859 lines
41 KiB
Python
859 lines
41 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, timedelta, timezone
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from core.utils import get_current_date_time
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import re
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import json
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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 OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE, BINANCE_MONITOR_CONFIG
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from core.db.db_market_data import DBMarketData
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from core.db.db_binance_data import DBBinanceData
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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, is_us_stock: bool = False, is_binance: bool = False):
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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_huge_volume_data = DBHugeVolumeData(self.db_url)
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self.is_us_stock = is_us_stock
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self.is_binance = is_binance
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if is_us_stock:
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self.symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["QQQ"]
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)
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self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"bars", ["5m"]
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)
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self.initial_date = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"initial_date", "2014-11-30 00:00:00"
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)
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self.db_market_data = DBMarketData(self.db_url)
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else:
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if is_binance:
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self.symbols = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["BTC-USDT"]
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)
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self.bars = ["30m", "1H"]
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self.initial_date = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"initial_date", "2017-08-16 00:00:00"
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)
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self.db_market_data = DBBinanceData(self.db_url)
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else:
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self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["XCH-USDT"]
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)
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self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"bars", ["5m", "15m", "30m", "1H"]
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)
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self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"initial_date", "2025-05-15 00:00:00"
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)
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self.db_market_data = DBMarketData(self.db_url)
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if len(self.initial_date) > 10:
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self.initial_date = self.initial_date[:10]
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self.end_date = get_current_date_time()
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self.trade_strategy_config = self.get_trade_strategy_config()
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self.main_strategy = self.trade_strategy_config.get("均线系统策略", None)
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def get_trade_strategy_config(self):
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with open("./json/trade_strategy.json", "r", encoding="utf-8") as f:
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trade_strategy_config = json.load(f)
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return trade_strategy_config
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def batch_statistics(self, strategy_name: str = "全均线策略"):
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if self.is_us_stock:
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self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/"
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self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/"
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elif self.is_binance:
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self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/"
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self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/"
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else:
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self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/"
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self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/"
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os.makedirs(self.stats_output_dir, exist_ok=True)
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os.makedirs(self.stats_chart_dir, exist_ok=True)
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ma_break_market_data_list = []
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market_data_pct_chg_list = []
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if strategy_name not in self.main_strategy.keys() or strategy_name is None:
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strategy_name = "全均线策略"
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for symbol in self.symbols:
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for bar in self.bars:
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logger.info(
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f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}"
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)
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ma_break_market_data, market_data_pct_chg = self.trade_simulate(
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symbol, bar, strategy_name
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)
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if (
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ma_break_market_data is not None
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and len(ma_break_market_data) > 0
