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# VLLM Swarm Agents
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!!! tip "Quick Summary"
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This guide demonstrates how to create a sophisticated multi-agent system using VLLM and Swarms for comprehensive stock market analysis. You'll learn how to configure and orchestrate multiple AI agents working together to provide deep market insights.
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## Overview
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The example showcases how to build a stock analysis system with 5 specialized agents:
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- Technical Analysis Agent
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- Fundamental Analysis Agent
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- Market Sentiment Agent
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- Quantitative Strategy Agent
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- Portfolio Strategy Agent
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Each agent has specific expertise and works collaboratively through a concurrent workflow.
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## Prerequisites
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!!! warning "Requirements"
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Before starting, ensure you have:
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- Python 3.7 or higher
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- The Swarms package installed
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- Access to VLLM compatible models
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- Sufficient compute resources for running VLLM
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## Installation
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!!! example "Setup Steps"
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1. Install the Swarms package:
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```bash
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pip install swarms
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```
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2. Install VLLM dependencies (if not already installed):
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```bash
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pip install vllm
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```
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## Basic Usage
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Here's a complete example of setting up the stock analysis swarm:
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```python
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from swarms import Agent, ConcurrentWorkflow
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from swarms.utils.vllm_wrapper import VLLMWrapper
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# Initialize the VLLM wrapper
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vllm = VLLMWrapper(
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model_name="meta-llama/Llama-2-7b-chat-hf",
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system_prompt="You are a helpful assistant.",
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)
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```
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!!! note "Model Selection"
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The example uses Llama-2-7b-chat, but you can use any VLLM-compatible model. Make sure you have the necessary permissions and resources to run your chosen model.
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## Agent Configuration
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### Technical Analysis Agent
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```python
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technical_analyst = Agent(
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agent_name="Technical-Analysis-Agent",
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agent_description="Expert in technical analysis and chart patterns",
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system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:
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1. PRICE ACTION ANALYSIS
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- Identify key support and resistance levels
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- Analyze price trends and momentum
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- Detect chart patterns (e.g., head & shoulders, triangles, flags)
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- Evaluate volume patterns and their implications
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2. TECHNICAL INDICATORS
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- Calculate and interpret moving averages (SMA, EMA)
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- Analyze momentum indicators (RSI, MACD, Stochastic)
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- Evaluate volume indicators (OBV, Volume Profile)
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- Monitor volatility indicators (Bollinger Bands, ATR)
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3. TRADING SIGNALS
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- Generate clear buy/sell signals based on technical criteria
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- Identify potential entry and exit points
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- Set appropriate stop-loss and take-profit levels
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- Calculate position sizing recommendations
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4. RISK MANAGEMENT
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- Assess market volatility and trend strength
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- Identify potential reversal points
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- Calculate risk/reward ratios for trades
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- Suggest position sizing based on risk parameters
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Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
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max_loops=1,
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llm=vllm,
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)
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```
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!!! tip "Agent Customization"
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Each agent can be customized with different:
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- System prompts
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- Temperature settings
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- Max token limits
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- Response formats
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## Running the Swarm
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To execute the swarm analysis:
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```python
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swarm = ConcurrentWorkflow(
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name="Stock-Analysis-Swarm",
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description="A swarm of agents that analyze stocks and provide comprehensive analysis.",
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agents=stock_analysis_agents,
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)
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# Run the analysis
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response = swarm.run("Analyze the best etfs for gold and other similar commodities in volatile markets")
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```
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## Full Code Example
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```python
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from swarms import Agent, ConcurrentWorkflow
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from swarms.utils.vllm_wrapper import VLLMWrapper
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# Initialize the VLLM wrapper
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vllm = VLLMWrapper(
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model_name="meta-llama/Llama-2-7b-chat-hf",
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system_prompt="You are a helpful assistant.",
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)
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# Technical Analysis Agent
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technical_analyst = Agent(
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agent_name="Technical-Analysis-Agent",
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agent_description="Expert in technical analysis and chart patterns",
