Pros of bt

• More flexible and customizable, allowing for complex trading strategies
• Better suited for professional-grade backtesting and portfolio analysis
• Supports multiple assets and portfolio rebalancing

Cons of bt

• Steeper learning curve and more complex API
• Less beginner-friendly documentation
• Slower performance for simple backtests

Code Comparison

bt:

strategy = bt.Strategy('s1', [bt.algos.RunMonthly(),
                              bt.algos.SelectAll(),
                              bt.algos.WeighEqually(),
                              bt.algos.Rebalance()])
backtest = bt.Backtest(strategy, data)
res = bt.run(backtest)

backtesting.py:

class Strategy(bt.Strategy):
    def init(self):
        self.sma = self.I(ta.SMA, self.data.Close, 20)
    
    def next(self):
        if self.data.Close[-1] > self.sma[-1]:
            self.buy()
        elif self.data.Close[-1] < self.sma[-1]:
            self.sell()

bt = Backtest(data, Strategy, cash=10000, commission=.002)
stats = bt.run()

Both libraries offer powerful backtesting capabilities, but they cater to different user needs. bt is more suitable for advanced users and complex strategies, while backtesting.py provides a simpler, more intuitive interface for beginners and straightforward backtests.

Pros of Backtrader

• More extensive documentation and community support
• Greater flexibility and customization options
• Supports a wider range of data feeds and brokers

Cons of Backtrader

• Steeper learning curve due to its complexity
• Slower execution speed compared to Backtesting.py

Code Comparison

Backtrader:

class MyStrategy(bt.Strategy):
    def __init__(self):
        self.sma = bt.indicators.SimpleMovingAverage(self.data.close, period=20)

    def next(self):
        if self.data.close[0] > self.sma[0]:
            self.buy()
        elif self.data.close[0] < self.sma[0]:
            self.sell()

Backtesting.py:

class MyStrategy(Strategy):
    def init(self):
        self.sma = self.I(SMA, self.data.Close, 20)

    def next(self):
        if self.data.Close > self.sma:
            self.buy()
        elif self.data.Close < self.sma:
            self.sell()

Both examples implement a simple moving average crossover strategy. Backtrader uses a more object-oriented approach with separate methods for initialization and trading logic, while Backtesting.py combines both in a single class. Backtrader's syntax is more verbose, but it offers greater flexibility for complex strategies.

Pros of QuantStats

• Comprehensive performance analytics and risk metrics
• Extensive visualization capabilities for portfolio analysis
• Integration with popular data sources like Yahoo Finance

Cons of QuantStats

• Primarily focused on analysis rather than strategy implementation
• May require additional tools for actual trading execution
• Less suitable for high-frequency trading scenarios

Code Comparison

QuantStats example:

import quantstats as qs

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

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

Backtesting.py example:

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

QuantStats focuses on performance analysis and reporting, while Backtesting.py provides a framework for implementing and testing trading strategies. QuantStats excels in generating comprehensive reports and visualizations, whereas Backtesting.py offers more flexibility in strategy development and execution simulation.

Pros of finmarketpy

• More comprehensive financial market analysis tools
• Supports a wider range of asset classes and trading strategies
• Offers advanced risk management and portfolio optimization features

Cons of finmarketpy

• Steeper learning curve due to its broader scope
• Less focused on backtesting specifically
• May be overkill for simple trading strategy testing

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()

finmarketpy:

from finmarketpy.backtest import Backtest
from finmarketpy.economics import Market
from finmarketpy.curve import IRCurve

market = Market(market_data=['libor'])
ir_curve = IRCurve()

backtest = Backtest()
backtest.calculate_trading_PnL(market, ir_curve)

This comparison highlights the different focus areas of the two libraries. backtesting.py is more streamlined for simple strategy backtesting, while finmarketpy offers a broader range of financial market analysis tools and supports more complex scenarios.

Pros of Lean

• More comprehensive and feature-rich platform for algorithmic trading
• Supports multiple asset classes and markets
• Offers live trading capabilities in addition to backtesting

Cons of Lean

• Steeper learning curve due to its complexity
• Requires more setup and configuration
• Less suitable for quick, simple backtests

Code Comparison

Lean (C#):

public class SimpleMovingAverageAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2012, 1, 1);
        SetEndDate(2012, 12, 31);
        SetCash(100000);
        AddEquity("SPY", Resolution.Daily);
    }
}

backtesting.py (Python):

class SmaCross(Strategy):
    def init(self):
        price = self.data.Close
        self.ma1 = self.I(SMA, price, 10)
        self.ma2 = self.I(SMA, price, 20)

    def next(self):
        if crossover(self.ma1, self.ma2):
            self.buy()
        elif crossover(self.ma2, self.ma1):
            self.sell()

Lean offers a more structured approach with built-in methods for setting up the backtest environment, while backtesting.py provides a simpler, more concise syntax for defining trading strategies. Lean's code is more verbose but offers greater flexibility and control over the backtesting process.

Pros of Catalyst

• More comprehensive ecosystem for crypto trading, including live trading capabilities
• Supports multiple exchanges and cryptocurrencies
• Offers advanced features like portfolio optimization and risk management

Cons of Catalyst

• Steeper learning curve due to its complexity
• Less active development and community support
• Requires more setup and configuration compared to Backtesting.py

Code Comparison

Catalyst example:

from catalyst import run_algorithm

def initialize(context):
    context.asset = symbol('btc_usdt')

def handle_data(context, data):
    order(context.asset, 1)

run_algorithm(capital_base=10000, data_frequency='daily', initialize=initialize, handle_data=handle_data)

Backtesting.py example:

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)
bt.run()