An algorithmic trading software uses advanced computer models and quantitative logic to analyze market data and execute trades based on the analysis. It evaluates real-time patterns such as price movement, momentum, and volatility to identify opportunities with precision. By automating decision-making, it eliminates emotional influence with the goal of ensuring disciplined execution. This technology helps users capture results using data-driven accuracy.
Selecting the right trading algorithm depends on your goals, preferred market exposure, and risk tolerance. Nurp licenses various quantitative and automated strategies tailored for conditions like volatility response, and risk tolerance. Each system is verified through backtesting and live data validation to test reliability. The best choice aligns with your desired balance between performance potential and risk control.
Nurp’s automated trading software is designed for all experience levels and requires no technical background. Built-in analytics allow you to monitor trades and performance in real time. The guided setup ensures a quick start for first-time users with full transparency and control.
All trading happens directly in your own brokerage account, so your funds always remain fully under your control. Nurp licenses the technology containing the algorithmic strategy, but never handles your money.
Nurp’s software connects with reputable international brokers that meet the requirements for automated trading. Many strategies are compatible with brokers offering at least 1:100 leverage and low spreads. Other strategies at Nurp, like Argos and Talos, are supported exclusively through certain brokers that provide the specific execution conditions those systems require.
Nurp’s automated trading algorithms are engineered to operate across diverse markets such as foreign exchange (“forex”) markets, commodities (silver, gold), and cryptocurrencies. Each algorithm is optimized with market neutral strategies uniquely to those sectors. Trading across multiple markets may allow diversification and smoother performance during changing economic conditions. This approach aims to enhance stability and broaden opportunity exposure, all depending on your goals.
Risk management is embedded within every Nurp algorithm through predefined rules. The systems aim to monitor volatility and position exposure in real time while using stop losses and adaptive sizing with the goal of minimizing drawdown. When market conditions become unstable, strategies can automatically pause or rebalance. These automated protections are designed to maintain consistency and long-term reliability. Users can override the default stop losses and risk exposure for additional risk management to fit their tolerance level. However, customers must always be mindful that overriding the default stop losses and risk exposure may increase the amount of risk and may result in the complete loss of your trading account balance.
Nurp support includes onboarding, educational resources, and ongoing technical help. Users receive step-by-step assistance for connecting brokers, analyzing algorithm data, and troubleshooting any setup issues. Customers are assigned a dedicated support team member, available via zoom, throughout.
All performance data from Nurp’s algorithms is independently verified through third-party platforms such as MyFXbook. These services track live trading accounts funded with real capital, providing full transparency into the performance of each strategy.
MyFXbook verification cannot be altered or manipulated, ensuring that every result accurately reflects real market performance. Users can review detailed metrics such as historical performance, profit factors, and win rates in real time. This process guarantees transparency, accountability, and boosts confidence in every algorithm Nurp offers.
First, reliability starts with thorough backtesting. Nurp’s algorithms undergo quantitative backtesting over multiple years of historical data. We update our AI based algorithm constantly following successful backtesting. However, the algorithms may be affected by certain market conditions that were not included in the backtesting. Furthermore, the adaptive nature of AI may result in a reduced ability to rely on historical data when backtesting.
Next, we deploy the algorithm and show live results in real-time monitoring via a third party, MyFXBook. Performance metrics such as drawdown, longs/shorts won, z-scores and more are available publicly. This multi-layered testing approach aims to provide confidence in live performance accuracy.
Multiple strategies are baked into each algorithm. AI-powered automated algorithms work in accordance with the coded strategies, and execute buy/sell, short/long trades. They detect evolving price behaviors, adjust parameters, and improve entry and exit precision automatically. Over time, these adaptive updates may help reduce errors and enhance returns. The goal is sustainable long-term consistency built on continuous learning. However, AI is always subject to various risks, including operational risks (i.e., risk of technology failures), hallucinations (e.g., false correlations between datasets), and limitations to its data. Please read our AI Risks stipulated in our Risk Disclosure page.
AI quant systems rely on dynamic machine learning models that analyze volatility and momentum in real time. When conditions shift, algorithms are designed to recalibrate trade logic, exposure, and execution timing. This automatic adaptation maintains performance even during unpredictable market phases. The continuous feedback loop is designed for precision without manual adjustments with default risk management built in. Risk parameters can further be customized by users. However, adjusting risk parameters may increase the amount of risk and may result in the complete loss of your trading account balance. However, AI is always subject to various risk factors, including operational risks (i.e., risk of technology failures), hallucinations (e.g., false correlations between datasets), and limitations to its data. Please read our AI Risks stipulated in our Risk Disclosure page.
AI in Nurp’s algorithms are designed to enhance trading reliability by adapting to changing market conditions in real time. Unlike purely technical strategies that rely only on chart data, AI systems process live market information, news events, and economic data to make more informed decisions.
Through independently operating subsystems, the AI can enable or disable specific strategies based on evolving conditions to help manage risk and maintain performance consistency. This intelligent adaptability allows the algorithms to perform across different market environments while still operating securely through encrypted broker connections that never handle user funds. However, AI is always subject to various risk, including operational risks (i.e., risk of technology failures), hallucinations (i.e., false correlations between datasets and/or presenting false, misleading or incorrect information as fact), and limitations to its data (i.e., incorrect or limited data availability). Please read our AI Risks stipulated in our Risk Disclosure page.
