Python使用pyecharts库构建股票交易 可交互可视化展示——基于Tushare大数据开放社区
Python使用pyecharts库构建股票交易 可交互可视化展示——基于Tushare大数据社区
1、前言
大家好！欢迎各位访问我的博客，今天给大家分享的是 python使用pyecharts库构建股票交易的可交互可视化展示，并简单设置股票买入卖出条件，统计股票收益率等操作。本次数据的来源是Tushare大数据开发社区（:D 感谢Tushare的数据支持，Tushare拥有丰富的数据内容，如股票、基金、期货、数字货币等行情数据，能够让我们解放双手，逃脱最痛苦的数据收集步骤），本次重点是利用pyecharts库绘制k线图，希望大家能够喜欢。
2、成品展示
3、源码分享
python
import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt
import json
import numpy as np
from matplotlib.pylab import date2num
import datetime
import mpl_finance as mpf
import pyecharts
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import *
import numpy as np
from openpyxl import Workbook
from openpyxl.styles import PatternFill
from openpyxl import load_workbook
# 设置Token
ts.set_token('******************') #请填入自己的Token
# 初始化接口
ts_api = ts.pro_api()
print("Tushare 接口调用正常，版本号:" + ts.__version__ +"\n")
print("pyecharts数据交互 接口调用正常，版本号:{}".format(pyecharts.__version__)+"\n")
class RobotTrade():
id = None
buyTime = None
buyPrice = None
sellTime = None
sellPrice = None
profit = None
def setId(self,id):
self.id = id
def buy(self,time,price):
self.buyTime = time
self.buyPrice = price
def sell(self,time,price):
self.sellTime = time
self.sellPrice = price
self.profit = round((self.sellPrice - self.buyPrice)/self.buyPrice,4)
def Out(self):
a = self.buyTime
b = self.sellTime
c = self.buyPrice
d = self.sellPrice
e = self.profit
f = self.id
return [a,b,c,d,e,f]
class Stock:
global returnList
isTodayBuy = ""
df = pd.DataFrame([0, 0])
code = ""
start = ""
end = ""
name = ""
list_BuyorSell = []
# 设置股票代码
def set_code(self,code):
stock_list = ts_api.stock_basic(exchange='', list_status='L',
fields='ts_code,symbol,name,area,industry,market,list_date')
a = stock_list["ts_code"].tolist()
for i in range(len(a)):
if a[i][0:6] == code:
self.code=a[i]
self.name=stock_list.loc[i,"name"]
# 请求股票列表
# 获取日线交易数据
def get_daily_trade(self): # 股票代码，开始日期20120101，结束日期20180101
# print("正在获取交易数据：\n --Start Download")
if self.start == "":
self.start = input("请输入交易开始日期：")
self.end = input("请输入交易结束日期：")
# 请求日线数据
# print(self.start)
df = ts_api.daily(ts_code = self.code, start_date = self.start, end_date = self.end)
# 按交易日升序排序
df.sort_values(by=["trade_date"], inplace=True)
df.reset_index(drop=True, inplace=True)
self.df = df
# print("已获取{} {}，交易数据，共计{}条交易数据\n".format(self.code,self.name, len(self.df)))
# 指数移动均线工具
def get_MA_EMA(self, n):
df = self.df
newname = "EMA" + str(n)
df[newname] = 0 # 初始化n天指数移动均线
for i in range(n - 1, len(df)): # 循环第n个数据开始，为之后的每个数据计算EMA
list_temp = []
list_ema = []
for d in range(n): # 添加数据
list_temp.append(df.loc[i - (n - d) + 1, "close"])
list_ema.append(0)
for d in range(n): # 进行计算
if d == 0:
list_ema[d] = list_temp[d]
