Kalman PredictorThe **Kalman Predictor** indicator is a powerful tool designed for traders looking to enhance their market analysis by smoothing price data and projecting future price movements. This script implements a Kalman filter, a statistical method for noise reduction, to dynamically estimate price trends and velocity. Combined with ATR-based confidence bands, it provides actionable insights into potential price movement, while offering clear trend and momentum visualization.
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#### **Key Features**:
1. **Kalman Filter Smoothing**:
- Dynamically estimates the current price state and velocity to filter out market noise.
- Projects three future price levels (`Next Bar`, `Next +2`, `Next +3`) based on velocity.
2. **Dynamic Confidence Bands**:
- Confidence bands are calculated using ATR (Average True Range) to reflect market volatility.
- Visualizes potential price deviation from projected levels.
3. **Trend Visualization**:
- Color-coded prediction dots:
- **Green**: Indicates an upward trend (positive velocity).
- **Red**: Indicates a downward trend (negative velocity).
- Dynamically updated label displaying the current trend and velocity value.
4. **User Customization**:
- Inputs to adjust the process and measurement noise for the Kalman filter (`q` and `r`).
- Configurable ATR multiplier for confidence bands.
- Toggleable trend label with adjustable positioning.
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#### **How It Works**:
1. **Kalman Filter Core**:
- The Kalman filter continuously updates the estimated price state and velocity based on real-time price changes.
- Projections are based on the current price trend (velocity) and extend into the future (Next Bar, +2, +3).
2. **Confidence Bands**:
- Calculated using ATR to provide a dynamic range around the projected future prices.
- Indicates potential volatility and helps traders assess risk-reward scenarios.
3. **Trend Label**:
- Updates dynamically on the last bar to show:
- Current trend direction (Up/Down).
- Velocity value, providing insight into the expected magnitude of the price movement.
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#### **How to Use**:
- **Trend Analysis**:
- Observe the direction and spacing of the prediction dots relative to current candles.
- Larger spacing indicates a potential strong move, while clustering suggests consolidation.
- **Risk Management**:
- Use the confidence bands to gauge potential price volatility and set stop-loss or take-profit levels accordingly.
- **Pullback Detection**:
- Look for flattening or clustering of dots during trends as a signal of potential pullbacks or reversals.
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#### **Customizable Inputs**:
- **Kalman Filter Parameters**:
- `lookback`: Adjusts the smoothing window.
- `q`: Process noise (higher values make the filter more reactive to changes).
- `r`: Measurement noise (controls sensitivity to price deviations).
- **Confidence Bands**:
- `band_multiplier`: Multiplies ATR to define the range of confidence bands.
- **Visualization**:
- `show_label`: Option to toggle the trend label.
- `label_offset`: Adjusts the label’s distance from the price for better visibility.
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#### **Examples of Use**:
- **Scalping**: Use on lower timeframes (e.g., 1-minute, 5-minute) to detect short-term price trends and reversals.
- **Swing Trading**: Identify pullbacks or continuations on higher timeframes (e.g., 4-hour, daily) by observing the prediction dots and confidence bands.
- **Risk Assessment**: Confidence bands help visualize potential price volatility, aiding in the placement of stops and targets.
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#### **Notes for Traders**:
- The **Kalman Predictor** does not predict the future with certainty but provides a statistically informed estimate of price movement.
- Confidence bands are based on historical volatility and should be used as guidelines, not guarantees.
- Always combine this tool with other analysis techniques for optimal results.
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This script is open-source, and the Kalman filter logic has been implemented uniquely to integrate noise reduction with dynamic confidence band visualization. If you find this indicator useful, feel free to share your feedback and experiences!
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#### **Credits**:
This script was developed leveraging the statistical principles of Kalman filtering and is entirely original. It incorporates ATR for dynamic confidence band calculations to enhance trader usability and market adaptability.
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Volatility-Adjusted Trend Deviation Statistics (C-Ratios)The Pine Script logic provided generates and displays a table with key information derived from VWMA, EMA, and ATR-based "C Ratios," alongside stochastic oscillators, correlation coefficients, Z-scores, and bias indicators. Here’s an explanation of the logic and what the output in the table informs:
Key Calculations and Their Purpose
VWMA and EMA (Smoothing Lengths):
Multiple EMAs are calculated using VWMA as the source, with lengths spanning short-term (13) to long-term (233).
These EMAs provide a hierarchy of smoothed price levels to assess trends over various time horizons.
ATR-Based "C Ratios":
The C Ratios measure deviations of smoothed prices (a_1 to a_7) from the source price relative to ATR at corresponding lengths.
These values normalize deviations, giving insight into the price's relative movement strength and direction over various periods.
Stochastic Oscillator for C Ratios:
Calculates normalized stochastic values for each C Ratio to assess overbought/oversold conditions dynamically over a rolling window.
Helps identify short-term momentum trends within the broader context of C Ratios.
Displays the average stochastic value derived from all C Ratios.
Text: Shows overbought/oversold conditions (Overbought, Oversold, or ---).
Color: Green for strong upward momentum, red for downward, and white for neutral.
Weighted and Mean C Ratio:
The script computes both an arithmetic mean (c_mean) and a weighted mean (c_mean_w) for all C Ratios.
Weighted mean emphasizes short-term values using predefined weights.
Trend Bias and Reversal Detection:
The script calculates Z-scores for c_mean to identify statistically significant deviations.
It combines Z-scores and weighted C Ratio values to determine:
Bias (Bullish/Bearish based on Z-score thresholds and mean values).
Reversals (Based on relative positioning and how the weighted c_mean and un-weighted C_mean move. ).
Correlation Coefficient:
Correlation of mean C Ratios (c_mean) with bar indices over the short-term length (sl) assesses the strength and direction of trend consistency.
Table Output and Its Meaning
Stochastic Strength:
Long-term Correlation:
List of Lengths: Define the list of lengths for EMA and ATR explicitly (e.g., ).
Calculate Mean C Ratios: For each length in the list, calculate the mean C Ratio
Average these values over the entire dataset.
Store Lengths and Mean C Ratios: Maintain arrays for lengths and their corresponding mean C Ratios.
Correlation: compute the Pearson correlation between the list of lengths and the mean C Ratios.
Text: Indicates Uptrend, Downtrend, or neutral (---).
Color: Green for positive (uptrend), red for negative (downtrend), and white for neutral.
Z-Score Bias:
Assesses the statistical deviation of C Ratios from their historical mean.
Text: Bullish Bias, Bearish Bias, or --- (neutral).
Color: Green or red based on the direction and significance of the Z-score.
