Multi Time Frame Normalized PriceEnhance Your Trading Experience with the Multi Time Frame Normalized Price Indicator
Introduction
As a trader, having a clear and informative chart is crucial for making informed decisions. In this post, we will introduce the Multi Time Frame Normalized Price (MTFNP) Indicator, an innovative trading tool that offers an insightful perspective on price action. The script creates a symmetric chart, with the time axis going from top to bottom, making it easier to identify potential tops and bottoms in various ranges. Let's dive deeper into this powerful tool to understand how it works and how it can improve your trading experience.
The Multi Time Frame Normalized Price Indicator
The MTFNP Indicator is designed to provide a comprehensive view of price action across multiple time frames. By plotting the normalized price levels for each time frame, traders can easily identify areas of support and resistance, as well as potential tops and bottoms in various ranges.
One of the key features of this indicator is the symmetry of the chart. Instead of the traditional horizontal time axis, the MTFNP Indicator plots the time axis vertically from top to bottom. This innovative approach makes it easier for traders to visualize the price action across different time frames, enabling them to make more informed decisions.
Benefits of a Symmetric Chart
There are several advantages to using a symmetric chart with a vertical time axis, such as:
Easier to read: The unique layout of the chart makes it easier to analyze price action across multiple time frames. The clear separation between each time frame helps traders avoid confusion and identify important price levels more effectively.
Identifying tops and bottoms: The symmetric presentation of price action enables traders to quickly spot potential tops and bottoms in various ranges. This can be particularly useful for identifying potential reversal points or areas of support and resistance.
Improved decision-making: By offering a comprehensive view of price action, the MTFNP Indicator helps traders make better-informed decisions. This can lead to improved trading strategies and ultimately, better results.
The MTFNP Indicator Script
The MTFNP Indicator script leverages several custom functions, including the Chebyshev Type I Moving Average, to provide a smooth and responsive signal. Additionally, the indicator uses the Spider Plot function to create a symmetric chart with the time axis going from top to bottom.
To customize the MTFNP Indicator to your preferences, you can adjust the input parameters, such as the standard deviation length, multiplier, axes color, bottom color, and top color. You can also change the scale to fit your desired chart size.
Exploring the Relationship between Min, Max Values and Time Frames
In the Multi Time Frame Normalized Price (MTFNP) script, it is crucial to understand the relationship between the min and max values across different time frames. By analyzing how these values relate to each other, traders can make more informed decisions about market trends and potential reversals. In this section, we will dive deep into the relationship between the current time frame's min and max values and those of the further-out time frames.
Interpreting Min and Max Values Across Time Frames
When analyzing the min and max values of the current time frame in relation to the further-out time frames, it is essential to keep in mind the following points:
All min values: If the current time frame and all further-out time frames have min values, this is a strong indication that the current price level is not just a local minimum. Instead, it is likely a more significant support level. In such cases, there is a higher probability that the price will bounce back upwards, making it a potentially favorable entry point for a long position.
All max values: Conversely, if the current time frame and all further-out time frames have max values, this suggests that the current price level is not just a local maximum. Instead, it is likely a more significant resistance level. In these situations, there is a higher probability that the price will reverse downwards, making it a potentially favorable entry point for a short position.
Neutral values with high current time frame: If the current time frame has a high value while the further-out time frames are more neutral, it could indicate that the trend may continue. This is because the high value in the current time frame may signify momentum in the market, whereas the neutral values in the further-out time frames suggest that the trend has not yet reached an extreme level. In this case, traders might consider following the trend and entering a position in the direction of the current movement.
Neutral values with low current time frame: If the current time frame has a low value while the further-out time frames are more neutral, it could indicate that the trend may reverse. This is because the low value in the current time frame may suggest a potential reversal point, whereas the neutral values in the further-out time frames imply that the trend has not yet reached an extreme level. In this case, traders might consider entering a counter-trend position, anticipating a potential reversal.
Balancing Different Time Frames for Optimal Decision Making
It is essential to remember that relying solely on min and max values across different time frames can lead to potential pitfalls. The market is influenced by a wide array of factors, and no single indicator or data point can provide a complete picture. To make the most informed decisions, traders should consider incorporating additional technical analysis tools and evaluating the overall market context.
Moreover, it is crucial to maintain a balance between the current time frame and the further-out time frames. While the current time frame provides information about the most recent market movements, the further-out time frames offer a broader perspective on the market's historical behavior. By combining insights from both types of time frames, traders can make more comprehensive assessments of potential opportunities and risks.
Conclusion
In conclusion, the Multi Time Frame Normalized Price (MTFNP) script offers traders valuable insights by analyzing the relationship between the current time frame and further-out time frames. By identifying potential trend reversals and continuations, traders can make better-informed decisions about market entry and exit points.
Understanding the relationship between min and max values across different time frames is an essential component of using the MTFNP script effectively. By carefully analyzing these relationships and incorporating additional technical analysis tools, traders can improve their decision-making process and enhance their overall trading strategy.
However, it is important to remember that relying solely on the MTFNP script or any single indicator can lead to potential pitfalls. The market is influenced by a wide array of factors, and no single indicator or data point can provide a complete picture. To make the most informed decisions, traders should consider using a combination of technical analysis tools, evaluating the overall market context, and maintaining a balance between the current time frame and the further-out time frames for a comprehensive understanding of the market's behavior. By doing so, they can increase their chances of success in the ever-changing and complex world of trading.
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Stochastic Chebyshev Smoothed With Zero Lag SmoothingFast and Smooth Stochastic Oscillator with Zero Lag
Introduction
In this post, we will discuss a custom implementation of a Stochastic Oscillator that not only smooths the signal but also does so without introducing any noticeable lag. This is a remarkable achievement, as it allows for a fast Stochastic Oscillator that is less prone to false signals without being slow and sluggish.
We will go through the code step by step, explaining the various functions and the overall structure of the code.
First, let's start with a brief overview of the Stochastic Oscillator and the problem it addresses.
Background
The Stochastic Oscillator is a momentum indicator used in technical analysis to determine potential overbought or oversold conditions in an asset's price. It compares the closing price of an asset to its price range over a specified period. However, the Stochastic Oscillator is susceptible to false signals due to its sensitivity to price movements. This is where our custom implementation comes in, offering a smoother signal without noticeable lag, thus reducing the number of false signals.
Despite its popularity and widespread use in technical analysis, the Stochastic Oscillator has its share of drawbacks. While it is a price scaler that allows for easier comparisons across different assets and timeframes, it is also known for generating false signals, which can lead to poor trading decisions. In this section, we will delve deeper into the limitations of the Stochastic Oscillator and discuss the challenges associated with smoothing to mitigate its drawbacks.
Limitations of the Stochastic Oscillator
False Signals: The primary issue with the Stochastic Oscillator is its tendency to produce false signals. Since it is a momentum indicator, it reacts to short-term price movements, which can lead to frequent overbought and oversold signals that do not necessarily indicate a trend reversal. This can result in traders entering or exiting positions prematurely, incurring losses or missing out on potential gains.
