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 .
在腳本中搜尋"algo"
Volume Profile cheap copyIn the absence of TradingView's open-source Volume Profile (hereinafter referred to as VP) indicator code, I have replicated it. However, because this code is classified as an "indicator" rather than a "tool," it cannot allow users to define the range according to their preferences. In the code, I have set different periods, and users can input 0, 1, or 2 to let the indicator calculate the volume distribution from the earliest candle to the latest candle within the daily, weekly, or monthly range, respectively.
How can we prove that this code is consistent with TradingView's algorithm?
Firstly, the calculation or drawing process of VP starts from the earliest candle in the selected range. After calling TradingView's built-in "Fixed Range Volume Profile" (FRVP) tool, you can enter the settings interface of the tool and check both "developing POC" and "Value Area (VA)." The paths of POC, VAH, and VAL will appear in the chart. These paths are the changes in the values of POC, VAH, and VAL as the number of candles increases. If the paths shown by my indicator are the same as those shown by TradingView's VP indicator, then it proves the algorithms are consistent. Since VP itself is calculated based on volume, the high and low points of candles, and the opening and closing prices, if the data sources are consistent, the calculation results (the paths of POC, VAH, and VAL) will remain consistent over time. This can be used to infer that the algorithms are consistent. Additionally, the parameters of the two indicators (number of rows and value area ratio) must be the same to verify consistency. The number of rows in the indicator is usually set to 100 by default, and the value area ratio is 70. Therefore, the parameters in FRVP should also be set to 100 rows and a value area volume of 70.
Why is there a noticeable discrepancy?
When the start and end points of the VP remain unchanged, reducing the chart's time frame can improve accuracy. For example, when calculating the weekly VP, switching from a 1-hour time frame to a 5-minute time frame can make the indicator more closely match TradingView's native VP. Tests have shown that TradingView's native VP may not use the data displayed on the current chart for its calculations. For instance, the VP may use data from the 5-minute time frame even if the chart is displayed in the 1-hour time frame. However, my replicated VP calculates based on the chart's data, so differences in time frames will affect accuracy.
Current algorithm deficiencies
This replicated VP code is merely a demo and does not handle data updates. In other words, after the latest candle closes, the VP needs to be recalculated, but this recalculation step is not handled, which will cause errors. To resolve this issue, you only need to switch the time frame or delete the indicator and re-add it.
HilalimSBHilalimSB A Wedding Gift 🌙
HilalimSB - Revealing the Secrets of the Trend
HilalimSB is a powerful indicator designed to help investors analyze market trends and optimize trading strategies. Designed to uncover the secrets at the heart of the trend, HilalimSB stands out with its unique features and impressive algorithm.
Hilalim Algorithm and Fixed ATR Value:
HilalimSB is equipped with a special algorithm called "Hilalim" to detect market trends. This algorithm can delve into the depths of price movements to determine the direction of the trend and provide users with the ability to predict future price movements. Additionally, HilalimSB uses its own fixed Average True Range (ATR) value. ATR is an indicator that measures price movement volatility and is often used to determine the strength of a trend. The fixed ATR value of HilalimSB has been tested over long periods and its reliability has been proven. This allows users to interpret the signals provided by the indicator more reliably.
ATR Calculation Steps
1.True Range Calculation:
+ The True Range (TR) is the greatest of the following three values:
1. Current high minus current low
2. Current high minus previous close (absolute value)
3. Current low minus previous close (absolute value)
2.Average True Range (ATR) Calculation:
-The initial ATR value is calculated as the average of the TR values over a specified period
(typically 14 periods).
-For subsequent periods, the ATR is calculated using the following formula:
ATRt=(ATRt−1×(n−1)+TRt)/n
Where:
+ ATRt is the ATR for the current period,
+ ATRt−1 is the ATR for the previous period,
+ TRt is the True Range for the current period,
+ n is the number of periods.
Pine Script to Calculate ATR with User-Defined Length and Multiplier
Here is the Pine Script code for calculating the ATR with user-defined X length and Y multiplier:
//@version=5
indicator("Custom ATR", overlay=false)
// User-defined inputs
X = input.int(14, minval=1, title="ATR Period (X)")
Y = input.float(1.0, title="ATR Multiplier (Y)")
// True Range calculation
TR1 = high - low
TR2 = math.abs(high - close )
TR3 = math.abs(low - close )
TR = math.max(TR1, math.max(TR2, TR3))
// ATR calculation
ATR = ta.rma(TR, X)
// Apply multiplier
customATR = ATR * Y
// Plot the ATR value
plot(customATR, title="Custom ATR", color=color.blue, linewidth=2)
This code can be added as a new Pine Script indicator in TradingView, allowing users to calculate and display the ATR on the chart according to their specified parameters.
HilalimSB's Distinction from Other ATR Indicators
HilalimSB emerges with its unique Average True Range (ATR) value, presenting itself to users. Equipped with a proprietary ATR algorithm, this indicator is released in a non-editable form for users. After meticulous testing across various instruments with predetermined period and multiplier values, it is made available for use.
ATR is acknowledged as a critical calculation tool in the financial sector. The ATR calculation process of HilalimSB is conducted as a result of various research efforts and concrete data-based computations. Therefore, the HilalimSB indicator is published with its proprietary ATR values, unavailable for modification.
The ATR period and multiplier values provided by HilalimSB constitute the fundamental logic of a trading strategy. This unique feature aids investors in making informed decisions.
Visual Aesthetics and Clear Charts:
HilalimSB provides a user-friendly interface with clear and impressive graphics. Trend changes are highlighted with vibrant colors and are visually easy to understand. You can choose colors based on eye comfort, allowing you to personalize your trading screen for a more enjoyable experience. While offering a flexible approach tailored to users' needs, HilalimSB also promises an aesthetic and professional experience.
Strong Signals and Buy/Sell Indicators:
After completing test operations, HilalimSB produces data at various time intervals. However, we would like to emphasize to users that based on our studies, it provides the best signals in 1-hour chart data. HilalimSB produces strong signals to identify trend reversals. Buy or sell points are clearly indicated, allowing users to develop and implement trading strategies based on these signals.
For example, let's imagine you wanted to open a position on BTC on 2023.11.02. You are aware that you need to calculate which of the buying or selling transactions would be more profitable. You need support from various indicators to open a position. Based on the analysis and calculations it has made from the data it contains, HilalimSB would have detected that the graph is more suitable for a selling position, and by producing a sell signal at the most ideal selling point at 08:00 on 2023.11.02 (UTC+3 Istanbul), it would have informed you of the direction the graph would follow, allowing you to benefit positively from a 2.56% decline.
Technology and Innovation:
HilalimSB aims to enhance the trading experience using the latest technology. With its innovative approach, it enables users to discover market opportunities and support their decisions. Thus, investors can make more informed and successful trades. Real-Time Data Analysis: HilalimSB analyzes market data in real-time and identifies updated trends instantly. This allows users to make more informed trading decisions by staying informed of the latest market developments. Continuous Update and Improvement: HilalimSB is constantly updated and improved. New features are added and existing ones are enhanced based on user feedback and market changes. Thus, HilalimSB always aims to provide the latest technology and the best user experience.
Social Order and Intrinsic Motivation:
Negative trends such as widespread illegal gambling and uncontrolled risk-taking can have adverse financial effects on society. The primary goal of HilalimSB is to counteract these negative trends by guiding and encouraging users with data-driven analysis and calculable investment systems. This allows investors to trade more consciously and safely.
Quarterly H/L [Dango]Introducing the Quarterly High and Low Indicator, a powerful and original tool designed to enhance your understanding of price action by identifying key turning points within quarterly cycles. This innovative script accurately determines the most significant highs and lows in each quarter, providing valuable insights for traders.
Key Features:
- Identifies and displays quarterly highs and lows on 90-minute, daily, weekly, monthly, and yearly timeframes
- Employs advanced algorithms and a deep understanding of cycle theory to precisely pinpoint key turning points
- Accounts for subtle nuances in price action and market dynamics
- Intended to be used in conjunction with the Quarterly Cycles Indicator for further confluence
How It Works:
The Quarterly High and Low Indicator utilizes a proprietary algorithm to meticulously analyze price action within each quarter. This advanced formula takes into account multiple factors, such as price momentum, volatility, and volume, to accurately identify the most significant high and low points.
The script employs a multi-step process to determine the quarterly highs and lows:
1. Cycle Isolation: The indicator first isolates the price action within each quarter, focusing on the specific time frame being analyzed (90-minute, daily, weekly, monthly, or yearly).
2. Momentum Analysis: The script then analyzes the price momentum within each quarter, identifying periods of strong bullish or bearish sentiment. This helps to narrow down potential high and low points.
3. Volatility and Volume Confirmation: To further refine the identification of key turning points, the indicator assesses the volatility and volume characteristics surrounding potential highs and lows. Significant changes in volatility and volume often accompany important price reversals.
