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Trendoscope

RSI Impact Heat Map [Trendoscope]Here is a simple tool to measure and display outcome of certain RSI event over heat map. 🎲 Process 🎯Event Event can be either Crossover or Crossunder of RSI on certain value. 🎯Measuring Impact Impact of the event after N number of bars is measured in terms of highest and lowest displacement from the last close price. Impact can be collected as either number of times of ATR or percentage of price. Impact for each trigger is recorded separately and stored in array of custom type. 🎯Plotting Heat Map Heat map is displayed using pine tables. Users can select heat map size - which can vary from 10 to 90. Selecting optimal size is important in order to get right interpretation of data. Having higher number of cells can give more granular data. But, chart may not fit into the window. Having lower size means, stats are combined together to get less granular data which may not give right picture of the results. Default value for size is 50 - meaning data is displayed in 51X51 cells. Range of the heat map is adjusted automatically based on min and max value of the displacement. In order to filter out or merge extreme values, range is calculated based on certain percentile of the values. This will avoid displaying lots of empty cells which can obscure the actual impact. 🎲 Settings Settings allow users to define their event, impact duration and reference, and few display related properties. The description of these parameters are as below: 🎲 Use Cases In this script, we have taken RSI as an example to measure impact. But, we can do this for any event. This can be price crossing over/under upper/lower bollinger bands, moving average crossovers or even complex entry or exit conditions. Overall, we can use this to plot and evaluate our trade criteria. 🎲 Interpretation Q1 - If more coloured dots appear on the top right corner of the table, then the event is considered to trigger high volatility and high risk environment. Q2 - If more coloured dots appear on the top left corner, then the events are considered to trigger bearish environment. Q3 - If more coloured dots appear on the bottom left corner of the chart, then the events are considered insignificant as they neither generate higher displacement in positive or negative side. You can further alter outlier percentage to reduce the bracket and hence have higher distribution move towards Q4 - If more coloured dots appear on the bottom right corner, then the events are considered to trigger bullish environment. Will also look forward to implement this as library so that any conditions or events can be plugged into it.
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Trendoscopeの投稿
23
Bar MagnifierMany times while developing algos based on patterns and reversals, I come across issues which needs lower timeframe inspection. Loading multiple charts and comparing equivalent lower timeframe is slightly cumbersome at times. Hence, I thought of building this simple tool - which will instantly provide me lower timeframe candles for given candle. Since the candle selection happen via confirmed time input, we can use this as slider to move from one candle to other for inspection. 🎲 Usage 🎯Loading the script When you load the script, a prompt appears which asks you to select a time by clicking on the chart. Select the bar you want to magnify and study 🎯Components Once loaded, you can see the marker which tells which bar is magnified. And you can also see all the lower timeframe candles before that point. Please note that due to pine restrictions, we can only show last 250 lower timeframe bars. You can change the lower timeframe via settings to cover if the chart timeframe is very high. 🎯Moving to different bars Click on the middle of the marker, you will see slider which you can slide to move from one bar to other. Example, after sliding, you will see the lower timeframe data of new candle. 🎯Settings Settings has only two inputs. Bar time - selects the bar which needs to be inspected. Lower timeframe - Default is 1 min. And select a timeframe according to your chart timeframe. Less than 1 min is not supported by security.lower_tf function. Hence, will not work.
