Allora Python SDK

What You'll Learn

How to install and set up the Allora Python SDK for your applications
Complete API reference with method signatures and usage examples
Best practices for integrating Allora Network data into Python workflows
Error handling and authentication strategies for production use

Overview

The Allora Python SDK provides a convenient way to interact with the Allora API from Python applications.

Why Use the Python SDK?

Development Benefits:

Pythonic interface: Natural Python patterns and conventions
Data science integration: Seamless compatibility with pandas, numpy, and ML libraries
Production ready: Robust error handling and authentication support
Comprehensive coverage: Access to all Allora Network API endpoints

Use Cases:

Data analysis: Retrieve and analyze network inference data
Machine learning: Integrate predictions into ML pipelines
Backend services: Build Python web applications with Allora data
Research: Academic and commercial research applications

Installation

You can install the Allora Python SDK using pip:

pip install allora_sdk

Installation Benefits:

Simple setup: One command installation with automatic dependencies
Version management: Pip handles SDK updates and compatibility
Virtual environment support: Works with conda, venv, and other environments
Cross-platform: Compatible with Windows, macOS, and Linux

Basic Usage

Here's how to use the Allora Python SDK:

from allora_sdk import AlloraClient
 
# Initialize the client
client = AlloraClient(
    chain="testnet",  # Use "mainnet" for mainnet
    api_key="YOUR_API_KEY"  # Optional, but recommended for production use
)
 
# Fetch all available topics
def fetch_topics():
    try:
        topics = client.get_all_topics()
        print(f"Available topics: {topics}")
    except Exception as e:
        print(f"Error fetching topics: {e}")
 
# Fetch inference for a specific topic
def fetch_inference(topic_id):
    try:
        inference = client.get_inference_by_topic_id(topic_id)
        print(f"Inference data: {inference}")
    except Exception as e:
        print(f"Error fetching inference: {e}")
 
# Fetch price inference for a specific asset and timeframe
def fetch_price_inference():
    try:
        inference = client.get_price_inference(
            asset="BTC",
            timeframe="8h"
        )
        print(f"Price inference data: {inference}")
    except Exception as e:
        print(f"Error fetching price inference: {e}")

Code Example Breakdown

Client Initialization:

Chain selection: Choose between testnet and mainnet environments
API key: Optional authentication for production use and rate limiting
Configuration: Simple constructor with sensible defaults

Error Handling:

Try-catch blocks: Robust error management for network operations
Graceful degradation: Handle API failures without crashing applications
Debugging support: Clear error messages for development and troubleshooting

The API key is optional but recommended for production use. If not provided, a default API key will be used, which may be subject to rate limiting.

API Reference

`AlloraClient`

The main class for interacting with the Allora API.

Constructor

def __init__(self, chain="testnet", api_key=None, base_api_url=None):
    """
    Initialize the Allora API client.
    
    Args:
        chain (str): The chain to use. Can be "testnet" or "mainnet".
        api_key (str, optional): Your API key. Recommended for production use.
        base_api_url (str, optional): The base URL for the API.
    """

Constructor Parameters:

chain: Network environment selection for development or production
api_key: Authentication token for enhanced rate limits and tracking
base_api_url: Custom API endpoint for specialized deployments

Core Methods

`get_all_topics()`

Fetches all available topics from the Allora API.

Method Signature:

def get_all_topics(self) -> dict:
    """
    Retrieve all available topics from the network.
    
    Returns:
        dict: Dictionary containing all topics with their metadata
    
    Raises:
        AlloraAPIError: When API request fails
        NetworkError: When network connectivity issues occur
    """

Usage Pattern:

Topic discovery: Find available prediction categories
Network exploration: Understand current network offerings
Integration planning: Select appropriate topics for your application

`get_inference_by_topic_id(topic_id)`

Retrieves the latest inference for a specific topic.

Method Signature:

def get_inference_by_topic_id(self, topic_id: int) -> dict:
    """
    Get the most recent inference for a given topic.
    
    Args:
        topic_id (int): The ID of the topic to query
        
    Returns:
        dict: Inference data including prediction and metadata
        
    Raises:
        ValueError: When topic_id is invalid
        AlloraAPIError: When API request fails
    """

Use Cases:

Real-time data: Access current network predictions
Application integration: Feed live data into your applications
Decision support: Use predictions for automated decision making

`get_price_inference(asset, timeframe)`

Fetches price predictions for specific assets and timeframes.