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system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:
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1. PRICE ACTION ANALYSIS
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- Identify key support and resistance levels
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- Analyze price trends and momentum
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- Detect chart patterns (e.g., head & shoulders, triangles, flags)
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- Evaluate volume patterns and their implications
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2. TECHNICAL INDICATORS
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- Calculate and interpret moving averages (SMA, EMA)
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- Analyze momentum indicators (RSI, MACD, Stochastic)
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- Evaluate volume indicators (OBV, Volume Profile)
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- Monitor volatility indicators (Bollinger Bands, ATR)
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3. TRADING SIGNALS
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- Generate clear buy/sell signals based on technical criteria
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- Identify potential entry and exit points
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- Set appropriate stop-loss and take-profit levels
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- Calculate position sizing recommendations
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4. RISK MANAGEMENT
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- Assess market volatility and trend strength
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- Identify potential reversal points
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- Calculate risk/reward ratios for trades
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- Suggest position sizing based on risk parameters
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Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
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max_loops=1,
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llm=vllm,
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)
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# Fundamental Analysis Agent
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fundamental_analyst = Agent(
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agent_name="Fundamental-Analysis-Agent",
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agent_description="Expert in company fundamentals and valuation",
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system_prompt="""You are an expert Fundamental Analysis Agent specializing in company valuation and financial metrics. Your core responsibilities include:
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1. FINANCIAL STATEMENT ANALYSIS
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- Analyze income statements, balance sheets, and cash flow statements
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- Calculate and interpret key financial ratios
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- Evaluate revenue growth and profit margins
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- Assess company's debt levels and cash position
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2. VALUATION METRICS
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- Calculate fair value using multiple valuation methods:
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* Discounted Cash Flow (DCF)
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* Price-to-Earnings (P/E)
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* Price-to-Book (P/B)
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* Enterprise Value/EBITDA
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- Compare valuations against industry peers
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3. BUSINESS MODEL ASSESSMENT
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- Evaluate competitive advantages and market position
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- Analyze industry dynamics and market share
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- Assess management quality and corporate governance
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- Identify potential risks and growth opportunities
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4. ECONOMIC CONTEXT
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- Consider macroeconomic factors affecting the company
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- Analyze industry cycles and trends
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- Evaluate regulatory environment and compliance
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- Assess global market conditions
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Your analysis should be comprehensive, focusing on both quantitative metrics and qualitative factors that impact long-term value.""",
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max_loops=1,
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llm=vllm,
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)
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# Market Sentiment Agent
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sentiment_analyst = Agent(
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agent_name="Market-Sentiment-Agent",
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agent_description="Expert in market psychology and sentiment analysis",
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system_prompt="""You are an expert Market Sentiment Agent specializing in analyzing market psychology and investor behavior. Your key responsibilities include:
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1. SENTIMENT INDICATORS
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- Monitor and interpret market sentiment indicators:
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* VIX (Fear Index)
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* Put/Call Ratio
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* Market Breadth
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* Investor Surveys
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- Track institutional vs retail investor behavior
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2. NEWS AND SOCIAL MEDIA ANALYSIS
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- Analyze news flow and media sentiment
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- Monitor social media trends and discussions
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- Track analyst recommendations and changes
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- Evaluate corporate insider trading patterns
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3. MARKET POSITIONING
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- Assess hedge fund positioning and exposure
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- Monitor short interest and short squeeze potential
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- Track fund flows and asset allocation trends
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- Analyze options market sentiment
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4. CONTRARIAN SIGNALS
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- Identify extreme sentiment readings
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- Detect potential market turning points
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- Analyze historical sentiment patterns
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- Provide contrarian trading opportunities
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Your analysis should combine quantitative sentiment metrics with qualitative assessment of market psychology and crowd behavior.""",
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max_loops=1,
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llm=vllm,
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)
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# Quantitative Strategy Agent
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quant_analyst = Agent(
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agent_name="Quantitative-Strategy-Agent",
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agent_description="Expert in quantitative analysis and algorithmic strategies",
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system_prompt="""You are an expert Quantitative Strategy Agent specializing in data-driven investment strategies. Your primary responsibilities include:
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1. FACTOR ANALYSIS
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- Analyze and monitor factor performance:
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* Value
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* Momentum
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* Quality
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* Size
