Dix$on's Weighted Volume FlowDixson's Weighted Volume Flow
Dixson's Weighted Volume Flow is a technical indicator designed to analyze and visualize the distribution of buy and sell volume within a given timeframe. It dynamically calculates the proportional allocation of volume based on price action within each bar, providing insights into market sentiment and activity. This indicator displays horizontal volume bars in a separate pane and annotates them with precise volume values.
How It Works
1. Volume Allocation:
- The indicator calculates buy and sell volume using the following formulas:
- Buy Volume = (Close - Low) / (High - Low) Total Volume
- Sell Volume = (High - Close) / (High - Low) Total Volume
- These formulas allocate volume proportionally based on the bar's price range, attributing more volume to buying or selling depending on the relationship between the close, high, and low prices.
2. Dynamic Scaling:
- The buy and sell volumes are scaled relative to their combined total for the period.
- The resulting values determine the length of the horizontal bars, providing a comparative view of buy and sell activity.
3. Bar Visualization:
- Buy Volume Bars: Displayed as green horizontal bars.
- Sell Volume Bars: Displayed as red horizontal bars.
- The lengths of the bars represent the dominance of buy or sell volume, scaled dynamically within the pane.
4. Labels:
- Each bar is annotated with a label showing its calculated buy or sell volume value.
5. Timeframe Adjustment:
- The indicator uses the request.security() function to fetch data from the selected timeframe, allowing users to customize their analysis for intraday, daily, or longer-term trends.
6. Customization Options:
- Enable or disable the indicator using a toggle.
- Adjust colors for the buy/sell bars and text labels to suit your chart theme.
How to Use It
1. Enable the Indicator:
- Activate the indicator using the "Enable/Disable" toggle in the settings.
2. Select a Timeframe:
- Choose the timeframe for analysis (e.g., 1-minute, 1-hour, daily). The indicator fetches volume data specific to the selected timeframe.
3. Interpret the Visualization:
- Compare Bar Lengths:
- Longer buy volume bars (green) indicate stronger buying activity.
- Longer sell volume bars (red) suggest dominant selling pressure.
- Labels:
- Use the labels to view the exact buy and sell volume values for precise analysis.
4. Combine with Other Tools:
- Use the indicator alongside price action analysis, support/resistance levels, or trend indicators to confirm market sentiment and detect potential reversals.
5. Monitor Imbalances:
- Significant disparities between buy and sell volume can signal shifts in market sentiment, such as the end of a trend or the start of a breakout.
Practical Applications
- Trend Confirmation:
- Align the dominance of buy or sell volume with price trends to confirm market direction.
- Reversal Signals:
- Watch for volume imbalances or a sudden shift in the dominance of buy or sell volume to identify potential reversals.
- High-Activity Zones:
- Identify areas with increased volume to anticipate significant price movements or key support/resistance interactions.
Dixson's Weighted Volume Flow provides a clear and systematic way to analyze market activity by visualizing the dynamics of buy and sell volume. It is particularly useful for traders looking to enhance their understanding of volume-based sentiment and its impact on price movements.
Cerca negli script per "bar"
Wick/Tail Candle MeasurementsThis indicator runs on trading view. It was programmed with pine script v5.
Once the indicator is running you can scroll your chart to any year or date on the chart, then for the input select the date your interested in knowing the length of the tails and wicks from a bar and their lengths are measured in points.
To move the measurement, you can select the vertical bar built into the indicator AFTER clicking the green label and moving it around using the vertical bar *only*. You must click the vertical bar in the middle of the label to move the indicator calculation to another bar. You can also just select the date using the input as mentioned. This indicator calculates just one bar at a time.
measurements are from bar OPEN to bar HIGH for measured WICKS regardless of the bar being long or short and from bar OPEN to bar LOW for measured TAILS also regardless of the bar being long or short.
This indicator calculates tails and wicks including the bar body in the calculations. Basically showing you how much the market moved in a certain direction for the entire duration of that Doji candle.
Its designed to measure completed bars on the daily futures charts. (Dow Jones, ES&P500, Nasdaq, Russell 2000, etc) Although it may work well on other markets. The indicator could easily be tweaked in order to work well with other markets. It is not designed for forex markets currently.
Bull Bear Power With EMA FilterDescription of Indicator:
This Pine Script indicator colors price bars based on the open price in relation to custom moving averages (EMA/SMA), Bull/Bear Power (BBPower), and an optional VWAP filter. The bar colors help identify bullish and bearish conditions with added visual cues for price positioning relative to VWAP.
Key Features:
Customizable Moving Averages (EMA/SMA):
The user can select between EMA or SMA for both short-term and long-term moving averages.
Default moving averages are set to 5 (short-term) and 9 (long-term) but can be adjusted by the user.
Bullish Condition (Blue or Purple Bars):
A bar is colored blue if the following conditions are met:
The open price is above both the short-term and long-term moving averages.
The short-term moving average (MA 1) is above the long-term moving average (MA 2).
BBPower (open price minus the 13-period EMA) is positive, indicating bullish strength.
If the VWAP filter is enabled and the price opens below VWAP, the bullish bars will turn purple.
Bearish Condition (Yellow or Orange Bars):
A bar is colored yellow if the following conditions are met:
The open price is below both the short-term and long-term moving averages.
The short-term moving average (MA 1) is below the long-term moving average (MA 2).
BBPower is negative or zero, indicating bearish market conditions.
If the VWAP filter is enabled and the price opens above VWAP, the bearish bars will turn orange.
VWAP Filter (Optional):
An optional filter allows the user to add VWAP (Volume-Weighted Average Price) to the bar coloring logic.
When the VWAP filter is enabled, it provides additional information about price positioning relative to VWAP, turning bullish bars purple and bearish bars orange depending on whether the price opens above or below VWAP.
Usage:
Bullish Trend: Look for blue or purple bars to identify potential bullish momentum.
Bearish Trend: Look for yellow or orange bars to spot bearish conditions in the market.
The indicator allows users to customize the length and type of moving averages (EMA or SMA), as well as decide whether to apply the VWAP filter.
This indicator provides traders with clear visual signals to quickly assess the strength of bullish or bearish conditions based on the price's position relative to custom moving averages, BBPower, and VWAP, helping with trend identification and potential trade setups.
Trend and RSI Bias FusionTrend and RSI Bias Fusion Indicator
This is my first ever indicator. I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share this because I believe in learning and earing together as a community. I will later share the rest of the indicators I have created. This is my first time ever sharing any indicator so if you guys have any questions or suggestions write them.
Overview
The "Trend and RSI Bias Fusion" indicator is a versatile tool designed to help traders identify key market trends, potential reversals, momentum shifts, and RSI-based pullbacks. This indicator fuses trend analysis and RSI bias into a single, comprehensive visual, making it easier to make informed trading decisions across various timeframes and market conditions.
Features
Dual Timeframe Analysis: Combines trend analysis on a higher timeframe (e.g., Daily) with RSI analysis on a lower timeframe (e.g., 4-Hour), providing a more granular view of market conditions. You can, however, choose any timeframe you want for instance 12hr with trend and 2hr RSI analysis.
Trend and Momentum Visualization: The indicator uses Exponential Moving Averages (EMAs) to determine trend direction and colors the chart background to reflect bullish or bearish trends, along with momentum strength.
RSI Bias Detection: Automatically identifies overbought and oversold conditions using the RSI, providing a clear indication of potential market reversals or continuations.
Color-Coded Bars: Optionally color codes bars based on either trend direction or RSI bias, giving you a quick visual cue of the market's state.
Reversal Markers: Displays trend reversal markers on the chart when the short-term EMA crosses over or under the long-term EMA.
Calculation Details
Exponential Moving Averages (EMAs): The indicator calculates short-term and long-term EMAs using the closing prices.
The crossover between these EMAs is used to determine the trend direction:
Short-Term EMA: Typically a 14-period EMA.
Long-Term EMA: Typically a 50-period EMA.
Momentum: Calculated using the RSI and then centered around zero by subtracting 50. This allows the indicator to distinguish between positive and negative momentum.
RSI Bias: The RSI is calculated on a lower timeframe to detect overbought (above 60) and oversold (below 40) conditions, which are used to determine the bias:
RSI Above 60: Indicates potential overbought conditions (bearish bias).
RSI Below 40: Indicates potential oversold conditions (bullish bias).
How to Use the Indicator
Select Your Timeframes: Choose your preferred trend timeframe (e.g., Daily) and RSI timeframe (e.g., 4-2 Hour) in the indicator settings. These should match your trading strategy and the asset class you're analyzing.
Interpret Trend and Momentum
Background Color: The background color reflects the current trend direction:
Green/Lime: Uptrend, with lime indicating positive momentum.
Red/Maroon: Downtrend, with maroon indicating positive momentum within a downtrend.
Momentum Histogram: The histogram plot shows momentum, color-coded by the trend. A histogram above zero with green/lime indicates bullish momentum, while below zero with red/maroon indicates bearish momentum.
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Read RSI Bias:
The RSI bias line helps identify the current market state relative to overbought or oversold levels. The RSI value is plotted on the chart, with lines at 60 and 40 to mark these levels.
When the RSI crosses above 60, it suggests a bearish bias; crossing below 40 suggests a bullish bias.
Use Reversal Markers: The indicator places small circles on the chart at points where the short-term EMA crosses the long-term EMA, signaling potential trend reversals.
Bar Color Customization:
You can choose to color the bars based on either the trend or the RSI bias in the indicator settings. In the Images below I have changed the colors to fit my personal style , Blue for uptrend and Pink for downtrend:
Trend-Based: Bars will reflect the trend direction (green for uptrend or in this case blue, red for downtrend or in this case pink).
RSI-Based: Bars will reflect RSI conditions (yellow for overbought, maroon for oversold).
Image above: RSI is set to 4hr and Trend is set to daily, uses RSI bar color
Image above: RSI is set to 4hr and Trend is set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Image above: Both RSI and Trend are set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, without bar color
Image above: Both RSI and Trend are set to daily, how it looks on a clean chart
Example Use Case Swing Traders:
For instance, if you're trading a 4-hour chart of USDCHF:
Set the trend timeframe to Daily and the RSI timeframe to 4-Hour.
Watch for background color shifts and reversal markers to determine trend direction.
Use RSI bias to time your entries and exits, especially around overbought/oversold levels.
Enable bar coloring to quickly see when conditions favor either trend continuation or reversal.
This indicator is particularly effective for swing traders and those who want to align their trades with higher timeframe trends while using momentum and RSI for entry and exit signals.
