add fastapi webapp

.env.example

@@ -9,3 +9,13 @@ BYBIT_BASE_URL=https://api-demo.bybit.com
 BYBIT_API_KEY=bybit_api_key_placeholder
 BYBIT_API_SECRET=bybit_api_secret_placeholder
 COIN_GECKO_API_BASE_URL=https://api.coingecko.com/api/v3
+
+# Model settings
+LLM_PROVIDER=openai
+BACKEND_URL=https://api.openai.com/v1
+DEEP_THINK_LLM=gpt-4o-mini
+QUICK_THINK_LLM=gpt-4o-mini
+
+# App settings
+APP_HOST=localhost
+APP_PORT=8000

README.md

@@ -239,3 +239,8 @@ Please reference our work if you find *TradingAgents* provides you with some hel
       url={https://arxiv.org/abs/2412.20138}, 
 }
 ```
+
+## How to Run
+```
+python3 webapp.py
+```

pyproject.toml

@@ -28,6 +28,8 @@ dependencies = [
     "rich>=14.0.0",
     "setuptools>=80.9.0",
     "stockstats>=0.6.5",
+    "fastapi>=0.115.0",
+    "uvicorn[standard]>=0.32.0",
     "tqdm>=4.67.1",
     "tushare>=1.4.21",
     "typing-extensions>=4.14.0",

requirements.txt

@@ -26,3 +26,5 @@ langchain_anthropic
 langchain-google-genai
 binance-sdk-spot
 telethon
+fastapi
+uvicorn[standard]

tradingagents/agents/utils/memory.py

@@ -11,7 +11,7 @@ class FinancialSituationMemory:
             self.embedding = "text-embedding-3-small"
         self.client = OpenAI(base_url=config["backend_url"])
         self.chroma_client = chromadb.Client(Settings(allow_reset=True))
-        self.situation_collection = self.chroma_client.create_collection(name=name)
+        self.situation_collection = self.chroma_client.get_or_create_collection(name=name)
 
     def get_embedding(self, text):
         """Get OpenAI embedding for a text"""

tradingagents/default_config.py

@@ -1,6 +1,10 @@
 import os
 
 DEFAULT_CONFIG = {
+    # App config
+    "APP_HOST": "localhost",
+    "APP_PORT": 8000,
+    # Directory settings
     "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
     "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
     "data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
@@ -9,10 +13,10 @@ DEFAULT_CONFIG = {
         "dataflows/data_cache",
     ),
     # LLM settings
-    "llm_provider": "openai",
-    "deep_think_llm": "o4-mini",
-    "quick_think_llm": "gpt-4o-mini",
-    "backend_url": "https://api.openai.com/v1",
+    "llm_provider":  os.getenv("LLM_PROVIDER", "openai"),
+    "deep_think_llm": os.getenv("DEEP_THINK_LLM", "gpt-4o-mini"),
+    "quick_think_llm": os.getenv("QUICK_THINK_LLM", "gpt-4o-mini"),
+    "backend_url": os.getenv("BACKEND_URL","https://api.openai.com/v1"),
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
@@ -29,9 +33,6 @@ DEFAULT_CONFIG = {
     "tool_vendors": {
         "get_global_news": "telegram"               # Override category default
     },
-    # Tool provider settings
-    "tool_providers": {
-    },
     "external": {
         "BINANCE_API_KEY": os.getenv("BINANCE_API_KEY", ""),
         "TAAPI_BASE_URL": os.getenv("TAAPI_BASE_URL", "https://api.taapi.io"),

webapp.py

@@ -0,0 +1,105 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import uvicorn
+from datetime import datetime
+import asyncio
+
+# Import your trading agents
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.dataflows.config import get_config
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+config = get_config()
+
+# Create FastAPI app instance
+app = FastAPI(
+    title="TradingAgents API",
+    description="API for TradingAgents financial trading framework",
+    version="0.1.0"
+)
+
+# Pydantic models for request/response
+class TradingRequest(BaseModel):
+    symbol: str
+    date: str
+
+class TradingResponse(BaseModel):
+    symbol: str
+    date: str
+    decision: dict
+    timestamp: str
+    status: str
+
+# Initialize trading agent once at startup
+def create_trading_agent():
+    """Create trading agent with fixed configuration"""
+    return TradingAgentsGraph(debug=True, config=config)
+
+# Create the trading agent instance once
+trading_agent = create_trading_agent()
+
+@app.get("/")
+async def root():
+    """Root endpoint"""
+    return {"message": "Welcome to TradingAgents API"}
+
+@app.get("/ping")
+async def ping():
+    """Simple ping endpoint that returns pong"""
+    return {"message": "pong"}
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {
+        "status": "healthy",
+        "timestamp": datetime.now().isoformat(),
+        "service": "tradingagents-api"
+    }
+
+@app.post("/trading/analyze", response_model=TradingResponse)
+async def analyze_trading_decision(request: TradingRequest):
+    """
+    Analyze trading decision for a given symbol and date
+    
+    Example usage:
+    POST /trading/analyze
+    {
+        "symbol": "NVDA",
+        "date": "2024-05-10"
+    }
+    """
+    try:
+        # Run the analysis (this might take a while, so we run it in a thread pool)
+        def run_analysis():
+            _, decision = trading_agent.propagate(request.symbol, request.date)
+            return decision
+        
+        # Run in thread pool to avoid blocking
+        loop = asyncio.get_event_loop()
+        decision = await loop.run_in_executor(None, run_analysis)
+        
+        return TradingResponse(
+            symbol=request.symbol,
+            date=request.date,
+            decision=decision,
+            timestamp=datetime.now().isoformat(),
+            status="success"
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Trading analysis failed: {str(e)}")
+
+
+if __name__ == "__main__":
+    # Run the server
+    uvicorn.run(
+        "webapp:app",
+        host=config.get("APP_HOST", "localhost"),
+        port=config.get("APP_PORT", 8000),
+        reload=True,
+        log_level="info"
+    )