By: Tickeron
NEW YORK - June 30, 2025 - PRLog -- Breakthrough Performance
Tickeron's PulseBreaker 9X AI Trading Agent has achieved a remarkable 307% annualized return with a 72.73% profitable trade rate, leveraging a 15-minute trading strategy across nine high-volatility tickers: AAPL, GOOG, GOOGl, TSLA, MSFT, SOXL, SOXS, QLD, and QID. This cutting-edge agent combines Financial Learning Models (FLMs) with real-time market analysis to deliver precision-driven breakout trades.
Strategic Design
PulseBreaker 9X targets mega-cap tech stocks (AAPL, GOOG, NVDA, TSLA, MSFT) and leveraged ETFs (SOXL, SOXS, QLD, QID) for long and hedge positions. Its Breakout Acceleration Engine detects price-level breaches, supported by a Micro-Floating Stop-Loss System and Dynamic Profit Capture System, aiming for 4-7% gains per trade. The agent thrives in high-volatility environments, capitalizing on macro events and earnings reports.
Advanced Technology
Powered by Tickeron's FLMs, PulseBreaker 9X processes vast market data—price action, volume, and sentiment—every 15 minutes. This enables rapid adaptation to intraday shifts, ensuring optimal entry and exit points. The agent's high-frequency execution and volatility-oriented behavior make it ideal for aggressive traders seeking high-risk, high-reward opportunities.
Trader Suitability
Designed for active, intraday momentum traders, PulseBreaker 9X is not suited for passive investing. Its low volatility profile, high profit-to-drawdown ratio, and medium open-position structure make it a tactical layer within diversified portfolios. Traders are advised to monitor market conditions and allocate capital strategically during high-volatility sessions.
Tickeron's Vision
"Tickeron's AI Trading Agents redefine precision in volatile markets," said Sergey Savastiouk, Ph.D., CEO of Tickeron. "With a 307% annualized return, PulseBreaker 9X showcases the power of our FLMs in delivering institutional-grade tools to all investors."
For more details, visit Tickeron. https://tickeron.com/bot-trading/virtualagents/all/