OPEN-SOURCE SCRIPT
Advanced Physics Financial Indicator
Each component represents a scientific theory and is applied to the price data in a way that reflects key principles from that theory.
Detailed Explanation
1. Fractal Geometry - High/Low Signal
Concept: Fractal geometry studies self-similar patterns that repeat at different scales. In markets, fractals can be used to detect recurring patterns or turning points.
Implementation: The script detects pivot highs and lows using ta.pivothigh and ta.pivotlow, representing local turning points in price. The fractalSignal is set to 1 for a pivot high, -1 for a pivot low, and 0 if there is no signal. This logic reflects the cyclical, self-similar nature of price movements.
Practical Use: This signal is useful for identifying local tops and bottoms, allowing traders to spot potential reversals or consolidation points where fractal patterns emerge.
2. Quantum Mechanics - Probabilistic Monte Carlo Simulation
Concept: Quantum mechanics introduces uncertainty and probability into systems, much like how future price movements are inherently uncertain. Monte Carlo simulations are used to model a range of possible outcomes based on random inputs.
Implementation: In this script, we simulate 100 random outcomes by generating a random number between -1 and 1 for each iteration. These random values are stored in an array, and the average of these values is calculated to represent the Quantum Signal.
Practical Use: This probabilistic signal provides a sense of randomness and uncertainty in the market, reflecting the possibility of price movement in either direction. It simulates the market’s chaotic nature by considering multiple possible outcomes and their average.
3. Thermodynamics - Efficiency Ratio Signal
Concept: Thermodynamics deals with energy efficiency and entropy in systems. The efficiency ratio in financial terms can be used to measure how efficiently the price is moving relative to volatility.
Implementation: The Efficiency Ratio is calculated as the absolute price change over n periods divided by the sum of absolute changes for each period within n. This ratio shows how much of the price movement is directional versus random, mimicking the concept of efficiency in thermodynamic systems.
Practical Use: A high efficiency ratio suggests that the market is trending smoothly (high efficiency), while a low ratio indicates choppy, non-directional movement (low efficiency, or high entropy).
4. Chaos Theory - ATR Signal
Concept: Chaos theory studies how complex systems are highly sensitive to initial conditions, leading to unpredictable behavior. In markets, chaotic price movements can often be captured through volatility indicators.
Implementation: The script uses a very long ATR period (1000) to reflect slow-moving chaos over time. The Chaos Signal is computed by measuring the deviation of the current price from its long-term average (SMA), normalized by ATR. This captures price deviations over time, hinting at chaotic market behavior.
Practical Use: The signal measures how far the price deviates from its long-term average, which can signal the degree of chaos or extreme behavior in the market. High deviations indicate chaotic or volatile conditions, while low deviations suggest stability.
5. Network Theory - Correlation with BTC
Concept: Network theory studies how different components within a system are interconnected. In markets, assets are often correlated, meaning that price movements in one asset can influence or be influenced by another.
Implementation: This indicator calculates the correlation between the asset’s price and the price of Bitcoin (BTC) over 30 periods. The Network Signal shows how connected the asset is to BTC, reflecting broader market dynamics.
Practical Use: In a highly correlated market, BTC can act as a leading indicator for other assets. A strong correlation with BTC might suggest that the asset is likely to move in line with Bitcoin, while a weak or negative correlation might indicate that the asset is moving independently.
6. String Theory - RSI & MACD Interaction
Concept: String theory attempts to unify the fundamental forces of nature into a single framework. In trading, we can view the RSI and MACD as interacting forces that provide insights into momentum and trend.
Implementation: The script calculates the RSI and MACD and combines them into a single signal. The formula for String Signal is (RSI - 50) / 100 + (MACD Line - Signal Line) / 100, normalizing both indicators to a scale where their contributions are additive. The RSI represents momentum, and MACD shows trend direction and strength.
Practical Use: This signal helps in detecting moments where momentum (RSI) and trend strength (MACD) align, giving a clearer picture of the asset's direction and overbought/oversold conditions. It unifies these two indicators to create a more holistic view of market behavior.
7. Fluid Dynamics - On-Balance Volume (OBV) Signal
Concept: Fluid dynamics studies how fluids move and flow. In markets, volume can be seen as a "flow" that drives price movement, much like how fluid dynamics describe the flow of liquids.
Implementation: The script uses the OBV (On-Balance Volume) indicator to track the cumulative flow of volume based on price changes. The signal is further normalized by its moving average to smooth out fluctuations and make it more reflective of price pressure over time.
Practical Use: The Fluid Signal shows how the flow of volume is driving price action. If the OBV rises significantly, it suggests that there is strong buying pressure, while a falling OBV indicates selling pressure. It’s analogous to how pressure builds in a fluid system.
8. Final Signal - Combining All Physics-Based Indicators
Implementation: Each of the seven physics-inspired signals is combined into a single Final Signal by averaging their values. This approach blends different market insights from various scientific domains, creating a comprehensive view of the market’s condition.
Practical Use: The final signal gives you a holistic, multi-dimensional view of the market by merging different perspectives (fractal behavior, quantum probability, efficiency, chaos, correlation, momentum/trend, and volume flow). This approach helps traders understand the market's dynamics from multiple angles, offering deeper insights than any single indicator.
9. Color Coding Based on Signal Extremes
Concept: The color of the final signal plot dynamically reflects whether the market is in an extreme state.
Implementation: The signal color is determined using percentiles. If the Final Signal is in the top 55th percentile of its range, the signal is green (bullish). If it is between the 45th and 55th percentiles, it is orange (neutral). If it falls below the 45th percentile, it is red (bearish).
Practical Use: This visual representation helps traders quickly identify the strength of the signal. Bullish conditions (green), neutral conditions (orange), and bearish conditions (red) are clearly distinguished, simplifying decision-making.
