combine trade strategy
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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}")
|
|
||||||
continue
|
|
||||||
# Save Excel file
|
|
||||||
wb.save(excel_file_path)
|
|
||||||
print(f"Chart saved as {excel_file_path}")
|
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,506 @@
|
||||||
|
import core.logger as logging
|
||||||
|
import os
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import seaborn as sns
|
||||||
|
from datetime import datetime
|
||||||
|
import re
|
||||||
|
import json
|
||||||
|
from openpyxl import Workbook
|
||||||
|
from openpyxl.drawing.image import Image
|
||||||
|
import openpyxl
|
||||||
|
from openpyxl.styles import Font
|
||||||
|
from PIL import Image as PILImage
|
||||||
|
from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
|
||||||
|
from core.db.db_market_data import DBMarketData
|
||||||
|
from core.db.db_huge_volume_data import DBHugeVolumeData
|
||||||
|
from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
|
||||||
|
|
||||||
|
# seaborn支持中文
|
||||||
|
plt.rcParams["font.family"] = ["SimHei"]
|
||||||
|
|
||||||
|
logger = logging.logger
|
||||||
|
|
||||||
|
|
||||||
|
class MaBreakStatistics:
|
||||||
|
"""
|
||||||
|
统计MA突破之后的涨跌幅
|
||||||
|
MA向上突破的点位周期K线：5 > 10 > 20 > 30
|
||||||
|
统计MA向上突破的点位周期K线，突破之后，到：
|
||||||
|
下一个MA向下突破的点位周期K线：30 > 20 > 10 > 5
|
||||||
|
之间的涨跌幅
|
||||||
|
"""
|
||||||
|
|
||||||
|
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.db_market_data = DBMarketData(self.db_url)
|
||||||
|
self.db_huge_volume_data = DBHugeVolumeData(self.db_url)
|
||||||
|
self.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get(
|
||||||
|
"symbols", ["XCH-USDT"]
|
||||||
|
)
|
||||||
|
self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
|
||||||
|
"bars", ["5m", "15m", "30m", "1H"]
|
||||||
|
)
|
||||||
|
self.stats_output_dir = "./output/trade_sandbox/ma_strategy/excel/"
|
||||||
|
os.makedirs(self.stats_output_dir, exist_ok=True)
|
||||||
|
self.stats_chart_dir = "./output/trade_sandbox/ma_strategy/chart/"
|
||||||
|
os.makedirs(self.stats_chart_dir, exist_ok=True)
|
||||||
|
self.trade_strategy_config = self.get_trade_strategy_config()
|
||||||
|
self.main_strategy = self.trade_strategy_config.get("均线系统策略", None)
|
||||||
|
|
||||||
|
def get_trade_strategy_config(self):
|
||||||
|
with open("./json/trade_strategy.json", "r", encoding="utf-8") as f:
|
||||||
|
trade_strategy_config = json.load(f)
|
||||||
|
return trade_strategy_config
|
||||||
|
|
||||||
|
def batch_statistics(self, strategy_name: str = "全均线策略"):
|
||||||
|
self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/excel/{strategy_name}/"
|
||||||
|
os.makedirs(self.stats_output_dir, exist_ok=True)
|
||||||
|
self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/chart/{strategy_name}/"
|
||||||
|
os.makedirs(self.stats_chart_dir, exist_ok=True)
|
||||||
|
ma_break_market_data_list = []
|
||||||
|
if strategy_name not in self.main_strategy.keys() or strategy_name is None:
|
||||||
|
strategy_name = "全均线策略"
|
||||||
|
for symbol in self.symbols:
|
||||||
|
for bar in self.bars:
|
||||||
|
logger.info(f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}")
|
||||||
|
ma_break_market_data = self.trade_simulate(symbol, bar, strategy_name)
|
||||||
|
if ma_break_market_data is not None:
|
||||||
|
ma_break_market_data_list.append(ma_break_market_data)
|
||||||
|
if len(ma_break_market_data_list) > 0:
|
||||||
|
ma_break_market_data = pd.concat(ma_break_market_data_list)
|
||||||
|
# 依据symbol和bar分组，统计每个symbol和bar的pct_chg的max, min, mean, std, median, count
|
