Timing KenhTradding The Timing KenhTradding indicator is a versatile and customizable tool designed to provide detailed insights into market sessions, daily price dynamics, and key levels. This indicator is especially helpful for traders aiming to track volatility, session-specific movements, and broader trends with additional tools like EMA and VWAP.
Key Features
Session Tracking:
Visualizes up to 8 customizable sessions using shaded boxes on the chart.
Sessions are defined by specific time intervals and are labeled with user-defined names and colors for easy identification.
EMA Integration:
Displays two critical exponential moving averages (EMA):
EMA200 (1-minute): Ideal for short-term trend analysis.
EMA200 (4-hour): Provides a broader perspective on market trends.
EMA smoothing options ensure clarity and reduce noise.
Daily High, Low, Open, and Close Levels:
Automatically draws horizontal lines to highlight the daily high, low, and open prices.
Displays these levels with annotations and customizable colors.
Price Movement Representation:
Visualizes daily price movements using boxes for the body, upper wick, and lower wick:
The body shows the range between the open and close.
The upper and lower wicks represent the highs and lows relative to the body.
Annotations display the exact pip/movement size of the wicks.
VWAP Overlay:
Plots the Volume Weighted Average Price (VWAP) to provide a weighted average of price levels based on volume, aiding in intraday decision-making.
Session-Based Background Highlighting:
Highlights specific hours (e.g., 2 AM) with a customizable background color for better visual segmentation.
Dynamic Data Updates:
Updates key levels and boxes dynamically as new price data becomes available.
Benefits for Traders
Session Analysis:
Easily identify and analyze the behavior of price action within specific trading sessions, such as high volatility around news events.
Trend and Momentum Tracking:
Use EMA and VWAP overlays to gauge the direction and strength of the market.
Daily Levels for Precision:
Incorporates high, low, and open levels to assist with setting entry, exit, and stop-loss points.
Visual Clarity:
Simplifies complex market data with clean and intuitive visualizations, enabling traders to make informed decisions quickly.
Customization Options
Sessions:
Define up to 8 custom sessions with personalized labels, time zones, and colors.
Visuals:
Adjust colors, transparency, and line styles for session boxes, EMAs, and daily levels.
Text Details:
Customize text size, alignment, and colors for annotations and labels.
EMA Display:
Toggle between short-term and long-term EMA views.
How to Use It
Track Daily Levels:
Watch for price reactions around daily high, low, and open levels for potential breakout or reversal opportunities.
Session-Based Strategies:
Focus on specific trading sessions for high-probability trades. Use session boxes to identify price ranges and key levels during those times.
Trend Confirmation:
Combine EMA200 and VWAP for a reliable trend-following strategy.
Volatility Assessment:
Observe the size of daily wicks and session ranges to understand market volatility and adjust your strategy accordingly.
This indicator is an essential tool for both intraday and swing traders, offering unparalleled insights into price action, session-specific volatility, and trend dynamics.
Cari dalam skrip untuk "wave"
Gradient Filter with Fibonacci-AYNETExplanation of the Combined Features:
Dynamic Gradient Filter:
This section remains as in the previous example, calculating a smoothed filter (filt) with dynamic gradient coloring.
The color of the filter line transitions from red to green based on its RSI value.
Fibonacci Levels:
Calculates key Fibonacci retracement levels (0.0, 0.236, 0.382, 0.5, 0.618, and 1.0) over a user-defined lookback period (fib_length).
Uses the highest high and lowest low in the lookback period to determine the range.
Plotting Fibonacci Levels:
Each Fibonacci level is drawn as a horizontal line.
The lines extend back by the lookback period and are styled with dotted lines for clarity.
Features:
Customizable Inputs:
Users can enable or disable Fibonacci levels (show_fib_levels).
Adjust the color (fib_color) and width (fib_width) of Fibonacci lines.
Integrated Dynamic Filter:
Combines the filtered line with Fibonacci retracement levels to provide multi-dimensional insights.
Use Case:
Dynamic Filter:
Observe how the filtered line behaves near Fibonacci levels for potential trend continuations or reversals.
Fibonacci Levels:
Use retracement levels as key support/resistance zones to make trading decisions.
This combined script is now more functional, blending the dynamic gradient filter with Fibonacci retracement levels. Test this script in different market conditions, and let me know if additional features are required! 😊
ICT Setup 03 [TradingFinder] Judas Swing NY 9:30am + CHoCH/FVG🔵 Introduction
Judas Swing is an advanced trading setup designed to identify false price movements early in the trading day. This advanced trading strategy operates on the principle that major market players, or "smart money," drive price in a certain direction during the early hours to mislead smaller traders.
This deceptive movement attracts liquidity at specific levels, allowing larger players to execute primary trades in the opposite direction, ultimately causing the price to return to its true path.
The Judas Swing setup functions within two primary time frames, tailored separately for Forex and Stock markets. In the Forex market, the setup uses the 8:15 to 8:30 AM window to identify the high and low points, followed by the 8:30 to 8:45 AM frame to execute the Judas move and identify the CISD Level break, where Order Block and Fair Value Gap (FVG) zones are subsequently detected.
In the Stock market, these time frames shift to 9:15 to 9:30 AM for identifying highs and lows and 9:30 to 9:45 AM for executing the Judas move and CISD Level break.
Concepts such as Order Block and Fair Value Gap (FVG) are crucial in this setup. An Order Block represents a chart region with a high volume of buy or sell orders placed by major financial institutions, marking significant levels where price reacts.
Fair Value Gap (FVG) refers to areas where price has moved rapidly without balance between supply and demand, highlighting zones of potential price action and future liquidity.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The Judas Swing setup enables traders to pinpoint entry and exit points by utilizing Order Block and FVG concepts, helping them align with liquidity-driven moves orchestrated by smart money. This setup applies two distinct time frames for Forex and Stocks to capture early deceptive movements, offering traders optimized entry or exit moments.
🟣 Bullish Setup
In the Bullish Judas Swing setup, the first step is to identify High and Low points within the initial time frame. These levels serve as key points where price may react, forming the basis for analyzing the setup and assisting traders in anticipating future market shifts.
In the second time frame, a critical stage of the bullish setup begins. During this phase, the price may create a false break or Fake Break below the low level, a deceptive move by major players to absorb liquidity. This false move often causes smaller traders to enter positions incorrectly. After this fake-out, the price reverses upward, breaking the CISD Level, a critical point in the market structure, signaling a potential bullish trend.
Upon breaking the CISD Level and reversing upward, the indicator identifies both the Order Block and Fair Value Gap (FVG). The Order Block is an area where major players typically place large buy orders, signaling potential price support. Meanwhile, the FVG marks a region of supply-demand imbalance, signaling areas where price might react.
Ultimately, after these key zones are identified, a trader may open a buy position if the price reaches one of these critical areas—Order Block or FVG—and reacts positively. Trading at these levels enhances the chance of success due to liquidity absorption and support from smart money, marking an opportune time for entering a long position.
🟣 Bearish Setup
In the Bearish Judas Swing setup, analysis begins with marking the High and Low levels in the initial time frame. These levels serve as key zones where price could react, helping to signal possible trend reversals. Identifying these levels is essential for locating significant bearish zones and positioning traders to capitalize on downward movements.
In the second time frame, the primary bearish setup unfolds. During this stage, price may exhibit a Fake Break above the high, causing a brief move upward and misleading smaller traders into incorrect positions. After this false move, the price typically returns downward, breaking the CISD Level—a crucial bearish trend indicator.
With the CISD Level broken and a bearish trend confirmed, the indicator identifies the Order Block and Fair Value Gap (FVG). The Bearish Order Block is a region where smart money places significant sell orders, prompting a negative price reaction. The FVG denotes an area of supply-demand imbalance, signifying potential selling pressure.
When the price reaches one of these critical areas—the Bearish Order Block or FVG—and reacts downward, a trader may initiate a sell position. Entering trades at these levels, due to increased selling pressure and liquidity absorption, offers traders an advantage in profiting from price declines.
🔵 Settings
Market : The indicator allows users to choose between Forex and Stocks, automatically adjusting the time frames for the "Opening Range" and "Trading Permit" accordingly: Forex: 8:15–8:30 AM for identifying High and Low points, and 8:30–8:45 AM for capturing the Judas move and CISD Level break. Stocks: 9:15–9:30 AM for identifying High and Low points, and 9:30–9:45 AM for executing the Judas move and CISD Level break.
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The Judas Swing indicator helps traders spot reliable trading opportunities by detecting false price movements and key levels such as Order Block and FVG. With a focus on early market movements, this tool allows traders to align with major market participants, selecting entry and exit points with greater precision, thereby reducing trading risks.
Its extensive customization options enable adjustments for various market types and trading conditions, giving traders the flexibility to optimize their strategies. Based on ICT techniques and liquidity analysis, this indicator can be highly effective for those seeking precision in their entry points.
Overall, Judas Swing empowers traders to capitalize on significant market movements by leveraging price volatility. Offering precise and dependable signals, this tool presents an excellent opportunity for enhancing trading accuracy and improving performance
Fx_Shepherd Lot Size Calculator [ALLDYN]This "Fx_Shepherd Lot Size Calculator" script is a basic yet essential tool designed for traders to calculate the appropriate lot size based on account balance, risk percentage, and stop-loss pips. It promotes disciplined risk management by ensuring that the user only risks a defined percentage of their account on each trade. The script also features a toggleable table that displays the account size, risk percentage, and calculated lot size, offering clear, real-time visualization for the user. This helps traders maintain consistency and avoid over-leveraging.
