OrderFlow Sentiment SwiftEdgeOrderFlow Sentiment SwiftEdge
Overview
OrderFlow Sentiment SwiftEdge is a visual indicator designed to help traders analyze market dynamics through a simulated orderbook and market sentiment display. It breaks down the current candlestick into 10 price bins, estimating buy and sell volumes, and presents this data in an orderbook table alongside a sentiment row showing the buy vs. sell bias. This tool provides a quick and intuitive way to assess orderflow activity and market sentiment directly on your chart.
How It Works
The indicator consists of two main components: an Orderbook Table and a Market Sentiment Row.
Orderbook Table:
Simulates buy and sell volumes for the current candlestick by distributing total volume into 10 price bins based on price movement and proximity to open/close levels.
Displays the price bins in a table with columns for Price, Buy Volume, and Sell Volume, sorted from highest to lowest price.
Highlights the current price level in orange for easy identification, while buy and sell dominance is indicated with green (buy) or red (sell) backgrounds.
Market Sentiment Row:
Calculates the overall buy and sell sentiment (as a percentage) for the current candlestick based on the simulated orderflow data.
Displays the sentiment above the orderbook table, with the background colored green if buyers dominate or red if sellers dominate.
Features
Customizable Colors: Choose colors for buy (default: green), sell (default: red), and current price (default: orange) levels.
Lot Scaling Factor: Adjust the volume scaling factor (default: 0.1 lots per volume unit) to simulate realistic lot sizes.
Table Position: Select the table position on the chart (Top, Middle, or Bottom; default: Middle).
Default Properties
Positive Color: Green
Negative Color: Red
Current Price Color: Orange
Lot Scaling Factor: 0.1
Table Position: Middle
Usage
This indicator is ideal for traders who want to visualize orderflow dynamics and market sentiment in real-time. The orderbook table provides a snapshot of buy and sell activity at different price levels within the current candlestick, helping you identify areas of high buying or selling pressure. The sentiment row offers a quick overview of market bias, allowing you to gauge whether buyers or sellers are currently dominating. Use this information to complement your trading decisions, such as identifying potential breakout levels or confirming trend direction.
Limitations
This indicator simulates orderflow data based on candlestick price movement and volume, as TradingView does not provide tick-by-tick data. The volume distribution is an approximation and should be used as a visual aid rather than a definitive measure of market activity.
The indicator operates on the chart's current timeframe and does not incorporate higher timeframe data.
The simulated volumes are scaled using a user-defined lot scaling factor, which may not reflect actual market lot sizes.
Disclaimer
This indicator is for informational purposes only and does not guarantee trading results. Always conduct your own analysis and manage risk appropriately. The simulated orderflow data is an estimation and may not reflect real market conditions.
חפש סקריפטים עבור "Table"
Swing Trade TarayıcıSwing Trade Scanner (v6) User Guide
1. Purpose:
This TradingView indicator (written in Pine Script v6) is designed to help identify swing trading opportunities. It calculates an overall trend strength and direction score by combining multiple technical analyses for up to 20 financial assets (stocks, cryptocurrencies, forex, etc.) that you specify. It presents the results in a customizable table, allowing you to quickly scan the market.
2. Analyses Used and Their Roles:
By default, the indicator uses the following 4 main technical analyses:
EMA Crossover (Default: 9/21): Used to capture short-term trend direction and potential momentum shifts. When the fast EMA (9) crosses above the slow EMA (21), it's considered a bullish signal; when it crosses below, it's a bearish signal. It's often one of the main entry/exit triggers.
RSI (Relative Strength Index - Default: 14): Measures the speed of price movements to identify overbought (OB) and oversold (OS) conditions. Reversals from the OB zone can signal potential downturns, while reversals from the OS zone can signal potential upturns. It also provides insight into the strength of the momentum.
MACD (Moving Average Convergence Divergence - Default: 12, 26, 9): A trend-following momentum indicator. The relationship between the MACD line and the signal line (crossovers) and the state of the histogram (position relative to the zero line) are used to confirm momentum shifts and trend strength.
ADX/DI (Average Directional Index - Default: 14, 14): Measures the strength (ADX) and direction (+DI/-DI lines) of a trend. Its main role is to filter signals from other indicators. A trend is considered to exist if the ADX is above a certain threshold (e.g., 25). +DI above -DI indicates an uptrend, and the reverse indicates a downtrend.
3. Scoring System:
The indicator calculates an individual score for each analysis and then combines these scores using weights you define to create a final Trend Score:
Individual Scores: Each analysis (EMA, RSI, MACD, ADX/DI) generates a decimal score between -1.0 (Strong Negative/Bearish) and +1.0 (Strong Positive/Bullish) based on its own rules. For example, RSI might score +1.0 if oversold, -1.0 if overbought, and 0.0 if neutral. MACD and ADX/DI scores can also take intermediate values reflecting both direction and strength (e.g., ±1.0, ±0.6, ±0.3, 0.0).
Weighting: In the settings, you can assign a weight (between 0.0 and 1.0) to each analysis, determining how much it influences the final score. For instance, you might give EMA crossover a higher weight and use RSI with a lower weight.
Total Score: The individual scores of the active analyses are multiplied by their assigned weights and summed up. This total is then divided by the sum of the weights of the active analyses to obtain a final, normalized Trend Score between -1.0 and +1.0. This score represents the combined view of all analyses.
4. Trend Determination:
The calculated final Trend Score is classified into an overall TREND status based on threshold values you set in the settings:
S.UP (Strong Up): Score > Strong Up Threshold (Default: 0.70)
UP: Up Threshold < Score <= Strong Up Threshold (Default: 0.35 < Score <= 0.70)
NEUTRAL: Down Threshold <= Score <= Up Threshold (Default: -0.35 <= Score <= 0.35)
DOWN: Strong Down Threshold <= Score < Down Threshold (Default: -0.70 <= Score < -0.35)
S.DOWN (Strong Down): Score < Strong Down Threshold (Default: -0.70)
This classification allows you to see at a glance which assets have strong trend potential.
5. Table Structure and Meanings:
The indicator displays the results in a table with the following columns:
TICKER: The symbol of the analyzed asset (abbreviated).
TREND: The overall trend status determined by the total score (S.UP, UP, NEUTRAL, DOWN, S.DOWN). The background color of this column also reflects this overall trend (Dark Green, Green, Gray, Red, Dark Red).
SCORE: The calculated weighted total score (between -1.00 and +1.00, with two decimal places). The background color of this column also reflects the overall trend.
EMA: Shows the result of only the EMA 9/21 analysis.
▲: EMA(9) > EMA(21) (Green Background)
▼: EMA(9) < EMA(21) (Red Background)
N: Neutral (Gray Background)
-: Analysis disabled or no data (Pale Gray Background)
RSI: Shows the result of only the RSI analysis.
OS: Oversold (RSI < 30) (Green Background)
M+: Mid Positive (30 <= RSI < 45) (Light Green/Lime Background)
N: Neutral (45 <= RSI <= 55) (Gray Background)
M-: Mid Negative (55 < RSI <= 70) (Orange Background)
OB: Overbought (RSI > 70) (Red Background)
-: Analysis disabled or no data (Pale Gray Background)
MACD: Shows the result of only the MACD analysis.
S+: Strong Positive (MACD > Signal AND Histogram > 0) (Green Background)
M+: Mid Positive (MACD > Signal BUT Histogram < 0) (Light Green/Lime Background)
N: Neutral (Other cases) (Gray Background)
M-: Mid Negative (MACD < Signal BUT Histogram > 0) (Orange Background)
S-: Strong Negative (MACD < Signal AND Histogram < 0) (Red Background)
-: Analysis disabled or no data (Pale Gray Background)
ADX/DI: Shows the result of only the ADX/DI analysis.
S+: Strong Uptrend (ADX > 40 AND +DI > -DI) (Green Background)
M+: Mid Uptrend (25 < ADX <= 40 AND +DI > -DI) (Light Green/Lime Background)
W: Weak Trend (ADX <= 25) (Gray Background)
M-: Mid Downtrend (25 < ADX <= 40 AND -DI > +DI) (Orange Background)
S-: Strong Downtrend (ADX > 40 AND -DI > +DI) (Red Background)
-: Analysis disabled or no data (Pale Gray Background)
6. Settings (Inputs):
You can customize the indicator's behavior using the following settings:
General Settings:
Analysis Timeframe: Select the timeframe for the scans (Leave blank to use the chart timeframe).
Auto Adjust Parameters and Weights: If checked, predefined parameters and weights based on the selected timeframe (1h, 4h, 1D, 1W) are used. If unchecked, or if an unsupported timeframe is selected, the manual settings below apply.
Strong/Normal Up/Down Thresholds: Adjust the score thresholds used to determine the TREND column.
Analysis Settings (Separate Group for Each Analysis):
Enable ... Analysis: Check to include the respective analysis in the score.
... Weight (Manual): If auto-adjust is off, set the weight of this analysis in the total score.
... Period/Level (Manual): If auto-adjust is off, adjust the parameters (period, level, etc.) of the respective indicator.
Symbols (1-10):
Checkbox: Check to include the respective symbol in the scan.
Text Box: Enter the symbol of the asset you want to analyze (e.g., "NASDAQ:AAPL", "BINANCE:BTCUSDT").
Table Settings:
Table Position: Choose where the table appears on the chart.
Cell Width: Adjust the width of the table cells.
Text Size: Select the general size of the text in the table (individual analysis columns are usually shown one size smaller).
7. How to Use:
Add the indicator to your TradingView chart.
Enter the indicator settings.
In the Symbols section, enter the symbols of the assets you want to analyze and check the boxes next to them.
In the General Settings section, select your desired Analysis Timeframe.
Decide whether the Auto Adjust Parameters and Weights option should be checked. If not, adjust the manual parameters and weights for each indicator in the Analysis Settings section according to your strategy.
Examine the table:
The TREND and SCORE columns give you a general overview. Focus on strong signals (S.UP, S.DOWN) or states that have just crossed thresholds (UP, DOWN).
The EMA, RSI, MACD, ADX/DI columns allow you to see in detail which analyses influenced the overall score. You can track confirming or conflicting signals here.
Combine these scan results with other rules of your swing trading strategy (support/resistance, patterns, risk management, etc.) to make trading decisions.
8. Important Notes:
This indicator does not constitute financial advice. It is merely a tool that combines technical analysis tools to help you scan the market.
The default parameters and weights in the indicator settings are for general use. For best results, it is strongly recommended that you optimize these settings by backtesting them on historical data according to your own strategy, the assets you trade, and market conditions.
No technical indicator or system generates 100% accurate signals. Always apply risk management principles and do not trade with money you cannot afford to lose.
I hope this guide helps you use the indicator effectively!
3CRGANG - TRUE RANGEThis indicator helps traders identify key support and resistance levels using dynamic True Range calculations, while also providing a multi-timeframe trend overview. It plots True Range levels as horizontal lines, marks breakouts with arrows, and displays trend directions across various timeframes in a table, making it easier to align trades with broader market trends.
What It Does
The 3CRGANG - TRUE RANGE indicator calculates dynamic support and resistance levels based on the True Range concept, updating them as price breaks out of the range. It also analyzes trend direction across multiple timeframes (M1 to M) and presents the results in a table, using visual cues to indicate bullish, bearish, or neutral conditions.
Why It’s Useful
This script combines True Range analysis with multi-timeframe trend identification to provide a comprehensive tool for traders. The dynamic True Range levels help identify potential reversal or continuation zones, while the trend table allows traders to confirm the broader market direction before entering trades. This dual approach reduces the need for multiple indicators, streamlining analysis across different timeframes and market conditions.
How It Works
The script operates in the following steps:
True Range Calculation: The indicator calculates True Range levels (support and resistance) using price data (close, high, low) from a user-selected timeframe. It updates these levels when price breaks above the upper range (bullish breakout) or below the lower range (bearish breakout).
Line Plotting: Two styles are available:
"3CR": Plots one solid line after a breakout (green for bullish, red for bearish) and removes the opposing line.
"RANGE": Plots both upper and lower range lines as dotted lines (green for support, red for resistance) until a breakout occurs, then solidifies the breakout line.
Multi-Timeframe Trend Analysis: The script analyzes trend direction on multiple timeframes (M1, M5, M15, M30, H1, H4, D, W, M) by comparing the current close to the True Range levels on each timeframe. A trend is:
Trend Table: A table displays the trend direction for each timeframe, with color-coded backgrounds (green for bullish, red for bearish) and triangles to indicate the trend state.