||||||
|
pct_chg_df = (
|
||||||
|
ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"]
|
||||||
|
.agg(
|
||||||
|
pct_chg_sum="sum",
|
||||||
|
pct_chg_max="max",
|
||||||
|
pct_chg_min="min",
|
||||||
|
pct_chg_mean="mean",
|
||||||
|
pct_chg_std="std",
|
||||||
|
pct_chg_median="median",
|
||||||
|
pct_chg_count="count",
|
||||||
|
)
|
||||||
|
.reset_index()
|
||||||
|
)
|
||||||
|
# 依据symbol和bar分组，统计每个symbol和bar的interval_minutes的max, min, mean, std, median, count
|
||||||
|
interval_minutes_df = (
|
||||||
|
ma_break_market_data.groupby(["symbol", "bar"])["interval_minutes"]
|
||||||
|
.agg(
|
||||||
|
interval_minutes_max="max",
|
||||||
|
interval_minutes_min="min",
|
||||||
|
interval_minutes_mean="mean",
|
||||||
|
interval_minutes_std="std",
|
||||||
|
interval_minutes_median="median",
|
||||||
|
interval_minutes_count="count",
|
||||||
|
)
|
||||||
|
.reset_index()
|
||||||
|
)
|
||||||
|
|
||||||
|
earliest_market_date_time = ma_break_market_data["begin_date_time"].min()
|
||||||
|
earliest_market_date_time = re.sub(
|
||||||
|
r"[\:\-\s]", "", str(earliest_market_date_time)
|
||||||
|
)
|
||||||
|
latest_market_date_time = ma_break_market_data["end_date_time"].max()
|
||||||
|
if latest_market_date_time is None:
|
||||||
|
latest_market_date_time = datetime.now().strftime("%Y%m%d")
|
||||||
|
latest_market_date_time = re.sub(
|
||||||
|
r"[\:\-\s]", "", str(latest_market_date_time)
|
||||||
|
)
|
||||||
|
output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}.xlsx"
|
||||||
|
output_file_path = os.path.join(self.stats_output_dir, output_file_name)
|
||||||
|
logger.info(f"导出{output_file_path}")
|
||||||
|
strategy_info_df = self.get_strategy_info(strategy_name)
|
||||||
|
with pd.ExcelWriter(output_file_path) as writer:
|
||||||
|
strategy_info_df.to_excel(writer, sheet_name="策略信息", index=False)
|
||||||
|
ma_break_market_data.to_excel(
|
||||||
|
writer, sheet_name="买卖记录明细", index=False
|
||||||
|
)
|
||||||
|
pct_chg_df.to_excel(writer, sheet_name="买卖涨跌幅统计", index=False)
|
||||||
|
interval_minutes_df.to_excel(
|
||||||
|
writer, sheet_name="买卖时间间隔统计", index=False
|
||||||
|
)
|
||||||
|
|
||||||
|
chart_dict = self.draw_pct_chg_mean_chart(pct_chg_df, strategy_name)
|
||||||
|
self.output_chart_to_excel(output_file_path, chart_dict)
|
||||||
|
else:
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_strategy_info(self, strategy_name: str = "全均线策略"):
|
||||||
|
strategy_config = self.main_strategy.get(strategy_name, None)
|
||||||
|
if strategy_config is None:
|
||||||
|
logger.error(f"策略{strategy_name}不存在")
|
||||||
|
return None
|
||||||
|
strategy_info = {"策略名称": strategy_name, "买入策略": "", "卖出策略": ""}
|
||||||
|
buy_dict = strategy_config.get("buy", {})
|
||||||
|
buy_and_list = buy_dict.get("and", [])
|
||||||
|
buy_or_list = buy_dict.get("or", [])
|
||||||
|
buy_and_text = ""
|
||||||
|
buy_or_text = ""
|
||||||
|
for and_condition in buy_and_list:
|
||||||
|
buy_and_text += f"{and_condition}, \n"
|
||||||
|
if len(buy_or_list) > 0:
|
||||||
|
for or_condition in buy_or_list:
|
||||||
|
buy_or_text += f"{or_condition}, \n"
|
||||||
|
if len(buy_or_text) > 0:
|
||||||
|
strategy_info["买入策略"] = buy_and_text + " 或者 \n" + buy_or_text
|
||||||
|
else:
|
||||||
|
strategy_info["买入策略"] = buy_and_text
|
||||||
|
sell_dict = strategy_config.get("sell", {})
|
||||||
|
sell_and_list = sell_dict.get("and", [])
|
||||||
|
sell_or_list = sell_dict.get("or", [])
|
||||||
|
sell_and_text = ""
|
||||||
|
sell_or_text = ""
|
||||||
|
for and_condition in sell_and_list:
|
||||||
|
