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51 lines
1.6 KiB
51 lines
1.6 KiB
from swarms import Agent
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from swarms.structs.mixture_of_agents import MixtureOfAgents
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from swarms.prompts.moa_prompt import MOA_AGGREGATOR_SYSTEM_PROMPT
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# Initialize the equity analyst agents
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equity_analyst_1 = Agent(
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agent_name="Equity-Analyst-1",
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agent_description="Equity research analyst focused on fundamental analysis",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="gpt-4.1",
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dynamic_temperature_enabled=True,
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)
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equity_analyst_2 = Agent(
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agent_name="Equity-Analyst-2",
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agent_description="Equity research analyst focused on technical analysis",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="gpt-4.1",
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dynamic_temperature_enabled=True,
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)
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equity_analyst_3 = Agent(
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agent_name="Equity-Analyst-3",
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agent_description="Equity research analyst focused on quantitative analysis and risk modeling",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="gpt-4.1",
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dynamic_temperature_enabled=True,
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)
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swarm = MixtureOfAgents(
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name="Equity-Research-Swarm",
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agents=[equity_analyst_1, equity_analyst_2, equity_analyst_3],
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output_type="dict",
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layers=1,
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aggregator_system_prompt=MOA_AGGREGATOR_SYSTEM_PROMPT,
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)
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out = swarm.run(
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task="Analyze Exchange-Traded Funds (ETFs) and stocks related to copper. Focus on fundamentals including supply/demand factors, production costs, major market participants, and recent price trends. Create a detailed analysis table in markdown.",
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)
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print(out)
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