[AIO] Multi Collection Moving Averages 140 MA TypesAll In One Multi Collection Moving Averages.
Since signing up 2 years ago, I have been collecting various Сollections.
I decided to get it into a decent shape and make it one of the biggest collections on TV, and maybe the entire internet.
And now I'm sharing my collection with you.
140 Different Types of Moving Averages are waiting for you.
Specifically :
"
AARMA | Adaptive Autonomous Recursive Moving Average
ADMA | Adjusted Moving Average
ADXMA | Average Directional Moving Average
ADXVMA | Average Directional Volatility Moving Average
AHMA | Ahrens Moving Average
ALF | Ehler Adaptive Laguerre Filter
ALMA | Arnaud Legoux Moving Average
ALSMA | Adaptive Least Squares
ALXMA | Alexander Moving Average
AMA | Adaptive Moving Average
ARI | Unknown
ARSI | Adaptive RSI Moving Average
AUF | Auto Filter
AUTL | Auto-Line
BAMA | Bryant Adaptive Moving Average
BFMA | Blackman Filter Moving Average
CMA | Corrected Moving Average
CORMA | Correlation Moving Average
COVEMA | Coefficient of Variation Weighted Exponential Moving Average
COVNA | Coefficient of Variation Weighted Moving Average
CTI | Coral Trend Indicator
DEC | Ehlers Simple Decycler
DEMA | Double EMA Moving Average
DEVS | Ehlers - Deviation Scaled Moving Average
DONEMA | Donchian Extremum Moving Average
DONMA | Donchian Moving Average
DSEMA | Double Smoothed Exponential Moving Average
DSWF | Damped Sine Wave Weighted Filter
DWMA | Double Weighted Moving Average
E2PBF | Ehlers 2-Pole Butterworth Filter
E2SSF | Ehlers 2-Pole Super Smoother Filter
E3PBF | Ehlers 3-Pole Butterworth Filter
E3SSF | Ehlers 3-Pole Super Smoother Filter
EDMA | Exponentially Deviating Moving Average (MZ EDMA)
EDSMA | Ehlers Dynamic Smoothed Moving Average
EEO | Ehlers Modified Elliptic Filter Optimum
EFRAMA | Ehlers Modified Fractal Adaptive Moving Average
EHMA | Exponential Hull Moving Average
EIT | Ehlers Instantaneous Trendline
ELF | Ehler Laguerre filter
EMA | Exponential Moving Average
EMARSI | EMARSI
EPF | Edge Preserving Filter
EPMA | End Point Moving Average
EREA | Ehlers Reverse Exponential Moving Average
ESSF | Ehlers Super Smoother Filter 2-pole
ETMA | Exponential Triangular Moving Average
EVMA | Elastic Volume Weighted Moving Average
FAMA | Following Adaptive Moving Average
FEMA | Fast Exponential Moving Average
FIBWMA | Fibonacci Weighted Moving Average
FLSMA | Fisher Least Squares Moving Average
FRAMA | Ehlers - Fractal Adaptive Moving Average
FX | Fibonacci X Level
GAUS | Ehlers - Gaussian Filter
GHL | Gann High Low
GMA | Gaussian Moving Average
GMMA | Geometric Mean Moving Average
HCF | Hybrid Convolution Filter
HEMA | Holt Exponential Moving Average
HKAMA | Hilbert based Kaufman Adaptive Moving Average
HMA | Harmonic Moving Average
HSMA | Hirashima Sugita Moving Average
HULL | Hull Moving Average
HULLT | Hull Triple Moving Average
HWMA | Henderson Weighted Moving Average
IE2 | Early T3 by Tim Tilson
IIRF | Infinite Impulse Response Filter
ILRS | Integral of Linear Regression Slope
JMA | Jurik Moving Average
KA | Unknown
KAMA | Kaufman Adaptive Moving Average & Apirine Adaptive MA
KIJUN | KIJUN
KIJUN2 | Kijun v2
LAG | Ehlers - Laguerre Filter
LCLSMA | 1LC-LSMA (1 line code lsma with 3 functions)
LEMA | Leader Exponential Moving Average
LLMA | Low-Lag Moving Average
LMA | Leo Moving Average
LP | Unknown
LRL | Linear Regression Line
LSMA | Least Squares Moving Average / Linear Regression Curve
LTB | Unknown
LWMA | Linear Weighted Moving Average
MAMA | MAMA - MESA Adaptive Moving Average
MAVW | Mavilim Weighted Moving Average
MCGD | McGinley Dynamic Moving Average
MF | Modular Filter
MID | Median Moving Average / Percentile Nearest Rank
MNMA | McNicholl Moving Average
MTMA | Unknown
MVSMA | Minimum Variance SMA
NLMA | Non-lag Moving Average
NWMA | Dürschner 3rd Generation Moving Average (New WMA)
PKF | Parametric Kalman Filter
PWMA | Parabolic Weighted Moving Average
QEMA | Quadruple Exponential Moving Average
QMA | Quick Moving Average
REMA | Regularized Exponential Moving Average
REPMA | Repulsion Moving Average
RGEMA | Range Exponential Moving Average
RMA | Welles Wilders Smoothing Moving Average
RMF | Recursive Median Filter
RMTA | Recursive Moving Trend Average
RSMA | Relative Strength Moving Average - based on RSI
RSRMA | Right Sided Ricker MA
RWMA | Regressively Weighted Moving Average
SAMA | Slope Adaptive Moving Average
SFMA | Smoother Filter Moving Average
SMA | Simple Moving Average
SSB | Senkou Span B
SSF | Ehlers - Super Smoother Filter P2
SSMA | Super Smooth Moving Average
STMA | Unknown
SWMA | Self-Weighted Moving Average
SW_MA | Sine-Weighted Moving Average
TEMA | Triple Exponential Moving Average
THMA | Triple Exponential Hull Moving Average
TL | Unknown
TMA | Triangular Moving Average
TPBF | Three-pole Ehlers Butterworth
TRAMA | Trend Regularity Adaptive Moving Average
