680 lines
27 KiB
Python
680 lines
27 KiB
Python
import numpy as np
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import math
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import akshare as ak
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import os
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from datetime import datetime, timedelta, date
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import pandas as pd
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import mplfinance as mpf
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import sqlite3
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import stock_database
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import mysql_database
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from EmailTest import send_email, parse_return_email
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from dataclasses import dataclass
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import time
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@dataclass
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class BuyState:
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trigger_time: float # 触发次数
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buy_price: float # 买入价格
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add_price: float # 加仓价格
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stop_price: float # 止损价格
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shares: int # 买入股数
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atr: int # ATR
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available_cash: float # 可用资金
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@dataclass
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class TradeLog:
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data: str # 时间
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type: str # 操作类型
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buy_price: float # 买入价格
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shares: int # 买入股数
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cost: float # 成本
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atr: int # ATR
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available_cash: float # 可用资金
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all_shares: float # 总股数
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all_cost: float # 总成本
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Net_value: float # 净值
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Net_return: float # 净收益
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def calc_sma_atr_pd(kdf,period):
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"""计算TR与ATR
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Args:
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kdf (_type_): 历史数据
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period (_type_): ATR周期
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Returns:
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_type_: 返回kdf，增加TR与ATR列
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"""
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kdf['最高'] = kdf['最高'].astype(float)
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kdf['最低'] = kdf['最低'].astype(float)
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kdf['收盘'] = kdf['收盘'].astype(float)
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kdf['HL'] = kdf['最高'] - kdf['最低']
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kdf['HC'] = np.abs(kdf['最高'] - kdf['收盘'].shift(1))
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kdf['LC'] = np.abs(kdf['最低'] - kdf['收盘'].shift(1))
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kdf['TR'] = np.round(kdf[['HL','HC','LC']].max(axis=1), 3)
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# ranges = pd.concat([high_low, high_close, low_close], axis=1)
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# true_range = np.max(ranges, axis=1)
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kdf['ATR'] = np.round(kdf['TR'].rolling(period).mean(), 3)
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return kdf.drop(['HL','HC','LC'], axis = 1)
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class TurtleTrading(object):
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"""对象范围较小，对某一个标的创建一个海龟，如513300，
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计算ATR、唐奇安通道线
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基础数据
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Args:
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object (_type_): _description_
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"""
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def __init__(self, TradeCode, type, riskcoe, Capital, cash) -> None:
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self.TradeCode = TradeCode
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self.type = type
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self.riskcoe = riskcoe
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self.Capital = Capital
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self.cash = cash
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self.TrigerTime = 0
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self.BuyStates = list[BuyState]
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self.tradeslog = list[TradeLog] # 交易记录
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def GetRecentData(self):
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"""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
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Returns:
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_type_: _description_
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"""
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Today = datetime.today()
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# print(Today)
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formatted_date = Today.strftime("%Y%m%d")
