Lessons learned building an ML trading system (tradientblog.com)

Article summary

The article discusses lessons learned from building a machine learning trading system, with the author sharing their experiences and insights from developing and running the system. The system uses a market-neutral strategy and has been successful in generating profits. However, the author notes that the system's performance is not solely due to its algorithmic trading, but also due to the author's manual intervention and risk management. The article's content is not directly available, but the discussion reveals various aspects of the system and its operation.

Main themes

• Machine Learning Trading
• Market Neutral Strategy
• Risk Management
• Cryptocurrency Trading
• High-Frequency Trading
• Value Creation

What commenters say

• The author's system has been successful in generating profits, but it is unclear whether this is due to the algorithm or luck.
• The use of machine learning in trading is not a guarantee of success and requires careful consideration of risk management and market conditions.
• Providing liquidity in cryptocurrency markets can be seen as creating value, but others argue that it does not generate real value and may even increase net societal harm.
• The distinction between 'value' and 'value' as defined by markets is important, with some arguing that creating value for wealthy interests is not the same as creating value for society as a whole.
• The author's system is mostly taking liquidity from the market, rather than providing it, which raises questions about its overall impact on the market.
• The development of such systems can be seen as intellectually interesting and a way to learn, even if they do not create direct value.
• Some argue that academic research and other projects that do not create direct value should be criticized and re-evaluated, while others see value in their intellectual curiosity and potential for future applications.