Sensitivity to Market Noise: The Stochastic Oscillator is highly sensitive to market noise, which can create erratic signals in volatile markets. This sensitivity can make it difficult for traders to discern between genuine trend reversals and temporary fluctuations.
Lack of Predictive Power: Although the Stochastic Oscillator can help identify potential overbought and oversold conditions, it does not provide any information about the future direction or strength of a trend. As a result, it is often used in conjunction with other technical analysis tools to improve its predictive power.
Challenges of Smoothing the Stochastic Oscillator
To address the limitations of the Stochastic Oscillator, many traders attempt to smooth the indicator by applying various techniques. However, these approaches are not without their own set of challenges:
Trade-off between Smoothing and Responsiveness: The process of smoothing the Stochastic Oscillator inherently involves reducing its sensitivity to price movements. While this can help eliminate false signals, it can also result in a less responsive indicator, which may not react quickly enough to genuine trend reversals. This trade-off can make it challenging to find the optimal balance between smoothing and responsiveness.
Increased Complexity: Smoothing techniques often involve the use of additional mathematical functions and algorithms, which can increase the complexity of the indicator. This can make it more difficult for traders to understand and interpret the signals generated by the smoothed Stochastic Oscillator.
Lagging Signals: Some smoothing methods, such as moving averages, can introduce a time lag into the Stochastic Oscillator's signals. This can result in late entry or exit points, potentially reducing the profitability of a trading strategy based on the smoothed indicator.
Overfitting: In an attempt to eliminate false signals, traders may over-optimize their smoothing parameters, resulting in a Stochastic Oscillator that is overfitted to historical data. This can lead to poor performance in real-time trading, as the overfitted indicator may not accurately reflect the dynamics of the current market.
In our custom implementation of the Stochastic Oscillator, we used a combination of Chebyshev Type I Moving Average and zero-lag Gaussian-weighted moving average filters to address the indicator's limitations while preserving its responsiveness. In this section, we will discuss the reasons behind selecting these specific filters and the advantages of using the Chebyshev filter for our purpose.
Filter Selection
Chebyshev Type I Moving Average: The Chebyshev filter was chosen for its ability to provide a smoother signal without sacrificing much responsiveness. This filter is designed to minimize the maximum error between the original and the filtered signal within a specific frequency range, effectively reducing noise while preserving the overall shape of the signal. The Chebyshev Type I Moving Average achieves this by allowing a specified amount of ripple in the passband, resulting in a more aggressive filter roll-off and better noise reduction compared to other filters, such as the Butterworth filter.
Zero-lag Gaussian-weighted Moving Average: To further improve the Stochastic Oscillator's performance without introducing noticeable lag, we used the zero-lag Gaussian-weighted moving average (GWMA) filter. This filter combines the benefits of a Gaussian-weighted moving average, which prioritizes recent data points by assigning them higher weights, with a zero-lag approach that minimizes the time delay in the filtered signal. The result is a smoother signal that is less prone to false signals and is more responsive than traditional moving average filters.
Advantages of the Chebyshev Filter
Effective Noise Reduction: The primary advantage of the Chebyshev filter is its ability to effectively reduce noise in the Stochastic Oscillator signal. By minimizing the maximum error within a specified frequency range, the Chebyshev filter suppresses short-term fluctuations that can lead to false signals while preserving the overall trend.
Customizable Ripple Factor: The Chebyshev Type I Moving Average allows for a customizable ripple factor, enabling traders to fine-tune the filter's aggressiveness in reducing noise. This flexibility allows for better adaptability to different market conditions and trading styles.
Responsiveness: Despite its effective noise reduction, the Chebyshev filter remains relatively responsive compared to other smoothing filters. This responsiveness allows for more accurate detection of genuine trend reversals, making it a suitable choice for our custom Stochastic Oscillator implementation.
Compatibility with Zero-lag Techniques: The Chebyshev filter can be effectively combined with zero-lag techniques, such as the Gaussian-weighted moving average filter used in our custom implementation. This combination results in a Stochastic Oscillator that is both smooth and responsive, with minimal lag.
Code Overview
The code begins with defining custom mathematical functions for hyperbolic sine, cosine, and their inverse functions. These functions will be used later in the code for smoothing purposes.
Next, the gaussian_weight function is defined, which calculates the Gaussian weight for a given 'k' and 'smooth_per'. The zero_lag_gwma function calculates the zero-lag moving average with Gaussian weights. This function is used to create a Gaussian-weighted moving average with minimal lag.
The chebyshevI function is an implementation of the Chebyshev Type I Moving Average, which is used for smoothing the Stochastic Oscillator. This function takes the source value (src), length of the moving average (len), and the ripple factor (ripple) as input parameters.
The main part of the code starts by defining input parameters for K and D smoothing and ripple values. The Stochastic Oscillator is calculated using the ta.stoch function with Chebyshev smoothed inputs for close, high, and low. The result is further smoothed using the zero-lag Gaussian-weighted moving average function (zero_lag_gwma).
Finally, the lag variable is calculated using the Chebyshev Type I Moving Average for the Stochastic Oscillator. The Stochastic Oscillator and the lag variable are plotted on the chart, along with upper and lower bands at 80 and 20 levels, respectively. A fill is added between the upper and lower bands for better visualization.
Conclusion
The custom Stochastic Oscillator presented in this blog post combines the Chebyshev Type I Moving Average and zero-lag Gaussian-weighted moving average filters to provide a smooth and responsive signal without introducing noticeable lag. This innovative implementation results in a fast Stochastic Oscillator that is less prone to false signals, making it a valuable tool for technical analysts and traders alike.
However, it is crucial to recognize that the Stochastic Oscillator, despite being a price scaler, has its limitations, primarily due to its propensity for generating false signals. While smoothing techniques, like the ones used in our custom implementation, can help mitigate these issues, they often introduce new challenges, such as reduced responsiveness, increased complexity, lagging signals, and the risk of overfitting.
The selection of the Chebyshev Type I Moving Average and zero-lag Gaussian-weighted moving average filters was driven by their combined ability to provide a smooth and responsive signal while minimizing false signals. The advantages of the Chebyshev filter, such as effective noise reduction, customizable ripple factor, and responsiveness, make it an excellent fit for addressing the limitations of the Stochastic Oscillator.
When using the Stochastic Oscillator, traders should be aware of these limitations and challenges, and consider incorporating other technical analysis tools and techniques to supplement the indicator's signals. This can help improve the overall accuracy and effectiveness of their trading strategies, reducing the risk of losses due to false signals and other limitations associated with the Stochastic Oscillator.