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two_years_ago = (date.today() - timedelta(days=365*2)).strftime("%Y%m%d")
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# print(formatted_date)
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Code = f"{self.TradeCode}"
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CurrentData = ak.fund_etf_hist_em(symbol=Code, period="daily", start_date=two_years_ago, end_date=formatted_date, adjust="")
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# 将日期列转换为datetime
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CurrentData = pd.DataFrame(CurrentData)
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CurrentData['日期'] = pd.to_datetime(CurrentData['日期'])
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# print(type(CurrentData['日期'].iloc[0]))
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CurrentData.set_index('日期', inplace=True)
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# CurrentData.reset_index(inplace=True)
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# print(type(CurrentData['日期'].iloc[0]))
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# create table
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# stock_database.create_table(Code)
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# stock_database.insert_data(Code, CurrentData)
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# mysql_database.insert_db(CurrentData, Code, True, "'日期'")
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self.CurrentData = CurrentData
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# return self.CurrentData
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def CalATR(self, data, ATRday):
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"""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
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Args:
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ATRday: 多少日ATR
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SaveOrNot (_type_): 是否保存.csv数据
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"""
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self.CurrentData = calc_sma_atr_pd(data, ATRday)
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self.N = self.CurrentData['ATR']
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# return self.N
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def ReadExistData(self, data):
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"""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
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Args:
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data (_type_): 本地csv名称
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"""
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self.CurrentData = pd.read_csv(data)
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def DrawKLine(self, days):
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"""画出k线图看看,画出最近days天的K线图
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"""
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# 日期部分
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# dates = pd.to_datetime(self.CurrentData['日期'][-days:])
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# # Klinedf['Data'] = pd.to_datetime(self.CurrentData['日期'])
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Klinedf = pd.DataFrame()
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# Klinedf.set_index = Klinedf['Data']
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# 其他数据
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Klinedf['Date'] = self.CurrentData['日期'][-days:]
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Klinedf['Open'] = self.CurrentData['开盘'][-days:].astype(float)
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Klinedf['High'] = self.CurrentData['最高'][-days:].astype(float)
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Klinedf['Low'] = self.CurrentData['最低'][-days:].astype(float)
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Klinedf['Close'] = self.CurrentData['收盘'][-days:].astype(float)
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Klinedf['Volume'] = self.CurrentData['成交量'][-days:].astype(float)
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Klinedf.set_index(pd.to_datetime(Klinedf['Date']), inplace=True)
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# 画图
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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")
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def calculate_donchian_channel_up(self, n):
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"""
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计算n日唐奇安上通道
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参数:
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self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，包含"High"
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n (int): 时间周期
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返回:self.Donchian
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DataFrame: 唐奇安通道的DataFrame，包含"Upper"
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"""
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Donchian = pd.DataFrame() # 创建一个空的DataFrame用于存储唐奇安通道数据
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# 计算最高价和最低价的N日移动平均线
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name = 'Donchian_' + str(n) + '_upper'
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Donchian[name] = 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
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# # 计算中间线
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# 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
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return Donchian # 返回包含唐奇安上通道的DataFrame
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def calculate_donchian_channel_down(self, n):
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"""
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计算n日唐奇安上通道
