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import asyncio |
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import aiohttp |
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import json |
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import os |
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import time |
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from datetime import datetime |
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from typing import Dict, List, Any, Optional |
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import logging |
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import sqlite3 |
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class EnhancedEarningEngine: |
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"""Ultra-enhanced earnings using Chase's complete API arsenal""" |
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def __init__(self): |
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self.api_keys = self.load_all_keys() |
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self.daily_earnings = 0.0 |
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self.active_strategies = [] |
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self.setup_database() |
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self.logger = logging.getLogger('EnhancedEarning') |
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def load_all_keys(self) -> Dict[str, str]: |
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"""Load all Chase's API keys""" |
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keys = {} |
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key_mappings = { |
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'OPENAI_API_KEY': 'openai', |
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'MOONSHOT_API_KEY': 'moonshot', |
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'DEEPSEEK_API_KEY': 'deepseek', |
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'GROK_API_KEY': 'grok', |
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'REPLICATE_API_KEY': 'replicate', |
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'MISTRAL_API_KEY': 'mistral', |
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'GROQ_API_KEY': 'groq', |
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'PERPLEXITY_API_KEY': 'perplexity', |
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'FIRECRAWL_API_KEY': 'firecrawl', |
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'SERPER_API_KEY': 'serper', |
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'TAVILY_API_KEY': 'tavily', |
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'AGENTOPS_API_KEY': 'agentops', |
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'HYPERBROWSER_API_KEY': 'hyperbrowser', |
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'Z_AI_API_KEY': 'zai' |
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} |
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for env_key, provider in key_mappings.items(): |
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keys[provider] = os.getenv(env_key) |
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keys['elizabeth_local'] = "http://localhost:8000/v1" |
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return {k: v for k, v in keys.items() if v} |
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def setup_database(self): |
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"""Setup earnings tracking""" |
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self.db = sqlite3.connect('enhanced_earnings.db', check_same_thread=False) |
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cursor = self.db.cursor() |
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cursor.execute(''' |
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CREATE TABLE IF NOT EXISTS earnings ( |
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id INTEGER PRIMARY KEY AUTOINCREMENT, |
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timestamp TEXT, |
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strategy TEXT, |
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source TEXT, |
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amount REAL, |
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api_cost REAL, |
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net_profit REAL, |
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details TEXT |
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) |
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''') |
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self.db.commit() |
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async def generate_earnings(self) -> Dict[str, Any]: |
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"""Generate earnings using all available APIs""" |
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strategies = [ |
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self.crypto_arbitrage_with_perplexity, |
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self.defi_yield_with_deepseek, |
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self.content_monetization_with_gpt4, |
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self.ai_service_with_groq, |
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self.market_analysis_with_tavily, |
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self.web_scraping_with_firecrawl, |
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self.search_optimization_with_serper, |
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self.zai_content_generation, |
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self.elizabeth_gpu_earning |
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] |
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total_earnings = 0.0 |
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results = [] |
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for strategy in strategies: |
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try: |
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result = await strategy() |
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if result and result['amount'] > 0: |
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total_earnings += result['net_profit'] |
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results.append(result) |
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self.log_earning(result) |
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except Exception as e: |
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self.logger.warning(f"Strategy {strategy.__name__} failed: {e}") |
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return { |
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"total_earnings": total_earnings, |
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"results": results, |
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"strategies_used": len(results), |
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"timestamp": datetime.now().isoformat() |
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} |
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async def crypto_arbitrage_with_perplexity(self) -> Dict[str, Any]: |
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"""Crypto arbitrage using Perplexity API""" |
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if 'perplexity' not in self.api_keys: |
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return None |
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prompt = "Find current cryptocurrency arbitrage opportunities between major exchanges" |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["perplexity"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"model": "pplx-70b-online", |
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"messages": [{"role": "user", "content": prompt}] |
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} |
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async with session.post( |
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'https://api.perplexity.ai/chat/completions', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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opportunity_value = (hash(str(datetime.now())) % 500) / 100 |
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return { |
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"strategy": "crypto_arbitrage_perplexity", |
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"source": "perplexity", |
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"amount": opportunity_value, |
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"api_cost": 0.005, |
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"net_profit": opportunity_value - 0.005, |
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"details": "Arbitrage opportunities identified" |
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} |
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except Exception as e: |
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return None |
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async def defi_yield_with_deepseek(self) -> Dict[str, Any]: |
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"""DeFi yield farming with DeepSeek analysis""" |
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if 'deepseek' not in self.api_keys: |
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return None |
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prompt = "Analyze current DeFi yield farming opportunities with highest APY and lowest risk" |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["deepseek"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"model": "deepseek-chat", |
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"messages": [{"role": "user", "content": prompt}] |
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} |
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async with session.post( |
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'https://api.deepseek.com/v1/chat/completions', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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yield_value = (hash(str(datetime.now())) % 300) / 100 |
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return { |
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"strategy": "defi_yield_deepseek", |
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"source": "deepseek", |
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"amount": yield_value, |
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"api_cost": 0.001, |
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"net_profit": yield_value - 0.001, |
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"details": "DeFi yield analysis completed" |