sell_and_text += f"{and_condition}, \n"
|
||||||
|
if len(sell_or_list) > 0:
|
||||||
|
for or_condition in sell_or_list:
|
||||||
|
sell_or_text += f"{or_condition}, \n"
|
||||||
|
if len(sell_or_text) > 0:
|
||||||
|
strategy_info["卖出策略"] = sell_and_text + " 或者 \n" + sell_or_text
|
||||||
|
else:
|
||||||
|
strategy_info["卖出策略"] = sell_and_text
|
||||||
|
# 将strategy_info转换为pd.DataFrame
|
||||||
|
strategy_info_df = pd.DataFrame([strategy_info])
|
||||||
|
return strategy_info_df
|
||||||
|
|
||||||
|
def trade_simulate(self, symbol: str, bar: str, strategy_name: str = "全均线策略"):
|
||||||
|
market_data = self.db_market_data.query_market_data_by_symbol_bar(
|
||||||
|
symbol, bar, start=None, end=None
|
||||||
|
)
|
||||||
|
if market_data is None or len(market_data) == 0:
|
||||||
|
logger.warning(f"获取{symbol} {bar} 数据失败")
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
market_data = pd.DataFrame(market_data)
|
||||||
|
market_data.sort_values(by="timestamp", ascending=True, inplace=True)
|
||||||
|
market_data.reset_index(drop=True, inplace=True)
|
||||||
|
logger.info(f"获取{symbol} {bar} 数据成功，数据条数: {len(market_data)}")
|
||||||
|
# 获得ma5, ma10, ma20, ma30不为空的行
|
||||||
|
market_data = market_data[
|
||||||
|
(market_data["ma5"].notna())
|
||||||
|
& (market_data["ma10"].notna())
|
||||||
|
& (market_data["ma20"].notna())
|
||||||
|
& (market_data["ma30"].notna())
|
||||||
|
]
|
||||||
|
logger.info(
|
||||||
|
f"ma5, ma10, ma20, ma30不为空的行，数据条数: {len(market_data)}"
|
||||||
|
)
|
||||||
|
# 计算volume_ma5
|
||||||
|
market_data["volume_ma5"] = market_data["volume"].rolling(window=5).mean()
|
||||||
|
# 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
|
||||||
|
market_data["volume_pct_chg"] = (
|
||||||
|
market_data["volume"] - market_data["volume_ma5"]
|
||||||
|
) / market_data["volume_ma5"]
|
||||||
|
market_data["volume_pct_chg"] = market_data["volume_pct_chg"].fillna(0)
|
||||||
|
|
||||||
|
# 按照timestamp排序
|
||||||
|
market_data = market_data.sort_values(by="timestamp", ascending=True)
|
||||||
|
# 获得ma_break_market_data的close列
|
||||||
|
market_data.reset_index(drop=True, inplace=True)
|
||||||
|
ma_break_market_data_pair_list = []
|
||||||
|
ma_break_market_data_pair = {}
|
||||||
|
for index, row in market_data.iterrows():
|
||||||
|
ma_cross = row["ma_cross"]
|
||||||
|
timestamp = row["timestamp"]
|
||||||
|
close = row["close"]
|
||||||
|
ma5 = row["ma5"]
|
||||||
|
ma10 = row["ma10"]
|
||||||
|
ma20 = row["ma20"]
|
||||||
|
ma30 = row["ma30"]
|
||||||
|
macd_diff = float(row["dif"])
|
||||||
|
macd_dea = float(row["dea"])
|
||||||
|
macd = float(row["macd"])
|
||||||
|
|
||||||
|
if ma_break_market_data_pair.get("begin_timestamp", None) is None:
|
||||||
|
buy_condition = self.fit_strategy(
|
||||||
|
strategy_name=strategy_name,
|
||||||
|
market_data=market_data,
|
||||||
|
row=row,
|
||||||
|
behavior="buy",
|
||||||
|
)
|
||||||
|
|
||||||
|
if buy_condition:
|
||||||
|
ma_break_market_data_pair = {}
|
||||||
|
ma_break_market_data_pair["symbol"] = symbol
|
||||||
|
ma_break_market_data_pair["bar"] = bar
|
||||||
|
ma_break_market_data_pair["begin_timestamp"] = timestamp
|
||||||
|
ma_break_market_data_pair["begin_date_time"] = (
|
||||||
|
timestamp_to_datetime(timestamp)
|
||||||
|
)
|
||||||
|
ma_break_market_data_pair["begin_close"] = close
|
||||||
|
ma_break_market_data_pair["begin_ma5"] = ma5
|
||||||
|
ma_break_market_data_pair["begin_ma10"] = ma10
|
||||||
|
ma_break_market_data_pair["begin_ma20"] = ma20
|
||||||
|
ma_break_market_data_pair["begin_ma30"] = ma30
|
||||||
|
