Ultimate Machine Learning MACD (Deep Learning Edition)This script is a "Deep Learning MACD" indicator that combines traditional MACD calculations with advanced machine learning techniques, including recursive feedback, adaptive learning rates, Monte Carlo simulations, and volatility-based adjustments. Here’s a breakdown of its key components:
Inputs
Lookback: The length of historical data (1000 by default) used for learning and volatility measurement.
Momentum and Volatility Weighting: Adjusts how much momentum and volatility contribute to the learning process (momentum weight: 1.2, volatility weight: 1.5).
MACD Lengths: Defines the range for MACD fast and slow lengths, starting at minimum of 1 and max of 1000.
Learning Rate: Defines how much the model learns from its predictions (very small learning rate by default).
Adaptive Learning: Enables dynamic learning rates based on market volatility.
Memory Factor: A feedback factor that determines how much weight past performance has in the current model.
Simulations: The number of Monte Carlo simulations used for probabilistic modeling.
Price Change: Calculated as the difference between the current and previous close.
Momentum: Measured using a lookback period (1000 bars by default).
Volatility: Standard deviation of closing prices.
ATR: Average true range over 14 periods for measuring market volatility.
Custom EMA Calculation
Implements an exponential moving average (EMA) formula from scratch using a recursive calculation with a smoothing factor.
Dynamic Learning Rate
Adjusts the learning rate based on market volatility. When volatility is high, the learning rate increases, and when volatility is low, it decreases. This makes the model more responsive during volatile markets and more stable during calm periods.
Error Calculation and Adjustment
Error Calculation: Measures the difference between the predicted value (via Monte Carlo simulations) and the true MACD value.
Adjust MACD Length: Uses the error to adjust the fast and slow MACD lengths dynamically, so the system can learn from market conditions.
Probabilistic Monte Carlo Simulation
Runs multiple simulations (200 by default) to generate probabilistic predictions. It uses random values weighted by momentum and volatility to simulate various market scenarios, enhancing
prediction accuracy.
MACD Calculation (Learning-Enhanced)
A custom MACD function that calculates:
Fast EMA and Slow EMA for MACD line.
Signal Line: An EMA of the MACD line.
Histogram: The difference between the MACD and signal lines.
Adaptive MACD Calculation
Adjusts the fast and slow MACD lengths based on the error from the Monte Carlo prediction.
Calculates the adaptive MACD, signal, and histogram using dynamically adjusted lengths.
Recursive Memory Feedback
Stores previous MACD values in an array (macdMemory) and averages them to create a feedback loop. This adds a "memory" to the system, allowing it to learn from past behaviors and refine future predictions.
Volatility-Based Reinforcement
Introduces a volatility reinforcement factor that influences the signal based on market conditions. It adds volatility awareness to the feedback system, making the system more reactive during high volatility periods.
Smoothed MACD
After all the adjustments, the MACD line is further smoothed based on the current market volatility, resulting in a final smoothed MACD.
Key Features
Monte Carlo Simulation: Runs multiple simulations to enhance predictions based on randomness and market behavior.
Adaptive Learning: Dynamic adjustments of learning rates and MACD lengths based on market conditions.
Recursive Feedback: Uses past data as feedback to refine the system’s predictions over time.
Volatility Awareness: Integrates market volatility into the system, making the MACD more responsive to market fluctuations.
This combination of traditional MACD with machine learning creates an adaptive indicator capable of learning from past behaviors and adjusting its sensitivity based on changing market conditions.
Cycles
Ultimate Machine Learning RSI (Deep Learning Edition)This script represents an advanced implementation of a Machine Learning-based Relative Strength Index (RSI) indicator in Pine Script, incorporating several sophisticated techniques to create a more adaptive, intelligent, and responsive RSI.
Key Components and Features:
Lookback Period: The period over which the indicator "learns" from past data, set to 1000 bars by default.
Momentum and Volatility Weighting: These factors control how much the momentum and volatility of the market influence the learning and signal generation.
RSI Length Range: The minimum and maximum values for the RSI length, allowing the algorithm to adjust the RSI length dynamically.
Learning Rate: Controls how quickly the system adapts to new data. An adaptive learning rate can change based on market volatility.
Memory Factor: Influences how much the system "remembers" previous performance when making adjustments.
Monte Carlo Simulations: Used for probabilistic modeling to create a more robust signal.
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Price Change: Tracks the difference between the current close and the previous close.
Momentum: A measure of the rate of change in the price over the lookback period.
Volatility: Calculated using the standard deviation of the close prices.
ATR (Average True Range): Tracks the volatility of the market over a short period to influence decisions.
Monte Carlo Simulation:
Probabilistic Signal: This uses multiple random simulations (Monte Carlo) to generate potential future signals. These simulations are weighted by the momentum and volatility of the market. A cluster factor further enhances the simulation based on volatility regimes.
Z-Score for Extreme Conditions:
Z-Score: Measures how extreme current price movements are compared to the historical average, providing context for identifying overbought and oversold conditions.
Dynamic Learning Rate:
The learning rate adjusts based on the volatility of the market, becoming more responsive in high-volatility periods and slower in low-volatility markets. This prevents the system from overreacting to noise but ensures responsiveness to significant shifts.
Recursive Learning and Feedback:
Error Calculation: The system calculates the difference between the true RSI and the predicted RSI, creating an error that is fed back into the system to adjust the RSI length and other parameters dynamically.
RSI Length Adjustment: Based on the error, the RSI length is adjusted, ensuring that the system evolves over time to better reflect market conditions.
Adaptive Smoothing:
In periods of high volatility, the indicator applies a Triple Exponential Moving Average (TEMA) for faster adaptation, while in quieter markets, it uses an Exponential Moving Average (EMA) for smoother adjustments.
Recursive Memory Feedback:
The system maintains a memory of past RSI values, which helps refine the output further. The memory factor influences how much weight is given to past performance versus the current adaptive signal.
