TrendLine Toolkit w/ Breaks (Real-Time)The TrendLine Toolkit script introduces an innovating capability by extending the conventional use of trendlines beyond price action to include oscillators and other technical indicators. This tool allows traders to automatically detect and display trendlines on any TradingView built-in oscillator or community-built script, offering a versatile approach to trend analysis. With breakout detection and real-time alerts, this script enhances the way traders interpret trends in various indicators.
🔲 Methodology
Trendlines are a fundamental tool in technical analysis used to identify and visualize the direction and strength of a price trend. They are drawn by connecting two or more significant points on a price chart, typically the highs or lows of consecutive price movements (pivots).
Drawing Trendlines:
Uptrend Line - Connects a series of higher lows. It signals an upward price trend.
Downtrend Line - Connects a series of lower highs. It indicates a downward price trend.
Support and Resistance:
Support Line - A trendline drawn under rising prices, indicating a level where buying interest is historically strong.
Resistance Line - A trendline drawn above falling prices, showing a level where selling interest historically prevails.
Identification of Trends:
Uptrend - Prices making higher highs and higher lows.
Downtrend - Prices making lower highs and lower lows.
Sideways (or Range-bound) - Prices moving within a horizontal range.
A trendline helps confirm the existence and direction of a trend, providing guidance in aligning with the prevailing market sentiment. Additionally, they are usually paired with breakout analysis, a breakout occurs when the price breaches a trendline. This signals a potential change in trend direction or an acceleration of the existing trend.
The script adapts this methodology to oscillators and other indicators. Instead of relying on price pivots, which can only be detected in retrospect, the script utilizes a trailing stop on the oscillator to identify potential swings in real-time, you may find more info about it here (SuperTrend toolkit) . We detect swings or pivots simply by testing for crosses between the indicator and its trailing stop.
type oscillator
float o = Oscillator Value
float s = Trailing Stop Value
oscillator osc = oscillator.new()
bool l = ta.crossunder(osc.o, osc.s) => Utilized as a formed high
bool h = ta.crossover (osc.o, osc.s) => Utilized as a formed low
This approach enables the algorithm to detect trendlines between consecutive pivot highs or lows on the oscillator itself, providing a dynamic and immediate representation of trend dynamics.
🔲 Breakout Detection
The script goes beyond trendline creation by incorporating breakout detection directly within the oscillator. After identifying a trendline, the algorithm continuously monitors the oscillator for potential breakouts, signaling shifts in market sentiment.
🔲 Setup Guide
A simple example on one of my public scripts, Z-Score Heikin-Ashi Transformed
🔲 Settings
Source - Choose an oscillator source of which to base the Toolkit on.
Zeroing - The Mid-Line value of the oscillator, for example RSI & MFI use 50.
Sensitivity - Calibrates the Sensitivity of which TrendLines are detected, higher values result in more detections.
🔲 Alerts
Bearish TrendLine
Bullish TrendLine
Bearish Breakout
Bullish Breakout
As well as the option to trigger 'any alert' call.
By integrating trendline analysis into oscillators, this Toolkit enhances the capabilities of technical analysis, bringing a dynamic and comprehensive approach to identifying trends, support/resistance levels, and breakout signals across various indicators.
Cerca negli script per "Heikin Ashi"
SVMKR_UT_Bot_HMA_UCS_LRSThis Pine Script code is a TradingView study script titled "SVMKR_UT_Bot_HMA_UCS_LRS". It combines two separate trading indicators: the UT Bot (Ultimate Trailing Stop Bot) and the UCS_LRS (Linear Regression Slope) indicator.
UT Bot (Ultimate Trailing Stop Bot):
The UT Bot is designed to provide buy and sell signals based on a trailing stop strategy.
It calculates the trailing stop level using the Average True Range (ATR) and Heikin Ashi candle signals if enabled.
Buy signals are generated when the price crosses above the trailing stop, while sell signals occur when the price crosses below the trailing stop.
Additionally, buy and sell signals are visually represented on the chart with corresponding labels and shapes.
The script also includes options to customize the sensitivity of the trailing stop and to color the bars based on buy or sell signals.
Hull Moving Average (HMA):
This section calculates and plots the Hull Moving Average, a type of moving average that reduces lag and improves smoothing compared to traditional moving averages.
