代码重构
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529
ProfitSimulation.py
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529
ProfitSimulation.py
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"""
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Profit Simulation - 重构版本
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按照Python最佳实践重构，模块化设计，支持配置化
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核心功能：
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1. 从数据库获取投注订单数据
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2. 计算投注收益、赔率、胜率等指标
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3. 模拟资金变化和收益率分析
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4. 生成可视化图表和分析报告
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"""
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import os
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import json
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from datetime import datetime
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from typing import List, Dict, Tuple, Optional
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from dataclasses import dataclass
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from abc import ABC, abstractmethod
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import pandas as pd
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import plotly.graph_objects as go
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from loguru import logger
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# 导入项目模块
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from dao.Database import Database
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from data_model import MysqlConfig, OddsJamOrder
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@dataclass
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class SimulationConfig:
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"""模拟配置类 - 支持环境变量配置"""
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# 数据库配置
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config_file_path: str = os.getenv("MYSQL_CONFIG_PATH", "./config/mysql_config.json")
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table_name: str = os.getenv("BET_TABLE_NAME", "oddsjam_order")
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# 模拟参数
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initial_balance: float = float(os.getenv("INITIAL_BALANCE", "1000"))
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market_width_min: float = float(os.getenv("MARKET_WIDTH_MIN", "20"))
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market_width_max: float = float(os.getenv("MARKET_WIDTH_MAX", "25"))
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# 文件路径
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data_dir: str = os.getenv("DATA_DIR", "./data/bet_simulation/")
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output_dir: str = os.getenv("OUTPUT_DIR", "./data/bet_simulation/")
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# 缓存配置
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enable_cache: bool = os.getenv("ENABLE_CACHE", "true").lower() == "true"
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def __post_init__(self):
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"""初始化后处理"""
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self.mysql_config = MysqlConfig.parse_file(self.config_file_path)
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# 确保目录存在
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os.makedirs(self.data_dir, exist_ok=True)
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os.makedirs(self.output_dir, exist_ok=True)
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class DataService:
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"""数据服务类 - 负责数据获取和缓存"""
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def __init__(self, config: SimulationConfig):
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self.config = config
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self.dao = Database(config.mysql_config)
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def get_oddsjam_order_data(self, load_from_cache: bool = None) -> pd.DataFrame:
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"""获取OddsJam订单数据"""
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if load_from_cache is None:
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load_from_cache = self.config.enable_cache
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current_date_str = datetime.now().strftime("%Y%m%d")
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cache_file_path = os.path.join(
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self.config.data_dir, f"oddsjam_order_data_{current_date_str}.feather"
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)
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# 尝试从缓存加载
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if load_from_cache and os.path.exists(cache_file_path):
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logger.info(f"从缓存加载数据: {cache_file_path}")
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return pd.read_feather(cache_file_path)
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# 从数据库获取数据
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logger.info("从数据库获取订单数据")
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select_query = f"SELECT * FROM bet.{self.config.table_name} where bet_status in ('won', 'lost')"
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raw_data_list = self.dao.fetchall(query=select_query)
