Physics CandlesPhysics Candles embed volume and motion physics directly onto price candles or market internals according to the cyclic pattern of financial securities. The indicator works on both real-time “ticks” and historical data using statistical modeling to highlight when these values, like volume or momentum, is unusual or relatively high for some periodic window in time. Each candle is made out of one or more sub-candles that each contain their own information of motion, which converts to the color and transparency, or brightness, of that particular candle segment. The segments extend throughout the entire candle, both body and wicks, and Thick Wicks can be implemented to see the color coding better. This candle segmentation allows you to see if all the volume or energy is evenly distributed throughout the candle or highly contained in one small portion of it, and how intense these values are compared to similar time periods without going to lower time frames. Candle segmentation can also change a trader’s perspective on how valuable the information is. A “low” volume candle, for instance, could signify high value short-term stopping volume if the volume is all concentrated in one segment.
The Candles are flexible. The physics information embedded on the candles need not be from the same price security or market internal as the chart when using the Physics Source option, and multiple Candles can be overlayed together. You could embed stock price Candles with market volume, market price Candles with stock momentum, market structure with internal acceleration, stock price with stock force, etc. My particular use case is scalping the SPX futures market (ES), whose price action is also dictated by the volume action in the associated cash market, or SPY, as well as a host of other securities. Physics allows you to embed the ES volume on the SPY price action, or the SPY volume on the ES price action, or you can combine them both by overlaying two Candle streams and increasing the Number of Overlays option to two. That option decreases the transparency levels of your coloring scheme so that overlaying multiple Candles converges toward the same visual color intensity as if you had one. The Candle and Physics Sources allows for both Symbols and Spreads to visualize Candle physics from a single ticker or some mathematical transformation of tickers.
Due to certain TradingView programming restrictions, each Candle can only be made out of a maximum of 8 candle segments, or an “8-bit” resolution. Since limits are just an opportunity to go beyond, the user has the option to stack multiple Candle indicators together to further increase the candle resolution. If you don’t want to see the Candles for some particular period of the day, you can hide them, or use the hiding feature to have multiple Candles calibrated to show multiple parts of the trading day. Securities tend to have low volume after hours with sharp spikes at the open or close. Multiple Candles can be used for multiple parts of the trading day to accommodate these different cycles in volume.
The Candles do not need be associated with the nominal security listed on the TV chart. The Candle Source allows the user to look at AAPL Candles, for instance, while on a TSLA or SPY chart, each with their respective volume actions integrated into the candles, for instance, to allow the user to see multiple security price and volume correlation on a single chart.
The physics information currently embeddable on Candles are volume or time, velocity, momentum, acceleration, force, and kinetic energy. In order to apply equations of motion containing a mass variable to financial securities, some analogous value for mass must be assumed. Traders often regard volume or time as inextricable variables to a securities price that can indicate the direction and strength of a move. Since mass is the inextricable variable to calculating the momentum, force, or kinetic energy of motion, the user has the option to assume either time or volume is analogous to mass. Volume may be a better option for mass as it is not strictly dependent on the speed of a security, whereas time is.
Data transformations and outlier statistics are used to color code the intensity of the physics for each candle segment relative to past periodic behavior. A million shares during pre-market or a million shares during noontime may be more intense signals than a typical million shares traded at the open, and should have more intense color signals. To account for a specific cyclic behavior in the market, the user can specify the Window and Cycle Time Frames. The Window Time Frame splits up a Cycle into windows, samples and aggregates the statistics for each window, then compares the current physics values against past values in the same window. Intraday traders may benefit from using a Daily Cycle with a 30-minute Window Time Frame and 1-minute Sample Time Frame. These settings sample and compare the physics of 1-minute candles within the current 30-minute window to the same 30-minute window statistics for all past trading days, up until the data limit imposed by TradingView, or until the Data Collection Start Date specified in the settings. Longer-term traders may benefit from using a Monthly Cycle with a Weekly Time Frame, or a Yearly Cycle with a Quarterly Time Frame.
Multiple statistics and data transformation methods are available to convey relative intensity in different ways for different trading signals. Physics Candles allows for both Normal and Log-Normal assumptions in the physics distribution. The data can then be transformed by Linear, Logarithmic, Z-Score, or Power-Law scoring, where scoring simply assigns an intensity to the relative physics value of each candle segment based on some mathematical transformation. Z-scoring often renders adequate detection by scoring the segment value, such as volume or momentum, according to the mean and standard deviation of the data set in each window of the cycle. Logarithmic or power-law transformation with a gamma below 1 decreases the disparity between intensities so more less-important signals will show up, whereas the power-law transformation with gamma values above 1 increases the disparity between intensities, so less more-important signals will show up. These scores are then converted to color and transparency between the Min Score and the Max Score Cutoffs. The Auto-Normalization feature can automatically pick these cutoffs specific to each window based on the mean and standard deviation of the data set, or the user can manually set them. Physics was developed with novices in mind so that most users could calibrate their own settings by plotting the candle segment distributions directly on the chart and fiddling with the settings to see how different cutoffs capture different portions of the distribution and affect the relative color intensities differently. Security distributions are often skewed with fat-tails, known as kurtosis, where high-volume segments for example, have a higher-probabilities than expected for a normal distribution. These distribution are really log-normal, so that taking the logarithm leads to a standard bell-shaped distribution. Taking the Z-score of the Log-Normal distribution could make the most statistical sense, but color sensitivity is a discretionary preference.
Background Philosophy
This indicator was developed to study and trade the physics of motion in financial securities from a visually intuitive perspective. Newton’s laws of motion are loosely applied to financial motion:
“A body remains at rest, or in motion at a constant speed in a straight line, unless acted upon by a force”.
Financial securities remain at rest, or in motion at constant speed up or down, unless acted upon by the force of traders exchanging securities.
“When a body is acted upon by a force, the time rate of change of its momentum equals the force”.
Momentum is the product of mass and velocity, and force is the product of mass and acceleration. Traders render force on the security through the mass of their trading activity and the acceleration of price movement.
“If two bodies exert forces on each other, these forces have the same magnitude but opposite directions.”
Force arises from the interaction of traders, buyers and sellers. One body of motion, traders’ capitalization, exerts an equal and opposite force on another body of motion, the financial security. A securities movement arises at the expense of a buyer or seller’s capitalization.
Volume
The premise of this indicator assumes that volume, v, is an analogous means of measuring physical mass, m. This premise allows the application of the equations of motion to the movement of financial securities. We know from E=mc^2 that mass has energy. Energy can be used to create motion as kinetic energy. Taking a simple hypothetical example, the interaction of one short seller looking to cover lower and one buyer looking to sell higher exchange shares in a security at an agreed upon price to create volume or mass, and therefore, potential energy. Eventually the short seller will actively cover and buy the security from the previous buyer, moving the security higher, or the buyer will actively sell to the short seller, moving the security lower. The potential energy inherent in the initial consolidation or trading activity between buy and seller is now converted to kinetic energy on the subsequent trading activity that moves the securities price. The more potential energy that is created in the consolidation, the more kinetic energy there is to move price. This is why point and figure traders are said to give price targets based on the level of volatility or size of a consolidation range, or why Gann traders square price and time, as time is roughly proportional to mass and trading activity. The build-up of potential energy between short sellers and buyers in GME or TSLA led to their explosive moves beyond their standard fundamental valuations.
Position
Position, p, is simply the price or value of a financial security or market internal.
Time
Time, t, is another means of measuring mass to discover price behavior beyond the time snapshots that simple candle charts provide. We know from E=mc^2 that time is related to rest mass and energy given the speed of light, c, where time ≈ distance * sqrt(mass/E). This relation can also be derived from F=ma. The more mass there is, the longer it takes to compute the physics of a system. The more energy there is, the shorter it takes to compute the physics of a system. Similarly, more time is required to build a “resting” low-volatility trading consolidation with more mass. More energy added to that trading consolidation by competing buyers and sellers decreases the time it takes to build that same mass. Time is also related to price through velocity.
Velocity = (p(t1) – p(t0)) / p(t0)
Velocity, v, is the relative percent change of a securities price, p, over a period of time, t0 to t1. The period of time is between subsequent candles, and since time is constant between candles within the same timeframe, it is not used to calculate velocity or acceleration. Price moves faster with higher velocity, and slower with slower velocity, over the same fixed period of time. The product of velocity and mass gives momentum.
Momentum = mv
This indicator uses physics definition of momentum, not finance’s. In finance, momentum is defined as the amount of change in a securities price, either relative or absolute. This is definition is unfortunate, pun intended, since a one dollar move in a security from a thousand shares traded between a few traders has the exact same “momentum” as a one dollar move from millions of shares traded between hundreds of traders with everything else equal. If momentum is related to the energy of the move, momentum should consider both the level of activity in a price move, and the amount of that price move. If we equate mass to volume to account for the level of trading activity and use physics definition of momentum as the product of mass and velocity, this revised definition now gives a thousand-times more momentum to a one-dollar price move that has a thousand-times more volume behind it. If you want to use finance’s volume-less definition of momentum, use velocity in this indicator.
Acceleration = v(t1) – v(t0)
Acceleration, a, is the difference between velocities over some period of time, t0 to t1. Positive acceleration is necessary to increase a securities speed in the positive direction, while negative acceleration is necessary to decrease it. Acceleration is related to force by mass.
Force = ma
Force is required to change the speed of a securities valuation. Price movements with considerable force have considerably more impact on future direction. A change in direction requires force.
Kinetic Energy = 0.5mv^2
Kinetic energy is the energy that a financial security gains from the change in its velocity by force. The built-up of potential energy in trading consolidations can be converted to kinetic energy on a breakout from the consolidation.
Cycle Theory and Relativity
Just as the physics of motion is relative to a point of reference, so too should the physics of financial securities be relative to a point of reference. An object moving at a 100 mph towards another object moving in the same direction at 100 mph will not appear to be moving relative to each other, nor will they collide, but from an outsider observer, the objects are going 100 mph and will collide with significant impact if they run into a stationary object relative to the observer. Similarly, trading with a hundred thousand shares at the open when the average volume is a couple million may have a much smaller impact on the price compared to trading a hundred thousand shares pre-market when the average volume is ten thousand shares. The point of reference used in this indicator is the average statistics collected for a given Window Time Frame for every Cycle Time Frame. The physics values are normalized relative to these statistics.
Examples
The main chart of this publication shows the Force Candles for the SPY. An intense force candle is observed pre-market that implicates the directional overtone of the day. The assumption that direction should follow force arises from physical observation. If a large object is accelerating intensely in a particular direction, it may be fair to assume that the object continues its direction for the time being unless acted upon by another force.
The second example shows a similar Force Candle for the SPY that counters the assumption made in the first example and emphasizes the importance of both motion and context. While it’s fair to assume that a heavy highly accelerating object should continue its course, if that object runs into an obstacle, say a brick wall, it’s course may deviate. This example shows SPY running into the 50% retracement wall from the low of Mar 2020, a significant support level noted in literature. The example also conveys Gann’s idea of “lost motion”, where the SPY penetrated the 50% price but did not break through it. A brick wall is not one atom thick and price support is not one tick thick. An object can penetrate only one layer of a wall and not go through it.
The third example shows how Volume Candles can be used to identify scalping opportunities on the SPY and conveys why price behavior is as important as motion and context. It doesn’t take a brick wall to impede direction if you know that the person driving the car tends to forget to feed the cats before they leave. In the chart below, the SPY breaks down to a confluence of the 5-day SMA, 20-day SMA, and an important daily trendline (not shown) after the bullish bounce from the 50% retracement days earlier. High volume candles on the SMA signify stopping volume that reverse price direction. The character of the day changes. Bulls become more aggressive than bears with higher volume on upswings and resistance, whiles bears take on a defensive position with lower volume on downswings and support. High volume stopping candles are seen after rallies, and can tell you when to take profit, get out of a position, or go short. The character change can indicate that its relatively safe to re-enter bullish positions on many major supports, especially given the overarching bullish theme from the large reaction off the 50% retracement level.
The last example emphasizes the importance of relativity. The Volume Candles in the chart below are brightest pre-market even though the open has much higher volume since the pre-market activity is much higher compared to past pre-markets than the open is compared to past opens. Pre-market behavior is a good indicator for the character of the day. These bullish Volume Candles are some of the brightest seen since the bounce off the 50% retracement and indicates that bulls are making a relatively greater attempt to bring the SPY higher at the start of the day.
Infrequently Asked Questions
Where do I start?
The default settings are what I use to scalp the SPY throughout most of the extended trading day, on a one-minute chart using SPY volume. I also overlay another Candle set containing ES future volume on the SPY price structure by setting the Physics Source to ES1! and the Number of Overlays setting to 2 for each Candle stream in order to account for pre- and post-market trading activity better. Since the closing volume is exponential-like up until the end of the regular trading day, adding additional Candle streams with a tighter Window Time Frame (e.g., 2-5 minute) in the last 15 minutes of trading can be beneficial. The Hide feature can allow you to set certain intraday timeframes to hide one Candle set in order to show another Candle set during that time.
How crazy can you get with this indicator?
I hope you can answer this question better. One interesting use case is embedding the velocity of market volume onto an internal market structure. The PCTABOVEVWAP.US is a market statistic that indicates the percent of securities above their VWAP among US stocks and is helpful for determining short term trends in the US market. When securities are rising above their VWAP, the average long is up on the day and a rising PCTABOVEVWAP.US can be viewed as more bullish. When securities are falling below their VWAP, the average short is up on the day and a falling PCTABOVEVWAP.US can be viewed as more bearish. (UPVOL.US - DNVOL.US) / TVOL.US is a “spread” symbol, in TV parlance, that indicates the decimal percent difference between advancing volume and declining volume in the US market, showing the relative flow of volume between stocks that are up on the day, and stocks that are down on the day. Setting PCTABOVEVWAP.US in the Candle Source, (UPVOL.US - DNVOL.US) / TVOL.US in the Physics Source, and selecting the Physics to Velocity will embed the relative velocity of the spread symbol onto the PCTABOVEVWAP.US candles. This can be helpful in seeing short term trends in the US market that have an increasing amount of volume behind them compared to other trends. The chart below shows Volume Candles (top) and these Spread Candles (bottom). The first top at 9:30 and second top at 10:30, the high of the day, break down when the spread candles light up, showing a high velocity volume transfer from up stocks to down stocks.
How do I plot the indicator distribution and why should I even care?
The distribution is visually helpful in seeing how different normalization settings effect the distribution of candle segments. It is also helpful in seeing what physics intensities you want to ignore or show by segmenting part of the distribution within the Min and Max Cutoff values. The intensity of color is proportional to the physics value between the Min and Max Cutoff values, which correspond to the Min and Max Colors in your color scheme. Any physics value outside these Min and Max Cutoffs will be the same as the Min and Max Colors.
Select the Print Windows feature to show the window numbers according to the Cycle Time Frame and Window Time Frame settings. The window numbers are labeled at the start of each window and are candle width in size, so you may need to zoom into to see them. Selecting the Plot Window feature and input the window number of interest to shows the distribution of physics values for that particular window along with some statistics.
A log-normal volume distribution of segmented z-scores is shown below for 30-minute opening of the SPY. The Min and Max Cutoff at the top of the graph contain the part of the distribution whose intensities will be linearly color-coded between the Min and Max Colors of the color scheme. The part of the distribution below the Min Cutoff will be treated as lowest quality signals and set to the Min Color, while the few segments above the Max Cutoff will be treated as the highest quality signals and set to the Max Color.
What do I do if I don’t see anything?
Troubleshooting issues with this indicator can involve checking for error messages shown near the indicator name on the chart or using the Data Validation section to evaluate the statistics and normalization cutoffs. For example, if the Plot Window number is set to a window number that doesn’t exist, an error message will tell you and you won’t see any candles. You can use the Print Windows option to show windows that do exist for you current settings. The auto-normalization cutoff values may be inappropriate for your particular use case and literally cut the candles out of the chart. Try changing the chart time frame to see if they are appropriate for your cycle, sample and window time frames. If you get a “Timeframe passed to the request.security_lower_tf() function must be lower than the timeframe of the main chart” error, this means that the chart timeframe should be increased above the sample time frame. If you get a “Symbol resolve error”, ensure that you have correct symbol or spread in the Candle or Physics Source.
How do I see a relative physics values without cycles?
Set the Window Time Frame to be equal to the Cycle Time Frame. This will aggregate all the statistics into one bucket and show the physics values, such as volume, relative to all the past volumes that TV will allow.
How do I see candles without segmentation?
Segmentation can be very helpful in one context or annoying in another. Segmentation can be removed by setting the candle resolution value to 1.
Notes
I have yet to find a trading platform that consistently provides accurate real-time volume and pricing information, lacking adequate end-user data validation or quality control. I can provide plenty of examples of real-time volume counts or prices provided by TradingView and other platforms that were significantly off from what they should have been when comparing against the exchanges own data, and later retroactively corrected or not corrected at all. Since no indicator can work accurately with inaccurate data, please use at your own discretion.
