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
M-oscillator
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.
ATR OSC and Volume Screener (ATROSCVS)In today's world of trading, having the right tools and indicators can make all the difference. With the vast number of cryptocurrencies available, I've found it challenging to keep track of the market's overall direction and make informed decisions. That's where the ATR OSC and Volume Screener comes in, a powerful Pine Script that I use to identify potential trading opportunities across multiple cryptocurrencies, all in one convenient place.
This script combines two essential components: the ATR Oscillator (ATR OSC) and a Volume Screener. It is designed to work with the TradingView platform. Let me explain how this script works and how it benefits my trading.
Firstly, the ATR Oscillator is an RSI-like oscillator that performs better under longer lookback periods. Unlike traditional RSI, the ATR OSC doesn't lose its min and max ranges with a long lookback period, as the scale remains intact. It calculates the true range by considering the high, low, open, and close prices of a financial instrument, and uses this true range instead of the standard deviation in a modified z-score calculation. This unique approach helps provide a more precise assessment of the market's volatility.
The Volume Screener, on the other hand, helps me identify unusual trading volumes across various cryptocurrencies. It employs a normalized volume calculation method, effectively filtering out outliers and highlighting potentially significant trading opportunities.
One feature I find particularly impressive about the ATR OSC and Volume Screener is its versatility and the way it displays information using color gradients. With support for over 30 different cryptocurrencies, including popular options like Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Dogecoin (DOGE), I can monitor a wide range of markets simultaneously. The color gradient on the grid is visually appealing and makes it easy to identify the strength of the indicators for each cryptocurrency, allowing me to make quick comparisons and spot potential trading opportunities.
The customizable input options allow me to fine-tune the script to suit my individual trading preferences and strategies. In summary, the ATR OSC and Volume Screener has been an invaluable tool for me as I navigate the ever-evolving world of cryptocurrencies. By combining the power of the ATR Oscillator with a robust Volume Screener, this Pine Script makes it easier than ever to identify promising trading opportunities and stay ahead of the game.
The color gradient in the ATR OSC and Volume Screener is essential for visually representing the data on the heatmap. It uses a range of colors to indicate the strength of the indicators for each cryptocurrency, making it easier to understand the market dynamics at a glance.
In the heatmap, the color gradient typically starts from a cooler color, such as blue or green, at the lower extremes (low ATR OSC values) and progresses towards warmer colors, like yellow, orange, or red, as the ATR OSC values approach the upper extremes (high ATR OSC values). This color-coding system enables me to quickly identify and interpret the data without having to examine individual numerical values.
For example, cooler colors (blue or green) might represent lower values of the ATR Oscillator, suggesting oversold conditions in the respective cryptocurrencies. On the other hand, warmer colors (yellow, orange, or red) indicate higher ATR OSC values, signaling overbought market conditions. This visual representation allows me to make rapid comparisons between different cryptocurrencies and spot potential trading opportunities more efficiently.
By utilizing the color gradient in the heatmap, the ATR OSC and Volume Screener simplifies the analysis of multiple cryptocurrencies, helping me to quickly identify market trends and make better-informed trading decisions.
I highly recommend testing the ATR OSC and Volume Screener and seeing the difference it can make in your trading decisions. Happy trading!
Intrabar Run Count Indicator [tbiktag]• OVERVIEW
Introducing the Intrabar Run Count Indicator , a tool designed to detect potential non-randomness in intrabar price data. It utilizes the statistical runs test to examine the number of sequences ( runs ) of positive and negative returns in the analyzed price series. As deviations from random-walk behavior of returns may indicate market inefficiencies , the Intrabar Run Count Indicator can help traders gain a better understanding of the price dynamics inside each chart bar and make more informed trading decisions.
• USAGE
The indicator line expresses the deviation between the number of runs observed in the dataset and the expected number of runs under the hypothesis of randomness. Thus, it gauges the degree of deviation from random-walk behavior. If, for a given chart bar, it crosses above the critical value or crosses below the negative critical value, this may indicate non-randomness in the underlying intrabar returns. These instances are highlighted by on-chart signals and bar coloring. The confidence level that defines the critical value, as well as the number of intrabars used for analysis, are selected in the input settings.