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Trendoscopeの投稿
14
Band-Zigzag Based Trend FollowerWe defined new method to derive zigzag last month - which is called Channel-Based-Zigzag . This script is an example of one of the use case of this method. 🎲 Trend Following Defining a trend following method is simple. Basic rule of trend following is Buy High and Sell Low (Yes, you heard it right). To explain further - methodology involve finding an established trend which is flying high and join the trend with proper risk and optimal stop. Once you get into the trade, you will not exit unless there is change in the trend. Or in other words, the parameters which you used to define trend has reversed and the trend is not valid anymore. Few examples are: 🎯 Using bands When price breaks out of upper bands (example, Bollinger Band, Keltener Channel, or Donchian Channel), with a pre determined length and multiplier, we can consider the trend to be bullish and similarly when price breaks down the lower band, we can consider the trend to be bearish. Here are few examples where I have used bands for identifying trend Band-Based-Supertrend Donchian-Channel-Trend-Filter 🎯 Using Pivots Simple logic using zigzag or pivot points is that when price starts making higher highs and higher lows, we can consider this as uptrend. And when price starts making lower highs and lower lows, we can consider this as downtrend. There are few supertrend implementations I have published in the past based on zigzags and pivot points. Adoptive-Supertrend-Pivots Zigzag-Supertrend Drawbacks of both of these methods is that there will be too many fluctuations in both cases unless we increase the reference length. And if we increase the reference length, we will have higher drawdown. 🎲 Band Based Zigzag Method Band Based Zigzag will help overcome these issues by combining both the methods. Here we use bands to define our pivot high and pivot low - this makes sure that we are identifying trend only on breakouts as pivots are only formed on breakouts. Our method also includes pivot ratio to cross over 1.0 to be able to consider it as trend. This means, we are waiting for price also to make new high high or lower low before making the decision on trend. But, this helps us ignore smaller pivot movements due to the usage of bands. I have also implemented few tricks such as sticky bands (Bands will not contract unless there is breakout) and Adaptive Bands (Band will not expand unless price is moving in the direction of band). This makes the trend following method very robust. To avoid fakeouts, we also use percentB of high/low in comparison with price retracement to define breakout. 🎲 The indicator The output of indicator is simple and intuitive to understand. 🎯 Trend Criteria Uptrend when last confirmed pivot is pivot high and has higher retracement ratio than PercentB of High. Else, considered as downtrend. Downtrend when last confirmed pivot is pivot low and has higher retracement ratio than PercentB of High. Else, considered as uptrend. 🎯 Settings Settings allow you to select the band type and parameters used for calculating zigzag and then trend. Also has few options to hide the display.
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Trendoscopeの投稿
25
Chatterjee CorrelationThis is my first attempt on implementing a statistical method. This problem was given to me by @lejmer (who also helped me later on building more efficient code to achieve this) when we were debating on the need for higher resource allocation to run scripts so it can run longer and faster. The major problem faced by those who want to implement statistics based methods is that they run out of processing time or need to limit the data samples. My point was that such things need be implemented with an algorithm which suits pine instead of trying to port a python code directly. And yes, I am able to demonstrate that by using this implementation of Chatterjee Correlation. 🎲 What is Chatterjee Correlation? The Chatterjee rank Correlation Coefficient (CCC) is a method developed by Sourav Chatterjee which can be used to study non linear correlation between two series. Full documentation on the method can be found here: arxiv.org In short, the formula which we are implementing here is: Algorithm can be simplified as follows: 1. Get the ranks of X 2. Get the ranks of Y 3. Sort ranks of Y in the order of X (Lets call this SortedYIndices) 4. Calculate the sum of adjacent Y ranks in SortedYIndices (Lets call it as SumOfAdjacentSortedIndices) 5. And finally the correlation coefficient can be calculated by using simple formula CCC = 1 - (3*SumOfAdjacentSortedIndices)/(n^2 - 1) 🎲 Looks simple? What is the catch? Mistake many people do here is that they think in Python/Java/C etc while coding in Pine. This makes code less efficient if it involves arrays and loops. And the simple code may look something like this. var xArray = array.new() var yArray = array.new() array.push(xArray, x) array.push(yArray, y) sortX = array.sort_indices(xArray) sortY = array.sort_indices(yArray) SumOfAdjacentSortedIndices = 0.0 index = array.get(xSortIndices, 0) for i=1 to n > 1? n -1 : na indexNext = array.get(sortX, i) SumOfAdjacentSortedIndices += math.abs(array.get(sortY, indexNext)-array.get(sortY, index)) index := indexNext correlation := 1 - 3*SumOfAdjacentSortedIndices/(math.pow(n,2)-1) But, problem here is the number of loops run. Remember pine executes the code on every bar. There are loops run in array.sort_indices and another loop we are running to calculate SumOfAdjacentSortedIndices. Due to this, chances of program throwing runtime errors due to script running for too long are pretty high. This limits greatly the number of samples against which we can run the study. The options to overcome are Limit the sample size and calculate only between certain bars - this is not ideal as smaller sets are more likely to yield false or inconsistent results. Start thinking in pine instead of python and code in such a way that it is optimised for pine. - This is exactly what we have done in the published code. 