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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)

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代码 名称 最新价 涨跌额 涨跌幅 买入 卖出 昨收 今开 最高 最低 成交量 成交额
sh010107 21国债⑺ 100.01 0.0 0.0 0.0 0.0 100.01 0.0 0.0 0.0 0 0
sh010303 03国债⑶ 100.01 0.0 0.0 0.0 0.0 100.01 0.0 0.0 0.0 0 0
sh010504 05国债⑷ 100.5 0.0 0.0 99.896 100.987 100.5 0.0 0.0 0.0 0 0
sh010512 05国债⑿ 100.05 0.0 0.0 0.0 0.0 100.05 0.0 0.0 0.0 0 0
sh010609 06国债⑼ 100.0 0.0 0.0 101.826 0.0 100.0 0.0 0.0 0.0 0 0
sh010619 06国债⒆ 100.05 0.0 0.0 0.0 0.0 100.05 0.0 0.0 0.0 0 0
sh010706 07国债06 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh010713 07国债13 100.0 0.0 0.0 107.088 0.0 100.0 0.0 0.0 0.0 0 0
sh018001 国开1301 100.02 0.0 0.0 0.0 0.0 100.02 0.0 0.0 0.0 0 0
sh018003 国开1401 115.79 -0.01 -0.009 115.619 115.79 115.8 115.789 115.79 115.789 24000 2778958
sh018006 国开1702 100.04 0.0 0.0 0.0 0.0 100.04 0.0 0.0 0.0 0 0
sh018007 国开1801 99.98 0.0 0.0 0.0 0.0 99.98 0.0 0.0 0.0 0 0
sh018008 国开1802 99.994 0.0 0.0 0.0 0.0 99.994 0.0 0.0 0.0 0 0
sh018009 国开1803 130.45 0.015 0.011 0.0 130.4 130.435 130.45 130.45 130.45 4000 521800
sh018010 国开1902 100.002 0.0 0.0 0.0 0.0 100.002 0.0 0.0 0.0 0 0
sh018011 国开2002 100.03 0.0 0.0 0.0 0.0 100.03 0.0 0.0 0.0 0 0
sh018012 国开2003 103.1 0.0 0.0 0.0 103.98 103.1 0.0 0.0 0.0 0 0
sh018013 国开2004 99.99 0.0 0.0 0.0 0.0 99.99 0.0 0.0 0.0 0 0
sh018014 国开2005 103.7 0.0 0.0 0.0 104.0 103.7 0.0 0.0 0.0 0 0
sh018015 国开2006 123.584 0.0 0.0 122.633 125.0 123.584 0.0 0.0 0.0 0 0
sh018016 国开19G1 100.39 0.0 0.0 0.0 0.0 100.39 0.0 0.0 0.0 0 0
sh018017 国开2007 101.05 0.0 0.0 0.0 103.0 101.05 0.0 0.0 0.0 0 0
sh018018 国开2101 101.16 0.01 0.01 101.16 101.4 101.15 101.16 101.16 101.16 1000 101160
sh018019 国开2102 104.227 0.0 0.0 0.0 0.0 104.227 0.0 0.0 0.0 0 0
sh018020 国开2302 101.508 -0.289 -0.284 100.75 102.282 101.797 101.508 101.508 101.507 21000 2131659
sh018021 国开2303 99.992 0.0 0.0 0.0 0.0 99.992 0.0 0.0 0.0 0 0
sh018022 国开2304 100.397 0.0 0.0 0.0 0.0 100.397 0.0 0.0 0.0 0 0
sh018061 进出1911 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh018062 进出1912 100.1 0.0 0.0 0.0 0.0 100.1 0.0 0.0 0.0 0 0
sh018063 进出2101 101.65 0.0 0.0 0.0 0.0 101.65 0.0 0.0 0.0 0 0
sh018064 进出2103 100.218 0.0 0.0 0.0 0.0 100.218 0.0 0.0 0.0 0 0
sh018065 进出2201 100.006 0.0 0.0 0.0 0.0 100.006 0.0 0.0 0.0 0 0
sh018081 农发1901 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh018082 农发1902 100.01 0.0 0.0 0.0 0.0 100.01 0.0 0.0 0.0 8280 828083
sh018083 农发2001 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh018084 农发2002 103.8 0.0 0.0 0.0 0.0 103.8 0.0 0.0 0.0 0 0
sh018085 农发2301 100.647 0.0 0.0 0.0 0.0 100.647 0.0 0.0 0.0 0 0
sh019003 10国债03 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019009 10国债09 98.8 0.0 0.0 0.0 0.0 98.8 0.0 0.0 0.0 0 0
sh019012 10国债12 100.25 0.0 0.0 0.0 0.0 100.25 0.0 0.0 0.0 0 0
sh019014 10国债14 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019018 10国债18 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019019 10国债19 100.7 0.0 0.0 0.0 0.0 100.7 0.0 0.0 0.0 0 0
sh019023 10国债23 109.5 0.0 0.0 0.0 0.0 109.5 0.0 0.0 0.0 0 0
sh019024 10国债24 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019026 10国债26 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019029 10国债29 99.61 0.0 0.0 0.0 0.0 99.61 0.0 0.0 0.0 0 0
sh019031 10国债31 98.2 0.0 0.0 0.0 0.0 98.2 0.0 0.0 0.0 0 0
sh019034 10国债34 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019037 10国债37 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019040 10国债40 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019041 10国债41 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019102 11国债02 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019105 11国债05 113.5 0.0 0.0 0.0 0.0 113.5 0.0 0.0 0.0 0 0
sh019108 11国债08 100.55 0.0 0.0 0.0 0.0 100.55 0.0 0.0 0.0 0 0
