Fusion MFI RSIHello fellas,
This superb indicator summons two monsters called Relative Strength Index (RSI) and Money Flow Index (MFI) and plays the Yu-Gi-Oh! card "Polymerization" to combine them.
Overview
The Fusion MFI RSI Indicator is an advanced analytical tool designed to provide a nuanced understanding of market dynamics by combining the Relative Strength Index (RSI) and the Money Flow Index (MFI). Enhanced with sophisticated smoothing techniques and the Inverse Fisher Transform (IFT), this indicator excels in identifying key market conditions such as overbought and oversold states, trends, and potential reversal points.
Key Features (Brief Overview)
Fusion of RSI and MFI: Integrates momentum and volume for a comprehensive market analysis.
Advanced Smoothing Techniques: Employs Hann Window, Jurik Moving Average (JMA), T3 Smoothing, and Super Smoother to refine signals.
Inverse Fisher Transform (IFT) Enhances the clarity and distinctiveness of indicator outputs.
Detailed Feature Analysis
Fusion of RSI and MFI
RSI (Relative Strength Index): Developed by J. Welles Wilder Jr., the RSI measures the speed and magnitude of directional price movements. Wilder recommended using a 14-day period and identified overbought conditions above 70 and oversold conditions below 30.
MFI (Money Flow Index): Created by Gene Quong and Avrum Soudack, the MFI combines price and volume to measure trading pressure. It is typically calculated using a 14-day period, with over 80 considered overbought and under 20 as oversold.
Application in Fusion: By combining RSI and MFI, the indicator leverages RSI's sensitivity to price changes with MFI's volume-weighted confirmation, providing a robust analysis tool. This combination is particularly effective in confirming the strength behind price movements, making the signals more reliable.
Advanced Smoothing Techniques
Hann Window: Traditionally used to reduce the abrupt data discontinuities at the edges of a sample, it is applied here to smooth the price data.
Jurik Moving Average (JMA): Known for preserving the timing and smoothness of the data, JMA reduces market noise effectively without significant lag.
T3 Smoothing: Developed to respond quickly to market changes, T3 provides a smoother response to price fluctuations.
Super Smoother: Filters out high-frequency noise while retaining important trends.
Application in Fusion: These techniques are chosen to refine the output of the combined RSI and MFI values, ensuring the indicator remains responsive yet stable, providing clearer and more actionable signals.
Inverse Fisher Transform (IFT):
Developed by John Ehlers, the IFT transforms oscillator outputs to enhance the clarity of extreme values. This is particularly useful in this fusion indicator to make critical turning points more distinct and actionable.
Mathematical Calculations for the Fusion MFI RSI Indicator
RSI (Relative Strength Index)
The RSI is calculated using the following steps:
Average Gain and Average Loss: First, determine the average gain and average loss over the specified period (typically 14 days). This is done by summing all the gains and losses over the period and then dividing each by the period.
Average Gain = (Sum of Gains over the past 14 periods) / 14
Average Loss = (Sum of Losses over the past 14 periods) / 14
Relative Strength (RS): This is the ratio of average gain to average loss.
RS = Average Gain / Average Loss
RSI: Finally, the RSI is calculated using the RS value:
RSI = 100 - (100 / (1 + RS))
MFI (Money Flow Index)
The MFI is calculated using several steps that incorporate both price and volume:
Typical Price: Calculate the typical price for each period.
Typical Price = (High + Low + Close) / 3
Raw Money Flow: Multiply the typical price by the volume for the period.
Raw Money Flow = Typical Price * Volume
Positive and Negative Money Flow: Compare the typical price of the current period to the previous period to determine if the money flow is positive or negative.
If today's Typical Price > Yesterday's Typical Price, then Positive Money Flow = Raw Money Flow; Negative Money Flow = 0
If today's Typical Price < Yesterday's Typical Price, then Negative Money Flow = Raw Money Flow; Positive Money Flow = 0
Money Flow Ratio: Calculate the ratio of the sum of Positive Money Flows to the sum of Negative Money Flows over the past 14 periods.
Money Flow Ratio = (Sum of Positive Money Flows over 14 periods) / (Sum of Negative Money Flows over 14 periods)
MFI: Finally, calculate the MFI using the Money Flow Ratio.
