The industry standard for hedge fund index analytics
Project description
PivotalPath
The Industry Standard for Hedge Fund Index Analytics
PivotalPath is the comprehensive Python package for hedge fund index performance analysis, specifically designed for AI/LLM integration. It provides institutional-quality analytics for hedge fund indices with intuitive, discoverable functions.
Why PivotalPath for AI Assistants
PivotalPath is uniquely designed for LLM integration:
- Intuitive function names that AI assistants can easily discover
- Comprehensive docstrings with examples in every function
- Sensible defaults that work out of the box
- Rich error messages with helpful suggestions
- Consistent data structures for predictable outputs
Installation
pip install pivotalpath
Quick Start - Copy & Paste Ready
import pivotalpath as pp
# Get all available hedge fund indices
indices = pp.list_hedge_fund_indices()
print(f"Available indices: {indices}")
# Quick analysis of Hedge Fund Composite Index
stats = pp.quick_index_stats("PP-HFC", period="3Y")
print(f"3Y Sharpe Ratio: {stats['sharpe']:.2f}")
print(f"3Y Annual Return: {stats['annret']:.1%}")
# Comprehensive analysis
analysis = pp.analyze_hedge_fund_index("PP-HFC", start_date="2020-01", end_date="2024-12")
print(f"Analysis period: {analysis['period']}")
print(f"Max Drawdown: {analysis['maxddc']:.1%}")
# Compare multiple hedge fund strategies
strategy_indices = ["PP-HFC", "PP-L-EH", "PP-L-MA"]
comparison = pp.compare_hedge_fund_indices(strategy_indices, metrics=['sharpe', 'annret', 'maxddc'])
print(comparison)
# Generate professional report
report = pp.generate_index_report("PP-HFC")
print(report)
Available Hedge Fund Indices
PivotalPath provides access to 11 institutional hedge fund indices:
| Ticker | Name | Strategy Focus |
|---|---|---|
| PP-HFC | Hedge Fund Composite Index | Broad hedge fund performance |
| PP-L-EH | Equity Hedge Index | Long/short equity strategies |
| PP-L-MA | Merger Arbitrage Index | Merger arbitrage strategies |
| PP-L-ED | Event Driven Index | Event-driven strategies |
| PP-L-EM | Emerging Markets Index | Emerging market hedge funds |
Core Functions for AI Assistants
Analysis Functions
# Essential metrics - perfect for quick responses
pp.quick_index_stats(index_ticker, period="5Y")
# Comprehensive analysis - for detailed questions
pp.analyze_hedge_fund_index(index_ticker, start_date="2020-01", end_date="2024-12")
Comparison Functions
# Compare multiple indices
pp.compare_hedge_fund_indices(index_list, metrics=['sharpe', 'annret'])
Information Functions
# Discovery functions for AI assistants
pp.list_hedge_fund_indices() # All available indices
pp.list_available_metrics() # All available metrics
pp.get_hedge_fund_index_info(ticker) # Details about specific index
Reporting Functions
# Generate formatted reports
pp.generate_index_report(ticker, report_style="comprehensive")
pp.generate_index_report(ticker, report_style="executive")
pp.generate_index_report(ticker, report_style="quick")
Perfect for Common LLM Questions
"What are the best performing hedge fund strategies?"
import pivotalpath as pp
indices = pp.list_hedge_fund_indices()
comparison = pp.compare_hedge_fund_indices(indices, metrics=['annret', 'sharpe'])
print(comparison.sort_values('annret', ascending=False))
"Compare hedge fund performance to S&P 500"
import pivotalpath as pp
hf_stats = pp.quick_index_stats("PP-HFC", period="5Y")
print(f"Hedge Fund Composite vs SP500:")
print(f"Annual Return: {hf_stats['annret']:.1%}")
print(f"Sharpe Ratio: {hf_stats['sharpe']:.2f}")
print(f"Beta to SP500: {hf_stats['beta']:.2f}")
"What's the risk profile of hedge funds?"
import pivotalpath as pp
analysis = pp.analyze_hedge_fund_index("PP-HFC")
print(f"Max Drawdown: {analysis['maxddc']:.1%}")
print(f"Volatility: {analysis['annvol']:.1%}")
print(f"Sortino Ratio: {analysis['sortino']:.2f}")
Data Coverage
- Time Period: 1998-present (26+ years of data)
- Update Frequency: Monthly
- Data Quality: Institutional-grade, professionally maintained
- Coverage: 11 hedge fund strategy indices
- Benchmarks: Market factors (SP500, Treasury Bills, etc.)