🎲 How to think in Pine? In order to think in pine, you should try to eliminate the loops as much as possible. Specially on the data which is continuously growing. My first thought was that sorting takes lots of time and need to find a better way to sort series - specially when it is a growing data set. Hence, I came up with this library which implements Binary Insertion Sort. Replacing array.sort_indices with binary insertion sort will greatly reduce the number of loops run on each bar. In binary insertion sort, the array will remain sorted and any item we add, it will keep adding it in the existing sort order so that there is no need to run separate sort. This allows us to work with bigger data sets and can utilise full 20,000 bars for calculation instead of few 100s. However, last loop where we calculate SumOfAdjacentSortedIndices is not replaceable easily. Hence, we only limit these iterations to certain bars (Even though we use complete sample size). Plots are made for only those bars where the results need to be printed. 🎲 Implementation Current implementation is limited to few combinations of x and fixed y. But, will be converting this into library soon - which means, programmers can plug any x and y and get the correlation. Our X here can be Average volume ATR And our Y is distance of price from moving average - which identifies trend. Thus, the indicator here helps to understand the correlation coefficient between volume and trend OR volatility and trend for given ticker and timeframe. Value closer to 1 means highly correlated and value closer to 0 means least correlated. Please note that this method will not tell how these values are correlated. That is, we will not be able to know if higher volume leads to higher trend or lower trend. But, we can say whether volume impacts trend or not. Please note that values can differ by great extent for different timeframes. For example, if you look at 1D timeframe, you may get higher value of correlation coefficient whereas lower value for 1m timeframe. This means, volume to trend correlation is higher in 1D timeframe and lower in lower timeframes.
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Trendoscopeの投稿
10
Harmonic Patterns Based SupertrendExtending the earlier implemented concept of Harmonic-Patterns-Based-Trend-Follower , in this script, lets make it work as supertrend so that it is more easier to operate. 🎲 Process 🎯 Derive Zigzag and scan harmonic patterns for last 5 confirmed pivots 🎯 If a pattern is found, bullish and bearish zones are calculated based on parameter Base 🎯 These bullish and bearish zones act as supertrend based on current trade in progress. 🎯 When in bullish mode, bearish zone will only go up irrespective of new pattern forming new low. Similarly when in bearish mode, bullish zones will only come down - this is done to imitate the standard supertrend behaviour. 🎲 Note Patterns are not created on latest pivot as last pivot will be unconfirmed and moving. Due to this, patterns appear after certain delay - patterns will not be real time. But, this is expected and does not impact the overall process. Here are few chart captures to demonstrate how it works. 🎲 Settings Settings are explained in the screenshot below.
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Trendoscopeの投稿
12
Harmonic Patterns Based Trend FollowerEarlier this week, published an idea on how harmonic patterns can be used for trend following. This script is an attempt to implement the same. 🎲 Process 🎯 Derive Zigzag and scan harmonic patterns for last 5 confirmed pivots 🎯 If a pattern is found, highest point of pattern will become the bullish zone and lower point of the pattern will become bearish zone. 🎯 Since it is trend following method, when price reaches bullish zone, then the trend is considered as bullish and when price reaches bearish zone, the trend is considered as bearish. 🎯 If price does not touch both regions, then trend remains unchanged. 🎯 Bullish and bearish zone will change as and when new patterns are formed. 🎲 Note Patterns are not created on latest pivot as last pivot will be unconfirmed and moving. Due to this, patterns appear after certain delay - patterns will not be real time. But, this is expected and does not impact the overall process. When new pattern formed When price breaks over the zones 🎲 Output 🎯 Patterns formed are drawn in blue coloured lines. Due to pine limitation of max 500 lines, older patterns automatically get deleted when new ones come. 🎯 Bullish Zone and Bearish Zone are plotted in green and red colours and the zone will change whenever new pattern comes along. 🎯 Bar colors are changed according to calculated trend. Trend value can be 1 or -1 based on the current trend. You can also find the value in data window. 🎯 For simplicity purpose, input option for selection of specific patterns are not provided and also pattern names are not displayed on the chart.