TSF | True Strength Force
TT3 | Tilson (3rd Degree) Moving Average
VAMA | Volatility Adjusted Moving Average
VAMAF | Volume Adjusted Moving Average Function
VAR | Vector Autoregression Moving Average
VBMA | Variable Moving Average
VHMA | Vertical Horizontal Moving Average
VIDYA | Variable Index Dynamic Average
VMA | Volume Moving Average
VSO | Unknown
VWMA | Volume Weighted Moving Average
WCD | Unknown
WMA | Weighted Moving Average
XEMA | Optimized Exponential Moving Average
ZEMA | Zero Lag Moving Average
ZLDEMA | Zero-Lag Double Exponential Moving Average
ZLEMA | Ehlers - Zero Lag Exponential Moving Average
ZLTEMA | Zero-Lag Triple Exponential Moving Average
ZSMA | Zero-Lag Simple Moving Average
"
Don't forget that you can use any Moving Average not only for the chart but also for any of your indicators without affecting the code as in my example.
But remember that some MAs are not designed to work with anything other than a chart.
All MA and Code lists are sorted strictly alphabetically by short name (A-Z).
Each MA has its own number (ID) by which you can display the Moving Average you need.
Next to the ID selection there are tooltips with short names and their numbers. Use them.
The panel below will help you to read the Name of the selected MA.
Because of the size of the collection I think this is the optimal and most convenient use. Correct me if this is not the case.
Unknown - Some MAs I collected so long ago that I lost the full real name and couldn't find the authors. If you recognize them, please let me know.
I have deliberately simplified all MAs to input just Source and Length.
Because the collection is so large, it would be quite inconvenient and difficult to customize all MA functions (multipliers, offset, etc.).
If you need or like any MA you will still have to take it from my collection for your code.
I tried to leave the basic MA settings inside function in first strings.
I have tried to list most of the authors, but since the bulk of the collection was created a long time ago and was not intended for public publication I could not find all of them.
Some of the features were created from scratch or may have been slightly modified, so please be careful.
If you would like to improve this collection, please write to me in PM.
Also Credits, Likes, Awards, Loves and Thanks to :
@alexgrover
@allanster
@andre_007
@auroagwei
@blackcat1402
@bsharpe
@cheatcountry
@CrackingCryptocurrency
@Duyck
@ErwinBeckers
@everget
@glaz
@gotbeatz26107
@HPotter
@io72signals
@JacobAmos
@JoshuaMcGowan
@KivancOzbilgic
@LazyBear
@loxx
@LuxAlgo
@MightyZinger
@nemozny
@NGBaltic
@peacefulLizard50262
@RicardoSantos
@StalexBot
@ThiagoSchmitz
@TradingView
— 𝐀𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 !
So just a Big Thank You to everyone who has ever and anywhere shared their codes.
Average
Geometrical Mean Moving AverageThe geometric moving average is a type of moving average that calculates the geometric mean of the previous n-periods of the price time series. Unlike the simple moving average that uses the arithmetic mean to continuously calculate the moving average as new price data comes in, the geometric moving average uses the geometric mean formula to get the moving average of the price data as new ones come in.
Why use a geometric moving average?
The geometric moving average differs from the simple moving average in how it is calculated. Most importantly, the geometric mean takes into account the compounding that occurs from period to period.
How can you use a geometric mean moving average?
You can use the GMMA just as you would use any other moving average indicator. You can use it to identify the direction of the trend, and in this case, it can also serve as a support level during an uptrend or a resistance level during a downtrend.
Drawbacks with a geometric moving average
Just like other moving average indicators, the GMA has limitations. Some of them are as follows:
It lags because it uses past price data.
It is pretty useless when the price action is choppy or moving predominantly sideways. During such periods, it can give multiple false signals.
Strategy Gaussian Anomaly DerivativeConcept behind this Strategy :
Considering a normal "buy/sell" situation, an asset would be bought in average at the median price following a Gaussian like concept. A higher or lower average trend would significate that the current perceived value is respectively higher or lower than the current median price, which mean that the buyers are evaluating the price underpriced or overpriced.
This behaviour would be even more relevent depending on its derivative evolution.
Therefore, this Strategy setup is based on this Gaussian like concept anomaly of average close positionning compare to high-low average derivative, such as the derivative of the following ploted basic signal : 1-(high+low)/(2*close).
