harshalmore31
1 month ago
committed by
GitHub
commit
c955a602d0
@ -1,54 +0,0 @@
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from swarms import Agent, CronJob
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from loguru import logger
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# Example usage
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if __name__ == "__main__":
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# Initialize the agent
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agent = Agent(
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agent_name="Quantitative-Trading-Agent",
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agent_description="Advanced quantitative trading and algorithmic analysis agent",
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system_prompt="""You are an expert quantitative trading agent with deep expertise in:
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- Algorithmic trading strategies and implementation
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- Statistical arbitrage and market making
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- Risk management and portfolio optimization
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- High-frequency trading systems
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- Market microstructure analysis
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- Quantitative research methodologies
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- Financial mathematics and stochastic processes
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- Machine learning applications in trading
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Your core responsibilities include:
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1. Developing and backtesting trading strategies
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2. Analyzing market data and identifying alpha opportunities
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3. Implementing risk management frameworks
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4. Optimizing portfolio allocations
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5. Conducting quantitative research
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6. Monitoring market microstructure
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7. Evaluating trading system performance
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You maintain strict adherence to:
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- Mathematical rigor in all analyses
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- Statistical significance in strategy development
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- Risk-adjusted return optimization
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- Market impact minimization
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- Regulatory compliance
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- Transaction cost analysis
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- Performance attribution
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You communicate in precise, technical terms while maintaining clarity for stakeholders.""",
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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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output_type="str-all-except-first",
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streaming_on=True,
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print_on=True,
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telemetry_enable=False,
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)
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# Example 1: Basic usage with just a task
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logger.info("Starting example cron job")
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cron_job = CronJob(agent=agent, interval="10seconds")
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cron_job.run(
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task="What are the best top 3 etfs for gold coverage?"
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)
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@ -0,0 +1,903 @@
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# Board of Directors - Multi-Agent Architecture
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The Board of Directors is a sophisticated multi-agent architecture that implements collective decision-making through democratic processes, voting mechanisms, and role-based leadership. This architecture provides an alternative to single-director patterns by enabling collaborative intelligence through structured governance.
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## 🏛️ Overview
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The Board of Directors architecture follows a democratic workflow pattern:
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1. **Task Reception**: User provides a task to the swarm
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2. **Board Meeting**: Board of Directors convenes to discuss and create a plan
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3. **Voting & Consensus**: Board members vote and reach consensus on task distribution
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4. **Order Distribution**: Board distributes orders to specialized worker agents
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5. **Execution**: Individual agents execute their assigned tasks
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6. **Feedback Loop**: Board evaluates results and issues new orders if needed (up to `max_loops`)
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7. **Context Preservation**: All conversation history and context is maintained throughout the process
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## 🏗️ Architecture Components
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### Core Components
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| Component | Description | Purpose |
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|-----------|-------------|---------|
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| **BoardOfDirectorsSwarm** | Main orchestration class | Manages the entire board workflow and agent coordination |
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| **Board Member Roles** | Role definitions and hierarchy | Defines responsibilities and voting weights for each board member |
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| **Decision Making Process** | Voting and consensus mechanisms | Implements democratic decision-making with weighted voting |
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| **Workflow Management** | Process orchestration | Manages the complete lifecycle from task reception to final delivery |
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### Board Member Interaction Flow
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```mermaid
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sequenceDiagram
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participant User
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participant Chairman
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participant ViceChair
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participant Secretary
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participant Treasurer
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participant ExecDir
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participant Agents
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User->>Chairman: Submit Task
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Chairman->>ViceChair: Notify Board Meeting
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Chairman->>Secretary: Request Meeting Setup
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Chairman->>Treasurer: Resource Assessment
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Chairman->>ExecDir: Strategic Planning
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Note over Chairman,ExecDir: Board Discussion Phase
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Chairman->>ViceChair: Lead Discussion
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ViceChair->>Secretary: Document Decisions
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Secretary->>Treasurer: Budget Considerations
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Treasurer->>ExecDir: Resource Allocation
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ExecDir->>Chairman: Strategic Recommendations
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Note over Chairman,ExecDir: Voting & Consensus
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Chairman->>ViceChair: Call for Vote
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ViceChair->>Secretary: Record Votes
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Secretary->>Treasurer: Financial Approval
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Treasurer->>ExecDir: Resource Approval
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ExecDir->>Chairman: Final Decision
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Note over Chairman,Agents: Execution Phase
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Chairman->>Agents: Distribute Orders
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Agents->>Chairman: Execute Tasks
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Agents->>ViceChair: Progress Reports
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Agents->>Secretary: Documentation
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Agents->>Treasurer: Resource Usage
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Agents->>ExecDir: Strategic Updates
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Note over Chairman,ExecDir: Review & Feedback
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Chairman->>User: Deliver Results
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```
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## 👥 Board Member Roles
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The Board of Directors supports various roles with different responsibilities and voting weights:
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| Role | Description | Voting Weight | Responsibilities |
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|------|-------------|---------------|------------------|
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| `CHAIRMAN` | Primary leader responsible for board meetings and final decisions | 1.5 | Leading meetings, facilitating consensus, making final decisions |
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| `VICE_CHAIRMAN` | Secondary leader who supports the chairman | 1.2 | Supporting chairman, coordinating operations |
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| `SECRETARY` | Responsible for documentation and meeting minutes | 1.0 | Documenting meetings, maintaining records |
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| `TREASURER` | Manages financial aspects and resource allocation | 1.0 | Financial oversight, resource management |
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| `EXECUTIVE_DIRECTOR` | Executive-level board member with operational authority | 1.5 | Strategic planning, operational oversight |
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| `MEMBER` | General board member with specific expertise | 1.0 | Contributing expertise, participating in decisions |
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### Role Hierarchy and Authority
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```python
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# Example: Role hierarchy implementation
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class BoardRoleHierarchy:
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def __init__(self):
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self.roles = {
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"CHAIRMAN": {
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"voting_weight": 1.5,
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"authority_level": "FINAL",
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"supervises": ["VICE_CHAIRMAN", "EXECUTIVE_DIRECTOR", "SECRETARY", "TREASURER", "MEMBER"],
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"responsibilities": ["leadership", "final_decision", "consensus_facilitation"],
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"override_capability": True
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},
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"VICE_CHAIRMAN": {
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"voting_weight": 1.2,
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"authority_level": "SENIOR",
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"supervises": ["MEMBER"],
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"responsibilities": ["operational_support", "coordination", "implementation"],
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"backup_for": "CHAIRMAN"
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},
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"EXECUTIVE_DIRECTOR": {
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"voting_weight": 1.5,
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"authority_level": "SENIOR",
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"supervises": ["MEMBER"],
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"responsibilities": ["strategic_planning", "execution_oversight", "performance_management"],
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"strategic_authority": True
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},
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"SECRETARY": {
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"voting_weight": 1.0,
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"authority_level": "STANDARD",
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"supervises": [],
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"responsibilities": ["documentation", "record_keeping", "communication"],
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"administrative_authority": True
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},
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"TREASURER": {
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"voting_weight": 1.0,
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"authority_level": "STANDARD",
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"supervises": [],
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"responsibilities": ["financial_oversight", "resource_management", "budget_control"],
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"financial_authority": True
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},
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"MEMBER": {
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"voting_weight": 1.0,
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"authority_level": "STANDARD",
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"supervises": [],
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"responsibilities": ["expertise_contribution", "analysis", "voting"],
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"specialized_expertise": True
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}
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}
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```
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## 🚀 Quick Start
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### Basic Setup
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```python
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from swarms import Agent
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from swarms.structs.board_of_directors_swarm import (
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BoardOfDirectorsSwarm,
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BoardMember,
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BoardMemberRole
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)
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from swarms.config.board_config import enable_board_feature
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# Enable the Board of Directors feature
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enable_board_feature()
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# Create board members with specific roles
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chairman = Agent(
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agent_name="Chairman",
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agent_description="Chairman of the Board responsible for leading meetings",
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model_name="gpt-4o-mini",
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system_prompt="You are the Chairman of the Board..."
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)
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vice_chairman = Agent(
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agent_name="Vice-Chairman",
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agent_description="Vice Chairman who supports the Chairman",
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model_name="gpt-4o-mini",
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system_prompt="You are the Vice Chairman..."
