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AI in Action: Pinpointing Undervalued Stocks in the Energy Industry

In the rapidly advancing domain of algorithmic trading, Tickeron stands out with its cutting-edge Financial Learning Models (FLMs). These proprietary models meticulously analyze and back-test a wealth of financial data from diverse sources, dynamically activating renowned trading strategies according to specific market conditions. This innovative approach parallels the adaptability seen in large language models like ChatGPT. By doing so, Tickeron consistently delivers algorithmic trading and predictive analytics that outperform major financial indices. A notable example is the Swing Trader, Long Only with Inverse robot, which targets undervalued energy stocks while utilizing Inverse ETFs such as SRTY and FAZ to hedge against market downturns. This AI-driven robot, limited to 35 open positions with an average trade duration of 10 days, incorporates Benjamin Graham’s classic valuation principles, analyzing metrics like earnings per share and book value to align stock prices with their intrinsic value. It identifies daily buy signals for undervalued energy stocks and leverages technical indicators with a trailing stop mechanism to maximize profits and manage risks. The platform's comprehensive tracking and strategic adaptability make it an invaluable tool for both novice and experienced traders seeking robust, AI-powered trading solutions.

Tickeron`s Robots Factory

Tickeron’s Robot Factory is anchored by its proprietary Financial Learning Models (FLMs), which analyze and back-test extensive financial data from multiple sources. These models dynamically activate well-known financial strategies based on their performance in specific market conditions, similar to ChatGPT’s Large Language Models. This adaptive approach enables Tickeron to deliver exclusive algorithmic trading and predictive analytics that consistently outperform major financial indices. Additionally, Tickeron offers the Swing Trader, Long Only with Inverse robot, tailored for traders focusing on undervalued energy stocks while safeguarding against market declines using Inverse ETFs like SRTY and FAZ. This AI-powered robot caps at 35 open positions with an average trading duration of 10 days. It leverages classic valuation principles from Benjamin Graham, analyzing metrics like earnings per share and book value to align stock prices with fair value. Each day, the robot identifies and signals buys for undervalued energy stocks, and utilizes inverse ETFs to hedge against market corrections. It employs a trailing stop mechanism with technical indicators to maximize profits and manage risk. Users can track orders and trading statistics through the platform, benefiting from the robot’s strategic features and dynamic market adaptation. This makes it suitable for both novice and experienced traders seeking robust, AI-driven trading solutions.

Multi-Level Backtesting

Advanced trading robots employ multi-level backtesting to ensure robustness across various market conditions. This process involves testing the algorithms on different timeframes, market scenarios, and asset classes to identify and mitigate potential weaknesses. For example, the Sector Rotation Strategy robot's backtests include extensive analysis of historical data, covering a period of over 20 years and incorporating more than 100 different market indicators. This ensures effective diversification and correlation-based trading. Additionally, these backtests simulate extreme market events, such as the 2008 financial crisis and the COVID-19 market crash, to evaluate the algorithm's performance under stress conditions. By stress-testing against such extreme events, traders can understand potential vulnerabilities and adjust their strategies accordingly. Moreover, multi-level backtesting allows for the integration of various algorithmic adjustments, such as machine learning enhancements, to improve predictive accuracy and adaptability. This comprehensive approach ensures that trading robots are well-equipped to handle a wide range of market environments, thus providing a more reliable and consistent performance over time. Recent simulated performance data from a backtesting period between May 1, 2018, and June 5, 2024, demonstrates significant profitability and resilience. With an average trade profit of $672.62 and an average trade loss of $383.17, the algorithm achieved a total net profit of $112,588.33 from 914 closed trades. The system maintained a 16.63% annualized return with a Sharpe ratio of 0.42, indicating moderate risk-adjusted returns. Despite a maximum consecutive loss streak of 11 trades, the largest profit and loss trades were $5,005.98 and $4,041.83, respectively, showcasing the system's capability to capitalize on favorable market conditions while managing downside risks.