This Strategy can actually be used like a trend change and continuation strength indicator aswell.
In the Setup Signal part :
You can define the filtering of the basis signal "1-(high+low)/(2*close)" on EMA or SMA as you wish.
You can define the corresponding period and the threathold as a mutiply of the average 1/3 of all time value of the basis signal.
You can define the SMA filtering period of the Derivative signal and the corresponding threathold on the same mutiply of the average 1/3 of all time value of the derivative.
In the Setup Strategy part :
You can set up your strategy assesment based on Long and/or Short. You can also define the considered period.
The most successful tuned strategies I did were based on the derivative indicator with periods on the basis signal and the derivative under 30, can be 1 to 3 of te derivative and 7 to 21 for the basis signal. The threathold depends on the asset volatility aswell, 1 is usually the most efficient but 0 to 10 can be relevent depending on the situation I met. You can find an example of tuning for this strategy based on Kering's case hereafter.
I hoping that you will enjoy using this Strategy, don't hesitate to comment, to question, to correct or complete it ! I would be very curious about similar famous approaches that would have already been made.
Thank to you !
Average Range LinesThis Average Range Lines indicator identifies high and low price levels based on a chosen time period (day, week, month, etc.) and then uses a simple moving average over the length of the lookback period chosen to project support and resistance levels, otherwise referred to as average range. The calculation of these levels are slightly different than Average True Range and I have found this to be more accurate for intraday price bounces.
Lines are plotted and labeled on the chart based on the following methodology:
+3.0: 3x the average high over the chosen timeframe and lookback period.
+2.5: 2.5x the average high over the chosen timeframe and lookback period.
+2.0: 2x the average high over the chosen timeframe and lookback period.
+1.5: 1.5x the average high over the chosen timeframe and lookback period.
+1.0: The average high over the chosen timeframe and lookback period.
+0.5: One-half the average high over the chosen timeframe and lookback period.
Open: Opening price for the chosen time period.
-0.5: One-half the average low over the chosen timeframe and lookback period.
-1.0: The average low over the chosen timeframe and lookback period.
-1.5: 1.5x the average low over the chosen timeframe and lookback period.
-2.0: 2x the average low over the chosen timeframe and lookback period.
-2.5: 2.5x the average low over the chosen timeframe and lookback period.
-3.0: 3x the average low over the chosen timeframe and lookback period.
Look for price to find support or resistance at these levels for either entries or to take profit. When price crosses the +/- 2.0 or beyond, the likelihood of a reversal is very high, especially if set to weekly and monthly levels.
This indicator can be used/viewed on any timeframe. For intraday trading and viewing on a 15 minute or less timeframe, I recommend using the 4 hour, 1 day, and/or 1 week levels. For swing trading and viewing on a 30 minute or higher timeframe, I recommend using the 1 week, 1 month, or longer timeframes. I don’t believe this would be useful on a 1 hour or less timeframe, but let me know if the comments if you find otherwise.
Based on my testing, recommended lookback periods by timeframe include:
Timeframe: 4 hour; Lookback period: 60 (recommend viewing on a 5 minute or less timeframe)
Timeframe: 1 day; Lookback period: 10 (also check out 25 if your chart doesn’t show good support/resistance at 10 days lookback – I have found 25 to be useful on charts like SPX)
Timeframe: 1 week; Lookback period: 14
Timeframe: 1 month; Lookback period: 10
The line style and colors are all editable. You can apply a global coloring scheme in the event you want to add this indicator to your chart multiple times with different time frames like I do for the weekly and monthly.
I appreciate your comments/feedback on this indicator to improve. Also let me know if you find this useful, and what settings/ticker you find it works best with!
Also check out my profile for more indicators!
Average True Range Trailing Mean [Alifer]Upgrade of the Average True Range default indicator by TradingView. It adds and plots a trailing mean to show periods of increased volatility more clearly.
ATR TRAILING MEAN
A trailing mean, also known as a moving average, is a statistical calculation used to smooth out data over time and identify trends or patterns in a time series.
In our indicator, it clearly shows when the ATR value spikes outside of it's average range, making it easier to identify periods of increased volatility.
Here's how the ATR Trailing Mean (atr_mean) is calculated:
atr_mean = ta.cum(atr) / (bar_index + 1) * atr_mult
The ta.cum() function calculates the cumulative sum of the ATR over all bars up to the current bar.
(bar_index + 1) represents the number of bars processed up to the current bar, including the current one.
By dividing the cumulative ATR ta.cum(atr) by (bar_index + 1) and then multiplying it by atr_mult (Multiplier), we obtain the ATR Trailing Mean value.
If atr_mult is set to 1.0, the ATR Trailing Mean will be equal to the simple average of the ATR values, and it will follow the ATR's general trend.
However, if atr_mult is increased, the ATR Trailing Mean will react more strongly to the ATR's recent changes, making it more sensitive to short-term fluctuations.
On the other hand, reducing atr_mult will make the ATR Trailing Mean less responsive to recent changes in ATR, making it smoother and less prone to reacting to short-term volatility.
In summary, adjusting the atr_mult input allows traders to fine-tune the ATR Trailing Mean's responsiveness based on their preferred level of sensitivity to recent changes in market volatility.
