Range Breakout [BigBeluga]Range Breakout is a dynamic channel-based indicator designed to identify breakout opportunities and price reactions within defined ranges. It automatically creates upper and lower bands with a midline, helping traders spot breakout zones, retests, and potential fakeouts.
🔵 Key Features:
Dynamic Channel Formation:
Automatically plots upper and lower channel bands with a midline based on ATR calculations.
Channels adjust upon breakout events or after a predefined number of bars to reflect new price ranges.
Breakout Detection:
Green circles appear when price breaks above the upper channel edge.
Red circles appear when price breaks below the lower channel edge.
A new channel is formed after each breakout, allowing traders to monitor evolving price ranges.
Retest Signals:
Upward-pointing green triangles signal a retest of the lower band, indicating potential support.
Downward-pointing red triangles indicate a retest of the upper band, suggesting possible resistance.
Filter Signals by Trends (New Feature):
Optional toggle to filter ▲ and ▼ signals based on channel breakout conditions.
When enabled:
In a bullish channel (confirmed by a green circle breakout), only ▲ signals are displayed.
In a bearish channel (confirmed by a red circle breakout), only ▼ signals are displayed.
Helps traders align retest signals with the prevailing trend for higher-quality trade setups.
Fakeout Identification:
'X' symbols appear when price breaks the upper or lower edge of the channel and quickly returns back inside.
Helps traders identify and avoid false breakouts.
🔵 Usage:
Breakout Trading: Use the green and red circle signals to identify potential breakout trades.
Retest Confirmation: Look for triangle markers to confirm retests of key levels, aiding in entry or exit decisions.
Fakeout Alerts: Utilize the 'X' signals to spot and avoid potential trap moves.
Dynamic Range Monitoring: Stay aware of changing market conditions with automatically updating channels.
Range Breakout is an essential tool for traders seeking to capitalize on range breakouts, retests, and fakeout scenarios. Its dynamic channels and clear visual signals provide a comprehensive view of market structure and potential trade setups.
스크립트에서 "band"에 대해 찾기
UM-Optimized Linear Regression ChannelDESCRIPTION
This indicator was inspired by Dr. Stoxx at drstoxx.com. Shout out to him and his services for introducing me to this idea. This indicator is a slightly different take on the standard linear regression indicator.
It uses two standard deviations to draw bands and dynamically attempts to best-fit the data lookback period using an R-squared statistical measure. The R-squared value ranges between zero and one with zero being no fit to the data at all and 1 being a 100% match of the data to linear regression line. The R-squared calculation is weighted exponentially to give more weight to the most recent data.
The label provides the number of periods identified as the optimal best-fit period, the type of loopback period determination (Manual or Auto) and the R-squared value (0-100, 100% being a perfect fit). >=90% is a great fit of the data to the regression line. <50% is a difficult fit and more or less considered random data.
The lookback mode can also be set manually and defaults to a value of 100 periods.
DEFAULTS
The defaults are 1.5 and 2.0 for standard deviation. This creates 2 bands above and below the regression line. The default mode for best-fit determination with "Auto" selected in the dropdown. When manual mode is selected, the default is 100. The modes, manual lookback periods, colors, and standard deviations are user-configurable.
HOW TO USE
Overlay this indicator on any chart of any timeframe. Look for turning points at extremes in the upper and lower bands. Look for crossovers of the centerline. Look at the Auto-determination for best fit. Compare this to your favorite Manual mode setting (Manual Mode is set to 100 by default lookback periods.)
When price is at an extreme, look for turnarounds or reversals. Use your favorite indicators, in addition to this indicator, to determine reversals. Try this indicator against your favorite securities and timeframes.
CHART EXAMPLE
The chart I used for an example is the daily chart of IWM. I illustrated the extremes with white text. This is where I consider proactively exiting an existing position and/or begin looking for a reversal.
Donchian and Keltner Channels Trend Following with Trailing StopLong Only Trend-following model based on Keltner Channels and Donchian Channels.
These indicators include a noise region, which allows prices to oscillate without requiring position adjustments.
When price trades above the upper band, it signals strength; when it trades below the lower band, it signals weakness.
Keltner Channels
Keltner Channels are volatility-based envelopes set above and below an exponential moving average. Keltner Channels use the Average True Range (ATR), which measures daily volatility, to set channel distance.
Donchian Channel
Donchian Channels are are used to identify market trends and volatility. The upper and lower bands are based on the highest high and lowest low of a specified period. When the price moves above the upper band, it indicates a bullish breakout, while a
move below the lower band indicates a bearish breakout. The distance between the upper and lower channel of the Donchian Channel indicates the asset’s volatility.
Trend Following Model
The default settings are:
Upper Keltner and Upper Donchian Channel Length : 20
Lower Keltner and Lower Donchian Channel Length : 40
Keltner ATR Multiplier: 2
Entries, Exits and Trailing Stop
Entry : When price exceeds the upper band of at least one of these indicators.
Exit : When price undercuts the lower band of at least one of these indicators.
Trailing Stop : See below.
Trailing Stop
This is a stop-loss order that moves with the price of the underlying. It is designed to “trail” the price up (in the case of a long position) or down (for a short position), locking in profits as the price moves in a favorable direction.
At the end of day t, there was a Trailing Stop level in place. For the next day (day t + 1), the Trailing Stop will be adjusted. The new Trailing Stop will be the higher of two values:
The Trailing Stop from the previous day (day t).
The Lower Band computed at the end of day t + 1.
Institutional Moves DetectorIndicator Name: Institutional Pattern Detector
What It Does:
Trend Following: It uses a Moving Average (MA) to understand the general direction of the price. The MA is like a smoothed-out line of the price over time, showing if the price trend is going up or down.
Volatility Measurement: The script employs Bollinger Bands (BB) to see how much the price is fluctuating. Bollinger Bands create an upper and lower "channel" around the price, which gets wider or narrower based on how volatile the price is.
Volume Check: It looks at trading volume to find times when there's unusually high activity, which could mean big players (institutions like banks or funds) are trading. It flags this when the volume is 1.5 times more than the average volume of the last 100 bars.
