5. Data Collection Methodology

At the core of QNAX's analytical engine lies a robust, multi-layered data collection system designed to capture, process, and interpret diverse economic indicators in real time. Our approach ensures that trading signals and insights are rooted in comprehensive, cross-market intelligence. QNAX aggregates data from high-frequency APIs, institutional-grade data feeds, and decentralized oracles, focusing on the following key domains:

5.1 BTC-S&P500 Correlation

QNAX continuously monitors the correlation coefficient between Bitcoin and the S&P 500 index using rolling-window statistical analysis (typically 30-day and 90-day intervals). By applying Pearson and Spearman correlation methods, the platform detects convergences and divergences in asset behavior, highlighting macro sentiment shifts and potential decoupling events. These insights are enhanced with machine learning regression models to forecast correlation inflection points.

5.2 DXY (US Dollar Index) Influence

We analyze the inverse relationship between BTC and DXY using macroeconomic overlays. QNAX fetches DXY movements through financial market APIs and correlates them with BTC price action via vector autoregression (VAR) models. These algorithms identify temporal lead-lag dynamics and potential directional bias in crypto markets driven by USD strength or weakness.

5.3 Liquidation Heatmaps

QNAX integrates real-time liquidation data from major derivatives exchanges. Using heatmap visualizations, we identify liquidation clusters and high-risk zones. Our proprietary algorithms monitor cascading liquidations and liquidity gaps, enabling early detection of short squeezes or long wipeouts. These are modeled through volume-weighted average liquidation zones and adaptive volatility thresholds.

5.4 Order Book Analytics

The QNAX engine ingests full-depth order book data to evaluate market depth, bid-ask walls, spoofing patterns, and sudden liquidity shifts. A combination of custom-built parsing algorithms and time-series anomaly detectors helps isolate manipulative behaviors and whale footprints, which are then translated into probabilistic trade signals.

5.5 Funding Rates and Open Interest (OI)

Funding rates are collected and compared across exchanges to detect trader sentiment and capital flow direction. QNAX applies exponential smoothing to detect sudden shifts in funding bias. For open interest, we track cumulative contract volume per asset and combine it with price action to determine whether OI increases are bullish or bearish in context. Our algorithms flag OI spikes that occur without corresponding price moves, indicating potential hidden accumulation or distribution.

Through this multi-dimensional approach, QNAX ensures a 360-degree perspective of the crypto-economy. Each indicator is not only tracked individually but also analyzed in conjunction with others to produce composite signals, enhancing the precision of trading strategies for QUXY users.