7bb2905cca
- Change SQLite database path from `/home/oib/windsurf/aitbc/data/` to `/opt/data/` - Fix foreign key references to use correct table names (users, wallets, gpu_registry) - Replace governance router with new governance and community routers - Add multi-modal RL router to main application - Simplify DEPLOYMENT_READINESS_REPORT.md to focus on production deployment status - Update governance router with decentralized DAO voting
810 lines
31 KiB
Python
810 lines
31 KiB
Python
#!/usr/bin/env python3
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"""
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Agent Economics Enhancement Tests
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Phase 8.3: OpenClaw Agent Economics Enhancement (Weeks 5-6)
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"""
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import pytest
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import asyncio
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import time
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import json
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import requests
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import statistics
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from typing import Dict, List, Any, Optional, Tuple
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta
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import logging
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from enum import Enum
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class AgentType(Enum):
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"""Agent types in the marketplace"""
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COMPUTE_PROVIDER = "compute_provider"
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COMPUTE_CONSUMER = "compute_consumer"
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POWER_TRADER = "power_trader"
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MARKET_MAKER = "market_maker"
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ARBITRAGE_AGENT = "arbitrage_agent"
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class ReputationLevel(Enum):
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"""Reputation levels for agents"""
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BRONZE = 0.0
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SILVER = 0.6
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GOLD = 0.8
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PLATINUM = 0.9
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DIAMOND = 0.95
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@dataclass
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class AgentEconomics:
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"""Agent economics data"""
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agent_id: str
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agent_type: AgentType
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aitbc_balance: float
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total_earned: float
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total_spent: float
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reputation_score: float
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reputation_level: ReputationLevel
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successful_transactions: int
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failed_transactions: int
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total_transactions: int
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average_rating: float
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certifications: List[str] = field(default_factory=list)
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partnerships: List[str] = field(default_factory=list)
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@dataclass
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class Transaction:
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"""Transaction record"""
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transaction_id: str
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from_agent: str
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to_agent: str
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amount: float
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transaction_type: str
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timestamp: datetime
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status: str
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reputation_impact: float
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@dataclass
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class RewardMechanism:
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"""Reward mechanism configuration"""
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mechanism_id: str
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mechanism_type: str
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performance_threshold: float
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reward_rate: float
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bonus_conditions: Dict[str, Any]
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@dataclass
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class TradingProtocol:
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"""Agent-to-agent trading protocol"""
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protocol_id: str
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protocol_type: str
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participants: List[str]
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terms: Dict[str, Any]
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settlement_conditions: List[str]
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class AgentEconomicsTests:
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"""Test suite for agent economics enhancement"""
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def __init__(self, marketplace_url: str = "http://127.0.0.1:18000"):
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self.marketplace_url = marketplace_url
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self.agents = self._setup_agents()
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self.transactions = []
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self.reward_mechanisms = self._setup_reward_mechanisms()
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self.trading_protocols = self._setup_trading_protocols()
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self.session = requests.Session()
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self.session.timeout = 30
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def _setup_agents(self) -> List[AgentEconomics]:
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"""Setup test agents with economics data"""
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agents = []
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# High-reputation provider
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agents.append(AgentEconomics(
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agent_id="provider_diamond_001",
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agent_type=AgentType.COMPUTE_PROVIDER,
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aitbc_balance=2500.0,
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total_earned=15000.0,
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total_spent=2000.0,
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reputation_score=0.97,
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reputation_level=ReputationLevel.DIAMOND,
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successful_transactions=145,
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failed_transactions=3,
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total_transactions=148,
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average_rating=4.9,
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certifications=["gpu_expert", "ml_specialist", "reliable_provider"],
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partnerships=["enterprise_client_a", "research_lab_b"]
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))
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# Medium-reputation provider
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agents.append(AgentEconomics(
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agent_id="provider_gold_001",
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agent_type=AgentType.COMPUTE_PROVIDER,
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aitbc_balance=800.0,
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total_earned=3500.0,
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total_spent=1200.0,
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reputation_score=0.85,
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reputation_level=ReputationLevel.GOLD,
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successful_transactions=67,
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failed_transactions=8,
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total_transactions=75,
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average_rating=4.3,
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certifications=["gpu_provider"],
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partnerships=["startup_c"]
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))
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# Consumer agent
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agents.append(AgentEconomics(
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agent_id="consumer_silver_001",
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agent_type=AgentType.COMPUTE_CONSUMER,
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aitbc_balance=300.0,
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total_earned=0.0,
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total_spent=1800.0,
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reputation_score=0.72,
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reputation_level=ReputationLevel.SILVER,
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successful_transactions=23,
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failed_transactions=2,
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total_transactions=25,
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average_rating=4.1,
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certifications=["verified_consumer"],
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partnerships=[]
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))
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# Power trader
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agents.append(AgentEconomics(
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agent_id="trader_platinum_001",
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agent_type=AgentType.POWER_TRADER,
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aitbc_balance=1200.0,
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total_earned=8500.0,
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total_spent=6000.0,
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reputation_score=0.92,
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reputation_level=ReputationLevel.PLATINUM,
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successful_transactions=89,
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failed_transactions=5,
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total_transactions=94,
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average_rating=4.7,
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certifications=["certified_trader", "market_analyst"],
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partnerships=["exchange_a", "liquidity_provider_b"]
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))
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# Arbitrage agent
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agents.append(AgentEconomics(
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agent_id="arbitrage_gold_001",
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agent_type=AgentType.ARBITRAGE_AGENT,
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aitbc_balance=600.0,
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total_earned=4200.0,
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total_spent=2800.0,
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reputation_score=0.88,
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reputation_level=ReputationLevel.GOLD,
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successful_transactions=56,
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failed_transactions=4,
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total_transactions=60,
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average_rating=4.5,
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certifications=["arbitrage_specialist"],
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partnerships=["market_maker_c"]
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))
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return agents
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def _setup_reward_mechanisms(self) -> List[RewardMechanism]:
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"""Setup reward mechanisms for testing"""
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return [
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RewardMechanism(
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mechanism_id="performance_bonus_001",