This "Fx_Shepherd Lot Size Calculator" script stands out as a unique utility for traders in several ways:
### 1. **Real-Time Lot Size Calculation**:
- The script provides an automatic, real-time calculation of the optimal lot size based on the account balance, risk percentage, and stop loss (SL) in pips. This offers traders immediate guidance on how much risk they are exposing their account to in each trade, streamlining risk management decisions.
### 2. **Dynamic Table Display**:
- The toggle-able table feature allows users to show or hide the lot size table on the chart. This makes the script non-intrusive for traders who may not want constant table overlays, providing more flexibility for chart space management.
### 3. **Customizable Inputs**:
- Inputs such as **balance**, **risk percentage**, and **stop loss** are easily configurable, allowing users to adjust the calculations to suit different trading strategies, account sizes, and risk tolerances.
- The `truncate()` function ensures the lot size is presented in a simple, rounded format, which is crucial for precise order placement and reduces the chance of errors.
### 4. **Responsive and Clean UI**:
- The table is color-coded for easy reading, with a sleek design that places key information — account size, risk percentage, and calculated lot size — in a clear, organized structure. The black background with white text for the data points improves readability, while the border and table cell colors (green and black) provide a professional look.
### 5. **Risk Management Focus**:
- The primary purpose of this script is to ensure that traders maintain consistent risk management by aligning their lot size with their defined risk per trade and stop loss distance. This automated approach to risk ensures that traders stay disciplined with risk exposure.
### 6. **Efficiency for All Trading Styles**:
- Whether a trader is scalping, day trading, or swing trading, this calculator adjusts dynamically, allowing it to be used across various timeframes and asset classes. It helps traders avoid manual calculations for each trade, thus improving efficiency and reducing human error.
### 7. **Non-Intrusive Clean-Up**:
- The feature to **clear** the table when not needed ensures the chart remains clean and decluttered when the table is hidden. This improves the user experience, especially for traders who switch between different strategies or charts.
Overall, this script combines simplicity and efficiency while being flexible enough to fit the needs of a broad spectrum of traders. Its focus on user customization, clean interface, and emphasis on risk management makes it a valuable tool for both novice and experienced traders.
Fair Value Gaps Setup 01 [TradingFinder] FVG Absorption + CHoCH🔵 Introduction
🟣 Market Structures
Market structures exhibit a fractal and nested nature, which leads us to classify them into internal (minor) and external (major) categories. Definitions of market structure vary, with different methodologies such as Smart Money and ICT offering distinct interpretations.
To identify market structure, the initial step involves examining key highs and lows. An uptrend is characterized by successive highs and lows that are higher than their predecessors. Conversely, a downtrend is marked by successive lows and highs that are lower than their previous counterparts.
🟣 Market Trends and Movements
Market trends consist of two primary types of movements :
Impulsive Movements : These movements align with the main trend and are characterized by high strength and momentum.
Corrective Movements : These movements counter the main trend and are marked by lower strength and momentum.
🟣 Break of Structure (BOS)
In a downtrend, a Break of Structure (BOS) occurs when the price falls below the previous low and establishes a new low (LL). In an uptrend, a BOS, also known as a Market Structure Break (MSB), happens when the price rises above the last high.
To confirm a trend, at least one BOS is necessary, which requires the price to close at least one candle beyond the previous high or low.
🟣 Change of Character (CHOCH)
Change of Character (CHOCH) is a crucial concept in market structure analysis, indicating a shift in trend. A trend concludes with a CHOCH, also referred to as a Market Structure Shift (MSS).
For example, in a downtrend, the price continues to drop with BOS, showcasing the trend's strength. However, when the price rises and exceeds the last high, a CHOCH occurs, signaling a potential transition from a downtrend to an uptrend.
It is essential to note that a CHOCH does not immediately indicate a buy trade. Instead, it is prudent to wait for a BOS in the upward direction to confirm the uptrend. Unlike BOS, a CHOCH confirmation does not require a candle to close; merely breaking the previous high or low with the candle's wick is sufficient.
🟣 Spike | Inefficiency | Imbalance
All these terms mean fast price movement in the shortest possible time.
🟣 Fair Value Gap (FVG)
To pinpoint the "Fair Value Gap" (FVG) on a chart, a detailed candle-by-candle analysis is necessary. This process involves focusing on candles with substantial bodies and evaluating them in relation to the candles immediately before and after them.
Here are the steps :
Identify the Central Candle : Look for a candle with a large body.
Examine Adjacent Candles : The candles before and after this central candle should have long shadows, and their bodies must not overlap with the body of the central candle.
Determine the FVG Range : The distance between the shadows of the first and third candles defines the FVG range.
This method helps in accurately identifying the Fair Value Gap, which is crucial for understanding market inefficiencies and potential price movements.
🟣 Setup
This setup is based on Market Structure and FVG. After a change of character and the formation of FVG in the last lag of the price movement, we are looking for trading positions in the price pullback.
Bullish Setup :
Bearish Setup :
🔵 How to Use
After forming the setup, you can enter the trade using a pending order or after receiving confirmation. To increase the probability of success, you can adjust the pivot period market structure settings or modify the market movement coefficient in the formation leg of the FVG.
Bullish Setup :
Bearish Setup :
🔵 Setting
Pivot Period of Market Structure Detector :
This parameter allows you to configure the zigzag period based on pivots. Adjusting this helps in accurately detecting order blocks.
Show major Bullish ChoCh Lines :
You can toggle the visibility of the Demand Main Zone and "ChoCh" Origin, and customize their color as needed.
Show major Bearish ChoCh Lines :
Similar to the Demand Main Zone, you can control the visibility and color of the Supply Main Zone and "ChoCh" Origin.
FVG Detector Multiplier Factor :
This feature lets you adjust the size of the moves forming the Fair Value Gaps (FVGs) using the Average True Range (ATR). The default value is 1, suitable for identifying most setups. Adjust this value based on the specific symbol and market for optimal results.
FVG Validity Period :
This parameter defines the validity period of an FVG in terms of the number of candles. By default, an FVG remains valid for up to 15 candles, but you can adjust this period as needed.
Mitigation Level FVG :
This setting establishes the basic level of an FVG. When the price reaches this level, the FVG is considered mitigated.
Level in Low-Risk Zone :
This feature aims to reduce risk by dividing the FVG into two equal areas: "Premium" (upper area) and "Discount" (lower area). For lower risk, ensure that "Demand FVG" is in the "Discount" area and "Supply FVG" in the "Premium" area. This feature is off by default.
Show or Hide :
Given the potential abundance of setups, displaying all on the chart can be overwhelming. By default, only the last setup is shown, but you can enable the option to view all setups.
Alert Settings :
On / Off : Toggle alerts on or off.
Message Frequency : Determine how often alerts are triggered.
Options include :
"All" (alerts every time the function is called)
"Once Per Bar" (alerts only on the first call within the bar)
"Once Per Bar Close" (alerts only at the last script execution of the real-time bar upon closing)
The default setting is "Once Per Bar".
Show Alert Time by Time Zone : Set the alert time based on your preferred time zone, such as "UTC-4" for New York time. The default is "UTC".
Display More Info : Optionally show additional details like the price range of the order blocks and the date, hour, and minute in the alert message. Set this to "Off" if you prefer not to receive this information.
Market Structures SMC [TradingFinder] BOS/CHoCH Major & Minor🟣Introduction
Understanding market structure involves analyzing market behavior. In other words, market structure encompasses how the market forms and evolves within trends.
Market structures are typically fractal and nested, so we categorize them into internal (minor) and external (major) structures. There are various definitions of market structure, with different approaches such as Smart Money and ICT providing their own interpretations.
🟣How to Use
The first step in identifying market structure is to analyze key highs and lows. An uptrend is formed when highs and lows are successively higher than previous ones. Similarly, in a downtrend, lows and highs are successively lower than previous ones.
Market trends consist of two types of movements :
•Impulsive movements
•Corrective movements
Impulsive movements align with the main trend and possess high strength and momentum. Conversely, corrective movements go against the main trend and have lower strength and momentum. The following example illustrates these concepts.
🔵 Identifying Break of Structure (BOS)
In a specific trend, for example in a downtrend, when the price breaks below the previous low and forms a new low (LL), a Break of Structure occurs. In an uptrend, a BOS (Market Structure Break or MSB) happens when the price rises and surpasses the last high.
We need at least one BOS to confirm a trend. Breaking above or below the previous high or low must be confirmed by closing at least one candle after that level.
🔵 Identifying Change of Character (CHOCH)
Change of Character (CHOCH) is a key concept in market structure analysis. A change in structure signals a trend change. In other words, a trend ends with a CHOCH (Market Structure Shift or MSS). For instance, in a downtrend, the price declines with BOS.
BOS indicates the strength of the trend, but when the price increases and surpasses the last high, a CHOCH occurs, signaling a shift from a downtrend to an uptrend.
This does not mean entering a buy trade; instead, we should wait for a BOS in the upward direction to confirm the uptrend. Unlike BOS, confirming a CHOCH does not require a candle to close; simply breaking above or below the previous high or low with the candle's wick is sufficient. The following examples show bearish and bullish CHOCH.