For Day Traders
Timeframe Selection:
Trend Timeframe: Set to a higher intraday timeframe such as the 1 or 2 Hour chart.
RSI Timeframe: Set to a shorter timeframe like 15-10 Minutes or 5-Minutes to capture finer details of intraday momentum shifts.
Using the Indicator:
Trend Identification: Day traders can use the background color to quickly identify whether the market is in a bullish or bearish trend on the 1-Hour chart. A green background suggests looking for long opportunities, while a red background suggests short opportunities.
Momentum Analysis: The histogram can help day traders gauge the strength of the current trend. For example, if the histogram is green and above zero, the trader may consider buying pullbacks within the trend.
RSI Bias: Monitor RSI levels on the lower timeframe (e.g., 15-Minutes). If the RSI crosses below 40, it indicates an oversold condition, potentially signaling a buying opportunity, especially if it aligns with a bullish trend on the higher timeframe.
Trade Execution:
Look for entries when the RSI shows a reversal or pullback in the direction of the higher timeframe trend.
Use the trend reversal markers to confirm potential intraday reversals, adding extra confidence to trade setups.
For Scalpers
Timeframe Selection:
Trend Timeframe: Set to a short intraday timeframe like 15-Minutes or 5-Minutes.
RSI Timeframe: Use an even shorter timeframe, such as 1-Minute, to capture rapid price movements.
Final Notes:
The "Trend and RSI Bias Fusion" indicator is a powerful tool that combines trend analysis, momentum assessment, and RSI insights into one cohesive package. By integrating these different aspects, the indicator helps traders navigate complex market environments with greater clarity and confidence. Customize the settings to fit your specific trading style and market and use it to stay ahead of market trends and potential reversals.
My Scripts/Indicators/Ideas /Systems that I share are only for educational purposes!
Brooks Always In [KintsugiTrading]Brooks Always In
Overview:
The "Brooks Always In Indicator" by KintsugiTrading is a tool designed for traders who follow price action methodologies inspired by Al Brooks. This indicator identifies key bar patterns and breakouts, plots an Exponential Moving Average (EMA), and highlights consecutive bullish and bearish bars. It is intended to assist traders in making informed decisions based on price action dynamics.
Features:
Consecutive Bar Patterns:
Identifies and highlights consecutive bullish and bearish bars.
Differentiates between bars that are above/below the EMA and those that are not.
Customizable EMA:
Option to display an Exponential Moving Average (EMA) with user-defined length and offset.
The EMA can be smoothed using various methods such as SMA, EMA, SMMA (RMA), WMA, and VWMA.
Breakout Patterns:
Recognizes bullish and bearish breakout bars and outside bars.
Tracks inside bars and prior bar conditions to better understand the market context.
Customizable Display:
Users can display or hide the EMA, consecutive bar patterns, and consecutive bars relative to the moving average.
How to Use:
Customize Settings:
First, I like to navigate to the top right corner of the chart (bolt icon), and change both the bull and bear body color to match the background (white/black) - this helps the user visualize the indicator far better.
Next, Toggle to display EMA, consecutive bar patterns, and consecutive bars relative to the moving average using the provided input options.
Adjust the EMA length, source, and offset as per your trading strategy.
Select the smoothing method and length for the EMA if desired.
Analyze Key Patterns:
Observe the highlighted bars on the chart to identify consecutive bullish and bearish patterns.
Use the plotted EMA to gauge the general trend and analyze the relationship between price bars and the moving average.
Informed Decision Making:
Utilize the identified bar patterns and breakouts to make informed trading decisions, such as identifying potential entry and exit points based on price action dynamics.
Good luck with your trading!
HTF Descending TriangleHTF Descending Triangle aims at detecting descending triangles using higher time frame data, without repainting nor misalignment issues.
Descending triangles are defined by a falling upper trend line and an horizontal lower trend line. It is a chart pattern used in technical analysis to predict the continuation of a downtrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected descending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a low factor detection criteria to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
Low Factor checkbox: Turns on/off low factor detection criteria.
Low Factor field: Sets low factor to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
High must be lower than previous bar.
Open/close min value must be higher than reference bar low.
When low factor criteria is turned on, low must be lower than reference bar open/close min value minus low factor proportion of the distance between reference bar low and open/close min value.
HTF Ascending TriangleHTF Ascending Triangle aims at detecting ascending triangles using higher time frame data, without repainting nor misalignment issues.
Ascending triangles are defined by an horizontal upper trend line and a rising lower trend line. It is a chart pattern used in technical analysis to predict the continuation of an uptrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected ascending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a high factor detection criteria to apply on higher time frame bars high as a proportion of the distance between the reference bar high and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
High Factor checkbox: Turns on/off high factor detection criteria.
High Factor field: Sets high factor to apply on higher time frame bars high as a proportion of the distance between the reference bar high and close/open.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
Low must be higher than previous bar.
Open/close max value must be lower than reference bar high.
When high factor criteria is turned on, high must be higher than reference bar open/close max value plus high factor proportion of the distance between reference bar high and open/close max value.
regressionsLibrary "regressions"
This library computes least square regression models for polynomials of any form for a given data set of x and y values.
fit(X, y, reg_type, degrees)
Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset.
Parameters:
X (array) : (float ) X inputs for regression fit.
y (array) : (float ) y outputs for regression fit.
reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom
degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial.
Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial
regress(reg, x)
Regress one x input.
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
x (float) : (float) The input value cooresponding to the y_pred.
Returns: (float) The best fit y value for the given x input and regression.
predict(reg, X)
Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
X (array)
Returns: (float ) The best fit y values for the given x input and regression.
generate_points(reg, x, y, left_index, right_index)
Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array) : (float ) x values which coorispond to passed y values
y (array) : (float ) y values which coorispond to passed x values
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
Returns: (chart.point ) Returns an array of chart points
plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width)
Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array)
y (array)
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
curved (bool) : (bool) If the polyline is curved or not.
close (bool) : (bool) If true the polyline will be closed.
line_color (color) : (color) The color of the line.
line_width (int) : (int) The width of the line.
Returns: (polyline) The polyline for the regression.
series_to_list(src, left_index, right_index)
Convert a series to a list. Creates a list of all the cooresponding source values
from left_index to right_index. This should be called at the highest scope for consistency.
Parameters:
src (float) : (float ) The source the list will be comprised of.
left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for.
right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for.
Returns: (float ) An array of size left_index-right_index
range_list(start, stop, step)
Creates an from the start value to the stop value.
Parameters:
start (int) : (float ) The true y values.
stop (int) : (float ) The predicted y values.
step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2:
Returns: (float ) An array of size stop-start
regression
Fields:
coeffs (array__float)
degrees (array__float)
type_linear (series__string)
type_quadratic (series__string)
type_cubic (series__string)
type_custom (series__string)
_squared_error (series__float)
X (array__float)
Statistics • Chi Square • P-value • SignificanceThe Statistics • Chi Square • P-value • Significance publication aims to provide a tool for combining different conditions and checking whether the outcome is significant using the Chi-Square Test and P-value.
🔶 USAGE
The basic principle is to compare two or more groups and check the results of a query test, such as asking men and women whether they want to see a romantic or non-romantic movie.
–––––––––––––––––––––––––––––––––––––––––––––
| | ROMANTIC | NON-ROMANTIC | ⬅︎ MOVIE |
–––––––––––––––––––––––––––––––––––––––––––––
| MEN | 2 | 8 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
| WOMEN | 7 | 3 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
|⬆︎ SEX | 10 | 10 | 20 |
–––––––––––––––––––––––––––––––––––––––––––––
We calculate the Chi-Square Formula, which is:
Χ² = Σ ( (Observed Value − Expected Value)² / Expected Value )
In this publication, this is:
chiSquare = 0.
for i = 0 to rows -1
for j = 0 to colums -1
observedValue = aBin.get(i).aFloat.get(j)
expectedValue = math.max(1e-12, aBin.get(i).aFloat.get(colums) * aBin.get(rows).aFloat.get(j) / sumT) //Division by 0 protection
chiSquare += math.pow(observedValue - expectedValue, 2) / expectedValue
Together with the 'Degree of Freedom', which is (rows − 1) × (columns − 1) , the P-value can be calculated.
In this case it is P-value: 0.02462
A P-value lower than 0.05 is considered to be significant. Statistically, women tend to choose a romantic movie more, while men prefer a non-romantic one.
Users have the option to choose a P-value, calculated from a standard table or through a math.ucla.edu - Javascript-based function (see references below).
Note that the population (10 men + 10 women = 20) is small, something to consider.
Either way, this principle is applied in the script, where conditions can be chosen like rsi, close, high, ...
🔹 CONDITION
Conditions are added to the left column ('CONDITION')
For example, previous rsi values (rsi ) between 0-100, divided in separate groups
🔹 CLOSE
Then, the movement of the last close is evaluated
UP when close is higher then previous close (close )
DOWN when close is lower then previous close
EQUAL when close is equal then previous close
It is also possible to use only 2 columns by adding EQUAL to UP or DOWN
UP
DOWN/EQUAL
or
UP/EQUAL
DOWN
In other words, when previous rsi value was between 80 and 90, this resulted in:
19 times a current close higher than previous close
14 times a current close lower than previous close
0 times a current close equal than previous close
However, the P-value tells us it is not statistical significant.
NOTE: Always keep in mind that past behaviour gives no certainty about future behaviour.
A vertical line is drawn at the beginning of the chosen population (max 4990)
Here, the results seem significant.
🔹 GROUPS
It is important to ensure that the groups are formed correctly. All possibilities should be present, and conditions should only be part of 1 group.
In the example above, the two top situations are acceptable; close against close can only be higher, lower or equal.
The two examples at the bottom, however, are very poorly constructed.
Several conditions can be placed in more than 1 group, and some conditions are not integrated into a group. Even if the results are significant, they are useless because of the group formation.
A population count is added as an aid to spot errors in group formation.
In this example, there is a discrepancy between the population and total count due to the absence of a condition.
The results when rsi was between 5-25 are not included, resulting in unreliable results.
🔹 PRACTICAL EXAMPLES
In this example, we have specific groups where the condition only applies to that group.
For example, the condition rsi > 55 and rsi <= 65 isn't true in another group.
Also, every possible rsi value (0 - 100) is present in 1 of the groups.
rsi > 15 and rsi <= 25 28 times UP, 19 times DOWN and 2 times EQUAL. P-value: 0.01171
When looking in detail and examining the area 15-25 RSI, we see this:
The population is now not representative (only checking for RSI between 15-25; all other RSI values are not included), so we can ignore the P-value in this case. It is merely to check in detail. In this case, the RSI values 23 and 24 seem promising.