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mechanism_type="performance_based",
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performance_threshold=0.90,
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reward_rate=0.10, # 10% bonus
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bonus_conditions={
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"min_transactions": 10,
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"avg_rating_min": 4.5,
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"uptime_min": 0.95
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}
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),
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RewardMechanism(
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mechanism_id="volume_discount_001",
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mechanism_type="volume_based",
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performance_threshold=1000.0, # 1000 AITBC volume
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reward_rate=0.05, # 5% discount
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bonus_conditions={
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"monthly_volume_min": 1000.0,
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"consistent_trading": True
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}
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),
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RewardMechanism(
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mechanism_id="referral_program_001",
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mechanism_type="referral_based",
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performance_threshold=0.80,
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reward_rate=0.15, # 15% referral bonus
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bonus_conditions={
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"referrals_min": 3,
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"referral_performance_min": 0.85
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}
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)
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]
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def _setup_trading_protocols(self) -> List[TradingProtocol]:
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"""Setup agent-to-agent trading protocols"""
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return [
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TradingProtocol(
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protocol_id="direct_p2p_001",
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protocol_type="direct_peer_to_peer",
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participants=["provider_diamond_001", "consumer_silver_001"],
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terms={
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"price_per_hour": 3.5,
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"min_duration_hours": 2,
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"payment_terms": "prepaid",
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"performance_sla": 0.95
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},
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settlement_conditions=["performance_met", "payment_confirmed"]
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),
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TradingProtocol(
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protocol_id="arbitrage_opportunity_001",
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protocol_type="arbitrage",
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participants=["arbitrage_gold_001", "trader_platinum_001"],
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terms={
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"price_difference_threshold": 0.5,
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"max_trade_size": 100.0,
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"settlement_time": "immediate"
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},
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settlement_conditions=["profit_made", "risk_managed"]
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)
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]
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def _get_agent_by_id(self, agent_id: str) -> Optional[AgentEconomics]:
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"""Get agent by ID"""
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return next((agent for agent in self.agents if agent.agent_id == agent_id), None)
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async def test_agent_reputation_system(self, agent_id: str) -> Dict[str, Any]:
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"""Test agent reputation system"""
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try:
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agent = self._get_agent_by_id(agent_id)
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if not agent:
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return {"error": f"Agent {agent_id} not found"}
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# Test reputation calculation
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reputation_payload = {
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"agent_id": agent_id,
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"transaction_history": {
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"successful": agent.successful_transactions,
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"failed": agent.failed_transactions,
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"total": agent.total_transactions
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},
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"performance_metrics": {
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"average_rating": agent.average_rating,
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"uptime": 0.97,
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"response_time_avg": 0.08
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},
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"certifications": agent.certifications,
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"partnerships": agent.partnerships
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}
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response = self.session.post(
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f"{self.marketplace_url}/v1/agents/reputation/calculate",
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json=reputation_payload,
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timeout=15
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)
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if response.status_code == 200:
