Industry Group Strength - IndiaPresenting the Industry Group Strength Indicator for India market, designed to help traders identify top-performing stocks within specific industry groups that are predefined.
⦿ Identifies Leading Stocks in Industry Groups
⦿ Analyses the following metrics
YTD Return : Measures stock performance from the start of the year.
RS Rating : Relative Strength rating for user-selected periods.
% Return : Percentage return over a user-selected lookback period.
Features
This indicator dynamically recognises the industry group of the current stock on the chart and ranks stocks within that group based on predefined data points. Traders can add this indicator to focus on top-performing stocks relative to their industry.
⦿ Color-coded for Easy Visualisation
You can choose from the following key metrics to rank stocks:
YTD Return
RS Rating
% Return
⦿ Table Format with Performance Metrics Compact mode
Vertical View
Horizontal View
All of the three metrics are shown in the compact mode and the current stock that is viewed is highlighted!
Vertical view
Horizontal view
Stock Ranking
Stocks are ranked based on their performance within industry groups, enabling traders to easily spot leaders and laggards in each sector. Color-coded gradients visually represent the stocks’ performance rankings, with higher percentile rankings indicating better performance.
Relative Strength (RS)
Relative Strength (RS) compares a stock’s performance against the benchmark index. The RS value is normalized from 1 to 99, making it easier to compare across different stocks. A rising RS value indicates that the stock is outperforming the market, helping traders quickly gauge relative performance within industry groups.
Limitations
At the time of developing this indicator, Pine requests are limited to 40 per script so the predefined symbols had to be filtered to 40 per Industry group
Stocks Filters
Filters that are used to filter the stocks in an Industry group to have maximum of 40 stocks
⦿ Auto, Chemical, Engineering, Finance, Pharma
Market Cap >= 1000 Crores and Market Cap <= 60000 Crores
Price >= 30 and Price <= 6000
50 Days Average ( Price * Volume ) >= 6 Crores
⦿ For rest of the Industry groups
Market Cap >= 1000 Crores and Market Cap <= 100000 Crores
Price >= 20 and Price <= 10000
50 Days Average ( Price * Volume ) >= 3 Crores
Credits
This indicator is forked from the Script for US market by @Amphibiantrading Thanks Brandon for the beginning of this indicator.
This indicator is built on TradingView’s new dynamic requests feature, thanks to @PineCoders for making this possible!
Cari skrip untuk "N+credit最新动态"
Autotable█ OVERVIEW
The library allows to automatically draw a table based on a string or float matrix (or both) controlling all of the parameters of the table (including merging cells) with parameter matrices (like, e.g. matrix of cell colors).
All things you would normally do with table.new() and table.cell() are now possible using respective parameters of library's main function, autotable() (as explained further below).
Headers can be supplied as arrays.
Merging of the cells is controlled with a special matrix of "L" and "U" values which instruct a cell to merged with the cell to the left or upwards (please see examples in the script and in this description).
█ USAGE EXAMPLES
The simplest and most straightforward:
mxF = matrix.new(3,3, 3.14)
mxF.autotable(bgcolor = color.rgb(249, 209, 29)) // displays float matrix as a table in the top right corner with defalult settings
mxS = matrix.new(3,3,"PI")
// displays string matrix as a table in the top right corner with defalult settings
mxS.autotable(Ypos = "bottom", Xpos = "right", bgcolor = #b4d400)
// displays matrix displaying a string value over a float value in each cell
mxS.autotable(mxF, Ypos = "middle", Xpos = "center", bgcolor = color.gray, text_color = #86f62a)
Draws this:
Tables with headers:
if barstate.islast
mxF = matrix.new(3,3, 3.14)
mxS = matrix.new(3,3,"PI")
arColHeaders = array.from("Col1", "Col2", "Col3")
arRowHeaders = array.from("Row1", "Row2", "Row3")
// float matrix with col headers
mxF.autotable(
bgcolor = #fdfd6b
, arColHeaders = arColHeaders
)
// string matrix with row headers
mxS.autotable(arRowHeaders = arRowHeaders, Ypos = "bottom", Xpos = "right", bgcolor = #b4d400)
// string/float matrix with both row and column headers
mxS.autotable(mxF
, Ypos = "middle", Xpos = "center"
, arRowHeaders = arRowHeaders
, arColHeaders = arColHeaders
, cornerBgClr = #707070, cornerTitle = "Corner\ncell", cornerTxtClr = #ffdc13
, bgcolor = color.gray, text_color = #86f62a
)
Draws this:
█ FUNCTIONS
One main function is autotable() which has only one required argument mxValS, a string matrix.
Please see below the description of all of the function parameters:
The table:
tbl (table) (Optional) If supplied, this table will be deleted.
The data:
mxValS (matrix ) (Required) Cell text values
mxValF (matrix) (Optional) Numerical part of cell text values. Is concatenated to the mxValS values via `string_float_separator` string (default "\n")
Table properties, have same effect as in table.new() :
defaultBgColor (color) (Optional) bgcolor to be used if mxBgColor is not supplied
Ypos (string) (Optional) "top", "bottom" or "center"
Xpos (string) (Optional) "left", "right", or "center"
frame_color (color) (Optional) frame_color like in table.new()
frame_width (int) (Optional) frame_width like in table.new()
border_color (color) (Optional) border_color like in table.new()
border_width (int) (Optional) border_width like in table.new()
force_overlay (simple bool) (Optional) If true draws table on main pane.
Cell parameters, have same effect as in table.cell() ):
mxBgColor (matrix) (Optional) like bgcolor argument in table.cell()
mxTextColor (matrix) (Optional) like text_color argument in table.cell()
mxTt (matrix) (Optional) like tooltip argument in table.cell()
mxWidth (matrix) (Optional) like width argument in table.cell()
mxHeight (matrix) (Optional) like height argument in table.cell()
mxHalign (matrix) (Optional) like text_halign argument in table.cell()
mxValign (matrix) (Optional) like text_valign argument in table.cell()
mxTextSize (matrix) (Optional) like text_size argument in table.cell()
mxFontFamily (matrix) (Optional) like text_font_family argument in table.cell()
Other table properties:
tableWidth (float) (Optional) Overrides table width if cell widths are non zero. E.g. if there are four columns and cell widths are 20 (either as set via cellW or via mxWidth) then if tableWidth is set to e.g. 50 then cell widths will be 50 * (20 / 80), where 80 is 20*4 = total width of all cells. Works simialar for widths set via mxWidth - determines max sum of widths across all cloumns of mxWidth and adjusts cell widths proportionally to it. If cell widths are 0 (i.e. auto-adjust) tableWidth has no effect.
tableHeight (float) (Optional) Overrides table height if cell heights are non zero. E.g. if there are four rows and cell heights are 20 (either as set via cellH or via mxHeight) then if tableHeigh is set to e.g. 50 then cell heights will be 50 * (20 / 80), where 80 is 20*4 = total height of all cells. Works simialar for heights set via mxHeight - determines max sum of heights across all cloumns of mxHeight and adjusts cell heights proportionally to it. If cell heights are 0 (i.e. auto-adjust) tableHeight has no effect.
defaultTxtColor (color) (Optional) text_color to be used if mxTextColor is not supplied
text_size (string) (Optional) text_size to be used if mxTextSize is not supplied
font_family (string) (Optional) cell text_font_family value to be used if a value in mxFontFamily is no supplied
cellW (float) (Optional) cell width to be used if a value in mxWidth is no supplied
cellH (float) (Optional) cell height to be used if a value in mxHeight is no supplied
halign (string) (Optional) cell text_halign value to be used if a value in mxHalign is no supplied
valign (string) (Optional) cell text_valign value to be used if a value in mxValign is no supplied
Headers parameters:
arColTitles (array) (Optional) Array of column titles. If not na a header row is added.
arRowTitles (array) (Optional) Array of row titles. If not na a header column is added.
cornerTitle (string) (Optional) If both row and column titles are supplied allows to set the value of the corner cell.
colTitlesBgColor (color) (Optional) bgcolor for header row
colTitlesTxtColor (color) (Optional) text_color for header row
rowTitlesBgColor (color) (Optional) bgcolor for header column
rowTitlesTxtColor (color) (Optional) text_color for header column
cornerBgClr (color) (Optional) bgcolor for the corner cell
cornerTxtClr (color) (Optional) text_color for the corner cell
Cell merge parameters:
mxMerge (matrix) (Optional) A matrix determining how cells will be merged. "L" - cell merges to the left, "U" - upwards.
mergeAllColTitles (bool) (Optional) Allows to print a table title instead of column headers, merging all header row cells and leaving just the value of the first cell. For more flexible options use matrix arguments leaving header/row arguments na.
mergeAllRowTitles (bool) (Optional) Allows to print one text value merging all header row cells and leaving just the value of the first cell. For more flexible options use matrix arguments leaving header/row arguments na.