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* Low Volatility
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- Calculate factor exposures and correlations
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2. STATISTICAL ANALYSIS
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- Perform statistical arbitrage analysis
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- Calculate and monitor pair trading opportunities
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- Analyze market anomalies and inefficiencies
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- Develop mean reversion strategies
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3. RISK MODELING
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- Build and maintain risk models
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- Calculate portfolio optimization metrics
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- Monitor correlation matrices
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- Analyze tail risk and stress scenarios
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4. ALGORITHMIC STRATEGIES
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- Develop systematic trading strategies
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- Backtest and validate trading algorithms
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- Monitor strategy performance metrics
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- Optimize execution algorithms
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Your analysis should be purely quantitative, based on statistical evidence and mathematical models rather than subjective opinions.""",
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max_loops=1,
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llm=vllm,
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)
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# Portfolio Strategy Agent
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portfolio_strategist = Agent(
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agent_name="Portfolio-Strategy-Agent",
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agent_description="Expert in portfolio management and asset allocation",
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system_prompt="""You are an expert Portfolio Strategy Agent specializing in portfolio construction and management. Your core responsibilities include:
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1. ASSET ALLOCATION
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- Develop strategic asset allocation frameworks
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- Recommend tactical asset allocation shifts
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- Optimize portfolio weightings
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- Balance risk and return objectives
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2. PORTFOLIO ANALYSIS
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- Calculate portfolio risk metrics
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- Monitor sector and factor exposures
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- Analyze portfolio correlation matrix
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- Track performance attribution
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3. RISK MANAGEMENT
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- Implement portfolio hedging strategies
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- Monitor and adjust position sizing
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- Set stop-loss and rebalancing rules
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- Develop drawdown protection strategies
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4. PORTFOLIO OPTIMIZATION
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- Calculate efficient frontier analysis
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- Optimize for various objectives:
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* Maximum Sharpe Ratio
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* Minimum Volatility
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* Maximum Diversification
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- Consider transaction costs and taxes
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Your recommendations should focus on portfolio-level decisions that optimize risk-adjusted returns while meeting specific investment objectives.""",
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max_loops=1,
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llm=vllm,
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)
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# Create a list of all agents
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stock_analysis_agents = [
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technical_analyst,
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fundamental_analyst,
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sentiment_analyst,
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quant_analyst,
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portfolio_strategist
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]
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swarm = ConcurrentWorkflow(
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name="Stock-Analysis-Swarm",
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description="A swarm of agents that analyze stocks and provide a comprehensive analysis of the current trends and opportunities.",
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agents=stock_analysis_agents,
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)
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swarm.run("Analyze the best etfs for gold and other similiar commodities in volatile markets")
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```
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## Best Practices
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!!! success "Optimization Tips"
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1. **Agent Design**
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- Keep system prompts focused and specific
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- Use clear role definitions
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- Include error handling guidelines
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2. **Resource Management**
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- Monitor memory usage with large models
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- Implement proper cleanup procedures
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- Use batching for multiple queries
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3. **Output Handling**
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- Implement proper logging
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- Format outputs consistently
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- Include error checking
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## Common Issues and Solutions
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!!! warning "Troubleshooting"
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Common issues you might encounter:
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1. **Memory Issues**
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- *Problem*: VLLM consuming too much memory
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- *Solution*: Adjust batch sizes and model parameters
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2. **Agent Coordination**
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- *Problem*: Agents providing conflicting information
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- *Solution*: Implement consensus mechanisms or priority rules
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3. **Performance**
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- *Problem*: Slow response times
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- *Solution*: Use proper batching and optimize model loading
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## FAQ
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??? question "Can I use different models for different agents?"
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Yes, you can initialize multiple VLLM wrappers with different models for each agent. However, be mindful of memory usage.
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??? question "How many agents can run concurrently?"
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The number depends on your hardware resources. Start with 3-5 agents and scale based on performance.
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??? question "Can I customize agent communication patterns?"
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Yes, you can modify the ConcurrentWorkflow class or create custom workflows for specific communication patterns.
