sirius-prediction-api 1.1.0

Python wrapper for the Sirius Prediction Market API — real-time prediction markets, trading signals, sentiment, sports odds, and more.

Sirius Prediction Market API

Python wrapper for the Sirius Prediction Market API on RapidAPI.

Access real-time prediction markets, AI-generated trading signals, market sentiment, sports odds, weather data, and cross-platform odds comparison — all from a single, zero-dependency package.

Installation

pip install sirius-prediction-api

Quick Start

from sirius_prediction_api import SiriusAPI

api = SiriusAPI("your-rapidapi-key")

# Check API status
print(api.health())

# Get active prediction markets
markets = api.markets()
for market in markets:
    print(market["question"], market["odds"])

Get Your API Key

1. Go to RapidAPI - Sirius Prediction Market API
2. Subscribe to a plan (free tier available)
3. Copy your X-RapidAPI-Key from the dashboard

Endpoints

Free Tier

api = SiriusAPI("your-key")

# Active prediction markets with odds
markets = api.markets()

# Trading signals with optional filters
signals = api.signals(limit=10, layer="L1", min_gap=0.10)

# Historical signals with outcomes
history = api.signals_history()

# Market sentiment — Fear & Greed + long/short ratios
sentiment = api.sentiment()

# Sports odds (ESPN + Kalshi)
sports = api.sports()

# Weather-related prediction data
weather = api.weather()

# Cross-platform odds comparison
odds = api.odds()

Basic Tier

# Bot performance — win rate, PnL, track record
performance = api.performance()

# Full trade history with entry/exit details
trades = api.trades()

Pro Tier

# AI brain — optimization decisions and reasoning
brain = api.brain()

Signal Layers

The API provides signals from multiple detection layers:

Layer	Source	Description
L1	Price lag	Cross-platform price discrepancies
L2	News	News-driven market moves
L3	Weather	Weather-based predictions
L4	Kalshi	Kalshi platform arbitrage
L5	Sports	ESPN + Kalshi sports signals

Filter by layer:

# Only price-lag signals with at least 10% gap
signals = api.signals(layer="L1", min_gap=0.10)

Error Handling

The library raises specific exceptions for different error types:

from sirius_prediction_api import (
    SiriusAPI,
    SiriusAuthError,
    SiriusForbiddenError,
    SiriusRateLimitError,
    SiriusAPIError,
)

api = SiriusAPI("your-key")

try:
    brain = api.brain()
except SiriusAuthError:
    print("Invalid API key")
except SiriusForbiddenError:
    print("Upgrade your plan for this endpoint")
except SiriusRateLimitError:
    print("Rate limited — slow down or upgrade")
except SiriusAPIError as e:
    print(f"API error [{e.status_code}]: {e.message}")

Exception	HTTP Code	Meaning
SiriusAuthError	401	Invalid or missing API key
SiriusForbiddenError	403	Endpoint not in your tier
SiriusNotFoundError	404	Resource not found
SiriusRateLimitError	429	Rate limit exceeded
SiriusServerError	5xx	Server-side error
SiriusAPIError	any	Base class for all errors

Configuration

api = SiriusAPI(
    api_key="your-key",
    timeout=60,       # request timeout in seconds (default: 30)
)

Requirements

• Python 3.8+
• Zero dependencies — uses only urllib.request from the standard library

License

MIT