MFI = 100 - (100 / (1 + Money Flow Ratio))
Fusion of RSI and MFI
The final Fusion MFI RSI value could be calculated by averaging the IFT-transformed values of RSI and MFI, providing a single oscillator value that reflects both momentum and volume-weighted price action:
Fusion MFI RSI = (MFI weight * MFI) + (RSI weight * RSI)
Suggested Settings and Trading Rules
Original Usage
RSI: Wilder suggested buying when the RSI moves above 30 from below (enter long) and selling when the RSI moves below 70 from above (enter short). He recommended exiting long positions when the RSI reaches 70 or higher and exiting short positions when the RSI falls below 30.
MFI: Quong and Soudack recommended buying when the MFI is below 20 and starts rising (enter long), and selling when it is above 80 and starts declining (enter short). They suggested exiting long positions when the MFI reaches 80 or higher and exiting short positions when the MFI falls below 20.
Fusion Application
Settings: Use a 14-day period for this indicator's calculations to maintain consistency with the original settings suggested by the inventors.
Trading Rules:
Enter Long Signal: Consider entering a long position when both RSI and MFI are below their respective oversold levels and begin to rise. This indicates strong buying pressure supported by both price momentum and volume.
Exit Long Signal: Exit the long position when either RSI or MFI reaches its respective overbought threshold, suggesting a potential reversal or decrease in buying pressure.
Enter Short Signal: Consider entering a short position when both indicators are above their respective overbought levels and begin to decline, suggesting that selling pressure is mounting.
Exit Short Signal: Exit the short position when either RSI or MFI falls below its respective oversold threshold, indicating diminishing selling pressure and a potential upward reversal.
How to Use the Indicator
Select Source and Timeframe: Choose the data source and the timeframe for analysis.
Configure Fusion Settings: Adjust the weights for RSI and MFI.
Choose Smoothing Technique: Select and configure the desired smoothing method to suit the market conditions and personal preference.
Enable Fisherization: Optionally apply the Inverse Fisher Transform to enhance signal clarity.
Customize Visualization: Set up gradient coloring, background plots, and bands according to your preferences.
Interpret the Indicator: Use the Fusion value and visual cues to identify market conditions and potential trading opportunities.
Conclusion
The Fusion MFI RSI Indicator integrates classical and modern technical analysis concepts to provide a comprehensive tool for market analysis. By combining RSI and MFI with advanced smoothing techniques and the Inverse Fisher Transform, this indicator offers enhanced insights, aiding traders in making more informed and timely trading decisions. Customize the settings to align with your trading strategy and leverage this powerful tool to navigate financial markets effectively.
Best regards,
simwai
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Credits to:
@loxx – T3
@everget – JMA
@cheatcountry – Hann Window
No
Wick %Heyo Fellas,
thanks for checking out my new indicator.
Introduction
Wick % is a simple indicator to compare wick size with body size (mode 1) and to compare wick size with candle size (mode 2).
Upper wicks are bullish when close is higher than open pricen.
Lower wicks are bearish when close is lower than open price.
Wick Theory
In general, big wick and small bodie on a bar means that bull and bears are fighting heavily.
A big wick below the body means the bulls are leading in that fight,
and a big wick above the body means the bears are leading in that fight.
Calculation Formula
Mode 1 – Percentual Increase Wick/Body:
upperWickPercentage = (upperWick / body) * 100 - 100
lowerWickPercentage = (lowerWick / body) * 100 - 100
Mode 2 – Percent Wick/Candlestick:
upperWickPercentage = (upperWick / (high - low)) * 100
lowerWickPercentage = (lowerWick / (high - low)) * 100
Usage
You can use it on every symbol and every timeframe.
The indicator repaints by default, but you can disable it in the settings.
When you disable repaint, it moves the label one bar to the right.
If you want to use the indicator for signals, you must disable repainting.
Best regards,
simwai
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
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Credits to:
@loxx
@Bjorgum
@Greeny
Ehlers Combo Strategy🚀 Presenting the Enhanced Ehlers Combo Strategy 🚀
Hello Traders! 👋 I'm thrilled to share the latest version of the Ehlers Combo Strategy v2.0. This powerful algorithm combines Ehlers Elegant Oscillator, Decycler, Instantaneous Trendline, Spearman Rank, and introduces the Signal to Noise Ratio for even more precise trading signals.
📊 Strategy Highlights:
Ehlers Elegant Oscillator: Captures market momentum and turning points.
Ehlers Decycler: Filters out market noise for clearer trend signals.
