
backtrader
A feature-rich Python framework for backtesting and trading.

A platform for quantitative trading strategy development and evaluation.

Quantiacs is a platform designed for the development, backtesting, and deployment of quantitative trading strategies. It provides a cloud-based environment with access to market data and tools for algorithm creation. Users can develop algorithms in Python, leveraging libraries for time series analysis and machine learning. The platform emphasizes a rigorous evaluation process, allowing users to backtest strategies against historical data and participate in live trading competitions. Successful strategies may be funded for live trading, generating revenue for both Quantiacs and the algorithm developer. The core value proposition is democratizing access to quantitative trading and incentivizing the creation of robust, profitable algorithms through a meritocratic funding model. The platform facilitates the entire lifecycle of a quant strategy, from initial research to live deployment.
Quantiacs is a platform designed for the development, backtesting, and deployment of quantitative trading strategies.
Explore all tools that specialize in backtesting. This domain focus ensures Quantiacs delivers optimized results for this specific requirement.
Provides advanced risk metrics, including Value at Risk (VaR) and Expected Shortfall, to quantify potential losses.
Leverages a scalable cloud infrastructure for parallel backtesting of trading strategies across multiple assets and time periods.
Simulates a real-world trading environment, allowing users to test their strategies and compete for funding.
Employs genetic algorithms and other optimization techniques to automatically refine trading strategy parameters.
Provides access to low-latency market data feeds from multiple exchanges, ensuring timely execution of trading signals.
Create a Quantiacs account.
Familiarize yourself with the platform's IDE and data access methods.
Develop a basic trading algorithm in Python.
Backtest the algorithm using historical data.
Optimize the algorithm based on backtesting results.
Submit the algorithm to live trading competitions.
Monitor the algorithm's performance in the live environment.
All Set
Ready to go
Verified feedback from other users.
"Generally positive reviews highlighting the platform's ease of use and comprehensive backtesting capabilities, with some concerns about data availability."
Post questions, share tips, and help other users.

A feature-rich Python framework for backtesting and trading.

Automate your crypto trading with advanced bots and tools to reduce stress and emotional mistakes.

Transform natural language into institutional-grade trading algorithms with AI-powered backtesting.

Build trading algorithms with AI, backtest them, then execute—all in one platform.

The scikit-learn of Time Series: A unified Python library for forecasting and anomaly detection.

A cutting-edge, unified API for quantitative research, backtesting, and live trading on the world's leading algorithmic trading platform.