Geometric Momentum Breakout with Monte CarloOverview
This experimental indicator uses geometric trendline analysis combined with momentum and Monte Carlo simulation techniques to help visualize potential breakout areas. It calculates support, resistance, and an aggregated trendline using a custom Geo library (by kaigouthro). The indicator also tracks breakout signals in a way that a new buy signal is triggered only after a sell signal (and vice versa), ensuring no repeated signals in the same direction.
Important:
This script is provided for educational purposes only. It is experimental and should not be used for live trading without proper testing and validation.
Key Features
Trendline Calculation:
Uses the Geo library to compute support and resistance trendlines based on historical high and low prices. The midpoint of these trendlines forms an aggregated trendline.
Momentum Analysis:
Computes the Rate of Change (ROC) to determine momentum. Breakout conditions are met only if the price and momentum exceed a user-defined threshold.
Monte Carlo Simulation:
Simulates future price movements to estimate the probability of bullish or bearish breakouts over a specified horizon.
Signal Tracking:
A persistent variable ensures that once a buy (or sell) signal is triggered, it won’t repeat until the opposite signal occurs.
Geometric Enhancements:
Calculates an aggregated trend angle and channel width (distance between support and resistance), and draws a perpendicular “breakout zone” line.
Table Display:
A built-in table displays key metrics including:
Bullish probability
Bearish probability
Aggregated trend angle (in degrees)
Channel width
Alerts:
Configurable alerts notify when a new buy or sell breakout signal occurs.
Inputs
Resistance Lookback & Support Lookback:
Number of bars to look back for determining resistance and support points.
Momentum Length & Threshold:
Period for ROC calculation and the minimum percentage change required for a breakout confirmation.
Monte Carlo Simulation Parameters:
Simulation Horizon: Number of future bars to simulate.
Simulation Iterations: Number of simulation runs.
Table Position & Text Size:
Customize where the table is displayed on the chart and the size of the text.
How to Use
Add the Script to Your Chart:
Copy the code into the Pine Script editor on TradingView and add it to your chart.
Adjust Settings:
Customize the inputs (e.g., lookback periods, momentum threshold, simulation parameters) to fit your analysis or educational requirements.
Interpret Signals:
A buy signal is plotted as a green triangle below the bar when conditions are met and the state transitions from neutral or sell.
A sell signal is plotted as a red triangle above the bar when conditions are met and the state transitions from neutral or buy.
Alerts are triggered only on the bar where a new signal is generated.
Examine the Table:
The table displays key metrics (breakout probabilities, aggregated trend angle, and channel width) to help evaluate current market conditions.
Disclaimer
This indicator is experimental and provided for educational purposes only. It is not intended as a trading signal or financial advice. Use this script at your own risk, and always perform your own research and testing before using any experimental tools in live trading.
Credit
This indicator uses the Geo library by kaigouthro. Special thanks to Cryptonerds and @Hazzantazzan for their contributions and insights.
Komut dosyalarını "track" için ara
Shadow Edge (Example)This script tracks hourly price extremes (highs/lows) and their equilibrium (midpoint), plotting them as dynamic reference lines on your chart. It helps visualize intraday support/resistance levels and potential price boundaries.
Key Features
Previous Hour Levels (Static Lines):
PH (Previous Hour High): Red line.
PL (Previous Hour Low): Green line.
P.EQ (Previous Hour Equilibrium): Blue midpoint between PH and PL.
Current Hour Levels (Dynamic/Dotted Lines):
MuEH (Current Hour High): Yellow dashed line (updates in real-time).
MuEL (Current Hour Low): Orange dashed line (updates in real-time).
Labels: Clear text labels on the right edge of the chart for easy readability.
How It Works
Hourly Tracking:
Detects new hours using the hour(time) function.
Resets high/low values at the start of each hour.
Stores the previous hour’s PH, PL, and P.EQ when a new hour begins.
Dynamic Updates:
Continuously updates MuEH and MuEL during the current hour to reflect the latest extremes.
Customization
Toggle visibility of lines via inputs:
Enable/disable PH, PL, P.EQ, MuEH, MuEL individually.
Adjustable colors and line styles (solid for previous hour, dashed for current hour).
