Liquidity IndicatorThe Liquidity Indicator helps identify key price levels where liquidity may be concentrated by highlighting local highs and local lows on the chart. These levels are calculated using a lookback period to determine the highest and lowest points in the recent price action.
Local Highs: Displayed as red lines, these indicate recent peaks where price has experienced rejection or a possible reversal point.
Local Lows: Displayed as green lines, these represent recent troughs where price may find support or experience a bounce.
This indicator is useful for spotting potential areas of interest for price reversal or continuation, as high liquidity zones may lead to more significant price movements.
Key Features:
Adjustable lookback period to define the scope for identifying local highs and lows.
Continuous plotting without any time restrictions, providing real-time insights into liquidity conditions.
Alerts available for when a local high or local low is detected, enabling timely market analysis.
Use Case:
This indicator can be used in conjunction with other technical analysis tools or strategies to help identify significant price levels where liquidity could impact price action. It is suitable for both intraday and swing traders looking for key price zones where potential reversals or continuations might occur.
Wskaźniki i strategie
Engulfing Candle IndicatorThis indicator helps identify Bullish and Bearish Engulfing candle patterns on your chart.
Bullish Engulfing: Occurs when a green candle completely engulfs the prior red candle, signaling potential upward momentum.
Bearish Engulfing: Occurs when a red candle completely engulfs the prior green candle, signaling potential downward momentum.
The script highlights these patterns with green triangles below the bars for Bullish Engulfing and red triangles above the bars for Bearish Engulfing.
This tool is helpful for traders who use candlestick patterns as part of their technical analysis strategy.
Numerical Volume with Bullish/Bearish Color CodingDescription: This indicator visually represents trading volume with color-coded lines to distinguish between bullish and bearish market conditions. The volume line is colored green for bullish periods (when the closing price is higher than the opening price), red for bearish periods (when the closing price is lower than the opening price), and gray for neutral periods (when the open and close prices are equal). The volume can be displayed with labels at specified intervals, offering a quick reference to the exact volume for each period. This tool helps to analyze volume trends in relation to price action, providing an easy-to-read overview of market sentiment.
Liquidity + Engulfment StrategyThis strategy identifies potential trading opportunities by combining bullish and bearish engulfing candle patterns with liquidity seal-off points. The logic is based on the concept of engulfing candles, which signal a shift in market sentiment, and liquidity lines, which represent local price extremes (highs and lows) that can indicate potential reversal or continuation points.
Key Features:
Mode Selection
The strategy allows for three modes: "Both", "Bullish Only", and "Bearish Only". Users can choose whether to trade both directions, only bullish setups, or only bearish setups.
Time Range
Users can define a specific time range for when the strategy is active, enabling tailored analysis and trade execution over a desired period.
Engulfing Candles
Bullish Engulfing: A candle that closes above the high of the previous bearish candle, signaling potential upward momentum.
Bearish Engulfing: A candle that closes below the low of the previous bullish candle, indicating a potential downtrend.
Liquidity Seal-Off Points
The strategy detects local highs and local lows within a specified lookback period, which can serve as critical support and resistance points.
A bullish signal is triggered when the price touches a lower liquidity point (local low), and a bearish signal is triggered at a higher liquidity point (local high).
Signal Confirmation
Signals are only triggered when both an engulfing candle and the price action at a liquidity seal-off point align. This helps filter out weaker signals.
Consecutive signals are prevented by locking the trade direction after an initial signal and waiting for the liquidity line to be broken before re-triggering a signal.
Entry and Exit Conditions
The strategy can enter both long (bullish) or short (bearish) positions based on the mode and signals.
Exit is based on opposing signals or reaching predefined stop-loss and take-profit levels.
Alerts
The strategy supports alert conditions to notify users when bullish engulfing after a lower liquidity touch or bearish engulfing after an upper liquidity touch is detected.
Trend Strength/DirectionThis is a really good, though complex indicator, so I will add two different explanations so to appease both the laymen and those who take the time to read thoroughly.
Simple Explanation
This indicator utilizes 6HMA's to display their angles
The greater the angle ---> the stronger the trend
If more angles are positive, then trend is very strong
If more are negative, then very negative
Comprehensive Explanation
6 angles, each of a different time frame are used to represent direction and trend strength. Angles are used because they intrinsically represent momentum and speed. An angle of 45 represents a perfect balance between something that can cover the furthest distance without compensating for speed. 1 of the 6 angles is intended(though customizable) to represent the 5 hma's angle. This is because the 5hma is very good at representing very near term price action.
Angle Levels
Its important to understand what the angle levels mean for the underlying hma's. The 0 level represents a hma that is horizontal. This is important because this is the point at which it decides to be bullish or bearish. +/- 45, as noted before, represent bullishness/bearishness that represent strong trends without compensating for speed. A continuous increase/decrease and or a cross of these levels generally indicate significant change in sentiment, of which trades may be taken.
Strategy
You should weigh your decision by those angles that represent the longer time frame. If more angles represent a certain sentiment, it is obviously unwise to fight against that long term sentiment. The purpose of this indicator was to provide a proper representation of trend direction and strength, but also solve the problem of when you should 'dip' buy.
For an example: if all angles are increase or decreasing, then you may use the 5hma's angle to find the proper points at which you will enter a position.
***NOTE: I dont think the +/- 45 bands should indicate 'overbought' or 'oversold' zones that some might assume. Instead you should wait for a crossing of this zone.
RSI BandsOverview
The RSI Bands indicator is a tool designed to calculate and display overbought, oversold, and middle bands based on the Relative Strength Index (RSI).
