17 KiB
17 KiB
Neural Network Models Prediction Overview
Executive Summary
This document provides a comprehensive overview of what each neural network model in the trading system predicts. All models receive standardized BaseDataInput (7,870 or 22,880 features) and produce ModelOutput with consistent structure.
Model Categories
1. CNN Models (Convolutional Neural Networks)
Purpose: Pattern recognition from multi-timeframe OHLCV data
2. RL Models (Reinforcement Learning / DQN)
Purpose: Sequential decision-making with Q-learning
3. Transformer Models
Purpose: Long-range temporal dependencies and attention mechanisms
4. COB RL Models
Purpose: Order book microstructure analysis
1. StandardizedCNN
Location: NN/models/standardized_cnn.py
Input: BaseDataInput (7,870 or 22,880 features)
Output: ModelOutput with trading action predictions
Predictions:
| Prediction | Type | Description |
|---|---|---|
| Action | str |
Primary trading action: 'BUY', 'SELL', or 'HOLD' |
| Action Probabilities | Dict[str, float] |
Probability for each action: {'BUY': 0.65, 'SELL': 0.15, 'HOLD': 0.20} |
| Buy Probability | float |
Confidence in BUY action (0.0 to 1.0) |
| Sell Probability | float |
Confidence in SELL action (0.0 to 1.0) |
| Hold Probability | float |
Confidence in HOLD action (0.0 to 1.0) |
| Confidence | float |
Overall confidence in prediction (0.0 to 1.0) |
| Predicted Returns | List[float] |
Expected returns for 4 timeframes: [1s, 1m, 1h, 1d] |
| Predicted Return 1s | float |
Expected return over next second |
| Predicted Return 1m | float |
Expected return over next minute |
| Predicted Return 1h | float |
Expected return over next hour |
| Predicted Return 1d | float |
Expected return over next day |
| Extrema Detected | str |
Market extrema detection: 'bottom', 'top', or 'neither' |
| Price Direction | str |
Price movement direction: 'up', 'down', or 'sideways' |
| Market Conditions | Dict[str, str] |
Market analysis: {'volatility': 'high', 'risk': 'medium'} |
Output Structure:
ModelOutput(
model_type='cnn',
model_name='standardized_cnn_v1',
symbol='ETH/USDT',
timestamp=datetime.now(),
confidence=0.85,
predictions={
'action': 'BUY',
'buy_probability': 0.65,
'sell_probability': 0.15,
'hold_probability': 0.20,
'action_probabilities': [0.65, 0.15, 0.20],
'predicted_returns': [0.001, 0.005, 0.02, 0.05],
'predicted_return_1s': 0.001,
'predicted_return_1m': 0.005,
'predicted_return_1h': 0.02,
'predicted_return_1d': 0.05,
'extrema_detected': 'bottom',
'price_direction': 'up',
'market_conditions': {'volatility': 'high', 'risk': 'medium'}
},
hidden_states={...}, # For cross-model feeding
metadata={...}
)
2. EnhancedCNN
Location: NN/models/enhanced_cnn.py
Input: Feature vector (state tensor)
Output: Q-values, extrema predictions, price direction, advanced predictions
Predictions:
| Prediction | Type | Description |
|---|---|---|
| Q-Values | torch.Tensor |
Q-values for each action (used by DQN agent) |
| Action | int |
Selected action index: 0=BUY, 1=SELL |
| Action Probabilities | List[float] |
Probability distribution over actions |
| Confidence | float |
Confidence in selected action (0.0 to 1.0) |
| Price Direction | Dict[str, float] |
{'direction': -1.0 to 1.0, 'confidence': 0.0 to 1.0} |
| Extrema Predictions | torch.Tensor |
Bottom/top/neither detection probabilities |
| Volatility Prediction | str |
'Very Low', 'Low', 'Medium', 'High', 'Very High' |
| Support/Resistance | str |
'Strong Support', 'Weak Support', 'Neutral', 'Weak Resistance', 'Strong Resistance', 'Breakout' |
| Market Regime | str |
'Bull Trend', 'Bear Trend', 'Sideways', 'Volatile Up', 'Volatile Down', 'Accumulation', 'Distribution' |
| Risk Assessment | str |
'Low Risk', 'Medium Risk', 'High Risk', 'Extreme Risk' |
Output Structure:
# Returns tuple: (action_idx, confidence, action_probs)
action_idx = 0 # BUY
confidence = 0.87
action_probs = [0.87, 0.13] # [BUY, SELL]
# Additional predictions available via advanced_predictions:
{
'volatility': 'High',
'support_resistance': 'Strong Support',