Feel free to use, modify, or improve upon this custom Stochastic Oscillator code in your trading strategies. We hope this detailed walkthrough of the custom Stochastic Oscillator, its limitations, challenges, and filter selection has provided you with valuable insights and a better understanding of how it works. Happy trading!
Stochastic RSI Strategy (with SMA and VWAP Filters)The strategy is designed to trade on the Stochastic RSI indicator crossover signals.
Below are all of the trading conditions:
-When the Stochastic RSI crosses above 30, a long position is entered.
-When the Stochastic RSI crosses below 70, a short position is entered.
-The strategy also includes two additional conditions for entry:
-Long entries must have a positive spread value between the 9 period simple moving average and the 21 period simple moving average.
-Short entries must have a negative spread value between the 9 period simple moving average and the 21 period simple moving average.
-Long entries must also be below the volume-weighted average price.
-Short entries must also be above the volume-weighted average price.
-The strategy includes stop loss and take profit orders for risk management:
-A stop loss of 20 ticks is placed for both long and short trades.
-A take profit of 25 ticks is placed for both long and short trades.
Strength between currencies using RSICalculate the RSI between currencies and summarize it in a table.
If the RSI between currencies is greater than or equal to 50, it will have a red background, and if it is less than 50, it will have a blue background.
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通貨間のRSIを計算し、表にまとめる。
通貨間のRSIが50以上の場合は赤色、50未満の場合は青色の背景にする。
Hull Suite Oscillator - Normalized | IkkeOmarThis script is based off the Hull Suite by @InSilico.
I made this script to provide and calculate the Hull Moving Average (HMA) based on the chosen variation (HMA, TMA, or EMA) and length to then normalize the HMA values to a range of 0 to 100. The normalized values are further smoothed using an exponential moving average (EMA).
The smoothed oscillator is plotted as a line, where values above 80 are colored red, values below 20 are colored green, and values between 20 and 80 are colored blue. Additionally, there are horizontal dashed lines at the levels of 20 and 80 to serve as reference points.
Explanation for the code:
The script uses the close price of the asset as the source for calculations. The modeSwitch parameter allows selecting the type of Hull variation: Hma, Thma, or Ehma. The length parameter determines the calculation period for the Hull moving averages. The lengthMult parameter is used to adjust the length for higher timeframes. The oscSmooth parameter determines the lookback period for smoothing the oscillator.
There are three functions defined for calculating different types of Hull moving averages: HMA, EHMA, and THMA. These functions take the source and length as inputs and return the corresponding Hull moving average.
The Mode function acts as a switch and selects the appropriate Hull variation based on the modeSwitch parameter. It returns the chosen Hull moving average.
The script calculates the Hull moving averages using the selected mode, source, and length. The main Hull moving average is stored in the _hull variable, and aliases are created for the main Hull moving average (HULL), the main Hull value (MHULL), and the secondary Hull value (SHULL).
To create the normalized oscillator values, the script finds the highest and lowest values of the Hull moving average within the specified length. It then normalizes the Hull values to a range of 0 to 100 using a formula. This normalized oscillator represents the strength of the trend.
To smooth out the oscillator values, an exponential moving average is applied using the oscSmooth parameter.
The smoothed oscillator is plotted as a line chart. The line color is determined based on the oscillator value using conditional statements. If the oscillator value is above or equal to 80, the line color is set to red. If it is below or equal to 20, the color is green. Otherwise, it is blue. The linewidth is set to 2.
Additionally, two horizontal reference lines are plotted at levels 20 and 80 for visual reference. They are displayed in gray and dashed style.
Momentum Channel - [Volume Filter]The indicator incorporates a volume filter to ensure that the RSI only moves when the volume is above the moving average of the volume.
The filtered RSI is then used to calculate the Bollinger Bands and moving averages, providing insights into the market dynamics.
It also gives you insight into the bigger timeframes so you can monitor momentum!
Volume Filter Length: Input parameter for the length of the volume filter moving average.
Overview of code:
rsiPeriod: Input parameter for the RSI period.
bandLength: Input parameter for the length of the Bollinger Bands.
lengthrsipl: Input parameter for the length of the fast moving average (MA) on the RSI.
volumeFilterLength: Input parameter for the length of the volume filter moving average.
volumeAvg: Calculates the moving average of the volume using the ta.sma() function with the specified volume filter length.
filteredRsi: Uses the ta.valuewhen() function to obtain the RSI value only when the volume is greater than or equal to the volume moving average. This creates a filtered RSI based on the volume filter.
offs: Calculates the offset value for the Bollinger Bands. It is derived by multiplying 1.6185 with the standard deviation of the filtered RSI using the ta.stdev() function.
Normalized KAMA Oscillator | Ikke OmarThis indicator demonstrates the creation of a normalized KAMA (Kaufman Adaptive Moving Average) oscillator with a table display. I will explain how the code works, providing a step-by-step breakdown. This is personally made by me:)
Input Parameters:
fast_period and slow_period: Define the periods for calculating the KAMA.
er_period: Specifies the period for calculating the Efficiency Ratio.
norm_period: Determines the lookback period for normalizing the oscillator.
Efficiency Ratio (ER) Calculation:
Measures the efficiency of price changes over a specified period.
Calculated as the ratio of the absolute price change to the total price volatility.
Smoothing Constant Calculation:
Determines the smoothing constant (sc) based on the Efficiency Ratio (ER) and the fast and slow periods.
The formula accounts for the different periods to calculate an appropriate smoothing factor.
KAMA Calculation:
Uses the Exponential Moving Average (EMA) and the smoothing constant to compute the KAMA.
Combines the fast EMA and the adjusted price change to adapt to market conditions.
Oscillator Normalization:
Normalizes the oscillator values to a range between -0.5 and 0.5 for better visualization and comparison.
Determines the highest and lowest values of the KAMA within the specified normalization period.
Transforms the KAMA values into a normalized range.
By incorporating the Efficiency Ratio, smoothing constant, and normalization techniques, the indicator actually allows for the identification of trends on different timeframes, even in extreme market conditions.
The normalization makes it much more adaptive than if you were to just use a normal KAMA line. This way you actually get a lot more data by looking at the histogram, rather than just the KAMA line.
I essentially made the KAMA into an oscillator! Please ask if you want me to code another indicator
I hope you enjoyed this.
Please ask if you have any questions<3
Forex RadarForex Radar Indicator: A Powerful Tool for Analyzing Currency Strength and Weakness
Introduction
The Forex Radar Indicator is an innovative tool that provides a visual representation of the relative strength and weakness of various currencies in the Forex market. This indicator is designed to help traders identify potential trading opportunities by analyzing the performance of different currency pairs. In this blog post, we will explore the features and benefits of the Forex Radar Indicator, and explain how to use it effectively in your trading strategy.
Features of the Forex Radar Indicator
1. Spider Plot Visualization
The Forex Radar Indicator uses a spider plot to display the relative strength and weakness of various currencies. A spider plot is a graphical representation of multivariate data, in which each variable is plotted on a separate axis that radiates from the center of the plot. The data points are connected by lines, forming a web-like pattern that makes it easy to compare the performance of different currencies.