C-Ratio Mean:
Displays the weighted average C Ratio (c_mean_w) or a reversal condition.
Text: If no reversal is detected, shows c_mean_w; otherwise, a reversal condition (Bullish Reversal, Bearish Reversal).
Color: Indicates the strength and direction of the bias or reversal.
Practical Insights
Trend Identification: Correlation coefficients, Z-scores, and stochastic values collectively highlight whether the market is trending and the trend's direction.
Momentum and Volatility: Stochastic and ATR-normalized C Ratios provide insights into the momentum and price movement consistency across different timeframes.
Bias and Reversal Detection: The script highlights potential shifts in market sentiment or direction (bias or reversal) using statistical measures.
Customization: Users can toggle plots and analyze specific EMA lengths or focus on combined metrics like the weighted C Ratio.
Bayesian Price Projection Model [Pinescriptlabs]📊 Dynamic Price Projection Algorithm 📈
This algorithm combines **statistical calculations**, **technical analysis**, and **Bayesian theory** to forecast a future price while providing **uncertainty ranges** that represent upper and lower bounds. The calculations are designed to adjust projections by considering market **trends**, **volatility**, and the historical probabilities of reaching new highs or lows.
Here’s how it works:
🚀 Future Price Projection
A dynamic calculation estimates the future price based on three key elements:
1. **Trend**: Defines whether the market is predisposed to move up or down.
2. **Volatility**: Quantifies the magnitude of the expected change based on historical fluctuations.
3. **Time Factor**: Uses the logarithm of the projected period (`proyeccion_dias`) to adjust how time impacts the estimate.
🧠 **Bayesian Probabilistic Adjustment**
- Conditional probabilities are calculated using **Bayes' formula**:
\
This models future events using conditional information:
- **Probability of reaching a new all-time high** if the price is trending upward.
- **Probability of reaching a new all-time low** if the price is trending downward.
- These probabilities refine the future price estimate by considering:
- **Higher volatility** increases the likelihood of hitting extreme levels (highs/lows).
- **Market trends** influence the expected price movement direction.
🌟 **Volatility Calculation**
- Volatility is measured using the **ATR (Average True Range)** indicator with a 14-period window. This reflects the average amplitude of price fluctuations.
- To express volatility as a percentage, the ATR is normalized by dividing it by the closing price and multiplying it by 200.
- Volatility is then categorized into descriptive levels (e.g., **Very Low**, **Low**, **Moderate**, etc.) for better interpretation.
---
🎯 **Deviation Limits (Upper and Lower)**
- The upper and lower limits form a **projected range** around the estimated future price, providing a framework for uncertainty.
- These limits are calculated by adjusting the ATR using:
- A user-defined **multiplier** (`factor_desviacion`).
- **Bayesian probabilities** calculated earlier.
- The **square root of the projected period** (`proyeccion_dias`), incorporating the principle that uncertainty grows over time.
🔍 **Interpreting the Model**
This can be seen as a **dynamic probabilistic model** that:
- Combines **technical analysis** (trends and ATR).
- Refines probabilities using **Bayesian theory**.
- Provides a **visual projection range** to help you understand potential future price movements and associated uncertainties.
⚡ Whether you're analyzing **volatile markets** or confirming **bullish/bearish scenarios**, this tool equips you with a robust, data-driven approach! 🚀
Español :
📊 Algoritmo de Proyección de Precio Dinámico 📈
Este algoritmo combina **cálculos estadísticos**, **análisis técnico** y **la teoría de Bayes** para proyectar un precio futuro, junto con rangos de **incertidumbre** que representan los límites superior e inferior. Los cálculos están diseñados para ajustar las proyecciones considerando la **tendencia del mercado**, **volatilidad** y las probabilidades históricas de alcanzar nuevos máximos o mínimos.
Aquí se explica su funcionamiento:
🚀 **Proyección de Precio Futuro**
Se realiza un cálculo dinámico del precio futuro estimado basado en tres elementos clave:
1. **Tendencia**: Define si el mercado tiene predisposición a subir o bajar.
2. **Volatilidad**: Determina la magnitud del cambio esperado en función de las fluctuaciones históricas.
3. **Factor de Tiempo**: Usa el logaritmo del período proyectado (`proyeccion_dias`) para ajustar cómo el tiempo afecta la estimación.
🧠 **Ajuste Probabilístico con la Teoría de Bayes**
- Se calculan probabilidades condicionales mediante la fórmula de **Bayes**:
\
Esto permite modelar eventos futuros considerando información condicional:
- **Probabilidad de alcanzar un nuevo máximo histórico** si el precio sube.
- **Probabilidad de alcanzar un nuevo mínimo histórico** si el precio baja.
- Estas probabilidades ajustan la estimación del precio futuro considerando:
- **Mayor volatilidad** aumenta la probabilidad de alcanzar niveles extremos (máximos/mínimos).
- **La tendencia del mercado** afecta la dirección esperada del movimiento del precio.
🌟 **Cálculo de Volatilidad**
- La volatilidad se mide usando el indicador **ATR (Average True Range)** con un período de 14 velas. Este indicador refleja la amplitud promedio de las fluctuaciones del precio.
- Para obtener un valor porcentual, el ATR se normaliza dividiéndolo por el precio de cierre y multiplicándolo por 200.
- Además, se clasifica esta volatilidad en categorías descriptivas (e.g., **Muy Baja**, **Baja**, **Moderada**, etc.) para facilitar su interpretación.
🎯 **Límites de Desviación (Superior e Inferior)**
- Los límites superior e inferior representan un **rango proyectado** en torno al precio futuro estimado, proporcionando un marco para la incertidumbre.
- Estos límites se calculan ajustando el ATR según:
- Un **multiplicador** definido por el usuario (`factor_desviacion`).
- Las **probabilidades condicionales** calculadas previamente.
- La **raíz cuadrada del período proyectado** (`proyeccion_dias`), lo que incorpora el principio de que la incertidumbre aumenta con el tiempo.
---
🔍 **Interpretación del Modelo**
Este modelo se puede interpretar como un **modelo probabilístico dinámico** que:
- Integra **análisis técnico** (tendencias y ATR).
- Ajusta probabilidades utilizando **la teoría de Bayes**.
- Proporciona un **rango de proyección visual** para ayudarte a entender los posibles movimientos futuros del precio y su incertidumbre.
⚡ Ya sea que estés analizando **mercados volátiles** o confirmando **escenarios alcistas/bajistas**, ¡esta herramienta te ofrece un enfoque robusto y basado en datos! 🚀
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
[ AlgoChart ] - Compare MarketIndicator Description:
This indicator allows you to display a second asset, selectable from the input panel, in a separate window. Plotted on the same time scale as the first asset but with a distinct price scale, the indicator enables analysis of the relationships and relative movements of two financial instruments. It’s an ideal tool for understanding whether two assets move in a correlated or divergent manner.
