Skip to content

Instantly share code, notes, and snippets.

@yhilpisch

yhilpisch/00_epat_january_2018.md

Last active January 19, 2024 05:14
Show Gist options
  • Save yhilpisch/8c9fda8a003fafa334cf3c16eaad1baf to your computer and use it in GitHub Desktop.
Save yhilpisch/8c9fda8a003fafa334cf3c16eaad1baf to your computer and use it in GitHub Desktop.
Download ZIP
Raw
00_epat_january_2018.md

Executive Program in Algorithmic Trading (QuantInsti)

Python Sessions by Dr. Yves J. Hilpisch | The Python Quants GmbH

Online, 27. & 28. January 2018

Short Link

https://goo.gl/gc6TYW

Resources

Slides & Materials

You find the introduction slides under http://hilpisch.com/epat.pdf

You find the materials about OOP under http://hilpisch.com/py4fi_oop_epat.html

Python

If you have either Miniconda or Anaconda already installed, there is no need to install anything new.

The code that follows uses Python 3.6. For example, download and install Miniconda 3.6 from https://conda.io/miniconda.html if you do not have conda already installed.

In any case, for Linux/Mac you should execute the following lines on the shell to create a new environment with the needed packages:

conda create -n epat python=3.6
source activate epat
conda install numpy pandas matplotlib statsmodels
pip install plotly cufflinks
conda install ipython jupyter
jupyter notebook

On Windows, execute the following lines on the command prompt:

conda create -n epat python=3.6
activate epat
conda install numpy pandas matplotlib statsmodels
pip install plotly cufflinks
pip install win-unicode-console
set PYTHONIOENCODING=UTF-8
conda install ipython jupyter
jupyter notebook

Read more about the management of environments under https://conda.io/docs/using/envs.html

Docker

To install Docker see https://docs.docker.com/install/

docker run -ti -p 9000:9000 -h epat -v /Users/yves/Temp/:/root/ ubuntu:latest /bin/bash

ZeroMQ

The major resource for the ZeroMQ distributed messaging package based on sockets is http://zeromq.org/

Cloud

Use this link to get a 10 USD bonus on DigitalOcean when signing up for a new account.

Books

Good book about everything important in Python data analysis: Python Data Science Handbook, O'Reilly

Good book covering object-oriented programming in Python: Fluent Python, O'Reilly