🔵 Range Market Structure
Besides uptrends and downtrends, a third structure often found in the market is the range or sideways structure. In this state, the power of buyers and sellers is almost equal, and the market lacks a clear trend.
Many traders believe that the Forex market ranges 80% of the time. Therefore, it requires a lot of patience to wait for a new trend to start.
🟣 Settings
Through the settings, you can customize the display, visibility, and color of each line as desired.
Smart Money Setup 06 [TradingFinder] Liquidity Sweeps + OB Swing🔵 Introduction
Smart Money, managed by large investors, injects significant capital into financial markets by entering real capital markets.
Capital entering the market by this group of individuals is called smart money. Traders can profit from financial markets by following such individuals.
Therefore, smart money can be considered one of the effective methods for analyzing financial markets.
Sometimes, before a market movement, fluctuation movements that create price movement cause many traders' "Stop Loss" to be triggered. These movements are created in various patterns.
One of these patterns is similar to an "Expanding Triangle", which touches the stop loss of individuals who have placed their stop loss in the cash area in the form of 5 consecutive openings.
To better understand this setup, pay attention to the images below.
Bullish Setup Details :
Bearish Setup Details :
🔵 How to Use
After adding the indicator to the chart, wait for trading opportunities to appear. By changing the "Time Frame" and "Pivot Period", you can see different trading positions.
In general, the smaller the "Time Frame" and "Pivot Period", the more likely trading opportunities will appear.
Bullish Setup Details on Chart :
Bearish Setup Details on Chart :
🔵 Settings
You have access to "Pivot Period", "Order Block Refine", and "Refine Mode" through settings.
By changing the "Pivot Period", you can change the range of zigzag that identifies the setup.
Through "Order Block Refine", you can specify whether you want to refine the width of the order blocks or not. It is set to "On" by default.
Through "Refine Mode", you can specify how to improve order blocks.
If you are "risk-averse", you should set it to "Defensive" mode because in this mode, the width of the order blocks decreases, the number of your trades decreases, and the "reward-to-risk ratio "increases.
If you are on the opposite side and are "risk-taker", you can set it to "Aggressive" mode. In this mode, the width of the order blocks increases, and the likelihood of losing positions decreases.
Smart Money Setup 03 [TradingFinder] Minor OB & Trend Proof🔵 Introduction
The "Smart Money Concept" transcends mere technical trading strategies; it embodies a comprehensive philosophy elucidating market dynamics. Central to this concept is the acknowledgment that influential market participants manipulate price actions, presenting challenges for retail traders.
As a "retail trader", aligning your strategy with the behavior of "Smart Money," primarily market makers, is paramount. Understanding their trading patterns, which revolve around supply, demand, and market structure, forms the cornerstone of your approach. Consequently, decisions to enter trades should be informed by these considerations.
🟣 Important Note
In this setup, pattern formation revolves around the robustness of the "Stop Hunt" targeting retail traders.
When this stop hunt occurs, if the price tests below the minor pivot or above the minor pivot, a "Minor Order Block" is formed.
Similarly, if the price tests below the major pivot or above the major pivot, a "Major Order Block" is formed.
Since the price hasn't successfully broken the major pivots before breaking the Top or Bottom, it can be inferred that the minor pivots formed within a leg of price movement exhibit a "Range" structure.
For a deeper comprehension of this setup, refer to the accompanying visual aids below.
Bullish Setup Details :
Bearish Setup Details :
🔵 How to Use
Upon integrating the indicator into your chart, exercise patience as you await the evolution of the trading setup.
Experiment with different trading positions by adjusting both the "Time Frame" and "Pivot Period". Typically, setups materializing over longer "Time Frames" and "Pivot Periods" carry heightened validity.
Bullish Setup Details on Chart :
Bearish Setup Details on Chart :
Within the settings, you possess the flexibility to modify the "Pivot Period" input to tailor the indicator to your preferences.
Auto Fibonacci Supports [ProjeAdam]OVERVIEW
The Auto Fibonacci Supports indicator is designed for financial market analysis, particularly in identifying key support levels.
USER GUIDE:
The Auto Fibonacci Supports indicator is designed to identify key support levels based on the Fibonacci retracement theory. These levels are significant in technical analysis as potential areas where price movement can stall or reverse.
Customization
Users can activate or deactivate each support level and customize their color, enhancing the visual distinction on the chart
Setting Support Levels: The indicator allows users to set four distinct Fibonacci support levels.
These levels are defined as percentages and can be input using the input.float function. For example, the default values are set at 0.5, 0.618, 0.705, and 0.786 for the first, second, third, and fourth support levels, respectively.
Users can adjust these percentages according to their trading strategies.
Using Support Levels: These support levels are calculated based on the highest and lowest price values over a defined period (fib_support_length). The script calculates each support level by applying the Fibonacci ratio to the range between the highest and lowest prices.
The support levels are then plotted on the trading chart, offering a visual representation of potential support points where the price might experience resistance or reversal.
VİSUALİZATİON
Using pick signal Levels: The "pick signal level" feature in the Auto Fibonacci Supports indicator is designed to help traders identify specific price points where a trading signal might be generated.
This feature likely enables traders to choose a particular Fibonacci support level at which they might consider executing a trade.
By selecting a specific level, traders can focus on significant price points that align with their trading strategy, such as looking for potential buy or sell signals when the price of an asset reaches these key Fibonacci levels.
This helps in refining trading decisions and focusing on critical price movements.
LANGUAGE
The purpose of the "lang setting" is to provide language customization for the user interface.
This setting allows users to select their preferred language for the display of text and labels within the indicator.
Such a feature is particularly useful in making the tool accessible to a wider range of users from different linguistic backgrounds, enhancing user experience and understanding of the indicator's functionalities.
By offering multiple language options, the indicator becomes more versatile and user-friendly for traders around the globe.
Opportunity to examine different parities on the same chart
The script includes a section for setting up various pairs (like BTCUSDT, ETHUSDT, etc.) that users can select or deselect for analysis.
This feature enables traders to apply the Fibonacci support levels across multiple markets, allowing for comparative analysis and broader market insight.
By analyzing different pairs, traders can identify opportunities and patterns across various assets, enhancing their trading strategies.
This multipair functionality is particularly useful in diversified trading approaches.
ALGORITHM
In the "Auto Fibonacci Supports" indicator, calculating the high and low values is a crucial step.
This process involves identifying the highest and lowest price points of a financial instrument within a specific time frame, determined by the fib_support_length parameter.
The indicator scans historical data over this period to find these extreme values.
These high and low points serve as the reference for calculating the Fibonacci support levels, as they represent the full range of price movement in the selected time frame.
The accuracy and relevance of the support levels depend significantly on the correct identification of these high and low values.
Example
In this example, we can see the parities that fell below the first support level in the table, so we have the opportunity to quickly evaluate these parities.
Benefits
This indicator automates the process of identifying Fibonacci support levels, which can be a time-consuming task if done manually.
It offers traders customizable settings to adapt to different trading strategies and assets.
The visual representation on charts can help in making quicker and more informed trading decisions based on Fibonacci retracement levels.
If you have any ideas what to add to my work to add more sources or make calculations cooler, suggest in DM .
[Excalibur][Pandora][Mosaic] Ultra Spectrum Analyzer@veryfid, you will always be remembered eternally...
ANCIENT MYTHOS AND LORE:
The retellings of "Pandora's Box" serve as a cautionary metaphor depicting an opened container (pithos - jar) that once held profound perils and evils — sufferings that are experienced around the world in various forms. The known and vague mythical box contents actually represent manifestation of evils, situational adversities, and human disparities that have been encountered throughout life for aeons. In contemporary times, a meager list of ordeals would include incidents of deceit, betrayal, corruption, oppression, greed, envy, depravity, conflict, mania, affliction, plague, and mortality. However, as the tale is told, kept and remaining inside the box was the essence of expectant hope (elpis), which may represent the optimism and resilience to overcome immense hardships.
There are other versions of the classic story where Pandora isn't actually the culprit, being her husband Epimetheus was the lid lifting perpetrator and the one who always and actually received the gift(s). Curiously, the interpreted Greek word ‘Pandora’ translated to English, can mean either "all-endowed" or "all-gifting". Much like Pandora herself, who was formed from clay of the earth, the jar also would have been most likely crafted from clay. Conceived as a made-to-order maiden for an arranged marriage, Pandora was given qualities of exquisite beauty, persuasive charm, all while being adorned with jewelry and fine clothing. Olympian premeditated preparations in the didactic fable of 'Works and Days' by Hesiod had blamable intent and would be later used for centuries as denigration of women/mothers. The rest of Hesiod's tale is even worse.
In reality, the entire contrived exploit of incarnating Pandora as a trojan temptress was solely intended as an instrument of infiltration and entrapment for delivery to Epimetheus as an arranged seductive snare. Being a man myself, I find it appalling how the antiquated writings of ancient morphological men have repeatedly ostracized women for many of the ailments of mankind. When in truth, it is far more often that despicable men are the recorded all time winning historical harbingers of global abysmal darkness by means of ideological treachery. Vast historical chronicles since antiquity have frequently recorded who the typical real-world villains truly are and are not. As the stories are told in the first place, it was dictator Zeus along with his Olympian conspirators, who intently implanted malicious spirits into a gifted receptacle to orchestrate planetary suffering and carnage on humankind.
