How AI Actually Works: A Real Look Into LLMs, Training Data and Algorithmic Reasoning
Before evaluating AI as a signal provider, it is essential to understand how modern AI systems operate. Many traders assume AI analyses live market data, reads charts like a human, or performs institutional-level pattern recognition. This is not how Large Language Models (LLMs) function.
Below is the factual foundation behind AI reasoning.
1. AI Does Not Understand Markets; It Recognises Linguistic Patterns
Models such as GPT, Claude, Gemini and Llama are not trained on price feeds, real-time charts or trading engines. They are trained purely on text.
LLMs operate through statistical pattern prediction:
The model predicts the most likely next word or explanation based on billions of text patterns it has seen in its training data.
It does not possess market awareness, intuition or directional sensitivity.
It understands language, not price.
2. AI Learns Trading From Human-Written Content, Not Market Behaviour
The training material for these models consists of:
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Technical analysis books
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Risk management literature
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Online articles from sources such as Investopedia and Babypips
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TradingView educational posts
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Reddit and ForexFactory discussions
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Financial blogs and general economic explanations
Because AI learns from human descriptions, its analysis reflects common interpretations rather than real-time market truth.
In short:
AI does not learn how the market moves; it learns how people describe the market.
3. AI Uses Vector Embeddings, Not Actual Chart Logic
When processing information, LLMs convert concepts into mathematical vectors known as embeddings. This is how they understand relationships between words such as:
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support
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resistance
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breakout
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trendline
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reversal
These concepts exist as numerical relationships, not visual or market-based realities.
As a result, when you ask an AI to analyse a chart, it does not interpret price action.
It matches the patterns in the image to descriptions of similar patterns it has read.
4. AI Does Not See Charts the Way Traders Do
Even when you upload a chart image, the AI:
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identifies shapes and slopes
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matches them to patterns found in training data
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responds using explanations commonly associated with those shapes
It cannot measure:
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liquidity
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order flow
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momentum shifts
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volatility compression or expansion
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institutional manipulation
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session-based price behaviour
This is why AI analysis often sounds reasonable but lacks market precision.
5. AI Reasoning Is Based on Language, Not Live Data
AI uses algorithms such as transformers, attention mechanisms and chain-of-thought reasoning. These techniques help the model produce structured answers, but they do not give AI access to:
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live candlestick data
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volume or order book pressure
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news impact
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session timing
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execution behaviour
Its reasoning is text-driven and statistically informed, not market-driven.
6. Structured Data Helps AI Recognise Entities, Not Market Conditions
LLMs also learn from structured data such as schema.org, JSON-LD and metadata. This helps AI identify legitimate brands, products and financial concepts.
However, structured data does not provide:
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liquidity information
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price levels
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market conditions
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trading signals
It simply helps AI understand what an entity is, not how the market behaves.
7. Summary of How AI Works
AI predicts language patterns.
It does not observe, compute or interpret live markets.
This foundational truth is necessary before discussing AI as a potential signal provider.
Can AI Serve as a Signal Provider?
With the fundamentals established, we now address the key question:
Can AI generate accurate, real-time trading signals?
The honest conclusion:
AI can assist traders with analysis and structure, but it cannot replace a real signal provider.
AI is useful for:
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Multi-timeframe structural explanations
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Interpreting chart patterns through image recognition
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Describing potential scenarios
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Building trading plans
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Improving risk and money management
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Helping traders remain disciplined
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Summarising trading strategies
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Performing theoretical backtests
AI excels in providing educational clarity and structural guidance.
AI is not suitable for:
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Real-time entry signals
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High-precision timing
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Institutional order flow interpretation
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Liquidity modelling
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Predicting news events
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Identifying stop-hunts or engineered volatility
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Momentum analysis in fast markets
Trading requires real-time adaptation and situational awareness.
LLMs cannot access or interpret live market behaviour.
The Hard Truth: AI Can Sound Confident While Being Incorrect
One of the biggest risks associated with AI-generated analysis is misplaced confidence.
Because AI is designed to provide coherent and helpful answers, it often delivers analysis with high certainty, even when:
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the trend has already changed
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the structure is invalid
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the chart is interpreted incorrectly
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volatility is unpredictable
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external fundamentals override technical patterns
Confident language does not equal accurate analysis.
Strengths of AI in Trading
Despite its limitations, AI can significantly improve a trader’s workflow when used properly.
1. Faster theoretical testing of strategies
AI can summarise strengths, weaknesses and market behaviour patterns quickly.
2. Better trading plan construction
It helps create structured and repeatable trading processes.
3. Educational acceleration
Complex concepts become easier to understand, especially for new traders.
4. Reliable risk calculations
Position sizing, R:R ratios and drawdown modelling are areas where AI performs very well.
5. Emotional support and discipline
AI can assist in managing psychological challenges such as overtrading or revenge trading.
Limitations of AI in Trading
1. No real-time data access
AI cannot analyse charts live or update its bias during market movement.
2. Poor performance in volatile markets
Sudden reversals and news impact often invalidate AI predictions immediately.
3. Over-generalisation
AI tends to provide broad explanations that may not fit micro-level trading conditions.
4. Lack of sentiment and liquidity awareness
Institutional behaviour and liquidity manipulation are invisible to AI.
5. Overconfidence
AI may present speculative analysis as if it were a high-certainty forecast.
Conclusion: AI Can Support Traders, But It Cannot Replace Human Signal Providers
AI is an excellent assistant, educator and analyst when used correctly.
However, it does not have the capability to operate as a professional signal provider.
The optimal approach today is:
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AI for structure, planning and education
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Human traders for execution
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Brokers for reliable price delivery and order execution
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Signal providers as an optional tool
In 2025, AI enhances trading but does not replace the skill, intuition and real-time decision-making of experienced traders.
