EMA GridThe EMA Grid indicator is a powerful tool that calculates the overall market sentiment by comparing the order of 20 different Exponential Moving Averages (EMAs) over various lengths. The indicator assigns a rating based on how well-ordered the EMAs are relative to each other, representing the strength and direction of the market trend. It also smooths out the macro movements using cumulative calculations and visually represents the market sentiment through color-coded bands.
EMA Calculation:
The indicator uses a series of EMAs with different lengths, starting from 5 and going up to 100. Each EMA is calculated either using the exponential moving averages.
The EMAs form the grid that the indicator uses to measure the order and distance between them.
Rating Calculation:
The indicator computes the relative distance between consecutive EMAs and sums these differences.
The cumulative sum is further smoothed using multiple EMAs with different lengths (from 3 to 21). This smooths out short-term fluctuations and helps identify broader trends.
Market Sentiment Rating:
The overall sentiment is calculated by comparing the values of these smoothing EMAs. If the shorter-term EMA is above the longer-term EMA, it contributes positively to the sentiment; otherwise, it contributes negatively.
The final rating is a normalized value based on the relationship between these EMAs, producing a sentiment score between 1 (bullish) and -1 (bearish).
Color Coding and Bands:
The indicator uses the sentiment rating to color the space between the 100 EMA and 200 EMA, representing the strength of the trend.
If the sentiment is bullish (rating > 0), the band is shaded green. If the sentiment is bearish (rating < 0), the band is shaded red.
The intensity of the color is based on the strength of the sentiment, with stronger trends resulting in more saturated colors.
Utility for Traders:
The EMA Grid is ideal for traders looking to gauge the broader market trend by analyzing the structure and alignment of multiple EMAs. The color-coded band between the 100 and 200 EMAs provides an at-a-glance view of market momentum, helping traders make informed decisions based on the trend's strength and direction.
This indicator can be used to identify bullish or bearish conditions and offers a smoothed perspective on market trends, reducing noise and highlighting significant trend shifts.
Bands and Channels
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
MTF SqzMom [tradeviZion]Credits:
John Carter for creating the TTM Squeeze and TTM Squeeze Pro.
Lazybear for the original interpretation of the TTM Squeeze: Squeeze Momentum Indicator.
Makit0 for evolving Lazybear's script by incorporating TTM Squeeze Pro upgrades – Squeeze PRO Arrows.
MTF SqzMom - Multi-Timeframe Squeeze & Momentum Tool
MTF SqzMom is a tool designed to help traders easily monitor squeeze and momentum signals across multiple timeframes in a simple, organized format. Built using Pine Script 5, it ensures that data remains consistent, even when switching between different time intervals on the chart.
Key Features:
Multi-Timeframe Monitoring: Track squeeze and momentum signals across various timeframes, all in one view. This includes key timeframes like 1-minute, 5-minute, hourly, and daily.
Dynamic Table Display: A color-coded table that automatically adjusts based on the selected timeframes, offering a clear view of market conditions.
Alerts for Key Market Events: Get notifications when a squeeze starts or fires across your chosen timeframes, so you can stay informed without needing to monitor the chart continuously.
Customizable Appearance: Tailor the look of the table by selecting colors for squeeze levels and momentum shifts, and choose the best position on your chart for easy access.
How It Works:
MTF SqzMom is based on the concept of the squeeze, which signals periods of lower volatility where price breakouts may occur. The tool tracks this by monitoring the contraction of Bollinger Bands within Keltner Channels. Along with this, it provides momentum analysis to help you gauge the potential direction of the market after a squeeze.
Squeeze Conditions: The script tracks four levels of squeeze conditions (no squeeze, low, mid, and high), each represented by a different color in the table.
Momentum Analysis: Momentum is visually represented by colors indicating four stages: up increasing, up decreasing, down increasing, and down decreasing. This color coding helps you quickly assess whether the market is gaining or losing momentum.
Using Alerts:
You can enable two types of alerts: when a squeeze starts (indicating consolidation) and when a squeeze fires (indicating a breakout). These alerts cover all timeframes you’ve selected, so you never miss important signals.
How to Set It Up:
1. Enable Alerts in Settings: Turn on "Alert for Squeeze Start" and "Alert for Squeeze Fire" in the settings.
2. Add Alerts to Your Chart:
Click the three dots next to the indicator name.
Select "Add alert on tradeviZion - MTF SqzMom."
3. Customize and Save: Adjust alert options, choose your notification type, and click "Create."
Why Use MTF SqzMom ?
Consistent Data: The tool ensures that squeeze and momentum data remain consistent, even when you switch between chart intervals.
Real-Time Alerts: Stay updated with alerts for squeeze conditions without needing to constantly watch the chart.
Simple to Use, Customizable to Fit: You can easily adjust the table’s look and choose the timeframes and colors that best suit your trading style.
Acknowledgment:
While this tool builds on the TTM Squeeze concept developed by John Carter of Simpler Trading, it offers added flexibility through multi-timeframe analysis, alerts, and customizability to make monitoring market conditions more accessible.
TechniTrend: Dynamic Local Fibonacci LevelsTechniTrend: Dynamic Local Fibonacci Levels
Description: The "Dynamic Local Fibonacci Levels" indicator dynamically displays Fibonacci levels only when the market is experiencing significant volatility. By detecting volatile price movements, this tool helps traders focus on Fibonacci retracement levels that are most relevant during high market activity, reducing noise from calm market periods.
Key Features:
Adaptive Fibonacci Levels: The indicator calculates and plots Fibonacci levels (from 0 to 1) only during periods of high volatility. This helps traders focus on actionable levels during significant price swings.
Customizable Chart Type: Users can choose between Candlestick charts (including shadows) or Line charts (excluding shadows) to determine the high and low price points for Fibonacci level calculations.
Volatility-Based Detection: The Average True Range (ATR) is used to detect significant volatility. Traders can adjust the ATR multiplier to fine-tune the sensitivity of the indicator to price movements.
Fully Customizable Fibonacci Levels: Traders can modify the default Fibonacci levels according to their preferences or trading strategies.
Real-Time Volatility Confirmation: Fibonacci levels are displayed only if the price range between the local high and low exceeds a user-defined volatility threshold, ensuring that these levels are only plotted when the market is truly volatile.
Customization Options:
Chart Type: Select between "Candles (Includes Shadows)" and "Line (Excludes Shadows)" for detecting price highs and lows.
Length for High/Low Detection: Choose the period for detecting the highest and lowest price in the given time frame.
ATR Multiplier for Volatility Detection: Adjust the sensitivity of the volatility threshold by setting the ATR multiplier.
Fibonacci Levels: Customize the specific Fibonacci levels to be displayed, from 0 to 1.
Usage Tips:
Focus on Key Levels During Volatility: This indicator is best suited for periods of high volatility. It can help traders identify potential support and resistance levels that may be more significant in turbulent markets.
Adjust ATR Multiplier: Depending on the asset you're trading, you might want to fine-tune the ATR multiplier to better suit the market conditions and volatility.
Recommended Settings:
ATR Multiplier: 1.5
Fibonacci Levels: Default levels set to 0.00, 0.114, 0.236, 0.382, 0.5, 0.618, 0.786, and 1.0
Length for High/Low Detection: 55
Use this indicator to detect key Fibonacci retracement levels in volatile market conditions and make more informed trading decisions based on price dynamics and volatility.
Breakout LevelsBreakout Levels Indicator
The Breakout Levels indicator is a tool designed to help traders identify potential breakout points based on a specified time range and market volatility. By combining user-defined time frames with Average True Range (ATR) calculations, it provides actionable entry and stop-loss levels for both upward and downward breakouts. Additionally, it includes risk management features to calculate appropriate position sizes based on your account capital and risk tolerance.
Key Features
Custom Time Range Selection: Define a specific period during which the indicator calculates the highest high and lowest low to establish breakout levels.
ATR-Based Calculations: Use the ATR to adjust entry and stop-loss levels according to market volatility.
Risk Management: Automatically calculate position sizes based on your account capital and desired risk per trade.
Indicator Inputs
Start Time : The beginning of the time range for calculating the highest high and lowest low.
End Time : The end of the time range.
Entry Multiplier: A factor that determines how far the entry level is from the breakout level, scaled by the ATR.
Stop-Loss Multiplier: A factor that determines the distance of the stop-loss from the entry level, scaled by the ATR.
Risk per Trade (%) : The percentage of your account capital you're willing to risk on each trade.
Account Capital : Your total trading capital used for position size calculations.
ATR Length : The number of periods over which the ATR is calculated.
Position Size Up / Down : Shows you Lot size to maintain no loss more than allowed percentage at that entry
Bitcoin Logarithmic Growth Curve 2024The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements.
The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity.
Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences.
The Bitcoin logarithmic growth curve has the following function:
y = 10^(a * log10(x) - b)
In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart.
How is it made (You can skip this section if you’re not a fan of math):
To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows:
113, 18.55
240, 1004.42
451, 19128.27
655, 65502.47
The same process was applied to the bear market low values:
103, 2.48
267, 211.03
471, 3192.87
676, 16255.15
Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values.
For the cycle peak (x,y) values:
2.053, 1.268
2.380, 3.002
2.654, 4.282
2.816, 4.816
And for the bear market low (x,y) values:
2.013, 0.394
2.427, 2.324
2.673, 3.504
2.830, 4.211
Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary).
Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship.
The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value.
Below are the results:
It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression.
These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063).
The final bull cycle function is:
y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324)
The final bear cycle function is:
y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063)
The main Criticisms of growth curve models:
The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak.
This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band.
Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post:
This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns.
Our recommendations:
Drawing on the concept of diminishing returns, we propose alternative settings for this model that we believe provide a more realistic forecast aligned with this theory. The adjusted parameters apply only to the top band: a-value: 3.637 ± 0.2343 and b-parameter: -5.369 ± 0.6264. However, please note that these values are highly subjective, and you should be aware of the model's limitations.
Conservative bull cycle model:
y = 10^(3.637 ± 0.2343 * log10(x) - 5.369 ± 0.6264)
ATR+StdTR Band and Trailing StopThis Pine Script code plots the "ATR+StdTR Band and Trailing Stop," serving as a tool for volatility-based risk management and trend detection. While bands are typically set using a multiple of ATR, this script uses StdTR (the True Range standard deviation) and sets the band width based on ±(ATR + n times StdTR). StdTR is a great tool for detecting price volatility and anomalies, allowing traders to adapt to rapid changes in extreme market conditions. This helps traders proactively manage risk during sudden market fluctuations.
The following features are provided:
Table Display
A table is shown on the chart, allowing traders to visually track the current ATR value, StdTR (σ), and the long/short stop-loss levels (±ATR ± nσ). This enables real-time monitoring of risk management data.
Band Plots
The script plots bands that combine ATR with StdTR (nσ).
The upper and lower bands are calculated using the previous candle’s closing price (the source is customizable) and are plotted as ±(ATR + nσ), providing a clear visual of the price range.
ATR ± nσ Trailing Stop
The trailing stop dynamically adjusts the stop-loss levels based on price movements. In an uptrend, the stop-loss rises, while in a downtrend, it lowers, helping traders lock in profits while minimizing losses during significant reversals.
Breakout Detection
Breakouts are detected when the price exceeds the upper band or drops below the lower band. A visual marker (X) is displayed on the chart, allowing traders to quickly recognize when the price has moved beyond normal volatility ranges, making it easier to respond to trend formations or reversals.
Customization Points:
The ATR period and StdTR (n) are fully customizable.
The source for ATR band calculation can be adjusted, allowing traders to choose from close, open, high, low, etc.
The table’s display position and design (text color, size, etc.) can be customized to present the information clearly and effectively.
Price Iterations with Pips*Script Name:* Price Iterations with Pips
*Description:* This script plots horizontal lines above and below a user-defined initial price, representing price iterations based on a specified number of pips.
*Functionality:*
1. Asks for user input:
- Initial Price
- Pips per Iteration
- Number of Iterations
2. Calculates the price change per pip.
3. Plots horizontal lines:
- Above the initial price (green)
- Below the initial price (red)
4. Extends lines dynamically to both sides.
*Use Cases:*
1. *Support and Resistance Levels:* Use the script to visualize potential support and resistance levels based on price iterations.
2. *Price Targets:* Set the initial price as a target and use the iterations to estimate potential profit/loss levels.
3. *Risk Management:* Utilize the script to visualize risk levels based on pip iterations.
4. *Technical Analysis:* Combine the script with other technical indicators to identify potential trading opportunities.
*Trading Platforms:* This script is designed for TradingView.
*How to Use:*
1. Add the script to your TradingView chart.
2. Set the initial price, pips per iteration, and number of iterations.
3. Adjust the colors and line styles as needed.
4. Zoom in/out and pan to see the lines adjust.
*Benefits:*
1. Visualize price iterations and potential support/resistance levels.
2. Simplify risk management and price target estimation.
3. Enhance technical analysis with customizable price levels.
BTC Power of Law x Central Bank LiquidityThis indicator combines Bitcoin's long-term growth model (Power Law) with global central bank liquidity to help identify potential buy and sell signals.
How it works:
Power Law Oscillator: This part of the indicator tracks how far Bitcoin's current price is from its expected long-term growth, based on an exponential model. It helps you see when Bitcoin may be overbought (too expensive) or oversold (cheap) compared to its historical trend.
Central Bank Liquidity: This measures the amount of money injected into the financial system by major central banks (like the Fed or ECB). When more money is printed, asset prices, including Bitcoin, tend to rise. When liquidity dries up, prices often fall.
By combining these two factors, the indicator gives you a more accurate view of Bitcoin's price trends.
How to interpret:
Green Line : Bitcoin is undervalued compared to its long-term growth, and the liquidity environment is supportive. This is typically a buy signal.
Yellow Line: Bitcoin is trading near its expected value, or there's uncertainty due to mixed liquidity conditions. This is a hold signal.
Red Line: Bitcoin is overvalued, or liquidity is tightening. This is a potential sell signal.
Zones:
The background will turn green when Bitcoin is in a buy zone and red when it's in a sell zone, giving you easy-to-read visual cues.
Options Series - Explode BB⭐ Bullish Zone:
⭐ Bearish Zone:
⭐ Neutral Zone:
The provided script integrates Bollinger Bands with different lengths (20 and 200 periods) and applies customized candle coloring based on certain conditions. Here's a breakdown of its importance and insights:
⭐ 1. Dual Bollinger Bands (BBs):
Bollinger Bands (BB) with 20-period length:
This is the standard setting for Bollinger Bands, with a 20-period simple moving average (SMA) as the central line and upper/lower bands derived from the standard deviation.
These bands are used to identify volatility. Wider bands indicate higher volatility, while narrower bands indicate low volatility.
200-period BB:
This is a longer-term indicator providing insight into the overall trend and long-term volatility.
The 200-period bands filter out noise and offer a "macro" view of price movements compared to the 20-period bands, which focus on short-term price actions.
⭐ 2. Overlay of Bollinger Bands and SMA:
The script plots the Bollinger Bands along with the SMA (Simple Moving Average) of the 200-period BB. This gives traders both a short-term (20-period) and long-term (200-period) perspective, which is valuable for detecting major trend shifts or key support and resistance zones.
Using multiple time frames (20-period for short-term and 200-period for long-term) can help traders spot both immediate opportunities and overarching trends.
⭐ 3. Candle Coloring Based on Key Conditions:
Bullish Signal (GreenFluroscent): When the price closes above the upper 200-period Bollinger Band, the candle turns green, indicating a potential bullish breakout.
Bearish Signal (RedFluroscent): If the price closes below the lower 200-period Bollinger Band, the candle turns red, suggesting a bearish breakout.
Neutral or Uncertain Market: Candles are gray when the price remains between the upper and lower bands, indicating a lack of a strong directional bias.
This color-coded visualization allows traders to quickly assess market sentiment based on the Bollinger Bands' extremes.
⭐ 4. Strategic Importance of the Setup:
Multi-timeframe Analysis: Combining short-term (20-period) and long-term (200-period) Bollinger Bands enables traders to assess the market's overall volatility and trend strength. The longer-term bands act as a reference for broader trend direction, while the shorter-term bands can signal shorter-term pullbacks or entry/exit points.
Breakout Identification: By color-coding the candles when prices cross either the upper or lower 200-period bands, the script makes it easier to spot potential breakouts. This can be particularly helpful in trading strategies that rely on volatility expansions or trend-following tactics.
⭐ 5. Customization and Flexibility:
Custom Colors: The script uses distinct fluorescent green and red colors to highlight key bullish and bearish conditions, providing clear visual cues.
Simplicity with Flexibility: Despite its simplicity, the script leaves room for customization, allowing traders to adjust the Bollinger Band multipliers or apply different conditions to candle coloring for more nuanced setups.
This script enhances standard Bollinger Band usage by introducing multi-timeframe analysis, breakout signals, and visual cues for trend strength, making it a powerful tool for both trend-following and mean-reversion strategies.
🚀 Conclusion:
This script effectively simplifies volatility analysis by visually marking bullish, bearish, and neutral zones, making it a robust tool for identifying trade opportunities across multiple timeframes. Its dual-band approach ensures both trend-following and mean-reversion strategies are supported.
Dynamic Resistance and Support LinesThis script is designed to dynamically plot support and resistance lines based on full-dollar and half-dollar price levels relative to the close price on a chart. The script is particularly useful for day traders and scalpers, as it helps visualize key psychological price levels that often act as support and resistance zones in volatile and fast-moving markets in real time.
Key Features:
Dynamic Resistance and Support Levels:
Full-dollar levels: These are calculated by rounding the close price to the nearest full dollar and then extending the levels by adding and subtracting increments of 1 (e.g., $1, $2, $3).
Half-dollar levels: These are calculated by adding and subtracting 0.5 increments to the nearest full-dollar price, providing additional reference points. The historical full-dollar levels remain where support and resistance may have occurred in the past.
Extend Lines:
You can toggle whether the support and resistance lines are extended to the right, left, or both directions. This allows flexibility in projecting potential future areas of support or resistance.
Custom Line Extension:
The user can set the number of bars (or time periods) that the support and resistance lines will extend, giving control over how long the levels remain on the chart.
Color-Coded Lines:
Red lines represent full-dollar resistance and support levels.
Blue lines represent half-dollar levels, making it easy to differentiate between key psychological price zones.
Line Flexibility:
The script allows the lines to extend both left and right on the chart, making it useful for analyzing historical price action or projecting future price movements. The number of bars for extension is customizable, allowing for tailored setups.
Nearest Full Dollar Plot:
The nearest full-dollar price level is plotted as a yellow circle on the chart. This serves as a quick visual cue for traders to monitor price proximity to critical levels.
Benefits in Day Trading, Scalping, and Volatile Markets:
Visualizing Key Psychological Levels:
Full-dollar and half-dollar price levels often act as psychological barriers for traders. This script helps traders easily identify these levels, which are important in both fast-moving markets and during sideways consolidation.
Improved Decision-Making:
By automatically drawing these support and resistance levels, the script helps day traders and scalpers make quicker and more informed decisions, especially in volatile markets where every second counts.
Adaptability to Market Conditions:
The flexibility of extending lines based on trader preferences allows the user to adapt the script to various market conditions, such as high volatility or trend-based trading, providing a clear view of potential breakout or reversal areas.
Better Risk Management:
Having predefined support and resistance levels helps traders better manage risk, as these levels can act as logical areas for setting stop losses or taking profits.
This script is especially valuable for traders looking to capitalize on quick market movements or identify key entry and exit points during market volatility.
Indicator 10**Indicator 10** is a sophisticated technical analysis tool designed for use on trading platforms that support Pine Script (version 5). This indicator is primarily focused on analyzing price movements over different timeframes, incorporating elements of ZigZag analysis, Fibonacci levels, and historical price range calculations. Below is a detailed description of its features and functionalities:
#### Key Features:
1. **Input Variables:**
- **Year_calc:** Specifies the number of years to consider for historical price range calculations.
- **Size_fibo:** Defines the size of the Fibonacci levels in points.
- **Dig:** Represents the minimum tick size for the instrument being analyzed.
- **ZigZag Parameters:**
- **Period (zigzag_len):** The length of the ZigZag indicator.
- **Depth (zigzag_depth):** The depth percentage for the ZigZag indicator.
- **Display Count (zigzag_hist):** The number of ZigZag points to display.
- **Font Size (font_size):** The size of the font used for labels.
2. **Historical Price Range Calculation:**
- The indicator calculates the average weekly and monthly price ranges over the specified number of years (`Year_calc`).
- These ranges are used to adjust the Fibonacci levels dynamically based on historical volatility.
3. **ZigZag Analysis:**
- The indicator employs a custom ZigZag function to identify significant price swings on different timeframes (H4, D1, W1).
- The ZigZag points are stored in arrays, allowing for the visualization of recent price swings.
4. **Fibonacci Adjustment:**
- The Fibonacci levels are adjusted based on the historical price ranges (`W1_Val`, `MN1_Val`, `D1_Val`).
- These adjusted levels are used to draw support and resistance lines on the chart.
5. **Visualization:**
- The indicator draws lines and labels on the chart to represent the ZigZag points and adjusted Fibonacci levels.
- Different colors are used to distinguish between upward and downward trends.
6. **Dynamic Updates:**
- The indicator continuously updates the ZigZag points and Fibonacci levels as new price data becomes available.
- It ensures that only the most recent ZigZag points are displayed, maintaining a clean and relevant chart.
#### How It Works:
1. **Initialization:**
- The indicator initializes variables for storing historical price ranges and ZigZag points.
- It sets the start date for historical calculations based on the current year minus the specified number of years (`Year_calc`).
2. **Historical Data Retrieval:**
- The indicator retrieves weekly and monthly high and low prices for the specified period.
- It calculates the total price range and the average range for each timeframe.
3. **ZigZag Calculation:**
- The custom ZigZag function identifies local highs and lows based on the specified period and depth.
- These points are stored in arrays for later visualization.
4. **Fibonacci Adjustment:**
- The Fibonacci levels are adjusted based on the historical price ranges and the specified Fibonacci size.
- These adjusted levels are used to draw lines on the chart.
5. **Visualization:**
- The indicator draws lines connecting ZigZag points and labels indicating the direction of the trend.
- It ensures that only the most recent ZigZag points are displayed, maintaining a clean and relevant chart.
6. **Continuous Updates:**
- The indicator continuously updates the ZigZag points and Fibonacci levels as new price data becomes available.
- It ensures that only the most recent ZigZag points are displayed, maintaining a clean and relevant chart.
#### Conclusion:
**Indicator 10** is a powerful tool for traders who rely on historical price analysis, ZigZag patterns, and Fibonacci levels to make trading decisions. Its dynamic and adaptive nature ensures that the chart remains relevant and useful, providing traders with a clear view of recent price movements and potential support/resistance levels.
Wedge BreakoutThe Wedge Breakout indicator is designed to identify and signal potential breakouts from a wedge pattern, a common technical analysis formation. A wedge pattern typically forms when the price moves within converging trendlines, indicating a potential upcoming breakout either upwards (bullish) or downwards (bearish).
Identifying Pivot Points:
The indicator first calculates pivot points, which are significant highs and lows that define the wedge's upper and lower boundaries.
Pivot Lows: It identifies the lowest price points over a specified length (input_len), which serves as the lower boundary of the wedge.
Pivot Highs: Similarly, it identifies the highest price points over the same length, forming the upper boundary of the wedge.
Drawing Trendlines:
The pivot points are connected to form dashed trendlines that represent the upper and lower boundaries of the wedge.
The indicator uses the SimpleTrendlines library to manage and draw these trendlines dynamically:
Green Trendline: Indicates an upward slope (bullish).
Red Trendline: Indicates a downward slope (bearish).
Calculating the Breakout Conditions:
A breakout is confirmed when the price action fulfills two conditions:
The candle's high exceeds the upper trendline's highest point.
The candle's low drops below the lower trendline's lowest point.
This condition suggests that the price is squeezing within the wedge pattern and is about to break out.
Determining Breakout Direction:
The direction of the breakout is determined by the candle's closing position relative to its opening:
Bullish Breakout (Upward): When the candle closes above its opening price (close > open) after breaching both trendlines, it suggests a bullish breakout. This condition is marked with a green upward triangle .
Bearish Breakout (Downward): When the candle closes below its opening price (close < open) after breaching both trendlines, it suggests a bearish breakout. This condition is marked with a red downward triangle.
Visual Representation:
Green Triangle Up: Plotted below the bar to indicate a potential bullish breakout.
Red Triangle Down: Plotted above the bar to indicate a potential bearish breakout.
Used library:
www.tradingview.com
Adaptive VWAP [QuantAlgo]Introducing the Adaptive VWAP by QuantAlgo 📈🧬
Enhance your trading and investing strategies with the Adaptive VWAP , a versatile tool designed to provide dynamic insights into market trends and price behavior. This indicator offers a flexible approach to VWAP calculations by allowing users to adapt it based on lookback periods or fixed timeframes, making it suitable for a wide range of market conditions.
🌟 Key Features:
🛠 Customizable VWAP Settings: Choose between an adaptive VWAP that adjusts based on a rolling lookback period, or switch to a fixed timeframe (e.g., daily, weekly, monthly) for a more structured approach. Adjust the VWAP to suit your trading or investing style.
💫 Dynamic Bands and ATR Filter: Configurable deviation bands with multipliers allow you to visualize price movement around VWAP, while an ATR-based noise filter helps reduce false signals during periods of market fluctuation.
🎨 Trend Visualization: Color-coded trend identification helps you easily spot uptrends and downtrends based on VWAP positioning. The indicator fills the areas between the bands for clearer visual representation of price volatility and trend strength.
🔔 Custom Alerts: Set up alerts for when price crosses above or below the VWAP, signaling potential uptrend or downtrend opportunities. Stay informed without needing to monitor the charts constantly.
✍️ How to Use:
✅ Add the Indicator: Add the Adaptive VWAP to your favourites and apply to your chart. Choose between adaptive or timeframe-based VWAP calculation, adjust the lookback period, and configure the deviation bands to your preferred settings.
👀 Monitor Bands and Trends: Watch for price interaction with the VWAP and its deviation bands. The color-coded signals and band fills help identify potential trend shifts or price extremes.
🔔 Set Alerts: Configure alerts for uptrend and downtrend signals based on price crossing the VWAP, so you’re always informed of significant market movements.
⚙️ How It Works:
The Adaptive VWAP adjusts its calculation based on the user’s chosen configuration, allowing for a flexible approach to market analysis. The adaptive setting uses a rolling lookback period to continuously adjust the VWAP, while the fixed timeframe option anchors VWAP to key timeframes like daily, weekly, or monthly periods. This flexibility enables traders and investors to use the tool in various market environments.
Deviation bands, calculated with customizable multipliers, provide a clear visual of how far the price has moved from the VWAP, helping you gauge potential overbought or oversold conditions. To reduce false signals, an ATR-based filter can be applied, ensuring that only significant price movements trigger trend confirmations.
The tool also includes a fast exponential smoothing function for the VWAP, helping smooth out price fluctuations without sacrificing responsiveness. Trend confirmation is reinforced by the number of bars that price stays above or below the VWAP, ensuring a more consistent trend identification process.
Disclaimer:
The Adaptive VWAP is designed to enhance your market analysis but should not be relied upon as the sole basis for trading or investing decisions. Always combine it with other analytical tools and practices. No statements or signals from this indicator constitute financial advice. Past performance is not indicative of future results.
Harish Algo 2The script "Harish Algo 2" is a Pine Script-based TradingView indicator that automatically identifies significant trendlines based on fractal points and tracks price interactions with those trendlines. Key features include:
Fractal Detection: The script identifies fractal highs and lows, using a configurable fractal period, to serve as pivot points for generating trendlines. Fractal highs are marked in blue, and fractal lows are marked in red.
Dynamic Trendlines: It draws trendlines between consecutive fractal points, with a limit on the maximum number of active trendlines. The trendlines can be extended either in both directions or to the right, as per user input. The line width can also be customized.
Support/Resistance Counting: Each trendline tracks how many times the price interacts with it. If the price approaches the line from above and touches or stays near it, the line is considered a support. If the price approaches from below, it is considered a resistance. These counts are used to modify the trendline's color and appearance.
Trendlines with 2 support interactions turn green.
Trendlines with 2 resistance interactions turn red.
Trendlines with 3 or more interactions turn black.
Trendline Styling: Trendlines that extend over a long period (more than 100 bars) change to a dotted style to highlight their persistence.
Break Detection: The script monitors if the price crosses a trendline, signaling a potential breakout or breakdown. Once a trendline is broken, it stops extending further.
Trendline Removal: The script ensures that only a limited number of trendlines are active at a time. If the maximum number of trendlines is reached, the oldest trendline is removed to make space for new ones.
This indicator is designed to help traders visualize important trendlines, spot potential support and resistance levels, and detect breakouts or breakdowns based on price movement.
EMA14 Second Time BUY/SELL AlertsEMA14 Crossover Strategy with Conditional BUY/SELL Alerts
This powerful script provides dynamic BUY and SELL alerts based on the interaction between price action and the EMA14 (Exponential Moving Average 14). Ideal for traders looking to capitalize on trend reversals and breakout patterns, this indicator helps you time entries and exits with precision.
Key Features:
Second-Time Crossover Alerts: The script tracks when the price crosses the EMA14 for the second time. This adds confirmation to price movements and helps filter out false signals.
Conditional BUY/SELL Alerts:
BUY Alert: Triggered when the price closes above the EMA14 after a previous SELL signal, indicating a potential trend reversal or breakout to the upside.
SELL Alert: Triggered when the price closes below the EMA14 after a previous BUY signal, signaling a possible shift to the downside.
Advanced Crossover Tracking:
The script counts each crossover of the price relative to the EMA14, generating a BUY or SELL signal on the second instance to provide additional confirmation of trend strength.
Visual Alerts: Labels are plotted directly on the chart to highlight when a BUY or SELL signal has occurred, providing immediate visual feedback for traders to react in real-time.
How It Works:
The script combines the simplicity of EMA14 with enhanced logic that tracks both crossovers and closes relative to the moving average. This ensures that the signals are based not only on quick movements but also on price confirmation, reducing noise and false breakouts.
This script is perfect for traders who rely on moving average strategies and want additional filtering to confirm trends and optimize trade timing.
Multiple Bollinger Bands + Volatility [AlgoTraderPro]This indicator helps traders visualize price ranges and volatility changes. Designed to assist in identifying potential consolidation zones, the indicator uses multiple layers of Bollinger Bands combined with volatility-based shading. This can help traders spot periods of reduced price movement, which are often followed by breakouts or trend reversals.
█ FEATURES
Multiple Bollinger Bands: Displays up to seven bands with customizable standard deviations, providing a layered view of price range activity.
Volatility Measurement: Tracks changes in Bollinger Band width to display volatility percentage and direction (increasing, decreasing, or neutral).
Volatility Shading: Uses color-coded shading between the outermost bands to indicate changes in volatility, helping to visualize potential consolidation zones.
Customizable Inputs: Modify lookback periods, moving average lengths, and standard deviations for each band to tailor the analysis to your strategy.
Volatility Table: Displays a table on the chart showing real-time volatility data and direction for quick reference.
█ HOW TO USE
Add the Indicator: Apply it to your TradingView chart.
Adjust Settings: Customize the Bollinger Bands’ parameters to suit your trading timeframe and strategy.
Analyze Consolidation Zones: Use the multiple bands and volatility shading to identify areas of reduced price activity, signaling potential breakouts.
Monitor Volatility: Refer to the volatility table to track real-time shifts in market volatility.
Use in Different Markets: Adapt the settings for various assets and timeframes to assess market conditions effectively.
█ NOTES
• The indicator is useful in consolidating markets where price movement is limited, offering insights into potential breakout areas.
• Adjust the settings based on asset and market conditions for optimal results.
Charan_Trading_IndicatorCharan_Trading_Indicator Overview:
The Charan_Trading_Indicator combines several technical analysis tools, including Bollinger Bands, RSI (Relative Strength Index), VWAP (Volume-Weighted Average Price), and ATR (Average True Range), to provide buy and sell signals. The script incorporates multiple strategies, such as crack snap setups, overbought/oversold levels, and trend continuation indicators, all tailored for precise market entry and exit points.
Key Components:
RSI (Relative Strength Index):
The indicator uses RSI to detect overbought (RSI > 70) and oversold (RSI < 30) market conditions.
Alerts are triggered when prices are within the specified buy/sell range and RSI crosses these thresholds.
Bollinger Bands:
Bollinger Bands are calculated based on a configurable moving average and standard deviation.
The script identifies potential buy signals when the price dips below the lower Bollinger Band and recovers, and sell signals when the price exceeds the upper Bollinger Band and retraces.
Crack Snap Strategies:
The indicator incorporates multiple variations of the crack snap strategy:
Buy Signals: Triggered when price opens below the lower Bollinger Band and closes above it, alongside certain conditions in previous candles.
Sell Signals: Triggered when price opens above the upper Bollinger Band and closes below it, with similar candle patterns.
Variations such as 3-candle (3C) and 4-candle (4C) versions refine the crack snap setups for more robust signals.
Isolated Candle Conditions:
The indicator tracks isolated candles, where the entire candle lies above or below the Bollinger Bands, to identify potential reversal points.
Trend Continuation Signals:
Conditions based on the candle range and previous highs/lows allow the indicator to generate signals for trend continuation:
Buy signals when price breaks above the previous two highs.
Sell signals when price breaks below the previous two lows.
VWAP (Volume-Weighted Average Price):
The indicator integrates VWAP to give additional support and resistance levels, ensuring signals align with volume trends.
ATR-Based Stop Loss:
For both buy and sell conditions, the script plots stop-loss levels based on the ATR (Average True Range), giving dynamic risk management levels.
Buy/Sell Ranges:
The user can set minimum and maximum price ranges for buy and sell signals, ensuring that the indicator only generates alerts within desired price ranges.
How It Works:
Buy Signals: The script generates buy signals based on multiple conditions, including the crack snap strategy, oversold RSI levels, and trend continuation setups. When these conditions are met, green triangles appear below the price bars, and an alert is triggered.
Sell Signals: Sell signals are triggered when the opposite conditions are met (overbought RSI, crack snap sell setups, trend breaks), and red triangles appear above the price bars.
Visual Indicators: The script plots upper and lower Bollinger Bands, stop loss levels, and VWAP on the chart, providing a comprehensive view of market conditions and support/resistance levels.
This indicator is versatile, combining multiple technical tools for robust decision-making in trading. It generates alerts, plots visual markers, and integrates risk management, making it a well-rounded tool for technical analysis.
This indicator is versatile, combining multiple technical tools for robust decision-making in trading. It generates alerts, plots visual markers, and integrates risk management, making it a well-rounded tool for technical analysis.
Volatility Trend Bands [UAlgo]The Volatility Trend Bands is a trend-following indicator that combines the concepts of volatility and trend detection. Built using the Average True Range (ATR) to measure volatility, this indicator dynamically adjusts upper and lower bands around price movements. The bands act as dynamic support and resistance levels, making it easier to identify trend shifts and potential entry and exit points.
With the ATR multiplier, this indicator effectively captures volatility-based shifts in the market. The use of midline values allows for accurate trend detection, which is displayed through color-coded signals on the chart. Additionally, this tool provides clear buy and sell signals, accompanied by intuitive graphical markers for ease of use.
The Volatility Trend Bands is ideal for traders seeking an adaptive trend-following method that responds to changing market conditions while maintaining robust volatility control.
🔶 Key Features
Dynamic Support and Resistance: The indicator utilizes volatility to create dynamic bands. The upper band acts as resistance, and the lower band acts as support for the price. Wider bands indicate higher volatility, while narrower bands indicate lower volatility.
Customizable Inputs
You can tailor the indicator to your strategy by adjusting the:
Price Source: Select the price data (e.g., closing price) used for calculations.
ATR Length: Define the lookback period for the Average True Range (ATR) volatility measure.
ATR Multiplier: This factor controls the width of the volatility bands relative to the ATR value.
Color Options: Choose colors for the bands and signal arrows for better visualization.
Visual Signals: Arrows ("▲" for buy, "▼" for sell) appear on the chart when the trend changes, providing clear entry point indications.
Alerts: Integrated alerts for both buy and sell conditions, allowing you to receive notifications for potential trade opportunities.
🔶 Interpreting Indicator
Upper and Lower Bands: The upper and lower bands are dynamic, adjusting based on market volatility using the ATR. These bands serve as adaptive support and resistance levels. When price breaks above the upper band, it indicates a potential bullish breakout, signaling a strong uptrend. Conversely, a break below the lower band signals a bearish breakout, indicating a downtrend.
Buy/Sell Signals: The indicator provides clear buy and sell signals at breakout points. A buy signal ("▲") is generated when the price breaks above the upper band, suggesting the start of a bullish trend. A sell signal ("▼") is triggered when the price breaks below the lower band, indicating the beginning of a bearish trend. These signals help traders identify potential entry and exit points at key breakout levels.
Color-Coded Bars: The bars on the chart change color based on the trend direction. Teal bars represent bullish momentum, while purple bars signify bearish momentum. This color coding provides a quick visual cue about the market's current direction.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Bollinger Bands with RSI Buy/Sell Signals (15 min) Bollinger Bands with RSI Buy/Sell Signals (15 Min)
Description:
The Bollinger Bands with RSI Buy/Sell Signals (15 Min) indicator is designed to help traders identify potential reversal points in the market using two popular technical indicators: Bollinger Bands and the Relative Strength Index (RSI).
How It Works:
Bollinger Bands:
Bollinger Bands consist of an upper band, lower band, and a middle line (Simple Moving Average). These bands adapt to market volatility, expanding during high volatility and contracting during low volatility.
This indicator monitors the 15-minute Bollinger Bands. If the price moves completely outside the bands, it signals that the market is potentially overextended.
Relative Strength Index (RSI):
RSI is a momentum indicator that measures the strength of price movements. RSI readings above 70 indicate an overbought condition, while readings below 30 suggest an oversold condition.
This indicator uses the RSI on the 15-minute time frame to further confirm overbought and oversold conditions.
Buy/Sell Signal Generation:
Buy Signal:
A buy signal is triggered when the market price crosses above the lower Bollinger Band on the 15-minute time frame, indicating that the market may be oversold.