Volatility-Based Reinforcement: Higher market volatility increases the impact of this memory feedback, making the model more reactive in volatile conditions.
Multi-Factor Dynamic Thresholds:
Dynamic Overbought/Oversold: Instead of fixed RSI levels (70/30), the thresholds adjust dynamically based on the Z-Score, making the system more sensitive to extreme market conditions.
Combined Multi-Factor Signal:
The final output signal is the result of combining the true RSI, adaptive RSI, and the probabilistic signal generated from the Monte Carlo simulations. This creates a robust, multi-factor signal that incorporates various market conditions and machine learning techniques.
Visual Representation:
The final combined signal is plotted in blue on the chart, along with reference lines at 55 (overbought), 10 (oversold), and 35 (neutral).
Alerts are set up to trigger when the combined signal crosses above the dynamic overbought level or below the dynamic oversold level.
Conclusion:
This "Ultimate Machine Learning RSI" script leverages multiple machine learning techniques—probabilistic modeling, adaptive learning, recursive feedback, and dynamic thresholds—to create an advanced, highly responsive RSI indicator. The result is an RSI that continuously learns from market conditions, adjusts itself in real-time, and provides a more nuanced and robust signal compared to traditional fixed-length RSI. This indicator pushes the boundaries of what's possible with Pine Script and introduces cutting-edge techniques for technical analysis.
Ultimate Multi-Physics Financial IndicatorThe Ultimate Multi-Physics Financial Indicator is an advanced Pine Script designed to combine various complex theories from physics, mathematics, and statistical mechanics to create a holistic, multi-dimensional approach to market analysis. Let’s break down the core concepts and how they’re applied in this script:
1. Fractal Geometry: Recursive Pattern Recognition
Purpose: This part of the script uses fractal geometry to recursively analyze price pivots (highs and lows) for detecting patterns.
Fractals: The fractalHigh and fractalLow signals represent key turning points in the market. The script goes deeper by recursively analyzing layers of pivot sequences, adding "depth" to the recognition of patterns.
Recursive Depth: It breaks down each detected pivot into smaller components, giving more nuance to market pattern recognition. This provides a broader context for how prices have behaved historically at various levels of recursion.
2. Quantum Mechanics: Adaptive Probabilistic Monte Carlo with Correlation
Purpose: This component integrates randomness (from Monte Carlo simulations) with current market behavior using correlation.
Randomness Weighted by Correlation: By generating random probabilities and weighting them based on how well the market aligns with recent trends, it creates a probabilistic signal. The random values are scaled by a correlation factor (close prices and their moving average), adding adaptive elements where randomness is adjusted by current market conditions.
3. Thermodynamics: Adaptive Efficiency Ratio (Entropy-Like Decay)
Purpose: This section uses principles from thermodynamics, where efficiency in price movement is dynamically adjusted by recent volatility and changes.
Efficiency Ratio: It calculates how efficiently the market is moving over a certain period. The "entropy decay factor" reflects how stable the market is. Higher entropy (chaos) results in lower efficiency, while stable periods maintain higher efficiency.
4. Chaos Theory: Lorenz-Driven Market Oscillation
Purpose: Instead of using a basic Average True Range (ATR) indicator, this section applies chaos theory (using a Lorenz attractor analogy) to describe complex market oscillations.
Lorenz Attractor: This models market behavior with a chaotic system that depends on the historical price changes at different time intervals. The attractor value quantifies the level of "chaos" or unpredictability in the market.
5. String Theory: Multi-Layered Dimensional Analysis of RSI and MACD
Purpose: Combines traditional indicators like the RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence) with momentum for multi-dimensional analysis.
Interaction of Layers: Each layer (RSI, MACD, and momentum) is treated as part of a multi-dimensional structure, where they influence one another. The final signal is a blended outcome of these key metrics, weighted and averaged for complexity.
6. Fluid Dynamics: Adaptive OBV (Pressure-Based)
Purpose: This section uses fluid dynamics to understand how price movement and volume create pressure over time, similar to how fluids behave under different forces.
Adaptive OBV: Traditional OBV (On-Balance Volume) is adapted by using statistical smoothing to measure the "pressure" exerted by volume over time. The result is a signal that shows where there might be building momentum or pressure in the market based on volume dynamics.
7. Recursive Synthesis of Signals
Purpose: After calculating all the individual signals (fractal, quantum, thermodynamic, chaos, string, and fluid), the script synthesizes them into one cohesive signal.
Recursive Feedback Loop: Each signal is recursively influenced by others, forming a feedback loop that allows the indicator to continuously learn from new data and self-adjust.
8. Signal Smoothing and Final Output
Purpose: To avoid noise in the output, the final combined signal is smoothed using an Exponential Moving Average (EMA), which helps stabilize the output for easier interpretation.
9. Dynamic Color Coding Based on Signal Extremes
Purpose: Visual clarity is enhanced by using color to highlight different levels of signal strength.
Color Coding: The script dynamically adjusts colors (green, orange, red) based on the strength of the final signal relative to its percentile ranking in historical data, making it easier to spot bullish, neutral, or bearish signals.
The "Ultimate Multi-Physics Financial Indicator" integrates a diverse array of scientific principles — fractal geometry, quantum mechanics, thermodynamics, chaos theory, string theory, and fluid dynamics — to provide a comprehensive market analysis tool. By combining probabilistic simulations, multi-dimensional technical indicators, and recursive feedback loops, this indicator adapts dynamically to evolving market conditions, giving traders a holistic view of market behavior across various dimensions. The result is an adaptive and flexible tool that responds to both short-term and long-term market changes
INDIA/NIFTY DOWN DAY MARKERINDIA/NIFTY DOWN DAY MARKER is indicator designed for Indian investors that provides visual cues on whole universe of stock charts marking volatile days based on the performance of selected Indian market indices. This indicator helps traders and investors assess the relative strength of individual stocks during extreme market movements
Key Features:
1) Index Selection: Users can choose from four major Indian indices: Nifty 50, Nifty Midcap 100, Nifty Smallcap 100, and Nifty MIDSMALLCAP 400. This flexibility allows for tailored analysis based on market focus.
