Start hacking finance data with Python
PyCon JP 2016 Talk#024 driller@patraqushe
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Slide
en: http://www.slideshare.net/drillan/pycon-jp-2016-talk054-en
ja: http://www.slideshare.net/drillan/pycon-jp-2016-talk024-ja -
Docker image
This image doesn't include packages related to Excel.- Linux or docker-machine(Docker Toolbox)
git clone https://github.com/drillan/pyconjp2016.git docker run -d --name pycon -p 8888:8888 -v $PWD/pyconjp2016:/notebooks driller/docker-pyconjp2016- Docker for Windows
git clone https://github.com/drillan/pyconjp2016.git docker run -d --name pycon -p 8888:8888 -v %CD%/pyconjp2016:/notebooks driller/docker-pyconjp2016
- Some code is redundant due to:
- Python 2/3 support
- Offline mode
- No license limitations
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Input formula into a Cell to generate random number with geometric Brownian motion(it satisfies the following stochastic differential equation)
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Copy above formula count of sample paths
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Classify the result into bin
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Count the number of each bins then visualize
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Very long code(especially histogram)
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When changing the layout of an Excel sheet, you have to change all the addresses of related cells(can handle by "name manager" partially)
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Very slow
Case1-3: Implement Monte-Carlo Simulation in Python
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Very short code(especially histogram)
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No need to consider data storage
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Faster than VBA
Case-1-4: Correlation between Economic indicator and exchange rate and stock price
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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
- Economic indicator
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Visualize by seaborn
Case1-5: Relationship ETF/J-REIT purchases and stock prices
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Open Excel files from BOJ site using pandas
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Load TSE REIT index and stock index price data from k-db site
- Stock Indices
- TOPIX
- JPX400
- Nikkei225
- Stock Indices
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Visualize relationship using seaborn
Case1-6: Download stock prices and store into Excel cells
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xlwings features
- Calling Python from Excel
- Put pandas DataFrame data into Excel cells
- Uses syntax close to VBA
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Get stock prices using pandas_datareader
Case1-7: Create User Defined Function by Python, and use it as Excel function
- 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)
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
- Specify various types when selecting a location
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
Case2-3: Compute the last trading day by 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
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
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