The first version is a beta version. Debugging and validating code in Pine script is difficult without proper unit testing. Please report any bugs with enough information to reproduce them and indicate why they are important. I also encourage you to export the data from TradingView and verify the calculations for your particular use case.
The indicator works on real-time updates that occur at a higher frequency than the candle time frame, which TV incorrectly refers to as ticks. They use this terminology inaccurately as updates are really aggregated tick data that can take place at different prices and may not accurately reflect the real tick price action. Consequently, this inaccuracy also impacts the real-time segmentation accuracy to some degree. TV does not provide a means of retaining “tick” information, so the higher granularity of information seen real-time will be lost on a disconnect.
TV does not provide time and sales information. The volume and price information collected using the Sample Time Frame is intraday, which provides only part of the picture. Intraday volume is generally 50 to 80% of the end of day volume. Consequently, the daily+ OHLC prices are intraday, and may differ significantly from exchanged settled OHLC prices.
The Cycle and Window Time Frames refer to calendar days and time, not trading days or time. For example, the first window week of a monthly cycle is the first seven days of the month, not the first Monday through Friday of trading for the month.
Chart Time Frames that are higher than the Window Time Frames average the normalized physics for price action that occurred within a given Candle segment. It does not average price action that did not occur.
One of the main performance bottleneck in TradingView’s Pine Script is client-side drawing and plotting. The performance of this indicator can be increased by lowering the resolution (the number of sub-candles this indicator plots), getting a faster computer, or increasing the performance of your computer like plugging your laptop in and eliminating unnecessary processes.
The statistical integrity of this indicator relies on the number of samples collected per sample window in a given cycle. Higher sample counts can be obtained by increasing the chart time frame or upgrading the TradingView plan for a higher bar count. While increasing the chart time frame doesn’t increase the visual number of bars plotted on the chart, it does increase the number of bars that can be pulled at a lower time frame, up to 100,000.
Due to a limitation in Pine Scripts request_lower_tf() function, using a spread symbol will only work for regular trading hours, not extended trading hours.
Ideally, velocity or momentum should be calculated between candle closes. To eliminate the need to deal with price gaps that would lead to an incorrect statistical distributions, momentum is calculated between candle open and closes as a percent change of the price or value, which should not be an issue for most liquid securities.
Komut dosyalarını "ha溢价率" için ara
GT 5.1 Strategy═════════════════════════════════════════════════════════════════════════
█ OVERVIEW
People often look an indicator in their technical analysis to enter a position. We may also need to look at the signals of one or more indicators to verify the signals given by some indicators. In this context, I developed a strategy to test whether it really works by choosing some of the indicators that capture trend changes with the same characteristics. Also, since the subject is to catch the trend change, I thought it would be right to include an indicator using the heikin ashi logic. By averaging and smoothing the market noise, Heiken Ashi makes it easier to detect the direction of the trend helps to see possible reversal points on the chart. However, it should be noted that Heiken Ashi is a lagging indicator.
I picked 5 different indicators (but their purpose are similar) and combined them to produce buy and sell signals based on your choice(not repaint). First of all let's get some information about our indicators. So you will understand me why i picked these indicators and what is the meaning of their signals.
1 — Coral Trend Indicator by LazyBear
Coral Trend Indicator is a linear combination of moving averages, all obtained by a triple or higher order exponential smoothing. The indicator comes with a trend indication which is based on the normalized slope of the plot. the usage of this indicator is simple. When the color of the line is green that means the market is in uptrend. But when the color is red that means the market is in downtrend.
As you see the original indicator it is simple to find is it in uptrend or downtrend.
So i added a code to find when the color of the line change. When it turns green to red my script giving sell signals, when it turns red to green it gives buy signals.
I hide the candles to show you more clearly what is happening when you choose only Coral Strategy. But sometimes it is not enough only using itself. Even if green dots turn to red it continues in uptrend. So we need a to look another indicator to approve our signal.
2 — SSL channel by ErwinBeckers
Known as the SSL , the Semaphore Signal Level channel is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. This indicator creates a band by calculating the high and low values according to the determined period. Simply if you decide 10 as period, it calculates a 10-period moving average on the latest 10 highs. Calculate a 10-period moving average on the latest 10 lows. If the price falls below the low band, the downtrend begins, if the price closes above the high band, the uptrend begins. Lets look the original form of indicator and learn how it using.
If the red line is below and the green band is above, it means that we are in uptrend, and if it is on the opposite side, it means that we are in downtrend. Therefore, it would be logical to enter a position where the trend has changed. So i added a code to find when the crossover has occured.
As you see in my strategy, it gives you signals when the trend has changed. But sometimes it is not enough only using this indicator itself. So lets look 2 indicator together in one chart.
Look circle SSL is saying it is in downtrend but Coral is saying it has entered in uptrend. if we just look to coral signal it can misleads us. So it can be better to look another indicator for validating our signals.
3 — Heikin Ashi RSI Oscillator by JayRogers
The Heikin-Ashi technique is used by technical traders to identify a given trend more easily. Heikin-Ashi has a smoother look because it is essentially taking an average of the movement. There is a tendency with Heikin-Ashi for the candles to stay red during a downtrend and green during an uptrend, whereas normal candlesticks alternate color even if the price is moving dominantly in one direction. This indicator actually recalculates the RSI indicator with the logic of heikin ashi. Due to smoothing, the bars are formed with a slight lag, reflecting the trend rather than the exact price movement. So lets look the original version to understand more clearly. If red bars turn to green bars it means uptrend may begin, if green bars turn to red it means downtrend may begin.
As you see HARSI giving lots of signal some of them is really good but some of them are not very well. Because it gives so much signals Now i will change time period and lets look same chart again.
Now results are better because of heikin ashi's logic. it is not suitable for day traders, it gives more accurate result when using the time period is longer. But it can be useful to use this indicator in short time periods using with other indicators. So you may catch the trend changes more accurately.
4 — MACD DEMA by ToFFF
This indicator uses a double EMA and MACD algorithm to analyze the direction of the trend. Though it might seem a tough task to manage the trades with the help of MACD DEMA once you know how the proper way to interpret the signal lines, it will be an easy task.
This indicator also smoothens the signal lines with the time series algorithm which eventually makes the higher time frame important. So, expecting better results in the lower time frame can result in big losses as the data reading from the MACD DEMA will not be accurate. In order to understand the function of this indicator, you have to know the functions of the EMA also.
The exponential moving average tends to give more priority to the recent price changes. So, expecting better results when the volatility is very high is a very risky approach to trade the market. Moreover, the MACD has some lagging issues compared to the EMA, so it is super important to use a trading method that focuses on the higher time frame only. What does MACD 12 26 Close 9 mean? When the DEMA-9 crosses above the MACD(12,26), this is considered a bearish signal. It means the trend in the stock – its magnitude and/or momentum – is starting to shift course. When the MACD(12,26) crosses above the DEMA-9, this is considered a bullish signal. Lets see this indicator on Chart.
When the blue line crossover red line it is good time to buy. As you see from the chart i put arrows where the crossover are appeared.
When the red line crossover blue line it is good time to sell or exit from position.
5 — WaveTrend Oscillator by LazyBear
This is a technical indicator that creates high and low bands between two values. It then creates a trend indicator that draws waves with highs and lows within these boundaries. WaveTrend is a widely used indicator for finding direction of an asset.
Calculation period: number of candles used to calculate WaveTrend, defaults to 10. Averaging period: number of candles used to average WaveTrend, defaults to 21.
As you see in chart when the lines crossover occured my strategy gives buy or sell signals.
═════════════════════════════════════════════════════════════════════════
█ HOW TO USE
I hope you understand how the indicators I mentioned above work and what they are used for. Now, I will explain in detail how to use the strategy I have created.
When you enter the settings section, you will see 5 types of indicators. If you want to use the signals of the indicators, simply tick the box next to the indicators. Also, under each option there is an area where you can set the "lookback". This setting is a field that will make the signals overlap when you select more than one option. If you are going to trade with only one option, you should make sure that this field is 0. Otherwise, it may continue to generate as many signals as you choose.
Lets see in chart for easy understanding.
As you see chart, if i chose only HARSI with lookback 0 (HARSI and CORAL should be 1 minumum because of algorithm-we looking 1 bar before, others 0 because we are looking crossovers), it will give signals only when harsı bar's color changed. But when i changed Lookback as 7 it will be like this in chart.
Now i will choose 2 indicator with settings of their lookback 0.
As you see it will give signals when both of them occurs same time. But HARSI is an indicator giving very early signal so we can enter position 5-6 bars after the first bar color change. So i will change HARSI Lookback settings as 7. Lets look what happens when we use lookback option.
So it wil be useful to change lookback settings to find best signals in each time period and in each symbol. But it shouldnt be too high. Because you can be late to catch trend's starting.
this is an image of MACD and WAVE trend used and lookback option are both 6.
Now lets see an example with 3 options are chosen with lookback option 11-1-5
Now lets talk about indicators settings. After strategy options you will see each indicators settings, you can change their settings as you desired. So each indicators signal will be changed according to your adjustment.
I left strategy options with default settings. You can change it manually as if you want.
═════════════════════════════════════════════════════════════════════════
█ LIMITATIONS: Don't rely on non-standard charts results. For example Heikin Ashi is a technical analysis method used with the traditional candlestick chart.Heikin Ashi vs. Candlestick Chart: The decisive visual difference between Heikin Ashi and the traditional chart is that Heikin Ashi flattens the traditional candlestick chart using a modified formula.
The primary advantage of Heikin Ashi is that it makes the chart more reader-friendly and helps users identify and analyze trends .
Because Heikin Ashi provides averaged price information rather than real-time price and reacts slowly to volatility — not suitable for scalpers and high-frequency traders. I added HARSI indicator as a supportive signal because it is useful with using CORAL and SSL channel indicators. If you change your candle types to Heikin Ashi , your profit will change in good way but dont rely on it.
═════════════════════════════════════════════════════════════════════════
█ THANKS:
Special thanks to authors of the scripts that i used.
@LazyBear and @ErwinBeckers and @JayRogers and @ToFFF
═════════════════════════════════════════════════════════════════════════
█ DISCLAIMER
Any trade decisions you make are entirely your own responsibility.
True Strength Indicator + Realtime DivergencesTrue Strength Indicator (TSI) + Realtime Divergences + Alerts + Lookback periods.
This version of the True Strength Indicator adds the following 5 additional features to the stock TSI by Tradingview:
- Optional divergence lines drawn directly onto the oscillator in realtime.
- Configurable alerts to notify you when divergences occur, as well as when the TSI and lagline bands crossover one another, when the oscillator begins heading up, or heading down.
- Configurable lookback periods to fine tune the divergences drawn in order to suit different trading styles and timeframes.
- Background colouring option to indicate when the two TSI bands, the TSI line and the TSI lagline, have crossed one another, either moving upwards or downwards, or optionally when the two TSI bands have crossed upwards and an external oscillator, which can be linked via the settings, has crossed above its centerline, and the TSI bands have crossed downwards and the external oscillator has crossed below its centerline.
- Alternate timeframe feature allows you to configure the oscillator to use data from a different timeframe than the chart it is loaded on.
This indicator adds additional features onto the stock TSI by Tradingview, whose core calculations remain unchanged, although this version has different settings as default to suit a shorter time period (it uses 6, 13, 4 by default, whereas the stock TSI typically ships with higher values, e.g. 25, 13, 13). Namely the configurable option to automatically, quickly and clearly draw divergence lines onto the oscillator for you as they occur in realtime. It also has the addition of unique alerts, so you can be notified when divergences occur without spending all day watching the charts. Furthermore, this version of the TSI comes with configurable lookback periods, which can be configured in order to adjust the sensitivity of the divergences, in order to suit shorter or higher timeframe trading approaches.
The True Strength Indicator
Tradingview describes the True Strength Indicator as follows:
“The True Strength Index (TSI) is a momentum oscillator that ranges between limits of -100 and +100 and has a base value of 0. Momentum is positive when the oscillator is positive (pointing to a bullish market bias) and vice versa. It was developed by William Blau and consists of 2 lines: the index line and an exponential moving average of the TSI, called the signal line. Traders may look for any of the following 5 types of conditions: overbought, oversold, centerline crossover, divergence and signal line crossover. The indicator is often used in combination with other signals..”
What are divergences?
Divergence is when the price of an asset is moving in the opposite direction of a technical indicator, such as an oscillator, or is moving contrary to other data. Divergence warns that the current price trend may be weakening, and in some cases may lead to the price changing direction.
There are 4 main types of divergence, which are split into 2 categories;
regular divergences and hidden divergences. Regular divergences indicate possible trend reversals, and hidden divergences indicate possible trend continuation.
Regular bullish divergence: An indication of a potential trend reversal, from the current downtrend, to an uptrend.
Regular bearish divergence: An indication of a potential trend reversal, from the current uptrend, to a downtrend.
Hidden bullish divergence: An indication of a potential uptrend continuation.
Hidden bearish divergence: An indication of a potential downtrend continuation.
Setting alerts.
With this indicator you can set alerts to notify you when any/all of the above types of divergences occur, on any chart timeframe you choose.
Configurable lookback values.
You can adjust the default lookback values to suit your prefered trading style and timeframe. If you like to trade a shorter time frame, lowering the default lookback values will make the divergences drawn more sensitive to short term price action.
How do traders use divergences in their trading?
A divergence is considered a leading indicator in technical analysis , meaning it has the ability to indicate a potential price move in the short term future.
Hidden bullish and hidden bearish divergences, which indicate a potential continuation of the current trend are sometimes considered a good place for traders to begin, since trend continuation occurs more frequently than reversals, or trend changes.
When trading regular bullish divergences and regular bearish divergences, which are indications of a trend reversal, the probability of it doing so may increase when these occur at a strong support or resistance level . A common mistake new traders make is to get into a regular divergence trade too early, assuming it will immediately reverse, but these can continue to form for some time before the trend eventually changes, by using forms of support or resistance as an added confluence, such as when price reaches a moving average, the success rate when trading these patterns may increase.
Typically, traders will manually draw lines across the swing highs and swing lows of both the price chart and the oscillator to see whether they appear to present a divergence, this indicator will draw them for you, quickly and clearly, and can notify you when they occur.
Disclaimer: This script includes code from the stock TSI by Tradingview as well as the Divergence for Many Indicators v4 by LonesomeTheBlue
Dynamic Zone Range on OMA [Loxx]Dynamic Zone Range on OMA is an One More Moving Average oscillator with Dynamic Zones.
What is the One More Moving Average (OMA)?
The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ...
The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course:
The extras:
it has to be smooth
it has to be able to "change speed" without length change
it has to be able to adapt or not (since it has to "imitate" the non-adaptive as well as the adaptive ones)
The steps:
Smoothing - compared are the simple moving average (that is the basis and the first step of this indicator - a smoothed simple moving average with as little lag added as it is possible and as close to the original as it is possible) Speed 1 and non-adaptive are the reference for this basic setup.