It is important to note that the readings of the Intrabar Run Count Indicator do not convey directional information and cannot predict future asset performance. Rather, they help distinguish between random and potentially tradable price movements, such as breakouts, reversals, and gap fillings.
• DETAILS
The efficient-market hypothesis implies that the distribution of returns should be random, reflecting the idea that all available information is already priced into the asset. However, in practice, financial markets may not always be perfectly efficient due to factors such as market frictions, information asymmetry, and irrational behavior of market participants. As a result, inefficiency (non-randomness) can occur, potentially creating opportunities for trading strategies.
To search for potential inefficiencies, the Intrabar Run Count Indicator analyzes the distribution of the signs of returns. The central assumption underlying the indicator's logic is that if the asset price follows a random-walk pattern, then the probability of the next return being positive or negative (i.e., the next price value being larger or smaller than the current value) follows a binomial distribution. In this case, the number of runs is also a random variable, and, for a large sample, its conditional distribution is approximately normal with a well-defined mean and variance (see this link for the exact expressions). Thus, the observed number of runs in the price series is indicative of whether or not the time series can be regarded as random. In simple words, if there are too few runs or too many runs, it is unlikely a random time series. A trivial example is a series with all returns of the same sign.
Quantitatively, the deviation from randomness can be gauged by calculating the test statistic of the runs test (that serves as an indicator line ). It is defined as the absolute difference between the observed number of runs and the expected number of runs under the null hypothesis of randomness, divided by the standard deviation of the expected number of runs. If the test statistic is negative and exceeds the negative critical value (at a given confidence level), it suggests that there are fewer runs than expected for a random-walking time series. Likewise, if the test statistic exceeds the positive critical value, it is indicative of more runs than expected for a random series. The sign of the test statistic can also be informative, as too few runs can be sometimes indicative of mean-reverting behavior.
• CONCLUSION
The Intrabar Run Count Indicator can be a useful tool for traders seeking to exploit market inefficiencies and gain a better understanding of price action within each chart bar. However, it is important to note that the runs test only evaluates the distributional properties of the data and does not provide any information on the underlying causes of the non-randomness detected. Additionally, like any statistical test, it can sometimes produce false-positive signals. Therefore, this indicator should be used in conjunction with other analytical techniques as part of a trading strategy.
True Range OscHey fellow traders! I've just published a new indicator called the True Range Oscillator. It's designed to help you better understand price movements and volatility. The indicator calculates the average true range of the price data and uses a modified z-score-like approach to normalize it. The main difference is that it uses true range instead of standard deviation for normalization.
This oscillator identifies the highest and lowest values within a specified range, excluding any outliers based on standard deviations. It then scales the output between 0 and 100, so you can easily see how the current price action compares to its historical range. You can use the True Range Oscillator to spot potential trend reversals and overbought/oversold conditions.
Here are some features to explore:
Customize your price data source (open, high, low, or close).
Adjust the length and smoothing settings for the average true range calculation.
Find outliers with standard deviations, and tweak the outlier_level and dev_lookback options.
Visualize price action with plotted lines for the upper range (70), lower range (30), and center line (50), along with a shaded area between the upper and lower ranges for added clarity.
I hope you find this indicator useful in your trading journey!
Volume Flow OscillatorIntroducing the "Volume Flow Oscillator" indicator, a powerful and adaptable tool that incorporates the PeacefulIndicators library to analyze price movement strength and volume in the market. This indicator is designed to assist you in detecting potential opportunities and improving your trading analysis.
The Volume Flow Oscillator indicator offers the following features:
Adjustable input parameters, allowing you to modify the source (HLCC4 by default) and the short length to match your trading style and preferences.
A visually appealing display, with the Volume Flow Oscillator line in orange, a zero line in gray, and filled areas between the 70 and -70 levels in blue, making it easy to interpret the indicator's signals.
The core functionality of the Volume Flow Oscillator indicator is powered by the volume_flow_oscillator function from the PeacefulIndicators library, ensuring accurate and reliable results.
To start using the Volume Flow Oscillator indicator in your trading analysis, simply add the script to your chart and customize the input parameters as needed. We hope this script, built upon the PeacefulIndicators library, proves to be a valuable addition to your trading strategy.
