websocket 達到百萬連接?(三) 端口 驗證

端口重複使用? 假象?

https://colobu.com/2019/02/23/1m-go-tcp-connection/
在看了下數據後 最下面貼文 發現了是不是只是 本地端 的假象?

來實驗一下
在使用docker 虛擬連接發現docker 創建的連線數是正常的嗎
開了兩台 client 模擬客戶端 每台上限65536 ，可以看到 server 確實達到
接近12萬連線數 所以 這個就取決的server的硬件是否強大囉

後面 其中一docker 可能掛了還是怎樣，連線數掉到剩一台client
數據 大概 滿仔數據 記憶體大概是 快接近 6g cpu接近10%左右，頻寬 回傳 1.4m

接近 剛才的 部落格實測數據

websocket 達到百萬連接?(二) Rabbitmq 基本安裝與設定

https://www.rabbitmq.com/which-erlang.html

https://www.rabbitmq.com/which-erlang.html

install erlang

https://github.com/rabbitmq/erlang-rpm/releases/download/v22.0.7/erlang-22.0.7-1.el7.x86_64.rpm

rabbitmq install

新增來源

rpm --import https://github.com/rabbitmq/signing-keys/releases/download/2.0/rabbitmq-release-signing-key.asc

#In order to use the Yum repository, a .repo file (e.g. rabbitmq.repo) has to 
#be added under the /etc/yum.repos.d/ directory. The contents of the file 
##will vary slightly between distributions (e.g. CentOS 7 vs. CentOS 6 vs. 
##OpenSUSE). The following example targets CentOS 7:

[bintray-rabbitmq-server]
name=bintray-rabbitmq-rpm
baseurl=https://dl.bintray.com/rabbitmq/rpm/rabbitmq-server/v3.7.x/el/7/
gpgcheck=0
repo_gpgcheck=0
enabled=1

yum clean all    #yum makecache


yum install rabbitmq-server
!!

基本設定

啟動服務

sudo systemctl start rabbitmq-server

查詢狀態

sudo systemctl status rabbitmq-server

設置為開機啟動

sudo systemctl enable rabbitmq-server

1. 添加用户并授权

新增使用者

sudo rabbitmqctl add_user admin pwd

設定使用者

sudo rabbitmqctl set_user_tags admin administrator

#tag（administrator，monitoring，policymaker，management）

設定使用者權限 (接收所有來自host 的所有操作)

sudo rabbitmqctl  set_permissions -p "/" admin '.*' '.*' '.*'  

查詢使用者權限

sudo rabbitmqctl list_user_permissions admin

2. 設定使用者遠端訪問

修改配置相關文件

sudo vi /etc/rabbitmq/rabbitmq.config 

儲存以下內容

[
{rabbit, [{tcp_listeners, [5672]}, {loopback_users, ["admin"]}]}
].

3、重啟 rabbitmq server

重新啟動服務

sudo systemctl restart rabbitmq-server

新增防火牆開放端口

sudo firewall-cmd --add-port=5672/tcp --permanent

重新加載防火牆設定

sudo firewall-cmd --reload

開啟後台

rabbitmq-plugins enable rabbitmq_management

教學參考

https://blog.csdn.net/VirBird/article/details/20706159
https://www.rabbitmq.com/which-erlang.html

入門級AI 電腦 Jetson nano

安裝 jetson nano

入門級ai電腦來八月 NVDIA 對外販售終於可以買來玩一下，剛好最近不太想再用VM再開虛擬機了，讓這台筆電好好休息一下，網卡出了點問題，等好了再來往下實作，裝個spark 在上面跑機器學習好惹...
買個觸控螢幕和jetson nano

主體!

intel 8265 網卡，要準備塞剛剛那片散熱片底下

csi camera

組裝外殼，外層貼紙不撕掉比較酷

螢幕外殼

通電測試看看

刷機刷機

okok

websocket 達到百萬連接?

在跟技術經理討論過一次，我們的推送 server，將會面臨到有沒有可能斷線
或者資料遺失的可能性，
假設一最多只能有 65536 個port 扣掉 端口後，剩下
可用端口可能兩三萬，那麼有沒有可能單台 server 就突破 65536 個 port呢 ?
emmmm…
在經過一番查詢是有的，那麼就是差實做囉， 我們來架設一波
首先呢我們要對 linux 進行 一些額外的設定
至於伺服器方面呢，我們把移到golang 的 websocket 伺服器

client 和 server 是run 在 同一台 虛擬機

client 端

docker搭建

https://joshhu.gitbooks.io/dockercommands/content/Containers/DockerRun.html

產生 n個 docker 映像檔並執行 client.go 編譯出來的 client  使用 docker 映射到 docker 容器裡 再讓容器 發起 m個連接到 指定 ip 上

#!/bin/bash
## This script helps regenerating multiple client instances in different network namespaces using Docker
## This helps to overcome the ephemeral source port limitation
## Usage: ./connect <connections> <number of clients> <server ip>
## Server IP is usually the Docker gateway IP address, which is 172.17.0.1 by default
## Number of clients helps to speed up connections establishment at large scale, in order to make the demo faster

CONNECTIONS=$1
REPLICAS=$2
IP=$3
#go build --tags "static netgo" -o client client.go
for (( c=0; c<${REPLICAS}; c++ ))
do
    docker run -v $(pwd)/client:/client --name 1mclient_$c -d  alpine /client -conn=${CONNECTIONS} -ip=${IP}
done
      

client 發起 10萬連接

sudo bash setup.sh 20000 5 192.168.109.129

刪除 迴圈產生出來的 docker 映像檔

sudo docker rm -f 1mclient_4
sudo docker rm -f 1mclient_3
sudo docker rm -f 1mclient_2
sudo docker rm -f 1mclient_1
sudo docker rm -f 1mclient_0

server 端

linux 調效篇

這篇講得不錯很容易懂
http://blog.udn.com/q928856957/25855941

https://blog.csdn.net/xiexingshishu/article/details/43373297

https://serverfault.com/questions/769978/sysctl-cannot-stat-proc-sys-net-ipv4-netfilter-ip-conntrack-max-no-such-file

sudo sysctl -w fs.file-max=2000500
sudo sysctl -w fs.nr_open=2000500
sudo sysctl -w net.nf_conntrack_max=2000500
sudo ulimit -n 2000500
sudo sysctl -w net.ipv4.tcp_mem='131072  262144  524288'
sudo sysctl -w net.ipv4.tcp_rmem='8760  256960  4088000'
sudo sysctl -w net.ipv4.tcp_wmem='8760  256960  4088000'
sudo sysctl -w net.core.rmem_max=16384
sudo sysctl -w net.core.wmem_max=16384
sudo sysctl -w net.core.somaxconn=2048
sudo sysctl -w net.ipv4.tcp_max_syn_backlog=2048
sudo sysctl -w /proc/sys/net/core/netdev_max_backlog=2048
sudo sysctl -w net.ipv4.tcp_tw_recycle=1
sudo sysctl -w net.ipv4.tcp_tw_reuse=1
sudo echo 2000500 > /proc/sys/fs/nr_open

https://www.google.com/search?q=centos+開啟+端口&rlz=1C1WPZA_enTW818TW818&oq=centos+開啟+端口&aqs=chrome..69i57.6505j0j7&sourceid=chrome&ie=UTF-8

Run server!

組態檔，我們把onlyTestConnect 設為 false

{
        "port": 8888,
        "delay": "1m",
        "interval": "1m",
        "totalSize": 1,
        "onlyTestConnect": false
}

server.go

package main

import (
 "container/list"
 "fmt"
 "golang.org/x/net/websocket"
 "net/http"
 "strconv"
 "time"
 "encoding/json"
 "os"
 "sync"
 "goserver"
 "math/rand"
 //"unsafe"
)

var Config goserver.Configuration
const n = 10
var wsList = [n]*list.List {list.New(),list.New(),list.New(),list.New(),list.New(),list.New(),list.New(),list.New(),list.New(),list.New()}
var locks = [n]sync.RWMutex {}


func wsHandler(ws *websocket.Conn) {
 //p := unsafe.Pointer(&ws)
 //index := ((int)(uintptr(p))) % n
 index := rand.Intn(n)
 lock := locks[index]
 lock.Lock()
 wsList[index].PushBack(ws)  
 lock.Unlock()
 
 for {
  var reply string
  if err := websocket.Message.Receive(ws, &reply); err != nil {
   fmt.Println("Can't receive because of " + err.Error())
   break
  }
 }
 
 lock.Lock()
 for e := wsList[index].Front(); e != nil; e = e.Next() {
  if e.Value.(*websocket.Conn) == ws {
   wsList[index].Remove(e)
   break
  }
 }
 lock.Unlock()
}

func load(configfile string) goserver.Configuration {
 config := goserver.Configuration{}
 file, _ := os.Open(configfile)
 decoder := json.NewDecoder(file)
 err := decoder.Decode(&config)
 if err != nil {
  panic(err.Error())
 }

 return config
}

func main() {
 seed := time.Now().UTC().UnixNano()
 rand.Seed(seed)

 Config = load("config.json")
 
 //http.Handle("/", websocket.Handler(wsHandler))
 http.HandleFunc("/", func(w http.ResponseWriter, req *http.Request) {
  s := websocket.Server{Handler: websocket.Handler(wsHandler)}
  s.ServeHTTP(w, req)
 })
 
 delay, _ := time.ParseDuration(Config.Delay)
 interval, _ := time.ParseDuration(Config.Interval)
 timer := time.NewTimer(delay)
 if !Config.OnlyTestConnect {
  go func() {
   for {
    <-timer.C
    timer.Reset(interval)
    totalLen := 0
    for i :=0; i < n; i++ {
     totalLen += wsList[i].Len()
    }
    if totalLen >= Config.TotalSize {
     fmt.Println("send timestamp to all")
     for i :=0; i < n; i++ {
      i := i
      go func() {
       for e := wsList[i].Front(); e != nil; e = e.Next() {
        var ws = e.Value.(*websocket.Conn)
        now := time.Now().UnixNano() / int64(time.Millisecond)
        err := websocket.Message.Send(ws, strconv.FormatInt(now, 10))
        if err != nil {
         panic("Error: " + err.Error())
        }
       }
      }()

     }     
    } else {
     fmt.Println("current websockets: " + strconv.Itoa(totalLen))
    }
    
   }
  }()
 }

 err := http.ListenAndServe(":"+strconv.Itoa(Config.Port), nil)
 if err != nil {
  panic("Error: " + err.Error())
 }
}

emm… emmemm. 看起來差不多調校好了??

