In the competitive world of trading, where the signal-to-noise ratio is frequently low, extracting every possible alpha to improve performance is critical. Efficient automated backtest pipelines incorporating machine learning (ML) are becoming a more popular method for improving trading performance, as the automation is more repeatable and scalable than traditional “hand-tuned” approaches, and a larger and more comprehensive search space can be covered. Much of the development of the platform lately has been focused on supporting the direction of this newer paradigm. Conducting trading strategy research can be a complex and timeconsuming process, especially when it involves working with large amounts of data. Some of the challenges that researchers using AI methods may face include: • Procuring data from various sources, which can be timeconsuming and costly. • Ensuring that data is in a format that is suitable for research and backtesting. This may involve cleaning, transforming, and pre-processing data in order to make it more useful and easier to work with. • Storing and organizing data in a way that is efficient and easy to access. • Efficiently loading data for analysis. NautilusTrader includes the necessary machinery to assist in achieving the above for large amounts of historical data through an easy to query data catalogue interface, helping researchers to organize, find and stream the needed data into backtest nodes. This is achieved in part through adopting the use of technologies such as the Parquet data format, and the Rust programming language. Accurate and realistic strategy backtesting is essential for traders and analysts to make informed decisions and even the most sophisticated models are imperfect simplifications of reality and should be used as tools rather than definitive representations. It’s important to consider factors such as network latency, and market impact - in particular, in the still-maturing cryptocurrency markets, it’s necessary to simulate order book depth as a liquidity taker. NautilusTrader is capable of simulating this data feed and execution network latency, as well as handling L2 (market by price) and L3 (market by order) data, which is the highest level of granularity and a virtually direct representation of raw exchange messages. This allows the most accurate simulation possible, and we plan to extend these simulation models in the future and offer hook points for custom extended implementations. Also, thanks to the open-source nature of our project, we’ve had the opportunity to work with trading firms that have put NautilusTrader through extensive testing. This has allowed us to identify and address edge cases, resulting in an even higher level of correctness and accuracy. In a field where the signal-to-noise ratio is often low, it’s essential to have confidence in the accuracy of your backtesting. Best Global High-Performance AI Trading Platform – APAC & FinTech Innovators of the Year – Australia NautilusTrader is an open-source algorithmic trading platform designed to help quantitative traders backtest and deploy automated trading strategies into production with ease. The platform is built to provide a highly performant and robust Python native environment, making it ideal for traders who want to optimize their performance with AI methods, and take their trading to the next level. Of course, it’s still critical to maintain intellectual honesty and avoid the common pitfalls such as look-ahead bias (unrealistic future sight) and fitting models too closely to historical data. Stepping back and considering the overall process of conceptualizing, implementing, and running a trading operation, this requires a wide range of skills and can be incredibly complex and time-consuming. For smaller teams or sole traders, the burden of mastering such a mountain of subject matter can be overwhelming. This is where platforms like NautilusTrader can provide value by alleviating some of this burden, so at least platform level infrastructure doesn’t need to be written from scratch and then maintained ongoing. Another way the platform helps traders is by ensuring that the strategy code and the vast majority of the systems code logic is identical for both backtesting and live trading. This removes the risks and overhead of having to adapt additional strategy code for live trading, allowing traders to focus on other aspects of their operation. By streamlining these processes, NautilusTrader makes it easier for traders to transition from backtesting to live trading with confidence. In the world of trading, there are many risks to consider beyond just the development of models, including operational and market risks. Operational risk refers to potential hazards that may arise from the servers, software, or the exchanges themselves. To reduce this risk, it’s important to ensure that the trading platform has been designed and developed with robustness in mind and is capable of operating in mission-critical live environments, not just for research. By addressing these and other operational risks, traders can have greater confidence in the reliability and stability of their trading platform. Market risk is another important consideration for traders and firms, both at the individual trading instance and firm wide levels. The Nautilus Cloud (coming soon) will include risk management facilities to help traders and firms manage market risk on a more ‘global’ scale. There are many different statistics and metrics which can be fed into a risk management systems model, so this is another area where AI methods may be particularly useful. By using AI to analyze and interpret data in this way, traders and firms can gain a deeper understanding of market conditions and trends and make more informed decisions about how to manage risk. This may involve setting limits on trade sizes or diversifying portfolios to reduce exposure to specific asset classes, sectors or markets, all of this being monitored and controlled through a modern frontend GUI. Cloud computing has revolutionized the way that technology is used and has had a major impact on a wide range of industries, including financial services and trading. We’re currently working with some early adopters to explore the benefits of cloud-based systems for data storage and processing, as well as parallel backtest pipelines Nautech Systems
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