Adaptive MACDIntroducing the "Adaptive MACD" indicator, an innovative and user-friendly script that utilizes the PeacefulIndicators library to provide traders with a dynamic and responsive version of the classic MACD indicator. This script effectively adapts the MACD calculation to account for the dominant market cycle, offering improved signals to help you make better-informed trading decisions.
The Adaptive MACD indicator incorporates the following features:
A selection of customizable input parameters, allowing you to adjust the short length, long length, signal length, and the dynamic high and low values to suit your individual trading preferences.
A visually appealing and informative display, using different colors to highlight MACD line crossovers and histogram bars, making it easier to interpret the indicator's signals.
The core functionality of the Adaptive MACD is powered by the macdDynamicLength function from the PeacefulIndicators library, ensuring accurate and reliable calculations.
To start using the Adaptive MACD indicator in your trading analysis, simply add the script to your chart, and customize the input parameters as needed. We hope this script, built upon the PeacefulIndicators library, proves to be a valuable addition to your trading strategy.
Put to Call Ratio CorrelationHello!
Excited to share this with the community!
This is actually a very simple indicator but actually usurpingly helpful, especially for those who trade indices such as SPX, IWM, QQQ, etc.
Before I get into the indicator itself, let me explain to you its development.
I have been interested in the use of option data to detect sentiment and potential reversals in the market. However, I found option data on its own is full of noise. Its very difficult if not impossible for a trader to make their own subjective assessment about how option data is reflecting market sentiment.
Generally speaking, put to call ratios generally range between 0.8 to 1.1 on average. Unless there is a dramatic pump in calls or puts causing an aggressive spike up to over this range, or fall below this range, its really difficult to make the subjective assessment about what is happening.
So what I thought about trying to do was, instead of looking directly at put to call ratio, why not see what happens when you perform a correlation analysis of the PTC ratio to the underlying stock.
So I tried this in pinescript, pulling for Tradingview's ticker PCC (Total Equity Put to Call Ratio) and using the ta.correlation function against whichever ticker I was looking at.
I played around with this idea a bit, pulled the data into excel and from this I found something interesting. When there is a very significant negative or positive correlation between PTC ratio and price movement, we see a reversal impending. In fact, a significant negative or positive correlation (defined as a R value of 0.8 or higher or -0.8 or lower) corresponded to a stock reversal about 92% of the time when data was pulled on a 5 minute timeframe on SPY.
But wait, what is a correlation?
If you are not already familiar, a correlation is simply a statistical relationship. It is defined with a Pearson R correlation value which ranges from 0 (no correlation) to 1 (significant positive correlation) and 0 to -1 (significant negative correlation).
So what does positive vs negative mean?
A significant positive correlation means the correlation is moving the same as the underlying. In the case of this indicator, if there is a significant positive correlation could mean the stock price is climbing at the same time as the PTC ratio.
Inversely, it could mean the stock price is falling as well as the PTC ratio.
A significant negative correlation means the correlation is moving in the opposite direction. So in this case, if the stock price is climbing and the PTC ratio is falling proportionately, we would see a significant negative correlation.
So how does this work in real life?
To answer this, let's get into the actual indicator!
In the image above, you will see the arrow pointing to an area of significant POSITIVE correlation.
The indicator will paint the bars on the actual chart purple (customizable of course) to signify this is an area of significant correlation.
So, in the above example this means that the PTC ratio is increase proportionately to the increase in the stock price in the SAME direction (Puts are going up proportionately to the stock price). Thus, we can make the assumption that the underlying sentiment is overwhelmingly BEARISH. Why? Because option trading activity is significantly proportionate to stock movement, meaning that there is consensus among the options being traded and the movement of the market itself.
And in the above example we will see, the stock does indeed end up selling:
In this case, IWM fell roughly 1 point from where there was bearish consensus in the market.
Let's use this same trading day and same example to show the inverse:
You will see a little bit later, a significant NEGATIVE correlation developed.
In this case identified, the stock wise RISING and the PTC ratio was FALLING.
This means that Puts were not being bought up as much as calls and the sentiment had shifted to bullish .
And from that point, IWM ended up going up an additional 0.75 points from where there was a significant INVERSE correlation.