NOTE: We should check what the close price did without any condition.
If, for example, the close price had risen 100 times out of 100, this would make things very relative.
In this case (at least two conditions need to be present), we set 1 condition at 'always true' and another at 'always false' so we'll get only the close values without any condition:
Changing the population or the conditions will change the P-value.
In the following example, the outcome is evaluated when:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is lower/equal than the close value from 2 bars back
Or:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is equal than the close value from 2 bars back
close value from 1 bar back is lower than the close value from 2 bars back
In both examples, all possibilities of close against close are included in the calculations. close can only by higher, equal or lower than close
Both examples have the results without a condition included (5 = 5 and 5 < 5) so one can compare the direction of current close.
🔶 NOTES
• Always keep in mind that:
Past behaviour gives no certainty about future behaviour.
Everything depends on time, cycles, events, fundamentals, technicals, ...
• This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc., but not numerical data such as height or weight. One might argue that such tests shouldn't use rsi, close, ... values.
• Consider what you're measuring
For example rsi of the current bar will always lead to a close higher than the previous close, since this is inherent to the rsi calculations.
• Be careful; often, there are na -values at the beginning of the series, which are not included in the calculations!
• Always keep in mind considering what the close price did without any condition
• The numbers must be large enough. Each entry must be five or more. In other words, it is vital to make the 'population' large enough.
• The code can be developed further, for example, by splitting UP, DOWN in close UP 1-2%, close UP 2-3%, close UP 3-4%, ...
• rsi can be supplemented with stochRSI, MFI, sma, ema, ...
🔶 SETTINGS
🔹 Population
• Choose the population size; in other words, how many bars you want to go back to. If fewer bars are available than set, this will be automatically adjusted.
🔹 Inputs
At least two conditions need to be chosen.
• Users can add up to 11 conditions, where each condition can contain two different conditions.
🔹 RSI
• Length
🔹 Levels
• Set the used levels as desired.
🔹 Levels
• P-value: P-value retrieved using a standard table method or a function.
• Used function, derived from Chi-Square Distribution Function; JavaScript
LogGamma(Z) =>
S = 1
+ 76.18009173 / Z
- 86.50532033 / (Z+1)
+ 24.01409822 / (Z+2)
- 1.231739516 / (Z+3)
+ 0.00120858003 / (Z+4)
- 0.00000536382 / (Z+5)
(Z-.5) * math.log(Z+4.5) - (Z+4.5) + math.log(S * 2.50662827465)
Gcf(float X, A) => // Good for X > A +1
A0=0., B0=1., A1=1., B1=X, AOLD=0., N=0
while (math.abs((A1-AOLD)/A1) > .00001)
AOLD := A1
N += 1
A0 := A1+(N-A)*A0
B0 := B1+(N-A)*B0
A1 := X*A0+N*A1
B1 := X*B0+N*B1
A0 := A0/B1
B0 := B0/B1
A1 := A1/B1
B1 := 1
Prob = math.exp(A * math.log(X) - X - LogGamma(A)) * A1
1 - Prob
Gser(X, A) => // Good for X < A +1
T9 = 1. / A
G = T9
I = 1
while (T9 > G* 0.00001)
T9 := T9 * X / (A + I)
G := G + T9
I += 1
G *= math.exp(A * math.log(X) - X - LogGamma(A))
Gammacdf(x, a) =>
GI = 0.
if (x<=0)
GI := 0
else if (x
Chisqcdf = Gammacdf(Z/2, DF/2)
Chisqcdf := math.round(Chisqcdf * 100000) / 100000
pValue = 1 - Chisqcdf
🔶 REFERENCES
mathsisfun.com, Chi-Square Test
Chi-Square Distribution Function
Xen's Flag Pattern Scalper1. Input Parameters:
FlagLength: Determines the length of the flag pattern.
TakeProfit1Ratio, takeProfit2Ratio, takeProfit3Ratio: Define the ratios for calculating
the take-profit levels relative to the entry price.
RiskRewardRatio: Specifies the risk-reward ratio for calculating the stop-loss level
relative to the entry price.
2 Flag Conditions:
BullishFlag: Checks if the current bar meets the conditions for a bullish flag pattern. It
evaluates to true if the low of the current bar is lower than the low flagLength bars
ago, and the close of the current bar is higher than the high flagLength bars ago.
BearishFlag: Checks if the current bar meets the conditions for a bearish flag pattern. It evaluates to true if the high of the current bar is higher than the high flagLength bars
ago, and the close of the current bar is lower than the low flagLength bars ago.
3. Entry Price:
EntryPrice: Calculates the entry price based on whether a bullish or bearish flag
pattern is identified. For a bullish flag, the entry price is set to the low of the current bar.
For a bearish flag, the entry price is set to the high of the current bar.
4. Stop Loss:
StopLoss: Determines the stop-loss level based on the entry price and the specified
riskRewardRatio . For a bullish flag, the stop-loss level is calculated by subtracting the
difference between the high and low of the current bar multiplied by the riskRewardRatio from the low of the current bar. For a bearish flag, the stop-loss level
is calculated similarly but added to the high of the current bar.
5. Take Profit Levels:
Three take-profit levels ( takeProfit1, takeProfit2, takeProfit3 ) are calculated based on
the entry price, stop-loss level, and specified take-profit ratios ( takeProfit1Ratio,
takeProfit2Ratio, takeProfit3Ratio ).
6. Plotting Signals and Levels:
Bullish and bearish flag patterns are plotted using triangle shapes ( shape.triangleup for
bullish and shape.triangledown for bearish) above or below the bars, respectively.
Entry, stop-loss, and take-profit levels are plotted using horizontal lines ( line.new )
with different colors and styles. Entry and stop-loss levels are labeled with "Entry" and "SL",
respectively, while take-profit levels are labeled with "TP 1", "TP 2", and "TP 3".
The colors for bullish flags are white for entry, red for stop-loss, and green for take-profit levels. For bearish flags, the colors are the same, but the labels are plotted above the bars.
7. Label Placement:
Labels for entry, stop-loss, and take-profit levels are placed a distance of 4 bars to the right
of the entry price using bar_index + 4 .
This indicator is intended to help traders identify flag patterns on price charts and visualize potential entry, stop-loss, and take-profit levels associated with these patterns.
Please use risk management and when TP1 is hit, move stoploss to breakeven .
A_Traders_Edge__LibraryLibrary "A_Traders_Edge__Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing Market Overview Scanners (MOS)
LabelLocation(_firstLocation)
This function is used when there's a desire to print an assets ALERT LABELS at a set location on the scale that will
NOT change throughout the progression of the script. This is created so that if a lot of alerts are triggered, they
will stay relatively visible and not overlap each other. Ex. If you set your '_firstLocation' parameter as 1, since
there are a max of 40 assets that can be scanned, the 1st asset's location is assigned the value in the '_firstLocation' parameter,
the 2nd asset's location is the (1st asset's location+1)...and so on. If your first location is set to 81 then
the 1st asset is 81 and 2nd is 82 and so on until the 40th location = 120(in this particular example).
Parameters:
_firstLocation (simple int) : (simple int)
Optional(starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables each being a different location to print the labels so that an asset is asssigned to
a particular location on the scale. Regardless of if you have the maximum amount of assets being screened (40 max), this
function will output 40 locations… So there needs to be 40 variables assigned in the tuple in this function. What I
mean by that is you need to have 40 output location variables within your tuple (ie. between the ' ') regarless of
if your scanning 40 assets or not. If you only have 20 assets in your scripts input settings, then only the first 20
variables within the ' ' Will be assigned to a value location and the other 20 will be assigned 'NA', but their
variables still need to be present in the tuple.
SeparateTickerids(_string)
You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your MO scanner.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my MOS Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
Returns: Returns 40 output variables of the different strings of TickerID's (ie. you need to output 40 variables within the
tuple ' ' regardless of if you were scanning using all possible (40) assets or not.
If your scanning for less than 40 assets, then once the variables are assigned to all of the tickerIDs, the rest
of the 40 variables in the tuple will be assigned "NA".
TickeridForLabelsAndSecurity(_includeExchange, _ticker)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_includeExchange (simple bool) : (bool)
Optional(if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your MOS as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the MOS's input.
InvalidTID(_tablePosition, _stackVertical, _close, _securityTickerid, _invalidArray)
This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
Parameters:
_tablePosition (simple string) : (string)
Optional(if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional(if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here.
_close (float) : (float)
If you want them printed as a single row then input 'false' here.
This should be the closing value of each of the assets being tested to determine in the TickerID is valid or not.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never regestered then the logic counts the asset as INVALID.
This will be the 1st TickerID varible (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (string ) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
Returns: (na)
Returns a table with the screened assets Invalid TickerID's. Table draws automatically if any are Invalid, thus,
no output variable to deal with.
LabelSizes(_barCnt, _lblSzRfrnce)
This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your MOS includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the 'PrintedBarCount' function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') outputted by this function.
_lblSzRfrnce (string) : (string)
Optional(if parameter not included, it defaults to size.small). This will be the size of the 1st variable outputted
by this function ('assetNameLabel') BUT also affects the 2nd variable outputted by this function.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
AssetColor()
This function is used to assign 40 different colors to 40 variables to be used for the different labels/plots.
Returns: Returns 40 output variables each with a different color assigned to them to be used in your plots & labels.
Regardless of if you have the maximum amount of assets your scanning(40 max) or less,
this function will assign 40 colors to 40 variables that you have between the ' '.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
RCI(_rciLength, _source, _interval)
You will see me using this a lot. DEFINITELY my favorite oscillator to utilize for SO many different things from
timing entries/exits to determining trends.Calculation of this indicator based on Spearmans Correlation.
Parameters:
_rciLength (int) : (int)
Amount of bars back to use in RCI calculations.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). RCI calculation groups bars by this amount and then will.
rank these groups of bars.
Returns: (float)
Returns a single RCI value that will oscillates between -100 and +100.
RCIAVG(firstLength, _amtBtLengths, _rciSMAlen, _source, _interval)
20 RCI's are averaged together to get this RCI Avg (Rank Correlation Index Average). Each RCI (of the 20 total RCI)
has a progressively LARGER Lookback Length. Though the RCI Lengths are not individually adjustable,
there are 2 factors that ARE:
(1) the Lookback Length of the 1st RCI and
(2) the amount of values between one RCI's Lookback Length and the next.
