Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
ボラティリティ
Likelihood of Winning - Probability Density FunctionIn developing the "Likelihood of Winning - Probability Density Function (PDF)" indicator, my aim was to offer traders a statistical tool to quantify the probability of reaching target prices. This indicator, grounded in risk assessment principles, enables users to analyze potential outcomes based on the normal distribution, providing insights into market dynamics.
The tool's flexibility allows for customization of the data series, lookback periods, and target settings for both long and short scenarios. It features a color-coded visualization to easily distinguish between probabilities of hitting specified targets, enhancing decision-making in trading strategies.
I'm excited to share this indicator with the trading community, hoping it will enhance data-driven decision-making and offer a deeper understanding of market risks and opportunities. My goal is to continuously improve this tool based on user feedback and market evolution, contributing to more informed trading practices.
This indicator leverages the "NormalDistributionFunctions" library, enabling easy integration into other indicators or strategies. Users can readily embed advanced statistical analysis into their trading tools, fostering innovation within the Pine Script community.
Bandwidth Bands - Silverman's rule of thumbWhat are Bandwidth Bands?
This indicator uses Silverman Rule of Thumb Bandwidth to estimate the width of bands around the rolling moving average which takes in the log transformation of price to remove most of price skewness for the rest of the volatility calculations and then a exp() function is performed to convert it back to a right skewed distribution. These bandwidths bands could offer insights into price volatility and trading extremes.
Silverman rule of thumb bandwidth:
The Silverman Rule of Thumb Bandwidth is a heuristic method used to estimate the optimal bandwidth for kernel density estimation, a statistical technique for estimating the probability density function of a random variable. In the context of financial analysis, such as in this indicator, it helps determine the width of bands around a moving average, providing insights into the level of volatility in the market. This method is particularly useful because it offers a quick and straightforward way to estimate bandwidth without requiring extensive computational resources or complex mathematical calculation
The bandwidth estimator automatically adjust to the characteristics of the data, providing a flexible and dynamic measure of dispersion that can capture variations in volatility over time. Standard deviations alone may not be as adaptive to changes in data distributions. The Bandwidth considers the overall shape and structure of the data distribution rather than just focusing on the spread of data points.
Settings
Source
Sample length
1-4 SD options to disable or enable each band
Rate of Change RSIIndicator Name: Rate of Change RSI
Description:
The Rate of Change (ROC) of the Relative Strength Index (RSI) is a technical indicator designed to provide insights into the momentum of an asset's price movement. It combines the Relative Strength Index (RSI), a popular momentum oscillator, with the Rate of Change (ROC) concept to assess the speed at which RSI values are changing.
How It Works:
Relative Strength Index (RSI): The RSI measures the magnitude of recent price changes to evaluate overbought or oversold conditions in an asset. It oscillates between 0 and 100, with readings above 70 typically indicating overbought conditions and readings below 30 indicating oversold conditions.
Rate of Change (ROC): The ROC calculates the percentage change in a given indicator over a specified period. In this indicator, we apply the ROC to the RSI values to determine how quickly the RSI is changing over time.
Key Features:
Acceleration and Deceleration: The ROC of RSI helps traders identify whether the momentum of the RSI is accelerating or decelerating. Positive values suggest increasing momentum, while negative values indicate decreasing momentum.
Dynamic Color Change: The color of the ROC RSI line changes dynamically based on the RSI level. When the RSI is between 0 and 40, the line color is blue, indicating potential oversold conditions. When the RSI is between 40 and 60, the line color is yellow, suggesting neutral conditions. When the RSI is above 60, the line color changes to green, indicating potential overbought conditions.
How to Use:
Acceleration: When the ROC RSI is positive and increasing while the RSI is above 60 (green), it may signal strong upward momentum.
Deceleration: Conversely, if the ROC RSI is negative and decreasing while the RSI is below 40 (blue), it may indicate weakening downward momentum.
Originality and Usefulness:
This indicator combines the RSI, a well-known momentum oscillator, with the ROC concept to provide a unique perspective on momentum dynamics. By dynamically adjusting the color of the ROC RSI line based on RSI levels, traders can quickly assess potential overbought or oversold conditions in the market.
Chart:
The chart displayed alongside this script provides a clean and easy-to-understand visualization of the ROC RSI indicator. The ROC RSI line color changes dynamically based on RSI levels, allowing traders to visually identify potential market conditions at a glance.