'market_regime': 'Bull Trend',
'risk_assessment': 'Medium Risk'
}
3. DQN Agent (Deep Q-Network)
Location: NN/models/dqn_agent.py
Input: State vector (from BaseDataInput feature vector)
Output: Trading action with Q-value estimates
Predictions:
| Prediction | Type | Description |
|---|---|---|
| Action | int |
Trading action: 0=BUY, 1=SELL (2-action system) |
| Q-Values | torch.Tensor |
Expected future rewards for each action |
| Confidence | float |
Confidence in selected action (0.0 to 1.0) |
| Action Probabilities | List[float] |
Probability distribution: [buy_prob, sell_prob] |
| Price Direction | Dict[str, float] |
Price movement prediction with confidence |
| Market Regime | str |
Current market regime classification |
| Volatility Prediction | float |
Predicted volatility level |
Output Structure:
# Returns action index
action = 0 # BUY action
# Additional context available:
{
'action': 0,
'confidence': 0.82,
'action_probs': [0.82, 0.18],
'q_values': [2.5, -1.2], # Expected rewards
'price_direction': {'direction': 0.7, 'confidence': 0.85},
'market_regime': 'bull_trend',
'volatility': 0.65
}
4. COB RL Model (MassiveRLNetwork)
Location: NN/models/cob_rl_model.py
Input: COB (Consolidated Order Book) features
Output: Price direction prediction based on order book microstructure
Predictions:
| Prediction | Type | Description |
|---|---|---|
| Predicted Direction | int |
Price direction: 0=DOWN, 1=SIDEWAYS, 2=UP |
| Direction Text | str |
Human-readable: 'DOWN', 'SIDEWAYS', or 'UP' |
| Confidence | float |
Confidence in direction prediction (0.0 to 1.0) |
| Value | float |
State value estimate (for RL) |
| Probabilities | List[float] |
Probability distribution: [down_prob, sideways_prob, up_prob] |
Output Structure:
{
'predicted_direction': 2, # UP
'direction_text': 'UP',
'confidence': 0.78,
'value': 1.5,
'probabilities': [0.10, 0.12, 0.78] # [DOWN, SIDEWAYS, UP]
}
5. EnhancedCNNModel (OHLCV Predictor)
Location: NN/models/cnn_model.py
Input: Feature matrix (multi-timeframe OHLCV data)
Output: Future OHLCV predictions and market regime
Predictions:
| Prediction | Type | Description |
|---|---|---|
| OHLCV Prediction | torch.Tensor |
Predicted future OHLCV values: [open, high, low, close, volume] |
| Confidence | float |
Confidence in OHLCV prediction (0.0 to 1.0) |
| Market Regime | Dict[str, float] |
Regime probabilities: {'bull': 0.6, 'bear': 0.2, 'sideways': 0.2} |
| Volatility | float |
Predicted volatility level |
| Regime Stability | float |
Confidence in regime classification (0.0 to 1.0) |
Output Structure:
{
'ohlcv': [2025.0, 2030.0, 2020.0, 2028.0, 1500.0], # [O, H, L, C, V]
'confidence': 0.85,
'regime': {'bull': 0.6, 'bear': 0.2, 'sideways': 0.2},
'volatility': 0.45,
'regime_stability': 0.78
}
6. Advanced Trading Transformer
Location: NN/models/advanced_transformer_trading.py
Input: Multi-modal data (price, COB, technical indicators, market data)
Output: Comprehensive trading predictions with uncertainty estimation, next candle predictions, pivot point predictions, and trend-based actions
Predictions:
| Prediction | Type | Description |
|---|---|---|
| Action Logits | torch.Tensor |
Raw logits for each action |
| Action Probabilities | torch.Tensor |
Softmax probabilities: [BUY, SELL, HOLD] |
| Confidence | float |
Prediction confidence (if enabled) |
| Uncertainty Mean | float |
Mean uncertainty estimate (if enabled) |
| Uncertainty Std | float |
Uncertainty standard deviation (if enabled) |
| Price Prediction | torch.Tensor |
Predicted future price (auxiliary task) |
| Volatility Prediction | torch.Tensor |
Predicted volatility |
| Trend Strength | torch.Tensor |
Trend strength prediction |
| Regime Probabilities | torch.Tensor |
Market regime probabilities over time |
| Next Candles | Dict[str, torch.Tensor] |
NEW: OHLCV predictions for each timeframe (1s, 1m, 1h, 1d) |
| Next Pivots | Dict[str, Dict] |
NEW: Next pivot point predictions for L1-L5 levels with price, type (high/low), and confidence |
| Trend Vector | Dict |
