00_epat_october_2017.md

Executive Program in Algorithmic Trading (QuantInsti)

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

Online, 22. & 29. October 2017

Resources

• http://tpq.io
• http://hilpisch.com
• http://twitter.com/dyjh
• http://certificate.tpq.io

Slides

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

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 fxcm python=3.6
source activate fxcm
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 fxcm python=3.6
activate fxcm
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

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 import in Python data analysis: Python Data Science Handbook, O'Reilly

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

01_vecback_oop.ipynb

02_event_based_backtesting.ipynb

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)

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

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)