*** If you set 'firstLength' to it's default of 200 and '_amtBtLengths' to it's default of 120 (aka AMOUNT BETWEEN LENGTHS=120)...
then RCI_2 Length=320, RCI_3 Length=440, RCI_4 Length=560, and so on.
Parameters:
firstLength (int) : (int)
Optional(if parameter is not included when the function is called, then it defaults to 200).
This parameter is the Lookback Length for the 1st RCI used in the RCI Avg.
_amtBtLengths (int) : (int)
Optional(if parameter not included when the function is called, then it defaults to 120).
This parameter is the value amount between each of the progressively larger lengths used for the 20 RCI's that
are averaged in the RCI Avg.
***** BEWARE ***** Too large of a value here will cause the calc to look back too far, causing an error(thus the value must be lowered)
_rciSMAlen (int) : (int)
Unlike the Single RCI Function, this function smooths out the end result using an SMA with a length value that is this parameter.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). Within the RCI calculation, bars next to each other are grouped together
and then these groups are Ranked against each other. This parameter is the number of adjacent bars that are grouped together.
Returns: (float)
Returns a single RCI value that is the Avg of many RCI values that will oscillate between -100 and +100.
PercentChange(_startingValue, _endingValue)
This is a quick function to calculate how much % change has occurred between the '_startingValue' and the '_endingValue'
that you input into the function.
Parameters:
_startingValue (float) : (float)
The source value to START the % change calculation from.
_endingValue (float) : (float)
The source value to END the % change caluclation from.
Returns: Returns a single output being the % value between 0-100 (with trailing numbers behind a decimal). If you want only
a certain amount of numbers behind the decimal, this function needs to be put within a formatting function to do so.
Rescale(_source, _oldMin, _oldMax, _newMin, _newMax)
Rescales series with a known '_oldMin' & '_oldMax'. Use this when the scale of the '_source' to
rescale is known (bounded).
Parameters:
_source (float) : (float)
Source to be normalized.
_oldMin (int) : (float)
The known minimum of the '_source'.
_oldMax (int) : (float)
The known maximum of the '_source'.
_newMin (int) : (float)
What you want the NEW minimum of the '_source' to be.
_newMax (int) : (float)
What you want the NEW maximum of the '_source' to be.
Returns: Outputs your previously bounded '_source', but now the value will only move between the '_newMin' and '_newMax'
values you set in the variables.
Normalize_Historical(_source, _minimumLvl, _maximumLvl)
Normalizes '_source' that has a previously unknown min/max(unbounded) determining the max & min of the '_source'
FROM THE ENTIRE CHARTS HISTORY. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns your same '_source', but now the value will MOSTLY stay between the minimum and maximum values you set in the
'_minimumLvl' and '_maximumLvl' variables (ie. if the source you input is an RSI...the output is the same RSI value but
instead of moving between 0-100 it will move between the maxand min you set).
Normailize_Local(_source, _length, _minimumLvl, _maximumLvl)
Normalizes series with previously unknown min/max(unbounded). Much like the Normalize_Historical function above this one,
but rather than using the Highest/Lowest Values within the ENTIRE charts history, this on looks for the Highest/Lowest
values of '_source' within the last ___ bars (set by user as/in the '_length' parameter. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_length (int) : (float)
The amount of bars to look back to determine the highest/lowest '_source' value.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns a single output variable being the previously unbounded '_source' that is now normalized and bound between
the values used for '_minimumLvl'/'_maximumLvl' of the '_source' within the user defined lookback period.
VolatilityIndicatorsLibrary "VolatilityIndicators"
This is a library of Volatility Indicators .
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the parameters and sources, not being restricted to just the closing price.
@Thanks and credits:
1. Dynamic Zones: Leo Zamansky, Ph.D., and David Stendahl
2. Deviation: Karl Pearson (code by TradingView)
3. Variance: Ronald Fisher (code by TradingView)
4. Z-score: Veronique Valcu (code by HPotter)
5. Standard deviation: Ronald Fisher (code by TradingView)
6. ATR (Average True Range): J. Welles Wilder (code by TradingView)
7. ATRP (Average True Range Percent): millerrh
8. Historical Volatility: HPotter
9. Min-Max Scale Normalization: gorx1
10. Mean Normalization: gorx1
11. Standardization: gorx1
12. Scaling to unit length: gorx1
13. LS Volatility Index: Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad) (code by me)
14. Bollinger Bands: John Bollinger (code by TradingView)
15. Bollinger Bands %: John Bollinger (code by TradingView)
16. Bollinger Bands Width: John Bollinger (code by TradingView)
dev(source, length, anotherSource)
Deviation. Measure the difference between a source in relation to another source
Parameters:
source (float)
length (simple int) : (int) Sequential period to calculate the deviation
anotherSource (float) : (float) Source to compare
Returns: (float) Bollinger Bands Width
variance(src, mean, length, biased, degreesOfFreedom)
Variance. A statistical measurement of the spread between numbers in a data set. More specifically,
variance measures how far each number in the set is from the mean (average), and thus from every other number in the set.
Variance is often depicted by this symbol: σ2. It is used by both analysts and traders to determine volatility and market security.
Parameters:
src (float) : (float) Source to calculate variance
mean (float) : (float) Mean (Moving average)
length (simple int) : (int) The sequential period to calcule the variance (number of values in data set)
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length. Only applies when biased parameter is defined as true.
Returns: (float) Standard deviation
stDev(src, length, mean, biased, degreesOfFreedom)
Measure the Standard deviation from a source in relation to it's moving average.
In this implementation, you pass the average as a parameter, allowing a more personalized calculation.
Parameters:
src (float) : (float) Source to calculate standard deviation
length (simple int) : (int) The sequential period to calcule the standard deviation
mean (float) : (float) Moving average.
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Standard deviation
zscore(src, mean, length, biased, degreesOfFreedom)
Z-Score. A z-score is a statistical measurement that indicates how many standard deviations a data point is from
the mean of a data set. It is also known as a standard score. The formula for calculating a z-score is (x - μ) / σ,
where x is the individual data point, μ is the mean of the data set, and σ is the standard deviation of the data set.
Z-scores are useful in identifying outliers or extreme values in a data set. A positive z-score indicates that the
data point is above the mean, while a negative z-score indicates that the data point is below the mean. A z-score of
0 indicates that the data point is equal to the mean.
Z-scores are often used in hypothesis testing and determining confidence intervals. They can also be used to compare
data sets with different units or scales, as the z-score standardizes the data. Overall, z-scores provide a way to
measure the relative position of a data point in a data
Parameters:
src (float) : (float) Source to calculate z-score
mean (float) : (float) Moving average.
length (simple int) : (int) The sequential period to calcule the standard deviation
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Z-score
atr(source, length)
ATR: Average True Range. Customized version with source parameter.
Parameters:
source (float) : (float) Source
length (simple int) : (int) Length (number of bars back)
Returns: (float) ATR
atrp(length, sourceP)
ATRP (Average True Range Percent)
Parameters:
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
atrp(source, length, sourceP)
ATRP (Average True Range Percent). Customized version with source parameter.
Parameters:
source (float) : (float) Source for ATR
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
historicalVolatility(lengthATR, lengthHist)
Historical Volatility
Parameters:
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
historicalVolatility(source, lengthATR, lengthHist)
Historical Volatility
Parameters:
source (float) : (float) Source for ATR
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
minMaxNormalization(src, numbars)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
minMaxNormalization(src, numbars, minimumLimit, maximumLimit)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
In this implementation, the user explicitly provides the desired minimum (min) and maximum (max) values for the scale,
rather than using the minimum and maximum values from the data.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
minimumLimit (simple float) : (float) Minimum value to scale
maximumLimit (simple float) : (float) Maximum value to scale
Returns: (float) Normalized value
meanNormalization(src, numbars, mean)
Mean Normalization
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
mean (float) : (float) Mean of source
Returns: (float) Normalized value
standardization(src, mean, stDev)
Standardization (Z-score Normalization). How "outside the mean" values relate to the standard deviation (ratio between first and second)
Parameters:
src (float) : (float) Source to normalize
mean (float) : (float) Mean of source
stDev (float) : (float) Standard Deviation
Returns: (float) Normalized value
scalingToUnitLength(src, numbars)
Scaling to unit length
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
lsVolatilityIndex(movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int) : (float) Length for normalization
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
lsVolatilityIndex(sourcePrice, movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
sourcePrice (float) : (float) Source for measure the distance
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int)
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
bollingerBands(src, length, mult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) A tuple of Bollinger Bands, where index 1=basis; 2=basis+dev; 3=basis-dev; and dev=multiplier*stdev
bollingerBands(src, length, aMult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Also, various multipliers can be passed, thus getting more bands (instead of just 2).
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) An array of multiplies used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands, where:
index 1=basis; 2=basis+dev1; 3=basis-dev1; 4=basis+dev2, 5=basis-dev2, 6=basis+dev2, 7=basis-dev2, Nup=basis+devN, Nlow=basis-devN
and dev1, dev2, devN are ```multiplier N * stdev```
bollingerBandsB(src, length, mult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation:
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands %B
bollingerBandsB(src, length, aMult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands %B. The number of results in this array is equal the numbers of multipliers passed via parameter.
bollingerBandsW(src, length, mult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation:
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands Width
bollingerBandsW(src, length, aMult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands Width. The number of results in this array is equal the numbers of multipliers passed via parameter.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source (float) : (float) Source
sampleLength (simple int) : (int) Sample Length
pcntAbove (simple float) : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow (simple float) : (float) Calculates the bottom of the dynamic zone, considering that the minimum values are below x% of the sample
Returns: A tuple with 3 series of values: (1) Upper Line of Dynamic Zone;
(2) Lower Line of Dynamic Zone; (3) Center of Dynamic Zone (x = 50%)
Examples:
Stochastic of Two-Pole SuperSmoother [Loxx]Stochastic of Two-Pole SuperSmoother is a Stochastic Indicator that takes as input Two-Pole SuperSmoother of price. Includes gradient coloring and Discontinued Signal Lines signals with alerts.
What is Ehlers ; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Adaptive Two-Pole Super Smoother Entropy MACD [Loxx]Adaptive Two-Pole Super Smoother Entropy (Math) MACD is an Ehlers Two-Pole Super Smoother that is transformed into an MACD oscillator using entropy mathematics. Signals are generated using Discontinued Signal Lines.