4. Proprietary Scoring System: The algorithm assigns scores to each potential high and low point based on a proprietary scoring system. This system takes into account the confluence of momentum, volatility, and volume factors to determine the most significant turning points within each quarter.
The Quarterly High and Low Indicator visually represents these key turning points on the chart, enabling traders to easily identify potential support and resistance levels, trend reversals, and optimal entry and exit points. By focusing on the most significant price levels within each quarter, the indicator helps traders cut through the noise and make more informed trading decisions.
Expected Usage:
The Quarterly High and Low Indicator is designed to be a valuable tool for traders seeking to gain a deeper understanding of price action and market dynamics. By mapping out the most significant high and low points within each quarter, the indicator provides users with key levels to watch for potential trend reversals, support, and resistance.
1. Identifying Pivots and Reversals: The quarterly highs and lows identified by the indicator serve as critical levels where price is more likely to pivot or reverse. Traders can use these levels to anticipate potential trend changes and adjust their trading strategies accordingly.
2. Backtesting and Historical Analysis: The indicator enables traders to analyze historical price action and assess how the market has reacted to quarterly high and low levels in the past. By backtesting their strategies using these key levels, traders can gain valuable insights into the effectiveness of their approach and make data-driven refinements.
3. Support and Resistance: Quarterly highs and lows often act as significant support and resistance levels. Traders can use the indicator to identify these key areas and plan their trades around them. For example, if price approaches a quarterly high, traders may watch for potential selling pressure and consider taking profits or initiating short positions.
4. Confirmation and Confluence: The Quarterly High and Low Indicator can be used in conjunction with other technical analysis tools to confirm trade setups and increase confidence in trading decisions. When multiple indicators or analysis techniques align with the quarterly highs and lows, it provides a stronger signal for potential trade entry or exit points.
5. Risk Management: By understanding the location of quarterly highs and lows, traders can make more informed decisions about stop-loss placement and position sizing. Setting stop-losses beyond these key levels can help mitigate the risk of getting stopped out prematurely due to short-term price fluctuations.
6. Combining with the Quarterly Cycles Indicator: The Quarterly High and Low Indicator is intended to be used alongside the Quarterly Cycles Indicator for further confluence and validation. By analyzing the relationship between the identified quarterly highs and lows and the underlying quarterly cycles, traders can gain a more comprehensive understanding of market dynamics and potential turning points. When the quarterly highs and lows align with the key phases of the quarterly cycles, it provides a stronger signal for potential trend changes and trading opportunities.
Incorporating the Quarterly High and Low Indicator into a trading strategy, along with the Quarterly Cycles Indicator, allows traders to develop a more comprehensive understanding of price action and make better-informed decisions. By backtesting and analyzing how price reacts around these key levels and cycles, traders can refine their approach and potentially improve their trading outcomes.
Limitations and Disclaimer:
While the Quarterly High and Low Indicator is a powerful tool, it should not be used in isolation. Traders should combine the insights gained from this indicator with other forms of analysis, such as the Quarterly Cycles Indicator, fundamental analysis, risk management, and sound trading psychology, to develop a well-rounded and effective trading approach.
Please note that the indicator's accuracy may be impacted by extreme market volatility or unusual events, and quarterly highs and lows should not be relied upon in isolation. As with any trading tool, individual results may vary, and past performance does not guarantee future outcomes. Traders should always exercise caution, use appropriate risk management techniques, and continuously educate themselves to adapt to changing market conditions.
This indicator is provided for educational purposes only and should not be considered financial advice. Always conduct your own due diligence and consult with a financial professional before making any trading decisions.
Privacy of Code:
The underlying logic and specific calculations used in the proprietary algorithm are not disclosed to protect the intellectual property of the script. The advanced formula and scoring system used to identify quarterly highs and lows are the result of extensive research, testing, and refinement. By keeping these details confidential, the script maintains its competitive edge and ensures the protection of its intellectual property.
ICT Concept [TradingFinder] Order Block | FVG | Liquidity Sweeps🔵 Introduction
The "ICT" style is one of the subsets of "Price Action" technical analysis. ICT is a method created by "Michael Huddleston", a professional forex trader and experienced mentor. The acronym ICT stands for "Inner Circle Trader".
The main objective of the ICT trading strategy is to combine "Price Action" and the concept of "Smart Money" to identify optimal entry points into trades. However, finding suitable entry points is not the only strength of this approach. With the ICT style, traders can better understand price behavior and adapt their trading approach to market structure accordingly.
Numerous concepts are discussed in this style, but the key practical concepts for trading in financial markets include "Order Block," "Liquidity," and "FVG".
🔵 How to Use
🟣Order Block
Order blocks are a specific type of "Supply and Demand" zones formed when a series of orders are placed in a block. These orders could be created by banks or other major players. Banks typically execute large orders in blocks during their trading sessions. If they were to enter the market directly with a small quantity, significant price movements would occur before the orders are fully executed, resulting in less profit. To avoid this, they divide their orders into smaller, manageable positions. Traders should look for "buy" opportunities in "demand order blocks" areas and "sell" opportunities in "supply order blocks".
🟣Liquidity
These levels are where traders aim to exit their trades. "Market Makers" or smart money usually collects or distributes their trading positions near levels where many retail traders have placed their "Stop Loss" orders. When the liquidity resulting from these losses is collected, the price often reverses direction.
A "Stop Hunt" is a move designed to neutralize liquidity generated by triggered stop losses. Banks often use significant news events to trigger stop hunts and acquire the liquidity released in the market. If, for example, they intend to execute heavy buy orders, they encourage others to sell through stop hunts.
As a result, if there is liquidity in the market before reaching the order block region, the credibility of that order block is higher. Conversely, if liquidity is near the order block, meaning the price reaches the order block before reaching the liquidity area, the credibility of that order block is lower.
🟣FVG (Fair Value Gap)
To identify the "Fair Value Gap" on the chart, one must analyze candle by candle. Focus on candles with large bodies, examining one candle and the one before it. The candles before and after this central candle should have long shadows, and their bodies should not overlap with the body of the central candle. The distance between the shadows of the first and third candles is called the FVG range.
These zone function in two ways :
•Supply and Demand zone: In this case, the price reacts to these zone, and its trend reverses.
•Liquidity zone: In this scenario, the price "fills" the zone and then reaches the order block.
Important Note: In most cases, FVG zone with very small width act as supply and demand zone, while zone with a significant width act as liquidity zone, absorbing the price.
🔵 Setting
🟣Order Block
Refine Order Block : When the option for refining order blocks is Off, the supply and demand zones encompass the entire length of the order block (from Low to High) in their standard state and remain unaltered. On the option for refining order blocks triggers the improvement of supply and demand zones using the error correction algorithm.
Refine Type : The enhancement of order blocks via the error correction algorithm can be executed through two methods: Defensive and Aggressive. In the Aggressive approach, the widest possible range is taken into account for order blocks.
Show High Levels : If major high levels are to be displayed, set the option for showing high level to Yes.
Show Low Levels : If major low levels are to be displayed, set the option for showing low level to Yes.
Show Last Support : If showing the last support is desired, set the option for showing last support to Yes.
Show Last Resistance : If showing the last resistance is desired, set the option for showing last resistance to Yes.
🟣 FVG
FVG Filter : When FVG filtering is activated, the number of FVG areas undergoes filtration based on the specified algorithm.
FVG Filter Types :
1. Very Aggressive : Apart from the initial condition, an additional condition is introduced. For an upward FVG, the maximum price of the last candle should exceed the maximum price of the middle candle. Similarly, for a downward FVG, the minimum price of the last candle should be lower than the minimum price of the middle candle. This mode eliminates a minimal number of FVGs.
2. Aggressive : In addition to the conditions of the Very Aggressive mode, this mode considers the size of the middle candle; it should not be small. Consequently, a larger number of FVGs are eliminated in this mode.
3. Defensive : Alongside the conditions of the Very Aggressive mode, this mode takes into account the size of the middle candle, which should be relatively large with the majority of it comprising the body. Furthermore, to identify upward FVGs, the second and third candles must be positive, whereas for downward FVGs, the second and third candles must be negative. This mode filters out a considerable number of FVGs, retaining only those of suitable quality.
4. Very Defensive : In addition to the conditions of the Defensive mode, the first and third candles should not be very small-bodied doji candles. This mode filters out the majority of FVGs, leaving only the highest quality ones. Show Demand FVG: Enables the display of demand-related boxes, which can be toggled between off and on. Show Supply FVG: Enables the display of supply-related boxes along the path, which can also be toggled between off and on.
🟣 Liquidity
Statics Liquidity Line Sensitivity : A value ranging from 0 to 0.4. Increasing this value reduces the sensitivity of the "Statics Liquidity Line Detection" function and increases the number of identified lines. The default value is 0.3.