IMPLEMENTATION IN A STRATEGY
You can easily implement this indicator in an existing strategy, to only enter positions when the ATR is above the ATR Trailing Mean (with Multiplier-adjusted sensitivity). To do so, add the following lines of codes.
Under Inputs:
length = input.int(title="Length", defval=20, minval=1)
atr_mult = input.float(defval=1.0, step = 0.1, title = "Multiplier", tooltip = "Adjust the sensitivity of the ATR Trailing Mean line.")
smoothing = input.string(title="Smoothing", defval="RMA", options= )
ma_function(source, length) =>
switch smoothing
"RMA" => ta.rma(source, length)
"SMA" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
=> ta.wma(source, length)
This will allow you to define the Length of the ATR (lookback length over which the ATR is calculated), the Multiplier to adjust the Trailing Mean's sensitivity and the type of Smoothing to be used for the ATR.
Under Calculations:
atr= ma_function(ta.tr(true), length)
atr_mean = ta.cum(atr) / (bar_index+1) * atr_mult
This will calculate the ATR based on Length and Smoothing, and the resulting ATR Trailing Mean.
Under Entry Conditions, add the following to your existing conditions:
and atr > atr_mean
This will make it so that entries are only triggered when the ATR is above the ATR Trailing Mean (adjusted by the Multiplier value you defined earlier).
ATR - DEFINITION AND HISTORY
The Average True Range (ATR) is a technical indicator used to measure market volatility, regardless of the direction of the price. It was developed by J. Welles Wilder and introduced in his book "New Concepts in Technical Trading Systems" in 1978. ATR provides valuable insights into the degree of price movement or volatility experienced by a financial asset, such as a stock, currency pair, commodity, or cryptocurrency, over a specific period.
ATR - CALCULATION AND USAGE
The ATR calculation involves three components:
1 — True Range (TR): The True Range is a measure of the asset's price movement for a given period. It takes into account the following factors:
The difference between the high and low prices of the current period.
The absolute value of the difference between the high price of the current period and the closing price of the previous period.
The absolute value of the difference between the low price of the current period and the closing price of the previous period.
Mathematically, the True Range (TR) for the current period is calculated as follows:
TR = max(high - low, abs(high - previous_close), abs(low - previous_close))
2 — ATR Calculation: The ATR is calculated as a Moving Average (MA) of the True Range over a specified period.
The ATR is calculated as follows:
ATR = MA(TR, length)
3 — ATR Interpretation: The ATR value represents the average volatility of the asset over the chosen period. Higher ATR values indicate higher volatility, while lower ATR values suggest lower volatility.
Traders and investors can use ATR in various ways:
Setting Stop Loss and Take Profit Levels: ATR can help determine appropriate stop-loss and take-profit levels in trading strategies. A larger ATR value might require wider stop-loss levels to allow for the asset's natural price fluctuations, while a smaller ATR value might allow for tighter stop-loss levels.
Identifying Market Volatility: A sharp increase in ATR might indicate heightened market uncertainty or the potential for significant price movements. Conversely, a decreasing ATR might suggest a period of low volatility and possible consolidation.
Comparing Volatility Between Assets: Since ATR uses absolute values, it shouldn't be used to compare volatility between different assets, as assets with higher prices will consistently have higher ATR values, while assets with lower prices will consistently have lower ATR values. However, the addition of a trailing mean makes such a comparison possible. An asset whose ATR is consistently close to its ATR Trailing Mean will have a lower volatility than an asset whose ATR continuously moves far above and below its ATR Trailing Mean. This can help traders and investors decide which markets to trade based on their risk tolerance and trading strategies.
Determining Position Size: ATR can be used to adjust position sizes, taking into account the asset's volatility. Smaller position sizes might be appropriate for more volatile assets to manage risk effectively.
Rough AverageThe Rough Average indicator is a unique technical tool that calculates a modified average to provide insights into market conditions. It incorporates a combination of mathematical operations and existing indicators to offer traders a different perspective on price movements.
The Rough Average indicator aims to capture market dynamics through a specific calculation method. It utilizes two main components: a check for the approximate scale of the price and a profile calculation based on the Relative Strength Index (RSI) of the closing price.
Methodology:
Approximate Scale: The indicator determines the approximate scale of the price by analyzing the magnitude of the closing price. This step involves a mathematical process that identifies the power of 10 that best represents the scale. This function reduces overall lag and gives a better smoothing to the output of the calculation
Profile Calculation: The indicator calculates a profile value by summing the absolute values of the RSI of the closing price over a specified period. The RSI provides insights into the strength or weakness of price movements. The profile calculation considers a range of prices based on the determined scale.
Indicator Calculation:
The Rough Average is derived by applying the Exponential Moving Average (EMA) to the calculated profile. The EMA is a smoothing technique that emphasizes recent price data. The resulting value represents the modified average of the indicator.
Utility:
The Rough Average indicator offers traders an alternative perspective on market conditions. By utilizing a modified average calculation, it can reveal potential trends, reversals, or periods of market strength or weakness. Traders can use the Rough Average to complement their analysis and identify possible trading opportunities.