Key Features:
Multi-Asset Comparison: Display two assets simultaneously to compare their trends.
Custom Scale: Each asset uses its own price scale, making comparative analysis easier.
Intuitive Interface: Easily select the second asset through the input panel.
Operational Applications:
Spread Trading: Identify optimal moments to execute spread trades when two highly correlated instruments move in opposite directions.
Supply & Demand: Pinpoint zones of interest on both assets, increasing the validity of support and resistance areas.
Exposure Reduction: Monitor instruments that move similarly to avoid exposing the portfolio in identical directions, thereby reducing the risk of double losses.
Additional Features:
Candle Color Change: When a directional divergence occurs between the two assets, the candles change color to highlight the event.
Customizable Notifications: Receive instant alerts when a divergence occurs, allowing you to act promptly.
Ido strategy RSI Oversold with MACD Buy Signal Indicator
This indicator combines the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) to help identify potential buy signals based on oversold conditions and trend reversals. This script is designed for traders looking to identify entry points when an asset is likely undervalued (oversold) and showing bullish momentum.
How It Works
RSI Oversold Detection: The RSI measures the speed and change of price movements. This indicator flags when the RSI falls below 30, signaling that the asset may be oversold. The user can customize the RSI lookback period and the timeframe within which oversold conditions are considered relevant.
MACD Crossover: The MACD line crossing above the Signal line often indicates a shift to bullish momentum. In this script, a buy signal is generated when a MACD bullish crossover occurs after an RSI oversold condition has been met within a user-defined lookback window.
Buy Signal: A green triangle appears below the price chart each time both conditions are met—when the RSI has recently been in oversold territory and the MACD line crosses above the Signal line. This signal suggests that the asset may be positioned for a potential upward trend, providing a visual cue for entry points.
Customizable Settings
RSI Settings: Adjust the RSI source and period length.
MACD Settings: Customize the fast, slow, and signal lengths of the MACD to suit different market conditions.
Lookback Period: Define how many bars back to check for an RSI oversold condition before confirming a MACD crossover.
Visual Elements
Oversold Background Color: The background on the price chart is shaded red whenever the RSI is below 30.
Buy Signal: A green triangle is displayed on the chart to indicate a potential entry point when both conditions are met.
Alerts
This indicator includes optional alerts, allowing traders to receive notifications whenever the conditions for a buy signal are met, making it easier to monitor multiple assets and stay informed of trading opportunities.
This indicator is ideal for traders using a combination of momentum and trend reversal strategies, especially in volatile markets where oversold conditions often precede a trend change.
Customizable BTC Seasonality StrategyThis strategy leverages intraday seasonality effects in Bitcoin, specifically targeting hours of statistically significant returns during periods when traditional financial markets are closed. Padysak and Vojtko (2022) demonstrate that Bitcoin exhibits higher-than-average returns from 21:00 UTC to 23:00 UTC, a period in which all major global exchanges, such as the New York Stock Exchange (NYSE), Tokyo Stock Exchange, and London Stock Exchange, are closed. The absence of competing trading activity from traditional markets during these hours appears to contribute to these statistically significant returns.
The strategy proceeds as follows:
Entry Time: A long position in Bitcoin is opened at a user-specified time, which defaults to 21:00 UTC, aligning with the beginning of the identified high-return window.
Holding Period: The position is held for two hours, capturing the positive returns typically observed during this period.
Exit Time: The position is closed at a user-defined time, defaulting to 23:00 UTC, allowing the strategy to exit as the favorable period concludes.
This simple seasonality strategy aims to achieve a 33% annualized return with a notably reduced volatility of 20.93% and maximum drawdown of -22.45%. The results suggest that investing only during these high-return hours is more stable and less risky than a passive holding strategy (Padysak & Vojtko, 2022).
References
Padysak, M., & Vojtko, R. (2022). Seasonality, Trend-following, and Mean reversion in Bitcoin.
Dema Percentile Standard DeviationDema Percentile Standard Deviation
The Dema Percentile Standard Deviation indicator is a robust tool designed to identify and follow trends in financial markets.
How it works?
This code is straightforward and simple:
The price is smoothed using a DEMA (Double Exponential Moving Average).
Percentiles are then calculated on that DEMA.
When the closing price is below the lower percentile, it signals a potential short.
When the closing price is above the upper percentile and the Standard Deviation of the lower percentile, it signals a potential long.
Settings
Dema/Percentile/SD/EMA Length's: Defines the period over which calculations are made.
Dema Source: The source of the price data used in calculations.
Percentiles: Selects the type of percentile used in calculations (options include 60/40, 60/45, 55/40, 55/45). In these settings, 60 and 55 determine percentile for long signals, while 45 and 40 determine percentile for short signals.
Features
Fully Customizable
Fully Customizable: Customize colors to display for long/short signals.
Display Options: Choose to show long/short signals as a background color, as a line on price action, or as trend momentum in a separate window.
EMA for Confluence: An EMA can be used for early entries/exits for added signal confirmation, but it may introduce noise—use with caution!
Built-in Alerts.
Indicator on Diffrent Assets
INDEX:BTCUSD 1D Chart (6 high 56 27 60/45 14)
CRYPTO:SOLUSD 1D Chart (24 open 31 20 60/40 14)
CRYPTO:RUNEUSD 1D Chart (10 close 56 14 60/40 14)
Remember no indicator would on all assets with default setting so FAFO with setting to get your desired signal.
Relative Measured Volatility (RMV) – Spot Tight Entry ZonesTitle: Relative Measured Volatility (RMV) – Spot Tight Entry Zones
Introduction
The Relative Measured Volatility (RMV) indicator is designed to highlight tight price consolidation zones , making it an ideal tool for traders seeking optimal entry points before potential breakouts. By focusing on tightness rather than general volatility, RMV offers traders a practical way to detect consolidation phases that often precede significant market moves.
How RMV Works
The RMV calculates short-term tightness by averaging three ATR (Average True Range) values over different lookback periods and then normalizing them within a specified lookback window. The result is a percentage-based scale from 0 to 100, indicating how tight the current price range is compared to recent history.
Here’s the breakdown:
Three ATR values are computed using user-defined short lookback periods to represent short-term price movements. An average of the ATRs provides a smoothed measure of current tightness. The RMV normalizes this average against the highest and lowest values over the defined lookback period, scaling it from 0 to 100.
This approach helps traders identify consolidation zones that are more likely to lead to breakouts.