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if not raw_data_list:
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logger.warning("未找到符合条件的订单数据")
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return pd.DataFrame()
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# 转换为DataFrame
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order_data_list = [OddsJamOrder(**data).model_dump() for data in raw_data_list]
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order_df = pd.DataFrame(order_data_list)
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# 保存到缓存
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if self.config.enable_cache:
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order_df.to_feather(cache_file_path)
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logger.info(f"数据已缓存到: {cache_file_path}")
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return order_df
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def filter_data_by_market_width(self, data_df: pd.DataFrame) -> pd.DataFrame:
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"""根据市场宽度过滤数据"""
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original_count = len(data_df)
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filtered_df = data_df[
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(data_df["market_width"] >= self.config.market_width_min)
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& (data_df["market_width"] <= self.config.market_width_max)
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]
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filtered_count = len(filtered_df)
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logger.info(f"市场宽度过滤: {original_count} -> {filtered_count} 条记录")
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return filtered_df
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class ProfitCalculator:
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"""收益计算服务类"""
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@staticmethod
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def calculate_benefit_by_order(order_info: Dict) -> float:
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"""根据订单信息计算收益"""
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home_or_away = order_info["home_or_away"]
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price = order_info[f"{home_or_away}_price"] / 100
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if order_info["outcome"] == -1:
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return -1
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if price >= 0:
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return price
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else:
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return 1 / abs(price)
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@staticmethod
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def calculate_odds(row: pd.Series) -> float:
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"""计算赔率"""
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home_or_away = row["home_or_away"]
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price = row[f"{home_or_away}_price"] / 100
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if price >= 0:
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return price
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else:
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return 1 / abs(price)
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@staticmethod
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def calculate_closing_balance(
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day_benefit_list: List[float], pre_balance: float = 1000, pre_benefit: float = 0
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) -> List[float]:
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"""计算日末余额"""
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closing_balance_list = []
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for benefit in day_benefit_list:
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closing_balance = pre_balance + pre_benefit / 3 + benefit * 2 / 3
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closing_balance_list.append(closing_balance)
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pre_balance = closing_balance
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pre_benefit = benefit
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return closing_balance_list
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@staticmethod
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def calculate_in_transit_funds_ratio(
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daily_investment_list: List[float],
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closing_balance_list: List[float],
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start_closing_balance: float = 1000,
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) -> List[float]:
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"""计算在途资金比例"""
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assert len(daily_investment_list) == len(closing_balance_list)
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ratio_list = []
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for i, daily_investment in enumerate(daily_investment_list):
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if i == 0:
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ratio = daily_investment / start_closing_balance
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else:
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ratio = daily_investment / closing_balance_list[i - 1]
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ratio_list.append(ratio)
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return ratio_list
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class SimulationEngine:
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"""模拟引擎类 - 负责收益模拟分析"""
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def __init__(self, config: SimulationConfig):
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self.config = config
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self.calculator = ProfitCalculator()
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def simulate_profit(
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self, data_df: pd.DataFrame, init_balance: float = None
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) -> Tuple[pd.DataFrame, float, float]:
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"""执行收益模拟"""
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if init_balance is None:
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init_balance = self.config.initial_balance
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logger.info(f"开始收益模拟，初始资金: {init_balance}")
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# 按日期聚合数据
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res_df = (
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data_df.groupby("date")
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.agg({"investment": "sum", "benefit": "sum"})
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.reset_index()
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)
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res_df = res_df.rename(columns={"investment": "当日投入", "benefit": "日收益"})
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# 计算收益率指标
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res_df["日收益率"] = res_df["日收益"] / res_df["当日投入"]
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res_df["累计收益"] = res_df["日收益"].cumsum()
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res_df["累计投入"] = res_df["当日投入"].cumsum()
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res_df["累计收益率"] = res_df["累计收益"] / res_df["累计投入"]
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# 计算日末余额
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day_benefit_list = res_df["日收益"].tolist()
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closing_balance_list = self.calculator.calculate_closing_balance(
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day_benefit_list=day_benefit_list, pre_balance=init_balance
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)
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res_df["日末余额(1.6天结算)"] = closing_balance_list
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# 计算在途资金比例
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daily_investment_list = res_df["当日投入"].tolist()
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res_df["在途资金比例"] = self.calculator.calculate_in_transit_funds_ratio(
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daily_investment_list=daily_investment_list,
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closing_balance_list=closing_balance_list,
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start_closing_balance=init_balance,
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)
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# 计算关键指标
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annualized_sharpe_ratio = self._calculate_annualized_sharpe_ratio(
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res_df, init_balance
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)
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roi = res_df["日收益"].sum() / res_df["当日投入"].sum()
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logger.info(
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f"模拟完成 - 年化夏普率: {annualized_sharpe_ratio:.4f}, ROI: {roi:.4f}"
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)
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return res_df, annualized_sharpe_ratio, roi
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def _calculate_annualized_sharpe_ratio(
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self, res_df: pd.DataFrame, init_balance: float
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) -> float:
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"""计算年化夏普率"""
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if res_df["日收益率"].std() == 0:
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return 0
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return (
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res_df["日收益"].sum()
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/ init_balance
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/ res_df["日收益率"].std()
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* ((365 / len(res_df)) ** 0.5)
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)
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def calculate_statistics(self, data_df: pd.DataFrame) -> Dict[str, float]:
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"""计算统计指标"""
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data_df["odds"] = data_df.apply(self.calculator.calculate_odds, axis=1)
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total_mean_odds = data_df["odds"].mean()
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won_rate = len(data_df[data_df["outcome"] == 1]) / len(data_df)
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logger.info(f"统计指标 - 平均赔率: {total_mean_odds:.4f}, 胜率: {won_rate:.4f}")
