Interactive Brokers
The professional gateway to global multi-asset trading with institutional-grade API execution.
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.
Supports discrete event simulation to model market impact and execution delays more accurately than vector-based backtesters.
The professional gateway to global multi-asset trading with institutional-grade API execution.
Active asset management powered by AI-driven alpha generation and proprietary risk analytics.
Institutional-grade financial data refined for machine learning and quantitative research.
The AI-powered quant trading platform for automated portfolio management without code.
Verified feedback from the global deployment network.
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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).
Identifying specific windows of time where G10 currencies consistently trend due to central bank liquidity.
Registry Updated:2/7/2026
Review results via Plotly heatmaps.
Validating a cross-asset carry strategy before capital allocation.
Analyzing historical price spikes in natural gas during winter months over 20 years.