310 lines
13 KiB
Python
310 lines
13 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 matplotlib.ticker import PercentFormatter
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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 OKX_MONITOR_CONFIG
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from core.trade.mean_reversion_sandbox import MeanReversionSandbox
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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 MeanReversionSandboxMain:
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def __init__(self, start_date: str, end_date: str, window_size: int, only_5m: bool = False, solution_list: list = None):
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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.only_5m = only_5m
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if only_5m:
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self.bars = ["5m"]
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else:
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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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if solution_list is None:
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self.solution_list = ["solution_1", "solution_2", "solution_3"]
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else:
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self.solution_list = solution_list
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self.start_date = start_date
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self.end_date = end_date
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self.window_size = window_size
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self.save_path = f"./output/trade_sandbox/mean_reversion/"
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os.makedirs(self.save_path, exist_ok=True)
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def batch_mean_reversion_sandbox(self):
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"""
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批量计算均值回归
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"""
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logger.info("开始批量计算均值回归交易策略")
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logger.info(
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f"开始时间: {self.start_date}, 结束时间: {self.end_date}, 窗口大小: {self.window_size}"
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)
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for solution in self.solution_list:
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data_list = []
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for symbol in self.symbols:
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for bar in self.bars:
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data = self.mean_reversion(symbol, bar, solution)
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if data is not None and len(data) > 0:
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data_list.append(data)
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if len(data_list) == 0:
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return None
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total_data = pd.concat(data_list)
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total_data.sort_values(by="buy_timestamp", ascending=True, inplace=True)
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total_data.reset_index(drop=True, inplace=True)
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stat_data = self.statistic_data(total_data)
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excel_save_path = os.path.join(self.save_path, solution, "excel")
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os.makedirs(excel_save_path, exist_ok=True)
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date_time_str = datetime.now().strftime("%Y%m%d%H%M%S")
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excel_file_path = os.path.join(
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excel_save_path, f"{solution}_{date_time_str}.xlsx"
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)
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with pd.ExcelWriter(excel_file_path) as writer:
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total_data.to_excel(writer, sheet_name="total_data", index=False)
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stat_data.to_excel(writer, sheet_name="stat_data", index=False)
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chart_dict = {}
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self.draw_chart(stat_data, chart_dict)
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self.output_chart_to_excel(excel_file_path, chart_dict)
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def mean_reversion(self, symbol: str, bar: str, solution: str):
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"""
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均值回归交易策略
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"""
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mean_reversion_sandbox = MeanReversionSandbox(solution)
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data = mean_reversion_sandbox.trade_sandbox(
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symbol, bar, self.window_size, self.start_date, self.end_date
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)
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return data
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def statistic_data(self, data: pd.DataFrame):
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"""
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统计数据
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"""
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data_list = []
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# 以symbol, bar分组，统计data的profit_pct>0的次数，并且获得：
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# profit_pct的最大值，最小值，平均值，profit_pct>0的平均值，以及profit_pct<0的平均值
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data_grouped = data.groupby(["symbol", "bar"])
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for symbol, bar in data_grouped:
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solution = bar["solution"].iloc[0]
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# 止盈次数
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take_profit_count = len(bar[bar["sell_type"] == "止盈"])
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take_profit_ratio = round((take_profit_count / len(bar)) * 100, 4)
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# 止损次数
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stop_loss_count = len(bar[bar["sell_type"] == "止损"])
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stop_loss_ratio = round((stop_loss_count / len(bar)) * 100, 4)
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profit_pct_gt_0_count = len(bar[bar["profit_pct"] > 0])
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profit_pct_gt_0_ratio = round((profit_pct_gt_0_count / len(bar)) * 100, 4)
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profit_pct_lt_0_count = len(bar[bar["profit_pct"] < 0])
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profit_pct_lt_0_ratio = round((profit_pct_lt_0_count / len(bar)) * 100, 4)
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profit_pct_max = bar["profit_pct"].max()
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profit_pct_min = bar["profit_pct"].min()
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profit_pct_mean = bar["profit_pct"].mean()
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profit_pct_sum = bar["profit_pct"].sum()
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profit_pct_gt_0_mean = bar[bar["profit_pct"] > 0]["profit_pct"].mean()
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profit_pct_lt_0_mean = bar[bar["profit_pct"] < 0]["profit_pct"].mean()
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symbol_name = bar["symbol"].iloc[0]
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bar_name = bar["bar"].iloc[0]
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logger.info(
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f"策略: {solution}, symbol: {symbol_name}, bar: {bar_name}, profit_pct>0的次数: {profit_pct_gt_0_count}, profit_pct<0的次数: {profit_pct_lt_0_count}, profit_pct最大值: {profit_pct_max}, profit_pct最小值: {profit_pct_min}, profit_pct平均值: {profit_pct_mean}, profit_pct>0的平均值: {profit_pct_gt_0_mean}, profit_pct<0的平均值: {profit_pct_lt_0_mean}"
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)
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data_list.append(
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{
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"solution": solution,
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"symbol": symbol_name,
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"bar": bar_name,
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"profit_pct_sum": profit_pct_sum,
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"take_profit_count": take_profit_count,
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"take_profit_ratio": take_profit_ratio,
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"stop_loss_count": stop_loss_count,
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"stop_loss_ratio": stop_loss_ratio,
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"profit_pct_gt_0_count": profit_pct_gt_0_count,
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"profit_pct_gt_0_ratio": profit_pct_gt_0_ratio,
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"profit_pct_lt_0_count": profit_pct_lt_0_count,
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"profit_pct_lt_0_ratio": profit_pct_lt_0_ratio,
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"profit_pct_mean": profit_pct_mean,
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"profit_pct_max": profit_pct_max,
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"profit_pct_min": profit_pct_min,
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"profit_pct_gt_0_mean": profit_pct_gt_0_mean,
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"profit_pct_lt_0_mean": profit_pct_lt_0_mean,
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}
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)
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stat_data = pd.DataFrame(data_list)
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stat_data.sort_values(by=["bar", "symbol"], inplace=True)