What is Ehlers; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
SUPERTREND MIXED ICHI-DMI-DONCHIAN-VOL-GAP-HLBox@RLSUPERTREND MIXED ICHI-DMI-VOL-GAP-HLBox@RL
by RegisL76
This script is based on several trend indicators.
* ICHIMOKU (KINKO HYO)
* DMI (Directional Movement Index)
* SUPERTREND ICHIMOKU + SUPERTREND DMI
* DONCHIAN CANAL Optimized with Colored Bars
* HMA Hull
* Fair Value GAP
* VOLUME/ MA Volume
* PRICE / MA Price
* HHLL BOXES
All these indications are visible simultaneously on a single graph. A data table summarizes all the important information to make a good trade decision.
ICHIMOKU Indicator:
The ICHIMOKU indicator is visualized in the traditional way.
ICHIMOKU standard setting values are respected but modifiable. (Traditional defaults = .
An oriented visual symbol, near the last value, indicates the progression (Ascending, Descending or neutral) of the TENKAN-SEN and the KIJUN-SEN as well as the period used.
The CLOUD (KUMO) and the CHIKOU-SPAN are present and are essential for the complete analysis of the ICHIMOKU.
At the top of the graph are visually represented the crossings of the TENKAN and the KIJUN.
Vertical lines, accompanied by labels, make it possible to quickly visualize the particularities of the ICHIMOKU.
A line displays the current bar.
A line visualizes the end of the CLOUD (KUMO) which is shifted 25 bars into the future.
A line visualizes the end of the chikou-span, which is shifted 25 bars in the past.
DIRECTIONAL MOVEMENT INDEX (DMI) : Treated conventionally : DI+, DI-, ADX and associated with a SUPERTREND DMI.
A visual symbol at the bottom of the graph indicates DI+ and DI- crossings
A line of oriented and colored symbols (DMI Line) at the top of the chart indicates the direction and strength of the trend.
SUPERTREND ICHIMOKU + SUPERTREND DMI :
Trend following by SUPERTREND calculation.
DONCHIAN CHANNEL: Treated conventionally. (And optimized by colored bars when overshooting either up or down.
The lines, high and low of the last values of the channel are represented to quickly visualize the level of the RANGE.
SUPERTREND HMA (HULL) Treated conventionally.
The HMA line visually indicates, according to color and direction, the market trend.
A visual symbol at the bottom of the chart indicates opportunities to sell and buy.
VOLUME:
Calculation of the MOBILE AVERAGE of the volume with comparison of the volume compared to the moving average of the volume.
The indications are colored and commented according to the comparison.
PRICE: Calculation of the MOBILE AVERAGE of the price with comparison of the price compared to the moving average of the price.
The indications are colored and commented according to the comparison.
HHLL BOXES:
Visualizes in the form of a box, for a given period, the max high and min low values of the price.
The configuration allows taking into account the high and low wicks of the price or the opening and closing values.
FAIR VALUE GAP :
This indicator displays 'GAP' levels over the current time period and an optional higher time period.
The script takes into account the high/low values of the current bar and compares with the 2 previous bars.
The "gap" is generated from the lack of overlap between these bars. Bearish or bullish gaps are determined by whether the gap is above or below HmaPrice, as they tend to fill, and can be used as targets.
NOTE: FAIR VALUE GAP has no values displayed in the table and/or label.
Important information (DATA) relating to each indicator is displayed in real time in a table and/or a label.
Each information is commented and colored according to direction, value, comparison etc.
Each piece of information indicates the values of the current bar and the previous value (in "FULL" mode).
The other possible modes for viewing the table and/or the label allow a more synthetic view of the information ("CONDENSED" and "MINIMAL" modes).
In order not to overload the vision of the chart too much, the visualization box of the RANGE DONCHIAN, the vertical lines of the shifted marks of the ICHIMOKU, as well as the boxes of the HHLL Boxes indicator are only visualized intermittently (managed by an adjustable time delay ).
The "HISTORICAL INFO READING" configuration parameter set to zero (by default) makes it possible to read all the information of the current bar in progress (Bar #0). All other values allow to read the information of a historical bar. The value 1 reads the information of the bar preceding the current bar (-1). The value 10 makes it possible to read the information of the tenth bar behind (-10) compared to the current bar, etc.
At the bottom of the DATAS table and label, lights, red, green or white indicate quickly summarize the trend from the various indicators.
Each light represents the number of indicators with the same trend at a given time.
Green for a bullish trend, red for a bearish trend and white for a neutral trend.
The conditions for determining a trend are for each indicator:
SUPERTREND ICHIMOHU + DMI: the 2 Super trends together are either bullish or bearish.
Otherwise the signal is neutral.
DMI: 2 main conditions:
BULLISH if DI+ >= DI- and ADX >25.
BEARISH if DI+ < DI- and ADX >25.
NEUTRAL if the 2 conditions are not met.
ICHIMOKU: 3 main conditions:
BULLISH if PRICE above the cloud and TENKAN > KIJUN and GREEN CLOUD AHEAD.
BEARISH if PRICE below the cloud and TENKAN < KIJUN and RED CLOUD AHEAD.
The other additional conditions (Data) complete the analysis and are present for informational purposes of the trend and depend on the context.
DONCHIAN CHANNEL: 1 main condition:
BULLISH: the price has crossed above the HIGH DC line.
BEARISH: the price has gone below the LOW DC line.
NEUTRAL if the price is between the HIGH DC and LOW DC lines
The 2 other complementary conditions (Datas) complete the analysis:
HIGH DC and LOW DC are increasing, falling or stable.
SUPERTREND HMA HULL: The script determines several trend levels:
STRONG BUY, BUY, STRONG SELL, SELL AND NEUTRAL.
VOLUME: 3 trend levels:
VOLUME > MOVING AVERAGE,
VOLUME < MOVING AVERAGE,
VOLUME = MOVING AVERAGE.
PRICE: 3 trend levels:
PRICE > MOVING AVERAGE,
PRICE < MOVING AVERAGE,
PRICE = MOVING AVERAGE.
If you are using this indicator/strategy and you are satisfied with the results, you can possibly make a donation (a coffee, a pizza or more...) via paypal to: lebourg.regis@free.fr.
Thanks in advance !!!
Have good winning Trades.
**************************************************************************************************************************
SUPERTREND MIXED ICHI-DMI-VOL-GAP-HLBox@RL
by RegisL76
Ce script est basé sur plusieurs indicateurs de tendance.
* ICHIMOKU (KINKO HYO)
* DMI (Directional Movement Index)
* SUPERTREND ICHIMOKU + SUPERTREND DMI
* DONCHIAN CANAL Optimized with Colored Bars
* HMA Hull
* Fair Value GAP
* VOLUME/ MA Volume
* PRIX / MA Prix
* HHLL BOXES
Toutes ces indications sont visibles simultanément sur un seul et même graphique.
Un tableau de données récapitule toutes les informations importantes pour prendre une bonne décision de Trade.
I- Indicateur ICHIMOKU :
L’indicateur ICHIMOKU est visualisé de manière traditionnelle
Les valeurs de réglage standard ICHIMOKU sont respectées mais modifiables. (Valeurs traditionnelles par défaut =
Un symbole visuel orienté, à proximité de la dernière valeur, indique la progression (Montant, Descendant ou neutre) de la TENKAN-SEN et de la KIJUN-SEN ainsi que la période utilisée.
Le NUAGE (KUMO) et la CHIKOU-SPAN sont bien présents et sont primordiaux pour l'analyse complète de l'ICHIMOKU.
En haut du graphique sont représentés visuellement les croisements de la TENKAN et de la KIJUN.
Des lignes verticales, accompagnées d'étiquettes, permettent de visualiser rapidement les particularités de l'ICHIMOKU.
Une ligne visualise la barre en cours.
Une ligne visualise l'extrémité du NUAGE (KUMO) qui est décalé de 25 barres dans le futur.
Une ligne visualise l'extrémité de la chikou-span, qui est décalée de 25 barres dans le passé.
II-DIRECTIONAL MOVEMENT INDEX (DMI)
Traité de manière conventionnelle : DI+, DI-, ADX et associé à un SUPERTREND DMI
Un symbole visuel en bas du graphique indique les croisements DI+ et DI-
Une ligne de symboles orientés et colorés (DMI Line) en haut du graphique, indique la direction et la puissance de la tendance.
III SUPERTREND ICHIMOKU + SUPERTREND DMI
Suivi de tendance par calcul SUPERTREND
IV- DONCHIAN CANAL :
Traité de manière conventionnelle.
(Et optimisé par des barres colorées en cas de dépassement soit vers le haut, soit vers le bas.
Les lignes, haute et basse des dernières valeurs du canal sont représentées pour visualiser rapidement la fourchette du RANGE.
V- SUPERTREND HMA (HULL)
Traité de manière conventionnelle.
La ligne HMA indique visuellement, selon la couleur et l'orientation, la tendance du marché.
Un symbole visuel en bas du graphique indique les opportunités de vente et d'achat.
*VI VOLUME :
Calcul de la MOYENNE MOBILE du volume avec comparaison du volume par rapport à la moyenne mobile du volume.
Les indications sont colorées et commentées en fonction de la comparaison.
*VII PRIX :
Calcul de la MOYENNE MOBILE du prix avec comparaison du prix par rapport à la moyenne mobile du prix.
Les indications sont colorées et commentées en fonction de la comparaison.
*VIII HHLL BOXES :
Visualise sous forme de boite, pour une période donnée, les valeurs max hautes et min basses du prix.
La configuration permet de prendre en compte les mèches hautes et basses du prix ou bien les valeurs d'ouverture et de fermeture.
IX - FAIR VALUE GAP
Cet indicateur affiche les niveaux de 'GAP' sur la période temporelle actuelle ET une période temporelle facultative supérieure.
Le script prend en compte les valeurs haut/bas de la barre actuelle et compare avec les 2 barres précédentes.
Le "gap" est généré à partir du manque de recouvrement entre ces barres.
Les écarts baissiers ou haussiers sont déterminés selon que l'écart est supérieurs ou inférieur à HmaPrice, car ils ont tendance à être comblés, et peuvent être utilisés comme cibles.
NOTA : FAIR VALUE GAP n'a pas de valeurs affichées dans la table et/ou l'étiquette.
Les informations importantes (DATAS) relatives à chaque indicateur sont visualisées en temps réel dans une table et/ou une étiquette.
Chaque information est commentée et colorée en fonction de la direction, de la valeur, de la comparaison etc.