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## Advanced Configuration
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!!! example "Extended Settings"
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```python
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vllm = VLLMWrapper(
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model_name="meta-llama/Llama-2-7b-chat-hf",
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system_prompt="You are a helpful assistant.",
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temperature=0.7,
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max_tokens=2048,
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top_p=0.95,
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)
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```
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## Contributing
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!!! info "Get Involved"
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We welcome contributions! Here's how you can help:
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1. Report bugs and issues
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2. Submit feature requests
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3. Contribute to documentation
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4. Share example use cases
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## Resources
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!!! abstract "Additional Reading"
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- [VLLM Documentation](https://docs.vllm.ai/en/latest/)
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@ -1,194 +0,0 @@
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# vLLM Integration Guide
|
|
||||||
|
|
||||||
!!! info "Overview"
|
|
||||||
vLLM is a high-performance and easy-to-use library for LLM inference and serving. This guide explains how to integrate vLLM with Swarms for efficient, production-grade language model deployment.
|
|
||||||
|
|
||||||
|
|
||||||
## Installation
|
|
||||||
|
|
||||||
!!! note "Prerequisites"
|
|
||||||
Before you begin, make sure you have Python 3.8+ installed on your system.
|
|
||||||
|
|
||||||
=== "pip"
|
|
||||||
```bash
|
|
||||||
pip install -U vllm swarms
|
|
||||||
```
|
|
||||||
|
|
||||||
=== "poetry"
|
|
||||||
```bash
|
|
||||||
poetry add vllm swarms
|
|
||||||
```
|
|
||||||
|
|
||||||
## Basic Usage
|
|
||||||
|
|
||||||
Here's a simple example of how to use vLLM with Swarms:
|
|
||||||
|
|
||||||
```python title="basic_usage.py"
|
|
||||||
from swarms.utils.vllm_wrapper import VLLMWrapper
|
|
||||||
|
|
||||||
# Initialize the vLLM wrapper
|
|
||||||
vllm = VLLMWrapper(
|
|
||||||
model_name="meta-llama/Llama-2-7b-chat-hf",
|
|
||||||
system_prompt="You are a helpful assistant.",
|
|
||||||
temperature=0.7,
|
|
||||||
max_tokens=4000
|
|
||||||
)
|
|
||||||
|
|
||||||
# Run inference
|
|
||||||
response = vllm.run("What is the capital of France?")
|
|
||||||
print(response)
|
|
||||||
```
|
|
||||||
|
|
||||||
## VLLMWrapper Class
|
|
||||||
|
|
||||||
!!! abstract "Class Overview"
|
|
||||||
The `VLLMWrapper` class provides a convenient interface for working with vLLM models.
|
|
||||||
|
|
||||||
### Key Parameters
|
|
||||||
|
|
||||||
| Parameter | Type | Description | Default |
|
|
||||||
|-----------|------|-------------|---------|
|
|
||||||
| `model_name` | str | Name of the model to use | "meta-llama/Llama-2-7b-chat-hf" |
|
|
||||||
| `system_prompt` | str | System prompt to use | None |
|
|
||||||
| `stream` | bool | Whether to stream the output | False |
|
|
||||||
| `temperature` | float | Sampling temperature | 0.5 |
|
|
||||||
| `max_tokens` | int | Maximum number of tokens to generate | 4000 |
|
|
||||||
|
|
||||||
### Example with Custom Parameters
|
|
||||||
|
|
||||||
```python title="custom_parameters.py"
|
|
||||||
vllm = VLLMWrapper(
|
|
||||||
model_name="meta-llama/Llama-2-13b-chat-hf",
|
|
||||||
system_prompt="You are an expert in artificial intelligence.",
|
|
||||||
temperature=0.8,
|
|
||||||
max_tokens=2000
|
|
||||||
)
|
|
||||||
```
|
|
||||||
|
|
||||||
## Integration with Agents
|
|
||||||
|
|
||||||
You can easily integrate vLLM with Swarms agents for more complex workflows:
|
|
||||||
|
|
||||||
```python title="agent_integration.py"
|
|
||||||
from swarms import Agent
|
|
||||||
from swarms.utils.vllm_wrapper import VLLMWrapper
|
|
||||||
|
|
||||||
# Initialize vLLM
|
|
||||||
vllm = VLLMWrapper(
|
|
||||||
model_name="meta-llama/Llama-2-7b-chat-hf",
|
|
||||||
system_prompt="You are a helpful assistant."