Speed changing - same chart only added one more average with "speeds" 2 and 3 (for comparison purposes only here)
Finally - adapting : same chart with SMA compared to one more average with speed 1 but adaptive (so this parameters would make it a "smoothed adaptive simple average") Adapting part is a modified Kaufman adapting way and this part (the adapting part) may be a subject for changes in the future (it is giving satisfactory results, but if or when I find a better way, it will be implemented here)
Some comparisons for different speed settings (all the comparisons are without adaptive turned on, and are approximate. Approximation comes from a fact that it is impossible to get exactly the same values from only one way of calculation, and frankly, I even did not try to get those same values).
speed 0.5 - T3 (0.618 Tilson)
speed 2.5 - T3 (0.618 Fulks/Matulich)
speed 1 - SMA , harmonic mean
speed 2 - LWMA
speed 7 - very similar to Hull and TEMA
speed 8 - very similar to LSMA and Linear regression value
Parameters:
Length - length (period) for averaging
Source - price to use for averaging
Speed - desired speed (i limited to -1.5 on the lower side but it even does not need that limit - some interesting results with speeds that are less than 0 can be achieved)
Adaptive - does it adapt or not
Variety Moving Averages w/ Dynamic Zones contains 33 source types and 35+ moving averages with double dynamic zones levels.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included
4 signal types
Bar coloring
Alerts
Channels fill
Counting Stars Overlay [Market Overview Series]Hi fellow tradeurs,
So it's always been my goal to provide one of my best scripts. This is from what I call my "Market Overview" series. It is a scanner for my second best script to date. Market Overview bc of its origins as a scanner of the Kucoin Margin Coins. I realize that there are more coins that there are more margin coins that Kucoin has but I wanted to have a solid 40 coins on each coin "set". If you are unfamiliar with what I mean by 'sets' then you can view my other scanner scripts on this account for futher elaboration but to sum it up....there are 4 sets of coins I have to choose from in the settings. Each set has 40 coins in them (as there is a cap of 40 security calls that can be made per each iteration of the script on the chart). That being said...if you have the capabilities then add this script 4 times to your chart and select a diff set for each copy of the script. This has the scanner in a way that I've yet to present in my others scripts. When the alert for a coin goes off then the coins name will be printed as a label over the main chart. BTW, this was built for the 1 min timeframe and have used it EXTENSIVELY and this is the best TF for how the settings are set. I will also publish another script that will be a visual aid for this one but will rather show all the plots associated with the code that is in this scanner. Know that for the scanner it'll be best to choose a coin that has at least 1 trade/update/printed candle per minute (to be safe use BTC or ETH chart or else some of the signals will be printed if the signal arrives at a point in time where the coin on the screen does not print a candle bc no new trade or update to trades occur in TradingView. For the visual aid script that I will add right after this, there will be 20 different plots that appear. When the AVG of all of these plots is beyond the OverBought line and then the AVG line is falling for 2 bars...THEN the long signal for that coin is generated (and vise versa for short signals) Lastly regarding the visual aid script, THAT ONE will ONLY show the 20 plots that are associated with the coin that the chart is selected for. So that one is not a scanner and is just a stand alone script (again) to show whats going on in the background of this scanner. Now, once you add it however many time you want to see however many sets of coins you want, I recommend merging the scales so that they are all on one scale. I prefer mine being on the left side but all you have to do is select the 3 dots in the scripts settings in the chart window and select the scale location line and it'll open another set of lines at which point you can select "merge to scale Z" (that will be the left scale) and will put all the scales together on the left. I forgot ****If you want to see a whole diff exchange's coins you much make changes to this original script and it is further described how to do so in one of my first publications**** I REALLY hope it becomes of some benefit to you in your trading as it abundantly has in my own. It is after all one of the best of my best. Ohh, I forgot to add alerts to this but will do so immediately following this. To finish, this script DOES NOT REPAINT as far as I have EVER seen (and I have extensively searched for it bc of how good the signals were, I figured I MUST HAVE made a mistake and it did so...but alas...it does not. If you notice something on the contrary do notify me immediately with the coin, exchange, TF, and time of the occurrence and we can go from there. If anyone has any great ideas for the script then please do also let me know and if I find anyone with some abilities that mingle well with my own then lets talk as I'm always looking for good ol chaps to help me out with other scripts bc if you think this is good....well....you must imagine that I've got better that I have not/am not publishing. Aaaaaanywho, goodluck to you all. I wish you the best. ***I've got good info on how to look out for false signals but I want to see what yall come up with first before I give away all my alpha.
AND if anyone asks questions that Ive already touched on in this description or already in the comments sections then maybe someone there would be willing to waste their time answering them bc I've done quite a bit of work here that I am HAPPY to hand over to the general public but if you are not willing to do the work in reading to possibly answer your inquiries that have already been answered then I am not willing to do that work for you again. Peace and love people...peace and love. Im out.
WAP Maverick - (Dual EMA Smoothed VWAP) - [mutantdog]Short Version:
This here is my take on the popular VWAP indicator with several novel features including:
Dual EMA smoothing.
Arithmetic and Harmonic Mean plots.
Custom Anchor feat. Intraday Session Sizes.
2 Pairs of Bands.
Side Input for Connection to other Indicator.
This can be used 'out of the box' as a replacement VWAP, benefitting from smoother transitions and easy-to-use custom alerts.
By design however, this is intended to be a highly customisable alternative with many adjustable parameters and a pseudo-modular input system to connect with another indicator. Well suited for the tweakers around here and those who like to get a little more creative.
I made this primarily for crypto although it should work for other markets. Default settings are best suited to 15m timeframe - the anchor of 1 week is ideal for crypto which often follows a cyclical nature from Monday through Sunday. In 15m, the default ema length of 21 means that the wap comes to match a standard vwap towards the end of Monday. If using higher chart timeframes, i recommend decreasing the ema length to closely match this principle (suggested: for 1h chart, try length = 8; for 4h chart, length = 2 or 3 should suffice).
Note: the use of harmonic mean calculations will cause problems on any data source incorporating both positive and negative values, it may also return unusable results on extremely low-value charts (eg: low-sat coins in /btc pairs).
Long version:
The development of this project was one driven more by experimentation than a specific end-goal, however i have tried to fine-tune everything into a coherent usable end-product. With that in mind then, this walkthrough will follow something of a development chronology as i dissect the various functions.
DUAL-EMA SMOOTHING
At its core this is based upon / adapted from the standard vwap indicator provided by TradingView although I have modified and changed most of it. The first mod is the dual ema smoothing. Rather than simply applying an ema to the output of the standard vwap function, instead i have incorporated the ema in a manner analogous to the way smas are used within a standard vwma. Sticking for now with the arithmetic mean, the basic vwap calculation is simply sum(source * volume) / sum(volume) across the anchored period. In this case i have simply applied an ema to each of the numerator and denominator values resulting in ema(sum(source * volume)) / ema(sum(volume)) with the ema length independent of the anchor. This results in smoother (albeit slower) transitions than the aforementioned post-vwap method. Furthermore in the case when anchor period is equal to current timeframe, the result is a basic volume-weighted ema.
The example below shows a standard vwap (1week anchor) in blue, a 21-ema applied to the vwap in purple and a dual-21-ema smoothed wap in gold. Notably both ema types come to effectively resemble the standard vwap after around 24 hours into the new anchor session but how they behave in the meantime is very different. The dual-ema transitions quite gradually while the post-vwap ema immediately sets about trying to catch up. Incidentally. a similar and slower variation of the dual-ema can be achieved with dual-rma although i have not included it in this indicator, attempted analogues using sma or wma were far less useful however.
STANDARD DEVIATION AND BANDS
With this updated calculation, a corresponding update to the standard deviation is also required. The vwap has its own anchored volume-weighted st.dev but this cannot be used in combination with the ema smoothing so instead it has been recalculated appropriately. There are two pairs of bands with separate multipliers (stepped to 0.1x) and in both cases high and low bands can be activated or deactivated individually. An example usage for this would be to create different upper and lower bands for profit and stoploss targets. Alerts can be set easily for different crossing conditions, more on this later.
Alongside the bands, i have also added the option to shift ('Deviate') the entire indicator up or down according to a multiple of the corrected st.dev value. This has many potential uses, for example if we want to bias our analysis in one direction it may be useful to move the wap in the opposite. Or if the asset is trading within a narrow range and we are waiting on a breakout, we could shift to the desired level and set alerts accordingly. The 'Deviate' parameter applies to the entire indicator including the bands which will remain centred on the main WAP.
CUSTOM (W)ANCHOR
Ever thought about using a vwap with anchor periods smaller than a day? Here you can do just that. I've removed the Earnings/Dividends/Splits options from the basic vwap and added an 'Intraday' option instead. When selected, a custom anchor length can be created as a multiple of minutes (default steps of 60 mins but can input any value from 0 - 1440). While this may not seem at first like a useful feature for anyone except hi-speed scalpers, this actually offers more interesting potential than it appears.
When set to 0 minutes the current timeframe is always used, turning this into the basic volume-weighted ema mentioned earlier. When using other low time frames the anchor can act as a pre-ema filter creating a stepped effect akin to an adaptive MA. Used in combination with the bands, the result is a kind of volume-weighted adaptive exponential bollinger band; if such a thing does not already exist then this is where you create it. Alternatively, by combining two instances you may find potential interesting crosses between an intraday wap and a standard timeframe wap. Below is an example set to intraday with 480 mins, 2x st.dev bands and ema length 21. Included for comparison in purple is a standard 21 ema.
I'm sure there are many potential uses to be found here, so be creative and please share anything you come up with in the comments.
ARITHMETIC AND HARMONIC MEAN CALCULATIONS
The standard vwap uses the arithmetic mean in its calculation. Indeed, most mean calculations tend to be arithmetic: sma being the most widely used example. When volume weighting is involved though this can lead to a slight bias in favour of upward moves over downward. While the effect of this is minor, over longer anchor periods it can become increasingly significant. The harmonic mean, on the other hand, has the opposite effect which results in a value that is always lower than the arithmetic mean. By viewing both arithmetic and harmonic waps together, the extent to which they diverge from each other can be used as a visual reference of how much price has changed during the anchored period.
Furthermore, the harmonic mean may actually be the more appropriate one to use during downtrends or bearish periods, in principle at least. Consider that a short trade is functionally the same as a long trade on the inverse of the pair (eg: selling BTC/USD is the same as buying USD/BTC). With the harmonic mean being an inverse of the arithmetic then, it makes sense to use it instead. To illustrate this below is a snapshot of LUNA/USDT on the left with its inverse 1/(LUNA/USDT) = USDT/LUNA on the right. On both charts is a wap with identical settings, note the resistance on the left and its corresponding support on the right. It should be easy from this to see that the lower harmonic wap on the left corresponds to the upper arithmetic wap on the right. Thus, it would appear that the harmonic mean should be used in a downtrend. In principle, at least...
In reality though, it is not quite so black and white. Rarely are these values exact in their predictions and the sort of range one should allow for inaccuracies will likely be greater than the difference between these two means. Furthermore, the ema smoothing has already introduced some lag and thus additional inaccuracies. Nevertheless, the symmetry warrants its inclusion.
SIDE INPUT & ALERTS
Finally we move on to the pseudo-modular component here. While TradingView allows some interoperability between indicators, it is limited to just one connection. Any attempt to use multiple source inputs will remove this functionality completely. The workaround here is to instead use custom 'string' input menus for additional sources, preserving this function in the sole 'source' input. In this case, since the wap itself is dependant only price and volume, i have repurposed the full 'source' into the second 'side' input. This allows for a separate indicator to interact with this one that can be used for triggering alerts. You could even use another instance of this one (there is a hidden wap:mid plot intended for this use which is the midpoint between both means). Note that deleting a connected indicator may result in the deletion of those connected to it.
Preset alertconditions are available for crossings of the side input above and below the main wap, alongside several customisable alerts with corresponding visual markers based upon selectable conditions. Alerts for band crossings apply only to those that are active and only crossings of the type specified within the 'crosses' subsection of the indicator settings. The included options make it easy to create buy alerts specific to certain bands with sell alerts specific to other bands. The chart below shows two instances with differing anchor periods, both are connected with buy and sell alerts enabled for visible bands.
Okay... So that just about covers it here, i think. As mentioned earlier this is the product of various experiments while i have been learning my way around PineScript. Some of those experiments have been branched off from this in order to not over-clutter it with functions. The pseudo-modular design and the 'side' input are the result of an attempt to create a connective framework across various projects. Even on its own though, this should offer plenty of tweaking potential for anyone who likes to venture away from the usual standards, all the while still retaining its core purpose as a traders tool.
Thanks for checking this out. I look forward to any feedback below.
ArrayExtLibrary "ArrayExt"
Array extensions
get(a, idx) Get element from the array at index, or na if index not found
Parameters:
a : The array
idx : The array index to get
Returns: The array item if exists or na
get(a, idx) Get element from the array at index, or na if index not found
Parameters:
a : The array
idx : The array index to get
Returns: The array item if exists or na
get(a, idx) Get element from the array at index, or na if index not found
Parameters:
a : The array
idx : The array index to get
Returns: The array item if exists or na
get(a, idx) Get element from the array at index, or na if index not found
Parameters:
a : The array
idx : The array index to get
Returns: The array item if exists or na
get(a, idx) Get element from the array at index, or na if index not found
Parameters:
a : The array
idx : The array index to get
Returns: The array item if exists or na
get(a, idx) Get element from the array at index, or na if index not found
Parameters:
a : The array
idx : The array index to get
Returns: The array item if exists or na
set(a, idx, val) Set array item at index, if array has no index at the specified index, the array is filled with na
Parameters:
a : The array
idx : The array index to set
val : The value to be set
set(a, idx, val) Set array item at index, if array has no index at the specified index, the array is filled with na
Parameters:
a : The array
idx : The array index to set
val : The value to be set
set(a, idx, val) Set array item at index, if array has no index at the specified index, the array is filled with na
Parameters:
a : The array
idx : The array index to set
val : The value to be set
set(a, idx, val) Set array item at index, if array has no index at the specified index, the array is filled with na
Parameters:
a : The array
idx : The array index to set
val : The value to be set
set(a, idx, val) Set array item at index, if array has no index at the specified index, the array is filled with na
Parameters:
a : The array
idx : The array index to set
val : The value to be set
set(a, idx, val) Set array item at index, if array has no index at the specified index, the array is filled with na
Parameters:
a : The array
idx : The array index to set
val : The value to be set
Webhook Starter Kit [HullBuster]
Introduction
This is an open source strategy which provides a framework for webhook enabled projects. It is designed to work out-of-the-box on any instrument triggering on an intraday bar interval. This is a full featured script with an emphasis on actual trading at a brokerage through the TradingView alert mechanism and without requiring browser plugins.
The source code is written in a self documenting style with clearly defined sections. The sections “communicate” with each other through state variables making it easy for the strategy to evolve and improve. This is an excellent place for Pine Language beginners to start their strategy building journey. The script exhibits many Pine Language features which will certainly ad power to your script building abilities.
This script employs a basic trend follow strategy utilizing a forward pyramiding technique. Trend detection is implemented through the use of two higher time frame series. The market entry setup is a Simple Moving Average crossover. Positions exit by passing through conditional take profit logic. The script creates ten indicators including a Zscore oscillator to measure support and resistance levels. The indicator parameters are exposed through 47 strategy inputs segregated into seven sections. All of the inputs are equipped with detailed tool tips to help you get started.
To improve the transition from simulation to execution, strategy.entry and strategy.exit calls show enhanced message text with embedded keywords that are combined with the TradingView placeholders at alert time. Thereby, enabling a single JSON message to generate multiple execution events. This is genius stuff from the Pine Language development team. Really excellent work!
This document provides a sample alert message that can be applied to this script with relatively little modification. Without altering the code, the strategy inputs can alter the behavior to generate thousands of orders or simply a few dozen. It can be applied to crypto, stocks or forex instruments. A good way to look at this script is as a webhook lab that can aid in the development of your own endpoint processor, impress your co-workers and have hours of fun.
By no means is a webhook required or even necessary to benefit from this script. The setups, exits, trend detection, pyramids and DCA algorithms can be easily replaced with more sophisticated versions. The modular design of the script logic allows you to incrementally learn and advance this script into a functional trading system that you can be proud of.
Design
This is a trend following strategy that enters long above the trend line and short below. There are five trend lines that are visible by default but can be turned off in Section 7. Identified, in frequency order, as follows:
1. - EMA in the chart time frame. Intended to track price pressure. Configured in Section 3.
2. - ALMA in the higher time frame specified in Section 2 Signal Line Period.
3. - Linear Regression in the higher time frame specified in Section 2 Signal Line Period.
4. - Linear Regression in the higher time frame specified in Section 2 Signal Line Period.
5. - DEMA in the higher time frame specified in Section 2 Trend Line Period.
The Blue, Green and Orange lines are signal lines are on the same time frame. The time frame selected should be at least five times greater than the chart time frame. The Purple line represents the trend line for which prices above the line suggest a rising market and prices below a falling market. The time frame selected for the trend should be at least five times greater than the signal lines.
Three oscillators are created as follows:
1. Stochastic - In the chart time frame. Used to enter forward pyramids.
2. Stochastic - In the Trend period. Used to detect exit conditions.
3. Zscore - In the Signal period. Used to detect exit conditions.
The Stochastics are configured identically other than the time frame. The period is set in Section 2.
Two Simple Moving Averages provide the trade entry conditions in the form of a crossover. Crossing up is a long entry and down is a short. This is in fact the same setup you get when you select a basic strategy from the Pine editor. The crossovers are configured in Section 3. You can see where the crosses are occurring by enabling Show Entry Regions in Section 7.
The script has the capacity for pyramids and DCA. Forward pyramids are enabled by setting the Pyramid properties tab with a non zero value. In this case add on trades will enter the market on dips above the position open price. This process will continue until the trade exits. Downward pyramids are available in Crypto and Range mode only. In this case add on trades are placed below the entry price in the drawdown space until the stop is hit. To enable downward pyramids set the Pyramid Minimum Span In Section 1 to a non zero value.
This implementation of Dollar Cost Averaging (DCA) triggers off consecutive losses. Each loss in a run increments a sequence number. The position size is increased as a multiple of this sequence. When the position eventually closes at a profit the sequence is reset. DCA is enabled by setting the Maximum DCA Increments In Section 1 to a non zero value.
It should be noted that the pyramid and DCA features are implemented using a rudimentary design and as such do not perform with the precision of my invite only scripts. They are intended as a feature to stress test your webhook endpoint. As is, you will need to buttress the logic for it to be part of an automated trading system. It is for this reason that I did not apply a Martingale algorithm to this pyramid implementation. But, hey, it’s an open source script so there is plenty of room for learning and your own experimentation.