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result = response.json()
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return {
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"agent_id": agent_id,
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"current_reputation": agent.reputation_score,
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"calculated_reputation": result.get("reputation_score"),
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"reputation_level": result.get("reputation_level"),
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"reputation_factors": result.get("factors"),
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"accuracy": abs(agent.reputation_score - result.get("reputation_score", 0)) < 0.05,
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"success": True
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}
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else:
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return {
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"agent_id": agent_id,
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"error": f"Reputation calculation failed with status {response.status_code}",
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"success": False
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}
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except Exception as e:
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return {
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"agent_id": agent_id,
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"error": str(e),
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"success": False
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}
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async def test_performance_based_rewards(self, agent_id: str, performance_metrics: Dict[str, Any]) -> Dict[str, Any]:
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"""Test performance-based reward mechanisms"""
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try:
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agent = self._get_agent_by_id(agent_id)
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if not agent:
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return {"error": f"Agent {agent_id} not found"}
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# Test performance reward calculation
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reward_payload = {
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"agent_id": agent_id,
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"performance_metrics": performance_metrics,
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"reward_mechanism": "performance_bonus_001",
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"calculation_period": "monthly"
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}
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response = self.session.post(
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f"{self.marketplace_url}/v1/rewards/calculate",
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json=reward_payload,
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timeout=15
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)
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if response.status_code == 200:
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result = response.json()
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return {
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"agent_id": agent_id,
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"performance_metrics": performance_metrics,
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"reward_amount": result.get("reward_amount"),
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"reward_rate": result.get("reward_rate"),
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"bonus_conditions_met": result.get("bonus_conditions_met"),
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"reward_breakdown": result.get("breakdown"),
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"success": True
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}
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else:
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return {
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"agent_id": agent_id,
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"error": f"Reward calculation failed with status {response.status_code}",
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"success": False
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}
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except Exception as e:
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return {
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"agent_id": agent_id,
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"error": str(e),
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"success": False
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}
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async def test_agent_to_agent_trading(self, protocol_id: str) -> Dict[str, Any]:
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"""Test agent-to-agent AI power trading protocols"""
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try:
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protocol = next((p for p in self.trading_protocols if p.protocol_id == protocol_id), None)
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if not protocol:
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return {"error": f"Protocol {protocol_id} not found"}
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# Test trading protocol execution
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trading_payload = {
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"protocol_id": protocol_id,
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"participants": protocol.participants,
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"terms": protocol.terms,
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"execution_type": "immediate"
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}
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response = self.session.post(
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f"{self.marketplace_url}/v1/trading/execute",
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json=trading_payload,
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timeout=20
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)
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if response.status_code == 200:
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result = response.json()
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# Record transaction
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transaction = Transaction(
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transaction_id=result.get("transaction_id"),