Pattern Detection for Trading Signals:
Entry Signal ("IN"): When there's high volume and the price is above the upper Bollinger Band, it suggests there might be strong buying from big institutions. This could mean the price might keep going up.
EXIT Signal ("OUT"): If there's high volume and the price falls below the lower Bollinger Band, it indicates possible strong selling pressure from institutions, suggesting the price might go down.
Visual Cues:
An orange label "IN" appears below the price bar for entry signals.
A red label "OUT" appears above the price bar for exit signals.
The moving average line is plotted on the chart in orange to help you see the trend.
Alerts: The script can alert you when these entry or exit signals occur, so you can get notifications without needing to stare at the chart all day.
For New Traders:
This indicator helps you spot when big traders might be influencing the market, potentially giving you a clue about when to enter or exit.
Remember, this is one tool among many. You should not base your trading solely on this; combine it with other analysis methods.
It's always wise to practice with a demo account before using real money to get a feel for how these signals work in actual market conditions.
[LeonidasCrypto]Volume Force IndexVolume Force Index (VFI)
Overview
The Volume Force Index (VFI) is a technical indicator that measures the balance between buying and selling pressure in the market by analyzing volume patterns. It helps traders identify potential trend reversals and confirm trend strength.
What It Measures
Buying vs. selling volume pressure
Market momentum
Potential overbought/oversold conditions
Volume trend strength
How to Read the Indicator
Main Components:
Main Line (Green/Red)
Green: Buying pressure is dominant
Red: Selling pressure is dominant
The steeper the slope, the stronger the pressure
Signal Line (Yellow)
Fast EMA that helps identify trend changes
Acts as an early warning system for potential reversals
Dynamic Bands (Red/Green lines)
Adapt to market volatility
Help identify extreme conditions
Based on actual market volatility rather than fixed levels
Signals to Watch
Trend Direction:
Rising oscillator = Increasing buying pressure
Falling oscillator = Increasing selling pressure
Signal Line Crossovers:
Main line crosses above signal line = Potential bullish signal
Main line crosses below signal line = Potential bearish signal
Band Touches:
Touching upper band = Possible buying exhaustion
Touching lower band = Possible selling exhaustion
Color Changes:
Green to Red = Shift to selling pressure
Red to Green = Shift to buying pressure
Best Practices
When to Use:
Trend confirmation
Identifying potential reversals
Volume analysis
Market strength assessment
Tips:
Use in conjunction with price action
Look for divergences with price
More reliable on higher timeframes
Consider market context
Default Settings:
MA Period: 14 (volume calculation)
Smooth Length: 3 (noise reduction)
EMA Period: 4 (signal line)
Volatility Period: 20 (band calculation)
Volatility Multiplier: 1.5 (band width)
Best Markets to Apply
Any market with reliable volume data
Summary
The VFI is a powerful tool that combines volume analysis with trend identification. Its adaptive nature makes it suitable for various market conditions, but it should be used as part of a complete trading strategy, not in isolation.
FON60DK by leventsahThe strategy generates buy and sell signals using the Tillson T3 and TOTT (Twin Optimized Trend Tracker) indicators. Additionally, the Williams %R indicator is used to filter the signals. Below is an explanation of the main components of the code:
1. Input Parameters:
Tillson T3 and TOTT parameters: Separate parameters are defined for both buy (AL) and sell (SAT) conditions. These parameters control the sensitivity and behavior of the indicators.
Williams %R period: The period for the Williams %R indicator is set to determine overbought and oversold levels.
2. Tillson T3 Calculation:
The Tillson T3 indicator is a smoothed moving average that uses an exponential moving average (EMA) with additional smoothing. The formula calculates a weighted average of multiple EMAs to produce a smoother line.
The t3 function computes the Tillson T3 value based on the close price and the input parameters.
3. TOTT Calculation (Twin Optimized Trend Tracker):
The TOTT indicator is a trend-following tool that adjusts its sensitivity based on market conditions. It uses a combination of price action and a volatility coefficient to determine trend direction.
The Var_Func function calculates the TOTT value, which is then used to derive the OTT (Optimized Trend Tracker) levels for both buy and sell conditions.
4. Williams %R Calculation:
Williams %R is a momentum oscillator that measures overbought and oversold levels. It is calculated using the highest high and lowest low over a specified period.
5. Buy and Sell Conditions:
Buy Condition: A buy signal is generated when the Tillson T3 value crosses above the TOTT upper band (OTTup) and the Williams %R is above -20 (indicating an oversold condition).
Sell Condition: A sell signal is generated when the Tillson T3 value crosses below the TOTT lower band (OTTdnS) and the Williams %R is above -70 (used to close long positions).
6. Strategy Execution:
The strategy.entry function is used to open a long position when the buy condition is met.
The strategy.close function is used to close the long position when the sell condition is met.
7. Visualization:
The bars on the chart are colored green when a long position is open.
The Tillson T3, TOTT upper band (OTTup), and TOTT lower band (OTTdn) are plotted on the chart for both buy and sell conditions.
8. Plots:
The Tillson T3 values for buy and sell conditions are plotted in blue.
The TOTT upper and lower bands are plotted in green and red, respectively, for both buy and sell conditions.
Summary:
This strategy combines trend-following indicators (Tillson T3 and TOTT) with a momentum oscillator (Williams %R) to generate buy and sell signals. The use of separate parameters for buy and sell conditions allows for fine-tuning the strategy based on market behavior. The visual elements, such as colored bars and plotted indicators, help traders quickly identify signals and trends on the chart.
Range Channel by Atilla YurtsevenThis script creates a dynamic channel around a user-selected moving average (MA). It calculates the relative difference between price and the MA, then finds the average of the positive differences and the negative differences separately. Using these averages, it plots upper and lower bands around the MA as well as a histogram-like oscillator to show when price moves above or below the average thresholds.
How It Works
Moving Average Selection
The indicator allows you to choose among multiple MA types (SMA, EMA, WMA, Linear Regression, etc.). Depending on your preference, it calculates the chosen MA for the selected lookback period.