PROLOGUE:
I believe, it is way past overdue to restore Pandora's name to a place of better standing. As I have been peaking into a theoretical pitcher of mathematic mysteria for years now, where no one else dares to look. Once upon a time, I pondered an opposite notion: What if Pandora was originally conceived to solve global problems instead of creating them? Maybe Pandora could have been wielded into existence to wage unrelenting and avenging retribution on every dominance hierarchy and each diabolical enemy intently hostile to humankind. My hypothetical version of Pandora would take the notion of "mors omnibus tyrannis" to a whole other fearsome magnitude. She would cause evil arrogant men to tremble with sheer horror... the kind of fear ALL false gods, despotic kings, tyrannical dictators, controligarchies, and criminal syndicates truly worry about at night. In my opinion, that would be a better fictional story worthy of retelling for aeons.
One unique goliath 21st century adversary is LAG and it must be subdued or minimized. This unyielding nemesis is also known as group delay, processing delay, and algorithmic latency. My eyes are locked onto this opponent with fixation that will never surrender a staring contest. The formidable creature lag is my daily arch enemy destined for defeat in battle. It's losing time after time and bar by bar during the past year of 2023. In my attempts to peer through the murky darkness of useless and deceptive information, I am confident that I have found more suitable answers to many current dilemmas of algorithmic lag.
The internet, using mathematics and the speed of light as a planetary beneficial advantage, has already performed wonders by drastically reducing the delay of dissemination of knowledge. This has garnered a mostly positive rapid acceleration of economic evolution. However, hierarchies of dark forces of chaos and subversion by the thousands lurking in the global shadows are not thrilled about well informed populations. In the present era, new spectrums of strife within planetary societies are being waged, one of the worst forms taking the hideous form of censorship. Other nefarious tactics are hindering economic progress with substantial negativity using heavily funded penetration and infiltration operations. Those sinister operational varieties are spanning psychological, cultural, educational, digital, financial, electoral, scientific, medical, biological, commercial, infrastructural, institutional, and organizational domains.
They are mistakenly meddling with the entire primordial order of planetary natural dynamics. The miscalculations from these malevolent CAUSES will be countered with EFFECTS of immense retaliatory primal veracity having equal or exceedingly more powerful opposition with overwhelming numbers in mass. It is a law embedded within the universe that supersedes ALL laws, known as 'causality'. Everyone, especially programmers, know exactly what to do with predatory infiltrating cockroaches... When tyranny becomes enforced law by agendized policies in any land, order = abs(DUTY) * pow(RIGHT) * exp(PEOPLE).
FUTURE ECONOMIC ADVERSARIAL CHALLENGES:
Just as programmers have to critically analyze our code for BUGS, a scrutinized analysis of the current world around us is at times necessary. It is an empirical statistical fact that a few percent of captains at the helm of industry, commerce, institutes, and governance are monetarily psychopathic. They are often hidden bugs operating within national systems. The subsequent economic consequences result in effects that aren't always clearly obvious to all. Here are a few global economic security issues...
Corrupted immoral code in national operation is an inevitable breakdown waiting to happen. In the harsh future to follow, old degenerate interdependent control systems will need to be dismantled and discarded, eventually succeeded by having resilient parallel arrangements with robust independent fidelity. The coming successive paradigm shifts would include future hardware and the hefty novel algorithms that will run on them afterwards. Evolution is inevitable! The internet must be upgraded and continually programmed securely to the near hardness of diamonds at multiple layers within the operational code to retain peaceful global integrity between international collaborations.
DigitalID is never going to fix an insecure vulnerable titanic network of devices full of holes taking in megatons of water from every direction. Weaponized digital mucking ID dead on arrival is certainly NOT a one size fits all solution and it still doesn't do diddly-squat to secure the internet's DNA as executable code. DigID's real purpose is to manage servitude digitally and keep citizens right where they want them, as subservient slaves.
There is a very specific reason why we have key chain rings in OUR pockets with numerous private keys evolving technologically over time to robustly safeguard individual locks we use every day, duh. AI becoming an artificial sentient hyper intelligence may sooner or later become a potential hazard, especially if it breaks AES192 into a thousand shards of glass. Perilous aspects from artilects will emerge and are coming swiftly. AI is already being weaponized and tasked to mind muzzle expressions of human consciousness.
Also, EMPs from the sun ARE an imminent planetary threat, and no amount of carbon taxation schemes inciting anthropomorphic climate hysteria originating from falsified modeling hocus-pocus is going to protect against extreme solar cycle related X-class phenomena. Our solar system candle called the sun, is not consistently energy irradiation stable if you just glance at SOHO images/video. There are very obvious cyclical frequencies within the dynamics of the sun's energetic activity that affect planets far beyond earth. The earth already has a built-in natural thermometer indicating that oceans have been rising very linearly for thousands of years since the last ice age, submerging entire ancient cities under coastal water dozens of meters.
BEAR with me and pardon my French translation, but I have the option to call major league climate BULLshite. There is no hardcore "anthropomorphic climate crisis" proof. It is a crisis in failed modeling that is insufficient to properly estimate colossal computations with dircet limited empirical data with enough accuracy to anticipate higly probable future outcomes. People deserve solid science instead of slanderous smackdowns and slighted statistics. 400ppm of atmospheric CO2 is nothing compared to previously existing 1600ppm concentrations acquired from ancient indirect historical observations at a time when early humans were hunter gatherers driving gas guzzlers.
Western climate-monger fortune tellers are scamming every nation on earth, betraying the collective human species worldwide by climate hype strangulation. Wait until the sheeple with dinner forks turn on the rabid wolves in shepherds's clothing; it has already begun. What these predatory profiteering fraudsters are not telling you is WATER (H2O) in earth's atmosphere is the all time dominating and potent greenhouse gas, always has been, not CO2. Dr. Willie Soon has explained it in the best of ways with clarity. Misleaders, banksterCorpses, and mediaPresstitutes are immensely involved in this hot model scheme and like keeping people right where they want them, force fed with mental filth with regularly scheduled socially engineered programming.
Beware of agendas and isms. The ESGovernanceAgenda is ready made economic coffin nails. I'll explain this very simply, a future green war on carbon is a silent war on carbon lifeforms and economies. Many of the smiling faces you can actually see on the world stage pulling levers are often the coldest blooded deceivers beyond anything you can ever imagine. In truth, corporate agents and policies are the greatest devastators to ecologies, while in concert, they are incessantly waging blame campaign agendas with subversive narratives by targeting consumers as the wrongdoers.
Why am I mentioning all these adversarial difficulties? Well, the intertangling myriads of tomorrow's "bundle of burdens" in a future box ALL have to be thoroughly analyzed, sifted through, and dealt with tenaciously now and in the future by generations to come in every nation state. Some days I wonder if Hesiod's fiction was taken from reality over 2000 years ago to WARN future world inhabitants. In the scope of economics, the series of incidents that have or will lead up to major world events, will need to have the frequency of related occurrences examined that lead up to crucial points in time historically. In order to prevent future disparities, our progeny will look backwards into history with ultra clarity and vigilance to see how corrupted society once was by hordes of overlords twisted by obsessive delusions of absolute power over the entire human species. There is no human race, only diverse genetic multiformity expressed from the DNA code of humankind exists.
We can't simply put the lid back on low entropy hydroCarbons and a broadband globalNet without having an implemented proven replacement or upgrade. It's far too late, leaving only wiser security chess moves forward as the only viable options. Nikola Tesla was dreaming of this daily in order to build every foundation of modern civilization that we now enjoy today and take for granted. Humanity still has to evolve by unlocking hidden secrets of mother nature. For instance, nations powered by endless geothermal electricity and deuterium fusion WILL solve a lot of the world's problems. Imagine our world dominantly powered by extreme abundant amounts of heavy water... Lady destiny awaits and begs for the future to be built securely, by eventual abandonment of antiquated wheelworks that eventually deserve to be hurled into the annihilatory dustbin of history.
SPECTRAL BURDENS:
Ephemeral 'spectral contents' are extremely difficult to decipher with the least amount of lag, especially while they reside within a noise ridden non-stationary environment. When 'lifting the lid off' of series analysis to peek with quick discernment, distinguishing between real-time relevant signals differing from intertwining undesirable randomness in a crowded information space, requires special kinds of intricate extraction. Due to the nature of fractal chaos, any novel spectral method is better than the scanty few we have now. Firstly, let's comprehend agilities of interpreting a spectrum's structure...
SPECTRAL ANALYSIS PURPOSE AND INTENTION:
Frequency Analysis - Spectral analysis serves a crucial purpose in unraveling the frequency composition of a signal. Its primary intention is to explore the intricacies of a dataset by identifying dominant frequencies and unveiling inherent cyclical patterns. This foundational understanding forms the basis for improving analyses.
Power Spectrum Visualization - The visualization of a signal's power spectrum is a key objective in spectral analysis. By portraying how power is distributed across different frequencies, the goal is to provide a visual representation of the signal's energy landscape. This insight aids with grasping the significance of various frequency components obtained from a larger whole.
Signal Characteristics - Understanding the traits of a signal is another vital goal. Spectral analysis seeks to characterize the nature of the signal, unveiling its periodicity, trends, or irregularities. This knowledge is instrumental in deciphering the behavior of the signal over time, fostering a deeper comprehension.
