BTC Prediction AI – Devlog

I’ve had the idea of building my own AI for BTC predictions for a while. Not that whole “AI will make you rich” stuff, but just a tool that gives me a second opinion on the chart. After a few months of tinkering, the first version is done – and I’ve released it on GitHub for anyone to test.

This isn’t a tutorial or financial advice. It’s just a devlog explaining what I built and why.

What does it do?

It loads Bitcoin candles from Binance (1-minute chart, ~250,000 candles) and displays them in a chart that feels like TradingView. Three small AI models look at the last 200 candles and try to predict the next single candle:

Close model: What will the price be at the end of the minute?
High/Low model: How much will the price swing back and forth during the minute?
Volume model: How many people are buying/selling in this minute?

All three work together: first Close, then High/Low as offsets to it, then Volume. The prediction is fed back as a new candle and the next step begins – using the same principle as ChatGPT, creating 100 predicted candles in one go.

Since it’s a replay, you can scroll through history, turn on the AI, and see if it was right back then. There’s also Paper Trading: Buy, Sell, Close – with a win/loss counter.

Features

Replay: Walk through 250,000 BTC candles at adjustable speed
AI Prediction: 100 candles into the future, shown as white/blue candles
Paper Trading: Long/Short with play money, including fee input and stats
Indicators: Volume Delta + SuperTrend Oscillator (toggle on/off)
Live Chart: Real Binance data + AI in real time (/live_chart)

Tech

Data: BTCUSDT 1m from Binance, ~250k candles, 10 features (Close, High, Low, Volume, MA10, MA20, BB, EMA, OBV)
Training: Close (18 epochs), Volume (36), High/Low (180) – trained separately on CPU
Server: Python HTTP server (no Flask etc.), port 8765
Frontend: lightweight-charts (TradingView-like), Vanilla JS, ~300 lines

Try it yourself git clone https://github.com/tradersquant/BTC-PredictionAI.git cd BTC-PredictionAI pip install -r requirements.txt python trading_server.py

Then open http://localhost:8765 in your browser.

What’s next

The current version is a first draft. Three simple nets with 10 features are far from the end goal:

More inputs: Order book data, funding rate, liquidation levels, macro data
Larger networks: Deeper architectures, maybe transformers or attention
Ensemble: Multiple models running in parallel, validating each other
Better features: Automated pipeline that tests what actually has predictive power

But for now, the foundation works. Everything runs locally, offline, no API costs, no cloud dependency.

Built by tradersquant – BTC Prediction AI, July 2026