yhilpisch

#
# TsTables -- High Frequency Times Series Data with PyTables
#
# to install -- pip install tstables
# Github repo -- https://github.com/afiedler/tstables
#
# Dr. Yves J. Hilpisch
#
# The Python Quants GmbH
# http://quant-platform.com

yhilpisch

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)

yhilpisch

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

yhilpisch

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

import numpy as np

yhilpisch

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

yhilpisch

#
# Python for Algorithmic Trading
# Quant Insights Bootcamp
#
# The Python Quants
#
import numpy as np
import pandas as pd
import seaborn as sns; sns.set()
from pandas_datareader import data as web

yhilpisch

yhilpisch

Welcome to the Algo Trading Bootcamp
====================================

WIFI
====
Ft1ch#2016#!

Quant Platform
==============

yhilpisch

#
# 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

yhilpisch

Streaming Financial Data & Plots with Ploty

This Gist hosts the code from my talk and brief coding session at Plotcon.

• tick data server with ZeroMQ
• tick data client with ZeroMQ
• tick data plotting with plotly
• Flask app embedding the streaming plot