So you can see that it is helpful for identifying reversals. But what is also can be used for is identifying areas of LOW conviction. Meaning, areas where there really is no relationship between option activity and stock movement. Let's take spy on the 1 hour timeframe for this example:
You can see in the above example there really is no consensus in the option trading activity with the overarching sentiment. The price action is choppy and so too is option trading activity. Option traders are not pushing too far in one direction or the other. We can also see the lack of conviction in the option trading activity by looking at the correlation SMA (the white line).
When a ticker is experiencing volatile and good movement up and down, the SMA will generally trade to the top of the correlation range (roughly + 1.0) and then make a move down to the bottom (roughly - 1.0), see the example below:
When the SMA is not moving much and accumulating around the centerline, it generally means a lot of indecision.
Additional Indicator Information:
As I have said, the indicator is very simple. It pulls the data from the ticker PCC and runs a correlation assessment against whichever ticker you are on.
PCC pulls averaged data from all equities within the market and is not limited to a single equity. As such, its helpful to use this with indices such as SPY, IWM and QQQ, but I have had success with using it on individual tickers such as NVDA and AMD.
The correlation length is defaulted to 14. You can modify it if you wish, but I do recommend leaving it at this as the default and the testing I have done with this have all been on the 14 correlation length.
You can chose to smooth the SMA over whichever length of period you wish as well.
When the indicator is approaching a significant negative or positive relationship, you will see the indicator flash red in the upper or lower band to signify the relationship. As well, the chart will change the bar colour to purple:
Everything else is pretty straight forward.
Let me know your questions/comments or suggestions around the indicator and its applications.
As always, no indicator is meant to provide a single, reliable strategy to your trading regimen and no indicator or group of indicators should be relied on solely. Be sure to do your own analysis and assessments of the stock prior to taking any trades.
Safe trades everyone!
RDX Relative Directional IndexRDX Relative Directional Index, Strength + Direction + Trend. This indicator is the combination of RSI and DMI or ADX. RDX aims at providing Relative direction of the price along with strength of the trend. This acts as both RSI and Average Directional Index. as the strength grows the RSI line becomes wider and when there is high volatility and market fluctuation the line becomes thinner. Color decides the Direction. This indicator provides sideways detection of RSI signal.
RDX Width: This determines the strength of RSI and Strength of ADX, The strength grows RDX band grows wider, as strength decreases band shrinks and merge into the RSI line. for exact working simply disable RSI plot on the indicator. when there is no strength the RSI vanishes..
Technical:
RSI : with default 14 period
ADX : Default 14 period
RDX=RSI+(ADX-20)/5
Color Code:
Red: Down Direction
Green: Up Direction
Sideways:
A rectangular channel is plotted on RSI 50 Level
Oversold Overbought:
Oversold and Overbought Levels are plotted for normal RSI Oversold and Overbought detection.
Buy/Sell:
Buy sell signals from ADX crossover are plotted and its easy to determine
Strength + Direction + Trend in one go
Hope the community likes this...
Contibute for more ideas and indicators..
RiverFlow ADX ScreenerRiverFlow ADX Screener, Scans ADX and Donchian Trend values across various Timeframes. This screener provides support to the Riverflow indicator. Riverflow concept is based on Two indicators. Donchian Channel and ADX or DMI.
How to implement?
1.Donchian Channel with period 20
2. ADX / DMI 14,14 threshold 20
Entry / Exit:
1. Buy/Sell Signal from ADX Crossovers.
2. Trend Confirmation Donchian Channel.
3. Major Trend EMA 200
Buy/Sell:
After a buy/sell is generated by ADX Crossover, Check for Donchian Trend. it has to be in same direction as trend. for FTT trades take 2x limit. for Forex and Stocks take 1:1.5, SL must be placed below recent swing. One can use Riverflow indicator for better results.
ADX Indicator is plotted with
Plus: Green line
Minus: Red Line
ADX strength: plotted as Background area.
TREND: Trend is represented by Green and Red Area around Threshold line
Table:
red indicates down trend
green indicates up trend
grey indicates sideways
Weak ADX levels are treated sideways and a channel is plotted on ADX and PLUS and MINUS lines . NO TRADES are to be TAKEN on within the SIDEWAYS region.
Settings are not required as it purely works on Default settings. However Donchian Length can be changed from settings.
Timeframes below 1Day are screened. Riverflow strategy works on timeframe 5M and above timeframe. so option is not provided for lower timeframes.
