247 lines
9.4 KiB
Python
247 lines
9.4 KiB
Python
import numpy as np
|
||
import math
|
||
import akshare as ak
|
||
import os
|
||
from datetime import datetime, timedelta, date
|
||
import pandas as pd
|
||
import mplfinance as mpf
|
||
import sqlite3
|
||
import stock_database
|
||
import mysql_database
|
||
|
||
def calc_sma_atr_pd(kdf,period):
|
||
"""计算TR与ATR
|
||
|
||
Args:
|
||
kdf (_type_): 历史数据
|
||
period (_type_): ATR周期
|
||
|
||
Returns:
|
||
_type_: 返回kdf，增加TR与ATR列
|
||
"""
|
||
kdf['最高'] = kdf['最高'].astype(float)
|
||
kdf['最低'] = kdf['最低'].astype(float)
|
||
kdf['收盘'] = kdf['收盘'].astype(float)
|
||
kdf['HL'] = kdf['最高'] - kdf['最低']
|
||
kdf['HC'] = np.abs(kdf['最高'] - kdf['收盘'].shift(1))
|
||
kdf['LC'] = np.abs(kdf['最低'] - kdf['收盘'].shift(1))
|
||
kdf['TR'] = np.round(kdf[['HL','HC','LC']].max(axis=1), 3)
|
||
# ranges = pd.concat([high_low, high_close, low_close], axis=1)
|
||
# true_range = np.max(ranges, axis=1)
|
||
kdf['ATR'] = np.round(kdf['TR'].rolling(period).mean(), 3)
|
||
|
||
return kdf.drop(['HL','HC','LC'], axis = 1)
|
||
|
||
class TurtleTrading(object):
|
||
"""对象范围较小，对某一个标的创建一个海龟，如513300，
|
||
计算ATR、唐奇安通道线
|
||
基础数据
|
||
Args:
|
||
object (_type_): _description_
|
||
"""
|
||
def __init__(self, TradeCode) -> None:
|
||
self.TradeCode = TradeCode
|
||
|
||
def GetRecentData(self):
|
||
"""获取某个标的的最近数据，从两年前到今天, 计算后的数据保存在self.CurrentData
|
||
|
||
Returns:
|
||
_type_: _description_
|
||
"""
|
||
Today = datetime.today()
|
||
# print(Today)
|
||
formatted_date = Today.strftime("%Y%m%d")
|
||
two_years_ago = (date.today() - timedelta(days=365*2)).strftime("%Y%m%d")
|
||
# print(formatted_date)
|
||
Code = f"{self.TradeCode}"
|
||
CurrentData = ak.fund_etf_hist_em(symbol=Code, period="daily", start_date=two_years_ago, end_date=formatted_date, adjust="")
|
||
|
||
# 将日期列转换为datetime
|
||
CurrentData = pd.DataFrame(CurrentData)
|
||
CurrentData['日期'] = pd.to_datetime(CurrentData['日期'])
|
||
# print(type(CurrentData['日期'].iloc[0]))
|
||
|
||
CurrentData.set_index('日期', inplace=True)
|
||
# CurrentData.reset_index(inplace=True)
|
||
# print(type(CurrentData['日期'].iloc[0]))
|
||
# create table
|
||
# stock_database.create_table(Code)
|
||
# stock_database.insert_data(Code, CurrentData)
|
||
# mysql_database.insert_db(CurrentData, Code, True, "'日期'")
|
||
self.CurrentData = CurrentData
|
||
# return self.CurrentData
|
||
|
||
|
||
def CalATR(self, data, ATRday):
|
||
"""计算某个标的的ATR，从上市日到今天, 计算后的数据保存在self.CurrentData
|
||
|
||
Args:
|
||
ATRday: 多少日ATR
|
||
SaveOrNot (_type_): 是否保存.csv数据
|
||
"""
|
||
|
||
self.CurrentData = calc_sma_atr_pd(data, ATRday)
|
||
self.N = self.CurrentData['ATR']
|
||
|
||