Instantaneous Trendline: Offers a dynamic view of the market trend.
Spearman Rank: Analyzes market rank correlations for enhanced insights.
Signal to Noise Ratio (SNR): Filters out noise for more accurate signals.
💡 Key Features & Customizations:
Adaptive Length: Enable adaptive length based on the market's current conditions.
SNR Threshold: Set your desired SNR threshold for filtering signals.
Exit Length: Define the length for exit signals.
📈 Trading Signals:
Long Entry: Elegant Oscillator and Decycler cross above 0, source crosses above Decycler, source is greater than an increasing Instantaneous Trendline, Spearman Rank is positive, and SNR exceeds the threshold.
Long Exit: Source crosses below the Instantaneous Trendline after entering a long position.
Short Entry: Elegant Oscillator and Decycler cross below 0, source crosses below Decycler, source is less than a decreasing Instantaneous Trendline, Spearman Rank is negative, and SNR exceeds the threshold.
Short Exit: Source crosses above the Instantaneous Trendline after entering a short position.
📊 Insights & Enhancements:
Dynamic Length: The strategy adapts its length dynamically based on market conditions.
Improved SNR: Signal to Noise Ratio ensures better filtering of signals.
Enhanced Visualization: The Elegant Oscillator now features improved color coding for a clearer interpretation.
🚨 Disclaimer:
Trading involves risk, and this script should be used judiciously. It's not a guaranteed profit machine, but with careful use, it can be a valuable addition to your toolkit.
Feel free to backtest, tweak, and make it your own! Let's conquer the markets together! 💪📈
🚀✨ Happy Trading! ✨🚀
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🙌 Credits:
A big shoutout to the original contributors:
@blackcat1402
@cheatcountry
@DasanC
Normalized Fisher Transformed VolumeGreetings Traders,
I am thrilled to introduce a game-changing tool that I've passionately developed to enhance your trading precision – the Normalized Fisher Transformed Volume indicator. Let's dive into the specifics and explore how this tool can empower you in the markets.
Unlocking Trading Precision:
Normalization and Transformation:
Normalize raw volume data to ensure a consistent scale for analysis.
The Fisher Transformation converts normalized volume data into a Gaussian distribution, providing enhanced insights into trend dynamics.
Flexible Modes for Tailored Strategies:
Choose from three distinct modes:
Volume T3 (MA) + Heatmap: Identify trends with T3 Moving Average and visualize volume strength with Heatmap.
Volume Percent Rank: Evaluate the position of current volume relative to historical data.
Volume T3 (MA) Percent Rank: Combine T3 Moving Average with percentile ranking for a comprehensive analysis.
Heatmap Visualization for Quick Insights:
Heatmap Zones and Lines visually represent volume strength relative to historical data.
Customize threshold multipliers and color options for precise Heatmap interpretation.
T3 Moving Average Integration:
Smoothed representation of volume trends with the T3 Moving Average enhances trend identification.
Percent Rank Analysis for Context:
Gauge the position of normalized volume within historical context using Percent Rank analysis.
User-Friendly Customization:
Easily adjust parameters such as length, T3 Moving Average length, Heatmap standard deviation length, and threshold multipliers.
Intuitive interface with colored bars and customizable background options for personalized analysis.
How to Use Effectively:
Mode Selection:
Identify your preferred trading strategy and select the mode that aligns with your approach.
Parameter Adjustment:
Fine-tune the indicator by adjusting parameters to match your preferred trading style.
Interpret Heatmap and T3 Analysis:
Leverage Heatmap and T3 Moving Average analysis to spot potential trend reversals, overbought/oversold conditions, and market sentiment shifts.
Conclusion:
The Normalized Fisher Transformed Volume indicator is not just a tool; it's your key to unlocking precision in trading. Crafted by Simwai, this indicator offers unique insights tailored to your specific trading needs. Dive in, explore its features, experiment with parameters, and let it guide you to more informed and precise trading decisions.
Trade wisely and prosper,
simwai
Smoothing R-Squared ComparisonIntroduction
Heyo guys, here I made a comparison between my favorised smoothing algorithms.
I chose the R-Squared value as rating factor to accomplish the comparison.
The indicator is non-repainting.
Description
In technical analysis, traders often use moving averages to smooth out the noise in price data and identify trends. While moving averages are a useful tool, they can also obscure important information about the underlying relationship between the price and the smoothed price.
