RSI MTF DashboardThis is an RSI dashboard, which allows you to see the current RSI value for five timeframes across up to 8 tickers of your choice. This is a useful tool to gauge momentum across multiple timeframes, where you would look to enter a buy with high RSI values across the timeframes (and vice versa for sell positions).
Conversely, some traders use RSI to identify potential areas for reversals, so you would look to buy with low RSI values (and vice versa for sell positions).
In the settings, please select which 5 timeframes you require. Then select which tickers you wish to see, and you will find a dashboard on your chart to show the RSI values. The dashboard can be highlighted when the RSI value shows bearish momentum (a value under 50, of your choice) and bullish momentum (a value over 50, again of your choice). These colours and values are fully customisable.
In the settings you can also select the location of the dashboard, as well as some colour and transparency settings to enable the best possible view on screen.
Oscillators
Average Trend with Deviation Bands v2TL;DR: An average based trend incl. micro trend spotting and multiple display options.
This script is basically an update of my "Average Trend with Deviation Bands" script. I made the following changes:
Not an overlay anymore - The amount of drawn lines makes the chart pretty messy. That's why I moved it to a pane. If you preferred the overlay you can use my "Average Trend with Deviation Bands" script. *This is also the reason why I publish this script instead of updating the existing one.
I added an EMA to represent the price movement instead of candles
I added a signal (SMA) to spot micro trends and early entry/exit signals
I added the option to switch between a "line view" which shows the average trend and deviation bands and an "oscillator view" which shows an oscillator and histogram (MACD style)
General usage:
1. The white line is the average trend (which is an average of the last N bars open, close, high, low price).
2. Bands around the average trend are standard deviations which can be adjusted in the options menu and are only visible in "lines view". Basically they are like the clouds in the Ichimoku Cloud indicator - In big deviation bands the price movement needs more "power" to break through the average trend and vice versa.
3. Indicator line (blue line) - This is the EMA which represents the price. Crossing the average trend from below indicates an uptrend and vice versa (crossing from above indicates a down trend).
4. Signal line (red line) - This is a smoothed version of the indicator line which can be used to predict the movement of the price when crossed by the indicator line (like at MACD and many other indicators).
Oscillator usage:
When switched to "oscillator view" the indicator line oscillates around a zero line which can be seen as the average trend. The usage is basically the same as described above. However there is also the histogram which shows the difference between the indicator and signal. Of course the histogram can be deactivated. Additionally a color filling can be added to easily spot entry/exit signals.
As always: Code is free do whatever you like. If you have any questions/comments/etc. just drop it in the comment section.
Open Interest OffsetThis indicator is used to display whether there has been an abnormal increase or decrease in recent contract positions. Its usage is similar to the RSI indicator.
Please note that this indicator uses fixed (customizable) thresholds of 0.4 and 0.6 to indicate when abnormal opening and closing occur respectively. For some altcoins, their values may far exceed 0.4 so please adjust accordingly based on your symbol.
(1) When there is an abnormal increase in recent contract positions, the value of the indicator will be above 0.4. This means that there may be a liquidation market situation occurring subsequently. If the market background at this time is rising, it may not be suitable to continue buying because the indicator shows that it is currently overbought. On the contrary, it may be appropriate to sell now.
(2) When there is an abnormal decrease in recent contract positions, the value of the indicator will be below -0.4. This means that a liquidation market situation has occurred recently. If the market background at this time is falling, it may not be suitable to continue shorting because the indicator shows that it is currently oversold. On the contrary, it may be appropriate to buy now.
Special thanks to the following TradingView community members for providing open-source indicators and contributing to the development of this indicator!
Open Interest Delta - By Leviathan - @LeviathanCapital
Regarding the relationship with the above-mentioned open source indicator:
Indicator Open Interest Delta - By Leviathan - @LeviathanCapital obtained OI data for Binance USDT perpetual contracts in the code. We refer to their method of obtaining OI data in our code.