Dynamics Liquidity Line Sensitivity : A value ranging from 0.4 to 1.95. Increasing this value enhances the sensitivity of the "Dynamics Liquidity Line Detection" function and decreases the number of identified lines. The default value is 1.
Statics Period Pivot : Default value is set to 8. By adjusting this value, you can specify the period for static liquidity line pivots.
Dynamics Period Pivot : Default value is set to 3. By adjusting this value, you can specify the period for dynamic liquidity line pivots.
You can activate or deactivate liquidity lines as necessary using the buttons labeled "Show Statics High Liquidity Line," "Show Statics Low Liquidity Line," "Show Dynamics High Liquidity Line," and "Show Dynamics Low Liquidity Line".
AB=CD [Real-Time] (Zeiierman)█ Overview
The AB=CD (Zeiierman) indicator is designed to automatically detect the ABCD pattern across any chart and timeframe as it unfolds. Activating when point C forms, it automatically draws the D line, giving traders immediate entry, stop-loss, and target signals.
The primary use of the ABCD pattern is to provide a structure to forecast where prices are likely to move next. It's grounded in the principle that history tends to repeat itself, and patterns in price movements are reflective of market psychology.
A simple yet powerful tool in the trader's toolkit, providing clear signals for entry, stop-loss, and profit-target levels, which are based on symmetrical price movements and Fibonacci mathematics. It is applicable in various markets including forex, stocks, and commodities.
█ How to Use
The ABCD pattern is one of the foundational chart patterns used in technical analysis. It's essentially a price structure where two price legs are equivalent in length. In other words, the distance price travels from A to B roughly equals the distance from C to D.
Trend Continuation: Suggests that after a pullback, the original market trend is likely to resume towards point D.
Entry Point: Typically at point C to capitalize on the movement towards D.
Profit Target: Set at point D, expected to mirror the length of the A to B leg.
Stop Loss: Placed just beyond point C to protect against pattern failure.
█ How It Works
The pattern is made up of three consecutive price swings:
AB: This is the first price leg. It can either be up or down.
BC: This is a corrective or retracement leg. If AB is up, BC will be down, and vice versa.
CD: This is the final price leg. It moves in the same direction as AB and is approximately equal in length.
The ABCD pattern algorithm identifies pivot points over a user-defined period, labeled as A, B, C, and D. These points are determined by finding the highest and lowest values (extremes) within the specified period. The direction of the pattern is then established based on the position of these extremes. Fibonacci retracement levels are calculated between these points to determine potential reversal zones (entry and stop levels) and extension levels (target zones). When the price crosses into these zones, the ABCD pattern becomes active, signaling potential trading opportunities.
█ Settings
Market Move: This setting allows traders to define the size of the market move they're interested in, ranging from small to traditional, to swing, or even a custom length. This adjusts the sensitivity and the period over which the ABCD pattern is detected.
Bias: Traders can set their bias to bullish, bearish, or both, which filters the patterns based on the anticipated market direction.
Entry Retracement: Defines the Fibonacci retracement level for potential entry points.
Stop Retracement: Sets the Fibonacci retracement level for stop loss placement.
Exit Retracement: Determines the Fibonacci extension level for the profit target.
Show Stoploss & Target: Toggles the display of stop loss and target lines on the chart.
Color Settings: Customize the colors for bullish and bearish patterns to improve visual distinction.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Eternal Moving AverageA moving average with absolutely no* settings??? Now that is a challenge.
* The only setting is for the user to change the calculation method of the dataset.
A trader must have their mind on recent price action. At the same time they must not miss the bigger picture. Instead of creating a moving average that takes some data into account (like 200 days), I decided to take all data into account. Each chart is analyzed separately. A custom algorithm generates moving averages, some slower, some faster.
In the future I may tweak the lengths of the algorithm. It is a hard process and it will take user-feedback as well as personal research for future alterations of the algorithm. It is however a complete, working product at the time of writing.
The basis of this moving average is EMA. It has the responsiveness of EMA, that takes more recent data into account. Contrary to some MAs, it preserves long-term trends.
As a hidden extra, with this moving average no candle is lost. Everything is analyzed without repainting.
This indicator does not provide any signals. The meaning of any lines crossing is left to the trader for explanation. This indicator helps trend analysts retain perspective of past price action.
WinningWave By Sercan V1Winningwave is a hurricane algorithm that works in all time frames and all transactions (stock exchange-coin), is too comprehensive to be explained in detail and includes many strategies.
To explain briefly; It is a layered oracle algorithm that gives signals by filtering the formations (Normal and Harmonic formations) created by multiple account movements containing many calculations and algorithms, based on the instantaneous momentum of the price and the overbought or oversold levels in a certain time period. Of course, formations refer to situations in which price movements occur in a certain order in financial markets. These patterns are specific patterns seen on the price chart and can often provide clues about future movements of prices. For example; Reverse Shoulder, Head and Shoulder, Symmetrical Triangle etc. Dozens of formation formation conditions and targets were filtered and made suitable for signaling. It also creates bands using YDK3 with the channel algorithm it contains. This band is usually calculated using the standard deviation method to measure price movements and indicate a specific deviation. The upper and lower bands obtained as a result of standard deviation calculations are drawn on the price chart. After a certain band is created, automatic expansion is carried out in order to predict possible movements of future prices. Additionally, Winningwave includes Ema calculations and has identified stop points after the main entry signal to help you in case you miss the main exit signal or choose a different strategy.
STRATEGY 1: As I mentioned in the general statement, the signals that emerged after many formations were filtered in 2 stages (SMI and CCI values served as filters for the formations) and the false signal rate was reduced to a minimum. You can combine signals into your own strategy using oscillators and tactics you trust.
It is important to remember that no indicator or tactic works 100% accurately. That's why filters and combinations are the right methods for you.
STRATEGY 2: Channel programs often create bands using the standard deviation method to indicate price movements and a specific deviation. Standard deviations are a measure of how far prices are generally from the mean. Channel programs draw price charts by creating upper and lower bands using these standard deviation values.
These bands can become very narrow depending on the playability of the price and the strength of the trends. In this way it can change the normal range of movement of prices and indicate potential overbought or oversold.
Once the channel is created, it is automatically expanded and gives us some clues about the direction of price movements. This expansion automatically signals the change according to the price movements of the bands. This feature becomes a predictive tool to predict price movements on the indicator.
Thus, using channel updates and standard deviation, the bands show the normal range of prices and these bands expand or contract dynamically, giving an idea about possible changes in prices. This can help investors gain insight into potential trend reversals or overbought or oversold prices.
In channel band strategy . It is a second strategy in which we calculate the profit rate with the most logical calculations when the prices touch the channel bottoms and channel tops and move up or down.
STRATEGY 3: We aimed to create a stop zone by blending the most appropriate ema values with buy signals. In some cases where you don't want to follow the signals or are confident in the transaction (written to filter out successive sell signals where price action generally rises without correction), it has created a more reliable stopping point for your trading strategy. It gives you a stopping point.
*** Calculations and mathematical settings will be in the menu. For healthy signals and filters, do not play with the numbers. For your personal use, color options or On-Off settings of each feature are available in the menu.
Fibonacci Structure & Trend Channel (Expo)█ Overview
The Fibonacci Structure & Trend Channel (Expo) is designed to identify trend direction and potential reversal levels and offer insights into price structure based on Fibonacci ratios. The algorithm plots a Fibonacci channel, making it easier for traders to identify potential retracement points. Additionally, the Fibonacci market structure is plotted to enhance traders' understanding of the underlying order flow.
█ How to Use
Identify Trends
Use the plotted Fibonacci Trend Line to identify the direction of the market trend. A green line typically signifies a bullish trend, while a red line signifies a bearish trend.
Retracement Levels
The plotted Fibonacci levels can act as potential support or resistance levels. Look for price action signs at these levels for entry or exit points.
Channel Trading
If you enable the Fibonacci channel, the upper and lower bounds can act as overbought or oversold levels.
Market Structure
The plotted Fibonacci market structure serves as a valuable tool for dissecting the underlying order flow and gauging the strength or weakness of a trend. By analyzing these structures, traders can identify key levels where supply and demand intersect, which often act as pivotal points for trend reversals or accelerations. This visual representation simplifies complex market dynamics. Whether you're looking to catch a new trend early or seeking confirmation for a potential reversal, understanding the market structure plotted by the Fibonacci ratios can provide actionable insights for various trading strategies.
Use the Table
The information table can provide quick insights into the current trend and when it started.
█ Settings
The Fibonacci settings allow traders to specify the Fibonacci retracement levels that will be used to calculate the trend and its channel.
The Fibonacci Structure Trend Channel structure settings enable traders to fine-tune how the indicator identifies and plots the underlying price structure.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
[blackcat] L5 Dragon-Void-Dragon for Spot TradingLevel: 5
Background
First of all, this L5 technical indicator is only suitable for spot trading. Because its algorithm is only designed for one-way long, and there is no algorithm for short-selling mechanism.