One way to evaluate this relationship is by calculating the R-squared value, which represents the proportion of the variance in the price that can be explained by the smoothed price in a linear regression model.
This PineScript code implements a smoothing R-squared comparison indicator.
It provides a comparison of different smoothing techniques such as Kalman filter, T3, JMA, EMA, SMA, Super Smoother and some special combinations of them.
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement.
The input parameters for the Kalman filter include the process noise covariance and the measurement noise covariance, which help to adjust the sensitivity of the filter to changes in the input data.
The T3 smoothing technique is a popular method used in technical analysis to remove noise from a signal.
The input parameters for the T3 smoothing method include the length of the window used for smoothing, the type of smoothing used (Normal or New), and the smoothing factor used to adjust the sensitivity to changes in the input data.
The JMA smoothing technique is another popular method used in technical analysis to remove noise from a signal.
The input parameters for the JMA smoothing method include the length of the window used for smoothing, the phase used to shift the input data before applying the smoothing algorithm, and the power used to adjust the sensitivity of the JMA to changes in the input data.
The EMA and SMA techniques are also popular methods used in technical analysis to remove noise from a signal.
The input parameters for the EMA and SMA techniques include the length of the window used for smoothing.
The indicator displays a comparison of the R-squared values for each smoothing technique, which provides an indication of how well the technique is fitting the data.
Higher R-squared values indicate a better fit. By adjusting the input parameters for each smoothing technique, the user can compare the effectiveness of different techniques in removing noise from the input data.
Usage
You can use it to find the best fitting smoothing method for the timeframe you usually use.
Just apply it on your preferred timeframe and look for the highlighted table cell.
Conclusion
It seems like the T3 works best on timeframes under 4H.
There's where I am active, so I will use this one more in the future.
Thank you for checking this out. Enjoy your day and leave me a like or comment. 🧙♂️
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Credits to:
▪@loxx – T3
▪@balipour – Super Smoother
▪ChatGPT – Wrote 80 % of this article and helped with the research
Adaptive Fusion ADX VortexIntroduction
The Adaptive Fusion ADX DI Vortex Indicator is a powerful tool designed to help traders identify trend strength and potential trend reversals in the market. This indicator uses a combination of technical analysis (TA) and mathematical concepts to provide accurate and reliable signals.
Features
The Adaptive Fusion ADX DI Vortex Indicator has several features that make it a powerful tool for traders. The Fusion Mode combines the Vortex Indicator and the ADX DI indicator to provide a more accurate picture of the market. The Hurst Exponent Filter helps to filter out choppy markets (inspired by balipour). Additionally, the indicator can be customized with various inputs and settings to suit individual trading strategies.
Signals
The enterLong signal is generated when the algorithm detects that it's a good time to buy a stock or other asset. This signal is based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value is above a certain threshold and there is a crossover between the plus and minus lines of the ADX indicator, then the algorithm will generate an enterLong signal.
Similarly, the enterShort signal is generated when the algorithm detects that it's a good time to sell a stock or other asset. This signal is also based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value is above a certain threshold and there is a crossunder between the plus and minus lines of the ADX indicator, then the algorithm will generate an enterShort signal.
The exitLong and exitShort signals are generated when the algorithm detects that it's a good time to close a long or short position, respectively. These signals are also based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value crosses above a certain threshold or there is a crossover between the minus and plus lines of the ADX indicator, then the algorithm will generate an exitLong signal.
Usage
Traders can use this indicator in a variety of ways, depending on their trading strategy and style. Short-term traders may use it to identify short-term trends and potential trade opportunities, while long-term traders may use it to identify long-term trends and potential investment opportunities. The indicator can also be used to confirm other technical indicators or trading signals. Personally, I prefer to use it for short-term trades.
Strengths
One of the strengths of the Adaptive Fusion ADX DI Vortex Indicator is its accuracy and reliability. The indicator uses a combination of TA and mathematical concepts to provide accurate and reliable signals, helping traders make informed trading decisions. It is also versatile and can be used in a variety of trading strategies.
Weaknesses
While this indicator has many strengths, it also has some weaknesses. One of the weaknesses is that it can generate false signals in choppy or sideways markets. Additionally, the indicator may lag behind the market, making it less effective in fast-moving markets. That's a reason why I included the Hurst Exponent Filter and special smoothing.