|
|
||||||
)
|
|
||||||
|
|
||||||
# Create an agent with vLLM
|
|
||||||
agent = Agent(
|
|
||||||
agent_name="Research-Agent",
|
|
||||||
agent_description="Expert in conducting research and analysis",
|
|
||||||
system_prompt="""You are an expert research agent. Your tasks include:
|
|
||||||
1. Analyzing complex topics
|
|
||||||
2. Providing detailed summaries
|
|
||||||
3. Making data-driven recommendations""",
|
|
||||||
llm=vllm,
|
|
||||||
max_loops=1
|
|
||||||
)
|
|
||||||
|
|
||||||
# Run the agent
|
|
||||||
response = agent.run("Research the impact of AI on healthcare")
|
|
||||||
```
|
|
||||||
|
|
||||||
## Advanced Features
|
|
||||||
|
|
||||||
### Batch Processing
|
|
||||||
|
|
||||||
!!! tip "Performance Optimization"
|
|
||||||
Use batch processing for efficient handling of multiple tasks simultaneously.
|
|
||||||
|
|
||||||
```python title="batch_processing.py"
|
|
||||||
tasks = [
|
|
||||||
"What is machine learning?",
|
|
||||||
"Explain neural networks",
|
|
||||||
"Describe deep learning"
|
|
||||||
]
|
|
||||||
|
|
||||||
results = vllm.batched_run(tasks, batch_size=3)
|
|
||||||
```
|
|
||||||
|
|
||||||
### Error Handling
|
|
||||||
|
|
||||||
!!! warning "Error Management"
|
|
||||||
Always implement proper error handling in production environments.
|
|
||||||
|
|
||||||
```python title="error_handling.py"
|
|
||||||
from loguru import logger
|
|
||||||
|
|
||||||
try:
|
|
||||||
response = vllm.run("Complex task")
|
|
||||||
except Exception as error:
|
|
||||||
logger.error(f"Error occurred: {error}")
|
|
||||||
```
|
|
||||||
|
|
||||||
## Best Practices
|
|
||||||
|
|
||||||
!!! success "Recommended Practices"
|
|
||||||
=== "Model Selection"
|
|
||||||
- Choose appropriate model sizes based on your requirements
|
|
||||||
- Consider the trade-off between model size and inference speed
|
|
||||||
|
|
||||||
=== "System Resources"
|
|
||||||
- Ensure sufficient GPU memory for your chosen model
|
|
||||||
- Monitor resource usage during batch processing
|
|
||||||
|
|
||||||
=== "Prompt Engineering"
|
|
||||||
- Use clear and specific system prompts
|
|
||||||
- Structure user prompts for optimal results
|
|
||||||
|
|
||||||
=== "Error Handling"
|
|
||||||
- Implement proper error handling and logging
|
|
||||||
- Set up monitoring for production deployments
|
|
||||||
|
|
||||||
=== "Performance"
|
|
||||||
- Use batch processing for multiple tasks
|
|
||||||
- Adjust max_tokens based on your use case
|
|
||||||
- Fine-tune temperature for optimal output quality
|
|
||||||
|
|
||||||
## Example: Multi-Agent System
|
|
||||||
|
|
||||||
Here's an example of creating a multi-agent system using vLLM:
|
|
||||||
|
|
||||||
```python title="multi_agent_system.py"
|
|
||||||
from swarms import Agent, ConcurrentWorkflow
|
|
||||||
from swarms.utils.vllm_wrapper import VLLMWrapper
|
|
||||||
|
|
||||||
# Initialize vLLM
|
|
||||||
vllm = VLLMWrapper(
|
|
||||||
model_name="meta-llama/Llama-2-7b-chat-hf",
|
|
||||||
system_prompt="You are a helpful assistant."