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参数:
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self.CurrentData (DataFrame): 包含价格数据的Pandas DataFrame，包含"High"
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n (int): 时间周期
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返回:self.Donchian
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DataFrame: 唐奇安通道的DataFrame，包含"Upper"
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"""
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Donchian = pd.DataFrame()
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# 计算最高价和最低价的N日移动平均线
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name = 'Donchian_' + str(n) + '_lower'
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Donchian[name] = self.CurrentData['最低'].rolling(n).min()
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# # 计算中间线
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# Donchian['Middle'] = (self.Donchian['Upper'] + self.Donchian['Lower']) / 2
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return Donchian
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def calc_atr_donchian_short(self):
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"""计算ATR、短期唐奇安通道
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"""
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# 计算ATR
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self.CalATR(self.CurrentData, 20)
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# 计算唐奇安通道
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self.Donchian_20_ups = self.calculate_donchian_channel_up(20)
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self.Donchian_50_ups = self.calculate_donchian_channel_up(50)
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self.Donchian_downs = self.calculate_donchian_channel_down(10)
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# 画图
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# self.DrawKLine(days)
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# 把self.N, self.Donchian_up, self.Donchian_down, 添加到self.CurrentData后面，保存到mysql数据库
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self.CurrentData = pd.concat([self.CurrentData, self.Donchian_20_ups, self.Donchian_50_ups, self.Donchian_downs], axis=1)
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def get_ready(self, days):
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"""创建一个turtle对象，获取数据，计算ATR，计算唐奇安通道
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Args:
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days (_type_): _description_
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n (_type_): _description_
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"""
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# if 不存在database
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if not mysql_database.check_db_table(f"{self.TradeCode}"):
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self.GetRecentData()
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self.calc_atr_donchian_short()
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Code = f"{self.TradeCode}"
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mysql_database.insert_db(self.CurrentData, Code, True, "日期")
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else:
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# 检查数据库最后一条的时间距离今天是否两天以上
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current_date = date.today()
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threshold_date = current_date - timedelta(days=2)
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last_update = mysql_database.check_db_table_last_date(f"{self.TradeCode}")
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if last_update < threshold_date:
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# 如果不存在，则从akshare获取数据并保存到mysql数据库
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mysql_database.delete_table(f"{self.TradeCode}")
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self.GetRecentData()
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self.calc_atr_donchian_short()
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Code = f"{self.TradeCode}"
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mysql_database.insert_db(self.CurrentData, Code, True, "日期")
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else:
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# 如果存在，则从mysql数据库中读取数据
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self.CurrentData = mysql_database.fetch_all_data(f"{self.TradeCode}")
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def CalPositionSize(self):
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"""根据风险系数、ATR，计算仓位大小, 存于self.IntPositionSize
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"""
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PositionSize = self.riskcoe * self.Capital /(self.N) # 默认用股票形式了 100
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self.IntPositionSize = int(PositionSize // 100) * 100
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def system1EnterNormal(self, PriceNow, TempDonchian20Upper, BreakOutLog):
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# 没有持仓且价格向上突破---此时包含两种情形：1 对某标的首次使用系统，2 已发生过突破，此时上次突破天然是失败的
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if self.TrigerTime == 0 and PriceNow > TempDonchian20Upper:
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# 买入
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return True
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elif PriceNow > TempDonchian20Upper:#todo !=0不会满足条件 先跳过
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self.system1BreakoutValid(PriceNow)
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if BreakOutLog[-1][5] == 'Lose': # TT!= 0且突破且上一次突破unseccessful
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return True
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else:
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return False
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else:
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return False
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def system1EnterSafe(self, PriceNow, TempDonchian55Upper):
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if PriceNow > TempDonchian55Upper[-1]: # 保底的55日突破
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return True
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else:
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return False
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def system1BreakoutValid(self, priceNow):
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"""判断前一次突破是否成功，是log[-1][5]写入“win”，否则写入“Lose”
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"""
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if priceNow < self.BreakOutLog[-1][3]:
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self.BreakOutLog[-1][5] = 'Lose'
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else:
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self.BreakOutLog[-1][5] = 'None'
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# 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
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def system_1_Out(self, PriceNow, TempDonchian10Lower):
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# 退出:低于20日最低价（多头方向）,空头以突破20日最高价为止损价格--有持仓且价格向下突破
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if self.TrigerTime != 0 and PriceNow < TempDonchian10Lower:
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# 退出
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return True
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else:
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return False
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def add(self, PriceNow):
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"""加仓
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"""
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if self.TrigerTime < 4 and PriceNow > self.BuyStates[self.TrigerTime - 1][2]:#todo BuyStates是空的
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# 买入
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return True
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else:
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return False
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def system_1_stop(self, PriceNow):
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"""止损判断：如果当前价格<上一次买入后的止损价格则止损
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"""
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if PriceNow < self.BuyStates[self.TrigerTime - 1][3]:
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# 买入
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return True
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else:
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return False
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def day_end(self):
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pass
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class TurtleTrading_OnTime(object):
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''' 实时监测主程序，可以处理多个turtle
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1、获取实时大盘数据
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2、根据turtles的代码，比较是否触发条件
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3、实时监测主流程
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'''
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def __init__(self, turtle: TurtleTrading, user_email):
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self.turtle = turtle
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self.user_email = user_email
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def get_stocks_data(self):
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"""获取实时股票、基金数据，不保存
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"""
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stock_data = ak.stock_zh_a_spot_em()
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stock_data = stock_data.dropna(subset=['最新价'])
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# # print(stock_zh_a_spot_df)
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# # stock_zh_a_spot_df第一列加上时间，精确到分钟
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# stock_zh_a_spot_df['时间'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# mysql_database.insert_db(stock_zh_a_spot_df, "stock_price", True, "代码")
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etf_data = ak.fund_etf_spot_em()
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# etf_data = ak.fund_etf_spot_ths()
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# etf_data = etf_data.dropna(subset=['当前-单位净值'])
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etf_data = etf_data.dropna(subset=['最新价'])
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# etf_data['时间'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# mysql_database.insert_db(etf_data, "etf_price", True, "代码")
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return stock_data, etf_data
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def Buy_stock(self, price_now):
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# 发送邮件 代码self.turtle.TradeCode, 建议买入价格price_now，买入份额self.turtle.IntPositionSize
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if self.turtle.TrigerTime == 0: # 第一次买入
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subject = "买入"
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body = f"{self.turtle.TradeCode}，价格{price_now}，份额{self.turtle.IntPositionSize} \n "
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body += "回复：实际买入价格-买入份额-手续费"
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send_email(subject, body, self.user_email)
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send_email_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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#每隔1分钟检测回信，解析邮件。
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parsed_email_flag = False
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while not parsed_email_flag:
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time.sleep(60) # 每次尝试前等待 60 秒
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parse_states, buy_price, buy_share, fee = parse_return_email(
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self.user_email, send_email_time
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)
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if parse_states:
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parsed_email_flag = True
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break
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# 成功买入
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self.turtle.TrigerTime += 1
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# 记录self.turtle.BuyStates
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add_price = buy_price + 1/2 * self.turtle.N
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stop_price = buy_price - 2 * self.turtle.N
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cost = buy_price * buy_share - fee
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available_cash = self.turtle.Capital - cost
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buy_this_time = BuyState(self.turtle.TrigerTime,
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buy_price,
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add_price,
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stop_price,
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buy_share,
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self.turtle.N,
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available_cash)
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self.turtle.BuyStates.append(buy_this_time)
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today = datetime.now().strftime("%Y-%m-%d")
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log_this_time = TradeLog(today,
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"买入",
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buy_price,
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buy_share,
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cost,
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self.turtle.N,
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available_cash,
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all_shares=buy_share,
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all_cost=cost,
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Net_value=buy_price * buy_share,
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Net_return=0)
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self.turtle.tradeslog.append(log_this_time)
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else:
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# 加仓
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subject = "加仓"
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body = f"{self.turtle.TradeCode}，价格{price_now}，份额{self.turtle.IntPositionSize} \n "
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body += "回复：实际买入价格-买入份额-手续费"
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send_email(subject, body, self.user_email)
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send_email_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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#每隔1分钟检测回信，解析邮件。
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parsed_email_flag = False
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while not parsed_email_flag:
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time.sleep(60) # 每次尝试前等待 60 秒
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parse_states, buy_price, buy_share, fee = parse_return_email(
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self.user_email, send_email_time
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)
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if parse_states:
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parsed_email_flag = True
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break
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# 成功买入
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self.turtle.TrigerTime += 1
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# 记录self.turtle.BuyStates
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add_price = buy_price + 1/2 * self.turtle.N
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stop_price = buy_price - 2 * self.turtle.N
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cost = buy_price * buy_share - fee
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available_cash = self.turtle.BuyStates[-1].available_cash - cost
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all_shares = buy_share + self.turtle.BuyStates[-1].all_shares
|
||
all_cost = cost + self.turtle.BuyStates[-1].all_cost
|
||
net_value = buy_price * all_shares
|
||
net_return = net_value - all_cost
|
||
buy_this_time = BuyState(self.turtle.TrigerTime,
|
||
buy_price,
|
||
add_price,
|
||
stop_price,
|
||
buy_share,
|
||
self.turtle.N,
|
||
available_cash)
|
||
|
||
self.turtle.BuyStates.append(buy_this_time)
|
||
|
||
today = datetime.now().strftime("%Y-%m-%d")
|
||
log_this_time = TradeLog(today,
|
||
"加仓",
|
||
buy_price,
|
||
buy_share,
|
||
cost,
|
||
self.turtle.N,
|
||
available_cash,
|
||
all_shares,
|
||
all_cost,
|
||
net_value,
|
||
net_return)
|
||
self.turtle.tradeslog.append(log_this_time)
|
||
pass
|
||
|
||
|
||
def stop_sale_stock(self, price_now):
|
||
"""止损卖出
|
||
|
||
Args:
|
||
price_now (_type_): 现价
|
||
"""
|
||
# 发送邮件 代码self.turtle.TradeCode, 建议卖出价格price_now，卖出份额self.turtle.IntPositionSize
|
||
subject = "止损卖出"
|
||
body = f"{self.turtle.TradeCode}，价格{price_now}，份额{self.turtle.IntPositionSize} \n "
|
||
body += "回复：实际卖出价格-卖出份额-手续费"
|
||
send_email(subject, body, self.user_email)
|
||
send_email_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
# 每隔1分钟检测回信，解析邮件。
|
||
|
||
parsed_email_flag = False
|
||
while not parsed_email_flag:
|
||
time.sleep(60) # 每次尝试前等待 60 秒
|
||