Breakout Arrows: When price breaks above the upper range, a green ▲ arrow appears below the bar (bullish). When price breaks below the lower range, a red ▼ arrow appears above the bar (bearish).
Bullish (▲): Price is above the upper range.
Bearish (▼): Price is below the lower range.
Neutral (△/▽): Price is within the range, with the last trend indicated by an empty triangle (△ for last bullish, ▽ for last bearish).
Alerts: Breakout alerts can be set for each timeframe, with options to filter by trading sessions (e.g., New York, London) or enable all-day alerts.
Underlying Concepts
The script uses the True Range concept to define dynamic support and resistance levels, which adjust based on price action to reflect the most relevant price zones. The multi-timeframe trend analysis leverages the same True Range logic to determine trend direction, providing a consistent framework across all timeframes. The combination of breakout signals and trend confirmation helps traders align their strategies with both short-term price movements and longer-term market trends.
Use Case
Breakout Trading: Use the True Range lines and arrows to identify breakouts. For example, a green ▲ arrow below a bar with price breaking above the upper range suggests a potential long entry.
Trend Confirmation: Check the trend table to ensure the breakout aligns with the broader trend. For instance, a bullish breakout on the 1H chart is more reliable if the D and W timeframes also show bullish trends (▲).
Range Trading: When price is within the True Range (dotted lines in "RANGE" style), consider range-bound strategies, buying near support and selling near resistance, while monitoring the table for potential trend shifts.
Settings
Input Timeframe: Select the timeframe for True Range calculations (default: chart timeframe).
True Range Style: Choose between "3CR" (single line after breakout) or "RANGE" (both lines until breakout) (default: 3CR).
Change Symbol: Compare a different ticker if needed (default: chart symbol).
Color Theme: Select "LIGHT THEME" or "DARK THEME" for colors, or enable custom colors (default: LIGHT THEME).
Table Position: Set the trend table’s position (center, right, left) (default: right).
Multi Res Alerts Setup: Enable/disable breakout alerts for each timeframe (default: enabled for most timeframes).
Sessions Alerts: Filter alerts by trading sessions (e.g., New York, London) or enable all-day alerts (default: most sessions enabled).
Chart Notes
The chart displays the script’s output on XAUUSD (1H timeframe), showing:
Candlesticks representing price action.
True Range lines (green for support, red for resistance) in "3CR" style, with solid lines after breakouts and dotted lines during range-bound periods.
Arrows (green ▲ below bars for bullish breakouts, red ▼ above bars for bearish breakouts) indicating range breakouts.
A trend table in the top-right corner labeled "TREND EA," showing trend directions across timeframes (M1 to M) with triangles (▲/▼ for active trends, △/▽ for last trend) and color-coded backgrounds (green for bullish, red for bearish).
Notes
The script uses the chart’s ticker by default but allows comparison with another symbol if enabled.
Trend data for higher timeframes (e.g., M) may not display if the chart’s history is insufficient.
Alerts are triggered only during selected trading sessions unless "ALL DAY ALERTS" is enabled.
Disclaimer
This indicator is a tool for analyzing market trends and does not guarantee trading success. Trading involves risk, and past performance is not indicative of future results. Always use proper risk management.
Statistical OHLC Projections [neo|]█ OVERVIEW
Statistical OHLC Projections is an indicator designed to offer users a customizable deep-dive on measuring historical price levels for any timeframe. The indicator separates price into two distinct levels, "Manipulation" and "Distribution", where the idea is that for higher timeframe candles, e.g. an up-close candle, the distance from the open to the bottom of the wick would constitute the Manipulation, and the rest would be considered the Distribution. By measuring out these levels, we can gain insight on how far the market may move from higher timeframe opens to their manipulations and distributions, and apply this knowledge to our analysis.
IMPORTANT: Since levels are based on the lookback available on your chart, if the levels aren't being displayed this likely means you don't have enough lookback for your selected timeframe. To check this, enable the stat table to see how many values are available for your timeframe, and either reduce the lookback or increase your chart timeframe.
█ CONCEPTS
The core concept revolves around understanding market behavior through the lens of historical candle structure. The indicator dissects OHLC data to provide statistical boundaries of expected price movement.
- Manipulation Levels: These represent the areas typically seen as liquidity grabs or false moves where price extends in one direction before reversing.
- Distribution Levels: These highlight where the bulk of directional movement tends to occur, often following the manipulation move.
The tool aggregates this data across your selected timeframe to inform you of potential levels associated with it.
█ FEATURES
Multiple Display Types: Display statistical data through two sleek styles, areas or lines. Where areas represent the area between two customizable lookback values, and lines represent one average value.
Adjustable Timeframe Selection: Whether you want to see data based on the 1D chart, or the 1W chart, anything is possible. Simply change the timeframe on the dropdown menu and if there is sufficient lookback the indicator will adjust to your requested timeframe.
Customizable Historical Lookback: By default, the indicator will measure the average 60 values of your requested timeframe, however this may be adjusted to be higher or lower based on your preference. If you want to measure recent moves, 10-20 lookback may be better for you, or if you want more data for less volatile instruments, a value of 100 may be better.
Historical Display: Prevent historical levels from being removed by unchecking the "Remove Previous Drawings" option, this will allow you to examine how the levels previously interacted with price.
NY Midnight Anchoring: By checking the "Use NY Midnight" option, you may see the projection anchored to the New York midnight open time, which is often a significant level on indices.
Alerts: You may enable alerts for any of the indicator's provided levels to stay informed, even when off the charts.
█ How to use
To use the indicator, simply apply it to your chart and modify any of your desired inputs.
By default, the indicator will provide levels for the "1D" timeframe, with a desired lookback of 60, on most instruments and plans this can be gotten when you are on the 30 minute timeframe or above.
When price reaches or extends beyond a manipulation level, observe how it reacts and whether it rejects from that level, if it does this may be an indication that the candle for the timeframe you selected may be reversing.
█ SETTINGS AND OPTIONS
Customize the indicator’s behavior, timeframe sources, and visual appearance to fit your analysis style. Each setting has been designed with flexibility in mind, whether you're working on lower or higher timeframes.
Display Mode: Switch between different display styles for levels: - Default: Shows all statistical levels as individual lines.
- Areas: Plots filled zones between two customizable lookbacks to represent the range between them.
This is ideal for visually mapping high-probability zones of price activity.
Timeframe Settings:
- Show First/Second Timeframe: Choose to show one or both timeframe projections simultaneously.
- First Timeframe / Second Timeframe: Define the higher timeframe candle you want to base calculations on (e.g., 1D, 1W).
- Use NY Midnight: When enabled and using the daily timeframe, the levels will be anchored to the New York Midnight Open (00:00 EST), a key institutional timing reference, especially useful for indices and forex.
Calculation Settings:
- Main Lookback Period: The number of historical candles used in the statistical calculations. A lower number focuses on recent price action, while a higher number smooths results across broader history.
- First Lookback / Second Lookback: Used when “Areas” mode is selected to define the range of the shaded zone. For example, an area from 20 to 60 candles creates a band between short- and long-term price behavior averages.
Visual Settings:
- Line Style: Set your preferred visual style: Solid, Dashed, or Dotted.
- Remove Previous Drawings: When enabled, only the most recent projection is shown on the chart. Disable to retain previous levels and visually backtest their reactions over time.
Color Settings:
Customize each level independently to match your chart theme:
- Manipulation High/Low
- Distribution High/Low
- Open Level
- Label Text Color
Premium/Discount Zones:
- Enable Premium/Discount Zones: Overlay price zones above and below equilibrium to visualize potential overbought (premium) and oversold (discount) areas.
- Premium/Discount Colors: Fully customizable zone colors for clarity and emphasis.
Table Settings:
- Show Statistics Table: Adds an on-chart table summarizing key levels from your active timeframe(s).
- Table Cell Color: Set the background color of the table cells for visibility.
- Table Position: Choose from preset chart locations to position the table where it works best for your layout.
Alerts:
Stay on top of price interactions with key levels even when you're away from the charts.
- Manipulation Hits (High)
- Manipulation Hits (Low)
- Distribution Hits (High)
- Distribution Hits (Low)
RSI Multi-Timeframe K2Indicator Name: RSI Multi-Timeframe Cross Indicator
Overview:
"RSI Multi-Timeframe Cross Indicator" is a versatile Pine Script (v5) tool developed for TradingView, designed for traders using multi-time frame analysis. It monitors the Relative Strength Index (RSI) cross its Simple Moving Average (SMA) on multiple time frames (1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 4-hour and daily) to identify bullish and bearish conditions. The indicator overlays the signals on the chart and provides a customizable table to visualize the time frame conditions.
Key Features:
RSI Crossover Detection:
Monitors when the RSI crosses above (bullish trend) or below (bearish trend) its SMA on each selected time frame.
Uses constant state tracking to maintain a bullish/bearish state until an opposite crossover occurs.
Configurable Parameters:
RSI Length: Configurable period for calculating RSI (default: 14).
MA Length: Configurable period for SMA applied to RSI (default: 20).
Time Frame Controls:
Logical Switches: Independent switches ( use1m , use5m , etc.) to include/exclude each time frame in the signal logic.
Visualization Switches: Separate switches ( show1m , show5m , etc.) to show/hide each time frame in the table without affecting the logic.
Visuals:
Triangles: Green ascending triangles below the bars indicate bullish signals, red descending triangles above the bars indicate bearish signals.
Labels : Long (green) or Short (red) labels on the last confirmed bar when all enabled timeframes match.
Dynamic Table : A centered table at the bottom of the chart displaying the status of each timeframe with colored boxes (green for bullish, red for bearish). The table size is adjustable based on the visible timeframes.
Alerts :
Trigger alerts when all enabled timeframes are bullish ("All RSI timeframes are bullish (green)!") or bearish ("All RSI timeframes are bearish (red)!").
Input Parameters:
RSI Settings :
RSI Length : Integer (min: 1, default: 14) — Period for RSI calculation.
MA Length : Integer (min: 1, default: 20) — Period for SMA RSI.
Timeframe Logic Settings:
Use 1M in Logic, Use 5M in Logic, etc.: Boolean (default: true) - Enable/Disable each timeframe in signal calculation.
Timeframe Visualization Settings:
Show 1M in Table, Show 5M in Table, etc.: Boolean (default: true) - Show/Hide each timeframe in the table display.
Logic:
Bullish Condition: RSI crosses above SMA on a given timeframe, setting a bullish condition until a bearish crossover occurs.
Bearish Condition: RSI crosses below SMA on a given timeframe, setting a bearish condition until a bullish crossover occurs.
Combination signal: A Long or Short signal is generated only when all enabled timeframes (use the * switches) line up in the same direction (bullish or bearish).
Visualization: The table displays the status of each timeframe, but only shows the fields for the timeframes with the Show* switch enabled.
Visual output:
Chart signals:
A green ascending triangle and a Long label when all enabled timeframes are bullish.
A red downward-pointing triangle and a Short label when all enabled timeframes are bearish.
Table:
Located in the lower center of the chart.
The bars dynamically adjust to the number of visible timeframes (1 to 7).
Each cell displays the time frame name (e.g. "1M", "5M") with a background color indicating its status (green for bullish, red for bearish).
Use:
Trend Confirmation: Used to confirm trends across multiple time frames based on RSI behavior.
Configure: Customize RSI and MA lengths to suit your trading strategy, and turn time frames on/off for both logic and visualization to focus on the relevant periods.
Alerts: Set up alerts to be notified when all selected time frames match, useful for automated trading systems or manual monitoring.
Notes:
The indicator does not display RSI or SMA lines directly on the chart, focusing instead on crossover events and signals.
If all visualization toggles are disabled, the table disappears, but signals and alerts continue to function based on the logic toggles.
Compatible with any chart timeframe, data from later timeframes is retrieved using request.security() .
Enhanced Volume Spike ScannerEnhanced Volume Spike Scanner – User Instructions
Overview
This TradingView indicator monitors the trading volume of a list of selected tickers and detects “volume spikes” by comparing the current volume against a simple moving average (SMA) of past volumes. It calculates a “spike strength” (the ratio of current volume to average volume) and assigns a color based on its intensity. The indicator then displays a sorted table on your chart with each ticker’s symbol, spike value, and current volume. Alerts can be triggered if a ticker’s spike strength reaches specific color-coded levels.
How It Works
1. Volume Calculation:
• For each ticker, the indicator fetches the current volume and calculates its simple moving average (SMA) over a user-defined lookback period.
• The ratio of the current volume to the SMA (spike strength) is computed.
