yhilpisch/01_overview.md

## 01_overview.md

      
    Raw
  

              01_overview.md
            
          
    For Python Quants Bootcamp

This is the Gist the FPQ bootcamp in New York, 08.-11. May 2017 (http://fpq.io)
Notebooks & Code Files

Additional files (eg Jupyter Notebooks) are found under:
https://gist.github.com/yhilpisch/64aeedbeb334c89aa05529a79881ceeb
Slides

Overview slides

http://hilpisch.com/bootcamp.pdf

Slides by Jorge Santos

Interactive Financial Charting — http://jorgesantos.io/slides/fpq.slides.html#/
TR Eikon Proxy & Python API — http://jorgesantos.io/slides/eikon_demo.slides.html#/

Twitter Handle

http://twitter.com/dyjh
Recommended Books


Jake VanderPlas (2016): Python Data Science Handbook. O'Reilly.
Wes McKinney (2017): Python for Data Analysis. 2nd ed., O'Reilly.
Luciano Ramalho (2015): Fluent Python. O'Reilly.

Python Cheat Sheets


Python: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
NumPy: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf
pandas: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf
SciPy: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf
matplotlib: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf


## 02_docker.md

      
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              02_docker.md
            
          
    Some Docker Basics

Linux/Mac

open a shell window and type
docker version

to test whether it is installed
to run a docker container type
docker run -ti -h fpq -p 8888:8888 ubuntu:latest /bin/bash

 
Windows

open the Docker Quickstart Terminal
to run a docker container type
docker run -ti -h fpq -p 8888:8888 -e MACHINE_IP=$(docker-machine ip) ubuntu:latest /bin/bash


## 03_environment.md

      
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              03_environment.md
            
          
    Setting Up the Python Environment

(Assuming Ubuntu running in a Docker container, for instance)
Linux

updates and installs
  apt-get update apt-get upgrade apt-get install wget bzip2 screen vim gcc  
Miniconda

to install it
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh

open new shell instance to activate the new Python install
Python

see http://conda.io
see https://conda.io/docs/_downloads/conda-cheatsheet.pdf
to create an environment
conda create -n fpq python=3.6
source activate fpq

to install Python packages
conda install ipython jupyter numpy pandas scikit-learn matplotlib pytables

TensorFlow (on Windows) for Py 3.6

http://www.lfd.uci.edu/~gohlke/pythonlibs/#tensorflow
Download this:
tensorflow‑1.1.0‑cp36‑cp36m‑win_amd64.whl

pip install <path/to/wheel>


## 04_accounts.md

      
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              04_accounts.md
            
          
    Links to Platforms

Quant Platform

http://pqp.io
DigitalOcean (just in case)

setting up an account with 10 USD starting balance:
https://m.do.co/c/fbe512dd3dac
Plotly

http://plot.ly
getting started: https://plot.ly/python/getting-started/
Oanda

general http://oanda.com
demo account https://www.oanda.com/register/#/sign-up/demo
Python package to be used: http://hilpisch.com/v20.zip
(download and unzip in working folder)

  
## 05_Titanic.txt
"","Class","Sex","Age","Survived","Freq"
"1","1st","Male","Child","No",0
"2","2nd","Male","Child","No",0
"3","3rd","Male","Child","No",35
"4","Crew","Male","Child","No",0
"5","1st","Female","Child","No",0
"6","2nd","Female","Child","No",0
"7","3rd","Female","Child","No",17
"8","Crew","Female","Child","No",0
"9","1st","Male","Adult","No",118
"10","2nd","Male","Adult","No",154
"11","3rd","Male","Adult","No",387
"12","Crew","Male","Adult","No",670
"13","1st","Female","Adult","No",4
"14","2nd","Female","Adult","No",13
"15","3rd","Female","Adult","No",89
"16","Crew","Female","Adult","No",3
"17","1st","Male","Child","Yes",5
"18","2nd","Male","Child","Yes",11
"19","3rd","Male","Child","Yes",13
"20","Crew","Male","Child","Yes",0
"21","1st","Female","Child","Yes",1
"22","2nd","Female","Child","Yes",13
"23","3rd","Female","Child","Yes",14
"24","Crew","Female","Child","Yes",0
"25","1st","Male","Adult","Yes",57
"26","2nd","Male","Adult","Yes",14
"27","3rd","Male","Adult","Yes",75
"28","Crew","Male","Adult","Yes",192
"29","1st","Female","Adult","Yes",140
"30","2nd","Female","Adult","Yes",80
"31","3rd","Female","Adult","Yes",76
"32","Crew","Female","Adult","Yes",20