2. Customizable Color Scheme
The Forex Radar Indicator allows users to customize the color scheme for each currency, making it easy to identify individual currencies on the spider plot. This feature can be particularly helpful for traders who prefer specific colors for each currency, or who want to use a color scheme that matches their trading platform or charting tools.
3. EMA Divergence and RSI Style Selection
The Forex Radar Indicator offers users the flexibility to choose between two different styles: EMA divergence and RSI. The EMA divergence style displays the difference between a short-term and long-term exponential moving average, while the RSI style shows the relative strength index of the currency pairs. By selecting the preferred style, traders can customize the indicator to suit their specific trading style and strategy.
4. Flexible Input Parameters
The Forex Radar Indicator offers flexible input parameters, allowing users to customize the indicator according to their trading preferences. These parameters include the length of the moving average, the filter value for the moving average, and the normalization length. By adjusting these parameters, traders can fine-tune the indicator to suit their specific trading style and strategy.
Using the Forex Radar Indicator in Your Trading Strategy
The Forex Radar Indicator can be a valuable tool in any trading strategy, as it provides a visual representation of the currency strength and weakness. Here are some tips on how to use the Forex Radar Indicator effectively in your trading:
1. Identify Currency Strength and Weakness
The main purpose of the Forex Radar Indicator is to help traders identify the strength and weakness of various currencies. By analyzing the spider plot, traders can quickly determine which currencies are performing well and which are underperforming. This information can be used to identify potential trading opportunities, as traders can focus on currency pairs that feature a strong currency against a weak one.
2. Choose Between EMA Divergence and RSI Style
Depending on your trading style and strategy, you can choose between the EMA divergence and RSI style options provided by the Forex Radar Indicator. Both styles offer valuable insights into currency strength and weakness, but they may highlight different aspects of the market. By selecting the style that best aligns with your trading approach, you can maximize the effectiveness of the indicator in your trading strategy.
3. Combine with Other Technical Analysis Tools
While the Forex Radar Indicator provides valuable insights into currency strength and weakness, it is important to remember that no single indicator can provide a complete picture of the market. To improve the accuracy and effectiveness of your trading strategy, consider combining the Forex Radar Indicator with other technical analysis tools, such as trend lines, support and resistance levels, and other indicators.
Conclusion
The Forex Radar Indicator is a powerful tool that can help traders gain a better understanding of the relative strength and weakness of various currencies in the Forex market. By incorporating the Forex Radar Indicator into your trading strategy, you can quickly identify potential trading opportunities and make more informed trading decisions. With its customizable color scheme, EMA divergence and RSI style options, and flexible input parameters, the Forex Radar Indicator is a versatile tool that can be adapted to suit any trading style or strategy.
Radar RiderThe Radar Rider indicator is a powerful tool that combines multiple technical indicators into a single spider plot, providing traders with a comprehensive view of market conditions. This article will delve into the workings of each built-in indicator and their arrangement within the spider plot. To better understand the structure of the script, let's first examine some of the primary functions and how they are utilized in the script.
Normalize Function: normalize(close, len)
The normalize function takes the close price and a length as arguments and normalizes the price data by scaling it between 0 and 1, making it easier to compare different indicators.
Exponential Moving Average (EMA) Filter: bes(source, alpha)
The EMA filter is used to smooth out data using an exponential moving average, with the given alpha value defining the level of smoothing. This helps reduce noise and enhance the trend-following characteristics of the indicators.
Maximum and Minimum Functions: max(src) and min(src)
These functions find the maximum and minimum values of the input data over a certain period, respectively. These values are used in the normalization process and can help identify extreme conditions in the market.
Min-Max Function: min_max(src)
The min-max function scales the input data between 0 and 100 by dividing the difference between the data point and the minimum value by the range between the maximum and minimum values. This standardizes the data, making it easier to compare across different indicators.
Slope Function: slope(source, length, n_len, pre_smoothing = 0.15, post_smoothing = 0.7)
The slope function calculates the slope of a given data source over a specified length, and then normalizes it using the provided normalization length. Pre-smoothing and post-smoothing values can be adjusted to control the level of smoothing applied to the data before and after calculating the slope.
Percent Function: percent(x, y)
The percent function calculates the percentage difference between two values, x and y. This is useful for comparing the relative change in different indicators.
In the given code, there are multiple indicators included. Here, we will discuss each of them in detail.
EMA Diff:
The Exponential Moving Average (EMA) Diff is the difference between two EMA values of different lengths. The EMA is a type of moving average that gives more weight to recent data points. The EMA Diff helps traders identify trends and potential trend reversals. In the code, the EMA Diff is calculated using the ema_diff() function, which takes length, close, filter, and len_norm as parameters.
Percent Rank EMA Diff:
The Percent Rank EMA Diff is the percentage rank of the EMA Diff within a given range. It helps traders identify overbought or oversold conditions in the market. In the code, the Percent Rank EMA Diff is calculated using the percent_rank_ema_diff() function, which takes length, close, filter, and len_norm as parameters.
EMA Diff Longer:
The EMA Diff Longer is the difference between two EMA values of different lengths, similar to EMA Diff but with a longer period. In the code, the EMA Diff Longer is calculated using the ema_diff_longer() function, which takes length, close, filter, and len_norm as parameters.
RSI Filter:
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. The RSI Filter is the RSI value passed through a filter to smooth out the data. In the code, the RSI Filter is calculated using the rsi_filter() function, which takes length, close, and filter as parameters.
RSI Diff Normalized:
The RSI Diff Normalized is the normalized value of the derivative of the RSI. It helps traders identify potential trend reversals in the market. In the code, the RSI Diff Normalized is calculated using the rsi_diff_normalized() function, which takes length, close, filter, len_mad, and len_norm as parameters.
Z Score:
The Z Score is a statistical measurement that describes a value's relationship to the mean of a group of values. In the context of the code, the Z Score is calculated for the closing price of a security. The z_score() function takes length, close, filter, and len_norm as parameters.
EMA Normalized:
The EMA Normalized is the normalized value of the EMA, which helps traders identify trends and potential trend reversals in the market. In the code, the EMA Normalized is calculated using the ema_normalized() function, which takes length, close, filter, and len_norm as parameters.
WMA Volume Normalized:
The Weighted Moving Average (WMA) Volume Normalized is the normalized value of the WMA of the volume. It helps traders identify volume trends and potential trend reversals in the market. In the code, the WMA Volume Normalized is calculated using the wma_volume_normalized() function, which takes length, volume, filter, and len_norm as parameters.
EMA Close Diff Normalized:
The EMA Close Diff Normalized is the normalized value of the derivative of the EMA of the closing price. It helps traders identify potential trend reversals in the market. In the code, the EMA Close Diff Normalized is calculated using the ema_close_diff_normalized() function, which takes length, close, filter, len_mad, and len_norm as parameters.