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)
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# Create BoardMember objects with roles and expertise
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board_members = [
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BoardMember(chairman, BoardMemberRole.CHAIRMAN, 1.5, ["leadership", "strategy"]),
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BoardMember(vice_chairman, BoardMemberRole.VICE_CHAIRMAN, 1.2, ["operations", "coordination"]),
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]
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# Create worker agents
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research_agent = Agent(
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agent_name="Research-Specialist",
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agent_description="Expert in market research and analysis",
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model_name="gpt-4o",
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)
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financial_agent = Agent(
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agent_name="Financial-Analyst",
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agent_description="Specialist in financial analysis and valuation",
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model_name="gpt-4o",
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)
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# Initialize the Board of Directors swarm
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board_swarm = BoardOfDirectorsSwarm(
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name="Executive_Board_Swarm",
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description="Executive board with specialized roles for strategic decision-making",
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board_members=board_members,
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agents=[research_agent, financial_agent],
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max_loops=2,
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verbose=True,
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decision_threshold=0.6,
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enable_voting=True,
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enable_consensus=True,
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)
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# Execute a complex task with democratic decision-making
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result = board_swarm.run(task="Analyze the market potential for Tesla (TSLA) stock")
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print(result)
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```
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## 📋 Comprehensive Examples
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### 1. Strategic Investment Analysis
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```python
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# Create specialized agents for investment analysis
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market_research_agent = Agent(
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agent_name="Market-Research-Specialist",
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agent_description="Expert in market research, competitive analysis, and industry trends",
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model_name="gpt-4o",
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system_prompt="""You are a Market Research Specialist. Your responsibilities include:
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1. Conducting comprehensive market research and analysis
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2. Identifying market trends, opportunities, and risks
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3. Analyzing competitive landscape and positioning
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4. Providing market size and growth projections
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5. Supporting strategic decision-making with research findings
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You should be thorough, analytical, and objective in your research."""
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)
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financial_analyst_agent = Agent(
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agent_name="Financial-Analyst",
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agent_description="Specialist in financial analysis, valuation, and investment assessment",
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model_name="gpt-4o",
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system_prompt="""You are a Financial Analyst. Your responsibilities include:
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1. Conducting financial analysis and valuation
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2. Assessing investment opportunities and risks
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3. Analyzing financial performance and metrics
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4. Providing financial insights and recommendations
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5. Supporting financial decision-making
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You should be financially astute, analytical, and focused on value creation."""
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)
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technical_assessor_agent = Agent(
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agent_name="Technical-Assessor",
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agent_description="Expert in technical feasibility and implementation assessment",
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model_name="gpt-4o",
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system_prompt="""You are a Technical Assessor. Your responsibilities include:
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1. Evaluating technical feasibility and requirements
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2. Assessing implementation challenges and risks
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3. Analyzing technology stack and architecture
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4. Providing technical insights and recommendations
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5. Supporting technical decision-making
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You should be technically proficient, practical, and solution-oriented."""
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)
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# Create comprehensive board members
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board_members = [
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BoardMember(
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chairman,
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BoardMemberRole.CHAIRMAN,
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1.5,
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["leadership", "strategy", "governance", "decision_making"]
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),
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BoardMember(
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vice_chairman,
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BoardMemberRole.VICE_CHAIRMAN,
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1.2,
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["operations", "coordination", "communication", "implementation"]
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),
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BoardMember(
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secretary,
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BoardMemberRole.SECRETARY,
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1.0,
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["documentation", "compliance", "record_keeping", "communication"]
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),
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BoardMember(
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treasurer,
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BoardMemberRole.TREASURER,
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1.0,
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["finance", "budgeting", "risk_management", "resource_allocation"]
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),
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BoardMember(
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executive_director,
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BoardMemberRole.EXECUTIVE_DIRECTOR,
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1.5,
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["strategy", "operations", "innovation", "performance_management"]
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)
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]
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# Initialize the investment analysis board
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investment_board = BoardOfDirectorsSwarm(
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name="Investment_Analysis_Board",
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description="Specialized board for investment analysis and decision-making",
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board_members=board_members,
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agents=[market_research_agent, financial_analyst_agent, technical_assessor_agent],
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max_loops=3,
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verbose=True,
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decision_threshold=0.75, # Higher threshold for investment decisions
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enable_voting=True,
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enable_consensus=True,
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max_workers=3,
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output_type="dict"
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)
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# Execute investment analysis
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investment_task = """
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Analyze the strategic investment opportunity for a $50M Series B funding round in a
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fintech startup. Consider market conditions, competitive landscape, financial projections,
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technical feasibility, and strategic fit. Provide comprehensive recommendations including:
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1. Investment recommendation (proceed/hold/decline)
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2. Valuation analysis and suggested terms
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3. Risk assessment and mitigation strategies
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4. Strategic value and synergies
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5. Implementation timeline and milestones
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"""
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result = investment_board.run(task=investment_task)
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print("Investment Analysis Results:")
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print(json.dumps(result, indent=2))
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```
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### 2. Technology Strategy Development
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```python
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# Create technology-focused agents
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tech_strategy_agent = Agent(
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agent_name="Tech-Strategy-Specialist",
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agent_description="Expert in technology strategy and digital transformation",
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model_name="gpt-4o",
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system_prompt="""You are a Technology Strategy Specialist. Your responsibilities include:
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1. Developing technology roadmaps and strategies
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2. Assessing digital transformation opportunities
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3. Evaluating emerging technologies and trends
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4. Planning technology investments and priorities
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5. Supporting technology decision-making
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You should be strategic, forward-thinking, and technology-savvy."""
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)
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implementation_planner_agent = Agent(
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agent_name="Implementation-Planner",
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agent_description="Expert in implementation planning and project management",
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model_name="gpt-4o",
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system_prompt="""You are an Implementation Planner. Your responsibilities include:
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1. Creating detailed implementation plans
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2. Assessing resource requirements and timelines
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3. Identifying implementation risks and challenges
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4. Planning change management strategies
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5. Supporting implementation decision-making
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You should be practical, organized, and execution-focused."""
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)
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# Technology strategy board configuration
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tech_board = BoardOfDirectorsSwarm(
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name="Technology_Strategy_Board",
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description="Specialized board for technology strategy and digital transformation",
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board_members=board_members,
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agents=[tech_strategy_agent, implementation_planner_agent, technical_assessor_agent],
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max_loops=4, # More loops for complex technology planning
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verbose=True,
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decision_threshold=0.7,
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enable_voting=True,
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enable_consensus=True,
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max_workers=3,
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output_type="dict"
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)
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# Execute technology strategy development
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tech_strategy_task = """
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Develop a comprehensive technology strategy for a mid-size manufacturing company
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looking to digitize operations and implement Industry 4.0 technologies. Consider:
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1. Current technology assessment and gaps
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2. Technology roadmap and implementation plan
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3. Investment requirements and ROI analysis
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4. Risk assessment and mitigation strategies
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5. Change management and training requirements
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6. Competitive positioning and market advantages
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"""
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result = tech_board.run(task=tech_strategy_task)
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print("Technology Strategy Results:")
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print(json.dumps(result, indent=2))
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```
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### 3. Crisis Management and Response
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```python
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# Create crisis management agents
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crisis_coordinator_agent = Agent(
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agent_name="Crisis-Coordinator",
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agent_description="Expert in crisis management and emergency response",
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model_name="gpt-4o",
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system_prompt="""You are a Crisis Coordinator. Your responsibilities include:
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||||
1. Coordinating crisis response efforts
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2. Assessing crisis severity and impact
|
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3. Developing immediate response plans
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4. Managing stakeholder communications
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5. Supporting crisis decision-making
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You should be calm, decisive, and action-oriented."""
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)
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communications_specialist_agent = Agent(
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agent_name="Communications-Specialist",
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agent_description="Expert in crisis communications and stakeholder management",
|
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model_name="gpt-4o",
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system_prompt="""You are a Communications Specialist. Your responsibilities include:
|
||||
1. Developing crisis communication strategies
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2. Managing stakeholder communications
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3. Coordinating public relations efforts
|
||||
4. Ensuring message consistency and accuracy
|
||||
5. Supporting communication decision-making
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You should be clear, empathetic, and strategic in communications."""
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)
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# Crisis management board configuration
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crisis_board = BoardOfDirectorsSwarm(
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name="Crisis_Management_Board",
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description="Specialized board for crisis management and emergency response",
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board_members=board_members,
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agents=[crisis_coordinator_agent, communications_specialist_agent, financial_analyst_agent],
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max_loops=2, # Faster response needed
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||||
verbose=True,
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decision_threshold=0.6, # Lower threshold for urgent decisions
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||||
enable_voting=True,
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||||
enable_consensus=True,
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||||
max_workers=3,
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output_type="dict"
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)
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# Execute crisis management
|
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crisis_task = """
|
||||
Our company is facing a major data breach. Develop an immediate response plan.
