a-practitioners-guide-to-the-ONC-algorithm

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

a-practitioners-guide-to-the-ONC-algorithm

★ 20 Open GitHub ↗

Code base for the practitioner's guide to the ONC algorithm paper published with the Journal of Financial Data Science

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# A-Practitioners-Guide-to-the-ONC-Algorithm
Code base for the Practitioner's Guide to the ONC Algorithm paper published with the Journal of Financial Data Science.

Identifying profitable investment strategies has been a long-standing challenge for
finance practitioners. The Optimal Number of clusters (ONC) algorithm is a reliable tool
used to evaluate backtest results affected by multiple testing. The algorithm is
necessary to calculate the deflated Sharpe Ratio (DSR), a popular metric that detects
potential false positive investment strategies. These methods are based on the
Familywise Error Rate (FWER) approach, which provides stringent control over the
overall error rate, reducing the likelihood of false discoveries and increasing the
reliability of findings. However, the ONC algorithm's time complexity poses a significant
challenge for practitioners. This study proposes a practical solution to reduce the
number of clusters tested by the ONC algorithm while maintaining accuracy. Results
from simulated data sets demonstrate that the proposed solution significantly reduces
the algorithm's runtime. Additionally, this study addresses the impact of outliers on the
ONC algorithm, showing that they can lead to non-optimal solutions, and provides a
simple solution to mitigate their effects. These findings contribute to the literature on
finance by enhancing the usability of the ONC algorithm.

a-practitioners-guide-to-the-ONC-algorithm

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README