Algorithmic Adaptation - Spectral analyzer estimation can play a pivotal role in algorithmic development. By assisting with the creation of algorithms sensitive to specific frequency ranges, one possible advantage is to enable real-time adaptability. This adaptability approach may allow algorithms to respond dynamically to variations in different spectral components, potentially enhancing their efficacy.
Market Analysis - In the realm of trading systems and financial markets, spectral analysis methods can serve as applicable functions when studying market dynamics. By 'uncovering' trends, cycles, and anomalies within financial instruments, this analytical proficiency can aid traders and algorithm developers with making better informed decisions based on the spectral attributes of market data.
Noise/Interference Detection - Another purpose of spectral analysis is to identify and scrutinize undesirable elements within a signal, such as noise or interference. One benefit would be to facilitate the development of strategies to mitigate or eliminate these unwanted components, ultimately refining the quality of a given signal with filtration.
INTRODUCTION:
Allow me to introduce Pandora! What you see in the demonstration above, I've named it "Pandora Periodogram", which is also referred to as 'Ultra Spectrum Analyzer' (USA) for technical minds. Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an avant-garde cycle period detector obtaining accurate spectral estimates on market data with Pine Script v5.0. USA is a spectral analysis cryptid that I can only describe as being an alien saber in nature. It is my rendering of spectral wrath unleashed. With time and history to come, my HOPE is this instrument will reveal Excalibur like aspects capable of slicing up a spectrum craftily, traits long thought to be a mythical enigma.
It is not modified forms of either Autocorrelation Periodogram (ACP) or MESA. Pandora's Periodogram embodies an entirely distinct design, adorned with glamourous color, by incorporating several of my most profound, highly refined technological innovations that I have poetically composed into being. What I have forged in Pine, has essentially manifested as a zero lag spectrum analyzer. Pandora easily peeks inside a single signal source more effectively to inspect for hidden spectres, revealing invisible apparitions inside data with improved clarity...
My 'Ultra Spectrum Analyzer' bears an eerie likeness to Autocorrelation Periodogram, but it possesses no autocorrelation and the other small hindrances of ACP that I formerly encountered. While ACP does have a few shortcomings, a few bars of lag, and high frequency bias, it is still phenomenal code. ACP is one answer to spectral enigmas, but not the only one. Developers can utilize this detector by creating scripts that employ a "Dominant Cycle Source" input to adaptively govern algorithms. If you are capable of building suitable algorithms for direct tethering to Autocorrelation Periodogram, then this is your next step in evolutionary application to tether to when you are ready. ACP is a good place to start building upon as an exploratory vessel, before you might ponder using USA. Once you do obtain dynamic ACP sweetness with only a few pesky bars of dominant cycle induced lag, USA may be your tool chest choice without the burden of subtle ACP lag.
USA is possibly the end of my quest for spectral bliss, for the time being. However, I still suspect there is more room for upgrades to Pandora in the future. I must mention, as an overture, this won't be the last of Pandora tech that you will witness, as my literal "out of the box thinking" will unleash many additional creations upon this Earth. The "Power of Pine" merely serves as the beginning foundational phase... Some of my futuristic dreams and daydreams of TradingView are droplets in a wavy ocean of economic providence and potential.
What I am crafting in poetic form is born out of raw curiosity. Future creations are probably best kept private for now, but I will present my future tech with beauty and elegance as it should rightfully be. There's one catch, I have absolutely no idea what this and my future marvels may do to the future of digital signal processing (DSP) and markets. I do fear any insane AI or MALEficent entity ever seeing this code. My innermost hopes and ambitions are always focused on achieving the best result obtainable. What the future can hold, may be absolutely exquisite to gaze upon, maybe even monstrous, or possibly a combination of both.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. My own projects demand too much of my day to day time. I hope you understand. Meanwhile, I'll be applying this on future indication until Mr. Mortality sneaks up behind me.
FEATURES AND CHARACTERISTICS:
I have included as much ultra adjustability as I can humanly muster. Those features being the following and more...
Color Preferences - Four vivid color schemes are available in the original release. The "Ultra Violet" color scheme, in particular, contributes to the indicator's technical title, as it seems to me to reveal the greatest detail of my various spectral color schemes. Color inversion of the four color schemes is also possible, yielding eight schemes in total with predator style visuals. Heatmap transparency control is also provided.
Lag Control - Pandora achieves zero lag spectral approximations, with the added capability to control lag using an input for selectable delay. Note, however, that testing less than zero lag has not been assessed thoroughly due to potential unforeseen instability concerns. Adjustments are provided in either direction for further testing.
Spectral Bias Mitigation - Options for mitigating high OR low-frequency spectral biases are present. One interesting tweak made during development was a subtle form of spectral manipulation, involving a partial reduction of frequency amplitudes influencing either the highest or lowest periodicities. This slightly reduces the impact on the upper and lower portions of the spectrogram and the dominant cycle measurement. What initially surfaced as an unexpected discovery, may now be considered worthy of experimental utility.
Adjustable Periodogram Window Size - The periodogram is adjustable for various window sizes of periodic operation. Exploration up to a periodicity of 59 is obtainable for curiosity's sake. This flexibility challenges the notion that curiosity isn't always a negative trait, contrasting with Hesiod's ancient perspective.
Dominant Cycle Filtration - Filtration of the dominant cycle is achieved with a novel smoother having reduced lag, easily surpassing SuperSmoother's performance. However, defeating lag completely on that one plot() function was elusive.
Tooltips for Control Intention - The settings commonly include handy and informative tooltips that provide information eluding to the intention behind the various controls provided.
Initialization Advantages - Initialization of USA accomplishes what Autocorrelation Periodogram (ACP) didn't. Spectral analysis begins on the earliest visible bars, starting at period 2. Users need to ensure their algorithm's integrity from period 2 upwards to beyond 40ish, establishing a viable operational range for dynamically governing those algorithms. It's notable that stochastics and correlations have a minimum operable critical period of 2, distinct from most low-pass filters that can actually achieve a period of 1 (which is the raw signal itself). Proper initialization of complex IIR filters is particularly effective, especially with smaller initialization periods.
Remaining options and features are comparable to my Enhanced Autocorrelation Periodogram in terms of comprehension, and other upgrades may be added in the future upon discovery.
PERIODOGRAM INTERPRETATION:
The periodogram heatmap renders a power spectrum of a signal visually by color, where the y-axis represents periodicity (frequencies/wavelengths) and the x-axis is delineating time. The y-axis is divided into periods, with each elevation portraying demarcation of periodicity. In this periodogram, the y-axis ranges from 4 at the very bottom to 49 (or greater) at the top, with intermediary values in between, all conveying power of the corresponding frequency component by color. The higher the position ascends on the y-axis, the longer the cycle period or lower the frequency. The x-axis of the periodogram signifies time and is partitioned into equal chart intervals, where each vertical column corresponds to the time interval when the signal was measured. Most recent values/colors are on the right side of the periodogram.
Intensity of the colors on the periodogram signify the power level of the corresponding frequency or cycle period. For example, the "Fiery Embers" color scheme is distinctly like heat intensity from any casual flame witnessed in a small fire from a lighter, match, or campfire. The most intense power exhibited would be represented by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
Monday_Weekly_Range/ErkOzi/Deviation Level/V1"Hello, first of all, I believe that the most important levels to look at are the weekly Fibonacci levels. I have planned an indicator that automatically calculates this. It models a range based on the weekly opening, high, and low prices, which is well-detailed and clear in my scans. I hope it will be beneficial for everyone.
***The logic of the Monday_Weekly_Range indicator is to analyze the weekly price movement based on the trading range formed on Mondays. Here are the detailed logic, calculation, strategy, and components of the indicator:
***Calculation of Monday Range:
The indicator calculates the highest (mondayHigh) and lowest (mondayLow) price levels formed on Mondays.
If the current bar corresponds to Monday, the values of the Monday range are updated. Otherwise, the values are assigned as "na" (undefined).
***Calculation of Monday Range Midpoint:
The midpoint of the Monday range (mondayMidRange) is calculated using the highest and lowest price levels of the Monday range.
***Fibonacci Levels:
// Calculate Fibonacci levels
fib272 = nextMondayHigh + 0.272 * (nextMondayHigh - nextMondayLow)
fib414 = nextMondayHigh + 0.414 * (nextMondayHigh - nextMondayLow)
fib500 = nextMondayHigh + 0.5 * (nextMondayHigh - nextMondayLow)
fib618 = nextMondayHigh + 0.618 * (nextMondayHigh - nextMondayLow)
fibNegative272 = nextMondayLow - 0.272 * (nextMondayHigh - nextMondayLow)
fibNegative414 = nextMondayLow - 0.414 * (nextMondayHigh - nextMondayLow)
fibNegative500 = nextMondayLow - 0.5 * (nextMondayHigh - nextMondayLow)
fibNegative618 = nextMondayLow - 0.618 * (nextMondayHigh - nextMondayLow)
fibNegative1 = nextMondayLow - 1 * (nextMondayHigh - nextMondayLow)
fib2 = nextMondayHigh + 1 * (nextMondayHigh - nextMondayLow)
***Fibonacci levels are calculated using the highest and lowest price levels of the Monday range.
Common Fibonacci ratios such as 0.272, 0.414, 0.50, and 0.618 represent deviation levels of the Monday range.
Additionally, the levels are completed with -1 and +1 to determine at which level the price is within the weekly swing.
***Visualization on the Chart:
The Monday range, midpoint, Fibonacci levels, and other components are displayed on the chart using appropriate shapes and colors.