This technical indicator is the main chart indicator of the integrated trend line, channel technology and moving average technology. Trendlines are straight lines connecting at least two significant highs or lows on a price chart, indicating the direction and strength of a trend. Channels are parallel lines that contain price action within a trend, showing the range and potential reversal points.
Function
Trend lines, channel indicators, and moving averages are all very good subjective technical indicators. However, I have found that if one of the three is used mechanically, or a combination of the three often does not achieve good trading results.Therefore, through continuous practice and summary, I implemented some subjective ideas through algorithms, which improved the winning rate after the integration of the three. Buy and sell points are also more accurate.This involves automatic drawing of trend lines and channel indicators. It is conceivable that if you want to draw relatively stable trend lines and channel indicators, you have to wait until the price trend is relatively stable to obtain stable trend lines and channel lines. The advantage of this is that the subsequent price may rely on this inertia to move up and down the trend line or in the channel, which can be the basis for trend reversal. On the other hand, the formation of trendlines and channel indicators requires price movements and time as prerequisites. This means that the process of waiting for the formation of the trend must also sacrifice part of the profit. This is a trade-off between corresponding characteristics and stable characteristics. What we need to do is to find a perfect balance between the two, and expand profits while keeping risks within a controllable range. Ultimately realize big wins and small losses, long-term compound interest accumulation.
The technical elements reflected in this indicator are: channel line, color of trend strength, double moving average. And through the calculation of the background algorithm, some labels for buying and selling are obtained as alarm signals.
Key Signal
Overall this indicator is quite intuitive and does not require a lot of intellect to understand how to use it. It can be summarized as:
1. If the channel is in a warm color and the direction points to the upper right corner, then go long; if the channel is in a cool color and the direction points to the lower right corner, then go short or close the position.
2. The color of the channel is changed from cool to warm. The extreme value of the cool color is dark blue, which means that it is extremely oversold; the extreme value of the warm color is purple, which means that it is extremely overbought; therefore, when you see channels in different directions, you should also pay attention to their colors, which means that the current channel is in the market Where, and if you need to be careful about price reversals.
3. Because this technical indicator is specially developed for spot trading. Therefore, if you want to enter the market, it is generally better to have the color of the channel and the candle be yellow and orange. Otherwise, it is just a rebound, and the price will repeat more later, and it is more necessary to continue to fall.
4. The double moving average system is also specially customized, mainly combined with Zen Theory's Kiss Saying. This double moving average system is a pressure and support system other than the channel. When an uptrend is relayed and continues to rise after a retracement, the Kiss, Wet-Kiss, and Fly-Kiss triggered by the double moving average will generate yellow and orange buy signal labels.
5. This system needs to wait for the price trend to stabilize before generating a buying and selling point, so there are not many buying and selling signals, and of course some entry opportunities will be missed. Of course, this is the result of sacrificing timeliness for transaction stability. So, be flexible. If your trading style is more aggressive, you can only use the buy and sell labels as auxiliary signals.
Remarks
1. It need time to stablize trendlines and channels, so "B"/"S" labels may not be so in time.
2. Closed-source, Invite-only, NOT free.
3. Highl recommended to use this indicator for >= 30min timeframe, which means this is powerful for swing trading.
4. If you are trading crypto, highly recommend use " L3 RS MSFIELD Crypto" indicator as a screener to find target is stronger than Bitcoin.
5. If you are trading CN A Share, highly recommend use " L3 RS MSFIELD CN A Share" indicator as a screener to find target is stronger than SSE Index.
Subscription
L4/L5 are not free indicators. Trail permissions can be given. Monthly and annual subscriptions are acceptable.
Filtered Volume Profile [ChartPrime]The "Filtered Volume Profile" is a powerful tool that offers insights into market activity. It's a technical analysis tool used to understand the behavior of financial markets. It uses a fixed range volume profile to provide a histogram representing how much volume occurred at distinct price levels.
Profile in action with various significant levels displayed
How to Use
The script is designed to analyze cumulative trading volumes in different price bins over a certain period, also known as `'lookback'`. This lookback period can be defined by the user and it represents the number of bars to look back for calculating levels of support and resistance.
The `'Smoothing'` input determines the degree to which the output is smoothed. Higher values lead to smoother results but may impede the responsiveness of the indicator to rapid changes in volatility.
The `'Peak Sensitivity'` input is used to adjust the sensitivity of the script's peak detection algorithm. Setting this to a lower value makes the algorithm more sensitive to local changes in trading volume and may result in "noisier" outputs.
The `'Peak Threshold'` input specifies the number of bins that the peak detection mechanism should account for. Larger numbers imply that more volume bins are taken into account, and the resultant peaks are based on wider intervals.
The `'Mean Score Length'` input is used for scaling the mean score range. This is particularly important in defining the length of lookback bars that will be used to calculate the average close price.
Sinc Filter
The application of the sinc-filter to the Filtered Volume Profile reduces the risk of viewing artefacts that may misrepresent the underlying market behavior. Sinc filtering is a high-quality and sharp filter that doesn't manifest any ringing effects, making it an optimal choice for such volume profiling.
Histogram
On the histogram, the volume profile is colored based on the balance of bullish to bearish volume. If a particular bar is more intense in color, it represents a larger than usual volume during a single price bar. This is a clear signal of a strong buying or selling pressure at a particular price level.
Threshold for Peaks
The `peak_thresh` input determines the number of bins the algorithm takes in account for the peak detection feature. The 'peak' represents the level where a significant amount of volume trading has occurred, and usually is of interest as an indicative of support or resistance level.
By increasing the `peak_thresh`, you're raising the bar for what the algorithm perceives as a peak. This could result in fewer, but more significant peaks being identified.
History of Volume Profiles and Evolution into Sinc Filtering
Volume profiling has a rich history in market analysis, dating back to the 1950s when Richard D. Wyckoff, a legendary trader, introduced the concept of volume studies. He understood the critical significance of volume and its relationship with market price movement. The core of Wyckoff's technical analysis suite was the relationship between prices and volume, often termed as "Effort vs Results".
Moving forward, in the early 1800s, the esteemed mathematician J. R. Carson made key improvements to the sinc function, which formed the basis for sinc filtering application in time series data. Following these contributions, trading studies continued to create and integrate more advanced statistical measures into market analysis.
This culminated in the 1980s with J. Peter Steidlmayer’s introduction of Market Profile. He suggested that markets were a function of continuous two-way auction processes thus introducing the concept of viewing markets in price/time continuum and price distribution forms. Steidlmayer's Market Profile was the first wide-scale operation of organized volume and price data.
However, despite the introduction of such features, challenges in the analysis persisted, especially due to noise that could misinform trading decisions. This gap has given rise to the need for smoothing functions to help eliminate the noise and better interpret the data. Among such techniques, the sinc filter has become widely recognized within the trading community.
The sinc filter, because of its properties of constructing a smooth passing through all data points precisely and its ability to eliminate high-frequency noise, has been considered a natural transition in the evolution of volume profile strategies. The superior ability of the sinc filter to reduce noise and shield against over-fitting makes it an ideal choice for smoothing purposes in trading scripts, particularly where volume profiling forms the crux of the market analysis strategy, such as in Filtered Volume Profile.
Moving ahead, the use of volume-based studies seems likely to remain a core part of technical analysis. As long as markets operate based on supply and demand principles, understanding volume will remain key to discerning the intent behind price movements. And with the incorporation of advanced methods like sinc filtering, the accuracy and insight provided by these methodologies will only improve.
Mean Score
The mean score in the Filtered Volume Profile script plays an important role in probabilistic inferences regarding future price direction. This score essentially characterizes the statistical likelihood of price trends based on historical data.
The mean score is calculated over a configurable `'Mean Score Length'`. This variable sets the window or the timeframe for calculation of the mean score of the closing prices.
Statistically, this score takes advantage of the concept of z-scores and probabilities associated with the t-distribution (a type of probability distribution that is symmetric and bell-shaped, just like the standard normal distribution, but has heavier tails).
The z-score represents how many standard deviations an element is from the mean. In this case, the "element" is the price level (Point of Control).
The mean score section of the script calculates standard errors for the root mean squared error (RMSE) and addresses the uncertainty in the prediction of the future value of a random variable.
The RMSE of a model prediction concerning observed values is used to measure the differences between values predicted by a model and the values observed.
The lower the RMSE, the better the model is able to predict. A zero RMSE means a perfect fit to the data. In essence, it's a measure of how concentrated the data is around the line of best fit.
Through the mean score, the script effectively predicts the likelihood of the future close price being above or below our identified price level.
Summary
Filtered Volume Profile is a comprehensive trading view indicator which utilizes volume profiling, peak detection, mean score computations, and sinc-filter smoothing, altogether providing the finer details of market behavior.