Relative Difference Calculation
It then computes the percentage difference between the source (typically the closing price) and the MA. (diff = (src / ma - 1) * 100)
Positive & Negative Averages
- Positive differences are averaged and represent how far the price typically moves above the MA.
- Negative differences are similarly averaged for when price moves below the MA.
Range Channel & Oscillator
- The channel is plotted around the MA using the average positive and negative differences (Upper Edge and Lower Edge).
- The “Untrended” histogram plots the difference (diff). Green bars occur when price is above the MA on average, and red bars when below. Two additional lines mark the upper and lower average thresholds on this histogram.
How to Use
Identify Overbought/Oversold Zones: The upper edge can serve as a dynamic overbought level, while the lower edge can suggest potential oversold conditions. When the histogram approaches or crosses these levels, it may signal price extremes relative to its average movement.
Trend Confirmation: Compare price action relative to the channel. If price and the histogram consistently remain above the MA and upper threshold, it could indicate a stronger bullish trend. If they remain below, it might signal a prolonged bearish trend.
Entry/Exit Timings:
- Entry: Traders can look for moments when price breaks back inside the channel from an extreme, anticipating a mean reversion.
- Exit: Watching how price interacts with these dynamic edges can help define stop-loss or take-profit points.
Because these thresholds adapt over time based on actual price behavior, they can be more responsive than fixed-percentage bands. However, like all indicators, it’s most effective when used in conjunction with other technical and fundamental tools.
Disclaimer
This script is provided for educational and informational purposes only. It does not guarantee any specific outcome or profit. Use it at your own discretion and risk.
Trade smart, stay safe.
Atilla Yurtseven
Daily BreadWhat it does:
This script uses specific multiple true ranges from a 30 EMA baseline to plot lines that represent 10% buying increments. Although the common period for ATR is 14, this script employs a period of 20 for smoothing that I have determined is more effective when used with a daily candle chart. It includes onscreen trend signals to identify an uptrend or downtrend when the 50 EMA crosses the 90 EMA and will also display a coloured directional signal at each candle beyond an EMA cross to identify the current trend.
The script plots a scale of percentage labels at the end of each line to identify the percent of an account intended to be in short or longer term trades.
How it does it:
The script uses a 30 EMA baseline and then multiplies ATR increments of +1, +2, +4 and -1 through -7. These ATR multiples and the EMA are plotted as 11 lines, 10 of which make up the range of 10% increments from 10% to 100% with the 11th line being the High Band representing the extreme high or expected sale of any holdings. The percentage label scale uses variable declarations to position and colour match a percentage label to each line.
Intended use:
It is intended to be used for short term trading or long term investing with a daily market index chart such as SPY and multiple exchange traded funds that track said market index. A different ETF is purchased when a daily SPY candle reaches a lower buy band using 10% of a total account value. The sale of any ETFs is at the discretion of the trader and dependent on investment strategy (short term trading or long term inventing) and the trend. When short term trading in a downtrend or when daily candles are below the 50 EMA, selling would be done every 2 to 3 bands above a buy to mitigate the risk of a significant portion of an account getting caught in a downtrend. In an uptrend the High Band would be used to sell any holdings.
Double Donchian Channels [CrossTrade]Dual Channel System
The indicator incorporates two Donchian Channels - the Inner Channel and the Outer Channel. These channels are adjustable, allowing users to define their lengths according to their trading strategy.
Inner Channel: With a default length of 100 periods, the Inner Channel provides a closer view of market trends and potential support and resistance areas. It includes an upper, lower, and middle line (average of the upper and lower), offering detailed insights into shorter-term price movements.
Outer Channel: Set with a default length of 300 periods, the Outer Channel offers a broader perspective, ideal for identifying long-term trends and stronger levels of support and resistance.
Dynamic Color Coding: The middle lines of both channels change color based on the relationship between the previous close and the channel's basis. This feature provides an immediate visual cue regarding market sentiment.
Touching Bars Highlighting: The indicator highlights bars that touch the upper or lower bands of either channel. This is particularly useful for identifying potential reversals or continuation patterns.
Pullback Identification: By differentiating between bars that touch the Inner Channel only and those that touch the Outer Channel, the indicator helps in identifying pullbacks within a broader trend.
Customizable Alert System: Users can set up alerts for specific conditions - a bar touching the bottom band of the Inner Channel (green), the bottom band of the Outer Channel (blue), the upper band of the Inner Channel (red), and the upper band of the Outer Channel (orange). These alerts assist in timely decision-making and can be tailored to individual trading styles.
The indicator is a versatile tool designed to adapt to various trading styles and timeframes. Its features make it suitable for trend analysis, identifying potential reversal points, and understanding market volatility.
Advanced Keltner Channel/Oscillator [MyTradingCoder]This indicator combines a traditional Keltner Channel overlay with an oscillator, providing a comprehensive view of price action, trend, and momentum. The core of this indicator is its advanced ATR calculation, which uses statistical methods to provide a more robust measure of volatility.
Starting with the overlay component, the center line is created using a biquad low-pass filter applied to the chosen price source. This provides a smoother representation of price than a simple moving average. The upper and lower channel lines are then calculated using the statistically derived ATR, with an additional set of mid-lines between the center and outer lines. This creates a more nuanced view of price action within the channel.
The color coding of the center line provides an immediate visual cue of the current price momentum. As the price moves up relative to the ATR, the line shifts towards the bullish color, and vice versa for downward moves. This color gradient allows for quick assessment of the current market sentiment.
The oscillator component transforms the channel into a different perspective. It takes the price's position within the channel and maps it to either a normalized -100 to +100 scale or displays it in price units, depending on your settings. This oscillator essentially shows where the current price is in relation to the channel boundaries.
The oscillator includes two key lines: the main oscillator line and a signal line. The main line represents the current position within the channel, smoothed by an exponential moving average (EMA). The signal line is a further smoothed version of the oscillator line. The interaction between these two lines can provide trading signals, similar to how MACD is often used.