## 06_Titanic.csv

          
            Class
            Sex
            Age
            Survived
            Freq

            
              1
              1st
              Male
              Child
              No
              0

            
              2
              2nd
              Male
              Child
              No
              0

            
              3
              3rd
              Male
              Child
              No
              35

            
              4
              Crew
              Male
              Child
              No
              0

            
              5
              1st
              Female
              Child
              No
              0

            
              6
              2nd
              Female
              Child
              No
              0

            
              7
              3rd
              Female
              Child
              No
              17

            
              8
              Crew
              Female
              Child
              No
              0

            
              9
              1st
              Male
              Adult
              No
              118

            
              10
              2nd
              Male
              Adult
              No
              154

            
              11
              3rd
              Male
              Adult
              No
              387

            
              12
              Crew
              Male
              Adult
              No
              670

            
              13
              1st
              Female
              Adult
              No
              4

            
              14
              2nd
              Female
              Adult
              No
              13

            
              15
              3rd
              Female
              Adult
              No
              89

            
              16
              Crew
              Female
              Adult
              No
              3

            
              17
              1st
              Male
              Child
              Yes
              5

            
              18
              2nd
              Male
              Child
              Yes
              11

            
              19
              3rd
              Male
              Child
              Yes
              13

            
              20
              Crew
              Male
              Child
              Yes
              0

            
              21
              1st
              Female
              Child
              Yes
              1

            
              22
              2nd
              Female
              Child
              Yes
              13

            
              23
              3rd
              Female
              Child
              Yes
              14

            
              24
              Crew
              Female
              Child
              Yes
              0

            
              25
              1st
              Male
              Adult
              Yes
              57

            
              26
              2nd
              Male
              Adult
              Yes
              14

            
              27
              3rd
              Male
              Adult
              Yes
              75

            
              28
              Crew
              Male
              Adult
              Yes
              192

            
              29
              1st
              Female
              Adult
              Yes
              140

            
              30
              2nd
              Female
              Adult
              Yes
              80

            
              31
              3rd
              Female
              Adult
              Yes
              76

            
              32
              Crew
              Female
              Adult
              Yes
              20

## 07_web_service.py
#
# Simple Web Service
# with Flask
#
from flask import Flask

app = Flask(__name__)

page = '''
For Python Quants Bootcamp.
'''

@app.route('/')
def home():
    return page

if __name__ == '__main__':
    app.run(port=7777, debug=True)

## 08_web_service.py
#
# Simple Web Service
# with Flask
#
from flask import Flask, request
from pandas_datareader import data as web

app = Flask(__name__)

page = '''
<style>body {font-family: "Arial"; text-align: center; color: #08088A;}
table {margin-right: auto; margin-left: auto;}
</style>
<h1>For Python Quants Bootcamp</h1>
<p>Symbol: %s</p>
'''

@app.route('/')
def home():
    symbol = request.args['symbol'].upper()
    data = web.DataReader(symbol, data_source='yahoo')
    html = page % symbol
    return html + data.head().to_html()

if __name__ == '__main__':
    app.run(port=7777, debug=True)

## 09_web_service.py
#
# Simple Web Service
# with Flask
#
# URL
# http://127.0.0.1:7777/?symbol=amzn&rows=100
#
import random
import pandas as pd
from pylab import plt
from flask import Flask, request
from pandas_datareader import data as web

plt.style.use('seaborn')

app = Flask(__name__)

page = '''
<style>body {font-family: "Arial"; text-align: center; color: #08088A;}
table {margin-right: auto; margin-left: auto;}
</style>
<h1>For Python Quants Bootcamp</h1>
<p>Symbol: %s</p>
<img src="static/plot.png?d=%s">
'''

@app.route('/')
def home():
    symbol = request.args['symbol'].upper()
    rows = int(request.args['rows'])
    data = web.DataReader(symbol, data_source='yahoo')
    plt.figure()
    data['Adj Close'].plot(title=symbol)
    plt.savefig('static/plot.png')
    rn = str(random.gauss(0, 1))[2:]
    html = page % (symbol, rn)
    return html + data.tail(rows).to_html()