2. Color Coding:
• A color is assigned based on the spike strength:
• >5.0: Orange
• >4.0: Yellow
• >3.0: Green
• >2.5: Lime
• >2.0: Blue
• Otherwise: Pink
• These colors serve as visual cues for the intensity of the volume spike.
3. Table Display:
• All tickers are listed in a table on the chart.
• The table is sorted in descending order of spike strength so that the tickers with the strongest volume spikes appear at the top.
• Each row displays the ticker symbol, its spike value (formatted to two decimal places), and the current volume.
4. Alerts:
• You can enable alerts for each color level (e.g., Orange for the most intense spikes).
• Alerts trigger once per bar for any ticker that meets the criteria, allowing you to react promptly to significant volume changes.
⸻
User Inputs and Customization
• Volume Spike Multiplier:
Set the threshold for what constitutes a spike (e.g., a value of 2.0 means the current volume must be at least twice the SMA to be considered a spike).
• Volume Lookback Period:
Define how many bars to use when calculating the SMA of the volume.
• Minimum Volume Filter:
Optionally enable a filter to ignore low-volume ticks. Specify the minimum volume that must be reached for a ticker to be considered.
• Table Settings:
Adjust parameters like column width, table number, and vertical positioning to suit your chart layout.
• Alert Settings:
Toggle alerts for each spike intensity level. Only tickers that meet the specified color threshold will trigger an alert.
• Color Inputs:
Customize the colors for each spike intensity level using the color input settings.
• Ticker Inputs:
Update the ticker symbols manually in the input section (Ticker 1, Ticker 2, etc.) to monitor the assets you are interested in.
⸻
Using the Indicator
1. Add the Indicator:
Once added to your chart, the table will appear at the top-right corner. You can reposition it using the “Table Number” or “Move Down” inputs.
2. Monitor the Table:
Watch the table update each bar:
• The Symbol column lists your selected tickers.
• The Spike column shows the spike strength along with its corresponding color (indicating intensity).
• The Volume column shows the current volume for each ticker.
3. React to Alerts:
If you have enabled alerts for a specific intensity, you’ll receive a notification (once per bar) when a ticker reaches that volume spike level.
4. Adjust Settings as Needed:
Experiment with the multipliers, lookback periods, and minimum volume thresholds to fine-tune the indicator for your trading style.
⸻
Uptrick Signal Density Cloud🟪 Introduction
The Uptrick Signal Density Cloud is designed to track market direction and highlight potential reversals or shifts in momentum. It plots two smoothed lines on the chart and fills the space between them (often called a “cloud”). The bars on the chart change color depending on bullish or bearish conditions, and small triangles appear when certain reversal criteria are met. A metrics table displays real-time values for easy reference.
🟩 Why These Features Have Been Linked Together
1) Dual-Line Structure
Two separate lines represent shorter- and longer-term market tendencies. Linking them in one tool allows traders to view both near-term changes and the broader directional bias in a single glance.
2) Smoothed Averages
The script offers multiple smoothing methods—exponential, simple, hull, and an optimized approach—to reduce noise. Using more than one type of moving average can help balance responsiveness with stability.
3) Density Cloud Concept
Shading the region between the two lines highlights the gap or “thickness.” A wider gap typically signals stronger momentum, while a narrower gap could indicate a weakening trend or potential market indecision. When the cloud is too wide and crosses a certain threshold defined by the user, it indicates a possible reversal. When the cloud is too narrow it may indicate a potential breakout.
🟪 Why Use This Indicator
• Trend Visibility: The color-coded lines and bars make it easier to distinguish bullish from bearish conditions.
• Momentum Tracking: Thicker cloud regions suggest stronger separation between the faster and slower lines, potentially indicating robust momentum.
• Possible Reversal Alerts: Small triangles appear within thick zones when the indicator detects a crossover, drawing attention to key moments of potential trend change.
• Quick Reference Table: A metrics table shows line values, bullish or bearish status, and cloud thickness without needing to hover over chart elements.
🟩 Inputs
1) First Smoothing Length (length1)
Default: 14
Defines the lookback period for the faster line. Lower values make the line respond more quickly to price changes.
2) Second Smoothing Length (length2)
Default: 28
Defines the lookback period for the slower line or one of the moving averages in optimized mode. It generally responds more slowly than the faster line.
3) Extra Smoothing Length (extraLength)
Default: 50
A medium-term period commonly seen in technical analysis. In optimized mode, it helps add broader perspective to the combined lines.
4) Source (source)
Default: close
Specifies the price data (for example, open, high, low, or a custom source) used in the calculations.
5) Cloud Type (cloudType)
Options: Optimized, EMA, SMA, HMA
Determines the smoothing method used for the lines. “Optimized” blends multiple exponential averages at different lengths.
6) Cloud Thickness Threshold (thicknessThreshold)
Default: 0.5
Sets the minimum separation between the two lines to qualify as a “thick” zone, indicating potentially stronger momentum.
🟪 Core Components
1) Faster and Slower Lines
Each line is smoothed according to user preferences or the optimized technique. The faster line typically reacts more quickly, while the slower line provides a broader overview.
2) Filled Density Cloud
The space between the two lines is filled to visualize in which direction the market is trending.
3) Color-Coded Bars
Price bars adopt bullish or bearish colors based on which line is on top, providing an immediate sense of trend direction.
4) Reversal Triangles
When the cloud is thick (exceeding the threshold) and the lines cross in the opposite direction, small triangles appear, signaling a possible market shift.
5) Metrics Table
A compact table shows the current values of both lines, their bullish/bearish statuses, the cloud thickness, and whether the cloud is in a “reversal zone.”
🟩 Calculation Process
1) Raw Averages
Depending on the mode, standard exponential, simple, hull, or “optimized” exponential blends are calculated.
2) Optimized Averages (if selected)
The faster line is the average of three exponential moving averages using length1, length2, and extraLength.
The slower line similarly uses those same lengths multiplied by 1.5, then averages them together for broader smoothing.
3) Difference and Threshold
The absolute gap between the two lines is measured. When it exceeds thicknessThreshold, the cloud is considered thick.
4) Bullish or Bearish Determination
If sma1 (the faster line) is above sma2 (the slower line), conditions are deemed bullish; otherwise, they are bearish. This distinction is reflected in both bar colors and cloud shading.
5) Reversal Markers
In thick zones, a crossover triggers a triangle at the point of potential reversal, alerting traders to a possible trend change.
🟪 Smoothing Methods
1) Exponential (EMA)
Prioritizes recent data for quicker responsiveness.
2) Simple (SMA)
Takes a straightforward average of the chosen period, smoothing price action but often lagging more in volatile markets.
3) Hull (HMA)
Employs a specialized formula to reduce lag while maintaining smoothness.
4) Optimized (Blended Exponential)
Combines multiple EMA calculations to strike a balance between responsiveness and noise reduction.
🟩 Cloud Logic and Reversal Zones
Cloud thickness above the defined threshold typically signals exceeding momentum and can lead to a quick reversal. During these thick periods, if the width exceeds the defined threshold, small triangles mark potential reversal points. In order for the reversal shape to show, the color of the cloud has to be the opposite. So, for example, if the cloud is bearish, and exceeds momentum, defined by the user, a bullish signal appears. The opposite conditions for a bullish signal. This approach can help traders focus on notable changes rather than minor oscillations.
🟪 Bar Coloring and Layered Lines
Bars take on bullish or bearish tints, matching the faster line’s position relative to the slower line. The lines themselves are plotted multiple times with varying opacities, creating a layered, glowing look that enhances visibility without affecting calculations.
🟩 The Metrics Table
Located in the top-right corner of the chart, this table displays:
• SMA1 and SMA2 current values.
• Bullish or bearish alignment for each line.
• Cloud thickness.
• Reversal zone status (in or out of zone).
This numeric readout allows for a quick data check without hovering over the chart.
🟪 Why These Specific Moving Average Lengths Are Used
Default lengths of 14, 28, and 50 are common in technical analysis. Fourteen captures near-term price movement without overreacting. Twenty-eight, roughly double 14, provides a moderate smoothing level. Fifty is widely regarded as a medium-term benchmark. Multiplying each length by 1.5 for the slower line enhances separation when combined with the faster line.
🟩 Originality and Usefulness
• Multi-Layered Smoothing. The user can select from several moving average modes, including a unique “optimized” blend, possibly reducing random fluctuations in the market data.
• Combined Visual and Numeric Clarity. Bars, clouds, and a real-time table merge into a single interface, enabling efficient trend analysis.
• Focus on Significant Shifts. Thick cloud zones and triangles draw attention to potentially stronger momentum changes and plausible reversals.
• Flexible Across Markets. The adjustable lengths and threshold can be tuned to different asset classes (stocks, forex, commodities, crypto) and timeframes.
By integrating multiple technical concepts—cloud-based trend detection, color coding, reversal markers, and an immediate reference table—the Uptrick Signal Density Cloud aims to streamline chart reading and decision-making.
🟪 Additional Considerations
• Timeframes. Intraday, daily, and weekly charts each yield different signals. Adjust the smoothing lengths and threshold to suit specific trading horizons.
• Market Types. Though applicable across asset classes, parameters might need tweaking to address the volatility of commodities, forex pairs, or cryptocurrencies.
• Confirmation Tools. Pairing this indicator with volume studies or support/resistance analysis can improve the reliability of signals.
• Potential Limitations. No indicator is foolproof; sudden market shifts or choppy conditions may reduce accuracy. Cautious position sizing and risk management remain essential.
🟩 Disclaimers
The Uptrick Signal Density Cloud relies on historical price data and may lag sudden moves or provide false positives in ranging conditions. Always combine it with other analytical techniques and sound risk management. This script is offered for educational purposes only and should not be considered financial advice.
🟪 Conclusion
The Uptrick Signal Density Cloud blends trend identification, momentum assessment, and potential reversal alerts in a single, user-friendly tool. With customizable smoothing methods and a focus on cloud thickness, it visually highlights important market conditions. While it cannot guarantee predictive accuracy, it can serve as a comprehensive reference for traders seeking both a quick snapshot of the current trend and deeper insights into market dynamics.
Dynamic Intensity Transition Oscillator (DITO)The Dynamic Intensity Transition Oscillator (DITO) is a comprehensive indicator designed to identify and visualize the slope of price action normalized by volatility, enabling consistent comparisons across different assets. This indicator calculates and categorizes the intensity of price movement into six states—three positive and three negative—while providing visual cues and alerts for state transitions.
Components and Functionality
1. Slope Calculation
- The slope represents the rate of change in price action over a specified period (Slope Calculation Period).
- It is calculated as the difference between the current price and the simple moving average (SMA) of the price, divided by the length of the period.
2. Normalization Using ATR
- To standardize the slope across assets with different price scales and volatilities, the slope is divided by the Average True Range (ATR).
- The ATR ensures that the slope is comparable across assets with varying price levels and volatility.
3. Intensity Levels
- The normalized slope is categorized into six distinct intensity levels:
High Positive: Strong upward momentum.
Medium Positive: Moderate upward momentum.
Low Positive: Weak upward movement or consolidation.
Low Negative: Weak downward movement or consolidation.
Medium Negative: Moderate downward momentum.
High Negative: Strong downward momentum.
4. Visual Representation
- The oscillator is displayed as a histogram, with each intensity level represented by a unique color:
High Positive: Lime green.
Medium Positive: Aqua.
Low Positive: Blue.
Low Negative: Yellow.
Medium Negative: Purple.
High Negative: Fuchsia.
Threshold levels (Low Intensity, Medium Intensity) are plotted as horizontal dotted lines for visual reference, with separate colors for positive and negative thresholds.
5. Intensity Table
- A dynamic table is displayed on the chart to show the current intensity level.
- The table's text color matches the intensity level color for easy interpretation, and its size and position are customizable.
6. Alerts for State Transitions
- The indicator includes a robust alerting system that triggers when the intensity level transitions from one state to another (e.g., from "Medium Positive" to "High Positive").
- The alert includes both the previous and current states for clarity.
Inputs and Customization
The DITO indicator offers a variety of customizable settings:
Indicator Parameters
Slope Calculation Period: Defines the period over which the slope is calculated.
ATR Calculation Period: Defines the period for the ATR used in normalization.
Low Intensity Threshold: Threshold for categorizing weak momentum.
Medium Intensity Threshold: Threshold for categorizing moderate momentum.
Intensity Table Settings
Table Position: Allows you to position the intensity table anywhere on the chart (e.g., "Bottom Right," "Top Left").