VWAP Bands @shrilssVWAP Bands Integrates VWAP with standard deviation bands to provide traders with insights into potential support and resistance levels based on volume dynamics. VWAP is a key metric used by institutional traders to gauge the average price a security has traded at throughout the trading day, taking into account both price and volume.
This script calculates the VWAP for each trading session and overlays it on the price chart as a solid line. Additionally, it plots multiple standard deviation bands around the VWAP to indicate potential areas of price extension or contraction. These bands are derived from multiplying the standard deviation of price by predetermined factors, offering traders a visual representation of potential price ranges.
Scalper's Volatility Filter [QuantraSystems]Scalpers Volatility Filter
Introduction
The 𝒮𝒸𝒶𝓁𝓅𝑒𝓇'𝓈 𝒱𝑜𝓁𝒶𝓉𝒾𝓁𝒾𝓉𝓎 𝐹𝒾𝓁𝓉𝑒𝓇 (𝒮𝒱𝐹) is a sophisticated technical indicator, designed to increase the profitability of lower timeframe trading.
Due to the inherent decrease in the signal-to-noise ratio when trading on lower timeframes, it is critical to develop analysis methods to inform traders of the optimal market periods to trade - and more importantly, when you shouldn’t trade.
The 𝒮𝒱𝐹 uses a blend of volatility and momentum measurements, to signal the dominant market condition - trending or ranging.
Legend
The 𝒮𝒱𝐹 consists of a signal line that moves above and below a central zero line, serving as the indication of market regime.
When the signal line is positioned above zero, it indicates a period of elevated volatility. These periods are more profitable for trading, as an asset will experience larger price swings, and by design, trend-following indicators will give less false signals.
Conversely, when the signal line moves below zero, a low volatility or mean-reverting market regime dominates.
This distinction is critical for traders in order to align strategies with the prevailing market behaviors - leveraging trends in volatile markets and exercising caution or implementing mean-reversion systems in periods of lower volatility.
Case Study
Here we can see the indicator's unique edge in action.
Out of the four potential long entries seen on the chart - displayed via bar coloring, two would result in losses.
However, with the power of the 𝒮𝒱𝐹 a trader can effectively filter false signals by only entering momentum-trades when the signal line is above zero.
In this small sample of four trades, the 𝒮𝒱𝐹 increased the win rate from 50% to 100%
Methodology
The methodology behind the 𝒮𝒱𝐹 is based upon three components:
By calculating and contrasting two ATR’s, the immediate market momentum relative to the broader, established trend is calculated. The original method for this can be credited to the user @xinolia
A modified and smoothed ADX indicator is calculated to further assess the strength and sustainability of trends.
The ‘Linear Regression Dispersion’ measures price deviations from a fitted regression line, adding further confluence to the signals representation of market conditions.
Together, these components synthesize a robust, balanced view of market conditions, enabling traders to help align strategies with the prevailing market environment, in order to potentially increase expected value and win rates.
RSI Volatility Bands [QuantraSystems]RSI Volatility Bands
Introduction
The RSI Volatility Bands indicator introduces a unique approach to market analysis by combining the traditional Relative Strength Index (RSI) with dynamic, volatility adjusted deviation bands. It is designed to provide a highly customizable method of trend analysis, enabling investors to analyze potential entry and exit points in a new and profound way.
The deviation bands are calculated and drawn in a manner which allows investors to view them as areas of dynamic support and resistance.
Legend
Upper and Lower Bands - A dynamic plot of the volatility-adjusted range around the current price.
Signals - Generated when the RSI volatility bands indicate a trend shift.
Case Study
The chart highlights the occurrence of false signals, emphasizing the need for caution when the bands are contracted and market volatility is low.
Juxtaposing this, during volatile market phases as shown, the indicator can effectively adapt to strong trends. This keeps an investor in a position even through a minor drawdown in order to exploit the entire price movement.
Recommended Settings
The RSI Volatility Bands are highly customisable and can be adapted to many assets with diverse behaviors.
The calibrations used in the above screenshots are as follows:
Source = close
RSI Length = 8
RSI Smoothing MA = DEMA
Bandwidth Type = DEMA
Bandwidth Length = 24
Bandwidth Smooth = 25
Methodology
The indicator first calculates the RSI of the price data, and applies a custom moving average.
The deviation bands are then calculated based upon the absolute difference between the RSI and its moving average - providing a unique volatility insight.