記憶體吃滿了先開 10w client 玩玩看 ，不過單台 確實突破 65536 的迷思 (?

中間能不能再夾一層 nginx 呢進行更多變化呢或優化呢… 看來要在 買一些洋垃圾組一台 server

端口重複使用? 假象?

https://colobu.com/2019/02/23/1m-go-tcp-connection/
在看了下數據後 最下面貼文 發現了是不是只是 本地端 的假象?

來實驗一下
在使用docker 虛擬連接發現docker 創建的連線數是正常的嗎
開了兩台 client 模擬客戶端 每台上限65536 ，可以看到 server 確實達到
接近12萬連線數 所以 這個就取決的server的硬件是否強大囉

後面 其中一docker 可能掛了還是怎樣，連線數掉到剩一台client
數據 大概 滿仔數據 記憶體大概是 快接近 6g cpu除了每個一分鐘回傳滿載
幾乎 接近10%左右，頻寬 回傳 1.4m

接近 剛才的 部落格實測數據

參考

https://colobu.com/2019/02/23/1m-go-tcp-connection/
https://github.com/eranyanay/1m-go-websockets
https://github.com/smallnest/C1000K-Servers

flask python vue 股票回測 (二) 隨機森林演算法

隨機森林演算法

翻到以前的code來補齊一下，新增隨機森林演算法
預測與權重誰比較重要
在漲跌來看，在一股價移動平均線5天來看最可以影響漲跌，
在股票不只股票，像是天氣下雨，濕度.....都可以使用隨機森林找到最有相關性的特徵!?

在判斷 當天和隔天的狀況下可以經由判斷前一天的close 收盤價
新增一個 新的一個特徵點 label假設為漲跌則設為1 否則為0

	sma_5 = talib.SMA(np.array(close), 5)
	sma_10 = talib.SMA(np.array(close), 10)
	sma_20 = talib.SMA(np.array(close), 20)
	sma_60 = talib.SMA(np.array(close), 60)
	macd = get_MACD(np.array(close))
	#
	data_list4 = []
	for x in range (len (randomforest_list)-1 , 33 ,-1 ):
	# 今天大於 昨天 等於 漲跌
	tmp =0
	if(x+1 <len (randomforest_list)):
	if((randomforest_list[x][4] > randomforest_list[x+1][4]) ):
	tmp=1
	else:
	tmp=0
	# datas2 = (t, ope[x], high[x], low[x],close[x],vol[x])
	#
	# data_list4.append({'account': str(data_list2[x][0]),data_list2[x][1],data_list2[x][2],data_list2[x][3],data_list2[x][4],data_list2[x][5],sma_5[x]
	# ,sma_10[x],sma_20[x],sma_20[x], macd[x] ,tmp})
	data_list4.append({ 'date': str(randomforest_list[x][0]).split()[0],'open': str(randomforest_list[x][1]) ,
	'high': str(randomforest_list[x][2]),'low': str(randomforest_list[x][3]) ,'close': str(randomforest_list[x][4]),'vol': str(randomforest_list[x][5])
	,'ma5': sma_5[x] ,'ma10':sma_10[x] ,'ma20': sma_20[x],'macd': macd[x],'label': tmp})
	df = pd.DataFrame( data_list4)
	df = df[['date','open','high','low','close','vol','ma5','ma10','ma20','macd','label']]
	#
	# df.set_index("date" , inplace=True)
	#
	# df.to_csv("./sss.csv", index=True)
	df = df.dropna() #剔除缺失值
	df['ts'] =pd.to_datetime(df['date'])
	df = df.drop('date', axis=1)
	# df = df.set_index(['ts'])
	df.to_csv("./sss.csv", index=True)
	#
	train_data = df[df['ts']<"2017-01-04"]
	test_data = df[df['ts']>="2017-01-04"]
	train_X = train_data.ix[:,'open':"ma20"].values
	train_y = train_data['label'].values
	test_X = test_data.ix[:,'open':"ma20"].values
	test_y = test_data['label'].values
	clf = RandomForestClassifier(max_depth=10,n_estimators=100 )
	clf.fit(train_X,train_y)
	print(accuracy_score(train_y,clf.predict(train_X)))
	print(accuracy_score(test_y,clf.predict(test_X)))
	importance = clf.feature_importances_
	indices = np.argsort(importance)[::-1]
	feat_labels = df.columns[1:]
	# features = train_X.columns
	test =[]
	for f in range(train_X.shape[1]):
	test.append([feat_labels[indices[f]], importance[indices[f]]])
	# data_list4=str(accuracy_score(train_y,clf.predict(train_X)))+"next :"+str(accuracy_score(test_y,clf.predict(test_X)))
	data_list4=test

view raw randomforest_list.py hosted with ❤ by GitHub

flask python vue 股票回測 (一) Vue移植

搬移模組目前大致完成進度50%
會延遲不是 Lag xd 是我用 gif 錄影 fps 調太低不然檔案很大

目前新增各模組

• bband
• macd 
• flask restful api

繪圖速度大概是 python matplotlib 好幾倍
來跟大數據結合一下好了看看
這邊將會把後端全部改為 hadoop?
機器學習部分改為用spark訓練
然後每天回傳固定股市，漲幅等等

以前用python 做的回測軟體，現在要全部搬移到 vue上面，
在之前前幾次有做了幾個教學就是golang架設伺服器篇
https://x8795278.blogspot.com/2018/09/golang-json.html

新的版面

預計全部塞到裡面，做個良好的回測 準備開工!