parse_states, sale_price, sale_share, fee = parse_return_email(
|
||
self.user_email, send_email_time
|
||
)
|
||
|
||
if parse_states:
|
||
parsed_email_flag = True
|
||
break
|
||
|
||
|
||
# 成功卖出
|
||
self.turtle.TrigerTime = 0
|
||
# 记录self.turtle.BuyStates
|
||
available_cash = self.turtle.BuyStates[-1].available_cash + sale_price * sale_share - fee
|
||
self.turtle.BuyStates = []
|
||
|
||
sale_this_time = TradeLog(datetime.now().strftime("%Y-%m-%d"),
|
||
"止损",
|
||
sale_price,
|
||
sale_share,
|
||
sale_price * sale_share - fee,
|
||
self.turtle.N,
|
||
available_cash,
|
||
all_shares=0,
|
||
all_cost=0,
|
||
Net_value=sale_price * sale_share,
|
||
Net_return=abs(self.turtle.Capital - available_cash))
|
||
self.turtle.tradeslog.append(sale_this_time)
|
||
|
||
def out_sale_stock(self, price_now):
|
||
"""止盈卖出
|
||
|
||
Args:
|
||
price_now (_type_): 现价
|
||
"""
|
||
|
||
# 发送邮件 代码self.turtle.TradeCode, 建议卖出价格price_now，卖出份额self.turtle.IntPositionSize
|
||
subject = "止盈卖出"
|
||
body = f"{self.turtle.TradeCode}，价格{price_now}，份额{self.turtle.IntPositionSize} \n "
|
||
body += "回复：实际卖出价格-卖出份额-手续费"
|
||
send_email(subject, body, self.user_email)
|
||
send_email_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
# 每隔1分钟检测回信，解析邮件。
|
||
|
||
parsed_email_flag = False
|
||
while not parsed_email_flag:
|
||
time.sleep(60) # 每次尝试前等待 60 秒
|
||
parse_states, sale_price, sale_share, fee = parse_return_email(
|
||
self.user_email, send_email_time
|
||
)
|
||
|
||
if parse_states:
|
||
parsed_email_flag = True
|
||
break
|
||
|
||
# 成功卖出
|
||
self.turtle.TrigerTime = 0
|
||
# 记录self.turtle.BuyStates
|
||
available_cash = self.turtle.BuyStates[-1].available_cash + sale_price * sale_share - fee
|
||
self.turtle.BuyStates = []
|
||
|
||
sale_this_time = TradeLog(datetime.now().strftime("%Y-%m-%d"),
|
||
"止盈",
|
||
sale_price,
|
||
sale_share,
|
||
sale_price * sale_share - fee,
|
||
self.turtle.N,
|
||
available_cash,
|
||
all_shares=0,
|
||
all_cost=0,
|
||
Net_value=sale_price * sale_share,
|
||
Net_return=abs(self.turtle.Capital - available_cash))
|
||
self.turtle.tradeslog.append(sale_this_time)
|
||
|
||
def run_short_trading_loop(self):
|
||
while True:
|
||
# 获取当前时间
|
||
now = datetime.now().time()
|
||
|
||
# 判断当前时间是否在交易时段内（9:30-11:30 或 13:00-15:00）
|
||
is_trading_time = (
|
||
(now.hour == 9 and now.minute >= 30) or
|
||
(now.hour == 10 and 0 <= now.minute <= 59) or
|
||
(now.hour == 11 and now.minute <= 30) or
|
||
(now.hour == 13 and 0 <= now.minute <= 59) or
|
||
(now.hour == 14 and 0 <= now.minute <= 59) or
|
||
(now.hour == 15 and now.minute <= 0)
|
||
)
|
||
|
||
# if not is_trading_time:
|
||
# # 非交易时间，等待 1 分钟后继续循环
|
||
# time.sleep(60)
|
||
# continue
|
||
|
||
# 获取股票和ETF数据
|
||
stock_data, etf_data = self.get_stocks_data()
|
||
|
||
# 根据类型获取当前价格
|
||
if self.turtle.type == "stock":
|
||
self.turtle.PriceNow = float(stock_data.loc[etf_data['代码'] == self.turtle.TradeCode, '最新价'].values[0])
|
||
|
||
elif self.turtle.type == "etf":
|
||
# self.turtle.PriceNow = float(etf_data.loc[etf_data['基金代码'] == self.turtle.TradeCode, '当前-单位净值'].values[0])
|
||
self.turtle.PriceNow = float(etf_data.loc[etf_data['代码'] == self.turtle.TradeCode, '最新价'].values[0])
|
||
|
||
# 判断当前仓位状态并执行相应操作
|
||
if self.turtle.TrigerTime == 0:
|
||
# 空仓状态
|
||
if self.turtle.system1EnterNormal(
|
||
self.turtle.PriceNow,
|
||
self.turtle.Donchian_20_up,
|
||
self.turtle.BreakOutLog
|
||
):
|
||
self.Buy_stock(self.turtle.PriceNow)
|
||
elif self.turtle.system1EnterSafe(
|
||
self.turtle.PriceNow,
|
||
self.turtle.Donchian_50_up
|
||
):
|
||
self.Buy_stock(self.turtle.PriceNow)
|
||
|
||
elif 1 <= self.turtle.TrigerTime <= 3:
|
||
# 加仓状态
|
||
if self.turtle.system1EnterNormal(
|
||
self.turtle.PriceNow,
|
||
self.turtle.Donchian_20_up,
|
||
self.turtle.BreakOutLog
|
||
):
|
||
self.Buy_stock(self.turtle.PriceNow)
|
||
elif self.turtle.system1EnterSafe(
|
||
self.turtle.PriceNow,
|
||
self.turtle.Donchian_50_up
|
||
):
|
||
self.Buy_stock(self.turtle.PriceNow)
|
||
elif self.turtle.add(self.turtle.PriceNow):
|
||
self.Buy_stock(self.turtle.PriceNow)
|
||
elif self.turtle.system_1_stop(self.turtle.PriceNow):
|
||
self.stop_sale_stock(self.turtle.PriceNow)
|
||
elif self.turtle.system_1_Out(
|
||
self.turtle.PriceNow,
|
||
self.turtle.Donchian_10_down
|
||
):
|
||
self.out_sale_stock(self.turtle.PriceNow)
|
||
|
||
elif self.turtle.TrigerTime == 4:
|
||
# 满仓状态
|
||
if self.turtle.system_1_stop(self.turtle.PriceNow):
|
||
self.stop_sale_stock(self.turtle.PriceNow)
|
||
elif self.turtle.system_1_Out(
|
||
self.turtle.PriceNow,
|
||
self.turtle.Donchian_10_down
|
||
):
|
||
self.out_sale_stock(self.turtle.PriceNow)
|
||
|
||
# 等待 1 分钟后下一次循环
|
||
time.sleep(60)
|
||
|
||
def Start_short_system(self):
|
||
"""启动short系统
|
||
"""
|
||
|
||
# ------------------准备阶段--------------------
|
||
# 获取数据或读取数据 -- 计算ATR Donchian 20 50 up, 20 down
|
||
self.turtle.get_ready(100)
|
||
self.turtle.N = float(self.turtle.CurrentData['ATR'].iloc[-1])
|
||
self.turtle.Donchian_20_up = float(self.turtle.CurrentData['Donchian_20_upper'].iloc[-1])
|
||
self.turtle.Donchian_50_up = float(self.turtle.CurrentData['Donchian_50_upper'].iloc[-1])
|
||
self.turtle.Donchian_10_down = float(self.turtle.CurrentData['Donchian_10_lower'].iloc[-1])
|
||
self.turtle.CalPositionSize()
|
||
# ------------------实时监测阶段--------------------
|
||
# 9:00 1、判断是否是新的一周，是则重新计算Position Size
|
||
# 判断是否是新的一周
|
||
if datetime.now().weekday() == 0:
|
||
self.turtle.CalPositionSize()
|
||
|
||
# 每分钟获取一次数据，判断是否触发条件 9:30-11:30 13:00-15:00
|
||
self.run_short_trading_loop()
|
||
# ------------------结束阶段--------------------
|
||
# 数据库更新当天数据，增加ATR、donchian数据
|
||
# 直接做个新表
|
||
mysql_database.delete_table(f"{self.turtle.TradeCode}")
|
||
self.turtle.get_ready(100)
|
||
time.sleep(16.5*600)
|
||
|
||
if __name__ == '__main__':
|
||
user_email = "guoyize2209@163.com"
|
||
t = TurtleTrading('513870', "etf", 0.0025, 100000, 200000)
|
||
# t.get_ready(100)
|
||
|
||
a = TurtleTrading_OnTime(t, user_email)
|
||
a.Start_short_system()
|
||
|
||
# # 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)
|
||
|