3. Agenda
Are you still exhausted from Excel?
Don’t be afraid of time series data
Using Jupyter Notebook
3
4. Why Python for Finance?
Analyzing data with simple code
Substantial libraries(especially Deep leaning)
pandas
Jupyter Notebook
Cooperating with:
Scraping, crawling
Web frame work
Infrastructure
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5. Why Python in my case
Developed trading tools using Excel
Adding new functions and changing rules of exchange became
gradually more difficult
Python is the most manageable(in my opinion)
Pandas is similar to Excel
Drastically cheaper using Jupyter Notebooks
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6. Are you still exhausted from
Excel?
~ Migrate from Excel to Python to improve productivity ~
6
7. Generate stock prices using Monte-Carlo
simulation
Stock price 1,000 yen
time remaining until
expiration
30 days
risk-free interest rate 0.1%
annual volatility of stock price 20%
sample paths 10,000 -> 50,000
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8. Case1-1: Implement Monte-Carlo
Simulation in Excel Function
1. Input formula into a Cell to generate random number with
geometric Brownian motion(it satisfies the following stochastic
differential equation)
𝑑𝑆𝑡 = 𝜇𝑆𝑡 𝑑𝑡 + 𝜎𝑆𝑡 𝐵𝑡
2. Copy above formula count of sample paths
3. Classify the result into bins
4. Count the number of each bins then visualize
Sample:
Case1-1_1-2.xlsm
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9. It's possible to implement Monte-Carlo
Simulations using only mathematical formula,
however:
To increase cell for increasing sample paths
To correspond existing cell for adding cells
To become heavy and slow by recalculation
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10. Case1-2: Implement Monte-Carlo
Simulation in VBA
Very long code(especially histogram)
When changing the layout of an Excel sheet, you have to change all
the addresses of related cells(can handle by "name manager"
partially)
Very slow
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Sample:
Case1-1_1-2.xlsm
11. Case1-3: Implement Monte-Carlo
Simulation in Python
Very short code(especially histogram)
No need to consider data storage
Faster than VBA
Sample:
Case1-3.ipynb
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12. Excel vs. Python
Lines of Code: 105
Wall Time: 7.89s
More complex
Lines of Code: 10
Wall Time: 0.83s
More simple
12
13. Though, Excel has the advantage of…
Easy to input
Easy to create a template
A huge number of users(high data compatibility)
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14. Python packages to work with Excel files
xlrd
xlwt
XlsxWriter
xlutils
openpyxl
xlwings
ExcelPython
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15. There are many packages, but…
pandas.read_excel() will almost always be what I want
Use other packages for the operation that pandas cannot do
Write data to an opened file
Operate Cell
Draw graphs…
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16. Case1-4: Relationship Economic indicator
and exchange rate and stock price
Open Economic indicator & Stock price Excel file @ vdata.nikkei.com
through pandas
Economic indicator
Real Gross Domestic Product
Diffusion Index
Currency Pair : USD/JPY
Stock: Nikkei Index
Visualize by seaborn
Sample:
Case1-4.ipynb
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17. Case 1-5: Relationship ETF/J-REIT
purchases and stock prices
Open Excel files @ boj.or.jp through pandas
Load TSE REIT index and stock Indices price data from k-db site
Stock Indices
TOPIX
JPX400
Nikkei225
Visualize relationship using seaborn
Sample:
Case1-5.ipynb
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18. Use xlwings
Read/Write open Excel files
Supports Numpy and pandas data types
Call Python script from Excel
Write Excel User Defined Functions(UDF) in Python
Use openpyxl for Cell & Chart operation
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21. UDF returns multiple values to each cells
Using array formula
(Ctrl + Shift + Enter)
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22. Case1-6: Download stock prices and
store in Excel Cells
xlwings features
Calling Python from Excel
Put pandas DataFrame data into Excel Cells
Uses syntax close to VBA
Get stock prices using pandas_datareader
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Sample:
Case1-6.xlsm
23. Case 1-7: Create User Defined Functions
using Python, and use it in Excel
Windows only
Install add-in
Call function written in Python like an Excel function
Fetch Excel Range(multiple Cells) as array data(pandas or Numpy) in
UDF function
Input multiple return values into Excel Range(multiple Cells)
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Sample:
Case1-7.xlsm
25. Why pandas?
Wes McKinney built pandas during his tenure at AQR(a quantitative
investment management firm)
Enable all these things in one place
Data structures with labeled axes supporting automatic or explicit data alignment
Integrated time series functionality
One data structure to handle both time series and non-time series data
Arithmetic operations and reductions
Flexible handling of missing data
Merge and other relational operations found in popular database databases(SQL
based, for example)
Pandas is developed by financial specialists, so it is well suited to analyse
financial data
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26. Case2-1: Use DatetimeIndex
pandas.date_range is very useful to create continuous data
Advantage of DatetimeIndex :
Specify various types when selecting a location
datetime.date, datetime.datetime, datetime.time, str, int and so on…
Able to parse most known formats(similar to parsing by dateutil.parser)
Allows slicing into year, month, etc
Handles missing values
Sample:
Case2-1_2.ipynb
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27. Case2-2: Create OHLC data and covert
time range
Not that easy to create OHLC
Convert time-series data into frequencies using the .resample()
method
.resample() performs resampling operations during frequency
conversion
Daily, Weekly, 30minute, 1hour, Quarter, etc
There are tips to convert between different OHLC data representations
Sample:
Case2-1_2.ipynb
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32. Handling the last trading day of
derivatives
Exchange JPX
Products Futures, Options
last trading day The 2nd Friday of every month
* If the 2nd Friday is holiday, the
day before
http://www.jpx.co.jp/derivatives/r
ules/last-trading-day/
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37. Dealing with Japanese public holidays
pandas.tseries.holiday only supports US holidays(as of Sep 22nd, 2016)
It's possible ot create your own holiday rules by inheriting
AbstractHolidayCalendar, but...
Does not solve holidays such as Spring/Autumn Equinox
Instead, use CustomBusinessDay to implement individual holidays
Implement Japanese holidays in pandas using existing calendar data
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38. Case2-3: Compute the last trading day
using the CustomBusinessDay class
Import holiday data from YAML file
Select the 2nd friday of evey month using
pandas.date_range(feq='WOM-2FRI')
Skip holidays using the CustomBusinessDay class
Sample:
Case2-3.ipynb
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40. Case3-1: Create own magic command
Search stock price using "line magic", and output it to
IPython.display.Iframe
Paste data in various formats into notebook cells using "cell magic" ,
and convert it into a pandas DataFrame
Save frequently used commands to a file and re-use them
using %load_ext
Sample:
Case3-1.ipynb
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41. Case3-2: ipywidgets is the easiest way to
create a UI
Easy to implement a UI using the ipywidgets.interact decorator
Automatically creates UI controls for function arguments
bool: check box
Int: slider
Creates interactive visualization of moving averages and Bollinger-
Bands
Sample:
Case3-2.ipynb
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42. Useful Nbextensions
Best installed using jupyter_contrib_nbextensions
https://github.com/ipython-contrib/jupyter_contrib_nbextensions
* Of course, it is possible to install Nbextention individually
Easy to enable/disable indiviudal extensions using the Nbextensions
edit menu
Create your own extensions using Javascript
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43. Today’s summary
Python >>> Excel
Suitable for handling time series data
Easy to create commands and UI
43
44. Sample code and files
Sample code and excel files on Github
https://github.com/drillan/pyconjp2016
Some code is redundant due to:
Python 2/3 support
Offline mode
No license limitations
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