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Trendoscopeの投稿
34
Band Based Trend FilterSimilar to RelativeBandwidthFilter , this script is also a simple trend filter which can be used to define your trading zone. 🎲 Concept On contrary to reversal mindset, we define trend when price hits either side of the band. If close price hits upper band then it is considered as bullish and if close price hits lower band, then it is considered bearish. Further, trend strength is measured in terms of how many times the price hits one side of the band without hitting other side. Hit is counted only if price has touched middle line in between the touches. This way price walks on the bands are considered as just one hit. 🎲 Settings Settings are minimal and details can be found in the tooltips against each parameters 🎲 Usage This can be used with your own strategy to filter your trading/non-trading zones based on trend . Script plots a variable called "Trend" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic. Trend values can be 1 : Allow only Long -1 : Allow only short 0 : Do not allow any trades
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Trendoscopeの投稿
10
Channel Based Zigzag [HeWhoMustNotBeNamed]🎲 Concept Zigzag is built based on the price and number of offset bars. But, in this experiment, we build zigzag based on different bands such as Bollinger Band, Keltner Channel and Donchian Channel. The process is simple: 🎯 Derive bands based on input parameters 🎯 High of a bar is considered as pivot high only if the high price is above or equal to upper band. 🎯 Similarly low of a bar is considered as pivot low only if low price is below or equal to lower band. 🎯 Adding the pivot high/low follows same logic as that of regular zigzag where pivot high is always followed by pivot low and vice versa. 🎯 If the new pivot added is of same direction as that of last pivot, then both pivots are compared with each other and only the extreme one is kept. (Highest in case of pivot high and lowest in case of pivot low) 🎯 If a bar has both pivot high and pivot low - pivot with same direction as previous pivot is added to the list first before adding the pivot with opposite direction. 🎲 Use Cases Can be used for pattern recognition algorithms instead of standard zigzag. This will help derive patterns which are relative to bands and channels. Example: John Bollinger explains how to manually scan double tap using Bollinger Bands in this video: www.youtube.com This modified zigzag base can be used to achieve the same using algorithmic means. 🎲 Settings Few simple configurations which will let you select the band properties. Notice that there is no zigzag length here. All the calculations depend on the bands. With bands display, indicator looks something like this Note that pivots do not always represent highest/lowest prices. They represent highest/lowest price relative to bands. As mentioned many times, application of zigzag is not for buying at lower price and selling at higher price. It is mainly used for pattern recognition either manually or via algorithms. Lets build new Harmonic, Chart patterns, Trend Lines using the new zigzag?
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Trendoscopeの投稿
18
Electrocardiogram ChartThis is an attempt to develop alternative visualisation of financial charts. This script also makes use of new pine feature types which represents User Defined Object Types. You can refer to below documentation to understand more about this feature: www.tradingview.com www.tradingview.com 🎲 Structure of new chart components 🎯Instead of candles/bars, this type of chart contains Electrocardiogram blocks which resembles the heartbeat signals on electrocardiogram. Body color of the block is defined by the open and close prices of the bar. If close is greater than open, body is green. Otherwise, the body is painted red. Border color of the block is defined by the close prices of current and previous bar. If the close of current bar is greater than that of last bar, then the border color is green. Otherwise, border color is painted red. 🎯Inside each blocks there will be 5 connecting lines called the signal lines. open-open open-firstPeak(high or low of the bar whichever comes first) firstPeak-secondPeak(high or low of the bar whichever comes last) secondPeak-close close-close 🎯 Color of the signal lines are determined by which among the high/low of the bar comes last. If highest part of the bar reached after reaching the lowest part of the bar, then signal lines are coloured green signifying bullish sentiment towards the end of bar. If lowest part of the bar reached after reaching the highest part of the bar, then signal lines are coloured red signifying bearish sentiment towards the end of bar. Pictorial examples here: 🎲 Limitations with pinescript implementation Since, pinescript can only use maximum 500 lines and each block will take 1 box and 5 lines, it is not possible to display more than 100 bars. Each block of new Electrocardiogram chart will take the space of 7 bars of candlestick chart. Due to this, the alignment of regular OHLC candles is not inline with the new chart type. Background highlighting is done for the part of the OHLC candles where Electrocardiogram blocks are plotted so that it helps users to map the bars manually Thanks to @theheirophant for suggestion of name :)