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return {
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"total_mean_odds": total_mean_odds,
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"won_rate": won_rate,
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"total_bets": len(data_df),
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}
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class VisualizationService:
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"""可视化服务类 - 负责图表生成"""
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def __init__(self, config: SimulationConfig):
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self.config = config
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def plot_won_lost_mean_odds(
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self, data_df: pd.DataFrame, output_path: str = None
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) -> None:
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"""绘制胜负数量和平均赔率图表"""
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if output_path is None:
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output_path = os.path.join(
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self.config.output_dir, "won_lost_mean_odds.html"
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)
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logger.info("生成胜负数量和平均赔率图表")
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data_df = data_df.sort_values(by="date")
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date_x = data_df["date"].tolist()
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fig = go.Figure()
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# 添加胜负柱状图
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cols = ["won", "lost"]
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for col in cols:
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if col in data_df.columns:
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y_data = data_df[col].tolist()
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fig.add_trace(go.Bar(x=date_x, y=y_data, name=col, yaxis="y1"))
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# 添加平均赔率折线图
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if "odds" in data_df.columns:
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fig.add_trace(
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go.Scatter(
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x=data_df["date"],
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y=data_df["odds"],
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mode="markers+lines",
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name="平均赔率",
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yaxis="y2",
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)
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)
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fig.update_layout(
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barmode="group",
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font=dict(family="Times New Roman"),
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title="每天胜负数量以及平均赔率",
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xaxis=dict(title="日期"),
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yaxis=dict(title="数量"),
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yaxis2=dict(title="赔率", overlaying="y", side="right"),
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)
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fig.write_html(output_path)
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logger.info(f"图表已保存到: {output_path}")
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def plot_profit_simulation(
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self, data_df: pd.DataFrame, title: str = None, output_path: str = None
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) -> None:
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"""绘制收益模拟图表"""
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if output_path is None:
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output_path = os.path.join(self.config.output_dir, "profit_simulation.html")
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if title is None:
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title = "收益模拟"
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logger.info("生成收益模拟图表")
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fig = go.Figure()
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# 添加日末余额柱状图
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fig.add_trace(
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go.Bar(
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x=data_df["date"],
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y=data_df["日末余额(1.6天结算)"],
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name="日末余额",
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yaxis="y1",
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)
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)
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# 添加收益率折线图
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for col in ["日收益率", "累计收益率", "在途资金比例"]:
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if col in data_df.columns:
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fig.add_trace(
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go.Scatter(
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x=data_df["date"],
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y=data_df[col],