It is important to note that the effectiveness of the Rough Average indicator may vary depending on the specific market and trading strategy. It is recommended to combine its analysis with other technical indicators and conduct thorough testing before making trading decisions.
Key Features:
Customizable OB\OS Levels
Bar coloring methods: Trend, Reversions, Extremities
Example Charts:
TimeLy Moving Average - TMAHello traders, I'm Only Fibonacci.
With this indicator, you will see the averages according to the hourly, weekly and monthly price movements in many periods on the chart.
This will show you the moving average values of the price over different periods in a progressive manner on the chart that is open to you.
Options and Usage
To see the hourly average, your chart's time range must be less than or equal to 60 minutes, otherwise it will produce a NaN value.
In order to see the daily average, your chart must be open for any minute period or (even if the second is open, it must be greater than 6 seconds). Otherwise, it does not produce any value.
Your chart must be larger than the second chart to see the weekly average. In other words, you can see the weekly average with at least 1 minute chart open.
In order to see the monthly average, your chart time interval must be above 10 minutes, otherwise you will not be able to see data again.
Settings
You choose the moving average type and the time interval value you want to see from the indicator settings.
You can also select a source for moving averages.
Enjoy it, you can make improvements on it.
Please do not forget to comment for various bug reports.
Anchored VWAP+This indicator is an enhanced version of the Anchored VWAP indicator with additional functions:
1. Anchored AP (average price). It removes the volume weighting step in Anchored VWAP, and can display the average price over a period of time. For example, if the price of the stock in the last 3 days is 100, 200, 300, then AP is their average value of 200
2. Anchored AC (average cost). The average cost over time can be displayed. For example, if the price of the stock in the last 2 days is 100,300, then AC is (1+1)/(1/100+1/300)=150
When using the indicator, you need to choose a starting point, then the indicator will start to calculate the subsequent VWAP, AP and AC from this starting point, and draw 3 lines in the graph
These three lines can be regarded as the average cost line of the market, with potential support and resistance effects
We have filled the shadow between VWAP and AP, which can be regarded as a potential support resistance band
===========================中文版本===========================
该指标为增强版本的Anchored VWAP指标。在Anchored VWAP基础上增加了额外功能：
1. Anchored AP。其去掉了Anchored VWAP中成交量加权的步骤，可以显示一段时间的平均价格。举个例子，假如股票最近3天的价格为100，200，300，那么AP为他们的平均值200
2. Anchored AC。可以显示一段时间的平均成本。举个例子，假如股票最近2天的价格为100，300，那么AC为(1+1)/(1/100+1/300)=150
使用指标时你需要先选择一个起点，随后指标将会以该起点开始计算后续的VWAP、AP和AC，并且在图中绘制3根线
这3根线均可以视作是市场的平均成本线，具有潜在的支撑和阻力效果
我们让VWAP和AP之间填充了阴影，该阴影可以视作潜在的支撑阻力带
Ratio To Average - The Quant ScienceRatio To Average - The Quant Science is a quantitative indicator that calculates the percentage ratio of the market price in relation to a reference average. The indicator allows the calculation of the ratio using four different types of averages: SMA, EMA, WMA, and HMA. The ratio is represented by a series of histograms that highlight periods when the ratio is positive (in green) and periods when the ratio is negative (in red).
What is the Ratio to Average?
The Ratio to Average is a measure that tracks the price movements with one of its averages, calculating how much the price is above or below its own average, in percentage terms.
USER INTERFACE
Lenght: it adjusts the number of bars to include in the calculation of the average.
Moving Average: it allows you to choose the type of average to use.
Color Up/Color Down : it allows you to choose the color of the indicator for positive and negative ratios.
MADI(Moving average deviation rate index)This script is "Moving average deviation rate" to Indexing.
index = average deviation rate / (Sigma * (input:SIgma)) * 100
It's for people who like simplicity.
Average Trend with Deviation Bands v2TL;DR: An average based trend incl. micro trend spotting and multiple display options.
This script is basically an update of my "Average Trend with Deviation Bands" script. I made the following changes:
Not an overlay anymore - The amount of drawn lines makes the chart pretty messy. That's why I moved it to a pane. If you preferred the overlay you can use my "Average Trend with Deviation Bands" script. *This is also the reason why I publish this script instead of updating the existing one.
I added an EMA to represent the price movement instead of candles
I added a signal (SMA) to spot micro trends and early entry/exit signals
I added the option to switch between a "line view" which shows the average trend and deviation bands and an "oscillator view" which shows an oscillator and histogram (MACD style)
General usage:
1. The white line is the average trend (which is an average of the last N bars open, close, high, low price).
2. Bands around the average trend are standard deviations which can be adjusted in the options menu and are only visible in "lines view". Basically they are like the clouds in the Ichimoku Cloud indicator - In big deviation bands the price movement needs more "power" to break through the average trend and vice versa.
3. Indicator line (blue line) - This is the EMA which represents the price. Crossing the average trend from below indicates an uptrend and vice versa (crossing from above indicates a down trend).
4. Signal line (red line) - This is a smoothed version of the indicator line which can be used to predict the movement of the price when crossed by the indicator line (like at MACD and many other indicators).