It uses the weighted moving average (WMA) to compute the HMA, which helps to identify trend direction and potential reversal points.
UCS_LRS (Linear Regression Slope):
The UCS_LRS indicator calculates the linear regression slope of the closing prices over a specified period.
It then applies exponential smoothing to the slope values and calculates an average slope.
Buy signals are generated when the current slope is greater than the average slope and positive, indicating an uptrend.
Conversely, sell signals are generated when the current slope is less than the average slope and negative, suggesting a downtrend.
The linear regression slope and its average are plotted on the chart, allowing traders to visually identify trend strength and potential reversal points.
Overall, this combined script provides traders with a comprehensive set of tools for trend following and momentum trading strategies, integrating trailing stop analysis, moving average smoothing, and linear regression slope analysis into a single script for technical analysis on TradingView charts.
Gap Removal IndicatorThis gap indicator shows the price of your chosen instrument as if no gaps had occurred overnight. It can be especially useful on highly-volatile exchange-listed instruments that track other 24/7 assets, because the normal candlestick chart of these instruments will create a large amount of noise that may decrease the accuracy of your indicators or make the trend harder to see.
Gaps are determined with the following code:
daychange = ta.change(dayofmonth)
gapup = daychange and open > math.max(open,close)
gapdown = daychange and open < math.min(open,close)
Whereas the gap value is determined by taking the overnight difference in prices:
downgap_change = math.min(open,close) - open
upgap_change = open - math.max(open,close)
The gap changes are cumulatively added and subtracted from the initial closing price to create the gap-adjusted price. The price will depend on how many bars your subscription allows, so pay more attention to the relative differences and/or trend than the cumulative gap-adjusted price itself.
The gap indicator comes pre-built with normal candlestick and Heikin-Ashi candle types, and four indicators (two EMAs, Bollinger bands, and a supertrend). All elements are configurable.
Anchored Progressive RangeIntroducing a simple script based off of the idea that ranges form from a point of origin that can be measured and produce interesting analysis indicating potential opportunities.
Specifically I use this on daily and weekly anchorage to find mid range retracements once range has developed.
Configure internal multipliers to provide potentially useful measurements between range high and mid point, as well as mid point and range low. By default it's standard .25 based multipliers but one could adjust to fib multipliers such as .615, .65, etc.
Anchored open price is plotted as continuous line as often times reversals will occur and open price will be tested on daily, weekly, monthly timeframes.
Once a bit of range is established and there's a rapid adjustment of range mid up or down, these can signal interesting breakouts. Also areas where the range stays flat due to no new high or low being printed can be indicative of consolidation, etc.
I've tested this with heikin ashi, renko, bars, line and regular candles through various markets such as futures, etfs and stocks and everything appears to anchor correctly, please feedback if experiencing otherwise.
I hope you enjoy this indicator as much as I enjoyed creating it, happy trading!
Supply Demand Profiles [LuxAlgo]The Supply Demand Profiles is a charting tool that measures the traded volume at all price levels on the market over a specified time period and highlights the relationship between the price of a given asset and the willingness of traders to either buy or sell it, in other words, highlights key concepts as significant supply & demand zones, the distribution of the traded volume, and market sentiment at specific price levels within a specified time period, allowing traders to reveal dominant and/or significant price levels and to analyze the trading activity of a particular user-selected range.
In other words, this tool highlights key concepts as significant supply & demand zones, the distribution of the traded volume, and market sentiment at specific price levels within a specified time period, allowing traders to reveal dominant and/or significant price levels and to analyze the trading activity of a particular user-selected range.
Besides having the tool as a combo tool, the uniqueness of this version of the tool compared to its early versions is its ability to benefit from different volume data sources and its ability to use a variety of different polarity methods, where polarity is a measure used to divide the total volume into either up volume (trades that moved the price up) or down volume (trades that moved the price down).