Table Size: Enables customization of table text size (e.g., "Small," "Large").
Use Cases
Trend Identification:
- Quickly assess the strength and direction of price movement with color-coded intensity levels.
Cross-Asset Comparisons:
- Use the normalized slope to compare momentum across different assets, regardless of price scale or volatility.
Dynamic Alerts:
- Receive timely alerts when the intensity transitions, helping you act on significant momentum changes.
Consolidation Detection:
- Identify periods of low intensity, signaling potential reversals or breakout opportunities.
How to Use
- Add the indicator to your chart.
- Configure the input parameters to align with your trading strategy.
Observe:
The Oscillator: Use the color-coded histogram to monitor price action intensity.
The Intensity Table: Track the current intensity level dynamically.
Alerts: Respond to state transitions as notified by the alerts.
Final Notes
The Dynamic Intensity Transition Oscillator (DITO) combines trend strength detection, cross-asset comparability, and real-time alerts to offer traders an insightful tool for analyzing market conditions. Its user-friendly visualization and comprehensive alerting make it suitable for both novice and advanced traders.
Disclaimer: This indicator is for educational purposes and is not financial advice. Always perform your own analysis before making trading decisions.
Average Price Range Screener [KFB Quant]Average Price Range Screener
Overview:
The Average Price Range Screener is a technical analysis tool designed to provide insights into the average price volatility across multiple symbols over user-defined time periods. The indicator compares price ranges from different assets and displays them in a visual table and chart for easy reference. This can be especially helpful for traders looking to identify symbols with high or low volatility across various time frames.
Key Features:
Multiple Symbols Supported:
The script allows for analysis of up to 10 symbols, such as major cryptocurrencies and market indices. Symbols can be selected by the user and configured for tracking price volatility.
Dynamic Range Calculation:
The script calculates the average price range of each symbol over three distinct time periods (default are 30, 60, and 90 bars). The price range for each symbol is calculated as a percentage of the bar's high-to-low difference relative to its low value.
Range Visualization:
The results are visually represented using:
- A color-coded table showing the calculated average ranges of each symbol and the current chart symbol.
- A line plot that visually tracks the volatility for each symbol on the chart, with color gradients representing the range intensity from low (red/orange) to high (blue/green).
Customizable Inputs:
- Length Inputs: Users can define the time lengths (default are 30, 60, and 90 bars) for calculating average price ranges for each symbol.
- Symbol Inputs: 10 symbols can be tracked at once, with default values set to popular crypto pairs and indices.
- Color Inputs: Users can customize the color scheme for the range values displayed in the table and chart.
Real-Time Ranking:
The indicator ranks symbols by their average price range, providing a clear view of which assets are exhibiting higher volatility at any given time.
Each symbol's range value is color-coded based on its relative volatility within the selected symbols (using a gradient from low to high range).
Data Table:
The table shows the average range values for each symbol in real-time, allowing users to compare volatility across multiple assets at a glance. The table is dynamically updated as new data comes in.
Interactive Labels:
The indicator adds labels to the chart, showing the average range for each symbol. These labels adjust in real-time as the price range values change, giving users an immediate view of volatility rankings.
How to Use:
Set Time Periods: Adjust the time periods (lengths) to match your trading strategy's timeframe and volatility preference.
Symbol Selection: Add and track the price range for your preferred symbols (cryptocurrencies, stocks, indices).
Monitor Volatility: Use the visual table and plot to identify symbols with higher or lower volatility, and adjust your trading strategy accordingly.
Interpret the Table and Chart: Ranges that are color-coded from red/orange (lower volatility) to blue/green (higher volatility) allow you to quickly gauge which symbols are most volatile.
Disclaimer: This tool is provided for informational and educational purposes only and should not be considered as financial advice. Always conduct your own research and consult with a licensed financial advisor before making any investment decisions.
Burst PowerThe Burst Power indicator is to be used for Indian markets where most stocks have a maximum price band limit of 20%.
This indicator is intended to identify stocks with high potential for significant price movements. By analysing historical price action over a user-defined lookback period, it calculates a Burst Power score that reflects the stock's propensity for rapid and substantial moves. This can be helpful for stock selection in strategies involving momentum bursts, swing trading, or identifying stocks with explosive potential.
Key Components
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Significant Move Counts:
5% Moves: Counts the number of days within the lookback period where the stock had a positive close-to-close move between 5% and 10%.
10% Moves: Counts the number of days with a positive close-to-close move between 10% and 19%.
19% Moves: Counts the number of days with a positive close-to-close move of 19% or more.
Maximum Price Move (%):
Identifies the largest positive close-to-close percentage move within the lookback period, along with the date it occurred.
Burst Power Score:
A composite score calculated using the counts of significant moves: Burst Power =(Count5%/5) +(Count10%/2) + (Count19%/0.5)
The score is then rounded to the nearest whole number.
A higher Burst Power score indicates a higher frequency of significant price bursts.
Visual Indicators:
Table Display: Presents all the calculated data in a customisable table on the chart.
Markers on Chart: Plots markers on the chart where significant moves occurred, aiding visual analysis.
Using the Lookback Period
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The lookback period determines how much historical data the indicator analyses. Users can select from predefined options:
3 Months
6 Months
1 Year
3 Years
5 Years
A shorter lookback period focuses on recent price action, which may be more relevant for short-term trading strategies. A longer lookback period provides a broader historical context, useful for identifying long-term patterns and behaviors.
Interpreting the Burst Power Score
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High Burst Power Score (≥15):
Indicates the stock frequently experiences significant price moves.
Suitable for traders seeking quick momentum bursts and swing trading opportunities.
Stocks with high scores may be more volatile but offer potential for rapid gains.
Moderate Burst Power Score (10 to 14):
Suggests occasional significant price movements.
May suit traders looking for a balance between volatility and stability.
Low Burst Power Score (<10):
Reflects fewer significant price bursts.
Stocks are more likely to exhibit longer, sustainable, but slower price trends.
May be preferred by traders focusing on steady growth or longer-term investments.
Note: Trading involves uncertainties, and the Burst Power score should be considered as one of many factors in a comprehensive trading strategy. It is essential to incorporate broader market analysis and risk management practices.
Customisation Options
_________________________
The indicator offers several customisation settings to tailor the display and functionality to individual preferences:
Display Mode:
Full Mode: Shows the detailed table with all components, including significant move counts, maximum price move, and the Burst Power score.
Mini Mode: Displays only the Burst Power score and its corresponding indicator (green, orange, or red circle).
Show Latest Date Column:
Toggle the display of the "Latest Date" column in the table, which shows the most recent occurrence of each significant move category.
Theme (Dark Mode):
Switch between Dark Mode and Light Mode for better visual integration with your chart's color scheme.
Table Position and Size:
Position: Place the table at various locations on the chart (top, middle, bottom; left, center, right).
Size: Adjust the table's text size (tiny, small, normal, large, huge, auto) for optimal readability.
Header Size: Customise the font size of the table headers (Small, Medium, Large).
Color Settings:
Disable Colors in Table: Option to display the table without background colors, which can be useful for printing or if colors are distracting.
Bullish Closing Filter:
Another customisation here is to count a move only when the closing for the day is strong. For this, we have an additional filter to see if close is within the chosen % of the range of the day. Closing within the top 1/3, for instance, indicates a way more bullish day tha, say, closing within the bottom 25%.
Move Markers on chart:
The indicator also marks out days with significant moves. You can choose to hide or show the markers on the candles/bars.
Practical Applications
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Momentum Trading: High Burst Power scores can help identify stocks that are likely to experience rapid price movements, suitable for momentum traders.
Swing Trading: Traders looking for short- to medium-term opportunities may focus on stocks with moderate to high Burst Power scores.
Positional Trading: Lower Burst Power scores may indicate steadier stocks that are less prone to volatility, aligning with long-term investment strategies.
Risk Management: Understanding a stock's propensity for significant moves can aid in setting appropriate stop-loss and take-profit levels.
Disclaimer: Trading involves significant risk, and past performance is not indicative of future results. The Burst Power indicator is intended for educational purposes and should not be construed as financial advice. Always conduct thorough research and consult with a qualified financial professional before making investment decisions.
US30 Challenge 3.0Purpose of the Script
This script is designed to provide advanced technical analysis for the US30 index by combining moving averages (MA and EMA) on different timeframes and a modified Keltner channel to analyze volatility. It visualizes trends across both daily and hourly charts and displays their relationship in a custom table, helping traders to make informed decisions based on the alignment of these indicators.
Explanation of the Key Features
User Input Parameters:
The script allows users to customize several parameters, such as whether to show the baseline moving average, which type of moving average to use (e.g., EMA, SMA, HMA), and the length of the moving average. These inputs make the script flexible, allowing users to adjust it to their trading style.
Moving Averages (MA and EMA):
Two types of moving averages are calculated: the baseline (which can be any of several moving average types) and two additional moving averages (SMA and EMA) based on user-defined periods. These are plotted on the chart to provide insight into the trend and momentum of the US30 price action.
The baseline moving average is central to the strategy, and its calculation can be customized by selecting different methods (e.g., SMA, EMA, or HMA), making it adaptable to different market conditions.
Volatility Bands (Keltner Channel):
The script calculates volatility bands using a method similar to the Keltner Channel. It can either use the True Range (ATR) or the simple high-low price difference to determine market volatility.
These bands are useful for identifying overbought and oversold conditions, as well as detecting periods of price contraction or expansion. The width of the bands is adjustable via a multiplier, allowing users to fine-tune their analysis.
Security Function for Higher Timeframes:
The script retrieves moving average values for the daily timeframe using the request.security() function, which allows it to display higher-timeframe information on lower-timeframe charts. This gives traders a multi-timeframe perspective, helping them align their shorter-term trades with the broader trend.
Trend and Cross Detection:
The script detects when the EMA crosses below or above the SMA on both the daily and hourly timeframes. These crossovers are significant for trend-following strategies, as they often signal shifts in market momentum.
It visually indicates whether the EMA is above or below the SMA for both timeframes using color-coded panels, providing an easy-to-read summary of market conditions.
Custom Table Display:
A custom table is created to summarize the trend information for both the daily and hourly timeframes. The table shows whether the EMA is above or below the SMA for each timeframe, with green or red background colors indicating bullish or bearish conditions, respectively.
This feature is particularly useful for traders who want a quick, at-a-glance confirmation of the trend across multiple timeframes without having to analyze the chart visually.
Visual Plotting:
The script plots the moving averages and volatility bands directly on the price chart, providing clear visual cues for traders. The baseline and bands help traders identify key support and resistance levels, while the additional moving averages help confirm the current trend direction.
How to Use the Script
Adjust Parameters:
Before using the script, traders can customize the type of baseline moving average, its length, and the volatility band multiplier to suit their specific strategy and market conditions. Users can also choose whether to use the True Range or high-low difference for the volatility calculation.
Multi-Timeframe Analysis:
The script combines information from both daily and hourly charts, making it ideal for traders who prefer to align their short-term trades with the broader market trend. The custom table provides a quick snapshot of the trend on both timeframes, allowing users to see if the EMA is above or below the SMA in both cases.
Visual Cues:
By watching the relationship between price and the plotted bands, traders can identify potential breakouts, consolidations, or reversals. The moving average crossovers provide a simple, yet powerful, signal for entering or exiting trades.
Trend Confirmation:
The color-coded custom table helps traders quickly confirm the trend without having to analyze the price action directly. If both the daily and hourly EMA are above their respective SMA, this indicates a strong bullish trend. Conversely, if the EMA is below the SMA on both timeframes, this signals a bearish trend.
Differences from Other Scripts
Multi-Timeframe Cross Detection: Unlike many scripts, this one focuses on detecting moving average crossovers across multiple timeframes (daily and hourly), providing traders with a more comprehensive view of the market.
Custom Volatility Band Calculation: It includes a customizable Keltner-like channel, offering flexibility in how volatility is calculated, which is not commonly found in standard indicators.
Visual Trend Table: The addition of a custom table to visually display trend confirmation across different timeframes sets this script apart from most others, making it easier for traders to digest the information.
******************************************************************** (Español)
Propósito del Script
Este script está diseñado para proporcionar un análisis técnico avanzado del índice US30, combinando medias móviles (MA y EMA) en diferentes marcos de tiempo y un canal Keltner modificado para analizar la volatilidad. Visualiza las tendencias tanto en gráficos diarios como horarios y muestra su relación en una tabla personalizada, ayudando a los traders a tomar decisiones informadas basadas en la alineación de estos indicadores.