The deviation bands are then adjusted with another smoothing function, providing clear visuals of the RSI’s trend within a volatility-adjusted context.
rsiVal = ta.rsi(close, rsiLength)
rsiEma = ma(rsiMA, rsiVal, bandLength)
bandwidth = ma(bandMA, math.abs(rsiVal - rsiEma), bandLength)
upperBand = ma(bandMA, rsiEma + bandwidth, smooth)
lowerBand = ma(bandMA, rsiEma - bandwidth, smooth)
long = upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50)
short= not (upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50))
By dynamically adjusting to market conditions, the RSI trend bands offer a unique perspective on market trends, and reversal zones.
Truncated ATRThis program uses SMA and Truncated Mean to calculate ATR.
A truncated mean or trimmed mean is a statistical measure of central tendency, much like the mean and median. It involves the calculation of the mean after discarding given parts of a probability distribution or sample at the high and low end, and typically discarding an equal amount of both.
TASC 2024.03 Rate of Directional Change█ OVERVIEW
This script implements the Rate of Directional Change (RODC) indicator introduced by Richard Poster in the "Taming The Effects Of Whipsaw" article featured in the March 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
In his article, Richard Poster discusses an approach to potentially reduce false trend-following strategy entry signals due to whipsaws in forex data. The RODC indicator is central to this approach. The idea behind RODC is that one can characterize market whipsaw as alternating up and down ZigZag segments. By counting the number of up and down segments within a lookback window, the RODC indicator aims to identify if the window contains a significant whipsaw pattern:
RODC = 100 * Segments / Window Size (bars)
Larger RODC values suggest elevated whipsaw in the calculation window, while smaller values signify trending price activity.
█ CALCULATIONS
• For each price bar, the script iterates through the lookback window to identify up and down segments.
• If the price change between subsequent bars within the window is in the direction opposite to the current segment and exceeds the specified threshold , the calculation interprets the condition as a reversal point and the start of a new segment.
• The script uses the number of segments within the window to calculate RODC according to the above formula.
• Finally, the script applies a simple moving average to smoothen the RODC data.
Users can change the length of the lookback window , the threshold value, and the smoothing length in the "Inputs" tab of the script's settings.
F.B_Consolidation Range Identifier
The "F.B_Consolidation Range Identifier" (F.B_CRI) is an indicator aimed at identifying consolidation areas in the price chart. Here is an explanation of the logic and usage of this indicator:
Calculation of Standard Deviation
This indicator analyzes the market's volatility by considering the standard deviation of price movements over a defined period. A higher standard deviation indicates larger price movement, while a lower standard deviation suggests potential consolidation, where price movements are limited.
Derivation of Standard Deviation
To track changes in volatility, the derivative of the standard deviation is calculated. Positive derivative values indicate increasing volatility, while negative values suggest a decrease in volatility. This allows for the identification of potential consolidation phases where volatility decreases, and the market may stabilize.
Identification of Consolidation Phase
The indicator signals potential consolidation phases when the standard deviation is low and/or the derivative of the standard deviation is negative. To represent consolidation phases on the chart, the standard deviation line, background, and candles are colored red. However, it's important to note that the display is customizable and can be configured according to individual needs.
🚨 Important 🚨
The indicator only indicates whether consolidation phases exist. If the standard deviation line, background, or candles are gray, it indicates that a trend exists in general, but not whether it is bullish or bearish. It is advisable to use other analytical tools to confirm the direction of the trend.
Supertrend & CCI Strategy ScalpThis strategy is based on 2 Super Trend Indicators along with CCI .
The longer factor length gives you the current trend and the deviation in the short factor length gives us the opportunity to enter in the trade .
CCI indicator is used to determine the overbought and oversold levels.
Setup :
Long : When atrLength1 > close and atrLength2 < close and CCI < -100 we look for long trades as the longer factor length will be bullish .
Short : When atrLength1 < close and atrLength2 > close and CCI > 100 we look for short trades as the longer factor length will be bearish .
Please tune the settings according to your use .
Trade what you see not what you feel .
Please consult with your financial advisor before you deploy any real money for trading .
Pivot Extremes BreakoutI created the "Pivot Extremes Breakout" (PEB) indicator to easily spot breakout zones using pivot points. This tool comes from my need to anticipate market direction and capitalize on breakouts. PEB uses the last two pivot points to predict price paths and highlights potential breakout areas, adjusting for any timeframe. It simplifies seeing where the market might move next with color-coded lines and zones, aiming to improve your trading decisions.
F.B_DI+ DI- Trend TrackerThe F.B_DI+ DI- Trend Tracker is an indicator developed based on Directional Movement and True Range to identify trends in the market and assess their strength. Here is the logic behind the indicator and how to use it for trading:
Direction Determination
Unlike traditional DI+ and DI- based on simple calculations, this indicator utilizes derived versions of these directional indicators. These derived versions offer a more precise measurement of price directional movements by specifically tailoring to market conditions and the chosen time frame.
Trend Strength
The derived directional indicators are generated by dividing smoothed Directional Movements by smoothed True Range and converting them into percentages. These values provide insights into the strength of the trend by considering directional movements relative to market volatility.
Identifying Trend Reversals
To capture changes in trend strength more accurately, the first derivative of DI+ and DI- is computed. A crossover of these derived versions could indicate a potential trend reversal, with a crossover of DI+ over DI- suggesting a possible uptrend and a crossover of DI- over DI+ indicating a potential downtrend.
Making Trading Decisions
Traders can use crossovers between the derived DI+ and DI- to receive signals for potential trend reversals. Additionally, changes in the color of candlesticks or background can be used as confirmation of the trend direction and its strength.
By utilizing these derived directional indicators, the F.B_DI+ DI- Trend Tracker indicator offers more precise trading signals and improved trend analysis, enabling traders to make informed trading decisions.
Volatility Adjusted Profit Target
In my 'Volatility Adjusted Profit Target' indicator, I've crafted a dynamic tool for calculating target profit percentages suitable for both long and short trading strategies. It evaluates the highest and lowest prices over the anticipated duration of your trade, establishing a profit target that shifts with market volatility. As volatility increases, the potential for profit follows, with the target percentage rising accordingly; conversely, it declines with decreasing volatility. As a trader, setting an optimal Take Profit level has always been a challenge. This indicator not only helps in determining that level but also dynamically adjusts it throughout the trade's duration, providing a strategic edge in volatile markets.
Implied Volatility and Historical VolatilityThis indicator provides a visualization of two different volatility measures, aiding in understanding market perceptions and actual price movements. Remember to combine it with other technical analysis tools and risk management strategies for informed trading decisions. The two measures of volatility:
Implied Volatility: Based on the standard deviation of recent price changes, it represents the market's expectation of future volatility.
Historical Volatility: Measured by the daily high-low range as a percentage of the closing price, it reflects the actual volatility experienced recently. It is intended to be used along side the Mean and Standard Deviation Lines indicator.
Inputs:
Period (Days): Defines the number of past bars used to calculate both types of volatility.
Calculations:
Interpretation:
Comparing the lines: Divergence between the lines can indicate potential mispricing:
If the Implied Volatility is higher than the Historical Volatility, the market might be overestimating future volatility.
Conversely, if the Implied Volatility is lower, the market might be underestimating future volatility.
Monitoring trends: Track changes in both lines over time to identify potential shifts in volatility expectations or actual market behavior.
Limitations:
Assumes normality in price distribution, which may not always hold true.
Historical Volatility only reflects past behavior, not future expectations.
Consider other factors like market sentiment and news events for comprehensive volatility analysis.
Trend Continuation Signals [AlgoAlpha]Introducing the Trend Continuation Signals by AlgoAlpha 🌟🚀
Elevate your trading game with this multipurpose indicator, designed to pinpoint trend continuation opportunities as well as highlight volatility and oversold/overbought conditions. Whether you're a trading novice or a seasoned market veteran, this tool offers intuitive visual cues to boost your decision-making and enhance your market analysis. Let's explore the key features, how to use it effectively, and delve into the operational mechanics that make this tool a game-changer in your trading arsenal:
Key Features:
🔥 Advanced Trend Detection : Leverages the Hull Moving Average (HMA) for superior trend tracking as compared to other MAs, offering unique insights into market momentum.
🌈 Volatility Bands : Implements adjustable bands around the trend line, which evolve with market conditions to highlight potential trading opportunities.
⚡ Trend Continuation Signals : Identifies bullish and bearish continuation signals, equipping you with actionable signals to exploit the prevailing market trend.
🎨 Intuitive Color Coding : Employs a vibrant color scheme to distinguish between uptrends, downtrends, and neutral phases, facilitating easy interpretation of the indicator's insights.