============= 中文版本 =============
该指标用于显示近期合约持仓量是否有异常的增加和减少。它的用法类似于RSI指标
请注意，该指标使用了固定的（可定制的）阈值0.4和0.6来提示异常开仓和平仓的发生。对于某些山寨币而言，指标的数值可能远大于0.4。请根据你所关注的标的自行调整
（1）当近期合约持仓量有异常的增加时，指标的值会在0.4以上。这意味着后续可能有清算行情的发生。若此时市场背景为上涨，此时可能不太适合继续做多，因为指标显示目前处于超买行情。相反，现在可能适合卖出
（2）当近期合约的持仓量有异常的减少时，指标的值会在-0.4以下。这意味着近期已经发生了清算行情。若此时市场背景为下跌，此时可能不太适合继续做空，因为指标显示目前处于超卖行情。相反，现在可能适合买入
特别感谢以下TradingView社区成员提供开源指标并为该指标的开发做出贡献！
Open Interest Delta - By Leviathan - @LeviathanCapital
与上述开源指标的关系：
指标Open Interest Delta - By Leviathan - @LeviathanCapital在代码中获取了Binance USDT永续合约的OI数据。我们在代码中参考他们获取OI数据的方式
RSI TrueLevel StrategyThis strategy is a momentum-based strategy that uses the Relative Strength Index (RSI) indicator and a TrueLevel envelope to generate trade signals.
The strategy uses user-defined input parameters to calculate TrueLevel envelopes for 14 different lengths. The TrueLevel envelope is a volatility-based technical indicator that consists of upper and lower bands. The upper band is calculated by adding a multiple of the standard deviation to a linear regression line of the price data, while the lower band is calculated by subtracting a multiple of the standard deviation from the same regression line.
The strategy generates long signals when the RSI crosses above the oversold level or when the price crosses above the selected lower band of the TrueLevel envelope. It generates short signals when the RSI crosses below the overbought level or when the price crosses below the selected upper band of the TrueLevel envelope.
The strategy allows for long and short trades and sets the trade size as a percentage of the account equity. The colors of the bands and fills are also customizable through user-defined input parameters.
In this strategy, the 12th TrueLevel band was chosen due to its ability to capture significant price movements while still providing a reasonable level of noise reduction. The strategy utilizes a total of 14 TrueLevel bands, each with varying lengths. The 12th band, with a length of 2646, strikes a balance between sensitivity to market changes and reducing false signals, making it a suitable choice for this strategy.
RSI Parameters:
In this strategy, the RSI overbought and oversold levels are set at 65 and 40, respectively. These values were chosen to filter out more noise in the market and focus on stronger trends. Traditional RSI overbought and oversold levels are set at 70 and 30, respectively. By raising the oversold level and lowering the overbought level, the strategy aims to identify more significant trend reversals and potential trade opportunities.
Of course, the parameters can be adjusted to suit individual preferences.
KDJ-RSI Buy/Sell Signal ver. 1It is an indicator combining the RSI indicator and KDJ indicator.
Buy signal will triggers when:
RSI signal positioning below 25
J value crosses below 0
Sell signal will triggers when:
RSI signal positioning above 85
J value crosses above 100
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Please take note that this indicator may be not accurate for every chart in the crypto market, but it is most appropriate to use it in BTC/USDT charts, mainly for 1h, 4h, and 1d candles. Not recommended to use it for 1m or 15m leverage trades, this indicator might be altered by FOMO sentiment.
T3 OscillatorTL;DR - An Oscillator based on T3 moving average
The T3 moving average is a well known moving average created by Tim TIllson. Oscillator values are created by using the simple formula "source (close by default) - T3 moving average". Tim Tillson used a "volume factor" of 0.7 in his original T3 calculation. I changed this value to 0.618 and added the option to change it if needed/wanted. I also added alarms for zero line crossing upwards and downward, a smoothing option and custom time frames.
Compared to other oscillators like TSI, MACD etc. I observed better signals, especially in trending market situations, from the T3 oscillator (I tested Forex and Crypto).
Usage is simple: If the oscillator is above 0 it indicates a bearish trend. If below 0 it indicates a bullish trend. -> Really simple to use. However it can also be used to determine micro trends and reversals when combined with price action analysis. To keeps things simple I have not added a moving average like many other oscillators because I think it is confusing and does not help (in this particular case).