It offers a customizable look back period, smoothing options, and peak sensitivity setting along with a uniquely set peak threshold. The application of the Sinc Filter ensures a high level of accuracy and noise reduction in volume profiling, making this script a reliable tool for gaining market insights.
Furthermore, the use of mean score calculations provides probabilistic insights into price movements, thus providing traders with a statistically sound foundation for their trading decisions. As trading markets advance, the use of such methodologies plays a pivotal role in formulating effective trading strategies and the Filtered Volume Profile is a successful embodiment of such advancements in the field of market analysis.
RibboNN Machine Learning [ChartPrime]The RibboNN ML indicator is a powerful tool designed to predict the direction of the market and display it through a ribbon-like visual representation, with colors changing based on the prediction outcome from a conditional class. The primary focus of this indicator is to assist traders in trend following trading strategies.
The RibboNN ML in action
Prediction Process:
Conditional Class: The indicator's predictive model relies on a conditional class, which combines information from both longcon (long condition) and short condition. These conditions are determined using specific rules and criteria, taking into account various market factors and indicators.
Direction Prediction: The conditional class provides the basis for predicting the direction of the market move. When the prediction value is greater than 0, it indicates an upward trend, while a value less than 0 suggests a downward trend.
Nearest Neighbor (NN): To attempt to enhance the accuracy of predictions, the RibboNN ML indicator incorporates a Nearest Neighbor algorithm. This algorithm analyzes historical data from the Ribbon ML's predictive model (RMF) and identifies patterns that closely resemble the current conditional prediction class, thereby offering more robust trend forecasts.
Ribbon Visualization:
The Ribbon ML indicator visually represents its predictions through a ribbon-like display. The ribbon changes colors based on the direction predicted by the conditional class. An upward trend is represented by a green color, while a downward trend is depicted by a red color, allowing traders to quickly identify potential market directions.
The introduction of the Nearest Neighbor algorithm provides the Ribbon ML indicator with unique and adaptive behaviors. By dynamically analyzing historical patterns and incorporating them into predictions, the indicator can adapt to changing market conditions and offer more reliable signals for trend following trading strategies.
Manipulation of the NN Settings:
Smaller Value of Neighbours Count:
When the value of "Neighbours Count" is small, the algorithm considers only a few nearest neighbors for making predictions.
A smaller value of "Neighbours Count" leads to more flexible decision boundaries, which can result in a more granular and sensitive model.
However, using a very small value might lead to overfitting, especially if the training data contains noise or outliers.
Larger Value of "Neighbours Count":
When the value of "Neighbours Count" is large, the algorithm considers a larger number of nearest neighbors for making predictions.
A larger value of "Neighbours Count" leads to smoother decision boundaries and helps capture the global patterns in the data.
However, setting a very large value might result in a loss of local patterns and make the model less sensitive to changes in the data.
MTF Fusion - High Volume Expansion Channel [TradingIndicators]Exceptionally high volume and rapid price expansion are key markers of powerful moves, especially when they occur during a breakout or breakdown. The High Volume Expansion Channel (HVEC) uses our multi-timeframe fusion and price compression/expansion algorithms to look for high volume and rapid expansion from multiple higher timeframes at once. It uses this info to determine a high volume and expansion 'grade', and then encodes this result into a colored channel. This channel coloring varies in intensity based on how exceptionally high volume is and how rapidly price is expanding in either direction.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates a 'high volume and expansion grade' (let's call it HVEG), as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful data and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the HVEG value is calculated by determining the HVEG for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the HVEG value from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused HVEG' as (HigherTF_HVEG_1 + HigherTF_HVEG_2 + HigherTF_HVEG_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the high volume and price expansion grade calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Included Features
MTF Fusion high volume and expansion coloring
MTF Fusion ATR-based channel for visual effect
Channel width customization and explanatory labels
Pre-built color stylings
Options
Show Channel Lines: Show/hide the upper and lower lines of the channel
Fill Channel: Fill the channel with coloring depicting the current degree of high volume and rapid price expansion
Channel Width Multiplier: Sets the width of the ATR-based channel
Explanatory Labels: Show/hide explanatory labels describing the visuals
Lookback: Select how you want the degree of high volume expansion to be calculated (longer = long-term high volume and expansion, shorter = short-term high volume and expansion)
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Price & Volume Profile (Expo)█ Overview
The Price & Volume Profile provides a holistic perspective on market dynamics by simultaneously tracking price action and trading volume across a range of price levels. So it is not only a volume-based indicator but also a price-based one. In addition to illustrating volume distribution, it quantifies how frequently the price has fallen within a particular range, thus offering a holistic perspective on market dynamics.
This unique and comprehensive approach to market analysis by considering both price action and trading volume, two crucial dimensions of market activity. Its distinctive methodology offers several advantages:
Holistic Market View: By simultaneously tracking the frequency of specific price ranges (Price Profile) and the volume traded at those ranges (Volume Profile), this indicator provides a more complete picture of market behavior. It shows not only where the market is trading but also how much it's trading, reflecting both price acceptance levels and market participation intensity.
Point of Control (POC): The POC, as highlighted by this indicator, serves as a significant reference point for traders. It identifies the price level with the highest trading activity, thus indicating a strong consensus among market participants about the asset's fair value. Observing how price interacts with the POC can offer valuable insights into market sentiment and potential trend reversals.
Support and Resistance Levels: Price levels with high trading activity often act as support or resistance in future price movements. The indicator visually represents these levels, enabling traders to anticipate potential price reactions.
Price Profile
Price and Volume Profile
█ Calculations
The algorithm analyzes both trade frequency and volume across different price levels. It identifies these levels within the visible chart range, then examines each bar to determine if the selected price falls within these levels. If so, it increases a counter and adds the trading volume. This process repeats across the visible range and is visualized as a horizontal histogram, each bar representing a price level and the bar length reflecting trade frequency and volume. Additionally, it calculates the Point of Control (POC), signifying the price level with the highest activity.
In summary: The histogram presents a dual perspective - not only the traded volume at each price level but also the frequency of the price hitting each range. The longer the bar, the more times the price has frequented that specific range, revealing key insights into price behavior and acceptance levels. These frequently visited areas often emerge as strong support or resistance zones, helping traders navigate market movements.
Please note that the indicator adjusts to the visible price range, making it adaptable to changing market conditions. This dynamic analysis can provide more relevant and timely information than static indicators.
█ How to use
This indicator is beneficial for traders as it offers insights into the distribution of trading activity across different price levels. It helps identify key areas of support and resistance and gives a visual representation of market sentiment and liquidity.
The point of control (POC) , which is the price level with the highest traded volume or frequency count, becomes even more crucial in this context. It marks the price at which the most trading activity occurred, signaling a strong consensus among market participants about the asset's fair value. If the market price deviates significantly from the POC, it could suggest an overbought or oversold condition, potentially leading to a price reversion.
Fair Price Areas/gaps are specific price levels or zones where an asset has spent limited time in the past. These areas are considered interesting or significant because they may have an impact on future price action.
Similar to the concept of fair value gaps, which refers to discrepancies between an asset's market price and its estimated intrinsic value, Fair Price Areas/gaps focus on price levels that have been relatively underutilized in terms of trading activity. When an asset's price reaches a Fair Price Area/gap, traders and investors pay attention because they expect the price to react in some way. The rationale behind this concept is that price tends to gravitate towards areas where it has spent less time in the past, as the market perceives them as significant levels.
█ Settings
The indicator is customizable, allowing users to define the number of price levels (rows), the offset, the data source, and whether to display volume or frequency count. It also adjusts dynamically to the visible price range on the chart, ensuring that the analysis remains relevant and timely with changing market conditions.
Source: The price to use for the calculation. Typically, this is the closing price. By considering the user-selected Source (typically the closing price), the indicator determines the frequency with which the price lands within each designated price level (row) over the selected period. In essence, the indicator provides a count of bars where the Source price falls within each range, essentially creating a "Price Profile."
Row Size: The number of price levels (rows) to divide the visible price range into.
Display: Choose whether to display the number of bars ("Counter") or the total volume ("Volume") for each price level.
Offset: The distance of the histogram from the price chart.
Point of Control (POC): If enabled, the indicator will highlight the price level with the most activity.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Volume Orderbook (Expo)█ Overview
The Volume Orderbook indicator is a volume analysis tool that visually resembles an order book. It's used for displaying trading volume data in a way that may be easier to interpret or more intuitive for certain traders, especially those familiar with order book analysis.
This indicator aggregate and display the total trading volume at different price levels over the entire range of data available on the chart, similar to how an order book displays current buy and sell orders at different price levels. However, unlike a real-time order book, it only considers historical trading data, not current bid and ask orders. This provides a 'historical order book' of sorts, indicating where most trading activities have taken place.
