Plain English Guide

AI Trading Explained —
How It Really Works

From pattern recognition to large language models — a clear explanation of how AI analyses markets, generates trade signals, and what its real strengths and limitations are.

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The Core Technologies

What "AI Trading" Actually Means

AI trading is not a single technology — it's a combination of different tools that each serve a specific function in market analysis.

Pattern Recognition
AI models can scan thousands of charts and identify technical patterns — breakouts, double bottoms, head-and-shoulders, divergences — with consistent precision, without fatigue or emotional bias.
Large Language Models
LLMs like DeepSeek and Groq's Llama-3 can interpret technical data and generate plain-English analysis explaining what indicators are showing and why a trade setup may or may not be valid.
Quantitative Scoring
Multi-factor scoring models assign a numeric score to each asset based on RSI, MACD, moving average position, momentum, and volume. These scores are objective and reproducible — no human inconsistency.
Real-Time Data Processing
AI can process live price feeds from multiple exchanges simultaneously, computing technical indicators in real time and updating signal outputs as market conditions change.
Step by Step

How TradeProview AI Generates a Signal

Every signal on TradeProview's AI Ideas desk goes through a transparent 4-step pipeline.

1

Live price data is pulled

Real-time prices, 24-hour changes, and volume data are fetched from CoinGecko (crypto) and Finnhub (stocks, ETFs, forex). Data is collected for each instrument in the scan.

2

Technical indicators are computed

RSI (14-period), MACD (12/26/9), moving average positions, and momentum scores are calculated for each asset. A composite 1–10 trend score is assigned based on the combined readings.

3

AI generates the analysis

The technical data is passed to a large language model (DeepSeek primary, Groq as fallback). The AI generates a plain-English rationale for the signal direction, interpreting what the indicators are showing in context.

4

Structured trade idea is output

Observed provider facts, disclosed calculations, evidence labels and an AI explanation are presented for review. The learner—not the model—writes any thesis, confirmation, invalidation and risk rule.

Honest Assessment

What AI Trading Can and Can't Do

Understanding the real limitations of AI trading tools makes you a more effective user of them.

What AI does well
Processes large amounts of data quickly. Applies indicators consistently without emotional bias. Generates structured trade setups with defined risk levels. Explains technical context in plain English. Available 24/7 across all markets and time zones.
What AI cannot do
Predict the future with certainty. Account for unexpected news events or black swan scenarios. Replace your own judgment about position sizing and market context. Guarantee profitable trades. AI is an analytical tool, not a trading strategy on its own.
Human + AI is best
The most effective approach is using AI as an analysis starting point — not a signal-following system. Review the reasoning, check the broader context, apply your own risk management rules, and make the final decision yourself.
Risk management is yours
An evidence label is not an instruction. Use TradeProview only for education and virtual practice, and seek appropriately qualified professional help for decisions involving real money.
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