🛠 How to Use "Trend Continuation Signals ":
🔍 Setting Up : Incorporate the indicator onto your chart and customize the indicator to suite your preferences.
👀 Reading the Signals : Pay attention to the color-coded trend lines and volatility bands. Green indicates an uptrend, red signifies a downtrend, and gray denotes a neutral market condition.
📈 Identifying Entry Points : Look for bullish (▲) and bearish (▼) continuation icons below or above the price bars as signals for potential entry points for long or short positions, respectively.
🔄 Confirmation : Validate your trades with further analysis or other indicators. The Trend Continuation Signals are most effective when complemented by other technical analysis tools or fundamental insights.
📉 Risk Management : Implement stop-loss orders in line with your risk appetite and adjust them based on the volatility bands provided by the indicator to safeguard your investments.
How It Operates:
The essence of the indicator is captured through the hull moving averages for both the primary and secondary lines, set at periods of 93 and 50, respectively, to reflect market trends and pullbacks that trigger the continuation signals every time price recovers from a detected pullback.
Volatility is quantified through the standard deviation of the midline, magnified by a factor, establishing the upper and lower trend band boundaries.
Further volatility bands are plotted around the main volatility band, providing a granular view of market volatility and potential breakout or breakdown zones.
Market trend direction is determined by comparing the HMA line's current position to its previous value, enhanced by the secondary line to identify continuation patterns.
Embrace the power of the Trend Continuation Signals to enhance your trading strategy! It is important to note that all indicators are best used in confluence with other forms of analysis, happy trading! 📊💥
Candle + Volume as HistogramNow plot Candle and Volume as Histogram Oscillator. It is a very simple but very effective indicator to see what is happening at Volume and Candle levels. It clearly plots whether the market is trending, volatile, what is going to happen in next candles etc. Above Zero is price candle and below Zero is Volume histogram.
SETTINGS:
EMA : 9 period EMA on price is default, which I find the best, but you can change as you likes. Below zero line shows market is in negative phase and above zero market is positive.
PRICE CANDLE : As price and volume have different numeric value, so you have to use multiplier to set the proper price candle, which is plotted above zero
VOLUME : As price and volume have different numeric value, so you have to use multiplier to set the Volume candle, which is plotted below zero.
ZERO LINE : use ‘Gap between Candle and Volume plotting’. Increase the value until you see a thin line between price and volume histogram.
NOTE: (i) When you will plot the indicator for the first time, either price candle may not be appearing properly or volume. It means that non-appearing histogram side needs multiplication, so keep on increasing value on proper side until both side histogram appears properly.
(ii) In other case. Price or volume histogram may appear as full Candle. In this case too, you need to increase multiplier value on opposite side till value becomes equal on both sides.
These settings are important because a share price can be 10 only whereas volume may be in millions. Indicator has been tested on many shares and indexes, so never feels that indicator is improper. Just try to change the setting.
Enjoy.
PresentTrend RMI Synergy - Strategy [presentTrading] █ Introduction and How it is Different
The "PresentTrend RMI Synergy Strategy" is the combined power of the Relative Momentum Index (RMI) and a custom presentTrend indicator. This strategy introduces a multifaceted approach, integrating momentum analysis with trend direction to offer traders a more nuanced and responsive trading mechanism.
BTCUSD 6h L/S Performance
Local
█ Strategy, How It Works: Detailed Explanation
The "PresentTrend RMI Synergy Strategy" intricately combines the Relative Momentum Index (RMI) and a custom SuperTrend indicator to create a powerful tool for traders.
🔶 Relative Momentum Index (RMI)
The RMI is a variation of the Relative Strength Index (RSI), but instead of using price closes against itself, it measures the momentum of up and down movements in price relative to previous prices over a given period. The RMI for a period length `N` is calculated as follows:
RMI = 100 - 100/ (1 + U/D)
where:
- `U` is the average upward price change over `N` periods,
- `D` is the average downward price change over `N` periods.
The RMI oscillates between 0 and 100, with higher values indicating stronger upward momentum and lower values suggesting stronger downward momentum.
RMI = 21
RMI = 42
For more information - RMI Trend Sync - Strategy :
🔶 presentTrend Indicator
The presentTrend indicator combines the Average True Range (ATR) with a moving average to determine trend direction and dynamic support or resistance levels. The presentTrend for a period length `M` and a multiplier `F` is defined as:
- Upper Band: MA + (ATR x F)
- Lower Band: MA - (ATR x F)
where:
- `MA` is the moving average of the close price over `M` periods,
- `ATR` is the Average True Range over the same period,
- `F` is the multiplier to adjust the sensitivity.