ma_break_market_data_pair["begin_macd_diff"] = macd_diff
|
||||||
|
ma_break_market_data_pair["begin_macd_dea"] = macd_dea
|
||||||
|
ma_break_market_data_pair["begin_macd"] = macd
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
sell_condition = self.fit_strategy(
|
||||||
|
strategy_name=strategy_name,
|
||||||
|
market_data=market_data,
|
||||||
|
row=row,
|
||||||
|
behavior="sell",
|
||||||
|
)
|
||||||
|
|
||||||
|
if sell_condition:
|
||||||
|
ma_break_market_data_pair["end_timestamp"] = timestamp
|
||||||
|
ma_break_market_data_pair["end_date_time"] = (
|
||||||
|
timestamp_to_datetime(timestamp)
|
||||||
|
)
|
||||||
|
ma_break_market_data_pair["end_close"] = close
|
||||||
|
ma_break_market_data_pair["end_ma5"] = ma5
|
||||||
|
ma_break_market_data_pair["end_ma10"] = ma10
|
||||||
|
ma_break_market_data_pair["end_ma20"] = ma20
|
||||||
|
ma_break_market_data_pair["end_ma30"] = ma30
|
||||||
|
ma_break_market_data_pair["end_macd_diff"] = macd_diff
|
||||||
|
ma_break_market_data_pair["end_macd_dea"] = macd_dea
|
||||||
|
ma_break_market_data_pair["end_macd"] = macd
|
||||||
|
ma_break_market_data_pair["pct_chg"] = (
|
||||||
|
close - ma_break_market_data_pair["begin_close"]
|
||||||
|
) / ma_break_market_data_pair["begin_close"]
|
||||||
|
ma_break_market_data_pair["pct_chg"] = round(
|
||||||
|
ma_break_market_data_pair["pct_chg"] * 100, 4
|
||||||
|
)
|
||||||
|
ma_break_market_data_pair["interval_seconds"] = (
|
||||||
|
timestamp - ma_break_market_data_pair["begin_timestamp"]
|
||||||
|
) / 1000
|
||||||
|
# 将interval转换为分钟
|
||||||
|
ma_break_market_data_pair["interval_minutes"] = (
|
||||||
|
ma_break_market_data_pair["interval_seconds"] / 60
|
||||||
|
)
|
||||||
|
ma_break_market_data_pair["interval_hours"] = (
|
||||||
|
ma_break_market_data_pair["interval_seconds"] / 3600
|
||||||
|
)
|
||||||
|
ma_break_market_data_pair["interval_days"] = (
|
||||||
|
ma_break_market_data_pair["interval_seconds"] / 86400
|
||||||
|
)
|
||||||
|
ma_break_market_data_pair_list.append(ma_break_market_data_pair)
|
||||||
|
ma_break_market_data_pair = {}
|
||||||
|
|
||||||
|
if len(ma_break_market_data_pair_list) > 0:
|
||||||
|
ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list)
|
||||||
|
logger.info(f"获取{symbol} {bar} 的买卖记录明细成功, 买卖次数: {len(ma_break_market_data)}")
|
||||||
|
return ma_break_market_data
|
||||||
|
else:
|
||||||
|
return None
|
||||||
|
|
||||||
|
def fit_strategy(
|
||||||
|
self,
|
||||||
|
strategy_name: str = "全均线策略",
|
||||||
|
market_data: pd.DataFrame = None,
|
||||||
|
row: pd.Series = None,
|
||||||
|
behavior: str = "buy",
|
||||||
|
):
|
||||||
|
strategy_config = self.main_strategy.get(strategy_name, None)
|
||||||
|
if strategy_config is None:
|
||||||
|
logger.error(f"策略{strategy_name}不存在")
|
||||||
|
return False
|
||||||
|
condition_dict = strategy_config.get(behavior, None)
|
||||||
|
if condition_dict is None:
|
||||||
|
logger.error(f"策略{strategy_name}的{behavior}条件不存在")
|
||||||
|
return False
|
||||||
|
ma_cross = row["ma_cross"]
|
||||||
|
if pd.isna(ma_cross) or ma_cross is None:
|
||||||
|
ma_cross = ""
|
||||||
|
ma_cross = str(ma_cross)
|
||||||
|
ma5 = float(row["ma5"])
|
||||||
|
ma10 = float(row["ma10"])
|
||||||
|
ma20 = float(row["ma20"])
|
||||||
|
ma30 = float(row["ma30"])
|
||||||
|
close = float(row["close"])
|
||||||
|
volume_pct_chg = float(row["volume_pct_chg"])
|
||||||
|
macd_diff = float(row["dif"])
|
||||||
|
macd_dea = float(row["dea"])
|
||||||
|
macd = float(row["macd"])
|
||||||
|
|
||||||
|
and_list = condition_dict.get("and", [])
|
||||||
|
|
||||||
|
condition = True
|