舊的

	# -*- coding: utf-8 -*-
	"""
	Created on Wed Dec 20 17:46:15 2017
	@author: user
	"""
	import sys
	from flask import Flask
	from flask_restful import reqparse, abort, Api, Resource
	import logging
	from logging.handlers import RotatingFileHandler
	import urllib.request, json
	from tkinter import *
	from tkinter import messagebox
	from tkinter import ttk
	import tkinter as tk
	import datetime
	from tkinter import HORIZONTAL
	from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
	from matplotlib.figure import Figure
	import numpy as np
	import pandas as pd
	import csv
	import talib
	import os
	import time
	import random
	import tkinter.filedialog as filedialog
	import threading
	import warnings
	warnings.filterwarnings('ignore')
	import urllib.request; #用來建立請求
	import urllib.parse;
	from bs4 import *
	import matplotlib.pyplot as plt
	from mpl_finance import candlestick_ohlc
	from mpl_finance import volume_overlay3
	import matplotlib.dates as dates
	from datetime import datetime
	import datetime as datetmp
	from stocker import Stocker
	import strategy.bband as bb
	import strategy.rsi as rsi
	import strategy.popular_model as popmoder
	from datetime import datetime
	plt.rcParams['font.sans-serif'] = ['DFKai-sb']
	plt.rcParams['font.family']='sans-serif'
	data_list = []
	data_list2 = []
	data_list3 = []
	#購買資料
	buy_list=[]
	sell_list=[]
	#root=tk.Tk()
	#var = DoubleVar(root,0)
	#var2 = DoubleVar(root,0)
	#var3 = tk.IntVar(root,0)
	#var4 = tk.IntVar(root,0)
	#
	#var5 = tk.StringVar(root,0)
	#var6 = tk.StringVar(root,0)
	#var9 = tk.StringVar(root,0)
	#var10 = tk.StringVar(root,0)
	#var11 = tk.StringVar(root,0)
	#var12 = tk.StringVar(root,0)
	#
	#var7 = tk.IntVar(root,0)
	#var8 = tk.IntVar(root,0)
	##個別參數
	global start
	global end
	global str_tmp
	global date
	global ope
	global close
	global high
	global low
	global vol
	global date
	global date_ex
	global BBand_up
	global BBand_sma
	global BBand_down
	global BBand_width
	global BBand_mid
	global catch_stock
	global catch_stockgarbage
	global fbpropht_data
	global deposit
	global draw_tmp
	global thread_bolean
	##模組設定
	global strategy_model
	#1216統一 3144新揚科
	num_48 = ["1216統一"]
	df=pd.DataFrame()
	df2=pd.DataFrame(num_48)
	#技術指標
	def get_MACD(close):
	MACD = talib.MACD(close,
	fastperiod=12, slowperiod=26, signalperiod=9)
	return [float(x) for x in MACD [1]]
	def get_DIF(close):
	DIF=[]
	EMA12=talib.EMA(close, timeperiod=12)
	EMA26=talib.EMA(close, timeperiod=26)
	for tmp in range(0,len(EMA12)):
	DIF.append(float(EMA12[tmp]-EMA26[tmp]))
	return DIF
	def get_BBand(close):
	#BBband
	BBand = talib.BBANDS(close,timeperiod=20,nbdevup=1.5,nbdevdn=1.5)
	return BBand
	def get_OSC(macd,dif):
	OSC=[]
	for tmp in range(0,len(macd),1):
	OSC.append(float(macd[tmp]-dif[tmp]))
	return OSC
	def get_SMA(close):
	SMA= talib.MA(np.array(close), timeperiod=10, matype=0)
	return SMA
	def get_fastktd(high,low,close):
	fastk, fastd = talib.STOCHF(high, low,close, fastk_period=60, fastd_period=7, fastd_matype=0)
	return (fastk,fastd)
	#def call__band()
	def backer(model_name):
	global start
	global end
	global str_tmp
	global ope
	global close
	global high
	global low
	global vol
	global date
	global date_ex
	global BBand_up
	global BBand_sma
	global BBand_width
	global BBand_down
	global deposit
	#交易策略參數
	str_tmp="回測模組:" + model_name +'\n'
	star=100000000 #初始金額無變動
	price=100000000 #變動初始金額
	star=price
	ex_handle=20 #一次股數上限
	#參數list
	data_param=[star,price,ex_handle,buy_list,sell_list,data_list ,data_list2 ,data_list3 ,start,end,str_tmp,ope,close,high,low,vol,date,date_ex]
	if (model_name == "BBand"):
	str_tmp += bb.bband(data_param)
	elif (model_name == "Rsi"):
	str_tmp += rsi.rsi(data_param)
	elif (model_name == "show_fbpropht"):
	show_fbpropht()
	def read_file(file):
	global start
	global end
	global str_tmp
	global date
	global ope
	global close
	global high
	global low
	global vol
	global date_ex
	global deposit
	global BBand
	global BBand_up
	global BBand_sma
	global BBand_down
	global BBand_mid
	global BBand_width
	# path_str=os.path.join("C:\\","Users","x2132","OneDrive","桌面","History_data",iii)
	# path_str=path_str+'.csv'
	path_str=file
	print (path_str)
	#收盤
	c=[row[1] for row in file] #要導入的列
	#開盤
	o=[row[2] for row in file] #要導入的列
	#最高
	h=[row[3] for row in file] #要導入的列
	#最低
	l=[row[4] for row in file] #要導入的列
	#成交量
	v=[row[5] for row in file] #要導入的列
	#日期
	d=[row[0] for row in file] #要導入的列
	date=[]
	low=[]
	high=[]
	close=[]
	ope=[]
	close=[]
	vol=[]
	date.clear()
	low.clear()
	high.clear()
	close.clear()
	ope.clear()
	vol.clear()
	ope = [float(x) for x in o]
	close = [float(x) for x in c]
	high = [float(x) for x in h]
	low = [float(x) for x in l]
	vol = [float(x) for x in v]
	date =d
	date_ex=[]
	date.reverse()
	low.reverse()
	high.reverse()
	close.reverse()
	ope.reverse()
	vol.reverse()
	#print (BBand_width)
	for x in range(0,len(ope),1):
	#datess =dates.date2num(d[x])
	#print (datetime.datetime(2017, 3, 13))
	#print (dates.date2num(datetime.datetime(2017, 3, 13, 12, 0)))
	#print (datetime.datetime(2017, 3, 13, 12, 0))
	# t = d[x].replace('/','-')
	t= datetime.fromtimestamp(d[x]/1000)
	#print ((dates.date2num(datetime.strptime(t, '%Y-%m-%d'))))
	# (type(datetime.datetime(2017, 3, 13, 12, 0)))
	#print (type(dates.date2num(datetime.datetime(2017, 3, 13, 12, 0))))
	#print(dates.date2num(datetime.datetime(2017, 3, 13, 12, 0)))
	datas = (t , ope[x], high[x], low[x],close[x])
	datas2 = (t, ope[x], high[x], low[x],close[x],vol[x])
	datas3 = (t,close[x])
	date_ex.append(t)
	data_list.append(datas)
	data_list2.append(datas2)
	data_list3.append(datas3)
	start=0
	end=len(data_list)
	# w=tk.Scale(frame2, from_=start, to=end-1, orient=HORIZONTAL, variable = var,length=200 ).grid(row=2,column=0)
	# w2=tk.Scale(frame2, from_=start, to=end-1, orient=HORIZONTAL, variable = var2,length=200).grid(row=2,column=1)
	# var2.set(int(end-1))
	df = pd.DataFrame(data_list3, columns=list(['date','deposit']))
	#print (df)
	# df=pd.DataFrame(datas, index=False)
	# df.insert(0,'股票代號',df2)
	print(df)
	df.to_csv("price.csv", index=False)
	drawPic()
	# tmp2=10
	# print (str(tmp2))
	# tmp =type(tmp2)
	# fig, ax = plt.subplots()
	# print (fig)
	# print (ax)
	# fig.subplots_adjust(bottom=0.2)
	# # 设置X轴刻度为日期时间
	# #data_list.reverse()
	# ax.xaxis_date()
	# plt.xticks(rotation=45)
	# plt.yticks()
	# plt.title("k線圖")
	# plt.xlabel("時間")
	# plt.ylabel("股價")
	# #print (len(data_list))
	# candlestick_ohlc(ax, data_list[1000:2000], width=0.5, colorup='r', colordown='g')
	# for x in range(0,len(buy_list),1):
	# ax.annotate('', xy=(buy_list[x][0],buy_list[x][1]), xytext=(-15, -27),
	# textcoords='offset points', ha="center",
	# arrowprops=dict(facecolor='black', color='red',width=1),
	# )
	# for x in range(0,len(sell_list),1):
	# ax.annotate('', xy=(sell_list[x][0],sell_list[x][1]), xytext=(-17, 20),
	# textcoords='offset points', ha="center",
	# arrowprops=dict(facecolor='black', color='green',width=1),
	# )
	# #print('hello')
	# pro_fit='獲利金額:'+str((int(fin_price-star)))
	# buy_count='交易次數 買/賣:'+str(len(buy_data))+'/'+str(len(self_data))
	# plt.figtext(0.1,0.92,pro_fit+'\n'+buy_count,color='black')
	# #plt.grid()
	# plt.savefig("filename.png")
	# #plt.show()
	# print (buy_list[x][1])
	# # print (buy_list)
	# # print (MACD[500])
	# # print (start)
	# # print (end)
	def data_deal():
	#Return the day of the week as an integer, where Monday is 0 and Sunday is 6.
	now = datetime.now()
	df_statements=[]
	tmp=[]
	print (now)
	#(datetime.datetime.now()+datetime.timedelta(days=-1)).strftime("%Y-%m-%d %H:%M:%S")
	for y in range (0,10000,1):
	now_stock_number=""
	tmp=[]
	for x in range (int(now.weekday()),2000,1):