A complete quantitative framework includes accurate data collection, statistical modeling, backtesting validation, prudent risk management and position sizing, and adaptive optimization. Each component is designed to contribute to systematic precision and performance resilience. By integrating quantitative analysis, the framework aims to become more responsive to real-time changes. Together, these elements aim to create a scalable and reliable algorithmic system.
Automated algorithm execution is the software that carries out trades the moment a strategy identifies an opportunity. It sends orders through secure broker connections with precise timing, ensuring accuracy even in fast-moving markets.
By reacting to changes in milliseconds, it aims to minimize slippage, applies risk controls, and executes trades based on predetermined rules. This real time precision allows users to benefit from opportunities that may be impossible to capture through manual trading.
AI within algo quant software aims to detect hidden correlations and evolving price structures that static models miss. By analyzing live and historical data simultaneously, it fine-tunes entry and exit points. This continual optimization aims to sharpen accuracy and reduce false signals. The outcome is a smarter trading strategy. However, AI is always subject to various risk factors, including operational risks (i.e., risk of technology failures), hallucinations (i.e., false correlations between datasets and/or presenting false, misleading or incorrect information as fact), and limitations to its data (i.e., incorrect or limited data availability). Please read our AI Risks stipulated in our Risk Disclosure page.
AI quantitative analysis processes massive datasets to uncover relationships among assets, risk factors, and timing signals. This analysis guides portfolio balancing and aims to enhance trade selection based on probability outcomes. By combining automation with analytical depth, users may achieve stronger diversification and stability. The process results in data-driven decision-making rather than subjective judgment. However, AI may be faced with issues involving its data sets, including hallucinations (i.e., false correlations between datasets and/or presenting false, misleading or incorrect information as fact), and limitations to its data (i.e., incorrect or limited data availability). Please read our AI Risks stipulated in our Risk Disclosure page.
Transparency allows users to verify performance and trust the technology they use. Nurp provides open access to performance analytics, trade records, and live verification tools. Users can independently confirm how each algorithm functions before and after activation. This openness strengthens accountability and distinguishes trustworthy trading providers.
Nurp differentiates itself by offering tested algorithms where users keep full custody of their funds in their own brokerage accounts. Every system is backed by third-party verification. Nurp also continuously evaluates algorithm performance and integrates AI developments. Unlike passive platforms, Nurp prioritizes real-time precision, disciplined risk control, and full transparency. This unique balance of control and technology sets it apart in automated trading. However, there may be limitations to the continuous evaluation of algorithm that may degrade performance and AI may be faced with issues involving its data sets, including hallucinations (i.e., false correlations between datasets and/or presenting false, misleading or incorrect information as fact), and limitations to its data (i.e., incorrect or limited data availability). Please read our AI Risks stipulated in our Risk Disclosure page.
Nurp’s research team continuously evaluates algorithm performance and integrates new AI developments. Machine learning updates and live optimization cycles aim to keep strategies aligned with evolving markets. The company reviews statistical outcomes regularly to maintain accuracy and efficiency. This constant evolution ensures the software remains cutting-edge and future ready.
Use of Nurp’s products involve many risks that may result in the loss of all of the balance of your trading account, including technology risk (e.g., software failure), market risk (e.g., price volatility), and software risk (e.g., loss or interrupted data). For details on each of these risks, visit our Risk Disclosures page.
AI Quantitative
Researcher
Bingham Zhou
Bingham Zhou, CFA, has over 15 years of experience as a quantitative researcher. His expertise spans systematic equity strategies, CTA trend-following, and interest rate proprietary trading in both U.S. and Asian markets. He holds advanced degrees from MIT, Carnegie Mellon, and Yale.
Greg Doscher was a CFO for many years who built out many quantitative strategies and investment tools to manage and enhance risk adjusted returns in the company’s pension plan. Prior to joining Nurp, he consolidated his skills in coding and discretionary trading to develop a comprehensive and fully automated algorithmic trading system deployed across 200+ futures markets and cryptocurrencies that encompassed all of the trading strategies he had honed over the last 22 years in finance
Marcin was Head of Quant IT at the USD 4bn+ CERN Pension Fund, where he spent nearly a decade building quantitative asset allocation systems and implementing algorithmic investment strategies for a multi-asset institutional portfolio.Before joining Nurp Marcin was also Senior Quant Strategist at Evooq, a Swiss-based fund managing four strategies across equities, gold, and equity derivatives.Marcin holds a degree in Computer Science an MBA from the University of Geneva and the Certificate in Quantitative Finance (CQF).
Abhayjit Anand
Abhay has worked with Nurp since 2022. As a Product Strategist, he focuses on building, refining, and commercializing algorithmic trading strategies. He brings seven years of experience in financial trading – combining macro research, technical analysis, quantitative strategy development, and market psychology. Alongside his work at Nurp, Abhay also serves as an Investment Analyst at Orca Capital. Before entering financial markets professionally, he spent eight years at IBM, including three years in the AI & data division as a Delivery Lead managing complex implementation projects.