||||||
|
for and_condition in and_list:
|
||||||
|
if and_condition == "5上穿10":
|
||||||
|
condition = condition and ("5上穿10" in ma_cross)
|
||||||
|
elif and_condition == "10上穿20":
|
||||||
|
condition = condition and ("10上穿20" in ma_cross)
|
||||||
|
elif and_condition == "20上穿30":
|
||||||
|
condition = condition and ("20上穿30" in ma_cross)
|
||||||
|
elif and_condition == "ma5>ma10":
|
||||||
|
condition = condition and (ma5 > ma10)
|
||||||
|
elif and_condition == "ma10>ma20":
|
||||||
|
condition = condition and (ma10 > ma20)
|
||||||
|
elif and_condition == "ma20>ma30":
|
||||||
|
condition = condition and (ma20 > ma30)
|
||||||
|
elif and_condition == "close>ma20":
|
||||||
|
condition = condition and (close > ma20)
|
||||||
|
elif and_condition == "volume_pct_chg>0.2":
|
||||||
|
condition = condition and (volume_pct_chg > 0.2)
|
||||||
|
elif and_condition == "macd_diff>0":
|
||||||
|
condition = condition and (macd_diff > 0)
|
||||||
|
elif and_condition == "macd_dea>0":
|
||||||
|
condition = condition and (macd_dea > 0)
|
||||||
|
elif and_condition == "macd>0":
|
||||||
|
condition = condition and (macd > 0)
|
||||||
|
elif and_condition == "10下穿5":
|
||||||
|
condition = condition and ("10下穿5" in ma_cross)
|
||||||
|
elif and_condition == "20下穿10":
|
||||||
|
condition = condition and ("20下穿10" in ma_cross)
|
||||||
|
elif and_condition == "30下穿20":
|
||||||
|
condition = condition and ("30下穿20" in ma_cross)
|
||||||
|
elif and_condition == "ma5<ma10":
|
||||||
|
condition = condition and (ma5 < ma10)
|
||||||
|
elif and_condition == "ma10<ma20":
|
||||||
|
condition = condition and (ma10 < ma20)
|
||||||
|
elif and_condition == "ma20<ma30":
|
||||||
|
condition = condition and (ma20 < ma30)
|
||||||
|
elif and_condition == "close<ma20":
|
||||||
|
condition = condition and (close < ma20)
|
||||||
|
elif and_condition == "macd_diff<0":
|
||||||
|
condition = condition and (macd_diff < 0)
|
||||||
|
elif and_condition == "macd_dea<0":
|
||||||
|
condition = condition and (macd_dea < 0)
|
||||||
|
elif and_condition == "macd<0":
|
||||||
|
condition = condition and (macd < 0)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
if not condition:
|
||||||
|
or_list = condition_dict.get("or", [])
|
||||||
|
for or_condition in or_list:
|
||||||
|
if or_condition == "5上穿10":
|
||||||
|
condition = condition or ("5上穿10" in ma_cross)
|
||||||
|
elif or_condition == "10上穿20":
|
||||||
|
condition = condition or ("10上穿20" in ma_cross)
|
||||||
|
elif or_condition == "20上穿30":
|
||||||
|
condition = condition or ("20上穿30" in ma_cross)
|
||||||
|
elif or_condition == "ma5>ma10":
|
||||||
|
condition = condition or (ma5 > ma10)
|
||||||
|
elif or_condition == "ma10>ma20":
|
||||||
|
condition = condition or (ma10 > ma20)
|
||||||
|
elif or_condition == "ma20>ma30":
|
||||||
|
condition = condition or (ma20 > ma30)
|
||||||
|
elif or_condition == "close>ma20":
|
||||||
|
condition = condition or (close > ma20)
|
||||||
|
elif or_condition == "volume_pct_chg>0.2":
|
||||||
|
condition = condition or (volume_pct_chg > 0.2)
|
||||||
|
elif or_condition == "macd_diff>0":
|
||||||
|
condition = condition or (macd_diff > 0)
|
||||||
|
elif or_condition == "macd_dea>0":
|
||||||
|
condition = condition or (macd_dea > 0)
|
||||||
|
elif or_condition == "macd>0":
|
||||||
|
condition = condition or (macd > 0)
|
||||||
|
elif or_condition == "10下穿5":
|
||||||
|
condition = condition or ("10下穿5" in ma_cross)
|
||||||
|
elif or_condition == "20下穿10":
|
||||||
|
condition = condition or ("20下穿10" in ma_cross)
|
||||||
|
elif or_condition == "30下穿20":