else:
list_ema[d] = (2 * list_temp[d] + (n - 1) * list_ema[d - 1]) / (n + 1)
df.loc[i, newname] = list_ema[n - 1]
# print(" --已计算指数移动均线EMA{}".format(n))
self.df = df
# 移动均线计算
def get_MA(self, n):
df = self.df
newname = "MA" + str(n)
df[newname] = None # 初始化n天指数移动均线
for i in range(n-1,len(df)):
list_temp = []
sum = 0
avg = 0
for d in range(n):
sum = sum + df.loc[ i-(n-d)+1 ,"close"]
avg = round(sum/n,2)
df.loc[i, newname] = avg
# print(" --已计算移动均线MA{}".format(n))
# 移动均线计算
def get_Vol_MA(self, n):
df = self.df
newname = "VolMA" + str(n)
df[newname] = None # 初始化n天指数移动均线
for i in range(n-1,len(df)):
list_temp = []
sum = 0
avg = 0
for d in range(n):
sum = sum + df.loc[ i-(n-d)+1 ,"vol"]
avg = round(sum/n,2)
df.loc[i, newname] = avg
# print(" --已计算成交量移动均线MA{}".format(n))
# Macd的快速线DIF计算
def get_Macd_DIF(self, n1, n2):
df = self.df
newname = "Macd_DIF"
df[newname] = 0 # 初始化n天指数移动均线
for i in range(n2,len(df)):
df.loc[i, "Macd_DIF"] = df.loc[i, "EMA" + str(n1)] - df.loc[i, "EMA" + str(n2)]
# print(" --已计算MACD 快速线DIF")
self.df = df
# Macd的慢速线DEA计算
def get_Macd_DEA(self, n):
df = self.df
newname = "Macd_DEA"
df[newname] = 0 # 初始化n天指数移动均线
for i in range(n - 1, len(df)): # 循环第n个数据开始，为之后的每个数据计算EMA
list_temp = []
list_dif = []
for d in range(n): # 添加数据
list_temp.append(df.loc[i - (n - d) + 1, "Macd_DIF"])
list_dif.append(0)
for d in range(n): # 进行计算
if d == 0:
list_dif[d] = list_temp[d]
else:
list_dif[d] = (2 * list_temp[d] + (n - 1) * list_dif[d - 1]) / (n + 1)
df.loc[i, newname] = list_dif[n - 1]
# print(" --已计算MACD 慢速线DEA")
self.df = df
# 计算MACD柱状线
def get_Macd_Bar(self,n):
df = self.df
for i in range(n,len(df)):
df.loc[i,"MACD_Bar"] = 2 * ( df.loc[i, "Macd_DIF"] - df.loc[i, "Macd_DEA"])
# print(" --已计算MACD 柱状线Bar")
self.df = df
# Macd计算
def get_Macd(self, a=12, b=26, c=9):
# print("正在计算MACD指标：")
# 计算EMA12、EMA26 数据
self.get_MA_EMA(a)
self.get_MA_EMA(b)
# 计算三条曲线
self.get_Macd_DIF(a,b)
self.get_Macd_DEA(c)
self.get_Macd_Bar(b)
# print(" --已完成MACD指标的计算")
# 交易策略
def set_Trading_Strategy(self):
df = self.df
list_BuyorSell = []
count = 0
temp = 0 # 买入后的世界线进行
for i in range(10,len(df)): # 偷懒从第十个交易日开始
i = i + temp
if i >=len(df):
break
lowThanma5 = 0 # 收盘价低于5日均线的天数
Is_skip = False # 是否条件不满足
Is_selled = False
# 条件0：买点的前2天收盘价均小于5日均线
for day in range(1,2):
if df.loc[i-day,"MA5"] < df.loc[i-day,"close"]:
Is_skip = True
if Is_skip==True:
continue
# 条件1：当天收盘价大于5日线
if df.loc[i,"close"] < df.loc[i,"MA5"]:
Is_skip = True
if Is_skip==True:
continue
# # 条件2：当天开盘价低于5日线
# if df.loc[i, "open"] > df.loc[i, "MA5"]:
# Is_skip = True
# if Is_skip == True:
# continue
# 条件3: 当日增幅超过3%
if ((df.loc[i,"close"]-df.loc[i,"open"]) / df.loc[i,"open"] ) <= 0.03:
Is_skip = True
if Is_skip==True:
continue
# 条件3:...
if Is_skip==True:
continue
# 买入条件均满足后买入
r = RobotTrade() # 设置交易机器人id
r.setId(count)
if i+1 == len(df): # 是否今天买入
self.isTodayBuy = "下个交易日开盘立刻买入"
break
r.buy(i+1,df.loc[i+1,"open"]) # 机器人后一天买入
have_price = df.loc[i+1,"open"] # 第二天开盘立刻买入
# 一但卖出条件成熟，机器人执行卖出
for day in range(1,31): # 最大持股时间30日
if i+1+day >= len(df) - 1: # 检查数组是否越界
r.sell(len(df) - 1, r.buyPrice)
list_BuyorSell.append(r.Out())
break
# 单次操作亏损达到-0.0666时卖出
if (df.loc[i+1+day,"close"]-r.buyPrice)/r.buyPrice <= -0.0666:
r.sell(i + 1 + day, r.buyPrice*0.9334)
list_BuyorSell.append(r.Out())
Is_selled = True
break
# 单次操作盈利达到+0.2888时卖出
if (df.loc[i + 1 + day, "high"] - r.buyPrice) / r.buyPrice >= 0.1888:
r.sell(i + 1 + day, r.buyPrice * 1.1888)
list_BuyorSell.append(r.Out())
Is_selled = True
break
# 5日均线小于10日均线的0.98 卖出
if df.loc[i+1+day,"MA10"]*0.98 > df.loc[i+1+day,"MA5"]:
r.sell(i+1+day,df.loc[i+1+day,"close"])
list_BuyorSell.append(r.Out())
Is_selled = True
break
# 连续3天收盘价小于5日均线 且 5日线小于10日线 卖出
if df.loc[i + 1 + day, "MA5"] > df.loc[i + 1 + day, "close"] and df.loc[i + 1 + day, "MA5"] < \
df.loc[i + 1 + day, "MA10"]:
lowThanma5 = lowThanma5 + 1
if lowThanma5 >= 3:
r.sell(i + 1 + day, df.loc[i + 1 + day, "close"])
list_BuyorSell.append(r.Out())
Is_selled = True
break
# 若卖出条件未出现，则持仓等候
a = 1+1 # 摸鱼
temp = temp + 1
if Is_selled == True:
continue
if i+31 >= len(df)-1: # 检查数组越界
r.sell(len(df)-1,r.buyPrice)
list_BuyorSell.append(r.Out())
break
# 如果最大持股时间达到时 还持有仓位，则强制卖出
if Is_selled != True:
r.sell(i + 31,df.loc[i + 31, "close"])
list_BuyorSell.append(r.Out())
Is_selled = True
# 结束一个周期
count = count +1
self.list_BuyorSell = list_BuyorSell
# print(list_BuyorSell)
# 绘制MA指标图，返回line_ma
def get_Pyecharts_MA(self,n,index,itheme="light"):
df = self.df
colorlist = ["rgb(47,79,79)","rgb(255,140,0)","rgb(0,191,255)","rgb(187, 102, 255)"]
icolor = colorlist[index-2]
line = (
Line(init_opts=opts.InitOpts(theme=itheme,animation_opts=opts.AnimationOpts(animation=False),))
# 添加x轴交易日期数据
.add_xaxis(df["trade_date"].tolist())
.add_yaxis("MA{}".format(n),df["MA{}".format(n)].tolist(),xaxis_index=index,yaxis_index=index,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 是否显示小圆点
itemstyle_opts=opts.ItemStyleOpts(color=icolor)) # 更改颜色
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_show=False,grid_index=index),
yaxis_opts=opts.AxisOpts(is_show=False,grid_index=index),
legend_opts=opts.LegendOpts(is_show=True), # 图例是否显示
)
)
return line
# 绘制成交量均值线
def get_Pyecharts_VolMA(self,n,index,itheme="light"):
df = self.df
colorlist = ["rgb(47,79,79)","rgb(255,140,0)"]
icolor = colorlist[index-6]
line = (
Line(init_opts=opts.InitOpts(theme=itheme,animation_opts=opts.AnimationOpts(animation=False),))
# 添加x轴交易日期数据
.add_xaxis(df["trade_date"].tolist())
.add_yaxis("VolMA{}".format(n),df["VolMA{}".format(n)].tolist(),xaxis_index=index,yaxis_index=index,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 是否显示小圆点
tooltip_opts=opts.TooltipOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=icolor)) # 更改颜色
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_show=False,grid_index=index),
yaxis_opts=opts.AxisOpts(is_show=False,grid_index=index),
# 图例是否显示
)
)
return line
# 绘制K线图,
def get_Pyecharts_Kline(self,itheme="light"):
tradeAction = [] # 交易输出记录
df = self.df
valueList = []
temp_data = [] # 用于标记区域
