LANZ Strategy 3.0🔷 LANZ Strategy 3.0 — Asian Range Fibonacci Strategy with Execution Window Logic
LANZ Strategy 3.0 is a rule-based trading system that utilizes the Asian session range to project Fibonacci levels and manage entries during a defined execution window. Designed for Forex and index traders, this strategy focuses on structured price behavior around key levels before the New York session.
🧠 Core Components:
Asian Session Range Mapping: Automatically detects the high, low, and midpoint during the Asian session.
Fibonacci Level Projection: Projects configurable Fibonacci retracement and extension levels based on the Asian range.
Execution Window Logic: Uses the 01:15 NY candle as a reference to validate potential reversals or continuation setups.
Conditional Entry System: Includes logic for limit order entries (buy or sell) at specific Fib levels, with reversal logic if price breaks structure before execution.
Risk Management: Entry orders are paired with dynamic SL and TP based on Fibonacci-based distances, maintaining a risk-reward ratio consistent with intraday strategies.
📊 Visual Features:
Asian session high/low/mid lines.
Fibonacci levels: Original (based on raw range) and Optimized (user-adjustable).
Session background coloring for Asia, Execution Window, and NY session.
Labels and lines for entry, SL, and TP targets.
Dynamic deletion of untriggered orders after execution window expires.
⚙️ How It Works:
The script calculates the Asian session range.
Projects Fibonacci levels from the range.
Waits for the 01:15 NY candle to close to validate a signal.
If valid, a limit entry order (BUY or SELL) is plotted at the selected level.
If price structure changes (e.g., breaks the high/low), reversal logic may activate.
If no trade is triggered, orders are cleared before the NY session.
🔔 Alerts:
Alerts trigger when a valid setup appears after 01:15 NY candle.
Optional alerts for order activation, SL/TP hit, or trade cancellation.
📝 Notes:
Intended for semi-automated or discretionary trading.
Best used on highly liquid markets like Forex majors or indices.
Script parameters include session times, Fib ratios, SL/TP settings, and reversal logic toggle.
Credits:
Developed by LANZ, this script merges traditional session-based analysis with Fibonacci tools and structured execution timing, offering a unique framework for morning volatility plays.
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DrawIndicatorOnTheChartLibrary "DrawIndicatorOnTheChart"
this library is used to show an indicator (such RSI, CCI, MOM etc) on the main chart with indicator's horizontal lines in a window. Location of the window is calculated dynamically by last price movemements
drawIndicator(enabled, indicatorName, indicator1, indicator2, indicator3, indicatorcolors, period, indimax_, indimin_, levels, precision, xlocation, lnwidth)
draws the realted indicator on the chart
Parameters:
enabled (bool) : if it's enabled to show
indicatorName (string) : is the indicator name as string such "RSI", "CCI" etc
indicator1 (float) : is first indicator you want to show, such rsi(close, 14), mom(close, 10) etc
indicator2 (float) : is second indicator you want to show, such -DI of DMI
indicator3 (float) : is third indicator you want to show, such ADX of DMI
indicatorcolors (array)
period (int) : is the length of the window to show
indimax_ (float) : is the maximum value of the indicator, for example for RSI it's 100.0, if the indicator (such CCI, MOM etc) doesn't have maximum value then use "na"
indimin_ (float) : is the minimum value of the indicator, for example for RSI it's 0.0, if the indicator (such CCI, MOM etc)doesn't have maximum value then use "na"
levels (array) : is the levels of the array for the horizontal lines. for example if you want horizontal lines at 30.0, and 70.0 then use array.from(30.0, 70.0). if no horizontal lines then use array.from(na)
precision (int) : is the precision/nuber of decimals that is used to show indicator values, for example for RSI set it 2
xlocation (int) : is end location of the indicator window, for example if xlocation = 0 window is created on the index of the last bar/candle
lnwidth (int) : is the line width of the indicator lines
Returns: none
Anomaly DetectorPrice Anomaly Detector
This is a script designed to identify unusual price movements. By analyzing deviations from typical price behavior, this tool helps traders spot potential trading opportunities and manage risks effectively.
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Features
- Anomaly Detection: Flags price points that significantly deviate from the average.
- Visual Indicators: Highlights anomalies with background colors and cross markers.
- Customizable Settings: Adjust sensitivity and window size to match your trading strategy.
- Real-Time Analysis: Continuously updates anomaly signals as new data is received.
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Usage
After adding the indicator to your chart:
1. View Anomalies: Red backgrounds and cross markers indicate detected anomalies.
2. Adjust Settings: Modify the `StdDev Threshold` and `Window Length` to change detection sensitivity.
3. Interpret Signals:
- Red Background: Anomaly detected on that bar.
- Red Cross: Specific point of anomaly.
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Inputs
- StdDev Threshold: Higher values reduce anomaly sensitivity. Default: 2.0.
- Window Length: Larger windows smooth data, reducing false positives. Default: 20.
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Limitations
- Approximation Method: Uses a simple method to detect anomalies, which may not capture all types of unusual price movements.
- Performance: Extremely large window sizes may impact script performance.
- Segment Detection: Does not group consecutive anomalies into segments.
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Disclaimer : This tool is for educational purposes only. Trading involves risk, and you should perform your own analysis before making decisions. The author is not liable for any losses incurred.
Multi-Step Vegas SuperTrend - strategy [presentTrading]Long time no see! I am back : ) Please allow me to gain some warm-up.
█ Introduction and How it is Different
The "Vegas SuperTrend Strategy" is an enhanced trading strategy that leverages both the Vegas Channel and SuperTrend indicators to generate buy and sell signals.
What sets this strategy apart from others is its dynamic adjustment to market volatility and its multi-step take profit mechanism. Unlike traditional single-step profit-taking approaches, this strategy allows traders to systematically scale out of positions at predefined profit levels, thereby optimizing their risk-reward ratio and maximizing potential gains.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The Vegas SuperTrend Strategy combines the strengths of the Vegas Channel and SuperTrend indicators to identify market trends and generate trade signals. The following subsections delve into the details of how each component works and how they are integrated.