Momentum Normalized:
The Momentum Normalized is the normalized value of the momentum, which measures the rate of change of a security's price. It helps traders identify trends and potential trend reversals in the market. In the code, the Momentum Normalized is calculated using the momentum_normalized() function, which takes length, close, filter, and len_norm as parameters.
Slope Normalized:
The Slope Normalized is the normalized value of the slope, which measures the rate of change of a security's price over a specified period. It helps traders identify trends and potential trend reversals in the market. In the code, the Slope Normalized is calculated using the slope_normalized() function, which takes length, close, filter, and len_norm as parameters.
Trend Intensity:
Trend Intensity is a measure of the strength of a security's price trend. It is based on the difference between the average of price increases and the average of price decreases over a given period. The trend_intensity() function in the code calculates the Trend Intensity by taking length, close, filter, and len_norm as parameters.
Volatility Ratio:
The Volatility Ratio is a measure of the volatility of a security's price, calculated as the ratio of the True Range (TR) to the Exponential Moving Average (EMA) of the TR. The volatility_ratio() function in the code calculates the Volatility Ratio by taking length, high, low, close, and filter as parameters.
Commodity Channel Index (CCI):
The Commodity Channel Index (CCI) is a momentum-based oscillator used to help determine when an investment vehicle is reaching a condition of being overbought or oversold. The CCI is calculated as the difference between the mean price of a security and its moving average, divided by the mean absolute deviation (MAD) of the mean price. In the code, the CCI is calculated using the cci() function, which takes length, high, low, close, and filter as parameters.
These indicators are combined in the code to create a comprehensive trading strategy that considers multiple factors such as trend strength, momentum, volatility, and overbought/oversold conditions. The combined analysis provided by these indicators can help traders make informed decisions and improve their chances of success in the market.
The Radar Rider indicator is a powerful tool that combines multiple technical indicators into a single, easy-to-read visualization. By understanding the inner workings of each built-in indicator and their arrangement within the spider plot, traders can better interpret market conditions and make informed trading decisions.
Spider VisionSpider Vision is an indicator that I created for trading view, which consists of a spider chart with 7 indicators built into it. This chart provides a visual representation of how these indicators are behaving, allowing traders to quickly assess the current market conditions.
The chart displays the following indicators:
RSI (Relative Strength Index): This is a momentum indicator that measures the strength of a security's price action. When the RSI is above 70, it is considered overbought, and when it is below 30, it is considered oversold.
Stochastic: This is another momentum indicator that compares the closing price of a security to its price range over a given time period. When the stochastic is above 80, it is considered overbought, and when it is below 20, it is considered oversold.
Momentum: This is a simple indicator that measures the change in a security's price over a given time period. When the momentum is positive, it indicates that the price is increasing, and when it is negative, it indicates that the price is decreasing.
BBW (Bollinger Bands Width): This indicator measures the width of the Bollinger Bands, which are a popular technical analysis tool used to identify potential trends and reversals. When the BBW is high, it suggests that the market is volatile, and when it is low, it suggests that the market is quiet.
DTO (Detrended Price Oscillator): This indicator measures the difference between the price of a security and its moving average. When the DTO is positive, it indicates that the price is above its moving average, and when it is negative, it indicates that the price is below its moving average.
Chop Zone: This indicator measures the choppiness of the market by comparing the average true range (ATR) to the difference between the high and low prices over a given time period. When the chop zone is high, it suggests that the market is choppy, and when it is low, it suggests that the market is trending.
Chaikin Oscillator: This is an oscillator that measures the accumulation/distribution of a security. When the Chaikin Oscillator is positive, it indicates that there is buying pressure in the market, and when it is negative, it indicates that there is selling pressure.
To use this indicator, traders can simply add it to their TradingView chart and adjust the input parameters to suit their trading style. The scale parameter can be used to adjust the size of the spider chart, while the color parameters can be used to customize the appearance of the chart. Traders can also adjust the length of each indicator to suit their preference.
Overall, the Spider Vision indicator provides a convenient way for traders to quickly assess the current market conditions and make more informed trading decisions.
JS-TechTrading: Supertrend-Strategy_Basic versionAre you looking for a reliable and profitable algorithmic trading strategy for TradingView? If so, you might be interested in our Supertrend basic strategy, which is based on three powerful indicators: Supertrend (ATR), RSI and EMA.
Supertrend is a trend-following indicator that helps you identify the direction and strength of the market. It also gives you clear signals for entry and exit points based on price movements.
RSI is a momentum indicator that measures the speed and change of price movements. It helps you filter out false signals and avoid overbought or oversold conditions.
EMA is a moving average indicator that smooths out price fluctuations and shows you the long-term trend of the market. It helps you confirm the validity of your trades and avoid trading against the trend.
Our Supertrend basic strategy combines these three indicators to give you a simple yet effective way to trade any market. Here's how it works:
- For long trades, you enter when the price is above Supertrend and pulls back below it (the low of the candle crosses Supertrend) and then rebounds above it (the high of the next candle goes above the pullback candle). You exit when the price closes below Supertrend or when you reach your target profit or stop loss.
- For short trades, you enter when the price is below Supertrend and pulls back above it (the high of the candle crosses Supertrend) and then drops below it (the low of the next candle goes below the pullback candle). You exit when the price closes above Supertrend or when you reach your target profit or stop loss.
- You can also use RSI and EMA filters to improve your results. For long trades, you only enter if RSI is above 50 and price is above 200 EMA. For short trades, you only enter if RSI is below 50 and price is below 200 EMA.
- You can set your stop loss and target profit as a percentage of your entry price or based on other criteria. You can also adjust the parameters of each indicator according to your preferences and risk tolerance.
Our Supertrend basic strategy is easy to use and has been tested on various markets and time frames. It can help you capture consistent profits while minimizing your losses.
Kalman RSIThis is a simplified version of Kalman RSI by onegreencandle.
Simplifications:
It shows the indicator for a single configurable length with a default of 14.
It does not color by region.
It allows selecting the source, with a default of close . The version by onegreencandle uses ohlc4 instead. Note that both versions also use high and low .
It uses the newer version (5) of Pine Script.
It sets bands at 85 and 15.
S & R RSi stratIn this updated version, a trend filter is applied using the Simple Moving Average (SMA) on the 4-hour timeframe. The trend is considered up when the 50-period SMA is below the 200-period SMA (ta.sma(trendFilterSource, 50) < ta.sma(trendFilterSource, 200)).
The buy condition (buyCondition) is triggered when the RSI crosses above the oversold threshold (ta.crossover(rsi, oversoldThreshold)), the trend filter confirms an uptrend (isUptrend is true), and the close price is greater than or equal to the support level (close >= supportLevel).