P.S. I haven't found a T3 oscillator on Trading View. Code is free - do whatever you want with it ;)
Trend Angle Candle ColorIntroduction:
As a trader, understanding the trend of the market is crucial for making informed decisions. One way to gain insight into the market trend is by using technical indicators, which are mathematical calculations that provide traders with valuable information about price action. In this post, we will explore a unique indicator called the "Trend Angle Candle Color" that not only identifies the trend but also visualizes it using color-coded candlesticks. We'll dive into the script, discuss its key components, and explain how you can benefit from using it in your trading strategy.
Script Overview:
The Trend Angle Candle Color Indicator is written in the Pine Script language for the TradingView platform. The indicator utilizes a combination of Exponential Moving Average (EMA), Average True Range (ATR), and Epanechnikov Kernel function to calculate the trend angle, which is then represented by color-coded candlesticks. The script offers several customizable inputs, such as the length of the lookback period, the scale (sensitivity), and the smoothing factor.
Key Components of the Script:
Inputs:
Length: Determines the lookback period for calculating the trend.
Scale: Adjusts the sensitivity of the indicator.
Smoothing: Controls the degree of smoothing applied to the angle calculation.
Smoothing Factor: Adjusts the weight of the Epanechnikov Kernel function.
Functions:
grad(src): A function that takes an input value and returns a corresponding color from a predefined gradient.
ema(source): An Exponential Moving Average function that smoothens the price data.
atan2(y, x) and degrees(float source): Functions that convert the slope into an angle in radians and then into degrees.
epanechnikov_kernel(_src, _size, _h, _r): A function that applies the Epanechnikov Kernel smoothing method to the angle data.
Calculations:
ATR: Calculates the Average True Range using the EMA function.
Slope: Determines the slope of the price change over the specified lookback period.
Angle_rad: Converts the slope into an angle in radians.
Degrees: Applies the Epanechnikov Kernel smoothing function to the angle data and scales it to a range between 0 to 100.
Visualization:
Colour: Assigns a color to each candlestick based on the calculated degree value using the grad() function.
Barcolor(colour) and plotcandle(): Functions that display the color-coded candlesticks on the chart.
Benefits of Using the Trend Angle Candle Color Indicator:
Easy Visualization: The color-coded candlesticks provide a simple and intuitive way to understand the market trend direction and strength at a glance.
Customizable Parameters: The customizable inputs allow traders to fine-tune the indicator to their preferred settings, suiting their trading style and strategy.
Versatility: The Trend Angle Candle Color Indicator can be used across various timeframes and financial instruments, making it a valuable addition to any trader's toolkit.
Conclusion:
The Trend Angle Candle Color Indicator is a powerful tool that can enhance your trading strategy by providing a visual representation of the market trend. The unique combination of EMA, ATR, and Epanechnikov Kernel smoothing helps create a more accurate and easy-to-understand trend angle calculation. By incorporating this indicator into your trading analysis, you can gain better insight into market dynamics and make more informed trading decisions.
Trend AngleIntroduction:
In today's post, we'll dive deep into the source code of a unique trading tool, the Trend Angle Indicator. The script is an indicator that calculates the trend angle for a given financial instrument. This powerful tool can help traders identify the strength and direction of a trend, allowing them to make informed decisions.
Overview of the Trend Angle Indicator:
The Trend Angle Indicator calculates the trend angle based on the slope of the price movement over a specified period. It uses an Exponential Moving Average (EMA) to smooth the data and an Epanechnikov kernel function for additional smoothing. The indicator provides a visual representation of the trend angle, making it easy to interpret for traders of all skill levels.
Let's break down the key components of the script:
Inputs:
Length: The number of periods to calculate the trend angle (default: 8)
Scale: A scaling factor for the ATR (Average True Range) calculation (default: 2)
Smoothing: The smoothing parameter for the Epanechnikov kernel function (default: 2)
Smoothing Factor: The radius of the Epanechnikov kernel function (default: 1)
Functions:
ema(): Exponential Moving Average calculation
atan2(): Arctangent function
degrees(): Conversion of radians to degrees
epanechnikov_kernel(): Epanechnikov kernel function for additional smoothing
Calculations:
atr: The EMA of the True Range
slope: The slope of the price movement over the given length
angle_rad: The angle of the slope in radians
degrees: The smoothed angle in degrees
Plotting:
Trend Angle: The trend angle, plotted as a line on the chart
Horizontal lines: 0, 90, and -90 degrees as reference points
How the Trend Angle Indicator Works:
The Trend Angle Indicator begins by calculating the Exponential Moving Average (EMA) of the True Range (TR) for a given financial instrument. This smooths the price data and provides a more accurate representation of the instrument's price movement.