2) Customizable Thresholds: Users can set their desired percentage thresholds for both rise and fall days, with default values of 2%. This customization enables users to adapt the indicator to their trading strategies.
3) Visual Indicators:
Rise Days: When the selected index rises by the specified percentage, the chart background turns green, indicating a bullish trend.
Fall Days: Conversely, if the index falls by the defined percentage, the background changes to red, signaling a bearish trend.
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.
Night Low Liquidity Congestions with 4 Trading SessionsThis indicator is designed to help traders visualize and analyze key market periods of low liquidity during the night and identify high-activity zones in the morning. It also includes customizable time sessions for major global markets, including the European and American sessions, as well as the London Close session.
T he main functionalities include:
- Night Low Liquidity Phase: This highlights periods with typically low market activity during the night (default: 20:01–5:59). It also displays the total range (in pips) during this phase, allowing traders to identify potential price consolidations.
- Morning Hot Zone: This focuses on high-activity periods in the early morning (default: 6:00–7:59), providing visual cues without altering bar colors.
- European Trading Session: Displays the European market’s open hours (default: 8:00–12:00), shaded in blue, to mark increased volatility typically seen during this period.
- American Trading Session: Marks the active hours of the U.S. market (default: 12:01–16:59), where market activity tends to peak.
- London Close Area: Highlights the closing hours of the London market (default: 17:00–20:00), allowing traders to track potential liquidity shifts.
Key Features:
1. Customizable Time Sessions:
- The indicator allows for full customization of the start and end times for each market session, making it adaptable to different instruments and trading style.
- Traders can choose their preferred color and opacity for each time zone to suit their charting preferences.
2. Night Low Liquidity Pip Range Calculation:
- Automatically calculates and displays the pip range for the Night Low Liquidity phase.
- The range is colored red if it exceeds the specified threshold and green if it remains below it.
3. Alarm System:
- Customizable alerts for H1, M15, and M5 timeframes.
- Traders can set alerts to trigger just before a bar closes during specific sessions (European, American, or London Close) and on selected days of the week (Monday–Friday).
- The alarm system allows for full customization of active hours and days, giving traders full control over their notifications.
4. Clear Visual Cues:
- The indicator uses transparent shading to differentiate market sessions, making it easy to spot different phases of the trading day.
- Each session is visually distinct and can be toggled on or off based on trader preferences.
Ideal For:
- Traders who focus on intraday strategies and want to understand how market sessions affect liquidity and volatility.
- Those looking to trade during specific time windows like the Night Low Liquidity or Morning Hot Zones.
- Traders who need to automate their alerts based on specific market hours and close events for major timeframes.
Parabolic (Brachistochrone) Curve IndicatorOverview of the Script
The script is designed to plot an approximation of the Brachistochrone curve between two points on a TradingView chart. The Brachistochrone curve represents the path of fastest descent under gravity between two points not aligned vertically. In physics, this curve is a segment of a cycloid.
Understanding the Brachistochrone Curve
Definition: The Brachistochrone curve is the curve along which a particle will descend from one point to another in the least time under gravity, without friction.
Mathematical Representation: The solution to the Brachistochrone problem is a cycloid, which is the curve traced by a point on the rim of a circular wheel as it rolls along a straight line.
Relevance to Trading: While the Brachistochrone curve originates from physics, plotting it on a price-time chart can offer a unique visual representation of the fastest possible movement between two price levels.
How the Script Works
Inputs
Start and End Bars:
startBar: The number of bars back from the current bar to define the starting point.
endBar: The number of bars back from the current bar to define the ending point.
Curve Customization:
numPoints: The number of points used to plot the curve (affects smoothness).
curveColor: The color of the curve.
curveWidth: The width of the curve lines.
Labels:
showTimeLabels: A toggle to display labels along the curve for reference.
Calculations
Determine Start and End Points:
The script calculates the coordinates (x_start, y_start, x_end, y_end) of the start and end points based on the specified bar offsets.
x_start and x_end correspond to bar indices (time).
y_start and y_end correspond to price levels.
Calculate Differences and Parameters:
Horizontal and Vertical Differences:
delta_x = x_end - x_start
delta_y = y_end - y_start
Ensure Descending Motion:
If the end point is higher than the start point (i.e., delta_y is positive), the script swaps the start and end points to ensure the curve represents a descent.
Cycloid Parameters:
Angle (theta): Calculated using theta = atan(delta_y / delta_x), representing the inclination of the curve.
Radius (R): The radius of the generating circle for the cycloid, calculated with R = delta_x / (π * cos(theta)).
Generate Points Along the Cycloid:
Parameter t: Varies from 0 to t_end, where t_end is set to π to represent half a cycloid (a common segment for the Brachistochrone).
Cycloid Equations:
Horizontal Component (x_t): x_t = R * (t - sin(t))
Vertical Component (y_t): y_t = R * (1 - cos(t))
Adjust Coordinates:
The script adjusts the cycloid coordinates to align with the chart's axes:
x_plot = x_start + x_t * cos(theta)
y_plot = y_start + y_t * sin(theta)
The x_plot values are converted to integer bar indices to match the chart's x-axis.
Plotting the Curve
Drawing Lines:
The script connects consecutive points using lines to form the curve.
It uses the line.new function, specifying the start and end coordinates of each line segment.
Adding Labels (Optional):
If showTimeLabels is enabled, the script places labels at intervals along the curve to indicate progress or parameter values.