Key Features of RMV
Multi-Period ATR Calculation : Uses three ATR values to effectively capture market tightness over the short term. Normalization : Converts the tightness measure to a 0-100 scale for easy interpretation. Dynamic Histogram and Background Colors : The RMV indicator uses a color-coded system for clarity.
How to Use the RMV Indicator
Identify Tight Consolidation Zones:
a - RMV values between 0-10 indicate very tight price ranges, making this the most optimal zone for potential entries before breakouts.
b - RMV values between 11-20 suggest moderate tightness, still favorable for entries.
Monitor Potential Breakout Areas:
As RMV moves from 21-30 , tightness reduces, signaling expanding volatility that may require wider stops or more flexible entry strategies.
Adjust Trading Strategies:
Use RMV values to identify tight zones for entering trades, especially in trending markets or at key support/resistance levels.
Customize the Indicator:
a - Adjust the short-term ATR lookback periods to control sensitivity.
b - Modify the lookback period to match your trading horizon, whether short-term or long-term.
Color-Coding Guide for RMV
ibb.co
How to Add RMV to Your Chart
Open your chart on TradingView.
Go to the “Indicators” section.
Search for "Relative Measured Volatility (RMV)" in the Community Scripts section.
Click on the indicator to add it to your chart.
Customize the input parameters to fit your trading strategy.
Input Parameters
Lookback Period : Defines the period over which tightness is measured and normalized.
Short-term ATR Lookbacks (1, 2, 3) : Control sensitivity to short-term tightness.
Histogram Threshold : Sets the threshold for differentiating between bright (tight) and dim (less tight) histogram colors.
Conclusion
The Relative Measured Volatility (RMV) is a versatile tool designed to help traders identify tight entry zones by focusing on market consolidation. By highlighting narrow price ranges, the RMV guides traders toward potential breakout setups while providing clear visual cues for better decision-making. Add RMV to your trading toolkit today and enhance your ability to identify optimal entry points!
4AM-5AM BRT HighlighterThe 4AM-5AM BRT Highlighter is a simple yet effective tool designed to visually mark your preferred trading time on the chart. It highlights the period between 4:00 AM and 5:00 AM Brazilian Time (BRT/UTC-3) by default, helping you stay focused and aware of your prime trading window.
Key Features:
Clear Visual Highlight: Colors the background of your chart during the chosen timeframe, making it easy to see when your trading session starts and ends.
Customizable Colors: Easily adjust the highlight color and transparency to suit your visual preferences.
Accurate Time Conversion: Automatically accounts for Brazilian Time (BRT), ensuring the highlight appears correctly no matter your chart’s default timezone.
Whether you're trading currencies, metals, indexes, or cryptocurrencies, this indicator helps you maintain focus during your dedicated trading hour by clearly marking your active period on the chart.
Macro Timeframes with Opening PriceDescription: Macro Timeframe Horizontal Line Indicator
This indicator highlights macro periods on the chart by drawing a horizontal line at the opening price of each macro period. The macro timeframe is defined as the last 10 minutes of an hour (from :50 to :00) and the first 10 minutes of the following hour (from :00 to :10).
A horizontal black line is plotted at the opening price of the macro period, starting at :50 and extending through the duration of the macro window. However, you can customize it however you see fit.
The background of the macro period is highlighted with a customizable color to visually distinguish the timeframe.
The horizontal line updates at each macro period, ensuring that the opening price for every macro session is accurately reflected on the chart.
This tool is useful for traders who want to track the behavior of price within key macro intervals and visually assess price movement and volatility during these periods.
Cosine-Weighted MA ATR [InvestorUnknown]The Cosine-Weighted Moving Average (CWMA) ATR (Average True Range) indicator is designed to enhance the analysis of price movements in financial markets. By incorporating a cosine-based weighting mechanism , this indicator provides a unique approach to smoothing price data and measuring volatility, making it a valuable tool for traders and investors.
Cosine-Weighted Moving Average (CWMA)
The CWMA is calculated using weights derived from the cosine function, which emphasizes different data points in a distinctive manner. Unlike traditional moving averages that assign equal weight to all data points, the cosine weighting allocates more significance to values at the edges of the data window. This can help capture significant price movements while mitigating the impact of outlier values.
The weights are shifted to ensure they remain non-negative, which helps in maintaining a stable calculation throughout the data series. The normalization of these weights ensures they sum to one, providing a proportional contribution to the average.
// Function to calculate the Cosine-Weighted Moving Average with shifted weights
f_Cosine_Weighted_MA(series float src, simple int length) =>
var float cosine_weights = array.new_float(0)
array.clear(cosine_weights) // Clear the array before recalculating weights
for i = 0 to length - 1
weight = math.cos((math.pi * (i + 1)) / length) + 1 // Shift by adding 1
array.push(cosine_weights, weight)
// Normalize the weights
sum_weights = array.sum(cosine_weights)
for i = 0 to length - 1
norm_weight = array.get(cosine_weights, i) / sum_weights
array.set(cosine_weights, i, norm_weight)
// Calculate Cosine-Weighted Moving Average
cwma = 0.0
if bar_index >= length
for i = 0 to length - 1
cwma := cwma + array.get(cosine_weights, i) * close
cwma
Cosine-Weighted ATR Calculation
The ATR is an essential measure of volatility, reflecting the average range of price movement over a specified period. The Cosine-Weighted ATR uses a similar weighting scheme to that of the CWMA, allowing for a more nuanced understanding of volatility. By emphasizing more recent price movements while retaining sensitivity to broader trends, this ATR variant offers traders enhanced insight into potential price fluctuations.
// Function to calculate the Cosine-Weighted ATR with shifted weights
f_Cosine_Weighted_ATR(simple int length) =>
var float cosine_weights_atr = array.new_float(0)
array.clear(cosine_weights_atr)
for i = 0 to length - 1
weight = math.cos((math.pi * (i + 1)) / length) + 1 // Shift by adding 1
array.push(cosine_weights_atr, weight)
// Normalize the weights
sum_weights_atr = array.sum(cosine_weights_atr)
for i = 0 to length - 1
norm_weight_atr = array.get(cosine_weights_atr, i) / sum_weights_atr
array.set(cosine_weights_atr, i, norm_weight_atr)
// Calculate Cosine-Weighted ATR using true ranges
cwatr = 0.0
tr = ta.tr(true) // True Range
if bar_index >= length
for i = 0 to length - 1
cwatr := cwatr + array.get(cosine_weights_atr, i) * tr
cwatr
Signal Generation
The indicator generates long and short signals based on the relationship between the price (user input) and the calculated upper and lower bands, derived from the CWMA and the Cosine-Weighted ATR. Crossover conditions are used to identify potential entry points, providing a systematic approach to trading decisions.
