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Integrated Agents - Quick Start

What Changed

✅ Screening Agent - Now a langgraph agent, part of the unified system ✅ Pump Detection Agent - Now a langgraph agent, part of the unified system ✅ Both agents work together with existing analysts and researchers ✅ Flexible enabling/disabling via parameters

Quick Usage

Minimal Example (1 Stock)

from tradingagents.graph.trading_graph import TradingAgentsGraph

# Create graph
graph = TradingAgentsGraph(
    include_pump_detection=True,  # Enable pump detection
    selected_analysts=["market"],  # Just market analyst
)

# Analyze one stock
final_state, signal = graph.propagate("NVDA", "2025-12-05")

# Get results
print(final_state.get("pump_report"))  # Pump analysis
print(final_state.get("market_report"))  # Technical analysis

Full Analysis (All Agents)

graph = TradingAgentsGraph(
    include_screening=True,           # Find candidates
    include_pump_detection=True,       # Detect pumps
    selected_analysts=[
        "market",
        "social", 
        "news",
        "fundamentals"
    ],
)

final_state, signal = graph.propagate("NVDA", "2025-12-05")

Just Screening

graph = TradingAgentsGraph(
    include_screening=True,
    selected_analysts=["market"],
)

# Get screening recommendations
final_state, signal = graph.propagate("NVDA", "2025-12-05")
print(final_state.get("screening_report"))

Key Agents

Agent	Purpose	Key Tools	Output
Screening	Find candidates	Market movers, trending, earnings	Ticker list
Pump Detection	Detect pre-pumps	Volume, price, social, RSI, catalyst	Pump score 0-100
Market	Technical analysis	RSI, MACD, moving averages	Technical trends
Social	Sentiment	Social media mentions	Sentiment report
News	News sentiment	News, insider activity	News impact
Fundamentals	Financial analysis	P/E, growth, statements	Financial health
Bull/Bear	Debate	Analysis synthesis	Perspectives
Research Manager	Synthesize	Bull/bear debate	Investment decision
Trader	Trade plan	Decision	Entry/stop/target
Risk	Risk assess	Trade plan	Final decision

State Keys

{
    # Inputs
    "company_of_interest": "NVDA",
    "trade_date": "2025-12-05",
    
    # Optional outputs
    "screening_report": "...",           # If include_screening=True
    "pump_report": "...",                # If include_pump_detection=True
    "market_report": "...",              # If "market" in selected_analysts
    "sentiment_report": "...",           # If "social" in selected_analysts
    "news_report": "...",                # If "news" in selected_analysts
    "fundamentals_report": "...",        # If "fundamentals" in selected_analysts
    
    # Always present
    "final_trade_decision": "BUY/HOLD/SELL",
    "trader_investment_plan": "Entry: $100, Stop: $97, Target: $105",
}

Parameters

TradingAgentsGraph(
    selected_analysts=["market", "social", "news", "fundamentals"],  # Which analysts to use
    debug=False,                  # Show detailed agent reasoning
    config=None,                  # Custom config dict
    include_screening=False,      # Enable screening agent
    include_pump_detection=False, # Enable pump detection agent
)

Execution Flow

START
  │
  ├─ Screening Agent (if enabled)
  │   └─ Returns: Candidate stocks
  │
  ├─ Pump Detection Agent (if enabled)
  │   └─ Returns: Pump score 0-100
  │
  ├─ Analysts (market, social, news, fundamentals)
  │   ├─ Market Analyst → technical trends
  │   ├─ Social Analyst → sentiment
  │   ├─ News Analyst → news impact
  │   └─ Fundamentals Analyst → financial health
  │
  ├─ Researchers (Bull + Bear)
  │   ├─ Bull Researcher → bullish case
  │   └─ Bear Researcher → bearish case
  │
  ├─ Research Manager
  │   └─ Synthesizes → Investment decision
  │
  ├─ Trader
  │   └─ Creates → Trading plan
  │
  ├─ Risk Managers (Risky, Neutral, Safe)
  │   └─ Final risk → Assessment
  │
  └─ END (returns final_trade_decision)

Common Use Cases

Case 1: Find and Analyze Pump Candidates

graph = TradingAgentsGraph(
    include_screening=True,
    include_pump_detection=True,
)
# Screening finds candidates, pump detection scores them

Case 2: Quick Technical Analysis

graph = TradingAgentsGraph(
    selected_analysts=["market"],
)
# Fast technical analysis only

Case 3: Deep Fundamental Research

graph = TradingAgentsGraph(
    selected_analysts=["fundamentals", "news", "market"],
)
# Focus on fundamentals with supporting analysis

Case 4: Full Due Diligence

graph = TradingAgentsGraph(
    include_screening=True,
    include_pump_detection=True,
    selected_analysts=["market", "social", "news", "fundamentals"],
)
# Complete analysis: screening → detection → analysis → decision

Files to Know

tradingagents/agents/screening_agent.py - Screening agent
tradingagents/agents/pump_detection_agent.py - Pump detection agent
tradingagents/graph/trading_graph.py - Main graph orchestrator
tradingagents/graph/setup.py - Graph setup and flow
INTEGRATION_GUIDE.md - Full integration documentation
PUMP_DETECTION_GUIDE.md - Pump detection details
integrated_agents_demo.py - Architecture demo

Troubleshooting

"ModuleNotFoundError" - Ensure agents are imported in __init__.py

"Node not found" - Check setup_graph() includes the agent

"Tool not found" - Verify tool is added to tool node

Slow execution - Normal: ~30sec-2min total, disable debug mode

API errors - Use yfinance (free) instead of Alpha Vantage

Next Steps

Read INTEGRATION_GUIDE.md for full details
Run python integrated_agents_demo.py to see architecture
Start with one agent, add more as needed
Customize agents for your trading strategy

Happy trading! 🚀

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