Adjustments for Accurate Visualization
Handling Ascending Paths:
To adhere to the physical definition of the Brachistochrone curve, the script ensures that the ending point is below the starting point in terms of price.
If not, it swaps the points to represent a descending path.
Parameter Constraints:
The script ensures that calculations involving trigonometric functions remain within valid ranges to prevent mathematical errors (e.g., division by zero or invalid arguments for acos).
Scaling Considerations:
Adjustments are made to account for the differences in scaling between time (x-axis) and price (y-axis) on the chart.
The script maps spatial coordinates to the chart's axes appropriately.
Limitations and Considerations
Theoretical Nature:
The Brachistochrone curve is a theoretical concept from physics and doesn't necessarily predict actual price movements in financial markets.
Chart Scaling:
The visual appearance of the curve may be affected by the chart's scaling settings. Users may need to adjust the chart's zoom or scale to view the curve properly.
Data Range:
The start and end bars must be within the range of available data on the chart. If the specified bars are out of range, the script may not plot the curve.
Computational Limits:
TradingView imposes limits on the number of drawing objects (lines, labels) that can be displayed. The script accounts for this, but extremely high numPoints values may lead to performance issues.
Usage Instructions
Adding the Indicator:
The script is added to the chart as a custom indicator in TradingView's Pine Script Editor.
Configuring Inputs:
Start and End Bars: Users specify the bar offsets for the start and end points. It's important that the end point is below the start point in price to represent a descent.
Curve Customization: Users can adjust the number of points for smoothness and customize the curve's color and width.
Labels: Users can choose to display or hide labels along the curve.
Observing the Curve:
After configuring the inputs, the curve will be plotted between the two specified points.
Users can observe the curve to understand the theoretical fastest descent between the two price levels.
Potential Applications
Educational Tool:
The script serves as a visual aid to understand the properties of the Brachistochrone curve and cycloid.
Analytical Insights:
While not predictive, the curve might inspire new ways of thinking about price movements, momentum, or acceleration in markets.
Visualization:
It provides a unique way to visualize the relationship between time and price over a specific interval.
Conclusion
The script effectively adapts the mathematical concept of the Brachistochrone curve to a financial chart by carefully mapping spatial coordinates to time and price axes. By accounting for the unique characteristics of TradingView charts and implementing necessary mathematical adjustments, the script plots the curve between two user-defined points, offering a novel and educational visualization.
Weierstrass Function (Fractal Cycles)THE WEIERSTRASS FUNCTION
f(x) = ∑(n=0)^∞ a^n * cos(b^n * π * x)
The Weierstrass Function is the sum of an infinite series of cosine functions, each with increasing frequency and decreasing amplitude. This creates powerful multi-scale oscillations within the range ⬍(-2;+2), resembling a system of self-repetitive patterns. You can zoom into any part of the output and observe similar proportions, mimicking the hidden order behind the irregularity and unpredictability of financial markets.
IT DOESN’T RELY ON ANY MARKET DATA, AS THE OUTPUT IS BASED PURELY ON A MATHEMATICAL FORMULA!
This script does not provide direct buy or sell signals and should be used as a tool for analyzing the market behavior through fractal geometry. The function is often used to model complex, chaotic systems, including natural phenomena and financial markets.
APPLICATIONS:
Timing Aspect: Identifies the phases of market cycles, helping to keep awareness of frequency of turning points
Price-Modeling features: The Amplitude, frequency, and scaling settings allow the indicator to simulate the trends and oscillations. Its nowhere-differentiable nature aligns with the market's inherent uncertainty. The fractured oscillations resemble sharp jumps, noise, and dips found in volatile markets.
SETTINGS
Amplitude Factor (a): Controls the size of each wave. A higher value makes the waves larger.
Frequency Factor (b): Determines how fast the waves oscillate. A higher value creates more frequent waves.
Ability to Invert the output: Just like any cosine function it starts its journey with a decline, which is not distinctive to the behavior of most assets. The default setting is in "inverted mode".
Scale Factor: Adjusts the speed at which the oscillations grow over time.
Number of Terms (n_terms): Increases the number of waves. More terms add complexity to the pattern.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
RSI from Rolling VWAP [CHE]Introducing the RSI from Rolling VWAP Indicator
Elevate your trading strategy with the RSI from Rolling VWAP —a cutting-edge indicator designed to provide unparalleled insights and enhance your decision-making on TradingView. This advanced tool seamlessly integrates the Relative Strength Index (RSI) with a Rolling Volume-Weighted Average Price (VWAP) to deliver precise and actionable trading signals.
Why Choose RSI from Rolling VWAP ?
- Clear Trend Detection: Our enhanced algorithms ensure accurate identification of bullish and bearish trends, allowing you to capitalize on market movements with confidence.
- Customizable Time Settings: Tailor the time window in days, hours, and minutes to align perfectly with your unique trading strategy and market conditions.
- Flexible Moving Averages: Select from a variety of moving average types—including SMA, EMA, WMA, and more—to smooth the RSI, providing clearer trend analysis and reducing market noise.
- Threshold Alerts: Define upper and lower RSI thresholds to effortlessly spot overbought or oversold conditions, enabling timely and informed trading decisions.
- Visual Enhancements: Enjoy a visually intuitive interface with color-coded RSI lines, moving averages, and background fills that make interpreting market data straightforward and efficient.
- Automatic Signal Labels: Receive immediate bullish and bearish labels directly on your chart, signaling potential trading opportunities without the need for constant monitoring.
Key Features
- Inspired by Proven Tools: Building upon the robust foundation of TradingView's Rolling VWAP, our indicator offers enhanced functionality and greater precision.
- Volume-Weighted Insights: By incorporating volume into the VWAP calculation, gain a deeper understanding of price movements and market strength.
- User-Friendly Configuration: Easily adjust settings to match your trading preferences, whether you're a novice trader or an experienced professional.