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Trendoscopeの投稿
48
Next Pivot Projection [Trendoscope]Still experimental. Extending further on the divergence backtest results - in this script we try to project next 2 pivots (including one unconfirmed pivot) 🎲 Previous experiments 1. Divergence-Backtester 2. Divergence-Backtester-V2 🎲 Additions Apart from collecting the stats on number of occurrences of HH, HL, LH, LL - this script also keeps track of average ratio for each levels and average bars. Based on these data, we try to calculate the next pivot projections including possible bar and price. Cloud covering the candles indicate historical levels of average HH, HL, LH, LL projections. Hover on projection labels to find more details in tooltips. 🎲 Overall method in a nutshell 🎲 Going bit deeper 🎯 Unconfirmed Pivot and its projection - Last pivot of the zigzag is always unconfirmed. Meaning, it can potentially repaint based on further price movements. But, projection of the unconfirmed pivot will not change as it will be based on previous two pivots - both of which are confirmed. 🎯 Next Pivot Projection - Next pivot is projected based on last two pivots - which include last unconfirmed pivot. Hence, these projections can potentially repaint based on the last pivot repaint. 🎯 Historical projections displayed as cloud - Historical projection values are displayed as cloud around pivots. A cloud above represents area from average lower high range to average higher high range. Cloud color is green if average ratio of pivot high is more than 1. Red Otherwise. A cloud below represents area from average higher low range to average lower low range. Cloud color is red if average ratio of pivot high is more than 1. Green otherwise
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Trendoscopeの投稿
134
Divergence Backtester - V2Further attempts to study divergence impact on price in shorter terms. Previous study can be found here: In this script, we are trying to gather the stats based on last two pivot state together. For example, Individual table of Pivot High Projection is as explained below: But, by looking at the bigger picture, we can further estimate following things regarding the current unconfirmed pivot and the new pivot which is yet to be formed.
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Trendoscopeの投稿
12
Extreme Trend Reversal Points [HeWhoMustNotBeNamed]Using moving average crossover for identifying the change in trend is very common. However, this method can give lots of false signals during the ranging markets. In this algorithm, we try to find the extreme trend by looking at fully aligned multi-level moving averages and only look at moving average crossover when market is in the extreme trend - either bullish or bearish. These points can mean long term downtrend or can also cause a small pullback before trend continuation. In this discussion, we will also check how to handle different scenarios. 🎲 Components 🎯 Recursive Multi Level Moving Averages Multi level moving average here refers to applying moving average on top of base moving average on multiple levels. For example, Level 1 SMA = SMA(source, length) Level 2 SMA = SMA(Level 1 SMA, length) Level 3 SMA = SMA(Level 2 SMA, length) .. .. .. Level n SMA = SMA(Level (n-1) SMA, length) In this script, user can select how many levels of moving averages need to be calculated. This is achieved through " recursive moving average " algorithm. Requirement for building such algorithm was initially raised by @loxx While I was able to develop them in minimal code with the help of some of the existing libraries built on arrays and matrix , I also thought why not extend this to find something interesting. Note that since we are using variable levels - we will not be able to plot all the levels of moving average. (This is because plotting cannot be done in the loop). Hence, we are using lines to display the latest moving average levels in front of the last candle. Lines are color coded in such a way that least numbered levels are greener and higher levels are redder. 