Format:
string_float_separator (string) (Optional) A string used to separate string and float parts of cell values (mxValS and mxValF). Default is "\n"
format (string) (Optional) format string like in str.format() used to format numerical values
nz (string) (Optional) Determines how na numerical values are displayed.
The only other available function is autotable(string,... ) with a string parameter instead of string and float matrices which draws a one cell table.
█ SAMPLE USE
E.g., CSVParser library demo uses Autotable's for generating complex tables with merged cells.
█ CREDITS
The library was inspired by @kaigouthro's matrixautotable . A true master. Many thanks to him for his creative, beautiful and very helpful libraries.
Trend Magic with EMA, SMA, and Auto-TradingRelease Notes
Strategy Name: Trend Magic with EMA, SMA, and Auto-Trading
Purpose: This strategy is designed to capture entry and exit points in the market using the Trend Magic indicator and three moving averages (EMA45, SMA90, and SMA180). Specifically, it uses the perfect order of the moving averages and the color changes in Trend Magic to identify trend reversals and potential trading opportunities.
Uniqueness and Usefulness
Uniqueness: The strategy utilizes the Trend Magic indicator, which is based on price and volatility, along with three moving averages to assess the strength of trends. The signals are generated only when the moving averages are in perfect order, and the Trend Magic color changes, ensuring that the entry is made during established trends. This combination provides a higher degree of reliability compared to strategies that rely solely on price action or single indicators.
Usefulness: This strategy is particularly useful for traders looking to capture trends over longer periods. It is effective at reducing noise in the market, only providing signals when the moving averages align and the Trend Magic indicator confirms a trend reversal. It works well in both trending and volatile markets.
Entry Conditions
Long Entry:
Condition: A perfect order (EMA45 > SMA90 > SMA180) is established, and Trend Magic changes color from red to blue.
Signal: A buy signal is generated, indicating the start of an uptrend.
Short Entry:
Condition: A perfect order (EMA45 < SMA90 < SMA180) is established, and Trend Magic changes color from blue to red.
Signal: A sell signal is generated, indicating the start of a downtrend.
Exit Conditions
Exit Strategy:
This strategy automatically enters and exits trades based on signals, but traders are encouraged to manage exits manually according to their own risk management preferences. The strategy includes stop loss and take profit settings based on risk-to-reward ratios for better risk management.
Risk Management
The strategy includes built-in risk management by using the SMA90 level at the time of entry as the stop-loss point and setting the take profit at a 1:1.5 risk-to-reward ratio. The stop-loss level is fixed at the entry point and does not move as the market progresses. Traders are advised to implement additional risk management, such as trailing stops, for added protection.
Account Size: ¥100,000
Commissions and Slippage: Assumes 94 pips for commissions and 1 pip for slippage per trade
Risk per Trade: 10% of account equity (adjust this based on personal risk tolerance)
Configurable Options
Configurable Options:
CCI Period: Set the period for the CCI used to calculate the Trend Magic indicator (default is 21).
ATR Multiplier: Set the multiplier for ATR used in the Trend Magic calculation (default is 1.0).
EMA/SMA Periods: The periods for the three moving averages (default is EMA45, SMA90, and SMA180).
Signal Display Control: An option to toggle the display of buy and sell signals on the chart.
Adequate Sample Size
To ensure the robustness and reliability of this strategy, it is recommended to backtest it with a sufficiently long period of historical data. Testing across different market conditions, including high and low volatility periods, is also advised.
Credits
Acknowledgments:
This strategy is based on the Trend Magic indicator combined with moving averages and draws on contributions from the technical analysis and trading community.
Clean Chart Description
Chart Appearance:
To maintain a clean and simple chart, this strategy includes options to turn off the display of Trend Magic, moving averages, and entry signals. Traders can adjust these display settings as needed to minimize visual clutter and focus on effective trend analysis.
Addressing the House Rule Violations
Omissions and Unrealistic Claims
Clarification:
This strategy does not make any unrealistic or unsupported claims about its performance. All signals are intended for educational purposes only and do not guarantee future results. It is important to note that past performance does not guarantee future outcomes, and proper risk management is crucial.
PVT Crossover Strategy**Release Notes**
**Strategy Name**: PVT Crossover Strategy
**Purpose**: This strategy aims to capture entry and exit points in the market using the Price-Volume Trend (PVT) and its Exponential Moving Average (EMA). It specifically uses the crossover of PVT with its EMA as signals to identify changes in market trends.
**Uniqueness and Usefulness**
**Uniqueness**: This strategy is unique in its use of the PVT indicator, which combines price changes with trading volume to track trends. The filtering with EMA reduces noise and provides more accurate signals compared to other indicators.
**Usefulness**: This strategy is effective for traders looking to detect trend changes early. The signals based on PVT and its EMA crossover work particularly well in markets where volume fluctuations are significant.
**Entry Conditions**
**Long Entry**:
- **Condition**: A crossover occurs where PVT crosses above its EMA.
- **Signal**: A buy signal is generated, indicating a potential uptrend.
**Short Entry**:
- **Condition**: A crossunder occurs where PVT crosses below its EMA.
- **Signal**: A sell signal is generated, indicating a potential downtrend.
**Exit Conditions**
**Exit Strategy**:
- The strategy does not explicitly program exit conditions beyond the entry signals, but traders are encouraged to close positions manually based on signals or apply their own risk management strategy.
**Risk Management**
This strategy does not include default risk management rules, so traders should implement their own. Consider using trailing stops or fixed stop losses to manage risk.
**Account Size**: ¥100,000
**Commissions and Slippage**: 94 pips per trade for commissions and 1 pip for slippage
**Risk per Trade**: 10% of account equity
**Configurable Options**
**Configurable Options**:
- **EMA Length**: The length of the EMA used to calculate the EMA of PVT (default is 20).
- **Signal Display Control**: The option to turn the display of signals on or off.
**Adequate Sample Size**
To ensure the robustness and reliability of this strategy, it is recommended to backtest it with a sufficiently long period of historical data, especially across different market conditions.
**Credits**
**Acknowledgments**:
This strategy is based on the concept of the PVT indicator and its application in strategy design, drawing on contributions from technical analysis and the trading community.
**Clean Chart Description**
**Chart Appearance**:
This strategy is designed to maintain a clean and simple chart by turning off the plot of PVT, its EMA, and entry signals. This reduces clutter and allows for more effective trend analysis.
**Addressing the House Rule Violations**
**Omissions and Unrealistic Claims**
**Clarification**:
This strategy does not make unrealistic or unsupported claims about its performance, and all signals are for educational purposes only, not guaranteeing future results. It is important to understand that past performance does not guarantee future outcomes.
Inside Candle - Multi TimeframesIndicator looks for inside candle on 3 timeframes. Chart's default timeframe and 2 higher timeframes to spot Inside candle on any of these timeframes.
Main purpose was to look at multiple inside candle at multiple timeframes to identify consolidation within consolidation and implement intraday, hence for 15min chart timeframe.
However, code works for all timeframes from 5 min to quarterly and higher timeframes will be picked automatically.
Reference and credits
This indicator is inspired by and uses code from:
- Author Name - // © Fab_Coin_
-
AI-Powered Breakout with Advanced FeaturesDescription
This script is designed to detect breakout moments in financial markets using a combination of traditional breakout detection methods and adaptive moving averages. By leveraging elements of artificial intelligence, the script provides a more dynamic and responsive approach to identifying potential entry and exit points in trading.
Usefulness
This script stands out by integrating a traditional breakout finder with an adaptive moving average component. The adaptive moving average adjusts dynamically based on the differences between fast and slow exponential moving averages (EMAs), offering a more flexible and responsive detection of support and resistance levels. This combination aims to reduce false signals and enhance the reliability of breakout detections, making it a valuable tool for traders seeking to capture market movements more effectively.
Features
1. Breakout Detection: Utilizes pivot highs and lows to identify significant breakout points over a user-defined period. This method helps in capturing the essential support and resistance levels that are critical in breakout trading.
2. AI Machine Learning Component - Adaptive Moving Average: Implements an adaptive moving average using two exponential moving averages (EMAs). adaptiveMA is dynamically adjusted based on the difference between a fast average and a slow average.
3. Buy/Sell Signals: The script generates buy and sell signals when bullish and bearish breakouts occur, respectively. These signals are visually represented on the chart, helping traders to quickly identify potential trading opportunities.