temp_data2 = [] # 用于标记线
totalProfit = 100
# 构建日交易金额数据list
for i in range(len(self.list_BuyorSell)):
profit = self.list_BuyorSell[i][4] # 0.05
temp_data.append(opts.MarkAreaItem(name="", x=(df.loc[self.list_BuyorSell[i][0],"trade_date"], df.loc[self.list_BuyorSell[i][1],"trade_date"])))
temp_data2.append([ { "xAxis": df.loc[self.list_BuyorSell[i][0],"trade_date"],
"yAxis": df.loc[self.list_BuyorSell[i][0],"open"],
"value": "盈利：{:.2f}%".format(profit*100)
},
{
"xAxis": df.loc[self.list_BuyorSell[i][1], "trade_date"],
"yAxis": self.list_BuyorSell[i][3],
}])
totalProfit = totalProfit*(1+profit)
# 输出Excel交易操作信息
tradeAction.append([ df.loc[self.list_BuyorSell[i][0],"trade_date"], df.loc[self.list_BuyorSell[i][1], "trade_date"], round(df.loc[self.list_BuyorSell[i][0],"open"],2),round(self.list_BuyorSell[i][3],2), "{:.2f}%".format(self.list_BuyorSell[i][4]*100)])
self.totalProfit = totalProfit
# name="交易{:0>2d}\n{:.2f}%".format(i+1,profit)
returnList.append([self.name, self.code, tradeAction, round((self.totalProfit-100)/100*100,2)])
for i in range(len(df)):
valueList.append([df.loc[i, "open"], df.loc[i, "close"], df.loc[i, "high"], df.loc[i, "low"],(df.loc[i,"close"]-df.loc[i,"open"])/df.loc[i,"open"] ])
x = df["trade_date"].tolist()
y = valueList
# 绘制K线图
kline = (
Kline(init_opts=opts.InitOpts(theme=itheme,animation_opts=opts.AnimationOpts(animation=True,animation_easing_update="backOut"))) # chalk
# 添加x轴交易日期数据
.add_xaxis(x)
# 添加y轴成交价格数据
.add_yaxis(series_name="Daily Trade Data", y_axis=y, itemstyle_opts=opts.ItemStyleOpts( # 风格设置
color="red",color0="green",
border_color="#ef232a",border_color0="#14b143", # 边框色彩
),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue"
),
data=temp_data2,
symbol=["none", "arrow"],
symbol_size=10,
linestyle_opts=opts.LineStyleOpts(color="blue",width=3)
),
)
# 设计标记区 x = 开始,结束
.set_series_opts(markarea_opts=opts.MarkAreaOpts(data=temp_data,
itemstyle_opts=opts.ItemStyleOpts(opacity=0.6, # 透明度
color={
"type":'linear',
"x":1,"y":1,"x2":0,"y2":0,
"colorStops": [{"offset": 0, "color": '#F55555'}, {"offset": 1, "color": '#FCCF31'}]
}
)
)
)
# 设置x、y轴显示信息
.set_global_opts(xaxis_opts=opts.AxisOpts(name='交易时间'))
# .set_global_opts(yaxis_opts=opts.AxisOpts(name='交易价格/元'))
# 固定y轴的范围
# .set_global_opts(yaxis_opts=opts.AxisOpts(min_=5, max_=10))
.set_global_opts(title_opts=opts.TitleOpts(title="{} {} 总盈利{:.2f}% {}".format(self.code,self.name,(self.totalProfit-100)/100*100,self.isTodayBuy), subtitle='日K线图 交易价格/元'), # 标题选项
legend_opts=opts.LegendOpts(is_show=True), # 图例选项
datazoom_opts=[ # 缩放选项
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0,1],
# 初始的框选范围
range_start=80,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0,1],
type_="slider",
pos_top="95%",
range_start=80,
range_end=100,
)
],
yaxis_opts=opts.AxisOpts( # 坐标轴配置项
is_scale=True, # 是否显示y轴
splitarea_opts=opts.SplitAreaOpts( # 分割区域配置项
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))
),
# 控制x轴label
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=JsCode(
"""
function (x) {
a = x.substring(0,4);
b = x.substring(4,6);
c = x.substring(6,8);
return a+'年'+b+'月'+c+'日';
}
""")
)),
tooltip_opts=opts.TooltipOpts( # 提示框配置项
trigger="axis", # 触发类型默认
axis_pointer_type="cross", # 鼠标指针指示器
background_color="rgba(245, 245, 245, 0.8)", # 提示框漂浮层背景色
border_width=1, # 提示框漂浮层边框
