Tickeron, Inc. has emerged as a pivotal player in the financial technology space by developing an AI financial platform designed for the seamless creation of thousands of Financial Learning Models (FLMs). These models are integral to algorithmic trading services, catering to both self-directed investors (SDIs) and hedge funds. This article delves into the nuances of Tickeron's AI offerings, the core concepts behind their Financial Learning Models, and the specific features of their Swing Trader, Popular Stocks: Price Action Trading Strategy (TA&FA). This specialized AI robot, tailored for swing traders who value manual trading and independent signal selection, consistently delivers accurate trading predictions, enabling informed decisions aligned with individual preferences. It identifies medium-term price impulses during market uptrends or downtrends, leveraging sharp volatility increases.

The algorithm’s distinct approach evaluates volatility and historical price patterns to pinpoint high-probability sustained movements and optimal entry points using proprietary indicators. Trades are managed with a fixed take profit of 3% and a medium-term trailing stop to accurately determine price reversals. The robot focuses on liquid, actively traded US stocks, informed by fundamental indicators to avoid low-quality shares and minimize risks. Its trading dynamics include medium open positions, high robot volatility, and a high universe diversification score, making it suitable for traders at all levels who seek either high profit or low drawdown opportunities. For detailed trading statistics and real-time trade monitoring, users can explore the "Open Trades" and "Closed Trades" tabs on the robot's page.

Tickeron`s Robots Factory

Tickeron, Inc. has developed an AI financial platform that facilitates the creation of thousands of Financial Learning Models (FLMs), which are integral to its Robots Factory AI capabilities. These proprietary models are utilized in algorithmic trading services for both self-directed investors (SDIs) and hedge funds. The core of Tickeron's AI offerings is its FLMs, which analyze and back-test extensive financial data from various sources, including stock prices, trading volumes, economic indicators, and corporate financial statements.

By incorporating historical data spanning decades and millions of trading points, these FLMs ensure robust and reliable predictions. Inspired by ChatGPT's Large Language Models (LLMs), Tickeron's main innovation is the dynamic activation of effective financial models based on their performance in specific market conditions. This dynamic adjustment is achieved through continuous monitoring and real-time data analysis, ensuring the models are responsive to market changes. As a result, Tickeron provides exclusive algorithmic trading and predictive analytics that consistently outperform major financial institutions, often by margins as high as 5-10% annually.

Enhancing Backtesting with Price Action Analysis

Incorporating price action analysis into backtesting can significantly enhance a strategy's robustness, offering traders a more comprehensive understanding of market dynamics beyond technical indicators alone. Price action analysis involves examining the movement of security prices over time to infer market sentiment and make informed decisions, such as by analyzing candlestick patterns, support and resistance levels, and trend lines to identify potential entry and exit points with greater accuracy. Historical data reveals that incorporating such elements can improve prediction accuracy, with studies indicating a potential increase in success rates by up to 20% when price action strategies are applied. Additionally, AI trading robots, like the specialized Swing Trader for popular stocks, provide tailored strategies for swing traders who prioritize independent signal selection and manual trading.

This AI robot leverages medium-term price impulses, sharp volatility increases, and a proprietary set of indicators to identify ideal entry points, ensuring trades align with market trends. It maintains a fixed take profit of 3% for both long and short positions and incorporates a medium-term trailing stop to enhance accuracy. The robot focuses on the most liquid and actively traded US stocks, informed by fundamental indicators to minimize risks. It supports diversified exposure and manages concentration risk with a high universe diversification score, making it suitable for all levels of traders aiming for high profit or low drawdown in high volatility markets. By combining backtesting with price action analysis and the strategic use of AI trading robots, traders can achieve a more nuanced interpretation of market trends, mitigate risks, and enhance the reliability of their trading strategies.