Best suits for INTRADAY and LONG TERM Trading
Moonhub IndexMoonhub Index combines several popular technical indicators to create an aggregated index that aims to give a clearer overall picture of the market. The index takes into account the current market condition (trending, ranging, or volatile) to adjust its calculations accordingly.
The indicators used in this composite index are:
Hull Moving Average (HMA)
Fisher Transform (FT)
Williams Alligator
Moving Average Convergence Divergence (MACD)
Average True Range (ATR)
On-Balance Volume (OBV)
Money Flow Index (MFI)
Accumulation/Distribution (AD)
Pivot Points
True Strength Index (TSI)
Volume-Weighted Average Price (VWAP)
The script calculates the values of each indicator and then normalizes and weighs them according to predefined weights. The composite index is formed by summing the weighted values of each indicator. The final Moon Index is plotted on the chart, along with several other related lines like the exponential moving averages (EMA) and simple moving averages (SMA) of the index.
This custom index can be used by traders to get a more comprehensive view of the market and make better-informed trading decisions based on the combined insights of multiple indicators.
Moonhub Cycle IndexMoonhub Cycle Index is a composite index derived from three popular technical analysis indicators: Moving Average Convergence Divergence (MACD), Schaff Trend Cycle (STC), and Detrended Price Oscillator (DPO). The indicator is designed to help identify potential trends and market sentiment by combining the unique characteristics of each indicator.
Key components of the indicator include:
Input Parameters:
COEMA Length (len_DIema): The length of the Exponential Moving Average (EMA) applied to the Custom Index. Default is set to 9.
COSMA Length (len_DIsma): The length of the Simple Moving Average (SMA) applied to the Custom Index. Default is set to 30.
Indicators:
MACD: A momentum oscillator that shows the relationship between two moving averages of a security's price. It is calculated using the difference between the 12-period and 26-period EMA, and a 9-period EMA (signal line) of the MACD.
STC: A cyclic indicator that identifies cyclical trends in the market. It is calculated using the Stochastic oscillator formula applied to the close, high, and low prices over a 10-period lookback window.
DPO: A price oscillator that eliminates the trend from price data to focus on underlying cycles. It is calculated using a custom function that shifts the price by half the length and subtracts the SMA from the shifted price.
Custom Index: The composite index is calculated by taking the average of the MACD line, STC, and DPO.
COEMA and COSMA: Exponential and Simple Moving Averages applied to the Custom Index using the lengths specified by the input parameters (len_DIema and len_DIsma).
Plots: The Custom Index, COEMA, and COSMA are plotted with different colors and line widths to visualize their interaction and provide insights into potential market trends.
This Custom Index Indicator can be useful for traders who want to analyze the market using a combination of these indicators to make more informed decisions. It can also help identify potential trends and market sentiment by combining the unique characteristics of each indicator.
Momentum Covariance Oscillator by TenozenWell, guess what? A new indicator is here! Again it's a coincidence, as I experiment with my formula. So far it's less noisy than Autoregressive Covariance Oscillator, so possibly this one is better. The formula is much simpler, care me to explain.
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Yt = close - previous average
Val = Yt/close
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Welp that's the formula lol. Funny thing is that it's so simple, but it's good! What matters is the use of it haha.
So how to use this Oscillator? If the value is above 0, we expect a bullish response, if the value is below 0 we expect a bearish response. That simple. Ciao.
(Any questions and suggestions? feel free to comment!)
Sakura 2The oscillator uses an adaptive moving average as input to another RSI oscillator and is designed to provide a way to minimize the impact of corrections on the output of the oscillator without significant lag.
An additional trigger line is present in order to provide entry points from intersections between the oscillator and the trigger line.
I'll be working on the code to add and describe the privileges and the best settings
Settings
=Lengthy : period of the oscillator
=Power : controls the sensitivity of the oscillator to retracements, with higher values minimizing the sensitivity to retracements.
=Src : source input of the indicator
The indicator also includes the following graphical settings:
=Gradient : Determines the color mode to use for the gradient, options include "Red To Green", "Red To Blue" and "None", with "None" displaying no gradient.
=Color fill : Determines whether to fill the area between the oscillator and the trigger line or not, by default "On".
=Circles : Determines whether to show circles highlighting the crosses between the oscillator and the trigger line.