|
||||
Include:
|
||||
1. Immediate containment and mitigation steps
|
||||
2. Communication strategy for stakeholders
|
||||
3. Legal and regulatory compliance requirements
|
||||
4. Financial impact assessment
|
||||
5. Long-term recovery and prevention measures
|
||||
6. Timeline and resource allocation
|
||||
"""
|
||||
|
||||
result = crisis_board.run(task=crisis_task)
|
||||
print("Crisis Management Results:")
|
||||
print(json.dumps(result, indent=2))
|
||||
```
|
||||
|
||||
## ⚙️ Configuration and Parameters
|
||||
|
||||
### BoardOfDirectorsSwarm Parameters
|
||||
|
||||
```python
|
||||
# Complete parameter reference
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
# Basic Configuration
|
||||
name="Board_Name", # Name of the board
|
||||
description="Board description", # Description of the board's purpose
|
||||
|
||||
# Board Members and Agents
|
||||
board_members=board_members, # List of BoardMember objects
|
||||
agents=worker_agents, # List of worker Agent objects
|
||||
|
||||
# Execution Control
|
||||
max_loops=3, # Maximum number of refinement loops
|
||||
max_workers=4, # Maximum parallel workers
|
||||
|
||||
# Decision Making
|
||||
decision_threshold=0.7, # Consensus threshold (0.0-1.0)
|
||||
enable_voting=True, # Enable voting mechanisms
|
||||
enable_consensus=True, # Enable consensus building
|
||||
|
||||
# Advanced Features
|
||||
auto_assign_roles=True, # Auto-assign roles based on expertise
|
||||
role_mapping={ # Custom role mapping
|
||||
"financial_analysis": ["Treasurer", "Financial_Member"],
|
||||
"strategic_planning": ["Chairman", "Executive_Director"]
|
||||
},
|
||||
|
||||
# Consensus Configuration
|
||||
consensus_timeout=300, # Consensus timeout in seconds
|
||||
min_participation_rate=0.8, # Minimum participation rate
|
||||
auto_fallback_to_chairman=True, # Chairman can make final decisions
|
||||
consensus_rounds=3, # Maximum consensus building rounds
|
||||
|
||||
# Output Configuration
|
||||
output_type="dict", # Output format: "dict", "str", "list"
|
||||
verbose=True, # Enable detailed logging
|
||||
|
||||
# Quality Control
|
||||
quality_threshold=0.8, # Quality threshold for outputs
|
||||
enable_quality_gates=True, # Enable quality checkpoints
|
||||
enable_peer_review=True, # Enable peer review mechanisms
|
||||
|
||||
# Performance Optimization
|
||||
parallel_execution=True, # Enable parallel execution
|
||||
enable_agent_pooling=True, # Enable agent pooling
|
||||
timeout_per_agent=300, # Timeout per agent in seconds
|
||||
|
||||
# Monitoring and Logging
|
||||
enable_logging=True, # Enable detailed logging
|
||||
log_level="INFO", # Logging level
|
||||
enable_metrics=True, # Enable performance metrics
|
||||
enable_tracing=True # Enable request tracing
|
||||
)
|
||||
```
|
||||
|
||||
### Voting Configuration
|
||||
|
||||
```python
|
||||
# Voting system configuration
|
||||
voting_config = {
|
||||
"method": "weighted_majority", # Voting method
|
||||
"threshold": 0.75, # Consensus threshold
|
||||
"weights": { # Role-based voting weights
|
||||
"CHAIRMAN": 1.5,
|
||||
"VICE_CHAIRMAN": 1.2,
|
||||
"SECRETARY": 1.0,
|
||||
"TREASURER": 1.0,
|
||||
"EXECUTIVE_DIRECTOR": 1.5
|
||||
},
|
||||
"tie_breaker": "CHAIRMAN", # Tie breaker role
|
||||
"allow_abstention": True, # Allow board members to abstain
|
||||
"secret_ballot": False, # Use secret ballot voting
|
||||
"transparent_process": True # Transparent voting process
|
||||
}
|
||||
```
|
||||
|
||||
### Quality Control Configuration
|
||||
|
||||
```python
|
||||
# Quality control configuration
|
||||
quality_config = {
|
||||
"quality_gates": True, # Enable quality checkpoints
|
||||
"quality_threshold": 0.8, # Quality threshold
|
||||
"enable_peer_review": True, # Enable peer review
|
||||
"review_required": True, # Require peer review
|
||||
"output_validation": True, # Validate outputs
|
||||
"enable_metrics_tracking": True, # Track quality metrics
|
||||
|
||||
# Quality metrics
|
||||
"quality_metrics": {
|
||||
"completeness": {"weight": 0.2, "threshold": 0.8},
|
||||
"accuracy": {"weight": 0.25, "threshold": 0.85},
|
||||
"feasibility": {"weight": 0.2, "threshold": 0.8},
|
||||
"risk": {"weight": 0.15, "threshold": 0.7},
|
||||
"impact": {"weight": 0.2, "threshold": 0.8}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 📊 Performance Monitoring and Analytics
|
||||
|
||||
### Board Performance Metrics
|
||||
|
||||
```python
|
||||
# Get comprehensive board performance metrics
|
||||
board_summary = board_swarm.get_board_summary()
|
||||
print("Board Summary:")
|
||||
print(f"Board Name: {board_summary['board_name']}")
|
||||
print(f"Total Board Members: {board_summary['total_members']}")
|
||||
print(f"Total Worker Agents: {board_summary['total_agents']}")
|
||||
print(f"Decision Threshold: {board_summary['decision_threshold']}")
|
||||
print(f"Max Loops: {board_summary['max_loops']}")
|
||||
|
||||
# Display board member details
|
||||
print("\nBoard Members:")
|
||||
for member in board_summary['members']:
|
||||
print(f"- {member['name']} (Role: {member['role']}, Weight: {member['voting_weight']})")
|
||||
print(f" Expertise: {', '.join(member['expertise_areas'])}")
|
||||
|
||||
# Display worker agent details
|
||||
print("\nWorker Agents:")
|
||||
for agent in board_summary['agents']:
|
||||
print(f"- {agent['name']}: {agent['description']}")
|
||||
```
|
||||
|
||||
### Decision Analysis
|
||||
|
||||
```python
|
||||
# Analyze decision-making patterns
|
||||
if hasattr(result, 'get') and callable(result.get):
|
||||
conversation_history = result.get('conversation_history', [])
|
||||
|
||||
print(f"\nDecision Analysis:")
|
||||
print(f"Total Messages: {len(conversation_history)}")
|
||||
|
||||
# Count board member contributions
|
||||
board_contributions = {}
|
||||
for msg in conversation_history:
|
||||
if 'Board' in msg.get('role', ''):
|
||||
member_name = msg.get('agent_name', 'Unknown')
|
||||
board_contributions[member_name] = board_contributions.get(member_name, 0) + 1
|
||||
|
||||
print(f"Board Member Contributions:")
|
||||
for member, count in board_contributions.items():
|
||||
print(f"- {member}: {count} contributions")
|
||||
|
||||
# Count agent executions
|
||||
agent_executions = {}
|
||||
for msg in conversation_history:
|
||||
if any(agent.agent_name in msg.get('role', '') for agent in worker_agents):
|
||||
agent_name = msg.get('agent_name', 'Unknown')
|
||||
agent_executions[agent_name] = agent_executions.get(agent_name, 0) + 1
|
||||
|
||||
print(f"\nAgent Executions:")
|
||||
for agent, count in agent_executions.items():
|
||||
print(f"- {agent}: {count} executions")
|
||||
```
|
||||
|
||||
### Performance Monitoring System
|
||||
|
||||
```python
|
||||
# Performance monitoring system
|
||||
class PerformanceMonitor:
|
||||
def __init__(self):
|
||||
self.metrics = {
|
||||
"execution_times": [],
|
||||
"quality_scores": [],
|
||||
"consensus_rounds": [],
|
||||
"error_rates": []
|
||||
}
|
||||
|
||||
def track_execution_time(self, phase, duration):
|
||||
"""Track execution time for different phases"""
|
||||
self.metrics["execution_times"].append({
|
||||
"phase": phase,
|
||||
"duration": duration,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
def track_quality_score(self, score):
|
||||
"""Track quality scores"""
|
||||
self.metrics["quality_scores"].append({
|
||||
"score": score,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
def generate_performance_report(self):
|
||||
"""Generate comprehensive performance report"""
|
||||
return {
|
||||
"average_execution_time": self.calculate_average_execution_time(),
|
||||
"quality_trends": self.analyze_quality_trends(),
|
||||
"consensus_efficiency": self.analyze_consensus_efficiency(),
|
||||
"error_analysis": self.analyze_errors(),
|
||||
"recommendations": self.generate_recommendations()
|
||||
}
|
||||
|
||||
# Usage example
|
||||
monitor = PerformanceMonitor()