	d2 =now+datetmp.timedelta(days=-x)
	file_path = "./history_data/"+d2.strftime("%Y%m%d")+"statements.csv"
	if (os.path.isfile(file_path)):
	print("File exists!")
	if (int(d2.weekday()) >=0 and int(d2.weekday()) <=5 and int(d2.weekday())!= int(now.weekday())):
	print (str(d2.strftime("%Y%m%d")))
	test = pd.read_csv(file_path)
	try:
	#print (test.loc[y,['證券代號','證券名稱']])
	now_stock_number=str(test.loc[y,['證券代號']][0])
	#print (test.loc[0,['證券代號']][0])
	test.loc[y,'日期']=str(d2.strftime("%Y/%m/%d"))
	tmp.append((test.loc[y,['日期','成交股數','成交筆數','成交金額','開盤價','最高價','最低價','收盤價','漲跌(+/-)','本益比']]))
	print (tmp)
	except:
	print("An exception occurred")
	continue
	continue
	df = pd.DataFrame(tmp, columns=list(['日期','成交股數','成交筆數','成交金額','開盤價','最高價','最低價','收盤價','漲跌(+/-)','本益比']))
	df.to_csv("./stock/"+now_stock_number+".csv", index=False)
	# df_statements=popmoder.get_today_tw(str(d2.strftime("%Y%m%d")))
	# x=random.randint(17, 20)
	# print (x)
	# time.sleep(x)
	#break
	#print (df_statements.loc[0,['證券代號','證券名稱','成交股數','成交筆數','成交金額','開盤價','最高價','最低價','收盤價','漲跌(+/-)','本益比']])
	#畫圖區~~~~~~~~~
	def get_statements():
	#Return the day of the week as an integer, where Monday is 0 and Sunday is 6.
	now = datetime.now()+datetmp.timedelta(days=-1420)
	df_statements=[]
	#(datetime.datetime.now()+datetime.timedelta(days=-1)).strftime("%Y-%m-%d %H:%M:%S")
	for x in range (int(now.weekday()),1060,1):
	d2 =now+datetmp.timedelta(days=-x)
	file_path = "./history_data/"+d2.strftime("%Y%m%d")+"statements.csv"
	if (os.path.isfile(file_path)):
	print("File exists!")
	continue
	if (int(d2.weekday()) >=0 and int(d2.weekday()) <=5 and int(d2.weekday())!= int(now.weekday())):
	print (str(d2.strftime("%Y%m%d")))
	df_statements=popmoder.get_today_tw(str(d2.strftime("%Y%m%d")))
	x=random.randint(17, 20)
	print (x)
	time.sleep(x)
	#break
	#print (df_statements.loc[0,['證券代號','證券名稱','成交股數','成交筆數','成交金額','開盤價','最高價','最低價','收盤價','漲跌(+/-)','本益比']])
	create_gride_statements(df_statements)
	def create_gride_statements(df_statements):
	## 篩選本益比
	#table grid
	height = 20
	width = 10
	namR = []
	parman=['證券代號','證券名稱','成交股數','成交筆數','成交金額','開盤價','最高價','最低價','收盤價','漲跌(+/-)','本益比']
	x=0
	z=0
	#df2 = df_statements[pd.to_numeric(df_statements['本益比'], errors='coerce') < 15]
	#df2.to_csv("./sss.csv", index=False)
	df_statements = pd.read_csv("20181224statements.csv")
	for i in range(height): #Rows
	z=0
	for j in range(width): #Columns
	namR.append(tk.Entry(frame6))
	namR[x].grid(row=i, column=j)
	namR[x].insert(0,str(df_statements.loc[x,parman[z]]))
	x+=1
	z+=1
	#for x in range (0,len(namR),1):
	# namR[x].insert(x,str(x))
	def show_fbpropht():
	global fbpropht_data
	df = pd.read_csv('price.csv', index_col='date', parse_dates=['date'])
	price = df.squeeze()
	price.head()
	tsmc = Stocker(price)
	model, model_data ,ax, fig= tsmc.create_prophet_model(days=90)
	tsmc.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2])
	fbpropht_data = model_data
	drawPic.f2.clf()
	drawPic.f2 = fig
	drawPic.canvas2 = FigureCanvasTkAgg(drawPic.f2, master=frame2)
	drawPic.a =ax
	drawPic.a.set_title('price')
	drawPic.f2.subplots_adjust(hspace=0)
	drawPic.canvas2.show()
	drawPic.canvas2.get_tk_widget().grid(row=0,column=5,columnspan=1 , rowspan=6)
	fbpropht_data0 = pd.read_csv('fbprophet0.csv')
	fbpropht_data1 = pd.read_csv('fbprophet1.csv')
	fbpropht_data2 = pd.read_csv('fbprophet2.csv')
	fbpropht_data3 = pd.read_csv('fbprophet3.csv')
	gain ( "fbpropht_data0",close [ len (close) -1 ],fbpropht_data0[fbpropht_data0['ds']>( date[len(date)-1]) ])
	gain ( "fbpropht_data1",close [ len (close) -1 ],fbpropht_data1[fbpropht_data1['ds']>( date[len(date)-1]) ])
	gain ( "fbpropht_data2",close [ len (close) -1 ],fbpropht_data2[fbpropht_data2['ds']>( date[len(date)-1]) ])
	gain ( "fbpropht_data3",close [ len (close) -1 ],fbpropht_data3[fbpropht_data3['ds']>( date[len(date)-1]) ])
	#print ( fbpropht_data0[fbpropht_data0['ds']>( date[len(date)-1]) ])
	#print ()
	tk.messagebox.showinfo("show_fbpropht","預言家模組開啟")
	def gain (name,inital,data):
	data.columns = range(len(data.columns))
	data.index = range(len(data.index))
	print (name)
	#print (data)
	print ( "前"+str((int(len(data)/4)) *1)+"筆漲幅" + str(float( ((data.loc [ int(len(data)/4) *1,1]-inital ) /inital )*100)))
	print ( "前"+str((int(len(data)/4)) *2)+"筆漲幅" + str(float( ((data.loc [ int(len(data)/4) *2,1]-inital ) /inital )*100)))
	print ( "前"+str((int(len(data)/4)) *3)+"筆漲幅" + str(float( ((data.loc [ int(len(data)/4) *3,1]-inital ) /inital )*100)))
	print ( "前"+str((int(len(data)/4)) *4)+"筆漲幅" + str(float( ((data.loc [ int(len(data)/4) *4,1]-inital ) /inital )*100)))
	#print ("60天漲幅" + str(float( ((data.loc [ int( 738+ 60) ,["trend"]]-inital) /inital )*100)))
	#print ("90天漲幅" + str(float( ((data.loc [ int(738+ 90) ,["trend"]]-inital) /inital )*100)))
	#print ("120天漲幅" + str(float( ((data.loc [ int(738+ 120) ,["trend"]]-inital) /inital )*100)))
	def show_lstm():
	print ("暫時")
	def show_info():
	global deposit
	global str_tmp
	#print (deposit)
	#tk.messagebox.showinfo("績效",str_tmp)
	def clock():
	global start
	global end
	t=time.strftime('%I:%M:%S',time.localtime())
	if t!='':
	#var5.set(date[int(var.get())])
	start=var.get()
	#var6.set(date[int(var2.get())])
	end=var2.get()
	root.after(500,clock)
	def drawPic():
	global deposit
	global str_tmp
	global date_ex
	global BBand_up
	global BBand_sma
	global BBand_down
	global BBand_width
	global BBand_mid
	global strategy_model
	global draw_tmp
	global deposit
	global str_tmp
	global thread_bolean
	global buy_list
	global sell_list
	buy_list=[]
	sell_list=[]
	# root.title("執行繪圖中..")
	# strategy_model =numberChosen.get()
	## 取得模組名稱
	backer("BBand")
	print("testend")
	# drawPic.f.clf()
	# draw_tmp.f.clf()
	#ax.set_xticks([])
	#frame.axes.get_yaxis().set_visible(False)
	#
	# drawPic.a =drawPic.f.add_axes([0.1,0.4,0.8,0.5])
	# drawPic.a2 = drawPic.f.add_axes([0.1,0.190,0.8,0.2])
	#
	# drawPic.f.subplots_adjust(hspace=0)
	# drawPic.f.tight_layout()
	# drawPic.a.xaxis_date()
	# drawPic.a2.xaxis_date()
	# for label in drawPic.a.xaxis.get_ticklabels():
	# label.set_rotation(45)
	# for label in drawPic.a2.xaxis.get_ticklabels():
	# label.set_rotation(45)
	# drawPic.a.set_title("K線圖", fontsize=20)
	# drawPic.a2.set_xlabel("時間", fontsize=10)
	# drawPic.a.set_ylabel("股價", fontsize=10)
	#drawPic.a2.set_ylabel("成交量", fontsize=10)
	# drawPic.a.set_title("K", fontsize=20)
	# drawPic.a2.set_xlabel("time", fontsize=10)
	# drawPic.a.set_ylabel("price", fontsize=10)
	# drawPic.a2.set_ylabel("volume", fontsize=10)
	# 设置X轴刻度为日期时间
	#data_list.reverse()
	sma_5 = talib.SMA(np.array(close), 5)
	sma_10 = talib.SMA(np.array(close), 10)
	sma_20 = talib.SMA(np.array(close), 20)
	sma_60 = talib.SMA(np.array(close), 60)
	#print (len(data_list))
	# start =int(var.get())
	# end=int(var2.get())
	# if(var4.get()==1):
	# drawPic.a.plot( date_ex[start:end:5],sma_5[start:end:5], label='SMA5')
	# drawPic.a.plot( date_ex[start:end:10],sma_10[start:end:10], label='SMA10')
	# drawPic.a.plot( date_ex[start:end:20],sma_20[start:end:20], label='SMA20')
	# drawPic.a.plot( date_ex[start:end:60],sma_60[start:end:60], label='SMA60')
	# drawPic.a.legend(loc='upper left')
	#
	# var5.set(date[start])
	# var6.set(date[end])
	# candlestick_ohlc(drawPic.a, data_list[start:end+1], width=0.5, colorup='r', colordown='g')
	#tupleto_listtmp=[]
	pos_d =[]
	pos_v =[]
	neg_d =[]
	neg_v =[]
	#開盤-收盤
	for x in data_list2[start:end+1]:
	if((x[1]-x[4])<=0):
	pos_d.append(x[0])
	pos_v.append(x[5])
	elif ((x[1]-x[4])>0):
	neg_d.append(x[0])
	neg_v.append(x[5])
	#
	# drawPic.a2.bar(pos_d,pos_v,color='red',width=0.5,align='center')