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mode="markers+lines",
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name=col,
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yaxis="y2",
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)
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)
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fig.update_layout(
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title=title,
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font=dict(family="Times New Roman"),
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xaxis=dict(title="日期"),
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yaxis=dict(title="金额"),
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yaxis2=dict(title="收益率", overlaying="y", side="right", tickformat=".1%"),
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)
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fig.write_html(output_path)
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logger.info(f"图表已保存到: {output_path}")
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class ProfitAnalysisApp:
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"""主应用类 - 协调各个服务完成分析"""
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def __init__(self, config: SimulationConfig = None):
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self.config = config or SimulationConfig()
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self.data_service = DataService(self.config)
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self.simulation_engine = SimulationEngine(self.config)
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self.visualization_service = VisualizationService(self.config)
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def run_analysis(self, load_from_cache: bool = None) -> Dict:
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"""运行完整的收益分析"""
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logger.info("开始收益分析")
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# 获取数据
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order_df = self.data_service.get_oddsjam_order_data(load_from_cache)
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if order_df.empty:
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logger.error("未获取到数据，分析终止")
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return {}
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# 数据预处理
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order_df = order_df[~order_df["home_or_away"].isna()]
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order_df["outcome"] = order_df["bet_status"].apply(
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lambda x: 1 if x == "won" else -1
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)
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order_df["benefit"] = order_df.apply(
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lambda row: ProfitCalculator.calculate_benefit_by_order(row.to_dict()),
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axis=1,
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)
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order_df["date"] = order_df["start_timestamp"].apply(
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lambda x: datetime.fromtimestamp(x // 1000).strftime("%Y-%m-%d")
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)
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# 过滤数据
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data_df = self.data_service.filter_data_by_market_width(order_df)
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if data_df.empty:
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logger.error("过滤后无数据，分析终止")
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return {}
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# 设置投资金额
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data_df["investment"] = 1
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# 执行模拟
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res_df, annualized_sharpe_ratio, roi = self.simulation_engine.simulate_profit(
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data_df
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)
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# 计算统计指标
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stats = self.simulation_engine.calculate_statistics(data_df)
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# 合并数据
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res_df = self._merge_additional_data(res_df, data_df)
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# 保存结果
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self._save_results(res_df, annualized_sharpe_ratio, roi, stats)
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# 生成图表
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self._generate_charts(res_df, annualized_sharpe_ratio, roi)
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# 返回分析结果
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result = {
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"summary": {
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"annualized_sharpe_ratio": annualized_sharpe_ratio,
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"roi": roi,
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"total_bets": stats["total_bets"],
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"won_rate": stats["won_rate"],
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"mean_odds": stats["total_mean_odds"],
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},
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"data": res_df,