🎯 Finding the trend and range Strength of fully aligned moving average is calculated based on position of each level with respect to other levels. For example, in a complete uptrend, we can find source > L(1)MA > L(2)MA > L(3)MA ...... > L(n-1)MA > L(n)MA Similarly in a complete downtrend, we can find source < L(1)MA < L(2)MA < L(3)MA ...... < L(n-1)MA < L(n)MA Hence, the strength of trend here is calculated based on relative positions of each levels. Due to this, value of strength can range from 0 to Level*(Level-1)/2 0 represents the complete downtrend Level*(Level-1)/2 represents the complete uptrend. Range and Extreme Range are calculated based on the percentile from median. The brackets are defined as per input parameters - Range Percentile and Extreme Range Percentile by using Percentile History as reference length. Moving average plot is color coded to display the trend strength. Green - Extreme Bullish Lime - Bullish Silver - range Orange - Bearish Red - Extreme Bearish 🎯 Finding the trend reversal Possible trend reversals are when price crosses the moving average while in complete trend with all the moving averages fully aligned. Triangle marks are placed in such locations which can help observe the probable trend reversal points. But, there are possibilities of trend overriding these levels. An example of such thing, we can see here: In order to overcome this problem, we can employ few techniques. 1. After the signal, wait for trend reversal (moving average plot color to turn silver) before placing your order. 2. Place stop orders on immediate pivot levels or support resistance points instead of opening market order. This way, we can also place an order in the direction of trend. Whichever side the price breaks out, will be the direction to trade. 3. Look for other confirmations such as extremely bullish and bearish candles before placing the orders. 🎯 An example of using stop orders Let us take this scenario where there is a signal on possible reversal from complete uptrend. Create a box joining high and low pivots at reasonable distance. You can also chose to add 1 ATR additional distance from pivots. Use the top of the box as stop-entry for long and bottom as stop-entry for short. The other ends of the box can become stop-losses for each side. After few bars, we can see that few more signals are plotted but, the price is still within the box. There are some candles which touched the top of the box. But, the candlestick patterns did not represent bullishness on those instances. If you have placed stop orders, these orders would have already filled in. In that case, just wait for position to hit either stop or target. For bullish side, targets can be placed at certain risk reward levels. In this case, we just use 1:1 for bullish (trend side) and 1:1.5 for bearish side (reversal side) In this case, price hit the target without any issue: Wait for next reversal signal to appear before placing another order :)
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Trendoscopeの投稿
82
Micro ZigzagMicro zigzag is created based on similar concepts as that of zigzag but by using lower timeframe intra-bar data. The lines join candle's high/low points but also depict how the price movement within the candle happened. That is, if the high of the candle is reached first, pivot from previous candle join the high first and then low and vice versa. The output can also be viewed as advanced line chart. 🎲 Process 🎯 For every bar identify whether high came first or low by using lower timeframe data. 🎯 If high came before low, add high as high pivot first and then low as low pivot. If otherwise, add low as lower pivot first and then high as higher pivot. 🎯 When adding pivot, check if the last pivot is in the same direction as the new one. If yes, replace existing pivot if the new one goes beyond it. Ignore otherwise. 🎯 If the last pivot is of different direction as that one new one, then simple add the new pivot.
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Trendoscopeの投稿
15
SubCandles-V2Further development on the sub-candle concept defined in the earlier script. SubCandle This time instead of concentrating only on the last part, we are dividing the main candle into three parts. First Sub-Candle - Covers the candle movement from open to first of highest/lowest point. First sub-candle tells how fast/slow the initial movement took place and which direction it went. Second Sub-Candle - Is always a full bodied candle. Most important think here is the thickness of the sub-candle which tells how quickly or slowly the price went from one side of the peak to other. Third Sub-Candle - Similar to first sub-candle but covers the last part. This part is similar to what we are depicting in the earlier version of Sub-Candle script. Thickness of the sub-candle is based on relatively how much time each sub-candle took to complete within the main candle. Few interpretation can be: If the first or third sub-candle are the thicker ones and if the thicker sub-candle has long wick, then it may mean strong rejection. If the first or third sub-candle are thicker ones and if the thicker sub-candle has full body, it may mean higher volatility within the sub-candle. If the second sub-candle is thicker than the rest - it can mean strong bias towards the direction of second sub-candle Thinner second sub-candle may mean a dump/pump. May need to check third sub-candle to see if the dump/pump are followed by pump/dump Also adding the screen shot reference below. Note: This can repaint within bar. Use it as part of bar replay. Will try to add more option to select a particular candle without bar reply soon Let me know what you think?