	# drawPic.a2.bar(neg_d,neg_v,color='green',width=0.5,align='center')
	#drawPic.a2.bar(pos[0][0],pos[0][1],color='green',width=1,align='center')
	#print (tupleto_listtmp)
	# for x in pos:
	# drawPic.a2.bar(x[0],x[1],color='green',width=1,align='center')
	#for x in neg:
	# drawPic.a2.bar(x[0],x[1],color='red',width=1,align='center')
	#drawPic.a2.bar(pos[:][0],pos[:][1],color='green',width=1,align='center')
	#drawPic.a2.bar(neg[:][0],neg[:][1],color='red',width=1,align='center')
	#volume_overlay3(drawPic.a2,data_list2[start:end], colorup='r', colordown='g', width=0.5, alpha=0.8)
	# if(var7.get()==1):
	BBand=popmoder.get_BBand(np.asarray(close))
	BBand_up=[]
	BBand_sma=[]
	BBand_down=[]
	BBand_up=[float(x) for x in BBand [0]]
	BBand_sma=[float(x) for x in BBand [1]]
	BBand_down=[float(x) for x in BBand [2]]
	# drawPic.a.plot( date_ex[start:end:1],BBand_sma[start:end:1], label='BBnad')
	# drawPic.a.plot( date_ex[start:end:1],BBand_up[start:end:1], label='BBnad_up')
	# drawPic.a.plot( date_ex[start:end:1],BBand_down[start:end:1], label='BBnad_down')
	# drawPic.a.legend(loc='upper left')
	# if(var8.get()==1):
	BBand_up=[float(x) for x in BBand [0]]
	BBand_sma=[float(x) for x in BBand [1]]
	BBand_down=[float(x) for x in BBand [2]]
	BBand_mid=[]
	BBand_width=[]
	for x in range(0,len(BBand_sma),1):
	BBand_mid.append((BBand_up[x]+BBand_down[x])/2)
	#print ((BBand_mid))
	for x in range(0,len(BBand_mid),1):
	BBand_width.append(100*((BBand_up[x]-BBand_down[x])/BBand_mid[x]))
	# drawPic.a.plot( date_ex[start:end:1],BBand_width[start:end:1], label='BBnad_width')
	# drawPic.a.legend(loc='upper left')
	# for x in range(start,end,1):
	# drawPic.a.plot( date_ex[start:end:1],BBand_sma[start:end:1], label='bb')
	# drawPic.a.plot(date_ex[x],BBand_sma[x], label='bb')
	# if(var3.get()==1):
	# for x in range(0,len(buy_list),1):
	# drawPic.a.annotate('', xy=(buy_list[x][0],buy_list[x][1]), xytext=(-15, -27),
	# textcoords='offset points', ha="center",
	# arrowprops=dict(facecolor='black', color='red',width=0.5),
	# )
	#
	# for x in range(0,len(sell_list),1):
	# drawPic.a.annotate('', xy=(sell_list[x][0],sell_list[x][1]), xytext=(-17, 20),
	# textcoords='offset points', ha="center",
	# arrowprops=dict(facecolor='black', color='green',width=0.5),
	# )
	#
	# drawPic.a.grid()
	# drawPic.a2.grid()
	# drawPic.canvas.show()
	df = pd.read_csv('price2.csv', index_col='date', parse_dates=['date'])
	price = df.squeeze()
	price.head()
	# drawPic.f2 = Figure(figsize=(10,4))
	# drawPic.canvas2 = FigureCanvasTkAgg(drawPic.f2, master=frame2)
	# drawPic.f2.clf()
	# drawPic.a =drawPic.f2.add_axes([0.1,0.1,0.8,0.8])
	# drawPic.a.set_title('price')
	# drawPic.f2.subplots_adjust(hspace=0)
	# drawPic.a.plot(price)
	# drawPic.a.text(0.13, 0.3, str_tmp,transform=drawPic.f2.transFigure, fontsize=15, color='red',)
	#
	# drawPic.canvas2.show()
	# #drawPic.f2.set_size_inches(7, 3, forward=True)
	# drawPic.canvas2.get_tk_widget().grid(row=0,column=5,columnspan=1 , rowspan=6)
	show_info()
	# root.title("回測工具")
	def insert_point():
	var = e.get(root)
	tk.messagebox.showinfo("績效",e.get())
	def get_historical_data(name, number_of_days):
	global catch_stockgarbage
	max_bb=0
	data = []
	url = "https://finance.yahoo.com/quote/" + name + "/history/"
	try:
	rows = BeautifulSoup(urllib.request.urlopen(url).read()).findAll('table')[0].tbody.findAll('tr')
	for each_row in rows:
	divs = each_row.findAll('td')
	if divs[1].span.text != 'Dividend': #Ignore this row in the table
	#I'm only interested in 'Open' price; For other values, play with divs[1 - 5]
	date_process=divs[0].span.text.replace(",", "")
	date_process=datetime.strptime(date_process, '%b %d %Y').date().strftime('%Y/%m/%d')
	t = date_process.replace('/','-')
	date_process = (dates.date2num(datetime.strptime(t, '%Y-%m-%d')))
	#date_process=datetime.datetime.strptime(date_process, '%b %d %Y').date().strftime('%m %d %Y')
	#data.append({'Date': str(date_process), 'Open': float(divs[1].span.text.replace(',',''))\
	# , 'High': float(divs[2].span.text.replace(',',''))\
	# , 'Low': float(divs[3].span.text.replace(',',''))\
	# , 'Close': float(divs[4].span.text.replace(',',''))\
	# , 'Volume': float(divs[6].span.text.replace(',',''))})
	#datas2 = (dates.date2num(datetime.strptime(t, '%Y-%m-%d')) , ope[x], high[x], low[x],close[x],vol[x])#
	data.append([ str(date_process), float(divs[1].span.text.replace(',',''))\
	, float(divs[2].span.text.replace(',',''))\
	, float(divs[3].span.text.replace(',',''))\
	, float(divs[4].span.text.replace(',',''))\
	, float(divs[6].span.text.replace(',',''))])
	#篩選指標
	date.clear()
	low.clear()
	high.clear()
	close.clear()
	ope.clear()
	vol.clear()
	for x in data:
	date.append(x[0])
	ope.append(x[1])
	high.append(x[2])
	low.append(x[3])
	close.append(x[4])
	vol.append(x[5])
	date.reverse()
	low.reverse()
	high.reverse()
	close.reverse()
	ope.reverse()
	vol.reverse()
	BBand=get_BBand()
	BBand_up=[float(x) for x in BBand [0]]
	BBand_sma=[float(x) for x in BBand [1]]
	BBand_down=[float(x) for x in BBand [2]]
	BBand_mid=[]
	BBand_width=[]
	for x in range(0,len(BBand_sma),1):
	BBand_mid.append((BBand_up[x]+BBand_down[x])/2)
	for x in range(0,len(BBand_mid),1):
	BBand_width.append(100*((BBand_up[x]-BBand_down[x])/BBand_mid[x]))
	#print (BBand_width)
	#print (max(BBand_width[20:]))
	print ('----------')
	max_vol=max(vol[-10:])
	#if(max(BBand_width[20:]))
	#return data[:number_of_days]
	if(max_vol>15*1000): #成交量大於1500
	max_bb=max(BBand_width[20:])
	#print (max(BBand_width[20:]))
	print (max_bb)
	print (max_vol)
	except Exception as e:
	global catch_stockgarbage
	#print ('找不到所選資料,或發生異常錯誤.')
	max_bb=0
	#catch_stockgarbage.append(name)
	return max_bb
	def read_file2():
	fname = filedialog.askopenfilename(title=u"選擇回測檔案",
	initialdir=(os.path.expanduser(default_dir)))
	read_file(fname)
	def call_download():
	global catch_stock
	global catch_stockgarbage
	catch_stock=[]
	#table grid
	height = len(catch_stock)
	width = 1
	count=0
	tree = ttk.Treeview(frame6, show="headings")
	tree["columns"]=('col1','col2')
	tree.column('col1', width=50)
	tree.column('col2', width=200)
	tree.heading('col1', text='num')
	tree.heading('col2', text='股票代號')
	tree.grid(row=0,column=0)
	#近三個月 bb寬度篩選
	# 建立 5 個子執行緒
	threads = []
	catch_stockgarbage=[]
	for x in range (1200,10000,2):
	for i in range(2):
	threads.append(threading.Thread(target = job, args = (x+i,)))
	threads[i].start()
	print (x+i)
	root.update()
	frame6.update()
	# 等待所有子執行緒結束
	for i in range(2):
	threads[i].join()
	threads.clear()
	# for x in range(1234,9999,1):
	# if(get_historical_data(str(x)+'.TW', 90)>20):
	# catch_stock.append(str(x)+'.TW')
	# tree.insert('',i,text=str(i),values=('',str(x)+'.TW'))
	# count=count+1
	print("Done.")
	print ('----------')
	print ('捕捉BBAND寬度>10股票')
	for i in tree.get_children():
	tree.delete(i)
	for x in range(0,len(catch_stock),1):
	tree.insert('',count,text=str(i),values=(str(count),catch_stock[count]))
	count=count+1
	print (catch_stockgarbage)
	print (catch_stock)
	# for i in range (0,len(catch_stock),1):
	# tree.insert('',i,text=str(i),values=(str(i),catch_stock[i]))
	# for x in catch_stock:
	# b = Entry(frame6, text="11")
	# b.grid(row=count, column=0)
	# b.insert(0,x[count])
	# count=count+1
	# for i in range(height): #Rows
	# for j in range(width): #Columns
	# b = tk.Entry(frame6, text="11")
	# b.grid(row=i, column=j)
	# b.insert(0,'50')
	# for i in get_historical_data(str(x)+'.TW', 90):
	# print (i)
	def print_selection():
	print( var.get())
	#default_dir = r"C:\Users\lenovo\Desktop" # 设置默认打开目录
	#fname = filedialog.askopenfilename(title=u"選擇回測檔案",
	# initialdir=(os.path.expanduser(default_dir)))