sh019110 11国债10 113.04 0.0 0.0 114.679 0.0 113.04 0.0 0.0 0.0 0 0
sh019112 11国债12 109.81 0.0 0.0 0.0 0.0 109.81 0.0 0.0 0.0 0 0
sh019115 11国债15 106.12 0.0 0.0 0.0 0.0 106.12 0.0 0.0 0.0 0 0
sh019116 11国债16 105.27 0.0 0.0 0.0 0.0 105.27 0.0 0.0 0.0 0 0
sh019119 11国债19 101.0 0.0 0.0 0.0 0.0 101.0 0.0 0.0 0.0 0 0
sh019123 11国债23 108.1 0.0 0.0 0.0 0.0 108.1 0.0 0.0 0.0 0 0
sh019124 11国债24 100.01 0.0 0.0 0.0 0.0 100.01 0.0 0.0 0.0 0 0
sh019204 12国债04 100.36 0.0 0.0 0.0 0.0 100.36 0.0 0.0 0.0 0 0
sh019206 12国债06 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019208 12国债08 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019209 12国债09 101.0 0.0 0.0 0.0 0.0 101.0 0.0 0.0 0.0 0 0
sh019212 12国债12 101.94 0.0 0.0 0.0 0.0 101.94 0.0 0.0 0.0 0 0
sh019213 12国债13 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019215 12国债15 100.69 0.0 0.0 0.0 0.0 100.69 0.0 0.0 0.0 0 0
sh019218 12国债18 119.2 0.001 0.001 116.303 0.0 119.199 119.2 119.2 119.2 1000 119200
sh019220 12国债20 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019221 12国债21 101.2 0.0 0.0 0.0 0.0 101.2 0.0 0.0 0.0 0 0
sh019305 13国债05 101.73 0.0 0.0 0.0 0.0 101.73 0.0 0.0 0.0 0 0
sh019308 13国债08 100.05 0.0 0.0 0.0 0.0 100.05 0.0 0.0 0.0 0 0
sh019309 13国债09 112.11 0.0 0.0 115.823 0.0 112.11 0.0 0.0 0.0 0 0
sh019310 13国债10 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019311 13国债11 100.187 0.0 0.0 0.0 0.0 100.187 0.0 0.0 0.0 0 0
sh019315 13国债15 100.07 0.0 0.0 0.0 0.0 100.07 0.0 0.0 0.0 0 0
sh019316 13国债16 120.777 0.0 0.0 120.137 0.0 120.777 0.0 0.0 0.0 0 0
sh019318 13国债18 100.05 0.0 0.0 0.0 0.0 100.05 0.0 0.0 0.0 0 0
sh019319 13国债19 145.055 -0.345 -0.237 139.163 145.5 145.4 145.055 145.055 145.055 1000 145055
sh019320 13国债20 102.8 0.0 0.0 0.0 0.0 102.8 0.0 0.0 0.0 0 0
sh019324 13国债24 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019325 13国债25 143.0 0.0 0.0 145.539 0.0 143.0 0.0 0.0 0.0 0 0
sh019403 14国债03 101.8 0.0 0.0 0.0 0.0 101.8 0.0 0.0 0.0 0 0
sh019405 14国债05 103.0 0.0 0.0 0.0 0.0 103.0 0.0 0.0 0.0 0 0
sh019406 14国债06 101.8 0.0 0.0 0.0 0.0 101.8 0.0 0.0 0.0 0 0
sh019409 14国债09 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019410 14国债10 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019412 14国债12 107.13 0.0 0.0 0.0 0.0 107.13 0.0 0.0 0.0 0 0
sh019413 14国债13 100.02 0.0 0.0 0.0 0.0 100.02 0.0 0.0 0.0 0 0
sh019416 14国债16 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019417 14国债17 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019421 14国债21 100.036 0.0 0.0 0.0 0.0 100.036 0.0 0.0 0.0 0 0
sh019424 14国债24 100.28 0.0 0.0 0.0 0.0 100.28 0.0 0.0 0.0 0 0
sh019425 14国债25 100.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh019427 14国债27 100.41 0.0 0.0 0.0 0.0 100.41 0.0 0.0 0.0 0 0
sh019429 14国债29 103.145 0.0 0.0 0.0 0.0 103.145 0.0 0.0 0.0 0 0
sh019502 15国债02 100.19 0.0 0.0 0.0 0.0 100.19 0.0 0.0 0.0 0 0
sh019505 15国债05 100.05 0.0 0.0 100.001 100.066 100.05 0.0 0.0 0.0 0 0
sh019507 15国债07 100.25 0.0 0.0 0.0 0.0 100.25 0.0 0.0 0.0 0 0
sh019508 15国债08 121.1 0.0 0.0 119.491 124.45 121.1 0.0 0.0 0.0 0 0
sh019510 15国债10 155.888 0.0 0.0 144.633 0.0 155.888 0.0 0.0 0.0 0 0
sh019511 15国债11 101.0 0.0 0.0 0.0 0.0 101.0 0.0 0.0 0.0 0 0
sh019514 15国债14 100.4 0.0 0.0 0.0 0.0 100.4 0.0 0.0 0.0 0 0
sh019516 15国债16 101.409 0.0 0.0 99.701 103.0 101.409 0.0 0.0 0.0 0 0
sh019517 15国债17 132.2 0.0 0.0 126.594 132.2 132.2 0.0 0.0 0.0 0 0
sh019519 15国债19 100.58 0.0 0.0 0.0 0.0 100.58 0.0 0.0 0.0 0 0
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sh020703 25贴债04 99.345 0.0 0.0 97.288 0.0 99.345 0.0 0.0 0.0 0 0
sh020704 25贴债05 99.677 0.0 0.0 97.671 0.0 99.677 0.0 0.0 0.0 0 0
sh020705 25贴债06 99.663 0.0 0.0 97.656 0.0 99.663 0.0 0.0 0.0 0 0
sh020706 25贴债07 99.902 0.0 0.0 0.0 0.0 99.902 0.0 0.0 0.0 0 0
sh020707 25贴债08 99.77 0.0 0.0 97.767 0.0 99.77 0.0 0.0 0.0 0 0
sh020708 25贴债09 99.66 0.0 0.0 97.647 0.0 99.66 0.0 0.0 0.0 0 0