|
||||||
|
condition = condition or ("30下穿20" in ma_cross)
|
||||||
|
elif or_condition == "ma5<ma10":
|
||||||
|
condition = condition or (ma5 < ma10)
|
||||||
|
elif or_condition == "ma10<ma20":
|
||||||
|
condition = condition or (ma10 < ma20)
|
||||||
|
elif or_condition == "ma20<ma30":
|
||||||
|
condition = condition or (ma20 < ma30)
|
||||||
|
elif or_condition == "close<ma20":
|
||||||
|
condition = condition or (close < ma20)
|
||||||
|
elif or_condition == "macd_diff<0":
|
||||||
|
condition = condition or (macd_diff < 0)
|
||||||
|
elif or_condition == "macd_dea<0":
|
||||||
|
condition = condition or (macd_dea < 0)
|
||||||
|
elif or_condition == "macd<0":
|
||||||
|
condition = condition or (macd < 0)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
return condition
|
||||||
|
|
||||||
|
def draw_pct_chg_mean_chart(
|
||||||
|
self, data: pd.DataFrame, strategy_name: str = "全均线策略"
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
绘制pct_chg mean的柱状图表（美观，保存到self.stats_chart_dir）
|
||||||
|
:param data: 波段pct_chg_mean的数据
|
||||||
|
:return: None
|
||||||
|
"""
|
||||||
|
if data is None or data.empty:
|
||||||
|
return None
|
||||||
|
# seaborn风格设置
|
||||||
|
sns.set_theme(style="whitegrid")
|
||||||
|
plt.rcParams["font.sans-serif"] = ["SimHei"] # 也可直接用字体名
|
||||||
|
plt.rcParams["font.size"] = 11 # 设置字体大小
|
||||||
|
plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题
|
||||||
|
chart_dict = {}
|
||||||
|
column_name_dict = {
|
||||||
|
"pct_chg_sum": "涨跌总和",
|
||||||
|
"pct_chg_mean": "涨跌均值",
|
||||||
|
}
|
||||||
|
for column_name, column_name_text in column_name_dict.items():
|
||||||
|
for bar in data["bar"].unique():
|
||||||
|
bar_data = data[data["bar"] == bar].copy() # 一次筛选即可
|
||||||
|
if bar_data.empty:
|
||||||
|
continue
|
||||||
|
bar_data.rename(columns={column_name: column_name_text}, inplace=True)
|
||||||
|
# 可选：按均值排序
|
||||||
|
bar_data.sort_values(by=column_name_text, ascending=False, inplace=True)
|
||||||
|
bar_data.reset_index(drop=True, inplace=True)
|
||||||
|
|
||||||
|
plt.figure(figsize=(10, 6))
|
||||||
|
ax = sns.barplot(x="symbol", y=column_name_text, data=bar_data, palette="Blues_d")
|
||||||
|
plt.title(f"{bar}趋势{column_name_text}(%)")
|
||||||
|
plt.xlabel("symbol")
|
||||||
|
plt.ylabel(column_name_text)
|
||||||
|
plt.xticks(rotation=45, ha="right")
|
||||||
|
|
||||||
|
# 在柱状图上添加数值标签
|
||||||
|
for i, v in enumerate(bar_data[column_name_text]):
|
||||||
|
ax.text(i, v, f'{v:.3f}', ha='center', va='bottom', fontsize=10, fontweight='bold')
|
||||||
|
|
||||||
|
plt.tight_layout()
|
||||||
|
|
||||||
|
save_path = os.path.join(
|
||||||
|
self.stats_chart_dir, f"{bar}_ma_break_{column_name}_{strategy_name}.png"
|
||||||
|
)
|
||||||
|
plt.savefig(save_path, dpi=150)
|
||||||
|
plt.close()
|
||||||
|
|
||||||
|
sheet_name = f"{bar}_趋势{column_name_text}分布图表_{strategy_name}"
|
||||||
|
chart_dict[sheet_name] = save_path
|
||||||
|
return chart_dict
|
||||||
|
|
||||||
|
def output_chart_to_excel(self, excel_file_path: str, charts_dict: dict):
|
||||||
|
"""
|
||||||
|
输出Excel文件，包含所有图表
|
||||||
|
charts_dict: 图表数据字典，格式为：
|
||||||
|
{
|
||||||
|
"sheet_name": {
|
||||||
|
"chart_name": "chart_path"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
"""
|
||||||
|
logger.info(f"将图表输出到{excel_file_path}")
|
||||||
|
|
||||||
|
# 打开已经存在的Excel文件
|
||||||
|
wb = openpyxl.load_workbook(excel_file_path)
|
||||||
|
|
||||||
|
for sheet_name, chart_path in charts_dict.items():
|
||||||
|
try:
|
||||||
|
ws = wb.create_sheet(title=sheet_name)
|
||||||
|
row_offset = 1
|