Its primary purpose is to provide traders with a clue on whether to place limit buy or limit sell orders, or to set stop-loss orders effectively. The bands represent the price levels the asset must reach for the RSI to align with specific thresholds:
Overbought Band: Displays the upper band representing the price level the asset must reach for the RSI to become overbought.
Oversold Band: Displays the lower band representing the price level the asset must reach for the RSI to become oversold.
Middle Band: Displays the middle band representing the price level the asset must reach for the RSI to hit the middle level. It uses both traditional RSI calculations and a dynamic period adjustment mechanism for improved adaptability to market conditions. The script also offers smoothing options for the bands.
Features
Calculates overbought, oversold, and middle bands using RSI values.
Dynamically adjusts the RSI period based on pivot points if enabled.
Offers smoothing options for the bands: EMA, SMA, or None.
Customizable input parameters for flexibility.
Inputs
Source Value: Selects the data source (e.g., close price) for RSI calculation.
Period: Sets the static RSI calculation period. Used if dynamic period is disabled.
Use Dynamic Period?: Toggles the use of a dynamic RSI period.
Pivot Left/Right Length: Determines the range of bars for pivot detection when using dynamic periods.
Dynamic Period Multiplier: Scales the dynamically calculated RSI period.
Overbought Level: RSI level that marks the overbought threshold.
Oversold Level: RSI level that marks the oversold threshold.
Middle Level: RSI level used as a midpoint reference.
Smoothing Type: Specifies the smoothing method for the bands (EMA, SMA, or None).
Smoothing Length: Length used for the selected smoothing method.
Key Calculations
RSI Calculation:
Computes RSI using gains and losses over the specified period (dynamic or static).
Incorporates a custom function for calculating RSI with dynamic periods.
Dynamic Period Adjustment:
Uses pivot points to determine an adaptive RSI period.
Multiplies the base dynamic period by the Dynamic Period Multiplier.
Band Calculation:
Calculates price changes (deltas) required to achieve the overbought, oversold, and middle RSI levels.
The price changes (deltas) are determined using an iterative approximation technique. For each target RSI level (overbought, oversold, or middle), the script estimates the required change in price by adjusting a hypothetical delta value until the calculated RSI aligns with the target RSI. This approximation ensures precise calculation of the price levels necessary for the RSI to reach the specified thresholds.
Computes the upper (overbought), lower (oversold), and middle bands by adding these deltas to the source price.
Smoothing:
Applies the selected smoothing method (EMA or SMA) to the calculated bands.
Plots
Overbought Band: Displays the upper band representing the price level the asset must reach for the RSI to become overbought.
Oversold Band: Displays the lower band representing the price level the asset must reach for the RSI to become oversold.
Middle Band: Displays the middle band representing the price level the asset must reach for the RSI to hit the middle level.
Usage
Choose the source value (e.g., close price).
Select whether to use a dynamic RSI period or a static one.
Adjust pivot lengths and multipliers for dynamic period calculation as needed.
Set the overbought, oversold, and middle RSI levels based on your analysis.
Configure smoothing options for the bands.
Observe the plotted bands and use them to identify potential overbought and oversold market conditions.
Adaptive MAAdaptive Moving Average (AMA)
Overview
The Adaptive Moving Average (AMA) script is designed to calculate and plot a moving average that adapts dynamically based on market conditions. This script uses pivot-based periods for its calculation, allowing it to adjust its behavior in response to market volatility and trends. It supports both Simple Moving Average (SMA) and Exponential Moving Average (EMA).
Features
Dynamic Period Calculation: Leverages the DynamicPeriodPublic library to compute periods based on pivot points, providing an adaptive length for the moving average.
Customizable Parameters: Users can choose predefined "Fast" and "Slow" settings or manually configure the parameters for greater control.
Supports SMA and EMA: Flexibility to choose between SMA and EMA for the moving average calculation.
Inputs
Source ( src ): Data source for the moving average (e.g., close price).
Default: close
Length Type ( length_type ): Determines the type of period calculation.
Options: Fast, Slow, Manual
MA Type ( ma_type ): Specifies the type of moving average to calculate.
Options: SMA, EMA
Manual Parameters (used when length_type is set to Manual):
Left Bars ( left_bars ): Number of left-hand bars for pivot detection.
Right Bars ( right_bars ): Number of right-hand bars for pivot detection.
Number of Pivots ( num_pivots ): Minimum number of pivots for dynamic period calculation.
Length Multiplier ( length_mult ): Multiplier applied to the calculated period.
Use Cases
Trend Analysis: Identify market trends with an average that adapts to changing conditions.
Volatility-Based Strategies: Adjust strategies dynamically in response to market volatility.
Custom Configurations: Fine-tune pivot parameters for specific markets or assets using the "Manual" mode.
Example Usage
Select the desired length type (Fast, Slow, or Manual).
If Manual is selected, configure the pivot detection parameters and length multiplier.
Choose the moving average type (SMA or EMA).
Observe the adaptive moving average plotted on the chart.
DynamicPeriodPublicDynamic Period Calculation Library
This library provides tools for adaptive period determination, useful for creating indicators or strategies that automatically adjust to market conditions.
Overview
The Dynamic Period Library calculates adaptive periods based on pivot points, enabling the creation of responsive indicators and strategies that adjust to market volatility.
Key Features
Dynamic Periods: Computes periods using distances between pivot highs and lows.
Customizable Parameters: Users can adjust detection settings and period constraints.
Robust Handling: Includes fallback mechanisms for cases with insufficient pivot data.