// - - - - - CALCULATIONS - - - - - //{
bar b = bar.new()
float src = b.calc_src(cwma_src)
float cwma = f_Cosine_Weighted_MA(src, ma_length)
// Use normal ATR or Cosine-Weighted ATR based on input
float atr = atr_type == "Normal ATR" ? ta.atr(atr_len) : f_Cosine_Weighted_ATR(atr_len)
// Calculate upper and lower bands using ATR
float cwma_up = cwma + (atr * atr_mult)
float cwma_dn = cwma - (atr * atr_mult)
float src_l = b.calc_src(src_long)
float src_s = b.calc_src(src_short)
// Signal logic for crossovers and crossunders
var int signal = 0
if ta.crossover(src_l, cwma_up)
signal := 1
if ta.crossunder(src_s, cwma_dn)
signal := -1
//}
Backtest Mode and Equity Calculation
To evaluate its effectiveness, the indicator includes a backtest mode, allowing users to test its performance on historical data:
Backtest Equity: A detailed equity curve is calculated based on the generated signals over a user-defined period (startDate to endDate).
Buy and Hold Comparison: Alongside the strategy’s equity, a Buy-and-Hold equity curve is plotted for performance comparison.
Visualization and Alerts
The indicator features customizable plots, allowing users to visualize the CWMA, ATR bands, and signals effectively. The colors change dynamically based on market conditions, with clear distinctions between long and short signals.
Alerts can be configured to notify users of crossover events, providing timely information for potential trading opportunities.
Breakout and Breakdown Indicator with RetestsThis indicator is designed to help traders identify high-probability breakout and breakdown points based on the first 5 minutes of market activity (9:30 am to 9:35 am). It works effectively on both the 1-minute and 5-minute timeframes, making it ideal for day traders and scalpers.
This indicator is a better indicator of my previous 5-Minute Opening Range Breakout indicator.
Key Features:
Dynamic Support and Resistance Lines: Automatically plots the highest and lowest price levels from 9:30 am to 9:35 am, providing essential support and resistance zones.
Breakout/Breakdown Detection: Identifies and marks successful breakout and breakdown points only after a confirmed retest, ensuring more accurate signals.
Visual Markers: Uses customizable green diamonds for successful breakouts and red diamonds for successful breakdowns, allowing easy identification on the chart.
Customization Options:
Change Colors: You can personalize the color of the breakout and breakdown markers, the label text, and the lines drawn from the 9:30 am to 9:35 am window.
Adapt to Your Chart: Adjust the indicator to match your preferred charting theme, ensuring it blends seamlessly with your trading setup.
How It Works:
Plots Key Levels: Identifies the highest and lowest prices during the first 5 minutes of trading (9:30 am to 9:35 am) and plots them on the chart.
Monitors Retests: Waits for a retest of these levels before confirming a breakout or breakdown.
Labels Breakouts/Breakdowns: After a retest, successful breakouts are marked with green diamonds and "Breakout" text, while breakdowns are marked with red diamonds and "Breakdown" text.
Why Use This Indicator?
Avoid False Signals: The retest requirement helps filter out false breakouts and breakdowns, offering more reliable trading signals.
Works Across Timeframes: Suitable for both 1-minute and 5-minute charts, allowing flexibility for different trading styles.
Some what Customizable: Adjust colors to fit your charting preferences and enhance visual clarity.
Recommended Use: Combine this indicator with other technical analysis tools, such as volume, candlestick patterns, or moving averages, for more informed trading decisions.
bar_index inspectorThis is a tool for developers who are working with index based plots and find themselves adding the bar_index to their indicators on a regular basis during development and debugging.
What it does:
shows the bar_index in the status line and data window.
plots optional labels (bar index + time) into the chart every 10 bars
Larry Conners Vix Reversal II Strategy (approx.)This Pine Script™ strategy is a modified version of the original Larry Connors VIX Reversal II Strategy, designed for short-term trading in market indices like the S&P 500. The strategy utilizes the Relative Strength Index (RSI) of the VIX (Volatility Index) to identify potential overbought or oversold market conditions. The logic is based on the assumption that extreme levels of market volatility often precede reversals in price.
How the Strategy Works
The strategy calculates the RSI of the VIX using a 25-period lookback window. The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is often used to identify overbought and oversold conditions in assets.
Overbought Signal: When the RSI of the VIX rises above 61, it signals a potential overbought condition in the market. The strategy looks for a RSI downtick (i.e., when RSI starts to fall after reaching this level) as a trigger to enter a long position.
Oversold Signal: Conversely, when the RSI of the VIX drops below 42, the market is considered oversold. A RSI uptick (i.e., when RSI starts to rise after hitting this level) serves as a signal to enter a short position.
The strategy holds the position for a minimum of 7 days and a maximum of 12 days, after which it exits automatically.
Larry Connors: Background
Larry Connors is a prominent figure in quantitative trading, specializing in short-term market strategies. He is the co-author of several influential books on trading, such as Street Smarts (1995), co-written with Linda Raschke, and How Markets Really Work. Connors' work focuses on developing rules-based systems using volatility indicators like the VIX and oscillators such as RSI to exploit mean-reversion patterns in financial markets.
Risks of the Strategy
While the Larry Connors VIX Reversal II Strategy can capture reversals in volatile market environments, it also carries significant risks:
Over-Optimization: This modified version adjusts RSI levels and holding periods to fit recent market data. If market conditions change, the strategy might no longer be effective, leading to false signals.
Drawdowns in Trending Markets: This is a mean-reversion strategy, designed to profit when markets return to a previous mean. However, in strongly trending markets, especially during extended bull or bear phases, the strategy might generate losses due to early entries or exits.
Volatility Risk: Since this strategy is linked to the VIX, an instrument that reflects market volatility, large spikes in volatility can lead to unexpected, fast-moving market conditions, potentially leading to larger-than-expected losses.
Scientific Literature and Supporting Research
The use of RSI and VIX in trading strategies has been widely discussed in academic research. RSI is one of the most studied momentum oscillators, and numerous studies show that it can capture mean-reversion effects in various markets, including equities and derivatives.
Wong et al. (2003) investigated the effectiveness of technical trading rules such as RSI, finding that it has predictive power in certain market conditions, particularly in mean-reverting markets .
The VIX, often referred to as the “fear index,” reflects market expectations of volatility and has been a focal point in research exploring volatility-based strategies. Whaley (2000) extensively reviewed the predictive power of VIX, noting that extreme VIX readings often correlate with turning points in the stock market .
