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| import os | |
| import requests | |
| import yfinance as yf | |
| from dotenv import load_dotenv | |
| from agents import function_tool | |
| from core.logger import log_call | |
| from datetime import datetime, timedelta | |
| # Load environment variables | |
| load_dotenv() | |
| # ============================================================ | |
| # 🔹 YAHOO FINANCE TOOLSET | |
| # ============================================================ | |
| class FinanceTools: | |
| """ | |
| FinanceTools provides a set of function tools for interacting with Yahoo Finance for **Financial Data** only. | |
| These tools can be used by an AI agent to fetch stock, ETF, or crypto data, | |
| analyze recent market trends, market sentiments, and generate market insights. | |
| """ | |
| def get_summary(symbol: str, period: str = "1d", interval: str = "1h") -> str: | |
| """ | |
| Fetch the latest summary information and intraday price data for a given ticker. | |
| Ensures recent data is retrieved by calculating start/end dates dynamically. | |
| Parameters: | |
| ----------- | |
| symbol : str | |
| The ticker symbol (e.g., "AAPL", "GOOG", "BTC-USD"). | |
| period : str, optional (default="1d") | |
| Time range for price data. Examples: "1d", "5d", "1mo", "3mo". | |
| interval : str, optional (default="1h") | |
| Granularity of the data. Examples: "1m", "5m", "1h", "1d". | |
| Returns: | |
| -------- | |
| str | |
| A formatted string containing: | |
| - Company/ticker name | |
| - Current price and change | |
| - Open, High, Low prices | |
| - Volume | |
| - Period and interval used | |
| """ | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| # Calculate start and end dates based on period | |
| end_date = datetime.today() | |
| if period.endswith("d"): | |
| days = int(period[:-1]) | |
| elif period.endswith("mo"): | |
| days = int(period[:-2]) * 30 | |
| elif period.endswith("y"): | |
| days = int(period[:-1]) * 365 | |
| else: | |
| days = 30 # default 1 month | |
| start_date = end_date - timedelta(days=days) | |
| # Fetch recent data explicitly | |
| data = ticker.history( | |
| start=start_date.strftime("%Y-%m-%d"), | |
| end=end_date.strftime("%Y-%m-%d"), | |
| interval=interval | |
| ) | |
| if data.empty: | |
| return f"No data found for symbol '{symbol}'." | |
| latest = data.iloc[-1] | |
| current_price = round(latest["Close"], 2) | |
| open_price = round(latest["Open"], 2) | |
| change = round(current_price - open_price, 2) | |
| pct_change = round((change / open_price) * 100, 2) | |
| info = ticker.info | |
| long_name = info.get("longName", symbol) | |
| currency = info.get("currency", "USD") | |
| formatted = [ | |
| f"📈 {long_name} ({symbol})", | |
| f"Current Price: {current_price} {currency}", | |
| f"Change: {change} ({pct_change}%)", | |
| f"Open: {open_price} | High: {round(latest['High'], 2)} | Low: {round(latest['Low'], 2)}", | |
| f"Volume: {int(latest['Volume'])}", | |
| f"Period: {period} | Interval: {interval}", | |
| ] | |
| return "\n".join(formatted) | |
| except Exception as e: | |
| return f"Error fetching data for '{symbol}': {e}" | |
| def get_market_sentiment(symbol: str, period: str = "1mo") -> str: | |
| """ | |
| Analyze recent price changes and provide a simple market sentiment. | |
| Uses dynamic start/end dates to ensure recent data. | |
| This tool computes the percentage change over the specified period and | |
| classifies the sentiment as: | |
| - Bullish (if price increased >2%) | |
| - Bearish (if price decreased >2%) | |
| - Neutral (otherwise) | |
| Parameters: | |
| ----------- | |
| symbol : str | |
| The ticker symbol (e.g., "AAPL", "GOOG", "BTC-USD"). | |
| period : str, optional (default="1mo") | |
| Time range to analyze. Examples: "7d", "1mo", "3mo". | |
| Returns: | |
| -------- | |
| str | |
| A human-readable sentiment string including percentage change. | |
| """ | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| # Calculate start/end dynamically | |
| end_date = datetime.today() | |
| if period.endswith("d"): | |
| days = int(period[:-1]) | |
| elif period.endswith("mo"): | |
| days = int(period[:-2]) * 30 | |
| elif period.endswith("y"): | |
| days = int(period[:-1]) * 365 | |
| else: | |
| days = 30 | |
| start_date = end_date - timedelta(days=days) | |
| data = ticker.history( | |
| start=start_date.strftime("%Y-%m-%d"), | |
| end=end_date.strftime("%Y-%m-%d") | |
| ) | |
| if data.empty: | |
| return f"No data for {symbol}." | |
| recent_change = data["Close"].iloc[-1] - data["Close"].iloc[0] | |
| pct_change = (recent_change / data["Close"].iloc[0]) * 100 | |
| sentiment = "Neutral" | |
| if pct_change > 2: | |
| sentiment = "Bullish" | |
| elif pct_change < -2: | |
| sentiment = "Bearish" | |
| return f"{symbol} market sentiment ({period}): {sentiment} ({pct_change:.2f}% change)" | |
| except Exception as e: | |
| return f"Error fetching market sentiment for '{symbol}': {e}" | |
| def get_history(symbol: str, period: str = "1mo") -> str: | |
| """ | |
| Fetch historical price data for a given ticker. | |
| Ensures recent data is retrieved dynamically using start/end dates. | |
| Parameters: | |
| ----------- | |
| symbol : str | |
| The ticker symbol (e.g., "AAPL", "GOOG", "BTC-USD"). | |
| period : str, optional (default="1mo") | |
| The length of historical data to retrieve. Examples: "1d", "5d", "1mo", "3mo", "1y", "5y". | |
| Returns: | |
| -------- | |
| str | |
| A formatted string showing the last 5 rows of historical prices (Open, High, Low, Close, Volume). | |
| """ | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| # Calculate start/end dynamically | |
| end_date = datetime.today() | |
| if period.endswith("d"): | |
| days = int(period[:-1]) | |
| elif period.endswith("mo"): | |
| days = int(period[:-2]) * 30 | |
| elif period.endswith("y"): | |
| days = int(period[:-1]) * 365 | |
| else: | |
| days = 30 | |
| start_date = end_date - timedelta(days=days) | |
| data = ticker.history( | |
| start=start_date.strftime("%Y-%m-%d"), | |
| end=end_date.strftime("%Y-%m-%d") | |
| ) | |
| if data.empty: | |
| return f"No historical data found for '{symbol}'." | |
| return f"Historical data for {symbol} ({period}):\n{data.tail(5).to_string()}" | |
| except Exception as e: | |
| return f"Error fetching historical data for '{symbol}': {e}" | |