Enterprise-grade Python library for modular backtesting and quantitative financial market analysis.
FinMarketPy is a robust Python-based framework developed by Cuemacro, designed for backtesting trading strategies and analyzing financial market data with high precision. In the 2026 landscape, it stands as a critical tool for quantitative analysts who require a transparent, non-black-box environment for strategy development. The architecture is highly modular, separating the data fetching (via findatapy), visualization (via chartpy), and core analytical logic. It supports both simple cash-based backtesting and complex portfolio-level simulations. Its technical edge lies in its specialized focus on FX and macro markets, offering built-in capabilities for seasonality analysis, event-driven trade analysis, and transaction cost modeling. By leveraging the SciPy stack (Pandas, NumPy) and modern visualization libraries like Plotly and Bokeh, FinMarketPy enables institutional-grade research workflows. Its open-source nature allows for deep customization, making it a preferred choice for hedge funds and independent researchers who need to integrate proprietary alpha signals with standard market indicators while maintaining full control over the execution logic and risk parameters.
FinMarketPy is a robust Python-based framework developed by Cuemacro, designed for backtesting trading strategies and analyzing financial market data with high precision.
Explore all tools that specialize in backtest trading strategies. This domain focus ensures finmarketpy delivers optimized results for this specific requirement.
Explore all tools that specialize in backtesting trading strategies. This domain focus ensures finmarketpy delivers optimized results for this specific requirement.
Supports discrete event simulation to model market impact and execution delays more accurately than vector-based backtesters.
Uses 'findatapy' to unify data access from Bloomberg, Reuters, Quandl, and Yahoo Finance into a single API.
Built-in functions to analyze recurring price patterns across specific hours, days, or months.
Module for calculating slippage, market impact, and commission costs based on historical liquidity data.
Integrated wrappers for common and exotic technical indicators optimized for Pandas performance.
Automated parameter sweeping and Monte Carlo simulations to test strategy robustness.
Deep integration with a high-level charting wrapper that supports multiple backends (Plotly, Bokeh, Matplotlib).
Install Python 3.9+ environment using Anaconda or venv.
Clone the repository from GitHub: git clone https://github.com/cuemacro/finmarketpy.git.
Install core dependencies: pip install findatapy chartpy pandas numpy.
Configure the 'DataConfig' file to specify local paths for market data storage.
Set up API keys for external data providers (Bloomberg, Reuters, or Quandl) within the environment variables.
Initialize the 'Backtest' class and define the trading strategy logic within the 'TradingStrategy' subclass.
Load historical data using findatapy wrappers.
Execute the backtest engine using the 'calculate_trading_signals' method.
Generate performance metrics including Sharpe Ratio and Drawdown via 'TradeAnalysis'.
Export visualization charts using the Chart object for stakeholder reporting.
All Set
Ready to go
Verified feedback from other users.
"Highly praised by quantitative professionals for its transparency and specific focus on FX/Macro, though noted for a steep learning curve for non-Python users."
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