🔶 USAGE
Supply & demand zones are presented as horizontal zones across the selected range, hence adding the ability to visualize the price interaction with them
By default, the right side of the profile is the volume profile which highlights the distribution of the traded activity at different price levels, emphasizing the value area, the range of price levels in which the specified percentage of all volume was traded during the time period, and levels of significance, such as developing point of control line, value area high/low lines, and profile high/low labels
The left side of the profile is the sentiment profile which highlights the market sentiment at specific price levels
🔶 DETAILS
🔹 Volume data sources
The users have the option to select volume data sources as either 'volume' (regular volume) or 'volume delta', where volume represents all the recorded trades that occur at a given bar and volume delta is the difference between the buying and the selling volume, that is, the net demand at a given bar
🔹 Polarity methods
The users are able to choose the methods of how the tool to take into consideration the polarity of the bar (the direction of a bar, green (bullish) or red (bearish) bar) among a variety of different options, such as 'bar polarity', 'bar buying/selling pressure', 'intrabar (chart bars at a lower timeframe than the chart's) polarity', 'intrabar buying/selling pressure', and 'heikin ashi bar polarity'.
Finally, the interactive mode of the tool is activated, as such users can easily modify the intervals of their interest just by selecting the indicator and moving the points on the chart
🔶 SETTINGS
The script takes into account user-defined parameters and plots the profiles and zones
🔹 Calculation Settings
Volume Data Source and Polarity: This option is to set the desired volume data source and polarity method
Lower Timeframe Precision: This option is applicable in case any of the 'Intrabar (LTF)' options are selected, please check the tooltip for further details
Value Area Volume %: Specifies the percentage for the value area calculation
🔹 Presentation Settings
Supply & Demand Zones: Toggles the visibility of the supply & demand zones
Volume Profile: Toggles the visibility of the volume profile
Sentiment Profile: Toggles the visibility of the sentiment profile
🔹 Presentation, Others
Value Area High (VAH): Toggles the visibility of the VAH line and color customization option
Point of Control (POC): Toggles the visibility of the developing POC line and color customization option
Value Area Low (VAL): Toggles the visibility of the VAL line and color customization option
🔹 Supply & Demand, Others
Supply & Demand Threshold %: This option is used to set the threshold value to determine supply & demand zones
Supply/Demand Zones: Color customization option
🔹 Volume Profile, Others
Profile, Up/Down Volume: Color customization option
Value Area, Up/Down Volume: Color customization option
🔹 Sentiment Profile, Others
Sentiment, Bullish/Bearish: Color customization option
Value Area, Bullish/Bearish: Color customization option
🔹 Others
Number of Rows: Specify how many rows the profile will have
Placment: Specify where to display the profile
Profile Width %: Alters the width of the rows in the profile, relative to the profile range
Profile Price Levels: Toggles the visibility of the profile price levels
Profile Background, Color: Fills the background of the profile range
Value Area Background, Color: Fills the background of the value area range
Start Calculation/End Calculation: The tool is interactive, where the user may modify the range by selecting the indicator and moving the points on the chart or can set the start/end time using these options
🔶 RELATED SCRIPTS
Volume-Profile
Volume-Profile-Maps
Volume-Delta
RedK Relative Strength Ribbon: RS Ribbon and RS ChartsRedK Relative Strength Ribbon (RedK RS_Ribbon) is TA tool that plots the Relative Strength of the current chart symbol against another symbol, or an index of choice. It enables us to see when a stock is gaining strength (or weakness) relative to (an index that represents) the market, and when it hits new highs or lows of that relative strength, which may lead to better trading decisions.
I searched TV for existing RS indicators but didn't find what I really wanted, so I put this together and added some additional features for my own use. It started as a simple RS line with new x-weeks Hi/Lo markers, then evolved into what you see here in v1.0 with the ability to plot a full RS chart in regular or HA candle types. Hope this will be useful to some other growth traders here on TV.
What is Relative Strength (RS)
------------------------------------
(RS is a comprehensive concept in TA, below is a quick summary - please research further if it's not already a familiar topic)
Relative Strength (RS) is a technical concept / indicator used mainly by growth / swing / momentum traders to compare the performance of one security or asset against another. RS measures the price performance of a specific security relative to a benchmark, such as an index or another asset. It's not to be confused with the famous Relative Strength Index (RSI) technical indicator
For example, In the context of comparing a stock's relative strength to the SPY (S&P 500) index, the relative strength calculation involves dividing the stock's price or price-related value (e.g., close price) by the corresponding value of the SPY index. The resulting ratio (and its trend over time) indicates the relative performance of the stock compared to the index.
Traders and investors use relative strength analysis to identify securities that have been showing relative strength or weakness compared to a benchmark, which can help in making investment decisions or identifying the "market leaders" and potential trading opportunities.