|
||
# return self.N
|
||
|
||
|
||
def ReadExistData(self, data):
|
||
"""除了通过发请求获取数据，也可以读本地的数据库，赋值给self.CurrentData
|
||
|
||
Args:
|
||
data (_type_): 本地csv名称
|
||
"""
|
||
self.CurrentData = pd.read_csv(data)
|
||
|
||
def DrawKLine(self, days):
|
||
"""画出k线图看看,画出最近days天的K线图
|
||
"""
|
||
|
||
# 日期部分
|
||
|
||
# dates = pd.to_datetime(self.CurrentData['日期'][-days:])
|
||
# # Klinedf['Data'] = pd.to_datetime(self.CurrentData['日期'])
|
||
Klinedf = pd.DataFrame()
|
||
# Klinedf.set_index = Klinedf['Data']
|
||
|
||
# 其他数据
|
||
Klinedf['Date'] = self.CurrentData['日期'][-days:]
|
||
Klinedf['Open'] = self.CurrentData['开盘'][-days:].astype(float)
|
||
Klinedf['High'] = self.CurrentData['最高'][-days:].astype(float)
|
||
Klinedf['Low'] = self.CurrentData['最低'][-days:].astype(float)
|
||
Klinedf['Close'] = self.CurrentData['收盘'][-days:].astype(float)
|
||
Klinedf['Volume'] = self.CurrentData['成交量'][-days:].astype(float)
|
||
|
||
Klinedf.set_index(pd.to_datetime(Klinedf['Date']), inplace=True)
|
||
# 画图
|
||
mpf.plot(Klinedf, type='candle', style='yahoo', volume=False, mav=(5,), addplot=[mpf.make_addplot(self.Donchian_up['Upper'][-days:]), mpf.make_addplot(self.Donchian_down['lower'][-days:])], title=f"{self.TradeCode} K线图")
|
||
|
||
def calculate_donchian_channel_up(self, n):
|
||
"""
|
||
计算n日唐奇安上通道
|
||
|
||
参数:
|
||
self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，包含"High"
|
||
n (int): 时间周期
|
||
|
||
返回:self.Donchian
|
||
DataFrame: 唐奇安通道的DataFrame，包含"Upper"
|
||
"""
|
||
Donchian = pd.DataFrame() # 创建一个空的DataFrame用于存储唐奇安通道数据
|
||
# 计算最高价和最低价的N日移动平均线
|
||
Donchian['Upper'] = self.CurrentData['最高'].rolling(n).max() # 使用rolling函数计算n日最高价的移动最大值
|
||
|
||
# # 计算中间线
|
||
# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2 # 计算上通道和下通道的中间线，但此行代码被注释掉了
|
||
|
||
return Donchian # 返回包含唐奇安上通道的DataFrame
|
||
|
||
def calculate_donchian_channel_down(self, n):
|
||
"""
|
||
计算n日唐奇安上通道
|
||
|
||
参数:
|
||
self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，包含"High"
|
||
n (int): 时间周期
|
||
|
||
返回:self.Donchian
|
||
DataFrame: 唐奇安通道的DataFrame，包含"Upper"
|
||
"""
|
||
Donchian = pd.DataFrame()
|
||
# 计算最高价和最低价的N日移动平均线
|
||
Donchian['lower'] = self.CurrentData['最低'].rolling(n).min()
|
||
|
||
# # 计算中间线
|
||
# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
|
||
|
||
return Donchian
|
||
|
||
def get_ready(self, days):
|
||
"""创建一个turtle对象，获取数据，计算ATR，计算唐奇安通道
|
||
|
||
Args:
|
||
days (_type_): _description_
|
||
n (_type_): _description_
|
||
"""
|
||
# 检查mysql数据库中是否存在该股票的数据 或者数据库最后一条的时间距离今天是否两天以上
|
||
current_date = date.today()
|
||
threshold_date = current_date - timedelta(days=2)
|
||
