Risk Appetite & Directional Bias [NariCapitalTrading]

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Guide to the Risk Appetite & Directional Bias Indicator [NariCapitalTrading]

This indicator is a tool designed to capture the relationship between Bitcoin and the S&P 500 (but could be any two assets of your choice, theoretically). This post aims to provide a detailed overview of the logic, components, and implementation of the indicator.

1. Introduction
This indicator leverages the relationship between Bitcoin and the S&P 500 to provide insights into the directional bias of the S&P 500 based on Bitcoin's movements. The premise is that Bitcoin, due to its 24/7 trading nature, often leads SP500 price movements. By dynamically adjusting the influence (beta) of Bitcoin based on historical data, this indicator aims to capture shifts in market sentiment or "risk appetite."

2. Core Concepts
a. Dynamic Weighting
The indicator uses a dynamic weighting mechanism to adjust the influence of Bitcoin on the S&P 500. The weight is based on the correlation between Bitcoin's and the S&P 500's returns, normalized by their respective volatilities.

// Calculate rolling correlation between Bitcoin and S&P 500
btcSp500Correlation = ta.correlation(btcChange, sp500Change, lookbackPeriod)

// Dynamic adjustment factor for Bitcoin influence on S&P 500
dynamicBtcWeight = btcWeightInput * btcSp500Correlation / normalizedBtcVolatility
b. Percentage Change and Volatility
Percentage change and volatility are critical components of the indicator. They are calculated for both Bitcoin and the S&P 500 to understand their respective behaviors over a defined lookback period.

// Function to calculate percentage change
f_change(src) =>
ta.change(src) * 100

// Function to calculate volatility
f_volatility(src, period) =>
ta.stdev(f_change(src), period)
These functions calculate the percentage change and standard deviation (volatility) of the asset prices.

c. Normalization
Normalization is applied to Bitcoin's volatility relative to the S&P 500's volatility to ensure that the influence of Bitcoin is appropriately scaled. This prevents Bitcoin's typically higher volatility from overwhelming the analysis.

// Normalize Bitcoin's volatility against S&P 500's volatility
normalizedBtcVolatility = sp500Volatility != 0 ? btcVolatility / sp500Volatility : na
3. Indicator Logic
The indicator's logic involves combining the historical change of the S&P 500 with the dynamically weighted influence of Bitcoin's change. The output is an "adjusted change" that reflects this combined impact.

// Combine the Bitcoin influence with S&P 500's historical change
adjustedChange = sp500Change + (dynamicBtcWeight * btcChange)
This adjusted change is used to determine the directional bias, categorized as "Bullish," "Bearish," or "Neutral."

4. Visualization
The indicator visualizes the predicted price of the S&P 500 based on the adjusted change. It uses different colors to represent different biases.

// Plot the predicted price with color indication based on bias
plotColor = bias == "Bullish" ? color.green : bias == "Bearish" ? color.red : color.blue
plot(predictedPrice, color=plotColor, title="Predicted SP500 Price", linewidth=2, style=plot.style_line)
Additionally, the adjusted change is plotted as a histogram.

5. Use Cases and Practical Applications
The indicator is particularly useful for day traders and swing traders who seek to anticipate market moves before they are fully reflected in traditional equity markets. This may/will require some parameter tuning and optimization on your part (the user).

For other researchers and quants: the dynamic weighting mechanism offers an example of how cross-asset relationships can be modeled and incorporated into pinescript studies.

6. Customization
Users can customize several aspects of the indicator:

Lookback Period: Defines the period over which correlation and volatility are calculated.
EMA Period: Adjusts the sensitivity of the indicator.
Initial Weight of Bitcoin Influence: Sets the starting point for Bitcoin's impact, which is then dynamically adjusted.