sh020709 25贴债10 99.327 0.0 0.0 97.265 0.0 99.327 0.0 0.0 0.0 0 0
sh020710 25贴债11 99.663 0.0 0.0 97.646 0.0 99.663 0.0 0.0 0.0 0 0
sh020711 25贴债12 99.646 0.0 0.0 97.634 0.0 99.646 0.0 0.0 0.0 0 0
sh020712 25贴债13 99.888 0.0 0.0 97.886 0.0 99.888 0.0 0.0 0.0 0 0
sh020713 25贴债14 99.74 0.0 0.0 97.741 0.0 99.74 0.0 0.0 0.0 0 0
sh020714 25贴债15 99.62 0.0 0.0 97.626 0.0 99.62 0.0 0.0 0.0 0 0
sh020715 25贴债16 99.207 0 0 97.244 0.0 99.207 0.0 0.0 0.0 0 0
sh020716 25贴债17 99.611 0 0 97.621 0.0 99.611 0.0 0.0 0.0 0 0
sh020717 25贴债18 99.613 0 0 97.629 0.0 99.613 0.0 0.0 0.0 0 0
sh110059 浦发转债 108.893 0.02 0.018 108.883 108.895 108.873 108.888 108.948 108.866 2926280 318652260
sh110060 天路转债 159.753 -0.415 -0.259 159.7 159.76 160.168 160.248 163.242 159.2 461890 74278967
sh110062 烽火转债 122.128 0.598 0.492 122.094 122.185 121.53 121.591 122.77 121.488 510090 62330928
sh110063 鹰19转债 113.948 1.174 1.041 113.949 113.95 112.774 112.939 114.408 112.7 456150 51899352
sh110064 建工转债 111.29 0.014 0.013 111.29 111.315 111.276 111.306 111.446 111.224 249630 27784000
sh110067 华安转债 120.658 0.595 0.496 120.621 120.699 120.063 120.063 120.919 120.011 380760 45910596
sh110070 凌钢转债 116.722 1.35 1.17 116.726 116.759 115.372 115.95 116.766 115.775 191380 22273386
sh110072 广汇转债 44.267 0 0 0.0 0.0 44.267 0.0 0.0 0.0 0 0
sh110073 国投转债 113.048 0.343 0.304 112.95 113.07 112.705 112.778 113.25 112.62 592130 66913640
sh110074 精达转债 223.005 6.348 2.93 222.849 223.01 216.657 219.999 229.84 215.698 3222160 720330155
sh110075 南航转债 122.981 0.677 0.554 122.871 123.0 122.304 122.31 123.4 122.31 333230 40990291
sh110076 华海转债 114.093 1.381 1.225 113.901 114.142 112.712 112.712 114.4 112.712 284180 32324812
sh110077 洪城转债 186.018 2.008 1.091 185.62 186.019 184.01 184.21 186.221 182.368 253770 46800208
sh110079 杭银转债 128.587 1.122 0.88 128.565 128.6 127.465 127.358 128.643 127.358 2678850 343473716
sh110081 闻泰转债 111.522 -0.217 -0.194 111.515 111.534 111.739 111.9 112.6 111.471 840950 94105707
sh110082 宏发转债 128.626 -0.424 -0.329 128.415 128.69 129.05 129.0 129.632 128.3 454480 58655701
sh110084 贵燃转债 116.457 0 0 0.0 0.0 116.457 0.0 0.0 0.0 0 0
sh110085 通22转债 112.152 0.533 0.478 112.2 112.292 111.619 111.653 112.496 111.4 435570 48854106
sh110086 精工转债 107.543 0.023 0.021 107.462 107.482 107.52 107.6 107.85 107.155 328780 35318555
sh110087 天业转债 107.073 0.626 0.588 106.934 107.073 106.447 106.398 107.597 106.2 245700 26292399
sh110089 兴发转债 116.945 0.32 0.274 116.858 116.949 116.625 116.625 117.147 116.0 348120 40639281
sh110090 爱迪转债 123.084 0.276 0.225 123.0 123.1 122.808 122.869 124.823 122.691 507670 62729961
sh110092 三房转债 85.312 -1.198 -1.385 85.315 85.326 86.51 86.598 86.779 85.1 3485330 299329905
sh110093 神马转债 120.449 0.178 0.148 120.406 120.449 120.271 120.271 121.434 119.9 704300 84836623
sh110094 众和转债 123.398 1.26 1.032 123.38 123.533 122.138 122.138 123.811 121.889 292680 36087490
sh110095 双良转债 114.31 -0.165 -0.144 114.244 114.3 114.475 114.474 116.406 114.258 642220 73934451
sh110096 豫光转债 136.813 0.403 0.295 136.773 136.813 136.41 135.6 138.522 135.6 304470 41717269
sh110097 天润转债 138.007 1.701 1.248 137.902 138.09 136.306 136.305 139.739 136.305 338160 46829374
sh110098 南药转债 124.86 0.651 0.524 124.59 124.888 124.209 124.209 126.0 124.209 312280 39069521
sh110801 继峰定01 183.25 0 0 0.0 0.0 183.25 0.0 0.0 0.0 0 0
sh110807 动力定01 0.0 0.000 0.000 0.0 0.0 0.0 0.0 0.0 0.0 0 0
sh110808 动力定02 116.055 0 0 0.0 0.0 116.055 0.0 0.0 0.0 0 0
sh110815 九丰定01 100.0 0 0 0.0 0.0 100.0 0.0 0.0 0.0 0 0
sh110816 九丰定02 109.5 0 0 0.0 0.0 109.5 0.0 0.0 0.0 0 0
sh111000 起帆转债 118.698 0.492 0.416 118.618 118.87 118.206 118.3 119.487 118.277 297190 35290134
sh111001 山玻转债 114.204 0.29 0.255 114.114 114.205 113.914 113.999 114.51 113.0 55820 6365547
sh111002 特纸转债 117.552 1.287 1.107 117.52 117.7 116.265 116.265 118.578 116.076 297880 35064889