Display the source blob
Display the rendered blob
Raw
01_object_orientation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EPAT Session 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Executive Program in Algorithmic Trading**\n",
"\n",
"**_Basics of Object Orientation_**\n",
"\n",
"Dr. Yves J. Hilpisch | The Python Quants GmbH | http://tpq.io\n",
"\n",
"<img src=\"http://hilpisch.com/images/tpq_bootcamp.png\" width=\"350px\" align=\"left\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class HumanBeing(object): # <1>\n",
" def __init__(self, first_name, eye_color): # <2>\n",
" self.first_name = first_name # <3>\n",
" self.eye_color = eye_color # <4>\n",
" self.position = 0 # <5>\n",
" def walk_steps(self, steps): # <6>\n",
" self.position += steps # <7>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Sandra = HumanBeing('Sandra', 'blue') # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Sandra.first_name # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Sandra.position # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Sandra.walk_steps(5) # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Sandra.position # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Sandra.walk_steps(-2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Sandra.position"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Brief Look at Standard Objects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### int"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 5 # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(n) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n.numerator # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n.bit_length() # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n + n # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2 * n # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n.__sizeof__() # <7>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = [1, 2, 3, 4] # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(l) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l[0] # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l.append(10) # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l + l # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2 * l # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sum(l) # <7>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l.__sizeof__() # <8>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ndarray"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = np.arange(16).reshape((4, 4)) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(a) # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.nbytes # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.sum() # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.cumsum(axis=0) # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a + a # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2 * a # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sum(a) # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sum(a) # <7>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.__sizeof__() # <8>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DataFrame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(a, columns=list('abcd')) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(df) # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.columns # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.sum() # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.cumsum() # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df + df # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2 * df # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sum(df) # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.__sizeof__() # <7>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Basics of Python Classes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class FinancialInstrument(object): # <1>\n",
" pass # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi = FinancialInstrument() # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(fi) # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.__str__() # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi.price = 100 # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.price # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class FinancialInstrument(object):\n",
" author = 'Yves Hilpisch' # <1>\n",
" def __init__(self, symbol, price): # <2>\n",
" self.symbol = symbol # <3>\n",
" self.price = price # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"FinancialInstrument.author # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aapl = FinancialInstrument('AAPL', 100) # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aapl.symbol # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aapl.author # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aapl.price = 105 # <7>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aapl.price # <7>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class FinancialInstrument(FinancialInstrument): # <1>\n",
" def get_price(self): # <2>\n",
" return self.price # <2>\n",
" def set_price(self, price): # <3>\n",
" self.price = price # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi = FinancialInstrument('AAPL', 100) # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.get_price() # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi.set_price(105) # <7>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.get_price() # <6>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.price # <8>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class FinancialInstrument(object):\n",
" def __init__(self, symbol, price):\n",
" self.symbol = symbol \n",
" self.__price = price # <1>\n",
" def get_price(self):\n",
" return self.__price\n",
" def set_price(self, price):\n",
" self.__price = price"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi = FinancialInstrument('AAPL', 100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.get_price() # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi.__price # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fi._FinancialInstrument__price # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi._FinancialInstrument__price = 105 # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fi.set_price(100) # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class PortfolioPosition(object):\n",
" def __init__(self, financial_instrument, position_size):\n",
" self.position = financial_instrument # <1>\n",
" self.__position_size = position_size # <2>\n",
" def get_position_size(self):\n",
" return self.__position_size\n",
" def update_position_size(self, position_size):\n",
" self.__position_size = position_size\n",
" def get_position_value(self):\n",
" return self.__position_size * \\\n",