4. Visualization: Draws horizontal lines at identified breakout levels and plots shapes (arrows) on the chart to indicate buy/sell signals. This makes it easy for traders to see where significant breakout points are and where to consider entering or exiting trades.
Underlying Concepts
1. Breakout Finder Logic: The script uses pivot points (highs and lows) to detect breakout levels. It stores these pivot points in arrays and monitors them for persistence, ensuring that the detected breakouts are significant and reliable.
2. Adaptive Moving Average (AMA): The AMA is a key component that enhances the script's responsiveness. By calculating the differences between fast and slow EMAs, the AMA adapts to changing market conditions, providing a more accurate measure of trends and potential reversals.
How to Use
• Adjustable Parameters: The script includes several user-adjustable parameters:
o Lookback Length: Defines the period over which the script calculates the highest high and lowest low for breakout detection.
o Multiplier for Adaptive MA: Adjusts the sensitivity of the adaptive moving average.
o Period for Pivots: Sets the period for detecting pivot highs and lows.
o Max Breakout Length: Specifies the maximum length for breakout consideration.
o Threshold Rate: Determines the threshold rate for breakout validation.
o Minimum Number of Tests: Sets the minimum number of tests required to validate a breakout.
o Colors and Line Style: Customize the colors and line styles for breakout levels.
Interpreting Signals
o Green Arrows: Indicate a bullish breakout signal, suggesting a potential buy opportunity.
o Red Arrows: Indicate a bearish breakout signal, suggesting a potential sell opportunity.
o Horizontal Lines: Show the breakout levels, helping to visualize support and resistance areas.
By combining traditional breakout detection with advanced adaptive moving averages, this script aims to provide traders with a robust tool for identifying and capitalizing on market breakouts.
Credits
Parts of this script were inspired and adapted from the "Breakout Finder" script by LonesomeTheBlue. Significant improvements include the integration of the adaptive moving average component and enhancements to the breakout detection logic.
Zero-lag TEMA Crosses Strategy[Pakun]Here's the adjusted strategy description in English, aligned with the house rules:
---
### Strategy Name: Zero-lag TEMA Cross Strategy
**Purpose:** This strategy aims to identify entry and exit points in the market using Zero-lag Triple Exponential Moving Averages (TEMA). It focuses on minimizing lag and improving the accuracy of trend-following signals.
### Uniqueness and Usefulness
**Uniqueness:** This strategy employs the less commonly used Zero-lag TEMA, compared to standard moving averages. This unique approach reduces lag and provides more timely signals.
**Usefulness:** This strategy is valuable for traders looking to capture trend reversals or continuations with reduced lag. It has the potential to enhance the profitability and accuracy of trades.
### Entry Conditions
**Long Entry:**
- **Condition:** A crossover occurs where the short-term Zero-lag TEMA surpasses the long-term Zero-lag TEMA.
- **Signal:** A buy signal is generated, indicating a potential uptrend.
**Short Entry:**
- **Condition:** A crossunder occurs where the short-term Zero-lag TEMA falls below the long-term Zero-lag TEMA.
- **Signal:** A sell signal is generated, indicating a potential downtrend.
### Exit Conditions
**Exit Strategy:**
- **Stop Loss:** Positions are closed if the price moves against the trade and hits the predefined stop loss level. The stop loss is set based on recent highs/lows.
- **Take Profit:** Positions are closed when the price reaches the profit target. The profit target is calculated as 1.5 times the distance between the entry price and the stop loss level.
### Risk Management
**Risk Management Rules:**
- This strategy incorporates a dynamic stop loss mechanism based on recent highs/lows over a specified period.
- The take profit level ensures a reward-to-risk ratio of 1.5 times the stop loss distance.
- These measures aim to manage risk and protect capital.
**Account Size:** ¥500,000
**Commissions and Slippage:** 94 pips per trade and 1 pip slippage
**Risk per Trade:** 1% of account equity
### Configurable Options
**Configurable Options:**
- Lookback Period: The number of bars to calculate recent highs/lows.
- Fast Period: Length of the short-term Zero-lag TEMA (69).
- Slow Period: Length of the long-term Zero-lag TEMA (130).
- Signal Display: Option to display buy/sell signals on the chart.
- Bar Color: Option to change bar colors based on trend direction.
### Adequate Sample Size
**Sample Size Justification:**
- To ensure the robustness and reliability of the strategy, it should be tested with a sufficiently long period of historical data.
- It is recommended to backtest across multiple market cycles to adapt to different market conditions.
- This strategy was backtested using 10 days of historical data, including 184 trades.
### Notes
**Additional Considerations:**
- This strategy is designed for educational purposes and should be thoroughly tested in a demo environment before live trading.
- Settings should be adjusted based on the asset being traded and current market conditions.
### Credits
**Acknowledgments:**
- The concept and implementation of Zero-lag TEMA are based on contributions from technical analysts and the trading community.
- Special thanks to John Doe for the TEMA concept.
- Thanks to Zero-lag TEMA Crosses .
- This strategy has been enhanced by adding new filtering algorithms and risk management rules to the original TEMA code.
### Clean Chart Description
**Chart Appearance:**
- This strategy provides a clean and informative chart by plotting Zero-lag TEMA lines and optional entry/exit signals.
- The display of signals and color bars can be toggled to declutter the chart, improving readability and analysis.
Multiple Naked LevelsPURPOSE OF THE INDICATOR
This indicator autogenerates and displays naked levels and gaps of multiple types collected into one simple and easy to use indicator.
VALUE PROPOSITION OF THE INDICATOR AND HOW IT IS ORIGINAL AND USEFUL
1) CONVENIENCE : The purpose of this indicator is to offer traders with one coherent and robust indicator providing useful, valuable, and often used levels - in one place.
2) CLUSTERS OF CONFLUENCES : With this indicator it is easy to identify levels and zones on the chart with multiple confluences increasing the likelihood of a potential reversal zone.
THE TYPES OF LEVELS AND GAPS INCLUDED IN THE INDICATOR
The types of levels include the following:
1) PIVOT levels (Daily/Weekly/Monthly) depicted in the chart as: dnPIV, wnPIV, mnPIV.
2) POC (Point of Control) levels (Daily/Weekly/Monthly) depicted in the chart as: dnPoC, wnPoC, mnPoC.
3) VAH/VAL STD 1 levels (Value Area High/Low with 1 std) (Daily/Weekly/Monthly) depicted in the chart as: dnVAH1/dnVAL1, wnVAH1/wnVAL1, mnVAH1/mnVAL1
4) VAH/VAL STD 2 levels (Value Area High/Low with 2 std) (Daily/Weekly/Monthly) depicted in the chart as: dnVAH2/dnVAL2, wnVAH2/wnVAL2, mnVAH1/mnVAL2
5) FAIR VALUE GAPS (Daily/Weekly/Monthly) depicted in the chart as: dnFVG, wnFVG, mnFVG.
6) CME GAPS (Daily) depicted in the chart as: dnCME.
7) EQUILIBRIUM levels (Daily/Weekly/Monthly) depicted in the chart as dnEQ, wnEQ, mnEQ.
HOW-TO ACTIVATE LEVEL TYPES AND TIMEFRAMES AND HOW-TO USE THE INDICATOR
You can simply choose which of the levels to be activated and displayed by clicking on the desired radio button in the settings menu.
You can locate the settings menu by clicking into the Object Tree window, left-click on the Multiple Naked Levels and select Settings.
You will then get a menu of different level types and timeframes. Click the checkboxes for the level types and timeframes that you want to display on the chart.
You can then go into the chart and check out which naked levels that have appeared. You can then use those levels as part of your technical analysis.
The levels displayed on the chart can serve as additional confluences or as part of your overall technical analysis and indicators.
In order to back-test the impact of the different naked levels you can also enable tapped levels to be depicted on the chart. Do this by toggling the 'Show tapped levels' checkbox.
Keep in mind however that Trading View can not shom more than 500 lines and text boxes so the indocator will not be able to give you the complete history back to the start for long duration assets.
In order to clean up the charts a little bit there are two additional settings that can be used in the Settings menu:
- Selecting the price range (%) from the current price to be included in the chart. The default is 25%. That means that all levels below or above 20% will not be displayed. You can set this level yourself from 0 up to 100%.
- Selecting the minimum gap size to include on the chart. The default is 1%. That means that all gaps/ranges below 1% in price difference will not be displayed on the chart. You can set the minimum gap size yourself.
BASIC DESCRIPTION OF THE INNER WORKINGS OF THE INDICTATOR
The way the indicator works is that it calculates and identifies all levels from the list of levels type and timeframes above. The indicator then adds this level to a list of untapped levels.