There are so many books and documentation about the RS concept and its importance to identify market leaders, especially when recovering from a bear market - if you're interested in the concept, please search more about it and review some of that literature. There's also a more detailed definition of Relative Strength in this article on Invstopedia
RedK RS_Ribbon features and options
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The indicator settings provide many options and features - see the settings box below
- Change / choose base symbol
The default is to use SPY as the base symbol - so we're comparing the chart's symbol to a proxy of the S&P 500 - Some traders may prefer to use the QQQ - or other index or ETF that acts as a proxy for the industry / sector / market they are trading
- RS Calculation / RS line
we use the simple form of the RS calculation,
RS = closing price of current chart symbol / closing price of the base symbol (default is SPY) * 100
some RS documentation will use the Rate of Change (RoC) - but that's not what we're using here.
- The RS_Ribbon
* Once the RS line is plotted, it made sense to add couple of moving averages to it, to make it easier to observe the trend of the RS and the changes in that trend as you can see in the sample chart on top.
* The RS_Ribbon is made up of a fast and slow moving averages and will change color (green / red) based on detected trend RS direction - the 2 MA types and lengths can be changed until you get the setup that provides the best view for you of the RS trend over time. My preferred settings are used as defaults here.
- Identifying New (x)Week Hi/Lo RS Values
* Most traders would be interested when the calculated RS hits a new 52-week high or low value.
* There are cases where we may want to see when a new RS Hi/Lo has been hit for a different period - for example, a quarter (13 weeks)
* the number of weeks can be changed as well as adjusting the numbers of trading days per week (if needed for certain symbols/exchanges)
- Working with Different Timeframes
* Now these "markers" will only be available in the daily and weekly timeframes and there is a good reason for that, it's not the fact that i'm lazy :) and that enabling this in timeframes lower than 1D would have been some heavy lifting, but the reality is that with RS, we're really interested if a "day's close" hits a new RS high or low value against the moving window of x weeks (and the weeks close also) - if you think of this more, at lower TF, RS can hit a lower value that never end up registering on the daily closing and that causes a lot of visual confusion. So i took the "cleaner way out" of that issue.
* note that you can choose a different timeframe for the RS_Ribbon than the chart - if you do, please make sure the chart is at a lower timeframe than the indicator's - (and in that case remember to hide the candles because they won't make much sense)
i wanted to leverage TV's built-in multi-Timeframe (MTF) support with the caveat that using the indicator at lower TF with a chart at a higher TF (example chart at 1Wk and indicator at 1D) will show inaccurate results. If this sounds confusing, keep the indicator TF same as the chart.
the example here shows a 2-Hr chart against 1D RS_Ribbon
- Using RS Charts and RS Candles
* Beside the ability to plot the RS "closing" value with the RS line, the indicator provides the ability to show a "full" RS Chart with candles that represent the relative values of open, high, low. and close against the base symbol.
* the RS Charts can be used for regular chart analysis, for example, we can identify common chart patterns like Cup & Handle, VCP, Head & Shoulder..etc using these charts .. which can provide some edge over the price charts
* for the Heikin Ashi fans, I added the ability to choose classic or HA candles for the chart. note you have to enable the option to show the RS candles first before you choose the option to switch to HA.
The chart below shows a side-by-side comparison on the 2 RS chart types
Closing remarks
-----------------------
* RS is a good way to identify market/sector leaders (who will usually recover from a bear market before others) - and enable us to see the strength that comes from the broader makrket versus the one that comes from the stock's own performance and identify good trading opportunities
* I'll continue to update this work and alerts will come in next version - but wanted to check initial reaction and value
* as usual, if you decide to use this in your chart analysis, it's necessary to combine with other momentum, trend, ...etc indicators and do not make trading decision only based on the signales from a single indicator
The Z-score The Z-score, also known as the standard score, is a statistical measurement that describes a value's relationship to the mean of a group of values. It's measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
The concept of Z-score was introduced by statistician Carl Friedrich Gauss as part of his "method of the least squares," which was an important step in the development of the normal distribution and Z-score tables. It's a key concept in statistics and is used in various statistical tests.
In financial analysis, Z-scores are used to determine whether a data point is usual or unusual. You can think of it as a measure of how many standard deviations an element is from the mean. For instance, a Z-score of 1.0 would denote a value that is one standard deviation from the mean. Z-scores are also used to predict probabilities, with Z-scores having a distribution that is expected to be normal.
