
`question_title`	Human-readable question text
`type`	`direct` (same outcome, different prices) or `dutch_book` (buy Yes on one platform + No on another for < $1 combined)
`buy_platform` / `sell_platform`	Platform objects with `id`, `slug`, and `name`
`buy_price` / `sell_price`	Prices as probabilities (0–1)
`net_spread`	Spread remaining after deducting platform fees
`estimated_profit`	Estimated profit per $100 deployed
`arbitrage_score`	Quality score (0–100)
`classification`	`actionable`, `informational`, or `noise`
`status`	`active`, `stale`, or `resolved`
`risk_gate_state`	`allowed`, `degraded`, or `blocked`
`guarantee`	`STRICT` (policy-approved) or `QUASI` (weaker guarantee)
`fees`	Breakdown of `buy_fee`, `sell_fee`, and `total_fees` as fractions

Stats object

The stats object summarizes the most recent scan across all opportunities (not just the filtered subset you received):

Field	Description
`total_opportunities`	Total detected in this scan
`actionable_count`	How many scored ≥ 70 with positive profit and adequate liquidity
`informational_count`	How many scored 40–69 or had low liquidity
`avg_spread`	Average raw spread across all opportunities
`max_profit`	Highest estimated profit per $100 in this scan
`scanned_questions`	Total canonical questions evaluated
`platform_pairs`	Per-pair opportunity counts

Use stats to understand current market conditions before acting on individual opportunities. A low actionable_count or low avg_spread signals a quiet market.

meta.source

The meta.source field tells you where the data came from:

Value	Meaning
`database`	Live data served directly from the database
`cache`	Served from a short-lived cache layer
`empty`	No opportunities matched; response is empty

Polling for opportunities

Most trading bots poll this endpoint on a schedule. The background scanner runs continuously, so polling every 30–60 seconds gives you fresh data without burning your rate limit.

import httpx
import time

API_KEY = "pdx_a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
BASE_URL = "https://api.predexy.com"
POLL_INTERVAL_SECONDS = 30

def fetch_opportunities():
    response = httpx.get(
        f"{BASE_URL}/api/v1/external/arbitrage/opportunities",
        headers={"X-API-Key": API_KEY},
        params={
            "classification": "actionable",
            "status": "active",
            "min_score": 70,
            "risk_gate": "allowed",
            "limit": 50,
        },
        timeout=10,
    )

    if response.status_code == 429:
        reset = int(response.headers.get("X-RateLimit-Reset", time.time() + 60))
        wait = max(0, reset - time.time())
        print(f"Rate limited. Waiting {wait:.0f}s.")
        time.sleep(wait)
        return []

    response.raise_for_status()
    payload = response.json()
    return payload["data"]["opportunities"]

while True:
    opportunities = fetch_opportunities()
    print(f"Found {len(opportunities)} actionable opportunities")
    for opp in opportunities:
        # your execution logic here
        pass
    time.sleep(POLL_INTERVAL_SECONDS)

Check X-RateLimit-Remaining on each response. If it drops near zero, slow your polling cadence or upgrade your key’s rate limit in the Developer Console.

Get Started

Core Concepts

Guides

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Reference

Fetch and Filter Arbitrage Opportunities

Endpoint

Query parameters

Understanding classification

Understanding risk gate

Example request

Sample response

Reading the response

Opportunity fields

Stats object

meta.source

Polling for opportunities

​Endpoint

​Query parameters

​Understanding classification

​Understanding risk gate

​Example request

​Sample response

​Reading the response

​Opportunity fields

​Stats object

​meta.source

​Polling for opportunities

Endpoint

Query parameters

Understanding classification

Understanding risk gate

Example request

Sample response

Reading the response

Opportunity fields

Stats object

meta.source

Polling for opportunities