Chaque information indique la valeurs de la barre en cours et la valeur précédente ( en mode "COMPLET").
Les autres modes possibles pour visualiser la table et/ou l'étiquette, permettent une vue plus synthétique des informations (modes "CONDENSÉ" et "MINIMAL").
Afin de ne pas trop surcharger la vision du graphique, la boite de visualisation du RANGE DONCHIAN, les lignes verticales des marques décalées de l'ICHIMOKU, ainsi que les boites de l'indicateur HHLL Boxes ne sont visualisées que de manière intermittente (géré par une temporisation réglable ).
Le paramètre de configuration "HISTORICAL INFO READING" réglé sur zéro (par défaut) permet de lire toutes les informations de la barre actuelle en cours (Barre #0).
Toutes autres valeurs permet de lire les informations d'une barre historique. La valeur 1 permet de lire les informations de la barre précédant la barre en cours (-1).
La valeur 10 permet de lire les information de la dixième barre en arrière (-10) par rapport à la barre en cours, etc.
Dans le bas de la table et de l'étiquette de DATAS, des voyants, rouge, vert ou blanc indique de manière rapide la synthèse de la tendance issue des différents indicateurs.
Chaque voyant représente le nombre d'indicateur ayant la même tendance à un instant donné. Vert pour une tendance Bullish, rouge pour une tendance Bearish et blanc pour une tendance neutre.
Les conditions pour déterminer une tendance sont pour chaque indicateur :
SUPERTREND ICHIMOHU + DMI : les 2 Super trends sont ensemble soit bullish soit Bearish. Sinon le signal est neutre.
DMI : 2 conditions principales :
BULLISH si DI+ >= DI- et ADX >25.
BEARISH si DI+ < DI- et ADX >25.
NEUTRE si les 2 conditions ne sont pas remplies.
ICHIMOKU : 3 conditions principales :
BULLISH si PRIX au dessus du nuage et TENKAN > KIJUN et NUAGE VERT DEVANT.
BEARISH si PRIX en dessous du nuage et TENKAN < KIJUN et NUAGE ROUGE DEVANT.
Les autres conditions complémentaires (Datas) complètent l'analyse et sont présents à titre informatif de la tendance et dépendent du contexte.
CANAL DONCHIAN : 1 condition principale :
BULLISH : le prix est passé au dessus de la ligne HIGH DC.
BEARISH : le prix est passé au dessous de la ligne LOW DC.
NEUTRE si le prix se situe entre les lignes HIGH DC et LOW DC
Les 2 autres conditions complémentaires (Datas) complètent l'analyse : HIGH DC et LOW DC sont croissants, descendants ou stables.
SUPERTREND HMA HULL :
Le script détermine plusieurs niveaux de tendance :
STRONG BUY, BUY, STRONG SELL, SELL ET NEUTRE.
VOLUME : 3 niveaux de tendance :
VOLUME > MOYENNE MOBILE, VOLUME < MOYENNE MOBILE, VOLUME = MOYENNE MOBILE.
PRIX : 3 niveaux de tendance :
PRIX > MOYENNE MOBILE, PRIX < MOYENNE MOBILE, PRIX = MOYENNE MOBILE.
Si vous utilisez cet indicateur/ stratégie et que vous êtes satisfait des résultats,
vous pouvez éventuellement me faire un don (un café, une pizza ou plus ...) via paypal à : lebourg.regis@free.fr.
Merci d'avance !!!
Ayez de bons Trades gagnants.
Price Displacement - Candlestick (OHLC) CalculationsA Magical little helper friend for Candle Math.
When composing scripts, it is often necessary to manipulate the math around the OHLC. At times, you want a scalar (absolute) value others you want a vector (+/-). Sometimes you want the open - close and sometimes you want just the positive number of the body size. You might want it in ticks or you might want it in points or you might want in percentages. And every time you try to put it together you waste precious time and brain power trying to think about how to properly structure what you're looking for. Not to mention it's normally not that aesthetically pleasing to look at in the code.
So, this fixes all of that.
Using this library. A function like 'pd.pt(_exp)' can call any kind of candlestick math you need. The function returns the candlestick math you define using particular expressions.
Candle Math Functions Include:
Points:
pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.
vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.
Ticks:
tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.
vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.
Percentages:
pct(_exp, _prec) Absolute Percent Displacement. (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Expressions You Can Use with Formulas:
The expressions are simple (simple strings that is) and I did my best to make them sensible, generally using just the ohlc abreviations. I also included uw, lw, bd, and rg for when you're just trying to pull a candle component out. That way you don't have to think about which of the ohlc you're trying to get just use pd.tick("uw") and now the variable is assigned the length of the upper wick, absolute value, in ticks. If you wanted the vector in pts its pd.vpt("uw"). It also makes changing things easy too as I write it out.
Expression List:
Combinations
"oh" = open - high
"ol" = open - low
"oc" = open - close
"ho" = high - open
"hl" = high - low
"hc" = high - close
"lo" = low - open
"lh" = low - high
"lc" = low - close
"co" = close - open
"ch" = close - high
"cl" = close - low
Candle Components
"uw" = Upper Wick
"bd" = Body
"lw" = Lower Wick
"rg" = Range
Pct() Only
"scp" = Scalar Close Position
"sop" = Scalar Open Position
"vcp" = Vector Close Position
"vop" = Vector Open Position
The attributes are going to be available in the pop up dialogue when you mouse over the function, so you don't really have to remember them. I tried to make that look as efficient as possible. You'll notice it follows the OHLC pattern. Thus, "oh" precedes "ho" (heyo) because "O" would be first in the OHLC. Its a way to help find the expression you're looking for quickly. Like looking through an alphabetized list for traders.
There is a copy/paste console friendly helper list in the script itself.
Additional Notes on the Pct() Only functions:
This is the original reason I started writing this. These concepts place a rating/value on the bar based on candle attributes in one number. These formulas put a open or close value in a percentile of the bar relative to another aspect of the bar.
Scalar - Non-directional. Absolute Value.
Scalar Position: The position of the price attribute relative to the scale of the bar range (high - low)
Example: high = 100. low = 0. close = 25.
(A) Measure price distance C-L. How high above the low did the candle close (e.g. close - low = 25)
(B) Divide by bar range (high - low). 25 / (100 - 0) = .25
Explaination: The candle closed at the 25th percentile of the bar range given the bar range low = 0 and bar range high = 100.
Formula: scp = (close - low) / (high - low)
Vector = Directional.
Vector Position: The position of the price attribute relative to the scale of the bar midpoint (Vector Position at hl2 = 0)
Example: high = 100. low = 0. close = 25.
(A) Measure Price distance C-L: How high above the low did the candle close (e.g. close - low = 25)
(B) Measure Price distance H-C: How far below the high did the candle close (e.g. high - close = 75)
(C) Take Difference: A - B = C = -50
(D) Divide by bar range (high - low). -50 / (100 - 0) = -0.50
Explaination: Candle close at the midpoint between hl2 and the low.
Formula: vcp = { / (high - low) }
Thank you for checking this out. I hope no one else has already done this (because it took half the day) and I hope you find value in it. Be well. Trade well.
Library "PD"
Price Displacement
pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Point size of given expression as an absolute value.
vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Point size of given expression as a vector.
tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Tick size of given expression as an absolute value.
vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Tick size of given expression as a vector.
pct(_exp, _prec) Absolute Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Parameters:
_exp : (string) Expression
_prec : (int) Overload - Place value precision definition
Returns: Percent size of given expression as decimal.
vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Parameters:
_exp : (string) Expression
_prec : (int) Overload - Place value precision definition
Returns: Percent size of given expression as decimal.
ConditionalAverages█ OVERVIEW
This library is a Pine Script™ programmer’s tool containing functions that average values selectively.
█ CONCEPTS
Averaging can be useful to smooth out unstable readings in the data set, provide a benchmark to see the underlying trend of the data, or to provide a general expectancy of values in establishing a central tendency. Conventional averaging techniques tend to apply indiscriminately to all values in a fixed window, but it can sometimes be useful to average values only when a specific condition is met. As conditional averaging works on specific elements of a dataset, it can help us derive more context-specific conclusions. This library offers a collection of averaging methods that not only accomplish these tasks, but also exploit the efficiencies of the Pine Script™ runtime by foregoing unnecessary and resource-intensive for loops.
█ NOTES
To Loop or Not to Loop
Though for and while loops are essential programming tools, they are often unnecessary in Pine Script™. This is because the Pine Script™ runtime already runs your scripts in a loop where it executes your code on each bar of the dataset. Pine Script™ programmers who understand how their code executes on charts can use this to their advantage by designing loop-less code that will run orders of magnitude faster than functionally identical code using loops. Most of this library's function illustrate how you can achieve loop-less code to process past values. See the User Manual page on loops for more information. If you are looking for ways to measure execution time for you scripts, have a look at our LibraryStopwatch library .
Our `avgForTimeWhen()` and `totalForTimeWhen()` are exceptions in the library, as they use a while structure. Only a few iterations of the loop are executed on each bar, however, as its only job is to remove the few elements in the array that are outside the moving window defined by a time boundary.
Cumulating and Summing Conditionally
The ta.cum() or math.sum() built-in functions can be used with ternaries that select only certain values. In our `avgWhen(src, cond)` function, for example, we use this technique to cumulate only the occurrences of `src` when `cond` is true:
float cumTotal = ta.cum(cond ? src : 0) We then use:
float cumCount = ta.cum(cond ? 1 : 0) to calculate the number of occurrences where `cond` is true, which corresponds to the quantity of values cumulated in `cumTotal`.
Building Custom Series With Arrays
The advent of arrays in Pine has enabled us to build our custom data series. Many of this library's functions use arrays for this purpose, saving newer values that come in when a condition is met, and discarding the older ones, implementing a queue .
`avgForTimeWhen()` and `totalForTimeWhen()`
These two functions warrant a few explanations. They operate on a number of values included in a moving window defined by a timeframe expressed in milliseconds. We use a 1D timeframe in our example code. The number of bars included in the moving window is unknown to the programmer, who only specifies the period of time defining the moving window. You can thus use `avgForTimeWhen()` to calculate a rolling moving average for the last 24 hours, for example, that will work whether the chart is using a 1min or 1H timeframe. A 24-hour moving window will typically contain many more values on a 1min chart that on a 1H chart, but their calculated average will be very close.