In trading, a Z-score is used to determine how often a trading system may produce a string of winners or losers. It can help a trader to understand whether the losses or profits they see are something that the system would most likely produce, or if it's a once in a blue moon situation. This helps traders make decisions about when to start or stop a system.
I just wanted to play a bit with the Z-score I guess.
Feel free to share your findings if you discover additional applications for this strategy or identify timeframes where it appears to perform more optimally.
How it works:
This strategy is based on a statistical concept called Z-score, which measures the number of standard deviations a data point is from the mean. In other words, it helps determine how unusual or usual a data point is.
In the context of this strategy, Z-score is applied to a 10-period EMA (Exponential Moving Average) of Heikin-Ashi candlestick close prices. The Z-score is calculated over a look-back period of 25 bars.
The EMA of the Z-score is then calculated over a 20-bar period, and the upper and lower thresholds (bounds for buy and sell signals) are defined using the 90th and 10th percentiles of this EMA score.
Long positions are taken when the Z-score crosses above the lower threshold or crosses above the mid-line (50th percentile). An additional long entry is made when the Z-score crosses above the highest value the EMA has been in the past 100 periods.
Short positions are initiated when the EMA crosses below the upper threshold, lower threshold or the highest value the EMA has been in the past 100 periods.
Positions are closed when opposing entry conditions are met, for example, a long position is closed when the short entry condition is true, and vice versa.
Set your desired start date for the strategy. This can be modified in the timestamp("YYYY MM DD") function at the top of the script.
HeikinashiLibrary "Heikinashi"
This library calculates "Heikinashi".
calc(_o, _h, _l, _c, _my_close)
This function calculates "Heikinashi".
Parameters:
_o : open
_h : high
_l : low
_c : close
_my_close : Specify if you want to force only the closing price to a real value.
Returns: TODO: add what function returns
MAGIC MACDMAGIC MACD ( MACD Indicator with Trend Filter and EMA Crossover confirmation and Momentum). This MACD uses Default Trading view MACD
from Technical indicators library and adding a second MACD along with 3 EMA's to detect Trend and confirm MACD Signal.
Eliminates usage of 3different indicators (Default MACD , MACD-2,EMA5, EMA20, EMA50)
Basic IDEA.
Idea is to filter Histogram when price is above or below 50EMA. Similar to QQE -mod oscillator but Has a EMA Filter
1.Take DEFAULT MACD crossover signals with lower period
2.check with a Higher MACD Histogram.
3.Enter upon EMA crossover signal and Histogram confirmation.
Histogram changes to GRAY when price is below EMA 50 or above EMA 50 (Follows Trend)
4.Exit on next Default MACD crossover signal.
Overview :
Moving Average Convergence Divergence Indicator Popularly Known as MACD is widely used. MACD Usually generates a lots of False signals
and noise in Lower Time Frames, making it difficult to enter a trade in sideways market. Divergence is a major issue along with sideways
movement and tangling of MACD and Signal Lines. There is no way to confirm a Default MACD signal, except to switch time frames and
verify.
Magic MACD Can be used to in combination with other signals.
This MACD uses two MACD Signals to verify the signal given by Default MACD . The Histogram Plot shown is of a higher period
MACD (close,5,50,30) values. When a signal is generated on a lower MACD it is verified by the histogram with higher time period.
Technicals Used:
1. Lower MACD-1 values 12,26 and signal-9 (crossover Signals)
2. Higher MACD-2 values 5,50 and signal-30 (Histogram)
3. EMA 50 (Histogram Filter to allow only if price above or below Ema 50)
4. EMA 5 and EMA 20 for crossover confirmation of trend
What's is in this Indicator?
1.Histogram-(higher period 5,50 and 30signal)
2. MACD crossover Signals-(lower period Default MACD setting)
3.Signal Lines-( EMA 5 & 20)
Implemented & Removed in this Indicator
1. Default MACD and Signal Lines are removed completely
2. MACD crossover are taken on lower periods and plotted as signals(Blue Triangle or Red Triangle)
3. Histogram is plotted from a higher Period providing a clear picture with Higher Time period
4. EMA 5 and EMA 20 are used for MACD signal confirmation
How to use?