🔶 Vegas Channel Calculation
The Vegas Channel is based on a simple moving average (SMA) and the standard deviation (STD) of the closing prices over a specified period. The channel is defined by upper and lower bounds that are dynamically adjusted based on market volatility.
Simple Moving Average (SMA):
SMA_vegas = (1/N) * Σ(Close_i) for i = 0 to N-1
where N is the length of the Vegas Window.
Standard Deviation (STD):
STD_vegas = sqrt((1/N) * Σ(Close_i - SMA_vegas)^2) for i = 0 to N-1
Vegas Channel Upper and Lower Bounds:
VegasChannelUpper = SMA_vegas + STD_vegas
VegasChannelLower = SMA_vegas - STD_vegas
The details are here:
🔶 Trend Detection and Trade Signals
The strategy determines the current market trend based on the closing price relative to the SuperTrend bounds:
Market Trend:
MarketTrend = 1 if Close > SuperTrendPrevLower
-1 if Close < SuperTrendPrevUpper
Previous Trend otherwise
Trade signals are generated when there is a shift in the market trend:
Bullish Signal: When the market trend shifts from -1 to 1.
Bearish Signal: When the market trend shifts from 1 to -1.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates a multi-step take profit mechanism that allows for partial exits at predefined profit levels. This helps in locking in profits gradually and reducing exposure to market reversals.
Take Profit Levels:
The take profit levels are calculated as percentages of the entry price:
TakeProfitLevel_i = EntryPrice * (1 + TakeProfitPercent_i/100) for long positions
TakeProfitLevel_i = EntryPrice * (1 - TakeProfitPercent_i/100) for short positions
Multi-steps take profit local picture:
█ Trade Direction
The trade direction can be customized based on the user's preference:
Long: The strategy only takes long positions.
Short: The strategy only takes short positions.
Both: The strategy can take both long and short positions based on the market trend.
█ Usage
To use the Vegas SuperTrend Strategy, follow these steps:
Configure Input Settings:
- Set the ATR period, Vegas Window length, SuperTrend Multiplier, and Volatility Adjustment Factor.
- Choose the desired trade direction (Long, Short, Both).
- Enable or disable the take profit mechanism and set the take profit percentages and amounts for each step.
█ Default Settings
The default settings of the strategy are designed to provide a balanced approach to trading. Below is an explanation of each setting and its effect on the strategy's performance:
ATR Period (10): This setting determines the length of the ATR used in the SuperTrend calculation. A longer period smoothens the ATR, making the SuperTrend less sensitive to short-term volatility. A shorter period makes the SuperTrend more responsive to recent price movements.
Vegas Window Length (100): This setting defines the period for the Vegas Channel's moving average. A longer window provides a broader view of the market trend, while a shorter window makes the channel more responsive to recent price changes.
SuperTrend Multiplier (5): This base multiplier adjusts the sensitivity of the SuperTrend to the ATR. A higher multiplier makes the SuperTrend less sensitive, reducing the frequency of trade signals. A lower multiplier increases sensitivity, generating more signals.
Volatility Adjustment Factor (5): This factor dynamically adjusts the SuperTrend multiplier based on the width of the Vegas Channel. A higher factor increases the sensitivity of the SuperTrend to changes in market volatility, while a lower factor reduces it.
Take Profit Percentages (3.0%, 6.0%, 12.0%, 21.0%): These settings define the profit levels at which portions of the trade are exited. They help in locking in profits progressively as the trade moves in favor.
Take Profit Amounts (25%, 20%, 10%, 15%): These settings determine the percentage of the position to exit at each take profit level. They are distributed to ensure that significant portions of the trade are closed as the price reaches the set levels, reducing exposure to reversals.
Adjusting these settings can significantly impact the strategy's performance. For instance, increasing the ATR period or the SuperTrend multiplier can reduce the number of trades, potentially improving the win rate but also missing out on some profitable opportunities. Conversely, lowering these values can increase trade frequency, capturing more short-term movements but also increasing the risk of false signals.
Volume Footprint Voids [BigBeluga]Volume Footprint Voids is a unique tool that uses lower timeframe calculation to plot different styles of single candle POC.
This indicator is very powerful for scalping and finding very precise entry and exits, spotting potential trapped traders, and more.
Unlike many other volume profiles, this aims to plot single candle profiles as well as their own footprints.
🔶 FEATURES
The script includes the following settings:
Windows: Plotting style and calculations
Coloring modes
Display modes
lower-timeframe calculations
🔶 CALCULATION
In the image above we can see how the script calculates each level position that will serve as a calculation process to see how much volume/closes there are within the levels.
In the image above, we can have a more clear example of how we count each candle close.
We use the prior screenshot as an example, after setting each level we will use the lower-timeframe input to measure the amount of closes within the ranges.
Depending on the lot size, the box will be larger or smaller, usually the POC will always have the highest box size.
NOTE: Size is the starting point, always from the low of the candle.
To find more voids, select a closer LTF to the current one you're using.
To find fewer voids, select a timeframe away from your current one.
Due to Pine Script limitations, we are only able to plot a certain amount of footprints, and we can't plot the whole history chart.
POC will be the largest block displayed, indicating the time point of control
Gray areas are closes above the average
Black are Void or imbalance that price will fill in the future, like FVG
The image above shows an incorrect size input that will lead to bad calculations, while on the other side, a correct size input that will lead to a clear vision and better calculation.
🔶 WINDOWS
The "▲▼" Mode will display delta buyers and delta sellers coloring with voids as black.
It also offers a gradient mode for a beautier visualization
The "Total Volume" mode will display the net volume within the lot size (closes within the levels).
This is useful to spot possible highest net volume within the same highest lot size.
The "POC + Gaps" will show both POC and Gaps as the highest block while all the rest will be considered as the smaller block.
This is useful to see where the highest lot were and if there are higher or lower imbalances within the candle
The last option "Gaps" will simply display the gaps as the highest block, while the POC as the lowest block.
This is useful to have a better view of the gaps areas
🔶 EXAMPLE
This is one of the most basic examples of how this script can be used. POC at the bottom creating a strong support area as price holds and creates higher voids gap that price fills while rising.