Weighted Momentum and Volatility Indicator (WMI)The Weighted Momentum and Volatility Indicator (WMI) is a composite technical analysis tool that combines momentum and volatility to identify potential trend changes in the underlying asset.
The WMI is displayed as an histogram that oscillates around a zero line, with increasing bars indicating a bullish trend and decreasing bars indicating a bearish trend.
The WMI is calculated by combining the Rate of Change (ROC) and Average True Range (ATR) indicators.
The ROC measures the percentage change in price over a set period of time, while the ATR measures the volatility of the asset over the same period.
The WMI is calculated by multiplying the normalized values of the ROC and ATR indicators, with the normalization process being used to adjust the values to a scale between 0 and 1.
Traders and investors can use the WMI to identify potential trend changes in the underlying asset, with increasing bars indicating a bullish trend and decreasing bars indicating a bearish trend.
The WMI can be used in conjunction with other technical analysis tools to develop a comprehensive trading strategy.
Do not hesitate to let me know your comments if you see any improvements to be made :)
Oscillator: Which follows Normal Distribution?When doing machine learning using oscillators, it would be better if the oscillators were normally distributed.
So I analyzed the distribution of oscillators.
The value of the oscillator was divided into 50 groups each from 0 to 100.
ex) if rsi value is 45.43 -> group_44, 58.23 -> group_58
Ocscillators : RSI, Stoch, MFI, WT, RVI, etc....
Caution: The normal distribution was verified through an empirical formula.
Fetch Buy And Hold StrategyThis script was created as an experiment using ChatGPT. I actually woudn't recommend using the ai program to help you with your Pinescripts, as it makes a fair amount of mistakes. It was a fun experiment however.
The script is a simple buy and hold tool. Here's what it does:
- Everytime the rsi enters below the set treshold, a counter increases.
- The second increase of the counter happens when the price goes above the treshold, and then dips below the treshold again.
- The program would fire off a buy signal when the counter hits the number 3.
- After the buy. the counter will reset.
Lets take a look at the following example where the rsi treshold is 30:
- So the rsi dips below 30 and the initial counter is set from 0 to 1.
- The price rises which brings the rsi back to 40.
- Then another dip happens and the rsi is now 25, increasing the counter from 1 two.
- Rsi now dips to 23 and nothing happens.
- Rsi goes back up to 31, and dips back to 28 which puts the counter at 3. A buy singal is now fired and the counter is set to 0.
Paranoia IndicatorThe Paranoia Indicator is a technical analysis tool that combines three popular indicators: Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Stochastic. The Paranoia Indicator formula is calculated by taking a weighted average of the three indicators, with the weights being 23.6%, 61.8%, and 14.6%, respectively.
The Paranoia Indicator is used to identify potential trend reversals and overbought/oversold conditions in the market. When the indicator is above zero, it is considered bullish, and when it is below zero, it is considered bearish. The Paranoia Indicator also has extreme bands that help to identify when the market is overbought or oversold.
Traders can use the Paranoia Indicator in conjunction with other technical analysis tools to confirm trading signals and make more informed trading decisions. The Paranoia Indicator is suitable for all types of markets, including stocks, forex, and commodities, and can be applied to any time frame.
Overall, the Paranoia Indicator is a useful tool for traders looking to identify potential trend reversals and overbought/oversold conditions in the market.
Local Model Kalman Market ModeIntroduction
Heyo guys, I made a new (repainting) indicator called Local Model Kalman Market Mode.
I created it, because I wanted a reliable market mode filter for a potential mean-reversion strategy (e. g. BB Scalping).
On the screenshot you can see an example of how to use it in a BB strategy.
E.g. you would enter long when you have bullish divergence, price is under lower BB, price is under PoC and this indicator here shows range-bound market phase.
You would exit long on cross of the middle band.
Description
The indicator attempts to model the underlying market using different local models (i.e., trending, range-bound, and choppy) and combines them using the T3 Six Pole Kalman Filter to generate an overall estimate of the market.
The Fisher Transform is applied on the price to reach a Gaussian distribution, which increases the accuracy of the indicator itself.
The script first defines state variables for each local model, which include trend direction, trend strength, upper and lower bounds of the range, volatility of the range, level of choppiness, and strength of noise.