The sell condition (sellCondition) is triggered when the RSI crosses below the overbought threshold (ta.crossunder(rsi, overboughtThreshold)), the trend filter confirms a downtrend (isUptrend is false), and the close price is less than or equal to the resistance level (close <= resistanceLevel).
With this implementation, the signals will only be generated in the direction of the trend on the 4-hour timeframe.
ATR OSC and Volume Screener (ATROSCVS)In today's world of trading, having the right tools and indicators can make all the difference. With the vast number of cryptocurrencies available, I've found it challenging to keep track of the market's overall direction and make informed decisions. That's where the ATR OSC and Volume Screener comes in, a powerful Pine Script that I use to identify potential trading opportunities across multiple cryptocurrencies, all in one convenient place.
This script combines two essential components: the ATR Oscillator (ATR OSC) and a Volume Screener. It is designed to work with the TradingView platform. Let me explain how this script works and how it benefits my trading.
Firstly, the ATR Oscillator is an RSI-like oscillator that performs better under longer lookback periods. Unlike traditional RSI, the ATR OSC doesn't lose its min and max ranges with a long lookback period, as the scale remains intact. It calculates the true range by considering the high, low, open, and close prices of a financial instrument, and uses this true range instead of the standard deviation in a modified z-score calculation. This unique approach helps provide a more precise assessment of the market's volatility.
The Volume Screener, on the other hand, helps me identify unusual trading volumes across various cryptocurrencies. It employs a normalized volume calculation method, effectively filtering out outliers and highlighting potentially significant trading opportunities.
One feature I find particularly impressive about the ATR OSC and Volume Screener is its versatility and the way it displays information using color gradients. With support for over 30 different cryptocurrencies, including popular options like Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Dogecoin (DOGE), I can monitor a wide range of markets simultaneously. The color gradient on the grid is visually appealing and makes it easy to identify the strength of the indicators for each cryptocurrency, allowing me to make quick comparisons and spot potential trading opportunities.
The customizable input options allow me to fine-tune the script to suit my individual trading preferences and strategies. In summary, the ATR OSC and Volume Screener has been an invaluable tool for me as I navigate the ever-evolving world of cryptocurrencies. By combining the power of the ATR Oscillator with a robust Volume Screener, this Pine Script makes it easier than ever to identify promising trading opportunities and stay ahead of the game.
The color gradient in the ATR OSC and Volume Screener is essential for visually representing the data on the heatmap. It uses a range of colors to indicate the strength of the indicators for each cryptocurrency, making it easier to understand the market dynamics at a glance.
In the heatmap, the color gradient typically starts from a cooler color, such as blue or green, at the lower extremes (low ATR OSC values) and progresses towards warmer colors, like yellow, orange, or red, as the ATR OSC values approach the upper extremes (high ATR OSC values). This color-coding system enables me to quickly identify and interpret the data without having to examine individual numerical values.
For example, cooler colors (blue or green) might represent lower values of the ATR Oscillator, suggesting oversold conditions in the respective cryptocurrencies. On the other hand, warmer colors (yellow, orange, or red) indicate higher ATR OSC values, signaling overbought market conditions. This visual representation allows me to make rapid comparisons between different cryptocurrencies and spot potential trading opportunities more efficiently.
By utilizing the color gradient in the heatmap, the ATR OSC and Volume Screener simplifies the analysis of multiple cryptocurrencies, helping me to quickly identify market trends and make better-informed trading decisions.
I highly recommend testing the ATR OSC and Volume Screener and seeing the difference it can make in your trading decisions. Happy trading!
EMA bridge and dashboard with color coding.
Summary:
This is a custom moving average indicator script that calculates and plots different Exponential Moving Averages (EMAs) based on user-defined input values. The script also displays MACD and RSI, and provides a table that displays the current trend of the market in a color-coded format.
Explanation:
- The script starts by defining the name of the indicator and the different inputs that the user can customize.
- The inputs include bridge values for three different EMAs (high, close, and low), and four other EMAs (5, 50, 100, and 200).
- The script assigns values to these inputs using the `ta.ema()` function.
- Additionally, the script calculates EMAs for higher timeframes (3m, 5m, 15m, and 30m).
- The script then plots the EMAs on the chart using different colors and line widths.
- The script defines conditions for going long or short based on the crossover of two EMAs.
- It plots triangles above or below bars to indicate the crossover events.
- The script also calculates and displays the RSI and MACD of the asset.
- Finally, the script creates a table that displays the current trend of the market in a color-coded format. The table can be positioned on the top, middle, or bottom of the chart and on the left, center, or right side of the chart.
Parameters:
- i_ema_h: Bridge value for high EMA (default=34)
- i_ema_c: Bridge value for close EMA (default=34)
- i_ema_l: Bridge value for low EMA (default=34)
- i_ema_5: Value for 5-period EMA (default=5)
- i_ema_50: Value for 50-period EMA (default=50)
- i_ema_100: Value for 100-period EMA (default=100)
- i_ema_200: Value for 200-period EMA (default=200)
- i_f_ema: Value for fast EMA used in MACD calculation (default=9)
- i_s_ema: Value for slow EMA used in MACD calculation (default=21)
- fastInput: Value for fast length used in MACD calculation (default=7)
- slowInput: Value for slow length used in MACD calculation (default=14)
- tableYposInput: Vertical position of the table (options: top, middle, bottom; default=middle)
- tableXposInput: Horizontal position of the table (options: left, center, right; default=right)
- bullColorInput: Color of the table cell for a bullish trend (default=green)
- bearColorInput: Color of the table cell for a bearish trend (default=red)
- neutColorInput: Color of the table cell for a neutral trend (default=white)
- neutColorLabelInput: Color of the label for neutral trend in the table (default=fuchsia)
Usage:
To use this script, simply copy and paste it into the Pine Editor on TradingView. You can then customize the input values to your liking or leave them at their default values. Once you have added the script to your chart, you can view the EMAs, MACD, RSI, and trend table on the chart. The trend table provides a quick way to assess the current trend of the market at a glance.
Intrabar Run Count Indicator [tbiktag]• OVERVIEW
Introducing the Intrabar Run Count Indicator , a tool designed to detect potential non-randomness in intrabar price data. It utilizes the statistical runs test to examine the number of sequences ( runs ) of positive and negative returns in the analyzed price series. As deviations from random-walk behavior of returns may indicate market inefficiencies , the Intrabar Run Count Indicator can help traders gain a better understanding of the price dynamics inside each chart bar and make more informed trading decisions.
• USAGE
The indicator line expresses the deviation between the number of runs observed in the dataset and the expected number of runs under the hypothesis of randomness. Thus, it gauges the degree of deviation from random-walk behavior. If, for a given chart bar, it crosses above the critical value or crosses below the negative critical value, this may indicate non-randomness in the underlying intrabar returns. These instances are highlighted by on-chart signals and bar coloring. The confidence level that defines the critical value, as well as the number of intrabars used for analysis, are selected in the input settings.