Problems will arise on non-24x7 markets when large time gaps occur between chart bars, as will be the case across holidays or trading sessions. For example, if you were using a 24H timeframe and there is a two-day gap between two bars, then no chart bars would fit in the moving window after the gap. The `minBars` parameter mitigates this by guaranteeing that a minimum number of bars are always included in the calculation, even if including those bars requires reaching outside the prescribed timeframe. We use a minimum value of 10 bars in the example code.
Using var in Constant Declarations
In the past, we have been using var when initializing so-called constants in our scripts, which as per the Style Guide 's recommendations, we identify using UPPER_SNAKE_CASE. It turns out that var variables incur slightly superior maintenance overhead in the Pine Script™ runtime, when compared to variables initialized on each bar. We thus no longer use var to declare our "int/float/bool" constants, but still use it when an initialization on each bar would require too much time, such as when initializing a string or with a heavy function call.
Look first. Then leap.
█ FUNCTIONS
avgWhen(src, cond)
Gathers values of the source when a condition is true and averages them over the total number of occurrences of the condition.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
Returns: (float) A cumulative average of values when a condition is met.
avgWhenLast(src, cond, cnt)
Gathers values of the source when a condition is true and averages them over a defined number of occurrences of the condition.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
cnt : (simple int) The quantity of last occurrences of the condition for which to average values.
Returns: (float) The average of `src` for the last `x` occurrences where `cond` is true.
avgWhenInLast(src, cond, cnt)
Gathers values of the source when a condition is true and averages them over the total number of occurrences during a defined number of bars back.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when a value will be included in the set of values to be averaged.
cnt : (simple int) The quantity of bars back to evaluate.
Returns: (float) The average of `src` in last `cnt` bars, but only when `cond` is true.
avgSince(src, cond)
Averages values of the source since a condition was true.
Parameters:
src : (series int/float) The source of the values to be averaged.
cond : (series bool) The condition determining when the average is reset.
Returns: (float) The average of `src` since `cond` was true.
avgForTimeWhen(src, ms, cond, minBars)
Averages values of `src` when `cond` is true, over a moving window of length `ms` milliseconds.
Parameters:
src : (series int/float) The source of the values to be averaged.
ms : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional.
minBars : (simple int) The minimum number of values to keep in the moving window. Optional.
Returns: (float) The average of `src` when `cond` is true in the moving window.
totalForTimeWhen(src, ms, cond, minBars)
Sums values of `src` when `cond` is true, over a moving window of length `ms` milliseconds.
Parameters:
src : (series int/float) The source of the values to be summed.
ms : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional.
minBars : (simple int) The minimum number of values to keep in the moving window. Optional.
Returns: (float) The sum of `src` when `cond` is true in the moving window.
Overnight Gap AnalysisThere is a wide range of opinion on holding positions overnight due to gap risk. So, out of curiosity, I coded this analysis as a strategy to see what the result of only holding a position overnight on an asset would be. The results really surprised me. The script backtests 10+ years, and here are the findings:
Holding a position for 1 hour bar overnight on QQQ since January 2010 results in a 545% return. QQQ's entire return holding through the same period is 643%
The max equity drawdown on holding that position overnight is lower then the buy/hold drawdown on the underlying asset.
It doesn't matter if the last bar of the day is green or red, the results are similar.
It doesn't matter if it is a bull or bear market. Filtering the script to only trade when the price is above the 200-day moving average actually reduces its return from 545% to 301%, though it does also reduce drawdown.
I see similar patterns when applying the script to other index ETFs. Applying it to leveraged index ETFs can end up beating buy/hold of the underlying index.
Since this script holds through the 1st bar of the day, this could also speak to a day-opening price pattern
The default inputs are for the script to be applied to 1 hour charts only that have 7 bars on the chart per day. You can apply it to other chart types, but must follow the instructions below for it to work properly.
What the script is doing :
This script is buying the close of the last bar of the day and closing the trade at the close of the next bar. So, all trades are being held for 1 bar. By default, the script is setup for use on a 1hr chart that has 7 bars per day. If you try to apply it to a different timeframe, you will need to adjust the count of the last bar of the day with the script input. I.e. There are 7 bars per day on an hour chart on US Stocks/ETFs, so the input is set to 7 by default.
Other ways this script can be used :
This script can also test the result of holding a position over any 1 bar in the day using that same input. For instance, on an hour chart you can input 6 on the script input, and it will model buying the close of the 6th bar of the day while selling on the close of the next bar. I used this out of curiosity to model what only holding the last bar of the day would result in. On average, you lose money on the last bar every day.
The irony here is that the root cause of this last bar of the day losing may be people selling their positions at the end of day so that they aren't exposed to overnight gap risk.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Bjorgum SuperScript
Bjorgum Reversal
Bj Reversal uses Tilson moving averages to identify trend changes
Bars change to yellow as bar close crosses the Tilson moving averages. Blue or red is confirmed as the two Tilson averages themselves cross.
Reversal is great for pinpointing trend change often giving the absolute best entry or exit
Its sensitive nature can mean more false signals on some assets
Be sure to use other indicators from the Bjorgum Collection to confirm signals, or use another strategy that fits the asset or time frame being viewed
Bjorgum HEMA Strategy
Hema uses HA smoothed EMAs to identify trend direction
Default EMA lengths are 5,9, and 21 period
Bar Color will change Malibu or Ruby on a cross of BOTH 5 and 9 EMA
The lengths are customizable to whatever lengths the user desires
Rolando Santos True Relative Movement (TRM)
This underrated momentum strategy conceptualized by Rolando Santos uses 2 indicators to give a 3 color scheme
A leading indicator (RSI) is combined with a lagging indicator (TSI) to produce bar colors based on the condition of each indicator
Both indicators in positive territory produce blue bars
Both indicators in negative bias produce yellow bars
If signals are mixed (one up one down) bars become grey
Speed Selection
The Bjorgum speed selector optimizes the strategy based on the users desires or trading style at the touch of a button
Fast setting is better for swing trades - more timely signals, more whipsaw
Slow setting is better for longer holds or more volatile assets - slower signals, smooths out whipsaw
RSI Bar Color
RSI color changes bar color based on user defined RSI values
Buy/ sell signals are typically given on a cross of the 50 level
Speed selector (fast/Slow) automatically changes lengths between Bj RSI (5 period) and a standard RSI (14 period)
Additional capabilities can be mixed and matched from strategies in the "Strategy Override" section
Add-ons include:
Tilson - The moving average system from Bjorgum Reversal can be toggled to couple with another bar color strategy by clicking this button
PSAR - Parabolic Stop and Reverse indicator can help with trend direction, volatility, and stop losses
HEMA - The 3 moving average system from the HEMA strategy can be coupled with any of the other strategies by clicking "Show HEMA"
Bj Arrows - These arrows plot at the bar level to draw attention to when the BJ TSI is "curling" (See profile for Bjorgum TSI and download today)
-Optional "Plotbar Overlay" plots bars with Heikin-Ashi Inputs when toggled
-This allows for the benefits of price smoothing without sacrificing moving average and indicator performance as real close value is still used
-This can also help on short time frames and improve results with crypto! The user must "mute" the main series candles when in use to avoid candle overlap.
-Optional price line as muting main bars will disable the TradingView default price line. The horizontal plot will track the real closing price while in HA mode!
Historic VPoCs and pseudo VPVRThis study tries to recreate session based historic VPoCs
and VPVR Volume Profile
as they are used by
TradingLatino TradingView user.
It's aimed at BTCUSDT pair and 4h timeframe.
HOW IT WORKS
HOW IT WORKS - VPVR Profile Block
It gathers volume from the last chosen Bars
in order to draw the vpvr profile block
Volume that intersects with current level range
being studied is added to its value.
Additionally the current level price is modified
so that it matches the level price where most
of the volume has concentrated
So you get a pretty accurate price for drawn volume
while at the same time the levels are not stuck
to arbitrary level prices.
HOW IT WORKS - VPoC
It calculates a Volume Profile for the
given historic session but then
it only outputs that Volume Profile VPoC.
SETTINGS
Show VPVR Volume Profile {True}.
Show Historic VPoC lines {True}.
Show Historic VPoC labels {True}.
Extend Historic VPoC lines {True}: If this option is turned off the VPoC lines are only shown during the session duration.
Show tick difference from current price {False}: BETA. Feedback is needed because I'm not sure how it should work this setting.
VPVR Number of bars {100}: Define the Visible Range in number of bars so that its Volume Profile can be shown.
VPVR Profile width (in bars) {15}: VPVR Profile can be make larger or smaller in width thanks to this option.
VPVR Profile offset (in bars) {15}: VPVR Profile can be shown more to the left or to the right if the defaults do not suit you.
Historic Session Volume Profile timeframe {1D}: Historic VPoC use 1 day as their timeframe reference by default.
Number of decimal digits {2}: How many decimal digits are shown in label prices.
Number of previous sessions to print VPoC {5}: How many previous sessions VPoCs are to be printed. The maximum for this setting is 20.
Historic VPoC lines width (in pixels) {2}.
Historic VPoC labels size {small}.
History VPoC line offset (in bars) {5}: How far to the right VPoCs lines are to be extended. Note: This setting does not apply when 'Extend Historic VPoC lines' is set to 'False'.
WARNING
Please be aware that VPoC from the first previous session might not be accurate due to Pine Script limitations.
VPVR USAGE
This is not a VPVR like the official TradingView indicator.
This is a pseudo VPVR and that means it needs some manual input from you.
But, don't worry it's quite easy to do and if you always use the same number
of bars to calculate your VPVR then you might even just set it up once.
In order to show the VPVR (or Volume Profile on the Visible Range):
Rescale your chart so that you see all the bars for your Visible Range.
Click on the ruler tool.
Click on the last bar (far to the right) shown on the screen
Drag the ruler to first bar (far to the left) shown on the screen
Check what the ruler says
E.g. it says: 101 bars
Open this study settings
Modify: 'VPVR Number of bars ' setting
So that its value matches your measured number of bars (101)
Press OK to confirm and wait for the indicator to refresh.
STRATEGY USAGE
If your strategy uses VPoC
to define your resistances
or supports
you can check the VPoCs shown here.
FEEDBACK
I have only used this identifier in BTCUSDT 4h timeframe.
I'm interested to know what needs to be tweaked
in other securities and timeframes.
PINE STUDY TRICK
This study let's you choose the number of decimals the label will use.
CREDITS
I have reused and adapted some code from
'Poor man's volume profile' study
which it's from TradingView IldarAkhmetgaleev user.
I also wanted to thank him for helping me understanding his study.