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from_agent=protocol.participants[0],
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to_agent=protocol.participants[1],
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amount=protocol.terms.get("price_per_hour", 0) * protocol.terms.get("min_duration_hours", 1),
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transaction_type=protocol.protocol_type,
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timestamp=datetime.now(),
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status="completed",
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reputation_impact=result.get("reputation_impact", 0.01)
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)
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self.transactions.append(transaction)
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return {
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"protocol_id": protocol_id,
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"transaction_id": transaction.transaction_id,
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"participants": protocol.participants,
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"trading_terms": protocol.terms,
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"execution_result": result,
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"reputation_impact": transaction.reputation_impact,
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"success": True
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}
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else:
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return {
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"protocol_id": protocol_id,
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"error": f"Trading execution failed with status {response.status_code}",
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"success": False
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}
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except Exception as e:
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return {
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"protocol_id": protocol_id,
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"error": str(e),
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"success": False
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}
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async def test_marketplace_analytics(self, time_range: str = "monthly") -> Dict[str, Any]:
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"""Test marketplace analytics and economic insights"""
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try:
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analytics_payload = {
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"time_range": time_range,
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"metrics": [
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"trading_volume",
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"agent_participation",
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"price_trends",
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"reputation_distribution",
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"earnings_analysis"
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]
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}
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response = self.session.post(
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f"{self.marketplace_url}/v1/analytics/marketplace",
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json=analytics_payload,
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timeout=15
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)
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if response.status_code == 200:
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result = response.json()
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return {
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"time_range": time_range,
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"trading_volume": result.get("trading_volume"),
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"agent_participation": result.get("agent_participation"),
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"price_trends": result.get("price_trends"),
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"reputation_distribution": result.get("reputation_distribution"),
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"earnings_analysis": result.get("earnings_analysis"),
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"economic_insights": result.get("insights"),
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"success": True
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}
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else:
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return {
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"time_range": time_range,
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"error": f"Analytics failed with status {response.status_code}",
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"success": False
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}
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except Exception as e:
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return {
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"time_range": time_range,
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"error": str(e),
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"success": False
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}
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async def test_agent_certification(self, agent_id: str, certification_type: str) -> Dict[str, Any]:
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"""Test agent certification and partnership programs"""
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try:
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agent = self._get_agent_by_id(agent_id)
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if not agent:
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return {"error": f"Agent {agent_id} not found"}
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# Test certification process
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certification_payload = {
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"agent_id": agent_id,
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"certification_type": certification_type,
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"current_certifications": agent.certifications,
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"performance_history": {
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"successful_transactions": agent.successful_transactions,
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"average_rating": agent.average_rating,