Explicación de las Características Clave
Parámetros de Entrada del Usuario:
El script permite a los usuarios personalizar varios parámetros, como si mostrar la media móvil base, qué tipo de media móvil usar (por ejemplo, EMA, SMA, HMA) y la longitud de la media móvil. Estos inputs hacen que el script sea flexible, permitiendo que los usuarios lo ajusten a su estilo de trading.
Medias Móviles (MA y EMA):
Se calculan dos tipos de medias móviles: la base (que puede ser de varios tipos) y dos medias adicionales (SMA y EMA) basadas en los períodos definidos por el usuario. Estas se trazan en el gráfico para proporcionar información sobre la tendencia y el impulso de la acción del precio del US30.
La media móvil base es central en la estrategia, y su cálculo se puede personalizar seleccionando diferentes métodos (por ejemplo, SMA, EMA, o HMA), lo que la hace adaptable a diferentes condiciones de mercado.
Bandas de Volatilidad (Canal Keltner):
El script calcula bandas de volatilidad usando un método similar al Canal Keltner. Puede usar el Rango Verdadero (ATR) o la simple diferencia entre el alto y el bajo del precio para determinar la volatilidad del mercado.
Estas bandas son útiles para identificar condiciones de sobrecompra y sobreventa, así como para detectar períodos de contracción o expansión del precio.
Función security() para Tiempos Superiores:
El script obtiene los valores de las medias móviles para el marco temporal diario, utilizando la función request.security(), lo que permite mostrar información de marcos temporales más largos en gráficos de marcos más cortos.
Detección de Cruces de Tendencia:
El script detecta cuando la EMA cruza por debajo o por encima de la SMA en los gráficos diarios y horarios. Estos cruces son significativos para estrategias de seguimiento de tendencias, ya que suelen señalar cambios en el impulso del mercado.
Tabla de Tendencias Personalizada:
Se crea una tabla personalizada para resumir la información de la tendencia en los gráficos diarios y horarios, mostrando si la EMA está por encima o por debajo de la SMA.
Trazado Visual:
El script traza las medias móviles y las bandas de volatilidad directamente en el gráfico de precios, proporcionando señales visuales claras para los traders.
Cómo usar el Script
Ajustar Parámetros.
Análisis Multi-Tiempo.
Señales Visuales.
Confirmación de Tendencia.
Diferencias con Otros Scripts
Detección Multi-Tiempo de Cruces.
Cálculo Personalizado de Bandas de Volatilidad.
Tabla Visual de Tendencia.
Saludos
VM y CS
Ticker Tape█ OVERVIEW
This indicator creates a dynamic, scrolling display of multiple securities' latest prices and daily changes, similar to the ticker tapes on financial news channels and the Ticker Tape Widget . It shows realtime market information for a user-specified list of symbols along the bottom of the main chart pane.
█ CONCEPTS
Ticker tape
Traditionally, a ticker tape was a continuous, narrow strip of paper that displayed stock prices, trade volumes, and other financial and security information. Invented by Edward A. Calahan in 1867, ticker tapes were the earliest method for electronically transmitting live stock market data.
A machine known as a "stock ticker" received stock information via telegraph, printing abbreviated company names, transaction prices, and other information in a linear sequence on the paper as new data came in. The term "ticker" in the name comes from the "tick" sound the machine made as it printed stock information. The printed tape provided a running record of trading activity, allowing market participants to stay informed on recent market conditions without needing to be on the exchange floor.
In modern times, electronic displays have replaced physical ticker tapes. However, the term "ticker" remains persistent in today's financial lexicon. Nowadays, ticker symbols and digital tickers appear on financial news networks, trading platforms, and brokerage/exchange websites, offering live updates on market information. Modern electronic displays, thankfully, do not rely on telegraph updates to operate.
█ FEATURES
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab allows users to list up to 40 symbols or ticker Identifiers. The indicator dynamically requests and displays information for each one. To add symbols to the list, enter their names separated by commas . For example: "BITSTAMP:BTCUSD, TSLA, MSFT".
Each item in the comma-separated list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script will raise a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Realtime updates
This indicator requests symbol descriptions, current market prices, daily price changes, and daily change percentages for each ticker from the user-specified list of symbols or ticker identifiers. It receives updated information for each security after new realtime ticks on the current chart.
After a new realtime price update, the indicator updates the values shown in the tape display and their colors.
The color of the percentages in the tape depends on the change in price from the previous day . The text is green when the daily change is positive, red when the value is negative, and gray when the value is 0.
The color of each displayed price depends on the change in value from the last recorded update, not the change over a daily period. For example, if a security's price increases in the latest update, the ticker tape shows that price with green text, even if the current price is below the previous day's closing price. This behavior allows users to monitor realtime directional changes in the requested securities.
NOTE: Pine scripts execute on realtime bars when new ticks are available in the chart's data feed. If no new updates are available from the chart's realtime feed, it may cause a delay in the data the indicator receives.
Ticker motion
This indicator's tape display shows a list of security information that incrementally scrolls horizontally from right to left after new chart updates, providing a dynamic visual stream of current market data. The scrolling effect works by using a counter that increments across successive intervals after realtime ticks to control the offset of each listed security. Users can set the initial scroll offset with the "Offset" input in the "Settings/Inputs" tab.
The scrolling rate of the ticker tape display depends on the realtime ticks available from the chart's data feed. Using the indicator on a chart with frequent realtime updates results in smoother scrolling. If no new realtime ticks are available in the chart's feed, the ticker tape does not move. Users can also deactivate the scrolling feature by toggling the "Running" input in the indicator's settings.
█ FOR Pine Script™ CODERS
• This script utilizes dynamic requests to iteratively fetch information from multiple contexts using a single request.security() instance in the code. Previously, `request.*()` functions were not allowed within the local scopes of loops or conditional structures, and most `request.*()` function parameters, excluding `expression`, required arguments of a simple or weaker qualified type. The new `dynamic_requests` parameter in script declaration statements enables more flexibility in how scripts can use `request.*()` calls. When its value is `true`, all `request.*()` functions can accept series arguments for the parameters that define their requested contexts, and `request.*()` functions can execute within local scopes. See the Dynamic requests section of the Pine Script™ User Manual to learn more.
• Scripts can execute up to 40 unique `request.*()` function calls. A `request.*()` call is unique only if the script does not already call the same function with the same arguments. See this section of the User Manual's Limitations page for more information.
• This script converts a comma-separated "string" list of symbols or ticker IDs into an array . It then loops through this array, dynamically requesting data from each symbol's context and storing the results within a collection of custom `Tape` objects . Each `Tape` instance holds information about a symbol, which the script uses to populate the table that displays the ticker tape.
• This script uses the varip keyword to declare variables and `Tape` fields that update across ticks on unconfirmed bars without rolling back. This behavior allows the script to color the tape's text based on the latest price movements and change the locations of the table cells after realtime updates without reverting. See the `varip` section of the User Manual to learn more about using this keyword.
• Typically, when requesting higher-timeframe data with request.security() using barmerge.lookahead_on as the `lookahead` argument, the `expression` argument should use the history-referencing operator to offset the series, preventing lookahead bias on historical bars. However, the request.security() call in this script uses barmerge.lookahead_on without offsetting the `expression` because the script only displays results for the latest historical bar and all realtime bars, where there is no future information to leak into the past. Instead, using this call on those bars ensures each request fetches the most recent data available from each context.
• The request.security() instance in this script includes a `calc_bars_count` argument to specify that each request retrieves only a minimal number of bars from the end of each symbol's historical data feed. The script does not need to request all the historical data for each symbol because it only shows results on the last chart bar that do not depend on the entire time series. In this case, reducing the retrieved bars in each request helps minimize resource usage without impacting the calculated results.
Look first. Then leap.
Uptrick: TimeFrame Trends: Performance & Sentiment Indicator### **Uptrick: TimeFrame Trends: Performance & Sentiment Indicator (TFT) - In-Depth Explanation**
#### **Overview**
The **Uptrick: TimeFrame Trends: Performance & Sentiment Indicator (TFT)** is a sophisticated trading tool designed to provide traders with a comprehensive view of market trends across multiple timeframes, combined with a sentiment gauge through the Relative Strength Index (RSI). This indicator offers a unique blend of performance analysis, sentiment evaluation, and visual signal generation, making it an invaluable resource for traders who seek to understand both the macro and micro trends within a financial instrument.
#### **Purpose**
The primary purpose of the TFT indicator is to empower traders with the ability to assess the performance of an asset over various timeframes while simultaneously gauging market sentiment through the RSI. By analyzing price changes over periods ranging from one week to one year, and complementing this with sentiment signals, TFT enables traders to make informed decisions based on a well-rounded analysis of historical price performance and current market conditions.
#### **Key Components and Features**
1. **Multi-Timeframe Performance Analysis:**
- **Performance Lookback Periods:**
- The TFT indicator calculates the percentage price change over several predefined timeframes: 7 days (1 week), 14 days (2 weeks), 30 days (1 month), 180 days (6 months), and 365 days (1 year). These timeframes provide a layered view of how an asset has performed over short, medium, and long-term periods.
- **Percentage Change Calculation:**
- The indicator computes the percentage change for each timeframe by comparing the current closing price to the closing price at the start of each period. This gives traders insight into the strength and direction of the trend over different periods, helping them identify consistent trends or potential reversals.
2. **Sentiment Analysis Using RSI:**
- **Relative Strength Index (RSI):**
- RSI is a widely-used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought or oversold conditions. In TFT, the RSI is calculated using a 14-period lookback, which is standard for most RSI implementations.
- **RSI Smoothing with EMA:**
- To refine the RSI signal and reduce noise, TFT applies a 10-period Exponential Moving Average (EMA) to the RSI values. This smoothed RSI is then used to generate buy, sell, and neutral signals based on its position relative to the 50 level:
- **Buy Signal:** Triggered when the smoothed RSI crosses above 50, indicating bullish sentiment.
- **Sell Signal:** Triggered when the smoothed RSI crosses below 50, indicating bearish sentiment.
- **Neutral Signal:** Triggered when the smoothed RSI equals 50, suggesting indecision or a balanced market.
3. **Visual Signal Generation:**
- **Signal Plots:**
- TFT provides clear visual cues directly on the price chart by plotting shapes at the points where buy, sell, or neutral signals are generated. These shapes are color-coded (green for buy, red for sell, yellow for neutral) and are positioned below or above the price bars for easy identification.
- **First Occurrence Trigger:**
- To avoid clutter and focus on significant market shifts, TFT only triggers the first occurrence of each signal type. This feature helps traders concentrate on the most relevant signals without being overwhelmed by repeated alerts.
4. **Customizable Performance & Sentiment Table:**
- **Table Display:**
- The TFT indicator includes a customizable table that displays the calculated percentage changes for each timeframe. This table is positioned on the chart according to user preference (top-left, top-right, bottom-left, bottom-right) and provides a quick reference to the asset’s performance across multiple periods.
- **Dynamic Text Color:**
- To enhance readability and provide immediate visual feedback, the text color in the table changes based on the direction of the percentage change: green for positive (upward movement) and red for negative (downward movement). This color-coding helps traders quickly assess whether the asset is in an uptrend or downtrend for each period.
- **Customizable Font Size:**
- Traders can adjust the font size of the table to fit their chart layout and personal preferences, ensuring that the information is accessible without being intrusive.
5. **Flexibility and Customization:**
- **Lookback Period Customization:**
- While the default lookback periods are set for common trading intervals (7 days, 14 days, etc.), these can be adjusted to match different trading strategies or market conditions. This flexibility allows traders to tailor the indicator to focus on the timeframes most relevant to their analysis.
- **RSI and EMA Settings:**
- The length of the RSI calculation and the smoothing EMA can also be customized. This is particularly useful for traders who prefer shorter or longer periods for their momentum analysis, allowing them to fine-tune the sensitivity of the indicator.
- **Table Position and Appearance:**
- The table’s position on the chart, along with its font size and colors, is fully customizable. This ensures that the indicator can be integrated seamlessly into any chart setup without obstructing key price data.
#### **Use Cases and Applications**
1. **Trend Identification and Confirmation:**
- **Short-Term Traders:**
- Traders focused on short-term movements can use the 7-day and 14-day performance metrics to identify recent trends and momentum shifts. The RSI signals provide additional confirmation, helping traders enter or exit positions based on the latest market sentiment.
- **Swing Traders:**
- For those holding positions over days to weeks, the 30-day and 180-day performance data are particularly useful. These metrics highlight medium-term trends, and when combined with RSI signals, they provide a robust framework for swing trading strategies.