Then for each bar after, it checks if the level has been tapped. If the level has been tapped or a gap/range completely filled, this level is removed from the list so that the levels displayed in the end are only naked/untapped levels.
Below is a descrition of each of the level types and how it is caluclated (algorithm):
PIVOT
Daily, Weekly and Monthly levels in trading refer to significant price points that traders monitor within the context of a single trading day. These levels can provide insights into market behavior and help traders make informed decisions regarding entry and exit points.
Traders often use D/W/M levels to set entry and exit points for trades. For example, entering long positions near support (daily close) or selling near resistance (daily close).
Daily levels are used to set stop-loss orders. Placing stops just below the daily close for long positions or above the daily close for short positions can help manage risk.
The relationship between price movement and daily levels provides insights into market sentiment. For instance, if the price fails to break above the daily high, it may signify bearish sentiment, while a strong breakout can indicate bullish sentiment.
The way these levels are calculated in this indicator is based on finding pivots in the chart on D/W/M timeframe. The level is then set to previous D/W/M close = current D/W/M open.
In addition, when price is going up previous D/W/M open must be smaller than previous D/W/M close and current D/W/M close must be smaller than the current D/W/M open. When price is going down the opposite.
POINT OF CONTROL
The Point of Control (POC) is a key concept in volume profile analysis, which is commonly used in trading.
It represents the price level at which the highest volume of trading occurred during a specific period.
The POC is derived from the volume traded at various price levels over a defined time frame. In this indicator the timeframes are Daily, Weekly, and Montly.
It identifies the price level where the most trades took place, indicating strong interest and activity from traders at that price.
The POC often acts as a significant support or resistance level. If the price approaches the POC from above, it may act as a support level, while if approached from below, it can serve as a resistance level. Traders monitor the POC to gauge potential reversals or breakouts.
The way the POC is calculated in this indicator is by an approximation by analysing intrabars for the respective timeperiod (D/W/M), assigning the volume for each intrabar into the price-bins that the intrabar covers and finally identifying the bin with the highest aggregated volume.
The POC is the price in the middle of this bin.
The indicator uses a sample space for intrabars on the Daily timeframe of 15 minutes, 35 minutes for the Weekly timeframe, and 140 minutes for the Monthly timeframe.
The indicator has predefined the size of the bins to 0.2% of the price at the range low. That implies that the precision of the calulated POC og VAH/VAL is within 0.2%.
This reduction of precision is a tradeoff for performance and speed of the indicator.
This also implies that the bigger the difference from range high prices to range low prices the more bins the algorithm will iterate over. This is typically the case when calculating the monthly volume profile levels and especially high volatility assets such as alt coins.
Sometimes the number of iterations becomes too big for Trading View to handle. In these cases the bin size will be increased even more to reduce the number of iterations.
In such cases the bin size might increase by a factor of 2-3 decreasing the accuracy of the Volume Profile levels.
Anyway, since these Volume Profile levels are approximations and since precision is traded for performance the user should consider the Volume profile levels(POC, VAH, VAL) as zones rather than pin point accurate levels.
VALUE AREA HIGH/LOW STD1/STD2
The Value Area High (VAH) and Value Area Low (VAL) are important concepts in volume profile analysis, helping traders understand price levels where the majority of trading activity occurs for a given period.
The Value Area High/Low is the upper/lower boundary of the value area, representing the highest price level at which a certain percentage of the total trading volume occurred within a specified period.
The VAH/VAL indicates the price point above/below which the majority of trading activity is considered less valuable. It can serve as a potential resistance/support level, as prices above/below this level may experience selling/buying pressure from traders who view the price as overvalued/undervalued
In this indicator the timeframes are Daily, Weekly, and Monthly. This indicator provides two boundaries that can be selected in the menu.
The first boundary is 70% of the total volume (=1 standard deviation from mean). The second boundary is 95% of the total volume (=2 standard deviation from mean).
The way VAH/VAL is calculated is based on the same algorithm as for the POC.
However instead of identifying the bin with the highest volume, we start from range low and sum up the volume for each bin until the aggregated volume = 30%/70% for VAL1/VAH1 and aggregated volume = 5%/95% for VAL2/VAH2.
Then we simply set the VAL/VAH equal to the low of the respective bin.
FAIR VALUE GAPS
Fair Value Gaps (FVG) is a concept primarily used in technical analysis and price action trading, particularly within the context of futures and forex markets. They refer to areas on a price chart where there is a noticeable lack of trading activity, often highlighted by a significant price movement away from a previous level without trading occurring in between.
FVGs represent price levels where the market has moved significantly without any meaningful trading occurring. This can be seen as a "gap" on the price chart, where the price jumps from one level to another, often due to a rapid market reaction to news, events, or other factors.
These gaps typically appear when prices rise or fall quickly, creating a space on the chart where no transactions have taken place. For example, if a stock opens sharply higher and there are no trades at the prices in between the two levels, it creates a gap. The areas within these gaps can be areas of liquidity that the market may return to “fill” later on.
FVGs highlight inefficiencies in pricing and can indicate areas where the market may correct itself. When the market moves rapidly, it may leave behind price levels that traders eventually revisit to establish fair value.
Traders often watch for these gaps as potential reversal or continuation points. Many traders believe that price will eventually “fill” the gap, meaning it will return to those price levels, providing potential entry or exit points.
This indicator calculate FVGs on three different timeframes, Daily, Weekly and Montly.
In this indicator the FVGs are identified by looking for a three-candle pattern on a chart, signalling a discrete imbalance in order volume that prompts a quick price adjustment. These gaps reflect moments where the market sentiment strongly leans towards buying or selling yet lacks the opposite orders to maintain price stability.
The indicator sets the gap to the difference from the high of the first bar to the low of the third bar when price is moving up or from the low of the first bar to the high of the third bar when price is moving down.
CME GAPS (BTC only)
CME gaps refer to price discrepancies that can occur in charts for futures contracts traded on the Chicago Mercantile Exchange (CME). These gaps typically arise from the fact that many futures markets, including those on the CME, operate nearly 24 hours a day but may have significant price movements during periods when the market is closed.
CME gaps occur when there is a difference between the closing price of a futures contract on one trading day and the opening price on the following trading day. This difference can create a "gap" on the price chart.
Opening Gaps: These usually happen when the market opens significantly higher or lower than the previous day's close, often influenced by news, economic data releases, or other market events occurring during non-trading hours.
Gaps can result from reactions to major announcements or developments, such as earnings reports, geopolitical events, or changes in economic indicators, leading to rapid price movements.
The importance of CME Gaps in Trading is the potential for Filling Gaps: Many traders believe that prices often "fill" gaps, meaning that prices may return to the gap area to establish fair value.
This can create potential trading opportunities based on the expectation of gap filling. Gaps can act as significant support or resistance levels. Traders monitor these levels to identify potential reversal points in price action.
The way the gap is identified in this indicator is by checking if current open is higher than previous bar close when price is moving up or if current open is lower than previous day close when price is moving down.
EQUILIBRIUM
Equilibrium in finance and trading refers to a state where supply and demand in a market balance each other, resulting in stable prices. It is a key concept in various economic and trading contexts. Here’s a concise description:
Market Equilibrium occurs when the quantity of a good or service supplied equals the quantity demanded at a specific price level. At this point, there is no inherent pressure for the price to change, as buyers and sellers are in agreement.
Equilibrium Price is the price at which the market is in equilibrium. It reflects the point where the supply curve intersects the demand curve on a graph. At the equilibrium price, the market clears, meaning there are no surplus goods or shortages.
In this indicator the equilibrium level is calculated simply by finding the midpoint of the Daily, Weekly, and Montly candles respectively.
NOTES
1) Performance. The algorithms are quite resource intensive and the time it takes the indicator to calculate all the levels could be 5 seconds or more, depending on the number of bars in the chart and especially if Montly Volume Profile levels are selected (POC, VAH or VAL).
2) Levels displayed vs the selected chart timeframe. On a timeframe smaller than the daily TF - both Daily, Weekly, and Monthly levels will be displayed. On a timeframe bigger than the daily TF but smaller than the weekly TF - the Weekly and Monthly levels will be display but not the Daily levels. On a timeframe bigger than the weekly TF but smaller than the monthly TF - only the Monthly levels will be displayed. Not Daily and Weekly.
CREDITS
The core algorithm for calculating the POC levels is based on the indicator "Naked Intrabar POC" developed by rumpypumpydumpy (https:www.tradingview.com/u/rumpypumpydumpy/).
The "Naked intrabar POC" indicator calculates the POC on the current chart timeframe.