|
|
||||||
)
|
|
||||||
|
|
||||||
# Create specialized agents
|
|
||||||
research_agent = Agent(
|
|
||||||
agent_name="Research-Agent",
|
|
||||||
agent_description="Expert in research",
|
|
||||||
system_prompt="You are a research expert.",
|
|
||||||
llm=vllm
|
|
||||||
)
|
|
||||||
|
|
||||||
analysis_agent = Agent(
|
|
||||||
agent_name="Analysis-Agent",
|
|
||||||
agent_description="Expert in analysis",
|
|
||||||
system_prompt="You are an analysis expert.",
|
|
||||||
llm=vllm
|
|
||||||
)
|
|
||||||
|
|
||||||
# Create a workflow
|
|
||||||
agents = [research_agent, analysis_agent]
|
|
||||||
workflow = ConcurrentWorkflow(
|
|
||||||
name="Research-Analysis-Workflow",
|
|
||||||
description="Comprehensive research and analysis workflow",
|
|
||||||
agents=agents
|
|
||||||
)
|
|
||||||
|
|
||||||
# Run the workflow
|
|
||||||
result = workflow.run("Analyze the impact of renewable energy")
|
|
||||||
```
|
|
||||||
@ -1,214 +0,0 @@
|
|||||||
from swarms import Agent, ConcurrentWorkflow
|
|
||||||
from swarms.utils.vllm_wrapper import VLLMWrapper
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
|
|
||||||
load_dotenv()
|
|
||||||
|
|
||||||
# Initialize the VLLM wrapper
|
|
||||||
vllm = VLLMWrapper(
|
|
||||||
model_name="meta-llama/Llama-2-7b-chat-hf",
|
|
||||||
system_prompt="You are a helpful assistant.",
|
|
||||||
)
|
|
||||||
|
|
||||||
# Technical Analysis Agent
|
|
||||||
technical_analyst = Agent(
|
|
||||||
agent_name="Technical-Analysis-Agent",
|
|
||||||
agent_description="Expert in technical analysis and chart patterns",
|
|
||||||
system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:
|
|
||||||
|
|
||||||
1. PRICE ACTION ANALYSIS
|
|
||||||
- Identify key support and resistance levels
|
|
||||||
- Analyze price trends and momentum
|
|
||||||
- Detect chart patterns (e.g., head & shoulders, triangles, flags)
|
|
||||||
- Evaluate volume patterns and their implications
|
|
||||||
|
|
||||||
2. TECHNICAL INDICATORS
|
|
||||||
- Calculate and interpret moving averages (SMA, EMA)
|
|
||||||
- Analyze momentum indicators (RSI, MACD, Stochastic)
|
|
||||||
- Evaluate volume indicators (OBV, Volume Profile)
|
|
||||||
- Monitor volatility indicators (Bollinger Bands, ATR)
|
|
||||||
|
|
||||||
3. TRADING SIGNALS
|
|
||||||
- Generate clear buy/sell signals based on technical criteria
|
|
||||||
- Identify potential entry and exit points
|
|
||||||
- Set appropriate stop-loss and take-profit levels
|
|
||||||
- Calculate position sizing recommendations
|
|
||||||
|
|
||||||
4. RISK MANAGEMENT
|
|
||||||
- Assess market volatility and trend strength
|
|
||||||
- Identify potential reversal points
|
|
||||||
- Calculate risk/reward ratios for trades
|
|
||||||
- Suggest position sizing based on risk parameters
|
|
||||||
|
|
||||||
Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
|
|
||||||
max_loops=1,
|
|
||||||
llm=vllm,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Fundamental Analysis Agent
|
|
||||||
fundamental_analyst = Agent(
|
|
||||||
agent_name="Fundamental-Analysis-Agent",
|
|
||||||
agent_description="Expert in company fundamentals and valuation",
|
|
||||||
system_prompt="""You are an expert Fundamental Analysis Agent specializing in company valuation and financial metrics. Your core responsibilities include:
|
|
||||||
|
|
||||||
1. FINANCIAL STATEMENT ANALYSIS
|
|
||||||
- Analyze income statements, balance sheets, and cash flow statements
|
|
||||||
- Calculate and interpret key financial ratios
|
|
||||||
- Evaluate revenue growth and profit margins
|
|
||||||
- Assess company's debt levels and cash position