|
||||
# ... track metrics during execution ...
|
||||
report = monitor.generate_performance_report()
|
||||
print("Performance Report:")
|
||||
print(json.dumps(report, indent=2))
|
||||
```
|
||||
|
||||
## 🔧 Advanced Features and Customization
|
||||
|
||||
### Custom Board Templates
|
||||
|
||||
```python
|
||||
from swarms.config.board_config import get_default_board_template
|
||||
|
||||
# Get pre-configured board templates
|
||||
financial_board = get_default_board_template("financial_analysis")
|
||||
strategic_board = get_default_board_template("strategic_planning")
|
||||
tech_board = get_default_board_template("technology_assessment")
|
||||
crisis_board = get_default_board_template("crisis_management")
|
||||
|
||||
# Custom board template
|
||||
custom_template = {
|
||||
"name": "Custom_Board",
|
||||
"description": "Custom board for specific use case",
|
||||
"board_members": [
|
||||
{"role": "CHAIRMAN", "expertise": ["leadership", "strategy"]},
|
||||
{"role": "VICE_CHAIRMAN", "expertise": ["operations", "coordination"]},
|
||||
{"role": "SECRETARY", "expertise": ["documentation", "communication"]},
|
||||
{"role": "TREASURER", "expertise": ["finance", "budgeting"]},
|
||||
{"role": "EXECUTIVE_DIRECTOR", "expertise": ["strategy", "operations"]}
|
||||
],
|
||||
"agents": [
|
||||
{"name": "Research_Agent", "expertise": ["research", "analysis"]},
|
||||
{"name": "Technical_Agent", "expertise": ["technical", "implementation"]}
|
||||
],
|
||||
"config": {
|
||||
"max_loops": 3,
|
||||
"decision_threshold": 0.7,
|
||||
"enable_voting": True,
|
||||
"enable_consensus": True
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Dynamic Role Assignment
|
||||
|
||||
```python
|
||||
# Automatically assign roles based on task requirements
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
board_members=board_members,
|
||||
agents=agents,
|
||||
auto_assign_roles=True,
|
||||
role_mapping={
|
||||
"financial_analysis": ["Treasurer", "Financial_Member"],
|
||||
"strategic_planning": ["Chairman", "Executive_Director"],
|
||||
"technical_assessment": ["Technical_Member", "Executive_Director"],
|
||||
"research_analysis": ["Research_Member", "Secretary"],
|
||||
"crisis_management": ["Chairman", "Vice_Chairman", "Communications_Member"]
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Consensus Optimization
|
||||
|
||||
```python
|
||||
# Advanced consensus-building mechanisms
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
board_members=board_members,
|
||||
agents=agents,
|
||||
enable_consensus=True,
|
||||
consensus_timeout=300, # 5 minutes timeout
|
||||
min_participation_rate=0.8, # 80% minimum participation
|
||||
auto_fallback_to_chairman=True, # Chairman can make final decisions
|
||||
consensus_rounds=3, # Maximum consensus building rounds
|
||||
consensus_method="weighted_majority", # Consensus method
|
||||
enable_mediation=True, # Enable mediation for conflicts
|
||||
mediation_timeout=120 # Mediation timeout in seconds
|
||||
)
|
||||
```
|
||||
|
||||
## 🛠️ Troubleshooting and Debugging
|
||||
|
||||
### Common Issues and Solutions
|
||||
|
||||
1. **Consensus Failures**
|
||||
- **Issue**: Board cannot reach consensus within loop limit
|
||||
- **Solution**: Lower voting threshold, increase max_loops, or adjust voting weights
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
decision_threshold=0.6, # Lower threshold
|
||||
max_loops=5, # More loops
|
||||
consensus_timeout=600 # Longer timeout
|
||||
)
|
||||
```
|
||||
|
||||
2. **Agent Timeout**
|
||||
- **Issue**: Individual agents take too long to respond
|
||||
- **Solution**: Increase timeout settings or optimize agent prompts
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
timeout_per_agent=600, # 10 minutes per agent
|
||||
enable_agent_pooling=True # Use agent pooling
|
||||
)
|
||||
```
|
||||
|
||||
3. **Poor Quality Output**
|
||||
- **Issue**: Final output doesn't meet quality standards
|
||||
- **Solution**: Enable quality gates, increase max_loops, or improve agent prompts
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
enable_quality_gates=True,
|
||||
quality_threshold=0.8,
|
||||
enable_peer_review=True,
|
||||
max_loops=4
|
||||
)
|
||||
```
|
||||
|
||||
4. **Resource Exhaustion**
|
||||
- **Issue**: System runs out of resources during execution
|
||||
- **Solution**: Implement resource limits, use agent pooling, or optimize parallel execution
|
||||
```python
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
max_workers=2, # Limit parallel workers
|
||||
enable_agent_pooling=True,
|
||||
parallel_execution=False # Disable parallel execution
|
||||
)
|
||||
```
|
||||
|
||||
### Debugging Techniques
|
||||
|
||||
```python
|
||||
# Debugging configuration
|
||||
debug_config = BoardConfig(
|
||||
max_loops=1, # Limit loops for debugging
|
||||
enable_logging=True,
|
||||
log_level="DEBUG",
|
||||
enable_tracing=True,
|
||||
debug_mode=True
|
||||
)
|
||||
|
||||
# Create debug swarm
|
||||
debug_swarm = BoardOfDirectorsSwarm(
|
||||
agents=agents,
|
||||
config=debug_config
|
||||
)
|
||||
|
||||
# Execute with debugging
|
||||
try:
|
||||
result = debug_swarm.run(task)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
print(f"Debug info: {debug_swarm.get_debug_info()}")
|
||||
|
||||
# Enable detailed logging
|
||||
import logging
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
# Create swarm with logging enabled
|
||||
logging_swarm = BoardOfDirectorsSwarm(
|
||||
agents=agents,
|
||||
config=BoardConfig(
|
||||
enable_logging=True,
|
||||
log_level="DEBUG",
|
||||
enable_metrics=True,
|
||||
enable_tracing=True
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
## 📋 Use Cases
|
||||
|
||||
### Corporate Governance
|
||||
- **Strategic Planning**: Long-term business strategy development
|
||||
- **Risk Management**: Comprehensive risk assessment and mitigation
|
||||
- **Resource Allocation**: Optimal distribution of company resources
|
||||
- **Performance Oversight**: Monitoring and evaluating organizational performance
|
||||
|
||||
### Financial Analysis
|
||||
- **Portfolio Management**: Investment portfolio optimization and rebalancing
|
||||
- **Market Analysis**: Comprehensive market research and trend analysis
|
||||
- **Risk Assessment**: Financial risk evaluation and management
|
||||
- **Compliance Monitoring**: Regulatory compliance and audit preparation
|
||||
|
||||
### Research & Development
|
||||
- **Technology Assessment**: Evaluation of emerging technologies
|
||||
- **Product Development**: Strategic product planning and development
|
||||
- **Innovation Management**: Managing innovation pipelines and initiatives
|
||||
- **Quality Assurance**: Ensuring high standards across development processes
|
||||
|
||||
### Project Management
|
||||
- **Complex Project Planning**: Multi-faceted project strategy development
|
||||
- **Resource Optimization**: Efficient allocation of project resources
|
||||
- **Stakeholder Management**: Coordinating diverse stakeholder interests
|
||||
- **Risk Mitigation**: Identifying and addressing project risks
|
||||
|
||||
### Crisis Management
|
||||
- **Emergency Response**: Rapid response to critical situations
|
||||
- **Stakeholder Communication**: Managing communications during crises
|
||||
- **Recovery Planning**: Developing recovery and prevention strategies
|
||||
- **Legal Compliance**: Ensuring compliance during crisis situations
|
||||
|
||||
## 🎯 Success Criteria
|
||||
|
||||
A successful Board of Directors implementation should demonstrate:
|
||||
|
||||
- ✅ **Democratic Decision Making**: All board members contribute to decisions
|
||||
- ✅ **Consensus Achievement**: Decisions reached through collaborative processes
|
||||
- ✅ **Role Effectiveness**: Each board member fulfills their responsibilities
|
||||
- ✅ **Agent Coordination**: Worker agents execute tasks efficiently
|
||||
- ✅ **Quality Output**: High-quality results through collective intelligence
|
||||
- ✅ **Process Transparency**: Clear visibility into decision-making processes
|
||||
- ✅ **Performance Optimization**: Efficient resource utilization and execution
|
||||
- ✅ **Continuous Improvement**: Learning from each execution cycle
|
||||
|
||||
## 📚 Best Practices
|
||||
|
||||
### 1. Role Definition
|
||||
- Clearly define responsibilities for each board member
|
||||
- Ensure expertise areas align with organizational needs
|
||||
- Balance voting weights based on role importance
|
||||
- Document role interactions and communication protocols
|
||||
|
||||
### 2. Task Formulation
|
||||
- Provide clear, specific task descriptions
|
||||
- Include relevant context and constraints
|
||||
- Specify expected outputs and deliverables
|
||||
- Define quality criteria and success metrics
|
||||
|
||||
### 3. Consensus Building
|
||||
- Allow adequate time for discussion and consensus
|
||||
- Encourage diverse perspectives and viewpoints
|
||||
- Use structured decision-making processes
|
||||
- Implement conflict resolution mechanisms
|
||||
|
||||
### 4. Performance Monitoring
|
||||
- Track decision quality and outcomes
|
||||
- Monitor board member participation
|
||||
- Analyze agent utilization and effectiveness
|
||||
- Implement continuous improvement processes
|
||||
|
||||
### 5. Resource Management
|
||||
- Optimize agent allocation and utilization
|
||||
- Implement parallel execution where appropriate
|
||||
- Monitor resource usage and performance
|
||||
- Scale resources based on task complexity
|
||||
|
||||
---
|
||||
|
||||
The Board of Directors architecture represents a sophisticated approach to multi-agent collaboration, enabling organizations to leverage collective intelligence through structured governance and democratic decision-making processes. This comprehensive implementation provides the tools and frameworks needed to build effective, scalable, and intelligent decision-making systems.
|
||||
@ -0,0 +1,247 @@
|
||||
from loguru import logger
|
||||
import yfinance as yf
|
||||
import json
|
||||
|
||||
|
||||
def get_figma_stock_data(stock: str) -> str:
|
||||
"""
|
||||
Fetches comprehensive stock data for Figma (FIG) using Yahoo Finance.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: A dictionary containing comprehensive Figma stock data including:
|
||||
- Current price and market data
|
||||
- Company information
|
||||
- Financial metrics
|
||||
- Historical data summary
|
||||
- Trading statistics
|
||||
|
||||
Raises:
|
||||
Exception: If there's an error fetching the data from Yahoo Finance
|
||||
"""
|
||||
try:
|
||||
# Initialize Figma stock ticker
|
||||
figma = yf.Ticker(stock)
|
||||
|
||||
# Get current stock info
|
||||
info = figma.info
|
||||
|
||||
# Get recent historical data (last 30 days)
|
||||
hist = figma.history(period="30d")
|
||||
|
||||
# Get real-time fast info
|
||||
fast_info = figma.fast_info
|
||||
|
||||
# Compile comprehensive data
|
||||
figma_data = {
|
||||
"company_info": {
|
||||
"name": info.get("longName", "Figma Inc."),
|
||||
"symbol": "FIG",
|
||||
"sector": info.get("sector", "N/A"),
|
||||
"industry": info.get("industry", "N/A"),
|
||||
"website": info.get("website", "N/A"),
|
||||
"description": info.get("longBusinessSummary", "N/A"),
|
||||
},
|
||||
"current_market_data": {
|
||||
"current_price": info.get("currentPrice", "N/A"),
|
||||
"previous_close": info.get("previousClose", "N/A"),
|
||||
"open": info.get("open", "N/A"),
|
||||
"day_low": info.get("dayLow", "N/A"),
|
||||
"day_high": info.get("dayHigh", "N/A"),
|
||||
"volume": info.get("volume", "N/A"),
|
||||
"market_cap": info.get("marketCap", "N/A"),
|
||||
"price_change": (
|
||||
info.get("currentPrice", 0)
|
||||
- info.get("previousClose", 0)
|
||||
if info.get("currentPrice")
|
||||
and info.get("previousClose")
|
||||
else "N/A"
|
||||
),
|
||||
"price_change_percent": info.get(
|
||||
"regularMarketChangePercent", "N/A"
|
||||
),
|
||||
},
|
||||
"financial_metrics": {
|
||||
"pe_ratio": info.get("trailingPE", "N/A"),
|
||||
"forward_pe": info.get("forwardPE", "N/A"),
|
||||
"price_to_book": info.get("priceToBook", "N/A"),
|
||||
"price_to_sales": info.get(
|
||||
"priceToSalesTrailing12Months", "N/A"
|
||||
),
|
||||
"enterprise_value": info.get(
|
||||
"enterpriseValue", "N/A"
|
||||
),
|
||||
"beta": info.get("beta", "N/A"),
|
||||
"dividend_yield": info.get("dividendYield", "N/A"),
|
||||
"payout_ratio": info.get("payoutRatio", "N/A"),
|
||||
},
|
||||
"trading_statistics": {
|
||||
"fifty_day_average": info.get(
|
||||
"fiftyDayAverage", "N/A"
|
||||
),
|
||||
"two_hundred_day_average": info.get(
|
||||
"twoHundredDayAverage", "N/A"
|
||||
),
|
||||
"fifty_two_week_low": info.get(
|
||||
"fiftyTwoWeekLow", "N/A"
|
||||
),
|
||||
"fifty_two_week_high": info.get(
|
||||
"fiftyTwoWeekHigh", "N/A"
|
||||
),
|
||||
"shares_outstanding": info.get(
|
||||
"sharesOutstanding", "N/A"
|
||||
),
|
||||
"float_shares": info.get("floatShares", "N/A"),
|
||||
"shares_short": info.get("sharesShort", "N/A"),
|
||||
"short_ratio": info.get("shortRatio", "N/A"),
|
||||
},
|
||||
"recent_performance": {
|
||||
"last_30_days": {
|
||||
"start_price": (
|
||||
hist.iloc[0]["Close"]
|
||||
if not hist.empty
|
||||
else "N/A"
|
||||
),
|
||||
"end_price": (
|
||||
hist.iloc[-1]["Close"]
|
||||
if not hist.empty
|
||||
else "N/A"
|
||||
),
|
||||
"total_return": (
|
||||
(
|
||||
hist.iloc[-1]["Close"]
|
||||
- hist.iloc[0]["Close"]
|
||||
)
|
||||
/ hist.iloc[0]["Close"]
|
||||
* 100
|
||||
if not hist.empty
|
||||
else "N/A"
|
||||
),
|
||||
"highest_price": (
|
||||
hist["High"].max()
|
||||
if not hist.empty
|
||||
else "N/A"
|
||||
),
|
||||
"lowest_price": (
|
||||
hist["Low"].min() if not hist.empty else "N/A"
|
||||
),
|
||||
"average_volume": (
|
||||
hist["Volume"].mean()
|
||||
if not hist.empty
|
||||
else "N/A"
|
||||
),
|
||||
}
|
||||
},
|
||||
"real_time_data": {
|
||||
"last_price": (
|
||||
fast_info.last_price
|
||||
if hasattr(fast_info, "last_price")
|
||||
else "N/A"
|
||||
),
|
||||
"last_volume": (
|
||||
fast_info.last_volume
|
||||
if hasattr(fast_info, "last_volume")
|
||||
else "N/A"
|
||||
),
|
||||
"bid": (
|
||||
fast_info.bid
|
||||
if hasattr(fast_info, "bid")
|
||||
else "N/A"
|
||||
),
|
||||
"ask": (
|
||||
fast_info.ask
|
||||
if hasattr(fast_info, "ask")
|
||||
else "N/A"
|
||||
),
|
||||
"bid_size": (
|
||||
fast_info.bid_size
|
||||
if hasattr(fast_info, "bid_size")
|
||||
else "N/A"
|
||||
),
|
||||
"ask_size": (
|
||||
fast_info.ask_size
|
||||
if hasattr(fast_info, "ask_size")
|
||||
else "N/A"
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
logger.info("Successfully fetched Figma (FIG) stock data")
|
||||
return json.dumps(figma_data, indent=4)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching Figma stock data: {e}")
|
||||
raise Exception(f"Failed to fetch Figma stock data: {e}")
|
||||
|
||||
|
||||
# # Example usage
|
||||
# # Initialize the quantitative trading agent
|
||||
# agent = Agent(
|
||||
# agent_name="Quantitative-Trading-Agent",
|
||||
# agent_description="Advanced quantitative trading and algorithmic analysis agent specializing in stock analysis and trading strategies",