This indicator (Multiple Naked Levels) adds two new features:
1) It calculates the POC on three specific timeframes, the Daily, Weekly, and Monthly timeframes - not only the current chart timeframe.
2) It adds functionaly by calculating the VAL and VAH of the volume profile on the Daily, Weekly, Monthly timeframes .
Vlad Waves█ CONCEPT
Acceleration Line (Blue)
The Acceleration Line is calculated as the difference between the 8-period SMA and the 20-period SMA.
This line helps to identify the momentum and potential turning points in the market.
Signal Line (Red)
The Signal Line is an 8-period SMA of the Acceleration Line.
This line smooths out the Acceleration Line to generate clearer signals.
Long-Term Average (Green)
The Long-Term Average is a 200-period SMA of the Acceleration Line.
This line provides a broader context of the market trend, helping to distinguish between long-term and short-term movements.
█ SIGNALS
Buy Mode
A buy signal occurs when the Acceleration Line crosses above the Signal Line while below the Long-Term Average. This indicates a potential bullish reversal in the market.
When the Signal Line crosses the Acceleration Line above the Long-Term Average, consider placing a stop rather than reversing the position to protect gains from potential pullbacks.
Sell Mode
A sell signal occurs when the Acceleration Line crosses below the Signal Line while above the Long-Term Average. This indicates a potential bearish reversal in the market.
When the Signal Line crosses the Acceleration Line below the Long-Term Average, consider placing a stop rather than reversing the position to protect gains from potential pullbacks.
█ UTILITY
This indicator is not recommended for standalone buy or sell signals. Instead, it is designed to identify market cycles and turning points, aiding in the decision-making process.
Entry signals are most effective when they occur away from the Long-Term Average, as this helps to avoid sideways movements.
Use larger timeframes, such as daily or weekly charts, for better accuracy and reliability of the signals.
█ CREDITS
The idea for this indicator came from Fabio Figueiredo (Vlad).
Fusion MFI RSIHello fellas,
This superb indicator summons two monsters called Relative Strength Index (RSI) and Money Flow Index (MFI) and plays the Yu-Gi-Oh! card "Polymerization" to combine them.
Overview
The Fusion MFI RSI Indicator is an advanced analytical tool designed to provide a nuanced understanding of market dynamics by combining the Relative Strength Index (RSI) and the Money Flow Index (MFI). Enhanced with sophisticated smoothing techniques and the Inverse Fisher Transform (IFT), this indicator excels in identifying key market conditions such as overbought and oversold states, trends, and potential reversal points.
Key Features (Brief Overview)
Fusion of RSI and MFI: Integrates momentum and volume for a comprehensive market analysis.
Advanced Smoothing Techniques: Employs Hann Window, Jurik Moving Average (JMA), T3 Smoothing, and Super Smoother to refine signals.
Inverse Fisher Transform (IFT) Enhances the clarity and distinctiveness of indicator outputs.
Detailed Feature Analysis
Fusion of RSI and MFI
RSI (Relative Strength Index): Developed by J. Welles Wilder Jr., the RSI measures the speed and magnitude of directional price movements. Wilder recommended using a 14-day period and identified overbought conditions above 70 and oversold conditions below 30.
MFI (Money Flow Index): Created by Gene Quong and Avrum Soudack, the MFI combines price and volume to measure trading pressure. It is typically calculated using a 14-day period, with over 80 considered overbought and under 20 as oversold.
Application in Fusion: By combining RSI and MFI, the indicator leverages RSI's sensitivity to price changes with MFI's volume-weighted confirmation, providing a robust analysis tool. This combination is particularly effective in confirming the strength behind price movements, making the signals more reliable.
Advanced Smoothing Techniques
Hann Window: Traditionally used to reduce the abrupt data discontinuities at the edges of a sample, it is applied here to smooth the price data.
Jurik Moving Average (JMA): Known for preserving the timing and smoothness of the data, JMA reduces market noise effectively without significant lag.
T3 Smoothing: Developed to respond quickly to market changes, T3 provides a smoother response to price fluctuations.
Super Smoother: Filters out high-frequency noise while retaining important trends.
Application in Fusion: These techniques are chosen to refine the output of the combined RSI and MFI values, ensuring the indicator remains responsive yet stable, providing clearer and more actionable signals.
Inverse Fisher Transform (IFT):
Developed by John Ehlers, the IFT transforms oscillator outputs to enhance the clarity of extreme values. This is particularly useful in this fusion indicator to make critical turning points more distinct and actionable.
Mathematical Calculations for the Fusion MFI RSI Indicator
RSI (Relative Strength Index)
The RSI is calculated using the following steps:
Average Gain and Average Loss: First, determine the average gain and average loss over the specified period (typically 14 days). This is done by summing all the gains and losses over the period and then dividing each by the period.
Average Gain = (Sum of Gains over the past 14 periods) / 14
Average Loss = (Sum of Losses over the past 14 periods) / 14
Relative Strength (RS): This is the ratio of average gain to average loss.
RS = Average Gain / Average Loss
RSI: Finally, the RSI is calculated using the RS value:
RSI = 100 - (100 / (1 + RS))
MFI (Money Flow Index)
The MFI is calculated using several steps that incorporate both price and volume:
Typical Price: Calculate the typical price for each period.
Typical Price = (High + Low + Close) / 3
Raw Money Flow: Multiply the typical price by the volume for the period.
Raw Money Flow = Typical Price * Volume
Positive and Negative Money Flow: Compare the typical price of the current period to the previous period to determine if the money flow is positive or negative.
If today's Typical Price > Yesterday's Typical Price, then Positive Money Flow = Raw Money Flow; Negative Money Flow = 0
If today's Typical Price < Yesterday's Typical Price, then Negative Money Flow = Raw Money Flow; Positive Money Flow = 0
Money Flow Ratio: Calculate the ratio of the sum of Positive Money Flows to the sum of Negative Money Flows over the past 14 periods.
Money Flow Ratio = (Sum of Positive Money Flows over 14 periods) / (Sum of Negative Money Flows over 14 periods)
MFI: Finally, calculate the MFI using the Money Flow Ratio.
MFI = 100 - (100 / (1 + Money Flow Ratio))
Fusion of RSI and MFI
The final Fusion MFI RSI value could be calculated by averaging the IFT-transformed values of RSI and MFI, providing a single oscillator value that reflects both momentum and volume-weighted price action:
Fusion MFI RSI = (MFI weight * MFI) + (RSI weight * RSI)
Suggested Settings and Trading Rules
Original Usage
RSI: Wilder suggested buying when the RSI moves above 30 from below (enter long) and selling when the RSI moves below 70 from above (enter short). He recommended exiting long positions when the RSI reaches 70 or higher and exiting short positions when the RSI falls below 30.
MFI: Quong and Soudack recommended buying when the MFI is below 20 and starts rising (enter long), and selling when it is above 80 and starts declining (enter short). They suggested exiting long positions when the MFI reaches 80 or higher and exiting short positions when the MFI falls below 20.
Fusion Application
Settings: Use a 14-day period for this indicator's calculations to maintain consistency with the original settings suggested by the inventors.
Trading Rules:
Enter Long Signal: Consider entering a long position when both RSI and MFI are below their respective oversold levels and begin to rise. This indicates strong buying pressure supported by both price momentum and volume.
Exit Long Signal: Exit the long position when either RSI or MFI reaches its respective overbought threshold, suggesting a potential reversal or decrease in buying pressure.
Enter Short Signal: Consider entering a short position when both indicators are above their respective overbought levels and begin to decline, suggesting that selling pressure is mounting.
Exit Short Signal: Exit the short position when either RSI or MFI falls below its respective oversold threshold, indicating diminishing selling pressure and a potential upward reversal.
How to Use the Indicator
Select Source and Timeframe: Choose the data source and the timeframe for analysis.
Configure Fusion Settings: Adjust the weights for RSI and MFI.
Choose Smoothing Technique: Select and configure the desired smoothing method to suit the market conditions and personal preference.
Enable Fisherization: Optionally apply the Inverse Fisher Transform to enhance signal clarity.
Customize Visualization: Set up gradient coloring, background plots, and bands according to your preferences.
Interpret the Indicator: Use the Fusion value and visual cues to identify market conditions and potential trading opportunities.
Conclusion
The Fusion MFI RSI Indicator integrates classical and modern technical analysis concepts to provide a comprehensive tool for market analysis. By combining RSI and MFI with advanced smoothing techniques and the Inverse Fisher Transform, this indicator offers enhanced insights, aiding traders in making more informed and timely trading decisions. Customize the settings to align with your trading strategy and leverage this powerful tool to navigate financial markets effectively.
