Pros of Lean

• More comprehensive and feature-rich platform for algorithmic trading
• Supports multiple asset classes (stocks, forex, crypto, options, futures)
• Larger community and ecosystem with extensive documentation

Cons of Lean

• Steeper learning curve due to its complexity
• Requires more setup and configuration
• Less focused on simplicity and ease of use for beginners

Code Comparison

Jesse:

@property
def hyperparameters(self):
    return [
        {'name': 'stop_loss', 'type': int, 'min': 1, 'max': 20, 'default': 10},
        {'name': 'take_profit', 'type': int, 'min': 1, 'max': 20, 'default': 10},
    ]

Lean:

public override void Initialize()
{
    SetStartDate(2013, 10, 07);
    SetEndDate(2013, 10, 11);
    SetCash(100000);
    AddEquity("SPY", Resolution.Minute);
}

Jesse focuses on simplicity and ease of use, with a Python-based approach that's more accessible to beginners. Lean offers a more comprehensive platform with support for multiple asset classes and languages, but requires more setup and has a steeper learning curve. Jesse's code example shows a straightforward way to define hyperparameters, while Lean's initialization demonstrates its more complex, but flexible, approach to algorithm setup.

Pros of Catalyst

• Built specifically for the Secret Network, offering privacy-preserving smart contract functionality
• Supports multiple programming languages including Rust, Go, and JavaScript
• Provides a comprehensive development environment with built-in testing and deployment tools

Cons of Catalyst

• Limited to the Secret Network ecosystem, reducing flexibility for other blockchain platforms
• Steeper learning curve due to the complexity of privacy-preserving computations
• Smaller community and ecosystem compared to more general-purpose trading frameworks

Code Comparison

Jesse:

from jesse.strategies import Strategy

class MyStrategy(Strategy):
    def should_long(self):
        return self.close > self.sma(10)

    def should_short(self):
        return self.close < self.sma(10)

Catalyst:

use cosmwasm_std::{Deps, Env, StdResult};
use secret_toolkit::viewing_key::{ViewingKey, ViewingKeyStore};

pub fn query_balance(deps: Deps, env: Env, address: String, key: String) -> StdResult<Balance> {
    ViewingKey::check(deps.storage, &address, &key)?;
    // Query balance logic here
}

Pros of QuantStats

• Focuses on performance analysis and risk metrics for quantitative trading strategies
• Provides extensive visualization tools for portfolio analysis
• Integrates well with pandas DataFrames for easy data manipulation

Cons of QuantStats

• Limited to post-trade analysis and doesn't provide trading execution capabilities
• Requires external data sources for historical price data
• Less suitable for live trading scenarios compared to Jesse

Code Comparison

Jesse:

from jesse.routes import router
router.set_routes([
    ('Binance', 'BTC-USDT', '4h', 'ExampleStrategy'),
])

QuantStats:

import quantstats as qs
qs.extend_pandas()
returns = pd.read_csv('returns.csv', index_col='date', parse_dates=True)
qs.reports.html(returns, output='stats.html')

Jesse is designed for creating and backtesting trading strategies, while QuantStats focuses on analyzing trading performance and generating reports. Jesse provides a more comprehensive framework for strategy development and execution, whereas QuantStats excels in post-trade analysis and visualization of trading results.

Pros of bt

• More comprehensive backtesting and analysis tools, including performance metrics and risk management features
• Supports a wider range of financial instruments, including stocks, options, and futures
• More active development and larger community support

Cons of bt

• Steeper learning curve due to more complex API and features
• Less focus on cryptocurrency trading compared to Jesse
• Requires more setup and configuration for basic use cases

Code Comparison

Jesse:

from jesse.strategies import Strategy

class MyStrategy(Strategy):
    def should_long(self):
        return self.close > self.sma(10)

    def should_short(self):
        return self.close < self.sma(10)

bt:

import bt

def sma_cross(data):
    sma_fast = data.rolling(10).mean()
    sma_slow = data.rolling(20).mean()
    return sma_fast > sma_slow

strategy = bt.Strategy('SMA_Cross', 
    [bt.algos.SelectWhere(sma_cross),
     bt.algos.WeighEqually(),
     bt.algos.Rebalance()])

Both Jesse and bt offer backtesting capabilities for trading strategies, but they cater to different user needs and preferences. Jesse is more focused on cryptocurrency trading with a simpler API, while bt provides a more comprehensive set of tools for various financial instruments and advanced analysis.

Pros of Backtrader

• More mature and established project with a larger community
• Supports a wider range of asset classes, including stocks and futures
• Offers more advanced features like multi-timeframe analysis and live trading

Cons of Backtrader

• Steeper learning curve due to its complexity and extensive features
• Less focus on cryptocurrency trading compared to Jesse
• Documentation can be overwhelming for beginners

Code Comparison

Jesse:

from jesse.strategies import Strategy

class MyStrategy(Strategy):
    def should_long(self):
        return self.close > self.sma(10)

    def should_short(self):
        return self.close < self.sma(10)

Backtrader:

import backtrader as bt

class MyStrategy(bt.Strategy):
    def __init__(self):
        self.sma = bt.indicators.SimpleMovingAverage(period=10)

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

Both frameworks allow for creating custom strategies, but Jesse's syntax is more concise and focused on cryptocurrency trading. Backtrader offers more flexibility and supports a wider range of assets, but requires more boilerplate code.

Pros of finmarketpy

• Broader focus on financial market analysis, including backtesting, market data analysis, and trading strategies
• More comprehensive documentation and examples for various financial market applications
• Integrates with popular data providers like Bloomberg and Quandl

Cons of finmarketpy

• Less specialized for cryptocurrency trading compared to Jesse
• May have a steeper learning curve due to its broader scope
• Not as actively maintained, with fewer recent updates

Code Comparison

Jesse:

from jesse.strategies import Strategy

class MyStrategy(Strategy):
    def should_long(self):
        return self.close > self.sma(10)

    def should_short(self):
        return self.close < self.sma(10)

finmarketpy:

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

market = Market(market_data=['EURUSD'])
strategy = Backtest(market_data=market, strategy_parameters={})
strategy.calculate_strategy()

Both libraries offer ways to implement trading strategies, but Jesse is more focused on cryptocurrency trading with a simpler API, while finmarketpy provides a more general framework for financial market analysis and backtesting across various asset classes.