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}
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logger.info("收益分析完成")
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return result
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def _merge_additional_data(
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self, res_df: pd.DataFrame, data_df: pd.DataFrame
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) -> pd.DataFrame:
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"""合并额外数据"""
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# 合并平均赔率
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odds_df = data_df.groupby("date").agg({"odds": "mean"}).reset_index()
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||||
res_df = pd.merge(res_df, odds_df, on="date", how="left")
|
||||
|
||||
# 合并胜负统计
|
||||
bet_status_df = pd.pivot_table(
|
||||
data_df,
|
||||
index=["date"],
|
||||
columns=["bet_status"],
|
||||
aggfunc="size",
|
||||
fill_value=0,
|
||||
).reset_index()
|
||||
res_df = pd.merge(res_df, bet_status_df, on="date", how="left")
|
||||
|
||||
# 计算胜率
|
||||
if "won" in res_df.columns and "lost" in res_df.columns:
|
||||
res_df["won rate"] = res_df["won"] / (res_df["won"] + res_df["lost"])
|
||||
|
||||
# 合并市场宽度
|
||||
if "market_width" in data_df.columns:
|
||||
market_width_df = (
|
||||
data_df.groupby("date").agg({"market_width": "mean"}).reset_index()
|
||||
)
|
||||
res_df = pd.merge(res_df, market_width_df, on="date", how="left")
|
||||
|
||||
return res_df
|
||||
|
||||
def _save_results(
|
||||
self,
|
||||
res_df: pd.DataFrame,
|
||||
annualized_sharpe_ratio: float,
|
||||
roi: float,
|
||||
stats: Dict,
|
||||
) -> None:
|
||||
"""保存分析结果"""
|
||||
output_path = os.path.join(self.config.output_dir, "profit_simulation.csv")
|
||||
res_df.to_csv(output_path, index=False, encoding="utf-8-sig")
|
||||
logger.info(f"分析结果已保存到: {output_path}")
|
||||
|
||||
# 保存摘要信息
|
||||
summary = {
|
||||
"analysis_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"annualized_sharpe_ratio": annualized_sharpe_ratio,
|
||||
"roi": roi,
|
||||
"total_bets": stats["total_bets"],
|
||||
"won_rate": stats["won_rate"],
|
||||
"mean_odds": stats["total_mean_odds"],
|
||||
"market_width_range": f"{self.config.market_width_min}-{self.config.market_width_max}",
|
||||
"initial_balance": self.config.initial_balance,
|
||||
}
|
||||
|
||||
summary_path = os.path.join(self.config.output_dir, "analysis_summary.json")
|
||||
with open(summary_path, "w", encoding="utf-8") as f:
|
||||
json.dump(summary, f, ensure_ascii=False, indent=2)
|
||||
logger.info(f"分析摘要已保存到: {summary_path}")
|
||||
|
||||
def _generate_charts(
|
||||
self, res_df: pd.DataFrame, annualized_sharpe_ratio: float, roi: float
|
||||
) -> None:
|
||||
"""生成图表"""
|
||||
title = f"收益模拟,年化夏普率: {annualized_sharpe_ratio:.4f}, ROI: {roi:.4f}"
|
||||
|
||||
self.visualization_service.plot_profit_simulation(data_df=res_df, title=title)
|
||||
self.visualization_service.plot_won_lost_mean_odds(data_df=res_df)
|
||||
|
||||
|
||||
def main():
|
||||
"""主入口函数"""
|
||||
# 创建配置
|
||||
config = SimulationConfig()
|
||||
|
||||
# 创建应用实例
|
||||
app = ProfitAnalysisApp(config)
|
||||
|
||||
# 运行分析
|
||||
result = app.run_analysis()
|
||||
|
||||
if result:
|
||||
summary = result["summary"]
|
||||
print(f"分析完成!")
|
||||
print(f"年化夏普率: {summary['annualized_sharpe_ratio']:.4f}")
|
||||
print(f"ROI: {summary['roi']:.4f}")
|
||||
print(f"总投注数: {summary['total_bets']}")
|
||||
print(f"胜率: {summary['won_rate']:.4f}")
|
||||
print(f"平均赔率: {summary['mean_odds']:.4f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,183 +0,0 @@
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from dao.Database import Database
|
||||
from data_model import MysqlConfig, OddsJamOrder, OddsjamBet
|
||||
from typing import List
|
||||
import json
|
||||
import os
|
||||
import plotly.graph_objects as go
|
||||
|
||||
|
||||
def get_oddsjam_order_data_from_db(load_from_local: bool = False) -> pd.DataFrame:
|
||||
current_date_str = datetime.now().strftime('%Y%m%d')
|
||||
file_path = os.path.join('data', f'oddsjam_order_data_{current_date_str}.csv')
|
||||
if load_from_local and os.path.exists(file_path):
|
||||
return pd.read_csv(file_path, low_memory=False)
|
||||
|
||||
config_file_path = 'config\mysql_config.json'
|
||||
mysql_config = MysqlConfig.parse_file(config_file_path)
|
||||
dao = Database(mysql_config)
|
||||
|
||||
select_query = "SELECT * FROM bet.oddsjam_order where bet_status in ('won', 'lost');"
|
||||
raw_data_list = dao.fetchall(query=select_query)
|
||||
order_data_list = [OddsJamOrder(**data).model_dump()
|
||||
for data in raw_data_list]
|
||||
order_df = pd.DataFrame(order_data_list)
|
||||
order_df.to_csv(file_path, index=False, encoding='utf-8-sig')
|
||||
return order_df
|
||||
|
||||
|
||||
def calc_benefit_by_order_info(order_info: dict) -> float:
|
||||
home_or_away = order_info['home_or_away']
|
||||
price = order_info[f'{home_or_away}_price'] / 100
|
||||
if order_info['outcome'] == -1:
|
||||
return -1
|
||||
if price >= 0:
|
||||
return price
|
||||
else:
|
||||
return 1 / abs(price)
|
||||
|
||||
|
||||
def calc_odds(row):
|
||||
home_or_away = row['home_or_away']
|
||||
price = row[f'{home_or_away}_price'] / 100
|
||||
if price >= 0:
|
||||
return price
|
||||
else:
|
||||
return 1 / abs(price)
|
||||
|
||||
|
||||
def clac_closing_balance(day_benefit_list: List, pre_balance: float = 1000, pre_benefit: float = 0) -> List:
|
||||
closing_balance_list = []
|
||||
for i, benefit in enumerate(day_benefit_list):
|
||||
closing_balance = pre_balance + pre_benefit / 3 + benefit * 2 / 3