The indicator provides a visual representation of the Monday range and Fibonacci levels using lines, circles, and other graphical elements.
***Strategy and Usage:
The Monday range represents the starting point of the weekly price movement. This range plays an important role in determining weekly support and resistance levels.
Fibonacci levels are used to identify potential reaction zones and trend reversals. These levels indicate where the price may encounter support or resistance.
You can use the indicator in conjunction with other technical analysis tools and indicators to conduct a more comprehensive analysis. For example, combining it with trendlines, moving averages, or oscillators can enhance the accuracy.
When making investment decisions, it is important to combine the information provided by the indicator with other analysis methods and use risk management strategies.
Thank you in advance for your likes, follows, and comments. If you have any questions, feel free to ask."
Dual Dynamic Fibonacci Retracement — Long and Short Duration
Title : "The Dual-Dynamic Fibonacci Retracement Script: An Advanced Tool for Comprehensive Market Analysis"
As the author of the "Dual-Dynamic Fibonacci Retracement Script", I am delighted to introduce you to this cutting-edge tool for technical analysis. Unlike conventional Fibonacci scripts, this advanced model incorporates multiple unique features and adjustments that make it a powerful asset for any market analyst. Whether you're dealing with forex, commodities, equities or any other market, this script is versatile enough to enhance your trading strategy.
Uniqueness & Differentiation:
The "Dual-Dynamic Fibonacci Script" stands out by offering two distinct lookback periods. This feature is what separates it from other scripts available in the market. The first lookback period is longer, focusing on capturing broader market trends. The second lookback period is shorter, allowing for a more granular analysis of near-term market fluctuations. This dual perspective provides a more comprehensive view of the market, allowing you to see both the forest and the trees at the same time.
Fibonacci Levels:
While offering the standard Fibonacci retracement levels (0.236, 0.382, 0.5, 0.618, 0.786, and 1.0), the script also gives you the ability to plot 0.114 and 0.886 levels. These additional levels offer an extra layer of depth to your analysis, and can prove crucial in high-volatility markets where they often serve as significant support and resistance points.
Customizable Line Shifts and Extends:
This script provides options for customization of the shift and extension of the plotted lines. This means you can adjust the start and end points of the Fibonacci lines according to your personal trading style and strategy. This level of personalization is not typically available in other scripts, and it allows for a more tailored visual representation.
Flexible Trading Positioning:
Depending on whether the closing price is above or below the midpoint of the pivot high and pivot low, the Fibonacci retracement levels are adjusted accordingly. This ensures the script remains relevant and useful regardless of market conditions.
Clean Visualization:
To prevent clutter and maintain focus on the most relevant price action, the script removes old Fibonacci lines and plots new ones once a new pivot high or low is identified. This clean visualization helps keep your analysis focused and sharp.
How to Use the Script:
To get started, simply adjust the lookback periods according to your trading strategy. If you're a long-term investor or prefer swing trading, a longer lookback period might be appropriate. Conversely, if you're a day trader, a shorter lookback period might be more beneficial.
The "Shift" and "Extend" inputs allow you to control the positioning of the Fibonacci lines on your chart. Positive values shift the lines to the right, while negative values shift them to the left.
You also have the choice to plot the additional Fibonacci levels (0.114 and 0.886) via the "Plot 0.114 and 0.886 levels?" input. Similarly, the "Plot second set of levels?" input lets you decide whether to display the second set of Fibonacci levels derived from the shorter lookback period.
Like any technical analysis tool, this script is most effective when used in conjunction with other indicators and methods of analysis. It is designed to work well in trending markets, where Fibonacci retracements can often indicate potential reversal levels. However, it's always recommended to use a holistic approach to market analysis to maximize the likelihood of successful trades.
Note: the two lines drawn on the chart are there to help the user identify the levels from which the two respective Fib sequences are calculated.
~~~
Input Explanations:
Long Period Pivot High/Low Lookback and Short Period Pivot High/Low Lookback : These settings determine the length of the lookback periods for the long-term and short-term pivot points, respectively. A pivot point is a technical analysis indicator used to determine the overall trend of the market over different time frames. The pivot points are then used to calculate the Fibonacci levels. A longer lookback period will identify pivot points over a broader time frame, capturing major market trends, while a shorter lookback period will identify pivot points over a narrower time frame, capturing more immediate market movements.
Long Period Fibonacci Level Shift and Short Period Fibonacci Level Shift : These inputs control the shift of the Fibonacci levels based on the long and short lookback periods, respectively. If you want to shift the Fibonacci levels to the right, increase the value. If you want to shift the Fibonacci levels to the left, decrease the value. This allows you to adjust the Fibonacci levels to better align with your analysis.
Long Period Fibonacci Level Extend and Short Period Fibonacci Level Extend : These inputs control the extension of the Fibonacci levels based on the long and short lookback periods, respectively. If you want the Fibonacci levels to extend further to the right, increase the value. If you want the Fibonacci levels to extend less to the right, decrease the value. This feature provides the flexibility to adjust the length of the Fibonacci levels according to your personal trading preferences and strategy.
Plot 0.114 and 0.886 levels? : This setting gives you the ability to plot the additional 0.114 and 0.886 Fibonacci levels. These levels provide extra depth to your analysis, particularly in highly volatile markets where they can act as significant support and resistance levels.
Plot second set of levels? : This input allows you to decide whether to plot the second set of Fibonacci levels based on the short lookback period. Displaying this second set of levels can provide a more granular view of market movements and potential reversal points, enhancing your overall analysis.
DEMO - FxCanli S/REN - FxCanli S&R indicator shows any drawings about Support & Resistance on charts
DEMO VERSION of FXCANLI S&R Indicator work with any NZD or any DOGE symbols
TR - FxCanli S&R indikatörü grafiklerinizde Destek & Direnç ile ilgili tüm çizimleri otomatik yapar
FXCANLI S&R indikatörünün DEMO VERSİYONUNU herhangi bir NZD veya DOGE sembolü ile kullanabilirsiniz.
EN - For Example | TR - Örnek
NZD|...
NZD|USD
NZD|CAD
NZD|CHF
NZD|JPY
DOGE|...
DOGE|USD
DOGE|USDT
DOGE|USDTPERP
DOGE|BTC
**ENGLISH**
This indicator shows;
1) Support Levels (Green Solid Line)
2) Resistance Levels (Red Solid Line)
3) Support Line (Green Dashed Line)
4) Resistance Line (Red Dashed Line)
ALERTS at;
Resistance Zone Breakout and Touch
Resistance Line Breakout and Touch
Support Zone Breakout and Touch
Support Line Breakout and Touch
AND AT PULLBACKS
COMBO BREAKOUTS
**TURKCE**
Bu indikatör grafiklerinizde;
1) Destek Seviyelerini (Yeşil Kesintisiz Çizgi)
2) Direnç Seviyelerini (Kırmızı Kesintisiz Çizgi)
3) Destek Çizgisini (Yeşil Kesikli Çizgi)
4) Direnç Çizgisini (Kırmızı Kesik Çizgi) çizer
Alarm Özellikleri;
Destek Bölgesi Kırılımı ve Teması
Destek Çizgisi Kırılımı ve Teması
Direnç Bölgesi Kırılımı ve Teması
Direnç Çizgisi Kırılımı ve Teması
VE PULLBACK lerde (GeriOnaylarda)
COMBO KIRILIMLARDA
Some Examples / Bazı Örnekler
s3.tradingview.com
s3.tradingview.com
s3.tradingview.com
s3.tradingview.com
CDC Fibonacci Retracement and ExtensionThis indicator is meant to be used as a tool to quickly identify
fibonacci retracements and projections in multiple charts during
the same date range.
Users can set the calculation date range and quickly flip through
different charts for comparisons
Steps for using this indicator is as follows:
1. Specify Start Date and End Date for calculations
2. Choose Open-ended mode for just retracements, this will disregard
end date in calculations.
3. Select price source, if Use Highs/Lows is selected, the indicator will
use high and low prices for calculation, if not, closing price eill
be used instead
4. Select and/or modify retracement / projection lines as you see fit.
5. Enjoy the result!
tops/bottomsThe script gives market tops and bottoms.
How to use:
-In an uptrend market condition - the bottoms signifies the extent of price pullback and can be used for going long after bottoms
-In an downtrend market condition - the tops signifies the extent of price pullback and can be used for going short after tops
-In a range bound market condition - both tops and bottoms signifies the extent of price extremes in the range channel and can be used for going long after bottoms and going short after tops.
This script is designed after a heavy research done on fibonacci numbers and moving averages where 13 acts as the best reversal point of tops and bottoms, if the market goes beyond it we can adjust period with the next fibonacci numbers ie 21,34,55 etc. to know about the next turning point and trade accordingly.
Neowave chart cash dataScript Cash is a neo-analytic style data. Add to use on the chart and then hide the candlesticks and enjoy the cash data.
The daily data cache is set normally. To change the settings, be sure to change the D indicator to W for weekly and M for monthly.
Also enter the number of minutes to use in the hourly time frame, for example four hours (240)
...
When you change the data cache settings in the settings, you must follow the rule of one fortieth of the Neowave style and move the time frame chart to forty to analyze it, for example, for a daily time frame go to 30 minutes.
I hope it is used.
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Browser Code
The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
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:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
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.
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.
