DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification (2507.01971v1)

Abstract: Support and resistance (SR) levels are central to technical analysis, guiding traders in entry, exit, and risk management. Despite widespread use, traditional SR identification methods often fail to adapt to the complexities of modern, volatile markets. Recent research has introduced machine learning techniques to address the following challenges, yet most focus on price prediction rather than structural level identification. This paper presents DeepSupp, a new deep learning approach for detecting financial support levels using multi-head attention mechanisms to analyze spatial correlations and market microstructure relationships. DeepSupp integrates advanced feature engineering, constructing dynamic correlation matrices that capture evolving market relationships, and employs an attention-based autoencoder for robust representation learning. The final support levels are extracted through unsupervised clustering, leveraging DBSCAN to identify significant price thresholds. Comprehensive evaluations on S&P 500 tickers demonstrate that DeepSupp outperforms six baseline methods, achieving state-of-the-art performance across six financial metrics, including essential support accuracy and market regime sensitivity. With consistent results across diverse market conditions, DeepSupp addresses critical gaps in SR level detection, offering a scalable and reliable solution for modern financial analysis. Our approach highlights the potential of attention-based architectures to uncover nuanced market patterns and improve technical trading strategies.

• The paper introduces DeepSupp, a novel deep-learning model that uses multi-head attention to dynamically identify support and resistance levels in financial time series.
• It integrates feature engineering, Spearman rank correlation, autoencoder representation, and DBSCAN clustering to extract nuanced market patterns.
• Experimental results demonstrate state-of-the-art performance with a score of 0.554 ± 0.039, underlining its potential for improved algorithmic trading strategies.

DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification

The paper "DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification" (2507.01971) introduces a novel deep-learning approach, named DeepSupp, for identifying financial support levels by integrating multi-head attention mechanisms to analyze spatial correlations and market microstructure relationships. This method addresses the limitations of traditional technical analysis techniques, which often struggle to adapt to the complexities of modern, volatile markets. The authors present a comprehensive evaluation framework using six fundamental financial metrics, demonstrating that DeepSupp captures subtle price-volume relationships and achieves state-of-the-art performance in support level detection.

Methodology

The DeepSupp methodology combines feature engineering with volume-weighted indicators, dynamic correlation analysis using Spearman rank correlation, multi-head attention processing for parallel relationship analysis, autoencoder-based representation learning, and DBSCAN clustering for market regime identification (Figure 1).

Figure 1: DeepSupp Architecture Overview: The methodology integrates feature engineering with volume-weighted indicators, dynamic correlation analysis using Spearman rank correlation, multi-head attention processing for parallel relationship analysis, autoencoder-based representation learning, and DBSCAN clustering for market regime identification.

The feature engineering process constructs a feature vector $\mathbf{F}_t$ that includes closing price ($\text{Close}_t$), Volume Weighted Average Price ($\text{VWAP}_t$), volume ($V_t$), price change weighted by volume ($\text{PriceChangeVolume}_t$), and volume ratio ($\text{VolumeRatio}_t$). These features are normalized using MinMax scaling to ensure numerical stability during training. The dynamic correlation analysis module captures evolving relationships between market variables using Spearman rank correlation within sliding windows of length $n=32$. The Spearman rank correlation $\rho_{ij}^{(t)}$ is calculated as:

$$\rho_{ij}^{(t)} = 1 - \frac{6\sum_{k=1}^{n} d_{k}^2}{n(n^2-1)}$$

where $d_k$ represents the difference between ranks of observations $i$ and $j$ at time $k$. This generates a sequence of correlation snapshots that are then fed into the attention mechanism.

A multi-head attention autoencoder processes the correlation matrices, leveraging the permutation invariance property of multi-head attention. This architecture employs 4 attention heads with an embedding dimension of 32, allowing each head to specialize in different types of market relationships. The encoder component consists of two linear layers with ReLU activations, compressing the attention-enhanced features from 32 dimensions to 16 dimensions. The decoder mirrors the encoder structure, reconstructing the original correlation matrices from the compressed embeddings using a symmetric architecture. Finally, support levels are extracted through unsupervised clustering of the learned embeddings using DBSCAN with parameters $\epsilon=0.1$ and a minimum samples threshold equal to 10% of the dataset size.

Experimental Results

The paper provides a standardized comparison framework for evaluating support level detection methods using six fundamental financial metrics. These metrics include Support Accuracy, Price Proximity, Volume Confirmation, Market Regime Sensitivity, Support Hold Duration, and False Breakout Rate.

DeepSupp was evaluated against six baseline methods: Hidden Markov Models (HMM), Local Minima detection, Fractal analysis, Fibonacci retracement, Moving Average analysis, and Quantile Regression. The experiments were conducted using S{content}P 500 tickers with historical price and volume data over a 2-year period.

The results, summarized in Table 1, demonstrate that DeepSupp achieves the highest overall score of 0.554 ± 0.039, indicating superior and consistent performance across the weighted combination of all metrics evaluated.

Figure 2: Evolution of Support Level Detection Methods.

DeepSupp's balanced performance across multiple metrics, combined with the lowest performance variability, underscores its robustness. The paper also analyzes the attention weights, revealing that DeepSupp automatically discovers the fundamental structure of market behavior without being explicitly programmed with financial theory.

Implications and Future Work

DeepSupp's capacity to capture complex non-linear temporal dependencies and market microstructure relationships has significant implications for algorithmic trading. The framework demonstrates the potential of attention-based architectures to uncover nuanced market patterns and improve technical trading strategies. Future research directions include experimenting with less liquid equities, fixed-income products, and cryptocurrencies to confirm portability. Additionally, the authors suggest inference time speedup for easier practical deployment scenarios.