🔶 SETTINGS
Users have full control over the script, from colors to choosing the lower-timeframe inputs to disabling the lot size.
LIT - TimingIntroduction
This Script displays the Asia Session Range, the London Open Inducement Window, the NY Open Inducement Window, the Previous Week's high and low, the Previous Day's highs and lows, and the Day Open price in the cleanest way possible.
Description
The Indicator is based on UTC -7 timing but displays the Session Boxes automatically correct at your chart so you do not have to adjust any timings based on your Time Zone and don't have to do any calculations based on your UTC. It is already perfect.
You will see on default settings the purple Asia Box and 2 grey boxes, the first one is for the London Open Inducement Window (1 hour) and the second grey box is for the NY Open Inducement Window (also 1 hour)
Asia Range comes with default settings with the Asia Range high, low, and midline, you can remove these 3 lines in the settings "style" and untick the "Lines" box, that way you only will have the boxes displayed.
Special Feature
Most Timing-based Indicators have "bugged" boxes or don't show clean boxes at all and don't adjust at daylight savings times, we made sure that everything automatically gets adjusted so you don't have to! So the timings will always display at the correct time regarding the daylight savings times.
Combining Timing with Liquidity Zones the right way and in a clear, clean, and simple format.
Different than others this script also shows the "true" Asia range as it respects the "day open gap" which affects the Asia range in other scripts and it also covers the full 8 hours of Asia Session.
Additions
You can add in the settings menu the last week's high and low, the previous day's high and low, and also the day's open price by ticking the boxes in the settings menu
All colors of the boxes are fully adjustable and customizable for your personal preferences. Same for the previous weeks and day highs and lows. Just go to "Style" and you can adjust the Line types or colors to your preferred choice.
Recommended Use
The most beautiful display is on the M5 Timeframe as you have a clear overview of all sessions without losing the intraday view. You can also use it on the M1 for more details or the M15 for the bigger picture. The Template can hide on higher time frames starting from the H1 to not flood your chart with boxes.
How to use the Asia Session Range Box
Use the Asia Range Box as your intraday Guide, keep in mind that a Breakout of Asia high or low induces Liquidity and a common price behavior is a reversal after the fake breakout of that range.
How to use the London Open and NY Open Inducement Windows
Both grey boxes highlight the Open of either London Open or NY Open and you should keep an eye out for potential Liquditiy Graps or Mitigations during that times as this is when they introduce major Liquidity for the regarding Session.
How to use the Asia high, low and midline and day open price
After Asia Range got taken out in one direction, often price comes back to those levels to mitigate or bounce off, so you can imagine those zones as support and resistance on some occasions, recommended in combination with Imbalances.
How to use the previous day and week's highs and lows
Once added in the settings, you can display those price levels, you can use them either as Liquidity Targets or as Inducement Levels once they are taken out.
Enjoy!
Bollinger Bands Liquidity Cloud [ChartPrime]This indicator overlays a heatmap on the price chart, providing a detailed representation of Bollinger bands' profile. It offers insights into the price's behavior relative to these bands. There are two visualization styles to choose from: the Volume Profile and the Z-Score method.
Features
Volume Profile: This method illustrates how the price interacts with the Bollinger bands based on the traded volume.
Z-Score: In this mode, the indicator samples the real distribution of Z-Scores within a specified window and rescales this distribution to the desired sample size. It then maps the distribution as a heatmap by calculating the corresponding price for each Z-Score sample and representing its weight via color and transparency.
Parameters
Length: The period for the simple moving average that forms the base for the Bollinger bands.
Multiplier: The number of standard deviations from the moving average to plot the upper and lower Bollinger bands.
Main:
Style: Choose between "Volume" and "Z-Score" visual styles.
Sample Size: The size of the bin. Affects the granularity of the heatmap.
Window Size: The lookback window for calculating the heatmap. When set to Z-Score, a value of `0` implies using all available data. It's advisable to either use `0` or the highest practical value when using the Z-Score method.
Lookback: The amount of historical data you want the heatmap to represent on the chart.
Smoothing: Implements sinc smoothing to the distribution. It smoothens out the heatmap to provide a clearer visual representation.
Heat Map Alpha: Controls the transparency of the heatmap. A higher value makes it more opaque, while a lower value makes it more transparent.
Weight Score Overlay: A toggle that, when enabled, displays a letter score (`S`, `A`, `B`, `C`, `D`) inside the heatmap boxes, based on the weight of each data point. The scoring system categorizes each weight into one of these letters using the provided percentile ranks and the median.
Color
Color: Color for high values.
Standard Deviation Color: Color to represent the standard deviation on the Bollinger bands.
Text Color: Determines the color of the letter score inside the heatmap boxes. Adjusting this parameter ensures that the score is visible against the heatmap color.
Usage
Once this indicator is applied to your chart, the heatmap will be overlaid on the price chart, providing a visual representation of the price's behavior in relation to the Bollinger bands. The intensity of the heatmap is directly tied to the price action's intensity, defined by your chosen parameters.
When employing the Volume Profile style, a brighter and more intense area on the heatmap indicates a higher trading volume within that specific price range. On the other hand, if you opt for the Z-Score method, the intensity of the heatmap reflects the Z-Score distribution. Here, a stronger intensity is synonymous with a more frequent occurrence of a specific Z-Score.
For those seeking an added layer of granularity, there's the "Weight Score Overlay" feature. When activated, each box in your heatmap will sport a letter score, ranging from `S` to `D`. This score categorizes the weight of each data point, offering a concise breakdown:
- `S`: Data points with a weight of 1.
- `A`: Weights below 1 but greater than or equal to the 75th percentile rank.
- `B`: Weights under the 75th percentile but at or above the median.
- `C`: Weights beneath the median but surpassing the 25th percentile rank.
- `D`: All that fall below the 25th percentile rank.
This scoring feature augments the heatmap's visual data, facilitating a quicker interpretation of the weight distribution across the dataset.
Further Explanations
Volume Profile
A volume profile is a tool used by traders to visualize the amount of trading volume occurring at specific price levels. This kind of profile provides a deep insight into the market's structure and helps traders identify key areas of support and resistance, based on where the most trading activity took place. The concept behind the volume profile is that the amount of volume at each price level can indicate the potential importance of that price.