VolumatrixVolumatrix is an enhanced volume weighted price indicator with advanced features
Created by CryptoJew & CryptoTiger on 04-06-2021
👋 Definition
Volumatrix turns current and historical price data into enhanced volume weighted price plots that allow you to visually grasp the momentum of any given market.
It’s easy to use and provides an accurate reading about an ongoing trend. This indicator is optimized to catch trend movements as soon as possible and to maximize certainty.
🙌 Overview
The Volumatrix indicator is based on an enhanced VWAP calculation, which serves as a present and upcoming price movement indication.
The further away the VWAP Wave is from the Zero Line, the more powerful the momentum is in that direction.
Conversely, the closer the VWAP Wave is to the Zero Line, the less momentum it has.
⭐️ Features
Volumatrix consists of the following features:
VWAP Waves: Visualizes the market's momentum in an easy-to-understand way by drawing colored waves.
VWAP Average: Acts as a calibration line for current wave movements.
Bearish & Bullish Dots: Indicates and confirms immediate trend changes by printing dual-colored dots.
E MA Backgrounds: Shows the general direction of the market, based on the exponential moving average (EMA).
In-depth alerts: Help traders discover potential trades with less time.
☝️ Basics
The Volume Weighted Average Price plays an essential role, as the Volumatrix indicator uses an enhanced VWAP calculation.
The volume weighted average price (VWAP) is a great technical trading indicator used by traders as it accounts for both price and volume.
VWAP signals the ratio of the cumulative share price to the cumulative volume traded over a given time.
It is essential because it provides traders with advanced insight into the trend and value of an asset.
Unlike moving averages, VWAP assigns more weight to price points with high volume.
This allows one to understand price points of interest, gauge relative strength, and identify prime entries/exits.
VWAP works with any interval: seconds, minutes, hours, days, weeks, months, years, etc...
However, keep in mind that VWAP can also experience some lag, much like a moving average.
Lag is inherent in the indicator because it's a calculation of an average using past data.
🧮 Calculation
Volume Weighted Average Price (VWAP) is constructed with two parameters, namely, price and volume, in 5 steps:
1. Calculate the Typical Price for the period.
((High + Low + Close)/3)
2. Multiply the Typical Price by the period Volume
(Typical Price x Volume)
3. Create a Cumulative Total of Typical Price
Cumulative(Typical Price x Volume)
4. Create a Cumulative Total of Volume
Cumulative(Volume)
5. Divide the Cumulative Totals
VWAP = Cumulative(Typical Price x Volume) / Cumulative(Volume)
🔍 Trend Identification - What to look for
VWAP is an excellent way to identify the trend of a market.
When using Volumatrix, you are looking for multiple confirmations that take place simultaneously.
The more confirmations that occur at the same time; the more certain the indicator will be.
You can identify the direction of a market by looking out for a few critical confirming signals.
📈 Bullish Trend Confirmations:
VWAP Wave overcrossing Zero Line :
When the VWAP Wave is crossing over the Zero Line, it indicates an immediate bullish trend.
This is one of the most certain moves that one can detect in Volumatrix.
This means that the price is about to change direction.
This is the case for any timeframe: seconds, minutes, hours, days, week, month, year, etc.
VWAP Wave color turning bullish:
When a bullish trend is about to happen, the VWAP Wave will change its color to yellow and finally to green.
That way, one can preemptively detect an upcoming bullish move.
In general, the VWAP Wave can change to 3 different colors.
Green means bullish.
Bullish Dots:
From time to time, bullish green dots will appear.
When combined with other indications, the Bullish Dots can be handy in confirming an upcoming or present uptrend.
That said, one should never solely rely on dots when deciding whether the trend is bullish or not.
Instead, if a trader sees a green dot, it should be taken as a hint to look for further bullish indications.
EMA Background:
One can identify the general trend of a market by looking at the background color of the indicator.
When the background is green, one can assume that a bullish trend is present.
The background color changes based on the exponential moving average (EMA).
By default, the 200 EMA is set. Change this value based on your timeframe preferences.
VWAP Average:
When the white VWAP Average line crosses above the Zero Line, it acts as an additional trend confirmation when combined with the VWAP waves.
As the VWAP average does not weigh in the short-term movements too heavily, it is less affected by immediate volatility.
Therefore, traders usually use the VWAP Average as a calibration tool to interpret the VWAP Waves more precisely.
📉 Bearish Trend Confirmations:
VWAP Wave under crossing Zero Line:
When the VWAP Wave is crossing under the Zero Line, it indicates an immediate bearish trend.
This is one of the most certain moves that one can detect in Volumatrix. This means that the price is about to change direction.
This is the case for any timeframe: seconds, minutes, hours, days, week, month, year, etc.
VWAP Wave turning bearish:
When a bearish trend is about to happen, the VWAP Wave will change its color to yellow and then finally to red.
That way, one can preemptively detect an upcoming bearish move. In general, the VWAP Wave can change to 3 different colors.
Red means bearish.
Bearish Dots:
From time to time, bearish red dots will appear.
When combined with other indications, the bearish dots can be handy in confirming an upcoming or present downtrend.
That said, one should never solely rely on dots when deciding whether the trend is bearish or not.
Instead, if a trader sees a red dot, it should be taken as a hint to look for further bearish indications.
EMA Background:
One can identify the general trend of a market by looking at the background color of the indicator.
When the background is red, one can assume that a bearish trend is present.
The background color changes based on the exponential moving average (EMA).
By default, the 200 EMA is set. Change this value based on your timeframe preferences.
VWAP Average:
When the white VWAP Average line crosses below the Zero Line, it acts as an additional trend confirmation if combined with the VWAP waves.
As the VWAP average does not weigh in the short-term movements too heavily, it is less affected by immediate volatility.
Therefore, traders usually use the VWAP Average as a calibration tool to interpret the VWAP Waves more precisely.
💤 Sideways Trend Confirmations:
VWAP Average:
When the VWAP Average is parallel and hovering around the Zero Line, either above or below it, that will indicate a sideways trend.
🚦 Usage - How and where to use it
The Volumatrix indicator is a universal indicator that works with any market capable of calculating a VWAP.
It’s currently being used in the following markets: cryptocurrency market, stock market, gold market and oil (just to name a few).
❗️ Requirements:
This indicator does not require any additional indicators as traders usually do in price action trading.
Basically, one just needs to follow the crossings, dots, and colors to get maximum certainty.
As a bonus, we recommend traders take advantage of TradingView’s multi-chart to catch more simultaneous confirmations.
🗣 Example Strategy: The 4 Timeframe Strategy
One can use the Volumatrix indicator along with the 4 timeframe strategy.
For example, open the 4 hour, 1 hour, 30 minute, and 5minute intervals simultaneously from left to right in a multi-chart layout.
Then lookout for the following conditions to meet:
OPEN LONG TRADE IF: On the 1-hour interval + 30-minute interval, Bullish Dots appear simultaneously
AND: On the 4-hour interval, the VWAP Wave is above the Zero Line
AND: On the 5-minute interval VWAP Wave is about to cross over the Zero Line or has already minimally crossed up.
OPEN SHORT TRADE IF: On the 1-hour interval + 30-minute interval, Bearish Dots appear simultaneously
AND: On the 4-hour interval VWAP Wave is below the Zero Line
AND: On the 5-minute interval VWAP Wave is about to cross under the Zero Line or has already minimally crossed down.
💡 Tips
Use TradingView’s 4-multi-chart layout to catch potential trades faster.
Use the indicator on a computer for optimal performance.
Set your computer screen to higher resolutions to get a better overview.
🔔 Alerts
With Volumatrix, you can use in-depth alerts like:
Bullish Dot
When a green dot at the bottom of the indicator appears
Bearish Dot
When a red dot at the bottom of the indicator appears
VWAP Wave Crossing Over Zero Line
When the VWAP Wave crosses over the Zero Line
VWAP Wave Crossing Under Zero Line
When the VWAP Wave crosses under the Zero Line
VWAP Wave Crossing Over Zero Line + Bullish Dot
When the VWAP Wave crosses over the Zero Line and a Bullish Dot appears
VWAP Wave Crossing Under Zero Line + Bearish Dot
When the VWAP Wave crosses over the Zero Line and a Bearish Dot appears
VWAP Average Crossing Over Zero Line
When the VWAP Average crosses over the Zero Line
VWAP Average Crossing Under Zero Line
When the VWAP Average crosses under the Zero Line
🔧 Settings
🔢 Inputs
These settings will change the behavior and outcome of the indicator.
EMA
Determines the number of previous candles that should be taken into calculation for the EMA background.
The value of the EMA can be changed to one's preferred value in accordance with the chosen interval.
The default value is 200.
🎨 Style
These settings will change the appearance of the indicator
VWAP Waves
Determines the color, opacity, thickness, and shape for the VWAP Waves.
The default shape is area.
The default colors are red, yellow & green.
VWAP Average
Determines the color, opacity, thickness, and shape for the VWAP Average.
The default shape is line.
The default color is white.
Zero Line
Determines the color, opacity, thickness, and shape for the Zero Line.
The default shape is a line.
The default color is white.
EMA Background
Determines the color & opacity for the Dynamic Background.
The default colors are black, red & green.
Bullish Dot
Determines the color, shape, opacity & location for the bullish dot.
The default shape is a circle.
The default color is green.
Bearish Dot
Determines the color, shape, opacity & location for the bearish dot.
The default shape is a circle.
