🤖 AI Crypto Trading Bot – Deep Learning with 3D CNN + GRU

A deep learning–powered crypto trading bot using a hybrid 3D CNN + GRU model to predict market signals (BUY, SELL, HOLD) and execute real-time trades via the Binance API.

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🧠 Key Features

• 🔍 Hybrid Deep Learning Model
  Combines 3D Convolutional Neural Networks and GRU layers to analyze spatio-temporal patterns in market data

• 🧾 Real-Time Trading
  Executes live trades using the Binance API, based on the model’s predictions

• 🛠️ Customizable Parameters
  Easily tune training parameters like epochs, batch size, learning rate, etc.

• 💾 Model State Management
  Saves and loads the best-performing model via PyTorch's state_dict

• 💹 Backtesting Module
  Run strategy simulations on historical data to evaluate performance

• 🔗 Binance Integration
  Fetches real-time market data (default: BTC/USDT) with easy support for other pairs

• 📦 Modular Codebase
  Designed for clarity and experimentation — each stage is separated and reusable

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🧠 Why 3D CNN + GRU?

• 3D Convolutional Neural Networks (3D CNNs) are excellent at capturing spatio-temporal features — that is, patterns across both indicators (features) and time.
• GRU (Gated Recurrent Units) are powerful for sequence modeling, allowing the model to remember trends and time dependencies.
• The combination enables the system to recognize complex multi-dimensional market patterns and short-term trends — essential for high-frequency crypto trading.

This hybrid model aims to improve predictive performance over traditional 2D CNNs or LSTMs used alone.

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⚙️ How It Works

The pipeline consists of five core Python scripts, each responsible for a key step in the trading workflow:

1. Data Preparation
   Collects raw crypto data from Binance, computes technical indicators, assigns labels (BUY, SELL, HOLD), normalizes inputs, and applies oversampling to balance the dataset.

2. Model Training
   Defines and trains the hybrid deep learning model using a combination of 3D CNN and GRU layers. Includes the training loop, loss tracking, and model saving.

3. Backtesting
   Tests the trained model on historical data to estimate profitability and performance over a selected time period. Outputs include trade logs and profit metrics.

4. Dynamic Optimizer
   Connects data prep, training, and backtesting in one loop. Automatically adjusts parameters (e.g., learning rate, batch size) every N iterations to search for better results.

5. Live Trading Bot
   Uses the trained model to make predictions in real-time and places trades via the Binance API based on the current market conditions.

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Install Dependencies

Install all required libraries with: pip install requests websocket-client pandas numpy ta torch scikit-learn python-binance python-dotenv