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and market_data_pct_chg is not None
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):
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ma_break_market_data_list.append(ma_break_market_data)
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logger.info(
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f"{symbol} {bar} 的市场价格变化, {market_data_pct_chg.get('pct_chg', 0)}%"
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)
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market_data_pct_chg_list.append(market_data_pct_chg)
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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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market_data_pct_chg_df = pd.DataFrame(market_data_pct_chg_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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ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
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ma_break_market_data.reset_index(drop=True, inplace=True)
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pct_chg_df = (
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ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"]
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.agg(
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pct_chg_sum="sum",
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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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)
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.reset_index()
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)
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pct_chg_df["strategy_name"] = strategy_name
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pct_chg_df["pct_chg_total"] = 0
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pct_chg_df["market_pct_chg"] = 0
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# 将pct_chg_total与market_pct_chg的值类型转换为float
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pct_chg_df["pct_chg_total"] = pct_chg_df["pct_chg_total"].astype(float)
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pct_chg_df["market_pct_chg"] = pct_chg_df["market_pct_chg"].astype(float)
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# 统计pct_chg_total
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# 算法要求，ma_break_market_data，然后pct_chg/100 + 1
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ma_break_market_data["pct_chg_total"] = (
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ma_break_market_data["pct_chg"] / 100 + 1
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)
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# 遍历symbol和bar，按照end_timestamp排序，计算pct_chg_total的值，然后相乘
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for symbol in pct_chg_df["symbol"].unique():
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for bar in pct_chg_df["bar"].unique():
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symbol_bar_data = ma_break_market_data[
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(ma_break_market_data["symbol"] == symbol)
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& (ma_break_market_data["bar"] == bar)
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].copy() # 创建副本避免SettingWithCopyWarning
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if len(symbol_bar_data) > 0:
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symbol_bar_data.sort_values(
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by="end_timestamp", ascending=True, inplace=True
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)
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symbol_bar_data.reset_index(drop=True, inplace=True)
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symbol_bar_data["pct_chg_total"] = symbol_bar_data[
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"pct_chg_total"
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].cumprod()
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# 将更新后的pct_chg_total数据同步更新到ma_break_market_data的对应数据行中
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for idx, row in symbol_bar_data.iterrows():
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mask = (ma_break_market_data["symbol"] == symbol) & \
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(ma_break_market_data["bar"] == bar) & \
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(ma_break_market_data["end_timestamp"] == row["end_timestamp"])
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ma_break_market_data.loc[mask, "pct_chg_total"] = row["pct_chg_total"]
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last_pct_chg_total = symbol_bar_data["pct_chg_total"].iloc[-1]
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last_pct_chg_total = (last_pct_chg_total - 1) * 100
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pct_chg_df.loc[
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(pct_chg_df["symbol"] == symbol)
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& (pct_chg_df["bar"] == bar),
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"pct_chg_total",
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] = last_pct_chg_total
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market_pct_chg = market_data_pct_chg_df.loc[
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(market_data_pct_chg_df["symbol"] == symbol)
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& (market_data_pct_chg_df["bar"] == bar),
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"pct_chg",
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].values[0]
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pct_chg_df.loc[
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(pct_chg_df["symbol"] == symbol)
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& (pct_chg_df["bar"] == bar),
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"market_pct_chg",
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] = market_pct_chg
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pct_chg_df = pct_chg_df[
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[
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"strategy_name",
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"symbol",
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"bar",
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"market_pct_chg",
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"pct_chg_total",
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"pct_chg_sum",
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"pct_chg_max",
