295 lines
15 KiB
Python
295 lines
15 KiB
Python
import core.logger as logging
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import os
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from datetime import datetime
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import re
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from openpyxl import Workbook
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from openpyxl.drawing.image import Image
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import openpyxl
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from openpyxl.styles import Font
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from PIL import Image as PILImage
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from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
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from core.db.db_market_data import DBMarketData
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from core.db.db_huge_volume_data import DBHugeVolumeData
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from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
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# seaborn支持中文
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plt.rcParams["font.family"] = ["SimHei"]
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logger = logging.logger
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class MaBreakStatistics:
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"""
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统计MA突破之后的涨跌幅
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MA向上突破的点位周期K线：5 > 10 > 20 > 30
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统计MA向上突破的点位周期K线，突破之后，到：
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下一个MA向下突破的点位周期K线：30 > 20 > 10 > 5
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之间的涨跌幅
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"""
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def __init__(self):
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mysql_user = MYSQL_CONFIG.get("user", "xch")
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mysql_password = MYSQL_CONFIG.get("password", "")
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if not mysql_password:
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raise ValueError("MySQL password is not set")
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mysql_host = MYSQL_CONFIG.get("host", "localhost")
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mysql_port = MYSQL_CONFIG.get("port", 3306)
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mysql_database = MYSQL_CONFIG.get("database", "okx")
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self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
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self.db_market_data = DBMarketData(self.db_url)
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self.db_huge_volume_data = DBHugeVolumeData(self.db_url)
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self.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["XCH-USDT"]
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)
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self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
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"bars", ["5m", "15m", "30m", "1H"]
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)
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self.stats_output_dir = "./output/statistics/excel/"
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os.makedirs(self.stats_output_dir, exist_ok=True)
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self.stats_chart_dir = "./output/statistics/chart/"
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os.makedirs(self.stats_chart_dir, exist_ok=True)
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def batch_statistics(self, all_change: bool = True):
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ma_break_market_data_list = []
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for symbol in self.symbols:
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for bar in self.bars:
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logger.info(f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计")
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ma_break_market_data = self.statistics(symbol, bar, all_change)
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if ma_break_market_data is not None:
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ma_break_market_data_list.append(ma_break_market_data)
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if len(ma_break_market_data_list) > 0:
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ma_break_market_data = pd.concat(ma_break_market_data_list)
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# 依据symbol和bar分组，统计每个symbol和bar的pct_chg的max, min, mean, std, median, count
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pct_chg_df = (ma_break_market_data
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.groupby(['symbol', 'bar'])['pct_chg']
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.agg(pct_chg_max='max',
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pct_chg_min='min',
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pct_chg_mean='mean',
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pct_chg_std='std',
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pct_chg_median='median',
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pct_chg_count='count')
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.reset_index())
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# 依据symbol和bar分组，统计每个symbol和bar的interval_minutes的max, min, mean, std, median, count
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interval_minutes_df = (ma_break_market_data
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.groupby(['symbol', 'bar'])['interval_minutes']
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.agg(interval_minutes_max='max',
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interval_minutes_min='min',
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interval_minutes_mean='mean',
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interval_minutes_std='std',
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interval_minutes_median='median',
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interval_minutes_count='count')
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.reset_index())
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earliest_market_date_time = ma_break_market_data["begin_date_time"].min()
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earliest_market_date_time = re.sub(r"[\:\-\s]", "", str(earliest_market_date_time))
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latest_market_date_time = ma_break_market_data["end_date_time"].max()
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if latest_market_date_time is None:
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latest_market_date_time = datetime.now().strftime("%Y%m%d")
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latest_market_date_time = re.sub(r"[\:\-\s]", "", str(latest_market_date_time))
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if all_change:
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output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_完全转势.xlsx"
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else:
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output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_部分转势.xlsx"
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output_file_path = os.path.join(self.stats_output_dir, output_file_name)
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logger.info(f"导出{output_file_path}")
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with pd.ExcelWriter(output_file_path) as writer:
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ma_break_market_data.to_excel(writer, sheet_name="ma_break_market_data", index=False)
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pct_chg_df.to_excel(writer, sheet_name="pct_chg_stats", index=False)
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interval_minutes_df.to_excel(writer, sheet_name="interval_minutes_stats", index=False)
