Pros of pyfolio

• More established and widely used in the quantitative finance community
• Offers more advanced risk metrics and analysis tools
• Better documentation and examples for complex use cases

Cons of pyfolio

• Less actively maintained (last update was in 2021)
• Heavier dependencies, which can make installation more challenging
• Steeper learning curve for beginners

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)

quantstats:

import quantstats as qs

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

Summary

While pyfolio offers more advanced features and is well-established in the quant finance community, quantstats provides a more user-friendly and actively maintained alternative. quantstats has a simpler API and lighter dependencies, making it easier for beginners to get started. However, pyfolio still holds an edge for more complex analysis needs and risk metrics. The choice between the two depends on the user's specific requirements and level of expertise in quantitative finance.

Pros of qlib

• More comprehensive AI-driven quantitative investment platform
• Supports advanced machine learning models and strategies
• Offers a modular and extensible architecture for customization

Cons of qlib

• Steeper learning curve due to its complexity
• Requires more computational resources for advanced features
• Less focused on performance reporting and visualization

Code Comparison

qlib example:

from qlib.contrib.model.pytorch_lstm import LSTMModel
from qlib.contrib.data.handler import Alpha158
from qlib.workflow import R

model = LSTMModel()
dataset = Alpha158()
R.run(dataset=dataset, model=model)

quantstats example:

import quantstats as qs

qs.extend_pandas()
stock = qs.utils.download_returns('AAPL')
qs.reports.html(stock, output='AAPL_report.html')

Summary

qlib is a more advanced platform for AI-driven quantitative investing, offering sophisticated models and strategies. It's highly customizable but requires more expertise and resources. quantstats, on the other hand, focuses on performance analysis and reporting, providing an easier-to-use solution for portfolio analytics and visualization. The choice between the two depends on the specific needs of the project and the user's level of expertise in quantitative finance and machine learning.

Pros of mlfinlab

• Focuses on machine learning applications in finance, offering advanced algorithms and techniques
• Provides comprehensive documentation and educational resources
• Includes implementations of academic research papers in financial machine learning

Cons of mlfinlab

• Steeper learning curve due to more complex algorithms and concepts
• May be overkill for basic portfolio analysis tasks
• Requires more computational resources for some advanced features

Code comparison

mlfinlab:

from mlfinlab.portfolio_optimization import MeanVarianceOptimisation

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

quantstats:

import quantstats as qs

qs.reports.html(returns, benchmark="SPY", output="analysis_report.html")

Summary

mlfinlab is more focused on advanced machine learning techniques for finance, offering implementations of cutting-edge research. It's well-suited for users looking to apply sophisticated algorithms to financial data. However, it may be more complex and resource-intensive than necessary for basic analysis tasks.

quantstats, on the other hand, excels in generating performance reports and metrics for portfolios. It's more user-friendly and suitable for quick analysis and visualization of trading strategies. While it may not offer the same depth of machine learning capabilities, it's excellent for portfolio performance evaluation and comparison.

The choice between the two depends on the specific needs of the user, with mlfinlab being better for advanced ML applications and quantstats for straightforward portfolio analysis and reporting.

Pros of ffn

• More established and mature project with a longer history
• Offers a wider range of financial metrics and analysis tools
• Better suited for complex portfolio analysis and risk management

Cons of ffn

• Less active development and fewer recent updates
• Documentation is less comprehensive and user-friendly
• Steeper learning curve for beginners

Code Comparison

ffn:

import ffn

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

QuantStats:

import quantstats as qs

qs.extend_pandas()
stock = qs.utils.download_returns('AAPL')
qs.reports.html(stock, output='AAPL_report.html')

ffn focuses on calculating various financial metrics and statistics, while QuantStats provides a more streamlined approach to generating performance reports. QuantStats offers easier-to-use functions for creating visual reports, while ffn provides more flexibility for custom analysis. Both libraries have their strengths, and the choice between them depends on the specific requirements of the financial analysis project.

Pros of backtesting.py

• More focused on backtesting strategies with a simple, intuitive API
• Includes built-in plotting and visualization tools
• Supports custom indicators and strategies with less boilerplate code

Cons of backtesting.py

• Less comprehensive performance metrics compared to QuantStats
• Limited to backtesting; doesn't offer portfolio analysis or risk management tools
• Smaller community and fewer contributors

Code Comparison

backtesting.py:

from backtesting import Backtest, Strategy
from backtesting.lib import crossover

class SmaCross(Strategy):
    def init(self):
        self.sma1 = self.I(SMA, self.data.Close, 10)
        self.sma2 = self.I(SMA, self.data.Close, 20)

    def next(self):
        if crossover(self.sma1, self.sma2):
            self.buy()
        elif crossover(self.sma2, self.sma1):
            self.sell()

bt = Backtest(data, SmaCross, cash=10000, commission=.002)
bt.run()
bt.plot()

QuantStats:

import quantstats as qs

# Assuming 'returns' is a pandas Series of strategy returns
qs.reports.html(returns, output='strategy_report.html')
qs.plots.snapshot(returns, title='Strategy Performance')

Both libraries offer valuable tools for quantitative finance, with backtesting.py focusing on strategy backtesting and QuantStats excelling in performance analysis and reporting.

Pros of Lean

• Comprehensive algorithmic trading platform with backtesting, live trading, and data management capabilities
• Supports multiple asset classes and markets
• Extensive documentation and active community support

Cons of Lean

• Steeper learning curve due to its complexity and extensive features
• Requires more setup and configuration compared to QuantStats
• May be overkill for simple portfolio analysis tasks

Code Comparison

QuantStats (Python):

import quantstats as qs

# Fetch stock data and calculate returns
stock = qs.utils.download_returns('AAPL')

# Generate tearsheet
qs.reports.html(stock, output='AAPL_tearsheet.html')

Lean (C#):

public class SimpleMovingAverageAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2015, 1, 1);
        SetEndDate(2018, 1, 1);
        SetCash(100000);
        AddEquity("SPY", Resolution.Daily);
    }

    public override void OnData(Slice data)
    {
        if (!Portfolio.Invested)
        {
            SetHoldings("SPY", 1);
        }
    }
}

QuantStats focuses on quick portfolio analysis and reporting, while Lean provides a full-fledged algorithmic trading environment with more advanced features and flexibility.