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stat_data.reset_index(drop=True, inplace=True)
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return stat_data
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def draw_chart(self, stat_data: pd.DataFrame, chart_dict: dict):
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"""
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绘制图表
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"""
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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
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plt.rcParams["figure.dpi"] = 150
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plt.rcParams["savefig.dpi"] = 150
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# 绘制各个solution的profit_pct_gt_0_ratio的柱状图
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# bar为5m, 15, 30m, 1H，共计四个分类，
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# 每一个bar为一张chart，构成2x2的画布
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# 要求y轴为百分比，x轴为symbol
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# 使用蓝色渐变色
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# 每一个solution保存为一张chart图片，保存到output/trade_sandbox/mean_reversion/chart/
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solution = stat_data["solution"].iloc[0]
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save_path = os.path.join(self.save_path, solution, "chart")
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os.makedirs(save_path, exist_ok=True)
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bars_in_order = [
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b for b in getattr(self, "bars", []) if b in stat_data["bar"].unique()
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]
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if not bars_in_order:
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bars_in_order = list(stat_data["bar"].unique())
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palette_name = "Blues_d"
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y_axis_fields = [
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"take_profit_ratio",
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"stop_loss_ratio",
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"profit_pct_sum",
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"profit_pct_mean",
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"profit_pct_gt_0_mean",
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"profit_pct_lt_0_mean",
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]
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sheet_name = f"{solution}_chart"
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chart_dict[sheet_name] = {}
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for y_axis_field in y_axis_fields:
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if self.only_5m:
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fig, axs = plt.subplots(1, 1, figsize=(10, 10))
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# 当只有一个子图时，将axs包装成数组以便统一处理
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axs = np.array([[axs]])
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else:
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# 绘制2x2的画布
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fig, axs = plt.subplots(2, 2, figsize=(10, 10))
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for j, bar in enumerate(bars_in_order):
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ax = axs[j // 2, j % 2]
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bar_data = stat_data[stat_data["bar"] == bar].copy()
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bar_data.sort_values(by=y_axis_field, ascending=False, inplace=True)
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bar_data.reset_index(drop=True, inplace=True)
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colors = sns.color_palette(palette_name, n_colors=len(bar_data))
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sns.barplot(
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x="symbol",
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y=y_axis_field,
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data=bar_data,
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palette=colors,
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ax=ax,
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)
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# 在柱子上方添加数值标签
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for i, (idx, row) in enumerate(bar_data.iterrows()):
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value = row[y_axis_field]
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# 根据数值类型格式化标签
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if "ratio" in y_axis_field:
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label = f"{value:.2f}%"
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else:
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label = f"{value:.4f}"
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# 在柱子上方显示数值
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ax.text(i, value, label,
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ha='center', va='bottom',
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fontsize=9, fontweight='bold')
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ax.set_ylabel(y_axis_field)
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ax.set_xlabel("symbol")
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ax.set_title(f"{solution} {bar}")
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if "ratio" in y_axis_field:
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ax.yaxis.set_major_formatter(PercentFormatter(100))
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ax.set_ylim(0, 100)
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for label in ax.get_xticklabels():
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label.set_rotation(45)
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label.set_horizontalalignment("right")
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# 隐藏未使用的subplot
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total_used = len(bars_in_order)
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if not self.only_5m:
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for k in range(total_used, 4):
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ax = axs[k // 2, k % 2]
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ax.axis("off")
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fig.tight_layout()
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file_name = f"{solution}_{y_axis_field}.png"
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fig.savefig(os.path.join(save_path, file_name))
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plt.close(fig)
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chart_dict[sheet_name][y_axis_field] = os.path.join(save_path, file_name)
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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_data_dict 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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for chart_name, chart_path in chart_data_dict.items():
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# Load image to get dimensions
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with PILImage.open(chart_path) as img:
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width_px, height_px = img.size
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# Convert pixel height to Excel row height (approximate: 1 point = 1.333 pixels, 1 row ≈ 15 points for 20 pixels)
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pixels_per_point = 1.333
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points_per_row = 15 # Default row height in points
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pixels_per_row = (
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points_per_row * pixels_per_point
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) # ≈ 20 pixels per row
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chart_rows = max(
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10, int(height_px / pixels_per_row)
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) # Minimum 10 rows for small charts
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# Add chart title
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# 支持中文标题
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ws[f"A{row_offset}"] = chart_name.encode("utf-8").decode("utf-8")
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ws[f"A{row_offset}"].font = openpyxl.styles.Font(bold=True, size=12)
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row_offset += 2 # Add 2 rows for title and spacing
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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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# Update row offset (chart height + padding)
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row_offset += (
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chart_rows + 5
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) # Add 5 rows for padding between charts
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except Exception as e:
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logger.error(f"输出Excel Sheet {sheet_name} 失败: {e}")
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continue
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# Save Excel file
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wb.save(excel_file_path)
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print(f"Chart saved as {excel_file_path}")
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if __name__ == "__main__":
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start_date = "2025-05-15 00:00:00"
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end_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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solution_list = ["solution_3"]
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mean_reversion_sandbox_main = MeanReversionSandboxMain(
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start_date=start_date, end_date=end_date, window_size=100, only_5m=True, solution_list=solution_list
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)
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mean_reversion_sandbox_main.batch_mean_reversion_sandbox()
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