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chart_dict = self.draw_pct_chg_mean_chart(pct_chg_df, all_change)
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self.output_chart_to_excel(output_file_path, chart_dict)
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else:
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return None
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def statistics(self, symbol: str, bar: str, all_change: bool = False):
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market_data = self.db_market_data.query_market_data_by_symbol_bar(symbol, bar, start=None, end=None)
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if market_data is None or len(market_data) == 0:
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logger.warning(f"获取{symbol} {bar} 数据失败")
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return
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else:
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market_data = pd.DataFrame(market_data)
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market_data.sort_values(by="timestamp", ascending=True, inplace=True)
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market_data.reset_index(drop=True, inplace=True)
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logger.info(f"获取{symbol} {bar} 数据成功，数据条数: {len(market_data)}")
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# 获得ma5, ma10, ma20, ma30不为空的行
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market_data = market_data[(market_data["ma5"].notna()) &
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(market_data["ma10"].notna()) &
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(market_data["ma20"].notna()) &
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(market_data["ma30"].notna())]
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logger.info(f"ma5, ma10, ma20, ma30不为空的行，数据条数: {len(market_data)}")
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# 计算volume_ma5
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market_data["volume_ma5"] = market_data["volume"].rolling(window=5).mean()
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# 获得5上穿10且ma5 > ma10 > ma20 > ma30且close > ma20的行,成交量较前5日均量放大20%以上
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market_data["volume_pct_chg"] = (market_data["volume"] - market_data["volume_ma5"]) / market_data["volume_ma5"]
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market_data["volume_pct_chg"] = market_data["volume_pct_chg"].fillna(0)
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if all_change:
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long_market_data = market_data[(market_data["ma_cross"] == "5上穿10") & (market_data["ma5"] > market_data["ma10"]) &
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(market_data["ma10"] > market_data["ma20"]) &
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(market_data["ma20"] > market_data["ma30"]) &
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(market_data["close"] > market_data["ma20"]) &
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(market_data["volume_pct_chg"] > 0.2)]
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logger.info(f"5上穿10, 且ma5 > ma10 > ma20 > ma30，并且close > ma20，并且成交量较前5日均量放大20%以上的行，数据条数: {len(long_market_data)}")
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else:
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long_market_data = market_data[(market_data["ma_cross"] == "5上穿10") & (market_data["ma5"] > market_data["ma10"]) &
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(market_data["volume_pct_chg"] > 0.2)]
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logger.info(f"5上穿10, 且ma5 > ma10，并且成交量较前5日均量放大20%以上的行，数据条数: {len(long_market_data)}")
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if len(long_market_data) == 0:
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return None
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if all_change:
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# 获得ma5 < ma10 < ma20 < ma30的行
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short_market_data = market_data[(market_data["ma5"] < market_data["ma10"]) &
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(market_data["ma10"] < market_data["ma20"]) &
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(market_data["ma20"] < market_data["ma30"])]
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logger.info(f"ma5 < ma10 < ma20 < ma30的行，数据条数: {len(short_market_data)}")
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else:
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# ma5 < ma10 or close < ma20
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short_market_data = market_data[(market_data["ma5"] < market_data["ma10"]) |
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(market_data["close"] < market_data["ma20"])]
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logger.info(f"ma5 < ma10 or close < ma20的行，数据条数: {len(short_market_data)}")
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# concat long_market_data和short_market_data
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ma_break_market_data = pd.concat([long_market_data, short_market_data])
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# 按照timestamp排序
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ma_break_market_data = ma_break_market_data.sort_values(by="timestamp", ascending=True)
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# 获得ma_break_market_data的close列
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ma_break_market_data.reset_index(drop=True, inplace=True)
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ma_break_market_data_pair_list = []
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ma_break_market_data_pair = {}
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for index, row in ma_break_market_data.iterrows():
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ma_cross = row["ma_cross"]
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timestamp = row["timestamp"]
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close = row["close"]
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ma5 = row["ma5"]
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ma10 = row["ma10"]
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ma20 = row["ma20"]
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ma30 = row["ma30"]
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if pd.notna(ma_cross) and ma_cross is not None:
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ma_cross = str(ma_cross)
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buy_condition = False
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if all_change:
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buy_condition = (ma_cross == "5上穿10") and (ma5 > ma10 and ma10 > ma20 and ma20 > ma30) and (close > ma20)
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else:
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buy_condition = (ma_cross == "5上穿10") and (ma5 > ma10)
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if buy_condition:
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ma_break_market_data_pair = {}
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ma_break_market_data_pair["symbol"] = symbol
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ma_break_market_data_pair["bar"] = bar
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ma_break_market_data_pair["begin_timestamp"] = timestamp
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ma_break_market_data_pair["begin_date_time"] = timestamp_to_datetime(timestamp)
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ma_break_market_data_pair["begin_close"] = close
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ma_break_market_data_pair["begin_ma5"] = ma5
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ma_break_market_data_pair["begin_ma10"] = ma10
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ma_break_market_data_pair["begin_ma20"] = ma20
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ma_break_market_data_pair["begin_ma30"] = ma30
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if all_change:
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change_condition = (ma5 < ma10 and ma10 < ma20 and ma20 < ma30)
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else:
