gogo2/docs/main/MODEL_INPUTS_OUTPUTS_REFERENCE.md

Model Inputs & Outputs Reference

Quick reference for all trading models in the system.

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1. Transformer (AdvancedTradingTransformer)

Type: Sequence-to-sequence transformer for multi-timeframe analysis
Size: 46M parameters
Architecture: 12 layers, 16 attention heads, 1024 model dimension

Inputs

price_data:   [batch, 150, 5]      # OHLCV sequences (150 candles)
cob_data:     [batch, 150, 100]    # Change of Bid features
tech_data:    [batch, 40]          # Technical indicators (SMA, returns, volatility)
market_data:  [batch, 30]          # Market context (volume, pivots, support/resistance)

Outputs

action_logits:        [batch, 3]         # Raw logits for BUY(1), SELL(2), HOLD(0)
action_probs:         [batch, 3]         # Softmax probabilities
confidence:           [batch, 1]         # Trade confidence (0-1)
price_prediction:     [batch, 1]         # Future price target
volatility_prediction:[batch, 1]         # Expected volatility
trend_strength:       [batch, 1]         # Trend strength (-1 to 1)

# Next candle predictions for each timeframe
next_candles: {
    '1s': [batch, 5],  # [open, high, low, close, volume]
    '1m': [batch, 5],
    '1h': [batch, 5],
    '1d': [batch, 5]
}

# Pivot point predictions (L1-L5)
next_pivots: {
    'L1': {
        'price': [batch, 1],
        'type_prob_high': [batch, 1],  # Probability of high pivot
        'type_prob_low': [batch, 1],   # Probability of low pivot
        'confidence': [batch, 1]
    },
    # ... L2, L3, L4, L5 (same structure)
}

# Trend vector analysis
trend_analysis: {
    'angle_radians': [batch, 1],    # Trend angle
    'steepness': [batch, 1],        # Trend steepness
    'direction': [batch, 1]         # Direction (-1 to 1)
}

Training Targets

actions:        [batch]      # Action labels (0=HOLD, 1=BUY, 2=SELL)
future_prices:  [batch]      # Price targets
trade_success:  [batch, 1]   # Success labels (0.0 or 1.0)

---

2. CNN (StandardizedCNN / EnhancedCNN)

Type: Convolutional neural network for pattern recognition
Size: ~5-10M parameters
Architecture: Multi-scale convolutions with attention

Inputs

# Via BaseDataInput.get_feature_vector()
feature_vector: [batch, 7834]  # Flattened features containing:
    - OHLCV ETH: 300 frames × 4 timeframes × 5 = 6000
    - OHLCV BTC: 300 frames × 5 = 1500
    - COB features: 184 (±20 buckets + MA imbalance)
    - Technical indicators: 100 (padded)
    - Last predictions: 50 (padded)

Outputs

action_logits:  [batch, 3]      # BUY, SELL, HOLD logits
action_probs:   [batch, 3]      # Softmax probabilities
confidence:     [batch, 1]      # Prediction confidence
hidden_states:  [batch, 1024]   # Feature embeddings (for cross-model feeding)
predicted_returns: [batch, 4]   # [return_1s, return_1m, return_1h, return_1d]

Training Targets

actions:    [batch]      # Action labels (0=HOLD, 1=BUY, 2=SELL)
returns:    [batch, 4]   # Actual returns per timeframe

---

3. DQN (Deep Q-Network Agent)

Type: Reinforcement learning agent for sequential decision making
Size: ~15M parameters
Architecture: Deep Q-Network with dueling architecture

Inputs

# Via BaseDataInput.get_feature_vector()
state: [batch, 7850]  # Full feature vector including:
    - Multi-timeframe OHLCV data
    - COB features
    - Technical indicators
    - Market regime indicators
    - Previous predictions

Outputs

q_values:       [batch, 3]      # Q-values for BUY, SELL, HOLD
action:         int             # Selected action (0, 1, 2)
confidence:     float           # Action confidence (0-1)

# Auxiliary outputs
regime_probs:   [batch, 4]      # [trending, ranging, volatile, mixed]
price_direction:[batch, 3]      # [down, neutral, up]
volatility:     [batch, 1]      # Predicted volatility
value:          [batch, 1]      # State value (V)
advantage:      [batch, 3]      # Action advantages (A)