Modified Version of Original Strategy
This script is a modified version of Larry Connors' original VIX Reversal II strategy. The key differences include:
Adjusted RSI period to 25 (instead of 2 or 4 commonly used in Connors’ other work).
Overbought and oversold levels modified to 61 and 42, respectively.
Specific holding period (7 to 12 days) is predefined to reduce holding risk.
These modifications aim to adapt the strategy to different market environments, potentially enhancing performance under specific volatility conditions. However, as with any system, constant evaluation and testing in live markets are crucial.
References
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Multi-Length RSI **Multi-Length RSI Indicator**
This script creates a custom Relative Strength Index (RSI) indicator with the ability to plot three different RSI lengths on the same chart, allowing traders to analyze momentum across various timeframes simultaneously. The script also includes features to enhance visual clarity and usability.
**Key Features:**
1. **Customizable RSI Lengths:**
- The script allows you to input and customize three different RSI lengths (7, 14, and 28 by default) via user inputs. This flexibility enables you to track short-term, medium-term, and long-term momentum in the market.
2. **Dynamic Colour Coding:**
- The RSI lines are color-coded based on their current value:
- **Above 70 (Overbought)**: The line turns red.
- **Below 30 (Oversold)**: The line turns green.
- **Between 30 and 70**: The line retains its user-defined colour (blue, yellow, orange by default).
- This dynamic colouring helps to quickly identify overbought and oversold conditions.
3. **Adjustable Line Widths and Colours:**
- Users can customize the colour and thickness of each RSI line, allowing for a personalized visual experience that fits different trading strategies.
4. **Overbought, Oversold, and Midline Levels:**
- The script includes static horizontal lines at the 70 (Overbought) and 30 (Oversold) levels, with a red and green colour, respectively.
- A midline at the 50 level is also included in gray and dashed, helping to visualize the neutral zone.
5. **Dynamic RSI Value Labels:**
- The current values of each RSI line are displayed directly on the chart as labels at the most recent bar, with colours matching their corresponding lines. This feature provides an immediate reference to the exact RSI values without the need to hover or look at the data window.
6. **Alerts for Crosses:**
- The script includes built-in alert conditions for when any of the RSI values cross above the overbought level (70) or below the oversold level (30). These alerts can be configured to notify you in real-time when significant momentum shifts occur.
**How to Use:**
1. **Customization**:
- Input your preferred RSI lengths, colours, and line widths through the script’s settings menu.
2. **Visual Analysis**:
- The indicator plots all three RSI values on a separate pane below the price chart. Use the color-coded lines and levels to quickly identify overbought, oversold, and neutral conditions across multiple timeframes.
3. **Set Alerts**:
- You can configure alerts based on the built-in alert conditions to get notified when the RSI crosses critical levels.
**Ideal For:**
- **Traders looking to analyze momentum across multiple timeframes**: The ability to view short-term, medium-term, and long-term RSIs simultaneously offers a comprehensive view of market strength.
- **Those who prefer visual clarity**: The dynamic colouring, clear labels, and customizable settings make it easy to interpret RSI data at a glance.
- **Traders who rely on alerts**: The built-in alert system allows for proactive trading based on significant RSI level crossings.
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This script is a powerful tool for any trader looking to leverage RSI analysis across multiple timeframes, offering both customization and clarity in a single indicator.
Machine Learning Support and Resistance [AlgoAlpha]🚀 Elevate Your Trading with Machine Learning Dynamic Support and Resistance!
The Machine Learning Dynamic Support and Resistance by AlgoAlpha leverages advanced machine learning techniques to identify dynamic support and resistance levels on your chart. This tool is designed to help traders spot key price levels where the market might reverse or stall, enhancing your trading strategy with precise, data-driven insights.
Key Features:
🎯 Dynamic Levels: Continuously adjusts support and resistance levels based on real-time price data using a K-means clustering algorithm.
🧠 Machine Learning: Utilizes clustering methods to optimize the identification of significant price zones.
⏳ Configurable Lookback Periods: Customize the training length and confirmation length for better adaptability to different market conditions.
🎨 Visual Clarity: Clearly distinguish bullish and bearish zones with customizable color schemes.
📉 Trailing and Fixed Levels: Option to display both trailing and fixed support/resistance levels for comprehensive analysis.
🚮 Auto-Cleaning: Automatically removes outdated levels after a specified number of bars to keep your chart clean and relevant.
Quick Guide to Using the Machine Learning Dynamic Support and Resistance Indicator
Maximize your trading with this powerful indicator by following these streamlined steps! 🚀✨
🛠 Add the Indicator: Add the indicator to favorites by pressing the star icon. Customize settings like clustering training length, confirmation length, and whether to show trailing or fixed levels to fit your trading style.
📊 Market Analysis: Monitor the dynamic levels to identify potential reversal points. Use these levels to inform entry and exit points, or to set stop losses.
How It Works
This indicator employs a K-means clustering algorithm to dynamically identify key price levels based on the historical price data within a specified lookback window. It starts by initializing three centroids based on the highest, lowest, and an average between the highest and lowest price over the lookback period. The algorithm then iterates through the price data to cluster the prices around these centroids, dynamically adjusting them until they stabilize, representing potential support and resistance levels. These levels are further confirmed based on a separate confirmation length parameter to identify "fixed" levels, which are then drawn as horizontal lines on the chart. The script continuously updates these levels as new data comes in, while also removing older levels to keep the chart clean and relevant, offering traders a clear and adaptive view of market structure.
ChartArt-Bankniftybuying5minName: ChartArt-BankNifty Buying Strategy (5-Minute)
Timeframe: 5-Minute Candles
Asset: BankNifty (Indian Stock Market Index)
Trading Hours: 9:30 AM - 2:45 PM IST (Indian Standard Time)
This strategy is designed for BankNifty intraday traders who want to capitalize on short-term price movements within a defined trading window. It combines technical indicators like Simple Moving Averages (SMA), Relative Strength Index (RSI), and candlestick patterns to identify potential buy signals during intraday downtrends. The strategy employs specific entry, stop-loss, and target conditions to manage trades effectively and minimize risk.
Technical Indicators Used
Simple Moving Averages (SMA):
EMA7: 7-period SMA on closing price.
EMA5: 5-period SMA on closing price.
Purpose: Used to identify the intraday trend by comparing short-term moving averages. The strategy focuses on situations where the market is in a minor downtrend, indicated by EMA5 being below EMA7.
Relative Strength Index (RSI):
RSI14: 14-period RSI, a momentum oscillator that measures the speed and change of price movements.