Oscillator usage:
When switched to "oscillator view" the indicator line oscillates around a zero line which can be seen as the average trend. The usage is basically the same as described above. However there is also the histogram which shows the difference between the indicator and signal. Of course the histogram can be deactivated. Additionally a color filling can be added to easily spot entry/exit signals.
As always: Code is free do whatever you like. If you have any questions/comments/etc. just drop it in the comment section.
Manual PnL (Profit and Loss) % Tracker - spot long only
This is a manual profit and loss tracker. It takes the user's manual input of total cost and quantity, and then outputs a table on the bottom right of the chart showing the profit or loss %, average purchase price, gross profit or loss, and market value.
Instructions:
1. Double click the indicator title at the top left of the chart
2. Select the "Inputs" tab and click the empty field next to "Symbol" to enter the traded symbol+exchange. This entry MUST be the same as the chart you are on, for example BTCUSDT/BINANCE (indicator will not display otherwise)
3. Enter the Total Cost and Qty of shares/coins owned
4. Optional - change positive or negative colors
5. Optional - under the "Style" tab, change the color of the average price (AVG) line
Note that for the average price (AVG) line to be shown/hidden you must enable/disable "Indicator and financials labels" in the scales settings.
For crypto or other tickers that have prices in many decimal places I would suggest, for the sake of accuracy, adjusting the decimal places in the code so that for prices under $1 you will display more info.
For example let's say you purchase x number of crypto at a price of 0.031558 you should change the code displaying "0.00" on line 44 to "0.000000"
This will ensure that the output table and plotted line will calculate an average price with the same number of decimals.
Ignition Cha Cha ChaIgnition Cha Cha Cha (ICCC) is a 3 color coded moving average indicator which numerically quantify the angle of their trends. I have labeled them as fast, medium and slow. The trend colors are Green for bullish, Red for bearish and Grey for sideways. The sideways movement can be user defined for all 3 in the settings under Threshold. If you regard for example anything under 10º as sideways then place 10 in the corresponding threshold and any angle under 10º will give a grey moving average and a grey labeled text. I use this chart in several ways. If you don't want moving averages all over your Chartistic Masterpiece you can turn off the plots and leave the numeric angles which will give you an overview of the trend. Conversely if you want to make the ultimate trend chart you can setup a 4 chart layout, Weekly, Daily, 12 hour and 4 hour and add the indicator with 200/50/25 moving averages and look for confluence. I find the best way for this is turn off the candles and use the moving averages with the numeric labels. You also have the ability to turn off and on different aspects of the indicator so that there is good control over its look. Also I have given the indicator lots of Alert presets for all 3 of the moving averages so you can avoid demented screen-stairing. Please forgive the name, my mother made me do Ballroom dancing lessons as a kid.
Average Cost (Costo Promedio)ENGLISH
This 'Average Cost' script allows the user to input and visualize profit or loss for different stocks (up to 50) with average cost and quantity data on a single chart. This is useful for tracking the profit or loss of each stock in real-time.
To use this script, the user should follow these steps:
1. Add the 'Average Cost' script to your TradingView chart.
2. In the script's configuration window, input the tickers, average costs, and quantity of shares for each ticker you want to monitor.
3. Click 'Accept' to apply the changes.
This script is primarily designed for stock markets, but can also be useful in other financial markets where the user is interested in tracking the performance of multiple assets.
ESPAÑOL
Este script de "Costo Promedio" permite al usuario ingresar y visualizar si hay ganancia o perdida para diferentes acciones (hasta 50) con los datos de costos promedio y cantidad de acciones en un solo gráfico. Esto es útil para realizar un seguimiento de la ganancia o pérdida de cada acción en tiempo real.
Para utilizar este script, el usuario debe seguir estos pasos:
1. Agregue el script "Costo Promedio" a su gráfico en TradingView.
2. En la ventana de configuración del script, ingrese los tickers, costos promedio y cantidad de acciones para cada ticker que desee monitorear.
3. Haga clic en "Aceptar" para aplicar los cambios.
Este script está diseñado principalmente para los mercados de acciones, pero también puede ser útil en otros mercados financieros donde el usuario esté interesado en rastrear el rendimiento de múltiples activos.
Volume+This volume indicator uses a long WMA to establish an average volume and calculates the standard deviation based on that average. Each deviation level from 1 to 3 is also plotted with the bar color gradually increasing in intensity when more than one standard deviation is exceeded.
Stock Data Table█ OVERVIEW
This is a table that shows some information about stocks. It is divided into four sections:
1) Correlation
2) Shares
3) Daily Data
4) Extended Session Data
The table is completely modular, which means you can add or remove each element from the settings menu, and it will automatically rearrange its spaces.
It is also highly customizable, to the extent that you can change almost any color, remove or change titles, invert section rows, and much more.
1) Correlation
The script checks if the stock is listed on NASDAQ, and if so, uses the QQQ (Nasdaq-100 ETF) as the reference index in the first cell; otherwise, it uses the SPY (S&P 500 ETF). The length of the correlation is shown in the second cell. The table then displays the correlation between the reference index and the other index, and the correlation between the reference index and the stock.
To make it easier to interpret the correlation values, each row's last cell is color-coded with a gradient to highlight the type of correlation, and the direction of the gradient can be customized.
The correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables, indicating how changes in one variable are associated with changes in the other variable, so it can be used to identify patterns and trends.
If you are interested in correlation, I suggest taking a look at my dedicated indicator:
2) Shares
This feature provides you with quick access to key information about shares and market capitalization.
On one row, you can view the total shares outstanding and the market capitalization for the fiscal year or the quarterly year. The total shares outstanding represents the total number of shares of the stock that have been issued and are currently outstanding, regardless of whether they are held by insiders or public investors. The market capitalization is a widely used measure of the company's value as determined by the stock market, calculated by multiplying its current stock price with the total number of outstanding shares.
The other row shows the float, which is the number of shares of a company that are available for public trading, and the corresponding free-float market cap, calculated by multiplying the company's current stock price with the float. Because Pine Script does not allow retrieving information about quarterly year float, you can view the float and the free-float market cap of the fiscal year only. The data can be displayed at all times or only when the difference between the total shares outstanding and the float is significant enough to result in a difference between the market cap and free-float market cap.
The classification for market cap and free-float market cap is set in this way:
Mega Cap: $200 billion or more
Large Cap: between $10 billion and $200 billion
Mid Cap: between $2 billion and $10 billion
Small Cap: between $300 million and $2 billion
Micro Cap: less than $300 million
Penny Stocks: less than $5 (customizable)
Comparing the free-float market cap to the market cap can provide insights into the liquidity of a stock. In fact, if the float is relatively small compared to the total shares outstanding, it may be more difficult to find buyers or sellers, which could lead to increased volatility. On the other hand, a larger float indicates that the stock is more liquid and may be easier to trade, potentially resulting in lower volatility. However, market conditions can change quickly and significantly, especially for intraday traders, and the free-float can also change as insiders or other large shareholders buy or sell shares. Therefore, comparing the data of the fiscal year with that of the quarterly year may not provide the most up-to-date and accurate information for making trading decisions. This limitation can be mitigated by combining those data with other indicators and tools, such as technical analysis or news events, to gain a better understand of the stock's performance and potential trading opportunities.
3) Daily Data
This section is available on daily charts only due to the lack of accuracy of real-time daily data on other time frames. Here, you can view the Average Daily Volume (ADV) over a preferred time range (20 days by default), and the Daily Change, which represents the percentage difference between the closing price on two consecutive trading days.
ADV is useful in measuring the stock's volatility, as it provides an indication of how much trading activity there is in it. Generally speaking, stocks with higher trading volume tend to be less volatile than stocks with lower trading volume. High trading volume means there are more buyers and sellers actively trading the stock, which makes it easier for investors to buy and sell shares at fair prices. This increased liquidity can help to stabilize the stock price, reducing the potential for large swings in either direction. On the other hand, stocks with lower trading volume may experience greater volatility, as there are fewer buyers and sellers actively trading the stock. This can result in larger price swings, as it may be more difficult for investors to buy or sell shares at fair prices.
The daily percentage change can provide an indication of the stock's volatility, with larger values indicating greater volatility and risk. It can also be compared to that of a benchmark such an index or other stocks in the same sector, helping to determine whether the stock is outperforming or underperforming relative to them.
4) Extended Session Data
The fourth section is available on intraday charts only. This section provides two pieces of information: the Extended Session Change and the Pre-Market Volume.
The Extended Session Change indicates the percentage difference between the previous day's closing price and the latest price in the extended session. This gives you the extent and the direction of the price gap that occurred during extended trading hours.
The Pre-Market Volume shows the sum of all shares traded during the pre-market session. This can be helpful in understanding how much interest the stock gained before the market opened.
By default, the two rows will be visible at all times. They will stop updating after the end of their respective time range, and resume updating when it starts again. However, you can choose to automatically hide them outside of their time ranges.
Both the extended session and pre-market time ranges can be customized. Please note that if you select time ranges outside of the regular market session (as set by default), you must enable the extended session to view the corresponding rows.
█ GENERAL NOTES
• Total Shares Outstanding, Float, Average Daily Volume and Pre-Market Volume cells use a customizable color system based on two thresholds, to help you quickly identify whether the value is "too low/acceptable/too high" or "too low/not enough high/acceptable".
• If you cannot see certain data, that simply means it is not available.
Premium Linear Regression - The Quant ScienceThis script calculates the average deviation of the source data from the linear regression. When used with the indicator, it can plot the data line and display various pieces of information, including the maximum average dispersion around the linear regression.
The code includes various user configurations, allowing for the specification of the start and end dates of the period for which to calculate linear regression, the length of the period to use for the calculation, and the data source to use.
The indicator is designed for multi-timeframe use and to facilitate analysis for traders who use regression models in their analysis. It displays a green linear regression line when the price is above the line and a red line when the price is below. The indicator also highlights areas of dispersion around the regression using circles, with bullish areas shown in green and bearish areas shown in red.
Quantitative Price Forecasting - The Quant ScienceThis script is a quantitative price forecasting indicator that forecasts price changes for a given asset.
