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