How does it work
The overall behavior of the script is governed by the Trading Mode selection in Section 1. It is the very first input so you should think about what behavior you intend for this strategy at the onset of the configuration. As previously discussed, this script is designed to be a trend follower. The trend being defined as where the purple line is predominately heading. In BiDir mode, SMA crossovers above the purple line will open long positions and crosses below the line will open short. If pyramiding is enabled add on trades will accumulate on dips above the entry price. The value applied to the Minimum Profit input in Section 1 establishes the threshold for a profitable exit. This is not a hard number exit. The conditional exit logic must be satisfied in order to permit the trade to close. This is where the effort put into the indicator calibration is realized. There are four ways the trade can exit at a profit:
1. Natural exit. When the blue line crosses the green line the trade will close. For a long position the blue line must cross under the green line (downward). For a short the blue must cross over the green (upward).
2. Alma / Linear Regression event. The distance the blue line is from the green and the relative speed the cross is experiencing determines this event. The activation thresholds are set in Section 6 and relies on the period and length set in Section 2. A long position will exit on an upward thrust which exceeds the activation threshold. A short will exit on a downward thrust.
3. Exponential event. The distance the yellow line is from the blue and the relative speed the cross is experiencing determines this event. The activation thresholds are set in Section 3 and relies on the period and length set in the same section.
4. Stochastic event. The purple line stochastic is used to measure overbought and over sold levels with regard to position exits. Signal line positions combined with a reading over 80 signals a long profit exit. Similarly, readings below 20 signal a short profit exit.
Another, optional, way to exit a position is by Bale Out. You can enable this feature in Section 1. This is a handy way to reduce the risk when carrying a large pyramid stack. Instead of waiting for the entire position to recover we exit early (bale out) as soon as the profit value has doubled.
There are lots of ways to implement a bale out but the method I used here provides a succinct example. Feel free to improve on it if you like. To see where the Bale Outs occur, enable Show Bale Outs in Section 7. Red labels are rendered below each exit point on the chart.
There are seven selectable Trading Modes available from the drop down in Section 1:
1. Long - Uses the strategy.risk.allow_entry_in to execute long only trades. You will still see shorts on the chart.
2. Short - Uses the strategy.risk.allow_entry_in to execute short only trades. You will still see long trades on the chart.
3. BiDir - This mode is for margin trading with a stop. If a long position was initiated above the trend line and the price has now fallen below the trend, the position will be reversed after the stop is hit. Forward pyramiding is available in this mode if you set the Pyramiding value in the Properties tab. DCA can also be activated.
4. Flip Flop - This is a bidirectional trading mode that automatically reverses on a trend line crossover. This is distinctively different from BiDir since you will get a reversal even without a stop which is advantageous in non-margin trading.
5. Crypto - This mode is for crypto trading where you are buying the coins outright. In this case you likely want to accumulate coins on a crash. Especially, when all the news outlets are talking about the end of Bitcoin and you see nice deep valleys on the chart. Certainly, under these conditions, the market will be well below the purple line. No margin so you can’t go short. Downward pyramids are enabled for Crypto mode when two conditions are met. First the Pyramiding value in the Properties tab must be non zero. Second the Pyramid Minimum Span in Section 1 must be non zero.
6. Range - This is a counter trend trading mode. Longs are entered below the purple trend line and shorts above. Useful when you want to test your webhook in a market where the trend line is bisecting the signal line series. Remember that this strategy is a trend follower. It’s going to get chopped out in a range bound market. By turning on the Range mode you will at least see profitable trades while stuck in the range. However, when the market eventually picks a direction, this mode will sustain losses. This range trading mode is a rudimentary implementation that will need a lot of improvement if you want to create a reliable switch hitter (trend/range combo).
7. No Trade. Useful when setting up the trend lines and the entry and exit is not important.
Once in the trade, long or short, the script tests the exit condition on every bar. If not a profitable exit then it checks if a pyramid is required. As mentioned earlier, the entry setups are quite primitive. Although they can easily be replaced by more sophisticated algorithms, what I really wanted to show is the diminished role of the position entry in the overall life of the trade. Professional traders spend much more time on the management of the trade beyond the market entry. While your trade entry is important, you can get in almost anywhere and still land a profitable exit.
If DCA is enabled, the size of the position will increase in response to consecutive losses. The number of times the position can increase is limited by the number set in Maximum DCA Increments of Section 1. Once the position breaks the losing streak the trade size will return the default quantity set in the Properties tab. It should be noted that the Initial Capital amount set in the Properties tab does not affect the simulation in the same way as a real account. In reality, running out of money will certainly halt trading. In fact, your account would be frozen long before the last penny was committed to a trade. On the other hand, TradingView will keep running the simulation until the current bar even if your funds have been technically depleted.
Entry and exit use the strategy.entry and strategy.exit calls respectfully. The alert_message parameter has special keywords that the endpoint expects to properly calculate position size and message sequence. The alert message will embed these keywords in the JSON object through the {{strategy.order.alert_message}} placeholder. You should use whatever keywords are expected from the endpoint you intend to webhook in to.
Webhook Integration
The TradingView alerts dialog provides a way to connect your script to an external system which could actually execute your trade. This is a fantastic feature that enables you to separate the data feed and technical analysis from the execution and reporting systems. Using this feature it is possible to create a fully automated trading system entirely on the cloud. Of course, there is some work to get it all going in a reliable fashion. Being a strategy type script place holders such as {{strategy.position_size}} can be embedded in the alert message text. There are more than 10 variables which can write internal script values into the message for delivery to the specified endpoint.
Entry and exit use the strategy.entry and strategy.exit calls respectfully. The alert_message parameter has special keywords that my endpoint expects to properly calculate position size and message sequence. The alert message will embed these keywords in the JSON object through the {{strategy.order.alert_message}} placeholder. You should use whatever keywords are expected from the endpoint you intend to webhook in to.
Here is an excerpt of the fields I use in my webhook signal:
"broker_id": "kraken",
"account_id": "XXX XXXX XXXX XXXX",
"symbol_id": "XMRUSD",
"action": "{{strategy.order.action}}",
"strategy": "{{strategy.order.id}}",
"lots": "{{strategy.order.contracts}}",
"price": "{{strategy.order.price}}",
"comment": "{{strategy.order.alert_message}}",
"timestamp": "{{time}}"
Though TradingView does a great job in dispatching your alert this feature does come with a few idiosyncrasies. Namely, a single transaction call in your script may cause multiple transmissions to the endpoint. If you are using placeholders each message describes part of the transaction sequence. A good example is closing a pyramid stack. Although the script makes a single strategy.close() call, the endpoint actually receives a close message for each pyramid trade. The broker, on the other hand, only requires a single close. The incongruity of this situation is exacerbated by the possibility of messages being received out of sequence. Depending on the type of order designated in the message, a close or a reversal. This could have a disastrous effect on your live account. This broker simulator has no idea what is actually going on at your real account. Its just doing the job of running the simulation and sending out the computed results. If your TradingView simulation falls out of alignment with the actual trading account lots of really bad things could happen. Like your script thinks your are currently long but the account is actually short. Reversals from this point forward will always be wrong with no one the wiser. Human intervention will be required to restore congruence. But how does anyone find out this is occurring? In closed systems engineering this is known as entropy. In practice your webhook logic should be robust enough to detect these conditions. Be generous with the placeholder usage and give the webhook code plenty of information to compare states. Both issuer and receiver. Don’t blindly commit incoming signals without verifying system integrity.
Setup
The following steps provide a very brief set of instructions that will get you started on your first configuration. After you’ve gone through the process a couple of times, you won’t need these anymore. It’s really a simple script after all. I have several example configurations that I used to create the performance charts shown. I can share them with you if you like. Of course, if you’ve modified the code then these steps are probably obsolete.
There are 47 inputs divided into seven sections. For the most part, the configuration process is designed to flow from top to bottom. Handy, tool tips are available on every field to help get you through the initial setup.
Step 1. Input the Base Currency and Order Size in the Properties tab. Set the Pyramiding value to zero.
Step 2. Select the Trading Mode you intend to test with from the drop down in Section 1. I usually select No Trade until I’ve setup all of the trend lines, profit and stop levels.
Step 3. Put in your Minimum Profit and Stop Loss in the first section. This is in pips or currency basis points (chart right side scale). Remember that the profit is taken as a conditional exit not a fixed limit. The actual profit taken will almost always be greater than the amount specified. The stop loss, on the other hand, is indeed a hard number which is executed by the TradingView broker simulator when the threshold is breached.
Step 4. Apply the appropriate value to the Tick Scalar field in Section 1. This value is used to remove the pipette from the price. You can enable the Summary Report in Section 7 to see the TradingView minimum tick size of the current chart.
Step 5. Apply the appropriate Price Normalizer value in Section 1. This value is used to normalize the instrument price for differential calculations. Basically, we want to increase the magnitude to significant digits to make the numbers more meaningful in comparisons. Though I have used many normalization techniques, I have always found this method to provide a simple and lightweight solution for less demanding applications. Most of the time the default value will be sufficient. The Tick Scalar and Price Normalizer value work together within a single calculation so changing either will affect all delta result values.
Step 6. Turn on the trend line plots in Section 7. Then configure Section 2. Try to get the plots to show you what’s really happening not what you want to happen. The most important is the purple trend line. Select an interval and length that seem to identify where prices tend to go during non-consolidation periods. Remember that a natural exit is when the blue crosses the green line.
Step 7. Enable Show Event Regions in Section 7. Then adjust Section 6. Blue background fills are spikes and red fills are plunging prices. These measurements should be hard to come by so you should see relatively few fills on the chart if you’ve set this up as intended. Section 6 includes the Zscore oscillator the state of which combines with the signal lines to detect statistically significant price movement. The Zscore is a zero based calculation with positive and negative magnitude readings. You want to input a reasonably large number slightly below the maximum amplitude seen on the chart. Both rise and fall inputs are entered as a positive real number. You can easily use my code to create a separate indicator if you want to see it in action. The default value is sufficient for most configurations.
Step 8. Turn off Show Event Regions and enable Show Entry Regions in Section 7. Then adjust Section 3. This section contains two parts. The entry setup crossovers and EMA events. Adjust the crossovers first. That is the Fast Cross Length and Slow Cross Length. The frequency of your trades will be shown as blue and red fills. There should be a lot. Then turn off Show Event Regions and enable Display EMA Peaks. Adjust all the fields that have the word EMA. This is actually the yellow line on the chart. The blue and red fills should show much less than the crossovers but more than event fills shown in Step 7.
Step 9. Change the Trading Mode to BiDir if you selected No Trades previously. Look on the chart and see where the trades are occurring. Make adjustments to the Minimum Profit and Stop Offset in Section 1 if necessary. Wider profits and stops reduce the trade frequency.
Step 10. Go to Section 4 and 5 and make fine tuning adjustments to the long and short side.
Example Settings
To reproduce the performance shown on the chart please use the following configuration: (Bitcoin on the Kraken exchange)
1. Select XBTUSD Kraken as the chart symbol.
2. On the properties tab set the Order Size to: 0.01 Bitcoin
3. On the properties tab set the Pyramiding to: 12
4. In Section 1: Select “Crypto” for the Trading Model
5. In Section 1: Input 2000 for the Minimum Profit
6. In Section 1: Input 0 for the Stop Offset (No Stop)
7. In Section 1: Input 10 for the Tick Scalar
8. In Section 1: Input 1000 for the Price Normalizer
9. In Section 1: Input 2000 for the Pyramid Minimum Span
10. In Section 1: Check mark the Position Bale Out
11. In Section 2: Input 60 for the Signal Line Period
12. In Section 2: Input 1440 for the Trend Line Period
13. In Section 2: Input 5 for the Fast Alma Length
14. In Section 2: Input 22 for the Fast LinReg Length
15. In Section 2: Input 100 for the Slow LinReg Length
16. In Section 2: Input 90 for the Trend Line Length
17. In Section 2: Input 14 Stochastic Length
18. In Section 3: Input 9 Fast Cross Length
19. In Section 3: Input 24 Slow Cross Length
20. In Section 3: Input 8 Fast EMA Length
21. In Section 3: Input 10 Fast EMA Rise NetChg
22. In Section 3: Input 1 Fast EMA Rise ROC
23. In Section 3: Input 10 Fast EMA Fall NetChg
24. In Section 3: Input 1 Fast EMA Fall ROC
25. In Section 4: Check mark the Long Natural Exit
26. In Section 4: Check mark the Long Signal Exit
27. In Section 4: Check mark the Long Price Event Exit
28. In Section 4: Check mark the Long Stochastic Exit
29. In Section 5: Check mark the Short Natural Exit
30. In Section 5: Check mark the Short Signal Exit
31. In Section 5: Check mark the Short Price Event Exit
32. In Section 5: Check mark the Short Stochastic Exit
33. In Section 6: Input 120 Rise Event NetChg
34. In Section 6: Input 1 Rise Event ROC
35. In Section 6: Input 5 Min Above Zero ZScore
36. In Section 6: Input 120 Fall Event NetChg
37. In Section 6: Input 1 Fall Event ROC
38. In Section 6: Input 5 Min Below Zero ZScore
In this configuration we are trading in long only mode and have enabled downward pyramiding. The purple trend line is based on the day (1440) period. The length is set at 90 days so it’s going to take a while for the trend line to alter course should this symbol decide to node dive for a prolonged amount of time. Your trades will still go long under those circumstances. Since downward accumulation is enabled, your position size will grow on the way down.
The performance example is Bitcoin so we assume the trader is buying coins outright. That being the case we don’t need a stop since we will never receive a margin call. New buy signals will be generated when the price exceeds the magnitude and speed defined by the Event Net Change and Rate of Change.
Feel free to PM me with any questions related to this script. Thank you and happy trading!
CFTC RULE 4.41
These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.
`security()` revisited [PineCoders]NOTE
The non-repainting technique in this publication that relies on bar states is now deprecated, as we have identified inconsistencies that undermine its credibility as a universal solution. The outputs that use the technique are still available for reference in this publication. However, we do not endorse its usage. See this publication for more information about the current best practices for requesting HTF data and why they work.
█ OVERVIEW
This script presents a new function to help coders use security() in both repainting and non-repainting modes. We revisit this often misunderstood and misused function, and explain its behavior in different contexts, in the hope of dispelling some of the coder lure surrounding it. The function is incredibly powerful, yet misused, it can become a dangerous WMD and an instrument of deception, for both coders and traders.
We will discuss:
• How to use our new `f_security()` function.
• The behavior of Pine code and security() on the three very different types of bars that make up any chart.
• Why what you see on a chart is a simulation, and should be taken with a grain of salt.
• Why we are presenting a new version of a function handling security() calls.
• Other topics of interest to coders using higher timeframe (HTF) data.
█ WARNING
We have tried to deliver a function that is simple to use and will, in non-repainting mode, produce reliable results for both experienced and novice coders. If you are a novice coder, stick to our recommendations to avoid getting into trouble, and DO NOT change our `f_security()` function when using it. Use `false` as the function's last argument and refrain from using your script at smaller timeframes than the chart's. To call our function to fetch a non-repainting value of close from the 1D timeframe, use:
f_security(_sym, _res, _src, _rep) => security(_sym, _res, _src )
previousDayClose = f_security(syminfo.tickerid, "D", close, false)
If that's all you're interested in, you are done.
If you choose to ignore our recommendation and use the function in repainting mode by changing the `false` in there for `true`, we sincerely hope you read the rest of our ramblings before you do so, to understand the consequences of your choice.
Let's now have a look at what security() is showing you. There is a lot to cover, so buckle up! But before we dig in, one last thing.
What is a chart?
A chart is a graphic representation of events that occur in markets. As any representation, it is not reality, but rather a model of reality. As Scott Page eloquently states in The Model Thinker : "All models are wrong; many are useful". Having in mind that both chart bars and plots on our charts are imperfect and incomplete renderings of what actually occurred in realtime markets puts us coders in a place from where we can better understand the nature of, and the causes underlying the inevitable compromises necessary to build the data series our code uses, and print chart bars.
Traders or coders complaining that charts do not reflect reality act like someone who would complain that the word "dog" is not a real dog. Let's recognize that we are dealing with models here, and try to understand them the best we can. Sure, models can be improved; TradingView is constantly improving the quality of the information displayed on charts, but charts nevertheless remain mere translations. Plots of data fetched through security() being modelized renderings of what occurs at higher timeframes, coders will build more useful and reliable tools for both themselves and traders if they endeavor to perfect their understanding of the abstractions they are working with. We hope this publication helps you in this pursuit.
█ FEATURES
This script's "Inputs" tab has four settings:
• Repaint : Determines whether the functions will use their repainting or non-repainting mode.
Note that the setting will not affect the behavior of the yellow plot, as it always repaints.
• Source : The source fetched by the security() calls.
• Timeframe : The timeframe used for the security() calls. If it is lower than the chart's timeframe, a warning appears.
• Show timeframe reminder : Displays a reminder of the timeframe after the last bar.
█ THE CHART
The chart shows two different pieces of information and we want to discuss other topics in this section, so we will be covering:
A — The type of chart bars we are looking at, indicated by the colored band at the top.
B — The plots resulting of calling security() with the close price in different ways.
C — Points of interest on the chart.
