gogo2/crypto/gogo2/_model.md

Neural Network Architecture Analysis for Trading Bot

Overview

This document provides a comprehensive analysis of the neural network architecture used in our trading bot system. The system consists of two main neural network components:

1. Price Prediction Model - Forecasts future price movements and extrema points
2. DQN (Deep Q-Network) - Makes trading decisions based on state representations

1. Price Prediction Model

Architecture

PricePredictionModel(nn.Module)
├── Input Layer: [batch_size, seq_len, 2] (price, volume)
├── LSTM Layers: 2 stacked layers with hidden_size=128
├── Attention Mechanism: Self-attention with linear projections
├── Linear Layer 1: hidden_size → hidden_size
├── ReLU Activation
├── Linear Layer 2: hidden_size → output_size (5 future prices)
└── Output: [batch_size, output_size]

Data Flow

Inputs:

• price_history: Sequence of historical prices [batch_size, seq_len]
• volume_history: Sequence of historical volumes [batch_size, seq_len]

Preprocessing:

• Normalization using MinMaxScaler (0-1 range)
• Reshaping to [batch_size, seq_len, 2] (price and volume features)

Forward Pass:

1. Input data passes through LSTM layers
2. Self-attention mechanism applied to LSTM outputs
3. Linear layers process the attended features
4. Output represents predicted prices for next 5 candles

Outputs:

• predicted_prices: Array of 5 future price predictions
• predicted_extrema: Binary indicators for potential price extrema points

2. DQN (Deep Q-Network)

Architecture

DQN(nn.Module)
├── Input Layer: [batch_size, state_size]
├── Linear Layer 1: state_size → hidden_size (384)
├── ReLU Activation
├── LSTM Layers: 2 stacked layers with hidden_size=384
├── Multi-Head Attention: 4 attention heads
├── Linear Layer 2: hidden_size → hidden_size
├── ReLU Activation
├── Linear Layer 3: hidden_size → action_size (4)
└── Output: [batch_size, action_size] (Q-values for each action)

Data Flow

Inputs:

• state: Current market state representation [batch_size, state_size]
  • Price features (normalized prices, returns, volatility)
  • Technical indicators (RSI, MACD, Stochastic,