When the oscillator line crosses above the signal line, it might indicate bullish momentum, especially if this occurs in the lower half of the oscillator range. Conversely, the oscillator line crossing below the signal line could signal bearish momentum, particularly if it happens in the upper half of the range.
The oscillator's position relative to its own range is also informative. Values near the top of the range (close to 100 if normalized) suggest that price is near the upper Keltner Channel band, indicating potential overbought conditions. Values near the bottom of the range (close to -100 if normalized) suggest proximity to the lower band, potentially indicating oversold conditions.
One of the strengths of this indicator is how the overlay and oscillator work together. For example, if the price is touching the upper band on the overlay, you'd see the oscillator at or near its maximum value. This confluence of signals can provide stronger evidence of overbought conditions. Similarly, the oscillator hitting extremes can draw your attention to price action at the channel boundaries on the overlay.
The mid-lines on both the overlay and oscillator provide additional nuance. On the overlay, price action between the mid-line and outer line might suggest strong but not extreme momentum. On the oscillator, this would correspond to readings in the outer quartiles of the range.
The customizable visual settings allow you to adjust the indicator to your preferences. The glow effects and color coding can make it easier to quickly interpret the current market conditions at a glance.
Overlay Component:
The overlay displays Keltner Channel bands dynamically adapting to market conditions, providing clear visual cues for potential trend reversals, breakouts, and overbought/oversold zones.
The center line is a biquad low-pass filter applied to the chosen price source.
Upper and lower channel lines are calculated using a statistically derived ATR.
Includes mid-lines between the center and outer channel lines.
Color-coded based on price movement relative to the ATR.
Oscillator Component:
The oscillator component complements the overlay, highlighting momentum and potential turning points.
Normalized values make it easy to compare across different assets and timeframes.
Signal line crossovers generate potential buy/sell signals.
Advanced ATR Calculation:
Uses a unique method to compute ATR, incorporating concepts like root mean square (RMS) and z-score clamping.
Provides both an average and mode-based ATR value.
Customizable Visual Settings:
Adjustable colors for bullish and bearish moves, oscillator lines, and channel components.
Options for line width, transparency, and glow effects.
Ability to display overlay, oscillator, or both simultaneously.
Flexible Parameters:
Customizable inputs for channel width multiplier, ATR period, smoothing factors, and oscillator settings.
Adjustable Q factor for the biquad filter.
Key Advantages:
Advanced ATR Calculation: Utilizes a statistical method to generate ATR, ensuring greater responsiveness and accuracy in volatile markets.
Overlay and Oscillator: Provides a comprehensive view of price action, combining trend and momentum analysis.
Customizable: Adjust settings to fine-tune the indicator to your specific needs and trading style.
Visually Appealing: Clear and concise design for easy interpretation.
The ATR (Average True Range) in this indicator is derived using a sophisticated statistical method that differs from the traditional ATR calculation. It begins by calculating the True Range (TR) as the difference between the high and low of each bar. Instead of a simple moving average, it computes the Root Mean Square (RMS) of the TR over the specified period, giving more weight to larger price movements. The indicator then calculates a Z-score by dividing the TR by the RMS, which standardizes the TR relative to recent volatility. This Z-score is clamped to a maximum value (10 in this case) to prevent extreme outliers from skewing the results, and then rounded to a specified number of decimal places (2 in this script).
These rounded Z-scores are collected in an array, keeping track of how many times each value occurs. From this array, two key values are derived: the mode, which is the most frequently occurring Z-score, and the average, which is the weighted average of all Z-scores. These values are then scaled back to price units by multiplying by the RMS.
Now, let's examine how these values are used in the indicator. For the Keltner Channel lines, the mid lines (top and bottom) use the mode of the ATR, representing the most common volatility state. The max lines (top and bottom) use the average of the ATR, incorporating all volatility states, including less common but larger moves. By using the mode for the mid lines and the average for the max lines, the indicator provides a nuanced view of volatility. The mid lines represent the "typical" market state, while the max lines account for less frequent but significant price movements.
For the color coding of the center line, the mode of the ATR is used to normalize the price movement. The script calculates the difference between the current price and the price 'degree' bars ago (default is 2), and then divides this difference by the mode of the ATR. The resulting value is passed through an arctangent function and scaled to a 0-1 range. This scaled value is used to create a color gradient between the bearish and bullish colors.
Using the mode of the ATR for this color coding ensures that the color changes are based on the most typical volatility state of the market. This means that the color will change more quickly in low volatility environments and more slowly in high volatility environments, providing a consistent visual representation of price momentum relative to current market conditions.
Using a good IIR (Infinite Impulse Response) low-pass filter, such as the biquad filter implemented in this indicator, offers significant advantages over simpler moving averages like the EMA (Exponential Moving Average) or other basic moving averages.
At its core, an EMA is indeed a simple, single-pole IIR filter, but it has limitations in terms of its frequency response and phase delay characteristics. The biquad filter, on the other hand, is a two-pole, two-zero filter that provides superior control over the frequency response curve. This allows for a much sharper cutoff between the passband and stopband, meaning it can more effectively separate the signal (in this case, the underlying price trend) from the noise (short-term price fluctuations).
The improved frequency response of a well-designed biquad filter means it can achieve a better balance between smoothness and responsiveness. While an EMA might need a longer period to sufficiently smooth out price noise, potentially leading to more lag, a biquad filter can achieve similar or better smoothing with less lag. This is crucial in financial markets where timely information is vital for making trading decisions.
Moreover, the biquad filter allows for independent control of the cutoff frequency and the Q factor. The Q factor, in particular, is a powerful parameter that affects the filter's resonance at the cutoff frequency. By adjusting the Q factor, users can fine-tune the filter's behavior to suit different market conditions or trading styles. This level of control is simply not available with basic moving averages.
Another advantage of the biquad filter is its superior phase response. In the context of financial data, this translates to more consistent lag across different frequency components of the price action. This can lead to more reliable signals, especially when it comes to identifying trend changes or price reversals.