The model aims to forecast future prices by analyzing past data within a selected time period. Mathematical probability is used to calculate whether starting from time X can lead to reaching prices Y1 and Y2. In this context, X represents the current selected time period, Y1 represents the selected percentage decrease, and Y2 represents the selected percentage increase. The probabilities are estimated using the simple average.
The simple average is displayed on the chart, showing in red the periods where the price is below the average and in green the periods where the price is above the average.
This powerful tool not only provides forecasts of future prices but also calculates the distribution of variations around the average. It then takes this information and creates an estimate of the average price variation around the simple average.
Using a mean-reverting logic, buying and selling opportunities are highlighted.
We recommend turning off the display of bars on your chart for a better experience when using this indicator.
Unlock the full potential of your trading strategy with our powerful indicator. By analyzing past price data, it provides accurate forecasts and calculates the probability of reaching specific price targets. Its mean-reverting logic highlights buying and selling opportunities, while the simple moving average displayed on the chart shows periods where the price is above or below the average. Additionally, it estimates the average variation of price around the simple average, giving you valuable insights into price movements. Don't miss out on this valuable tool that can take your trading to the next level
Fibonacci Step IndicatorThe Fibonacci Step Indicator assumes irregularity in calculating a moving average. It is measured as the mean of the previous lows and highs situated at Fibonacci past periods. For example, the mean of the lows from 2, 3, 5, 8, etc. periods ago form the Fibonacci step indicator.
The indicator uses the formula for the first twelve Fibonacci numbers on highs and lows so that it creates a moving support/resistance zone. Afterwards, the zone is stabilized by taking the highest highs of the upper indicator and the lowest lows of the lower indicator part.
The indicator is used as a trend following way. It can be compared to the Ichimoku Kinko Hyo cloud (without the future projection). The zone form a support and resistance area. During ranging periods, the market will fluctuate within the area which is a bad time to follow the trend (if any).
Strategy Myth-Busting #4 - LSMA+HULL Crossover - [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our fourth one we are automating is one of the strategies from "I Found The Best 1 Minute Scalping Strategy That Actually Works! ( Beginner Friendly )" from "Trade Domination" who claims to have made 366% profit on the 1 min chart of Solona despite having a 31% win rate in just a few weeks. As you can see from the backtest results below, I was unable to substantiate anything close to that that claim on the same symbol ( SOLUSD ), timeframe (1m) with identical instrument settings that "Trade Domination" was demonstrating with. Strategy Busted.
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
This strategy uses a combination of 2 open-source public indicators:
LSMA
Hull Suite by InSilico
Trading Rules
1 min candles
Stop Loss on recent swing High/Low
1:5 Risk Ratio
Enter Long
LSMA cross above Red Hull Suite line
Price has to be above Hull Suite Line
Enter Short
LSMA crosses under green Hull Suite Line
Price has to be below Hull Suite Line
ATR Oscillator - Index (Average True range Oscillator)The purpose of converting the ATR value indicator to an oscillator;
It is known that the ATR value is not between the two specified values. So it is not compressed between 0 and 100 like RSI and %B etc. Therefore, conditions such as "A condition if ATR value is X, B condition if ATR value is Y" cannot be created. In order to create these conditions, the max and min value range of the ATR value must be determined. This indicator converts the ATR values into a percentage number according to the maximum and minimum ATR values in the period you will choose. Max value is 100, min value is 0. The considered ATR value, on the other hand, corresponds to the % of the difference between the max and min value in the selected period.
In this way, conditions such as "If the ATR Oscillator value is greater than 10 or 20 or 30" can now be created, or the value of another indicator can be calculated based on the ATR Oscillator value. For example; Let's say we want the standard deviation of BBand to change according to the value of the ATR Oscillator. If BBand Standard Deviation is 3 if ATRO value is 100, BBand Standard Deviation is 2 if ATRO value is 0, and BBand Standard Deviation is 2.5 when ATRO value is 50;
We can encode it as BBand_Std_Dev=((ATRO*0.01)+2 )
If the ATRO value is between .... and ...., you can make improvements such as plot color X.
Anchored VWAP BandSimple script to anchor vwap to a drag and drop spot on the chart and display it as a band instead of a line.
the AVAP Band displays:
1. The AVWAP using High as the source
2. The AVWAP using OHLC4 as the source
3. The AVWAP using Low as the source
This is just a different way of visualising VWAP from an anchored point in time (Band vs Line)
Correlated ATR MA | AdulariHow do I use it?
Never use this indicator as standalone trading signal, it should be used as confluence.
When the price is above the moving average this shows the bullish trend is strong.
When the price is below the moving average this shows the bearish trend is strong.
When the moving average is purple, the trend is bullish, when it is gray, the trend is bearish.
Features:
Purple line for bullish trend and gray line for bearish trend.
Custom formula combining an ATR and Hull MA to clearly indicate trend strength and direction.
Unique approach to moving averages by taking the average of 3 types of MA's combined with custom ATR's.
How does it work?
1 — ATR value is calculated, then the correlation between the source and ATR is calculated.
2 — Signal value is calculated from the difference between the previous source and ATR values.
3 — Final value is being calculated using the following formula:
cor * target + (1 - cor) * nz(atr , target)
4 — Moving average is calculated by getting the average of 3 values: a normal HMA, HMA plus final value, and HMA minus final value.
