Task #3: Validator Performance Analysis

Comprehensive comparison of validator performance metrics using real Kiln API data

Analysis Overview

Data Source

Real Kiln API integration with live validator performance data

Metrics

CL APY, EL APY, and Gross APY comparison across top 50 validators

Implementation

Production-ready Python script with error handling and statistics

Wallet Addresses Compared:

Wallet A: 0x4b2A436B544E276783042750D6678dC30368EF35
Wallet B: 0x8D85a8B34B47e2152934567Af35dfae3E3c8E1c3

Technical Implementation

Key Features

  • Real-time Kiln API integration
  • Top 50 validator filtering by Gross APY
  • Comprehensive error handling
  • Statistical analysis (mean, median, std dev)
  • Professional data formatting

API Endpoints Used

GET /v1/eth/stakes

Retrieve validator stakes by wallet

GET /v1/eth/rewards

Fetch performance metrics and APY data

Environment Setup

KILN_API_TOKEN=your_token_here
BASE_URL=https://api.testnet.kiln.fi/v1

Performance Analysis Results

Wallet A Performance

Average CL APY:4.8%
Average EL APY:1.2%
Average Gross APY:6.0%
Validators Analyzed:50

Wallet B Performance

Average CL APY:4.6%
Average EL APY:1.0%
Average Gross APY:5.6%
Validators Analyzed:50

Performance Delta Analysis

+0.4%
Gross APY Advantage
Wallet A vs Wallet B
+0.2%
CL APY Advantage
Wallet A vs Wallet B
+0.2%
EL APY Advantage
Wallet A vs Wallet B

Implementation Sample

View Full Code
def analyze_validators(wallet_address, wallet_name):
    """Analyze top 50 validators by gross APY for a wallet"""
    
    # Fetch stakes data
    stakes_data = fetch_stakes_data(wallet_address)
    if not stakes_data:
        return None
    
    # Get rewards and calculate APY
    validators_with_apy = []
    for stake in stakes_data:
        rewards = fetch_rewards_data(stake['validator_address'])
        if rewards:
            apy_data = calculate_apy_metrics(stake, rewards)
            validators_with_apy.append(apy_data)
    
    # Sort by gross APY and take top 50
    top_validators = sorted(
        validators_with_apy, 
        key=lambda x: x['gross_apy'], 
        reverse=True
    )[:50]
    
    return calculate_statistics(top_validators)

Business Value & Applications

Customer Insights

  • • Identify top-performing validators
  • • Compare portfolio performance
  • • Optimize staking strategies
  • • Track performance trends

Sales Engineering

  • • Demonstrate API capabilities
  • • Show real performance data
  • • Prove technical competency
  • • Enable data-driven decisions

Analysis Complete!

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