247 lines
9.4 KiB
Python
247 lines
9.4 KiB
Python
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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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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) -> None:
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self.TradeCode = TradeCode
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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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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
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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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Donchian['lower'] = 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 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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# 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
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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 not mysql_database.check_db_table(f"{self.TradeCode}") or 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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if mysql_database.check_db_table(f"{self.TradeCode}") and last_update < threshold_date:
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mysql_database.delete_table(f"{self.TradeCode}")
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self.GetRecentData()
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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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# 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_up = self.calculate_donchian_channel_up(20)
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self.Donchian_down = 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_up, self.Donchian_down], axis=1)
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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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# 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 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):
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pass
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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_zh_a_spot_df = ak.stock_zh_a_spot_em()
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stock_zh_a_spot_df = stock_zh_a_spot_df.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['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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if __name__ == '__main__':
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# t = TurtleTrading('513300')
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# t.get_ready(100)
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a = TurtleTrading_OnTime()
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a.get_stocks_data()
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# # 全是股票
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# stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
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# # stock_zh_a_spot_df.to_csv("stock_zh_a_spot.txt", sep="\t", index=False, encoding="utf-8")
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# stock_zh_a_spot_df = stock_zh_a_spot_df.dropna(subset=['最新价'])
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# print(stock_zh_a_spot_df)
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# # 全是基金
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# etf_data = ak.fund_etf_spot_em()
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# etf_data = etf_data.dropna(subset=['最新价'])
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# etf_data.to_csv("fund_etf_spot.txt", sep="\t", index=False, encoding="utf-8")
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# print(etf_data)
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