Detailed Explanation
1. Fractal Geometry - High/Low Signal
Concept: Fractal geometry studies self-similar patterns that repeat at different scales. In markets, fractals can be used to detect recurring patterns or turning points.
Implementation: The script detects pivot highs and lows using ta.pivothigh and ta.pivotlow, representing local turning points in price. The fractalSignal is set to 1 for a pivot high, -1 for a pivot low, and 0 if there is no signal. This logic reflects the cyclical, self-similar nature of price movements.
Practical Use: This signal is useful for identifying local tops and bottoms, allowing traders to spot potential reversals or consolidation points where fractal patterns emerge.
2. Quantum Mechanics - Probabilistic Monte Carlo Simulation
Concept: Quantum mechanics introduces uncertainty and probability into systems, much like how future price movements are inherently uncertain. Monte Carlo simulations are used to model a range of possible outcomes based on random inputs.
Implementation: In this script, we simulate 100 random outcomes by generating a random number between -1 and 1 for each iteration. These random values are stored in an array, and the average of these values is calculated to represent the Quantum Signal.
Practical Use: This probabilistic signal provides a sense of randomness and uncertainty in the market, reflecting the possibility of price movement in either direction. It simulates the market’s chaotic nature by considering multiple possible outcomes and their average.
3. Thermodynamics - Efficiency Ratio Signal
Concept: Thermodynamics deals with energy efficiency and entropy in systems. The efficiency ratio in financial terms can be used to measure how efficiently the price is moving relative to volatility.
Implementation: The Efficiency Ratio is calculated as the absolute price change over n periods divided by the sum of absolute changes for each period within n. This ratio shows how much of the price movement is directional versus random, mimicking the concept of efficiency in thermodynamic systems.
Practical Use: A high efficiency ratio suggests that the market is trending smoothly (high efficiency), while a low ratio indicates choppy, non-directional movement (low efficiency, or high entropy).
4. Chaos Theory - ATR Signal
Concept: Chaos theory studies how complex systems are highly sensitive to initial conditions, leading to unpredictable behavior. In markets, chaotic price movements can often be captured through volatility indicators.
Implementation: The script uses a very long ATR period (1000) to reflect slow-moving chaos over time. The Chaos Signal is computed by measuring the deviation of the current price from its long-term average (SMA), normalized by ATR. This captures price deviations over time, hinting at chaotic market behavior.
Practical Use: The signal measures how far the price deviates from its long-term average, which can signal the degree of chaos or extreme behavior in the market. High deviations indicate chaotic or volatile conditions, while low deviations suggest stability.
5. Network Theory - Correlation with BTC
Concept: Network theory studies how different components within a system are interconnected. In markets, assets are often correlated, meaning that price movements in one asset can influence or be influenced by another.
Implementation: This indicator calculates the correlation between the asset’s price and the price of Bitcoin (BTC) over 30 periods. The Network Signal shows how connected the asset is to BTC, reflecting broader market dynamics.
Practical Use: In a highly correlated market, BTC can act as a leading indicator for other assets. A strong correlation with BTC might suggest that the asset is likely to move in line with Bitcoin, while a weak or negative correlation might indicate that the asset is moving independently.
6. String Theory - RSI & MACD Interaction
Concept: String theory attempts to unify the fundamental forces of nature into a single framework. In trading, we can view the RSI and MACD as interacting forces that provide insights into momentum and trend.
Implementation: The script calculates the RSI and MACD and combines them into a single signal. The formula for String Signal is (RSI - 50) / 100 + (MACD Line - Signal Line) / 100, normalizing both indicators to a scale where their contributions are additive. The RSI represents momentum, and MACD shows trend direction and strength.
Practical Use: This signal helps in detecting moments where momentum (RSI) and trend strength (MACD) align, giving a clearer picture of the asset's direction and overbought/oversold conditions. It unifies these two indicators to create a more holistic view of market behavior.
7. Fluid Dynamics - On-Balance Volume (OBV) Signal
Concept: Fluid dynamics studies how fluids move and flow. In markets, volume can be seen as a "flow" that drives price movement, much like how fluid dynamics describe the flow of liquids.
Implementation: The script uses the OBV (On-Balance Volume) indicator to track the cumulative flow of volume based on price changes. The signal is further normalized by its moving average to smooth out fluctuations and make it more reflective of price pressure over time.
Practical Use: The Fluid Signal shows how the flow of volume is driving price action. If the OBV rises significantly, it suggests that there is strong buying pressure, while a falling OBV indicates selling pressure. It’s analogous to how pressure builds in a fluid system.
8. Final Signal - Combining All Physics-Based Indicators
Implementation: Each of the seven physics-inspired signals is combined into a single Final Signal by averaging their values. This approach blends different market insights from various scientific domains, creating a comprehensive view of the market’s condition.
Practical Use: The final signal gives you a holistic, multi-dimensional view of the market by merging different perspectives (fractal behavior, quantum probability, efficiency, chaos, correlation, momentum/trend, and volume flow). This approach helps traders understand the market's dynamics from multiple angles, offering deeper insights than any single indicator.
9. Color Coding Based on Signal Extremes
Concept: The color of the final signal plot dynamically reflects whether the market is in an extreme state.
Implementation: The signal color is determined using percentiles. If the Final Signal is in the top 55th percentile of its range, the signal is green (bullish). If it is between the 45th and 55th percentiles, it is orange (neutral). If it falls below the 45th percentile, it is red (bearish).
Practical Use: This visual representation helps traders quickly identify the strength of the signal. Bullish conditions (green), neutral conditions (orange), and bearish conditions (red) are clearly distinguished, simplifying decision-making.
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