A — Chart bars
The colored band at the top shows the three types of bars that any chart on a live market will print. It is critical for coders to understand the important distinctions between each type of bar:
1 — Gray : Historical bars, which are bars that were already closed when the script was run on them.
2 — Red : Elapsed realtime bars, i.e., realtime bars that have run their course and closed.
The state of script calculations showing on those bars is that of the last time they were made, when the realtime bar closed.
3 — Green : The realtime bar. Only the rightmost bar on the chart can be the realtime bar at any given time, and only when the chart's market is active.
Refer to the Pine User Manual's Execution model page for a more detailed explanation of these types of bars.
B — Plots
The chart shows the result of letting our 5sec chart run for a few minutes with the following settings: "Repaint" = "On" (the default is "Off"), "Source" = `close` and "Timeframe" = 1min. The five lines plotted are the following. They have progressively thinner widths:
1 — Yellow : A normal, repainting security() call.
2 — Silver : Our recommended security() function.
3 — Fuchsia : Our recommended way of achieving the same result as our security() function, for cases when the source used is a function returning a tuple.
4 — White : The method we previously recommended in our MTF Selection Framework , which uses two distinct security() calls.
5 — Black : A lame attempt at fooling traders that MUST be avoided.
All lines except the first one in yellow will vary depending on the "Repaint" setting in the script's inputs. The first plot does not change because, contrary to all other plots, it contains no conditional code to adapt to repainting/no-repainting modes; it is a simple security() call showing its default behavior.
C — Points of interest on the chart
Historical bars do not show actual repainting behavior
To appreciate what a repainting security() call will plot in realtime, one must look at the realtime bar and at elapsed realtime bars, the bars where the top line is green or red on the chart at the top of this page. There you can see how the plots go up and down, following the close value of each successive chart bar making up a single bar of the higher timeframe. You would see the same behavior in "Replay" mode. In the realtime bar, the movement of repainting plots will vary with the source you are fetching: open will not move after a new timeframe opens, low and high will change when a new low or high are found, close will follow the last feed update. If you are fetching a value calculated by a function, it may also change on each update.
Now notice how different the plots are on historical bars. There, the plot shows the close of the previously completed timeframe for the whole duration of the current timeframe, until on its last bar the price updates to the current timeframe's close when it is confirmed (if the timeframe's last bar is missing, the plot will only update on the next timeframe's first bar). That last bar is the only one showing where the plot would end if that timeframe's bars had elapsed in realtime. If one doesn't understand this, one cannot properly visualize how his script will calculate in realtime when using repainting. Additionally, as published scripts typically show charts where the script has only run on historical bars, they are, in fact, misleading traders who will naturally assume the script will behave the same way on realtime bars.
Non-repainting plots are more accurate on historical bars
Now consider this chart, where we are using the same settings as on the chart used to publish this script, except that we have turned "Repainting" off this time:
The yellow line here is our reference, repainting line, so although repainting is turned off, it is still repainting, as expected. Because repainting is now off, however, plots on historical bars show the previous timeframe's close until the first bar of a new timeframe, at which point the plot updates. This correctly reflects the behavior of the script in the realtime bar, where because we are offsetting the series by one, we are always showing the previously calculated—and thus confirmed—higher timeframe value. This means that in realtime, we will only get the previous timeframe's values one bar after the timeframe's last bar has elapsed, at the open of the first bar of a new timeframe. Historical and elapsed realtime bars will not actually show this nuance because they reflect the state of calculations made on their close , but we can see the plot update on that bar nonetheless.
► This more accurate representation on historical bars of what will happen in the realtime bar is one of the two key reasons why using non-repainting data is preferable.
The other is that in realtime, your script will be using more reliable data and behave more consistently.
Misleading plots
Valiant attempts by coders to show non-repainting, higher timeframe data updating earlier than on our chart are futile. If updates occur one bar earlier because coders use the repainting version of the function, then so be it, but they must then also accept that their historical bars are not displaying information that is as accurate. Not informing script users of this is to mislead them. Coders should also be aware that if they choose to use repainting data in realtime, they are sacrificing reliability to speed and may be running a strategy that behaves very differently from the one they backtested, thus invalidating their tests.
When, however, coders make what are supposed to be non-repainting plots plot artificially early on historical bars, as in examples "c4" and "c5" of our script, they would want us to believe they have achieved the miracle of time travel. Our understanding of the current state of science dictates that for now, this is impossible. Using such techniques in scripts is plainly misleading, and public scripts using them will be moderated. We are coding trading tools here—not video games. Elementary ethics prescribe that we should not mislead traders, even if it means not being able to show sexy plots. As the great Feynman said: You should not fool the layman when you're talking as a scientist.
You can readily appreciate the fantasy plot of "c4", the thinnest line in black, by comparing its supposedly non-repainting behavior between historical bars and realtime bars. After updating—by miracle—as early as the wide yellow line that is repainting, it suddenly moves in a more realistic place when the script is running in realtime, in synch with our non-repainting lines. The "c5" version does not plot on the chart, but it displays in the Data Window. It is even worse than "c4" in that it also updates magically early on historical bars, but goes on to evaluate like the repainting yellow line in realtime, except one bar late.
Data Window
The Data Window shows the values of the chart's plots, then the values of both the inside and outside offsets used in our calculations, so you can see them change bar by bar. Notice their differences between historical and elapsed realtime bars, and the realtime bar itself. If you do not know about the Data Window, have a look at this essential tool for Pine coders in the Pine User Manual's page on Debugging . The conditional expressions used to calculate the offsets may seem tortuous but their objective is quite simple. When repainting is on, we use this form, so with no offset on all bars:
security(ticker, i_timeframe, i_source )
// which is equivalent to:
security(ticker, i_timeframe, i_source)
When repainting is off, we use two different and inverted offsets on historical bars and the realtime bar:
// Historical bars:
security(ticker, i_timeframe, i_source )
// Realtime bar (and thus, elapsed realtime bars):
security(ticker, i_timeframe, i_source )
The offsets in the first line show how we prevent repainting on historical bars without the need for the `lookahead` parameter. We use the value of the function call on the chart's previous bar. Since values between the repainting and non-repainting versions only differ on the timeframe's last bar, we can use the previous value so that the update only occurs on the timeframe's first bar, as it will in realtime when not repainting.
In the realtime bar, we use the second call, where the offsets are inverted. This is because if we used the first call in realtime, we would be fetching the value of the repainting function on the previous bar, so the close of the last bar. What we want, instead, is the data from the previous, higher timeframe bar , which has elapsed and is confirmed, and thus will not change throughout realtime bars, except on the first constituent chart bar belonging to a new higher timeframe.
After the offsets, the Data Window shows values for the `barstate.*` variables we use in our calculations.
█ NOTES
Why are we revisiting security() ?
For four reasons:
1 — We were seeing coders misuse our `f_secureSecurity()` function presented in How to avoid repainting when using security() .
Some novice coders were modifying the offset used with the history-referencing operator in the function, making it zero instead of one,
which to our horror, caused look-ahead bias when used with `lookahead = barmerge.lookahead_on`.
We wanted to present a safer function which avoids introducing the dreaded "lookahead" in the scripts of unsuspecting coders.
2 — The popularity of security() in screener-type scripts where coders need to use the full 40 calls allowed per script made us want to propose
a solid method of allowing coders to offer a repainting/no-repainting choice to their script users with only one security() call.
3 — We wanted to explain why some alternatives we see circulating are inadequate and produce misleading behavior.
4 — Our previous publication on security() focused on how to avoid repainting, yet many other considerations worthy of attention are not related to repainting.
Handling tuples
When sending function calls that return tuples with security() , our `f_security()` function will not work because Pine does not allow us to use the history-referencing operator with tuple return values. The solution is to integrate the inside offset to your function's arguments, use it to offset the results the function is returning, and then add the outside offset in a reassignment of the tuple variables, after security() returns its values to the script, as we do in our "c2" example.
Does it repaint?
We're pretty sure Wilder was not asked very often if RSI repainted. Why? Because it wasn't in fashion—and largely unnecessary—to ask that sort of question in the 80's. Many traders back then used daily charts only, and indicator values were calculated at the day's close, so everybody knew what they were getting. Additionally, indicator values were calculated by generally reputable outfits or traders themselves, so data was pretty reliable. Today, almost anybody can write a simple indicator, and the programming languages used to write them are complex enough for some coders lacking the caution, know-how or ethics of the best professional coders, to get in over their heads and produce code that does not work the way they think it does.
As we hope to have clearly demonstrated, traders do have legitimate cause to ask if MTF scripts repaint or not when authors do not specify it in their script's description.
► We recommend that authors always use our `f_security()` with `false` as the last argument to avoid repainting when fetching data dependent on OHLCV information. This is the only way to obtain reliable HTF data. If you want to offer users a choice, make non-repainting mode the default, so that if users choose repainting, it will be their responsibility. Non-repainting security() calls are also the only way for scripts to show historical behavior that matches the script's realtime behavior, so you are not misleading traders. Additionally, non-repainting HTF data is the only way that non-repainting alerts can be configured on MTF scripts, as users of MTF scripts cannot prevent their alerts from repainting by simply configuring them to trigger on the bar's close.
Data feeds
A chart at one timeframe is made up of multiple feeds that mesh seamlessly to form one chart. Historical bars can use one feed, and the realtime bar another, which brokers/exchanges can sometimes update retroactively so that elapsed realtime bars will reappear with very slight modifications when the browser's tab is refreshed. Intraday and daily chart prices also very often originate from different feeds supplied by brokers/exchanges. That is why security() calls at higher timeframes may be using a completely different feed than the chart, and explains why the daily high value, for example, can vary between timeframes. Volume information can also vary considerably between intraday and daily feeds in markets like stocks, because more volume information becomes available at the end of day. It is thus expected behavior—and not a bug—to see data variations between timeframes.
Another point to keep in mind concerning feeds it that when you are using a repainting security() plot in realtime, you will sometimes see discrepancies between its plot and the realtime bars. An artefact revealing these inconsistencies can be seen when security() plots sometimes skip a realtime chart bar during periods of high market activity. This occurs because of races between the chart and the security() feeds, which are being monitored by independent, concurrent processes. A blue arrow on the chart indicates such an occurrence. This is another cause of repainting, where realtime bar-building logic can produce different outcomes on one closing price. It is also another argument supporting our recommendation to use non-repainting data.
Alternatives
There is an alternative to using security() in some conditions. If all you need are OHLC prices of a higher timeframe, you can use a technique like the one Duyck demonstrates in his security free MTF example - JD script. It has the great advantage of displaying actual repainting values on historical bars, which mimic the code's behavior in the realtime bar—or at least on elapsed realtime bars, contrary to a repainting security() plot. It has the disadvantage of using the current chart's TF data feed prices, whereas higher timeframe data feeds may contain different and more reliable prices when they are compiled at the end of the day. In its current state, it also does not allow for a repainting/no-repainting choice.
When `lookahead` is useful
When retrieving non-price data, or in special cases, for experiments, it can be useful to use `lookahead`. One example is our Backtesting on Non-Standard Charts: Caution! script where we are fetching prices of standard chart bars from non-standard charts.
Warning users
Normal use of security() dictates that it only be used at timeframes equal to or higher than the chart's. To prevent users from inadvertently using your script in contexts where it will not produce expected behavior, it is good practice to warn them when their chart is on a higher timeframe than the one in the script's "Timeframe" field. Our `f_tfReminderAndErrorCheck()` function in this script does that. It can also print a reminder of the higher timeframe. It uses one security() call.
Intrabar timeframes
security() is not supported by TradingView when used with timeframes lower than the chart's. While it is still possible to use security() at intrabar timeframes, it then behaves differently. If no care is taken to send a function specifically written to handle the successive intrabars, security() will return the value of the last intrabar in the chart's timeframe, so the last 1H bar in the current 1D bar, if called at "60" from a "D" chart timeframe. If you are an advanced coder, see our FAQ entry on the techniques involved in processing intrabar timeframes. Using intrabar timeframes comes with important limitations, which you must understand and explain to traders if you choose to make scripts using the technique available to others. Special care should also be taken to thoroughly test this type of script. Novice coders should refrain from getting involved in this.
█ TERMINOLOGY
Timeframe
Timeframe , interval and resolution are all being used to name the concept of timeframe. We have, in the past, used "timeframe" and "resolution" more or less interchangeably. Recently, members from the Pine and PineCoders team have decided to settle on "timeframe", so from hereon we will be sticking to that term.
Multi-timeframe (MTF)
Some coders use "multi-timeframe" or "MTF" to name what are in fact "multi-period" calculations, as when they use MAs of progressively longer periods. We consider that a misleading use of "multi-timeframe", which should be reserved for code using calculations actually made from another timeframe's context and using security() , safe for scripts like Duyck's one mentioned earlier, or TradingView's Relative Volume at Time , which use a user-selected timeframe as an anchor to reset calculations. Calculations made at the chart's timeframe by varying the period of MAs or other rolling window calculations should be called "multi-period", and "MTF-anchored" could be used for scripts that reset calculations on timeframe boundaries.
Colophon
Our script was written using the PineCoders Coding Conventions for Pine .
The description was formatted using the techniques explained in the How We Write and Format Script Descriptions PineCoders publication.
Snippets were lifted from our MTF Selection Framework , then massaged to create the `f_tfReminderAndErrorCheck()` function.
█ THANKS
Thanks to apozdnyakov for his help with the innards of security() .
Thanks to bmistiaen for proofreading our description.
Look first. Then leap.
TrendMaAlignmentStrategy - Long term tradesThis is another strategy based on moving average alignment and HighLow periods. This is more suitable for long term trend traders and mainly for stocks.
Candle is colored lime if : Lookback Period has at least one bar with moving averages fully aligned OR None of the bars in Lookback periods has negatively aligned moving averages (More than half are positively aligned).
Candle is colored orange if : Lookback Period has at least one bar with moving averages fully aligned in negative way OR none of the bars in lookback has positively aligned moving averages (More than half are negatively aligned).
If either of above conditions are met, candle is colored silver.
Moving average alignment parameters:
Moving Average Type : MA Type for calculating Aligned Moving Average Index
Lookback Period : Lookback period to check highest and lowest Moving Average index.
HighLow parameters:
Short High/Low Period: Short period to check highs and lows
Long High/Low Period: Longer Period to check highs and lows.
If short period high == long period high, which means, instrument has made new high in the short period.
ATR Parameters:
ATR Length: ATR periods
StopMultiplyer: To set stop loss.
ReentryStopMultiplyer: This is used when signal is green buy stop loss on previous trade is hit. In such cases, new order will not be placed until it has certain distance from stop line.
Trade Prameters:
Exit on Signal : To be used with caution. Enabling it will allow us to get out on bad trades early and helps exit trades in long consolidation periods. But, this may also cause early exit in the trend. If instrument is trending nicely, it is better to keep this setting unchecked.
Trade direction : Default is long only. Short trades are not so successful in backtest. Use it with caution.
Backtest years : limit backtesting to certain years.
Part of the logic used from study's below:
Other strategies based on these two studies are below (which are meant for short - medium terms):
Slim Ribbon Volume BarsThe Slim Ribbon Volume Bars indicator is intended to be paired with the Slim Ribbon. The Slim Ribbon is also available for free in TradingView. The Slim Ribbon Volume Bars indicator changes the color of the volume bars based on the momentum condition of the Slim Ribbon. When the Ribbons have a bullish condition, the indicator colors the volume bars green. When the Ribbons have a bearish condition, the indicator colors the volume bars red. Finally, when the Ribbons have a neutral condition, the indicator colors the volume bars gray. See below for an overview of the Slim Ribbon.
The Slim Ribbon was developed by Steve Miller. Steve Miller is a 46-year veteran stock, futures and options trader. His badge on the trading floor was his initials, “SLM” and has since gone by the nickname Slim.
The Slim Ribbon is a momentum indicator . It is composed of 3 exponential moving averages (8, 13 and 21). A bullish condition occurs when the 8 period MA is above the 13 period MA and the 13 period MA is above the 21 period MA. A bearish condition occurs when the 8 period MA is below the 13 period MA and the 13 period MA is below the 21 period MA. A neutral condition occurs when the Ribbons are not in alignment.
The Slim Ribbon also notifies you when we transition from one condition to another. A green up arrow indicates that the Slim Ribbon has shifted from a neutral condition to a bullish condition. A red down arrow indicates that the Slim Ribbon has shifted from a neutral condition to a bearish condition. A blue up arrow indicates that we have shifted from a bearish condition to a neutral condition. Lastly, a blue down arrow indicates that we have shifted from a bullish condition to a neutral condition.
We would recommend using the Slim Ribbon on a candlestick chart. Steve Miller believes in the importance of visualizing trends. As a result, we have designed the Slim Ribbon to change the color of the candlesticks based on the condition of the ribbon. When the Slim Ribbon has a bullish condition, the candlesticks will turn green. When the Slim Ribbon has a bearish condition, the candlesticks will turn red. When the Slim Ribbon has a neutral condition, the candlesticks will turn gray.