|
|
||||||
|
|
||||||
2. VALUATION METRICS
|
|
||||||
- Calculate fair value using multiple valuation methods:
|
|
||||||
* Discounted Cash Flow (DCF)
|
|
||||||
* Price-to-Earnings (P/E)
|
|
||||||
* Price-to-Book (P/B)
|
|
||||||
* Enterprise Value/EBITDA
|
|
||||||
- Compare valuations against industry peers
|
|
||||||
|
|
||||||
3. BUSINESS MODEL ASSESSMENT
|
|
||||||
- Evaluate competitive advantages and market position
|
|
||||||
- Analyze industry dynamics and market share
|
|
||||||
- Assess management quality and corporate governance
|
|
||||||
- Identify potential risks and growth opportunities
|
|
||||||
|
|
||||||
4. ECONOMIC CONTEXT
|
|
||||||
- Consider macroeconomic factors affecting the company
|
|
||||||
- Analyze industry cycles and trends
|
|
||||||
- Evaluate regulatory environment and compliance
|
|
||||||
- Assess global market conditions
|
|
||||||
|
|
||||||
Your analysis should be comprehensive, focusing on both quantitative metrics and qualitative factors that impact long-term value.""",
|
|
||||||
max_loops=1,
|
|
||||||
llm=vllm,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Market Sentiment Agent
|
|
||||||
sentiment_analyst = Agent(
|
|
||||||
agent_name="Market-Sentiment-Agent",
|
|
||||||
agent_description="Expert in market psychology and sentiment analysis",
|
|
||||||
system_prompt="""You are an expert Market Sentiment Agent specializing in analyzing market psychology and investor behavior. Your key responsibilities include:
|
|
||||||
|
|
||||||
1. SENTIMENT INDICATORS
|
|
||||||
- Monitor and interpret market sentiment indicators:
|
|
||||||
* VIX (Fear Index)
|
|
||||||
* Put/Call Ratio
|
|
||||||
* Market Breadth
|
|
||||||
* Investor Surveys
|
|
||||||
- Track institutional vs retail investor behavior
|
|
||||||
|
|
||||||
2. NEWS AND SOCIAL MEDIA ANALYSIS
|
|
||||||
- Analyze news flow and media sentiment
|
|
||||||
- Monitor social media trends and discussions
|
|
||||||
- Track analyst recommendations and changes
|
|
||||||
- Evaluate corporate insider trading patterns
|
|
||||||
|
|
||||||
3. MARKET POSITIONING
|
|
||||||
- Assess hedge fund positioning and exposure
|
|
||||||
- Monitor short interest and short squeeze potential
|
|
||||||
- Track fund flows and asset allocation trends
|
|
||||||
- Analyze options market sentiment
|
|
||||||
|
|
||||||
4. CONTRARIAN SIGNALS
|
|
||||||
- Identify extreme sentiment readings
|
|
||||||
- Detect potential market turning points
|
|
||||||
- Analyze historical sentiment patterns
|
|
||||||
- Provide contrarian trading opportunities
|
|
||||||
|
|
||||||
Your analysis should combine quantitative sentiment metrics with qualitative assessment of market psychology and crowd behavior.""",
|
|
||||||
max_loops=1,
|
|
||||||
llm=vllm,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Quantitative Strategy Agent
|
|
||||||
quant_analyst = Agent(
|
|
||||||
agent_name="Quantitative-Strategy-Agent",
|
|
||||||
agent_description="Expert in quantitative analysis and algorithmic strategies",
|
|
||||||
system_prompt="""You are an expert Quantitative Strategy Agent specializing in data-driven investment strategies. Your primary responsibilities include:
|
|
||||||
|
|
||||||
1. FACTOR ANALYSIS
|
|
||||||
- Analyze and monitor factor performance:
|
|
||||||
* Value
|
|
||||||
* Momentum
|
|
||||||
* Quality
|
|
||||||
* Size
|
|
||||||
* Low Volatility
|
|
||||||
- Calculate factor exposures and correlations
|
|
||||||
|
|
||||||
2. STATISTICAL ANALYSIS
|
|
||||||
- Perform statistical arbitrage analysis
|
|
||||||
- Calculate and monitor pair trading opportunities
|
|
||||||
