capital_history
Retrieve historical capital flow data for Chinese stocks, including daily inflow and outflow metrics, to analyze market trends and investment patterns.
Instructions
获取历史资金流如流出数据，每日数据
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| stock_code | No | SZ000002 |
Implementation Reference
- main.py:113-117 (handler)The handler function for the 'capital_history' tool. It fetches historical capital flow data (daily) for a given stock code using the pysnowball library, processes timestamps, and returns the result as a dictionary.
@mcp.tool() def capital_history(stock_code: str="SZ000002") -> dict: """获取历史资金流如流出数据，每日数据""" result = ball.capital_history(stock_code) return process_data(result) - main.py:113-113 (registration)The @mcp.tool() decorator registers the capital_history function as an MCP tool.
@mcp.tool() - main.py:114-114 (schema)Function signature defines the input schema (stock_code: str with default) and output (dict), used by FastMCP for tool schema generation.
def capital_history(stock_code: str="SZ000002") -> dict: - main.py:34-61 (helper)Helper function that processes the raw data from pysnowball, including converting timestamps to readable datetime strings. Used by all tools including capital_history.
def process_data(data, process_config=None): """ 通用数据处理函数，可扩展添加各种数据处理操作 Args: data: 原始数据 process_config: 处理配置字典，用于指定要执行的处理操作 例如: {'convert_timestamps': True, 'other_process': params} Returns: 处理后的数据 """ if process_config is None: # 默认配置 process_config = { 'convert_timestamps': True } # 如果开启了时间戳转换 if process_config.get('convert_timestamps', True): data = convert_timestamps(data) # 在这里可以添加更多的数据处理逻辑 # 例如: # if 'format_numbers' in process_config: # data = format_numbers(data, **process_config['format_numbers']) return data - main.py:14-31 (helper)Supporting utility recursively converts timestamp fields in the data to formatted datetime strings, called by process_data.
def convert_timestamps(data): """递归地将数据中的所有 timestamp 转换为 datetime 字符串""" if isinstance(data, dict): for key, value in list(data.items()): if key == 'timestamp' and isinstance(value, (int, float)) and value > 1000000000000: # 毫秒级时间戳 data[key] = datetime.datetime.fromtimestamp(value/1000).strftime('%Y-%m-%d %H:%M:%S') elif key == 'timestamp' and isinstance(value, (int, float)) and value > 1000000000: # 秒级时间戳 data[key] = datetime.datetime.fromtimestamp(value).strftime('%Y-%m-%d %H:%M:%S') elif key.endswith('_date') and isinstance(value, (int, float)) and value > 1000000000000: # 毫秒级时间戳 data[key] = datetime.datetime.fromtimestamp(value/1000).strftime('%Y-%m-%d %H:%M:%S') elif key.endswith('_date') and isinstance(value, (int, float)) and value > 1000000000: # 秒级时间戳 data[key] = datetime.datetime.fromtimestamp(value).strftime('%Y-%m-%d %H:%M:%S') elif isinstance(value, (dict, list)): data[key] = convert_timestamps(value) elif isinstance(data, list): for i, item in enumerate(data): data[i] = convert_timestamps(item) return data