After years of searching for my true calling, I discovered algorithmic trading in 2017. Initially exploring traditional technical analysis and various trading strategies, I tested over 50 different approaches - from classic technical analysis to Wolf trading, Gann methods, Elliott Wave theory, and Price Action. However, these traditional methods resulted in consistent losses.
In 2019, I shifted my focus to hedge fund strategies like pair trading, basket trading, and arbitrage. This led me to programming and machine learning - the only approach that proved consistently profitable among the 300+ algorithms I developed.
The Power of Machine Learning in Trading
Unlike traditional trading robots with rigid algorithms, modern ML-based systems can identify patterns in vast amounts of market data. My most complex models contain up to 60 million lines of code, processing millions of market states and patterns beyond human perception.
Current Achievements and Future Goals
I've successfully developed a portfolio of four ML-based trading models, selected through genetic algorithms. This portfolio has demonstrated stable performance over a 20-year backtest period without a single losing year. Currently, I'm managing a significant prop trading account, with plans to scale up to $1M by 2025.
Trading Philosophy
My approach focuses on:
- Developing ML-based trading algorithms
- Minimizing human emotional factors
- Implementing sophisticated risk management
- Creating sustainable, long-term profitable strategies
Long-term Vision
My ultimate goal is to establish a world-class, high-tech hedge fund. The vision extends beyond pure trading - I aim to create an economic model that generates additional income streams for regular people while investing long-term profits into the real economy.
Key Insights Learned
- Success in trading requires significant time and resources
- Machine learning algorithms today are like technical indicators in the 1970s - highly effective but requiring constant innovation
- Maintaining strategy secrecy is crucial for long-term success
- Emotional detachment is essential for consistent profitability
Machine learning has opened new possibilities in automated trading. Instead of limited rigid algorithms, we can now utilize flexible, self-learning AI models that achieve stable profitability with lower drawdown risks.
Note: Past performance does not guarantee future results. Trading involves significant risks.