The computational efficiency of biquad filters is also worth noting. Despite their more complex mathematical foundation, biquad filters can be implemented very efficiently, often requiring only a few operations per sample. This makes them suitable for real-time applications and high-frequency trading scenarios.
Furthermore, the use of a more sophisticated filter like the biquad can help in reducing false signals. The improved noise rejection capabilities mean that minor price fluctuations are less likely to cause unnecessary crossovers or indicator movements, potentially leading to fewer false breakouts or reversal signals.
In the specific context of a Keltner Channel, using a biquad filter for the center line can provide a more stable and reliable basis for the entire indicator. It can help in better defining the overall trend, which is crucial since the Keltner Channel is often used for trend-following strategies. The smoother, yet more responsive center line can lead to more accurate channel boundaries, potentially improving the reliability of overbought/oversold signals and breakout indications.
In conclusion, this advanced Keltner Channel indicator represents a significant evolution in technical analysis tools, combining the power of traditional Keltner Channels with modern statistical methods and signal processing techniques. By integrating a sophisticated ATR calculation, a biquad low-pass filter, and a complementary oscillator component, this indicator offers traders a comprehensive and nuanced view of market dynamics.
The indicator's strength lies in its ability to adapt to varying market conditions, providing clear visual cues for trend identification, momentum assessment, and potential reversal points. The use of statistically derived ATR values for channel construction and the implementation of a biquad filter for the center line result in a more responsive and accurate representation of price action compared to traditional methods.
Furthermore, the dual nature of this indicator – functioning as both an overlay and an oscillator – allows traders to simultaneously analyze price trends and momentum from different perspectives. This multifaceted approach can lead to more informed decision-making and potentially more reliable trading signals.
The high degree of customization available in the indicator's settings enables traders to fine-tune its performance to suit their specific trading styles and market preferences. From adjustable visual elements to flexible parameter inputs, users can optimize the indicator for various trading scenarios and time frames.
Ultimately, while no indicator can predict market movements with certainty, this advanced Keltner Channel provides traders with a powerful tool for market analysis. By offering a more sophisticated approach to measuring volatility, trend, and momentum, it equips traders with valuable insights to navigate the complex world of financial markets. As with any trading tool, it should be used in conjunction with other forms of analysis and within a well-defined risk management framework to maximize its potential benefits.
MA MACD BB BackTesterOverview:
This Pine Script™ code provides a comprehensive backtesting tool that combines Moving Average (MA), Moving Average Convergence Divergence (MACD), and Bollinger Bands (BB). It is designed to help traders analyze market trends and make informed trading decisions by testing various strategies over historical data.
Key Features:
1. Customizable Indicators:
Moving Average (MA): Smooths out price data for clearer trend direction.
MACD: Measures trend momentum through MACD Line, Signal Line, and Histogram.
Bollinger Bands (BB): Identifies overbought or oversold conditions with upper and lower bands.
2. Flexible Trading Direction: Choose between long or short positions to adapt to different market conditions.
3. Risk Management: Efficiently allocate your capital with customizable position sizes.
4. Signal Generation:
Buy Signals: Triggered by crossovers for MACD, MA, and BB.
Sell Signals: Triggered by crossunders for MACD, MA, and BB.
5. Automated Trading: Automatically enter and exit trades based on signal conditions and strategy parameters.
How It Works:
1. Indicator Selection: Select your preferred indicator (MA, MACD, BB) and trading direction (Long/Short).
2. Risk Management Configuration: Set the percentage of capital to allocate per position to manage risk effectively.
3.Signal Detection: The algorithm identifies and plots buy/sell signals directly on the chart based on the chosen indicator.
4. Trade Execution: The strategy automatically enters and exits trades based on signal conditions and configured strategy parameters.
Use Cases:
- Backtesting: Evaluate the effectiveness of trading strategies using historical data to understand potential performance.
- Strategy Development: Customize and expand the strategy to incorporate additional indicators or conditions to fit specific trading styles.
ADDONS That Affect Strategy:
1. Indicator Parameters:
Adjustments to the settings of MACD (e.g., fast length, slow length), MA (e.g., length), and BB (e.g., length, multiplier) will directly impact the detection of signals and the strategy's performance.
2. Trading Direction:
Changing the trading direction (Long/Short) will alter the entry and exit conditions based on the detected signals.
3. Risk Management Settings:
Modifying the position size percentage affects capital allocation and overall risk exposure per trade.
ADDONS That Do Not Affect Strategy:
1. Visual Customizations:
Changes to the color, shape, and style of the plotted lines and signals do not impact the core functionality of the strategy but enhance visual clarity.
2. Text and Labels:
Modifying text labels for the signals (such as renaming "Buy MACD" to "MACD Buy Signal") is purely cosmetic and does not influence the strategy’s logic or outcomes.
Notes:
- Customization: The indicator is highly customizable to fit various trading styles and market conditions.
- Risk Management: Adjust position sizes and risk parameters according to your risk tolerance and account size.
- Optimization: Regularly backtest and optimize parameters to adapt to changing market dynamics for better performance.
Getting Started:
-Add the script to your chart.
-Adjust the input parameters to suit your analysis preferences.
-Observe the marked buy and sell signals on your chart to make informed trading decisions.
Bitcoin Wave RainbowThis Bitcoin Wave Rainbow model is a powerful tool designed to help traders of all levels understand and navigate the Bitcoin market. It works only with BTC in any timeframe, but better looks in dayly or weekly timeframes. It provides valuable insights into historical price behavior and offers forecasts for the next decade, making it an essential asset for both short-term and long-term strategies.
How the Model Works
The model is built on a logarithmic trend, also known as a power law, represented by the green line on the chart. This line illustrates the expected price trajectory of Bitcoin over time. The model also incorporates a range of price fluctuations around this trend, represented by colored bands.
The width of these bands narrows over time, indicating that the model becomes increasingly accurate as it progresses. This is due to the exponential decrease in the range of price fluctuations, making the model a reliable tool for predicting future price movements.
Understanding the Zones
Blue Zone: This zone signifies that the price is below its trend, making it a recommended area for buying Bitcoin. It represents a level where the price is unlikely to fall further, providing a potential opportunity for accumulation.
