The integration of artificial intelligence (AI) in financial markets has revolutionized trading strategies, offering unprecedented opportunities for investors. AI robots and trading bots, powered by sophisticated algorithms, conduct both technical analysis (TA) and fundamental analysis (FA), or a combination of both (TA&FA), to make informed trading decisions. This article delves into the most popular trading ideas in the field, categorizing them based on various criteria such as risk levels, trading frequency, market strategies, and sectors targeted.


The Spectrum of AI Robots in Trading

AI robots in trading span a wide spectrum, catering to various investment strategies and risk appetites.

For Beginners: Certain AI robots are designed with user-friendly interfaces and simplified decision-making processes, ideal for beginners.

For Experienced Traders: Advanced bots offer detailed analytics and customization options, catering to experienced traders seeking granular control over their trading strategies.

Incorporating the development of AI trading robots by Tickeron for different asset classes, the spectrum of AI robots in trading can be further expanded to include specific strategies and tools tailored to Stocks, Forex, and Crypto markets. Each of these asset classes has its unique characteristics and requires a distinct approach for successful trading.
Here's an overview of the categories:

Trading Frequency and Strategy

Day Traders and Swing Traders: AI robots tailored for day trading execute numerous trades within a single day, while swing trading bots aim for gains over a few days to weeks.

Market Neutral and Choppy Market Strategies: These bots excel in maintaining a neutral position to hedge against market volatility or in navigating choppy markets by capitalizing on small price movements.

Sector-Specific Bots

AI trading bots often specialize in specific sectors, such as hi-tech, consumer goods, or finance, leveraging sector-specific insights and trends for targeted investments.
 

TA and FA in AI Trading

Technical Analysis (TA): TA involves analyzing statistical trends gathered from trading activity, such as price movement and volume. AI robots utilizing TA predict future market movements based on historical data, employing indicators like moving averages, Relative Strength Index (RSI), and Bollinger Bands.

 

Fundamental Analysis (FA): FA focuses on evaluating a company's intrinsic value by examining related economic, financial, and other qualitative and quantitative factors. AI bots using FA assess earnings reports, market share, industry conditions, and the overall economy to make trading decisions.

 

Combining TA and FA: The most sophisticated AI trading robots leverage both TA and FA, offering a holistic approach to market analysis. This hybrid model provides a comprehensive view, encompassing both market trends and the underlying economic factors influencing stock prices.

 

High Win Rate and Pending Orders

Some AI robots specialize in achieving a high win rate, focusing on precision and efficiency. Others excel in managing pending orders, optimizing entry and exit points for trades.

 

Risk Management: High, Moderate, Low

AI trading bots are designed to cater to different risk tolerances: high risk for aggressive traders seeking substantial returns, moderate risk for balanced portfolios, and low risk for conservative investors prioritizing capital preservation.
 

Popular Long only Strategies

Long Only and Long Only with Inverse: Some AI robots focus on long-only strategies, betting on stock prices to rise, while others may also include inverse strategies to profit from declines.

Diversified Portfolios: Diversification is a key strategy for many AI bots, spreading investments across various sectors and asset classes to mitigate risk.

New and Top 10 AI Robots

Innovative AI trading robots frequently enter the market, boasting cutting-edge algorithms. The top 10 AI robots are those that have demonstrated exceptional performance, reliability, and user satisfaction.

 

Addressing Different Market Conditions

AI robots excel in adapting to various market conditions:

 

Trend Traders: These bots thrive in markets with clear trends, using algorithms to follow the direction of the market.

Choppy Market: Bots designed for choppy markets detect and capitalize on volatile, short-term price movements.

As AI technology continues to evolve, trading bots become increasingly sophisticated, capable of analyzing vast amounts of data in real-time. This progression promises to open new opportunities for personalized and automated trading strategies, further democratizing access to financial markets.
Piotroski F-score Model (FA)

At the core of the algorithm lies the well-established and time-tested method for evaluating a company's financial strength—the Piotroski F-score. Originating from the insights of Stanford University professor Joseph Piotroski, this score is widely embraced by professional hedge funds and value investors. The score assesses crucial financial indicators such as Return on Assets, Operating Cash Flow, and Asset Turnover Ratio. Following data processing, each company receives a score ranging from 0 to 9, where 0 signifies a weak financial position suitable for opening short positions, and 9 represents excellence, making them prime candidates for long positions.

On a daily basis, our mathematical prowess scans companies' financial statements, selecting those with the most favorable aggregate scores for long positions and the lowest scores for short positions. There are two exit strategies:

1. In the event of a significant change in the company's score.
2. When the stock price reaches 20% of the fixed stop-loss level from the trade's opening price.

An example of this type of robot: tickeron.com

Valuation & Hurst Model

The Edwin Hurst indicator, better known as the Hurst exponent, optimized by our quant team, has been added to the analysis. Hurst exponent is a statistical measure which is used to study scaling properties in time series. The scaling property in time series refers to the patterns in financial asset prices which are repeated at different time scales and is studied in a number of past researches.

After the valuation algorithm identifies a list of undervalued companies, the price dynamics of their shares are analyzed using the Hurst coefficient. If the ratio shows a buy signal, then a long position is opened.

Exit a position either by a fixed stop loss or by a trailing stop.

An example of this type of robot: tickeron

A Glimpse into the Future

In the last paragraph of our exploration, Tikeron emerges as a noteworthy mention. Although detailed information on Tikeron remains beyond the scope of this article, it represents the continuous innovation within the field of AI trading. As new platforms and technologies like Tikeron develop, they underscore the dynamic nature of the financial trading landscape, where AI continues to play a pivotal role in shaping the future of investment strategies.