Use Cases
Adaptive Indicators: Build tools that respond to market volatility by adjusting their periods dynamically.
Dynamic Strategies: Enhance trading strategies by integrating pivot-based period adjustments.
Function: `dynamic_period`
Description
Calculates a dynamic period based on the average distances between pivot highs and lows.
Parameters
`left` (default: 5): Number of left-hand bars for pivot detection.
`right` (default: 5): Number of right-hand bars for pivot detection.
`numPivots` (default: 5): Minimum pivots required for calculation.
`minPeriod` (default: 2): Minimum allowed period.
`maxPeriod` (default: 50): Maximum allowed period.
`defaultPeriod` (default: 14): Fallback period if no pivots are found.
Returns
A dynamic period calculated based on pivot distances, constrained by `minPeriod` and `maxPeriod`.
Example
//@version=6
import CrimsonVault/DynamicPeriodPublic/1
left = input.int(5, "Left bars", minval = 1)
right = input.int(5, "Right bars", minval = 1)
numPivots = input.int(5, "Number of Pivots", minval = 2)
period = DynamicPeriodPublic.dynamic_period(left, right, numPivots)
plot(period, title = "Dynamic Period", color = color.blue)
Implementation Notes
Pivot Detection: Requires sufficient historical data to identify pivots accurately.
Edge Cases: Ensures a default period is applied when pivots are insufficient.
Constraints: Limits period values to a user-defined range for stability.
DCA Order Info PlannerDescription :
This script is a Dollar-Cost Averaging (DCA) order planner designed for SPOT, LONG, and SHORT markets. It automatically calculates the optimal price levels for your orders based on configurable parameters, while also considering leverage and liquidation price.
🔹 Key Features:
1. Automatic Order Planning:
- The script calculates price levels for your orders based on an adjustable scaling coefficient (default: 1.5).
- You can set the percentage interval between each order (default: 2%).
- Displays the number of units to buy/sell at each level.
2.Leverage Management:
- Integrates a configurable leverage and computes the liquidation price for LONG and SHORT positions.
3.Clear Visual Display:
- Markers on the chart indicating order levels with customizable labels.
- A summary table shows price levels and corresponding quantities.
- Visualizes Stop Loss and Take Profit levels if defined.
4.Automatic Alerts:
- Sends alerts when the price reaches an order level.
🔹 Customizable Parameters:
- Starting Price: Initial price for calculating orders.
- Budget: Total budget for DCA orders.
- Leverage: Multiplier for LONG/SHORT positions.
- Scaling Coefficient: Adjusts the spacing between order levels.
- Maximum DCA Levels: Limits the number of generated orders.
🔹 How to Use:
1. Configure the parameters according to your strategy.
2. The script displays order levels and quantities on the chart.
3. Use the summary table to manually input orders on your favorite trading platform.
This script is particularly useful in volatile market conditions to average your entry or exit price and manage risk effectively.
Overnight High/LowThe script identifies the Overnight High (the highest price) and Overnight Low (the lowest price) for a trading instrument during a specified overnight session. It then plots these levels on the chart for reference in subsequent trading sessions.
Key Features:
Time Settings:
The script defines the start (startHour) and end (endHour + endMinute) times for the overnight session.
The session spans across two calendar days, such as 5:00 PM (17:00) to 9:30 AM (09:30).
Tracking High and Low:
During the overnight session, the script dynamically tracks:
Overnight High: The highest price reached during the session.
Overnight Low: The lowest price reached during the session.
Reset Mechanism:
After the overnight session ends (at the specified end time), the script resets the overnightHigh and overnightLow variables, preparing for the next session.
Visual Representation:
The script uses horizontal dotted lines to plot:
A green line for the Overnight High.
A red line for the Overnight Low.
These lines extend to the right of the chart, providing visual reference points for traders.
How It Works:
Session Detection:
The script checks whether the current time falls within the overnight session:
If the hour is greater than or equal to the start hour (e.g., 17:00).
Or if the hour is less than or equal to the end hour (e.g., 09:30), considering the next day.
The end minute (e.g., 30 minutes past the hour) is also considered for precision.
High and Low Calculation:
During the overnight session:
If the overnightHigh is not yet defined, it initializes with the current candle's high.
If already defined, it updates by comparing the current candle's high to the existing overnightHigh using the math.max function.
Similarly, overnightLow is initialized or updated using the math.min function.
Post-Session Reset:
After the session ends, the script clears the overnightHigh and overnightLow variables by setting them to na (not available).
Line Drawing:
The script draws horizontal dotted lines for the Overnight High and Low during and after the session.
The lines extend indefinitely to the right of the chart.
Benefits:
Visual Aid: Helps traders quickly identify overnight support and resistance levels, which are critical for intraday trading.
Automation: Removes the need for manually plotting these levels each day.
Customizable: Time settings can be adjusted to match different markets or trading strategies.
This script is ideal for traders who use the overnight range as part of their analysis for breakouts, reversals, or trend continuation strategies.
IU Opening range Breakout StrategyIU Opening Range Breakout Strategy
This Pine Script strategy is designed to capitalize on the breakout of the opening range, which is a popular trading approach. The strategy identifies the high and low prices of the opening session and takes trades based on price crossing these levels, with built-in risk management and trade limits for intraday trading.
Key Features:
1. Risk Management:
- Risk-to-Reward Ratio (RTR):
Set a customizable risk-to-reward ratio to calculate target prices based on stop-loss levels.
Default: 2:1
- Max Trades in a Day:
Specify the maximum number of trades allowed per day to avoid overtrading.