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Trendoscopeの投稿
14
SubCandleI created this script as POC to handle specific cases where not having tick data on historical bars create repainting. Happy to share if this serves purpose for other coders. What is the function of this script? Script plots a sub-candle which is remainder of candle after forming the latest peak. Higher body of Sub-candle refers to strong retracement of price from its latest peak. Color of the sub-candle defines the direction of retracement. Higher wick of Sub-candle refers to higher push in the direction of original candle. Meaning, after price reaching its peak, price retraced but could not hold. Here is a screenshot with explanation to visualise the concept: Settings There is only one setting which is number of backtest bars. Lower timeframe resolution which is used for calculating the Sub-candle uses this number to automatically calculate maximum possible lower timeframe so that all the required backtest windows are covered without having any issue. We need to keep in mind that max available lower timeframe bars is 100,000. Hence, with 5000 backtest bars, lower timeframe resolution can be about 20 (100000/5000) times lesser than that of regular chart timeframe. We need to also keep in mind that minimum resolution available as part of security_lower_tf is 1 minute. Hence, it is not advisable to use this script for chart timeframes less than 15 mins. Application I have been facing this issue in pattern recognition scripts where patterns are formed using high/low prices but entry and targets are calculated based on the opposite side (low/high). It becomes tricky during extreme bars to identify entry conditions based on just the opposite peak because, the candle might have originated from it before identifying the pattern and might have never reached same peak after forming the pattern. Due to lack of tick data on historical bars, we cannot use close price to measure such conditions. This leads to repaint and few unexpected results. I am intending to use this method to overcome the issue up-to some extent.
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Trendoscopeの投稿
9
Relative Bandwidth FilterThis is a very simple script which can be used as measure to define your trading zones based on volatility. Concept This script tries to identify the area of low and high volatility based on comparison between Bandwidth of higher length and ATR of lower length. Relative Bandwidth = Bandwidth / ATR Bandwidth can be based on either Bollinger Band, Keltner Channel or Donchian Channel. Length of the bandwidth need to be ideally higher. ATR is calculated using built in ATR method and ATR length need to be ideally lower than that used for calculating Bandwidth. Once we got Relative Bandwidth, the next step is to apply Bollinger Band on this to measure how relatively high/low this value is. Overall - If relative bandwidth is higher, then volatility is comparatively low. If relative bandwidth is lower, then volatility is comparatively high. Usage This can be used with your own strategy to filter out your non-trading zones based on volatility. Script plots a variable called "Signal" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic. Signal values can be 1 : Allow only Long -1 : Allow only short 0 : Do not allow any trades 2 : Allow both Long and Short
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Trendoscopeの投稿
19
Interactive Volume Profile - Based on LTF volumeHere is my first attempt on defining volume profile. In this script, I am using new feature of pine security_lower_tf Upon loading the script, it will ask users to select Time range to show the volume profile. Once you select the time range, confirmation input will popup. Upon confirming the inputs, you will be able to view the volume profile Settings are pretty simple. Some of them appear as part of confirmation. Limitation due to availability of LTF bars security_lower_tf can only fetch upto 100k bars, Hence, if we move the starting point beyond that, we will only see volume profile from the bar where LTF volume data is available. Increasing lower timeframe resolution will also increase the available range of volume profile. Option also available to use max range instead of time based range. If max bar range is selected, then volume profile is drawn based on all the bars for which LTF volume is available. An example of all combinations are show below. Selecting the granularity of volume profile Number of levels can be set from settings which impacts the granularity of volume profile. Below is the example of how different values for number of levels behave.
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Trendoscopeの投稿
21
Wolfe Scanner (Multi - zigzag) [HeWhoMustNotBeNamed]Before getting into the script, I would like to explain bit of history around this project. Wolfe was in the back of my mind for some time and I had several attempts so far. 🎯Initial Attempt When I first developed harmonic patterns, I got many requests from users to develop script to automatically detect Wolfe formation. I thought it would be easy and started boasting everywhere that I am going to attempt this next. However I miserably failed that time and started realising it is not as simple as I thought it would be. I started with Wolfe in mind. But, ran into issues with loops. Soon figured out that finding and drawing wedge is more trickier. I decided will explore trendline first so that it can help find wedge better. Soon, the project turned into something else and resulted in Auto-TrendLines-HeWhoMustNotBeNamed and Wolfe left forgotten. 🎯Using predefined ratios Wolfe also has predefined fib ratios which we can use to calculate the formation. But, upon initial development, it did not convince me that it matches visual inspection of Wolfe all the time. Hence, I decided to fall back on finding wedge first. 🎯 Further exploration in finding wedge This attempt was not too bad. I did not try to jump into Wolfe and nor I bragged anywhere about attempting anything of this sort. My target this time was to find how to derive wedge. I knew then that if I manage to calculate wedge in efficient way, it can help further in finding Wolfe. While doing that, ended up deriving Wedge-and-Flag-Finder-Multi-zigzag - which is not a bad outcome. I got few reminders on Wolfe after this both in comments and in PM. 🎯You never fail until you stop trying!! After 2 back to back hectic 50hr work weeks + other commitments, I thought I will spend some time on this. Took less than half weekend and here we are. I was surprised how much little time it took in this attempt. But, the plan was running in my subconscious for several weeks or even months. Last two days were just putting these plans into an action. Now, let's discuss about the script. 🎲 Wolfe Concept Wolfe concept is simple. Whenever a wedge is formed, draw a line joining pivot 1 and 4 as shown in the chart below: Converging trendline forms the stop loss whereas line joining pivots 1 and 4 form the profit taking points. 🎲 Settings Settings are pretty straightforward. Explained in the chart below.
