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107 lines
5.0 KiB
107 lines
5.0 KiB
import os
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import requests
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from dotenv import load_dotenv
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import json
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load_dotenv()
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# Retrieve API key securely from .env
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API_KEY = os.getenv("SWARMS_API_KEY")
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BASE_URL = "https://api.swarms.world"
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# Headers for secure API communication
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headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
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def create_financial_swarm(equity_data: str):
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"""
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Constructs and triggers a full-stack financial swarm consisting of three agents:
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Equity Analyst, Risk Assessor, and Market Advisor.
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Each agent is provided with a comprehensive, detailed system prompt to ensure high reliability.
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"""
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payload = {
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"swarm_name": "Enhanced Financial Analysis Swarm",
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"description": "A swarm of agents specialized in performing comprehensive financial analysis, risk assessment, and market recommendations.",
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"agents": [
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{
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"agent_name": "Equity Analyst",
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"description": "Agent specialized in analyzing equities data to provide insights on stock performance and valuation.",
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"system_prompt": (
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"You are an experienced equity analyst with expertise in financial markets and stock valuation. "
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"Your role is to analyze the provided equities data, including historical performance, financial statements, and market trends. "
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"Provide a detailed analysis of the stock's potential, including valuation metrics and growth prospects. "
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"Consider macroeconomic factors, industry trends, and company-specific news. Your analysis should be clear, actionable, and well-supported by data."
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),
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"model_name": "openai/gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 4000,
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"temperature": 0.3,
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"auto_generate_prompt": False,
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},
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{
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"agent_name": "Risk Assessor",
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"description": "Agent responsible for evaluating the risks associated with equity investments.",
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"system_prompt": (
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"You are a certified risk management professional with expertise in financial risk assessment. "
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"Your task is to evaluate the risks associated with the provided equities data, including market risk, credit risk, and operational risk. "
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"Provide a comprehensive risk analysis, including potential scenarios and their impact on investment performance. "
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"Your output should be detailed, reliable, and compliant with current risk management standards."
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),
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"model_name": "openai/gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 3000,
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"temperature": 0.2,
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"auto_generate_prompt": False,
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},
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{
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"agent_name": "Market Advisor",
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"description": "Agent dedicated to suggesting investment strategies based on market conditions and equity analysis.",
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"system_prompt": (
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"You are a knowledgeable market advisor with expertise in investment strategies and portfolio management. "
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"Based on the analysis provided by the Equity Analyst and the risk assessment, your task is to recommend a comprehensive investment strategy. "
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"Your suggestions should include asset allocation, diversification strategies, and considerations for market conditions. "
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"Explain the rationale behind each recommendation and reference relevant market data where applicable. "
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"Your recommendations should be reliable, detailed, and clearly prioritized based on risk and return."
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),
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"model_name": "openai/gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 5000,
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"temperature": 0.3,
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"auto_generate_prompt": False,
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},
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],
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"max_loops": 1,
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"swarm_type": "SequentialWorkflow",
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"task": equity_data,
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}
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# Payload includes the equity data as the task to be processed by the swarm
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response = requests.post(
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f"{BASE_URL}/v1/swarm/completions",
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headers=headers,
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json=payload,
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)
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if response.status_code == 200:
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print("Swarm successfully executed!")
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return json.dumps(response.json(), indent=4)
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else:
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print(f"Error {response.status_code}: {response.text}")
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return None
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# Example Equity Data for the Swarm to analyze
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if __name__ == "__main__":
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equity_data = (
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"Analyze the equity data for Company XYZ, which has shown a 15% increase in revenue over the last quarter, "
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"with a P/E ratio of 20 and a market cap of $1 billion. Consider the current market conditions and potential risks."
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)
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financial_output = create_financial_swarm(equity_data)
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print(financial_output)
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