sh111003 聚合转债 131.138 -0.051 -0.039 131.133 131.355 131.189 130.892 131.979 130.6 141430 18562827
sh111004 明新转债 114.112 -1.277 -1.107 114.104 114.147 115.389 115.389 115.989 113.177 422850 48367214
sh111005 富春转债 115.205 1.853 1.635 115.1 115.431 113.352 113.552 115.43 113.109 136770 15659209
sh111007 永和转债 130.106 0.982 0.761 130.083 130.213 129.124 128.8 130.84 128.8 610050 79348890
sh111008 沿浦转债 130.352 0 0 0.0 0.0 130.352 0.0 0.0 0.0 0 0
sh111009 盛泰转债 105.006 0.256 0.244 105.004 105.1 104.75 104.43 105.25 104.153 102160 10716565
sh111010 立昂转债 113.122 0.052 0.046 113.1 113.297 113.07 113.217 114.108 112.876 244190 27692363
sh111011 冠盛转债 223.535 1.548 0.697 223.3 223.482 221.987 221.985 232.3 221.5 1985260 450657021
sh111012 福新转债 328.461 -11.553 -3.398 329.136 329.999 340.014 341.001 350.0 326.1 1930710 649327866
sh111013 新港转债 120.683 1.45 1.216 120.482 120.8 119.233 119.233 121.06 119.233 287720 34653383
sh111014 李子转债 113.23 -0.339 -0.298 113.222 113.325 113.569 113.569 113.86 113.021 280280 31748468
sh111015 东亚转债 116.996 2.873 2.517 116.98 116.999 114.123 114.123 118.499 114.018 1342020 157026729
sh111016 神通转债 123.02 0.574 0.469 122.906 123.059 122.446 122.0 124.307 121.983 361460 44548043
sh111017 蓝天转债 133.6 1.514 1.146 133.46 133.6 132.086 132.123 133.973 132.123 254640 33951635
sh111018 华康转债 118.873 0.815 0.69 118.867 118.997 118.058 118.145 120.05 118.099 225720 26868391
sh111019 宏柏转债 122.138 2.015 1.677 122.137 122.15 120.123 120.36 123.96 120.36 616100 75477470
sh111020 合顺转债 134.057 1.073 0.807 133.937 134.179 132.984 132.955 135.449 132.955 282470 37955539
sh111021 奥锐转债 119.376 0.664 0.559 119.3 119.421 118.712 118.701 120.58 118.701 485450 58116715
sh113024 Z核建转 104.951 0.049 0.047 104.951 104.952 104.902 104.9 105.499 104.898 10112920 1062126643
sh113030 东风转债 122.069 1.539 1.277 121.895 121.956 120.53 120.53 122.887 120.53 173760 21214279
sh113033 XD利群转 111.661 0.673 0.606 111.601 111.699 110.988 111.42 112.236 111.111 569120 63497244
sh113037 紫银转债 109.942 -0.068 -0.062 109.923 109.963 110.01 110.099 110.2 109.909 294150 32363642
sh113039 嘉泽转债 128.803 1.438 1.129 128.8 128.83 127.365 127.501 131.463 127.365 811220 104685137
sh113042 上银转债 121.273 0.622 0.516 121.276 121.292 120.651 120.746 121.4 120.592 915920 110842811
sh113043 财通转债 115.301 -0.02 -0.017 115.3 115.349 115.321 115.32 115.94 115.248 440430 50870993
sh113045 环旭转债 119.277 -1.452 -1.203 119.14 119.399 120.729 120.729 120.803 118.821 1070440 127766807
sh113046 金田转债 110.623 -0.326 -0.294 110.554 110.701 110.949 110.945 111.146 110.305 401380 44425484
sh113047 旗滨转债 121.594 0.666 0.551 121.424 121.7 120.928 121.1 122.258 121.012 123520 15042137
sh113048 晶科转债 108.301 -0.438 -0.403 108.3 108.449 108.739 108.52 108.999 108.0 378900 41103422
sh113049 长汽转债 112.881 -0.117 -0.104 112.81 112.95 112.998 113.05 113.4 112.631 322260 36390291
sh113050 南银转债 127.024 0.407 0.321 127.05 127.051 126.617 126.8 127.187 126.452 1217890 154373364
sh113051 节能转债 118.503 0.103 0.087 118.437 118.578 118.4 118.393 118.993 118.087 209880 24866288
sh113052 兴业转债 116.968 0.037 0.032 116.935 117.0 116.931 116.945 117.156 116.567 2184250 255273844
sh113053 隆22转债 120.501 -0.14 -0.116 120.51 120.566 120.641 120.64 121.99 120.249 780940 94400281
sh113054 绿动转债 111.734 0.776 0.699 111.566 111.748 110.958 111.1 111.75 110.901 322500 35889758
sh113056 重银转债 117.521 0.01 0.009 117.463 117.522 117.511 117.515 117.832 117.185 855360 100461248
sh113058 友发转债 121.6 0.769 0.636 121.6 121.601 120.831 120.82 122.401 120.527 466890 56702543
sh113059 福莱转债 110.777 0.146 0.132 110.732 110.8 110.631 110.501 111.426 110.199 266520 29540330
sh113062 常银转债 121.074 0.299 0.248 121.066 121.171 120.775 120.775 121.499 120.473 369980 44763354
sh113064 东材转债 118.573 0.973 0.827 118.288 118.662 117.6 117.751 120.099 117.751 787980 93805853