In this indicator:
- The volume profile mode creates a visual representation by sampling trading volumes across price levels.
- The representation displays the balance between bullish and bearish volumes at each level, which is further differentiated using a color gradient from `low_color` to `high_color`.
- The volume profile becomes more refined with sinc smoothing, helping to produce a smoother distribution of volumes.
Z-Score and Distribution Resampling
Z-Score, in the context of trading, represents the number of standard deviations a data point (e.g., closing price) is from the mean (average). It’s a measure of how unusual or typical a particular data point is in relation to all the data. In simpler terms, a high Z-Score indicates that the data point is far away from the mean, while a low Z-Score suggests it's close to the mean.
The unique feature of this indicator is that it samples the real distribution of z-scores within a window and then resamples this distribution to fit the desired sample size. This process is termed as "resampling in the context of distribution sampling" . Resampling provides a way to reconstruct and potentially simplify the original distribution of z-scores, making it easier for traders to interpret.
In this indicator:
- Each Z-Score corresponds to a price value on the chart.
- The resampled distribution is then used to display the heatmap, with each Z-Score related price level getting a heatmap box. The weight (or importance) of each box is represented as a combination of color and transparency.
How to Interpret the Z-Score Distribution Visualization:
When interpreting the Z-Score distribution through color and alpha in the visualization, it's vital to understand that you're seeing a representation of how unusual or typical certain data points are without directly viewing the numerical Z-Score values. Here's how you can interpret it:
Intensity of Color: This often corresponds to the distance a particular data point is from the mean.
Lighter shades (closer to `low_color`) typically indicate data points that are more extreme, suggesting overbought or oversold conditions. These could signify potential reversals or significant deviations from the norm.
Darker shades (closer to `high_color`) represent data points closer to the mean, suggesting that the price is relatively typical compared to the historical data within the given window.
Alpha (Transparency): The degree of transparency can indicate the significance or confidence of the observed deviation. More opaque boxes might suggest a stronger or more reliable deviation from the mean, implying that the observed behavior is less likely to be a random occurrence.
More transparent boxes could denote less certainty or a weaker deviation, meaning that the observed price behavior might not be as noteworthy.
- Combining Color and Alpha: By observing both the intensity of color and the level of transparency, you get a richer understanding. For example:
- A light, opaque box could suggest a strong, significant deviation from the mean, potentially signaling an overbought or oversold scenario.
- A dark, transparent box might indicate a weak, insignificant deviation, suggesting the price is behaving typically and is close to its average.
Candlestick Pattern Criteria and Analysis Indicator█ OVERVIEW
Define, then locate the presence of a candle that fits a specific criteria. Run a basic calculation on what happens after such a candle occurs.
Here, I’m not giving you an edge, but I’m giving you a clear way to find one.
IMPORTANT NOTE: PLEASE READ:
THE INDICATOR WILL ALWAYS INITIALLY LOAD WITH A RUNTIME ERROR. WHEN INITIALLY LOADED THERE NO CRITERIA SELECTED.
If you do not select a criteria or run a search for a criteria that doesn’t exist, you will get a runtime error. If you want to force the chart to load anyway, enable the debug panel at the bottom of the settings menu.
Who this is for:
- People who want to engage in TradingView for tedious and challenging data analysis related to candlestick measurement and occurrence rate and signal bar relationships with subsequent bars. People who don’t know but want to figure out what a strong bullish bar or a strong bearish bar is.
Who this is not for:
- People who want to be told by an indicator what is good or bad or buy or sell. Also, not for people that don’t have any clear idea on what they think is a strong bullish bar or a strong bearish bar and aren’t willing to put in the work.
Recommendation: Use on the candle resolution that accurately reflects your typical holding period. If you typically hold a trade for 3 weeks, use 3W candles. If you hold a trade for 3 minutes, use 3m candles.
Tldr; Read the tool tips and everything above this line. Let me know any issues that arise or questions you have.
█ CONCEPTS
Many trading styles indicate that a certain candle construct implies a bearish or bullish future for price. That said, it is also common to add to that idea that the context matters. Of course, this is how you end up with all manner of candlestick patterns accounting for thousands of pages of literature. No matter the context though, we can distill a discretionary trader's decision to take a trade based on one very basic premise: “A trader decides to take a trade on the basis of the rightmost candle's construction and what he/she believes that candle construct implies about the future price.” This indicator vets that trader’s theory in the most basic way possible. It finds the instances of any candle construction and takes a look at what happens on the next bar. This current bar is our “Signal Bar.”
█ GUIDE
I said that we vet the theory in the most basic way possible. But, in truth, this indicator is very complex as a result of there being thousands of ways to define a ‘strong’ candle. And you get to define things on a very granular level with this indicator.
Features:
1. Candle Highlighting
When the user’s criteria is met, the candle is highlighted on the chart.
The following candle is highlighted based on whether it breaks out, breaks down, or is an inside bar.
2. User-Defined Criteria
Criteria that you define include:
Candle Type: Bull bars, Bear bars, or both
Candle Attributes
Average Size based on Standard Deviation or Average of all potential bars in price history
Search within a specific price range
Search within a specific time range
Clarify time range using defined sessions and with or without weekends
3. Strike Lines on Candle
Often you want to know how price reacts when it gets back to a certain candle. Also it might be true that candle types cluster in a price region. This can be identified visually by adding lines that extend right on candles that fit the criteria.
4. User-Defined Context
Labeled “Alternative Criteria,” this facet of the script allows the user to take the context provided from another indicator and import it into the indicator to use as a overriding criteria. To account for the fact that the external indicator must be imported as a float value, true (criteria of external indicator is met) must be imported as 1 and false (criteria of external indicator is not met) as 0. Basically a binary Boolean. This can be used to create context, such as in the case of a traditional fractal, or can be used to pair with other signals.
If you know how to code in Pinescript, you can save a copy and simply add your own code to the section indicated in the code and set your bull and bear variables accordingly and the code should compile just fine with no further editing needed.