- Hypothesis-Driven Analysis: Utilize hypothetical results to backtest strategies, understanding that past performance does not guarantee future outcomes.
How It Works
1. Data Integration: Utilizes the `hlc3` (average of high, low, and close) as the default data source, with customization options available to suit your trading needs.
2. Dynamic Time Window: Automatically calculates the optimal time window based on an auto timeframe or allows for fixed time periods, ensuring flexibility and adaptability.
3. Rolling VWAP Calculation: Accurately computes the Rolling VWAP by balancing price and volume over the specified time window, providing a reliable benchmark for price action.
4. RSI Analysis: Measures momentum through RSI based on Rolling VWAP changes, smoothed with your chosen moving average for enhanced trend clarity.
5. Actionable Signals: Detects and labels bullish and bearish conditions when RSI crosses predefined thresholds, offering clear indicators for potential market entries and exits.
Seamless Integration with Your TradingView Experience
Adding the RSI from Rolling VWAP to your TradingView charts is straightforward:
1. Add to Chart: Simply copy the Pine Script code into TradingView's Pine Editor and apply it to your desired chart.
2. Customize Settings: Adjust the Source Settings, Time Settings, RSI Settings, MA Settings, and Color Settings to align with your trading strategy.
3. Monitor Signals: Watch for RSI crossings above or below your set thresholds, accompanied by clear labels indicating bullish or bearish trends.
4. Optimize Your Trades: Leverage the visual and analytical strengths of the indicator to make informed buy or sell decisions, maximizing your trading potential.
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Get Started Today
Transform your trading approach with the RSI from Rolling VWAP indicator. Experience the synergy of momentum and volume-based analysis, and unlock the potential for more accurate and profitable trades.
Download now and take the first step towards a more informed and strategic trading journey!
For further inquiries or support, feel free to contact
Best regards
Chervolino
Inspired by the acclaimed Rolling VWAP by TradingView
(MA-EWMA) with ChannelsHamming Windowed Volume-Weighted Bidirectional Momentum-Adaptive Exponential Weighted Moving Average
This script is an advanced financial indicator that calculates a Hamming Windowed Volume-Weighted Bidirectional Momentum-Adaptive Exponential Weighted Moving Average (MA-EWMA). It adapts dynamically to market conditions, adjusting key parameters like lookback period, momentum length, and volatility sensitivity based on price volatility.
Key Components:
Dynamic Adjustments: The indicator adjusts its lookback and momentum length using the ATR (Average True Range), making it more responsive to volatile markets.
Volume Weighting: It incorporates volume data, weighting the moving average based on the volume activity, adding further sensitivity to price movement.
Bidirectional Momentum: It calculates upward and downward momentum separately, using these values to determine the directional weighting of the moving average.
Hamming Window: This technique smooths the price data by applying a Hamming window, which helps to reduce noise in the data and enhances the accuracy of the moving average.
Channels: Instead of plotting a single line, the script creates dynamic channels, providing more context for support and resistance levels based on the market's behavior.
The result is a highly adaptive and sophisticated moving average indicator that responds dynamically to both price momentum and volume trends.
VIDYA with Dynamic Length Based on ICPThis script is a Pine Script-based indicator that combines two key concepts: the Instantaneous Cycle Period (ICP) from Dr. John Ehlers and the Variable Index Dynamic Average (VIDYA). Here's an overview of how the script works:
Components:
Instantaneous Cycle Period (ICP):
This part of the indicator uses Dr. John Ehlers' approach to detect the market cycle length dynamically. It calculates the phase of price movement by computing the in-phase and quadrature components of the price detrended over a specific period.
The ICP helps adjust the smoothing length dynamically, giving a real-time estimate of the dominant cycle in price action. The script uses a phase calculation, adjusts it for cycle dynamics, and smoothes it for more reliable readings.
VIDYA (Variable Index Dynamic Average):
VIDYA is a moving average that dynamically adjusts its smoothing length based on the market conditions, in this case, using the RSI (Relative Strength Index) as a weight.
The length of VIDYA is determined by the dynamically calculated ICP, allowing it to adapt to changing market cycles.
This indicator performs several recursive layers of VIDYA smoothing (applying VIDYA multiple times) to provide a more refined result.
Key Features:
Dynamic Length: The length for the VIDYA calculation is derived from the smoothed ICP value, meaning that the smoothing adapts to the detected cycle length in real-time, making the indicator more responsive to market conditions.
Multiple VIDYA Layers: The script applies multiple layers of VIDYA smoothing (up to 5 iterations), further refining the output to smooth out market noise while maintaining responsiveness.
Plotting: The final smoothed VIDYA value and the smoothed ICP length are plotted. Additionally, overbought (70) and oversold (30) horizontal lines are provided for visual reference.
Application:
This indicator helps identify trends, smooths out price data, and adapts dynamically to market cycles. It's useful for detecting shifts in momentum and trends, and traders can use it to identify overbought or oversold conditions based on dynamically calculated thresholds.
Optimized WaveletsThe script, High-Resolution Volume-Price Pressure Indicator with Wavelets, utilizes wavelet transforms and high-resolution data to analyze market pressure based on volume and price dynamics. The approach combines volume data from smaller timeframes (1 second) with non-linear transformation techniques to generate a refined view of market conditions. Here’s a detailed breakdown of how it works:
Key Components:
Wavelet Transform:
A wavelet function is applied to the price and volume data to capture patterns over a set time period. This technique helps identify underlying structures in the data that might be missed with traditional moving averages.
High-Resolution Data:
The indicator fetches 1-second high-resolution data for price movements and volume. This allows the strategy to capture granular price and volume changes, crucial for short-term trading decisions.
Normalized Difference:
The script calculates the normalized difference in price and volume data. By comparing changes over the selected length, it standardizes these movements to help detect sudden shifts in market pressure.