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# change_condition = (ma5 < ma10 or ma10 < ma20 or ma20 < ma30)
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change_condition = (ma5 < ma10) or (close < ma20)
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if change_condition:
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if ma_break_market_data_pair.get("begin_timestamp", None) is None:
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continue
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ma_break_market_data_pair["end_timestamp"] = timestamp
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ma_break_market_data_pair["end_date_time"] = timestamp_to_datetime(timestamp)
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ma_break_market_data_pair["end_close"] = close
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ma_break_market_data_pair["end_ma5"] = ma5
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ma_break_market_data_pair["end_ma10"] = ma10
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ma_break_market_data_pair["end_ma20"] = ma20
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ma_break_market_data_pair["end_ma30"] = ma30
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ma_break_market_data_pair["pct_chg"] = (close - ma_break_market_data_pair["begin_close"]) / ma_break_market_data_pair["begin_close"]
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ma_break_market_data_pair["pct_chg"] = round(ma_break_market_data_pair["pct_chg"] * 100, 4)
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ma_break_market_data_pair["interval_seconds"] = (timestamp - ma_break_market_data_pair["begin_timestamp"]) / 1000
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# 将interval转换为分钟
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ma_break_market_data_pair["interval_minutes"] = ma_break_market_data_pair["interval_seconds"] / 60
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ma_break_market_data_pair["interval_hours"] = ma_break_market_data_pair["interval_seconds"] / 3600
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ma_break_market_data_pair["interval_days"] = ma_break_market_data_pair["interval_seconds"] / 86400
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ma_break_market_data_pair_list.append(ma_break_market_data_pair)
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ma_break_market_data_pair = {}
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if len(ma_break_market_data_pair_list) > 0:
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ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list)
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return ma_break_market_data
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else:
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return None
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def draw_pct_chg_mean_chart(self, data: pd.DataFrame, all_change: bool = True):
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"""
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绘制pct_chg mean的柱状图表（美观，保存到self.stats_chart_dir）
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:param data: 波段pct_chg_mean的数据
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:return: None
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"""
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if data is None or data.empty:
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return None
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# seaborn风格设置
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sns.set_theme(style="whitegrid")
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plt.rcParams["font.sans-serif"] = ["SimHei"] # 也可直接用字体名
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plt.rcParams["font.size"] = 11 # 设置字体大小
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plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题
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chart_dict = {}
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for bar in data["bar"].unique():
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bar_data = data[data["bar"] == bar].copy() # 一次筛选即可
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if bar_data.empty:
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continue
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bar_data.rename(columns={"pct_chg_mean": "涨跌幅均值"}, inplace=True)
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# 可选：按均值排序
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bar_data.sort_values(by="涨跌幅均值", ascending=False, inplace=True)
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bar_data.reset_index(drop=True, inplace=True)
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plt.figure(figsize=(10, 6))
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sns.barplot(x="symbol", y="涨跌幅均值", data=bar_data, palette="Blues_d")
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plt.title(f"{bar}趋势涨跌幅均值分布")
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plt.xlabel("symbol")
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plt.ylabel("涨跌幅均值")
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plt.xticks(rotation=45, ha="right")
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plt.tight_layout()
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if all_change:
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save_path = os.path.join(self.stats_chart_dir, f"{bar}_ma_break_pct_chg_mean_all_change.png")
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else:
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save_path = os.path.join(self.stats_chart_dir, f"{bar}_ma_break_pct_chg_mean_part_change.png")
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plt.savefig(save_path, dpi=150)
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plt.close()
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if all_change:
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sheet_name = f"{bar}_趋势涨跌幅均值分布图表_完全转势"
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else:
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sheet_name = f"{bar}_趋势涨跌幅均值分布图表_部分转势"
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chart_dict[sheet_name] = save_path
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return chart_dict
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def output_chart_to_excel(self, excel_file_path: str, charts_dict: dict):
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"""
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输出Excel文件，包含所有图表
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charts_dict: 图表数据字典，格式为：
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{
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"sheet_name": {
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"chart_name": "chart_path"
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}
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}
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"""
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logger.info(f"将图表输出到{excel_file_path}")
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# 打开已经存在的Excel文件
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wb = openpyxl.load_workbook(excel_file_path)
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for sheet_name, chart_path in charts_dict.items():
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try:
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ws = wb.create_sheet(title=sheet_name)
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row_offset = 1
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# Insert chart image
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img = Image(chart_path)
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ws.add_image(img, f"A{row_offset}")
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except Exception as e:
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logger.error(f"输出Excel Sheet {sheet_name} 失败: {e}")
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continue
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# Save Excel file
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wb.save(excel_file_path)
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print(f"Chart saved as {excel_file_path}") |