Training Targets

# RL uses experience replay
experience: {
    'state': [7850],
    'action': int,
    'reward': float,
    'next_state': [7850],
    'done': bool
}

---

4. COB RL Model (MassiveRLNetwork)

Type: Specialized RL for Change of Bid (COB) data
Size: ~3M parameters
Architecture: Deep network focused on order book dynamics

Inputs

cob_features: [batch, input_size]  # COB-specific features:
    - Bid/ask imbalance
    - Order book depth
    - Price level changes
    - Volume at price levels
    - Moving averages of imbalance

Outputs

price_logits:   [batch, 3]      # Direction logits [DOWN, SIDEWAYS, UP]
price_probs:    [batch, 3]      # Direction probabilities
confidence:     [batch, 1]      # Prediction confidence
value:          [batch, 1]      # State value estimate
predicted_direction: int        # 0=DOWN, 1=SIDEWAYS, 2=UP

Training Targets

targets: {
    'direction': [batch],       # Direction labels (0, 1, 2)
    'value': [batch],          # Value targets
    'confidence': [batch]      # Confidence targets
}

---

5. Extrema Trainer

Type: Pivot point detection and prediction
Size: ~1M parameters (lightweight)
Architecture: Statistical + ML hybrid

Inputs

# Context data (200 candles)
context: {
    'symbol': str,
    'candles': deque[200],      # Recent OHLCV candles
    'features': array,          # Extracted features
    'last_update': datetime
}

# For prediction
current_price: float
now: datetime

Outputs

# Detected extrema
extrema: {
    'type': str,                # 'high' or 'low'
    'price': float,
    'timestamp': datetime,
    'confidence': float,        # 0-1
    'window_size': int
}

# Predicted pivot
predicted_pivot: {
    'type': str,                # 'high' or 'low'
    'price': float,             # Predicted price level
    'timestamp': datetime,      # Predicted time
    'confidence': float,        # 0-1
    'horizon_seconds': int      # Time until pivot (30-300s)
}

Training Data

# Historical extrema for validation
historical_extrema: List[{
    'price': float,
    'timestamp': datetime,
    'type': str,
    'detected': bool
}]

---

Common Patterns

Action Encoding (All Models)

0 = HOLD      # No action / maintain position
1 = BUY       # Enter long / close short
2 = SELL      # Enter short / close long

Confidence Scores

• Range: 0.0 to 1.0
• Typical threshold: 0.6 (60%)
• High confidence: > 0.8
• Low confidence: < 0.4

Batch Sizes

• Training: Usually 1 (annotation-based) or 32-128 (batch training)
• Inference: Usually 1 (real-time prediction)

Device Management

All models support:

• CPU: torch.device('cpu')
• CUDA: torch.device('cuda')
• Automatic device selection based on availability

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Model Selection Guide

Use Case	Recommended Model	Why
Multi-timeframe analysis	Transformer	Handles 150-candle sequences across timeframes
Pattern recognition	CNN	Excellent at visual pattern detection
Sequential decisions	DQN	Learns optimal action sequences via RL
Order book dynamics	COB RL	Specialized for bid/ask imbalance
Pivot detection	Extrema	Lightweight, fast pivot predictions

---

Integration Example

# Get base data input
base_input = data_provider.get_base_data_input(symbol, timestamp)

# CNN prediction
cnn_features = base_input.get_feature_vector()
cnn_output = cnn_model(cnn_features)
cnn_action = torch.argmax(cnn_output['action_probs'])

# Transformer prediction
transformer_batch = prepare_transformer_batch(base_input)
transformer_output = transformer_model(**transformer_batch)
transformer_action = torch.argmax(transformer_output['action_probs'])

# DQN prediction
dqn_state = base_input.get_feature_vector()
dqn_output = dqn_agent.select_action(dqn_state)
dqn_action = dqn_output['action']

# Ensemble decision
final_action = majority_vote([cnn_action, transformer_action, dqn_action])

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Notes

1. Shape Conventions: [batch, ...] indicates batch dimension first
2. Dtype: All tensors use torch.float32 unless specified
3. Gradients: Only training targets require gradients
4. Normalization: Features are typically normalized to [-1, 1] or [0, 1]
5. Missing Data: Padded with zeros or last known values