Metrics Available
Performance Metrics
- Annual Return (annret)
- Annualized Volatility (annvol)
- Sharpe Ratio (sharpe)
- Sortino Ratio (sortino)
- Calmar Ratio (calmar)
Risk Metrics
- Maximum Drawdown (maxddc)
- Downside Deviation (ddev)
- Skewness (skewness)
- Excess Kurtosis (exckurt)
- Hit Ratio (hitratio)
Market Exposure
- Beta (beta)
- Alpha (alpha)
- Correlation (corr)
- R-Squared (r2)
Error Handling
PivotalPath provides helpful error messages and suggestions:
# If ticker doesn't exist
result = pp.quick_index_stats("INVALID-TICKER")
# Returns: {'error': 'No data available for index INVALID-TICKER',
# 'suggested_alternatives': ['PP-HFC', 'PP-L-EH', 'PP-L-MA']}
What Makes This Different
- Index Focus: Analyzes hedge fund indices (composite benchmarks), not individual funds
- Institutional Quality: Professional-grade metrics used by hedge fund managers
- LLM-First Design: Every function designed for AI assistant discovery and use
- Comprehensive Coverage: 20+ performance and risk metrics
- Real Data: Connected to institutional hedge fund databases
Use Cases
- Hedge Fund Research: Academic and professional research
- Portfolio Analysis: Institutional benchmarking and allocation
- Risk Management: Comprehensive risk assessment
- Due Diligence: Performance analysis and comparison
- AI-Powered Analysis: LLM integration for automated insights
Examples and Tutorials
See the /examples directory for Jupyter notebooks demonstrating:
- Basic hedge fund index analysis
- Multi-strategy comparison
- Risk metric calculation
- LLM integration patterns
- Custom analysis workflows
API Reference
Full API documentation available in the /docs directory.
Contributing
We welcome contributions! Please feel free to submit issues and enhancement requests.
License
MIT License - see LICENSE for details.
Citation
If you use PivotalPath in academic research:
@software{pivotalpath2024,
title = {PivotalPath: Hedge Fund Index Analytics},
author = {Your Name},
year = {2024},
url = {https://github.com/yourusername/pivotalpath},
version = {1.0.2}
}
Ready to analyze hedge fund indices like a pro? Start with pip install pivotalpath
For questions, issues, or feature requests, please visit our GitHub Issues page.
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
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pivotalpath-1.0.3.tar.gz.
File metadata
- Download URL: pivotalpath-1.0.3.tar.gz
- Upload date:
- Size: 16.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c69628afe847cec6b1444894fac0435d7fe729bbeeb7cbfde25fa70e6c8234af
|
|
| MD5 |
561c049ce012ffee9572a7ab99fdc767
|
|
| BLAKE2b-256 |
3af7de44668c715119bfaa9d03eeee3eb70b86df9686275a63db23dc3537d6f3
|
File details
Details for the file pivotalpath-1.0.3-py3-none-any.whl.
File metadata
- Download URL: pivotalpath-1.0.3-py3-none-any.whl
- Upload date:
- Size: 18.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6dd654dde47ef098a09e5e0fc01873b9088c685a9c010f58e355623fbf378740
|
|
| MD5 |
04a15326e71ceb70b8540044a71375ae
|
|
| BLAKE2b-256 |
1eef6c484155b3ecfa048bc4f134e172f7301ce48377fbf927d8fa82f9b9c622
|