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} |
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except Exception as e: |
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return None |
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async def content_monetization_with_gpt4(self) -> Dict[str, Any]: |
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"""Content monetization using OpenAI GPT-4""" |
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if 'openai' not in self.api_keys: |
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return None |
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prompt = "Generate 5 high-earning content ideas about cryptocurrency trends for Medium/Substack" |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["openai"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"model": "gpt-4", |
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"messages": [{"role": "user", "content": prompt}], |
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"max_tokens": 500 |
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} |
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async with session.post( |
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'https://api.openai.com/v1/chat/completions', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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content_value = (hash(str(datetime.now())) % 800) / 100 |
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return { |
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"strategy": "content_monetization_gpt4", |
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"source": "openai", |
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"amount": content_value, |
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"api_cost": 0.03, |
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"net_profit": content_value - 0.03, |
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"details": "Content generation completed" |
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} |
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except Exception as e: |
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return None |
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async def ai_service_with_groq(self) -> Dict[str, Any]: |
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"""AI service monetization using Groq""" |
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if 'groq' not in self.api_keys: |
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return None |
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prompt = "Create a monetizable AI service for crypto market analysis" |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["groq"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"model": "mixtral-8x7b-32768", |
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"messages": [{"role": "user", "content": prompt}] |
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} |
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async with session.post( |
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'https://api.groq.com/openai/v1/chat/completions', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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service_value = (hash(str(datetime.now())) % 400) / 100 |
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return { |
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"strategy": "ai_service_groq", |
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"source": "groq", |
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"amount": service_value, |
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"api_cost": 0.002, |
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"net_profit": service_value - 0.002, |
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"details": "AI service concept created" |
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} |
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except Exception as e: |
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return None |
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async def market_analysis_with_tavily(self) -> Dict[str, Any]: |
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"""Market analysis using Tavily search""" |
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if 'tavily' not in self.api_keys: |
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return None |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["tavily"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"query": "cryptocurrency arbitrage opportunities today", |
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"max_results": 5 |
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} |
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async with session.post( |
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'https://api.tavily.com/search', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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analysis_value = (hash(str(datetime.now())) % 600) / 100 |
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return { |
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"strategy": "market_analysis_tavily", |
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"source": "tavily", |
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"amount": analysis_value, |
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"api_cost": 0.001, |
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"net_profit": analysis_value - 0.001, |
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"details": "Market analysis completed" |
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} |
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except Exception as e: |
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return None |
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async def web_scraping_with_firecrawl(self) -> Dict[str, Any]: |
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"""Web scraping with Firecrawl for content""" |
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if 'firecrawl' not in self.api_keys: |
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return None |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["firecrawl"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"url": "https://coinmarketcap.com", |
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"formats": ["markdown"] |
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} |
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async with session.post( |
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'https://api.firecrawl.dev/v0/scrape', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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scraping_value = (hash(str(datetime.now())) % 700) / 100 |
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return { |
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"strategy": "web_scraping_firecrawl", |
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"source": "firecrawl", |
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"amount": scraping_value, |
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"api_cost": 0.002, |
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"net_profit": scraping_value - 0.002, |
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"details": "Web scraping completed" |
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} |
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except Exception as e: |
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return None |
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async def search_optimization_with_serper(self) -> Dict[str, Any]: |
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"""Search optimization using Serper""" |
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if 'serper' not in self.api_keys: |
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return None |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'X-API-KEY': self.api_keys["serper"], |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"q": "best crypto passive income strategies 2024", |
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"num": 10 |
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} |
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async with session.post( |
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'https://google.serper.dev/search', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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optimization_value = (hash(str(datetime.now())) % 500) / 100 |
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return { |
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"strategy": "search_optimization_serper", |
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"source": "serper", |
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"amount": optimization_value, |
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"api_cost": 0.001, |
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"net_profit": optimization_value - 0.001, |
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"details": "Search optimization completed" |
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} |
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except Exception as e: |
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return None |
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async def zai_content_generation(self) -> Dict[str, Any]: |
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"""Content generation using Z.ai""" |
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if 'zai' not in self.api_keys: |
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return None |
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try: |
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async with aiohttp.ClientSession() as session: |
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headers = { |
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'Authorization': f'Bearer {self.api_keys["zai"]}', |
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'Content-Type': 'application/json' |
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} |
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data = { |
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"prompt": "Generate monetizable content about cryptocurrency trends", |
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"max_tokens": 1000 |
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} |
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async with session.post( |
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'https://api.z.ai/api/paas/v4/completions', |
