Как искусственный интеллект помогает делать прибыль - Анатолий Радченко

Anatoly Radchenko, a professional trader with 18 years of experience, explains how he uses artificial intelligence in trading: from news and chart analysis to backtesting, strategy optimization, and trade selection in the American market. This episode offers an honest analysis of the capabilities and limitations of LLM: why neural networks don't automatically make traders smarter, how AI helps find signals, where self-deception and unrealistic backtesting begin, and why humans are still the ultimate bottleneck. We discussed news trading, slope, pre-trade filter, grid research, 1.7 leverage, bull market strategies, small and mid-caps, 50-day moving averages, and the real-world challenges of automation. Tickets for the SmartLab conference in Moscow on October 31st go on sale July 16th! Get a 20% discount with promo code MSK26! https://confa.smart-lab.ru/msk26/ All conference news and ticket giveaways here: https://t.me/smartlab_conf Speaker presentations, free lectures on investing, and other bonuses here: t.me/mozgovikbot 00:00 Introduction: 21 years in trading and a new challenge – AI 00:52 How artificial intelligence is changing trading 01:45 How to use AI without technical skills 04:04 Trading automation and news strategy 05:55 Why LLM doesn't always work in trading 06:33 How LLM analyzes news, charts, and market reaction 08:19 Backtesting, optimization, and strategy profitability 12:36 Why a trader without AI can lose money 13:06 Grid research and strategy parameter selection 14:04 Market correlations and reducing overvaluations Results 15:00 Where LLM Really Helps Traders 16:57 How to Properly Communicate with LLM When Analyzing Stocks 17:54 Yield Optimization, Leverage, and External Factors 19:22 LLM's Main Advantage: Memory, Search, and Processing Information 20:18 Limitations of AI: Why Humans Are the Main Bottleneck 23:03 Strategy: 50-Day Moving Average, Small/Mid Caps, and 90-Day Hold 24:28 How LLM Helps Automate Trading 25:19 New AI Models and Their Impact on Trading 26:18 AI in Biotech, Cybersecurity, and Simulations 27:18 Why Using AI Can Get Explosively More Expensive 28:12 Backtesting, Technical Analysis, and the Risk of Self-Deception 29:30 Real Strategy Results and a Portfolio with 1.7 Leverage 30:28 Conclusion #algotrading #radchenko #trading