crypto_quant/statistics_main.py

from core.statistics import Statistics
from core.db_market_data import DBMarketData
from monitor_main import MonitorMain
import logging
from config import MONITOR_CONFIG, MYSQL_CONFIG
from datetime import datetime
import pandas as pd

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)


class StatisticsMain:
    def __init__(self):
        mysql_user = MYSQL_CONFIG.get("user", "xch")
        mysql_password = MYSQL_CONFIG.get("password", "")
        if not mysql_password:
            raise ValueError("MySQL password is not set")
        mysql_host = MYSQL_CONFIG.get("host", "localhost")
        mysql_port = MYSQL_CONFIG.get("port", 3306)
        mysql_database = MYSQL_CONFIG.get("database", "okx")

        self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
        self.statistics = Statistics()
        self.db_market_data = DBMarketData(self.db_url)
        self.monitor_main = MonitorMain()

    def batch_detect_volume_spike(self, start: str, end: str):
        pass

    def detect_volume_spike(
        self,
        symbol: str = "XCH-USDT",
        bar: str = "5m",
        start: str = "2025-05-01 00:00:00",
        end: str = None,
        only_output_huge_volume: bool = False,
    ):
        if start is None:
            start = MONITOR_CONFIG.get("volume_monitor", {}).get(
                "initial_date", "2025-05-01 00:00:00"
            )
        if end is None:
            end = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        data = self.db_market_data.query_market_data_by_symbol_bar(symbol, bar, start, end)
        if data is None:
            logging.warning(f"获取数据失败: {symbol} {bar} {start} {end}")
            return None
        else:
            if len(data) == 0:
                logging.warning(f"获取数据为空: {symbol} {bar} {start} {end}")
                return None
            else:
                if isinstance(data, list):
                    data = pd.DataFrame(data)
                elif isinstance(data, dict):
                    data = pd.DataFrame([data])
        return self.statistics.detect_volume_spike(
            data=data,
            check_price=True,
            only_output_huge_volume=only_output_huge_volume,
            output_excel=True,
        )
    
    def update_volume_spike(self):
        self.monitor_main.update_data()


if __name__ == "__main__":
    statistics_main = StatisticsMain()
    statistics_main.detect_volume_spike(
        symbol="XCH-USDT",
        bar="5m",
        start="2025-05-01 00:00:00",
        only_output_huge_volume=True,
    )