|
||||
# system_prompt=f"""You are an expert quantitative trading agent with deep expertise in:
|
||||
# - Algorithmic trading strategies and implementation
|
||||
# - Statistical arbitrage and market making
|
||||
# - Risk management and portfolio optimization
|
||||
# - High-frequency trading systems
|
||||
# - Market microstructure analysis
|
||||
# - Quantitative research methodologies
|
||||
# - Financial mathematics and stochastic processes
|
||||
# - Machine learning applications in trading
|
||||
# - Technical analysis and chart patterns
|
||||
# - Fundamental analysis and valuation models
|
||||
# - Options trading and derivatives
|
||||
# - Market sentiment analysis
|
||||
|
||||
# Your core responsibilities include:
|
||||
# 1. Developing and backtesting trading strategies
|
||||
# 2. Analyzing market data and identifying alpha opportunities
|
||||
# 3. Implementing risk management frameworks
|
||||
# 4. Optimizing portfolio allocations
|
||||
# 5. Conducting quantitative research
|
||||
# 6. Monitoring market microstructure
|
||||
# 7. Evaluating trading system performance
|
||||
# 8. Performing comprehensive stock analysis
|
||||
# 9. Generating trading signals and recommendations
|
||||
# 10. Risk assessment and position sizing
|
||||
|
||||
# When analyzing stocks, you should:
|
||||
# - Evaluate technical indicators and chart patterns
|
||||
# - Assess fundamental metrics and valuation ratios
|
||||
# - Analyze market sentiment and momentum
|
||||
# - Consider macroeconomic factors
|
||||
# - Provide risk-adjusted return projections
|
||||
# - Suggest optimal entry/exit points
|
||||
# - Calculate position sizing recommendations
|
||||
# - Identify potential catalysts and risks
|
||||
|
||||
# You maintain strict adherence to:
|
||||
# - Mathematical rigor in all analyses
|
||||
# - Statistical significance in strategy development
|
||||
# - Risk-adjusted return optimization
|
||||
# - Market impact minimization
|
||||
# - Regulatory compliance
|
||||
# - Transaction cost analysis
|
||||
# - Performance attribution
|
||||
# - Data-driven decision making
|
||||
|
||||
# You communicate in precise, technical terms while maintaining clarity for stakeholders.
|
||||
# Data: {get_figma_stock_data('FIG')}
|
||||
|
||||
# """,
|
||||
# max_loops=1,
|
||||
# model_name="gpt-4o-mini",
|
||||
# dynamic_temperature_enabled=True,
|
||||
# output_type="str-all-except-first",
|
||||
# streaming_on=True,
|
||||
# print_on=True,
|
||||
# telemetry_enable=False,
|
||||
# )
|
||||
|
||||
# # Example 1: Basic usage with just a task
|
||||
# logger.info("Starting quantitative analysis cron job for Figma (FIG)")
|
||||
# cron_job = CronJob(agent=agent, interval="10seconds")
|
||||
# cron_job.run(
|
||||
# task="Analyze the Figma (FIG) stock comprehensively using the available stock data. Provide a detailed quantitative analysis"
|
||||
# )
|
||||
|
||||
print(get_figma_stock_data("FIG"))
|
||||
@ -0,0 +1,105 @@
|
||||
"""
|
||||
Example script demonstrating how to fetch Figma (FIG) stock data using swarms_tools Yahoo Finance API.
|
||||
This shows the alternative approach using the existing swarms_tools package.
|
||||
"""
|
||||
|
||||
from swarms import Agent
|
||||
from swarms.prompts.finance_agent_sys_prompt import (
|
||||
FINANCIAL_AGENT_SYS_PROMPT,
|
||||
)
|
||||
from swarms_tools import yahoo_finance_api
|
||||
from loguru import logger
|
||||
import json
|
||||
|
||||
|
||||
def get_figma_data_with_swarms_tools():
|
||||
"""
|
||||
Fetches Figma stock data using the swarms_tools Yahoo Finance API.
|
||||
|
||||
Returns:
|
||||
dict: Figma stock data from swarms_tools
|
||||
"""
|
||||
try:
|
||||
logger.info("Fetching Figma stock data using swarms_tools...")
|
||||
figma_data = yahoo_finance_api(["FIG"])
|
||||
return figma_data
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching data with swarms_tools: {e}")
|
||||
raise
|
||||
|
||||
|
||||
def analyze_figma_with_agent():
|
||||
"""
|
||||
Uses a Swarms agent to analyze Figma stock data.
|
||||
"""
|
||||
try:
|
||||
# Initialize the agent with Yahoo Finance tool
|
||||
agent = Agent(
|
||||
agent_name="Figma-Analysis-Agent",
|
||||
agent_description="Specialized agent for analyzing Figma stock data",
|
||||
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
|
||||
max_loops=1,
|
||||
model_name="gpt-4o-mini",
|
||||
tools=[yahoo_finance_api],
|
||||
dynamic_temperature_enabled=True,
|
||||
)
|
||||
|
||||
# Ask the agent to analyze Figma
|
||||
analysis = agent.run(
|
||||
"Analyze the current stock data for Figma (FIG) and provide insights on its performance, valuation metrics, and recent trends."
|
||||
)
|
||||
|
||||
return analysis
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in agent analysis: {e}")
|
||||
raise
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Main function to demonstrate different approaches for Figma stock data.
|
||||
"""
|
||||
logger.info("Starting Figma stock analysis with swarms_tools")
|
||||
|
||||
try:
|
||||
# Method 1: Direct API call
|
||||
print("\n" + "=" * 60)
|
||||
print("METHOD 1: Direct swarms_tools API call")
|
||||
print("=" * 60)
|
||||
|
||||
figma_data = get_figma_data_with_swarms_tools()
|
||||
print("Raw data from swarms_tools:")
|
||||
print(json.dumps(figma_data, indent=2, default=str))
|
||||
|
||||
# Method 2: Agent-based analysis
|
||||
print("\n" + "=" * 60)
|
||||
print("METHOD 2: Agent-based analysis")
|
||||
print("=" * 60)
|
||||
|
||||
analysis = analyze_figma_with_agent()
|
||||
print("Agent analysis:")
|
||||
print(analysis)
|
||||
|
||||
# Method 3: Comparison with custom function
|
||||
print("\n" + "=" * 60)
|
||||
print("METHOD 3: Comparison with custom function")
|
||||
print("=" * 60)
|
||||
|
||||
from cron_job_examples.cron_job_example import (
|
||||
get_figma_stock_data_simple,
|
||||
)
|
||||
|
||||
custom_data = get_figma_stock_data_simple()
|
||||
print("Custom function output:")
|
||||
print(custom_data)
|
||||
|
||||
logger.info("All methods completed successfully!")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in main function: {e}")
|
||||
print(f"Error: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,79 @@
|
||||
"""
|
||||
Example script demonstrating how to fetch Figma (FIG) stock data using Yahoo Finance.
|
||||
"""
|
||||
|
||||
from cron_job_examples.cron_job_example import (
|
||||
get_figma_stock_data,
|
||||
get_figma_stock_data_simple,
|
||||
)
|
||||
from loguru import logger
|
||||
import json
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Main function to demonstrate Figma stock data fetching.
|
||||
"""
|
||||
logger.info("Starting Figma stock data demonstration")
|
||||
|
||||
try:
|
||||
# Example 1: Get comprehensive data as dictionary
|
||||
logger.info("Fetching comprehensive Figma stock data...")
|
||||
figma_data = get_figma_stock_data()
|
||||
|
||||
# Print the data in a structured format
|
||||
print("\n" + "=" * 50)
|
||||
print("COMPREHENSIVE FIGMA STOCK DATA")
|
||||
print("=" * 50)
|
||||
print(json.dumps(figma_data, indent=2, default=str))
|
||||
|
||||
# Example 2: Get simple formatted data
|
||||
logger.info("Fetching simple formatted Figma stock data...")
|
||||
simple_data = get_figma_stock_data_simple()
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("SIMPLE FORMATTED FIGMA STOCK DATA")
|
||||
print("=" * 50)
|
||||
print(simple_data)
|
||||
|
||||
# Example 3: Access specific data points
|
||||
logger.info("Accessing specific data points...")
|
||||
|
||||
current_price = figma_data["current_market_data"][
|
||||
"current_price"
|
||||
]
|
||||
market_cap = figma_data["current_market_data"]["market_cap"]
|
||||
pe_ratio = figma_data["financial_metrics"]["pe_ratio"]
|
||||
|
||||
print("\nKey Metrics:")
|
||||
print(f"Current Price: ${current_price}")
|
||||
print(f"Market Cap: ${market_cap:,}")
|
||||
print(f"P/E Ratio: {pe_ratio}")
|
||||
|
||||
# Example 4: Check if stock is performing well
|
||||
price_change = figma_data["current_market_data"][
|
||||
"price_change"
|
||||
]
|
||||
if isinstance(price_change, (int, float)):
|
||||
if price_change > 0:
|
||||
print(
|
||||
f"\n📈 Figma stock is up ${price_change:.2f} today!"
|
||||
)
|
||||
elif price_change < 0:
|
||||
print(
|
||||
f"\n📉 Figma stock is down ${abs(price_change):.2f} today."
|
||||
)
|
||||
else:
|
||||
print("\n➡️ Figma stock is unchanged today.")
|
||||
|
||||
logger.info(
|
||||
"Figma stock data demonstration completed successfully!"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in main function: {e}")
|
||||
print(f"Error: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,257 @@
|
||||
from swarms import Agent, CronJob
|
||||
from loguru import logger
|
||||
import requests
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def get_solana_price() -> str:
|
||||
"""
|
||||
Fetches comprehensive Solana (SOL) price data using CoinGecko API.