Then, likelihood functions are defined for each local model based on the state variables.
Next, the script calculates weights for each local model based on their likelihoods and uses them to calculate state variables for the overall estimate.
Finally, the script combines the state variables using the T3 Six Pole Kalman Filter to generate the overall estimate of the market, which is plotted in blue.
Fundamental Knowledge
To understand the explanation of the indicator and the script, there are a few fundamental concepts that you need to know:
Market: A market is a place where buyers and sellers come together to exchange goods or services.
In the context of trading, the market refers to the exchange where financial instruments such as stocks, currencies, and commodities are bought and sold.
Local models: Local models are statistical models that attempt to capture the characteristics of a particular market regime.
For example, a trending market may have different characteristics than a range-bound market or a choppy market.
The indicator uses different local models to capture the different market regimes.
Trend direction and strength: The trend direction refers to the direction in which the market is moving, either up or down.
The trend strength refers to the magnitude of the trend and how likely it is to continue.
Range-bound market: A range-bound market is a market where prices are trading within a specific range, with a clear upper and lower bound.
Choppiness: Choppiness refers to the degree of irregularity in price movements, often seen in sideways or range-bound markets.
Volatility: Volatility refers to the degree of variation in the price of an asset over time. High volatility implies larger price swings, while low volatility implies smaller price swings.
Kalman filter: A Kalman filter is a mathematical algorithm used to estimate an unknown variable from a series of noisy measurements.
In the context of the indicator, the Kalman filter is used to generate an overall estimate of the market by combining the local models.
T3 Six Pole Kalman Filter: The T3 Six Pole Kalman Filter is a specific type of Kalman filter that is used to smooth and filter time-series data, such as the price data of a financial instrument.
Fisher Transform: The Fisher Transform is a mathematical formula used to transform any probability distribution into a Gaussian normal distribution. It is commonly used in technical analysis to transform non-Gaussian indicators into ones that are more suitable for statistical analysis.
By understanding these fundamental concepts, you should have a basic understanding of how the indicator works and how it generates an overall estimate of the market.
Usage
You can use this indicator on every timeframe.
Users can customize the parameters of the T3 Six Pole Kalman Filter (T3 length, alpha, beta, gamma, and delta) using input functions.
Try out different parameter combinations and use the one you like most.
Thank you for checking this out. Leave me a comment or boost the script, when you wanna support me! 👌
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Credits to:
▪@HPotter - Fisher Transform
▪@loxx - T3
▪ChatGPT - Helped me to make the research for this indicator and helped to build the core algorithm.
Autoregressive Covariance Oscillator by TenozenWell to be honest I don't know what to name this indicator lol. But anyway, here is my another original work! Gonna give some background of why I create this indicator, it's all pretty much a coincidence when I'm learning about time series analysis.
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Well, the formula of Auto-covariance is:
E{(X(t)-(t) * (X(t-s)-(t-s))}= Y_s
But I don't multiply both values but rather subtract them:
E{(X(t)-(t) - (X(t-s)-(t-s))}= Y_s?
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For arm_vald, the equation is as follows:
arm_vald = val_mu + mu_plus_lsm + et
val_mu --> mean of time series
mu_plus_lsm --> val_mu + LSM
et --> error term
As you can see, val_mu^2. I did this so the oscillator is much smoother.
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After I get the value, I normalize them:
aco = Y_s? / arm_vald
So by this calculation, I get something like an oscillator!
(more details in the code)
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So how to use this indicator? It's so easy! If the value is above 0, we gonna expect a bullish response, if the value is below 0, we gonna expect a bearish response; that simple. Be aware that you should wait for the price to be closed before executing a trade.
Well, try it out! So far this is the most powerful indicator that I've created, hope it's useful. Ciao.
(more updates for the indicator if needed)
Hull PressureThis amazing oscillator displays the difference between the hull average calculated on the close of the candles and the one calculated between the average of the highs and lows.
This allows the user to identify the pressure of the closing price over the average, useful to identify trends, divergences, and reversals.
This indicator also has two dynamic overbought and oversold areas, calculated over the past extreme highs and lows of the oscillator.
Rolling Candle Closes Summationscript to sum rolling 20 (default) period's prices together
use on volume indicators to get the likes of McClellan Summation
User selection: rolling periods to add