if __name__ == '__main__':
    app.run(port=7777, debug=True)
	"","Class","Sex","Age","Survived","Freq"
	"1","1st","Male","Child","No",0
	"2","2nd","Male","Child","No",0
	"3","3rd","Male","Child","No",35
	"4","Crew","Male","Child","No",0
	"5","1st","Female","Child","No",0
	"6","2nd","Female","Child","No",0
	"7","3rd","Female","Child","No",17
	"8","Crew","Female","Child","No",0
	"9","1st","Male","Adult","No",118
	"10","2nd","Male","Adult","No",154
	"11","3rd","Male","Adult","No",387
	"12","Crew","Male","Adult","No",670
	"13","1st","Female","Adult","No",4
	"14","2nd","Female","Adult","No",13
	"15","3rd","Female","Adult","No",89
	"16","Crew","Female","Adult","No",3
	"17","1st","Male","Child","Yes",5
	"18","2nd","Male","Child","Yes",11
	"19","3rd","Male","Child","Yes",13
	"20","Crew","Male","Child","Yes",0
	"21","1st","Female","Child","Yes",1
	"22","2nd","Female","Child","Yes",13
	"23","3rd","Female","Child","Yes",14
	"24","Crew","Female","Child","Yes",0
	"25","1st","Male","Adult","Yes",57
	"26","2nd","Male","Adult","Yes",14
	"27","3rd","Male","Adult","Yes",75
	"28","Crew","Male","Adult","Yes",192
	"29","1st","Female","Adult","Yes",140
	"30","2nd","Female","Adult","Yes",80
	"31","3rd","Female","Adult","Yes",76
	"32","Crew","Female","Adult","Yes",20
	Class	Sex	Age	Survived	Freq
1	1st	Male	Child	No	0
2	2nd	Male	Child	No	0
3	3rd	Male	Child	No	35
4	Crew	Male	Child	No	0
5	1st	Female	Child	No	0
6	2nd	Female	Child	No	0
7	3rd	Female	Child	No	17
8	Crew	Female	Child	No	0
9	1st	Male	Adult	No	118
10	2nd	Male	Adult	No	154
11	3rd	Male	Adult	No	387
12	Crew	Male	Adult	No	670
13	1st	Female	Adult	No	4
14	2nd	Female	Adult	No	13
15	3rd	Female	Adult	No	89
16	Crew	Female	Adult	No	3
17	1st	Male	Child	Yes	5
18	2nd	Male	Child	Yes	11
19	3rd	Male	Child	Yes	13
20	Crew	Male	Child	Yes	0
21	1st	Female	Child	Yes	1
22	2nd	Female	Child	Yes	13
23	3rd	Female	Child	Yes	14
24	Crew	Female	Child	Yes	0
25	1st	Male	Adult	Yes	57
26	2nd	Male	Adult	Yes	14
27	3rd	Male	Adult	Yes	75
28	Crew	Male	Adult	Yes	192
29	1st	Female	Adult	Yes	140
30	2nd	Female	Adult	Yes	80
31	3rd	Female	Adult	Yes	76
32	Crew	Female	Adult	Yes	20
	#
	# Simple Web Service
	# with Flask
	#
	from flask import Flask

	app = Flask(__name__)

	page = '''
	For Python Quants Bootcamp.
	'''

	@app.route('/')
	def home():
	return page

	if __name__ == '__main__':
	app.run(port=7777, debug=True)
	#
	# Simple Web Service
	# with Flask
	#
	from flask import Flask, request
	from pandas_datareader import data as web

	app = Flask(__name__)

	page = '''
	<style>body {font-family: "Arial"; text-align: center; color: #08088A;}
	table {margin-right: auto; margin-left: auto;}
	</style>
	<h1>For Python Quants Bootcamp</h1>
	<p>Symbol: %s</p>
	'''

	@app.route('/')
	def home():
	symbol = request.args['symbol'].upper()
	data = web.DataReader(symbol, data_source='yahoo')
	html = page % symbol
	return html + data.head().to_html()

	if __name__ == '__main__':
	app.run(port=7777, debug=True)
	#
	# Simple Web Service
	# with Flask
	#
	# URL
	# http://127.0.0.1:7777/?symbol=amzn&rows=100
	#
	import random
	import pandas as pd
	from pylab import plt
	from flask import Flask, request
	from pandas_datareader import data as web

	plt.style.use('seaborn')

	app = Flask(__name__)

	page = '''
	<style>body {font-family: "Arial"; text-align: center; color: #08088A;}
	table {margin-right: auto; margin-left: auto;}
	</style>
	<h1>For Python Quants Bootcamp</h1>
	<p>Symbol: %s</p>
	<img src="static/plot.png?d=%s">
	'''

	@app.route('/')
	def home():
	symbol = request.args['symbol'].upper()
	rows = int(request.args['rows'])
	data = web.DataReader(symbol, data_source='yahoo')
	plt.figure()
	data['Adj Close'].plot(title=symbol)
	plt.savefig('static/plot.png')
	rn = str(random.gauss(0, 1))[2:]
	html = page % (symbol, rn)
	return html + data.tail(rows).to_html()

	if __name__ == '__main__':
	app.run(port=7777, debug=True)