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"pct_chg_min",
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"pct_chg_mean",
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"pct_chg_std",
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"pct_chg_median",
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"pct_chg_count",
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]
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]
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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 = (
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ma_break_market_data.groupby(["symbol", "bar"])["interval_minutes"]
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.agg(
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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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)
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.reset_index()
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)
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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(
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r"[\:\-\s]", "", str(earliest_market_date_time)
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)
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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 = get_current_date_time()
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latest_market_date_time = re.sub(
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r"[\:\-\s]", "", str(latest_market_date_time)
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)
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output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}.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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strategy_info_df = self.get_strategy_info(strategy_name)
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with pd.ExcelWriter(output_file_path) as writer:
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strategy_info_df.to_excel(writer, sheet_name="策略信息", index=False)
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ma_break_market_data.to_excel(
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writer, sheet_name="买卖记录明细", index=False
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)
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pct_chg_df.to_excel(writer, sheet_name="买卖涨跌幅统计", index=False)
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interval_minutes_df.to_excel(
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writer, sheet_name="买卖时间间隔统计", index=False
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)
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chart_dict = self.draw_quant_pct_chg_bar_chart(pct_chg_df, strategy_name)
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self.output_chart_to_excel(output_file_path, chart_dict)
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chart_dict = self.draw_quant_line_chart(ma_break_market_data, strategy_name)
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self.output_chart_to_excel(output_file_path, chart_dict)
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return pct_chg_df
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else:
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return None
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def get_strategy_info(self, strategy_name: str = "全均线策略"):
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strategy_config = self.main_strategy.get(strategy_name, None)
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if strategy_config is None:
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logger.error(f"策略{strategy_name}不存在")
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return None
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strategy_info = {"策略名称": strategy_name, "买入策略": "", "卖出策略": ""}
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buy_dict = strategy_config.get("buy", {})
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buy_and_list = buy_dict.get("and", [])
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buy_or_list = buy_dict.get("or", [])
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buy_and_text = ""
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buy_or_text = ""
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for and_condition in buy_and_list:
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buy_and_text += f"{and_condition}, \n"
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if len(buy_or_list) > 0:
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for or_condition in buy_or_list:
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buy_or_text += f"{or_condition}, \n"
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if len(buy_or_text) > 0:
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strategy_info["买入策略"] = buy_and_text + " 或者 \n" + buy_or_text
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else:
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strategy_info["买入策略"] = buy_and_text
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sell_dict = strategy_config.get("sell", {})
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sell_and_list = sell_dict.get("and", [])
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sell_or_list = sell_dict.get("or", [])
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sell_and_text = ""
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sell_or_text = ""
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for and_condition in sell_and_list:
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sell_and_text += f"{and_condition}, \n"
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if len(sell_or_list) > 0:
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for or_condition in sell_or_list:
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sell_or_text += f"{or_condition}, \n"
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if len(sell_or_text) > 0:
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strategy_info["卖出策略"] = sell_and_text + " 或者 \n" + sell_or_text
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else:
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strategy_info["卖出策略"] = sell_and_text
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# 将strategy_info转换为pd.DataFrame
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strategy_info_df = pd.DataFrame([strategy_info])
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return strategy_info_df
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def trade_simulate(self, symbol: str, bar: str, strategy_name: str = "全均线策略"):