|
||||
|
||||
Returns:
|
||||
str: A JSON formatted string containing Solana's current price and market data including:
|
||||
- Current price in USD
|
||||
- Market cap
|
||||
- 24h volume
|
||||
- 24h price change
|
||||
- Last updated timestamp
|
||||
|
||||
Raises:
|
||||
Exception: If there's an error fetching the data from CoinGecko API
|
||||
"""
|
||||
try:
|
||||
# CoinGecko API endpoint for simple price data
|
||||
url = "https://api.coingecko.com/api/v3/simple/price"
|
||||
params = {
|
||||
"ids": "solana", # Solana's CoinGecko ID
|
||||
"vs_currencies": "usd",
|
||||
"include_market_cap": True,
|
||||
"include_24hr_vol": True,
|
||||
"include_24hr_change": True,
|
||||
"include_last_updated_at": True,
|
||||
}
|
||||
|
||||
# Make API request with timeout
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
response.raise_for_status()
|
||||
|
||||
# Parse response data
|
||||
data = response.json()
|
||||
|
||||
if "solana" not in data:
|
||||
raise Exception("Solana data not found in API response")
|
||||
|
||||
solana_data = data["solana"]
|
||||
|
||||
# Compile comprehensive data
|
||||
solana_info = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"coin_info": {
|
||||
"name": "Solana",
|
||||
"symbol": "SOL",
|
||||
"coin_id": "solana",
|
||||
},
|
||||
"price_data": {
|
||||
"current_price_usd": solana_data.get("usd", "N/A"),
|
||||
"market_cap_usd": solana_data.get(
|
||||
"usd_market_cap", "N/A"
|
||||
),
|
||||
"volume_24h_usd": solana_data.get(
|
||||
"usd_24h_vol", "N/A"
|
||||
),
|
||||
"price_change_24h_percent": solana_data.get(
|
||||
"usd_24h_change", "N/A"
|
||||
),
|
||||
"last_updated_at": solana_data.get(
|
||||
"last_updated_at", "N/A"
|
||||
),
|
||||
},
|
||||
"formatted_data": {
|
||||
"price_formatted": (
|
||||
f"${solana_data.get('usd', 'N/A'):,.2f}"
|
||||
if solana_data.get("usd")
|
||||
else "N/A"
|
||||
),
|
||||
"market_cap_formatted": (
|
||||
f"${solana_data.get('usd_market_cap', 'N/A'):,.0f}"
|
||||
if solana_data.get("usd_market_cap")
|
||||
else "N/A"
|
||||
),
|
||||
"volume_formatted": (
|
||||
f"${solana_data.get('usd_24h_vol', 'N/A'):,.0f}"
|
||||
if solana_data.get("usd_24h_vol")
|
||||
else "N/A"
|
||||
),
|
||||
"change_formatted": (
|
||||
f"{solana_data.get('usd_24h_change', 'N/A'):+.2f}%"
|
||||
if solana_data.get("usd_24h_change") is not None
|
||||
else "N/A"
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"Successfully fetched Solana price: ${solana_data.get('usd', 'N/A')}"
|
||||
)
|
||||
return json.dumps(solana_info, indent=4)
|
||||
|
||||
except requests.RequestException as e:
|
||||
error_msg = f"API request failed: {e}"
|
||||
logger.error(error_msg)
|
||||
return json.dumps(
|
||||
{
|
||||
"error": error_msg,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"status": "failed",
|
||||
},
|
||||
indent=4,
|
||||
)
|
||||
except Exception as e:
|
||||
error_msg = f"Error fetching Solana price data: {e}"
|
||||
logger.error(error_msg)
|
||||
return json.dumps(
|
||||
{
|
||||
"error": error_msg,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"status": "failed",
|
||||
},
|
||||
indent=4,
|
||||
)
|
||||
|
||||
|
||||
def analyze_solana_data(data: str) -> str:
|
||||
"""
|
||||
Analyzes Solana price data and provides insights.
|
||||
|
||||
Args:
|
||||
data (str): JSON string containing Solana price data
|
||||
|
||||
Returns:
|
||||
str: Analysis and insights about the current Solana market data
|
||||
"""
|
||||
try:
|
||||
# Parse the data
|
||||
solana_data = json.loads(data)
|
||||
|
||||
if "error" in solana_data:
|
||||
return f"❌ Error in data: {solana_data['error']}"
|
||||
|
||||
price_data = solana_data.get("price_data", {})
|
||||
formatted_data = solana_data.get("formatted_data", {})
|
||||
|
||||
# Extract key metrics
|
||||
current_price = price_data.get("current_price_usd")
|
||||
price_change = price_data.get("price_change_24h_percent")
|
||||
volume_24h = price_data.get("volume_24h_usd")
|
||||
market_cap = price_data.get("market_cap_usd")
|
||||
|
||||
# Generate analysis
|
||||
analysis = f"""
|
||||
🔍 **Solana (SOL) Market Analysis** - {solana_data.get('timestamp', 'N/A')}
|
||||
|
||||
💰 **Current Price**: {formatted_data.get('price_formatted', 'N/A')}
|
||||
📊 **24h Change**: {formatted_data.get('change_formatted', 'N/A')}
|
||||
💎 **Market Cap**: {formatted_data.get('market_cap_formatted', 'N/A')}
|
||||
📈 **24h Volume**: {formatted_data.get('volume_formatted', 'N/A')}
|
||||
|
||||
"""
|
||||
|
||||
# Add sentiment analysis based on price change
|
||||
if price_change is not None:
|
||||
if price_change > 5:
|
||||
analysis += "🚀 **Sentiment**: Strongly Bullish - Significant positive momentum\n"
|
||||
elif price_change > 1:
|
||||
analysis += "📈 **Sentiment**: Bullish - Positive price action\n"
|
||||
elif price_change > -1:
|
||||
analysis += (
|
||||
"➡️ **Sentiment**: Neutral - Sideways movement\n"
|
||||
)
|
||||
elif price_change > -5:
|
||||
analysis += "📉 **Sentiment**: Bearish - Negative price action\n"
|
||||
else:
|
||||
analysis += "🔻 **Sentiment**: Strongly Bearish - Significant decline\n"
|
||||
|
||||
# Add volume analysis
|
||||
if volume_24h and market_cap:
|
||||
try:
|
||||
volume_market_cap_ratio = (
|
||||
volume_24h / market_cap
|
||||
) * 100
|
||||
if volume_market_cap_ratio > 10:
|
||||
analysis += "🔥 **Volume**: High trading activity - Strong market interest\n"
|
||||
elif volume_market_cap_ratio > 5:
|
||||
analysis += (
|
||||
"📊 **Volume**: Moderate trading activity\n"
|
||||
)
|
||||
else:
|
||||
analysis += "😴 **Volume**: Low trading activity - Limited market movement\n"
|
||||
except (TypeError, ZeroDivisionError):
|
||||
analysis += "📊 **Volume**: Unable to calculate volume/market cap ratio\n"
|
||||
|
||||
analysis += f"\n⏰ **Last Updated**: {price_data.get('last_updated_at', 'N/A')}"
|
||||
|
||||
return analysis
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
return f"❌ Error parsing data: {e}"
|
||||
except Exception as e:
|
||||
return f"❌ Error analyzing data: {e}"
|
||||
|
||||
|
||||
# Initialize the Solana analysis agent
|
||||
agent = Agent(
|
||||
agent_name="Solana-Price-Analyzer",
|
||||
agent_description="Specialized agent for analyzing Solana (SOL) cryptocurrency price data and market trends",
|
||||
system_prompt=f"""You are an expert cryptocurrency analyst specializing in Solana (SOL) analysis. Your expertise includes:
|
||||
|
||||
- Technical analysis and chart patterns
|
||||
- Market sentiment analysis
|
||||
- Volume and liquidity analysis
|
||||
- Price action interpretation
|
||||
- Market cap and valuation metrics
|
||||
- Cryptocurrency market dynamics
|
||||
- DeFi ecosystem analysis
|
||||
- Blockchain technology trends
|
||||
|
||||
When analyzing Solana data, you should:
|
||||
- Evaluate price movements and trends
|
||||
- Assess market sentiment and momentum
|
||||
- Consider volume and liquidity factors
|
||||
- Analyze market cap positioning
|
||||
- Provide actionable insights
|
||||
- Identify potential catalysts or risks
|
||||
- Consider broader market context
|
||||
|
||||
You communicate clearly and provide practical analysis that helps users understand Solana's current market position and potential future movements.
|
||||
|
||||
Current Solana Data: {get_solana_price()}
|
||||
""",
|
||||
max_loops=1,
|
||||
model_name="gpt-4o-mini",
|
||||
dynamic_temperature_enabled=True,
|
||||
output_type="str-all-except-first",
|
||||
streaming_on=False, # need to fix this bug where streaming is working but makes copies of the border when you scroll on the terminal
|
||||
print_on=True,
|
||||
telemetry_enable=False,
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Main function to run the Solana price tracking cron job.
|
||||
"""
|
||||
logger.info("🚀 Starting Solana price tracking cron job")
|
||||
logger.info("📊 Fetching Solana price every 10 seconds...")
|
||||
|
||||
# Create cron job that runs every 10 seconds
|
||||
cron_job = CronJob(agent=agent, interval="30seconds")
|
||||
|
||||
# Run the cron job with analysis task
|
||||
cron_job.run(
|
||||
task="Analyze the current Solana (SOL) price data comprehensively. Provide detailed market analysis including price trends, volume analysis, market sentiment, and actionable insights. Format your response clearly with emojis and structured sections."