Up Signal
1. MACD Default (12,26,30) up signals are shown in Blue
2. Wait till the Histogram changes Blue
3. Look for EMA signals crossover near by
Down Signal
1. MACD Default (12,26,30) up signals are shown in Red
2. Wait till the Histogram changes Red
3. Look for EMA signals crossover near by
Do's
Consider only opposite color as signals
1. Red Triangle on Blue Histogram(likely to move down direction)
2. Blue Triangle on Red Histogram (Likely to move up direction)
Don'ts
1.Ignore Blue Signal on Blue Histogram (pull back signals can be used to enter trade if you miss first crossover)
2.Ignore Red Signal on Red Histogram(pull back signals can be used to enter trade if you miss first crossover)
3.Ignore Up and Down signals till Gray or Blacked out area is finished in Histogram
Tips:
1. EMA plot also shows pull back areas along with signals
2.side by side opposite signals shows sides ways movement
3. EMA 5,20 is plotted on MACD Histogram for Additional Benefit
Thanks & Credits
To Tradingview Team for allowing me to use their default MACD version and coding it in to a MAGIC MACD by adding a few lines of code that
makes it more enhanced.
Warning...!
This is purely for Educational purpose only. Not to be used as a stand alone indicator. Usage is at your own Risk. Please get familiar with its working before implementing. Its not a Financial Advice or Suggestion . Any losses or gains is at your own risk.
[-_-] Volatility Calibrated ATRDescription:
An indicator based on ATR adjusted for volatility of the market. It uses Heikin Ashi data to find short and long opportunities and displays a dynamic stop loss level. Additionally, it has alerts for when the trend changes (which is an entry signal).
How it works:
It works by dynamically calculating the Period for ATR which depends on current volatility level that is calculated by a function that uses Standard Deviation of price. ATR is then smoothed by Weighted Moving Average and multiplied by ATR Factor, resulting in a plot that changes its colour to red when we're in a downtrend and green when in an uptrend. This plot should be used as a dynamic Stop Loss level. Trend change is determined by price crossing the dynamic Stop Loss level. The squared red and green labels appear when the trend changes, and should be used as Entry signals.
Parameters:
- Source -> data used for calculations
- ATR Factor -> higher values produce less noise and longer trends, lower values give more signals
Rate of Change Candle Standardized (ROCCS)ROCCS is a standardized rate of change oscillator with "error bars". Rate of change helps traders gauge momentum in a market by comparing the current price with the price "n" periods ago. What makes this special is you get to see the momentum of the momentum via the candle view. The candle transformation utilizes a moving average to smooth the signal however this is only used for the close price. The high and low prices are not smoothed. The moving average has an adjustable period, and so does the standardization.
I hope you can find great use in this upgraded roc indicator.
Volatility Inverse Correlation CandleThis is an educational tool that can help you find direct or inverse relations between two assets.
In this case I am using VIX and SPX .
The way it works is the next one :
So I am looking at the current open value of VIX in comparison with the previous close ( if it either above or below) and after on the SPX I am looking into the history and see for example which type of candle we had in respect with the opening value from VIX .
So for example, lets imagine that today is monday, and the weekly open value from VIX was higher than previous friday close value. Now I am going to see with the inverse correlation , if based on this idea, the current weekly candle from SPX finished in a bear candle.
The same can be applied for the bearish situation, so if we had an open from VIX lower than previous close, we are looking to check the SPX bull candle accuracy.
At the same time, for a different type of calculation I have added an internal lookup into heikin ashi values.
If you have any questions please let me know !
Breakout Candles + RSIHello!
This is my firt script :)
This indicator looks for candles that are significantly larger than the previous X candle.
It is possible to set the following:
Multiplier: deviation from the size of the previous X candle (if set to 3 the size of the actual candle's body /abs(open - close)/ must be larger than the size of the bigger candle from the prevous X candles)
Previous candles: the number of previous candles to size check
Upper RSI limit: if the RSI14 close higher than the specified number, the candle will ignore
Lower RSI limit: if the RSI14 close lower than the specified number, the candle will ignore
Without dojis: if checked, watches candles only that do not have a bottom spike (bullish) or top spike (bearish). Useful for Heikin-Ashi candles
Feel free to left any suggestion!
Thank You!