It is important to note that the readings of the Intrabar Run Count Indicator do not convey directional information and cannot predict future asset performance. Rather, they help distinguish between random and potentially tradable price movements, such as breakouts, reversals, and gap fillings.
• DETAILS
The efficient-market hypothesis implies that the distribution of returns should be random, reflecting the idea that all available information is already priced into the asset. However, in practice, financial markets may not always be perfectly efficient due to factors such as market frictions, information asymmetry, and irrational behavior of market participants. As a result, inefficiency (non-randomness) can occur, potentially creating opportunities for trading strategies.
To search for potential inefficiencies, the Intrabar Run Count Indicator analyzes the distribution of the signs of returns. The central assumption underlying the indicator's logic is that if the asset price follows a random-walk pattern, then the probability of the next return being positive or negative (i.e., the next price value being larger or smaller than the current value) follows a binomial distribution. In this case, the number of runs is also a random variable, and, for a large sample, its conditional distribution is approximately normal with a well-defined mean and variance (see this link for the exact expressions). Thus, the observed number of runs in the price series is indicative of whether or not the time series can be regarded as random. In simple words, if there are too few runs or too many runs, it is unlikely a random time series. A trivial example is a series with all returns of the same sign.
Quantitatively, the deviation from randomness can be gauged by calculating the test statistic of the runs test (that serves as an indicator line ). It is defined as the absolute difference between the observed number of runs and the expected number of runs under the null hypothesis of randomness, divided by the standard deviation of the expected number of runs. If the test statistic is negative and exceeds the negative critical value (at a given confidence level), it suggests that there are fewer runs than expected for a random-walking time series. Likewise, if the test statistic exceeds the positive critical value, it is indicative of more runs than expected for a random series. The sign of the test statistic can also be informative, as too few runs can be sometimes indicative of mean-reverting behavior.
• CONCLUSION
The Intrabar Run Count Indicator can be a useful tool for traders seeking to exploit market inefficiencies and gain a better understanding of price action within each chart bar. However, it is important to note that the runs test only evaluates the distributional properties of the data and does not provide any information on the underlying causes of the non-randomness detected. Additionally, like any statistical test, it can sometimes produce false-positive signals. Therefore, this indicator should be used in conjunction with other analytical techniques as part of a trading strategy.
True Range OscHey fellow traders! I've just published a new indicator called the True Range Oscillator. It's designed to help you better understand price movements and volatility. The indicator calculates the average true range of the price data and uses a modified z-score-like approach to normalize it. The main difference is that it uses true range instead of standard deviation for normalization.
This oscillator identifies the highest and lowest values within a specified range, excluding any outliers based on standard deviations. It then scales the output between 0 and 100, so you can easily see how the current price action compares to its historical range. You can use the True Range Oscillator to spot potential trend reversals and overbought/oversold conditions.
Here are some features to explore:
Customize your price data source (open, high, low, or close).
Adjust the length and smoothing settings for the average true range calculation.
Find outliers with standard deviations, and tweak the outlier_level and dev_lookback options.
Visualize price action with plotted lines for the upper range (70), lower range (30), and center line (50), along with a shaded area between the upper and lower ranges for added clarity.
I hope you find this indicator useful in your trading journey!
Volume Flow OscillatorIntroducing the "Volume Flow Oscillator" indicator, a powerful and adaptable tool that incorporates the PeacefulIndicators library to analyze price movement strength and volume in the market. This indicator is designed to assist you in detecting potential opportunities and improving your trading analysis.
The Volume Flow Oscillator indicator offers the following features:
Adjustable input parameters, allowing you to modify the source (HLCC4 by default) and the short length to match your trading style and preferences.
A visually appealing display, with the Volume Flow Oscillator line in orange, a zero line in gray, and filled areas between the 70 and -70 levels in blue, making it easy to interpret the indicator's signals.
The core functionality of the Volume Flow Oscillator indicator is powered by the volume_flow_oscillator function from the PeacefulIndicators library, ensuring accurate and reliable results.
To start using the Volume Flow Oscillator indicator in your trading analysis, simply add the script to your chart and customize the input parameters as needed. We hope this script, built upon the PeacefulIndicators library, proves to be a valuable addition to your trading strategy.
Adaptive MACDIntroducing the "Adaptive MACD" indicator, an innovative and user-friendly script that utilizes the PeacefulIndicators library to provide traders with a dynamic and responsive version of the classic MACD indicator. This script effectively adapts the MACD calculation to account for the dominant market cycle, offering improved signals to help you make better-informed trading decisions.
The Adaptive MACD indicator incorporates the following features:
A selection of customizable input parameters, allowing you to adjust the short length, long length, signal length, and the dynamic high and low values to suit your individual trading preferences.
A visually appealing and informative display, using different colors to highlight MACD line crossovers and histogram bars, making it easier to interpret the indicator's signals.
The core functionality of the Adaptive MACD is powered by the macdDynamicLength function from the PeacefulIndicators library, ensuring accurate and reliable calculations.
To start using the Adaptive MACD indicator in your trading analysis, simply add the script to your chart, and customize the input parameters as needed. We hope this script, built upon the PeacefulIndicators library, proves to be a valuable addition to your trading strategy.
Put to Call Ratio CorrelationHello!
Excited to share this with the community!
This is actually a very simple indicator but actually usurpingly helpful, especially for those who trade indices such as SPX, IWM, QQQ, etc.
Before I get into the indicator itself, let me explain to you its development.
I have been interested in the use of option data to detect sentiment and potential reversals in the market. However, I found option data on its own is full of noise. Its very difficult if not impossible for a trader to make their own subjective assessment about how option data is reflecting market sentiment.
Generally speaking, put to call ratios generally range between 0.8 to 1.1 on average. Unless there is a dramatic pump in calls or puts causing an aggressive spike up to over this range, or fall below this range, its really difficult to make the subjective assessment about what is happening.
So what I thought about trying to do was, instead of looking directly at put to call ratio, why not see what happens when you perform a correlation analysis of the PTC ratio to the underlying stock.
So I tried this in pinescript, pulling for Tradingview's ticker PCC (Total Equity Put to Call Ratio) and using the ta.correlation function against whichever ticker I was looking at.
I played around with this idea a bit, pulled the data into excel and from this I found something interesting. When there is a very significant negative or positive correlation between PTC ratio and price movement, we see a reversal impending. In fact, a significant negative or positive correlation (defined as a R value of 0.8 or higher or -0.8 or lower) corresponded to a stock reversal about 92% of the time when data was pulled on a 5 minute timeframe on SPY.
But wait, what is a correlation?
If you are not already familiar, a correlation is simply a statistical relationship. It is defined with a Pearson R correlation value which ranges from 0 (no correlation) to 1 (significant positive correlation) and 0 to -1 (significant negative correlation).
So what does positive vs negative mean?