- **Long-Term Investors:**
- Long-term investors can benefit from the 1-year performance data to gauge the overall health and direction of an asset. The indicator’s ability to track performance across different periods helps in identifying long-term trends and potential reversal points.
2. **Sentiment Analysis and Market Timing:**
- **Market Sentiment Tracking:**
- By using RSI in conjunction with performance metrics, TFT provides a clear picture of market sentiment. Traders can use this information to time their entries and exits more effectively, aligning their trades with periods of strong bullish or bearish sentiment.
- **Avoiding False Signals:**
- The smoothing of RSI helps reduce noise and avoid false signals that are common in volatile markets. This makes the TFT indicator a reliable tool for identifying true market trends and avoiding whipsaws that can lead to losses.
3. **Comprehensive Market Analysis:**
- **Multi-Timeframe Analysis:**
- TFT’s ability to analyze multiple timeframes simultaneously makes it an excellent tool for comprehensive market analysis. Traders can compare short-term and long-term performance to understand the broader market context, making it easier to align their trading strategies with the overall trend.
- **Performance Benchmarking:**
- The percentage change metrics provide a clear benchmark for an asset’s performance over time. This information can be used to compare the asset against broader market indices or other assets, helping traders make more informed decisions about where to allocate their capital.
4. **Custom Strategy Development:**
- **Tailoring to Specific Markets:**
- TFT can be customized to suit different markets, whether it’s stocks, forex, commodities, or cryptocurrencies. For instance, traders in volatile markets may opt for shorter lookback periods and more sensitive RSI settings, while those in stable markets may prefer longer periods for a smoother analysis.
- **Integrating with Other Indicators:**
- TFT can be used alongside other technical indicators to create a more comprehensive trading strategy. For example, combining TFT with moving averages, Bollinger Bands, or MACD can provide additional layers of confirmation and reduce the likelihood of false signals.
#### **Best Practices for Using TFT**
- **Regularly Adjust Lookback Periods:**
- Depending on the market conditions and the asset being traded, it’s important to regularly review and adjust the lookback periods for the performance metrics. This ensures that the indicator remains relevant and responsive to current market trends.
- **Combine with Volume Analysis:**
- While TFT provides a solid foundation for trend and sentiment analysis, combining it with volume indicators can further enhance its effectiveness. Volume can confirm the strength of a trend or signal potential reversals when divergences occur.
- **Use RSI with Other Momentum Indicators:**
- Although RSI is a powerful tool on its own, using it alongside other momentum indicators like Stochastic Oscillator or MACD can provide additional confirmation and help refine entry and exit points.
- **Customize Table Settings for Clarity:**
- Ensure that the performance table is positioned and sized appropriately on the chart. It should be easily readable without obstructing important price data. Adjust the text size and colors as needed to maintain clarity.
- **Monitor Multiple Timeframes:**
- Utilize the multi-timeframe analysis feature of TFT to monitor trends across different periods. This helps in identifying the dominant trend and avoiding trades that go against the broader market direction.
#### **Conclusion**
The **Uptrick: TimeFrame Trends: Performance & Sentiment Indicator (TFT)** is a comprehensive and versatile tool that combines the power of multi-timeframe performance analysis with sentiment gauging through RSI. Its ability to customize and adapt to various trading strategies and markets makes it a valuable asset for traders at all levels. By offering a clear visual representation of trends and market sentiment, TFT empowers traders to make more informed and confident trading decisions, whether they are focusing on short-term price movements or long-term investment opportunities. With its deep integration of performance metrics and sentiment analysis, TFT stands out as a must-have indicator for any trader looking to gain a holistic understanding of market dynamics.
Uptrick: Volume-Weighted EMA Signal### **Uptrick: Volume-Weighted EMA Signal (UVES) Indicator - Comprehensive Description**
#### **Overview**
The **Uptrick: Volume-Weighted EMA Signal (UVES)** is an advanced, multifaceted trading indicator meticulously designed to provide traders with a holistic view of market trends by integrating Exponential Moving Averages (EMA) with volume analysis. This indicator not only identifies the direction of market trends through dynamic EMAs but also evaluates the underlying strength of these trends using real-time volume data. UVES is a versatile tool suitable for various trading styles and markets, offering a high degree of customization to meet the specific needs of individual traders.
#### **Purpose**
The UVES indicator aims to enhance traditional trend-following strategies by incorporating a critical yet often overlooked component: volume. Volume is a powerful indicator of market strength, providing insights into the conviction behind price movements. By merging EMA-based trend signals with detailed volume analysis, UVES offers a more nuanced and reliable approach to identifying trading opportunities. This dual-layer analysis allows traders to differentiate between strong trends supported by significant volume and weaker trends that may be prone to reversals.
#### **Key Features and Functions**
1. **Dynamic Exponential Moving Average (EMA):**
- The core of the UVES indicator is its dynamic EMA, calculated over a customizable period. The EMA is a widely used technical indicator that smooths price data to identify the underlying trend. In UVES, the EMA is dynamically colored—green when the current EMA value is above the previous value, indicating an uptrend, and red when below, signaling a downtrend. This visual cue helps traders quickly assess the trend direction without manually calculating or interpreting raw data.
2. **Comprehensive Moving Average Customization:**
- While the EMA is the default moving average in UVES, traders can select from various other moving average types, including Simple Moving Average (SMA), Smoothed Moving Average (SMMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). Each type offers unique characteristics:
- **SMA:** Provides a simple average of prices over a specified period, suitable for identifying long-term trends.
- **EMA:** Gives more weight to recent prices, making it more responsive to recent market movements.
- **SMMA (RMA):** A slower-moving average that reduces noise, ideal for capturing smoother trends.
- **WMA:** Weighs prices based on their order in the dataset, making recent prices more influential.
- **VWMA:** Integrates volume data, emphasizing price movements that occur with higher volume, making it particularly useful in volume-sensitive markets.
3. **Signal Line for Trend Confirmation:**
- UVES includes an optional signal line, which applies a secondary moving average to the primary EMA. This signal line can be used to smooth out the EMA and confirm trend changes. The signal line’s color changes based on its slope—green for an upward slope and red for a downward slope—providing a clear visual confirmation of trend direction. Traders can adjust the length and type of this signal line, allowing them to tailor the indicator’s responsiveness to their trading strategy.
4. **Buy and Sell Signal Generation:**
- UVES generates explicit buy and sell signals based on the interaction between the EMA and the signal line. A **buy signal** is triggered when the EMA transitions from a red (downtrend) to a green (uptrend), indicating a potential entry point. Conversely, a **sell signal** is triggered when the EMA shifts from green to red, suggesting an exit or shorting opportunity. These signals are displayed directly on the chart as upward or downward arrows, making them easily identifiable even during fast market conditions.
5. **Volume Analysis with Real-Time Buy/Sell Volume Table:**
- One of the standout features of UVES is its integration of volume analysis, which calculates and displays the volume attributed to buying and selling activities. This analysis includes:
- **Buy Volume:** The portion of the total volume associated with price increases (close higher than open).
- **Sell Volume:** The portion of the total volume associated with price decreases (close lower than open).
- **Buy/Sell Ratio:** A ratio of buy volume to sell volume, providing a quick snapshot of market sentiment.
- These metrics are presented in a real-time table positioned in the top-right corner of the chart, with customizable colors and formatting. The table updates with each new bar, offering continuous feedback on the strength and direction of the market trend based on volume data.
6. **Customizable Settings and User Control:**
- **EMA Length and Source:** Traders can specify the lookback period for the EMA, adjusting its sensitivity to price changes. The source for EMA calculations can also be customized, with options such as close, open, high, low, or other custom price series.
- **Signal Line Customization:** The signal line’s length, type, and width can be adjusted to suit different trading strategies, allowing traders to optimize the balance between trend detection and noise reduction.
- **Offset Adjustment:** The offset feature allows users to shift the EMA and signal line forward or backward on the chart. This can help align the indicator with specific price action or adjust for latency in decision-making processes.
- **Volume Table Positioning and Formatting:** The position, size, and color scheme of the volume table are fully customizable, enabling traders to integrate the table seamlessly into their chart setup without cluttering the visual workspace.
7. **Versatility Across Markets and Trading Styles:**
- UVES is designed to be effective across a wide range of financial markets, including Forex, stocks, cryptocurrencies, commodities, and indices. Its adaptability to different markets is supported by its comprehensive customization options and the inclusion of volume analysis, which is particularly valuable in markets where volume plays a crucial role in price movement.
#### **How Different Traders Can Benefit from UVES**
1. **Trend Followers:**
- Trend-following traders will find UVES particularly beneficial for identifying and riding trends. The dynamic EMA and signal line provide clear visual cues for trend direction, while the volume analysis helps confirm the strength of these trends. This combination allows trend followers to stay in profitable trades longer and exit when the trend shows signs of weakening.
2. **Volume-Based Traders:**
- Traders who focus on volume as a key indicator of market strength can leverage the UVES volume table to gain insights into the buying and selling pressure behind price movements. By monitoring the buy/sell ratio, these traders can identify periods of strong conviction (high buy volume) or potential reversals (high sell volume) with greater accuracy.
3. **Scalpers and Day Traders:**
- For traders operating on shorter time frames, UVES provides quick and reliable signals that are essential for making rapid trading decisions. The ability to customize the EMA length and type allows scalpers to fine-tune the indicator for responsiveness, while the volume analysis offers an additional layer of confirmation to avoid false signals.
4. **Swing Traders:**
- Swing traders, who typically hold positions for several days to weeks, can use UVES to identify medium-term trends and potential entry and exit points. The indicator’s ability to filter out market noise through the signal line and volume analysis makes it ideal for capturing significant price movements without being misled by short-term volatility.
5. **Position Traders and Long-Term Investors:**
- Even long-term investors can benefit from UVES by using it to identify major trend reversals or confirm the strength of long-term trends. The flexibility to adjust the EMA and signal line to longer periods ensures that the indicator remains relevant for detecting shifts in market sentiment over extended time frames.
#### **Optimal Settings for Different Markets**
- **Forex Markets:**
- **EMA Length:** 9 to 14 periods.
- **Signal Line:** Use VWMA or WMA for the signal line to incorporate volume data, which is crucial in the highly liquid Forex markets.
- **Best Use:** Short-term trend following, with an emphasis on identifying rapid changes in market sentiment.
- **Stock Markets:**
- **EMA Length:** 20 to 50 periods.
- **Signal Line:** SMA or EMA with a slightly longer length (e.g., 50 periods) to capture broader market trends.
- **Best Use:** Medium to long-term trend identification, with volume analysis confirming the strength of institutional buying or selling.
- **Cryptocurrency Markets:**
- **EMA Length:** 9 to 12 periods, due to the high volatility in crypto markets.
- **Signal Line:** SMMA or EMA for smoothing out extreme price fluctuations.
- **Best Use:** Identifying entry and exit points in volatile markets, with the volume table providing insights into market manipulation or sudden shifts in trader sentiment.
- **Commodity Markets:**
- **EMA Length:** 14 to 21 periods.
- **Signal Line:** WMA or VWMA, considering the impact of trading volume on commodity prices.
- **Best Use:** Capturing medium-term price movements and confirming trend strength with volume data.
#### **Customization for Advanced Users**
- **Advanced Offset Usage:** Traders can experiment with different offset values to see how shifting the EMA and signal line impacts the timing of buy/sell signals. This can be particularly useful in markets with known latency or for strategies that require a delayed confirmation of trend changes.
- **Volume Table Integration:** The position, size, and colors of the volume table can be adjusted to fit seamlessly into any trading setup. For example, a trader might choose to position the table in the bottom-right corner and use a smaller size to keep the focus on price action while still having access to volume data.
- **Signal Filtering:** By combining the signal line with the primary EMA, traders can filter out false signals during periods of low volatility or when the market is range-bound. Adjusting the length of the signal line allows for greater control over the sensitivity of the trend detection.
#### **Conclusion**
The **Uptrick: Volume-Weighted EMA Signal (UVES)** is a powerful and adaptable indicator designed for traders who demand more from their technical analysis tools. By integrating dynamic EMA trend signals with real-time volume analysis, UVES offers a comprehensive view of market conditions, making it an invaluable resource for identifying trends, confirming signals, and understanding market sentiment. Whether you are a day trader, swing trader, or long-term investor, UVES provides the versatility, precision, and customization needed to make more informed and profitable trading decisions. With its ability to adapt to various markets and trading styles, UVES is not just an indicator but a complete trend analysis solution.