Default: 2 trades in a day.
- End-of-Day Close:
Automatically closes all open positions at a user-defined session end time to ensure no overnight exposure.
Default: 3:15 PM
2. Opening Range Identification
- Opening Range High and Low:
The script detects the high and low of the first trading session using Pine Script's session functions.
These levels are plotted as visual guides on the chart:
- High: Lime-colored circles.
- Low: Red-colored circles.
3. Trade Entry Logic
- Long Entry:
A long trade is triggered when the price closes above the opening range high.
- Entry condition: Crossover of the price above the opening range high.
-Short Entry:
A short trade is triggered when the price closes below the opening range low.
- Entry condition: Crossunder of the price below the opening range low.
Both entries are conditional on the absence of an existing position.
4. Stop Loss and Take Profit
- Long Position:
- Stop Loss: Previous candle's low.
- Take Profit: Calculated based on the RTR.
- **Short Position:**
- **Stop Loss:** Previous candle's high.
- **Take Profit:** Calculated based on the RTR.
The strategy plots these levels for visual reference:
- Stop Loss: Red dashed lines.
- Take Profit: Green dashed lines.
5. Visual Enhancements
-Trade Level Highlighting:
The script dynamically shades the areas between the entry price and SL/TP levels:
- Red shading for the stop-loss region.
- Green shading for the take-profit region.
- Entry Price Line:
A silver-colored line marks the average entry price for active trades.
How to Use:
1.Input Configuration:
Adjust the Risk-to-Reward ratio, max trades per day, and session end time to suit your trading preferences.
2.Visual Cues:
Use the opening range high/low lines and shading to identify potential breakout opportunities.
3.Execution:
The strategy will automatically enter and exit trades based on the conditions. Review the plotted SL and TP levels to monitor the risk-reward setup.
Important Notes:
- This strategy is designed for intraday trading and works best in markets with high volatility during the opening session.
- Backtest the strategy on your preferred market and timeframe to ensure compatibility.
- Proper risk management and position sizing are essential when using this strategy in live markets.
Zuzukinho//@version=5
indicator("Gelişmiş Al/Sat Botu", overlay=true)
// Parametreler
rsiLength = input(14, title="RSI Uzunluğu")
macdFast = input(12, title="MACD Hızlı EMA")
macdSlow = input(26, title="MACD Yavaş EMA")
macdSignal = input(9, title="MACD Sinyal")
bbLength = input(20, title="Bollinger Band Uzunluğu")
bbMult = input(2.0, title="Bollinger Band Çarpanı")
maLength = input(50, title="Hareketli Ortalama Uzunluğu")
hacimCizelge = input(true, title="Hacim Çizelgesi Göster")
// RSI Hesaplama
rsiValue = ta.rsi(close, rsiLength)
// MACD Hesaplama
= ta.macd(close, macdFast, macdSlow, macdSignal)
macdHist = macdLine - signalLine
// Bollinger Bantları Hesaplama
basis = ta.sma(close, bbLength)
deviation = ta.stdev(close, bbLength)
upperBand = basis + bbMult * deviation
lowerBand = basis - bbMult * deviation
// Hareketli Ortalama Hesaplama
ma = ta.sma(close, maLength)
// Hacim Filtreleme
ortalamaHacim = ta.sma(volume, rsiLength)
yuksekHacim = volume > ortalamaHacim
// Alım ve Satım Sinyalleri
alSinyali = ta.crossover(rsiValue, 30) and macdHist > 0 and close < lowerBand and close > ma and yuksekHacim
satSinyali = ta.crossunder(rsiValue, 70) and macdHist < 0 and close > upperBand and close < ma and yuksekHacim
// Grafikte Gösterim
plotshape(alSinyali, title="Al Sinyali", location=location.belowbar, color=color.green, style=shape.labelup, size=size.small)
plotshape(satSinyali, title="Sat Sinyali", location=location.abovebar, color=color.red, style=shape.labeldown, size=size.small)
// Bollinger Bantları Çizimi
plot(upperBand, title="Üst Bollinger Bandı", color=color.new(color.blue, 50))
plot(lowerBand, title="Alt Bollinger Bandı", color=color.new(color.blue, 50))
// Hareketli Ortalama Çizimi
plot(ma, title="Hareketli Ortalama", color=color.orange)
// Hacim Çizelgesi
hacimPlot = hacimCizelge ? volume : na
plot(hacimPlot, color=color.new(color.blue, 50), title="Hacim")
Katalyst's Opening Range BreakoutKatalyst's Opening Range Breakout + No Trade Zone
📜 Overview:
This indicator allows traders to visualize the high and low of the opening range for a user-selected timeframe (e.g., 30s, 1m, 5m, 15m). It features fully customizable lines, labels, and an optional **No Trade Zone** fill to help you identify breakout levels with ease.
---
🎯 Key Features:
1. **Customizable Opening Range**:
- Select your preferred opening range duration: **30 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes, or 15 minutes**.
- The indicator calculates and plots the **high** and **low** of the selected opening range.
2. **Dynamic Line Styling**:
- Choose the **line color**, **transparency**, and **style**: **Solid, Dashed, or Dotted**.
- Lines extend to the right of the chart for clarity.
3. **No Trade Zone** *(Optional / Disabled by default)*:
- When enabled, fills the area between the high and low lines with a customizable **color and transparency**.
- Helps visually identify consolidation areas where trading might be avoided.
4. **Labels for Precision**:
- Clearly displays the **Opening Range High** and **Low** values.
- Labels are color-coded and positioned dynamically for easy interpretation.