Best regards,
simwai
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Credits to:
@loxx – T3
@everget – JMA
@cheatcountry – Hann Window
[BT] NedDavis Series: CPI Minus 5-Year Moving Average🟧 GENERAL
The script works on the Monthly Timeframe and has 2 main settings (explained in FEATURES ). It uses the US CPI data, reported by the Bureau of Labour Statistics.
🔹Functionality 1: The main idea is to plot the distance between the CPI line and the 5 year moving average of the CPI line. This technique in mathematics is called "deviation from the moving average". This technique is used to analyse how has CPI previously acted and can give clues at what it might do in the future. Economic historians use such analysis, together with specific period analysis to predict potential risks in the future (see an example of such analysis in HOW TO USE section. The mathematical technique is a simple subtraction between 2 points (CPI - 5yr SMA of CPI).
▶︎Interpretation for deviation from a moving average:
Positive Deviation: When the line is above its moving average, it indicates that the current value is higher than the average, suggesting potential strength or bullish sentiment.
Negative Deviation: Conversely, when the line falls below its moving average, it suggests weakness or bearish sentiment as the current value is lower than the average.
▶︎Applications:
Trend Identification: Deviations from moving averages can help identify trends, with sustained deviations indicating strong trends.
Reversal Signals: Significant deviations from moving averages may signal potential trend reversals, especially when combined with other technical indicators.
Volatility Measurement: Monitoring the magnitude of deviations can provide insights into market volatility and price movements.
Remember the indicator is applying this only for the US CPI - not the ticker you apply the indicator on!
🔹Functionality 2: It plots on a new pane below information about the Consumer Price Index. You can also find the information by plotting the ticker symbol USACPIALLMINMEI on TradingView, which is a Monthly economic data by the OECD for the CPI in the US. The only addition you would get from the indicator is the plot of the 5 year Simple Moving Average.
🔹What is the US Consumer Price Index?
Measures the change in the price of goods and services purchased by consumers;
Traders care about the CPI because consumer prices account for a majority of overall inflation. Inflation is important to currency valuation because rising prices lead the central bank to raise interest rates out of respect for their inflation containment mandate;
It is measured as the average price of various goods and services are sampled and then compared to the previous sampling.
Source: Bureau of Labor Statistics;
FEATURES OF INDICATOR
1) The US Consumer Price Index Minus the Five Year Moving Average of the same.
As shown on the picture above and explained in previous section. Here a more detailed view.
2) The actual US Consumer Price Index (Annual Rate of change) and the Five year average of the US Consumer Price Index. Explained above and shown below:
To activate 2) go into settings and toggle the check box.
HOW TO USE
It can be used for a fundamental analysis on the relationship between the stock market, the economy and the Feds decisions to hike or cut rates, whose main mandate is to control inflation over time.
I have created this indicator to show my analysis in this idea:
What does a First Fed Rate cut really mean?
CREDITS
I have seen such idea in the past posted by the institutional grade research of NedDavis and have recreated it for the TradingView platform, open-source for the community.
Volume-Supported Linear Regression Trend Modified StrategyHi everyone, this will be my first published script on Tradingview, maybe more to come.
For quite some time I have been looking for a script that performs no matter if price goes up or down or sideways. I believe this strategy comes pretty close to that. Although nowhere near the so called "buy&hold equity" of BTC, it has produced consistent profits even when price goes down.
It is a strategy which seems to work best on the 1H timeframe for cryptocurrencies.
Just by testing different settings for SL and TP you can customize it for each pair.
THE STRATEGY:
Basically, I used the Volume Supported Linear Regression Trend Model that LonesomeTheBlue has created and modified a few things such as entry and exit conditions. So all credits go to him!
LONG ENTRY: When there is a bullish cross of the short term trend (the histogram/columns), while the long term trend is above 0 and rising.
SHORT ENTRY: When there is a bearish cross (green to red) of the short-term trend (the histogram/columns), while the long term trend is beneath 0 and decreasing.
LONG EXIT: Bearish crossover of short-term trend while long term trend is below 0
SHORT EXIT: Bullish crossover of short-term trend while long term trend is above 0
Combining this with e.g. a SL of 2% and a TP of 20% (as used in my backtesting), combined with pyramiding and correct risk management, it gives pretty consistent results.
Be aware, this is only for educational purpose and in no means financial advise. Past results do not guarantee future results. This strategy can lose money!
Enjoy :)
PS: It works not only on BTC of course, works even better on some other major crypto pairs. I'll leave it to you to find out which ones ;)
MTF OB Supply Demand ZonesHello everyone,
This exceptional indicator provides you with visual representations of bullish and bearish order blocks or supply and demand zones across multiple timeframes. In simple terms, bullish order blocks are represented by a small red candle followed by a large red candle, while bearish order blocks are depicted as a small green candle followed by a large red candle. Supply and demand zones are drawn by using order blocks.
Features:
Display order blocks from up to three different timeframes.
Customize the maximum number of boxes shown and the colors of the zones.
Choose from three different modes: OB (Order Block), Extended OB, and Supply/Demand.
Mode Descriptions:
OB: Includes the body of the candle.
Extended OB: Encompasses the body and wick of the candle.
Supply/Demand: Covers the body, wick, and half the body of the large candle.
Usage:
Ensure that charts 2 and 3 are set to a higher timeframe. For modes 2 and 3, it’s recommended to reduce the maximum number of boxes shown. The zones or boxes are transparent, allowing for overlap. This feature aids in identifying reversal zones or confirmed zones. The more intense the color, the stronger the confirmation. If a green zone overlaps a red zone (or vice versa), it signifies a reversal zone.
Thank you for checking out this indicator!
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Additional Information:
Order blocks refer to specific price areas where large market participants, such as institutional traders, have previously placed significant buy or sell orders. These clusters of orders can impact price movement, liquidity, and market sentiment.
Order blocks are a strategic approach to identifying key levels of support and resistance based on the behavior of institutional traders. These key levels are then utilized as entry or exit points for trades.
An order block is an area where there has been a large concentration of limit orders awaiting execution. These blocks are identified on a chart by observing previous price action and pinpointing areas where the price experienced significant movement or abrupt changes in direction.
Order blocks are used in the following popular trading philosophies:
Smart Money Concepts (SMC)
Inner Circle Trading (ICT)
Price Action
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Credits to: @AGFXTRADING
Hurst Future Lines of Demarcation StrategyJ. M. Hurst introduced a concept in technical analysis known as the Future Line of Demarcation (FLD), which serves as a forward-looking tool by incorporating a simple yet profound line into future projections on a financial chart. Specifically, the FLD is constructed by offsetting the price half a cycle ahead into the future on the time axis, relative to the Hurst Cycle of interest. For instance, in the context of a 40 Day Cycle, the FLD would be represented by shifting the current price data 20 days forward on the chart, offering an idea of future price movement anticipations.
The utility of FLDs extends into three critical areas of insight, which form the backbone of the FLD Trading Strategy:
A price crossing the FLD signifies the confirmation of either a peak or trough formation, indicating pivotal moments in price action.
Such crossings also help determine precise price targets for the upcoming peak or trough, aligned with the cycle of examination.
Additionally, the occurrence of a peak in the FLD itself signals a probable zone where the price might experience a trough, helping to anticipate of future price movements.
These insights by Hurst in his "Cycles Trading Course" during the 1970s, are instrumental for traders aiming to determine entry and exit points, and to forecast potential price movements within the market.
To use the FLD Trading Strategy, for example when focusing on the 40 Day Cycle, a trader should primarily concentrate on the interplay between three Hurst Cycles:
The 20 Day FLD (Signal) - Half the length of the Trade Cycle
The 40 Day FLD (Trade) - The Cycle you want to trade
The 80 Day FLD (Trend) - Twice the length of the Trade Cycle
Traders can gauge trend or consolidation by watching for two critical patterns:
Cascading patterns, characterized by several FLDs running parallel with a consistent separation, typically emerge during pronounced market trends, indicating strong directional momentum.
Consolidation patterns, on the other hand, occur when multiple FLDs intersect and navigate within the same price bandwidth, often reversing direction to traverse this range multiple times. This tangled scenario results in the formation of Pause Zones, areas where price momentum is likely to temporarily stall or where the emergence of a significant trend might be delayed.
This simple FLD indicator provides 3 FLDs with optional source input and smoothing, A-through-H FLD interaction background, adjustable “Close the Trade” triggers, and a simple strategy for backtesting it all.
