Yves Hilpisch yhilpisch

## client.py
import zmq

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://0.0.0.0:8888')
socket.setsockopt_string(zmq.SUBSCRIBE, u'value')

while True:
    data = socket.recv_string()
    print(data)

## jupyter_notebook_config.py
c.NotebookApp.certfile='/cert.pem'
c.NotebookApp.keyfile='/cert.key'
c.NotebookApp.password='sha1:768ce7b87bf6:fa6013a02f7053bc014ef7c65be0ef34f77f21fd'

c.NotebookApp.ip='*'
c.NotebookApp.port=8888

c.NotebookApp.open_browser=False

## StrategyOptimizer.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from pandas_datareader import data as web

class StrategyOptimizer(object):
    def __init__(self, symbol, start, end, t1, t2):
        self.symbol = symbol
        self.start = start

## 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

## RegPred.py
#
# Stock Market Prediction
# with Linear Regression
#
# The Python Quants GmbH
#
import numpy as np
import pandas as pd
from pandas_datareader import data as web
import seaborn as sns

## 00_epat_june_2017.md

      
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    Executive Program in Algorithmic Trading (QuantInsti)

Python Sessions by Dr. Yves J. Hilpisch | The Python Quants GmbH
Online, 03. & 04. June 2017


## 00_bc_day_1_section_02
3 + 4
3 * 4
3 / 4
type(3)
type(4)
3 ** 4
sqrt(3)
3 ** 0.5
import math
math.sqrt(3)

## 00_epat_october_2017.md

      
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    Executive Program in Algorithmic Trading (QuantInsti)

Python Sessions by Dr. Yves J. Hilpisch | The Python Quants GmbH
Online, 22. & 29. October 2017


## 01_bc_day_01.md

      
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              December 18, 2017 23:58
            
          
    For Python Quants Bootcamp

This is the Gist the For Python Quants Bootcamp in London 21. November 2017 (http://fpq.io)
This introductory boocamp day is about Finance with Python.

Topics

  
## ScikitBacktest.py
#
# Backtesting Algo Strategies based on
# Logistic Regression with scikit-learn
#
# Yves Hilpisch
# ODSC London 2016
# The Python Quants GmbH
#

import numpy as np
	import zmq

	context = zmq.Context()
	socket = context.socket(zmq.SUB)
	socket.connect('tcp://0.0.0.0:8888')
	socket.setsockopt_string(zmq.SUBSCRIBE, u'value')

	while True:
	data = socket.recv_string()
	print(data)
	c.NotebookApp.certfile='/cert.pem'
	c.NotebookApp.keyfile='/cert.key'
	c.NotebookApp.password='sha1:768ce7b87bf6:fa6013a02f7053bc014ef7c65be0ef34f77f21fd'

	c.NotebookApp.ip='*'
	c.NotebookApp.port=8888

	c.NotebookApp.open_browser=False
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns; sns.set()
	from pandas_datareader import data as web

	class StrategyOptimizer(object):
	def __init__(self, symbol, start, end, t1, t2):
	self.symbol = symbol
	self.start = start
	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
	#
	# Stock Market Prediction
	# with Linear Regression
	#
	# The Python Quants GmbH
	#
	import numpy as np
	import pandas as pd
	from pandas_datareader import data as web
	import seaborn as sns
	3 + 4
	3 * 4
	3 / 4
	type(3)
	type(4)
	3 ** 4
	sqrt(3)
	3 ** 0.5
	import math
	math.sqrt(3)
	#
	# Backtesting Algo Strategies based on
	# Logistic Regression with scikit-learn
	#
	# Yves Hilpisch
	# ODSC London 2016
	# The Python Quants GmbH
	#

	import numpy as np