Green Zone: This zone represents a fair price range, where the price is relatively close to its trend. In this zone, the price may continue to go up or down, depending on the halving season. ransiting up around any halving and transiting down around 2 years after each halving.
Yellow Zone: This zone indicates that the price is somewhat overheated, often due to the hype following a halving event. While there may still be room for the price to rise, traders should exercise caution in this zone, as a price correction could occur.
Red Zone: This zone represents a strong overbought condition, where the price is significantly above its trend. Traders should be extremely cautious in this zone and consider reducing their positions, as the price is likely to revert back towards the trend or even lower.
Using the Model in Your Trading Strategy
This indicator can be used in conjunction with the Bitcoin Wave Model, which complements it by showing harmonic price fluctuations associated with halving events. Together, these indicators provide a comprehensive view of the Bitcoin market, allowing traders to make informed decisions based on both historical data and future projections.
Benefits for Traders
This Bitcoin price model offers numerous benefits for traders, including:
Clear Visualization: The model provides a clear and concise visual representation of Bitcoin's price behavior, making it easy to understand and interpret.
Accurate Forecasting: The model's accuracy increases over time, providing reliable forecasts for future price movements.
Risk Management: The model helps traders identify overbought and oversold conditions, allowing them to manage their risk more effectively.
Strategic Decision-Making: By understanding the different zones and their implications, traders can make more informed decisions about when to buy, sell, or hold Bitcoin.
By incorporating this Bitcoin price model into your trading strategy, you can gain a deeper understanding of the market dynamics and improve your chances of success.
Bullish Candlestick Patterns With Filters [TradeDots]The "Bullish Candlestick Patterns With Filters" is a trading indicator that identifies 6 core bullish candlestick patterns. This is further enhanced by applying channel indicator as filters, designed to further increase the accuracy of the recognized patterns.
6 CANDLESTICK PATTERNS
Hammer
Inverted Hammer
Bullish Engulfing
The Piercing Line
The Morning Star
The 3 White Soldiers
SIGNAL FILTERING
The indicator incorporates with 2 primary methodologies aimed at filtering out lower accuracy signals.
Firstly, it comes with a "Lowest period" parameter that examines whether the trough of the bullish candlestick configuration signifies the lowest point within a specified retrospective bar length. The longer the period, the higher the probability that the price will rebound.
Secondly, the channel indicators, the Keltner Channels or Bollinger Bands. This indicator examines whether the lowest point of the bullish candlestick pattern breaches the lower band, indicating an oversold signal. Users have the flexibility to modify the length and band multiplier, enabling them to custom-tune signal sensitivity.
Without Filtering:
With Filtering
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
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.
CCI based support and resistance strategy
WARNING:
Commissions and slippage has not been considered! Don’t take it easy adding commissions and slippage could turns a fake-profitable strategy to a real disaster.
We consider account size as 10k and we enter 1000 for each trade.
Less than 100 trades is too small sample community and it’s not reliable, Also the performance of the past do not guarantee future performance. This result was handpicked by author and will differ by other timeframes, instruments and settings.
*PLEASE SHARE YOUR SETTINGS THAT WORK WITH THE COMMUNITY.
Introduction:
The CCI-based dynamic support and resistance is a "Bands and Channels" kind of indicator consisting an upper and lower band. This is a strategy which uses CCI-based (Made by me) indicator to execute trades.
SL and TP are calculated based on max ATR during last selected time period. You can edit strategy settings using "Ksl", "Ktp" and the other button for time period. “KSL” and “KTP” are 2.5 and 5 by default.
Bands are calculated regarding CCI previous high and low pivot. CCI length, right pivot length and left pivot length are 50.
A dynamic support and resistance has been calculated using last upper-cci minus a buffer and last lower-cci plus the buffer. The buffer is 10.
If "Trend matter?" button is on you can detect trend by color of the upper and lower line. Green is bullish and red is bearish! "Trend matter?" is on.
The "show mid?" button makes mid line visible, which is average of upper and lower lines, visible. The button is not active by default.
Reaction to the support could be a buy signal while a reaction to the resistance could interpreted as a sell signal.
How this strategy work?
Donald Lambert, a technical analyst, created the CCI, or Commodity Channel Index, which he first published in 1980. CCI is calculated regarding CCI can be used both as trend-detector or an oscillator. As an oscillator most traders believe in static predefined levels. Overbought and oversold candles which are clear in the chart could be used as sell and buy signals.
During my trading career I’ve noticed that there might be some reversal points for the CCI. I believe CCI could have to potential to reverse more from lately reversal point. Of course, just like other trading strategies we are talking about probabilities. We do not expect a win trade each time.
On price chart
Now this the question! What price should the instrument reach that CCI turns to be equal to our reversing aim for CCI? Imagine we have found last important bearish reversal of CCI in 200. Now, if we need the CCI to be 200 what price should we wait for?
How to calculate?
This is the CCI formula:
CCI = (Typical Price - SMA of TP) / (0.015 x Mean Deviation)
Where, Typical Price (TP) = (High + Low + Close)/3
For probable reversing points, high and low pivots of 50 bars have been used.
So we do have an Upper CCI and a Lower CCI. They are valid until the next pivot is available.
By relocating factors in CCI formula you can reach the “Typical Price”.
“
Typical Price = CCI (0.015 * Mean Deviation) + SMA of TP
So we could have a Support or Resistance by replacing CCI with Upper and Lower CCI.
A buy signal is valid if the trend is bullish (or “trend matter” is off) and lowest low of last 2 candles is lower than support and close is greater than both support and open.
A Sell signal is produced in opposite situation.
There are 2+1 options for trend!
Trend matter box is on by default, which means we’ll just open trades in direction of the trend. It’s available to turn it off.
Other 2 options are cross and slope. Cross calculated by comparing fast SMA and slow SMA. The slope one differentiate slow SMA to last “n” one.
Considering last day and today highest ATR as the ATR to calculating SL and TP is our unique technique.