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"reputation_score": agent.reputation_score
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}
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}
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response = self.session.post(
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f"{self.marketplace_url}/v1/certifications/evaluate",
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json=certification_payload,
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timeout=15
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)
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if response.status_code == 200:
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result = response.json()
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return {
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"agent_id": agent_id,
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"certification_type": certification_type,
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"certification_granted": result.get("granted", False),
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"certification_level": result.get("level"),
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"valid_until": result.get("valid_until"),
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"requirements_met": result.get("requirements_met"),
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"benefits": result.get("benefits"),
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"success": True
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}
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else:
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return {
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"agent_id": agent_id,
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"certification_type": certification_type,
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"error": f"Certification failed with status {response.status_code}",
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"success": False
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}
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except Exception as e:
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return {
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"agent_id": agent_id,
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"certification_type": certification_type,
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"error": str(e),
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"success": False
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}
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async def test_earnings_analysis(self, agent_id: str, period: str = "monthly") -> Dict[str, Any]:
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"""Test agent earnings analysis and projections"""
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try:
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agent = self._get_agent_by_id(agent_id)
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if not agent:
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return {"error": f"Agent {agent_id} not found"}
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# Test earnings analysis
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earnings_payload = {
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"agent_id": agent_id,
|
|
"analysis_period": period,
|
|
"historical_data": {
|
|
"total_earned": agent.total_earned,
|
|
"total_spent": agent.total_spent,
|
|
"transaction_count": agent.total_transactions,
|
|
"average_transaction_value": (agent.total_earned + agent.total_spent) / max(agent.total_transactions, 1)
|
|
}
|
|
}
|
|
|
|
response = self.session.post(
|
|
f"{self.marketplace_url}/v1/analytics/earnings",
|
|
json=earnings_payload,
|
|
timeout=15
|
|
)
|
|
|
|
if response.status_code == 200:
|
|
result = response.json()
|
|
|
|
return {
|
|
"agent_id": agent_id,
|
|
"analysis_period": period,
|
|
"current_earnings": agent.total_earned,
|
|
"earnings_trend": result.get("trend"),
|
|
"projected_earnings": result.get("projected"),
|
|
"earnings_breakdown": result.get("breakdown"),
|
|
"optimization_suggestions": result.get("suggestions"),
|
|
"success": True
|
|
}
|
|
else:
|
|
return {
|
|
"agent_id": agent_id,
|
|
"analysis_period": period,
|
|
"error": f"Earnings analysis failed with status {response.status_code}",
|
|
"success": False
|
|
}
|
|
|
|
except Exception as e:
|
|
return {
|
|
"agent_id": agent_id,
|
|
"analysis_period": period,
|
|
"error": str(e),
|
|
"success": False
|
|
}
|
|
|
|
async def test_trust_system_accuracy(self) -> Dict[str, Any]:
|
|
"""Test trust system accuracy and reliability"""
|
|
try:
|
|
# Test trust system across all agents
|
|
trust_results = []
|
|
|
|
for agent in self.agents:
|
|
trust_payload = {
|
|
"agent_id": agent.agent_id,
|
|
"reputation_score": agent.reputation_score,
|
|
"transaction_history": {
|
|
"successful": agent.successful_transactions,
|
|
"failed": agent.failed_transactions,
|
|
"total": agent.total_transactions
|
|
},
|
|
"certifications": agent.certifications,
|
|
"partnerships": agent.partnerships
|
|
}
|
|
|
|
response = self.session.post(
|
|
f"{self.marketplace_url}/v1/trust/evaluate",
|
|
json=trust_payload,
|
|
timeout=10
|
|
)
|
|
|
|
if response.status_code == 200:
|
|
result = response.json()
|
|
trust_results.append({
|
|
"agent_id": agent.agent_id,
|
|
"actual_reputation": agent.reputation_score,
|
|
"predicted_trust": result.get("trust_score"),
|
|
"accuracy": abs(agent.reputation_score - result.get("trust_score", 0)),
|
|
"confidence": result.get("confidence", 0)
|
|
})
|
|
|
|
if trust_results:
|
|
avg_accuracy = statistics.mean([r["accuracy"] for r in trust_results])
|
|
avg_confidence = statistics.mean([r["confidence"] for r in trust_results])
|
|
|
|
return {
|
|
"total_agents_tested": len(trust_results),
|
|
"average_accuracy": avg_accuracy,
|
|
"target_accuracy": 0.95, # 95% accuracy target
|
|
"meets_target": avg_accuracy <= 0.05, # Within 5% error margin
|
|
"average_confidence": avg_confidence,
|
|
"trust_results": trust_results,
|
|
"success": True
|
|
}
|
|
else:
|
|
return {
|
|
"error": "No trust results available",
|
|
"success": False
|
|
}
|
|
|
|
except Exception as e:
|
|
return {"error": str(e), "success": False}
|
|
|
|
# Test Fixtures
|
|
@pytest.fixture
|
|
async def agent_economics_tests():
|
|
"""Create agent economics test instance"""
|
|
return AgentEconomicsTests()
|
|
|
|
@pytest.fixture
|
|
def sample_performance_metrics():
|
|
"""Sample performance metrics for testing"""
|
|
return {
|
|
"uptime": 0.98,
|
|
"response_time_avg": 0.07,
|
|
"task_completion_rate": 0.96,
|
|
"gpu_utilization_avg": 0.89,
|
|
"customer_satisfaction": 4.8,
|
|
"monthly_volume": 1500.0
|
|
}
|
|
|
|
# Test Classes
|
|
class TestAgentReputationSystem:
|
|
