119 lines
4.7 KiB
Python
119 lines
4.7 KiB
Python
from core.db_manager import query_market_data_by_symbol_bar
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from pandas import DataFrame
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import logging
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import os
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import re
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import pandas as pd
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
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)
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class Statistics:
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def __init__(self, output_folder: str = "./output"):
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self.output_folder = output_folder
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os.makedirs(self.output_folder, exist_ok=True)
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def detect_volume_spike(
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self,
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data: DataFrame,
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threshold: float = 2.0,
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window: int = 50,
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check_price: bool = False,
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only_output_huge_volume: bool = False,
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output_excel: bool = False,
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):
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"""
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detect_volume_spike的函数逻辑：
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1. 根据window滑动行情数据
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2. 每一个window的最新的volume是否高于该window的volume的均值+2倍标准差，如果满足条件，则增加一列：huge_volume，值为1
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3. 如果check_price为True，则检查:
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a. 每一个window的close是否处于该window的80%分位数及以上
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b. 每一个window的close是否处于该window的20%分位数及以下
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Args:
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data: 包含成交量数据的DataFrame
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threshold: 标准差倍数，默认为2.0（即成交量超过均值+2倍标准差）
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window: 计算移动窗口的大小，默认50个周期
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check_price: 是否检查价格处于windows内的80%分位数以上，或20%分位数以下，默认False
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Returns:
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DataFrame: 包含异常检测结果的DataFrame
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"""
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if data is None or len(data) == 0:
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logging.warning("数据为空，无法进行成交量异常检测")
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return None
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if "volume" not in data.columns:
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logging.error("数据中缺少volume列")
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return None
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# 按时间戳排序
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data = data.sort_values(by="timestamp", ascending=True).copy()
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# 计算移动窗口的成交量均值和标准差
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data["volume_ma"] = data["volume"].rolling(window=window, min_periods=1).mean()
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data["volume_std"] = data["volume"].rolling(window=window, min_periods=1).std()
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# 计算成交量阈值（均值 + threshold倍标准差）
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data["volume_threshold"] = data["volume_ma"] + threshold * data["volume_std"]
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# 判断当前成交量是否超过阈值
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data["huge_volume"] = (data["volume"] > data["volume_threshold"]).astype(int)
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# 计算成交量比率
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data["volume_ratio"] = data["volume"] / data["volume_ma"]
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# 计算异常强度
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data["spike_intensity"] = data["volume_ratio"] - 1
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# 如果check_price为True，检查价格分位数
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if check_price:
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if "close" not in data.columns:
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logging.error("数据中缺少close列，无法进行价格检查")
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return data
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# 计算移动窗口的收盘价分位数
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data["close_80_percentile"] = (
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data["close"].rolling(window=window, min_periods=1).quantile(0.8)
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)
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data["close_20_percentile"] = (
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data["close"].rolling(window=window, min_periods=1).quantile(0.2)
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)
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# 检查收盘价是否在80%分位数及以上或20%分位数及以下
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data["price_high"] = (data["close"] >= data["close_80_percentile"]).astype(
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int
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)
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data["price_low"] = (data["close"] <= data["close_20_percentile"]).astype(
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int
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)
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# 综合判断：成交量异常且价格处于极端位置
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data["volume_price_spike"] = (
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(data["huge_volume"] == 1)
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& ((data["price_high"] == 1) | (data["price_low"] == 1))
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).astype(int)
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if only_output_huge_volume:
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data = data[data["huge_volume"] == 1]
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if output_excel:
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# 检查数据是否为空
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if len(data) == 0:
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logging.warning("数据为空，无法导出Excel文件")
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return data
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start_date = data["date_time"].iloc[0]
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end_date = data["date_time"].iloc[-1]
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# remove punctuation from start_date and end_date
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start_date = re.sub(r"[\:\-\s]", "", str(start_date))
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end_date = re.sub(r"[\:\-\s]", "", str(end_date))
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symbol = data["symbol"].iloc[0]
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bar = data["bar"].iloc[0]
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file_name = f"volume_spike_{symbol}_{bar}_{start_date}_{end_date}.xlsx"
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with pd.ExcelWriter(os.path.join(self.output_folder, file_name)) as writer:
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data.to_excel(writer, sheet_name="volume_spike", index=False)
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return data
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