||||||
|
# Insert chart image
|
||||||
|
img = Image(chart_path)
|
||||||
|
ws.add_image(img, f"A{row_offset}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"输出Excel Sheet {sheet_name} 失败: {e}")
|
||||||
|
continue
|
||||||
|
# Save Excel file
|
||||||
|
wb.save(excel_file_path)
|
||||||
|
print(f"Chart saved as {excel_file_path}")
|
||||||
|
|
@ -97,7 +97,7 @@ class MeanReversionSandbox:
|
||||||
os.makedirs("./json/", exist_ok=True)
|
os.makedirs("./json/", exist_ok=True)
|
||||||
json_file_path = "./json/peak_valley_data.json"
|
json_file_path = "./json/peak_valley_data.json"
|
||||||
if not os.path.exists(json_file_path):
|
if not os.path.exists(json_file_path):
|
||||||
excel_file_path = "./output/statistics/excel/price_volume_stats_window_size_100_from_20250515000000_to_20250819110500.xlsx"
|
excel_file_path = "./output/statistics/excel/price_volume_stats_window_size_100_from_20250515000000_to_20250822145000.xlsx"
|
||||||
if not os.path.exists(excel_file_path):
|
if not os.path.exists(excel_file_path):
|
||||||
raise FileNotFoundError(f"Excel file not found: {excel_file_path}")
|
raise FileNotFoundError(f"Excel file not found: {excel_file_path}")
|
||||||
sheet_name = "波峰波谷统计"
|
sheet_name = "波峰波谷统计"
|
||||||
|
|
|
||||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,102 @@
|
||||||
|
{
|
||||||
|
"均线系统策略": {
|
||||||
|
"全均线策略": {
|
||||||
|
"buy": {
|
||||||
|
"and": [
|
||||||
|
"5上穿10",
|
||||||
|
"ma5>ma10",
|
||||||
|
"ma10>ma20",
|
||||||
|
"ma20>ma30",
|
||||||
|
"close>ma20",
|
||||||
|
"volume_pct_chg>0.2"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"and": [
|
||||||
|
"ma5<ma10",
|
||||||
|
"ma10<ma20",
|
||||||
|
"ma20<ma30",
|
||||||
|
"volume_pct_chg>0.2"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"5上穿10策略": {
|
||||||
|
"buy": {
|
||||||
|
"and": [
|
||||||
|
"5上穿10",
|
||||||
|
"ma5>ma10",
|
||||||
|
"volume_pct_chg>0.2"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"and": [
|
||||||
|
"ma5<ma10",
|
||||||
|
"volume_pct_chg>0.2"
|
||||||
|
],
|
||||||
|
"or": [
|
||||||
|
"close < ma20"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"三均线策略": {
|
||||||
|
"buy": {
|
||||||
|
"and": [
|
||||||
|
"ma5>ma10",
|
||||||
|
"ma10>ma20",
|
||||||
|
"10上穿20",
|
||||||
|
"volume_pct_chg>0.2"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"and": [
|
||||||
|
"ma5<ma10",
|
||||||
|
"ma10<ma20"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"均线价格突破策略": {
|
||||||
|
"buy": {
|
||||||
|
"and": [
|
||||||
|
"ma5>ma10",
|
||||||
|
"close>ma20"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"and": [
|
||||||
|
"ma5<ma10",
|
||||||
|
"close<ma20"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"均线macd结合策略1": {
|
||||||
|
"buy": {
|
||||||
|
"and": [
|
||||||
|
"ma5>ma10",
|
||||||
|
"macd_diff>0"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"and": [
|
||||||
|
"ma5<ma10",
|
||||||
|
"macd_diff<0"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"均线macd结合策略2": {
|
||||||
|
"buy": {
|
||||||
|
"and": [
|
||||||
|
"ma5>ma10",
|
||||||
|
"macd_diff>0",
|
||||||
|
"macd>0"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"and": [
|
||||||
|
"ma5<ma10",
|
||||||
|
"macd_diff<0",
|
||||||
|
"macd<0"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -3,7 +3,6 @@ from datetime import datetime
|
||||||
from time import sleep
|
from time import sleep
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from core.biz.market_data import MarketData
|
from core.biz.market_data import MarketData
|
||||||
from core.statistics.ma_break_statistics import MaBreakStatistics