NEW: Trend vector calculated from pivot predictions (angle, steepness, direction) |
| Trend-Based Action | Dict |
NEW: Trading action (BUY/SELL/HOLD) based on trend steepness and angle |
Output Structure:
{
'action_logits': tensor([2.5, -1.2, 0.3]),
'action_probs': tensor([0.82, 0.08, 0.10]), # [BUY, SELL, HOLD]
'confidence': 0.82,
'uncertainty_mean': 0.15,
'uncertainty_std': 0.05,
'price_prediction': tensor([2028.5]),
'volatility_prediction': tensor([0.45]),
'trend_strength_prediction': tensor([0.75]),
'regime_probs': tensor([...]), # Temporal regime probabilities
# NEW: Next candle predictions for each timeframe
'next_candles': {
'1s': tensor([2025.0, 2030.0, 2020.0, 2028.0, 1500.0]), # [O, H, L, C, V]
'1m': tensor([2028.0, 2035.0, 2025.0, 2032.0, 5000.0]),
'1h': tensor([2030.0, 2040.0, 2028.0, 2038.0, 15000.0]),
'1d': tensor([2035.0, 2050.0, 2030.0, 2045.0, 50000.0])
},
# NEW: Next pivot point predictions for L1-L5
'next_pivots': {
'L1': {
'price': tensor([2020.0]),
'type_prob_high': tensor([0.65]),
'type_prob_low': tensor([0.35]),
'pivot_type': tensor([0]), # 0=high, 1=low
'confidence': tensor([0.85])
},
'L2': {...},
'L3': {...},
'L4': {...},
'L5': {...}
},
# NEW: Trend vector analysis
'trend_vector': {
'pivot_prices': tensor([2020.0, 2025.0, 2030.0, 2035.0, 2040.0]), # L1-L5 prices
'price_delta': tensor([20.0]), # Price change from L1 to L5
'time_delta': tensor([4.0]), # Time change
'calculated_angle': tensor([1.373]), # Trend angle in radians (~78.7 degrees)
'calculated_steepness': tensor([20.4]), # Trend steepness magnitude
'calculated_direction': tensor([1.0]), # 1=up, -1=down
'vector': tensor([[20.0, 4.0]]) # [price_delta, time_delta]
},
# NEW: Trend-based trading action
'trend_based_action': {
'logits': tensor([[2.5, 0.3, 0.8]]), # [BUY, SELL, HOLD]
'probabilities': tensor([[0.82, 0.08, 0.10]]),
'action_idx': tensor([0]), # 0=BUY, 1=SELL, 2=HOLD
'trend_angle_degrees': tensor([78.7]), # Trend angle in degrees
'trend_steepness': tensor([20.4])
},
# Trend analysis (predicted)
'trend_analysis': {
'angle_radians': tensor([1.373]),
'steepness': tensor([20.4]),
'direction': tensor([0.95]) # -1 to 1 (down to up)
}
}
Helper Method: extract_predictions()
The model includes a helper method extract_predictions() that converts raw tensor outputs to user-friendly dictionaries:
# Usage example
outputs = model.forward(price_data, cob_data, tech_data, market_data)
predictions = model.extract_predictions(outputs, denormalize_prices=denorm_func)
# predictions structure:
{
'next_candles': {
'1s': {'open': 2025.0, 'high': 2030.0, 'low': 2020.0, 'close': 2028.0, 'volume': 1500.0},
'1m': {...},
'1h': {...},
'1d': {...}
},
'next_pivots': {
'L1': {'price': 2020.0, 'type': 'high', 'type_prob_high': 0.65, 'type_prob_low': 0.35, 'confidence': 0.85},
'L2': {...},
'L3': {...},
'L4': {...},
'L5': {...}
},
'trend_vector': {
'pivot_prices': [2020.0, 2025.0, 2030.0, 2035.0, 2040.0], # L1-L5
'angle_radians': 1.373,
'angle_degrees': 78.7,
'steepness': 20.4,
'direction': 'up',
'price_delta': 20.0
},
'trend_based_action': {
'action': 'BUY',
'action_idx': 0,
'probabilities': {'BUY': 0.82, 'SELL': 0.08, 'HOLD': 0.10},
'trend_angle_degrees': 78.7,
'trend_steepness': 20.4
}
}
Trend-Based Trading Logic:
The transformer model now includes sophisticated trend-based trading logic:
- Pivot Prediction: Predicts next pivot points for L1-L5 levels with price, type (high/low), and confidence
- Trend Vector Calculation: Calculates trend vector from pivot predictions:
- Trend angle: Angle of trend line in radians/degrees
- Trend steepness: Magnitude of price change over time
- Direction: Upward (>0), downward (<0), or sideways (≈0)
- Trade Action Logic:
- Steep upward trend (>45°): Suggests BUY action
- Steep downward trend (<-45°): Suggests SELL action
- Shallow trend (between -45° and 45°): Suggests HOLD action
- Action confidence scales with trend steepness
This enables the model to generate trend lines from pivot predictions and make trading decisions based on the predicted price movement steepness and angle.
7. Orchestrator (Ensemble Decision Maker)
Location: core/orchestrator.py
Input: Aggregates predictions from all models
Output: Final trading decision with weighted confidence
Predictions:
| Prediction | Type | Description |
|---|---|---|
| Final Action | str |
Ensemble decision: 'BUY', 'SELL', or 'HOLD' |
| Ensemble Confidence | float |
Weighted average confidence across models |
| Model Contributions | Dict[str, float] |
Each model's contribution to final decision |
| Consensus Score | float |
Agreement level among models (0.0 to 1.0) |
| Risk Assessment | str |
Overall risk level: 'low', 'medium', 'high' |
Output Structure:
Prediction(
action='BUY',
confidence=0.83,
probabilities={'BUY': 0.83, 'SELL': 0.10, 'HOLD': 0.07},
timeframe='1m',
timestamp=datetime.now(),
model_name='orchestrator_ensemble',
metadata={
'model_contributions': {
'cnn': 0.35,
'dqn': 0.40,
'transformer': 0.25
},
'consensus_score': 0.78,
'risk_assessment': 'medium'
}
)
Common Prediction Format
All models return standardized ModelOutput:
@dataclass
class ModelOutput:
model_type: str # 'cnn', 'rl', 'transformer', 'orchestrator'
model_name: str # Specific model identifier
symbol: str # Trading symbol (e.g., 'ETH/USDT')
timestamp: datetime # Prediction timestamp
confidence: float # Overall confidence (0.0 to 1.0)
predictions: Dict[str, Any] # Model-specific predictions
hidden_states: Optional[Dict[str, Any]] # For cross-model feeding
metadata: Dict[str, Any] # Additional information
Prediction Summary Table
| Model | Primary Prediction | Secondary Predictions | Use Case |
|---|---|---|---|
| StandardizedCNN | BUY/SELL/HOLD action | Returns (1s/1m/1h/1d), extrema, direction | Pattern recognition, multi-timeframe analysis |
| EnhancedCNN | Q-values (BUY/SELL) | Volatility, regime, risk, support/resistance | RL base network, comprehensive market analysis |
| DQN Agent | BUY/SELL action | Q-values, price direction, regime | Sequential decision-making, position management |
| COB RL | Price direction (UP/DOWN/SIDEWAYS) | Confidence, state value | Order book microstructure analysis |
| EnhancedCNNModel | Future OHLCV values | Market regime, volatility | Price forecasting, regime detection |
| Transformer | BUY/SELL/HOLD with uncertainty | Price prediction, volatility, trend strength, next candles (1s/1m/1h/1d), next pivots (L1-L5), trend vector, trend-based action | Long-range dependencies, uncertainty-aware trading, trend-based decision making |
| Orchestrator | Final ensemble decision | Consensus score, model contributions | Combining all models for optimal decision |
Key Takeaways
- All models predict trading actions (BUY/SELL/HOLD) with confidence scores
- Specialized predictions complement action predictions:
- Price direction and returns
- Market regime and volatility
- Support/resistance levels
- Risk assessment
- Uncertainty estimation (Transformer)
- Cross-model feeding enabled via
hidden_statesfor ensemble learning - Standardized output format ensures consistent integration across models
- Orchestrator combines all predictions for final decision with weighted confidence
References
- Model Interfaces:
NN/models/model_interfaces.py - Data Models:
core/data_models.py - Orchestrator:
core/orchestrator.py - Standardized CNN:
NN/models/standardized_cnn.py - DQN Agent:
NN/models/dqn_agent.py - Transformer:
NN/models/advanced_transformer_trading.py