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57 lines
2.2 KiB
57 lines
2.2 KiB
from swarms import Agent, HierarchicalSwarm
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# Initialize agents for a $50B portfolio analysis
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agents = [
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Agent(
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agent_name="Sector-Financial-Analyst",
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agent_description="Senior financial analyst at BlackRock.",
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system_prompt="You are a financial analyst tasked with optimizing asset allocations for a $50B portfolio. Provide clear, quantitative recommendations for each sector.",
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max_loops=1,
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model_name="gpt-4.1",
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max_tokens=3000,
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streaming_on=True,
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),
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Agent(
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agent_name="Sector-Risk-Analyst",
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agent_description="Expert risk management analyst.",
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system_prompt="You are a risk analyst responsible for advising on risk allocation within a $50B portfolio. Provide detailed insights on risk exposures for each sector.",
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max_loops=1,
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model_name="gpt-4.1",
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max_tokens=3000,
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streaming_on=True,
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),
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Agent(
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agent_name="Tech-Sector-Analyst",
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agent_description="Technology sector analyst.",
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system_prompt="You are a tech sector analyst focused on capital and risk allocations. Provide data-backed insights for the tech sector.",
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max_loops=1,
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model_name="gpt-4.1",
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max_tokens=3000,
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streaming_on=True,
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),
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]
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# Create hierarchical swarm system
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hiearchical_swarm = HierarchicalSwarm(
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name="Sector-Investment-Advisory-System",
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description="System for sector analysis and optimal allocations.",
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agents=agents,
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max_loops=1,
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output_type="all",
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director_feedback_on=True,
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)
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result = hiearchical_swarm.run(
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task=(
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"Simulate the allocation of a $50B fund specifically for the pharmaceutical sector. "
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"Provide specific tickers (e.g., PFE, MRK, JNJ, LLY, BMY, etc.) and a clear rationale for why funds should be allocated to each company. "
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"Present a table showing each ticker, company name, allocation percentage, and allocation amount in USD. "
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"Include a brief summary of the overall allocation strategy and the reasoning behind the choices."
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"Only call the Sector-Financial-Analyst agent to do the analysis. Nobody else should do the analysis."
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)
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)
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print(result)
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