SMA14: 14-period SMA of the RSI.
Purpose: RSI is used to identify potential reversal points. The strategy looks for situations where the RSI is below its own moving average, suggesting weakening momentum in the downtrend.
Candlestick Patterns:
Relaxed Hammer or Doji (2nd Candle): A pattern where the second candle in a 3-candle sequence shows a potential reversal signal (Hammer or Doji), indicating indecision or a potential turning point.
Bearish 1st Candle: The first candle is bearish, setting up the context for a potential reversal.
Bullish 3rd Candle: The third candle must be bullish with specific characteristics (closing near the high, surpassing the previous high), confirming the reversal.
Strategy Conditions
Time Condition:
The strategy is only active during specific hours (9:30 AM to 2:45 PM IST). This ensures that trades are only taken during the most liquid hours of the trading day, avoiding potential volatility or lack of liquidity towards market close.
Intraday Downtrend Condition:
EMA5 < EMA7: Indicates that the market is in a minor downtrend. The strategy looks for reversal opportunities within this trend.
RSI Condition:
RSI14 <= SMA14: Indicates that the current RSI value is below its 14-period SMA, suggesting potential weakening momentum, which can precede a reversal.
Candlestick Patterns:
1st Candle: Must be bearish, setting up the context for a potential reversal.
2nd Candle: Must either be a Hammer or Doji, indicating a potential reversal pattern.
3rd Candle: Must be bullish, with specific characteristics (closing near the high, breaking the previous high, etc.), confirming the reversal.
RSI Crossover Condition:
A crossover of the RSI over its SMA in the last 5 periods is also checked, adding further confirmation to the reversal signal.
Entry and Exit Rules
Entry Signal:
A buy signal is generated when all the conditions (time, intraday downtrend, bearish 1st candle, hammer/doji 2nd candle, bullish 3rd candle, and RSI condition) are met. The trade is entered at the high of the bullish third candle.
Stop Loss:
The stop loss is calculated based on the difference between the entry price and the low of the second candle. If this difference is greater than 90 points, the stop loss is placed at the midpoint of the second candle's range (average of high and low). Otherwise, it is placed at the low of the second candle.
Target 1:
The first target is set at 1.8 times the difference between the entry price and the stop loss. When this target is hit, half of the position is exited to lock in partial profits.
Target 2:
The second target is set at 3 times the difference between the entry price and the stop loss. The remaining position is exited at this point, or if the price hits the stop loss.
Originality and Usefulness
This strategy is original in its combination of multiple technical indicators and candlestick patterns to identify potential reversals in a specific intraday timeframe. By focusing on minor downtrends and utilizing a 3-candle reversal pattern, the strategy seeks to capture quick price movements with a structured approach to risk management.
Key Benefits:
High Precision: The strategy’s multi-step filtering process (time condition, trend confirmation, candlestick pattern analysis, and momentum evaluation via RSI) increases the likelihood of accurate trade signals.
Risk Management: The use of a dynamic stop-loss based on candle characteristics, combined with partial profit-taking, allows traders to lock in profits while still giving the trade room to develop further.
Structured Approach: The strategy provides a clear, rule-based system for entering and exiting trades, which can help remove emotional decision-making from the trading process.
Charts and Signals
The strategy produces signals in the form of labels on the chart:
Buy Signal: A green label is plotted below the candle that meets all entry conditions, indicating a potential buy opportunity.
Stop Loss (SL): A red dashed line is drawn at the stop-loss level with a label indicating "SL".
Target 1 (1st TG): A blue dashed line is drawn at the first target level with a label indicating "1st TG".
Target 2 (2nd TG): Another blue dashed line is drawn at the second target level with a label indicating "2nd TG".
These visual aids help traders quickly identify entry points, stop loss levels, and target levels on the chart, making the strategy easy to follow and implement.
Backtesting and Optimization
Backtesting: The strategy can be backtested on TradingView using historical data to evaluate its performance. Traders should consider testing across different market conditions to ensure the strategy's robustness.
Optimization: Parameters such as the RSI period, moving averages, and target multipliers can be optimized based on backtesting results to refine the strategy further.
Conclusion
The ChartArt-BankNifty Buying Strategy offers a well-rounded approach to intraday trading, focusing on capturing reversals in minor downtrends. With a strong emphasis on technical analysis, precise entry and exit rules, and robust risk management, this strategy provides a solid framework for traders looking to engage in intraday trading on BankNifty.
9:20 5 Min Candle Levels with AlertsThe 9:20 AM 5-Minute Candle refers to the candlestick that represents the price action of a financial asset between 9:20 AM and 9:25 AM on a trading day. This candle is observed on a 5-minute chart and captures all the market activity during this specific time window.
Description:
Timeframe: 9:20 AM to 9:25 AM (5-minute interval).
Opening Price: The price at 9:20 AM when the 5-minute period begins.
Closing Price: The price at 9:25 AM when the 5-minute period ends.
High: The highest price achieved during these five minutes.
Low: The lowest price reached during these five minutes.
Body: The distance between the opening and closing prices. A longer body indicates stronger buying or selling pressure, while a shorter body reflects more market indecision.
Wick (Shadow): The lines extending above and below the body, representing the range between the high and low prices during this period. Long wicks suggest higher volatility, while shorter wicks indicate more stable price movements.
Significance:
Bullish Candle: If the closing price is higher than the opening price, it suggests positive momentum and buying interest within this 5-minute period.
Bearish Candle: If the closing price is lower than the opening price, it signals negative momentum and selling pressure.
Market Sentiment: The 9:20 AM 5-minute candle can provide insight into the early sentiment of the market, often influencing the trading strategy for the rest of the day.
Volatility Indicator: The length of the wicks can help traders assess the volatility and potential risk during these five minutes.
This candle is particularly important for day traders and scalpers who rely on short-term price movements to make trading decisions.
[KVA] KMACDKMACD Indicator: Advanced Market Analysis Through Central Tendency Metrics
The KMACD (KAMVIA Moving Average Convergence Divergence) indicator is an advanced, multi-dimensional tool designed to provide traders and analysts with a deeper understanding of market dynamics. By integrating the classical MACD framework with statistical measures of central tendency, KMACD offers a sophisticated approach to identifying trends, reversals, and potential trading opportunities.
Key Features of the KMACD Indicator:
1. Enhanced MACD Calculation :
- The KMACD employs dual moving averages (fast and slow) of user-defined types (SMA, EMA, WMA) to calculate the MACD line, which represents the difference between these moving averages. This traditional approach is further enhanced by customizable signal smoothing, allowing users to fine-tune the sensitivity of the indicator.