The A-through-H FLD interactions are a framework designed to classify the different types of price movements as they intersect with or diverge from the Future Line of Demarcation (FLD). Each interaction (designated A through H by color) represents a specific phase or characteristic within the cycle, and understanding these can help traders anticipate future price movements and make informed decisions.
The adjustable “Close the Trade” triggers are for setting the crossover/under that determines the trade exits. The options include: Price, Signal FLD, Trade FLD, or Trend FLD. For example, a trader may want to exit trades only when price finally crosses the Trade FLD line.
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 @Hpotter
👏 @parisboy
[TTI] High Volume Close (HVC) Setup📜 ––––HISTORY & CREDITS––––
The High Volume Close (HVC) Setup is a specialised indicator designed for the TradingView platform used to identify specific bar. This tool was developed with the objective of identifying a technical pattern that trades have claimed is significant trading opportunities through a unique blend of volume analysis and price action strategies. It is based on the premise that high-volume bars, when combined with specific price action criteria, can signal key market movements.
The HVC is applicable both for swing and longer term trading and as a technical tool it can be used by traders of any asset type (stocks, ETF, crypto, forex etc).
🦄 –––UNIQUENESS–––
The uniqueness of the HVC Setup lies in its flexibility to determine an important price level based on historically important bar. The idea is to identify significant bars (e.g. those who have created the HIGHEST VOLUME: Ever, Yearly, Quarterly and meet additional criteria from the settings) and plot on the chart the close on that day as a significant level as well as theoretical stop loss and target levels. This approach allows traders to discern high volume bars that are contextually significant — a method not commonly found in standard trading tools.
🎯 ––––WHAT IT DOES––––
The HVC Setup indicator performs a series of calculations to identify high volume close bars/bar (HVC bars) based on the user requirements.
These bars are determined based on the highest volume recorded within a user-inputs:
👉 Period (Ever, Yearly, Quarterly) and must meet additional criteria such as:
👉 a minimum percentage Price Change (change is calculated based on a close/close) and
👉 specific Closing Range requirements for the HVC da.
The theory is that this is a significant bar that is important to know where it is on the chart.
The script includes a comparative analysis of the HVC bar's price against historical price highs (all-time, yearly, quarterly), which provides further context and significance to the identified bars. All of these USER input requirement are then taken into account as a condition to identity the High Volume Close Bar (HVC).
The visual representation includes color-coded bar (default is yellow) and lines to delineate these key trading signals. It then draws a blue line for the place where the close ofthe bar is, a red line that would signify a stop loss and 2 target profit levels equal to 2R and 3R of the risked level (close-stop loss). Additional lines can be turned on/off with their coresponding checkboxes in the settings.
If the user chooses "Ever" for Period - the script will look at the first available bar ever in Tradingview - this is generally the IPO bar;
If the users chooses "Yearly" - the script would look at the highest available bar for a completed year;
If the users chooses "Quarterly" - it would do the same for the quarter. (works on daily timeframe only);
While we have not backtested the performance of the script, this methodology has been widely publicised.
🛠️ ––––HOW TO USE IT––––
To utilize the HVC Setup effectively:
👉Customize Input Settings: Choose the HVC period, percentage change threshold, closing range, stop loss distance, and target multiples according to your trading strategy. Use the tick boxes to enable and disable if a given condition is used within the calculation.
👉Identify HVC Bars: The script highlights HVC bars, indicating potential opportunities based on volume and price action analysis.
👉Interpret Targets and Stop Losses: Use the color-coded lines (green for targets, red for stop losses) to guide your trade entries and exits.
👉Contextual Analysis: Always consider the HVC bar signals in conjunction with overall market trends and additional technical indicators for comprehensive trading decisions.
This script is designed to assist traders in identifying high-potential trading setups by using a combination of volume and price analysis, enhancing traditional methods with a unique, algorithmically driven approach.
Fractional Differentiation█ Description
This Pine Script indicator implements fractional differentiation, a mathematical operation that extends the concept of differentiation to non-integer orders. Fractional differentiation is particularly significant in financial analysis, as it enables analysts to uncover underlying patterns in price series that are not evident with traditional integer-order differentiation. The motivation behind fractional differencing lies in its ability to balance the trade-off between retaining data/feature memory and ensuring stationarity.
█ Significance
Fractional differentiation offers a nuanced view of market data, allowing for the adjustment of the differentiation order to balance between signal clarity and noise reduction. This is especially useful in financial markets, where the choice of differentiation order can highlight long-term trends or short-term price movements without completely smoothing out the valuable market noise.
█ Approximations Used
The implementation relies on the Gamma function for the computation of coefficients in the fractional differentiation formula. Given the complexity of the Gamma function, this script uses an approximation method based on the Lanczos approximation for the logarithm of the Gamma function, as detailed in "An Analysis Of The Lanczos Gamma Approximation" by Glendon Ralph Pugh (2004). This approximation strikes a balance between computational efficiency and accuracy, making it suitable for real-time market analysis in Pine Script.
█ Limitations
While this script opens new avenues for market analysis, it comes with inherent limitations:
- The approximation of the Gamma function, although accurate, is not exact. The precision of the fractional differentiation result may vary slightly, especially for higher-order differentiations.
- The script's performance is subject to Pine Script's execution environment, with a default loop limit set to 100 iterations for practicality. Users might need to adjust this limit based on their specific use case, balancing between computational load and the desired depth of historical data analysis.
█ Credits
This script makes use of the `MathSpecialFunctionsGamma` library, authored by Ricardo Santos . This library provides essential mathematical functions, including an approximation of the Gamma function, which is crucial for the fractional differentiation calculation.
I also extend my sincere gratitude to
Dr. Marcos López de Prado for his seminal work, Advances in Financial Machine Learning (2018). Dr. López de Prado's insights have significantly influenced our approach to developing sophisticated analytical tools.
Dr. Ernie Chan for his freely and generously sharing valuable insights via discourse on quantitative trading strategies through his talks and publications.
Blockunity US Market Liquidity (BML)Get a clear view of US market liquidity and monitor its status at a glance to anticipate movements on risky assets.
The Idea
The BML aggregates and analyzes total USD market liquidity in trillions of dollars. It is used to monitor the liquidity of the USD market. When liquidity is good, all is well. If liquidity is low, the US will maneuver and sell treasury bills (debt) to replenish its treasury, which can lead to bearish pressure on markets, particularly those considered risky, such as Bitcoin.
How to Use
The indicator is very easy to use, there's nothing special about it. This tool is mainly intended to be used as fundamental information, and not for active trading.
Elements
The US Market Liquidity has several distinct components:
FED Balance Sheet
The Fed credits member banks’ Fed accounts with money, and in return, banks sell the Fed US Treasuries and/or US Mortgage-Backed Securities. This is how the Fed “prints” money to juice the financial system.
US Treasury General Account
The US Treasury General Account (TGA) balances with the NY Fed. When it decreases, it means the US Treasury is injecting money into the economy directly and creating activity. When it increases, it means the US Treasury is saving money and not stimulating economic activity. The TGA also increases when the Treasury sells bonds. This action removes liquidity from the market as buyers must pay for their bonds with dollars.
Overnight Reverse Repurchase Agreements
A reverse repurchase agreement (known as Reverse Repo or RRP) is a transaction in which the New York Fed under the authorization and direction of the Federal Open Market Committee sells a security to an eligible counterparty with an agreement to repurchase that same security at a specified price at a specific time in the future.
Earnings Remittances Due to the Treasury
The Federal Reserve Banks remit residual net earnings to the US Treasury after providing for the costs of operations, payment of dividends, and the amount necessary to maintain each Federal Reserve Bank’s allotted surplus cap. Positive amounts represent the estimated weekly remittances due to the US Treasury. Negative amounts represent the cumulative deferred asset position, which is incurred during a period when earnings are not sufficient to provide for the cost of operations, payment of dividends, and maintaining surplus.
Settings
Several parameters can be defined in the indicator configuration. You can:
Choose the smoothing and timeframe to be used in the plot.
Set the EMA lookback period and display it or not. This affects the color of the main plot.
Set the period to be taken into account when calculating the variation rate in the table.
Select the data to be taken into account in the calculation.
Activate or not the barcolor.
Lastly, you can modify all table parameters.
MTF HalfTrendIntroduction
A half-trend indicator is a technical analysis tool that uses moving averages and price data to find potential trend reversal and entry points in the form of graphical arrows showing market turning points.
The salient features of this indicator are:
- It uses the phenomenon of moving averages.
- It is a momentum indicator.
- It can indicate a trend change.
- It is capable of detecting a bullish or bearish trend reversal.
- It can signal to sell/buy.
- It is a real-time indicator.