Sigmoid Transformation:
After combining the price and volume wavelet data, a sigmoid function is applied to smooth out the resulting values. This non-linear transformation helps highlight significant moves while filtering out minor fluctuations.
Volume-Price Pressure:
The up and down volume differences, together with price movements, are combined to create a "Volume-Price Pressure Score." The final indicator reflects the pressure exerted on the market by both buyers and sellers.
Indicator Plot:
The final transformed score is plotted, showing how price and volume dynamics, combined through wavelet transformation, interact. The indicator can be used to identify potential market turning points or pressure buildups based on volume and price movement patterns.
This approach is well-suited for traders looking for advanced signal detection based on high-frequency data and can provide insight into areas where typical indicators may lag or overlook short-term volatility.
Sweep + MSS# Sweep + MSS Indicator
This indicator identifies market sweeps and Market Structure Shifts (MSS) to help traders recognize potential trend changes and market manipulations.
How it works:
1. Sweep Detection:
- Identifies when price briefly moves beyond a recent high/low (pivot point) and then reverses.
- Bullish sweep: Price drops below a recent low, then closes above it.
- Bearish sweep: Price rises above a recent high, then closes below it.
2. Market Structure Shift (MSS):
- Occurs when price action invalidates a previous sweep level.
- Bullish MSS: Price closes above a bearish sweep level.
- Bearish MSS: Price closes below a bullish sweep level.
Key Features:
- Customizable pivot lookback length for sweep detection
- Minimum bar requirement after a sweep before MSS can trigger
- One MSS per sweep level to avoid multiple signals
- Visual representation with lines connecting sweep points to MSS triggers
- Emoji labels for easy identification (🐂-MSS for bullish, 🐻-MSS for bearish)
Logic Behind MSS:
The MSS aims to identify potential trend changes by recognizing when the market invalidates a previous sweep level. This often indicates a shift in market structure, suggesting that the previous trend may be weakening or reversing.
- A bullish MSS occurs when the price closes above a bearish sweep level, potentially signaling a shift from bearish to bullish sentiment.
- A bearish MSS occurs when the price closes below a bullish sweep level, potentially signaling a shift from bullish to bearish sentiment.
By requiring a minimum number of bars between the sweep and the MSS, the indicator helps filter out noise and focuses on more significant structural changes in the market.
This indicator can be a valuable tool for traders looking to identify potential trend changes and entry/exit points based on market structure analysis.
Fluid Dynamics-Inspired Indicator with Bidirectional ScalingThe "Enhanced Fluid Dynamics-Inspired Indicator with Bidirectional Scaling" is a sophisticated technical analysis tool that draws inspiration from the principles of fluid dynamics to measure both upward and downward price movements, while also incorporating volatility and momentum into its calculations. The indicator aims to provide traders with a clear understanding of market dynamics by analyzing "streamflow" (price and volume movements) in both directions, enhanced with adaptive scaling techniques.
Key Features:
Bidirectional Price Momentum:
The indicator separately calculates positive and negative momentum using the price's rate of change. This allows for independent analysis of upward and downward price movements, providing a balanced view of the market's direction.
Streamflow Model:
The "streamflow" is calculated by multiplying volume flow with price momentum. This approach treats the market as a fluid system, where the momentum and volume of trades influence the flow of prices in both upward and downward directions. Streamflow is calculated independently for each direction.
Adaptive Volatility Scaling:
Volatility is dynamically calculated using the Average True Range (ATR) and is weighted to adjust to varying market conditions. An adaptive logarithmic scaling factor is applied to the volatility to capture the dynamic nature of market environments.
DRMA (Displaced Rolling Moving Average):
The indicator uses the DRMA function to smooth out price and volume data, improving the accuracy of its measurements. This allows the indicator to capture longer-term trends while still being responsive to short-term fluctuations.
Non-Linear Scaling and Normalization:
To ensure that the output values are within a usable range, the indicator employs a sigmoid-based non-linear scaling function. This helps normalize the composite output, making it easier to interpret overbought and oversold conditions.
Visual Representation:
The indicator plots two separate lines for upward and downward market movements, making it easy to distinguish between bullish and bearish trends. Background colors are also used to highlight periods of strong upward or downward momentum, as well as high volatility.
Overbought/Oversold Conditions:
Upper and lower thresholds are used to signal potential overbought and oversold conditions. Alerts are triggered when the market moves into extreme levels, helping traders identify potential entry and exit points.
Usage:
This indicator is designed for traders who are looking for a more nuanced and dynamic tool to measure both bullish and bearish trends. By using bidirectional scaling, it provides clearer signals for market direction, while adaptive volatility and momentum adjustments ensure the indicator responds to different market environments. The alert conditions make it especially useful for timing trades in highly volatile conditions or when price movements reach extreme levels.
Fractal & Entropy Market Dynamics with Mexican Hat WaveletThis indicator combines fractal analysis, entropy, and wavelet theory to model market dynamics using a customized approach. It integrates advanced mathematical techniques to assess the complexity and structure of price action, while also incorporating volume and price volatility.
Key Concepts and Features:
Volume-Weighted Price:
The script calculates a volume-adjusted price using a moving average of volume to give more weight to periods with higher volume. This allows the indicator to account for the impact of trading volume on price movements, enhancing its sensitivity to significant price shifts.
Mexican Hat Wavelet Approximation:
The script employs the Mexican Hat Wavelet, a mathematical tool that approximates price movements based on the Laplacian of the price series. This helps capture localized oscillations in price, acting as a filter to highlight certain price dynamics over the specified length. This wavelet is commonly used to identify key inflection points and trends in financial data.
Fractal Dimension Calculation:
The fractal dimension is calculated to quantify the market's complexity. It measures how price moves between intervals, with higher values indicating chaotic or more volatile market behavior. This dimension captures the self-similarity in price movements across different time frames, a key feature of fractals.