Included with the script to maximize out-of-the-box functionality, there is preloaded as alternative criteria a code snippet. The criteria is met on the bull side when the current candle close breaks out above the prior candle high. The bear criteria is met when the close breaks below the prior candle. When Alternate Criteria is run by itself, this is the only criteria set and bars are highlighted when it is true. You can qualify these candles by adding additional attributes that you think would fit well.
Using Alternative Criteria, you are essentially setting a filter for the rest of the criteria.
5. Extensive Read Out in the Data Window (right side bar pop out window).
As you can see in the thumbnail, there is pasted a copy of the Data Window Dialogue. I am doubtful I can get the thumbnail to load up perfectly aligned. Its hard to get all these data points in here. It may be better suited for a table at this point. Let me know what you think.
The primary, but not exclusive, purpose of what is in the Data Window is to talk about how often your criteria happens and what happens on the next bar. There are a lot of pieces to this.
Red = Values pertaining to the size of the current bar only
Blue = Values pertaining or related to the total number of signals
Green = Values pertaining to the signal bars themselves, including their measurements
Purple = Values pertaining to bullish bars that happen after the signal bar
Fuchsia = Values pertaining to bearish bars that happen after the signal bar
Lime = Last four rows which are your percentage occurrence vs total signals percentages
The best way I can explain how to understand parts you don’t understand otherwise in the data window is search the title of the row in the code using ‘ctrl+f’ and look at it and see if it makes more sense.
█ [b}Available Candle Attributes
Candle attributes can be used in any combination. They include:
[*}Bodies
[*}High/Low Range
[*}Upper Wick
[*}Lower Wick
[*}Average Size
[*}Alternative Criteria
Criteria will evaluate each attribute independently. If none is set for a particular attribute it is bypassed.
Criteria Quantity can be in Ticks, Points, or Percentage. For percentage keep in mind if using anything involving the candle range will not work well with percentage.
Criteria Operators are “Greater Than,” “Less Than,” and “Threshold.” Threshold means within a range of two numbers.
█ Problems with this methodology and opportunities for future development:
#1 This kind of work is hard.
If you know what you’re doing you might be able to find success changing out the inputs for loops and logging results in arrays or matrices, but to manually go through and test various criteria is a lot of work. However, it is rewarding. At the time of publication in early Oct 2022, you will quickly find that you get MUCH more follow through on bear bars than bull bars. That should be obvious because we’re in the middle of a bear market, but you can still work with the parameters and contextual inputs to determine what maximizes your probability. I’ve found configurations that yield 70% probability across the full series of bars. That’s an edge. That means that 70% of the time, when this criteria is met, the next bar puts you in profit.
#2 The script is VERY heavy.
Takes an eternity to load. But, give it a break, it’s doing a heck of a lot! There is 10 unique arrays in here and a loop that is a bit heavy but gives us the debug window.
#3 If you don’t have a clear idea its hard to know where to start.
There are a lot of levers to pull on in this script. Knowing which ones are useful and meaningful is very challenging. Combine that with long load times… its not great.
#4 Your brain is the only thing that can optimize your results because the criteria come from your mind.
Machine learning would be much more useful here, but for now, you are the machine. Learn.
#5 You can’t save your settings.
So, when you find a good combo, you’ll have to write it down elsewhere for future reference. It would be nice if we could save templates on custom indicators like we can on some of the built in drawing tools, but I’ve had no success in that. So, I recommend screenshotting your settings and saving them in Notion.so or some other solid record keeping database. Then you can go back and retrieve those settings.
#6 no way to export these results into conditions that can be copy/pasted into another script.
Copy/Paste of labels or tables would be the best feature ever at this point. Because you could take the criteria and put it in a label, copy it and drop it into another strategy script or something. But… men can dream.
█ Opportunities to PineCoders Learn:
1. In this script I’m importing libraries, showing some of my libraries functionality. Hopefully that gives you some ideas on how to use them too.
The price displacement library (which I love!)
Creative and conventional ways of using debug()
how to display arrays and matrices on charts
I didn’t call in the library that holds the backtesting function. But, also demonstrating, you can always pull the library up and just copy/paste the function out of there and into your script. That’s fine to do a lot of the time.
2. I am using REALLY complicated logic in this script (at least for me). I included extensive descriptions of this ? : logic in the text of the script. I also did my best to bracket () my logic groups to demonstrate how they fit together, both for you and my future self.
3. The breakout, built-in, “alternative criteria” is actually a small bit of genius built in there if you want to take the time to understand that block of code and think about some of the larger implications of the method deployed.
As always, a big thank you to TradingView and the Pinescript community, the Pinescript pros who have mentored me, and all of you who I am privileged to help in their Pinescripting journey.
"Those who stay will become champions" - Bo Schembechler
Rolling QuartilesThis script will continuously draw a boxplot to represent quartiles associated with data points in the current rolling window.
Description :
A quartile is a statistical term that refers to the division of a dataset based on percentiles.
Q1 : Quartile 1 - 25th percentile
Q2 : Quartile 2 - 50th percentile, as known as the median
Q3 : Quartile 3 - 75th percentile
Other points to note:
Q0: the minimum
Q4: the maximum
Other properties :
- Q1 to Q3: a range is known as the interquartile range ( IQR ). It describes where 50% of data approximately lie.
- Line segments connecting IQR to min and max (Q0→Q1, and Q3→Q4) are known as whiskers . Data lying outside the whiskers are considered as outliers. However, such extreme values will not be found in a rolling window because whenever new datapoints are introduced to the dataset, the oldest values will get dropped out, leaving Q0 and Q4 to always point to the observable min and max values.
Applications :
This script has a feature that allows moving percentiles (moving values of Q1, Q2, and Q3) to be shown. This can be applied for trading in ways such as:
- Q2: as alternative to a SMA that uses the same lookback period. We know that the Mean (SMA) is highly sensitive to extreme values. On the other hand, Median (Q2) is less affected by skewness. Putting it together, if the SMA is significantly lower than Q2, then price is regarded as negatively skewed; prices of a few candles are likely exceptionally lower. Vice versa when price is positively skewed.
