Day: July 20, 2019

Recent Posts

“””
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
yum -y install git
“””
安裝docker和git(如未安裝)

git clone https://github.com/etaf/shadowsocks-docker.git && cd shadowsocks-docker
在github下載Dockerfile和相關設置

cat shadowsocks_config.json
查看設置,有需要就修改

{
“server”:”::”,
“server_port”: 8838,
“local_address”:”127.0.0.1″,
“local_port”:1080,
“password”:”abcdefg”,
“timeout”:300,
“method”:”aes-256-cfb”,
“fast_open”:true
}

systemctl stop firewalld
用各種方式停止防火墻和開啟端口,這裡只使用firewalld

docker build -t etaf/shadowsocks ./
把你的設置和原來的image一起構建成docker image

sudo service docker restart
重啟docker

sudo docker run –restart=always -d -p 8838:8838 etaf/shadowsocks
運行Container和開機自啟動

docker logs $(docker ps -ql)
查看Contianer裡的輸出

Matplotlib是Python語言中比較流行的畫圖的包,用於製作靜態圖比較合適。

交易產品的蠟燭圖所需要的源數據也就是Open High Low Close的價格,還有就是日期時間,現在給出一個data.csv作為Sample

import numpy as np
import csv
import matplotlib.pyplot as plt
from matplotlib import dates, ticker
import matplotlib as mpl
from mpl_finance import candlestick_ohlc

mpl.style.use(‘default’)

fname = ‘data.csv’

# Empty lists to extract the data from csv files
date_data = []
open_data = []
high_data = []
low_data= []
close_data= []
trade = []
turn = []

# Extracting data
with open(fname, ‘r’) as csvfile:
data = csv.reader(csvfile, delimiter=’,’)
for line in data:
date_data.append(line[0])
open_data.append(line[1])
high_data.append(line[2])
low_data.append(line[3])
close_data.append(line[4])
trade.append(line[5])
turn.append(line[6])
# removing ‘-‘ indata
trade[18] = 0
turn[18] = 0


# Conversion to numpy arrays
open_val = np.array(open_data[1:], dtype=np.float64)
high_val = np.array(high_data[1:], dtype=np.float64)
low_val = np.array(low_data[1:], dtype=np.float64)
close_val = np.array(close_data[1:], dtype=np.float64)
trade_val = np.array(trade[1:], dtype=np.float64)
turn_val = np.array(turn[1:], dtype=np.float64)

# Matplotlib needs dataes in floating numbers to plot them under the hood
data_dates=[]
for date in date_data[1:]:
new_Date = dates.datestr2num(date)
data_dates.append(new_Date)


i = 0
ohlc_data = []
while i < len(data_dates):
stats_1_day = data_dates[i], open_val[i], high_val[i], low_val[i], close_val[i]
ohlc_data.append(stats_1_day)
i += 1

fig, ax1 = plt.subplots()
candlestick_ohlc(ax1, ohlc_data, width=0.5, colorup=’g’, colordown=’r’, alpha=0.8)
plt.show()

得出以下結果:

PYTHON