The trend direction switches when the price crosses the presentTrend bands, signaling potential entry or exit points.
presentTrend length = 3
presentTrend length = 10
For more information - PresentTrend - Strategy :
🔶 Strategy Logic
Entry Conditions:
- Long Entry: Triggered when the RMI exceeds a threshold, say 60, indicating a strong bullish momentum, and when the price is above the presentTrend, confirming an uptrend.
- Short Entry: Occurs when the RMI drops below a threshold, say 40, showing strong bearish momentum, and the price is below the present trend, indicating a downtrend.
Exit Conditions with Dynamic Trailing Stop:
- Long Exit: Initiated when the price crosses below the lower presentTrend band or when the RMI falls back towards a neutral level, suggesting a weakening of the bullish momentum.
- Short Exit: Executed when the price crosses above the upper presentTrend band or when the RMI rises towards a neutral level, indicating a reduction in bearish momentum.
Equations for Dynamic Trailing Stop:
- For Long Positions: The exit price is set at the lower SuperTrend band once the entry condition is met.
- For Short Positions: The exit price is determined by the upper SuperTrend band post-entry.
These dynamic trailing stops adjust as the market moves, providing a method to lock in profits while allowing room for the position to grow.
This strategy's strength lies in its dual analysis approach, leveraging RMI for momentum insights and presentTrend for trend direction and dynamic stops. This combination offers traders a robust framework to navigate various market conditions, aiming to capture trends early and exit positions strategically to maximize gains and minimize losses.
█ Trade Direction
The strategy provides flexibility in trade direction selection, offering "Long," "Short," or "Both" options to cater to different market conditions and trader preferences. This adaptability ensures that traders can align the strategy with their market outlook, risk tolerance, and trading goals.
█ Usage
To utilize the "PresentTrend RMI Synergy Strategy," traders should input their preferred settings in the Pine Script™ and apply the strategy to their charts. Monitoring RMI for momentum shifts and adjusting positions based on SuperTrend signals can optimize entry and exit points, enhancing potential returns while managing risk.
█ Default Settings
1. RMI Length: 21
The 21-period RMI length strikes a balance between capturing momentum and filtering out market noise, offering a medium-term outlook on market trends.
2. Super Trend Length: 7
A SuperTrend length of 7 periods is chosen for its responsiveness to price movements, providing a dynamic framework for trend identification without excessive sensitivity.
3. Super Trend Multiplier: 4.0
The multiplier of 4.0 for the SuperTrend indicator widens the trend bands, focusing on significant market moves and reducing the impact of minor fluctuations.
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The "PresentTrend RMI Synergy Strategy" represents a significant step forward in trading strategy development, blending momentum and trend analysis in a unique way. By providing a detailed framework for understanding market dynamics, this strategy empowers traders to make more informed decisions.
Gap Removal IndicatorThis gap indicator shows the price of your chosen instrument as if no gaps had occurred overnight. It can be especially useful on highly-volatile exchange-listed instruments that track other 24/7 assets, because the normal candlestick chart of these instruments will create a large amount of noise that may decrease the accuracy of your indicators or make the trend harder to see.
Gaps are determined with the following code:
daychange = ta.change(dayofmonth)
gapup = daychange and open > math.max(open,close)
gapdown = daychange and open < math.min(open,close)
Whereas the gap value is determined by taking the overnight difference in prices:
downgap_change = math.min(open,close) - open
upgap_change = open - math.max(open,close)
The gap changes are cumulatively added and subtracted from the initial closing price to create the gap-adjusted price. The price will depend on how many bars your subscription allows, so pay more attention to the relative differences and/or trend than the cumulative gap-adjusted price itself.
The gap indicator comes pre-built with normal candlestick and Heikin-Ashi candle types, and four indicators (two EMAs, Bollinger bands, and a supertrend). All elements are configurable.
Vo-S-Di-T-I - Volatility Scaled Directional Trend IndicatorThis code represents just the foundation for what's to come. It lays the groundwork for a more sophisticated quant trading model, offering a glimpse into the potential of future developments. I hope my contribution to this community will be valued. I'm here for idea exchanges and coding together, with the key emphasis on ensuring everything we do is grounded on a solid statistical basis.