TrailingTakeProfit exampleQuite recently I came upon a concept of Trailing Take Profit and I couldn't find a PineScript which implements it for the fastest possible execution, so here it is :)
Everybody knows Trailing StopLoss - an invisible mechanism follows the price and exits the trade once the price retreats too much from its recent most extended favourable value. Trailing TakeProfit does the similar thing, but at the opposite end - the trade gets closed if a price moves too well, in too favourable extent.
Why close the trade if it is going so good? Well, whatever goes up, must go down and vice versa. It is expected, that after fast rally a market will soon fall and after a dump it will go up. So Trailing TakeProfit's role is to secure profits.
But how does Trailing TakeProfit differ from the standard one? "Trailing" means, the exit level is moving. Its role is to be executed only after a rapid favourable move within 1-2 candles, not more. We never know when a rapid move happens, but when it does, we wanna catch those pips and quickly exit without looking back.
Visually Trailing TakeProfit levels are... bands. In this script example these are ATR multiplied bands (aka Keltner Channel), but they could also be Bollinger Bands or something else.
The code is simple just to focus on this single functionality, so you can quickly copy-paste it into your script. Entries are triggered by default SMA crosses.
P.S. I wouldn't be myself, if I didn't add alert messages compatible with the syntax of recently revamped TradingConnector - both in the code already and in the table showing them.
Nifty 50 5mint Strategy
The script defines a specific trading session based on user inputs. This session is specified by a time range (e.g., "1000-1510") and selected days of the week (e.g., Monday to Friday). This session definition is crucial for trading only during specific times.
Lookback and Breakout Conditions:
The script uses a lookback period and the highest high and lowest low values to determine potential breakout points. The lookback period is user-defined (default is 10 periods).
The script also uses Bollinger Bands (BB) to identify potential breakout conditions. Users can enable or disable BB crossover conditions. BB consists of an upper and lower band, with the basis.
Additionally, the script uses Dema (Double Exponential Moving Average) and VWAP (Volume Weighted Average Price) . Users can enable or disable this condition.
Buy and Sell Conditions:
Buy conditions are met when the close price exceeds the highest high within the specified lookback period, Bollinger Bands conditions are satisfied, Dema-VWAP conditions are met, and the script is within the defined trading session.
Sell conditions are met when the close price falls below the lowest low within the lookback period, Bollinger Bands conditions are satisfied, Dema-VWAP conditions are met, and the script is within the defined trading session.
When either condition is met, it triggers a "long" or "short" position entry.
Trailing Stop Loss (TSL):
Users can choose between fixed points ( SL by points ) or trailing stop (Profit Trail).
For fixed points, users specify the number of points for the stop loss. A fixed stop loss is set at a certain distance from the entry price if a position is opened.
For Profit Trail, users can enable or disable this feature. If enabled, the script uses a "trail factor" (lookback period) to determine when to adjust the stop loss.
If the price moves in the direction of the trade and reaches a certain level (determined by the trail factor), the stop loss is adjusted, trailing behind the price to lock in profits.
If the close price falls below a certain level (lowest low within the trail factor(lookback)), and a position is open, the "long" position is closed (strategy.close("long")).
If the close price exceeds a certain level (highest high within the specified trail factor(lookback)), and a position is open, the "short" position is closed (strategy.close("short")).
Positions are also closed if they are open outside of the defined trading session.
Background Color:
The script changes the background color of the chart to indicate buy (green) and sell (red) signals, making it visually clear when the strategy conditions are met.
In summary, this script implements a breakout trading strategy with various customizable conditions, including Bollinger Bands, Dema-VWAP crossovers, and session-specific rules. It also includes options for setting stop losses and trailing stop losses to manage risk and lock in profits. The "trail factor" helps adjust trailing stops dynamically based on recent price movements. Positions are closed under certain conditions to manage risk and ensure compliance with the defined trading session.
CE=Buy, CE_SL=stoploss_buy, tCsl=Trailing Stop_buy.
PE=sell, PE_SL= stoploss_sell, tpsl=Trailing Stop_sell.
Remember that trading involves inherent risks, and past performance is not indicative of future results. Exercise caution, manage risk diligently, and consider the advice of financial experts when using this script or any trading strategy.
VCC SmtmWorks better for Cryptos (1W and greater than) timeframes.
This strategy incorporates multiple indicators to make informed trading signals. It leverages the Stochastic indicator to assess price momentum, utilizes the Bollinger Band to identify potential oversold and overbought conditions, and closely monitors Moving Averages to gauge the trend's bullish or bearish nature.
A long signal will be displayed if the following conditions are met:
The Stochastic D and Stochastic K both indicate an oversold condition, with Stochastic K being lower than Stochastic D.
The current Price Low is below the Bollinger Lower Band.
The Price Close is currently below all Moving Averages.
A Death Cross pattern has formed among the Moving Averages.
A short signal will be displayed if the opposite of the long conditions are true:
The Stochastic D and Stochastic K both indicate an overbought condition, with Stochastic K being higher than Stochastic D.
The current Price High is above the Bollinger Upper Band.
The Price Close is currently above all Moving Averages.
A Golden Cross pattern has formed among the Moving Averages.
Adaptive Price Channel (log scale)The field of technical analysis is consistently expanding, with numerous indicators used for market forecasting. Amongst them, a novel indicator dubbed the Adaptive Price Channel (log scale), inspired by the renowned Nadaraya-Watson Envelope (LuxAlgo) from LuxAlgo, is gaining traction for its distinctive features and versatility. Unlike its predecessor, the Adaptive Price Channel (log scale) is applicable on a logarithmic scale, thereby allowing it to be utilized on both smaller and larger timeframes.
1. Key Features
The Adaptive Price Channel (log scale) is founded on the trading view Pinescript language, version 5, with its primary aim to maximize the versatility and scalability of trading indicators. It allows traders to adapt it according to their preferred timeframe, thereby making it applicable for a wide range of trading strategies.
Its bandwidth can be adjusted through the input parameters, offering traders the flexibility to manipulate the indicator according to their strategic requirements. Furthermore, it provides an option for repainting smoothing. This option enables users to control the repainting effect in which the historical output of the indicator may change over time. When disabled, the indicator provides the endpoints of the calculations, ensuring consistency in historical values.