5. **Clean and Efficient Updates**:
- The indicator deletes old lines, labels, and fills before creating new ones, ensuring a clutter-free chart.
---
⚙️ How to Use:
1. **Select Your Timeframe**:
- From the settings, choose your desired opening range duration: 30s, 1m, 2m, 5m, 10m, or 15m.
2. **Customize the Visuals**:
- Adjust line color, style, and transparency.
- Enable the **No Trade Zone** for a transparent background fill between the high and low lines.
3. **Interpret the Breakout**:
- Watch for price movements above or below the **opening range** to identify potential breakout opportunities.
---
🛠 Settings:
Opening Range Duration: Select the timeframe for the opening range (30s, 1m, 2m, 5m, 10m, 15m).
Line Color: Set the color of the range lines.
Line Transparency: Adjust the transparency of the lines (0 = solid, 100 = invisible).
Line Style: Choose line style: Solid, Dashed, or Dotted.
Label Colors: Customize the label colors for the high and low values.
Enable No Trade Zone: Fill the area between high and low lines with a transparent color.
No Trade Zone Color: Set the fill color for the no trade zone.
No Trade Zone Transparency: Adjust the transparency of the no trade zone fill.
---
📈 Ideal For
Day traders and scalpers looking to trade **breakouts**.
Traders who want to identify areas of consolidation visually.
Anyone who relies on the **opening range** for their trading strategy.
---
🔍 Example Usage:
Set the opening range to **5 minutes** and enable the **No Trade Zone** with a light red fill.
Watch for price to break above or below the high/low lines to signal potential trade opportunities.
---
✨ Why Use This Indicator?
This script simplifies your breakout strategy by providing a clear, visually appealing representation of the opening range. The flexible customization options and the optional **No Trade Zone** make it a powerful tool for identifying high-probability trades.
---
Let me know if you need any additional tweaks or clarifications for this description. It's all set to help traders understand and use your powerful script! 🚀📈
Market Anomaly Detector (MAD)Market Anomaly Detector (MAD) Indicator - Detailed Description:
The Market Anomaly Detector (MAD) Indicator is a unique tool designed to identify potential market anomalies by combining several price action-based and momentum indicators. This indicator is especially useful for traders who seek to identify significant market shifts and anomalies before they become visible in conventional technical indicators.
Key Features of the MAD Indicator:
1. Z-Score Threshold for Anomaly Detection:
• The Z-Score measures how far a current price is from its average over a defined period, normalized by standard deviation. This allows the MAD indicator to detect outliers or anomalies in price movements.
• By adjusting the Z-Score Threshold, traders can tune the sensitivity of the indicator to capture only the most significant price deviations, filtering out noise and reducing false signals.
2. Volume and Liquidity Filter:
• Volume is a key indicator of market participation and sentiment. The MAD Indicator uses a volume multiplier to assess when price movements are supported by sufficient trading volume.
• A volume spike is identified when the current volume exceeds the average volume by a certain multiplier. This ensures that only high-confidence signals are generated, particularly useful for spotting trend reversals and breakout opportunities.
3. Signal Cooldown Period:
• To prevent overfitting and reduce false signals, a signal cooldown period is implemented. Once a buy or sell signal is triggered, the indicator waits for a specified number of bars (e.g., 5) before triggering another signal, even if the price action meets the criteria for a new signal. This helps maintain a cleaner trading environment and avoids confusion when the market is volatile.
4. Upper and Lower Bands for Trend Confirmation:
• The MAD Indicator uses bands based on the mean price and standard deviation, similar to Bollinger Bands. These upper and lower bands help to define the expected price range for a given period, indicating overbought or oversold conditions.
• The combination of Z-Score, volume, and band analysis helps pinpoint when the price breaks out of expected ranges, providing early warning signs for potential market shifts.
5. Trend Confirmation from Higher Timeframes:
• The MAD Indicator includes a multi-timeframe approach to trend confirmation, using the 50-period EMA on a higher timeframe (e.g., 1-hour chart). This ensures that signals are aligned with the overall market trend, enhancing the reliability of buy and sell signals.
How It Works:
• The MAD Indicator continuously monitors price action, volume, and statistical anomalies, using the Z-Score to determine when the price is significantly deviating from its historical average.
• When the price breaks above the upper band and a bullish anomaly is detected, a buy signal is generated. (Green Background)
• Similarly, when the price breaks below the lower band and a bearish anomaly is detected, a sell signal is triggered. (Red Background
• By filtering signals based on volume and using the cooldown period, the MAD Indicator ensures that only high-quality trades are signaled.
How to Use the MAD Indicator:
• Buy Signal: Occurs when the price breaks above the upper band and there is a significant deviation from the mean (bullish anomaly).
• Sell Signal: Occurs when the price breaks below the lower band and there is a significant deviation from the mean (bearish anomaly).
• Volume Confirmation: Ensure that the buy/sell signals are supported by a volume spike, indicating strong market participation.
• Signal Cooldown Period: After a signal is triggered, the indicator waits for the cooldown period to avoid triggering multiple signals in quick succession.
Why It’s Worth Paying For:
The MAD Indicator combines advanced statistical analysis (Z-Score), price action, and volume analysis to identify market anomalies and breakouts before they are visible on standard indicators. By leveraging the power of mean reversion and statistical anomalies, this tool provides traders with high-confidence signals that can lead to profitable trades, especially in volatile markets. The integration of a multi-timeframe trend filter ensures that signals are aligned with the overall market trend, reducing the likelihood of false breakouts.