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"), # 提示框文字选项
formatter=JsCode(
"""
function (x) {
date = x[0].axisValue.substring(0,4)+ '年' + x[0].axisValue.substring(4,6)+ '月' +x[0].axisValue.substring(6,8)+ '日';
open = x[0].data[1];
close = x[0].data[2];
return date + '<br>' + '开盘价：' + open + '<br>' +'收盘价：' + close + '<br>' +'涨跌幅：' + Math.round((close-open)/close*100*100)/100 + '%<br>'+ x[1].seriesName +' ：'+ x[1].data[1] + '<br>' + x[2].seriesName +'：'+ x[2].data[1] + '<br>'+ x[3].seriesName +'：'+ x[3].data[1] + '<br>'+ x[4].seriesName +'：'+ x[4].data[1] + '<br>';
}
""")
),
axispointer_opts=opts.AxisPointerOpts( # 坐标轴指示器配置项
is_show=True,
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts( # 区域选择组建配置项
x_axis_index="all", # 指定哪些 xAxisIndex 可以被刷选
brush_link="all", # 不同系列间，选中的项可以联动。
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
return kline
# 绘制成交量图形
def get_Pyecharts_Bar(self,itheme="light"):
df = self.df
valueList = []
# 构建日交易金额数据list
for i in range(len(df)):
valueList.append([df.loc[i, "open"], df.loc[i, "close"], df.loc[i, "high"], df.loc[i, "low"]])
# 绘制成交量柱状图
bar = (
Bar()
.add_xaxis(xaxis_data=df["trade_date"].tolist())
.add_yaxis(series_name="Volume",y_axis=df["vol"].tolist(),label_opts=opts.LabelOpts(is_show=False),
# 设置多图联动
xaxis_index=1,
yaxis_index=1,
tooltip_opts=opts.TooltipOpts(is_show=False),)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
# 控制x轴label
axislabel_opts=opts.LabelOpts(formatter=JsCode(
"""
function (x) {
a = x.substring(0,4);
b = x.substring(4,6);
c = x.substring(6,8);
return a+'年'+b+'月'+c+'日';
}
""")
),
type_="category",
is_scale=True,
grid_index=1,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(is_show=True),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=False),),
legend_opts=opts.LegendOpts(is_show=False),
)
)
return bar
# 绘制主图并输出页面
def Print_Main_index(self,kline,bar_volumn,line_ma=None,line_ma2=None,line_ma3=None,line_ma4 = None,itheme="light"):
bar = bar_volumn
kline.overlap(line_ma)
kline.overlap(line_ma2)
kline.overlap(line_ma3)
kline.overlap(line_ma4)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1200px", height="580px",
animation_opts=opts.AnimationOpts(animation=True,animation_easing="linear"),
theme=itheme, page_title="Pyecharts_Demo",
)
)
# 添加上图
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="10%", height="60%"),
)
# 添加下图
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="10%", pos_top="75%", height="16%"),
)
grid_chart.render(path = "C:/Users/***/Desktop/StockAnalysis/pyecharts-{}-{}.html".format(self.code[0:6],self.name))
import os
print("已保存文件至{}".format("C:/Users/***/Desktop/StockAnalysis/pyecharts-{}-{}.html".format(self.code[0:6],self.name)))
# os.system("start C:/Users/***/Desktop/pyecharts-{}-{}.html".format(self.code[0:6],self.name))
def begin(self):
self.get_daily_trade()
# self.get_Macd()
self.get_MA(5)
self.get_MA(10)
self.get_MA(20)
self.get_MA(150)
self.get_Vol_MA(5)
self.get_Vol_MA(10)
self.set_Trading_Strategy()
self.line_ma5 = self.get_Pyecharts_MA(5,2) # index由2开始
self.line_ma10 = self.get_Pyecharts_MA(10,3)
self.line_ma20 = self.get_Pyecharts_MA(20,4)
self.line_ma50 = self.get_Pyecharts_MA(150,5)
self.line_volma5 = self.get_Pyecharts_VolMA(5, 6)
self.line_volma10 = self.get_Pyecharts_VolMA(10, 7)
self.kline = self.get_Pyecharts_Kline()