Summary
This is a volume-based indicator that shows the volume traded at specific price levels, highlighting areas of high and low activity.
█ Calculations
The algorithm operates by calculating the cumulative volume traded in each specific price zone within the range of data displayed on the chart. The length of each horizontal bar corresponds to the total volume of trades that occurred within that particular price zone.
In essence, when the price is in a specific zone, the volume is added to the bar representing that zone. A thicker bar implies a larger price zone, meaning that more volume is accumulated within that bar. Therefore, the thickness of the bar visually indicates the amount of trading activity that took place within the associated price zone.
█ How to use
The Volume Orderbook indicator serves as a beneficial tool for traders by identifying key price levels with a significant amount of trading activity. These high-volume areas could represent potential support or resistance levels due to the large number of orders situated there. The indicator's ability to spotlight these zones might be particularly advantageous in pinpointing breakouts or breakdowns when prices move beyond these high-volume regions. Moreover, the indicator could also assist traders in recognizing anomalies, such as when an unusually large volume of trades occurs at unconventional price levels.
Identify Key Price Levels: The indicator highlights high-volume areas where a significant number of trades have occurred, which could act as potential support or resistance levels. This is based on the notion that many traders have established positions at these prices, so these levels may serve as significant areas for market activity in the future.
Volume Nodes: These are the peaks (high-volume areas) and troughs (low-volume areas) seen on the indicator. High-volume nodes represent price levels at which a large amount of volume has been traded, typically areas of strong support or resistance. Conversely, low-volume nodes, where very little volume has been traded, indicate price levels that traders have shown little interest in the past and could potentially act as barriers to price. It's important to note that while high trading volume can imply significant market interest, it doesn't always mean the price will stop or reverse at these levels. Sometimes, prices can quickly move through high-volume areas if there are no current orders (demand) to match with the new orders (supply).
Analyze Market Psychology: The distribution of volume across different price levels can provide insights into the market's psychology, revealing the balance of power between buyers and sellers.
Highlight Potential Reversal Points: The indicator can help identify price levels with high traded volume where the market might be more likely to reverse since these levels have previously attracted significant interest from traders.
Validate Breakouts or Breakdowns: If the price moves convincingly past a high-volume node, it could indicate a strong trend, suggesting a potential breakout or breakdown. Conversely, if the price struggles to move past a high-volume node, it could suggest that the trend is weak and might potentially reverse.
Trade Reversals: High-volume areas could also indicate potential turning points in the market. If the price reaches these levels and then starts to move away, it might suggest a possible price reversal.
Confirm Other Signals: As with all technical indicators, the "Volume Orderbook" should ideally be used in conjunction with other forms of technical and fundamental analysis to confirm signals and increase the odds of successful trades.
Summary
The Volume Orderbook indicator allows traders to identify key price levels, analyze market psychology, highlight potential reversal points, validate breakouts or breakdowns, confirm other trading signals, and anticipate possible trade reversals, thereby serving as a robust tool for trading analysis.
█ Settings
Source: The user can select the source, the default of which is "close." This implies that volume is added to the volume order book when the closing price falls within a specific zone. Users can modify this to any indicator present on their chart. For example, if it's set to an SMA (Simple Moving Average) of 20, the volume will be added to the volume order book when the SMA 20 falls within the specific zone.
Rows and width: These settings allow users to adjust the representation of volume order book zones. "ROWS" pertains to the number of volume order book zones displayed, while "WIDTH" refers to the breadth of each zone.
Table and Grid: These settings allow traders to customize the Volume order-book's position and appearance. By adjusting the "left" parameter, users can shift the position of the Volume order book on the chart; a higher value pushes the order book further to the right. Additionally, users can enable "Table Border" and "Table Grid" options to add gridlines or borders to the Volume order book for easier viewing and interpretation.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Algorganic Buy / Sell / X-Exit Signal [UOI]The " Algorganic Buy / Sell / X-Exit Signal " indicator is an Algorithmic Machine Learning-based superpack indicator that generates buy and sell signals for trading in financial markets. It is packed with conditional statemnets and filters to avoid false signals and utilizes Nearest Neighbors Model (NNM) algorithm with a distance metric to determine the direction of the price movement and make predictions according to the next past 12 bars for the next 4 to 8 bars in whatever chart frame the trader is using. Ideal time frames are 2, 3, 5 and 15 minutes for option traders and scalpers can use it on the 1 minute chart.
The indicator takes into account various technical indicators such as Relative Strength Index (RSI), Average Directional Index (ADX), CCI, Stochastic, ATR and major EMAs and has two optimizer for confirmation. These indicators are used as features to train the Machine Learning model and at the same time to provide better buy and sell signals with multiple "if" conditions.
The NNM algorithm calculates the distance between the current data point and historical data points. It works like a mixture of ATR and ADX. By considering the nearest neighbors, the model predicts the direction of future price movement. The predictions are filtered using additional criteria, including volatility, trend detection, and, ATR and ADX values.
The indicator provides visual signals on the chart, indicating when to enter a long (buy) or short (sell) position but traders should also be mindful of support and resistance levels and oversold and overbought conditions and the higher timeframe signal. It also offers options for dynamic exits based on specific conditions or fixed exits after a predefined number of bars.
Additionally, the indicator includes filters based on EMA (Exponential Moving Average), SMA (Simple Moving Average), and a kernel regression technique. These filters help to refine the signals and reduce noise in the predictions.
The indicator also includes alert functionalities to notify traders of entry and exit points.
The Algorganic is a versatile trading indicator that provides buy and sell signals based on the analysis of various popular technical indicators in combination with Machine Learning techniques with technical analysis and support and resistance levels to generate trading signals, helping traders make informed decisions. This powerful tool overlays on your price chart and can be used across different markets and timeframes.
Key Features:
1. Dynamic EMA Support and Resistance Levels: You can define the top and bottom lines as either 'Support' or 'Resistance'. These levels are calculated using an Exponential Moving Average (EMA) and Average True Range (ATR) inputs.
2. Exponential Moving Average (EMA): The EMA is calculated based on the EMA length input provided by the user, with a default setting of 21 periods.
3. Average True Range (ATR): The ATR is calculated with a default length of 14 periods and is used in determining the support and resistance levels.
4. Buy/Sell Signals: The indicator provides buy and sell signals when the price hits the defined support or resistance levels. These signals are represented by X-shapes plotted on the chart, with green indicating a hit on support (buy signal), and red indicating a hit on resistance (sell signal).
5. Trend Strength Analysis: It uses a unique combination of technical indicators like MACD, RSI, Velocity, CCI, Stochastic, and a custom trend strength indicator. The settings for each of these indicators can be customized according to user preference.
6. Bull/Bear Tug of War: This feature paints the little triangles green if the majority of the indicators are bullish, and red if the majority are bearish. This is a powerful feature to visualize the overall market sentiment.
7. Buy/Sell Alert: The script generates alerts for potential buy and sell signals. Alerts contain information about the signal type, ticker symbol, and current price.
8. Plot EMA Line: This indicator includes an option to display an additional EMA line on the chart, which can be toggled on or off as per the user's choice.
How to use it:
You basically need to master riding this machine. There are a lot of conditions that have been added to make sure novice traders do not make a mistake. The image below shows how to use the indicator. Pay attention to colors:
Longer time frame you should pay attention to the EMA lines and over bought and oversold levels in the optimizers. here is an example:
And another example on 15 min timeframe:
On top of all the above, this indicator has a built-in advanced support and resistance tool that dynamically identifies pivot points and their corresponding support and resistance zones based on the historical data of a given asset. So what this means is that you should ignore a buy signal very close to a resistance and only enter when the resistance is broken.
Here are the configurable support and resistance parameters:
1. Pivot Period : The period considered for pivot detection. The range is between 4 to 30 days with a default value of 25.
2. Source: The price point to be used as the source for pivot detection. You can choose between 'High/Low' and 'Close/Open'.
3. Maximum Number of Pivot: This defines the maximum number of pivot points that the algorithm will store. This can be anywhere from 5 to 100, with 45 as the default value.
4. Maximum Channel Width % : This sets the maximum width of the support/resistance channel as a percentage. Minimum value is 1, with a default value of 10. Higher numbers capture longer timeframe and lower number shorter timeframes. For scalping use 5 or 8 for swing use 12 or 14.
5. Maximum Number of Lines: This sets the maximum number of support/resistance lines displayed on the chart. It ranges from 1 to 15 with a default of 10.
6. Minimum Strength: This is the minimum strength of the support or resistance line, defined by the number of times price touches it. It ranges from 1 to 10 with a default of 2.
7. Line Style: This option allows the user to choose the line style between 'Solid', 'Dotted', and 'Dashed'.
8. Line Width: This allows users to choose the width of the line ranging from 1 to 4.
9. Resistance Color and Support Color: These define the colors for the resistance and support lines.
The script also includes functions to calculate if the price has crossed over or under a support or resistance line.