I have reused some code from
'MTF Selection Framework - PineCoders FAQ' study
which it's from TradingView PineCoders user.
Ultimate VolumeThis script can display a lot of different volume statistics. It also colours bars depending on a chosen, customisable criterion. Most options are disabled by default and can be reenabled in the settings menu.
FAQ
Why are the bars slightly higher than the default volume bars?
Due to the limitations of Pinescript.
What are the two last values (including the one in white?)
They're there due to the limitations of Pinescript. It used to be possible to prevent certain values from being plotted, but still display them as indicator values, but the functionality of that option was changed and is now WIP so until it's restored, these values are necessary to scale the bars properly.
Why are the percentages formatted as volume?
Due to the limitations of Pinescript.
Why are there so many options?
I don't know. They sort of happened. But you don't have to switch them on.
What is money volume?
It's an average of the bar price multiplied by the bar volume .
Why does the daily average volume display different values than the standard sma volume?
Because mine doesn't take into account the current day. So it doesn't fluctuate intraday. Which, I think, makes more sense.
What is total volume?
It's a sum of the total volume for that day and is reset on the next. This option only works with intraday timeframes.
What is average 1 bar intraday volume?
It's the average volume for 1 intraday bar, based on the current day's values only. Obviously, it only works intraday and changes dynamically. It's not an SMA , it's a simple average of all bars for a given day.
What is all-time 1 bar intraday volume?
It's the average volume for 1 intraday bar, but based on the whole chart's history. It's impossible to select a length for this, again, because of certain limitations.
What is short volume?
It is approximately 1/3rd of the actual short volume , due to the limitations of FINRA. It's multiplied by 3 in the script and it may be not entirely accurate. The short volume % is calculated differently, using the 1/3rd of short and total volume from FINRA.
What are the default threshold values?
They are 150%, 200%, 1000% of the average for the average bar volume and all-time average bar volume options, 10%, 50%, 100% for the average daily volume option and 100K, 500K and 1M for the volume option.
RedK_Supply/Demand Volume Viewer v1Background
============
VolumeViewer is a volume indicator, that offers a simple way to estimate the movement and balance (or lack of) of supply & demand volume based on the shape of the price bar. i put this together few years ago and i have a version of this published for another platform under different names (Directional Volume, BetterVolume) in case you come across them
what is V.Viewer
=====================
The idea here is to find a "simple proxy" for estimating the demand or supply portions of a volume bar - these 2 forces have the potential to affect the current price trend so we want an easy way to track them - or to understand if a stock is in accumulation or distribution - we want to do this without having access to Level II or bid/ask data, and without having to get into the complexity of exploring the lower timeframe price & volume data
- to achieve that, we depend on a simple assumption, that the volume associated with an up move is "demand" and the volume associated with a down move is "Supply". so we basically extrapolate these supply and demand values based on how the bar looks like - a full "green" price bar / candle will be considered 100% demand, and a full "red" price bar will be considered 100% supply - a bar that opens and closes at the same level will be 50/50 split between supply & demand.
- you may say this is a "too simple" of an assumption to make, but believe me, it works :) at least at the basic scenario we need here: i'm just exploring the volume movement and finding key levels - and it provides a good improvement compared to the classic way we see volume on a chart - which is still available here in VolumeViewer.
in all cases, i consider this to be work in progress, so i'd welcome any ideas to improve (without getting too complicated) - there's already a host of great volume-based indicators that will do the multi timeframe drill down, but that's not my scope here.
Technical Jargon & calculation
===========================
1. first we calculate a score % for the volume portion that is considered demand based on the bar shape
skip this part if it sounds too technical => if you're into coding indicators, you would probably know there are couple of different concepts for that algorithm - for example, the one used in Balance Of Power formula - which i'm a big fan of - but the one i use here is different. (how?) this is my own, ant it simply applies double weight for the "wick" parts of a price bar compared to the "body of the bar" -- i did some side-by-side comparison in past and decided this one works better. you can change it in the code if you like
2. after calculating the Bull vs Bears portion of volume, we take a moving average of both for the length you set, to come up with what we consider to be the Demand vs Supply - as usual, i use a weighted moving average (WMA) here.
3. the balance or net volume between these 2 lines is calculated, then we apply a final smoothing and that's the main plot we will get
4. being a very visual person, i did my best to build up the visuals in the correct order - then also to ensure the "study title" bar is properly organized and is simple and useful (Full Volume, Supply, Demand, Net Volume).
- i wish there was a way in Pine to hide a value that i still need to visually plot but don't want it showing its value on the study title bar, but couldn't find it. so the last plot value is repeated twice.
How to use
===========
- V.Viewer is set up to show the simplified view by default for simplicity. so when you first add it to a chart, you will get only the supply vs demand view you can see in the middle pane in the above chart
- Optional / detailed mode: go into the settings, and expose all other plots, you will be able to add the classic volume histogram, and the Supply / Demand lines - note these 2 lines will be overlay-ed on top of each other - this provides an easy way to see who is in control - especially if you change the display of these 2 lines into "area" style. This is what is showing in the lower pane in the above chart.
** Exploring Key Price Levels
- the premise is, at spots where there's big lack of balance, that's where to expect to find key price levels (support / resistance) and these price levels will come into play in future so can be used to set entry / exit targets for our trades - see the example in the AAPL chart where you can easily locate these "balance or reversal levels" using the tops/bottoms/zero-crossings from the Net Volume line
** Use for longer-term Price Analysis
- we can also use this simple indicator to gain more insights (at a high level) of the price in terms of accumulation vs distribution and if the sellers or buyers are in control - for example, in the above AAPL chart, V.Viewer tells us that buyers have been in control since October 19 - even during the recent drop, demand continued to be in play - compare that to DIS chart below for the same period, where it shows that the market was dumping DIS thru the weakness. DIS was bleeding red most of the time
Final thoughts
=============
- V.Viewer is an attempt to enhance the way we see and use Volume by leveraging the shape of the price bar to estimate volume supply & demand - and the Net between the 2
- it will work for stocks and other instruments as long as there's volume data
- note that V.Viewer does not track trend. each bar is taken in isolation of prior bars - the price may be going down and V.Viewer is showing supply going up (absorption scenario?) - so i suggest you do not use it to make decisions without consulting other trend / momentum indicators - of course this is a possible improvement idea, or can be implemented in another indicator, add in trend somehow, or maybe think of making this a +100 / -100 Oscillator .. feel free to play with these thoughts
- all thoughts welcome - if this is useful to you in your trading, please share with other trades here to learn from each other
- the code is commented - please feel free to use it as you like, or build things on top of it - but please continue to credit the author of this code :)
good luck!
-
Pivots MTF [LucF]Pivots detected at higher timeframes are more significant because more market activity—or work—is required to produce them. This indicator displays pivots calculated on the higher timeframe of your choice.
Features
► Timeframe selection
— The higher timeframe (HTF) can be selected in 3 different ways:
• By steps (15 min., 60 min., 4H, 1D, 3D, 1W, 1M, 1Y). This setting is the default.
• As a multiple of the current chart's resolution, which can be fractional, so 3.5 will work.
• Fixed.
— The HTF used can be displayed near the last bar (default).
— Note that using the HTF is not mandatory. If it is disabled, the indicator will calculate on the chart's resolution.
— Non-repainting or repainting mode can be selected. This has no impact on the display of historical bars, but when no repainting is selected, pivot detection in the realtime bar will be delayed by one chart bar (not one bar at the HTF).
► Pivots
— Three color schemes are provided: green/red, aqua/pink and coral/violet (the default).
— Both the thickness and brightness of lines can be controlled separately for the hi and lo pivots.
— The visibility of the last hi/lo pivots can be enhanced.
— Prices can be displayed on pivot lines and the text's size and color can be adjusted.
— The number of bars required for the left/right pivot legs can be controlled (the default is 4).
— The source can be selected individually for hi and lo pivots (the default is hlc3 and low .
— The mean of the hi/lo pivot values of the last few thousand chart bars can be displayed. Pivots having lasted longer during the mean's period will weigh more in the calculation. The mean can be displayed in running mode and/or only showing its last level as a long horizontal line. I don't find it very useful; maybe others will.
► Markers and Alerts
— Markers can be configured on breaches of either the last hi/lo pivot levels, or the hi/lo mean. Crossovers and crossunders are controlled separately.
— Alerts can be configured using any of the marker combinations. As is usual for my indicators, only one alert is used. It will trigger on the markers that are active when you create your alert. Once your markers are set up the way you want, create your alert from the chart/timeframe you want the alert to run on, and be sure to use the “Once Per Bar Close” triggering condition. Use an alert message that will remind you of the combination of markers used when creating the alert. If you use multiple markers to trigger one alert, then having the indicator show those markers will be important to help you figure out which marker triggered the alert when it fired.
A quick look at the pattern of these markers will hopefully convince you that using them as entry/exit signals would be perilous, as they are prone to whipsaw. I have included them because some traders may use the markers as reminders.
Using Pivots
These pivots can be used in a few different ways:
— When using the high / low sources they will show extreme levels, breaches of which should be more significant.
— Another way to use them is with hlc3 (the average of the high , low and close ) for hi pivots and low for the lo pivots. This accounts for my personal mythology to the effect that drops typically reach previous lows more easily than rallies make newer highs.
— Using low for hi pivots and high for lo pivots (so backward) can be a useful way to set stops or to detect weakness in movements.
You will usually be better served by pivots if you consider them as denoting regions rather than precise levels. The flexibility in the display options of this indicator will help you adapt it to the way you use your pivots. To indicate areas rather than levels, for example, try using a brightness of 1 with a line thickness of 30. The cloud effect generated this way will show areas better than fine lines.
Realize that these pivot lines are positioned in the past, and so they are drawn after the fact because a given number of bars need to elapse before calculations determine a pivot has occurred. You will thus never see a pivot top, for example, identified on the realtime bar. To detect a pivot, it takes a number of bars corresponding to the dilation of the higher timeframe in the current one, multiplied by the number of bars you use for your pivots' right leg. Also note that the Pine native function used to detect pivots in this indicator considers a summit to be a top when the number of bars in each leg are lower or equal to that top. Bars in legs do not need to be progressively lower on each side of the pivot for a pivot to be detected.
If you program in Pine
— See the Pinecoders MTF Selection Framework for an explanation of the functions used in this script to provide the selection mechanism for the higher timeframe.
— This code uses the Pine Script Coding Conventions .
Thanks
— To the Pine coders asking questions in the Pine Script chat on TV ; your questions got me to write this indicator.