	#w, h = root.winfo_screenwidth(), root.winfo_screenheight()
	#root.geometry("%dx%d+0+0" % (1360, 800))
	#
	#root.title("回測工具")
	#notebook = ttk.Notebook(root)
	#notebook2 = ttk.Notebook(root)
	#
	#frame1 = ttk.Frame(notebook)
	#frame4 = ttk.Frame(notebook)
	#frame5 = ttk.Frame(notebook)
	#frame6 = ttk.Frame(notebook)
	##
	#frame2 = ttk.Frame(notebook2)
	#frame3 = ttk.Frame(notebook2)
	#frame7 = ttk.Frame(notebook2)
	#frame8 = ttk.Frame(notebook2)
	#
	#notebook.add(frame1, text='回測技術分析')
	#notebook.add(frame5, text='即時技術分析')
	#notebook.add(frame4, text='即時監控')
	#notebook.add(frame6, text='股票快篩')
	#notebook2.add(frame2, text='工具箱')
	#notebook2.add(frame3, text='策略微調')
	#notebook2.add(frame7, text='快篩微調')
	#notebook2.add(frame8, text='機器學習微調')
	#工具箱
	#clock()
	#notebook.grid(row=0,column=0,sticky="nsew")
	#notebook2.grid(sticky="nsew",row=1,column=0, columnspan=3, padx=10, pady=10,fill = None)
	#drawPic.f = Figure(figsize=(18.3,6))
	#drawPic.canvas = FigureCanvasTkAgg(drawPic.f, master=frame1)
	#drawPic.f.clf()
	#drawPic.canvas.show()
	#drawPic.canvas.get_tk_widget().grid(row=0)
	#
	#tk.Button(frame2,text='回測',command=drawPic).grid(row=0,column=0,columnspan=1)
	#tk.Button(frame2,text='績效',command=show_info).grid(row=0,column=1,columnspan=1)
	#tk.Button(frame2,text='特殊模組開啟',command=show_fbpropht).grid(row=0,column=3,columnspan=1)
	#tk.Button(frame2,text='數據處理',command=data_deal).grid(row=0,column=4,columnspan=1)
	#
	#
	#number = tk.StringVar()
	#numberChosen = ttk.Combobox(frame2, width=12, textvariable=number)
	#numberChosen['values'] = ("BBand","Rsi", "Arron","ATR","KD","T3",
	# "一紅吃一黑","紅三兵","辰星","跳空買進","arr","fbpropht",
	# "keras", "google_trend") # 设置下拉列表的值
	#numberChosen.grid(row=0,column=2,columnspan=1) # 设置其在界面中出现的位置 column代表列 row 代表行
	#numberChosen.current(0) # 设置下拉列表默认显示的值，0为 numberChosen['values'] 的下标值
	#
	#drawPic.f2 = Figure(figsize=(10,4))
	#drawPic.canvas2 = FigureCanvasTkAgg(drawPic.f2, master=frame2)
	#drawPic.f2.clf()
	#
	#drawPic.canvas2.show()
	#drawPic.canvas2.get_tk_widget().grid(row=0,column=5,columnspan=1 , rowspan=6)
	#
	#draw_tmp = drawPic
	#c1 = tk.Checkbutton(frame2, text='買賣點', variable=var3, onvalue=1, offvalue=0,
	# ).grid(row=2,column=2)
	#c2 = tk.Checkbutton(frame2, text='SMA', variable=var4, onvalue=1, offvalue=0,
	# ).grid(row=3,column=2)
	#c3 = tk.Checkbutton(frame2, text='BBnad', variable=var7, onvalue=1, offvalue=0,
	# ).grid(row=4,column=2)
	#c4 = tk.Checkbutton(frame2, text='BBnad_width', variable=var8, onvalue=1, offvalue=0,
	# ).grid(row=5,column=2)
	##tk.Label(frame2,text='请输入样本数量：').grid(row=1,column=0)
	##inputEntry=tk.Entry(frame2)
	##inputEntry.grid(row=1,column=1)
	##inputEntry.insert(0,'50')
	#
	#data_label = tk.Label(frame2, text='-----', textvariable=var5).grid(row=3,column=0)
	#data_label2 = tk.Label(frame2, text='-----', textvariable=var6).grid(row=3,column=1)
	#e = tk.Entry(frame2,show='*').grid(row=4,column=0)
	##快篩微調
	#e2 = tk.Entry(frame7, textvariable=var9).grid(row=0,column=1)
	#e3 = tk.Entry(frame7, textvariable=var10).grid(row=0,column=3)
	#e3 = tk.Entry(frame7, textvariable=var11).grid(row=1,column=1)
	#e4 = tk.Entry(frame7, textvariable=var12).grid(row=1,column=3)
	#var9.set('1000')
	#var10.set('9999')
	#var11.set('<')
	#var12.set('15')
	#b1 = tk.Button(frame2,text="read_file",width=15,height=2,command=read_file2).grid(row=4,column=1)
	#b2 = tk.Button(frame7,text="篩選",command=call_download).grid(row=0,column=4)
	#tk.Button(frame7,text='取得財務報表',command=get_statements).grid(row=1,column=4,columnspan=2)
	#L1 = tk.Label(frame7, text="運算子").grid(row=1,column=0)
	#L2 = tk.Label(frame7, text="Value").grid(row=1,column=2)
	#L1 = tk.Label(frame7, text="起始範圍").grid(row=0,column=0)
	#L2 = tk.Label(frame7, text="結束範圍").grid(row=0,column=2)
	#機器學習微調
	#tk.Button(frame8,text='呼叫lstm',command=show_lstm).grid(row=1,column=4,columnspan=2)
	#drawPic()
	# 子執行緒的工作函數
	def job(num):
	global catch_stock
	tmp=''
	if(get_historical_data(str(num)+'.TW',60)>20):
	catch_stock.append(str(num)+'.TW')
	tmp=str(num)+'.TW'
	print (tmp)
	# 子執行緒的工作函數
	app = Flask(__name__)
	api = Api(app)
	TODOS = {
	'todo1': {'task': 'build an API'},
	'todo2': {'task': '?????'},
	'todo3': {'task': 'profit!'},
	}
	def abort_if_todo_doesnt_exist(todo_id):
	if todo_id not in TODOS:
	abort(404, message="Todo {} doesn't exist".format(todo_id))
	parser = reqparse.RequestParser()
	parser.add_argument('task')
	# Todo
	# shows a single todo item and lets you delete a todo item
	class Todo(Resource):
	def get(self, todo_id):
	abort_if_todo_doesnt_exist(todo_id)
	# json.load(urllib.request.urlopen("http://localhost:9000/321"))
	test2222 ="";
	url = "http://localhost:9000/321"
	response = urllib.request.urlopen(url)
	data = json.loads(response.read().decode('utf-8'))
	print(data[0])
	# return TODOS[todo_id]
	# read_file(fname)
	# default_dir = r"C:\Users\lenovo\Desktop" # 设置默认打开目录
	# fname = filedialog.askopenfilename(title=u"選擇回測檔案",
	# initialdir=(os.path.expanduser(default_dir)))
	# read_file()
	test_data = []
	for x in data :
	test_data.append([x[0],x[1],x[2],x[3],x[4],x[5]])
	read_file(test_data)
	drawPic()
	# app.logger.info('Info' +str_tmp)
	# app.logger.warning('A warning occurred (%d apples)', 42)
	# app.logger.error('An error occurred')
	# app.logger.info('Info')
	# b = bytes(str_tmp, 'utf-8')
	# str_tmp = str_tmp.encode('big5').decode('utf8')
	# return str_tmp.encode('utf8').decode('utf8','ignore')
	# // tmp = str_tmp.encode('big5').decode('unicode-escape')
	# u = unicode(str_tmp, "utf-8")
	stock_data ={};
	stock_data.clear()
	stock_data['bbandup']= list()
	stock_data['bbanddown']= list()
	stock_data['bbandwidth']= list()
	stock_data['buy_list']= list()
	stock_data['sell_list']= list()
	BBand_width
	for x in range (len(BBand_up)-1 , 0 , -1):
	if( ~np.isnan(BBand_up[x])):
	# time.mktime(datetime.datetime.strptime(str(buy_list[x][0]), '%Y-%m-%d %H:%M:%S').timetuple())
	# time.mktime(datetime.datetime.strptime(str(buy_list[x][0]), "%d/%m/%Y").timetuple())
	stock_data['bbandup'].append([date[x],BBand_up[x]])
	stock_data['bbanddown'].append([date[x],BBand_down[x]])
	stock_data['bbandwidth'].append([date[x],BBand_width[x]])
	for x in range (0, len(buy_list), 1):
	stock_data['buy_list'].append([ str(buy_list[x][0]).split()[0],buy_list[x][1]])
	for x in range (0, len(sell_list), 1):
	stock_data['sell_list'].append([ str(sell_list[x][0]).split()[0],sell_list[x][1]])
	# for x in range (len(sell_list)-1,0 , -1):
	# stock_data['sell_list'].append([sell_list[x]])
	# tmp.append( [date[x],BBand_up[x]])
	# tmp.
	return stock_data
	def delete(self, todo_id):
	abort_if_todo_doesnt_exist(todo_id)
	del TODOS[todo_id]
	return '', 204
	def put(self, todo_id):
	args = parser.parse_args()
	task = {'task': args['task']}
	TODOS[todo_id] = task
	return task, 201
	# TodoList
	# shows a list of all todos, and lets you POST to add new tasks
	class TodoList(Resource):
	def get(self):
	return TODOS
	def post(self):
	args = parser.parse_args()
	todo_id = int(max(TODOS.keys()).lstrip('todo')) + 1
	todo_id = 'todo%i' % todo_id
	TODOS[todo_id] = {'task': args['task']}
	return TODOS[todo_id], 201
	##
	## Actually setup the Api resource routing here
	##
	api.add_resource(TodoList, '/todos')
	api.add_resource(Todo, '/todos/<todo_id>')
	# 跨域支持
	def after_request(resp):
	resp.headers['Access-Control-Allow-Origin'] = 'http://172.16.7.21:8080'
	resp.headers['Access-Control-Allow-Credentials'] = 'true'
	return resp
	app.after_request(after_request)
	if __name__ == '__main__':
	handler = RotatingFileHandler('foo.log', maxBytes=10000, backupCount=1)
	handler.setLevel(logging.INFO)
	app.logger.addHandler(handler)
	app.run(debug=True)
	_job = None