Concepts
The Adaptive ADX DI Vortex Indicator with Fusion Mode and Hurst Filter is based on several key concepts. The Average Directional Index (ADX) is used to measure trend strength, while the Vortex Indicator is used to identify trend reversals. The Hurst Exponent is used to filter out noise and provide a more accurate picture of the market.
In conclusion, the Adaptive Fusion ADX DI Vortex Indicator is a versatile and powerful tool for traders. By combining technical analysis and mathematical concepts, this indicator provides accurate and reliable signals for identifying trend strength and potential trend reversals. While it has some weaknesses, its many strengths and features make it a valuable addition to any trader's toolbox.
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Credits to:
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@balipour – Hurst Exponent Filter
VHF Adaptive Linear Regression KAMAIntroduction
Heyo, in this indicator I decided to add VHF adaptivness, linear regression and smoothing to a KAMA in order to squeeze all out of it.
KAMA:
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
VHF:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Linear Regression Curve:
A line that best fits the prices specified over a user-defined time period.
This is very good to eliminate bad crosses of KAMA and the pric.
Usage
You can use this indicator on every timeframe I think. I mostly tested it on 1 min, 5 min and 15 min.
Signals
Enter Long -> crossover(close, kama) and crossover(kama, kama )
Enter Short -> crossunder(close, kama) and crossunder(kama, kama )
Thanks for checking this out!
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Credits to
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@LucF – Gradient
Adaptive Fisherized CMOIntroduction
Heyo, here is another no-repaint adaptive fisherized indicator.
I added Inverse Fisher Transform, Ehlers dominant cycle analysis and smoothing to the Chande Momentum Oscillator (CMO).
Usage
The CMO is a momentum oscillator which shows the usual movement of an asset.
I recommend to use it from a lower timeframe with a higher timeframe set.
Signals
(Signal mode will come soon.)
Zero Line
CMO crosses above zero line => enter long
CMO cross below zero line => ente short
Overbought/Oversold
CMO crosses above bottom band => enter long
CMO crosses under top band => enter short
MA (Maybe this signals will vary. Then, check update notes.)
CMO crosses above MA => enter long
CMO crosses below MA => enter short
Enjoy and share your experience with it!
More to read: CMO Explanationsp
Chandelier Exit ZLSMA StrategyIntroduction
Heyo guys, I recently checked out some eye-catching trading strategy videos on YT and found one to test.
This indicator is based on the video.
Usage
The recommended timeframe is 5 min.
Signals
Long Entry => L Label
Price crosses above ZLSMA and Chandelier Exit shows Buy
Long Exit => green circle
Price crosses below ZLSMA
Short Entry => S Label
Price crosses below ZLSMA and Chandelier Exit shows Sell
Short Exit => orange circle
Prices crosses above ZLSMA
Ty for checking this out. Enjoy!
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Credits to
@netweaver2011 - ZLSMA
@everget – Chandelier Exit
Adaptive Fisherized Trend Intensity Index Introduction
Here, I modified the script "Trend Intensity Index" (TII) of @everyget.
TTI was developed by M.H. Pee, who also published other trend analysis indicators like the Trend Trigger/Continuation Factor
It helps to determine how strong the current trend is.
The stronger the trend, the higher the chance the price may continue moving in the current direction.
Features
Adaptive mode (based on Ehlers dominant cycle determination) => automatically determines the length
Inverse Fisher Transform => gives sharper signals
Customizable MA Types => discover the impact of different ma bases
Hann Window and NET smoothing => state-of-the-art smoothing
Trend Visualization => shows you the up/down/side trend
Usage
This indicator here offers a perfect trend filtering system. It is capable of up/down/side trend detection.
There are a lot of trend indicators which don't respect sidetrends, which makes this indicator pretty useful.
A lot of traders use trend-following trading systems.
A trader will usually make his/her entry in the market during a strong trend and ride it, until the TII provides an indication of a reversal.
For mean-revertive trading systems, you could use TII to just trade in side trend.
A lot of mean-revertive signal emitters like Bollinger Bands or RSI work most of the times better in side trend.
Furthermore, every timeframe could be used, but higher timeframes have more impact because trends are stronger there.
Signals
Green zone (Top) => Etablished bullish trend
"Peachy" Zone (Middle) => Sidetrend/flat market
Red Zone (Bottom) => Etablished bearish trend
Enjoy guys!
(Let me know your opinions!)