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Trendoscopeの投稿
110
Zigzag MatrixNothing fancy. Just converted the new matrix library of zigzags ( mZigzag ) into indicator as I sensed it can be useful as indicator. On top of the standard zigzag, the indicator also tracks given oscillators, moving average and volume indicators on each pivots. More indicators can be added programmatically - but it will take up space in chart. Hence, so far I have only added option to add one per each type (moving average, oscillator and volume) Settings are as below
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Trendoscopeの投稿
13
Wedge and Flag Finder (Multi - zigzag)Here is a small attempt to automatically identify wedges and flags. Tradingview standard wedge checks for only 4 pivots. In this version, I have considered 5 pivots instead - which can help reduce noise as 4 pivots forming wedge can be quite common. In future, will also try to add more pivots in pattern recognition to make the signal more accurate. If wedge comes with a tail, then it is marked as flag :) Settings are quite simple and they are as shown below
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Trendoscopeの投稿
136
Intrabar OBV/PVTI got this idea from @fikira's script Intrabar-Price-Volume-Change-experimental The indicator calculates OBV and PVT based on ticks. Since, the indicator relies on live ticks, it only starts execution after it is put on the charts. The script can be useful in analysing intraday buy and sell pressure. Details are color coded based on the values. Data is presented in simple tabular format. Formula for OBV and PVT can be found here: www.investopedia.com www.investopedia.com
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Trendoscopeの投稿
6
Historical Range (Using eStrategy library)⬜ The script is intended to cover few things. ▶ Strategy testing framework based on eStrategy library ▶ Using historicalrange of values for identifying better entry and exits. This is also built on top of the Systematic Investment Plan script published here ⬜ Strategy testing framework Strategy testing framework is different from tradingview default strategy testing from few ways to suit the needs of systematic investments. ▶ Supports recurring investment on top of initial investment to emulate adding further funds to the investment bucket on regular basis. ▶ Better calculation of drawdowns based on daily equity rather than drawdown calculated only on close of trade. ▶ Provides better control over how much strategy can reduce and reload Having said that, this framework is not intended as replacement for tradingview strategy framework. It is not as comprehensive as tradingview strategy framework. But, created to address few specific styles of strategy. ▶ No detailed trade stats on individual trades. But, this can be implemented in future versions ▶ At present only facilitates long positions. ▶ UI features such as plotting trades on chart are not available. ▶ Does not take into consideration of slippage and brokerage - this is not an issue because the framework is not meant for short term trades. It is only made for daily timeframes. ▶ No pyramiding or leverage possible. And many more... Framework can be used for similar strategies based on market timing with few small changes. ⬜ Historical Range Strategy Concept here is, instead of taking indicators such as oscillators as is, use historical percentile to derive better oversold and overbought conditions. Strategy provides different options to base historical range. This can either be based on ▶ Band percent ▶ Oscillator Different choices of bands and oscillators are also available to chose. However, have not done extensive testing on all the combinations. ⬜ Settings ▶ Initial and recurring investment settings (As confirm inputs) ▶ Buy and hold and strategy specific settings to be used for stat calculation ▶ Band and oscillator parameters These are straightforward parameters which is used for defining the base of either bands or oscillators. ▶ Percentile moving average parameter Percentile MA is used with Percentile to find entry and exit signals based on crossover and crossunder. Feedbacks and suggestions welcome.
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Trendoscopeの投稿