Porfolio Finder¶
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