Slim RibbonThe Slim Ribbon was developed by Steve Miller. Steve Miller is a 46-year veteran stock, futures and options trader. His badge on the trading floor was his initials, “SLM” and has since gone by the nickname Slim.
The Slim Ribbon is a momentum indicator . It is composed of 3 exponential moving averages (8, 13 and 21). A bullish condition occurs when the 8 period MA is above the 13 period MA and the 13 period MA is above the 21 period MA. A bearish condition occurs when the 8 period MA is below the 13 period MA and the 13 period MA is below the 21 period MA. A neutral condition occurs when the Ribbons are not in alignment.
The Slim Ribbon also notifies you when we transition from one condition to another. A green up arrow indicates that the Slim Ribbon has shifted from a neutral condition to a bullish condition. A red down arrow indicates that the Slim Ribbon has shifted from a neutral condition to a bearish condition. A blue up arrow indicates that we have shifted from a bearish condition to a neutral condition. Lastly, a blue down arrow indicates that we have shifted from a bullish condition to a neutral condition.
We would recommend using the Slim Ribbon on a candlestick chart. Steve Miller believes in the importance of visualizing trends. As a result, we have designed the Slim Ribbon to change the color of the candlesticks based on the condition of the ribbon. When the Slim Ribbon has a bullish condition, the candlesticks will turn green. When the Slim Ribbon has a bearish condition, the candlesticks will turn red. When the Slim Ribbon has a neutral condition, the candlesticks will turn gray.
This indicator is designed to be paired with the Slim Ribbon Volume Bars indicator, which is also available for free in TradingView.
S&P Bear Warning IndicatorTHIS SCRIPT HAS BEEN BUILT TO BE USED AS A S&P500 SPY CRASH INDICATOR ON A DAILY TIME FRAME (should not be used as a strategy).
THIS SCRIPT HAS BEEN BUILT AS A STRATEGY FOR VISUALIZATION PURPOSES ONLY AND HAS NOT BEEN OPTIMIZED FOR PROFIT.
The script has been built to show as a lower indicator and also gives visual SELL signal on top when conditions are met. BARE IN MIND NO STOP LOSS, NOR ADVANCED EXIT STRATEGY HAS BEEN BUILT.
As well as the chart SELL signal an alert option has also been built into this script.
The script utilizes a VIX indicator (maroon line) and 50 period Momentum (blue line) and Danger/No trade zone(pink shading).
When the Momentum line crosses down across the VIX this is a sell off but in order to only signal major sell offs the SELL signal only triggers if the momentum continues down through the danger zone.
A SELL signal could be given earlier by removing the need to wait for momentum to continue down through the Danger Zone however this is designed only to catch major market weakness not small sell offs.
As you can see from the picture between the big October 2018 and March 2020 market declines only 2 additional SELLS were triggered.
To use this indicator to identify ideal buying then you should only buy when Momentum line is crossed above the VIX and the Momentum line is above the Danger Zone (ideally 3 - 5 days above danger zone)
Strategija 2Hello
This strategy is based on Steve Primo's No. 4 with added entry conditions. I will describe long trades only, conversely is valid for shorts.
1. Price has to be above basis SMA and fast EMA
2. Fast EMA has to be above basis EMA
3. ADX has to be above 20 (settings 14,20, fixed)
4. RSI has to be above 50 and above its 21 EMA
5. A pullback has to occur with the touch of the fast EMA
6. A bounce from that level has to occur and close above the control EMA
7. We compare N (1) bars for reverse for fast EMA bounce
Please use it on your own risk. Will add some strategy rules afterwards. My proposal is to use it on Daily and Weekly TF.
Gap Filling Strategy Gaps are market prices structures that appear frequently in the stock market, and can be detected when the opening price is different from the previous closing price, this is why gaps are also called "opening price jumps". While gaps can occur frequently, some of them are more significant than others, and can be observed when looking at a long term chart.
The following strategy is based on the exploitation of significant gaps occurring during a new session, and posses various options that can return a wide variety of results.
Type Of Gaps And Occurence
I'am not a professional when it comes to gaps, but as you know the stock market close for the day, however it is still possible to place orders, your broker will hold them until the market open back. Once the market reopen the broker execute the pending orders, and when many orders where pending the market register really high volume and the price might differ from the precedent close.
Gaps are generally broken down into four types:
Common : Gaps occurring within a certain price range, mostly occurs during ranging markets.
Break Away : Gaps breaking a support and resistance, making a new higher high/lower low.
Runaway : Gaps occurring within a trend, followed by a continuation of the trend.
Exhaustion : Gaps occurring at the end of a trend, followed by a reversal.
As said before, some gaps are more significant than others, the significance of a gap can be determined by comparing the opening price with the previous high/low price and by looking at volume. Significant up gaps will have an opening price greater than the previous high, while significant down gap will have an opening price lower than the previous low with both high volume accompanying them.
After a gap, when the price go back to the point previous to the gap we say that it has been "filled", this characteristic is what will be exploited in this strategy.
Strategy Rules & Logic
In this strategy, the significance of a gap is determined by the position of the opening price relative to the previous high/low and make sure the bar following the gap don't fill it.
When the setting invert is set to false the strategy interpret the detected gaps as being exhaustion gaps, therefore when an up gap occur a short position is opened, when a down gap occur a long position is opened. When invert is set to true gaps are considered to be runaway or break away gaps, therefore the contrary positions are opened. Positions are exited when the gap has been filled, which in the chart is show'n when the price cross the red level who act as either a take profit (invert = false) or as a stop loss (invert = true).
There are various closing conditions available that the user can select from the "close when" setting.
New Session : This option close all previous positions when the market is in a new session.
New Gap : This option close all previous position when a new gap has been detected.
Reverse Position : This option close all previous position when a contrary position to the current one is opened. This option would reduce the number of trades.
Testing On Some Stocks
The analysis will be tested in different tech stocks with a main TF of 15 minutes with no spread and commissions applied. Default settings will be used. We'll be making our first analysis using AMD, who has recently formed a full reverse HS pattern, where the neckline has been crossed by the price. (by the way i have a bad feeling about it, hey ! feeling filling ! Lame jokes!)
Profit: $ -12.22
Trades: 272
Profitability: 65.07 %
We can see negative results, with an heavily decreasing balance. Using invert would return positive results.
We will now test the strategy on NVDA, the company is one of the biggest when it comes to the Gpu market.
Profit: $ -215.54
Trades: 297
Profitability: 60.27 %
Not better, using invert would of course create better results. Like AMD the balance is heavily decreasing.
Finally we will test the strategy on Seagate technology, a company mostly known for their mechanical hard drives.
Profit: $ -4.32
Trades: 261
Profitability: 65.9 %
Here the balance does not appear so heavily decreasing and even managed to reach back the initial balance before going down again.
Summary
A strategy based on gap filling has been briefly introduced and tested with 3 tech stocks. The results show that using invert option might be better. The advantage of this strategy against ones using technical indicators is that this one does not heavily depend on user settings, which make it way more efficient, this a big advantage of patterns based strategies.
Thx to LucF for helping with the "process_orders_on_close" element, since i had to use closing price i had to remove it tho, was afraid results would differ even more from a more realistic backtest. And thx for those who continuously support me, more cool stuff is coming up.
Thx for reading and i hope you'll have learned something new today !
Delta Volume Columns Pro [LucF]█ OVERVIEW
This indicator displays volume delta information calculated with intrabar inspection on historical bars, and feed updates when running in realtime. It is designed to run in a pane and can display either stacked buy/sell volume columns or a signal line which can be calculated and displayed in many different ways.
Five different models are offered to reveal different characteristics of the calculated volume delta information. Many options are offered to visualize the calculations, giving you much leeway in morphing the indicator's visuals to suit your needs. If you value delta volume information, I hope you will find the time required to master Delta Volume Columns Pro well worth the investment. I am confident that if you combine a proper understanding of the indicator's information with an intimate knowledge of the volume idiosyncrasies on the markets you trade, you can extract useful market intelligence using this tool.
█ WARNINGS
1. The indicator only works on markets where volume information is available,
Please validate that your symbol's feed carries volume information before asking me why the indicator doesn't plot values.
2. When you refresh your chart or re-execute the script on the chart, the indicator will repaint because elapsed realtime bars will then recalculate as historical bars.
3. Because the indicator uses different modes of calculation on historical and realtime bars, it's critical that you understand the differences between them. Details are provided further down.
4. Calculations using intrabar inspection on historical bars can only be done from some chart timeframes. See further down for a list of supported timeframes.
If the chart's timeframe is not supported, no historical volume delta will display.
█ CONCEPTS
Chart bars
Three different types of bars are used in charts:
1. Historical bars are bars that have already closed when the script executes on them.
2. The realtime bar is the current, incomplete bar where a script is running on an open market. There is only one active realtime bar on your chart at any given time.
The realtime bar is where alerts trigger.
3. Elapsed realtime bars are bars that were calculated when they were realtime bars but have since closed.
When a script re-executes on a chart because the browser tab is refreshed or some of its inputs are changed, elapsed realtime bars are recalculated as historical bars.
Why does this indicator use two modes of calculation?
Historical bars on TradingView charts contain OHLCV data only, which is insufficient to calculate volume delta on them with any level of precision. To mine more detailed information from those bars we look at intrabars , i.e., bars from a smaller timeframe (we call it the intrabar timeframe ) that are contained in one chart bar. If your chart Is running at 1D on a 24x7 market for example, most 1D chart bars will contain 24 underlying 1H bars in their dilation. On historical bars, this indicator looks at those intrabars to amass volume delta information. If the intrabar is up, its volume goes in the Buy bin, and inversely for the Sell bin. When price does not move on an intrabar, the polarity of the last known movement is used to determine in which bin its volume goes.
In realtime, we have access to price and volume change for each update of the chart. Because a 1D chart bar can be updated tens of thousands of times during the day, volume delta calculations on those updates is much more precise. This precision, however, comes at a price:
— The script must be running on the chart for it to keep calculating in realtime.
— If you refresh your chart you will lose all accumulated realtime calculations on elapsed realtime bars, and the realtime bar.
Elapsed realtime bars will recalculate as historical bars, i.e., using intrabar inspection, and the realtime bar's calculations will reset.
When the script recalculates elapsed realtime bars as historical bars, the values on those bars will change, which means the script repaints in those conditions.
— When the indicator first calculates on a chart containing an incomplete realtime bar, it will count ALL the existing volume on the bar as Buy or Sell volume,
depending on the polarity of the bar at that point. This will skew calculations for that first bar. Scripts have no access to the history of a realtime bar's previous updates,
and intrabar inspection cannot be used on realtime bars, so this is the only to go about this.
— Even if alerts only trigger upon confirmation of their conditions after the realtime bar closes, they are repainting alerts
because they would perhaps not have calculated the same way using intrabar inspection.
— On markets like stocks that often have different EOD and intraday feeds and volume information,
the volume's scale may not be the same for the realtime bar if your chart is at 1D, for example,
and the indicator is using an intraday timeframe to calculate on historical bars.
— Any chart timeframe can be used in realtime mode, but plots that include moving averages in their calculations may require many elapsed realtime bars before they can calculate.
You might prefer drastically reducing the periods of the moving averages, or using the volume columns mode, which displays instant values, instead of the line.
Volume Delta Balances
This indicator uses a variety of methods to evaluate five volume delta balances and derive other values from those balances. The five balances are:
1 — On Bar Balance : This is the only balance using instant values; it is simply the subtraction of the Sell volume from the Buy volume on the bar.
2 — Average Balance : Calculates a distinct EMA for both the Buy and Sell volumes, and subtracts the Sell EMA from the Buy EMA.
3 — Momentum Balance : Starts by calculating, separately for both Buy and Sell volumes, the difference between the same EMAs used in "Average Balance" and
an SMA of double the period used for the "Average Balance" EMAs. The difference for the Sell side is subtracted from the difference for the Buy side,
and an RSI of that value is calculated and brought over the −50/+50 scale.
4 — Relative Balance : The reference values used in the calculation are the Buy and Sell EMAs used in the "Average Balance".
From those, we calculate two intermediate values using how much the instant Buy and Sell volumes on the bar exceed their respective EMA — but with a twist.
If the bar's Buy volume does not exceed the EMA of Buy volume, a zero value is used. The same goes for the Sell volume with the EMA of Sell volume.
Once we have our two intermediate values for the Buy and Sell volumes exceeding their respective MA, we subtract them. The final "Relative Balance" value is an ALMA of that subtraction.
The rationale behind using zero values when the bar's Buy/Sell volume does not exceed its EMA is to only take into account the more significant volume.
If both instant volume values exceed their MA, then the difference between the two is the signal's value.
The signal is called "relative" because the intermediate values are the difference between the instant Buy/Sell volumes and their respective MA.
This balance flatlines when the bar's Buy/Sell volumes do not exceed their EMAs, which makes it useful to spot areas where trader interest dwindles, such as consolidations.
The smaller the period of the final value's ALMA, the more easily you will see the balance flatline. These flat zones should be considered no-trade zones.
5 — Percent Balance : This balance is the ALMA of the ratio of the "On Bar Balance" value, i.e., the volume delta balance on the bar (which can be positive or negative),
over the total volume for that bar.
From the balances and marker conditions, two more values are calculated:
1 — Marker Bias : It sums the up/down (+1/‒1) occurrences of the markers 1 to 4 over a period you define, so it ranges from −4 to +4, times the period.
Its calculation will depend on the modes used to calculate markers 3 and 4.
2 — Combined Balances : This is the sum of the bull/bear (+1/−1) states of each of the five balances, so it ranges from −5 to +5.
█ FEATURES
The indicator has two main modes of operation: Columns and Line .
Columns
• In Columns mode you can display stacked Buy/Sell volume columns.
• The buy section always appears above the centerline, the sell section below.
• The top and bottom sections can be colored independently using eight different methods.
• The EMAs of the Buy/Sell values can be displayed (these are the same EMAs used to calculate the "Average Balance").
Line
• Displays one of seven signals: the five balances or one of two complementary values, i.e., the "Marker Bias" or the "Combined Balances".
• You can color the line and its fill using independent calculation modes to pack more information in the display.
You can thus appraise the state of 3 different values using the line itself, its color and the color of its fill.
• A "Divergence Levels" feature will use the line to automatically draw expanding levels on divergence events.
Default settings
Using the indicator's default settings, this is the information displayed:
• The line is calculated on the "Average Balance".
• The line's color is determined by the bull/bear state of the "Percent Balance".
• The line's fill gradient is determined by the advances/declines of the "Momentum Balance".
• The orange divergence dots are calculated using discrepancies between the polarity of the "On Bar Balance" and the chart's bar.
• The divergence levels are determined using the line's level when a divergence occurs.
• The background's fill gradient is calculated on advances/declines of the "Marker Bias".
• The chart bars are colored using advances/declines of the "Relative Balance". Divergences are shown in orange.
• The intrabar timeframe is automatically determined from the chart's timeframe so that a minimum of 50 intrabars are used to calculate volume delta on historical bars.
Alerts
The configuration of the marker conditions explained further is what determines the conditions that will trigger alerts created from this script. Note that simply selecting the display of markers does not create alerts. To create an alert on this script, you must use ALT-A from the chart. You can create multiple alerts triggering on different conditions from this same script; simply configure the markers so they define the trigger conditions for each alert before creating the alert. The configuration of the script's inputs is saved with the alert, so from then on you can change them without affecting the alert. Alert messages will mention the marker(s) that triggered the specific alert event. Keep in mind, when creating alerts on small chart timeframes, that discrepancies between alert triggers and markers displayed on your chart are to be expected. This is because the alert and your chart are running two distinct instances of the indicator on different servers and different feeds. Also keep in mind that while alerts only trigger on confirmed conditions, they are calculated using realtime calculation mode, which entails that if you refresh your chart and elapsed realtime bars recalculate as historical bars using intrabar inspection, markers will not appear in the same places they appeared in realtime. So it's important to understand that even though the alert conditions are confirmed when they trigger, these alerts will repaint.
Let's go through the sections of the script's inputs.
Columns
The size of the Buy/Sell columns always represents their respective importance on the bar, but the coloring mode for tops and bottoms is independent. The default setup uses a standard coloring mode where the Buy/Sell columns are always in the bull/bear color with a higher intensity for the winning side. Seven other coloring modes allow you to pack more information in the columns. When choosing to color the top columns using a bull/bear gradient on "Average Balance", for example, you will have bull/bear colored tops. In order for the color of the bottom columns to continue to show the instant bar balance, you can then choose the "On Bar Balance — Dual Solid Colors" coloring mode to make those bars the color of the winning side for that bar. You can display the averages of the Buy and Sell columns. If you do, its coloring is controlled through the "Line" and "Line fill" sections below.
Line and Line fill
You can select the calculation mode and the thickness of the line, and independent calculations to determine the line's color and fill.
Zero Line
The zero line can display dots when all five balances are bull/bear.