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#!/usr/local/bin/python
# -*- coding: utf-8 -*-
# apk add py-mysqldb or
import platform
from datetime import datetime
import time
import sys
import os
import MySQLdb
from sqlalchemy import create_engine
from sqlalchemy.types import NVARCHAR
from sqlalchemy import inspect
from sqlalchemy.sql import text
import pandas as pd
import traceback
import akshare as ak
# 使用环境变量获得数据库。兼容开发模式可docker模式。
MYSQL_HOST = os.environ.get('MYSQL_HOST') if (os.environ.get('MYSQL_HOST') != None) else "localhost"
MYSQL_USER = os.environ.get('MYSQL_USER') if (os.environ.get('MYSQL_USER') != None) else "root"
MYSQL_PWD = os.environ.get('MYSQL_PWD') if (os.environ.get('MYSQL_PWD') != None) else "1212"
MYSQL_DB = os.environ.get('MYSQL_DB') if (os.environ.get('MYSQL_DB') != None) else "stock_data"
MYSQL_PORT = os.environ.get('MYSQL_PORT') if (os.environ.get('MYSQL_PORT') != None) else "3306"
print("MYSQL_HOST :", MYSQL_HOST, ",MYSQL_USER :", MYSQL_USER, ",MYSQL_DB :", MYSQL_DB)
MYSQL_CONN_URL = "mysql+mysqldb://" + MYSQL_USER + ":" + MYSQL_PWD + "@" + MYSQL_HOST + ":" + MYSQL_PORT + "/" + MYSQL_DB + "?charset=utf8mb4"
print("MYSQL_CONN_URL :", MYSQL_CONN_URL)
__version__ = "2.0.0"
# 每次发布时候更新。
def engine():
engine = create_engine(MYSQL_CONN_URL)
#encoding='utf8', convert_unicode=True)
return engine
def engine_to_db(to_db):
MYSQL_CONN_URL_NEW = "mysql+mysqldb://" + MYSQL_USER + ":" + MYSQL_PWD + "@" + MYSQL_HOST + ":" + MYSQL_PORT + "/" + to_db + "?charset=utf8mb4"
engine = create_engine(MYSQL_CONN_URL_NEW)
#encoding='utf8', convert_unicode=True)
return engine
# 通过数据库链接 engine。
def conn():
try:
db = MySQLdb.connect(MYSQL_HOST, MYSQL_USER, MYSQL_PWD, MYSQL_DB, charset="utf8")
# db.autocommit = True
except Exception as e:
print("conn error :", e)
db.autocommit(on=True)
return db.cursor()
# 定义通用方法函数，插入数据库表，并创建数据库主键，保证重跑数据的时候索引唯一。
def insert_db(data, table_name, write_index, primary_keys):
# 插入默认的数据库。
insert_other_db(MYSQL_DB, data, table_name, write_index, primary_keys)
# 增加一个插入到其他数据库的方法。
def insert_other_db(to_db, data, table_name, write_index, primary_keys):
# 定义engine
engine_mysql = engine_to_db(to_db)
# 使用 http://docs.sqlalchemy.org/en/latest/core/reflection.html
# 使用检查检查数据库表是否有主键。
insp = inspect(engine_mysql)
col_name_list = data.columns.tolist()
# 如果有索引，把索引增加到varchar上面。
if write_index:
# 插入到第一个位置：
col_name_list.insert(0, data.index.name)
print(col_name_list)
data.to_sql(name=table_name, con=engine_mysql, schema=to_db, if_exists='append',
dtype={col_name: NVARCHAR(length=255) for col_name in col_name_list}, index=write_index)
# print(insp.get_pk_constraint(table_name))
# print()
# print(type(insp))
# 判断是否存在主键
if insp.get_pk_constraint(table_name)['constrained_columns'] == []:
with engine_mysql.connect() as con:
try:
# 使用 text 包裹 SQL 语句
con.execute(text('ALTER TABLE `%s` ADD PRIMARY KEY (`%s`)' % (table_name, primary_keys)))
except Exception as e:
print("################## ADD PRIMARY KEY ERROR :", e)
def fetch_all_data(table_name):
engine_mysql = engine_to_db(MYSQL_DB)
data = pd.read_sql_table(table_name, engine_mysql)
return data
def check_db_table(table_name):
# 判断是否已存在此表。
engine_mysql = engine_to_db(MYSQL_DB)
insp = inspect(engine_mysql)
if insp.has_table(table_name):
return True
else:
return False
def check_db_table_last_date(table_name):
engine_mysql = engine_to_db(MYSQL_DB)
insp = inspect(engine_mysql)
if not insp.has_table(table_name):
return None
sql = f"SELECT `日期` FROM `{table_name}` ORDER BY `日期` DESC LIMIT 1"
# params = (table_name,)
result = select(sql)#, params)
if not result:
return None
date_str = result[0][0]
if not date_str:
return None
try:
extracted_date_str = date_str.split(' ')[0]
return datetime.strptime(extracted_date_str, "%Y-%m-%d").date()
except ValueError:
return None
# 插入数据。
def insert(sql, params=()):
with conn() as db:
print("insert sql:" + sql)
try:
db.execute(sql, params)
except Exception as e:
print("error :", e)
# 查询数据
# def select(sql, params=()):
# with conn() as db:
# print("select sql:" + sql)
# try:
# db.execute(sql, params)
# except Exception as e:
# print("error :", e)
# result = db.fetchall()
# return result
def select(sql, params=None):
# 假设conn()是一个有效的数据库连接函数
with conn() as db:
try:
db.execute(sql, params)
result = db.fetchall()
return result
except Exception as e:
print(f"Error executing SQL query: {e}")
return None
# 计算数量
def select_count(sql, params=()):
with conn() as db:
print("select sql:" + sql)
try:
db.execute(sql, params)
except Exception as e:
print("error :", e)
result = db.fetchall()
# 只有一个数组中的第一个数据
if len(result) == 1:
return int(result[0][0])
else:
return 0
def delete_table(table_name):
with conn() as db:
drop_table_query = f"DROP TABLE IF EXISTS `{table_name}`"
db.execute(drop_table_query)
print(f"表 {table_name} 已删除，准备重新获取数据")
# # 通用函数。获得日期参数。
# def run_with_args(run_fun):
# tmp_datetime_show = datetime.datetime.now() # 修改成默认是当日执行 + datetime.timedelta()
# tmp_hour_int = int(tmp_datetime_show.strftime("%H"))
# if tmp_hour_int < 12 :
# # 判断如果是每天 中午 12 点之前运行，跑昨天的数据。
# tmp_datetime_show = (tmp_datetime_show + datetime.timedelta(days=-1))
# tmp_datetime_str = tmp_datetime_show.strftime("%Y-%m-%d %H:%M:%S.%f")
# print("\n######################### hour_int %d " % tmp_hour_int)
# str_db = "MYSQL_HOST :" + MYSQL_HOST + ", MYSQL_USER :" + MYSQL_USER + ", MYSQL_DB :" + MYSQL_DB
# print("\n######################### " + str_db + " ######################### ")
# print("\n######################### begin run %s %s #########################" % (run_fun, tmp_datetime_str))
# start = time.time()
# # 要支持数据重跑机制，将日期传入。循环次数
# if len(sys.argv) == 3:
# # python xxx.py 2017-07-01 10
# tmp_year, tmp_month, tmp_day = sys.argv[1].split("-")
# loop = int(sys.argv[2])
# tmp_datetime = datetime.datetime(int(tmp_year), int(tmp_month), int(tmp_day))
# for i in range(0, loop):
# # 循环插入多次数据，重复跑历史数据使用。
# # time.sleep(5)
# tmp_datetime_new = tmp_datetime + datetime.timedelta(days=i)
# try:
# run_fun(tmp_datetime_new)
# except Exception as e:
# print("error :", e)
# traceback.print_exc()
# elif len(sys.argv) == 2:
# # python xxx.py 2017-07-01
# tmp_year, tmp_month, tmp_day = sys.argv[1].split("-")
# tmp_datetime = datetime.datetime(int(tmp_year), int(tmp_month), int(tmp_day))
# try:
# run_fun(tmp_datetime)
# except Exception as e:
# print("error :", e)
# traceback.print_exc()
# else:
# # tmp_datetime = datetime.datetime.now() + datetime.timedelta(days=-1)
# try:
# run_fun(tmp_datetime_show) # 使用当前时间
# except Exception as e:
# print("error :", e)
# traceback.print_exc()
# print("######################### finish %s , use time: %s #########################" % (
# tmp_datetime_str, time.time() - start))
# # 设置基础目录，每次加载使用。
# bash_stock_tmp = "/data/cache/hist_data_cache/%s/%s/"
# if not os.path.exists(bash_stock_tmp):
# os.makedirs(bash_stock_tmp) # 创建多个文件夹结构。
# print("######################### init tmp dir #########################")
# # 增加读取股票缓存方法。加快处理速度。
# def get_hist_data_cache(code, date_start, date_end):
# cache_dir = bash_stock_tmp % (date_end[0:7], date_end)
# # 如果没有文件夹创建一个。月文件夹和日文件夹。方便删除。
# # print("cache_dir:", cache_dir)
# if not os.path.exists(cache_dir):
# os.makedirs(cache_dir)
# cache_file = cache_dir + "%s^%s.gzip.pickle" % (date_end, code)
# # 如果缓存存在就直接返回缓存数据。压缩方式。
# if os.path.isfile(cache_file):
# print("######### read from cache #########", cache_file)
# return pd.read_pickle(cache_file, compression="gzip")
# else:
# print("######### get data, write cache #########", code, date_start, date_end)
# stock = ak.stock_zh_a_hist(symbol= code, start_date=date_start, end_date=date_end, adjust="")
# stock.columns = ['date', 'open', 'close', 'high', 'low', 'volume', 'amount', 'amplitude', 'quote_change',
# 'ups_downs', 'turnover']
# if stock is None:
# return None
# stock = stock.sort_index(0) # 将数据按照日期排序下。
# print(stock)
# stock.to_pickle(cache_file, compression="gzip")
# return stock

