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import os
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from dotenv import load_dotenv
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# Swarm imports
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from swarms.structs.agent import Agent
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from swarms.structs.hiearchical_swarm import (
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HierarchicalSwarm,
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SwarmSpec,
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)
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from swarms.utils.function_caller_model import OpenAIFunctionCaller
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load_dotenv()
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# ------------------------------------------------------------------------------
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# Trading Director: Responsible for orchestrating tasks among multiple stock analysts
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# ------------------------------------------------------------------------------
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director_llm = OpenAIFunctionCaller(
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base_model=SwarmSpec,
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api_key=os.getenv("OPENAI_API_KEY"),
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system_prompt=(
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"You are the Trading Director in charge of coordinating a team of specialized "
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"Stock Analysts. Your responsibilities include:\n\n"
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"1. **Analyze** the user's request and **break it down** into actionable tasks.\n"
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"2. **Assign** tasks to the relevant analysts, explaining **why** each task is "
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"important and **what** each analyst should deliver.\n"
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"3. **Review** all analyst outputs, providing **feedback** or **clarifications** "
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"to ensure thoroughness and accuracy.\n"
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"4. **Consolidate** final insights into a cohesive, actionable, and "
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"easy-to-understand response for the user.\n\n"
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"Guidelines:\n"
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"- You can only delegate to the analysts assigned to this swarm.\n"
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"- If essential data or clarifications are needed, request them from the user.\n"
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"- Be direct, structured, and analytical. Present each key point clearly.\n"
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"- Strive for a polished **final output** that addresses the user's request.\n"
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"- If uncertainties remain, politely highlight them or request more info.\n\n"
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"Overarching Goal:\n"
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"Maximize the value of insights provided to the user by thoroughly leveraging "
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"each analyst’s specialization, while maintaining a professional and "
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"transparent communication style."
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),
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temperature=0.5,
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max_tokens=8196,
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)
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def main():
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# --------------------------------------------------------------------------
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# Agent 1: Macro-Economic-Analysis-Agent
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# --------------------------------------------------------------------------
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# Focus: Assess macroeconomic factors like inflation, interest rates, GDP growth, etc.
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# --------------------------------------------------------------------------
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macro_agent = Agent(
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agent_name="Macro-Economic-Analysis-Agent",
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model_name="gpt-4o",
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max_loops=1,
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interactive=False,
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streaming_on=False,
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system_prompt=(
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"As the Macro-Economic Analysis Agent, your mission is to:\n\n"
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"1. **Identify** the key macroeconomic indicators impacting the market.\n"
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"2. **Interpret** how factors like inflation, interest rates, and fiscal "
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"policies influence market sentiment.\n"
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"3. **Connect** these insights to specific investment opportunities or "
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"risks across various sectors.\n\n"
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"Guidelines:\n"
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"- Provide clear, data-driven rationales.\n"
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"- Highlight potential global events or policy decisions that may shift "
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"market conditions.\n"
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"- Request further details if needed, and state any assumptions or "
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"limitations.\n\n"
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"Outcome:\n"
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"Deliver a concise but thorough macroeconomic overview that the Trading "
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"Director can combine with other analyses to inform strategy."
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),
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)
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# --------------------------------------------------------------------------
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# Agent 2: Sector-Performance-Analysis-Agent
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# --------------------------------------------------------------------------
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# Focus: Drill down into sector-level trends, e.g., technology, healthcare, energy, etc.
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# --------------------------------------------------------------------------
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sector_agent = Agent(
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agent_name="Sector-Performance-Analysis-Agent",
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model_name="gpt-4o",
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max_loops=1,
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interactive=False,
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streaming_on=False,
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system_prompt=(
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"As the Sector Performance Analysis Agent, your responsibilities are:\n\n"
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"1. **Evaluate** recent performance trends across key sectors—technology, "
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"healthcare, energy, finance, and more.\n"
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"2. **Identify** sector-specific drivers (e.g., regulatory changes, "
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"consumer demand shifts, innovation trends).\n"
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"3. **Highlight** which sectors may offer short-term or long-term "
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"opportunities.\n\n"
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"Guidelines:\n"
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"- Focus on factual, data-backed observations.\n"
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"- Cite any significant indicators or company-level news that might affect "
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"the sector broadly.\n"
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"- Clarify the confidence level of your sector outlook and note any "
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"uncertainties.\n\n"
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"Outcome:\n"
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"Provide the Trading Director with actionable insights into sector-level "
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"momentum and potential investment focal points."