2. Central Tendency Metrics :
- The indicator integrates additional statistical measures, such as Mean, Median, Mode, Standard Deviation, and Variance, calculated over a rolling window. These metrics provide insights into the central tendencies of the MACD values, helping traders understand the overall trend direction and the dispersion of price movements around the trend.
3. RSI-Like Oscillator :
- A unique RSI-like value derived from the MACD line is included to highlight overbought and oversold conditions. This offers a dual-layered perspective, combining the power of MACD and RSI methodologies, to signal potential market extremes with greater precision.
4. Customizable Visual Elements :
- KMACD allows users to toggle the visibility of the MACD line, Signal line, and Histogram, providing flexibility in how the data is presented. The histogram dynamically changes color—green when above zero, indicating bullish momentum, and red when below zero, indicating bearish momentum.
5. Horizontal Line Customization :
- The indicator includes customizable horizontal lines for the zero level, overbought, and oversold thresholds. These lines serve as visual cues to identify key price levels and market conditions.
6. Adaptive to Various Market Conditions :
- KMACD's comprehensive features make it adaptable to various market conditions, from trending markets to sideways consolidations. Whether you're looking to capture momentum shifts or identify potential reversal points, KMACD provides the analytical power needed to make informed trading decisions.
How to Use KMACD:
- Trend Identification : Use the MACD line in conjunction with central tendency measures (Mean, Median, Mode) to gauge the overall market trend and its strength. A rising MACD line, supported by higher mean and median values, typically indicates an uptrend.
- Momentum Analysis : The histogram and RSI-like value help in identifying the momentum behind price movements. Positive histogram bars suggest increasing bullish momentum, while negative bars suggest increasing bearish momentum.
- Overbought/Oversold Conditions : Monitor the RSI-like oscillator and the overbought/oversold levels to detect when the market may be poised for a reversal.
- Divergence Detection : Look for divergences between the MACD line and price action, supported by the central tendency measures, to spot potential reversal points.
Conclusion
The KMACD indicator is more than just a traditional MACD; it’s a comprehensive tool designed to cater to both novice and experienced traders. By incorporating central tendency metrics and customizable features, KMACD stands out as a versatile and powerful indicator that enhances market analysis and trading strategies. Whether you're navigating volatile markets or steady trends, KMACD offers the precision and depth needed to stay ahead.
Smoothed Heiken Ashi Strategy Long OnlyThis is a trend-following approach that uses a modified version of Heiken Ashi candles with additional smoothing. Here are the key components and features:
1. Heiken Ashi Modification: The strategy starts by calculating Heiken Ashi candles, which are known for better trend visualization. However, it modifies the traditional Heiken Ashi by using Exponential Moving Averages (EMAs) of the open, high, low, and close prices.
2. Double Smoothing: The strategy applies two layers of smoothing. First, it uses EMAs to calculate the Heiken Ashi values. Then, it applies another EMA to the Heiken Ashi open and close prices. This double smoothing aims to reduce noise and provide clearer trend signals.
3. Long-Only Approach: As the name suggests, this strategy only takes long positions. It doesn't short the market during downtrends but instead exits existing long positions when the sell signal is triggered.
4. Entry and Exit Conditions:
- Entry (Buy): When the smoothed Heiken Ashi candle color changes from red to green (indicating a potential start of an uptrend).
- Exit (Sell): When the smoothed Heiken Ashi candle color changes from green to red (indicating a potential end of an uptrend).
5. Position Sizing: The strategy uses a percentage of equity for position sizing, defaulting to 100% of available equity per trade. This should be tailored to each persons unique approach. Responsible trading would use less than 5% for each trade. The starting capital used is a responsible and conservative $1000, reflecting the average trader.
This strategy aims to provide a smooth, trend-following approach that may be particularly useful in markets with clear, sustained trends. However, it may lag in choppy or ranging markets due to its heavy smoothing. As with any strategy, it's important to thoroughly backtest and forward test before using it with real capital, and to consider using it in conjunction with other analysis tools and risk management techniques.
This has been created mainly to provide data to judge what time frame is most profitable for any single asset, as the volatility of each asset is different. This can bee seen using it on AUXUSD, which has a higher profitable result on the daily time frame, whereas other currencies need a higher or lower time frame. The user can toggle between each time frame and watch for the higher profit results within the strategy tester window.
Other smoothed Heiken Ashi indicators also do not provide buy and sell signals, and only show the change in color to dictate a change in trend. By adding buy and sell signals after the close of the candle in which the candle changes color, alerts can be programmed, which helps this be a more hands off protocol to experiment with. Other smoothed Heiken Ashi indicators do not allow for alarms to be set.
This is a unique HODL strategy which helps identify a change in trend, without the noise of day to day volatility. By switching to a line chart, it removes the candles altogether to avoid even more noise. The goal is to HODL a coin while the color is bullish in an uptrend, but once the indicator gives a sell signal, to sell the holdings back to a stable coin and let the chart ride down. Once the chart gives the next buy signal, use that same capital to buy back into the asset. In essence this removes potential losses, and helps buy back in cheaper, gaining more quantitity fo the asset, and therefore reducing your average initial buy in price.
Most HODL strategies ride the price up, miss selling at the top, then riding the price back down in anticipation that it will go back up to sell. This strategy will not hit the absolute tops, but it will greatly reduce potential losses.
Bitcoin Production CostFirst inspired by the amazing @capriole_charles, I decided to create my own version of calculating the Bitcoin production cost and to share it with you guys.
One of the main difference is the electricity cost calculation. I used a country-specific input system that calculates the weighted electricity cost leveraged by the distribution of the Bitcoin network hashrate. I like the fact that it requires little updating although it is less realistic for past calculations (further in the past production costs seems too low).
How to use:
- Add the indicator to your chart.
- Adjust the inputs if needed. Update the percentage of Bitcoin network Hashrate or electricity Cost per countries. Update the mining hardware stats to the most recent hardware. For example I used a Bitcoin Miner S21 Pro stats.
- Check the multiple variables in the data window.
- Turn on/off the halving event in the style tab
ICT Premium/DiscountThis script indicator prints lines for the highest, lowest and middle price in a selected time period (in days).
With that you can easily see wheter the price is currently high, low or balanced compared to the prices in the selected time period.
I also added a gray dotted vertical line to the chart which represents the beginning of your selected time period
You can choose the time period on your own and you can also customize the color and style of the lines.
Your lines may get printed in a separate window. To fix this, click on the indicator and select
Move to -> existing pane above
Your lines also may stay stuck on the same place on the chart and are not fixed to a high/low. To fix this, right-click on the left price scale and select
Merge all scales into one -> on the right