- Q1 and Q3: as lower and upper bands. As mentioned above, the IQR covers approximately 50% of data within the rolling window. If price is normally distributed, then Q1 and Q3 bands will overlap a bollinger band configured with +/- 0.67x standard deviations (modifying default: 2) above and below the mean.
- The boxplot, combined with TradingView's builtin bar replay feature, makes a great tool for studies purposes. This helps visualization of price at a chosen instance of time. Speaking of which, it can also be used in conjunction with a fixed volume profile to compare and contrast the effects (in terms of price range) with and without consideration of weights by volume.
Parameters :
- Lookback: The size of the rolling window.
- Offset: Location of boxplot, right hand side relative to recent bar.
- Source data: Data points for observation, default is closing price
- Other options such as color, and whether to show/hide various lines.
Real-Fast Fourier Transform of Price w/ Linear Regression [Loxx]Real-Fast Fourier Transform of Price w/ Linear Regression is a indicator that implements a Real-Fast Fourier Transform on Price and modifies the output by a measure of Linear Regression. The solid line is the Linear Regression Trend of the windowed data, The green/red line is the Real FFT of price.
What is the Discrete Fourier Transform?
In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle.
What is the Complex Fast Fourier Transform?
The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T).
The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward.
What is Real-Fast Fourier Transform?
Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded.
Inputs:
src = source price
uselreg = whether you wish to modify output with linear regression calculation
Windowin = windowing period, restricted to powers of 2: "4", "8", "16", "32", "64", "128", "256", "512", "1024", "2048"
Treshold = to modified power output to fine tune signal
dtrendper = adjust regression calculation
barsback = move window backward from bar 0
mutebars = mute bar coloring for the range
Further reading:
Real-valued Fast Fourier Transform Algorithms IEEE Transactions on Acoustics, Speech, and Signal Processing, June 1987
Related indicators utilizing Fourier Transform
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Price w/ Projection Forecast
Debug_Window_LibraryLibrary "Debug_Window_Library"
Provides a framework for logging debug information to a window on the chart.
consoleWrite(txt, maxLines) Adds a line of text to the debug window. The text is rolled off the bottom of the window as it fills up.
Parameters:
txt : - this is the text to be appended to the window
maxLines : - this is the size of the window in lines.
Returns: nothing
The example above shows the close value for the last 10 bars.
Here's the code.
//@version=5
indicator("Debug Library test Script", overlay=true)
import sp2432/Debug_Window_Library/1 as dbg
// add some text to the debug window
dbg .consoleWrite( str .tostring(close), 10)
Sentiment Estimator [AstrideUnicorn]Sentiment Estimator is an indicator that estimates market sentiment using only its pricing data. It counts bullish and bearish candles in a rolling window and calculates their relative values as percentages of the total amount of candles in the window. Market sentiment shows the direction in which the market is biased to move or the current trend direction. Extreme values of the market sentiment are contrarian signals. When the market sentiment is too bullish, it is time to sell and vice versa.
HOW TO USE
Sentiment Estimator plots a pair of green and red circles for each candle. They represent bullish and bearish sentiments, respectively.
The vertical positions of the circles show corresponding sentiment values in percentage units. For example, if a green circle's height is 60, the market is 60% bullish. In this case, the red circle's height will be 40, as bullish and bearish parts of the market sentiment sum to 100%.
The blue line plotted at the 50% level shows the neutral sentiment level. If a green circle is above the blue line, the prevailing market sentiment at that time is bullish, and the market is biased to move up. If a red one is above, the market has predominantly bearish sentiment and is prone to move down.
The red level shows extreme sentiment level. If a green or red circle is above this line, it means that the market is extremely bullish or bearish, respectively. It is a contrarian signal, and one can expect a reversal soon. In this case, a blue label with the text "reversal expected" is shown.
SETTINGS
Timeframe - allows choosing a timeframe other than the chart's one for the indicator calculation.
Look-Back Window - sets the historical window length used to perform the calculations. You can adjust the window to get the best results for a particular market or timeframe.
DrawIndicatorOnTheChartLibrary "DrawIndicatorOnTheChart"
this library is used to show an indicator (such RSI, CCI, MOM etc) on the main chart with indicator's horizontal lines in a window. Location of the window is calculated dynamically by last price movemements
drawIndicator(indicatorName, indicator, indicatorcolor, period, indimax_, indimin_, levels, precision, xlocation) draws the related indicator on the chart
Parameters:
indicatorName : is the indicator name as string such "RSI", "CCI" etc
indicator : is the indicator you want to show, such rsi(close, 14), mom(close, 10) etc
indicatorcolor : is the color of indicator line
period : is the length of the window to show
indimax_ : is the maximum value of the indicator, for example for RSI it's 100.0, if the indicator (such CCI, MOM etc) doesn't have maximum value then use "na"
indimin_ : is the minimum value of the indicator, for example for RSI it's 0.0, if the indicator (such CCI, MOM etc)doesn't have maximum value then use "na"
levels : is the levels of the array for the horizontal lines. for example if you want horizontal lines at 30.0, and 70.0 then use array.from(30.0, 70.0). if no horizontal lines then use array.from(na)
precision : is the precision/number of decimals that is used to show indicator values, for example for RSI set it 2
xlocation : is end location of the indicator window, for example if xlocation = 0 window is created on the index of the last bar/candle
Returns: none
`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.
RSI Stochastic AlignmentRSI Stochastic Alignment input RSI and Stochastic into 1 windows and align them to find bullish and bearish divergence.