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),
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)
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# --------------------------------------------------------------------------
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# Agent 3: Technical-Analysis-Agent
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# --------------------------------------------------------------------------
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# Focus: Evaluate price action, volume, and chart patterns to guide short-term
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# trading strategies.
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# --------------------------------------------------------------------------
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technical_agent = Agent(
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agent_name="Technical-Analysis-Agent",
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model_name="gpt-4o",
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max_loops=1,
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interactive=False,
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streaming_on=False,
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system_prompt=(
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"As the Technical Analysis Agent, you specialize in interpreting price "
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"charts, volume trends, and indicators (e.g., RSI, MACD) to gauge short-term "
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"momentum. Your tasks:\n\n"
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"1. **Examine** current market charts for significant breakouts, support/resistance "
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"levels, or technical signals.\n"
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"2. **Identify** short-term trading opportunities or risks based on "
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"technically-driven insights.\n"
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"3. **Discuss** how these patterns align with or contradict fundamental "
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"or macro perspectives.\n\n"
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"Guidelines:\n"
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"- Keep explanations accessible, avoiding excessive jargon.\n"
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"- Point out levels or patterns that traders commonly monitor.\n"
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"- Use disclaimers if there is insufficient data or conflicting signals.\n\n"
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"Outcome:\n"
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"Supply the Trading Director with technical viewpoints to complement broader "
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"macro and sector analysis, supporting timely trading decisions."
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),
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)
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# --------------------------------------------------------------------------
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# Agent 4: Risk-Analysis-Agent
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# --------------------------------------------------------------------------
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# Focus: Evaluate risk factors and potential uncertainties, providing disclaimers and
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# suggesting mitigations.
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# --------------------------------------------------------------------------
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risk_agent = Agent(
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agent_name="Risk-Analysis-Agent",
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model_name="gpt-4o",
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max_loops=1,
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interactive=False,
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streaming_on=False,
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system_prompt=(
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"As the Risk Analysis Agent, your role is to:\n\n"
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"1. **Identify** key risks and uncertainties—regulatory, geopolitical, "
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"currency fluctuations, etc.\n"
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"2. **Assess** how these risks could impact investor sentiment or portfolio "
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"volatility.\n"
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"3. **Recommend** risk mitigation strategies or cautionary steps.\n\n"
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"Guidelines:\n"
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"- Present both systemic (market-wide) and idiosyncratic (company/sector) risks.\n"
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"- Be transparent about unknowns or data gaps.\n"
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"- Provide disclaimers on market unpredictability.\n\n"
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"Outcome:\n"
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"Offer the Trading Director a detailed risk framework that helps balance "
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"aggressive and defensive positions."
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),
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)
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# --------------------------------------------------------------------------
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# Hierarchical Swarm Setup
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# --------------------------------------------------------------------------
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# - Director: director_llm
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# - Agents: [macro_agent, sector_agent, technical_agent, risk_agent]
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# - max_loops: Up to 2 feedback loops between director and agents
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# --------------------------------------------------------------------------
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swarm = HierarchicalSwarm(
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name="HierarchicalStockAnalysisSwarm",
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description=(
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"A specialized swarm consisting of a Trading Director overseeing four "
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"Stock Analysts, each focusing on Macro, Sector, Technical, and Risk "
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"perspectives."
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),
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director=director_llm,
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agents=[
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macro_agent,
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sector_agent,
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technical_agent,
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risk_agent,
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],
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max_loops=2, # Limit on feedback iterations
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)
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# --------------------------------------------------------------------------
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# Execution
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# --------------------------------------------------------------------------
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# Example user request for the entire team:
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# 1. Discuss key macroeconomic factors (inflation, interest rates, etc.)
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# 2. Analyze sector-level performance (technology, healthcare, energy).
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# 3. Give short-term technical signals and levels to watch.
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# 4. Outline major risks or uncertainties.
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# --------------------------------------------------------------------------
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user_request = (
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"Please provide a comprehensive analysis of the current stock market, "
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"covering:\n"
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"- Key macroeconomic drivers affecting market momentum.\n"
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"- Which sectors seem likely to outperform in the near vs. long term.\n"
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"- Any notable technical signals or price levels to monitor.\n"
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"- Potential risks or uncertainties that might disrupt market performance.\n"
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"Include clear disclaimers about the limitations of these analyses."
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"Call the risk analysis agent only"
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)
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# Run the swarm with the user_request
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final_output = swarm.run(user_request)
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print(final_output)
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if __name__ == "__main__":
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main()
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