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| import datetime | |
| import pytz | |
| import yaml | |
| import yfinance as yf | |
| from smolagents import CodeAgent, HfApiModel, load_tool, tool | |
| from tools.final_answer import FinalAnswerTool | |
| from Gradio_UI import GradioUI | |
| # Tool to fetch the current stock price using yfinance. | |
| def get_current_stock_price(symbol: str) -> str: | |
| """Fetches the current stock price for the given stock symbol. | |
| Args: | |
| symbol: The stock ticker symbol (e.g., 'AAPL'). | |
| Returns: | |
| A string reporting the current stock price. | |
| """ | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| price = ticker.info.get("regularMarketPrice", None) | |
| if price is None: | |
| return f"Could not fetch current price for {symbol}." | |
| return f"The current price of {symbol} is {price:.2f} USD." | |
| except Exception as e: | |
| return f"Error fetching current price for {symbol}: {str(e)}" | |
| # Tool to fetch historical stock data for a given timeline using yfinance. | |
| def get_stock_historical_data(symbol: str, timeline: str) -> str: | |
| """Fetches historical stock data for a given symbol and timeline. | |
| Args: | |
| symbol: The stock ticker symbol (e.g., 'TSLA'). | |
| timeline: The timeframe for data. Acceptable values: '1h', '1day', '1week', '1year'. | |
| Returns: | |
| A summary string of the historical price data. | |
| """ | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| if timeline == "1h": | |
| # For 1h data, fetch 1 day of data at 1-minute intervals and take the last 60 minutes. | |
| df = ticker.history(period="1d", interval="1m").tail(60) | |
| elif timeline == "1day": | |
| df = ticker.history(period="1d", interval="5m") | |
| elif timeline == "1week": | |
| df = ticker.history(period="7d", interval="30m") | |
| elif timeline == "1year": | |
| df = ticker.history(period="1y", interval="1d") | |
| else: | |
| return f"Timeline '{timeline}' is not supported. Use '1h', '1day', '1week', or '1year'." | |
| if df.empty: | |
| return f"No historical data available for {symbol} over timeline {timeline}." | |
| start_price = df['Close'].iloc[0] | |
| end_price = df['Close'].iloc[-1] | |
| min_price = df['Close'].min() | |
| max_price = df['Close'].max() | |
| summary = ( | |
| f"For {symbol} over {timeline}:\n" | |
| f"Start Price: {start_price:.2f} USD\n" | |
| f"End Price: {end_price:.2f} USD\n" | |
| f"Min Price: {min_price:.2f} USD\n" | |
| f"Max Price: {max_price:.2f} USD" | |
| ) | |
| return summary | |
| except Exception as e: | |
| return f"Error fetching historical data for {symbol} over {timeline}: {str(e)}" | |
| # Final answer tool (must be included) | |
| final_answer = FinalAnswerTool() | |
| # Define the model configuration. | |
| # Here we reduce max_tokens to help keep the total token count below the limit. | |
| model = HfApiModel( | |
| max_tokens=1000, # Reduced from 2096 to reduce total token count | |
| temperature=0.5, | |
| model_id='Qwen/Qwen2.5-Coder-32B-Instruct', | |
| custom_role_conversions=None, | |
| ) | |
| # Optionally, load an additional tool from the Hub (e.g., a text-to-image tool) | |
| image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True) | |
| # Load prompt templates from YAML. | |
| with open("prompts.yaml", 'r') as stream: | |
| prompt_templates = yaml.safe_load(stream) | |
| # Initialize the CodeAgent with the new yfinance tools. | |
| agent = CodeAgent( | |
| model=model, | |
| tools=[final_answer, get_current_stock_price, get_stock_historical_data], | |
| max_steps=3, # Reduced steps to limit the chain-of-thought length | |
| verbosity_level=1, | |
| grammar=None, | |
| planning_interval=None, | |
| name="TradingAgent", | |
| description="An AI agent that analyzes stock data using yfinance and advises on long-term trading decisions.", | |
| prompt_templates=prompt_templates | |
| ) | |
| # Launch the Gradio UI to interact with the agent. | |
| GradioUI(agent).launch() | |