Shannon Entropy Calculation:
Shannon Entropy is used to measure the randomness or uncertainty in the price action. It calculates the degree of unpredictability based on the price changes, providing insight into the market's informational efficiency. Higher entropy indicates more randomness, while lower entropy suggests more predictable trends.
Custom Normalization:
The script includes a custom normalization function that processes the composite score (derived from fractal dimension and entropy). This normalization helps scale the values into a consistent range, making it easier to interpret the output. The smoothing factor and RSI-based approach ensure that the normalized value reacts smoothly to the changes in market dynamics.
Composite Score:
The composite score is a weighted combination of the fractal dimension and entropy. This score aims to provide a holistic view of the market by combining the structural complexity (fractal) and randomness (entropy) into one unified metric.
Plotting and Visuals:
The indicator plots the normalized composite score on a scale where a baseline of 50 is provided for reference. The resulting plot helps traders visualize market dynamics, with the score fluctuating based on changes in the market's fractal dimension and entropy. A score above or below the baseline of 50 indicates potential market shifts.
Use Case:
The "Enhanced Fractal and Entropy Market Dynamics with Mexican Hat Wavelet" is useful for traders looking to identify market conditions where there is a balance between price structure and randomness. By integrating wavelets, fractals, and entropy, the indicator can provide insights into market complexity, helping traders recognize potential trend reversals, periods of consolidation, or increased volatility. This can be particularly effective for those employing swing trading or trend-following strategies
Bernoulli Price Dynamics with IntraBar Volume (Bidirectional)This indicator adapts the principles of Bernoulli’s equation from fluid dynamics to analyze price and volume dynamics in the market. By incorporating intrabar volume data and splitting price movements into upward and downward components, it provides a bidirectional view of the market's kinetic and potential energies. This approach helps assess market pressure in both upward and downward directions, offering insights into potential price movement with energy-based mechanics.
Key Features:
Intrabar Volume Integration: The indicator collects up and down volume data from a lower timeframe, such as seconds or minutes, to provide more granular insights.
Bidirectional Market Pressure: By separating upward and downward price movements, it calculates market pressure in both directions, which is akin to fluid pressure. The separation enables tracking of distinct upward and downward energy flows in the market.
Energy Calculation:
Kinetic Energy: This represents the "movement" aspect of the price, weighted by volume. It is calculated for both upward and downward movements based on price velocity squared.
Potential Energy: This represents the "position" aspect of the price, calculated as the product of volume and the current price level. It is also separated into upward and downward components.
Market Pressure: The difference between the total energy (sum of kinetic and potential energies) and the highest observed total energy over a defined period (N). This provides an insight into the current momentum of price movement in both directions.
Visualization:
Market Pressure Up/Down: Plots the calculated market pressure for upward (green) and downward (red) movements.
Kinetic and Potential Energies: Provides individual plots for kinetic and potential energy in both directions to analyze the behavior of price and volume in more detail.
This indicator can be used to track market momentum and potential reversals by understanding the energy and pressure dynamics in both upward and downward price movements.
Earnings Surprise Indicator (Post-Earnings Announcement Drift)What It Does:
- Displays a company's actual earnings vs. analysts' estimates over time
- Shows "earnings surprises" - when actual results beat or miss expectations
- Helps identify trends in a company's financial performance
How It Works:
- Green bars: Positive surprise (earnings beat estimates)
- Red bars: Negative surprise (earnings missed estimates)
- Yellow line: Analysts' earnings estimates
Correlation with Post Earnings Announcement Drift (PEAD): PEAD is the tendency for a stock's price to drift in the direction of an earnings surprise for several weeks or months after the announcement.
Why It Matters:
- Positive surprises often lead to upward price drift
- Negative surprises often lead to downward price drift
- This drift can create trading opportunities
How to Use It:
1. Spot Trends:
- Consistent beats may indicate strong company performance
- Consistent misses may signal underlying issues
2. Gauge Market Expectations:
- Large surprises may lead to significant price movements
3. Timing Decisions:
- Consider long positions after positive surprises
- Consider short positions or exits after negative surprises
4. Risk Management:
- Be cautious of reversal if the drift seems excessive
- Use in conjunction with other technical and fundamental analysis
Key Takeaways:
- Earnings surprises can be fundamental-leading indicators of future stock performance, especially when correlated with analyst projections
- PEAD suggests that markets often underreact to earnings news initially
- This indicator helps visualize the magnitude and direction of surprises
- It can be a valuable tool for timing entry and exit points in trades
Profitable Mondays & Losing FridaysHere's a Pine Script that marks profitable Mondays and losing Fridays for a given stock:
Explanation
Input Parameter: The script allows you to input the stock symbol, defaulting to SPX.
Daily Returns: It calculates the daily return based on the closing price.
Day Identification: It checks if the current day is Monday or Friday.
Conditions:
Profitable Mondays: Marks with a green background if Monday's return is positive.
Losing Fridays: Marks with a red background if Friday's return is negative.
Visualization: Uses bgcolor to highlight the respective days on the chart.
You can adjust the stockSymbol input to analyze different stocks.
RSI Ignoring Gaps Between DaysThe RSI Ignoring Gaps Between Days indicator is an advanced modification of the traditional Relative Strength Index (RSI) designed to exclude price gaps that occur between the last bar of one trading day and the first bar of the next. This ensures that the RSI calculations remain focused on the actual price action during the trading session, avoiding distortions caused by overnight price gaps.
Key Features:
Gap Ignoring Mechanism: The indicator detects when a new day begins and skips the price change between the last bar of the previous day and the first bar of the current day. This ensures that only the intra-day price changes are included in the RSI calculation.
Intra-day Price Movement: The RSI calculations are based on real price changes within each trading day, providing a clearer reflection of momentum without interference from overnight events.
Dynamic RSI Calculation: The traditional RSI formula is preserved, but gains and losses are recalculated based on price changes from bar to bar within the same day.