Expected Move PlotterI get a lot of requests about my indicators that I use. Unfortunately, at this time I cannot make those public but I thought about creating a makeshift alternative people could use as a reference.
I came up with this very simple yet extremely effective indicator. I call it the average or expected move plotter, but its essentially the average move plotter.
All it does is it averages out the move from open to high and low on a monthly, weekly and daily basis over the past 5 days and plots the expected move.
It really is that simple!
I have broken it down by month, week and day, so you can see the average expected move on whichever time frame you prefer.
I will use TSLA as the example.
Here is the daily:
Here is the weekly:
And here is the monthly:
You can switch between whichever timeframe you are working on and it permits all traders (day traders and swing traders) to assist in setting realistic target prices within their desired time frame.
It works on any stock, index, commodity or future.
I have also ensured that it will work with Heikin Ashi candles, for those (like myself) who are fond of those candles.
Let me know if you have any questions and if you like it!
Take care everyone and trade safe!
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt [Loxx]STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt is a normalized Cardinal Sine Filter Kernel Weighted Fir Filter that uses Ehler's FIR filter calculation instead of the general FIR filter calculation. This indicator has Kalman Velocity lag reduction, a standard deviation filter, a clutter filter, and a kernel noise filter. When calculating the Kernels, the both sides are calculated, then smoothed, then sliced to just the Right side of the Kernel weights. Lastly, blackman windowing is used for our purposes here. You can read about blackman windowing here:
Blackman window
Advantages of Blackman Window over Hamming Window Method for designing FIR Filter
The Kernel amplitudes are shown below with their corresponding values in yellow:
This indicator is intended to be used with Heikin-Ashi source inputs, specially HAB Median. You can read about this here:
Moving Average Filters Add-on w/ Expanded Source Types
What is a Finite Impulse Response Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response.
Ehlers FIR Filter
Ehlers Filter (EF) was authored, not surprisingly, by John Ehlers. Read all about them here: Ehlers Filters
What is Normalized Cardinal Sine?
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
TDI w/ Variety RSI, Averages, & Source Types [Loxx]This hybrid indicator is developed to assist traders in their ability to decipher and monitor market conditions related to trend direction, market strength, and market volatility. Even though comprehensive, the Traders Dynamic Index (TDI) is easy to read and use. This version of TDI has 7 different types of RSI, 38 different types of Moving Averages, 33 source types, and 5 types of signals as well as alerts and coloring. Default RSI type is set to Jurik's RSX. This indicator can be used on any timeframe.
Green/Red line = RSI Price line
White line = Trade Signal line
Dark Green/Red lines = Volatility Band
Yellow line = Market Base Line
Gray dashed lines = Horizontal boundary lines, oversold/overbought
5 Signal Types w/ Alerts
Signal Crosses = Green/Red line crosses over or under White line
Floating Boundary Crosses = Green/Red line crosses over or under upper Dark Green/ lower Red lines
Horizontal Boundary Crosses = Green/Red line crosses over or under Gray dashed upper/lower lines
Floating Middle Crosses = Green/Red line crosses over or under Yellow line
Horizontal Middle Crosses = Green/Red line crosses over or under Gray dashed middle line
Manual Signal Types (no alerts included, this requires manual analysis)
Volatility Band Signals (Dark Green/Red lines) = When the Dark Green/Red lines are expanding, the market is strong and trending. When Dark Green/Red lines are constricting, the market is weak and in a range. When the Dark Green/Red lines are extremely tight in a narrow range, expect an economic announcement or other market condition to spike the market
Beyond these simple signal rules, there are various other signals or methods that can be used to derive long/short/exit signals from TDI included slope of the Green/Red line and bounces off the Yellow line.
Included
Loxx's Expanded Source Types
Loxx's Variety RSI
Loxx's Moving Averages
Signals
Alerts
Bar coloring
10-Year Bond Yields (Interest Rate Differential)With this little script, I have attempted to incorporate fundamental data (in this case, 10-year bond yields) into technical analysis . When pairing two currencies, the one with a higher bond interest rate usually appreciates when the interest rate differential widens, or, to use a simple example: in a currency pair A vs. B, with A showing a higher bond yield than B, a widening interest rate gap is likely to help A and create a buying opportunity (shown as a blue square at the bottom of the chart), while the opposite is true when the gap tightens (sell signal, red square).
