investlib 0.0.6

InvestLib is a Python package for backtesting ETF investments

InvestLib

InvestLib is a Python package for backtesting ETF investments. It allows you to create strategies, filter ETFs, and allocate capital based on your personal preferences within a rebalancing period.

Flow

1. Choose etfs
2. Choose rebalancing timer (every month, every 3 month at a custom interval)
3. Filter certain ETFs for the next period.
4. Allocate the current budget based on your chosen allocation

Rebalance

MonthlyTimer class allow you to set number of month to execute the rebalancing and on which day. Common possibilities includes FirstFriday.

Filters

Filters allow you to select a group of etfs in your list based on specific conditions, to create for example a momentum selection every month: - Percentage variation in month/days - Sharpe ratio (assuming risk-free rate as zero)

Allocations

You can allocate percentages between the chosen ETFs using: - Fixed allocation - Statically - Dinamically based on the number of filtered ETFs - PctChangeRank Allow you to make a ranking on filtered ETF and allocate money based on your preference

Data

The current unique data source is Tiingo. InvestLib automatically manages cash to avoid making repeated data calls. To use this feature, you need to create a free account on Tiingo and set the following environment variables:

• tiingo_api_key: the API token provided by Tiingo upon subscription
• tiingo_backup_path: the folder where you want to save the end-of-day CSV data for all the ETFs you need (default '/tmp/Tiingo').

Installation

I raccomand to use virtualenv:

pip install investlib

Example

Here's some code to test out some famous portfolios:

60/40 (60 stocks/40 bond)

import os

from investlib.strategy import Strategy
from investlib.rebalance import FixedAllocation
from investlib.rebalance import MonthlyTimer
from investlib.rebalance import FirstFriday
from investlib.report.draw import generate_report

os.environ['tiingo_api_key'] = '<tiingo_api_key>'

start = '2007-01-01'
end = '2021-06-04'
tickers = ['VTI', 'IEF']

# We create a strategy that rebalances the current money (invested + cash) to 60% VTI and 40% IEF
# every month on the first Friday.
s = Strategy(
    tickers, 
    start=start, 
    end=end, 
    timer=MonthlyTimer(months=1, day=FirstFriday()),
    allocation_class=FixedAllocation({'VTI':0.6,'IEF':0.4})
)
s.run()

generate_report(s, path='/tmp/6040.pdf', name='60/40')

Futures

- Add more stats
- Add more source of data
- Currency
- Tax
- Portfolio over Strategy