This indicator is ideal for trend-following traders looking for high-probability entries and mean-reversion traders aiming to capture price deviations. The signal cooldown period and volume filter provide an additional layer of precision, ensuring that you only act on the strongest market signals.
Crypto$ure EMA with 4H Trend TableThe Crypto AMEX:URE EMA indicator provides a clear, multi-timeframe confirmation setup to help you align your shorter-term trades with the broader market trend.
Key Features:
4-Hour EMA Trend Insight:
A table, displayed at the top-right corner of your chart, shows the current 4-hour EMA value and whether the 4-hour trend is Bullish, Bearish, or Neutral. This gives you a reliable, higher-timeframe perspective, making it easier to understand the general market direction.
Lower Timeframe Signals (e.g., 25m or 15m):
On your chosen chart timeframe, the indicator plots two EMAs (Fast and Slow).
A Buy Signal (an up arrow) appears when the Fast EMA crosses above the Slow EMA, indicating potential upward momentum.
A Sell Signal (a down arrow) appears when the Fast EMA crosses below the Slow EMA, indicating potential downward momentum.
Manual Confirmation for Better Accuracy:
While the Buy/Sell signals come directly from the shorter timeframe, you can use the 4-hour trend information from the table to confirm or filter these signals. For example, if the 4-hour trend is Bullish, the Buy signals on the shorter timeframe may carry more weight. If it’s Bearish, then the Sell signals might be more reliable.
How to Use:
Add the Crypto AMEX:URE EMA indicator to your chart.
Check the top-right table to see the current 4-hour EMA trend.
Watch for Buy (up arrow) or Sell (down arrow) signals on your current timeframe.
For added confidence, consider taking Buy signals only when the 4-hour trend is Bullish and Sell signals when the 4-hour trend is Bearish.
Note:
This indicator does not generate trading orders. Instead, it provides actionable insights to help guide your discretionary decision-making. Always consider additional market context, risk management practices, and personal trading rules before acting on any signal.
BTC/USDT Volume-Based StrategyOverview
There is a distinct difference between the buying pressure exerted by individual investors and the buying pressure of institutional or "whale" traders. Monitoring volume data over a shorter period of time is crucial to distinguish these subtle differences. When whale investors or other significant market players signal price increases, volume often surges noticeably. Indeed, volume often acts as an important leading indicator in market dynamics.
Key Features
This metric, calibrated with a 5-minute Bitcoin spot chart, identifies a significant inflow of trading volume. For every K-plus surge in trading volume, those candles are shown in a green circle.
When a green circle appears, consider active long positions in subsequent declines and continue to accumulate long positions despite temporary price declines. Pay attention to the continuity of the increase in volume before locking in earnings even after the initial bullish wave.
Conversely, it may be wise to reevaluate the long position if the volume is not increasing in parallel and the price is rising. Under these conditions, starting a partial short position may be advantageous until a larger surge in volume reappears.
COIN/BTC Volume-Weighted DivergenceThe COIN/BTC Volume-Weighted Divergence indicator identifies buy and sell signals by analyzing deviations between Coinbase and Bitcoin prices relative to their respective VWAPs (Volume-Weighted Average Price). This method isolates points of potential trend reversals, overextensions, or relative mispricing based on volume-adjusted price benchmarks.
The indicator leverages Coinbase’s high beta relative to Bitcoin in bull markets. A buy signal occurs when Coinbase is below VWAP (indicating undervaluation) while Bitcoin is above VWAP (signaling strong broader momentum). A sell signal is generated when Coinbase trades above VWAP (indicating overvaluation) while Bitcoin moves below VWAP (indicating weakening momentum).
This divergence logic enables traders to identify misalignment between Bitcoin-driven market trends and Coinbase’s price behavior. The indicator effectively identifies undervalued entry points and signals exits before speculative extensions are correct. It provides a systematic approach to trading during trending conditions, aligning decisions with volume-weighted price dynamics and inter-asset relationships.
How It Works
1. VWAP:
“fair value” benchmark combining price and volume.
• Above VWAP: Bullish momentum.
• Below VWAP: Bearish momentum.
2. Divergence:
• Coinbase Divergence: close - coin_vwap (distance from COIN’s VWAP).
• Bitcoin Divergence: btc_price - btc_vwap (distance from BTC’s VWAP).
3. Signals:
• Buy: Coinbase is below VWAP (potentially oversold), and Bitcoin is above VWAP (broader bullish trend).
• Sell: Coinbase is above VWAP (potentially overbought), and Bitcoin is below VWAP (broader bearish trend).
4. Visualization:
• Green triangle: Buy signal.
• Red triangle: Sell signal.
Strengths
• Combines price and volume for reliable insights.
• Highlights potential trend reversals or overextensions.
• Exploits correlations between Coinbase and Bitcoin.
Limitations
• Struggles in sideways markets.
• Sensitive to volume spikes, which may distort VWAP.
• Ineffective in strong trends where divergence persists.
Improvements
1. Z-Scores: Use statistical thresholds (e.g., ±2 std dev) for stronger signals.
2. Volume Filter: Generate signals only during high-volume periods.
3. Momentum Confirmation: Combine with RSI or MACD for better reliability.
4. Multi-Timeframe VWAP: Use intraday, daily, and weekly VWAPs for deeper analysis.
Complementary Tools
• Momentum Indicators: RSI, MACD for trend validation.
• Volume-Based Metrics: OBV, cumulative delta volume.
• Support/Resistance Levels: Enhance reversal accuracy.
Quantify [Entry Model] | FractalystWhat’s the indicator’s purpose and functionality?
Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.
➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.
Can the indicator be applied to any market approach/trading strategy?
Absolutely, all trading strategies share one fundamental element: Directional Bias
Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.
The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How can this indicator help me to refine my trading strategy?
1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.
2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.
3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.
4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.
5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.
6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.
How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.
4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.
5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.
How does the indicator calculate the current range?
The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).
What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
What does the multi-timeframe functionality offer?
You can incorporate up to 4 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
How does the Indicator Identifies Positive Expected Values?
Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).
To determine a positive EV setup, the indicator uses the formula:
EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))
where:
- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.
How can I know that the setup I'm going to trade with has a positive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
What machine learning techniques are used in Quantify?
Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.
2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.
How does machine learning affect the expected value calculations in Quantify?
Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
What is a trailing stoploss, and how does it work in Quantify?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?
Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.
2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.
3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.
🎲 Becoming the House: Gaining an Edge Over the Market
In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.
From a Trader’s Perspective
In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.
This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.
🎰 Applying the Concept to Trading
Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.
By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.
What Makes the Quantify Indicator Original?
1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.
2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.
3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.
4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.
5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.
6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.
7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Salience Theory Crypto Returns (AiBitcoinTrend)The Salience Theory Crypto Returns Indicator is a sophisticated tool rooted in behavioral finance, designed to identify trading opportunities in the cryptocurrency market. Based on research by Bordalo et al. (2012) and extended by Cai and Zhao (2022), it leverages salience theory—the tendency of investors, particularly retail traders, to overemphasize standout returns.
In the crypto market, dominated by sentiment-driven retail investors, salience effects are amplified. Attention disproportionately focused on certain cryptocurrencies often leads to temporary price surges, followed by reversals as the market stabilizes. This indicator quantifies these effects using a relative return salience measure, enabling traders to capitalize on price reversals and trends, offering a clear edge in navigating the volatile crypto landscape.
👽 How the Indicator Works
Salience Measure Calculation :
👾 The indicator calculates how much each cryptocurrency's return deviates from the average return of all cryptos over the selected ranking period (e.g., 21 days).
👾 This deviation is the salience measure.
👾 The more a return stands out (salient outcome), the higher the salience measure.
Ranking:
👾 Cryptos are ranked in ascending order based on their salience measures.
👾 Rank 1 (lowest salience) means the crypto is closer to the average return and is more predictable.
👾 Higher ranks indicate greater deviation and unpredictability.
Color Interpretation:
👾 Green: Low salience (closer to average) – Trending or Predictable.
👾 Red/Orange: High salience (far from average) – Overpriced/Unpredictable.
👾 Text Gradient (Teal to Light Blue): Helps visualize potential opportunities for mean reversion trades (i.e., cryptos that may return to equilibrium).
👽 Core Features
Salience Measure Calculation
The indicator calculates the salience measure for each cryptocurrency by evaluating how much its return deviates from the average market return over a user-defined ranking period. This measure helps identify which assets are trending predictably and which are likely to experience a reversal.
Dynamic Ranking System
Cryptocurrencies are dynamically ranked based on their salience measures. The ranking helps differentiate between:
Low Salience Cryptos (Green): These are trending or predictable assets.
High Salience Cryptos (Red): These are overpriced or deviating significantly from the average, signaling potential reversals.
👽 Deep Dive into the Core Mathematics
Salience Theory in Action
Salience theory explains how investors, particularly in the crypto market, tend to prefer assets with standout returns (salient outcomes). This behavior often leads to overpricing of assets with high positive returns and underpricing of those with standout negative returns. The indicator captures these deviations to anticipate mean reversions or trend continuations.
Salience Measure Calculation
// Calculate the average return
avgReturn = array.avg(returns)
// Calculate salience measure for each symbol
salienceMeasures = array.new_float()
for i = 0 to array.size(returns) - 1
ret = array.get(returns, i)
salienceMeasure = math.abs(ret - avgReturn) / (math.abs(ret) + math.abs(avgReturn) + 0.1)
array.push(salienceMeasures, salienceMeasure)
Dynamic Ranking
Cryptos are ranked in ascending order based on their salience measures:
Low Ranks: Cryptos with low salience (predictable, trending).
High Ranks: Cryptos with high salience (unpredictable, likely to revert).
👽 Applications
👾 Trend Identification
Identify cryptocurrencies that are currently trending with low salience measures (green). These assets are likely to continue their current direction, making them good candidates for trend-following strategies.
👾 Mean Reversion Trading
Cryptos with high salience measures (red to light blue) may be poised for a mean reversion. These assets are likely to correct back towards the market average.
👾 Reversal Signals
Anticipate potential reversals by focusing on high-ranked cryptos (red). These assets exhibit significant deviation and are prone to price corrections.
👽 Why It Works in Crypto
The cryptocurrency market is dominated by retail investors prone to sentiment-driven behavior. This leads to exaggerated price movements, making the salience effect a powerful predictor of reversals.
👽 Indicator Settings
👾 Ranking Period : Number of bars used to calculate the average return and salience measure.
Higher Values: Smooth out short-term volatility.
Lower Values: Make the ranking more sensitive to recent price movements.
👾 Number of Quantiles : Divide ranked assets into quantile groups (e.g., quintiles).
Higher Values: More detailed segmentation (deciles, percentiles).
Lower Values: Broader grouping (quintiles, quartiles).
👾 Portfolio Percentage : Percentage of the portfolio allocated to each selected asset.
Enter a percentage (e.g., 20 for 20%), automatically converted to a decimal (e.g., 0.20).