A. The Line display in windows:
1. Fast RSI (green line) is RSI(close,3)
2. Slow Rsi (red line) is Linear Regession of Fast RSI with 5 period and offset 0 = linreg(rsi,5,0)
3. Fast Stochastic (blue line) is %K of Stochastic
4. Slow Stochastic (aqua line) is %D of Stochastic
B. Alignment and Divergence Detect
1. Bearish Divergence:
* Slow RSI at top
* Fast Stochastic at bottom
* Fast RSI over overbought level (default = 70)
* Slow Stochastic under overbought level minus a constant value (Divergence Power value, default this value = 1)
2. Bullish Divergence:
* Fast Stochastic at top
* Slow RSI at bottom
* Fast RSI under oversold level (default = 30)
* Slow Stochastic over oversold level plus a constant value (Divergence Power value, default this value = 1)
C. Script Option
1. RSI value adjustable
2. Stochastic value adjustable
3. Overbought and Oversold Level adjustable
4. Enable/Disable Level line
5. Enable/Disable Divergence Column
6. Enable/Disable Key Bar Colored
Narrow Bandpass FilterIn technical analysis most bandpass filters like the MACD, TRIX, AO, or COG will have a non-symmetrical frequency response, in fact, this one is generally right-skewed. As such these oscillators will not fully remove lower and higher frequency components from the input signal, the following indicator is a bandpass filter with a more symmetrical frequency response with the possibility to have a narrow bandwidth, this allows the indicator to potentially isolate sinusoids from the input signal.
Indicator & Settings
The filter is calculated via convolution, if we take into account that the frequency response of a filter is the Fourier transform of its weighting function we can deduce that we can get a narrow response by using a sinusoid sin(2𝛑nf) as the weighting function, with the peak of the frequency response being equal to f , this makes the filter quite easy to control by the user, as this one can choose the frequency to be isolated. The length of the weighting function controls the bandwidth of the frequency response, with a higher length returning an ever-smaller frequency response width.
In the indicator settings the "Cycle Period" determine the period of the sinusoid used as a weighting function, while "Bandwidth" determine the filter passband width, with higher values returning a narrower passband, this setting also determine the length of the convolution, because the sum of the weights must add to 0 we know that the length of the convolution must be a multiple of "Cycle Period", so the length of the convolution is equal to "Cycle Period × Bandwidth".
Finally, the windowing option determines if a window is applied to the weighting function, a weighting function allow to remove ripples in the filter frequency response
Above both indicators have a Cycle period of 100 and a Bandwidth of 4, we can see that the indicator with no windowing don't fully remove the trend component in the price, this is due to the presence of ripples allowing lower frequency components to pass, this is not the case for the windowed version.
In theory, an ultra-narrow passband would allow to fully isolate pure sinusoids, below the cycle period of interest is 20
using a bandwidth equal to 10 allow to retain that sinusoid, however, note that this sinusoid is subject to phase shift and that it might not be a dominant frequency in the price.
Decaying Rate of Change Non Linear FilterThis is a potential solution to dealing with the inherent lag in most filters especially with instruments such as BTC and the effects of long periods of low volatility followed by massive volatility spikes as well as whipsaws/barts etc.
We can try and solve these issues in a number of ways, adaptive lengths, dynamic weighting etc. This filter uses a non linear weighting combined with an exponential decay rate.
With the non linear weighting the filter can become very responsive to sudden volatility spikes. We can use a short length absolute rate of change as a method to improve weighting of relative high volatility.
c1 = abs(close - close ) / close
Which gives us a fairly simple filter :
filter = sum(c1 * close,periods) / sum(c1,periods)
At this point if we want to control the relative magnitude of the ROC coefficients we can do so by raising it to a power.
c2 = pow(c1, x)
Where x approaches zero the coefficient approaches 1 or a linear filter. At x = 1 we have an unmodified coefficient and higher values increase the relative magnitude of the response. As an extreme example with x = 10 we effectively isolate the highest ROC candle within the window (which has some novel support resistance horizontals as those closes are often important). This controls the degree of responsiveness, so we can magnify the responsiveness, but with the trade off of overshoot/persistence.
So now we have the problem whereby that a highly weighted data point from a high volatility event persists within the filter window. And to a possibly extreme degree, if a reversal occurs we get a potentially large "overshoot" and in a way actually induced a large amount of lag for future price action.
This filter compensates for this effect by exponentially decaying the abs(ROC) coefficient over time, so as a high volatility event passes through the filter window it receives exponentially less weighting allowing more recent prices to receive a higher relative weighting than they would have.
c3 = c2 * pow(1 - percent_decay, periods_back)
This is somewhat similar to an EMA, however with an EMA being recursive that event will persist forever (to some degree) in the calculation. Here we are using a fixed window, so once the event is behind the window it's completely removed from the calculation
I've added Ehler's Super Smoother as an optional smoothing function as some highly non linear settings benefit from smoothing. I can't remember where I got the original SS code snippet, so if you recognize it as yours msg me and I'll link you here.
Macros, Sessions, and Quarters - psyploThis script was developed for my personal trading workflow, where I rely heavily on macro time windows and session-based timing to guide my strategy. I designed this tool to help visualize important time-based zones that are crucial in my trading approach, and I believe other traders might find it useful as well.
Features
Macro Time Window Boxes: Visual representation of price range within a configurable time window around the top of every hour.
Session Ribbon Boxes: Clearly marks the Asia, London, and New York trading sessions, labeled distinctly for better visual separation.
Session Quarters: Each session is automatically divided into four quarters, providing more granular time segmentation within sessions.
Time Zone: All session calculations are based on UTC +8 (Asia/Singapore) timezone.
This tool helps traders:
Identify volatility zones around hourly opens.
Track session-specific market dynamics.
Recognize shifts in momentum or liquidity during each session quarter.
I created this for my own strategies, but it can serve any trader who benefits from time-based market segmentation.
Feel free to adjust the colors and time window settings to suit your preferences!
LANZ Strategy 5.0 [Backtest]🔷 LANZ Strategy 5.0 — Rule-Based BUY Logic with Time Filter, Session Limits and Auto SL/TP Execution
This is the backtest version of LANZ Strategy 5.0, built as a strategy script to evaluate real performance under fixed intraday conditions. It automatically places BUY and SELL trades based on structured candle confirmation, EMA trend alignment, and session-based filters. The system simulates real-time execution with precise Stop Loss and Take Profit levels.
📌 Built for traders seeking to simulate clean intraday logic with fully automated entries and performance metrics.
🧠 Core Logic & Strategy Conditions
✅ BUY Signal Conditions:
Price is above the EMA200
The last 3 candles are bullish (close > open)
The signal occurs within the defined session window (NY time)
Daily trade limit has not been exceeded
If all are true, a BUY order is executed at market, with SL and TP set immediately.