"""Test agent reputation and trust systems"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_reputation_calculation_accuracy(self, agent_economics_tests):
|
|
"""Test reputation calculation accuracy"""
|
|
test_agents = ["provider_diamond_001", "provider_gold_001", "trader_platinum_001"]
|
|
|
|
for agent_id in test_agents:
|
|
result = await agent_economics_tests.test_agent_reputation_system(agent_id)
|
|
|
|
assert result.get("success", False), f"Reputation calculation failed for {agent_id}"
|
|
assert result.get("accuracy", False), f"Reputation calculation inaccurate for {agent_id}"
|
|
assert "reputation_level" in result, f"No reputation level for {agent_id}"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_trust_system_reliability(self, agent_economics_tests):
|
|
"""Test trust system reliability across all agents"""
|
|
result = await agent_economics_tests.test_trust_system_accuracy()
|
|
|
|
assert result.get("success", False), "Trust system accuracy test failed"
|
|
assert result.get("meets_target", False), "Trust system does not meet accuracy target"
|
|
assert result.get("average_accuracy", 1.0) <= 0.05, "Trust system accuracy too low"
|
|
assert result.get("average_confidence", 0) >= 0.8, "Trust system confidence too low"
|
|
|
|
class TestRewardMechanisms:
|
|
"""Test performance-based reward mechanisms"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_performance_based_rewards(self, agent_economics_tests, sample_performance_metrics):
|
|
"""Test performance-based reward calculation"""
|
|
test_agents = ["provider_diamond_001", "trader_platinum_001"]
|
|
|
|
for agent_id in test_agents:
|
|
result = await agent_economics_tests.test_performance_based_rewards(
|
|
agent_id,
|
|
sample_performance_metrics
|
|
)
|
|
|
|
assert result.get("success", False), f"Reward calculation failed for {agent_id}"
|
|
assert "reward_amount" in result, f"No reward amount for {agent_id}"
|
|
assert result.get("reward_amount", 0) >= 0, f"Negative reward for {agent_id}"
|
|
assert "bonus_conditions_met" in result, f"No bonus conditions for {agent_id}"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_volume_based_rewards(self, agent_economics_tests):
|
|
"""Test volume-based reward mechanisms"""
|
|
high_volume_metrics = {
|
|
"monthly_volume": 2500.0,
|
|
"consistent_trading": True,
|
|
"transaction_count": 150
|
|
}
|
|
|
|
result = await agent_economics_tests.test_performance_based_rewards(
|
|
"trader_platinum_001",
|
|
high_volume_metrics
|
|
)
|
|
|
|
assert result.get("success", False), "Volume-based reward test failed"
|
|
assert result.get("reward_amount", 0) > 0, "No volume reward calculated"
|
|
|
|
class TestAgentToAgentTrading:
|
|
"""Test agent-to-agent AI power trading protocols"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_direct_p2p_trading(self, agent_economics_tests):
|
|
"""Test direct peer-to-peer trading protocol"""
|
|
result = await agent_economics_tests.test_agent_to_agent_trading("direct_p2p_001")
|
|
|
|
assert result.get("success", False), "Direct P2P trading failed"
|
|
assert "transaction_id" in result, "No transaction ID generated"
|
|
assert result.get("reputation_impact", 0) > 0, "No reputation impact calculated"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_arbitrage_trading(self, agent_economics_tests):
|
|
"""Test arbitrage trading protocol"""
|
|
result = await agent_economics_tests.test_agent_to_agent_trading("arbitrage_opportunity_001")
|
|
|
|
assert result.get("success", False), "Arbitrage trading failed"
|
|
assert "transaction_id" in result, "No transaction ID for arbitrage"
|
|
assert result.get("participants", []) == 2, "Incorrect number of participants"
|
|
|
|
class TestMarketplaceAnalytics:
|
|
"""Test marketplace analytics and economic insights"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_monthly_analytics(self, agent_economics_tests):
|
|
"""Test monthly marketplace analytics"""
|
|
result = await agent_economics_tests.test_marketplace_analytics("monthly")
|
|
|
|
assert result.get("success", False), "Monthly analytics test failed"
|
|
assert "trading_volume" in result, "No trading volume data"
|
|
assert "agent_participation" in result, "No agent participation data"
|
|
assert "price_trends" in result, "No price trends data"
|
|
assert "earnings_analysis" in result, "No earnings analysis data"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_weekly_analytics(self, agent_economics_tests):
|
|
"""Test weekly marketplace analytics"""
|
|
result = await agent_economics_tests.test_marketplace_analytics("weekly")
|
|
|
|
assert result.get("success", False), "Weekly analytics test failed"
|
|
assert "economic_insights" in result, "No economic insights provided"
|
|
|
|
class TestAgentCertification:
|
|
"""Test agent certification and partnership programs"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_gpu_expert_certification(self, agent_economics_tests):
|
|
"""Test GPU expert certification"""
|
|
result = await agent_economics_tests.test_agent_certification(
|
|
"provider_diamond_001",
|
|
"gpu_expert"
|
|
)
|
|
|
|
assert result.get("success", False), "GPU expert certification test failed"
|
|
assert "certification_granted" in result, "No certification result"
|
|
assert "certification_level" in result, "No certification level"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_market_analyst_certification(self, agent_economics_tests):
|
|
"""Test market analyst certification"""
|
|
result = await agent_economics_tests.test_agent_certification(
|
|
"trader_platinum_001",
|
|
"market_analyst"
|
|
)
|
|
|
|
assert result.get("success", False), "Market analyst certification test failed"
|
|
assert result.get("certification_granted", False), "Certification not granted"
|
|
|
|
class TestEarningsAnalysis:
|
|
"""Test agent earnings analysis and projections"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_monthly_earnings_analysis(self, agent_economics_tests):
|
|
"""Test monthly earnings analysis"""
|
|
result = await agent_economics_tests.test_earnings_analysis(
|
|
"provider_diamond_001",
|
|
"monthly"
|
|
)
|
|
|
|
assert result.get("success", False), "Monthly earnings analysis failed"
|
|
assert "earnings_trend" in result, "No earnings trend provided"
|
|
assert "projected_earnings" in result, "No earnings projection provided"
|
|
assert "optimization_suggestions" in result, "No optimization suggestions"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_earnings_projections(self, agent_economics_tests):
|
|
"""Test earnings projections for different agent types"""
|
|
test_agents = ["provider_diamond_001", "trader_platinum_001", "arbitrage_gold_001"]
|
|
|
|
for agent_id in test_agents:
|
|
result = await agent_economics_tests.test_earnings_analysis(agent_id, "monthly")
|
|
|
|
assert result.get("success", False), f"Earnings analysis failed for {agent_id}"
|
|
assert result.get("projected_earnings", 0) > 0, f"No positive earnings projection for {agent_id}"
|
|
|
|
if __name__ == "__main__":
|
|
pytest.main([__file__, "-v", "--tb=short"])
|