Bearish vs Bullish ArgumentsThe Bearish vs Bullish Arguments Indicator is a tool designed to help traders visually assess and compare the number of bullish and bearish arguments based on their custom inputs. This script enables users to input up to five bullish and five bearish arguments, dynamically displaying the bias on a clean and customizable table on the chart. This provides traders with a clear, visual representation of the market sentiment they have identified.
Key Features:
Customizable Inputs: Users can input up to five bullish and five bearish arguments, which are displayed in a table on the chart.
Bias Calculation: The script calculates the bias (Bullish, Bearish, or Neutral) based on the number of bullish and bearish arguments provided.
Color Customization: Users can customize the colors for the table background, text, and headers, ensuring the table fits seamlessly into their charting environment.
Reset Functionality: A reset switch allows users to clear all input arguments with a single click, making it easy to start fresh.
How It Works:
Input Fields: The script provides input fields for up to five bullish and five bearish arguments. Each input is a simple text field where users can describe their arguments.
Bias Calculation: The script counts the number of non-empty bullish and bearish arguments and determines the overall bias. The bias is displayed in the table with a dynamically changing color to indicate whether the market sentiment is bullish, bearish, or neutral.
Customizable Table: The table is positioned on the chart according to the user's preference (top-left, top-right, bottom-left, bottom-right) and can be customized in terms of background color and text color.
How to Use:
Add the Indicator: Add the Bearish vs Bullish Arguments Indicator to your chart.
Input Arguments: Enter up to five bullish and five bearish arguments in the provided input fields in the script settings.
Customize Appearance: Adjust the table's background color, text color, and position on the chart to fit your preferences.
Example Use Case:
A trader might use this indicator to visually balance their arguments for and against a particular trade setup. By entering their reasons for a bullish outlook in the bullish argument fields and their reasons for a bearish outlook in the bearish argument fields, they can quickly see which side has more supporting points and make a more informed trading decision.
This script was inspired by Arjoio's concepts
Ohlson O-Score IndicatorThe Ohlson O-Score is a financial metric developed by Olof Ohlson to predict the probability of a company experiencing financial distress. It is widely used by investors and analysts as a key tool for financial analysis.
Inputs:
Period: Select the financial period for analysis, either "FY" (Fiscal Year) or "FQ" (Fiscal Quarter).
Country: Specify the country for Gross Net Product data. This helps in tailoring the analysis to specific economic conditions.
Gross Net Product : Define the number of years back for the index to be set at 100. This parameter provides a historical context for the analysis.
Table Display : Customize the display of various tables to suit your preference and analytical needs.
Key Features:
Predictive Power : The Ohlson O-Score is renowned for its predictive power in assessing the financial health of a company. It incorporates multiple financial ratios and indicators to provide a comprehensive view.
Financial Distress Prediction : Use the O-Score to gauge the likelihood of a company facing financial distress in the future. It's a valuable tool for risk assessment.
Country-Specific Analysis : Tailor the analysis to the economic conditions of a specific country, ensuring a more accurate evaluation of financial health.
Historical Context : Set the Gross Net Product index at a specific historical point, allowing for a deeper understanding of how a company's financial health has evolved over time.
How to Use:
Select Period : Choose either Fiscal Year or Fiscal Quarter based on your preference.
Specify Country : Input the country for country-specific Gross Net Product data.
Set Historical Context : Determine the number of years back for the index to be set at 100, providing historical context to your analysis.
Custom Table Display : Personalize the display of various tables to focus on the metrics that matter most to you.
Calculation and component description
Here is the description of O-score components as found in orginal Ohlson publication :
1. SIZE = log(total assets/GNP price-level index). The index assumes a base value of 100 for 1968. Total assets are as reported in dollars. The index year is as of the year prior to the year of the balance sheet date. The procedure assures a real-time implementation of the model. The log transform has an important implication. Suppose two firms, A and B, have a balance sheet date in the same year, then the sign of PA - Pe is independent of the price-level index. (This will not follow unless the log transform is applied.) The latter is, of course, a desirable property.
2. TLTA = Total liabilities divided by total assets.
3. WCTA = Working capital divided by total assets.
4. CLCA = Current liabilities divided by current assets.
5. OENEG = One if total liabilities exceeds total assets, zero otherwise.
6. NITA = Net income divided by total assets.
7. FUTL = Funds provided by operations divided by total liabilities
8. INTWO = One if net income was negative for the last two years, zero otherwise.
9. CHIN = (NI, - NI,-1)/(| NIL + (NI-|), where NI, is net income for the most recent period. The denominator acts as a level indicator. The variable is thus intended to measure change in net income. (The measure appears to be due to McKibben ).
Interpretation
The foundational model for the O-Score evolved from an extensive study encompassing over 2000 companies, a notable leap from its predecessor, the Altman Z-Score, which examined a mere 66 companies. In direct comparison, the O-Score demonstrates significantly heightened accuracy in predicting bankruptcy within a 2-year horizon.
While the original Z-Score boasted an estimated accuracy of over 70%, later iterations reached impressive levels of 90%. Remarkably, the O-Score surpasses even these high benchmarks in accuracy.
It's essential to acknowledge that no mathematical model achieves 100% accuracy. While the O-Score excels in forecasting bankruptcy or solvency, its precision can be influenced by factors both internal and external to the formula.
For the O-Score, any results exceeding 0.5 indicate a heightened likelihood of the firm defaulting within two years. The O-Score stands as a robust tool in financial analysis, offering nuanced insights into a company's financial stability with a remarkable degree of accuracy.
Price Cross Time Custom Range Interactive█ OVERVIEW
This indicator was a time-based indicator and intended as educational purpose only based on pine script v5 functions for ta.cross() , ta.crossover() and ta.crossunder() .
I realised that there is some overlap price with the cross functions, hence I integrate them into Custom Range Interactive with value variance and overlap displayed into table.
This was my submission for Pinefest #1 , I decided to share this as public, I may accidentally delete this as long as i keep as private.
█ INSPIRATION
Inspired by design, code and usage of CAGR. Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
█ FEATURES
1. Custom Range Interactive
2. Label can be resize and change color.
3. Label show tooltip for price and time.
4. Label can be offset to improve readability.
5. Table can show price variance when any cross is true.
6. Table can show overlap if found crosss is overlap either with crossover and crossunder.
7. Table text color automatically change based on chart background (light / dark mode).
8. Source 2 is drawn as straight line, while Source 1 will draw as label either above line for crossover, below line for crossunder and marked 'X' if crossing with Source 2's line.
9. Cross 'X' label can be offset to improve readability.
10. Both Source 1 and Source 2 can select Open, Close, High and Low, which can be displayed into table.
█ LIMITATIONS
1. Table is limited to intraday timeframe only as time format is not accurate for daily timeframe and above. Example daily timeframe will give result less 1 day from actual date.
2. I did not include other sources such external source or any built in sources such as hl2, hlc3, ohlc4 and hlcc4.
█ CODE EXPLAINATION
I pretty much create custom function with method which returns tuple value.
method crossVariant(float price = na, chart.point ref = na) =>
cross = ta.cross( price, ref.price)
over = ta.crossover( price, ref.price)
under = ta.crossunder(price, ref.price)
Unfortunately, I unable make the labels into array which i plan to return string value by getting the text value from array label, hence i use label.all and add incremental int value as reference.
series label labelCross = na, labelCross.delete()
var int num = 0
if over
num += 1
labelCross := label.new()
if under
num += 1
labelCross := label.new()
if cross
num += 1
labelCross := label.new()
I realised cross value can be overlap with crossover and crossunder, hence I add bool to enable force overlap and add additional bools.
series label labelCross = na, labelCross.delete()
var int num = 0
if forceOverlap
if over
num += 1
labelCross := label.new()
if under
num += 1
labelCross := label.new()
if cross
num += 1
labelCross := label.new()
else
if cross and over
num += 1
labelCross := label.new()
if cross and under
num += 1
labelCross := label.new()
if cross and not over and not under
num += 1
labelCross := label.new()
█ USAGE / EXAMPLES
dashboard MTF,EMA User Guide: Dashboard MTF EMA
Script Installation:
Copy the script code.
Go to the script window (Pine Editor) on TradingView.
Paste the code into the script window.
Save the script.
Adding the Script to the Chart:
Return to your chart on TradingView.
Look for the script in the list of available scripts.
Add the script to the chart.
Interpreting the Table:
On the right side of the chart, you will see a table labeled "EMA" with arrows.
The rows correspond to different timeframes: 5 minutes (5M), 15 minutes (15M), 1 hour (1H), 4 hours (4H), and 1 day (1D).
Understanding the Arrows:
Each row of the table has two columns: "EMA" and an arrow.
"EMA" indicates the trend of the Exponential Moving Average (EMA) for the specified period.
The arrow indicates the direction of the trend: ▲ for bullish, ▼ for bearish.
Table Colors:
The colors of the table reflect the current trend based on the comparison between fast and slow EMAs.
Blue (▲) indicates a bullish trend.
Red (▼) indicates a bearish trend.
Table Theme:
The table has a dark (Dark) or light (Light) theme according to your preference.
The background, frame, and colors are adjusted based on the selected theme.
Usage:
Use the table as a quick indicator of trends on different timeframes.
The arrows help you quickly identify trends without navigating between different time units.
Designed to simplify analysis and avoid cluttering the chart with multiple indicators.
Major and Minor Trend Indicator by Nikhil34a V 2.2Title: Major and Minor Trend Indicator by Nikhil34a V 2.2
Description:
The Major and Minor Trend Indicator v2.2 is a comprehensive technical analysis script designed for use with the TradingView platform. This powerful tool is developed in Pine Script version 5 and helps traders identify potential buying and selling opportunities in the stock market.
Features:
SMA Trend Analysis: The script calculates two Simple Moving Averages (SMAs) with user-defined lengths for major and minor trends. It displays these SMAs on the chart, allowing traders to visualize the prevailing trends easily.
Surge Detection: The indicator can detect buying and selling surges based on specific conditions, such as volume, RSI, MACD, and stochastic indicators. Both Buying and Selling surges are marked in black on the chart.
Option Buy Zone Detection: The script identifies the option buy zone based on SMA crossovers, RSI, and MACD values. The buy zone is categorized as "CE Zone" or "PE Zone" and displayed in the table along with the trigger time.
Two-Day High and Low Range: The script calculates the highest high and lowest low of the previous two trading days and plots them on the chart. The area between these points is shaded in semi-transparent green and red colors.
Crossover Analysis: The script analyzes moving average crossovers on multiple timeframes (2-minute, 3-minute, and 5-minute) and displays buy and sell signals accordingly.
Trend Identification: The script identifies the major and minor trends as either bullish or bearish, providing valuable insights into the overall market sentiment.
Usage:
Customize Major and Minor SMA Periods: Adjust the lengths of major and minor SMAs through input parameters to suit your trading preferences.
Enable/Disable Moving Averages: Choose which SMAs to display on the chart by toggling the "showXMA" input options.
Set Surge and Option Buy Zone Thresholds: Modify the surgeThreshold, volumeThreshold, RSIThreshold, and StochThreshold inputs to refine the surge and buy zone detection.
Analyze Crossover Signals: Monitor the crossover signals in the table, categorized by timeframes (2-minute, 3-minute, and 5-minute).
Explore Market Bias and Distance to 2-Day High/Low: The table provides information on market bias, current price movement relative to the previous two-day high and low, and the option buy zone status.
Additional Use Cases:
Surge Indicator:
The script includes a Surge Indicator that detects sudden buying or selling surges in the market. When a buying surge is identified, the "BSurge" label will appear below the corresponding candle with black text on a white background. Similarly, a selling surge will display the "SSurge" label in white text on a black background. These indicators help traders quickly spot strong buying or selling activities that may influence their trading decisions. These surges can be used to identify sudden premium dump zones.
Option Buy Zone:
The Option Buy Zone is an essential feature that identifies potential zones for buying call options (CE Zone) or put options (PE Zone) based on specific technical conditions. The indicator evaluates SMA crossovers, RSI, and MACD values to determine the current market sentiment. When the option buy zone is triggered, the script will display the respective zone ("CE Zone" or "PE Zone") in the table, highlighted with a white background. Additionally, the time when the buy zone was triggered will be shown under the "Option Buy Zone Trigger Time" column.