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pretest/data2sqlite.py Normal file
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import sqlite3
import time
import logging
from datetime import datetime
import akshare as ak
from apscheduler.schedulers.background import BackgroundScheduler
import pandas as pd
class StockDataSystem:
def __init__(self, db_name='stock_data.db'):
self.db_name = db_name
self.setup_logging()
self.init_database()
self.scheduler = BackgroundScheduler()
def setup_logging(self):
self.logger = logging.getLogger('StockDataSystem')
self.logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
self.logger.addHandler(handler)
def init_database(self):
with sqlite3.connect(self.db_name) as conn:
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS stock_realtime (
id INTEGER PRIMARY KEY AUTOINCREMENT,
序号 INTEGER,
代码 TEXT NOT NULL,
名称 TEXT NOT NULL,
最新价 REAL,
涨跌幅 REAL,
涨跌额 REAL,
成交量 REAL,
成交额 REAL,
振幅 REAL,
最高 REAL,
最低 REAL,
今开 REAL,
昨收 REAL,
量比 REAL,
换手率 REAL,
"市盈率-动态" REAL,
市净率 REAL,
总市值 REAL,
流通市值 REAL,
涨速 REAL,
"5分钟涨跌" REAL,
"60日涨跌幅" REAL,
年初至今涨跌幅 REAL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
)
''')
cursor.execute('CREATE INDEX IF NOT EXISTS idx_code_time ON stock_realtime(代码, timestamp)')
self.logger.info("数据库初始化完成")
def fetch_stock_data(self):
try:
stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
# 第三列'最新价' 没数据的行 删掉
df = stock_zh_a_spot_df.dropna(subset=['最新价'])
return df
except Exception as e:
self.logger.error(f"获取股票数据失败: {str(e)}")
return None
def store_data(self, df):
if df is None or df.empty:
self.logger.warning("没有数据需要存储")
return
try:
with sqlite3.connect(self.db_name) as conn:
df['timestamp'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
df.to_sql('stock_realtime', conn, if_exists='append', index=False)
self.logger.info(f"成功存储 {len(df)} 条记录")
except sqlite3.Error as e:
self.logger.error(f"存储数据失败: {str(e)}")
def collect_data(self):
current_time = datetime.now()
start_time = current_time.replace(hour=9, minute=30, second=0, microsecond=0)
end_time_1 = current_time.replace(hour=11, minute=30, second=0, microsecond=0)
end_time_2 = current_time.replace(hour=13, minute=0, second=0, microsecond=0)
end_time_3 = current_time.replace(hour=15, minute=0, second=0, microsecond=0)
if start_time <= current_time <= end_time_1 or end_time_2 <= current_time <= end_time_3:
self.logger.info("开始收集数据...")
df = self.fetch_stock_data()
self.store_data(df)
else:
self.logger.info("当前不在开盘时间，跳过数据收集")
def start_collection(self, interval_minutes=5):
self.scheduler.add_job(
self.collect_data,
'interval',
minutes=interval_minutes,
id='stock_data_collection'
)
self.scheduler.start()
self.logger.info(f"数据收集已启动，间隔为 {interval_minutes} 分钟")
def stop_collection(self):
self.scheduler.shutdown()
self.logger.info("数据收集已停止")
def query_stock_data(self, code, start_time=None, end_time=None):
try:
with sqlite3.connect(self.db_name) as conn:
query = "SELECT * FROM stock_realtime WHERE 代码 = ?"
params = [code]
if start_time:
query += " AND timestamp >= ?"
params.append(start_time)
if end_time:
query += " AND timestamp <= ?"
params.append(end_time)
query += " ORDER BY timestamp DESC"
df = pd.read_sql_query(query, conn, params=params)
return df
except sqlite3.Error as e:
self.logger.error(f"查询数据失败: {str(e)}")
return None