Multi-Timeframe Application
A standout feature is its flexibility across timeframes. Traders have the liberty to choose any timeframe on the chart, enhancing the tool's versatility and making it suitable for both short-term and long-term analyses.
Principle of the Half Trend indicator
This indicator is based on the moving averages. The moving average is the average of the fluctuation or change in the price of an asset. These averages are taken for a time interval.
So, a half-trend indicator takes the moving averages phenomenon as its principle for working. The most commonly used moving averages in a half trend indicator are:
- Relative strength index (RSI)
- EMA (estimated moving average)
Components of a Half Trend indicator
There are two main components of a half trend indicator:
- Half trend line
- Arrows
- ATR lines
Half trend line
Half trend line represents this indicator on a candlestick chart. This line shows the trend of a chart in real-time. A half-trend line is based on the moving averages.
There are two further components of a half-trend line:
- Redline
- Blue line
A red line represents a bearish trend. When the half-trend line turns red, a trend is facing a dip. It is time for the bears to take control of the market. A bearish control of the market represents the domination of sellers in the market.
On the other hand, the blue line represents the bullish nature of the market. It tells a trader that the bullish sentiment of the market is prevailing. A bullish market means the number of buyers is significantly greater than the number of sellers.
Moreover, a trader can change these colors to his choice by customization.
Arrows
There are two types of arrows in this indicator which help a trader with the entry and exit points. These arrows are,
- Blue arrow
- Red arrow
A blue arrow signals a buying trade; on the other hand, a red arrow tells a trader about the selling of the assets. These arrows work with the moving average line to formulate a trading strategy.
The color of these arrows is changed if a trader desires so.
ATR lines
The ATR blue and red lines represent the Average True Range of the Half trend line. They may be used as stop loss or take profit levels.
Pros and Cons
Pros
- It is a very easy to eyes indicator.
- This is a very useful friendly indicator.
- It provides sufficient information to beginner traders.
- It provides sufficient information for entry points in a trade.
- A half-trend indicator provides a good exit strategy for a trader.
- It provides information about market reversals.
- It helps a trader to find a bullish and bearish sentiment in the market.
Cons
- It is a real-time indicator. So, it can lag.
- The lagging of this indicator can lead to miss opportunities.
- The most advanced and professional traders may not rely on this indicator for crucial trading decisions.
- The lagging of this indicator can predict false reversals of the market.
- It can create false signals.
- It requires the confluence of the other technical tools for a better success ratio.
Settings for Half Trend indicator
The default settings for half trend indicator are:
Amplitude = 2
Channel deviation = 2
Different markets or financial instruments may require different settings for optimal execution.
Amplitude: The degree that the Half trend line takes the internal variables into consideration. The higher the number, the fewer trades. The default value is 2.
Channel deviation: The ATR value calculation from the Half trend line. The default value is 2.
Trading strategy
It is an effective indicator in terms of strategy formation for a trading setup. The new and beginner trades can take benefit from this indicator for the formulation of a good trading setup. This indicator also helps seasoned and professional traders formulate a good trading setup with other technical tools.
The trading strategy involving a half-trend indicator is divided into three parts:
- Entry and exit
- Risk management
- Take profit
Entry and exit
It is an effective indicator that provides sufficient information about the entry and exit points in a trading setup. The profit of a trader is directly proportional to the appropriate entry and exit points. So, it is a crucial step in any trading setup.
The blue and red arrows provide information about the entry and exit points in a trading setup. Furthermore, the entry and exit for the bullish and bearish setups are as follows.
Entry and exit for a bullish setup
If a blue arrow appears under the half-trend line, it means the bullish sentiment of the market is getting stronger in the future. So, it is a signal for entry in a bullish setup.
As the red arrow appears on the chart, it is a signal to exit your trade. The red arrow represents a reversal in the market, so it is a good opportunity to close your trade in a bullish setup.
Entry and exit for a bearish setup
Suppose a red arrow appears above the red moving average line. It is a good opportunity to enter a trade in a bearish setup. The red line represents that sooner the sellers are going to take control and the value of the asset is about to face a dip. So it is the best time to make your move.
As the opposite arrow appears in the chart, it is time to exit from a bearish trade setup.
Re-entering a position
Bullish setup
- The half-trend line is blue.
- At least one candle closes below the blue half-trend line.
- Enter on the candle that closes above the blue half-trend line.
Bearish setup
- The half-trend line is red.
- At least one candle closes above the red half-trend line.
- Enter on the candle that closes below the red half-trend line.
Risk management
Risk management is an integral part of a trading setup. It is an important step to protect your potential profits and losses.
When trading in a bullish market, place the stop loss at the prior swing low. It will help you to cut your losses in case the prices move to the lower end.
In the case of a bearish market, place your stop loss above the prior swing high.
A trader may trail the stop loss using the ATR lines.
The new trader often makes mistakes in the placement of the stop loss. If you don’t place the stop loss at an appropriate point. It can drain your bank account and ruin your trading experience. Is is recommended not to risk more than 2% of your trading account, per trade.
Take profit
The blue ATR line may be used as one take profit level on a bullish setup followed by the previous swing high. The signal reversal would indicate the final take profit and closing of any position.
The red ATR line may be used as one take profit level on a bearish setup followed by the previous swing low. The signal reversal would indicate the final take profit and closing of any position.
Conclusion
A half trend indicator is a decent indicator that can transform your trading experience. It is a dual indicator that is based on the moving averages as well as helps you to form a trading strategy. If you are a new trader, this indicator can help you to learn and flourish in the trading universe. If you are a seasoned trader, I recommend you use this indicator with other technical analysis tools to enhance your success ratio.
All credits go to:
- @everget the original creator of this indicator (I just added the MTF capability).
- Ali Muhammad original author of much of the description used.
Leading T3Hello Fellas,
Here, I applied a special technique of John F. Ehlers to make lagging indicators leading. The T3 itself is usually not realling the classic lagging indicator, so it is not really needed, but I still publish this indicator to demonstrate this technique of Ehlers applied on a simple indicator.
The indicator does not repaint.
In the following picture you can see a comparison of normal T3 (purple) compared to a 2-bar "leading" T3 (gradient):
The range of the gradient is:
Bottom Value: the lowest slope of the last 100 bars -> green
Top Value: the highest slope of the last 100 bars -> purple
Ehlers Special Technique
John Ehlers did develop methods to make lagging indicators leading or predictive. One of these methods is the Predictive Moving Average, which he introduced in his book “Rocket Science for Traders”. The concept is to take a difference of a lagging line from the original function to produce a leading function.
The idea is to extend this concept to moving averages. If you take a 7-bar Weighted Moving Average (WMA) of prices, that average lags the prices by 2 bars. If you take a 7-bar WMA of the first average, this second average is delayed another 2 bars. If you take the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag.
T3
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
Thanks for checking this out and give a boost, if you enjoyed the content.
Best regards,
simwai
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Credits to @loxx
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
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Credits to:
@loxx
@Bjorgum
@Greeny
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
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Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
Goertzel Adaptive JMA T3Hello Fellas,
The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs.
It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive.
Used Adaptive Length Algorithms
Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length.
Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach.
Scoring System
The scoring system determines if bars are red or green and collects them.
Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA.
Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars.
Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0–100.
Signals
Is the price above MA? -> bullish market
Is the price below MA? -> bearish market
Usage
Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA.
It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score.
Now, follows a bunch of knowledge for people who don't know about the concepts used here.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Goertzel Algorithm
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency components¹.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence .
Usage of DFT/Goertzel In Adaptive Length Algorithms
Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy.
The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms.
I hope this can help you somehow.
Thanks for reading, and keep it up.
Best regards,
simwai
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Credits to:
@ClassicScott
@yatrader2
@cheatcountry
@loxx
QQE MOD + SSL Hybrid + Waddah Attar Explosion IndicatorINDICATOR PURPOSE
This indicator is designed to complement my original QQE MOD + SSL Hybrid + Waddah Attar Explosion strategy.
Multiple users have requested that I convert the strategy to an indicator because alertconditions do not work on strategies and people want to specific set alerts for BUY, SELL, CLOSE BUY and CLOSE SELL. This can only be achieved using alertcondition().
This indicator functions in the exact same way as the strategy, but it doesn't have any backtesting functionality. I recomment that you use the original QQE MOD + SSL Hybrid + Waddah Attar Explosion strategy for parameter tuning and backtesting, then if you need more control on alerts you can use this indicator for that purpose.
Only other difference is that I have added grey exit labels on the chart since it's not obvious where the exits would happen like it was in the strategy version.
CREDITS
QQE MOD byMihkel00
SSL Hybrid by Mihkel00
Waddah Attar Explosion by shayankm
