get_historical_stock_prices
Retrieve historical stock price data, including date, open, high, low, close, and volume, for a specified ticker over a defined period and interval using Yahoo Finance data.
Instructions
获取指定股票的历史价格，返回日期、开盘价、最高价、最低价、收盘价和成交量。 参数说明： ticker: str 股票代码，例如 "AAPL" period : str 支持的周期：1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max； 也可以使用开始和结束日期；默认 "1mo" interval : str 支持的间隔：1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo； 分钟级数据最多可追溯60天；默认 "1d"
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| interval | No | 1d | |
| period | No | 1mo | |
| ticker | Yes |
Implementation Reference
- server.py:68-124 (handler)The async handler function that implements the tool logic: fetches data from Financial Modeling Prep API based on interval, processes with pandas DataFrame, filters by period, and returns JSON.
async def get_historical_stock_prices( ticker: str, period: str = "1mo", interval: str = "1d" ) -> str: """Get historical stock prices for a given ticker symbol""" api_key = os.environ.get("FMP_API_KEY") if not api_key: return "Error: FMP_API_KEY environment variable not set." base = "https://financialmodelingprep.com/api/v3" try: if interval in ["1min", "5min", "15min", "30min", "1hour", "4hour"]: url = f"{base}/historical-chart/{interval}/{ticker}" resp = requests.get(url, params={"apikey": api_key}, timeout=10) resp.raise_for_status() data = resp.json() else: url = f"{base}/historical-price-full/{ticker}" resp = requests.get(url, params={"apikey": api_key}, timeout=10) resp.raise_for_status() data = resp.json().get("historical", []) except Exception as e: return f"Error: getting historical stock prices for {ticker}: {e}" df = pd.DataFrame(data) if not df.empty: if "date" in df.columns: df.rename(columns={"date": "Date"}, inplace=True) df = df[["Date", "open", "high", "low", "close", "volume"]] df.columns = [c.title() for c in df.columns] # Convert the Date column to datetime for proper comparison df["Date"] = pd.to_datetime(df["Date"]) period_map = { "1d": pd.Timedelta(days=1), "5d": pd.Timedelta(days=5), "1mo": pd.Timedelta(days=30), "3mo": pd.Timedelta(days=90), "6mo": pd.Timedelta(days=180), "1y": pd.Timedelta(days=365), "2y": pd.Timedelta(days=730), "5y": pd.Timedelta(days=1825), "10y": pd.Timedelta(days=3650), } if period == "ytd": start = pd.Timestamp.now().replace(month=1, day=1) elif period != "max" and period in period_map: start = pd.Timestamp.now() - period_map[period] else: start = None if start is not None and not df.empty: df = df[df["Date"] >= start] return df.to_json(orient="records", date_format="iso") - server.py:54-67 (registration)Tool registration decorator @fmp_server.tool with name and detailed description including input parameters.
@fmp_server.tool( name="get_historical_stock_prices", description="""获取指定股票的历史价格，返回日期、开盘价、最高价、最低价、收盘价和成交量。 参数说明： ticker: str 股票代码，例如 "AAPL" period : str 支持的周期：1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max； 也可以使用开始和结束日期；默认 "1mo" interval : str 支持的间隔：1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo； 分钟级数据最多可追溯60天；默认 "1d" """, ) - server.py:68-70 (schema)Function signature providing type hints for input parameters (ticker, period, interval) and return type (str).
async def get_historical_stock_prices( ticker: str, period: str = "1mo", interval: str = "1d" ) -> str: