Transform natural language into institutional-grade trading algorithms with AI-powered backtesting.
Algoriz is a sophisticated AI-driven platform designed to democratize quantitative finance by bridging the gap between natural language and complex algorithmic trading. At its core, the platform utilizes a proprietary Natural Language Processing (NLP) engine that translates English sentences into executable trading logic, eliminating the need for proficiency in Python or C++. The technical architecture supports multi-asset classes, including Equities and Cryptocurrencies, providing users with a sandbox for rigorous backtesting against high-fidelity historical data. By 2026, Algoriz has positioned itself as a critical middleware for retail and semi-professional traders, offering seamless integration with major brokerages like Interactive Brokers. The system's competitive edge lies in its 'Strategy Visualizer,' which decomposes complex logic into modular components, and its optimization suite that uses genetic algorithms to refine parameters. Its enterprise readiness is bolstered by cloud-native execution and secure API handling, ensuring that strategy intellectual property remains encrypted while maintaining low-latency order routing.
Uses specialized financial LLMs to map English intent to technical functions (e.g., SMA, EMA, MACD).
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Iteratively tests thousands of parameter combinations to find the highest Sharpe Ratio.
Simultaneous testing of strategies across correlated assets using vectorized computations.
A unified API that standardizes order types across multiple different broker backends.
Simulates real-world market impact and execution delays during backtests.
A node-based interface for visualizing the flow of trading decisions and conditions.
Optional modules to incorporate social media and news sentiment into logic triggers.
Retail traders miss entries due to emotional hesitation or lack of screen time.
Registry Updated:2/7/2026
High-frequency opportunities in crypto are impossible for humans to track 24/7.
Coding a backtester from scratch takes weeks for professional analysts.