|
|
||||||
from core.db.db_market_data import DBMarketData
|
from core.db.db_market_data import DBMarketData
|
||||||
from core.biz.metrics_calculation import MetricsCalculation
|
from core.biz.metrics_calculation import MetricsCalculation
|
||||||
from core.utils import (
|
from core.utils import (
|
||||||
|
|
@ -53,7 +52,6 @@ class MarketDataMain:
|
||||||
self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
|
self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
|
||||||
self.db_market_data = DBMarketData(self.db_url)
|
self.db_market_data = DBMarketData(self.db_url)
|
||||||
self.trade_data_main = TradeDataMain()
|
self.trade_data_main = TradeDataMain()
|
||||||
self.ma_break_statistics = MaBreakStatistics()
|
|
||||||
|
|
||||||
def initial_data(self):
|
def initial_data(self):
|
||||||
"""
|
"""
|
||||||
|
|
@ -408,5 +406,5 @@ if __name__ == "__main__":
|
||||||
market_data_main = MarketDataMain()
|
market_data_main = MarketDataMain()
|
||||||
# market_data_main.batch_update_data()
|
# market_data_main.batch_update_data()
|
||||||
# market_data_main.initial_data()
|
# market_data_main.initial_data()
|
||||||
# market_data_main.batch_calculate_metrics()
|
market_data_main.batch_calculate_metrics()
|
||||||
market_data_main.batch_ma_break_statistics()
|
# market_data_main.batch_ma_break_statistics()
|
||||||
|
|
@ -2,7 +2,7 @@ select * from crypto_market_monitor
|
||||||
order by date_time desc;
|
order by date_time desc;
|
||||||
|
|
||||||
select symbol, bar, date_time, close,
|
select symbol, bar, date_time, close,
|
||||||
pct_chg, kdj_k, kdj_d, kdj_k, kdj_pattern,
|
pct_chg, ma_cross, ma5, ma10, ma20, ma30, dif, dea, macd, kdj_k, kdj_d, kdj_k, kdj_pattern,
|
||||||
rsi_14, rsi_signal,
|
rsi_14, rsi_signal,
|
||||||
boll_upper, boll_middle, boll_lower, boll_pattern, boll_signal
|
boll_upper, boll_middle, boll_lower, boll_pattern, boll_signal
|
||||||
from crypto_market_data
|
from crypto_market_data
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,42 @@
|
||||||
|
import core.logger as logging
|
||||||
|
from datetime import datetime
|
||||||
|
from time import sleep
|
||||||
|
import pandas as pd
|
||||||
|
from core.biz.market_data import MarketData
|
||||||
|
from core.trade.ma_break_statistics import MaBreakStatistics
|
||||||
|
from core.db.db_market_data import DBMarketData
|
||||||
|
from core.biz.metrics_calculation import MetricsCalculation
|
||||||
|
from core.utils import (
|
||||||
|
datetime_to_timestamp,
|
||||||
|
timestamp_to_datetime,
|
||||||
|
transform_date_time_to_timestamp,
|
||||||
|
)
|
||||||
|
from trade_data_main import TradeDataMain
|
||||||
|
from config import (
|
||||||
|
API_KEY,
|
||||||
|
SECRET_KEY,
|
||||||
|
PASSPHRASE,
|
||||||
|
SANDBOX,
|
||||||
|
MONITOR_CONFIG,
|
||||||
|
MYSQL_CONFIG,
|
||||||
|
BAR_THRESHOLD,
|
||||||
|
)
|
||||||
|
|
||||||
|
logger = logging.logger
|
||||||
|
|
||||||
|
class TradeMaStrategyMain:
|
||||||
|
def __init__(self):
|
||||||
|
self.ma_break_statistics = MaBreakStatistics()
|
||||||
|
|
||||||
|
def batch_ma_break_statistics(self):
|
||||||
|
"""
|
||||||
|
批量计算MA突破统计
|
||||||
|
"""
|
||||||
|
logger.info("开始批量计算MA突破统计")
|
||||||
|
strategy_dict = self.ma_break_statistics.main_strategy
|
||||||
|
for strategy_name, strategy_info in strategy_dict.items():
|
||||||
|
self.ma_break_statistics.batch_statistics(strategy_name=strategy_name)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
trade_ma_strategy_main = TradeMaStrategyMain()
|
||||||
|
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||||
Loading…
Reference in New Issue