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Credits to:
@blackcat1402
@DasanC
@cheatcountry
@everget
Adaptive VWAP Stdev BandsIntroduction
Heyo, here are some adaptive VWAP Standard Deviation Bands with nice colors.
I used Ehlers dominant cycle theories and ZLSMA smoothing to create this indicator.
You can choose between different algorithms to determine the dominant cycle and this will be used as reset period.
Everytime bar_index can be divided through the dominant cycle length and the result is zero VWAP resets if have chosen an adaptive mode in the settings.
The other reset event you can use is just a simple time-based event, e.g. reset every day.
Usage
I think people buy/sell when it reaches extreme zones.
Enjoy!
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Credits to:
@SandroTurriate - VWAP Stdev Bands
@blackcat1402 - Dominant Cycle Analysis
@DasanC - Dominant Cycle Analysis
@veryfid - ZLSMA
(Sry, too lazy for linking)
I took parts of their code. Ty guys for your work! Just awesome.
Adaptive Fisherized CMFIntroduction
Heyo, here I made a normalized Chaikin Money Flow (CMF) indicator with Inverse Fisher Transform (IFT) and some smoothing techniques.
I had to normalize the indicator in order to fit it to the IFT range (-1 -> 1).
Moreover, the good old adaptive mode is also included in this indicator. It uses Ehlers superb dominant cycle techniques.
It also has divergence detection, several options for individualisation and doesn't repaint.
Usage
www.investopedia.com
Signals
CMF above 0 => bullish market
CMF below 0 => bearish market
(You can also use the inner bands instead of the zero line, to make these signals more precise)
Bullish regular/hidden divergence => long
Bearish regular/hidden divergence => short
Enjoy guys!
PS: I really would like to hear some feedback of you.
Adaptive Fisherized Stochastic Center of GravityIntroduction
I modified the script "Fisher Stochastic Center of Gravity" of @DasanC for this indicator.
I added inverse Fisher transform, cycle period adaptiveness mode (Ehlers) and smoothing to it.
Moreover, I added buy and sell and beautified some stuff.
Lastly, it is also non-repainting!
Usage
This indicator can be used like a normal stochastic, but I don't recommend divergence analysis on it.
That fisherization stuff seems to make the graphs unuseable for that because it tries.
It works well on every timeframe I would say, but lower timeframes are recommended, because of the fast nature of stochastic.
Usually it does a good job on entry confirmation for reversals and trend continuation trades.
Recommended indicator to combine with this indicator is RSI cyclic smoothed v2 .
This is the best RSI version I know. In trending market it is recommended to look more on the inner bands and in flat market it is recommended to look more on the outer bands.
When RSI shows oversold and this indicator shows a crossover of the Center of Gravity plot through the bottom line -> Long entry is confirmed
When RSI shows overbought and this indicator shows a crossunder of the Center of Gravity plot through the top line -> Short entry is confirmed
Settings
The adaptive mode is enabled by default to give you straight the whole indicator experience.
The default settings are optimized, but should be changed depending on the market.
An example:
Market has a low volatility and a high momentum -> I want a slower/higher length to catch the slower new highs and lows.
Market has higher volatility and a low momentum, -> I want a faster/lower length to catch the faster new highs and lows
Signals
Crossover
Buy -> cog crossover signalLine
Sell -> cog crossunder signalLine
Overbought/Oversold Crossover
Buy -> cog crossover lowerBand
Sell -> cog crossunder lowerBand
I use this indicator a lot, because I don't know a better stochastic on this community here.
@DasanC did an awesome work with his version I used as base for this script.
Enjoy this indicator and let the profit roll! 🔥
Candlestick OB FinderIntroduction
Hello, this here is a non-repainting candlestick indicator which is able to detect OB looking candlestick formations.
Usage
It can be used to confirm entries, but be aware that it produces a lot of false signals.
Somehow the swings tend to reverse at these points.
I recommend the 10–15 minutes timeframe.
I hope you enjoy this small indicator. :)
Fisherized CCIIntroduction
This here is a non-repainting indicator where I use inverse Fisher transformation and smoothing on the well-known CCI (Commdity Channel Index) momentum indicator.
"The Inverse Fisher Transform" describes the calculation and use of the inverse Fisher transform by Dr . Ehlers in 2004. The transform is applied to any indicator with a known probability distribution function. It enables to transform an indicator signal into the range between +1 and -1. This can help to eliminate the noise of an indicator.
The CCI is an momentum indicator which describes the distance of the price to the average price.
