Portfolio analysis tools

Introduction to Portfolio Analysis in Python

Charlotte Werger

Data Scientist

Professional portfolio analysis tools

Screenshot of professional portfolio management tool

Back-testing your strategy

Back-testing: run your strategy on historic data and see how it would have performed
Strategy works on historic data: not guaranteed to work well on future data -> changes in markets

Portfolio back-test results plotted against an index

Quantopian's pyfolio tool

$ $

Quantopian logo

$ $ Pyfolio logo

¹ Github: https://github.com/quantopian/pyfolio

Performance and risk analysis in Pyfolio

# Install the package
!pip install pyfolio
# Import the package
import pyfolio as pf

# Read the data as a pandas series
returns=pd.Series(pd.read_csv('pf_returns.csv')
returns.index=pd.to_datetime(returns.index)

# Create a tear sheet on returns
pf.create_returns_tear_sheet(returns)

# If you have backtest and live data 
pf.create_returns_tear_sheet(returns, live_start_date='2018-03-01')

Pyfolio's tear sheet

Returns table in Pyfolio tear sheet output

Rolling Sharpe ratio in Pyfolio tear sheet output

Drawdown chart in Pyfolio tear sheet output

Holdings and exposures in Pyfolio

# define our sector mappings
sect_map = {'COST': 'Consumer Goods',
            'INTC': 'Technology', 
            'CERN': 'Healthcare', 
            'GPS': 'Technology',
            'MMM': 'Construction', 
            'DELL': 'Technology', 
            'AMD': 'Technology'}

pf.create_position_tear_sheet(returns, positions, 
                             sector_mappings=sect_map)

Exposure tear sheet results

Top 10 position in Pyfolio tear sheet output

$ $

Sector allocation in Pyfolio tear sheet output

Let's practice!

Introduction to Portfolio Analysis in Python