Use Case
Intraday Traders: Identify hourly ranges, breakout/retracement opportunities, or mean-reversion setups.
Visual Reference: Quickly see where price is relative to recent hourly activity.
Technical Notes
Overlay: Plots directly on the price chart.
Efficiency: Uses var variables to preserve values between bars.
Labels: Only appear on the latest bar to avoid clutter.
This tool simplifies intraday price action analysis by combining historical and real-time hourly data into a single visual framework.
SMA Strategy Builder: Create & Prove Profitability📄 Pine Script Strategy Description (For Publishing on TradingView)
🎯 Strategy Title:
SMA Strategy Builder: Create & Prove Profitability
✨ Description:
This tool is designed for traders who want to build, customize, and prove their own SMA-based trading strategies. The strategy tracks capital growth in real-time, providing clear evidence of profitability after each trade. Users can adjust key parameters such as SMA period, take profit levels, and initial capital, making it a flexible solution for backtesting and strategy validation.
🔍 Key Features:
✅ SMA-Based Logic:
Core trading logic revolves around the Simple Moving Average (SMA).
SMA period is fully adjustable to suit various trading styles.
🎯 Customizable Take Profit (TP):
User-defined TP percentages per position.
TP line displayed as a Step Line with Breaks for clear segmentation.
Visual 🎯TP label for quick identification of profit targets.
💵 Capital Tracking (Proof of Profitability):
Initial capital is user-defined.
Capital balance updates after each closed trade.
Shows both absolute profit/loss and percentage changes for every position.
Darker green profit labels for better readability and dark red for losses.
📈 Capital Curve (Performance Visualization):
Capital growth curve available (hidden by default, can be enabled via settings).
📏 Dynamic Label Positioning:
Label positions adjust dynamically based on the price range.
Ensures consistent visibility across low and high-priced assets.
⚡ How It Works:
Long Entry:
Triggered when the price crosses above the SMA.
TP level is calculated as a user-defined percentage above the entry price.
Short Entry:
Triggered when the price crosses below the SMA.
TP level is calculated as a user-defined percentage below the entry price.
TP Execution:
Positions close immediately once the TP level is reached (no candle close confirmation needed).
🔔 Alerts:
🟩 Long Signal Alert: When the price crosses above the SMA.
🟥 Short Signal Alert: When the price crosses below the SMA.
🎯 TP Alert: When the TP target is reached.
⚙️ Customization Options:
📅 SMA Period: Choose the moving average period that best fits your strategy.
🎯 Take Profit (%): Adjust TP percentages for flexible risk management.
💵 Initial Capital: Set the starting capital for realistic backtesting.
📈 Capital Curve Toggle: Enable or disable the capital curve to track overall performance.
🌟 Why Use This Tool?
🔧 Flexible Strategy Creation: Adjust core parameters and create tailored SMA-based strategies.
📈 Performance Proof: Capital tracking acts as real proof of profitability after each trade.
🎯 Immediate TP Execution: No waiting for candle closures; profits lock in as soon as targets are hit.
💹 Comprehensive Performance Insights: Percentage-based and absolute capital tracking with dynamic visualization.
🏦 Clean Visual Indicators: Strategy insights made clear with dynamic labeling and adjustable visuals.
⚠️ Disclaimer:
This script is provided for educational and informational purposes only. Trading financial instruments carries risk, and past performance does not guarantee future results. Always perform your own due diligence before making any trading decisions.
VFV Correction Levels
This Pine Script, "VFV Correction Levels," identifies significant daily price corrections and calculates corresponding investments based on fixed thresholds (paliers). Key features include:
Six predefined correction levels trigger investments between $150 and $600 based on the percentage drop.
Larger corrections correspond to higher investment amounts.
Graphical Indicators:
Visual labels mark correction levels and display investment amounts directly on the chart.
Investment Tracking:
Calculates total invested and tracks performance (yield percentage) relative to the initial correction price.
Sunil BB Blast Heikin Ashi StrategySunil BB Blast Heikin Ashi Strategy
The Sunil BB Blast Heikin Ashi Strategy is a trend-following trading strategy that combines Bollinger Bands with Heikin-Ashi candles for precise market entries and exits. It aims to capitalize on price volatility while ensuring controlled risk through dynamic stop-loss and take-profit levels based on a user-defined Risk-to-Reward Ratio (RRR).