While long-term investors know about and make use of the importance of bond yield fluctuations, most short-term traders tend to dismiss the idea of using fundamental data, mostly for lack of quantifiability and limited impact in an intraday environment. After extensive backtesting on daily and intraday charts (6-12 hours), however, I realized this indicator still managed to produce useful results (less useful than on monthly and yearly charts, to be fair, but still useful enough), especially when paired with simple price-driven indicators, such as Heikin Ashi or linear regression .
My personal (and thus subjective) thoughts: worth a try. Buy and sell signals frequently contradicted both more popular indicators and my gut feeling and managed to take out losing trades that I had considered trades with a high winning probability. In other words, when the market lures traders into seemingly promising trading decisions, this indicator might give you an early warning, especially when you manage to adjust period and continuity parameters to your trading strategy.
Currency pairs used in this script are all possible combinations of the eight majors. Each security has been assigned a name ("inst01" to "inst08" in the code) and a broker; if you make changes to the code, be sure not to mess with currency and broker names as this would render the entire script useless. Good luck trading, and feel free to suggest improvements!
Reverse Ehler Instantaneous Trendline - TraderHalaiThis script uses a reverse function of the famous Ehler Instantaneous Trendline to calculate the source price required in order to change from Bullish to bearish
From my analysis, the reverse price does appear to be rather choppy, though it is 100% accurate. This is because Ehler's Instantaneous Trendline tends to remain trending for longer periods of time with above average hold periods.
The main suitability for this would be higher level timeframes, such as Weekly, 5 daily, 3 daily. From my findings Smoothed Heikin Ashi Trend, tends to provide better risk-adjusted returns across most timeframes (Higher return to drawdown ratio)
As I have spent a bit of time getting the reverse function mathematics to work, I decided to publish this as open source for the benefit, scrutiny and for further development by the TradingView community anyways.
Enjoy!
Hodrick-Prescott MACD [Loxx]Hodrick-Prescott MACD is a MACD indicator using a Hodrick-Prescott Filter.
What is Hodrick–Prescott filter?
The Hodrick–Prescott filter (also known as Hodrick–Prescott decomposition) is a mathematical tool used in macroeconomics, especially in real business cycle theory, to remove the cyclical component of a time series from raw data. It is used to obtain a smoothed-curve representation of a time series, one that is more sensitive to long-term than to short-term fluctuations. The adjustment of the sensitivity of the trend to short-term fluctuations is achieved by modifying a multiplier Lambda.
The filter was popularized in the field of economics in the 1990s by economists Robert J. Hodrick and Nobel Memorial Prize winner Edward C. Prescott, though it was first proposed much earlier by E. T. Whittaker in 1923.
There are some drawbacks to use the HP filter than you can read here: en.wikipedia.org
Included
Bar coloring
3 types of signals
Alerts
Loxx's Expanded Source Types
HTF CandlesThis Indicator overlay candles from a timeframe input.
Use it to see Higher Time candles on your current chart
Heikin ashi optionnal
Stepped Moving Average of CCI [Loxx]Stepped Moving Average of CCI is a CCI that applies a stepping algorithm to smooth CCI. This allows for noice reduction and better identification of breakouts/breakdowns/reversals.
What is CCI?
The Commodity Channel Index ( CCI ) measures the current price level relative to an average price level over a given period of time. CCI is relatively high when prices are far above their average. CCI is relatively low when prices are far below their average. Using this method, CCI can be used to identify overbought and oversold levels.
Included:
Bar coloring
4 signal variations w/ alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
T3 Velocity [Loxx]T3 Velocity is a simple velocity indicator using T3 moving average that uses gradient colors to better identify trends.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
PPO w/ Discontinued Signal Lines [Loxx]PPO w/ Discontinued Signal Lines is a Percentage Price Oscillator with some upgrades. This indicator has 33 source types and 35+ moving average types as well as Discontinued Signal Lines and divergences. These additions reduce noise and increase hit rate.
What is the Price Percentage Oscillator?
The percentage price oscillator (PPO) is a technical momentum indicator that shows the relationship between two moving averages in percentage terms. The moving averages are a 26-period and 12-period exponential moving average (EMA).
The PPO is used to compare asset performance and volatility, spot divergence that could lead to price reversals, generate trade signals, and help confirm trend direction.
Included:
Bar coloring
3 signal variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