The S/R assist uses these inputs to calculate pivot highs and lows, create support and resistance zones, and plot these on the chart. When the price crosses a support or resistance line, the script can identify this as a possible trading signal. The lines' strengths are also calculated, and only those with strengths above the user-defined minimum are drawn on the chart.
MTF Fusion - SuperTrend [TradingIndicators]SuperTrend is undoubtedly one of the most popular and influential indicators ever developed, and by combining it with our MTF Fusion algorithm, we believe we have made it more useful and powerful than ever with MTF Fusion SuperTrend .
Let's start with a brief review of what the original SuperTrend indicator is and how it works.
What is SuperTrend?
The SuperTrend indicator is a popular technical analysis tool used in financial markets to identify the direction of a trend and potential entry and exit points for trading. It was developed by Olivier Seban, a French trader, and first introduced in his book "Tout le monde peut gagner en bourse" ("Everyone Can Win in the Stock Market") published in 2008.
SuperTrend is based on the concept of Average True Range (ATR) and uses two parameters: the multiplier and the period. The ATR measures the volatility of a financial instrument, and the SuperTrend indicator utilizes this information to plot a line above or below the price chart. It is an 'AITM' (Always In The Market) indicator, which, in its original form, is always 'long' or 'short' - and never 'flat'.
Here's a brief overview of how the SuperTrend indicator works:
Calculation of the ATR: The ATR is calculated using historical price data over a specified period. It measures the average range between high and low prices, reflecting the market's volatility.
Calculation of the upward (long/bullish) and downward (short/bearish) SuperTrend lines: The SuperTrend indicator multiplies the ATR by a specified multiplier (typically 2 or 3) and adds/subtracts the result from the current closing price. This calculation determines the upward and downward SuperTrend lines.
Plotting the Indicator: The SuperTrend indicator plots a line above the price chart when the price is trending upwards, and below the price chart when the price is trending downwards. The distance between the price and the indicator line provides insights into the strength of the trend.
Traders commonly use the SuperTrend indicator to identify potential buy or sell signals. For example, a buy signal may be generated when the price crosses above the indicator line, indicating an uptrend. Conversely, a sell signal may be triggered when the price crosses below the indicator line, signaling a downtrend.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates SuperTrend lines, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of SuperTrend lines are calculated by determining the value of the SuperTrend indicator for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a SuperTrend line from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_SuperTrend_1 + HigherTF_SuperTrend_2 + HigherTF_SuperTrend_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the SuperTrend calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Unlike in the original indicator, flat/'No Trend' areas exist in MTF Fusion SuperTrend!
MTF Fusion SuperTrend only shows a Fusion SuperTrend when the majority of SuperTrends from higher timeframes are in agreement and signaling the same trend direction . So, unlike the original SuperTrend indicator, MTF Fusion SuperTrend sometimes shows no SuperTrend line at all - typically in flat or indecisive areas, which we think is beneficial and helps to filter out noise on smaller timeframes.
Included Features
Fusion SuperTrend lines
Dynamic Multi-Timeframe SuperTrends
Filled zones to highlight trends
Full customization of SuperTrend parameters
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion SuperTrends calculated from multiple higher timeframes
MTF View: Show/hide the SuperTrends from multiple higher timeframes used to calculate the Fusion SuperTrends
Fill Trending Zones: Show/hide the fill for 'trending zones' between price and the Fusion SuperTrends
Multiplier: Sets the multiplier for all SuperTrend calculations
ATR Period: Sets the ATR period for all SuperTrend calculations
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Oscillator Toolkit (Expo)█ Overview
The Oscillators Toolkit stands at the forefront of technical trading tools, offering a comprehensive suite of sophisticated, adaptive, and unique oscillators. This toolkit has been thoughtfully designed to cater to all trading styles, ensuring versatility and utility for every trader. The toolkit features our flagship oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and Bellcurves. Furthermore, it offers many great features such as trend recognition, market impulses, and trend changes; all consolidated into a single, easy-to-use indicator.
Access to these high-quality oscillators and tools can elevate your trading strategy, providing you with insightful market analysis and potential trading opportunities. In addition, these tools help traders and investors to identify and interpret various market trends, momentum, and volatility patterns more efficiently.
The Oscillator toolkit works in any market and timeframe for discretionary analysis and includes many oscillators and features:
█ Oscillators
WaveTrend Momentum
The WaveTrend Momentum oscillator is a significant component of the toolkit. It factors in both the direction and the momentum of market trends. The waves within this system are both quick and responsive, operating independently to offer the most pertinent insights at the most opportune moments. Their rapid response time ensures that traders receive timely information, which is essential in the fast-paced, dynamic world of trading.
Example of how to use the WaveTrend Momentum Oscialltor
The WaveTrend Momentum is proficient at identifying trend reversals and pullbacks, allowing traders to enter or exit trades at optimal moments.
Leading RSI
The Leading Relative Strength Index (RSI) is a type of momentum oscillator that is commonly used in technical analysis to predict price movements. As the name suggests, it is an advanced form of the traditional Relative Strength Index (RSI), and it provides traders with more timely signals for market entries and exits.
The Leading RSI works on similar principles but is designed to provide signals ahead of the traditional RSI. This is achieved through more advanced mathematical modeling and calculations, which aim to identify shifts in market momentum before they happen. It takes into account not only the current price action but also considers historical data in a way that can foresee changes in trend directions.
Example of how to use the Leading RSI
The Leading RSI is an enhanced version of the traditional Relative Strength Index, offering more timely indications of divergences and overbought or oversold market conditions.
Momentum Oscillator
This oscillator measures the amount that a security's price has changed over a given time span. It is an excellent tool for understanding the strength of a trend and its potential endurance. When the momentum oscillator rises, it suggests that the price is moving upwards and vice versa.
The Momentum Oscillator is an advanced technical analysis tool that helps traders identify the rate of change or the momentum of the market. It is typically used to determine the strength or speed at which the price of an asset increases or decreases for a set of returns. This oscillator is considered 'fast-moving' and 'sensitive' because it responds quickly to changes in price momentum. The fast-moving nature of this oscillator helps traders to get early signals for potential market entry or exit points.
The Momentum Oscillator analyzes the current price compared to the previous price and adds two additional layers of analysis: 'Buy & Sell moves' and 'Extremes.'
Buy & Sell Moves: This layer of the oscillator helps identify the buying and selling pressure in the market. This can provide traders with valuable information about the possible direction of future price moves. When there is high buying pressure (demand), the price tends to rise, and when there is high selling pressure (supply), the price tends to fall.
Extremes: This layer helps to identify extreme overbought or oversold conditions. When the oscillator enters the overbought territory, it could indicate that the price is at a high and could potentially reverse. Conversely, if the oscillator enters the oversold territory, it could suggest that the price is at a low and could potentially rebound.
Example of how to use the Momentum Oscillator
The Momentum Oscillator is a sensitive and fast-moving oscillator that adapts quickly to price changes while keeping track of the long-term momentum, making it easier to spot buying or selling opportunities in trends.
Bellcurves
The Bellcurves indicator is a powerful tool for traders that uses statistical analysis to help identify potential market reversals and key support and resistance levels by leveraging the principles of statistical analysis to measure market impulses. The concept behind this tool is the normal distribution, also known as the bell curve, which is a fundamental statistical concept signifying that data tends to cluster around the average or mean value. The "impulses" in the market context refer to significant price movements driven by a high volume of trading activity. These are typically sharp and swift moves either upwards (bullish impulse) or downwards (bearish impulse). These impulses often signify a strong sentiment in the market and can result at the beginning of a new trend or the continuation of an existing one.
In effect, the Bellcurve indicator is designed to filter out minor price fluctuations or 'noise,' allowing traders to focus solely on significant market impulses. This makes it easier for traders to identify key market movements.
Example of how to use the Bellcurve
The Bellcurves uses the principles of statistical analysis to identify significant market impulses and potential market reversals.
█ Why is this Oscillator Toolkit Needed?
The Oscillator Toolkit is a vital asset for traders for several reasons:
Insight into Market Trends: The Oscillator Toolkit provides valuable insight into current market trends. This includes understanding whether the market is bullish (rising) or bearish (falling), as well as identifying potential future price movements.
Identification of Overbought or Oversold Conditions: Oscillators like those in the toolkit can help traders identify when an asset is overbought (potentially overvalued) or oversold (potentially undervalued). This can signal potential market reversals.
Confirmation of Price Patterns: The oscillators in the toolkit can confirm price patterns and trends. For example, if a price pattern suggests a bullish trend, an oscillator can help confirm this by showing rising momentum.
Versatility Across Markets and Timeframes: The Oscillator Toolkit is designed to work across a variety of markets, including stocks, forex, commodities, and cryptocurrencies. It's also effective across different timeframes, from short-term day trading to longer-term investment strategies.