Example of Backtesting for Long Only with Inverse: Valuation Model for Energy Sector (TA&FA)

The backtesting of the Long Only with Inverse Valuation Model for the Energy Sector, incorporating both technical analysis (TA) and fundamental analysis (FA), demonstrates a substantial return on investment over a simulated period spanning from May 1, 2018, to June 5, 2024. Throughout this period of 2226 days, the strategy executed trades valued at $10,000 each, culminating in an overall net profit of $112,588.33. This figure includes $1,429.27 in open trades and $111,159.06 from closed trades. Notably, the strategy displayed resilience, with a Sharpe Ratio of 0.42, reflecting a favorable risk-adjusted return.

The detailed statistics for closed trades reveal that out of 914 trades, 477 resulted in losses, accounting for 52.19% of the total, while 437 trades were profitable, representing 47.81%. The annualized return stands at 16.63%, with an average profit per trade of $121.62 over an average trade duration of 7 days. The profit factor of 1.61 indicates that the strategy generated $1.61 for every dollar lost. However, the strategy also experienced significant drawdowns, with the absolute drawdown reaching $26,482.45 and the maximal drawdown per trade at $5,565.19. Despite these drawdowns, the profit-to-drawdown ratio of 4.25 demonstrates the strategy's overall effectiveness in navigating the volatility of the energy sector.

Analyzing individual trades, the strategy achieved its highest profit from a single trade at $5,005.98, while the largest loss per trade amounted to $4,041.83. The most profitable trade involved a holding period of approximately 40 days, whereas the shortest holding period was around 1 day and 19 hours. This analysis underscores the strategy's capability to leverage both short-term and long-term opportunities within the energy sector, guided by comprehensive technical and fundamental analysis. The backtest results affirm the robustness of the Long Only with Inverse Valuation Model in generating consistent returns amidst market fluctuations.

 

In the ever-evolving landscape of stock trading, Tickeron Inc., a leader in AI-driven trading tools, has advanced significantly. Sergey Savastiouk, Ph.D., CEO and Founder of Tickeron, has introduced a feature to simplify quantitative stock analysis. Tickeron leads in algorithmic AI trading, serving individual investors and neural network developers. Their AI platform creates thousands of Financial Learning Models (FLMs) for algorithmic trading, analyzing and back-testing extensive financial data to activate effective models dynamically. This approach consistently outpaces major financial institutions in returns. Recently, Tickeron launched the Swing Trader, Long Only with Inverse: Valuation Model for the Energy Sector (TA&FA). This AI-powered robot is designed for traders favoring long-only positions in undervalued energy stocks, using Inverse ETFs to hedge against market declines. It caps at 35 open positions with an average trading duration of 10 days, and its strategy is based on Benjamin Graham's valuation approach. The robot analyzes U.S. stocks daily, generating buy signals for undervalued energy stocks and using SRTY and FAZ ETFs to hedge against market corrections. Orders are detailed in the "Pending Orders" tab for effective monitoring and real trading, making it suitable for both novice and seasoned traders.

Conclusion

The Future of Financial Learning Models. As financial markets grow increasingly complex, the role of AI and machine learning in trading is poised to expand significantly. Tickeron’s Financial Learning Models represent the forefront of this evolution, showcasing how AI can revolutionize trading and investment strategies. Tickeron's innovative approach, which includes dynamic activation and deactivation of financial models based on performance in specific market conditions, offers a sophisticated method for algorithmic trading. The platform’s comprehensive backtesting, incorporating price action analysis and multi-level testing, ensures strategies are effective and resilient across diverse market scenarios. A notable addition to their offerings is the Swing Trader, Long Only with an Inverse Valuation Model for the Energy Sector. This AI-powered robot is designed for traders favoring long-only positions in undervalued energy stocks, with protection against sudden market downturns using Inverse ETFs. With a maximum of 35 open positions and an average trading duration of 10 days, it simplifies tracking for both seasoned and novice traders. Rooted in Benjamin Graham's classic valuation principles, it employs machine learning algorithms to analyze earnings per share and book value, ensuring stock prices reflect their fair value. As AI continues to evolve, platforms like Tickeron's will likely play an increasingly vital role in the world of trading and financial analytics, driving greater accuracy and efficiency in algorithmic trading and solidifying their leadership in the fintech industry.

Disclaimers and Limitations

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