view raw flask_api.py hosted with ❤ by GitHub

	package main
	import (
	"database/sql"
	"encoding/json"
	"fmt"
	"log"
	"net/http"
	//"github.com/go-sql-driver/mysql"
	"github.com/gorilla/mux"
	"reflect"
	"time"
	"strings"
	//"strconv"
	)
	import _ "github.com/go-sql-driver/mysql"
	var people []Person
	type Person struct {
	ID string `json:"id,omitempty"`
	Name string `json:"name,omitempty"`
	Address *Address `json:"address,omitempty"`
	}
	type Stock struct {
	Date string `json:"date"`
	Open string `json:"open"`
	High string `json:"high"`
	Low string `json:"low"`
	Close string `json:"close"`
	Volume string `json:"volume"`
	}
	type Address struct {
	Country string `json:"country,omitempty"`
	City string `json:"city,omitempty"`
	}
	func DefaultHandler(w http.ResponseWriter, r *http.Request) {
	fmt.Fprint(w, "Home")
	fmt.Fprint(w, "Home")
	fmt.Fprint(w, "Home")
	}
	// 定义一个结构体, 需要大写开头哦, 字段名也需要大写开头哦, 否则json模块会识别不了
	// 结构体成员仅大写开头外界才能访问
	type User struct {
	User string `json:"user"`
	Password string `json:"password"`
	Host string `json:"host"`
	}
	func PeopleHandler(w http.ResponseWriter, r *http.Request) {
	w.Header().Set("Access-Control-Allow-Origin", "*")
	json.NewEncoder(w).Encode(people)
	}
	func PersonHandler(w http.ResponseWriter, r *http.Request) {
	w.Header().Set("Access-Control-Allow-Origin", "*")
	params := mux.Vars(r)
	for _, item := range people {
	if item.ID == params["id"] {
	json.NewEncoder(w).Encode(item)
	}
	}
	}
	func HttpFileHandler(response http.ResponseWriter, request *http.Request) {
	//fmt.Fprintf(w, "Hi from e %s!", r.URL.Path[1:])
	http.ServeFile(response, request, "index.html")
	}
	func HttpFileHandler2(response http.ResponseWriter, request *http.Request) {
	//fmt.Fprintf(w, "Hi from e %s!", r.URL.Path[1:])
	http.ServeFile(response, request, "details.html")
	}
	func HttpFileHandler3(response http.ResponseWriter, request *http.Request) {
	//fmt.Fprintf(w, "Hi from e %s!", r.URL.Path[1:])
	http.ServeFile(response, request, "123.html")
	}
	func getFieldString(e *Stock, field string) string {
	r := reflect.ValueOf(e)
	f := reflect.Indirect(r).FieldByName(field)
	return f.String()
	}
	func checkCount(rows *sql.Rows) (count int) {
	for rows.Next() {
	err:= rows.Scan(&count)
	checkErr(err)
	}
	return count
	}
	func checkErr(err error) {
	if err != nil {
	panic(err)
	}
	}
	func HttpFileHandler4(w http.ResponseWriter, r *http.Request) {
	w.Header().Set("Access-Control-Allow-Origin", "http://172.16.7.21:8080")
	w.Header().Set("Access-Control-Allow-Credentials", "true")
	db, e := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if e != nil { //如果连接出错,e将不是nil的
	print("ERROR?")
	return
	}
	// 提醒一句, 运行到这里, 并不代表数据库连接是完全OK的, 因为发送第一条SQL才会校验密码 汗~!
	_, e2 := db.Query("select 1")
	if e2 == nil {
	println("DB OK")
	ttt,err := db.Exec("USE stock")
	if err != nil {
	print("ERROR?")
	return
	}
	fmt.Print(ttt)
	rows, e := db.Query("SELECT date,open,high,low,close,volume FROM `1216tw`;")
	if e != nil {
	fmt.Print("query error!!%v\n", e)
	return
	}
	fmt.Print(rows)
	if rows == nil {
	print("Rows is nil")
	return
	}
	var first int
	first=0
	fmt.Fprintln(w,"[")
	for rows.Next() { //跟java的ResultSet一样,需要先next读取
	if first!=0{
	if rows.Next()!=false{
	fmt.Fprintln(w,",")
	}
	}
	if first==0{
	first=1
	}
	stock := new(Stock)
	// rows貌似只支持Scan方法 继续汗~! 当然,可以通过GetColumns()来得到字段顺序
	row_err := rows.Scan(&stock.Date,&stock.Open,&stock.High,&stock.Low,&stock.Close,&stock.Volume)
	if row_err != nil {
	//print("Row error!!")
	fmt.Fprintln(w,"]")
	return
	}
	fmt.Fprint(w,"[")
	tm2, _ := time.Parse("2006-01-02 15:04:05", strings.Replace(getFieldString(stock, "Date"), "/", "-", 2)+" 00:00:00")
	//fmt.Println(fmt.Sprint((int64(tm2.Unix()))))
	//fmt.Print(int32(tm2.Unix()))
	//fmt.Println(strings.Replace(getFieldString(stock, "Date"), "/", "-", 2)+" 00:00:00")
	//fmt.Print(int32(tm2.Unix()))
	fmt.Fprint(w,fmt.Sprint((int64(tm2.Unix()*1000)))+",")
	fmt.Fprint(w,getFieldString(stock, "Open")+",")
	fmt.Fprint(w,getFieldString(stock, "High")+",")
	fmt.Fprint(w,getFieldString(stock, "Low")+",")
	fmt.Fprint(w,getFieldString(stock, "Close")+",")
	fmt.Fprint(w,getFieldString(stock, "Volume"))
	fmt.Fprintln(w,"]")
	// fmt.Fprintln(w,checkCount(&rows))
	// b, _ := json.Marshal(stock)
	// fmt.Println(string(b)) // 这里没有判断错误, 呵呵, 一般都不会有错吧
	//fmt.Fprint(w,string(b))
	}
	fmt.Fprintln(w,"]")
	}
	}
	func getFromdatabase() {
	db, e := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if e != nil {
	print("ERROR?")
	return
	}
	_, e2 := db.Query("select 1")
	if e2 == nil {
	println("DB OK")
	rows, e := db.Query("select user,password,host from mysql.user")
	if e != nil {
	fmt.Print("query error!!%v\n", e)
	return
	}
	if rows == nil {
	print("Rows is nil")
	return
	}
	for rows.Next() {
	user := new(User)
	row_err := rows.Scan(&user.User,&user.Password, &user.Host)
	if row_err != nil {
	print("Row error!!")
	return
	}
	b, _ := json.Marshal(user)
	fmt.Println(string(b))
	}
	}
	}
	func getFromdatabase2() {
	db, e := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if e != nil { //如果连接出错,e将不是nil的
	print("ERROR?")
	return
	}
	// 提醒一句, 运行到这里, 并不代表数据库连接是完全OK的, 因为发送第一条SQL才会校验密码 汗~!
	_, e2 := db.Query("select 1")
	if e2 == nil {
	println("DB OK")
	ttt,err := db.Exec("USE stock")
	if err != nil {
	print("ERROR?")
	return
	}
	fmt.Print(ttt)
	rows, e := db.Query("SELECT date,open,high,low,close,volume FROM `1216tw`;")
	if e != nil {
	fmt.Print("query error!!%v\n", e)
	return
	}
	fmt.Print(rows)
	if rows == nil {
	print("Rows is nil")
	return
	}
	for rows.Next() { //跟java的ResultSet一样,需要先next读取
	stock := new(Stock)
	// rows貌似只支持Scan方法 继续汗~! 当然,可以通过GetColumns()来得到字段顺序
	row_err := rows.Scan(&stock.Date,&stock.Open,&stock.High,&stock.Low,&stock.Close,&stock.Volume)
	if row_err != nil {
	print("Row error!!")
	return
	}
	b, _ := json.Marshal(stock)
	fmt.Println(string(b)) // 这里没有判断错误, 呵呵, 一般都不会有错吧
	}
	}
	}
	//插入demo
	func insert() {
	db, err := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	stmt, err := db.Prepare(`INSERT user (Host,user,password) values (?,?,?)`)
	if err != nil {
	panic(err)
	}
	res, err := stmt.Exec("tony", 20, 1)
	if err != nil {
	panic(err)
	}
	id, err := res.LastInsertId()
	if err != nil {
	panic(err)
	}
	fmt.Println(id)
	}
	func create(name string) {
	db, err := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if err != nil {
	panic(err)
	}
	defer db.Close()
	_,err = db.Exec("USE stock")
	if err != nil {
	panic(err)
	}
	var query string
	query="CREATE TABLE "+name+" (data varchar(32) ,open float, high float, low float ,close float , volume float)"
	fmt.Println(query)
	_,err = db.Exec(query)
	if err != nil {
	panic(err)
	}
	}
	func update() {
	db, err := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if err != nil {
	panic(err)
	}
	stmt, err := db.Prepare(`UPDATE user SET user_age=?,user_sex=? WHERE user_id=?`)
	if err != nil {
	panic(err)
	}
	res, err := stmt.Exec(21, 2, 1)
	if err != nil {
	panic(err)
	}
	num, err := res.RowsAffected()
	if err != nil {
	panic(err)
	}
	fmt.Println(num)
	}
	//删除数据
	func remove() {
	db, err := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if err != nil {
	panic(err)
	}
	stmt, err := db.Prepare(`DELETE FROM user WHERE user_id=?`)
	if err != nil {
	panic(err)
	}
	res, err := stmt.Exec(1)
	if err != nil {
	panic(err)
	}
	num, err := res.RowsAffected()
	if err != nil {
	panic(err)
	}
	fmt.Println(num)
	}
	func main() {
	route := mux.NewRouter()
	people = append(people, Person{ID: "1", Name: "sojib", Address: &Address{Country: "bangladesh", City: "Dhaka"}})
	people = append(people, Person{ID: "2", Name: "hasan", Address: &Address{Country: "china", City: "C"}})
	people = append(people, Person{ID: "3", Name: "kamal", Address: &Address{Country: "japan", City: "J"}})
	route.HandleFunc("/", DefaultHandler).Methods("Get")
	route.HandleFunc("/people", PeopleHandler).Methods("Get")
	route.HandleFunc("/people/{id}", PersonHandler).Methods("Get")
	route.HandleFunc("/index", HttpFileHandler)
	route.HandleFunc("/details", HttpFileHandler2)
	route.HandleFunc("/123", HttpFileHandler3)
	route.HandleFunc("/321", HttpFileHandler4)
	//route.Handle("/details", http.FileServer(http.Dir("/mnt/c/golangtmp")))
	//getFromdatabase2()
	//create("2015tw")
	//create("qwdwqd")
	log.Fatal(http.ListenAndServe(":9000", route))
	}

