Skip to main content

Tool to help find an optimal portfolio allocation

Project description

Porfolio Finder

PyPI version fury.io PyPI pyversions Build Status Documentation Status

A Python library, based primarily around pandas, to identify an optimal portfolio allocation through back-testing.

API Documentation is available on Read the Docs.

Example Usage

Each of these examples make use of data.csv which provides returns for a handful of funds over 1970-2019.

Find best portfolio allocation to minimize the required timeframe to achieve a target value

from portfoliofinder import Allocations

Allocations(0.05, ['USA_TSM', 'WLDx_TSM', 'USA_INT', 'EM'])\
    .filter('USA_TSM>=0.6 & WLDx_TSM<=0.2 & USA_INT>=0.3')\
    .with_returns("data.csv")\
    .with_regular_contributions(100000, 10000)\
    .get_backtested_timeframes(target_value=1000000)\
    .get_statistics(['min', 'max', 'mean', 'std'])\
    .filter_by_min_of('max')\
    .filter_by_max_of('min')\
    .get_allocation_which_min_statistic('std')

Output

Statistic
min     14.000000
max     22.000000
mean    16.965517
std      2.809204
Name: Allocation(USA_TSM=0.65, WLDx_TSM=0.0, USA_INT=0.3, EM=0.05), dtype: float64

Find best portfolio allocation to maximize value with minimal risk over a fixed timeframe

from portfoliofinder import Allocations

Allocations(0.05, ['USA_TSM', 'WLDx_TSM', 'USA_INT', 'EM'])\
    .filter('USA_TSM>=0.6 & WLDx_TSM<=0.2 & USA_INT>=0.3')\
    .with_returns("data.csv")\
    .with_regular_contributions(100000, 10000)\
    .get_backtested_values(timeframe=10)\
    .get_statistics(['mean', 'std'])\
    .filter_by_gte_percentile_of(90, 'mean')\
    .get_allocation_which_min_statistic('std')

Output

Statistic
mean    446560.590088
std     117448.007302
Name: Allocation(USA_TSM=0.6, WLDx_TSM=0.0, USA_INT=0.3, EM=0.1), dtype: float64

Graph statistics from multiple portfolio allocations to visualize their efficient frontier

from portfoliofinder import Allocations

Allocations(0.05, ['USA_TSM', 'WLDx_TSM', 'USA_INT', 'EM'])\
    .filter('USA_TSM>=0.2 & USA_INT>=0.2')\
    .with_returns("data.csv")\
    .with_regular_contributions(100000, 10000)\
    .get_backtested_values(timeframe=10)\
    .get_statistics()\
    .graph('std', 'mean')

Output

efficient_frontier

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

portfoliofinder-0.2.4.tar.gz (2.8 kB view hashes)

Uploaded Source

Built Distribution

portfoliofinder-0.2.4-py3-none-any.whl (3.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page