How AI works
Revolutionizing AI, Automation & Finance
AI can help us make smarter decisions by learning from past data and predicting future outcomes. Here’s how it works step by step:
1. Preprocess the Data (Get the data ready)
Before AI can learn, it needs clean and organized data.
Load historical price data: We collect past prices of coins or assets (like Bitcoin prices over the last few years).
Fix missing or messy data: If some prices are missing or recorded incorrectly, we correct or remove them.
Normalize the data: Different indicators (like price, volume, etc.) can have very different scales. Normalizing makes everything comparable, like converting all values to the same “unit size.”
2. Create Useful Indicators (Add smart clues)
AI works better when it has the right “clues.” We create indicators (like moving averages, RSI, MACD) that give insights about market trends and price momentum. These indicators help AI understand patterns, not just raw numbers.
3. Feature Selection (Choose the most important clues)
Not every indicator matters. Some might add noise instead of value.
We check which indicators have the strongest relationship with price movements (using statistics and math).
Then, we use algorithms to rank indicators from most to least useful.
4. Train the AI Model (Teach the AI)
Now, we give the AI all the useful indicators and historical data so it can learn patterns.
The model tries to understand: When this combination of indicators happens, what usually happens to the price next?
After training, the AI can predict possible future price movements based on what it learned.
5. Identify the Best Indicators & Settings (Fine-tune for accuracy)
Once the AI is trained:
We check which indicators had the biggest impact on accurate predictions (these are your strongest signals).
We adjust the settings (called parameters) to make the model even more accurate.
Why This Matters
By doing this, we can predict market trends more accurately, make smarter trading decisions, and reduce guesswork. AI doesn’t guarantee profits, but it gives you a data-driven edge instead of relying on gut feelings.
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