For smoothing I used the Hann Window and NET (Noise Elimination Technique) methods.
Additional Features
Divergence Analysis
Trend-adaptive Histogram
Timeframe selection
Usage
It is usually used to spot potential trend reverals or mean-reversion (against the trend) trades on lower timeframes. IMO it can be even used to spot trend-following trades. It always depends on which settings you have, which timeframe do you use and which indicators you combine with it.
The suggested timeframe for this indicator is 15 min (with the length setting on 50).
The histogram with adaptive mode enabled could be used as filter applied on the buy and sell signals.
The divergence analysis can help to spot additional entries/exits or confirm the buy and sell signals.
Always try to find the best settings! This indicators has a lot of customization options you should take advantage of.
Signals
The indicator uses the following logic to generate the buy and sell signals:
Normal
Buy -> When CCI and MA go above the top band (usually +100) and cross
Sell -> When CCI and MA go below the the bottom band (usually -100) and cross
Fisherized
Buy -> When CCI and MA go above the the zero line and cross
Sell -> When CCI and MA go below the the zero line and cross
Have fun with the indicator! I am open for feedback and questions. :)
No gaps candlesThis indicator repaints the candles so that every candle's open price is the previous candle's close price. This helps visualize stocks and ETFs that have big gaps, usually between trading days.
You should hide visibility of the ticker for this to be displayed properly.
Multi Supertrend with no-repaint and HTF optionThis indicator has 2 Supertrends to filter the trend.
The Default one uses the same timeframe as chart.
The additional Supertrend is non-repaint type and can run on higher timeframes.
It has an auto-higher timeframe selection option, thanks to LonesomeTheBlue, the original author.
It is accurate on current timeframe also.
How to avoid repainting when using security() - PineCoders FAQNOTE
The non-repainting technique in this publication that relies on bar states is now deprecated, as we have identified inconsistencies that undermine its credibility as a universal solution. The outputs that use the technique are still available for reference in this publication. However, we do not endorse its usage. See this publication for more information about the current best practices for requesting HTF data and why they work.
This indicator shows how to avoid repainting when using the security() function to retrieve information from higher timeframes.
What do we mean by repainting?
Repainting is used to describe three different things, in what we’ve seen in TV members comments on indicators:
1. An indicator showing results that change during the realtime bar, whether the script is using the security() function or not, e.g., a Buy signal that goes on and then off, or a plot that changes values.
2. An indicator that uses future data not yet available on historical bars.
3. An indicator that uses a negative offset= parameter when plotting in order to plot information on past bars.
The repainting types we will be discussing here are the first two types, as the third one is intentional—sometimes even intentionally misleading when unscrupulous script writers want their strategy to look better than it is.
Let’s be clear about one thing: repainting is not caused by a bug ; it is caused by the different context between historical bars and the realtime bar, and script coders or users not taking the necessary precautions to prevent it.
Why should repainting be avoided?
Repainting matters because it affects the behavior of Pine scripts in the realtime bar, where the action happens and counts, because that is when traders (or our systems) take decisions where odds must be in our favor.
Repainting also matters because if you test a strategy on historical bars using only OHLC values, and then run that same code on the realtime bar with more than OHLC information, scripts not properly written or misconfigured alerts will alter the strategy’s behavior. At that point, you will not be running the same strategy you tested, and this invalidates your test results , which were run while not having the additional price information that is available in the realtime bar.
The realtime bar on your charts is only one bar, but it is a very important bar. Coding proper strategies and indicators on TV requires that you understand the variations in script behavior and how information available to the script varies between when the script is running on historical and realtime bars.
How does repainting occur?
Repainting happens because of something all traders instinctively crave: more information. Contrary to trader lure, more information is not always better. In the realtime bar, all TV indicators (a.k.a. studies ) execute every time price changes (i.e. every tick ). TV strategies will also behave the same way if they use the calc_on_every_tick = true parameter in their strategy() declaration statement (the parameter’s default value is false ). Pine coders must decide if they want their code to use the realtime price information as it comes in, or wait for the realtime bar to close before using the same OHLC values for that bar that would be used on historical bars.
Strategy modelers often assume that using realtime price information as it comes in the realtime bar will always improve their results. This is incorrect. More information does not necessarily improve performance because it almost always entails more noise. The extra information may or may not improve results; one cannot know until the code is run in realtime for enough time to provide data that can be analyzed and from which somewhat reliable conclusions can be derived. In any case, as was stated before, it is critical to understand that if your strategy is taking decisions on realtime tick data, you are NOT running the same strategy you tested on historical bars with OHLC values only.