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market_data = self.get_full_data(symbol, bar)
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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 None, None
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else:
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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[
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(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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]
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logger.info(
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f"ma5, ma10, ma20, ma30不为空的行，数据条数: {len(market_data)}"
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)
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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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market_data["volume_pct_chg"] = (
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market_data["volume"] - market_data["volume_ma5"]
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) / market_data["volume_ma5"]
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market_data["volume_pct_chg"] = market_data["volume_pct_chg"].fillna(0)
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# 按照timestamp排序
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market_data = 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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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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if self.is_us_stock:
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date_time_field = "date_time_us"
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else:
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date_time_field = "date_time"
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for index, row in market_data.iterrows():
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ma_cross = row["ma_cross"]
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timestamp = row["timestamp"]
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date_time = row[date_time_field]
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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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macd_diff = float(row["dif"])
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macd_dea = float(row["dea"])
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macd = float(row["macd"])
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if ma_break_market_data_pair.get("begin_timestamp", None) is None:
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buy_condition = self.fit_strategy(
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strategy_name=strategy_name,
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market_data=market_data,
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row=row,
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behavior="buy",
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)
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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"] = date_time
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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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ma_break_market_data_pair["begin_macd_diff"] = macd_diff
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ma_break_market_data_pair["begin_macd_dea"] = macd_dea
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ma_break_market_data_pair["begin_macd"] = macd
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continue
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else:
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sell_condition = self.fit_strategy(
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strategy_name=strategy_name,
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market_data=market_data,
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row=row,
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behavior="sell",
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)
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if sell_condition:
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ma_break_market_data_pair["end_timestamp"] = timestamp
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ma_break_market_data_pair["end_date_time"] = date_time
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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
|
||
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)
|
||
# sort by end_timestamp
|
||
ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
|
||
ma_break_market_data.reset_index(drop=True, inplace=True)
|
||
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)}"
|
||
)
|
||
# 量化期间，市场的波动率：
|
||
# ma_break_market_data(最后一条数据的end_close - 第一条数据的begin_close) / 第一条数据的begin_close * 100
|
||
pct_chg = (
|
||
(ma_break_market_data["end_close"].iloc[-1] - ma_break_market_data["begin_close"].iloc[0])
|
||
/ ma_break_market_data["begin_close"].iloc[0]
|
||
* 100
|
||
)
|
||
pct_chg = round(pct_chg, 4)
|
||
market_data_pct_chg = {"symbol": symbol, "bar": bar, "pct_chg": pct_chg}
|
||
return ma_break_market_data, market_data_pct_chg
|
||
else:
|
||
return None, None
|
||
|
||
def get_full_data(self, symbol: str, bar: str = "5m"):
|
||
"""
|
||
分段获取数据，并将数据合并为完整数据
|
||
分段依据：如果end_date与start_date相差超过一年，则每次取一年数据
|
||
"""
|
||
data = pd.DataFrame()
|
||
start_date = datetime.strptime(self.initial_date, "%Y-%m-%d")
|
||
end_date = datetime.strptime(self.end_date, "%Y-%m-%d") + timedelta(days=1)
|
||
fields = [
|
||
"symbol",
|
||
"bar",
|
||
"timestamp",
|
||
"date_time",
|
||
"date_time_us",
|
||
"open",
|
||
"high",
|
||
"low",
|
||
"close",
|
||
"volume",
|
||
"sar_signal",
|
||
"ma5",
|
||
"ma10",
|
||
"ma20",
|
||
"ma30",
|
||
"ma_cross",
|
||
"dif",
|
||
"dea",
|
||
"macd",
|
||
]
|
||
while start_date < end_date:
|
||
current_end_date = min(start_date + timedelta(days=180), end_date)
|
||
start_date_str = start_date.strftime("%Y-%m-%d")
|
||
current_end_date_str = current_end_date.strftime("%Y-%m-%d")
|
||
logger.info(
|
||
f"获取{symbol}数据：{start_date_str}至{current_end_date_str}"
|
||
)
|
||
current_data = self.db_market_data.query_market_data_by_symbol_bar(
|
||
symbol, bar, fields, start=start_date_str, end=current_end_date_str
|
||
)
|
||
if current_data is not None and len(current_data) > 0:
|
||
current_data = pd.DataFrame(current_data)
|
||
data = pd.concat([data, current_data])
|
||
start_date = current_end_date