Example of Backtesting for Price Action (TA&FA)

The swing trading strategy, which leverages both technical analysis (TA) and fundamental analysis (FA), has been backtested over a period spanning 2209 days, from May 18, 2018, to June 5, 2024. Each trade executed within this strategy involved an investment of $10,000. The simulated performance data reveals substantial insights into the effectiveness and profitability of this trading approach.

Over the specified date range, the strategy demonstrated remarkable performance, resulting in a total net profit of $3,009,580.74. This figure includes closed trades profit of $3,008,594.74 and open trades profit of $986.00. The strategy engaged in 47,205 trades with a maximum of 136 trades open at any given time. The Sharpe ratio, a key indicator of risk-adjusted return, stands at 0.97, highlighting the strategy's efficiency in generating returns relative to its risk.

The strategy's annualized return was an impressive 56.86%, with an average trade profit of $63.78. The average duration for holding each trade was two days, indicating the short-term nature of the swing trading approach. Profitable trades constituted 47.10% of the total trades, while the loss trades accounted for 52.90%. The profit factor, which is the ratio of gross profit to gross loss, was 1.50, signifying that the strategy made 1.5 times more profit than it lost.

Further analysis shows that the average trade profit was $408.41, whereas the average trade loss was $243.09. The strategy performed better in short positions, winning 53.77% of the time compared to 42.72% for long positions. The absolute drawdown, which measures the largest drop from a peak in the portfolio value, was $181,356.42, and the maximal drawdown per trade was $192,685.14. The profit to drawdown ratio was 16.59, indicating a strong capacity to generate profit relative to the drawdown risks.

The strategy's maximum consecutive wins and losses were 85 trades ($61,781.44) and 59 trades ($92,784.36), respectively. The largest profit and loss in a single trade were $19,750.94 and $91,356.38, respectively, showcasing the potential volatility and reward of individual trades.

This comprehensive backtesting analysis underscores the viability and robustness of the swing trading strategy, combining both technical and fundamental analyses to navigate and capitalize on market movements effectively.

In the rapidly changing field of stock trading, Tickeron Inc., a pioneer in AI-driven trading tools, has achieved a noteworthy advancement. Sergey Savastiouk, Ph.D., CEO and Founder of Tickeron, introduces their newest feature aimed at streamlining quantitative stock analysis. Tickeron leads the way in algorithmic AI trading, serving both individual investors and developers of proprietary neural networks. Tickeron, Inc. offers an AI financial platform that enables the effortless creation of thousands of Financial Learning Models (FLMs). These models are employed in algorithmic trading services for both self-directed investors (SDIs) and hedge funds. The cornerstone of Tickeron's AI solutions is proprietary FLMs, which scrutinize and back-test vast amounts of financial data from diverse sources to identify market conditions under which well-known financial models perform optimally. Inspired by ChatGPT LLMs, the primary concept is to dynamically activate well-known financial models when they are effective and deactivate them when they are not. Consequently, Tickeron delivers exclusive algorithmic trading and predictive analytics, consistently surpassing the returns of major financial institutions.

Conclusion

As financial markets become increasingly complex, the role of AI and machine learning in trading is set to grow. Tickeron's Financial Learning Models (FLMs) represent the future of this evolution, providing a glimpse into how AI can transform trading and investment strategies. Tickeron's innovative use of FLMs and the rigorous process of backtesting underscore the potential of AI in transforming trading strategies. By dynamically activating and deactivating financial models based on their performance in specific market conditions, Tickeron's platform offers a sophisticated approach to algorithmic trading. The incorporation of comprehensive backtesting, including price action analysis and multi-level testing, ensures that these strategies are not only effective but also resilient across diverse market scenarios. The ongoing development and refinement of FLMs will likely lead to even greater accuracy and efficiency in algorithmic trading, further solidifying Tickeron’s position as a leader in the fintech industry. As AI continues to evolve, the capabilities of platforms like Tickeron's will likely play an increasingly vital role in the world of trading and financial analytics.

Disclaimers and Limitations