How do we avoid repainting?
It comes down to using reliable information and properly configuring alerts, if you use them. Here are the main considerations:
1. If your code is using security() calls, use the syntax we propose to obtain reliable data from higher timeframes.
2. If your script is a strategy, do not use the calc_on_every_tick = true parameter unless your strategy uses previous bar information to calculate.
3. If your script is a study and is using current timeframe information that is compared to values obtained from a higher timeframe, even if you can rely on reliable higher timeframe information because you are correctly using the security() function, you still need to ensure the realtime bar’s information you use (a cross of current close over a higher timeframe MA, for example) is consistent with your backtest methodology, i.e. that your script calculates on the close of the realtime bar. If your system is using alerts, the simplest solution is to configure alerts to trigger Once Per Bar Close . If you are not using alerts, the best solution is to use information from the preceding bar. When using previous bar information, alerts can be configured to trigger Once Per Bar safely.
What does this indicator do?
It shows results for 9 different ways of using the security() function and illustrates the simplest and most effective way to avoid repainting, i.e. using security() as in the example above. To show the indicator’s lines the most clearly, price on the chart is shown with a black line rather than candlesticks. This indicator also shows how misusing security() produces repainting. All combinations of using a 0 or 1 offset to reference the series used in the security() , as well as all combinations of values for the gaps= and lookahead= parameters are shown.
The close in the call labeled “BEST” means that once security has reached the upper timeframe (1 day in our case), it will fetch the previous day’s value.
The gaps= parameter is not specified as it is off by default and that is what we need. This ensures that the value returned by security() will not contain na values on any of our chart’s bars.
The lookahead security() to use the last available value for the higher timeframe bar we are using (the previous day, in our case). This ensures that security() will return the value at the end of the higher timeframe, even if it has not occurred yet. In our case, this has no negative impact since we are requesting the previous day’s value, with has already closed.
The indicator’s Settings/Inputs allow you to set:
- The higher timeframe security() calls will use
- The source security() calls will use
- If you want identifying labels printed on the lines that have no gaps (the lines containing gaps are plotted using very thick lines that appear as horizontal blocks of one bar in length)
For the lines to be plotted, you need to be on a smaller timeframe than the one used for the security() calls.
Comments in the code explain what’s going on.
Look first. Then leap.
14/28 Day SMA Divergence and RSI - No RepaintIf you are interested in purchasing my algorithmic trading bot that receives Tradingview indicator alerts via email and then executes them in Bittrex, please visit my product page here: ilikestocks.com Additionally, I would love to create video/blog guides on creating Tradingview scripts or strategies. If you are a knowledgeable in finance or other related fields and would like to be featured on my page, please contact me at [email protected].
No crossovers were used in this script, and this is likely the reason for the no repaint(Correct me if wrong).
This strategy script uses a 14-day SMA signal line, a 28-day SMA and RSI. The strategy works by determining whether the (14-day SMA is above the 28-day SMA and the RSI levels are overbought(below 30)) or RSI is very overbought(below 13 or so). Once either of these conditions have been met, a long position is opened.
The initial long position must be partially closed by the take profit first and then the final close is executed if the 14-day signal SMA is below the 28-day SMA; you may also exclusively use take profit to close positions.
The green plotted spikes are the initial long position conditions. The orange plotted spikes are take profit signals once a long position is opened. The red plotted spikes are plotted when the SMA 14-day is below the 28-day SMA.
Please do leave constructive criticism or comments below because it helps me better create scripts!
GMAE Original (By Kevin Manrrique)This script is called GMAE Original by me (Kevin Manrrique). I'm publishing this to the public because we are all traders and we need to support each other as a TVcommunity. This is something I built for fun. This script uses a series of EMA's. NO REPAINT, NO LAGGING! It works better for short-term trends as you can see. Please leave the copyright on the script at all times even if you rebuild it. If you need any help or have questions please inbox me privately. If you interested in joining up and building an indicator or strategy please inbox me as well. Thank you and I hope you enjoy this script as much as I do.
Remember there are no holy grails. The only holy grail there is are indicators built together to stop faulty signals and be as accurate as possible and this is one of them.
Sincerely,
Kevin Manrrique