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The developed code is based on a rigorous quantitative approach for analyzing price trends in the equity sector, utilizing advanced statistical methodology to scale returns based on the volatility observed over predefined periods of 20 and 50 days. This technique for normalizing returns allows us to eliminate distortions due to the intrinsic variability of prices and focus on the underlying structure of price behavior. The primary goal of the code is not to speculatively predict future market movements but rather to identify potential reversal trend signals through price dynamics analysis, within an optimized risk and return context.
Our approach is distinguished by the use of statistical decomposition techniques and time series analysis to interpret price variations as indicators of possible shifts in market behavior. This allows distinguishing between random or short-term price movements and true trend changes, providing a solid foundation for more informed investment decisions.
The current code represents the initial phase of a broader project that envisages the integration of machine learning algorithms to further refine the ability to detect significant changes in price trends. Through the application of predictive models and machine learning techniques, we intend to explore complex patterns in historical price data that may precede trend reversals, always respecting the principles of rigorous statistical analysis and risk management. This development and learning path will allow us to continuously improve investment strategies, leveraging the analytical capabilities of modern data science algorithms applied to the financial sector.
HOW TO READ
Simply put, Z values above 0 indicate an uptrend, while values below indicate a downtrend. IMPORTANT: It is not necessary to consider any crosses between Z-Short and Z-Long, but only potential crosses with 0.
The initial values are set at 20 and 50, but everyone is free to choose the most suitable periods, as long as all choices have valid statistical significance. My advice is to use R or MatLab to explore the best correlation between N and price movements. The reason I have set two values for N (Short and Long) is because it's interesting to assess short-term and medium-to-long-term trends to understand if price movements can lead to reversals only in the short term or also in the medium to long term. This idea came to me because I believe all other trend determination systems have too much lag and unpredictability.
Hamilton - Wick Length PredictionWick Length Prediction is a Pine Script indicator crafted to empower traders by predicting the potential length and direction of the next candle's wick based on historical price action. By analyzing previous candles' wick sizes, this tool provides valuable foresight into future price dynamics, enhancing decision-making for traders.
Key Features:
Wick Percentage Analysis : Calculates the percentages of the upper and lower wicks from the previous candle relative to its total range, offering a predictive insight into the next wick’s potential direction and size.
Directional Bias Indicator : Identifies the longer wick between the previous candle's upper and lower wicks to suggest a directional bias—green indicates an upward prediction, while red suggests downward.
Targeted Plotting : Marks a horizontal line at the anticipated wick position for the forthcoming candle, aiding traders in identifying potential price rejection zones ahead of time.
Strategic Insights for Traders:
Understanding Market Pressure : Recognizes that wicks typically indicate pressure in the opposite direction of their occurrence, presenting potential targets for price movement towards the opposite side. This insight is invaluable for identifying reversal zones or continuation patterns.
Optimal for Scalping : Especially beneficial for scalpers, this tool helps in pinpointing precise entry and exit points by forecasting where the price might face opposition and potentially reverse or absorb the wick.
Timeframe Flexibility : While best suited for higher timeframes, it also delivers on lower timeframes during aggressive market movements, making it a versatile addition to your trading arsenal.
Application Tips :
Leverage in combination with other indicators and support/resistance levels to refine your trading strategy.
Ideal for enhancing price action analysis, providing a clearer understanding of potential market movements.
Use as a strategic complement to your existing approach, mindful of its predictive nature to inform better trading decisions.
Disclaimer: Trading involves significant risk. This tool aims to support a diversified trading strategy, but it's crucial to perform your own analysis and adopt appropriate risk management practices.
Dynamic Price Targets @shrilssDynamic Price Targets is a designed to provide traders with a comprehensive view of dynamic price levels based on Volume Weighted Moving Average (VWMA) and standard deviation. This script allows users to identify potential support and resistance zones, aiding in strategic decision-making during market analysis.
The script calculates the VWMA of a chosen price source over a specified length, establishing a dynamic baseline for market trends. The standard deviation is then used to derive multiple upper and lower targets, each representing a certain deviation from the VWMA. These levels are color-coded for clarity, with upper targets displayed in shades of red and lower targets in shades of green.
SAT - Sentiment Analysis Tool by Csth97This code calculates the correlation between the ATR and the OBV of the price, and plots the difference between the positive and negative OBV correlations as a line. It also plots horizontal lines at the UP and DOWN levels, and signals bullish and bearish crosses when the difference line crosses those levels.
