数据获取/单指标平均收益分布.py

import choose
from pymongo import MongoClient
import pandas as pd
import numpy as np
import datetime
import os

client = MongoClient(
    "mongodb://wth000:[email protected]:27017/dbname?authSource=wth000")
db = client["wth000"]
# 设置参数
# names = ["可转债","COIN", "股票", "指数", "行业", "ETF",]
names = ["COIN",]
mubiao = f"{5}日量比"
a = 20  # 将数据划分成a个等距离的区间
# 获取当前.py文件的绝对路径
file_path = os.path.abspath(__file__)
# 获取当前.py文件所在目录的路径
dir_path = os.path.dirname(file_path)
# 获取当前.py文件所在目录的上两级目录的路径
dir_path = os.path.dirname(os.path.dirname(dir_path))
files = os.listdir(dir_path)
for file in files:
    for filename in names:
        if (filename in file):
            try:
                print(f"{mubiao}")
                # 获取文件名和扩展名
                name, extension = os.path.splitext(file)
                path = os.path.join(dir_path, f"{name}.csv")
                print(name)
                df = pd.read_csv(path)
                # df = df.reset_index()
                # dflanchou = pd.read_csv(f"每日蓝筹.csv")
                # dflanchou = dflanchou.iloc[:, :2]
                # dflanchou.columns = ['日期', '代码']
                # # dflanchou.rename(columns={dflanchou.columns[0]: '日期', dflanchou.columns[1]: '代码'}, inplace=True)
                # df = df.merge(dflanchou[['日期', '代码']], on=['日期', '代码'])
                # print(df)
                # dfkaipan = pd.read_csv('非ST股票（分钟）开盘后五分钟.csv')
                # dfkaipan.columns = ['日期', '代码', '开盘(开盘后五分钟)', '最高(开盘后五分钟)', '最低(开盘后五分钟)',
                #                     '收盘(开盘后五分钟)', '昨收(开盘后五分钟)', '成交额(开盘后五分钟)', '涨跌幅（今收今开）(开盘后五分钟)']
                # df = df.merge(dfkaipan, on=['日期', '代码'])  # 增加设置基本面因子
                # dfshoupan = pd.read_csv('非ST股票（分钟）收盘前五分钟.csv')
                # dfshoupan.columns = ['日期', '代码', '开盘(收盘前五分钟)', '最高(收盘前五分钟)', '最低(收盘前五分钟)',
                #                           '收盘(收盘前五分钟)', '昨收(收盘前五分钟)', '成交额(收盘前五分钟)', '涨跌幅（今收今开）(收盘前五分钟)']
                # df = df.merge(dfshoupan, on=['日期', '代码'])  # 增加设置基本面因子
                watchtime = 1999
                # start_date = datetime.datetime(
                #     watchtime, int(1), int(1)).strftime("%Y-%m-%d %H:%M:%S")
                # end_date = datetime.datetime(datetime.datetime.strptime(
                #     start_date, "%Y-%m-%d %H:%M:%S").year + 8, int(1), int(1)).strftime("%Y-%m-%d %H:%M:%S")
                # df = df[(df["日期"] >= start_date) & (df["日期"] <= end_date)]
                df = df.dropna()
                # # 量比达到低点，观察是否反弹
                # df = df[df["成交量"] == df[f"{40}日成交量低点"]]
                df = df[df["成交量"] == df[f"{40}日成交量高点"]]
                # 价格突破高点，观察量比分布
                # df = df[df["收盘"] == df[f"{5}日收盘高点"]]

                # df = df.groupby("代码", group_keys=False).apply(choose.technology)
                df, m, n = choose.choose(name, df)
                if ("股票" in name):
                    for i in range(1, n+1):
                        df = df[df[f"{i}日后总涨跌幅（未来函数）"] <= 3*(1+0.1*n)]
                else:
                    for i in range(1, n+1):
                        df = df[df[f"{i}日后总涨跌幅（未来函数）"] <= 20*(1+0.1*n)]
                df = df.dropna()
                df.to_csv(f"实际统计数据{name}_{mubiao}_{watchtime}年.csv")
                sorted_data = np.sort(df[f"{mubiao}"])
                indices = np.linspace(
                    0, len(df[f"{mubiao}"]), num=a+1, endpoint=True, dtype=int)
                # 得到每一个区间的上界，并作为该部分对应的区间范围
                ranges = []
                for i in range(len(indices) - 1):
                    start_idx = indices[i]
                    end_idx = indices[i+1] if i != len(indices) - \
                        2 else len(df[f"{mubiao}"])  # 最后一段需要特殊处理
                    upper_bound = sorted_data[end_idx-1]  # 注意索引从0开始，因此要减1
                    ranges.append((sorted_data[start_idx], upper_bound))
                result_dicts = []
                day = n  # 观察不同的持仓周期的涨跌分布
                for n in range(1, day):
                    for rank_range in ranges:
                        sub_df = df.copy()[(df[f"{mubiao}"] >= rank_range[0]) &
                                           (df[f"{mubiao}"] <= rank_range[1])]
                        future_returns = np.array(sub_df[f"{n}日后总涨跌幅（未来函数）"])
                        # 括号注意大小写的问题，要不就会报错没这个参数
                        up_rate = len(
                            future_returns[future_returns >= 0]) / len(future_returns)
                        avg_return = np.mean(future_returns)
                        result_dict = {
                            f"{mubiao}": f"from{rank_range[0]}to{rank_range[1]}",
                            f"未来{n}日上涨次数": len(future_returns[future_returns >= 0]),
                            f"未来{n}日上涨概率": up_rate,
                            f"未来{n}日平均涨跌幅": avg_return,
                        }
                        result_dicts.append(result_dict)
                # 将结果持久化
                result_df = pd.DataFrame(result_dicts)
                for n in range(1, day):
                    cols_to_shift = [f"未来{n}日上涨概率",
                                     f"未来{n}日上涨次数", f"未来{n}日平均涨跌幅"]
                    result_df[cols_to_shift] = result_df[cols_to_shift].shift(
                        -a*(n-1))
                # result_df = result_df.dropna()  # 删除含有空值的行
                path = os.path.join(os.path.abspath("."), "资产单指标平均收益分布")
                if not os.path.exists(path):
                    os.makedirs(path)
                result_df.round(decimals=6).to_csv(
                    f"{path}/{name}{mubiao}持有{n}日{str(watchtime)}平均收益分布.csv", index=False)
                print(name, "已完成")
            except Exception as e:
                print(f"发生bug: {e}")