A significant positive correlation means the correlation is moving the same as the underlying. In the case of this indicator, if there is a significant positive correlation could mean the stock price is climbing at the same time as the PTC ratio.
Inversely, it could mean the stock price is falling as well as the PTC ratio.
A significant negative correlation means the correlation is moving in the opposite direction. So in this case, if the stock price is climbing and the PTC ratio is falling proportionately, we would see a significant negative correlation.
So how does this work in real life?
To answer this, let's get into the actual indicator!
In the image above, you will see the arrow pointing to an area of significant POSITIVE correlation.
The indicator will paint the bars on the actual chart purple (customizable of course) to signify this is an area of significant correlation.
So, in the above example this means that the PTC ratio is increase proportionately to the increase in the stock price in the SAME direction (Puts are going up proportionately to the stock price). Thus, we can make the assumption that the underlying sentiment is overwhelmingly BEARISH. Why? Because option trading activity is significantly proportionate to stock movement, meaning that there is consensus among the options being traded and the movement of the market itself.
And in the above example we will see, the stock does indeed end up selling:
In this case, IWM fell roughly 1 point from where there was bearish consensus in the market.
Let's use this same trading day and same example to show the inverse:
You will see a little bit later, a significant NEGATIVE correlation developed.
In this case identified, the stock wise RISING and the PTC ratio was FALLING.
This means that Puts were not being bought up as much as calls and the sentiment had shifted to bullish .
And from that point, IWM ended up going up an additional 0.75 points from where there was a significant INVERSE correlation.
So you can see that it is helpful for identifying reversals. But what is also can be used for is identifying areas of LOW conviction. Meaning, areas where there really is no relationship between option activity and stock movement. Let's take spy on the 1 hour timeframe for this example:
You can see in the above example there really is no consensus in the option trading activity with the overarching sentiment. The price action is choppy and so too is option trading activity. Option traders are not pushing too far in one direction or the other. We can also see the lack of conviction in the option trading activity by looking at the correlation SMA (the white line).
When a ticker is experiencing volatile and good movement up and down, the SMA will generally trade to the top of the correlation range (roughly + 1.0) and then make a move down to the bottom (roughly - 1.0), see the example below:
When the SMA is not moving much and accumulating around the centerline, it generally means a lot of indecision.
Additional Indicator Information:
As I have said, the indicator is very simple. It pulls the data from the ticker PCC and runs a correlation assessment against whichever ticker you are on.
PCC pulls averaged data from all equities within the market and is not limited to a single equity. As such, its helpful to use this with indices such as SPY, IWM and QQQ, but I have had success with using it on individual tickers such as NVDA and AMD.
The correlation length is defaulted to 14. You can modify it if you wish, but I do recommend leaving it at this as the default and the testing I have done with this have all been on the 14 correlation length.
You can chose to smooth the SMA over whichever length of period you wish as well.
When the indicator is approaching a significant negative or positive relationship, you will see the indicator flash red in the upper or lower band to signify the relationship. As well, the chart will change the bar colour to purple:
Everything else is pretty straight forward.
Let me know your questions/comments or suggestions around the indicator and its applications.
As always, no indicator is meant to provide a single, reliable strategy to your trading regimen and no indicator or group of indicators should be relied on solely. Be sure to do your own analysis and assessments of the stock prior to taking any trades.
Safe trades everyone!
Dynamic Stop Loss DemoWhat does this script do ?
This script is for pine script programmers and explains how to implement a dynamic stop-loss strategy. It is different from trailing stop-loss. Trailing stop-loss can only set the retracement value, but this script can take profit on part of the position at a fixed price and allows users to decide whether to take profit on all positions based on whether a certain track is breached or other conditions author want. In this demo, it use rsi crossover and crossunder to decide the strategy condition, and use close price as open price, and use lowest low / highest high as stop price, and use 1.5 risk ratio to calculate the fixed first profit price. It will take 50% position size when the first profit price was reached. Then it will close all rest positions when the inverse condition come out or the dynamic stop(calculated by ATR) breached or when the price back to the open price or the stop price.
How is this script implemented
When start strategy by strategy.entry , it gives a custom id which contains direction, openPrice, stopPrice, profitPrice, qty, etc. It can be get from the global variable strategy.posiition_entry_name .
RDX Relative Directional IndexRDX Relative Directional Index, Strength + Direction + Trend. This indicator is the combination of RSI and DMI or ADX. RDX aims at providing Relative direction of the price along with strength of the trend. This acts as both RSI and Average Directional Index. as the strength grows the RSI line becomes wider and when there is high volatility and market fluctuation the line becomes thinner. Color decides the Direction. This indicator provides sideways detection of RSI signal.
RDX Width: This determines the strength of RSI and Strength of ADX, The strength grows RDX band grows wider, as strength decreases band shrinks and merge into the RSI line. for exact working simply disable RSI plot on the indicator. when there is no strength the RSI vanishes..
Technical:
RSI : with default 14 period
ADX : Default 14 period
RDX=RSI+(ADX-20)/5
Color Code:
Red: Down Direction
Green: Up Direction
Sideways:
A rectangular channel is plotted on RSI 50 Level
Oversold Overbought:
Oversold and Overbought Levels are plotted for normal RSI Oversold and Overbought detection.
Buy/Sell:
Buy sell signals from ADX crossover are plotted and its easy to determine
Strength + Direction + Trend in one go
Hope the community likes this...
Contibute for more ideas and indicators..
RiverFlow ADX ScreenerRiverFlow ADX Screener, Scans ADX and Donchian Trend values across various Timeframes. This screener provides support to the Riverflow indicator. Riverflow concept is based on Two indicators. Donchian Channel and ADX or DMI.
How to implement?
1.Donchian Channel with period 20
2. ADX / DMI 14,14 threshold 20
Entry / Exit:
1. Buy/Sell Signal from ADX Crossovers.
2. Trend Confirmation Donchian Channel.
3. Major Trend EMA 200
Buy/Sell:
After a buy/sell is generated by ADX Crossover, Check for Donchian Trend. it has to be in same direction as trend. for FTT trades take 2x limit. for Forex and Stocks take 1:1.5, SL must be placed below recent swing. One can use Riverflow indicator for better results.
ADX Indicator is plotted with
Plus: Green line
Minus: Red Line
ADX strength: plotted as Background area.
TREND: Trend is represented by Green and Red Area around Threshold line
Table:
red indicates down trend
green indicates up trend
grey indicates sideways
Weak ADX levels are treated sideways and a channel is plotted on ADX and PLUS and MINUS lines . NO TRADES are to be TAKEN on within the SIDEWAYS region.
Settings are not required as it purely works on Default settings. However Donchian Length can be changed from settings.
Timeframes below 1Day are screened. Riverflow strategy works on timeframe 5M and above timeframe. so option is not provided for lower timeframes.
Best suits for INTRADAY and LONG TERM Trading