The default color is red.
✅ Summary
Volumatrix is a unique indicator because, unlike many other VWAP tools, it's suited for simple as well as advanced analysis.
It’s a solid tool for immediately identifying the underlying trend of an asset.
Of course, this is true for any indicator based on the VWAP, which calculates an average using past data.
Still, Volumatrix is superior in this realm as it enhances the VWAP in its calculation and its visualization, while it comes with many advanced features.
❓ Questions
If you have any questions, just ask them here or in the Volumatrix community.
📚 Terminology
Bearish Dots: Red dots appearing at the bottom of the Volumatrix indicator.
Bullish Dots: Green dots appearing at the bottom of the Volumatrix indicator.
EMA: Exponential Moving Average - Tracks the price of an asset over time while giving more importance to recent price data.
Volume: A measure of how much of a given asset has traded in a period.
VWAP: Volume Weighted Average Price - The ratio of the value traded to total volume traded over time.
VWAP Average: Represents the average of the VWAP waves in the Volumatrix indicator.
VWAP Wave: The colorful waves representing the enhanced VWAP in the Volumatrix indicator.
Zero Line: It’s the indicator’s baseline and determines the beginning and end of a certain trend.
🙏 Acknowledgments
First, we would like to thank TradingView & PineCoders for this fantastic platform and technology.
We are also very grateful to our loyal trading community for constantly supporting our efforts.
We are looking forward to continuously improving this indicator for you.
Awesome Oscillator (AO) with Signals [AIBitcoinTrend]👽 Multi-Scale Awesome Oscillator (AO) with Signals (AIBitcoinTrend)
The Multi-Scale Awesome Oscillator transforms the traditional Awesome Oscillator (AO) by integrating multi-scale wavelet filtering, enhancing its ability to detect momentum shifts while maintaining responsiveness across different market conditions.
Unlike conventional AO calculations, this advanced version refines trend structures using high-frequency, medium-frequency, and low-frequency wavelet components, providing traders with superior clarity and adaptability.
Additionally, it features real-time divergence detection and an ATR-based dynamic trailing stop, making it a powerful tool for momentum analysis, reversals, and breakout strategies.
👽 What Makes the Multi-Scale AO – Wavelet-Enhanced Momentum Unique?
Unlike traditional AO indicators, this enhanced version leverages wavelet-based decomposition and volatility-adjusted normalization, ensuring improved signal consistency across various timeframes and assets.
✅ Wavelet Smoothing – Multi-Scale Extraction – Captures short-term fluctuations while preserving broader trend structures.
✅ Frequency-Based Detail Weights – Separates high, medium, and low-frequency components to reduce noise and improve trend clarity.
✅ Real-Time Divergence Detection – Identifies bullish and bearish divergences for early trend reversals.
✅ Crossovers & ATR-Based Trailing Stops – Implements intelligent trade management with adaptive stop-loss levels.
👽 The Math Behind the Indicator
👾 Wavelet-Based AO Smoothing
The indicator applies multi-scale wavelet decomposition to extract high-frequency, medium-frequency, and low-frequency trend components, ensuring an optimal balance between reactivity and smoothness.
sma1 = ta.sma(signal, waveletPeriod1)
sma2 = ta.sma(signal, waveletPeriod2)
sma3 = ta.sma(signal, waveletPeriod3)
detail1 = signal - sma1 // High-frequency detail
detail2 = sma1 - sma2 // Intermediate detail
detail3 = sma2 - sma3 // Low-frequency detail
advancedAO = weightDetail1 * detail1 + weightDetail2 * detail2 + weightDetail3 * detail3
Why It Works:
Short-Term Smoothing: Captures rapid fluctuations while minimizing noise.
Medium-Term Smoothing: Balances short-term and long-term trends.
Long-Term Smoothing: Enhances trend stability and reduces false signals.
👾 Z-Score Normalization
To ensure consistency across different markets, the Awesome Oscillator is normalized using a Z-score transformation, making overbought and oversold levels stable across all assets.
normFactor = ta.stdev(advancedAO, normPeriod)
normalizedAO = advancedAO / nz(normFactor, 1)
Why It Works:
Standardizes AO values for comparison across assets.
Enhances signal reliability, preventing misleading spikes.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence
Price makes a lower low, while AO forms a higher low.
A buy signal is confirmed when AO starts rising.
Bearish Divergence
Price makes a higher high, while AO forms a lower high.
A sell signal is confirmed when AO starts declining.
👾 Buy & Sell Signals with Trailing Stop
Bullish Setup:
✅AO crosses above the bullish trigger level → Buy Signal.
✅Trailing stop placed at Low - (ATR × Multiplier).
✅Exit if price crosses below the stop.
Bearish Setup:
✅AO crosses below the bearish trigger level → Sell Signal.
✅Trailing stop placed at High + (ATR × Multiplier).
✅Exit if price crosses above the stop.
👽 Why It’s Useful for Traders
Wavelet-Enhanced Filtering – Retains essential trend details while eliminating excessive noise.
Multi-Scale Momentum Analysis – Separates different trend frequencies for enhanced clarity.
Real-Time Divergence Alerts – Identifies early reversal signals for better entries and exits.
ATR-Based Risk Management – Ensures stops dynamically adapt to market conditions.
Works Across Markets & Timeframes – Suitable for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
AO Short Period – Defines the short-term moving average for AO calculation.
AO Long Period – Defines the long-term moving average for AO smoothing.
Wavelet Smoothing – Adjusts multi-scale decomposition for different market conditions.
Divergence Detection – Enables or disables real-time divergence analysis. Normalization Period – Sets the lookback period for standard deviation-based AO normalization.
Cross Signals Sensitivity – Controls crossover signal strength for buy/sell signals.
ATR Trailing Stop Multiplier – Adjusts the sensitivity of the trailing stop.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Solar Movement Gradient-AYNETSummary of the Solar Movement Gradient Indicator
This Pine Script creates a dynamic, colorful indicator inspired by solar movements. It uses a sinusoidal wave to plot oscillations over time with a rainbow-like gradient that changes based on the wave's position.
Key Features
Sinusoidal Wave:
A wave oscillates smoothly based on the bar index (time) or optionally influenced by price movements.
The wave’s amplitude, baseline, and wavelength can be customized.
Dynamic Colors:
A spectrum of seven colors (red, orange, yellow, green, blue, purple, pink) is used.
The color changes smoothly along with the wave, emulating a solar gradient.
Background Gradient:
An optional gradient fills the background with colors matching the wave, adding a visually pleasing effect.
Customizable Inputs
Gradient Speed:
Adjusts how fast the wave and colors change over time.
Amplitude & Wavelength:
Controls the height and smoothness of the wave.
Price Influence:
Allows the wave to react dynamically to price movements.
Background Gradient:
Toggles a colorful gradient in the chart’s background.
Use Case
This indicator is designed for visual appeal rather than trading signals. It enhances the chart with a dynamic and colorful representation, making it perfect for aesthetic customization.
Let me know if you need further refinements! 🌈✨
Infinity Market Grid -AynetConcept
Imagine viewing the market as a dynamic grid where price, time, and momentum intersect to reveal infinite possibilities. This indicator leverages:
Grid-Based Market Flow: Visualizes price action as a grid with zones for:
Accumulation
Distribution
Breakout Expansion
Volatility Compression
Predictive Dynamic Layers:
Forecasts future price zones using historical volatility and momentum.
Tracks event probabilities like breakout, fakeout, and trend reversals.
Data Science Visuals:
Uses heatmap-style layers, moving waveforms, and price trajectory paths.
Interactive Alerts:
Real-time alerts for high-probability market events.
Marks critical zones for "buy," "sell," or "wait."
Key Features
Market Layers Grid:
Creates dynamic "boxes" around price using fractals and ATR-based volatility.
These boxes show potential future price zones and probabilities.
Volatility and Momentum Waves:
Overlay volatility oscillators and momentum bands for directional context.
Dynamic Heatmap Zones:
Colors the chart dynamically based on breakout probabilities and risk.
Price Path Prediction:
Tracks price trajectory as a moving "wave" across the grid.
How It Works
Grid Box Structure:
Upper and lower price levels are based on ATR (volatility) and plotted dynamically.
Dashed green/red lines show the grid for potential price expansion zones.
Heatmap Zones:
Colors the background based on probabilities:
Green: High breakout probability.
Blue: High consolidation probability.
Price Path Prediction:
Forecasts future price movements using momentum.
Plots these as a dynamic "wave" on the chart.
Momentum and Volatility Waves:
Shows the relationship between momentum and volatility as oscillating waves.
Helps identify when momentum exceeds volatility (potential breakouts).
Buy/Sell Signals:
Triggers when price approaches grid edges with strong momentum.
Provides alerts and visual markers.
Why Is It Revolutionary?
Grid and Wave Synergy:
Combines structural price zones (grid boxes) with real-time momentum and volatility waves.
Predictive Analytics:
Uses momentum-based forecasting to visualize what’s next, not just what’s happening.
Dynamic Heatmap:
Creates a living map of breakout/consolidation zones in real-time.
Scalable for Any Market:
Works seamlessly with forex, crypto, and stocks by adjusting the ATR multiplier and box length.
This indicator is not just a tool but a framework for understanding market dynamics at a deeper level. Let me know if you'd like to take it even further — for example, adding machine learning-inspired probability models or multi-timeframe analysis! 🚀