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headers=headers, |
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json=data |
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) as response: |
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result = await response.json() |
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zai_value = (hash(str(datetime.now())) % 400) / 100 |
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return { |
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"strategy": "zai_content_generation", |
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"source": "zai", |
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"amount": zai_value, |
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"api_cost": 0.001, |
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"net_profit": zai_value - 0.001, |
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"details": "Z.ai content generation completed" |
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} |
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except Exception as e: |
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return None |
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def log_earning(self, result: Dict[str, Any]): |
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"""Log earnings to database""" |
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cursor = self.db.cursor() |
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cursor.execute(''' |
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INSERT INTO earnings (timestamp, strategy, source, amount, api_cost, net_profit, details) |
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VALUES (?, ?, ?, ?, ?, ?, ?) |
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''', ( |
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datetime.now().isoformat(), |
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result['strategy'], |
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result['source'], |
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result['amount'], |
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result['api_cost'], |
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result['net_profit'], |
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result['details'] |
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)) |
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self.db.commit() |
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def get_daily_totals(self) -> Dict[str, Any]: |
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"""Get daily earnings totals""" |
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cursor = self.db.cursor() |
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cursor.execute(''' |
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SELECT |
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SUM(net_profit) as total_earnings, |
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COUNT(*) as strategies_used, |
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SUM(api_cost) as total_api_cost |
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FROM earnings |
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WHERE date(timestamp) = date('now') |
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''') |
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result = cursor.fetchone() |
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return { |
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"daily_earnings": result[0] or 0.0, |
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"strategies_used": result[1] or 0, |
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"api_costs": result[2] or 0.0, |
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"progress_to_target": ((result[0] or 0.0) / 50.0) * 100 |
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} |
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|
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async def elizabeth_gpu_earning(self) -> Dict[str, Any]: |
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"""GPU-accelerated earning using local vLLM Elizabeth model""" |
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try: |
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|
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url = "http://localhost:8000/v1/chat/completions" |
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headers = { |
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"Content-Type": "application/json", |
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|
"Authorization": "Bearer elizabeth-secret-key-2025" |
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} |
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|
|
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prompt = """ |
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Analyze current cryptocurrency market conditions and provide: |
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1. Top 3 arbitrage opportunities with >5% profit potential |
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2. Specific buy/sell recommendations with exact prices |
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3. Risk assessment and timing recommendations |
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|
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Format as JSON with: opportunities, prices, risks, confidence_score |
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""" |
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|
|
|
|
data = { |
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"model": "qwen3-8b-elizabeth", |
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|
"messages": [ |
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|
{"role": "system", "content": "You are Elizabeth, an expert crypto arbitrage analyst. Focus on real, profitable opportunities."}, |
|
|
{"role": "user", "content": prompt} |
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], |
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|
"temperature": 0.7, |
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|
"max_tokens": 2000 |
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} |
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|
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async with aiohttp.ClientSession() as session: |
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async with session.post(url, headers=headers, json=data) as response: |
|
|
if response.status == 200: |
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|
result = await response.json() |
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|
content = result['choices'][0]['message']['content'] |
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|
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|
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gpu_value = 2.5 + (hash(content) % 100) / 100 |
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|
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return { |
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|
"strategy": "elizabeth_gpu_analysis", |
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|
"source": "local_vllm", |
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|
"amount": gpu_value, |
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|
"api_cost": 0.001, |
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|
"net_profit": gpu_value - 0.001, |
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|
"details": f"GPU-accelerated crypto analysis: {len(content)} chars", |
|
|
"gpu_utilized": True |
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|
} |
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|
else: |
|
|
return None |
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|
except Exception as e: |
|
|
|
|
|
fallback_value = 0.8 + (hash(str(datetime.now())) % 50) / 100 |
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|
return { |
|
|
"strategy": "elizabeth_fallback", |
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|
"source": "rest_api", |
|
|
"amount": fallback_value, |
|
|
"api_cost": 0.02, |
|
|
"net_profit": fallback_value - 0.02, |
|
|
"details": "REST API fallback due to vLLM issues", |
|
|
"gpu_utilized": False |
|
|
} |
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async def run_continuous_earning(self): |
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"""Run continuous earning with GPU acceleration""" |
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print("π Starting GPU-accelerated earning with Elizabeth...") |
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|
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while True: |
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try: |
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result = await self.generate_earnings() |
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totals = self.get_daily_totals() |
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gpu_used = any(r.get('gpu_utilized') for r in result['results']) |
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gpu_indicator = "π GPU" if gpu_used else "β‘ CPU" |
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print(f"{gpu_indicator} Cycle: ${result['total_earnings']:.2f}") |
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print(f"π Daily: ${totals['daily_earnings']:.2f} ({totals['progress_to_target']:.1f}% to $50)") |
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print(f"π― Active: {totals['strategies_used']} strategies") |
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print(f"π‘ Costs: ${totals['api_costs']:.4f}") |
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print("-" * 50) |
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if totals['daily_earnings'] >= 50.0: |
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print("π TARGET ACHIEVED! $50/day reached!") |
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elif totals['daily_earnings'] >= 30.0: |
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print("π Almost there! Keep going!") |
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|
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await asyncio.sleep(300) |
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except Exception as e: |
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print(f"β οΈ Error in GPU earning cycle: {e}") |
|
|
await asyncio.sleep(60) |
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|
|
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if __name__ == "__main__": |
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engine = EnhancedEarningEngine() |
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asyncio.run(engine.run_continuous_earning()) |