Divergences
You first select the detection mode. Divergences occur whenever the up/down direction of the signal does not match the up/down polarity of the bar. Divergences are used in three components of the indicator's visuals: the orange dot, colored chart bars, and to calculate the divergence levels on the line. The divergence levels are dynamic levels that automatically build from the line's values on divergence events. On consecutive divergences, the levels will expand, creating a channel. This implementation of the divergence levels corresponds to my view that divergences indicate anomalies, hesitations, points of uncertainty if you will. It precludes any attempt to identify a directional bias to divergences. Accordingly, the levels merely take note of divergence events and mark those points in time with levels. Traders then have a reference point from which they can evaluate further movement. The bull/bear/neutral colors used to plot the levels are also congruent with this view in that they are determined by the line's position relative to the levels, which is how I think divergences can be put to the most effective use. One of the coloring modes for the line's fill uses advances/declines in the line after divergence events.
Background
The background can show a bull/bear gradient on six different calculations. As with other gradients, you can adjust its brightness to make its importance proportional to how you use it in your analysis.
Chart bars
Chart bars can be colored using seven different methods. You have the option of emptying the body of bars where volume does not increase, as does my TLD indicator, and you can choose whether you want to show divergences.
Intrabar Timeframe
This is the intrabar timeframe that will be used to calculate volume delta using intrabar inspection on historical bars. You can choose between four modes. The three "Auto-steps" modes calculate, from the chart's timeframe, the intrabar timeframe where the said number of intrabars will make up the dilation of chart bars. Adjustments are made for non-24x7 markets. "Fixed" mode allows you to select the intrabar timeframe you want. Checking the "Show TF" box will display in the lower-right corner the intrabar timeframe used at any given moment. The proper selection of the intrabar timeframe is important. It must achieve maximal granularity to produce precise results while not unduly slowing down calculations, or worse, causing runtime errors. Note that historical depth will vary with the intrabar timeframe. The smaller the timeframe, the shallower historical plots you will be.
Markers
Markers appear when the required condition has been confirmed on a closed bar. The configuration of the markers when you create an alert is what determines when the alert will trigger. Five markers are available:
• Balances Agreement : All five balances are either bullish or bearish.
• Double Bumps : A double bump is two consecutive up/down bars with +/‒ volume delta, and rising Buy/Sell volume above its average.
• Divergence confirmations : A divergence is confirmed up/down when the chosen balance is up/down on the previous bar when that bar was down/up, and this bar is up/down.
• Balance Shifts : These are bull/bear transitions of the selected signal.
• Marker Bias Shifts : Marker bias shifts occur when it crosses into bull/bear territory.
Periods
Allows control over the periods of the different moving averages used to calculate the balances.
Volume Discrepancies
Stock exchanges do not report the same volume for intraday and daily (or higher) resolutions. Other variations in how volume information is reported can also occur in other markets, namely Forex, where volume irregularities can even occur between different intraday timeframes. This will cause discrepancies between the total volume on the bar at the chart's timeframe, and the total volume calculated by adding the volume of the intrabars in that bar's dilation. This does not necessarily invalidate the volume delta information calculated from intrabars, but it tells us that we are using partial volume data. A mechanism to detect chart vs intrabar timeframe volume discrepancies is provided. It allows you to define a threshold percentage above which the background will indicate a difference has been detected.
Other Settings
You can control here the display of the gray dot reminder on realtime bars, and the display of error messages if you are using a chart timeframe that is not greater than the fixed intrabar timeframe, when you use that mode. Disabling the message can be useful if you only use realtime mode at chart timeframes that do not support intrabar inspection.
█ RAMBLINGS
On Volume Delta
Volume is arguably the best complement to interpret price action, and I consider volume delta to be the most effective way of processing volume information. In periods of low-volatility price consolidations, volume will typically also be lower than normal, but slight imbalances in the trend of the buy/sell volume balance can sometimes help put early odds on the direction of the break from consolidation. Additionally, the progression of the volume imbalance can help determine the proximity of the breakout. I also find volume delta and the number of divergences very useful to evaluate the strength of trends. In trends, I am looking for "slow and steady", i.e., relatively low volatility and pauses where price action doesn't look like world affairs are being reassessed. In my personal mythology, this type of trend is often more resilient than high-volatility breakouts, especially when volume balance confirms the general agreement of traders signaled by the low-volatility usually accompanying this type of trend. The volume action on pauses will often help me decide between aggressively taking profits, tightening a stop or going for a longer-term movement. As for reversals, they generally occur in high-volatility areas where entering trades is more expensive and riskier. While the identification of counter-trend reversals fascinates many traders to no end, they represent poor opportunities in my view. Volume imbalances often precede reversals, but I prefer to use volume delta information to identify the areas following reversals where I can confirm them and make relatively low-cost entries with better odds.
On "Buy/Sell" Volume
Buying or selling volume are misnomers, as every unit of volume transacted is both bought and sold by two different traders. While this does not keep me from using the terms, there is no such thing as “buy only” or “sell only” volume. Trader lingo is riddled with peculiarities.
Divergences
The divergence detection method used here relies on a difference between the direction of a signal and the polarity (up/down) of a chart bar. When using the default "On Bar Balance" to detect divergences, however, only the bar's volume delta is used. You may wonder how there can be divergences between buying/selling volume information and price movement on one bar. This will sometimes be due to the calculation's shortcomings, but divergences may also occur in instances where because of order book structure, it takes less volume to increase the price of an asset than it takes to decrease it. As usual, divergences are points of interest because they reveal imbalances, which may or may not become turning points. To your pattern-hungry brain, the divergences displayed by this indicator will — as they do on other indicators — appear to often indicate turnarounds. My opinion is that reality is generally quite sobering and I have no reliable information that would tend to prove otherwise. Exercise caution when using them. Consequently, I do not share the overwhelming enthusiasm of traders in identifying bullish/bearish divergences. For me, the best course of action when a divergence occurs is to wait and see what happens from there. That is the rationale underlying how my divergence levels work; they take note of a signal's level when a divergence occurs, and it's the signal's behavior from that point on that determines if the post-divergence action is bullish/bearish.
Superfluity
In "The Bed of Procrustes", Nassim Nicholas Taleb writes: To bankrupt a fool, give him information . This indicator can display lots of information. While learning to use a new indicator inevitably requires an adaptation period where we put it through its paces and try out all its options, once you have become used to it and decide to adopt it, rigorously eliminate the components you don't use and configure the remaining ones so their visual prominence reflects their relative importance in your analysis. I tried to provide flexible options for traders to control this indicator's visuals for that exact reason — not for window dressing.
█ LIMITATIONS
• This script uses a special characteristic of the `security()` function allowing the inspection of intrabars — which is not officially supported by TradingView.
It has the advantage of permitting a more robust calculation of volume delta than other methods on historical bars, but also has its limits.
• Intrabar inspection only works on some chart timeframes: 3, 5, 10, 15 and 30 minutes, 1, 2, 3, 4, 6, and 12 hours, 1 day, 1 week and 1 month.
The script’s code can be modified to run on other resolutions.
• When the difference between the chart’s timeframe and the intrabar timeframe is too great, runtime errors will occur. The Auto-Steps selection mechanisms should avoid this.
• All volume is not created equally. Its source, components, quality and reliability will vary considerably with sectors and instruments.
The higher the quality, the more reliably volume delta information can be used to guide your decisions.
You should make it your responsibility to understand the volume information provided in the data feeds you use. It will help you make the most of volume delta.
█ NOTES
For traders
• The Data Window shows key values for the indicator.
• While this indicator displays some of the same information calculated in my Delta Volume Columns ,
I have elected to make it a separate publication so that traders continue to have a simpler alternative available to them. Both code bases will continue to evolve separately.
• All gradients used in this indicator determine their brightness intensities using advances/declines in the signal—not their relative position in a pre-determined scale.
• Volume delta being relative, by nature, it is particularly well-suited to Forex markets, as it filters out quite elegantly the cyclical volume data characterizing the sector.
If you are interested in volume delta, consider having a look at my other "Delta Volume" indicators:
• Delta Volume Realtime Action displays realtime volume delta and tick information on the chart.
• Delta Volume Candles builds volume delta candles on the chart.
• Delta Volume Columns is a simpler version of this indicator.
For coders
• I use the `f_c_gradientRelativePro()` from the PineCoders Color Gradient Framework to build my gradients.
This function has the advantage of allowing begin/end colors for both the bull and bear colors. It also allows us to define the number of steps allowed for each gradient.
I use this to modulate the gradients so they perform optimally on the combination of the signal used to calculate advances/declines,
but also the nature of the visual component the gradient applies to. I use fewer steps for choppy signals and when the gradient is used on discrete visual components
such as volume columns or chart bars.
• I use the PineCoders Coding Conventions for Pine to write my scripts.
• I used functions modified from the PineCoders MTF Selection Framework for the selection of timeframes.
█ THANKS TO:
— The devs from TradingView's Pine and other teams, and the PineCoders who collaborate with them. They are doing amazing work,
and much of what this indicator does could not be done without their recent improvements to Pine.
— A guy called Kuan who commented on a Backtest Rookies presentation of their Volume Profile indicator using a `for` loop.
This indicator started from the intrabar inspection technique illustrated in Kuan's snippet.
— theheirophant , my partner in the exploration of the sometimes weird abysses of `security()`’s behavior at intrabar timeframes.
— midtownsk8rguy , my brilliant companion in mining the depths of Pine graphics.
Vanilla ABCD PatternPatterns makes parts of the many predictive tools available to technical analysts, the most simples ones can be easily detected by using scripts. The proposed tool detect the simple (vanilla) form of the ABCD pattern, a pattern aiming to detect potential price swings. The script can use an additional confirmation condition that aim to filter potential false signals.
ABCD PATTERN
The ABCD pattern is not meant to be detected by analyzing individuals closing price observations but by analyzing longer term movements, this is done by using tools such as zig-zag. Like any pattern the ABCD one comes in different flavors, the simplest being based on the following structures:
Once price reach D we can expect a reversal. The classical pattern has the following conditions : BC = 0.618*AB and CD = 1.618*AB , as you can see this condition is based on 0.618 which is a ratio in the Fibonacci sequence. Other conditions are for AB to equal to CD or for CD to be 161.8% longer than AB. Why these conditions ? Cause Fibonacci of course .
The ABCD pattern that the proposed tool aim to detect is not based on the zig-zag but only on individual price observations and don't make use of any of the previously described conditions, thus becoming more like a candle pattern.
When the label is blue it means that the tool has detected a bullish ABCD pattern, while a red label means that the tool has detected a bearish ABCD pattern.
We can't expect patterns based on the analysis of 1,2,3 or 4 closing price observations to predict the reversal of mid/long term movements, this can be seen on the above chart, but we can see some signals predicting short term movements.
Since the pattern is based on a noisy variation, using smoother input can result in less signals.
Above the tool on BTCUSD using closing price as input. Below the tool using ohlc4 as input:
TOOL OPTIONS
Being to early can be as devastating as being to late, therefore a confirmation point can be beneficial, the tool allow you to wait for confirmation thus having a potentially better timing. Below is a chart of AMD with no confirmation:
As we can see there are many signals with some of them occurring to early, we can fix this by checking the confirmation option:
The confirmation is simply based on the candle color, for example if a bearish ABCD pattern has been detected in the past and the closing price is greater than the opening price then the tool return a buy signal. The same apply with a bearish ABCD pattern.
The "last bar repaint" option is true by default, this in order to show the bar where the D point of the pattern has been detected, since the closing price of the last bar is constantly changing the signals on the last bar can be constantly appearing/disappearing, unchecking the option will fix this but will no longer the bar where the D point of the pattern has been detected.
SUMMARY
The pattern is simple and can sometimes be accurate when predicting the direction of future short terms movements. The tool was a request, as it seems i don't post many pattern detectors, well thats true, and the reason is that for me patterns are not super significant, and their detection can be extremely subjective, this is why simple patterns are certainly the only ones worth a look.
Don't expect me to post many pattern related indicators in the future ^^'
BITCOIN KILL ZONES v2Kill Zones
Kill zones are really liquidity events. Many different market participants often come together and act around these events. The activity itself may be event driven (margin calls or options exercise related activity), portfolio management driven (buy-on-close and asset allocation rebalancing orders) or institutionally driven (larger players needing liquidity to get filled in size) or a combination of any/all three. The point is, this intense cross current of activity at a very specific point in time often occurs near significant technical levels and trends established coming out of these events often persist until the next Kill Zone in approached/entered.
Specifically, there are three Kill Zones and each has its own importance/significance.
1. Asian Kill Zone (1900 - 2300 EST) Considered the "institutional" zone, this zone represents both the launch pad for new trends and also too a reloading area from the post American session. It is the start of a new day (or week) for the world and as such it makes sense this zone will often set the tone for the rest of the world's trading day. Since it is very wide (4 hours) one should pay attention to the Tokyo open (2100 EST) the Beijing open (2120 EST) and the Sydney open (0650 EST previous day).
2. London Kill Zone (0200 - 0400 EST) Considered the center of the financial universe for more than 500 years, Europe still carries a lot of influence within the banking world. Many larger players use the Euro session to establish their positions. As such, the London open often sees the most significant trend establishment activity through any given trading day. Indeed, it has been suggested 80% of all weekly trends are established through Tuesday's London Kill Zone.
3. New York Kill Zone (0830 - 1030 EST) The United States is still by far the world's largest economy and so by default New York's open carries a lot of weight and often comes with a big injection of liquidity. Indeed, most of the world's trade-able assets are priced in US dollars which gives even more significance to political and economic activity within this region. Because it comes relatively late in the globe's trading day, this Kill Zone often sees violent price swings within it's first hour leading to the time tested adage "never trust the first hour of North American trading.
Additional notes:
It has become apparent these Kill Zones are evolving over time and the course of world history. Since the end of the second world war, New York has slowly encroached on London's place as the global center for commercial banking. So much so through the later part of the 20th century New York was considered indeed, the new center of the financial universe. With the end of the cold war that leadership seems to have shifted back toward Europe and away from The United States. Additionally, Japan has slowly lost its former predominance within the global economic landscape while Beijing's has risen dramatically.
Only time will tell how these kill zones will evolve given each region's ever changing political, economic and socioeconomic influences.
Trading Notes:
If you have specific levels of interest odds are the bigger players have the same levels too. If it is indeed a solid level, look for price to trade to your level through the kill zone because the zone is a liquidity event where the bigger players can find enough size to get their big orders filled.
Try to avoid taking positions heading into Kill Zones and look for confirmation of your levels coming out of the event. For the more advanced trader, look to take positions on those level hits through the zone but understand higher time frame players often have far deeper pockets then day traders and can endure far more volatility then us little guys.
Thanks for the contribution to @CRInvestor and @ICT_MHuddleston
Bitcoin: Pi Cycle Top & Bottom Indicator Z ScoreIndicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs within 3 days.
It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
Note: The multiple is of the price values of the 350DMA, not the number of days.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking.
It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. However, in this instance, it does so with a high degree of accuracy over Bitcoin's adoption phase of growth.
Bitcoin Price Prediction Using This Tool
The Pi Cycle Top Indicator forecasts the cycle top of Bitcoin’s market cycles. It attempts to predict the point where Bitcoin price will peak before pulling back. It does this on major high time frames and has picked the absolute tops of Bitcoin’s major price moves throughout most of its history.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter-term moving average, which is the 111-day moving average, has reached an x2 multiple of the 350-day moving average. Historically, it has proved advantageous to sell Bitcoin around this time in Bitcoin's price cycles.
It is also worth noting that this indicator has worked during Bitcoin's adoption growth phase, the first 15 years or so of Bitcoin's life. With the launch of Bitcoin ETF's and Bitcoin's increased integration into the global financial system, this indicator may cease to be relevant at some point in this new market structure.
Added the Z-Score metric for easy classification of the value of Bitcoin according to this indicator.
Created for TRW
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
[Pandora] Laguerre Ultimate Explorations MulticatorIt's time to begin demonstrations differentiating the difference between known and actual feasibility beyond imagination... Welcome to my algorithmic twilight zone .
INTRODUCTION:
Hot off my press, I present this Laguerre multicator employing PSv6.0, originally formulated by John Ehlers for TASC - July 2025 Traders Tips. Basically I transcended Ehlers' notions of transversal filtration with an overhaul of his Laguerre design with my "what if" Pandora notions included. Striving beyond John Ehlers' original intended design. This action packed indicator is a radically revamped version of his original filter using novel techniques. My aim was to explore whether providing even more enhanced responsiveness and lesser lag is possible and how. Presented here is my mind warping results to witness.
EHLERS' LAGUERRE EXPLAINED:
First and foremost, the concept of Ehlers' Laguerre-izing method deserves a comprehensive deep dive. Ehlers' Laguerre filter design, as it functions originally, begins with his Ultimate Smoother (US) followed by a gang of four LERP (jargon for Linear intERPolation) filters. Following a myriad of cascading LERPs is a window-like FIR filter tapped into the LERP delay values to provide extra smoothness via the output.
On a side note, damping factor controlled LERP filters resemble EMAs indeed, but aren't exactly "periodic" filters that would have a period/length parameter and their subsequent calculations. I won't go into fine-grained relationship details, but EMA and LERP are indeed related in approach, being cousins of similar pedigree.