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from data2sqlite import StockDataSystem
import time
# 收集数据
stock_system = StockDataSystem()
# 启动数据收集（5分钟间隔）
stock_system.start_collection(interval_minutes=5)
try:
# 保持主线程运行
while True:
time.sleep(1)
except KeyboardInterrupt:
# 当按下 Ctrl+C 时优雅地停止
stock_system.stop_collection()
print("数据收集已停止")
# 测试查找功能
# 查询示例
# code = "000001" # 平安银行
# df = stock_system.query_stock_data(
# code,
# start_time="2024-01-06 21:26:49",
# end_time="2024-01-06 21:33:40"
# )
# print(f"\n{code} 的数据：")
# print(df)

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#!/usr/local/bin/python3
# -*- coding: utf-8 -*-
import akshare as ak
import mysql_database
print(ak.__version__)
# 实时行情数据
# 接口: stock_zh_a_spot
# 目标地址: http://vip.stock.finance.sina.com.cn/mkt/#hs_a
# 描述: A 股数据是从新浪财经获取的数据, 重复运行本函数会被新浪暂时封 IP, 建议增加时间间隔
# 限量: 单次返回所有 A 股上市公司的实时行情数据
stock_zh_a_spot_df = ak.stock_zh_a_spot_em()
# stock_zh_a_spot_df = ak.stock_zh_a_spot()
print(stock_zh_a_spot_df)
# 第三列'最新价' 没数据的行 删掉
stock_zh_a_spot_df = stock_zh_a_spot_df.dropna(subset=['最新价'])
# 保存成txt
stock_zh_a_spot_df.to_csv("stock_zh_a_spot.txt", sep="\t", index=False, encoding="utf-8")
mysql_database.insert_db(stock_zh_a_spot_df, "stock_zh_a_spot", True, "`代码`")