Next, the indicator calculates the slope of the price movement over the specified length. This slope is then divided by the scaled ATR to normalize the trend angle based on the instrument's volatility. The angle is calculated using the atan2() function, which computes the arctangent of the slope.
The final step in the process is to smooth the trend angle using the Epanechnikov kernel function. This function provides additional smoothing to the trend angle, making it easier to interpret and reducing the impact of short-term price fluctuations.
Conclusion:
The Trend Angle Indicator is a powerful trading tool that allows traders to quickly and easily determine the strength and direction of a trend. By combining the Exponential Moving Average, ATR, and Epanechnikov kernel function, this indicator provides an accurate and easily interpretable representation of the trend angle. Whether you're an experienced trader or just starting, the Trend Angle Indicator can provide valuable insights into the market and help improve your trading decisions.
Strategy Creator5 indicators. Backtesting available. Uses ADX, RSI, Stochastic, MACD, and crossing EMAs (1,2, or 3). This strategy creator allows you to turn on or off these indicators and adjust the parameters for each indicator. It allows you to make one trade at a time e.g the next trade doesn't open until the last one closes. (You are also able to enter how many trades in one direction you want for example if you want only 2 long trades in a row, then the strategy waits for the next short position without making anymore long trades. Once there are 2 short positions in a row, it waits for a long position). The code can be edited to for automated trading by editing the comment in the source code for the strategy parameters. This took many hours to finish. ENJOY.
Fetch ATR + MA StrategyA trend following indicator that allows traders/investors to enter trades for the long term, as it is mainly tested on the daily chart. The indicator fires off buy and sell signals. The sell signals can be turned off as trader can decide to use this indicator for long term buy signals. The buy signals are indicated by the green diamonds, and the red diamonds show the points on then chart where the asset can be sold.
The indicator uses a couple indicators in order to generate the buy signals:
- ADX
- ATR
- Moving Average of ATR
- 50 SMA
- 200 SMA
The buy signal is generated at the cross overs of the 50 and 200 SMA's while the ATR is lower than then Moving Average of the ATR. The buy signal is fired when these conditions are met and if the ADX is lower than 30.
The thought process is as follows:
When the ATR is lower than its moving average, the price should be in a low volatilty environment. An ADX between 25 and 50 signals a Strong trend. Every value below 25 is an absent or weak trend. So entering a trade when the volatilty is still low but increasing, you'll be entering a trade at the start of a new uptrend. This mechanism also filters out lots of false signals of the simple cross overs.
The sell signals are fired every time the 50 SMA drops below the 200 SMA.
RSI Trending with DivergencesThis script uses the RSI and RSI divergences to mark signals where the rsi is both below/above the 50, below/above its moving average, and where the last regular or hidden divergence matches that state. The RSI is built into the indicator, so you don't need it in your bottom pane if you don't want it, I just put one there for illustrative purposes. Please note it will not print the same signal consecutively, as it is meant to show an overall direction, not the in and out fluctuations. I suggest using it in conjunction with some moving averages so you can ignore signals not in the trend.
RSI Exponential Smoothing (Expo)█ Background information
The Relative Strength Index (RSI) and the Exponential Moving Average (EMA) are two popular indicators. Traders use these indicators to understand market trends and predict future price changes. However, traders often wonder which indicator is better: RSI or EMA.
What if these indicators give similar results? To find out, we wanted to study the relationship between RSI and EMA. We focused on a hypothesis: when the RSI goes above 50, it might be similar to the price crossing above a certain length of EMA. Similarly, when the RSI goes below 50, it might be similar to the price crossing below a certain length of EMA.
Our goal was simple: to figure out if there is any connection between RSI and EMA.
Conclusion: Yes, it seems that there is a correlation between RSI and EMA, and this indicator clearly displays that relationship. Read more about the study here:
█ Overview of the indicator
The RSI Exponential Smoothing indicator displays RSI levels with clear overbought and oversold zones, shown as easy-to-understand moving averages, and the RSI 50 line as an EMA. Another excellent feature is the added FIB levels. To activate, open the settings and click on "FIB Bands." These levels act as short-term support and resistance levels which can be used for scalping.