self.bar_volumn = self.get_Pyecharts_Bar().overlap(self.line_volma5).overlap(self.line_volma10)
self.Print_Main_index(self.kline, self.bar_volumn, self.line_ma5, self.line_ma10, self.line_ma20, self.line_ma50)
class ExcelWriter:
path = None
wb = None
ws = None
def setLocation(self,Path):
self.path = Path
def newFile(self):
wb = Workbook()
ws = wb.active
self.wb = wb
self.ws = ws
def outList(self):
list = []
a = -100
size = 5
for i in range(70):
a = a
b = a + size
list.append([[a, b], "{}%->{}%".format(a, b), 0])
a = a + size
return list
def inList(self,n,list):
n = int(n)
for i in list:
# print(type(n), type(i[0][0]))
if int(i[0][0]) <= n and n < int(i[0][1]):
i[2] = i[2] + 1
return list
def writeData(self,Lists): # List 单个元素内容 [stockName,stockCode,[操作1,操作2,...],总盈利]
dictProfit = self.outList() # 收益率计数列表
countColumn = 1 # 控制列的输出
countRow = 1 # 控制行的输出
Avprofit = 0
for stock in Lists:
countRow = 1
# 输出股票名称及代码
self.ws.cell(row=countRow,column=countColumn).value = stock[0] + stock[1]
countRow = countRow+1
# 输出操作信息
for action in stock[2]:
self.ws.cell(row=countRow, column=countColumn).value = "{} {} {} {} {}".format(action[0],action[1],action[2],action[3],action[4])
countRow = countRow+1
self.ws.cell(row=25, column=countColumn).value = "个股收益："
self.ws.cell(row=26, column=countColumn).value = str(stock[3]) + "%"
dictProfit = self.inList(stock[3],dictProfit)
Avprofit = Avprofit + stock[3]
countColumn = countColumn + 1
# print(profit)
self.ws.cell(row=27, column=1).value = "平均收益："
self.ws.cell(row=28, column=1).value = str(round(Avprofit/len(Lists),2)) + "%"
self.ws.cell(row=30, column=1).value = "收益分布分析："
countColumn2 = 1
for i in dictProfit:
self.ws.cell(row=31, column=countColumn2).value = i[2]
self.ws.cell(row=32, column=countColumn2).value = i[1]
countColumn2 = countColumn2 + 1
def saveExcel(self):
print("保存文件至 -> {}".format(self.path))
self.wb.save(self.path)
import os
os.system("start C:/Users/***/Desktop/StockAnalysis/totalAnalysis.xlsx")
if __name__ == '__main__':
returnList = []
codeList = []
with open("C:/Users/***/Desktop/StockAnalysis/code.txt", 'r') as file:
data = file.readlines()
for i in data:
codeList.append(i.replace("\n",""))
print(codeList)
# for i in range(10):
# code = input("请输入证券代码：")
# codeList.append(code)
for i in codeList:
stock1 = Stock()
stock1.set_code(i)
stock1.start = "20210101"
stock1.end = "20211201"
stock1.begin()
ew = ExcelWriter()
ew.setLocation("C:/Users/***/Desktop/StockAnalysis/totalAnalysis.xlsx")
ew.newFile()
ew.writeData(returnList)
ew.saveExcel()
4、开发日志
交易系统开发日志
10月19日：构思完成，寻求股票交易数据。
10月21日：计划使用Tushare平台获取交易数据。
10月23日：申请Tushare高校学生权限。
10月24日：获取Tushare 1000积分，构建股票信息DataFrame。
10月26日：学习matplotlib绘制K线图。
10月27日：绘制第一张K线图。
10月31日：学习技术指标公式，尝试构建Macd指标（MACD计算结果错误，原因未核查）。
11月5日：开始接触pyecharts绘图库。
11月9日：理清pyecharts库的多图交互逻辑Grid、Overlap、Page。
11月12日：做出第一张基于Pyecharts库的可交互K线图。
11月14日：完成成交量与K线图的页面叠加。
11月18日：完成代码系统化构建。
11月25日：完成MA均线的计算与绘制。
11月26日：
完成交互的动画效果优化、
完成x轴日期格式化输出、
新增RobotTrade类：
新增自动交易机器人模型
新增set_Trading_Strategy函数：
现已支持设置自定义买入卖出条件
新增MarkArea标记域的父类继承设置：
能够自动标出买入卖出区域、自动计算当前收益率
11月28日：
重构Tooltip提示框：
删除不必要的信息
新增标记线：
现已支持显示机器人操作的收益率
新增区域标识透明度设置
12月3日：
新增ExcelWriter类，支持将结果输出到Excel：
已实现统计自选股收益信息，并将其保存。
新增对下一个交易日的行动安排
新增TxtRead方法：
现已支持导入外部自选股列表批量分析。
12月4日：
优化程序输出、
新增inList 和outList 方法：
现已支持收益率的数频统计
好啦本次的分享就到这，欢迎大家留言哟！
WeiRonbbin 😉