|
||
data.drop_duplicates(inplace=True)
|
||
if self.is_us_stock:
|
||
date_time_field = "date_time_us"
|
||
else:
|
||
date_time_field = "date_time"
|
||
data.sort_values(by=date_time_field, inplace=True)
|
||
data.reset_index(drop=True, inplace=True)
|
||
return data
|
||
|
||
|
||
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_quant_pct_chg_bar_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_total": "量化策略涨跌", "pct_chg_mean": "量化策略涨跌均值"}
|
||
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)
|
||
if column_name == "pct_chg_total":
|
||
bar_data.rename(columns={"market_pct_chg": "市场自然涨跌"}, 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)
|
||
|
||
# 如果column_name_text是"量化策略涨跌"，则柱状图，同时绘制量化策略涨跌与市场自然涨跌的柱状图，并绘制在同一个图表中
|
||
if column_name == "pct_chg_total":
|
||
plt.figure(figsize=(12, 7))
|
||
|
||
# 设置x轴位置，为并列柱状图做准备
|
||
x = np.arange(len(bar_data))
|
||
width = 0.35 # 柱状图宽度
|
||
|
||
# 确保symbol列是字符串类型，避免matplotlib警告
|
||
bar_data["symbol"] = bar_data["symbol"].astype(str)
|
||
|
||
# 绘制量化策略涨跌柱状图（蓝色渐变色）
|
||
bars1 = plt.bar(x - width/2, bar_data[column_name_text], width,
|
||
label=column_name_text,
|
||
color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data))))
|
||
|
||
# 绘制市场自然涨跌柱状图（绿色渐变色）
|
||
bars2 = plt.bar(x + width/2, bar_data["市场自然涨跌"], width,
|
||
label="市场自然涨跌",
|
||
color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data))))
|
||
|
||
# 设置图表标题和标签
|
||
plt.title(f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)", fontsize=14, fontweight='bold')
|
||
plt.xlabel("Symbol", fontsize=12)
|
||
plt.ylabel("涨跌幅(%)", fontsize=12)
|
||
plt.xticks(x, bar_data['symbol'], rotation=45, ha='right')
|
||
plt.legend()
|
||
plt.grid(True, alpha=0.3)
|
||
|
||
# 在量化策略涨跌柱状图上方添加数值标签
|
||
for i, (bar1, value1) in enumerate(zip(bars1, bar_data[column_name_text])):
|
||
plt.text(bar1.get_x() + bar1.get_width()/2, value1 + (0.01 if value1 >= 0 else -0.01),
|
||
f'{value1:.3f}%', ha='center', va='bottom' if value1 >= 0 else 'top',
|
||
fontsize=9, fontweight='bold', color='darkblue')
|
||
|
||
# 在市场自然涨跌柱状图上方添加数值标签
|
||
for i, (bar2, value2) in enumerate(zip(bars2, bar_data["市场自然涨跌"])):
|
||
plt.text(bar2.get_x() + bar2.get_width()/2, value2 + (0.01 if value2 >= 0 else -0.01),
|
||
f'{value2:.3f}%', ha='center', va='bottom' if value2 >= 0 else 'top',
|
||
fontsize=9, fontweight='bold', color='darkgreen')
|
||
|
||
else:
|
||
plt.figure(figsize=(10, 6))
|
||
|
||
# 确保symbol列是字符串类型，避免matplotlib警告
|
||
bar_data["symbol"] = bar_data["symbol"].astype(str)
|
||
|
||
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}_bar_chart_{column_name}_{strategy_name}.png",
|
||
)
|
||
plt.savefig(save_path, dpi=150)
|
||
plt.close()
|
||
|
||
sheet_name = f"{bar}_趋势{column_name_text}柱状图_{strategy_name}"
|
||
chart_dict[sheet_name] = save_path
|
||
return chart_dict
|
||
|
||
def draw_quant_line_chart(self, data: pd.DataFrame, strategy_name: str = "全均线策略"):
|
||
"""
|
||
根据量化策略买卖明细记录，绘制量化策略涨跌与市场自然涨跌的折线图
|
||
:param data: 量化策略买卖明细记录
|
||
:param strategy_name: 策略名称
|
||
:return: None
|
||
"""
|
||
symbols = data["symbol"].unique()
|
||
bars = data["bar"].unique()
|
||
chart_dict = {}
|
||
for symbol in symbols:
|
||
for bar in bars:
|
||
symbol_bar_data = data[(data["symbol"] == symbol) & (data["bar"] == bar)]
|
||
if symbol_bar_data.empty:
|
||
continue
|
||
|
||
# 获取第一行数据作为基准
|
||
first_row = symbol_bar_data.iloc[0].copy()
|
||
|
||
# 创建初始化行，设置基准值
|
||
init_row = first_row.copy()
|
||
init_row.loc["pct_chg_total"] = 1.0 # 量化策略初始值为1
|
||
init_row.loc["end_timestamp"] = first_row["begin_timestamp"]
|
||
init_row.loc["end_date_time"] = first_row["begin_date_time"]
|
||
init_row.loc["end_close"] = first_row["begin_close"]
|
||
init_row.loc["end_ma5"] = first_row["begin_ma5"]
|
||
init_row.loc["end_ma10"] = first_row["begin_ma10"]
|
||
init_row.loc["end_ma20"] = first_row["begin_ma20"]
|
||
init_row.loc["end_ma30"] = first_row["begin_ma30"]
|
||
init_row.loc["end_macd_diff"] = first_row["begin_macd_diff"]
|
||
init_row.loc["end_macd_dea"] = first_row["begin_macd_dea"]
|
||
init_row.loc["end_macd"] = first_row["begin_macd"]
|
||
init_row.loc["pct_chg"] = 0
|
||
init_row.loc["interval_seconds"] = 0
|
||
init_row.loc["interval_minutes"] = 0
|
||
init_row.loc["interval_hours"] = 0
|
||
init_row.loc["interval_days"] = 0
|
||
|
||
# 将初始化行添加到数据开头
|
||
symbol_bar_data = pd.concat([pd.DataFrame([init_row]), symbol_bar_data])
|
||
symbol_bar_data.sort_values(by="end_timestamp", ascending=True, inplace=True)
|
||
symbol_bar_data.reset_index(drop=True, inplace=True)
|
||
|
||
# 确保时间列是datetime类型，避免matplotlib警告
|
||
symbol_bar_data["end_date_time"] = pd.to_datetime(symbol_bar_data["end_date_time"])
|
||
|
||
# 计算市场价位归一化数据（相对于初始价格）
|
||
symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close"] / init_row["end_close"]
|
||
symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close_to_1"].round(4)
|
||
|
||
# 绘制折线图
|
||
plt.figure(figsize=(12, 7))
|
||
|
||
# 绘制量化策略涨跌线（蓝色）
|
||
plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["pct_chg_total"],
|
||
label="量化策略涨跌", color='blue', linewidth=2, marker='o', markersize=4)
|
||
|
||
# 绘制市场自然涨跌线（绿色）
|
||
plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["end_close_to_1"],
|
||
label="市场自然涨跌", color='green', linewidth=2, marker='s', markersize=4)
|
||
|
||
plt.title(f"{symbol} {bar} 量化与市场折线图_{strategy_name}",
|
||
fontsize=14, fontweight='bold')
|
||
plt.xlabel("时间", fontsize=12)
|
||
plt.ylabel("涨跌变化", fontsize=12)
|
||
plt.legend(fontsize=11)
|
||
plt.grid(True, alpha=0.3)
|
||
|
||
# 设置x轴标签，避免matplotlib警告
|
||
# 选择合适的时间间隔显示标签，避免过于密集
|
||
if len(symbol_bar_data) > 30:
|
||
# 如果数据点较多，选择间隔显示，但确保第一条和最后一条始终显示
|
||
step = max(1, len(symbol_bar_data) // 30)
|
||
|
||
# 创建标签索引列表，确保包含首尾数据
|
||
label_indices = [0] # 第一条
|
||
|
||
# 添加中间间隔的标签
|
||
for i in range(step, len(symbol_bar_data) - 1, step):
|
||
label_indices.append(i)
|
||
|
||
# 添加最后一条（如果还没有包含的话）
|
||
if len(symbol_bar_data) - 1 not in label_indices:
|
||
label_indices.append(len(symbol_bar_data) - 1)
|
||
|
||
# 设置x轴标签
|
||
plt.xticks(symbol_bar_data["end_date_time"].iloc[label_indices],
|
||
symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%Y%m%d %H:%M'),
|
||
rotation=45, ha='right')
|
||
else:
|
||
# 如果数据点较少，全部显示
|
||
plt.xticks(symbol_bar_data["end_date_time"],
|
||
symbol_bar_data["end_date_time"].dt.strftime('%Y%m%d %H:%M'),
|
||
rotation=45, ha='right')
|
||
|
||
plt.tight_layout()
|
||
|
||
save_path = os.path.join(
|
||
self.stats_chart_dir,
|
||
f"{symbol}_{bar}_line_chart_{strategy_name}.png",
|
||
)
|
||
plt.savefig(save_path, dpi=150, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
sheet_name = f"{symbol}_{bar}_折线图_{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}")
|