Last week, after a series of unprofitable trades, the robots managed to turn things around and ultimately earned over 3% on $CEI stocks.

Last week, there was quite an interesting situation with CEI (CAMBER ENERGY INC) stocks. Initially, the robots Swing Trader Medium Volatility Stocks for Active Trading TA-FA and Swing Trader High Volatility Stocks for Active Trading TA-FA showed a loss on these stocks, but after making additional transactions, they managed to turn the situation around and earned over 3% in profits. The Swing Trader Deep Trend Analysis v.2 TA robot also participated in trading this stock.

These robots use advanced trend analysis algorithms with a unique combination of several types of moving averages. This allows traders to more accurately determine the direction of the current trend for each stock and select the most effective algorithm for generating signals to open trades.

To enhance performance, these robots employ a wide diversification strategy, enabling them to choose the most appropriate system for each stock. Additionally, the AI Robot starts trading against the trend during the trend period and vice versa.

The average trade duration is one day. After entering a trade, the AI Robot sets a "Take Profit" order, the distance of which depends on the current market volatility. To exit a position, the robot uses two options: a 3% stop loss from the position's opening price and a flexible trailing stop that allows preserving most of the profit if the market reverses.

The robot's trading results are shown without using margin. For a complete trading statistics and equity chart, click on the "show more" button on the robot's page. In the "Open Trades" tab, traders can observe in real-time how the AI Robot selects equities, enters, and exits paper trades. In the "Closed Trades" tab, users can review all previous trades made by the AI Robot.

Contributor

Harry Richardson|Expert

Harry Richardson — Algorithmic Trader & Strategy Developer Harry is an algorithmic trader specializing in impulse and breakout trading strategies across cryptocurrency and equity markets. With more than 10 years of experience in developing automated trading systems, he focuses on building structured algorithms designed to capture momentum while maintaining strict risk control. His approach combines quantitative analysis, real-market execution, and continuous performance monitoring. Vitalii prioritizes risk management, drawdown control, and strategy stability over short-term optimization, ensuring algorithms are adaptable to changing market conditions. He has developed and tested hundreds of automated strategies, working extensively with live trading environments, forward testing, and portfolio-level algorithm management. His work centers on transforming trading ideas into fully operational, scalable automated systems.