Files and Resources for Quant Insights Bootcamp (DAY 1)

exercises.txt

Vectorized Backtesting
----------------------
Retrieve data for Apple (AAPL) from 2005 on until yesterday.
Calculate the SMA42 and SMA252 and store them as new columns.
Generate positiongs based on the relationship between the two SMAs.
Compare the performance of the strategy with the market performance.

qi_algo_notes.txt

QUANT INSIGHTS BOOTCAMP
=======================

WIFI
----
Ft1ch#2016#!

MINICONDA
---------
http://conda.pydata.org/miniconda.html
http://conda.pydata.org/docs/using/envs.html

IPYTHON MAGIC COMMANDS
----------------------
https://ipython.org/ipython-doc/3/interactive/magics.html

QUANT PLATFORM
--------------
http://training.pqp.io
--> follow registration link
[special ports used are 8000 and 5565]

qi_bootcamp_intro.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": [
    "# Python for Algorithmic Trading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quant Insights Bootcamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dr. Yves J. Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[yves@tpq.io](mailto:yves@tpq.io) | [http://tpq.io](http://tpq.io) | [@dyjh](http://twitter.com/dyjh)\n",
    "\n",
    "The Python Quants GmbH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Python Quants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" width=\"60%\">\n",
    "\n",
    "http://tpq.io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/pqp_overview.png\" width=\"90%\">\n",
    "\n",
    "http://pqp.io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://camo.githubusercontent.com/ae62a985112cd05290e6f5392215ba664b14dc87/687474703a2f2f68696c70697363682e636f6d2f64785f646f635f30322e706e67\" width=\"90%\">\n",
    "\n",
    "http://dx-analytics.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/images/python_for_finance.png\" width=\"40%\">\n",
    "\n",
    "http://python-for-finance.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/images/derivatives_analytics_front.jpg\" width=\"40%\">\n",
    "\n",
    "http://derivatives-analytics-with-python.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/images/lvvd_cover.png\" width=\"40%\">\n",
    "\n",
    "http://lvvd.tpq.io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"http://hilpisch.com/images/pff_certificate.png\">\n",
    "\n",
    "http://training.tpq.io/pythonforfinance.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For the curious:\n",
    "\n",
    "* http://tpq.io (company Web site)\n",
    "* http://pqp.io (Quant Platform)\n",
    "* http://datapark.io (data science in the browser)\n",
    "* http://fpq.io (For Python Quants conference)\n",
    "* http://meetup.com/Python-for-Quant-Finance-London/ (1,600+ members)\n",
    "* http://pff.tpq.io | http://dawp.tpq.io | http://lvvd.tpq.io\n",
    "* http://hilpisch.com (all my talks and more)\n",
    "* http://twitter.com/dyjh (events, talks, finance news)\n",
    "* http://training.tpq.io (online training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Agenda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menu of topic options for the bootcamp:\n",
    "\n",
    "* **Setting up the Local Environment**\n",
    "* **pandas for Vectorized Backtesting**\n",
    "* **Object-Oriented Programming for Event-based Backtesting**\n",
    "* **Stock Market Prediction with Regression & Machine Learning**\n",
    "* **Sockets, Real-Time Data and Stream Plotting**\n",
    "* **Oanda for Automated, Algorithmic Trading**\n",
    "* **Deployment in the Cloud**\n",
    "* ** ... ** (= many more)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Major Question 1\n",
    "\n",
    "> Where do you stand today?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Major Question 2\n",
    "\n",
    "> Where do you want to go (until when)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Major Question 3\n",
    "\n",
    "> How does this bootcamp fit in?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Major Question 4\n",
    "\n",
    "> When exactly would you consider the bootcamp to be a success?"
   ]
  },
  {
   "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>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:team@tpq.io\">team@tpq.io</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Listed Volatility and Variance Derivatives** |\n",
    "<a href=\"http://lvvd.tpq.io\" target=\"_blank\">Listed VV Derivatives @ Wiley Finance</a>\n",
    "\n",
    "**Python Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">Python for Finance University Certificate</a>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda env:py3]",
   "language": "python",
   "name": "conda-env-py3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