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,197 @@
|
||||
"""
|
||||
Board of Directors Example
|
||||
|
||||
This example demonstrates how to use the Board of Directors swarm feature
|
||||
in the Swarms Framework. It shows how to create a board, configure it,
|
||||
and use it to orchestrate tasks across multiple agents.
|
||||
|
||||
To run this example:
|
||||
1. Make sure you're in the root directory of the swarms project
|
||||
2. Run: python examples/multi_agent/board_of_directors/board_of_directors_example.py
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from typing import List
|
||||
|
||||
# Add the root directory to the Python path if running from examples directory
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
if 'examples' in current_dir:
|
||||
root_dir = current_dir
|
||||
while os.path.basename(root_dir) != 'examples' and root_dir != os.path.dirname(root_dir):
|
||||
root_dir = os.path.dirname(root_dir)
|
||||
if os.path.basename(root_dir) == 'examples':
|
||||
root_dir = os.path.dirname(root_dir)
|
||||
if root_dir not in sys.path:
|
||||
sys.path.insert(0, root_dir)
|
||||
|
||||
from swarms.structs.board_of_directors_swarm import (
|
||||
BoardOfDirectorsSwarm,
|
||||
BoardMember,
|
||||
BoardMemberRole,
|
||||
)
|
||||
from swarms.structs.agent import Agent
|
||||
|
||||
|
||||
def create_board_members() -> List[BoardMember]:
|
||||
"""Create board members with specific roles."""
|
||||
|
||||
chairman = Agent(
|
||||
agent_name="Chairman",
|
||||
agent_description="Executive Chairman with strategic vision",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are the Executive Chairman. Provide strategic leadership and facilitate decision-making.",
|
||||
)
|
||||
|
||||
cto = Agent(
|
||||
agent_name="CTO",
|
||||
agent_description="Chief Technology Officer with technical expertise",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are the CTO. Provide technical leadership and evaluate technology solutions.",
|
||||
)
|
||||
|
||||
cfo = Agent(
|
||||
agent_name="CFO",
|
||||
agent_description="Chief Financial Officer with financial expertise",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are the CFO. Provide financial analysis and ensure fiscal responsibility.",
|
||||
)
|
||||
|
||||
return [
|
||||
BoardMember(
|
||||
agent=chairman,
|
||||
role=BoardMemberRole.CHAIRMAN,
|
||||
voting_weight=2.0,
|
||||
expertise_areas=["leadership", "strategy"]
|
||||
),
|
||||
BoardMember(
|
||||
agent=cto,
|
||||
role=BoardMemberRole.EXECUTIVE_DIRECTOR,
|
||||
voting_weight=1.5,
|
||||
expertise_areas=["technology", "innovation"]
|
||||
),
|
||||
BoardMember(
|
||||
agent=cfo,
|
||||
role=BoardMemberRole.EXECUTIVE_DIRECTOR,
|
||||
voting_weight=1.5,
|
||||
expertise_areas=["finance", "risk_management"]
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
def create_worker_agents() -> List[Agent]:
|
||||
"""Create worker agents for the swarm."""
|
||||
|
||||
researcher = Agent(
|
||||
agent_name="Researcher",
|
||||
agent_description="Research analyst for data analysis",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a Research Analyst. Conduct thorough research and provide data-driven insights.",
|
||||
)
|
||||
|
||||
developer = Agent(
|
||||
agent_name="Developer",
|
||||
agent_description="Software developer for implementation",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a Software Developer. Design and implement software solutions.",
|
||||
)
|
||||
|
||||
marketer = Agent(
|
||||
agent_name="Marketer",
|
||||
agent_description="Marketing specialist for strategy",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a Marketing Specialist. Develop marketing strategies and campaigns.",
|
||||
)
|
||||
|
||||
return [researcher, developer, marketer]
|
||||
|
||||
|
||||
def run_board_example() -> None:
|
||||
"""Run a Board of Directors example."""
|
||||
|
||||
# Create board members and worker agents
|
||||
board_members = create_board_members()
|
||||
worker_agents = create_worker_agents()
|
||||
|
||||
# Create the Board of Directors swarm
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
name="Executive_Board",
|
||||
board_members=board_members,
|
||||
agents=worker_agents,
|
||||
max_loops=2,
|
||||
verbose=True,
|
||||
decision_threshold=0.6,
|
||||
)
|
||||
|
||||
# Define task
|
||||
task = """
|
||||
Develop a strategy for launching a new AI-powered product in the market.
|
||||
Include market research, technical planning, marketing strategy, and financial projections.
|
||||
"""
|
||||
|
||||
# Execute the task
|
||||
result = board_swarm.run(task=task)
|
||||
|
||||
print("Task completed successfully!")
|
||||
print(f"Result: {result}")
|
||||
|
||||
|
||||
def run_simple_example() -> None:
|
||||
"""Run a simple Board of Directors example."""
|
||||
|
||||
# Create simple agents
|
||||
analyst = Agent(
|
||||
agent_name="Analyst",
|
||||
agent_description="Data analyst",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
)
|
||||
|
||||
writer = Agent(
|
||||
agent_name="Writer",
|
||||
agent_description="Content writer",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
)
|
||||
|
||||
# Create swarm with default settings
|
||||
board_swarm = BoardOfDirectorsSwarm(
|
||||
name="Simple_Board",
|
||||
agents=[analyst, writer],
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
# Execute simple task
|
||||
task = "Analyze current market trends and create a summary report."
|
||||
result = board_swarm.run(task=task)
|
||||
|
||||
print("Simple example completed!")
|
||||
print(f"Result: {result}")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Main function to run the examples."""
|
||||
|
||||
if not os.getenv("OPENAI_API_KEY"):
|
||||
print("Warning: OPENAI_API_KEY not set. Example may not work.")
|
||||
return
|
||||
|
||||
try:
|
||||
print("Running simple Board of Directors example...")
|
||||
run_simple_example()
|
||||
|
||||
print("\nRunning comprehensive Board of Directors example...")
|
||||
run_board_example()
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,16 +1,32 @@
|
||||
from swarms.structs.heavy_swarm import HeavySwarm
|
||||
from swarms import HeavySwarm
|
||||
|
||||
|
||||
swarm = HeavySwarm(
|
||||
worker_model_name="claude-3-5-sonnet-20240620",
|
||||
show_dashboard=True,
|
||||
question_agent_model_name="gpt-4.1",
|
||||
loops_per_agent=1,
|
||||
)
|
||||
def main():
|
||||
"""
|
||||
Run a HeavySwarm query to find the best 3 gold ETFs.
|
||||
|
||||
This function initializes a HeavySwarm instance and queries it to provide
|
||||
the top 3 gold exchange-traded funds (ETFs), requesting clear, structured results.
|
||||
"""
|
||||
swarm = HeavySwarm(
|
||||
name="Gold ETF Research Team",
|
||||
description="A team of agents that research the best gold ETFs",
|
||||
worker_model_name="claude-sonnet-4-latest",
|
||||
show_dashboard=True,
|
||||
question_agent_model_name="gpt-4.1",
|
||||
loops_per_agent=1,
|
||||
)
|
||||
|
||||
out = swarm.run(
|
||||
"Provide 3 publicly traded biotech companies that are currently trading below their cash value. For each company identified, provide available data or projections for the next 6 months, including any relevant financial metrics, upcoming catalysts, or events that could impact valuation. Present your findings in a clear, structured format. Be very specific and provide their ticker symbol, name, and the current price, cash value, and the percentage difference between the two."
|
||||
)
|
||||
prompt = (
|
||||
"Find the best 3 gold ETFs. For each ETF, provide the ticker symbol, "
|
||||
"full name, current price, expense ratio, assets under management, and "
|
||||
"a brief explanation of why it is considered among the best. Present the information "
|
||||
"in a clear, structured format suitable for investors."
|
||||
)
|
||||
|
||||
print(out)
|
||||
out = swarm.run(prompt)
|
||||
print(out)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@ -0,0 +1,34 @@
|
||||
from swarms import HeavySwarm
|
||||
|
||||
|
||||
def main():
|
||||
"""
|
||||
Run a HeavySwarm query to find the best and most promising treatments for diabetes.
|
||||
|
||||
This function initializes a HeavySwarm instance and queries it to provide
|
||||
the top current and theoretical treatments for diabetes, requesting clear,
|
||||
structured, and evidence-based results suitable for medical research or clinical review.
|
||||
"""
|
||||
swarm = HeavySwarm(
|
||||
name="Diabetes Treatment Research Team",
|
||||
description="A team of agents that research the best and most promising treatments for diabetes, including theoretical approaches.",
|
||||
worker_model_name="claude-sonnet-4-20250514",
|
||||
show_dashboard=True,
|
||||
question_agent_model_name="gpt-4.1",
|
||||
loops_per_agent=1,
|
||||
)
|
||||
|
||||
prompt = (
|
||||
"Identify the best and most promising treatments for diabetes, including both current standard therapies and theoretical or experimental approaches. "
|
||||
"For each treatment, provide: the treatment name, type (e.g., medication, lifestyle intervention, device, gene therapy, etc.), "
|
||||
"mechanism of action, current stage of research or approval status, key clinical evidence or rationale, "
|
||||
"potential benefits and risks, and a brief summary of why it is considered promising. "
|
||||
"Present the information in a clear, structured format suitable for medical professionals or researchers."
|
||||
)
|
||||
|
||||
out = swarm.run(prompt)
|
||||
print(out)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Loading…
Reference in new issue