last_update = mysql_database.check_db_table_last_date(f"{self.TradeCode}")
|
||
|
||
if not mysql_database.check_db_table(f"{self.TradeCode}") or last_update < threshold_date:
|
||
# 如果不存在，则从akshare获取数据并保存到mysql数据库
|
||
if mysql_database.check_db_table(f"{self.TradeCode}") and last_update < threshold_date:
|
||
mysql_database.delete_table(f"{self.TradeCode}")
|
||
|
||
self.GetRecentData()
|
||
else:
|
||
# 如果存在，则从mysql数据库中读取数据
|
||
self.CurrentData = mysql_database.fetch_all_data(f"{self.TradeCode}")
|
||
|
||
|
||
# 计算ATR
|
||
self.CalATR(self.CurrentData, 20)
|
||
# 计算唐奇安通道
|
||
self.Donchian_up = self.calculate_donchian_channel_up(20)
|
||
self.Donchian_down = self.calculate_donchian_channel_down(10)
|
||
# 画图
|
||
# self.DrawKLine(days)
|
||
|
||
# 把self.N, self.Donchian_up, self.Donchian_down, 添加到self.CurrentData后面，保存到mysql数据库
|
||
self.CurrentData = pd.concat([self.CurrentData, self.Donchian_up, self.Donchian_down], axis=1)
|
||
|
||
Code = f"{self.TradeCode}"
|
||
mysql_database.insert_db(self.CurrentData, Code, True, "日期")
|
||
|
||
# 一天结束，计算ATR，计算唐奇安通道，追加到已有的mysql数据库中
|
||
def day_end(self):
|
||
pass
|
||
|
||
class TurtleTrading_OnTime(object):
|
||
''' 实时监测主程序，可以处理多个turtle
|
||
|
||
1、获取实时大盘数据
|
||
2、根据turtles的代码，比较是否触发条件
|
||
3、实时监测主流程
|
||
'''
|
||
|
||
def __init__(self):
|
||
pass
|
||
def get_stocks_data(self):
|
||
"""获取实时股票、基金数据，不保存
|
||
"""
|
||
stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
|
||
stock_zh_a_spot_df = stock_zh_a_spot_df.dropna(subset=['最新价'])
|
||
# # print(stock_zh_a_spot_df)
|
||
# # stock_zh_a_spot_df第一列加上时间，精确到分钟
|
||
# stock_zh_a_spot_df['时间'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
# mysql_database.insert_db(stock_zh_a_spot_df, "stock_price", True, "代码")
|
||
|
||
# etf_data = ak.fund_etf_spot_em()
|
||
etf_data = ak.fund_etf_spot_ths()
|
||
etf_data = etf_data.dropna(subset=['当前-单位净值'])
|
||
# etf_data['时间'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
# mysql_database.insert_db(etf_data, "etf_price", True, "代码")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# t = TurtleTrading('513300')
|
||
# t.get_ready(100)
|
||
|
||
a = TurtleTrading_OnTime()
|
||
a.get_stocks_data()
|
||
|
||
# # 全是股票
|
||
# stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
|
||
# # stock_zh_a_spot_df.to_csv("stock_zh_a_spot.txt", sep="\t", index=False, encoding="utf-8")
|
||
# stock_zh_a_spot_df = stock_zh_a_spot_df.dropna(subset=['最新价'])
|
||
# print(stock_zh_a_spot_df)
|
||
|
||
# # 全是基金
|
||
# etf_data = ak.fund_etf_spot_em()
|
||
# etf_data = etf_data.dropna(subset=['最新价'])
|
||
# etf_data.to_csv("fund_etf_spot.txt", sep="\t", index=False, encoding="utf-8")
|
||
# print(etf_data)
|
||
|