Price Movement Relative to 2-Day High/Low:
The script calculates the highest high and lowest low of the previous two trading days (high2DaysAgo and low2DaysAgo) and plots these points on the chart. The area between these two points is shaded in semi-transparent green and red colors. The green region indicates the price range between the highpricetoconsider (highest high of the previous two days) and the lower value between highPreviousDay and high2DaysAgo. Similarly, the red region represents the price range between the lowpricetoconsider (lowest low of the previous two days) and the higher value between lowPreviousDay and low2DaysAgo.
Entry Time and Current Zone:
The script identifies potential entry times for trades within the option buy zone. When a valid buy zone trigger occurs, the script calculates the entryTime by adding the durationInMinutes (user-defined) to the startTime. The entryTime will be displayed in the "Entry Time" column of the table. Depending on the comparison between optionbuyzonetriggertime and entryTime, the background color of the entry time will change. If optionbuyzonetriggertime is greater than entryTime, the background color will be yellow, indicating that a new trigger has occurred before the specified duration. Otherwise, the background color will be green, suggesting that the entry time is still within the defined duration.
Current Zone Indicator:
The script further categorizes the current zone as either "CE Zone" (call option zone) or "PE Zone" (put option zone). When the market is trending upwards and the minor SMA is above the major SMA, the currentZone will be set to "CE Zone." Conversely, when the market is trending downwards and the minor SMA is below the major SMA, the currentZone will be "PE Zone." This information is displayed in the "Current Zone" column of the table.
These additional use cases empower traders with valuable insights into market trends, buying and selling surges, option buy zones, and potential entry times. Traders can combine this information with their analysis and risk management strategies to make informed and confident trading decisions.
Note:
The script is optimized for identifying trends and potential trade opportunities. It is crucial to perform additional analysis and risk management before executing any trades based on the provided signals.
Happy Trading!
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.
LNL Smart TICKLNL Smart TICK
This study is mostly beneficial for intraday traders. It is basically a user-friendly "colorful" representation of the $TICK chart with highlighted $TICK extremes. This indicator also includes: a simple trend gauge that can visualize the bias for the day, cumulative tick cloud which is showing the cumulative strength of either longs & shorts on the day.
$TICK Trend Gauge
Although it is just a exponential moving average. This average (default set on 20) works quite well as an overall gauge for the day. Whenever the gauge is green (above zero), any negative $TICK values below -500 can offer great pullback opportunities. Same applies for the red gauge. 20 EMA is below zero ? Great time to fade any +500 or +1000 tick readings. Obviously the gauge can be ajdusted to any number based on personal style.
$TICK Extremes (little triangles)
These little triangles are triggered anytime $TICK jumps above or below the pre-set values of +1000 or -1000. By just simply observing the $TICK triangles during the day can tell you how much volaility or pressure there is. Sometimes there will be 20 green triangles and only 2 red ones. That obviously mean there is a strong bearish pressure. But there will be days when you are not going to see any triangles at all which can mean there is either a low volatility or the price is stuck in the indecisive market.
Cumulative $TICK Cloud
Cumulative $TICK by itself is a great study for day traders. It is basically running "counting" $TICK that is adding the previous $TICK values from previous bars. Cumulative $TICK can create a direct picture of the current market sentiment. It is not just a simple green / red line but a cloud that can really show you the depth on the $TICK. Some days, the cloud will be quite wide which is a good sign for the strength to one side, but sometimes the cloud will be so narrow it will practically disappear. This would be telling you the exact opposite - not much conviction to any side. Of course the depth as well as the color of the cloud can change during the day.
$TICK & Cumulative $TICK Tables
By just looking at these tables. You can immidiately tell the state of the current $TICK. They both can be red or green. It all depends whether the values are positive or negative. The tables are just a little visual addition to the whole $TICK study.
Hope it helps.
Swing Counter [theEccentricTrader]█ OVERVIEW
This indicator counts the number of confirmed swing high and swing low scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Peak and Trough Prices (Advanced)
• The advanced peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the highest preceding green candle high price, depending on which is higher.
• The advanced trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the lowest preceding red candle low price, depending on which is lower.
Green and Red Peaks and Troughs
• A green peak is one that derives its price from the green candle/s that constitute the swing high.
• A red peak is one that derives its price from the red candle that completes the swing high.
• A green trough is one that derives its price from the green candle that completes the swing low.
• A red trough is one that derives its price from the red candle/s that constitute the swing low.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
█ FEATURES
Inputs
• Start Date
• End Date
• Position
• Text Size
• Show Sample Period
• Show Plots
• Show Lines
Table
The table is colour coded, consists of three columns and nine rows. Blue cells denote neutral scenarios, green cells denote return line uptrend and uptrend scenarios, and red cells denote downtrend and return line downtrend scenarios.
The swing scenarios are listed in the first column with their corresponding total counts to the right, in the second column. The last row in column one, row nine, displays the sample period which can be adjusted or hidden via indicator settings.
Rows three and four in the third column of the table display the total higher peaks and higher troughs as percentages of total peaks and troughs, respectively. Rows five and six in the third column display the total lower peaks and lower troughs as percentages of total peaks and troughs, respectively. And rows seven and eight display the total double-top peaks and double-bottom troughs as percentages of total peaks and troughs, respectively.
Plots
I have added plots as a visual aid to the swing scenarios listed in the table. Green up-arrows with ‘HP’ denote higher peaks, while green up-arrows with ‘HT’ denote higher troughs. Red down-arrows with ‘LP’ denote higher peaks, while red down-arrows with ‘LT’ denote lower troughs. Similarly, blue diamonds with ‘DT’ denote double-top peaks and blue diamonds with ‘DB’ denote double-bottom troughs. These plots can be hidden via indicator settings.
Lines
I have also added green and red trendlines as a further visual aid to the swing scenarios listed in the table. Green lines denote return line uptrends (higher peaks) and uptrends (higher troughs), while red lines denote downtrends (lower peaks) and return line downtrends (lower troughs). These lines can be hidden via indicator settings.
█ HOW TO USE
This indicator is intended for research purposes and strategy development. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe. It can, for example, give you an idea of any inherent biases such as a greater proportion of higher peaks to lower peaks. Or a greater proportion of higher troughs to lower troughs. Such information can be very useful when conducting top down analysis across multiple timeframes, or considering entry and exit methods.
What I find most fascinating about this logic, is that the number of swing highs and swing lows will always find equilibrium on each new complete wave cycle. If for example the chart begins with a swing high and ends with a swing low there will be an equal number of swing highs to swing lows. If the chart starts with a swing high and ends with a swing high there will be a difference of one between the two total values until another swing low is formed to complete the wave cycle sequence that began at start of the chart. Almost as if it was a fundamental truth of price action, although quite common sensical in many respects. As they say, what goes up must come down.
The objective logic for swing highs and swing lows I hope will form somewhat of a foundational building block for traders, researchers and developers alike. Not only does it facilitate the objective study of swing highs and swing lows it also facilitates that of ranges, trends, double trends, multi-part trends and patterns. The logic can also be used for objective anchor points. Concepts I will introduce and develop further in future publications.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
The green and red candle calculations are based solely on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with. Alternatively, you can replace the scenarios with your own logic to account for the gap anomalies, if you are feeling up to the challenge.
The sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
█ NOTES
I feel it important to address the mention of advanced peak and trough price logic. While I have introduced the concept, I have not included the logic in my script for a number of reasons. The most pertinent of which being the amount of extra work I would have to do to include it in a public release versus the actual difference it would make to the statistics. Based on my experience, there are actually only a small number of cases where the advanced peak and trough prices are different from the basic peak and trough prices. And with adequate multi-timeframe analysis any high or low prices that are not captured using basic peak and trough price logic on any given time frame, will no doubt be captured on a higher timeframe. See the example below on the 1H FOREXCOM:USDJPY chart (Figure 1), where the basic peak price logic denoted by the indicator plot does not capture what would be the advanced peak price, but on the 2H FOREXCOM:USDJPY chart (Figure 2), the basic peak logic does capture the advanced peak price from the 1H timeframe.
Figure 1.
Figure 2.
█ RAMBLINGS
“Never was there an age that placed economic interests higher than does our own. Never was the need of a scientific foundation for economic affairs felt more generally or more acutely. And never was the ability of practical men to utilize the achievements of science, in all fields of human activity, greater than in our day. If practical men, therefore, rely wholly on their own experience, and disregard our science in its present state of development, it cannot be due to a lack of serious interest or ability on their part. Nor can their disregard be the result of a haughty rejection of the deeper insight a true science would give into the circumstances and relationships determining the outcome of their activity. The cause of such remarkable indifference must not be sought elsewhere than in the present state of our science itself, in the sterility of all past endeavours to find its empirical foundations.” (Menger, 1871, p.45).
█ BIBLIOGRAPHY
Menger, C. (1871) Principles of Economics. Reprint, Auburn, Alabama: Ludwig Von Mises Institute: 2007.
ASE Additionals v1ASE Additionals is a statistics-driven indicator that combines multiple features to provide traders with valuable statistics to help their trading. This indicator offers a customizable table that includes statistics for VWAP with customizable standard deviation waves.
Per the empirical rule, the following is a schedule for what percent of volume should be traded between the standard deviation range:
+/- 1 standard deviation: 68.26% of volume should be trading within this range
+/- 2 standard deviation: 95.44% of volume should be trading within this range
+/- 3 standard deviation: 99.73% of volume should be trading within this range
+/- 4 standard deviation: 99.9937% of volume should be trading within this range
+/- 5 standard deviation: 99.999943% of volume should be trading within this range
+/- 6 standard deviation: 99.9999998% of volume should be trading within this range
The statistics table presents five different pieces of data
Volume Analyzed: Amount of contracts analyzed for the statistics
Volume Traded Inside Upper Extreme: Calculated by taking the amount of volume traded inside the Upper Extreme band divided by the total amount of contracts analyzed
Volume Traded Inside Lower Extreme: Calculated by taking the amount of volume traded inside the Lower Extreme band divided by the total amount of contracts analyzed
Given the user’s inputs, they will see the upper and lower extremes of the day. For example, if the user changed the inner st. dev input to 2, 95.44% of the volume should be traded within the inner band. If the user changed the outer st. dev input to 3, 99.73% of the volume should be traded within the outer band. Thus, statistically, 2.145% ((99.73%-95.44%)/2) of volume should be traded between the upper and lower band fill.
In the chart above, the bands are the 2nd and 3rd standard deviation inputs. We notice that out of the 151 Million Contracts , the actual percentage of volume traded in the upper extreme was 2.7% , and the actual percentage of the volume traded in the lower extreme was 3.3% . Given the empirical rule, about 2.145% of the volume should be traded in the upper extreme band, and 2.145% of the volume should be traded in the lower extreme band. Based on the statistics table, the empirical rule is true when applied to the volume-weighted average price.
The trader should recognize that statistics is all about probability and there is a margin for error, so the bands should be used as a bias, not an entry. For example, given the +/-2 and 3 standard deviations, statistically, if 2.145% of the volume is traded within the upper band extreme, you shouldn’t look for a long trade if the current price is in the band. Likewise, if 2.145% of the volume is traded within the lower band extreme, you shouldn’t look for a short trade if the current price is in the band.
Additionally, we provide traders with the Daily, Weekly, and Monthly OHLC levels. Open, High, Low, and Close are significant levels, especially on major timeframes. Once price has touched the level, the line changes from dashed/dotted to solid.
Features
VWAP Price line and standard deviation waves to analyze the equilibrium and extremes of the sessions trend
Previous Day/WEEK/Month OHLC levels provide Major timeframe key levels
Settings
VWAP Equilibrium: Turn on the VWAP line
VWAP Waves: Turn on the VWAP standard deviation waves
Inner St. Dev: Changes the inner band standard deviation to show the percentage of volume traded within
Outer St. Dev: Changes the outer band standard deviation to show the percentage of volume traded within
Upper Extreme: Change the color of the upper VWAP wave
Lower Extreme: Change the color of the lower VWAP wave
Wave Opacity: Change the opacity of the waves (0= completely transparent, 100=completely solid)
Statistics Table: Turn on or off the statistics table
Statistics Table Settings: Change the Table Color, Text Color, Text Size, and Table Position
Previous Day/Week/Month OHLC: Choose; All, Open, Close, High, Low, and the color of the levels
OHLC Level Settings: Change the OHLC label color, line style, and line width
How to Use
The VWAP price line acts as the 'Fair Value' or the 'Equilibrium' of the price session. Just as the VWAP Waves show the session's upper and lower extreme possibilities. While we can find entries from VWAP , our analysis uses it more as confirmation. OHLC levels are to be used as support and resistance levels. These levels provide us with great entry and target opportunities as they are essential and can show pivots in price action.
