用python量化交易 -- 笔记 1
jupyter notebook f:/stock/book/abu-master_20200318
视频教程:
https://v.qq.com/x/page/g0555b9k6ge.html
page181
如果你的Anaconda可以运行，那么试着在cmd中运行 conda install ipython-notebook ，anaconda会把ipython notebook安装在你的电脑上，然后继续在cmd中运行 ipython notebook ，应该就可以了
import matplotlib.pyplot as plt
from collections import namedtuple
from collections import OrderedDict
from functools import reduce
import numpy as np
plt.plot(a_investor)
import matplotlib.pyplot as plt
from abupy import ABuSymbolPd
tsla_df = ABuSymbolPd.make_kl_df(‘usTSLA’, n_folds=2)
tsla_df.tail()
使用的环境是:
Ipython Notebook
https://github.com/bbfamily/abu
page5
www.hzbook.com
微信公众号:abu_quant
virtualenvwrapper-win
1.安装 virtualenvwrapper-win
pip3 install virtualenvwrapper-win
再次运行workon，目录中没有虚拟环境了，因为默认目录已经改变
https://blog.csdn.net/sophiezjz/article/details/82838951
虚拟环境搭建
IPython与IPython Notebook（Jupyter）安装
https://blog.csdn.net/sophiezjz/article/details/82838951
python -m pip install --upgrade pip
部署
推荐使用Anaconda部署Python环境，详见 量化环境部署
测试
import abupy
https://www.anaconda.com/
https://www.anaconda.com/distribution/#download-section
Anaconda Prompt(Anaconda3)
我选择了加入系统变量，我们可以看到我的python显示为Anaconda版
anconda使用conda作为包管理工具，也就是anaconda把一些常用的python包统一管理，可以方便的安装、更新和卸载包。
conda常用命令
conda --version 查看conda版本
conda -V
conda --help 获取帮助
conda -h
conda update --help
conda remove --help
Anaconda环境管理
接下来我们先研究单一环境下的Anaconda环境管理。
conda env --help
刚刚系统默认创建了名叫base的默认环境，我们可以使用conda命令查看当前有多少环境
conda env list 查看所有环境
或者
conda info --envs
anaconda ipython notebook 在哪
如果你的Anaconda可以运行，那么试着在cmd中运行 conda install ipython-notebook ，anaconda会把ipython notebook安装在你的电脑上，然后继续在cmd中运行 ipython notebook ，应该就可以了
https://www.cnblogs.com/rangger/p/9520123.html
ipython notebook教程
page33
i=1
type(i)
int
f=1.1
type(f)
float
b = ( 1 > 2)
print(b)
type(b)
False
bool
price_str = '30.14, 29.58, 26.36, 32.56, 32.82'
type(price_str)
print(price_str)
30.14, 29.58, 26.36, 32.56, 32.82
print(id(price_str))
85943664
price_str = price_str.replace(' ','')
print(id(price_str))
print(price_str)
85972352
30.14,29.58,26.36,32.56,32.82
price_array = price_str.split(',')
print(price_array)
['30.14', '29.58', '26.36', '32.56', '32.82']
print('hello')
hello
price_array.append('32.82')
print(price_array)
['30.14', '29.58', '26.36', '32.56', '32.82', '32.82']
print(set(price_array))
{'32.82', '32.56', '26.36', '29.58', '30.14'}
print(price_array)
['30.14', '29.58', '26.36', '32.56', '32.82', '32.82']
price_array.remove('32.82')
print(price_array)
['30.14', '29.58', '26.36', '32.56', '32.82']
date_array = []
date_base = 20170118
for _ in range(0, len(price_array)):
date_array.append(str(date_base))
date_base += 1
date_array
['20170118', '20170119', '20170120', '20170121', '20170122']
date_array = []
date_base = 20170118
price_cnt = len(price_array) -1
while price_cnt > 0 :
date_array.append(str(date_base))
date_base += 1
price_cnt -= 1
date_array
['20170118', '20170119', '20170120', '20170121']
date_base = 20170118
date_array = [str(date_base + ind) for ind, _ in enumerate(price_array)]
date_array
['20170118', '20170119', '20170120', '20170121', '20170122']
stock_tuple_list = [(date,price) for date, price in zip(date_array, price_array)]
stock_tuple_list
[('20170118', '30.14'),
('20170119', '29.58'),
('20170120', '26.36'),
('20170121', '32.56'),
('20170122', '32.82')]
from collections import namedtuple
stock_namedtuple = namedtuple('stock', ('date', 'price'))
stock_namedtuple_list = [stock_namedtuple(date,price) for date,price in zip(date_array, price_array)]
stock_namedtuple_list
[stock(date='20170118', price='30.14'),
stock(date='20170119', price='29.58'),
stock(date='20170120', price='26.36'),
stock(date='20170121', price='32.56'),
stock(date='20170122', price='32.82')]
stock_dict = {date: price for date, price in zip(date_array, price_array)}
stock_dict
{'20170118': '30.14',
'20170119': '29.58',
'20170120': '26.36',
'20170121': '32.56',
'20170122': '32.82'}
stock_dict.keys(), stock_dict.values()
(dict_keys(['20170118', '20170119', '20170120', '20170121', '20170122']),
dict_values(['30.14', '29.58', '26.36', '32.56', '32.82']))
from collections import OrderedDict
stock_dict = OrderedDict(
(date,price) for date,price in zip(date_array,price_array))
stock_dict.keys()
odict_keys(['20170118', '20170119', '20170120', '20170121', '20170122'])
min(stock_dict)
'20170118'
min(zip(stock_dict.values(),stock_dict.keys()))
('26.36', '20170120')
def find_second_max(dict_array):
stock_prices_sorted = sorted(zip(dict_array.values(), dict_array.keys()))
return stock_prices_sorted[-2]
if callable(find_second_max):
print(find_second_max(stock_dict))
('32.56', '20170121')
find_second_max_lambda = lambda dict_array:\
sorted(zip(dict_array.values(),dict_array.keys() ))[-2]
find_second_max_lambda(stock_dict)
('32.56', '20170121')
def find_max_and_min(dict_array):
stock_prices_sorted = sorted(
zip(dict_array.values(), dict_array.keys()) )
return stock_prices_sorted[0], stock_prices_sorted[-1]
find_max_and_min(stock_dict)
(('26.36', '20170120'), ('32.82', '20170122'))
