Pros of QuantStats

• More actively maintained with frequent updates
• Broader range of performance metrics and risk measures
• Includes tear sheets for stocks and ETFs, not just portfolios

Cons of QuantStats

• Less comprehensive documentation compared to PyFolio
• Steeper learning curve for beginners
• Fewer built-in plotting options for custom visualizations

Code Comparison

PyFolio:

import pyfolio as pf

pf.create_full_tear_sheet(returns, 
                          benchmark_rets=benchmark_returns,
                          positions=positions,
                          transactions=transactions)

QuantStats:

import quantstats as qs

qs.reports.html(returns, 
                benchmark=benchmark_returns,
                output='tearsheet.html')

Both libraries offer similar functionality for generating performance reports, but QuantStats provides a more streamlined API for creating HTML tear sheets. PyFolio offers more granular control over individual components of the analysis, while QuantStats focuses on producing comprehensive reports with a single function call.

QuantStats is generally more suitable for quick analysis and report generation, while PyFolio may be preferred for more customized and in-depth portfolio analysis. The choice between the two depends on the specific requirements of the project and the user's familiarity with each library.

Pros of qlib

• More comprehensive and feature-rich, offering a complete AI-driven quantitative investment platform
• Actively maintained with frequent updates and contributions from the community
• Provides advanced machine learning models and strategies for quantitative trading

Cons of qlib

• Steeper learning curve due to its complexity and extensive features
• May be overkill for simple portfolio analysis tasks
• Requires more computational resources for advanced features

Code Comparison

pyfolio:

import pyfolio as pf

returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(backtest)
pf.create_full_tear_sheet(returns, positions=positions, transactions=transactions)

qlib:

from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord

with R.start(experiment_name="workflow"):
    SR = SignalRecord(model=model, dataset=dataset)
    PAR = PortAnaRecord(recorder=SR)
    analysis = PAR.generate()

Summary

While pyfolio focuses on portfolio performance analysis and visualization, qlib offers a more comprehensive suite of tools for quantitative investment, including data processing, model training, and strategy development. pyfolio is simpler to use for basic portfolio analysis, while qlib provides more advanced features at the cost of increased complexity.

Pros of mlfinlab

• Broader scope, covering machine learning techniques for financial applications
• More active development and frequent updates
• Extensive documentation and educational resources

Cons of mlfinlab

• Steeper learning curve due to more complex algorithms
• May require more computational resources for some functions
• Less focus on traditional portfolio analysis techniques

Code Comparison

mlfinlab:

from mlfinlab.portfolio_optimization import MeanVarianceOptimisation

mvo = MeanVarianceOptimisation()
weights = mvo.optimize(expected_returns, covariance_matrix)

pyfolio:

import pyfolio as pf

pf.create_full_tear_sheet(returns, positions=positions, transactions=transactions)

Summary

mlfinlab offers a wider range of machine learning-based financial analysis tools, making it suitable for advanced quantitative research. It's actively maintained and provides extensive documentation. However, it may be more complex to use and resource-intensive compared to pyfolio.

pyfolio, on the other hand, focuses on traditional portfolio analysis and performance metrics. It's simpler to use and provides quick insights into portfolio performance, but may lack some of the advanced features found in mlfinlab.

The choice between the two depends on the specific needs of the project, with mlfinlab being more suitable for machine learning-driven financial research, while pyfolio excels in straightforward portfolio analysis and visualization.

Pros of ffn

• Lightweight and focused on financial functions and analytics
• Includes performance metrics not found in pyfolio (e.g., Sortino ratio, Calmar ratio)
• Offers data visualization capabilities with minimal dependencies

Cons of ffn

• Less comprehensive portfolio analysis tools compared to pyfolio
• Smaller community and less frequent updates
• Limited documentation and examples compared to pyfolio's extensive resources

Code Comparison

ffn:

import ffn

returns = ffn.get('SPY,AAPL', start='2010-01-01')
stats = returns.calc_stats()
print(stats.display())

pyfolio:

import pyfolio as pf

returns, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(backtest)
pf.create_full_tear_sheet(returns, positions=positions, transactions=transactions)

The ffn code focuses on quick performance calculations, while pyfolio provides a more comprehensive analysis with its tear sheet functionality. ffn is more suitable for quick, specific calculations, whereas pyfolio excels in detailed portfolio analysis and reporting.