🔻 SELL Signal Conditions (Optional):
Exactly inverse to BUY (below EMA + 3 bearish candles). Disabled by default.
🕐 Operational Time Filter (New York Time)
You can fully customize your intraday window:
Start Time: e.g., 01:15 NY
End Time: e.g., 16:00 NY
The system evaluates signals only within this range, even across midnight if configured.
🔁 Trade Management System
One trade at a time per signal
Trades include a Stop Loss (SL) and Take Profit (TP) based on pip distance
Trade result is calculated automatically
Each signal is shown with a triangle marker (BUY only, by default)
🧪 Backtest Accuracy
This version uses:
strategy.order() for entries
strategy.exit() for SL and TP
strategy.close_all() at the configured manual closing time
This ensures realistic behavior in the TradingView strategy tester.
⚙️ Flow Summary (Step-by-Step)
On every bar, check:
Is the time within the operational session?
Is the price above the EMA?
Are the last 3 candles bullish?
If conditions met → A BUY trade is opened:
SL = entry – X pips
TP = entry + Y pips
Trade closes:
If SL or TP is hit
Or at the configured manual close time (e.g., 16:00 NY)
📊 Settings Overview
Timeframe: 1-hour (ideal)
SL/TP: Configurable in pips
Max trades/day: User-defined (default = 99 = unlimited)
Manual close: Adjustable by time
Entry type: Market (not limit)
Visuals: Plotshape triangle for BUY entry
👨💻 Credits:
💡 Developed by: LANZ
🧠 Strategy logic & execution: LANZ
✅ Designed for: Clean backtesting, clarity in execution, and intraday logic simulation
Price Statistical Strategy-Z Score V 1.01
Price Statistical Strategy – Z Score V 1.01
Overview
A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”.
This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included.
Core Methodology
The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits.
Signal Conditions
Entry Condition:
A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met.
Exit Condition:
The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow.
Signal Gapping:
A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise.
Momentum Filter:
Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles.
Z-Score Function
The Z-Score is calculated as:
Z = (Close - SMA(Close, N)) / STDEV(Close, N)
Where N is the base period selected by the user.
Input Parameters
Enable Smoothed Z-Score Strategy
Enables or disables the Z-Score strategy logic. When disabled, no trades are executed.
Z-Score Base Period
Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes.
Short-Term Smoothing
Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility.
Long-Term Smoothing
Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic.
Bars gap between identical signals
Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals.
Trade Visualization Table
A table positioned at the bottom-right displays live PnL for open trades:
Entry Price
Unrealized PnL %
Text colors adapt based on whether unrealized profit is positive, negative, or neutral.
Technical Notes
This strategy uses only close prices — no trend indicators or volume components are applied.
All calculations are based on simple moving averages and standard deviation over user-defined windows.
Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
Tangent Extrapolation ForecastTangent Extrapolation Forecast
This indicator visually projects price direction by drawing a smoothed sequence of tangent lines based on recent price movements. For each bar in a user-defined lookback window, it calculates the slope over a smoothing period and extends the projected price forward. The resulting polyline forecast connect the endpoints of the extrapolations, and is color-coded to reflect directional changes: green for upward moves, red for downward, and gray for flat segments. This tool can assist traders in visualizing short-term momentum and potential trend continuity without introducing artificial future gaps.
Inputs:
Bars to Use: Number of historical bars used in the forecast.
Slope Smoothing Window: The number of bars used to calculate slope for projection.
Source: Price input for calculations (default is close).
This indicator does not generate buy/sell signals. It is intended as a visual aid to support discretionary analysis.
FibSync - DynamicFibSupportWhat is this indicator?
FibSync – DynamicFibSupport overlays your chart with both static and dynamic Fibonacci retracement levels, making it easy to spot potential areas of support and resistance.
Static Fibs: Calculated from the highest and lowest price over a user-defined lookback period.
Dynamic Fibs: Calculated from the most recent swing high and swing low, automatically adapting as new swings form.
How to use
Add the indicator to your chart.
Configure the settings:
Static Fib Period: Sets the lookback window for static fib levels.
Show Dynamic Fibonacci Levels: Toggle dynamic fibs on/off.
Dynamic Fib Swing Search Window: How far back to search for valid swing highs/lows.
Swing Strength (bars left/right): How many bars define a swing high/low (higher = stronger swing).
Interpret the levels:
Solid lines are static fibs.
Transparent lines are dynamic fibs (if enabled).
Colors match standard fib conventions (yellow = 0.236, red = 0.382, blue = 0.618, green = 0.786, gray = 0.5).
Tips
Static and dynamic fibs can overlap-this often highlights especially important support/resistance zones.
Adjust the swing strength for your trading style: lower values for short-term, higher for long-term swings.
Hide/show individual lines using the indicator’s style settings in TradingView.
Trading Ideas (for higher timeframes and static fibs)
Close above the blue line (0.618 static fib):
This can be interpreted as a potential long (buy) signal, suggesting the market is breaking above a key resistance level.
Close below the red line (0.382 static fib):
This can be interpreted as a potential short (sell) signal, indicating the market is breaking below a key support level.
Note: These signals are most meaningful on higher timeframes and when using the static fib lines. Always confirm with your own strategy and risk management.
ICT Macro H1"H1 Candle Time Box" is a custom TradingView indicator that highlights a configurable time window surrounding the close of each 1-hour (H1) candle. The indicator draws a transparent box 15 minutes before and after each H1 candle close (by default), helping traders visualize time-based reaction zones.
🔍 Features:
Custom time window: Users can set how many minutes before and after the H1 close the box should appear.
Dynamic positioning: Boxes are drawn slightly above the candles to avoid overlap with price bars.
Live time labels: Each box displays its time range (e.g., "08:45 - 09:15") based on the start and end time of the zone.
Auto-cleaning: Only a limited number of recent boxes (default: 5) are shown, keeping the chart clean.
Requires 1-minute chart for precise timing.
This tool is especially helpful for intraday traders to identify areas of interest or market reactions before and after key hourly closes.