EXAMINING LAGUERRE:
I focused firstly on US initialization obstacles at Pine's bar_index==0 with nz() in abundance. The next primary notion of intrigue I mostly wondered about was, why are there four LERP elements instead of fewer or more. Why not three or why not two LERPs, etc... 1-4-6-4-1, I remember seeing those coefficients before in high pass filters.
Gathering my thoughts from that highpass knowledge base, I devised other tapped configuration modes to inspect their behavior out of curiosity. Eureka! There is actually more to Laguerre than Ehlers' mind provided, now that I had formulated additional modes. Each mode exhibits it's own lag/smoothness characteristics better than the quad LERPed version. I narrowed it down to a total of 5 modes for exploration. Mode 0 is just the raw US by itself.
ANALYZING FILTER BEHAVIORS:
Which option might be possibly superior, and how may I determine that? Fortunately, I have a custom-built analyzer allowing me to thoroughly examine transient responses across multiple periodicities simultaneously, providing remarkable visual insights.
While Ehlers has meagerly touched upon presenting general frequency responses in his books, I have excelled far beyond that. This robust filter analysis capability enables me to observe finer aspects hidden to others, ultimately leading to the deprecation of numerous existing filters. Not only this, but inventing entirely new species of filtration whether lowpass, highpass, or bandpass is already possible with a thorough comprehensive evaluation.
Revealing what's quirky with each filter and having the ability to discover what filters may be lacking in performance, is one of it's implications. I'm just going to explain this: For example US has a little too much overshoot to my liking, along with nonconformant cutoff frequency compliance with the period parameter. Perhaps Ehlers should inspect US coefficients a bit closer... I hope stating this is not received in an ill manner, as it's not my intention here.
What this technically eludes to is that UltimateSmoother can be further improved, analogous to my Laguerre alterations described above. I will also state Laguerre can indeed be reformulated to an even greater extent concerning group delay, from what I have already discussed. Another exciting time though... More investigative research is warranted.
LAGUERRE CONCLUSIONS:
After analyzing Laguerre's frequency compliance, transient responses, amplitudes, lag, symmetry across periodicities, noise rejection, and smoothness... I favor mode 3 for a multitude of reasons over the mode 4 configuration, but mostly superb smoothing with less lag, AND I also appreciated mode 1 & 2 for it's lower lag performance options.
Each mode and lag (phase shift) damping value has it's own unique characteristics at extremes, yet they demonstrate additional finesse in it's new hybrid form without adding too much more complexity. This multicator has a bunch of Laguerre filters in the overlay chart over many periodicities so you can easily witness it's differing periodic symmetries on an input signal while adjusting lag and mode.
LAGUERRE OSCILLATOR:
The oscillator is integrated into the laguerreMulti() function for the intention of posterity only. I performed no evaluation on it, only providing the code in Pine. That wasn't part of my intended exploration adventure, as I'm more TREND oriented for the time being, focusing my efforts there.
Market analysis has two primary aspects in my observations, one cyclic while the other is trending dynamics... There's endless oscillators, but my expectations for trend analysis seems a little lesser explored in my opinion, hence my laborious trend endeavors. Ehlers provided both indicator facets this time around, and I hope you find the filtration aspect more intriguing after absorption of this reading.
FUNCTION MODULES EXPLAINED:
The Ultimate Smoother is an advanced IIR lowpass smoothing filter intended to minimize noise in time series data with minimal group delay, similar to a traditional biquad filter. This calculation helps to create a smoother version of the original signal without the distortions of short-term fluctuations and with minimal lag, adjustable by period.
The Modified Laguerre Lowpass Filter (MLLF) enhances the functionality of US by introducing a Laguerre mode parameter along side the lag parameter to refine control over the amount of additional smoothing/lag applied to the signal. By tethering US with this LERPed lag mechanism, MLLF achieves an effective balance between responsiveness and smoothness, allowing for customizable lag adjustments via multiple inputs. This filter ends with selecting from a choice of weighted averages derived from a gang of up to four cascading LERP calculations, resulting with smoother representations of the data.
The Laguerre Oscillator is a momentum-like indicator derived from the output of US and a singular LERPed lowpass filter. It calculates the difference between the US data and Laguerre filter data, normalizing it by the root mean square (RMS). This quasi-normalization technique helps to assess the intensity of the momentum on any timeframe within an expected bound range centered around 0.0. When the Laguerre Oscillator is positive, it suggests that the smoothed data is trending upward, while a negative value indicates a downward trend. Adjustability is controlled with period, lag, Laguerre mode, and RMS period.
Inflection PointInflection Point - The Adaptive Confluence Reversal Engine
This is not just another peak and valley indicator; it is a complete and total reimagining of how market turning points are detected, qualified, and acted upon. Born from the foundational concepts explored in systems like my earlier creation, DAFE - Turning Point, Inflection Point is a ground-up engineering feat designed for the modern trader. It moves beyond static rules and simple pattern recognition into the realm of dynamic, multi-factor confluence analysis and adaptive machine learning.
Where other indicators provide a guess, Inflection Point provides a probability. It meticulously analyzes the market's deepest currents—momentum, exhaustion, and reversal velocity—and fuses them into a single, unified "Confluence Score." This is not a simple combination of indicators; it is an intelligent, weighted system where each component works in concert, creating an analytical engine that is orders of magnitude more sophisticated and reliable than any standard reversal tool.
Furthermore, Inflection Point learns. Through its advanced Adaptive Learning Engine, it constantly monitors its own performance, adjusting its confidence and selectivity in real-time based on its recent success rate. This allows it to adapt its behavior to any security, on any timeframe, with remarkable success.
Theoretical Foundation - Confluence Core
Inflection Point's predictive power does not come from a single, magical formula. It comes from the intelligent synthesis of three critical market phenomena, weighted and scored in real-time to generate a single, high-conviction probability rating.
1. Factor One: Pre-Reversal Momentum State (RSI Analysis)
Instead of reacting to a simple RSI cross, Inflection Point proactively scans for the build-up of momentum that precedes a reversal.
• Formulaic Concept: It measures the highest RSI value over a lookback period for peaks and the lowest RSI for valleys. A signal is only considered valid if significant momentum has been established before the turn, indicating a stretched market condition ripe for reversal.
• Asymmetric Sophistication: The engine uses different, optimized thresholds for bull and bear momentum, recognizing that markets often fall faster than they rise.
2. Factor Two: Volatility Exhaustion (Bollinger Band Analysis)
A true reversal often occurs when price makes a final, exhaustive push into unsustainable territory.
• Formulaic Concept: The engine detects when price has significantly pierced the outer Bollinger Bands. This is not just a touch, but a statistical deviation from the mean that signals volatility exhaustion, where the energy for the current move is likely depleted.
3. Factor Three: Reversal Strength (Rate of Change Analysis)
The character of a reversal matters. A sharp, decisive turn is more significant than a slow, meandering one.
• Formulaic Concept: Using a short-term Rate of Change (ROC), the engine measures the velocity of the reversal itself. A higher ROC score adds significant weight to the final probability, confirming that the new direction has conviction.
4. The Final Calculation: The Adaptive Learning Engine
This is the system's "brain." It maintains a history of its past signals and calculates its real-time win rate. This hitRate is then used to generate an adaptiveMultiplier.
• Self-Correction: In "Quality Control" mode, a high win rate makes the indicator more selective, demanding a higher probability score to issue a signal, thereby protecting streaks. A lower win rate makes it slightly less selective to ensure it continues learning from new market conditions.
• The result is a system that is not static, but a living, breathing tool that adapts its personality to the unique rhythm of any chart.
Why Inflection Point is a Paradigm Shift
Inflection Point is fundamentally different from other reversal indicators for three key reasons:
Confluence Over Isolation: Standard indicators look at one thing (e.g., RSI > 70). Inflection Point simultaneously analyzes momentum, volatility, and velocity, understanding that true reversals are a product of multiple converging factors. It answers not just "if," but "why" a reversal is likely.
Probabilistic Over Binary: Other tools give you a simple "yes" or "no." Inflection Point provides a probability score from 0-100, allowing you to gauge the conviction of every potential signal. This empowers you to differentiate between a weak setup and an A+ opportunity.
Adaptive Over Static: Every other indicator uses the same rules forever. Inflection Point's Adaptive Engine means it is constantly refining its own logic based on what is actually working in the current market, on the specific asset you are trading. It is tailored to the now.
The Inputs Menu - Your Command Center
Every setting is a lever of control, allowing you to tune the engine to your precise trading style and market focus.
🧠 Neural Core Engine
Analysis Depth: This is the primary lookback for the Bollinger Band and other core calculations. A shorter depth makes the indicator faster and more sensitive, ideal for scalping. A longer depth makes it slower and more stable, ideal for swing trading.
Minimum Probability %: This is your master signal filter. It sets the minimum Confluence Score required to plot a signal. Higher values (85-95) will give you only the highest-conviction A+ setups. Lower values (70-80) will show more potential opportunities.
🤖 Adaptive Neural Learning
Enable Adaptive Learning Engine: Toggles the entire learning system. Disabling it will make the indicator's logic static.
Peak/Valley Success Threshold (ATR): This defines what constitutes a "successful" trade for the learning engine. A value of 1.5 means price must move 1.5x the ATR in your favor for the signal to be marked as a win. Adjust this to match your personal take-profit strategy.
Adaptive Mode: This dictates how the engine uses its hitRate. "Quality Control" is recommended for its intelligent filtering. "Aggressive" will always boost signal scores, useful for finding more setups in a known, trending environment.
Asymmetric Balance: Allows you to apply a "boost" to either peak (short) or valley (long) signals. If you find the market you're trading has stronger long reversals, you can increase the "Valley Signal Boost" to catch them more effectively.
🛡️ Elite Filters
Market Noise Filter: An exceptional tool for avoiding choppy markets. It counts the number of directional changes in the last 5 bars. If the market is whipping back and forth too much, it will block the signal. Lower the "Max Direction Changes" to be extremely selective.
Volume Filter: Requires signal confirmation from a significant volume spike. The "Volume Multiplier" dictates how large this spike must be (e.g., 1.2 = 20% above average volume). This is invaluable for filtering out low-conviction moves in stocks and crypto.
The Dashboard - Your Analytical Co-Pilot
The dashboard is not just a set of numbers; it is a holistic overview of the market's health and the engine's current state.
Unified AI Score: This section provides the most critical, at-a-glance information. "Total Score" is the current probability reading, while "Quality" gives you a human-readable interpretation. "Win Rate" shows the real-time performance of the Adaptive Engine.
Order Flow (OFPI): This measures the "weight" of money behind recent price moves by analyzing price change relative to volume. A high positive OFPI suggests strong buying pressure, while a high negative value suggests strong selling pressure. It gives you a peek into the market's underlying flow.
Component Analysis: This allows you to see the individual "Peak" and "Valley" confidence scores before they are filtered, giving you insight into building momentum before a signal forms.
Market Structure: This panel assesses the broader environment. "HTF Trend" tells you the direction of the larger trend (based on EMAs), while "Vol Regime" tells you if the market is in a high, medium, or low volatility state. Use this to align your signals with the broader market context.
Filter & Engine Statistics: Available on the "Large" dashboard, this provides deep insight into how many signals are being blocked by your filters and the current status of the Adaptive Engine's multiplier.
The Visual Interface - A Symphony of Data
Every visual element on the chart is designed for instant interpretation and insight.
Signal Markers: Simple, clean triangles mark the exact bar of a valid signal. A box is drawn around the high/low of the signal bar to highlight the precise point of inflection.
Dynamic Support/Resistance Zones: These are the glowing lines on your chart. They are not static lines; they are dynamic levels that represent the current battlefield between buyers and sellers.
Cyber Cyan (Valley Blue): This is the current Support Zone. This is the price level the market is currently trying to defend.
Neural Pink (Peak Red): This is the current Resistance Zone. This is the price level the market is currently trying to break through.
Grey (Next Level): This line is a projection, based on the current momentum and the size of the S/R range, of where the next major level of conflict will likely be. It acts as a potential price target.
Development & Philosophy
Inflection Point was not assembled; it was engineered. It represents hundreds of hours of research into market dynamics, statistical analysis, and machine learning principles. The goal was to create a tool that moves beyond the limitations of traditional technical analysis, which often fails in modern, algorithm-driven markets. By building a system based on multi-factor confluence and self-adaptive logic, Inflection Point provides a quantifiable, statistical edge that is simply unattainable with simpler tools. This is the result of a relentless pursuit of a better, more intelligent way to trade.
Universal Applicability
The principles of momentum, exhaustion, and velocity are universal to all freely traded markets. Because of its adaptive core and robust filtering options, Inflection Point has proven to be exceptionally effective on any security (stocks, crypto, forex, indices, futures) and on any timeframe (from 1-minute scalping charts to daily swing trading charts).
" Markets are constantly in a state of uncertainty and flux and money is made by discounting the obvious and betting on the unexpected. "
— George Soros
Trade with insight. Trade with anticipation.
— Dskyz, for DAFE Trading Systems
Volume Point of Control with Fib Based Profile🍀Description:
This indicator is a comprehensive volume profile analysis tool designed to identify key price levels based on trading activity within user-defined timeframes. It plots the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL), along with dynamically calculated Fibonacci levels derived from the developing period's range. It offers extensive customization for both historical and developing levels.
🍀Core Features:
Volume Profiling (POC, VAH, VAL):
Calculates and plots the POC (price level with the highest volume), VAH, and VAL for a selected timeframe (e.g., Daily, Weekly).
The Value Area percentage is configurable. 70% is common on normal volume profiles, but this script allows you to configure multiple % levels via the fib levels. I recommend using 2 versions of this indicator on a chart, one has Value Area at 1 (100% - high and low of lookback) and the second is a specified VA area (i.e. 70%) like in the chart snapshot above. See examples at the bottom.
Historical Levels:
Plots POC, VAH, and VAL from previous completed periods.
Optionally displays only "Unbroken" levels – historical levels that price has not yet revisited, which can act as stronger magnets or resistance/support.
The user can manage the number of historical lines displayed to prevent chart clutter.
Developing Levels:
Shows the POC, VAH, and VAL as they form in real-time during the current, incomplete period. This provides insight into intraday/intra-period value migration.
Dynamic Fibonacci Levels:
Calculates and plots Fibonacci retracement/extension levels based dynamically on the range between the developing POC and the developing VAH/VAL.
Offers 8 configurable % levels above and below POC that can be toggled on/off.
Visual Customization:
Extensive options for colors, line styles, and widths for all plotted levels.
Optional gradient fill for the Value Area that visualizes current price distance from POC - option to invert the colors as well.
Labels for developing levels and Fibonacci levels for easy identification.
🍀Characteristics:
Volume-Driven: Levels are derived from actual trading volume, reflecting areas of high participation and price agreement/disagreement.
Timeframe Specific: The results are entirely dependent on the chosen profile timeframe.
Dynamic & Static Elements: Developing levels and Fibs update live, while historical levels remain fixed once their period closes.
Lagging (Historical) & Potentially Leading: Historical levels are based on the past, but are often respected by future price action. Developing levels show current dynamics.
🍀How to Use It:
Identifying Support & Resistance: Historical and developing POCs, VAHs, and VALs are often key areas where price may react. Unbroken levels are particularly noteworthy.
Market Context & Sentiment: Trading above the POC suggests bullish strength/acceptance of higher prices, while trading below suggests bearishness/acceptance of lower prices.
Entry/Exit Zones: Interactions with these levels (rejections, breakouts, tests) can provide potential entry or exit signals, especially when confirming with other analysis methods.
Dynamic Targets: The Fibonacci levels calculated from the developing POC-VA range offer potential intraday/intra-period price targets or areas of interest.
Understanding Value Migration: Observing the movement of the developing POC/VAH/VAL throughout the period reveals where value is currently being established.
🍀Potential Drawbacks:
Input Sensitivity: The choice of timeframe, Value Area percentage, and volume resolution heavily influences the generated levels. Experimentation is needed for optimal settings per instrument/market. (I've found that Range Charts can provide very accurate volume levels on TV since the time element is removed. This helps to refine the accuracy of price levels with high volume.)
Volume Data Dependency: Requires accurate volume data. May be less reliable on instruments with sparse or questionable volume reporting.
Chart Clutter: Enabling all features simultaneously can make the chart busy. Utilize the line management inputs and toggle features as needed.
Not a Standalone Strategy: This indicator provides context and key levels. It should be used alongside other technical analysis tools and price action reading for robust decision-making.
Developing Level Fluctuation: Developing POC/VA/Fib levels can shift considerably, especially early in a new period, before settling down as more volume accumulates and time passes.
🍀Recommendations/Examples:
I recommend have this indicator on your chart twice, one has the VA set at 1 (100%) and has the fib levels plotted. The second has the VA set to 0.7 (70%) to highlight the defined VA.
Here is an example with 3 on a chart. VA of 100%, VA of 80%, and VA of 20%