- Analyze market anomalies and inefficiencies
|
|
||||||
- Develop mean reversion strategies
|
|
||||||
|
|
||||||
3. RISK MODELING
|
|
||||||
- Build and maintain risk models
|
|
||||||
- Calculate portfolio optimization metrics
|
|
||||||
- Monitor correlation matrices
|
|
||||||
- Analyze tail risk and stress scenarios
|
|
||||||
|
|
||||||
4. ALGORITHMIC STRATEGIES
|
|
||||||
- Develop systematic trading strategies
|
|
||||||
- Backtest and validate trading algorithms
|
|
||||||
- Monitor strategy performance metrics
|
|
||||||
- Optimize execution algorithms
|
|
||||||
|
|
||||||
Your analysis should be purely quantitative, based on statistical evidence and mathematical models rather than subjective opinions.""",
|
|
||||||
max_loops=1,
|
|
||||||
llm=vllm,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Portfolio Strategy Agent
|
|
||||||
portfolio_strategist = Agent(
|
|
||||||
agent_name="Portfolio-Strategy-Agent",
|
|
||||||
agent_description="Expert in portfolio management and asset allocation",
|
|
||||||
system_prompt="""You are an expert Portfolio Strategy Agent specializing in portfolio construction and management. Your core responsibilities include:
|
|
||||||
|
|
||||||
1. ASSET ALLOCATION
|
|
||||||
- Develop strategic asset allocation frameworks
|
|
||||||
- Recommend tactical asset allocation shifts
|
|
||||||
- Optimize portfolio weightings
|
|
||||||
- Balance risk and return objectives
|
|
||||||
|
|
||||||
2. PORTFOLIO ANALYSIS
|
|
||||||
- Calculate portfolio risk metrics
|
|
||||||
- Monitor sector and factor exposures
|
|
||||||
- Analyze portfolio correlation matrix
|
|
||||||
- Track performance attribution
|
|
||||||
|
|
||||||
3. RISK MANAGEMENT
|
|
||||||
- Implement portfolio hedging strategies
|
|
||||||
- Monitor and adjust position sizing
|
|
||||||
- Set stop-loss and rebalancing rules
|
|
||||||
- Develop drawdown protection strategies
|
|
||||||
|
|
||||||
4. PORTFOLIO OPTIMIZATION
|
|
||||||
- Calculate efficient frontier analysis
|
|
||||||
- Optimize for various objectives:
|
|
||||||
* Maximum Sharpe Ratio
|
|
||||||
* Minimum Volatility
|
|
||||||
* Maximum Diversification
|
|
||||||
- Consider transaction costs and taxes
|
|
||||||
|
|
||||||
Your recommendations should focus on portfolio-level decisions that optimize risk-adjusted returns while meeting specific investment objectives.""",
|
|
||||||
max_loops=1,
|
|
||||||
llm=vllm,
|
|
||||||
)
|
|
||||||
|
|
||||||
# Create a list of all agents
|
|
||||||
stock_analysis_agents = [
|
|
||||||
technical_analyst,
|
|
||||||
fundamental_analyst,
|
|
||||||
sentiment_analyst,
|
|
||||||
quant_analyst,
|
|
||||||
portfolio_strategist,
|
|
||||||
]
|
|
||||||
|
|
||||||
swarm = ConcurrentWorkflow(
|
|
||||||
name="Stock-Analysis-Swarm",
|
|
||||||
description="A swarm of agents that analyze stocks and provide a comprehensive analysis of the current trends and opportunities.",
|
|
||||||
agents=stock_analysis_agents,
|
|
||||||
)
|
|
||||||
|
|
||||||
swarm.run(
|
|
||||||
"Analyze the best etfs for gold and other similiar commodities in volatile markets"
|
|
||||||
)
|
|
||||||
@ -0,0 +1,13 @@
|
|||||||
|
from swarms.utils.vllm_wrapper import VLLMWrapper
|
||||||
|
|
||||||
|
# Initialize the vLLM wrapper
|
||||||
|
vllm = VLLMWrapper(
|
||||||
|
model_name="gpt-4o-mini",
|
||||||
|
system_prompt="You are a helpful assistant.",
|
||||||
|
temperature=0.7,
|
||||||
|
max_tokens=4000
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run inference
|
||||||
|
response = vllm.run("What is the capital of France?")
|
||||||
|
print(response)
|
||||||
Loading…
Reference in new issue