OpenBB

Pros of yfinance

• Lightweight and focused solely on Yahoo Finance data retrieval
• Simple to use with minimal setup required
• Actively maintained with frequent updates

Cons of yfinance

• Limited to Yahoo Finance as the sole data source
• Lacks advanced financial analysis tools and visualizations
• Prone to occasional issues due to changes in Yahoo Finance's structure

Code Comparison

yfinance

import yfinance as yf

ticker = yf.Ticker("AAPL")
hist = ticker.history(period="1mo")
print(hist.tail())

OpenBB

from openbb_terminal.sdk import openbb

aapl_data = openbb.stocks.load("AAPL")
hist = openbb.stocks.hist(aapl_data, start_date="2023-01-01")
print(hist.tail())

Summary

yfinance is a lightweight library focused on retrieving data from Yahoo Finance, making it easy to use for simple tasks. However, it lacks the comprehensive features and multiple data sources offered by OpenBB. OpenBB provides a more robust platform for financial analysis, including advanced tools and visualizations, but may require more setup and learning curve compared to yfinance's simplicity.

Pros of Qlib

• More focused on AI-driven quantitative investment strategies
• Extensive documentation and research papers supporting its methodologies
• Stronger emphasis on model training and backtesting capabilities

Cons of Qlib

• Steeper learning curve for users without a strong background in machine learning
• Less comprehensive in terms of general financial analysis tools
• More limited in terms of data sources and asset classes covered

Code Comparison

OpenBB:

from openbb_terminal.sdk import openbb

# Fetch stock data
aapl_data = openbb.stocks.load("AAPL")

# Perform technical analysis
rsi = openbb.ta.rsi(aapl_data["Adj Close"])

Qlib:

from qlib.contrib.model.gbdt import LGBModel
from qlib.contrib.data.handler import Alpha158

# Initialize data handler
handler = Alpha158()

# Train model
model = LGBModel()
model.fit(handler)

Both repositories offer powerful tools for financial analysis, but they cater to different user needs. OpenBB provides a more comprehensive suite of financial tools accessible to a wider range of users, while Qlib focuses on advanced quantitative strategies using AI and machine learning techniques.

Pros of Lean

• More comprehensive backtesting and live trading capabilities
• Supports multiple asset classes (stocks, forex, crypto, options)
• Larger community and ecosystem with extensive documentation

Cons of Lean

• Steeper learning curve for beginners
• Less focus on fundamental data and analysis tools
• Requires more setup and configuration for local use

Code Comparison

OpenBB:

from openbb_terminal.sdk import openbb

# Fetch stock data
aapl_data = openbb.stocks.load("AAPL")

# Plot stock price
openbb.stocks.candle(data=aapl_data)

Lean:

public class MyAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2020, 1, 1);
        SetCash(100000);
        AddEquity("AAPL", Resolution.Daily);
    }

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

OpenBB focuses on providing a user-friendly interface for financial data analysis and visualization, while Lean is geared towards algorithmic trading and backtesting. OpenBB is more accessible for beginners and offers a wide range of data sources, whereas Lean provides a more robust framework for developing and testing trading strategies across multiple asset classes.

Pros of TensorTrade

• Focused specifically on reinforcement learning for algorithmic trading
• Modular architecture allows for easy customization of trading environments
• Integrates well with popular RL libraries like OpenAI Gym

Cons of TensorTrade

• Less comprehensive in terms of general financial analysis tools
• Smaller community and fewer contributors compared to OpenBB
• More specialized, which may limit its use cases for broader financial applications

Code Comparison

TensorTrade example:

from tensortrade.env import TradingEnvironment
from tensortrade.feed import CSVFeed
from tensortrade.oms import instruments

environment = TradingEnvironment(
    feed=CSVFeed(data_frame=df),
    portfolio=Portfolio(USD=10000),
    action_scheme="managed-risk",
    reward_scheme="risk-adjusted",
    window_size=20
)

OpenBB example:

from openbb import obb

# Fetch stock data
stock_data = obb.stocks.load("AAPL", start_date="2022-01-01")

# Perform technical analysis
sma = obb.ta.sma(stock_data["Close"], 20)
rsi = obb.ta.rsi(stock_data["Close"])

The code examples highlight the different focus areas of each project. TensorTrade is geared towards creating trading environments for reinforcement learning, while OpenBB provides a broader set of financial analysis tools and data retrieval functions.

Pros of finmarketpy

• Focused specifically on financial market analysis and trading strategies
• Includes advanced features like backtesting and market microstructure analysis
• Lightweight and modular design for easy integration into existing workflows

Cons of finmarketpy

• Smaller community and less frequent updates compared to OpenBB
• More limited scope in terms of data sources and financial instruments covered
• Less comprehensive documentation and tutorials for beginners

Code Comparison

finmarketpy:

from finmarketpy.backtest import Backtest
from finmarketpy.economics import Calculator

backtest = Backtest(start_date='2010-01-01', finish_date='2020-12-31')
calculator = Calculator()
sharpe = calculator.calculate_sharpe_ratio(backtest.returns)

OpenBB:

from openbb_terminal.sdk import openbb

aapl_data = openbb.stocks.load("AAPL")
ma = openbb.ta.ma(aapl_data["Adj Close"], 50, 200)
rsi = openbb.ta.rsi(aapl_data["Adj Close"])

The code examples showcase the different focus areas of each library. finmarketpy emphasizes backtesting and financial calculations, while OpenBB provides a broader range of financial data retrieval and analysis tools.