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Risk Matrix [QuantraSystems]Risk Matrix The Risk Matrix is a sophisticated tool that aggregates a variety of fundamental inputs, primarily external (non-crypto) market data is used to assess investor risk appetite. By combining external macroeconomic factors and proxies for liquidity data with specific signals from the cryptomarket - the Risk Matrix provides a holistic view of market risk conditions. These insights are designed to help traders and investors make informed decisions on when to adopt a risk-on or risk-off approach. Core Concept The Risk Matrix functions as a dynamic risk assessment tool that integrates both fundamental and technical market indicators to generate an aggregated Z-score. This score helps traders to identify where the market is in a risk-off or risk-on state, The system provides both binary risk signals and a more nuanced “risk seasonality” mode for deeper analysis. Key Features Global Liquidity Aggregate - The Liquidity score is a custom measure of global liquidity, built by combining a variety of traditional financial metrics. These include data from central bank balance sheets, reverse repo operations and credit availability. This data is sourced from organizations such as the U.S. Federal Reserve, the European Central Bank, and the People’s Bank of China. The purpose of this aggregate is to gauge how much liquidity is available in the global financial system - which often correlates with risk sentiment. Rising liquidity tends to boost risk-on appetite, while liquidity contractions signal increased caution (risk-off) in the markets. The data sources used in this global liquidity aggregate include: - U.S. Commercial Bank Credit data - Federal Reserve balance sheet and reverse repo operations - Liquidity from major central banks including the Fed, Bank of Japan, ECB, and PBoC - Asset performance from major global financial indices such as the S&P 500, TLT, DXY (U.S. Dollar Index), MOVE (bond market volatility), and commodities like gold and oil. Other key Z-scores (measured individually) - The Risk Matrix also incorporates other major Z-scores that represent different facets of the financial markets: - Collateral Risk - A measure of US bond volatility, where higher values indicate higher interest rate risk - leading to potential market instability and cautious market behaviors. - Stablecoin Dominance - The dominance of stablecoins in the crypto markets - which can signal risk aversion the total capital allocated to stables increases relative to other cryptocurrencies. - US Currency Strength - The U.S. Dollar Index Z-score reflects currency market strength, with higher values typically indicating risk aversion as investors sell more volatile assets and flock to the dollar. - Trans-pacific Monetary Bias - Signals capital flow and monetary trends that link between the East and West, heavily influencing global risk sentiment. - Total - A measure of the total cryptocurrency market cap, signaling broader risk sentiment with the crypto market. Neural Network Synthesis - The NNSYNTH component adds a machine learning inspired layer to the Risk Matrix. This custom indicator synthesizes inputs from various technical indicators (such as RSI, MACD, Bollinger Bands, and others) to generate a composite signal that reflects the health of the cryptomarket. While highly complex in its design, the NNSYNTH ultimately helps detect market shifts early by synthesizing multiple signals into one cohesive output. This score is particularly useful for gauging momentum and identifying potential turning points in market trends. Because the NNSYNTH is a closed source indicator, and it is included here, the Risk Matrix by extension is a closed source indicator. How it Works Z-score Aggregation - The Risk Matrix computes a final risk score by aggregating several Z-scores from different asset classes and data sources, all of which contribute proportionally to the overall market risk assessment. Each input is equally weighted - normalization allows for direct comparisons across global liquidity trends, currency fluctuations, bond market volatility and crypto market conditions. Furthermore, this system employs multi-calibration aggregation - where each individual matrix is itself an aggregate of multiple Z-scores derived from various timeframes. This ensures that each matrix captures a distinct average across different time horizons before being combined into the overall Risk Matrix. This layered, multi timeframe approach enhances the precision and robustness of the final Z-score. Risk-On / Risk-Off Mode - The system’s binary mode provides a clear Risk On and Off signal. This nature of this signal is determined by the behavior of the Z-score relative to the midline, or Standard Deviation Bands, depending on specific conditions: Risk-On is signaled when the aggregated final Z-score crosses above 0. However, in extreme oversold conditions, Risk-On can trigger early if the upper standard deviation band falls below the zero line. In such cases, the Risk-On signal is triggered when the z-score crosses the upper standard deviation band - without waiting to cross the midline. Risk-Off is signaled when the final Z-score moves below 0. Similarly, Risk-Off can also be triggered early if the lower standard deviation band rises above the midline. In this instance, Risk-Off is triggered when the Z-score crosses below the lower band. Risk Seasonality Mode - This mode offers a more gradual transition between risk states, measuring the change in the Z-score to visualize the shifts in risk appetite over time. It's useful for traders seeking to understand broader market cycles and risk phases. The seasonality view breaks down the market into the following phases: Risk-On - High risk appetite where risk/cyclical markets are generally bullish. Weakening - Markets showing signs of cooling off, here the higher beta assets tend to sell off first. Risk-Off - Investors pull back, and bearish sentiment prevails. Recovery - Signs of bottoming out, potential for market re-entry. Component Matrices - Each individual Z-score is visualized as part of the component matrices - scaled to a 3 Sigma range. These component matrices allow traders to view how each data source is contributing to the overall risk assessment in real time - offering transparency and granularity. Visuals and UI Main Risk Matrix - The aggregated Z-Score is displayed saliently in the main risk matrix. Traders and investors can quickly see what season the Risk Matrix is signaling and adjust their strategies accordingly. Overview Table - A detailed overview table shows the current confirmed Z-scores for each component, along with values from 2, and 3 bars back. This helps traders spot trends and the rate of change (RoC) between signals, offering additional insights for shorter-term risk management. Customizability - Users can customize the visual elements of the matrix, including color palettes, table sizes, and positions. This allows for optimal integration into any trader’s existing workspace. Usage Summary The Risk Matrix is an incredibly versatile tool. It is especially valuable as a means of achieving a cross-market view of risk, incorporating both crypto-specific and macroeconomic factors. Some key use cases include: Adjusting Capital Allocation Based on Risk Seasons - Traders can use the Risk Matrix to adjust their capital allocation dynamically. During Risk-On periods, they might increase exposure to long positions, capitalizing on stronger market conditions. Conversely, during Risk-Off periods, traders could reduce or hedge long positions and potentially scale up short positions or move into safer assets. Complementing Other Trading Systems - The Risk Matrix can work alongside other technical systems to provide context to market moves. For instance, a trend-following strategy might suggest an entry, but the Risk Matrix could be used to verify whether the broader market conditions support this trade. If the Matrix is in a Risk-Off period, a trader might opt for more conservative trade sizes or avoid the trade entirely. This flexibility allows traders to adjust their strategies and portfolio risk dynamically, enhancing decision making based on broader market conditions - as indicated by external macroeconomic factors, liquidity, and risk sentiment. Important Note The Risk Matrix always uses the most up-to-date data available, ensuring analysis reflects the latest market conditions and macroeconomic inputs. In rare cases, governments or financial institutions revise past data - and the Risk Matrix will adjust accordingly. This behavior can only be seen in the Liquidity Matrix. and can affect the final score. While this is uncommon, it highlights the benefit of using a system that adapts in real-time, incorporating the most accurate and current information to enhance decision making processes.
Pine Script™ indicator
by QuantraSystems
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