There are 137,000 Python libraries.
But you only need 11 for quant finance:
But you only need 11 for quant finance:
pandas
• Allows quick data cleaning and preprocessing
• Provides DataFrame structure for time-series data
• Enables complex data operations with simple syntax
Use it for:
Manipulating and transforming financial market data.
• Allows quick data cleaning and preprocessing
• Provides DataFrame structure for time-series data
• Enables complex data operations with simple syntax
Use it for:
Manipulating and transforming financial market data.
NumPy
• Advanced statistical functions for analysis
• Efficient vectorized operations for large datasets
• Powerful random number generators for simulations
Use it for:
Fast and versatile scientific computing.
• Advanced statistical functions for analysis
• Efficient vectorized operations for large datasets
• Powerful random number generators for simulations
Use it for:
Fast and versatile scientific computing.
SciPy
• Advanced stats and hypothesis testing
• Linear algebra for portfolio optimization
• Efficient numerical routines and model calibration
Use it for:
Optimization and calibration.
• Advanced stats and hypothesis testing
• Linear algebra for portfolio optimization
• Efficient numerical routines and model calibration
Use it for:
Optimization and calibration.
Matplotlib
• Integrates with pandas
• Plot financial time series data
• Generate histogram, scatter plots
Use it for:
Fast plotting of market data and summary statistics.
• Integrates with pandas
• Plot financial time series data
• Generate histogram, scatter plots
Use it for:
Fast plotting of market data and summary statistics.
Statsmodels
• Hypothesis testing to test trading strategy efficacy
• Regression analysis for factor analysis and hedging
• Time series analysis with ARIMA and VAR models for forecasting
Use it for:
Regression and statistical analysis.
• Hypothesis testing to test trading strategy efficacy
• Regression analysis for factor analysis and hedging
• Time series analysis with ARIMA and VAR models for forecasting
Use it for:
Regression and statistical analysis.
vectorbt
• High performance to run millions of simulations
• Flexible using NumPy and pandas under the hood
• Easy to use once to get up and running fast
Use it for:
Prototyping trading strategies and optimizing parameters.
• High performance to run millions of simulations
• Flexible using NumPy and pandas under the hood
• Easy to use once to get up and running fast
Use it for:
Prototyping trading strategies and optimizing parameters.
Zipline Reloaded
• Create custom data pipelines
• Backtest strategies with realistic data
• Access a vast array of trading algorithms
Use it for:
A realistic market simulation for backtesting.
• Create custom data pipelines
• Backtest strategies with realistic data
• Access a vast array of trading algorithms
Use it for:
A realistic market simulation for backtesting.
PyFolio Reloaded
• Detailed portfolio risk analysis
• Integrates with Zipline for backtesting
• Supports custom tear sheets for personalized analytics
Use it for:
Risk and performance metrics.
• Detailed portfolio risk analysis
• Integrates with Zipline for backtesting
• Supports custom tear sheets for personalized analytics
Use it for:
Risk and performance metrics.
AlphaLens Reloaded
• Visualizes key metrics effectively
• Robust statistical analysis of factors' long-term trends
• Streamlines performance analysis of predictive alpha factors
Use it for:
Alpha factor analysis.
• Visualizes key metrics effectively
• Robust statistical analysis of factors' long-term trends
• Streamlines performance analysis of predictive alpha factors
Use it for:
Alpha factor analysis.
OpenBB
• Access financial market data from one place
• Fast Terminal environment for investment research
• SDK for programmatic access to all OpenBB functions
Use it for:
Research and data acquisition.
• Access financial market data from one place
• Fast Terminal environment for investment research
• SDK for programmatic access to all OpenBB functions
Use it for:
Research and data acquisition.
RiskFolio-Lib
• Optimize asset allocation with 20+ risk measures
• Perform scenario, stress testing, and backtesting
• Tools for risk budgeting and risk contribution
Use it for:
Advanced portfolio optimization and risk management.
• Optimize asset allocation with 20+ risk measures
• Perform scenario, stress testing, and backtesting
• Tools for risk budgeting and risk contribution
Use it for:
Advanced portfolio optimization and risk management.
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