█ Benefits of using this indicator instead of regular RSI
The findings about the Relative Strength Index (RSI) and the Exponential Moving Average (EMA) highlight that both indicators are equally accurate (when it comes to crossings), meaning traders can choose either one without compromising accuracy. This empowers traders to pick the indicator that suits their personal preferences and trading style.
█ How it works
Crossings over/under the value of 50
The EMA line in the indicator acts as the corresponding 50 line in the RSI. When the RSI crosses the value 50 equals when Close crosses the EMA line.
Bouncess from the value 50
In this example, we can see that the EMA line on the chart acts as support/resistance equals when RSI rejects the 50 level.
Overbought and Oversold
The indicator comes with overbought and oversold bands equal when RSI becomes overbought or oversold.
█ How to use
This visual representation helps traders to apply RSI strategies directly on the price chart, potentially making RSI trading easier for traders.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Donchian Channel Oscillator (DonOsc) Preface
DonOsc stands for Donchian Channel Oscillator. This channel envelopes all prices, so if you set the height of the channel to 100 percent, you can plot the prices as percent in between, creating this sub-pane oscillator. For clarity the example chart shows a Donchian channel in the main-pane with the same look-back as the DonOsc, this way you can see how both are related.
Price River
Not only the close is plotted, but also the high and the low of the bar. Thus you get a structure that can be associated with a river, streaming from left to right, in which the price moves between the left bank (i.e. the plotted highs) and the right bank (i.e. the plotted lows), which meanders between the high border (100%) and the low border (0%) of the oscillator. The surface of the price river is gray. The price line is blue when up and dark red when down. The river has also color patches dark red, light red, blue and aqua. Stochastic patches; up: aqua, down: light red
If you look at the price river, you may notice that the price line is closer to the left bank (highs) when moving up and to the right bank (lows) when moving down. Because this phenomenon is used in the stochastic indicator, I named these stochastic patches. These are depicted on the wide side for visibility, so the aqua patches are to the right of the price line and the light-red patches to the left.
Widening patches; up: blue, down: red
If you look at tops or bottoms in bar charts, you may notice that long bars (wide range) tend to be there. You may say that prices turn with a ‘range bang’. This causes a widening of the price river, depicted as a patch on the wide side.
Channel Features
High (76.4 %) and low (23.6 %) Fibonacci levels.
In the oscillator there is no need to calculate Fibonacci levels, we can just plot them. If the price is above 50% the low level is shown with a green color, when below the high level with a pink color. When the price river crosses a level a ‘near border’ highlighter will flash, lime near the high border and orange near the low one.
New high and new low markers.
A flaw in the oscillator is that is doesn’t show actual new lows and new highs in the Donchian Channel, because everything is made relative. This is ‘repaired’ by adding markers, dark red for new low depicted between the high fib and border, blue for new high depicted between low fib and border. Used are the same colors as in the widening patches, because new highs and lows also lead to widening of the actual Channel.
Uptrend and downtrend highlighters.
If in the actual Channel the bars run in the upper half, an uptrend is happening as long as these remain there, a downtrend when the bars remain in the lower half. In the oscillator a yellow highlighter flashes when the price is higher than 50%, a red highlighter below 50%.
Interpretation of the DonOsc
This sub-pane indicator provides a wealth of useful information about what is going on in the market. First of all you immediately see whether there is an up or down trend and whether these lead to new highs or lows. Second of all you can estimate the importance of price movements in the context of the look-back period. Thirdly the width of the price river reveals the emotions in the market. The higher the emotions run, the more risk is involved in a postilion in the charted instrument.
Settings of the DonOsc
Look-back settings.
By default the script sets the look-back, depending on the time frame. This overrules the standard manual setting. If you switch this off, the manual setting will work. A feed-back label can by shown which informs about the current setting.
Smoothing
This concerns the price river. Default is 2, if you increase this setting, the river will loose its touch with the channel borders. O.t.o.h. the river wil be wider and better visible. Maximum setting is 5.
Colors
The momentum colors set both the river widening patches and new high and low markers.
Take care, Eykpunter.
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.
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.