" self.position.get_price() # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pp = PortfolioPosition(fi, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pp.get_position_size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pp.get_position_value() # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pp.position.get_price() # <4>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pp.position.set_price(105) # <5>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pp.get_position_value() # <6>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Python Data Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(object):\n",
" def __init__(self, x=0, y=0, z=0): # <1>\n",
" self.x = x # <1>\n",
" self.y = y # <1>\n",
" self.z = z # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector(1, 2, 3) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(Vector):\n",
" def __repr__(self):\n",
" return 'Vector(%r, %r, %r)' % (self.x, self.y, self.z)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector(1, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(v) # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"abs(-2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"int(False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"int(True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bool(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bool(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bool(-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(Vector):\n",
" def __abs__(self):\n",
" return (self.x ** 2 + self.y ** 2 +\n",
" self.z ** 2) ** 0.5 # <1>\n",
" \n",
" def __bool__(self):\n",
" return bool(abs(self))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector(1, 2, -1) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"abs(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"bool(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector() # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"v # <3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"abs(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"bool(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"v + v"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"2 * v"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(Vector):\n",
" def __add__(self, other):\n",
" x = self.x + other.x\n",
" y = self.y + other.y\n",
" z = self.z + other.z\n",
" return Vector(x, y, z) # <1>\n",
" \n",
" def __mul__(self, scalar):\n",
" return Vector(self.x * scalar,\n",
" self.y * scalar,\n",
" self.z * scalar) # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector(1, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v + Vector(2, 3, 4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v * 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(Vector):\n",
" def __len__(self):\n",
" return 3 # <1>\n",
" \n",
" def __getitem__(self, i):\n",
" if i in [0, -3]: return self.x\n",
" elif i in [1, -2]: return self.y\n",
" elif i in [2, -1]: return self.z\n",
" else: raise IndexError('Index out of range.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector(1, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"len(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"v[-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v[3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(Vector):\n",
" def __iter__(self):\n",
" for i in range(len(self)):\n",
" yield self[i]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"v = Vector(1, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in range(3): # <1>\n",
" print(v[i]) # <1>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"for coordinate in v: # <2>\n",
" print(coordinate) # <2>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Vector(object):\n",
" def __init__(self, x=0, y=0, z=0):\n",
" self.x = x\n",
" self.y = y\n",
" self.z = z\n",
" \n",
" def __repr__(self):\n",
" return 'Vector(%r, %r, %r)' % (self.x, self.y, self.z)\n",
" \n",
" def __abs__(self):\n",
" return (self.x ** 2 + self.y ** 2 + self.z ** 2) ** 0.5\n",
" \n",
" def __bool__(self):\n",
" return bool(abs(self))\n",
" \n",
" def __add__(self, other):\n",
" x = self.x + other.x\n",
" y = self.y + other.y\n",
" z = self.z + other.z\n",
" return Vector(x, y, z)\n",
" \n",
" def __mul__(self, scalar):\n",
" return Vector(self.x * scalar,\n",
" self.y * scalar,\n",
" self.z * scalar)\n",
" \n",
" def __len__(self):\n",
" return 3\n",
" \n",
" def __getitem__(self, i):\n",
" if i in [0, -3]: return self.x\n",
" elif i in [1, -2]: return self.y\n",
" elif i in [2, -1]: return self.z\n",
" else: raise IndexError('Index out of range.')\n",
" \n",
" def __iter__(self):\n",
" for i in range(len(self)):\n",
" yield self[i]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Display the source blob
Display the rendered blob
Raw
02_vecback_oop.ipynb
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Display the source blob
Display the rendered blob
Raw
03_event_based_backtesting.ipynb
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Raw
tick_client.py
#
# Simple Tick Data Client
#
import zmq
import datetime
context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')
socket.setsockopt_string(zmq.SUBSCRIBE, '')
while True:
msg = socket.recv_string()
t = datetime.datetime.now()
print(str(t) + ' | ' + msg)
Raw
tick_collector.py
#
# Simple Tick Data Collector
#
import zmq
import datetime
import pandas as pd
context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')
socket.setsockopt_string(zmq.SUBSCRIBE, '')
raw = pd.DataFrame()
while True:
msg = socket.recv_string()
t = datetime.datetime.now()
print(str(t) + ' | ' + msg)
symbol, price = msg.split()
raw = raw.append(pd.DataFrame({'SYM': symbol, 'PRICE': price}, index=[t]))
data = raw.resample('5s', label='right').last()
if len(data) % 4 == 0:
print(50 * '=')
print(data.tail())
print(50 * '=')
# simple way of storing data, needs to be adjusted for your purposes
if len(data) % 20 == 0:
# h5 = pd.HDFStore('database.h5', 'a')
# h5['data'] = data
# h5.close()
pass
Raw
tick_plot.py
#
# Simple Tick Data Plotter with ZeroMQ & http://plot.ly
#
import zmq
import datetime
import plotly.plotly as ply
from plotly.graph_objs import *
import configparser
# credentials
c = configparser.ConfigParser()
c.read('../pyalgo.cfg')
stream_ids = c['plotly']['api_tokens'].split(',')
# socket
context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')
socket.setsockopt_string(zmq.SUBSCRIBE, '')
# plotting
s = Stream(maxpoints=100, token=stream_ids[0])
tr = Scatter(x=[], y=[], name='tick data', mode='lines+markers', stream=s)
d = Data([tr])
l = Layout(title='EPAT Tick Data Example')
f = Figure(data=d, layout=l)
ply.plot(f, filename='epat_example', auto_open=True)
st = ply.Stream(stream_ids[0])
st.open()
while True:
msg = socket.recv_string()
t = datetime.datetime.now()
print(str(t) + ' | ' + msg)
sym, value = msg.split()
st.write({'x': t, 'y': float(value)})
Raw
tick_server.py
#
# Simple Tick Data Server
#
import zmq
import time
import random
context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.bind('tcp://127.0.0.1:5555')
AAPL = 100.
while True:
AAPL += random.gauss(0, 1) * 0.5
msg = 'AAPL %.3f' % AAPL
socket.send_string(msg)
print(msg)
time.sleep(random.random() * 2)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment