AI Pattern Recognition In Trading

import torch
import torch.nn as nn
import torch.nn.functional as F

class ChartPatternCNN(nn.Module):
    """
    Enterprise-grade 2D Convolutional Neural Network designed to classify 
    spatial market geometries (e.g., Ascending Triangles, Bullish Flags, Head & Shoulders) 
    encoded as Gramian Angular Fields (GAF) or multi-channel candle heatmaps.
    """
    def __init__(self, num_classes: int = 5):
        super(ChartPatternCNN, self).__init__()
        
        # Layer 1: Micro-wick and localized price reversal detection
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        
        # Layer 2: Intermediate consolidation boundary mapping
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        
        # Layer 3: High-level structural abstraction & liquidity field isolation
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.dropout = nn.Dropout(p=0.4)
        
        # Classification dense head
        self.fc1 = nn.Linear(128 * 8 * 8, 256)
        self.fc2 = nn.Linear(256, num_classes)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Input shape: [batch_size, 3, 64, 64] (RGB GAF Tensor Array)
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        
        x = x.view(x.size(0), -1)  # Flatten spatial feature maps
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        logits = self.fc2(x)
        return F.softmax(logits, dim=1)

import numpy as np
from fastdtw import fastdtw
from scipy.spatial.distance import euclidean

def validate_pattern_dtw_confluence(
    current_price_window: np.ndarray,
    historical_pattern_template: np.ndarray,
    cvd_series: np.ndarray,
    orderbook_imbalance: float,
    max_allowed_distance: float = 12.5
) -> dict:
    """
    Evaluates non-linear geometric similarity using Fast Dynamic Time Warping (FastDTW)
    and cross-validates structural probability with real-time Order Flow metrics.
    """
    # 1. Z-score normalization to remove absolute price scale bias
    norm_current = (current_price_window - np.mean(current_price_window)) / (np.std(current_price_window) + 1e-8)
    norm_template = (historical_pattern_template - np.mean(historical_pattern_template)) / (np.std(historical_pattern_template) + 1e-8)
    
    # 2. Compute non-linear temporal distance via FastDTW
    dtw_distance, path = fastdtw(norm_current, norm_template, dist=euclidean)
    
    # 3. Calculate Cumulative Volume Delta (CVD) slope trend
    cvd_slope = np.polyfit(np.arange(len(cvd_series)), cvd_series, 1)[0]
    
    # 4. Multi-modal confluence gating
    is_geometrically_valid = dtw_distance <= max_allowed_distance
    is_orderflow_confirmed = (cvd_slope > 0) and (orderbook_imbalance > 0.15)
    
    confidence_score = max(0.0, 100.0 - (dtw_distance * 4.0)) if is_geometrically_valid else 0.0
    if is_orderflow_confirmed:
        confidence_score = min(99.9, confidence_score * 1.25)
        
    return {
        "dtw_distance": round(dtw_distance, 4),
        "is_geometrically_valid": is_geometrically_valid,
        "is_orderflow_confirmed": is_orderflow_confirmed,
        "final_confidence_percentage": round(confidence_score, 2),
        "execution_signal": "BUY_EXECUTE" if (is_geometrically_valid and is_orderflow_confirmed) else "REJECT"
    }

Role: Quantitative Market Architecture Validator
Context: A Convolutional Neural Network has identified a high-probability bullish compression breakout pattern on the SOL/USDT pair. You must evaluate the concurrent structural metrics to verify the absence of an institutional distribution sweep.

Input Parameters for Analysis:
- Target Asset: SOL/USDT
- Identified Pattern Profile: 4-Hour Symmetrical Triangle Breakout
- Real-Time Volume Expansion Factor: 2.8x above the 20-period rolling median
- Ask-Side Liquidity Thickness Change: -14.2% (Thinning overhead resistance)
- Cumulative Volume Delta (CVD) Slope: Positive and accelerating
- Cross-Exchange Open Interest Delta: +$115M over 15 minutes

Validation Rules:
1. Classify PATTERN_EXECUTION as "CONFIRMED" only if the Volume Expansion Factor exceeds 2.0x AND the CVD slope mirrors the upward price acceleration.
2. If the Open Interest tracks upward excessively while the Ask-Side Liquidity thickness remains completely flat or increases, classify this configuration as an over-leveraged retail trap and return "ABORT".

Output Constraints:
Return exclusively a minified, valid JSON structure without markdown wrapping or conversational commentary.

Target JSON Structure:
{
  "pattern_validated": true,
  "confidence_percentage": 94.5,
  "execution_risk_profile": "LOW",
  "target_extension_multiplier": 1.618,
  "primary_structural_justification": "Volume expansion 2.8x aligned with CVD slope acceleration and ask-side depth depletion."
}