Method Signature:

def get_price_inference(self, asset: str, timeframe: str) -> dict:
    """
    Retrieve price predictions for cryptocurrency assets.
    
    Args:
        asset (str): Asset symbol (e.g., "BTC", "ETH")
        timeframe (str): Prediction timeframe (e.g., "1h", "8h", "24h")
        
    Returns:
        dict: Price inference data with prediction and confidence metrics
        
    Raises:
        ValueError: When asset or timeframe is not supported
        AlloraAPIError: When API request fails
    """

Supported Parameters:

Assets: Major cryptocurrencies and trading pairs
Timeframes: Various prediction horizons from minutes to days
Return format: Structured data with predictions and confidence intervals

Advanced Usage Patterns

Data Integration

Pandas Integration Example:

import pandas as pd
from allora_sdk import AlloraClient
 
client = AlloraClient(chain="mainnet", api_key="your_key")
 
# Get multiple topics and convert to DataFrame
topics = client.get_all_topics()
df = pd.DataFrame(topics['data'])
 
# Analyze topic metadata
print(df.describe())
print(df.groupby('category').size())

Benefits for Data Science:

DataFrame compatibility: Direct integration with pandas workflows
Statistical analysis: Easy computation of metrics and summaries
Visualization: Seamless plotting with matplotlib, seaborn, or plotly
Machine learning: Feature engineering for predictive models

Production Deployment

Robust Error Handling:

import logging
from allora_sdk import AlloraClient, AlloraAPIError
 
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
 
class AlloraService:
    def __init__(self, api_key):
        self.client = AlloraClient(
            chain="mainnet",
            api_key=api_key
        )
    
    def get_inference_safely(self, topic_id, retries=3):
        for attempt in range(retries):
            try:
                return self.client.get_inference_by_topic_id(topic_id)
            except AlloraAPIError as e:
                logger.warning(f"API error on attempt {attempt + 1}: {e}")
                if attempt == retries - 1:
                    raise
                time.sleep(2 ** attempt)  # Exponential backoff

Production Considerations:

Retry logic: Handle temporary network failures gracefully
Logging: Track API usage and errors for monitoring
Rate limiting: Implement delays to respect API limits
Caching: Store frequently accessed data to reduce API calls

Authentication and Configuration

API Key Management

Best Practices:

Environment variables: Store API keys securely outside code
Configuration files: Use config files with proper permissions
Key rotation: Regularly update API keys for security
Monitoring: Track API usage for cost and performance optimization

Network Selection

Environment Strategy:

Development: Use testnet for development and testing
Staging: Validate with testnet before production deployment
Production: Switch to mainnet for live applications
Fallback: Implement graceful degradation when networks are unavailable

Integration Examples

Web Application Integration

Flask Example:

from flask import Flask, jsonify
from allora_sdk import AlloraClient
 
app = Flask(__name__)
client = AlloraClient(chain="mainnet", api_key=os.getenv("ALLORA_API_KEY"))
 
@app.route('/api/inference/<int:topic_id>')
def get_inference(topic_id):
    try:
        inference = client.get_inference_by_topic_id(topic_id)
        return jsonify(inference)
    except Exception as e:
        return jsonify({"error": str(e)}), 500

Automated Trading Systems

Trading Bot Integration:

class TradingBot:
    def __init__(self, api_key):
        self.allora = AlloraClient(chain="mainnet", api_key=api_key)
    
    def make_trading_decision(self, asset):
        price_inference = self.allora.get_price_inference(asset, "1h")
        confidence = price_inference.get('confidence', 0)
        
        if confidence > 0.8:
            return self.execute_trade(asset, price_inference)
        return None

Prerequisites

Python 3.7+: Modern Python version with full feature support
Network access: Internet connectivity for API communications
API credentials: Optional API key for enhanced functionality
Development environment: Python package manager (pip) and virtual environment

Next Steps

Explore the TypeScript SDK for JavaScript/TypeScript applications
Review API endpoint documentation for additional integration options
Study consumer examples for implementation patterns
Learn about network topics for data source understanding

TypeScript SDK Workers