Additionally, the RSI must be below 30, confirming an oversold condition.
A "Buy" label appears below the price when this condition is met.
Sell Signal:
A sell signal is triggered when the market price crosses below the upper Bollinger Band on the 15-minute time frame, indicating that the market may be overbought.
The RSI must be above 70, confirming an overbought condition.
A "Sell" label appears above the price when this condition is met.
Simplified Market ProfileVolume Bins: This script divides the price range into num_bins equal price levels. Each bin holds the cumulative volume for that price range.
Profile Length: The number of past bars that the profile considers for building the volume histogram.
Bin Size: The price range between bins is determined by dividing the difference between the highest and lowest prices over the specified range.
Volume Calculation: The script iterates over each bar within the specified range, determining which price bin the bar’s volume should be added to.
Plotting: The script visualizes the volume profile as lines plotted horizontally at different price levels, with thickness proportional to the volume traded at that level.
Sinc Bollinger BandsKaiser Windowed Sinc Bollinger Bands Indicator
The Kaiser Windowed Sinc Bollinger Bands indicator combines the advanced filtering capabilities of the Kaiser Windowed Sinc Moving Average with the volatility measurement of Bollinger Bands. This indicator represents a sophisticated approach to trend identification and volatility analysis in financial markets.
Core Components
At the heart of this indicator is the Kaiser Windowed Sinc Moving Average, which utilizes the sinc function as an ideal low-pass filter, windowed by the Kaiser function. This combination allows for precise control over the frequency response of the moving average, effectively separating trend from noise in price data.
The sinc function, representing an ideal low-pass filter, provides the foundation for the moving average calculation. By using the sinc function, analysts can independently control two critical parameters: the cutoff frequency and the number of samples used. The cutoff frequency determines which price movements are considered significant (low frequency) and which are treated as noise (high frequency). The number of samples influences the filter's accuracy and steepness, allowing for a more precise approximation of the ideal low-pass filter without altering its fundamental frequency response characteristics.
The Kaiser window is applied to the sinc function to create a practical, finite-length filter while minimizing unwanted oscillations in the frequency domain. The alpha parameter of the Kaiser window allows users to fine-tune the trade-off between the main-lobe width and side-lobe levels in the frequency response.
Bollinger Bands Implementation
Building upon the Kaiser Windowed Sinc Moving Average, this indicator adds Bollinger Bands to provide a measure of price volatility. The bands are calculated by adding and subtracting a multiple of the standard deviation from the moving average.
Advanced Centered Standard Deviation Calculation
A unique feature of this indicator is its specialized standard deviation calculation for the centered mode. This method employs the Kaiser window to create a smooth deviation that serves as an highly effective envelope, even though it's always based on past data.
The centered standard deviation calculation works as follows:
It determines the effective sample size of the Kaiser window.
The window size is then adjusted to reflect the target sample size.
The source data is offset in the calculation to allow for proper centering.
This approach results in a highly accurate and smooth volatility estimation. The centered standard deviation provides a more refined and responsive measure of price volatility compared to traditional methods, particularly useful for historical analysis and backtesting.
Operational Modes
The indicator offers two operational modes:
Non-Centered (Real-time) Mode: Uses half of the windowed sinc function and a traditional standard deviation calculation. This mode is suitable for real-time analysis and current market conditions.
Centered Mode: Utilizes the full windowed sinc function and the specialized Kaiser window-based standard deviation calculation. While this mode introduces a delay, it offers the most accurate trend and volatility identification for historical analysis.
Customizable Parameters
The Kaiser Windowed Sinc Bollinger Bands indicator provides several key parameters for customization:
Cutoff: Controls the filter's cutoff frequency, determining the divide between trends and noise.
Number of Samples: Sets the number of samples used in the FIR filter calculation, affecting the filter's accuracy and computational complexity.
Alpha: Influences the shape of the Kaiser window, allowing for fine-tuning of the filter's frequency response characteristics.
Standard Deviation Length: Determines the period over which volatility is calculated.
Multiplier: Sets the number of standard deviations used for the Bollinger Bands.
Centered Alpha: Specific to the centered mode, this parameter affects the Kaiser window used in the specialized standard deviation calculation.
Visualization Features
To enhance the analytical value of the indicator, several visualization options are included:
Gradient Coloring: Offers a range of color schemes to represent trend direction and strength for the moving average line.
Glow Effect: An optional visual enhancement for improved line visibility.
Background Fill: Highlights the area between the Bollinger Bands, aiding in volatility visualization.
Applications in Technical Analysis
The Kaiser Windowed Sinc Bollinger Bands indicator is particularly useful for:
Precise trend identification with reduced noise influence
Advanced volatility analysis, especially in the centered mode
Identifying potential overbought and oversold conditions
Recognizing periods of price consolidation and potential breakouts
Compared to traditional Bollinger Bands, this indicator offers superior frequency response characteristics in its moving average and a more refined volatility measurement, especially in centered mode. These features allow for a more nuanced analysis of price trends and volatility patterns across various market conditions and timeframes.
Conclusion
The Kaiser Windowed Sinc Bollinger Bands indicator represents a significant advancement in technical analysis tools. By combining the ideal low-pass filter characteristics of the sinc function, the practical benefits of Kaiser windowing, and an innovative approach to volatility measurement, this indicator provides traders and analysts with a sophisticated instrument for examining price trends and market volatility.
Its implementation in Pine Script contributes to the TradingView community by making advanced signal processing and statistical techniques accessible for experimentation and further development in technical analysis. This indicator serves not only as a practical tool for market analysis but also as an educational resource for those interested in the intersection of signal processing, statistics, and financial markets.
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