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Loacally Weighted MA (LWMA) Direction HistogramThe Locally Weighted Moving Average (LWMA) Direction Histogram indicator is designed to provide traders with a visual representation of the price momentum and trend direction. This Pine Script, written in version 6, calculates an LWMA by assigning higher weights to recent data points, emphasizing the most current market movements. The script incorporates user-defined input parameters, such as the LWMA length and a direction lookback period, making it flexible to adapt to various trading strategies and preferences.
The histogram visually represents the difference between the current LWMA and a previous LWMA value (based on the lookback period). Positive values are colored blue, indicating upward momentum, while negative values are yellow, signaling downward movement. Additionally, the script colors candlesticks according to the histogram's value, enhancing clarity for users analyzing market trends. The LWMA line itself is plotted on the chart but hidden by default, enabling traders to toggle its visibility as needed. This blend of histogram and candlestick visualization offers a comprehensive tool for identifying shifts in momentum and potential trading opportunities.
ATR for Aggregated Bars (2 Bars)Range Bar ATR Indicator: Detailed Description and Usage Guide
This script is a custom indicator designed specifically for Range Bar charts , tailored to help traders understand and navigate market conditions by utilizing the Average True Range (ATR) concept. The indicator adapts the traditional ATR to work effectively with Range Bar charts, where bars have a fixed range rather than being time-based.
How It Works
1. ATR Calculation on Range Bars :
- Unlike time-based charts, Range Bar charts focus on price movement within a fixed range.
- The indicator calculates ATR by pairing consecutive bars, treating every two bars as a single unit . This pairing ensures that the ATR reflects price movement effectively on Range Bar charts.
2. Short and Long Period ATR Values :
- The script displays two ATR values :
- A short-period ATR , calculated over a smaller number of paired bars.
- A long-period ATR , calculated over a larger number of paired bars.
- These values provide a dynamic view of both recent and longer-term market volatility.
Why Use This Indicator?
The primary goal is to provide a meaningful adaptation of the ATR indicator for Range Bar charts, allowing traders to make informed decisions similar to using ATR on traditional time-based charts.
Key Applications
Determine a Better Custom Range :
- Analyze the ATR values to choose an optimal range size for Range Bar charts, ensuring better alignment with market conditions.
Assess Market Volatility :
- Rising volatility : When the short-period ATR value is higher than the long-period value, it signals increasing volatility.
- Decreasing volatility : When the short-period ATR value is lower, it indicates declining volatility.
Risk and Stop Loss Management :
- Use the higher ATR value (e.g., the long-period ATR) to calculate minimum stop loss levels. Multiply the ATR by 1.5 or 2 to set a safe buffer against market fluctuations.
How to Use It
1. Add the script to a Range Bar chart.
2. Configure the short and long ATR periods to suit your trading style and preferences.
3. Observe the displayed ATR values:
- Use these values to analyze market conditions and adapt your strategy accordingly.
4. Apply insights from the ATR values for:
- Determining custom Range Bar settings.
- Evaluating volatility trends.
- Setting effective risk parameters like stop loss levels.
Benefits
- Provides a tailored ATR tool for Range Bar charts, addressing the unique challenges of fixed-range trading.
- Offers both short-term and long-term perspectives on volatility.
- Enhances decision-making for range settings, volatility analysis, and risk management.
This indicator bridges the gap between traditional ATR indicators and the specific needs of Range Bar chart users, making it a versatile tool for traders.
ka66: Candle Range MarkThis is a simple trailing stop loss tool using bar ranges, to be used with some discretion and understanding of basic price action.
Given a configurable percentage value, e.g. 25%:
A bullish bar (close > open) will be marked at the lower 25%
A bearish bar (close < open) will be marked at the upper 25%
The idea is to move your stop loss after each completed bar in the direction of the trade, at the configured percentage value.
If you have an inside bar, or something very close to it, or a doji-type bar, don't trail that, because there is no clarity of what the bar means, we can only wait.
The chart shows an example use, with trailing at 10% of the bar, from the initial stop loss after entry, trailing till we get stopped out. Some things to note:
Because this example focuses on a short trade, we ignore the bullish candles, and keep our trailing stop at the last bearish candle.
We ignore doji-esque candles and inside bars, where the body is in the range of the prior candle. Some definitions of inside bars include the wicks as well. I don't have a strong opinion, and this example is just for illustration. Furthermore, the inside bar will likely be the opposite of the swing bars (e.g. bullish bar in a range of bearish bars), so our stop remains unchanged.
One could use this semi-systematic approach in scalping on any timeframe, for example to maximise gains, adjusting the bar percentage as needed.
Zero-Lag MA CandlesThe Zero-Lag MA Candles indicator combines the efficiency of a Zero-Lag Moving Average (ZLMA) with dynamic candlestick coloring to provide a clear visual representation of market trends. By leveraging a dual EMA-based calculation, the ZLMA achieves reduced lag, enhancing its responsiveness to price changes. The indicator plots candles on the chart with colors determined by the trend direction of the ZLMA over a user-defined lookback period. Blue candles signify an uptrend, while yellow candles indicate a downtrend, offering traders an intuitive way to identify market sentiment.
This indicator is particularly useful for trend-following strategies, as the crossover and crossunder between the ZLMA and the standard EMA highlight potential reversal points or trend continuation zones. With customizable inputs for ZLMA length, trend lookback period, and color schemes, it caters to diverse trading preferences. Its ability to plot directly on the chart ensures seamless integration with other analysis tools, making it a valuable addition to a trader's toolkit.
Happy trading...