|
||||
closing_balance_list.append(closing_balance)
|
||||
pre_balance = closing_balance
|
||||
pre_benefit = benefit
|
||||
return closing_balance_list
|
||||
|
||||
|
||||
def calc_in_transit_funds_ratio(daily_investment_list: List, closing_balance_list: List, start_closing_balance: float = 1000) -> List:
|
||||
assert len(daily_investment_list) == len(closing_balance_list)
|
||||
ratio_list = []
|
||||
for i, daily_investment in enumerate(daily_investment_list):
|
||||
if i == 0:
|
||||
ratio = daily_investment / start_closing_balance
|
||||
else:
|
||||
ratio = daily_investment / closing_balance_list[i-1]
|
||||
ratio_list.append(ratio)
|
||||
return ratio_list
|
||||
|
||||
|
||||
def simulate_profit(data_df: pd.DataFrame, init_balance=1000) -> pd.DataFrame:
|
||||
res_df = data_df.groupby('date').agg({'investment': 'sum', 'benefit': 'sum'}).reset_index()
|
||||
# res_df['日期'] = pd.to_datetime(res_df['date'])
|
||||
res_df = res_df.rename(columns={'investment': '当日投入', 'benefit': '日收益'})
|
||||
|
||||
res_df['日收益率'] = res_df['日收益'] / res_df['当日投入']
|
||||
res_df['累计收益'] = res_df['日收益'].cumsum()
|
||||
res_df['累计投入'] = res_df['当日投入'].cumsum()
|
||||
res_df['累计收益率'] = res_df['累计收益'] / res_df['累计投入']
|
||||
|
||||
day_benefit_list = res_df['日收益'].tolist()
|
||||
closing_balance_list = clac_closing_balance(day_benefit_list=day_benefit_list,
|
||||
pre_balance=init_balance)
|
||||
res_df['日末余额(1.6天结算)'] = closing_balance_list
|
||||
daily_investment_list = res_df['当日投入'].tolist()
|
||||
res_df['在途资金比例'] = calc_in_transit_funds_ratio(daily_investment_list=daily_investment_list,
|
||||
closing_balance_list=closing_balance_list,
|
||||
start_closing_balance=init_balance)
|
||||
annualized_sharpe_ratio = res_df['日收益'].sum() / init_balance / res_df['日收益率'].std() * ((365 / len(res_df))**0.5)
|
||||
roi = res_df['日收益'].sum() / res_df['当日投入'].sum()
|
||||
return res_df, annualized_sharpe_ratio, roi
|
||||
|
||||
|
||||
def plot_won_lost_mean_odds(data_df: pd.DataFrame):
|
||||
|
||||
data_df = data_df.sort_values(by='date')
|
||||
date_x = data_df['date'].tolist()
|
||||
fig = go.Figure()
|
||||
cols = ['won', 'lost']
|
||||
for col in cols:
|
||||
y_data = data_df[col].tolist()
|
||||
fig.add_trace(go.Bar(x=date_x, y=y_data, name=col, yaxis='y1'))
|
||||
|
||||
fig.add_trace(go.Scatter(x=data_df['date'], y=data_df['odds'], mode='markers+lines', name='平均赔率', yaxis='y2'))
|
||||
fig.update_layout(
|
||||
barmode='group',
|
||||
font=dict(family="Times New Roman"),
|
||||
title='每天胜负数量以及平均赔率',
|
||||
xaxis=dict(title='日期'),
|
||||
yaxis=dict(title='数量'),
|
||||
yaxis2=dict(title='赔率', overlaying='y', side='right'),
|
||||
)
|
||||
fig.write_html('data/won_lost_mean_odds.html')
|
||||
|
||||
|
||||
def plot_profit_simulation(data_df: pd.DataFrame, title: str = None):
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Bar(x=data_df['date'], y=data_df['日末余额(1.6天结算)'], name='日末余额', yaxis='y1'))
|
||||
for col in ['日收益率', '累计收益率', '在途资金比例']:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=data_df['date'],
|
||||
y=data_df[col],
|
||||
mode='markers+lines',
|
||||
name=col,
|
||||
yaxis='y2'))
|
||||
|
||||
if title is None:
|
||||
title = '收益模拟'
|
||||
fig.update_layout(
|
||||
title=title,
|
||||
font=dict(family="Times New Roman"),
|
||||
xaxis=dict(title='日期'),
|
||||
yaxis=dict(title='金额'),
|
||||
yaxis2=dict(title='收益率', overlaying='y', side='right', tickformat='.1%'))
|
||||
fig.write_html('data/profit_simulation.html')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
order_df = get_oddsjam_order_data_from_db(load_from_local=True)
|
||||
# order_df = pd.read_excel('data/PEV 3.11-10.26.xlsx', sheet_name='原始数据')
|
||||
|
||||
order_df['outcome'] = order_df['bet_status'].apply(lambda x: 1 if x == 'won' else -1)
|
||||
|
||||
order_df['benefit'] = order_df.apply(lambda row: calc_benefit_by_order_info(row.to_dict()), axis=1)
|
||||
order_df['date'] = order_df['start_timestamp'].apply(
|
||||
lambda x: datetime.fromtimestamp(x // 1000).strftime("%Y-%m-%d"))
|
||||
|
||||
data_df = order_df.copy()
|
||||
|
||||
data_df = data_df[data_df['market_width'] <= 25]
|
||||
data_df = data_df[data_df['market_width'] >= 20]
|
||||
market_width_df = data_df.groupby('date').agg({'market_width': 'mean'}).reset_index()
|
||||
|
||||
data_df['investment'] = 1
|
||||
res_df, annualized_sharpe_ratio, roi = simulate_profit(data_df)
|
||||
print(f'年化夏普率: {annualized_sharpe_ratio}')
|
||||
print(f'ROI: {roi}')
|
||||
|
||||
data_df['odds'] = data_df.apply(calc_odds, axis=1)
|
||||
total_mean_odds = data_df['odds'].mean()
|
||||
print(f'{len(data_df)} 场比赛平均赔率: {total_mean_odds}')
|
||||
won_rate = len(data_df[data_df['outcome'] == 1]) / len(data_df)
|
||||
print(f'{len(data_df)} 场比赛胜率: {won_rate}')
|
||||
|
||||
odds_df = data_df.groupby('date').agg({'odds': 'mean'}).reset_index()
|
||||
res_df = pd.merge(res_df, odds_df, on='date', how='left')
|
||||
|
||||
bet_status_df = pd.pivot_table(data_df, index=['date'], columns=[
|
||||
'bet_status'], aggfunc='size', fill_value=0).reset_index()
|
||||
res_df = pd.merge(res_df, bet_status_df, on='date', how='left')
|
||||
res_df['won rate'] = res_df['won'] / (res_df['won'] + res_df['lost'])
|
||||
|
||||
if 'market_width' in data_df.columns:
|
||||
res_df = pd.merge(res_df, market_width_df, on='date', how='left')
|
||||
|
||||
res_df.to_csv('data/profit_simulation.csv', index=False, encoding='utf-8-sig')
|
||||
title = f'收益模拟,年化夏普率: {annualized_sharpe_ratio}, ROI: {roi}'
|
||||
plot_profit_simulation(data_df=res_df, title=title)
|
||||
plot_won_lost_mean_odds(data_df=res_df)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user