Overbought/Oversold Levels: The indicator retains standard RSI overbought (70) and oversold (30) levels, allowing traders to easily identify potential reversal zones.
Alerts for Crossovers: Built-in alert conditions trigger when the RSI crosses key levels (30 or 70), signaling potential buying or selling opportunities.
This indicator is particularly useful for traders looking to focus on intra-day price action and avoid the influence of gaps caused by overnight market activity. It is suitable for intraday trading strategies where consistency in price movement measurement is crucial.
AndreundCristianIndicator Overview:
The "Trade Signals with Volume" indicator is a custom script that generates buy and sell signals based on the crossover of two moving averages (a fast one and a slow one) and adds a volume filter to validate these signals. It plots these signals directly on the chart, using arrows or labels to indicate where buy and sell signals occur.
Key Features:
Moving Averages (MA):
The indicator uses two Simple Moving Averages (SMA): a fast SMA and a slow SMA.
A buy signal is triggered when the fast MA crosses above the slow MA, signaling potential bullish momentum.
A sell signal is triggered when the fast MA crosses below the slow MA, indicating potential bearish momentum.
Volume Filter:
To ensure that signals are more reliable, the indicator only triggers a buy or sell signal if the volume is above a certain threshold. This threshold can be adjusted by the user in the input settings.
For example, if the volume exceeds 100,000 (or any set value), and a crossover occurs, the signal is validated.
Visual Representation:
Buy signals are represented with green labels or arrows below the price bars.
Sell signals are represented with red labels or arrows above the price bars.
The MAs are also plotted on the chart for visual reference.
Input Parameters:
Fast Moving Average Length: The number of periods for the fast SMA (default is 9 periods).
Slow Moving Average Length: The number of periods for the slow SMA (default is 21 periods).
Volume Threshold: The minimum volume required to validate a buy or sell signal (default is 100,000).
NYSE, Euronext, and Shanghai Stock Exchange Hours IndicatorNYSE, Euronext, and Shanghai Stock Exchange Hours Indicator
This script is designed to enhance your trading experience by visually marking the opening and closing hours of major global stock exchanges: the New York Stock Exchange (NYSE), Euronext, and Shanghai Stock Exchange. By adding vertical lines and background fills during trading sessions, it helps traders quickly identify these critical periods, potentially informing better trading decisions.
Features of This Indicator:
NYSE, Euronext, and Shanghai Stock Exchange Hours: Displays vertical lines at market open and close times for these three exchanges. You can easily switch between showing or hiding the different exchanges to customize the indicator for your needs.
Background Fill: Highlights the trading hours of these exchanges using faint background colors, making it easy to spot when markets are in session. This feature is crucial for timing trades around overlapping trading hours and volume peaks.
Customizable Visuals: Adjust the color, line style (solid, dotted, dashed), and line width to match your preferences, making the indicator both functional and visually aligned with your chart's aesthetics.
How to Use the Indicator:
Add the Indicator to Your Chart: Add the script to your chart from the TradingView script library. Once added, the indicator will automatically plot vertical lines at the opening and closing times of the NYSE, Euronext, and Shanghai Stock Exchange.
Customize Display Settings: Choose which exchanges to display by enabling or disabling the NYSE, Euronext, or Shanghai sessions in the indicator settings. This allows you to focus only on the exchanges that are relevant to your trading strategy.
Adjust Visual Properties: Customize the appearance of the vertical lines and background fill through the settings. Modify the color of each exchange, adjust the line style (solid, dotted, dashed), and control the line thickness to suit your chart preferences. The background fill can also be customized to clearly highlight active trading sessions.
Identify Key Market Hours: Use the vertical lines and background fills to identify the market open and close times. This is particularly useful for understanding how price action changes during specific trading hours or for finding high liquidity periods when multiple markets are open simultaneously.
Adapt Trading Strategies: By knowing when major stock exchanges are open, you can adapt your trading strategy to take advantage of potential price movements, increased volatility, or volume. This can help you avoid low-liquidity times and capitalize on more active trading periods.
This indicator is especially valuable for traders focusing on cross-market dynamics or those interested in understanding how different sessions influence market liquidity and price action. With this tool, you can gain insight into market conditions and adapt your trading strategies accordingly. The clean visual separation of session times helps you maintain context, whether you're trading Forex, stocks, or cryptocurrencies.
Disclaimer: This script is intended for informational and educational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument. Always conduct your own research and consult with a licensed financial advisor before making any trading decisions. Trading involves risk, and past performance is not indicative of future results.
Prometheus Fractal-Based TrendThe Fractal-Based Trend indicator is a tool that uses fractals to try and detect which direction an underlying will continue to go.
Calculation:
A bullish fractal occurs when the current bar's high is lower than the previous bar high, and the previous bar's high is higher than both the high from two bars ago and the high from three bars ago.
A bearish fractal happens when the current bar's low is higher than the previous bar's low, and the previous bar's low is lower than both the low from two bars ago and the low from three bars ago.
When a bullish or bearish fractal forms, the corresponding value stored is the previous bar high for a bearish fractal or the previous bar's low for a bullish fractal.
The trade scenarios are when these fractals occur, a green or red label being plotted on the chart for whatever direction it predicts.
Trade examples:
We see on this daily chart of AMEX:SPY that the fractals represent the potential for a directional trade that can last a few days. The more volatile a chart is the more of these fractals we can see.
We see on this 5 minute chart for NASDAQ:TSLA there is way more activity, there are more sporadic candles on a lower time frame, so we can see more anomalies in the price action.
We see this to be true for BITSTAMP:BTCUSD even on a daily time frame, since it is very volatile. There are a lot of these labels plotted.
This is the perspective we aim to provide. We encourage traders to not follow indicators blindly. No indicator is 100% accurate. This one can give you a different perspective of price strength with volatility. We encourage any comments about desired updates or criticism!