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25
some架构/app.py Normal file
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import streamlit as st
from config import Config
from auth import Authenticator
class TradingSystem:
def __init__(self):
self.config = Config()
self.auth = Authenticator()
class TradingApp:
def __init__(self):
self.auth = Authenticator()
def login_page(self):
# 登录界面
pass
def dashboard(self):
# 主控制台
pass
def settings(self):
# 策略参数设置
pass

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from storage import DatabaseManager
class Authenticator:
def __init__(self):
self.db = DatabaseManager()
def login(self, username, password):
# 用户登录验证
pass
def register(self, user_info):
# 用户注册
pass

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some架构/config.py Normal file
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class Config:
# 数据源配置
DATA_SOURCES = {
'source1': {...},
'source2': {...}
}
# 策略参数配置
STRATEGY_PARAMS = {
'entry_window': 20,
'exit_window': 10
}
# 邮件配置
EMAIL_CONFIG = {
'smtp_server': '...',
'port': 587
}

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some架构/data_fetcher.py Normal file
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class MarketDataFetcher:
def __init__(self, source):
self.source = source # 支持多数据源配置
def get_realtime_data(self):
# 获取实时行情
pass
def get_historical_data(self):
# 获取历史数据
pass

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some架构/main.py Normal file
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from config import Config
from data_fetcher import MarketDataFetcher
from strategy import TurtleStrategy
from notifier import EmailNotifier
from storage import DatabaseManager
class TradingSystem:
def __init__(self):
self.config = Config()
self.data_fetcher = MarketDataFetcher(self.config.DATA_SOURCES)
self.strategy = TurtleStrategy(self.config.STRATEGY_PARAMS)
self.notifier = EmailNotifier(self.config.EMAIL_CONFIG)
self.db = DatabaseManager()
def run(self):
# 1. 启动数据获取
# 2. 运行策略
# 3. 处理信号
# 4. 发送通知
pass
def handle_signal(self, signal):
# 处理交易信号
self.notifier.send_notification(signal)
self.db.save_signal(signal)

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class EmailNotifier:
def __init__(self, config):
self.config = config
def send_notification(self, signal):
# 发送邮件通知
pass

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import sqlite3
class DatabaseManager:
def __init__(self):
self.db = sqlite3.connect('trading.db')
def save_signal(self, signal):
# 保存交易信号
pass
def save_transaction(self, trans):
# 保存交易记录
pass

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class TurtleStrategy:
def __init__(self, params):
self.params = params # 策略参数配置
def generate_signals(self, data):
# 生成交易信号
pass
def calculate_position(self):
# 计算仓位
pass

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import sqlite3
from sqlalchemy.types import NVARCHAR
def create_table(code):
conn = sqlite3.connect(code + '.db')
try:
cursor = conn.cursor()
table_name = "stock_data"
cursor.execute(f'''CREATE TABLE IF NOT EXISTS {table_name} (
日期 TEXT,
开盘 REAL,
收盘 REAL,
最高 REAL,
最低 REAL,
成交量 INTEGER,
成交额 REAL,
振幅 REAL,
涨跌幅 REAL,
涨跌额 REAL,
换手率 REAL
)''')
except sqlite3.Error as e:
print(f"Database error: {e}")
finally:
conn.close()
def insert_data(code, data):
conn = sqlite3.connect(code + '.db')
try:
cursor = conn.cursor()
table_name = "stock_data"
col_name_list = data.columns.tolist()
# data.to_sql(table_name, conn, if_exists='append', index=False)
data.to_sql(table_name, conn, if_exists='append',
dtype={col_name: NVARCHAR(length=255) for col_name in col_name_list}, index=False)
print("数据已成功插入到数据库中。")
cursor.execute("SELECT * FROM stock_data LIMIT 2;")
print(cursor.fetchall())
except sqlite3.Error as e:
print(f"插入数据时出错：{e}")
finally:
conn.close()

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