Timely Trade Signals: By providing real-time insights into market conditions and price momentum, the Oscillator Toolkit offers timely signals for trade entries and exits.
Enhancing Trading Strategy: Every trader has a unique approach to the market. The Oscillator Toolkit, with its suite of different oscillators, provides a robust set of tools that can be customized to enhance any trading strategy, whether it's a trend following, swing trading, scalping, or any other approach.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ How are the Oscillators calculated? - Overview
The Toolkit combines many of our existing premium indicators and new technical analysis algorithms to analyze the market. This overview covers how the main features are calculated.
WaveTrend Momentum
The WaveTrend Momentum oscillator operates at its core by comparing the current price to previous prices. If the current price is higher than the previous price, the oscillator value will rise, indicating an uptrend. Conversely, if the current price is lower than the previous price, the oscillator value will fall, indicating a downtrend. To make it unique and useful normalized weighting functions are added.
Leading RSI
The Leading RSI is based on the traditional Relative Strength Index, with an added exploration function that takes into account historical price movements.
Momentum Oscillator
The Momentum oscillator measures how quickly the price is changing, on average, over a certain period, relative to the variability of the price over that same period. It gives higher values when the price is changing rapidly in one direction and lower values when the price is fluctuating or changing more slowly. In addition, other functions, such as market extremes and buying/selling pressure, are factored in.
Bellcurves
The Bellcurves assume that some common historical price data is normally distributed, and once these patterns or moves are found the in the price data, a Bellcurve is formed.
█ In conclusion , the Oscillator Toolkit is an advanced, versatile, and indispensable asset for traders across various markets and timeframes. This innovative collection includes different oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and the Bellcurves Indicator, each serving a unique function in providing valuable insights into the market's behavior.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
MTF Fusion - PSAR [TradingIndicators]MTF Fusion PSAR intelligently adapts to whatever timeframe you're trading - dynamically calculating Parabolic SAR (Stop and Reverse) levels combined from four appropriate higher timeframes to give you a much broader view of the market and an edge in your trading decisions. It is the third indicator in our MTF Fusion series, and leverages our MTF Fusion algorithm - only this time to visualize J. Welles Wilder Jr.'s famous Parabolic SAR indicator.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates PSAR levels, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of PSAR levels are calculated by determining the value of the PSAR indicator for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a PSAR level from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_PSAR_Level_1 + HigherTF_PSAR_Level_2 + HigherTF_PSAR_Level_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the PSAR calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
What is the PSAR indicator?
The Parabolic SAR (Stop and Reverse) indicator is a technical analysis tool that helps identify potential trend reversals in price movements. It was developed by J. Welles Wilder Jr. and is widely used by traders to determine entry and exit points in the market. It consists of levels that are plotted above or below current price. The position of these plots relative to the price provides valuable information about the prevailing trend and potential reversal points.
Here's how the original PSAR indicator works:
Upward Trend: When the Parabolic SAR level is plotted below the price, it indicates an upward trend in the market. The level generally moves closer to the price as the trend progresses. This creates a parabolic curve that rises with time. Traders typically interpret this as a bullish signal, suggesting that it may be a good time to buy or hold positions.
Downward Trend: Conversely, when the Parabolic SAR level is plotted above the price, it indicates a downward trend in the market. The plot generally moves closer to the price as the trend continues, forming a parabolic curve that declines with time. This is considered a bearish signal, suggesting that it may be a suitable time to sell or avoid taking long positions.
Reversal Points: The primary purpose of the Parabolic SAR indicator is to identify potential trend reversals. When the price crosses above or below the Parabolic SAR level, it indicates a possible reversal in the trend.
The Parabolic SAR indicator is versatile and can be used in various market conditions and timeframes. It is particularly useful in trending markets, where it helps traders ride the trend and capture potential profits. However, it's important to note that the Parabolic SAR may generate false signals or provide delayed indications in sideways or choppy markets.
Included Features
Fusion PSAR levels
Filled zones to highlight trends
Full customization of PSAR parameters
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion PSAR levels calculated from multiple higher timeframes
Fill Trending Zones: Show/hide the fill for 'trending zones' between price and the Fusion PSAR levels
Start: Defines the rate at which the PSAR levels move closer to the price during the initial stages of a trend (higher = faster convergence, lower = slower convergence)
Increment: Controls the rate at which the acceleration factor increases or decreases as the trend continues (higher = faster convergence, lower = slower convergence)
Max: Sets a limit on the maximum value that the acceleration factor can reach
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
MTF Fusion - S/R Levels [TradingIndicators]MTF Fusion S/R Levels intelligently adapt to whatever timeframe you're trading - dynamically calculating pivot-based support and resistance levels combined from four appropriate higher timeframes to give you a much broader view of the market and an edge in your trading decisions. It is the second indicator in our MTF Fusion series, and leverages our MTF Fusion algorithm - only this time to visualize pivot-based S/R levels and zones.
These levels are not programmed to repaint - so you can use them in real-time just as they appeared historically.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates S/R Levels based on pivot points, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of support and resistance levels are calculated by determining pivot points for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a support level from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_Support_Level_1 + HigherTF_Support_Level_2 + HigherTF_Support_Level_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the lookbacks used for pivot and S/R level calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Included Features
Fusion Support and Resistance Levels
Dynamic Multi-Timeframe S/R Levels
Breakaway Zone fills to highlight breakouts and breakdowns from the Fusion S/R Levels
Customizable lookback approach
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion S/R Levels calculated from multiple higher timeframes
MTF View: Show/hide the S?R levels from multiple higher timeframes used to calculate the Fusion S/R Levels
Breakaway Zones: Show/hide the fill for zones where price breaks away from the Fusion S/R Levels
Lookback: Select how you want your S/R Levels to be calculated (longer = long-term levels, shorter = short-term levels)
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Complete Discrete Fourier Transform ToolkitThis is an expansion from my Discrete Fourier Transform Overlay indicator which offers various features that may be useful for traders wishing to apply frequency analysis or integral transform to their trading. For those unfamiliar with the concept, the discrete Fourier transform decomposes wave or wave-like data into functions depending on frequency. This can be helpful in demonstrating or interpreting trends and periodic frequencies in time-series price data, or oscillating indicators.
This toolkit has the following features:
Fourier bands (deviation cloud): The deviation cloud expresses the uncertainty in the DFT algorithm, as well as the relative change in frequency of the curve.
Fourier supertrend: The supertrend is applied as a product of the DFT algorithm, instead of onto the price data itself. This filters the supertrend from infrequent periodicities. For trading, this means that the supertrend will not be affected by false breakouts or breakdowns. See the image below for an example:
Future updates may include:
Projection of the probabilistic uncertainty principle. In a nutshell, the concept can be used to project uncertainties forwards through price data to forecast the path of least resistance, or, the most probable frequency.
Machine learning capabilities. Justin Doherty has done the Pine Script community a great service in introducing kNN algorithms with Lorentzian distance calculations; however, this is only the start of relativistic mechanics that can be applied to time series data. The DFT algorithm essentially filters data into its periodicities; this data can be inserted into a relativistic kNN algorithm - Lorenz or otherwise - to possibly improve accuracy.
Pattern Forecast (Expo)█ Overview
The Pattern Forecast indicator is a technical analysis tool that scans historical price data to identify common chart patterns and then analyzes the price movements that followed these patterns. It takes this information and projects it into the future to provide traders with potential price actions that may occur if the same pattern is identified in real-time market data. This projection helps traders to understand the possible outcomes based on the previous occurrences of the pattern, thereby offering a clearer perspective of the market scenario. By analyzing the historical data and understanding the subsequent price movements following the appearance of a specific pattern, the indicator can provide valuable insights into potential future market behavior.
█ Calculations
The indicator works by scanning historical price data for various candlestick patterns. It includes all in-built TradingView patterns, credit to TradingView that has coded them.
Essentially, the indicator takes the historical price moves that followed the pattern to forecast what might happen next.
█ Example
In this example, the algorithm is set to search for the Inverted Hammer Bullish candlestick pattern. If the pattern is found, the historical outcome is then projected into the future. This helps traders to understand how the past pattern evolved over time.
█ How to use
Providing traders with a comprehensive understanding of historical patterns and their implications for future price action allows them to assess the likelihood of specific market scenarios objectively. For example, suppose the pattern forecast indicator suggests that a particular pattern is likely to lead to a bullish move in the market. A trader might consider going long if the same pattern is identified in the real-time market. Similarly, a trader might consider shorting the asset if the indicator suggests a bearish move is likely, if the same pattern is identified in the real-time market.
█ Settings
Pattern
Select the pattern that the indicator should scan for. All inbuilt TradingView patterns can be selected.
Forecast Candles
Number of candles to project into the future.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
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Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
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Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
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