view raw main.go hosted with ❤ by GitHub

	package main
	import (
	"encoding/csv"
	"fmt"
	"io"
	"os"
	"database/sql"
	)
	import _ "github.com/go-sql-driver/mysql"
	func typeof(v interface{}) string {
	return fmt.Sprintf("%T", v)
	}
	func create(name string) {
	db, err := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if err != nil {
	panic(err)
	}
	defer db.Close()
	_,err = db.Exec("USE stock")
	if err != nil {
	panic(err)
	}
	var query string
	query="CREATE TABLE "+name+" (date date ,open float, high float, low float ,close float , volume float)"
	fmt.Println(query)
	_,err = db.Exec(query)
	if err != nil {
	//panic(err)
	print (err)
	}
	}
	//插入demo
	func insert(name string,date string,open string,high string,low string,close string,volume string) {
	db, err := sql.Open("mysql", "root:1234@tcp(localhost:3306)/mysql?charset=utf8")
	if err != nil {
	panic(err)
	}
	_,err = db.Exec("USE stock")
	if err != nil {
	panic(err)
	}
	stmt, err := db.Prepare("INSERT "+name+" (date,open,high,low,close,volume) values (?,?,?,?,?,?)")
	if err != nil {
	panic(err)
	}
	res, err := stmt.Exec(date,open,high,low,close,volume)
	if err != nil {
	panic(err)
	}
	id, err := res.LastInsertId()
	if err != nil {
	fmt.Println(id)
	}
	db.Close()
	}
	func main() {
	file, err := os.Open("1216統一.csv")
	if err != nil {
	fmt.Println("Error:", err)
	return
	}
	create("1216tw")
	defer file.Close()
	reader := csv.NewReader(file)
	for {
	record, err := reader.Read()
	if err == io.EOF {
	break
	} else if err != nil {
	fmt.Println("Error:", err)
	return
	}
	insert("1216tw",record[0],record[1],record[2],record[3],record[4],record[5])
	//fmt.Println(record) // record has the type []string
	//fmt.Println("get:", record[0])
	//fmt.Println(strings.Replace(string(record), " ", ",", 2))
	}
	//insert("1216tw","1","1","1","1","1","1")
	fmt.Println("ok")
	}

view raw main2.go hosted with ❤ by GitHub

	<template>
	<div class="act-form">
	<!-- <iframe :src="src" ref="iframe" width="100%" height="700" frameborder="0"></iframe>-->
	<button @click="test()">darw pic</button>
	<button @click="test2()">darw bband</button>
	<div id="container" style="height:600px; min-width: 310px"></div>
	</div>
	</template>
	<script>
	export default {
	data() {
	return {
	stock_data: [],
	bband_up: [],
	bband_down: [],
	bband_width: [],
	buy_data: [],
	sell_data: []
	};
	},
	mounted() {
	// 这里就拿到了iframe的对象
	console.log(this.$refs.iframe);
	},
	methods: {
	test2() {
	this.getRequest("http://localhost:5000/todos/todo3").then(resp => {
	this.stock_data = this.bband_up = this.bband_down = this.buy_data = [];
	this.bband_up = resp.data["bbandup"];
	this.bband_down = resp.data["bbanddown"];
	this.buy_data = resp.data["buy_list"];
	this.sell_data = resp.data["sell_list"];
	this.bband_width = resp.data["bbandwidth"];
	console.log("test");
	this.test();
	});
	},
	test() {
	this.getRequest("/321").then(resp => {
	//const items = resp.data.data.items;
	this.stock_data = resp.data;
	let tmp_data = [];
	console.log(tmp_data);
	let series_tmp = [
	{
	type: "candlestick",
	name: "AAPL Stock Price",
	id: "aapl",
	zIndex: 1,
	data: this.stock_data,
	dataGrouping: {
	units: [
	[
	"week", // unit name
	[1] // allowed multiples
	],
	["month", [1, 2, 3, 4, 6]]
	]
	}
	},
	{
	type: "macd",
	yAxis: 1,
	linkedTo: "aapl"
	},
	{
	name: "bband_up",
	data: this.bband_up,
	type: "spline",
	tooltip: {
	valueDecimals: 2
	},
	color: "green"
	},
	{
	name: "bband_down",
	data: this.bband_down,
	type: "spline",
	tooltip: {
	valueDecimals: 2
	},
	color: "green"
	},
	{
	name: "bband_width",
	data: this.bband_width,
	type: "spline",
	tooltip: {
	valueDecimals: 2
	},
	color: "blue"
	}
	];
	console.log(series_tmp);
	let tmp = [];
	this.buy_data.forEach(function(element) {
	let array = {
	type: "flags",
	data: [
	{
	x: new Date(element[0]).getTime(),
	y: element[1],
	title: "買入" + element[1],
	text: 'Shape: "squarepin"'
	}
	],
	onSeries: "dataseries",
	shape: "squarepin",
	width: 16,
	style: {
	// text style
	color: "red"
	}
	};
	series_tmp.push(array);
	});
	tmp = [];
	this.sell_data.forEach(function(element) {
	let array = {
	type: "flags",
	data: [
	{
	x: new Date(element[0]).getTime(),
	y: element[1],
	title: "賣出" + element[1],
	text: 'Shape: "squarepin"'
	}
	],
	onSeries: "dataseries",
	shape: "squarepin",
	width: 16,
	style: {
	// text style
	color: "green"
	}
	};
	series_tmp.push(array);
	});
	console.log(this.buy_data);
	console.log(series_tmp);
	Highcharts.stockChart("container", {
	title: {
	text: "AAPL Stock Price"
	},
	yAxis: [
	{
	height: "75%",
	resize: {
	enabled: true
	},
	labels: {
	align: "right",
	x: -3
	},
	title: {
	text: "AAPL"
	}
	},
	{
	top: "75%",
	height: "25%",
	labels: {
	align: "right",
	x: -3
	},
	offset: 0,
	title: {
	text: "MACD"
	}
	}
	],
	series: series_tmp
	});
	});
	}
	}
	};
	</script>

view raw stock.vue hosted with ❤ by GitHub