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
        self.end = end
        self.t1 = t1
        self.t2 = t2
        self.get_data()

    def get_data(self):
        d = web.DataReader(self.symbol, data_source='yahoo',
                           start=self.start, end=self.end)['Adj Close']
        d = pd.DataFrame(d)
        d.columns = [self.symbol]
        d['returns'] = np.log(d / d.shift(1))
        self.data = d

    def plot_data(self):
        plt.figure()
        self.data[self.symbol].plot(title='%s' % self.symbol)
    
    def plot_returns(self):
        plt.figure()
        self.data['returns'].hist(bins=35)

    def run_strategy(self, plot=False):
        self.data['t1'] = self.data[self.symbol].rolling(self.t1).mean()
        self.data['t2'] = self.data[self.symbol].rolling(self.t2).mean()
        self.data['position'] = np.where(self.data.t1 > self.data.t2, 1, -1)
        self.data['strategy'] = self.data.position.shift(1) * self.data.returns
        if plot is True:
            self.data.dropna()[['returns', 'strategy']].cumsum().apply(np.exp).plot()
        perf = self.data.dropna()[['returns', 'strategy']].cumsum().apply(np.exp).ix[-1]
        return perf

    def generate_heat_meap(self, range_t1, range_t2):
        results = np.zeros((len(range_t1), len(range_t2)))
        for i, t1 in enumerate(range_t1):
            for j, t2 in enumerate(range_t2):
                self.t1 = t1
                self.t2 = t2
                perf = self.run_strategy()
                net_perf = perf.loc['strategy'] - perf.loc['returns']
                results[i, j] = net_perf
        heat_map = pd.DataFrame(results, index=range_t1, columns=range_t2)
        return heat_map

vectorized_backtesting.py

#
# Python for Algorithmic Trading
# Quant Insights Bootcamp
#
# The Python Quants
#
import sys
import numpy as np
from pandas_datareader import data as web

sym = 'AAPL'
start = '2010-1-1'
t1 = 42
t2 = 252
if len(sys.argv) > 1:
    start = sys.argv[1]
    sym = sys.argv[2]
    t1 = int(sys.argv[3])
    t2 = int(sys.argv[4])

data = web.DataReader(sym, data_source='yahoo', start=start)

data['SMAshort'] = data['Adj Close'].rolling(t1).mean()
data['SMAlong'] = data['Adj Close'].rolling(t2).mean()
data['Position'] = np.where(data['SMAshort'] > data['SMAlong'], 1, -1)
data['Returns'] = np.log(data['Adj Close'] / data['Adj Close'].shift(1))
data['Strategy'] = data['Position'].shift(1) * data['Returns']
data.dropna(inplace=True)

data[['Adj Close', 'SMAshort', 'SMAlong']].plot(
    title='%s | SMAs = %d & %d' % (sym, t1, t2))
data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(title=sym)

zmqClient.py

#
# Python for Algorithmic Trading
# Quant Insights Bootcamp
#
# The Python Quants
#
import zmq
import datetime

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://0.0.0.0:6666')
socket.setsockopt_string(zmq.SUBSCRIBE, 'AAPL')

while True:
    data = socket.recv_string()
    print(str(datetime.datetime.now())+ ' | ' + data)

zmqCollector.py

#
# Python for Algorithmic Trading
# Quant Insights Bootcamp
#
# The Python Quants
#
import zmq
import pandas as pd
import datetime

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://0.0.0.0:6666')
socket.setsockopt_string(zmq.SUBSCRIBE, 'AAPL')

df = pd.DataFrame()

for i in range(20):
    data = socket.recv_string()
    sym, value = data.split()
    timestamp = datetime.datetime.now()
    df = df.append(pd.DataFrame({sym: value}, index=[timestamp]))
    print(i, data)

print(df)

zmqServer.py

#
# Python for Algorithmic Trading
# Quant Insights Bootcamp
#
# The Python Quants
#
import zmq
import time
import random

context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.bind('tcp://0.0.0.0:6666')

AAPL = 100.

while True:
    AAPL += random.gauss(0, 1)
    out = 'AAPL %f' % AAPL
    print(out)
    socket.send_string(out)
    time.sleep(random.random() * 3)

import zmq
import datetime

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://0.0.0.0:6666')
socket.setsockopt_string(zmq.SUBSCRIBE, 'AAPL')

while True:
    data = socket.recv_string()
    print(str(datetime.datetime.now())+ ' | ' + data)

zmqTrends.py

#
# Python for Algorithmic Trading
# Quant Insights Bootcamp
#
# The Python Quants
#
import zmq
import numpy as np
import pandas as pd
import datetime

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://0.0.0.0:6666')
socket.setsockopt_string(zmq.SUBSCRIBE, 'AAPL')

df = pd.DataFrame()

for i in range(20):
    data = socket.recv_string()
    sym, value = data.split()
    timestamp = datetime.datetime.now()
    df = df.append(pd.DataFrame({sym: value}, index=[timestamp]))
    if i >= 9:
        df['t1'] = df[sym].rolling(5).mean()
        df['t2'] = df[sym].rolling(10).mean()
        df['position'] = np.where(df.t1 > df.t2, 1, -1)
        # place trading logic here
    print(i, df.ix[-1])

print(df)