The Python Quants

How Open Source, Open Data and the Cloud are Reshaping Finance Education and the Financial Industry

PyCon Singapore

Singapore, 24. June 2016

Dr. Yves J. Hilpisch

About Me — The Python Quant

Our Vision

To revolutionize financial education, research and practice through Python.

Overview & Agenda

Today's talk covers the following topics:

  • Data Science & Finance Today
  • Open Data Science & The Cloud
  • The Importance of the Python Ecosystem
  • Why Python for Finance?
  • How to Learn and Do Python for Finance?
  • A "Complete Package" Example

Data Science & Finance Today

Data Science Defined

For instance, Wikipedia defines the field as follows (cf.

Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics, ...

Explosion in Data

Real-Time Economy & High Frequency

Established Tools Cannot Cope

"Big data is data that does not fit into an Excel spreadsheet." Unknown.

Traditional (IT) Processes Inappropriate

“If I had asked people what they wanted, they would have said faster horses.” Henry Ford

The Rise of Open Data Science

Open Source as the New Standard

Open (Financial) Data Up and Coming

Open Communities Replace Old Networks

The Browser as Operating System

Cloud Storage Going Corporate

Unlimited, Affordable Compute Power

The Importance of the Python Ecosystem Today

In Education

Data Science Languages

Programming Languages in General

Technology Giants

Financial Giants

Why Python for Finance?

Financial Algorithm Example

In Python, you generally start with the import of some libraries and packages.

In [2]:
from pandas import *
from pylab import *
from pandas_datareader import data as web
from seaborn import *; set()
%matplotlib inline

Consider the stochastic differential equation of a geometric Brownian motion as used by Black-Scholes-Merton (1973), describing the evolution of a stock index over time, for example.

$$ dS_t = rS_td_t+ \sigma S_tdZ_t $$

A possible Euler discretization to simulate the time $T$ value of $S$ is given by the difference equation

$$ S_T = S_0 e^{ \left( r - \frac{\sigma^2}{2} \right) T + \sigma \sqrt{T} z } $$

with $z$ being a standard normally distributed random variable.

The simulation of 10,000,000 time $T$ values for the index with Python is efficient and fast, the syntax is really concise.

In [3]:
S0 = 100.; r = 0.05; sigma = 0.2; T = 1.
z = standard_normal(10000000)
ST = S0 * exp((r - 0.5 * sigma ** 2) * T + sigma * sqrt(T) * z)
CPU times: user 787 ms, sys: 165 ms, total: 952 ms
Wall time: 1.33 s

We would expect for the mean value $\bar{S}_T = S_0 \cdot e^{r T} \approx 105.1$.

In [4]:
S0 * exp(r  * T)
In [5]:

The histogram of the simulated values with mean value (red line).

In [6]:
figure(figsize=(10, 6))
hist(ST, bins=45);
axvline(ST.mean(), color='r');

In addition, option pricing by Monte Carlo simulation is also easily implemented.

In [7]:
strike = 105.  # option strike
payoff = maximum(ST - strike, 0)  # payoff at maturity
C0 = exp(-r * T) * payoff.mean()  # MCS estimator
print 'Europen call option value is %.2f.' % C0
Europen call option value is 8.02.
CPU times: user 151 ms, sys: 57.7 ms, total: 209 ms
Wall time: 333 ms

Financial Data Science Example

We read historical daily closing data for the S&P 500 index.

In [8]:
spx = web.DataReader('^GSPC', data_source='yahoo')['Close']
In [9]:
2016-06-14    2075.320068
2016-06-15    2071.500000
2016-06-16    2077.989990
2016-06-17    2071.219971
2016-06-20    2083.250000
Name: Close, dtype: float64

A simple method call allows us to visualize the time series data.

In [10]:
spx.plot(figsize=(10, 6));

We also read historical closing data for the VIX volatility index ...

In [11]:
vix = web.DataReader('^VIX', data_source='yahoo')['Close']
In [12]:
2016-06-14    20.500000
2016-06-15    20.139999
2016-06-16    19.370001
2016-06-17    19.410000
2016-06-20    18.370001
Name: Close, dtype: float64

... and visualize it.

In [13]:
vix.plot(figsize=(10, 6));

First, we combine the two data sets into one.

In [14]:
data = DataFrame({'SPX': spx, 'VIX': vix})
In [15]:
2016-06-14 2075.320068 20.500000
2016-06-15 2071.500000 20.139999
2016-06-16 2077.989990 19.370001
2016-06-17 2071.219971 19.410000
2016-06-20 2083.250000 18.370001

Second, let us plot the data into a single diagram.

In [16]:
data.plot(figsize=(10, 6), secondary_y='VIX', title='');

Let us calculate the log returns for the two time series. This task is accomplished by a highly vectorized operation ("no looping").

In [17]:
rets = log(data / data.shift(1))
In [18]:
2010-01-04 NaN NaN
2010-01-05 0.003111 -0.035038
2010-01-06 0.000545 -0.009868
2010-01-07 0.003993 -0.005233
2010-01-08 0.002878 -0.050024

Now we can, for instance, calculate the correlation between the two time series.

In [19]:
SPX 1.000000 -0.828457
VIX -0.828457 1.000000

"Use a picture. It's worth a thousand words." Tass Flanders (1911)

In [20]:
jointplot(rets['SPX'], rets['VIX'], kind='reg', size=7);

How to Learn and Do Python for Finance?

Python Books

2nd edition in the making ...

Python Training

Python Conferences

Saturday here in Singapore at PyCon as well ...

For Python Quants Series

Online & Corporate Training

University Degrees

1st Python For Finance University Certificate

Master of Science Degree

Currently in the planning phase:

Master of Science in Financial Data Science and Computational Finance

Python-based Financial Research

Open Communities

Open Source Libraries




DX Analytics



Quant Platform


A "Complete Package" Example

Derivatives Analytics with Python


Github Repository

Code Hosting

DX Analytics as Library also Hosted

Online Training

Python for Finance

Python has become the English of programming languages (for finance).

If you have only time to learn one programming language for finance (thoroughly), learn Python.

The Python Quants | @dyjh |

Python Quant Platform |

Python for Finance | Python for Finance @ O'Reilly

Derivatives Analytics with Python | Derivatives Analytics @ Wiley Finance

Listed Volatility and Variance Derivatives | Listed VV Derivatives @ Wiley Finance