Moreover, the Adaptive Price Channel (log scale) allows for color customization, thereby improving visibility and user-friendliness. The colors of the indicator's upward and downward directions can be changed according to the user's preference.
2. Working Mechanism
The Adaptive Price Channel (log scale) uses the logarithm of the source, which is typically the closing price of a trading instrument. It leverages a Gaussian function that exponentially decreases the further the price moves away from the mean, accounting for both positive and negative values. The bandwidth of the Gaussian function can be adjusted to adapt to different market conditions.
Additionally, the Adaptive Price Channel (log scale) features an array of 500 lines for each bar, which helps in defining the boundaries or envelope for price movements. The calculations are executed using the Nadaraya-Watson estimator, which uses kernel regression for non-parametric analysis.
The calculated values for the upper and lower bounds of the envelope are then converted back from the logarithmic scale using the exponential function. This calculation process continues for each bar until the last bar in the data set.
To ensure optimal performance, the Adaptive Price Channel (log scale) uses dynamic repainting. If the repainting mode is enabled, it adjusts the smoothing of the indicator for the entire historical data, making the results more accurate.
3. Visualization and Alerts
The Adaptive Price Channel (log scale) offers an array of visual aids, including labels and plots. The upper and lower bounds of the envelope are plotted, and the indicator triggers labels at points where the closing price crosses these boundaries. These labels serve as alerts for potential trading opportunities.
4. Conclusion
The Adaptive Price Channel (log scale) is an innovative and adaptable trading indicator, drawing inspiration from its predecessor but introducing unique features to increase its versatility. By providing a repainting option, it ensures consistent historical values, thereby enhancing the reliability of the indicator. Furthermore, the capability to operate on a logarithmic scale broadens its usability for different timeframes. The Adaptive Price Channel (log scale) is a powerful tool for any trader, facilitating a better understanding of market dynamics, and enabling more informed decision-making.
Grid Strategy with MA0. Preface
Hello traders,
This is a strategy script that allows you to utilize a Grid Strategy using moving averages.
It is very simple, but I decided to post it because it was hard to find such shared open-source codes in Pine Script.
1. Main
This is a very simple trading method.
Based on the moving average line you set, if the price drops by a certain ATR (or percent) below it, you buy, and when it goes back up, you sell.
In basic settings, you choose the moving average line and its length, and decide how much to set the distance between each grid through the 'Band Multiplier/Percent' item.
I believe that it is advantageous to widen the bandwidth for stocks with strong upward momentum.
2. Conclusion
I have confirmed that this works better in the stock market than in the crypto market,
and that it is suitable for use on index stocks like NASDAQ because it follows trends.
In addition, through backtesting, I have confirmed that this grid strategy is more suitable for buying strategies than selling strategies, so I uploaded it as a strategy focused on buying strategies.
Personally, I have developed my own strategy by adjusting buying and selling strategies according to trends and managing risks.
I hope you can use this to create a script that suits you.
Thank you.
Range of a source displayed in thirdsThis indicator will take the value of any external source input and display how it has changed over time (the lookback period in settings). For the purposes of display here I'm using the WT1 line from Wavetrend with Crosses by LazyBear to provide a source input.
The highest and lowest value of the source over the lookback period are used to determine the highest and lowest point - the green and red lines at the top and bottom of the bands. This region is then mathematically split into three, such that the source (and its optional moving average line) can be defined as being in the top third, the middle or the bottom third.
Applications for this could be in risk management where you may wish to take on a larger position size when a certain indicator is in the top third, or decide that you want to enter / leave positions when the source crosses in / out of the extreme points.
WillyCycle Oscillator&DoubleMa/ErkOzi/version 2This oscillator can be customized by adjusting the length of the Willy period, the length of Willy's EMA, and the upper and lower bands. The upper and lower bands help traders identify overbought and oversold conditions.
The WillyCycle Oscillator is a technical analysis tool used to measure the momentum of an asset and identify overbought and oversold conditions based on the price range of a specific period and calculating the percentage of the closing price in that range. The WillyCycle Oscillator consists of two main components: Willy and Willy's EMA. The Willy component is the percentage calculation of the asset's price range, and Willy's EMA is the exponential moving average of the Willy component. Willy's EMA is used to smooth out the Willy component and make it easier to identify trends.
*** When the oscillator is above the 80 level, it indicates that the asset is overbought, and when it is below the 20 level, it indicates that the asset is oversold. Traders can use these levels as a guide for buying and selling signals.
***Traders can also use the WillyCycle Oscillator to identify trend reversals. When the oscillator rises above the 50 level, it signals a potential uptrend, and when it falls below the 50 level, it signals a potential downtrend.
***I have added a smoothed line option to the WillyCycle Oscillator, which allows traders to see a more smoothed version of the oscillator. This option can be enabled by setting the 'smoothed' input to true. The default value for the smoothed line is 15.
***We have also changed the value range of the WillyCycle Oscillator from -100 to 100 to 0 to 100. This change was made to make the oscillator more user-friendly and easier to read.
In conclusion, the WillyCycle Oscillator is a versatile tool that can help traders identify potential trading opportunities and trend reversals. Traders can customize the oscillator to fit their trading style and preferences. Adding a smoothed line and changing the value range can enhance the user experience and make the oscillator easier to use.
Price Heat MapWhat does this chart show? Take the highest high and lowest low of 200 bars. Divide that into 20 chunks. The more time the price spends in one of those 1/20th pockets, the brighter it is lit up on the chart. Number of bars back can be modified to around 500. It starts to chug beyond that. Brightness level of heat map can be adjusted. 0.5 is default. 1 = brighter, 0 = dimmer. Use on any time frame. When price moves out of a hot zone, it can move very quickly. There's no trading strategy here, just something to help you visualize recent price action. The blue band shows the price at the center of the current "hottest" band. The yellow band is the ema (exponential moving average) of the price using the "bars back" input. --enjoy!
