Outline
- Proven Ai Use Cases
- Institutions vs. Retails
- Challenges in Profitability
- Important Missing Parameters for Training AI
- The Reality of Retail Ai Systems
Proven Ai Use Cases
We see many AI trading bots on the internet, but only a small range of AI concepts actually work effectively for retail traders to improve their trading!
Sentiment Analysis :
This type of AI analyzes news and social media sentiment, requiring significant costs to run and maintain. It cannot typically be developed by one person alone; instead, it demands a professional team with substantial funding to ensure the AI remains efficient.
Risk Management :
The most efficient choice for integrating AI into your trading is to customize and train it based on your personal strategy. You can even train it using your trading statements to make informed decisions about how much volume and money to risk.
Pattern Recognition :
While machine learning models can detect patterns, building an AI solely for pattern detection is not an effective approach. You can automate pattern recognition with simple code and train an AI to determine the best time to enter the market based on your identified patterns.
Institutions vs. Retails
Imagine you run an institute and invest hundreds of dollars to build an efficient AI model for forex trading to generate profits. Would you sell it to retail traders for a $200 monthly subscription?
The answer is No.
However, there is another side to this. Most successful companies in forex, especially those leveraging AI, have access to data that retail traders can never obtain. (We will discuss this further in the section: Important Missing Parameters for Training AI.)
Challenges in Profitability of Ai Trading Bot
- Market Efficiency: Forex is a highly efficient market where most information is already priced in, making it difficult for retail-focused AI to find consistent arbitrage opportunities.
- Adaptability: Market conditions change rapidly, and an AI model needs to continuously adapt. Many pre-packaged AIs lack this flexibility.
- Slippage and Costs: Even if an AI finds profitable trades, execution issues such as slippage, spreads, and commissions can erode profitability.
Important Missing Parameters for Training AI
While retail traders work with simple OHLC data from past market events, successful forex institutes purchase retail traders’ statements and train their AI models on this data to understand how retail traders make decisions.
Some of them train AI models using real market volume and order flow to analyze how the market behaves in different situations based on specific levels of buy or sell volume.
This creates an unfair competition for retail traders who attempt AI trading alone and without sufficient funding.
The Reality of Retail Ai Systems
Many AI-based forex trading bots and tools are available in the industry, often showcasing profitable results. While AI is not a simple tool to use, even if we assume their AI is functional, there are several reasons why these claims cannot be entirely true.
- No one sells their money-printing machine!
- Many systems fail to disclose their underlying methodologies, making it difficult to verify their claims.
- Lack of Continuous improvement based on live performance.
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