Key Features:
Trading Window:
The strategy operates within a user-defined time window (e.g., from 09:20 to 15:00) to align with market hours or other preferred trading sessions.
Trade Direction:
Users can select between Long Only, Short Only, or Long/Short trade directions, allowing flexibility depending on market conditions.
Bollinger Bands:
Bollinger Bands are used to identify potential breakout or breakdown zones. The strategy enters trades when price breaks through the upper or lower Bollinger Band, indicating a possible trend continuation.
Heikin-Ashi Candles:
Heikin-Ashi candles help smooth price action and filter out market noise. The strategy uses these candles to confirm trend direction and improve entry accuracy.
Risk Management (Risk-to-Reward Ratio):
The strategy automatically adjusts the take-profit (TP) level and stop-loss (SL) based on the selected Risk-to-Reward Ratio (RRR). This ensures that trades are risk-managed effectively.
Automated Alerts and Webhooks:
The strategy includes automated alerts for trade entries and exits. Users can set up JSON webhooks for external execution or trading automation.
Active Position Tracking:
The strategy tracks whether there is an active position (long or short) and only exits when price hits the pre-defined SL or TP levels.
Exit Conditions:
The strategy exits positions when either the take-profit (TP) or stop-loss (SL) levels are hit, ensuring risk management is adhered to.
Default Settings:
Trading Window:
09:20-15:00
This setting confines the strategy to the specified hours, ensuring trading only occurs during active market hours.
Strategy Direction:
Default: Long/Short
This allows for both long and short trades depending on market conditions. You can select "Long Only" or "Short Only" if you prefer to trade in one direction.
Bollinger Band Length (bbLength):
Default: 19
Length of the moving average used to calculate the Bollinger Bands.
Bollinger Band Multiplier (bbMultiplier):
Default: 2.0
Multiplier used to calculate the upper and lower bands. A higher multiplier increases the width of the bands, leading to fewer but more significant trades.
Take Profit Multiplier (tpMultiplier):
Default: 2.0
Multiplier used to determine the take-profit level based on the calculated stop-loss. This ensures that the profit target aligns with the selected Risk-to-Reward Ratio.
Risk-to-Reward Ratio (RRR):
Default: 1.0
The ratio used to calculate the take-profit relative to the stop-loss. A higher RRR means larger profit targets.
Trade Automation (JSON Webhooks):
Allows for integration with external systems for automated execution:
Long Entry JSON: Customizable entry condition for long positions.
Long Exit JSON: Customizable exit condition for long positions.
Short Entry JSON: Customizable entry condition for short positions.
Short Exit JSON: Customizable exit condition for short positions.
Entry Logic:
Long Entry:
The strategy enters a long position when:
The Heikin-Ashi candle shows a bullish trend (green close > open).
The price is above the upper Bollinger Band, signaling a breakout.
The previous candle also closed higher than it opened.
Short Entry:
The strategy enters a short position when:
The Heikin-Ashi candle shows a bearish trend (red close < open).
The price is below the lower Bollinger Band, signaling a breakdown.
The previous candle also closed lower than it opened.
Exit Logic:
Take-Profit (TP):
The take-profit level is calculated as a multiple of the distance between the entry price and the stop-loss level, determined by the selected Risk-to-Reward Ratio (RRR).
Stop-Loss (SL):
The stop-loss is placed at the opposite Bollinger Band level (lower for long positions, upper for short positions).
Exit Trigger:
The strategy exits a trade when either the take-profit or stop-loss level is hit.
Plotting and Visuals:
The Heikin-Ashi candles are displayed on the chart, with green candles for uptrends and red candles for downtrends.
Bollinger Bands (upper, lower, and basis) are plotted for visual reference.
Entry points for long and short trades are marked with green and red labels below and above bars, respectively.
Strategy Alerts:
Alerts are triggered when:
A long entry condition is met.
A short entry condition is met.
A trade exits (either via take-profit or stop-loss).
These alerts can be used to trigger notifications or webhook events for automated trading systems.
Notes:
The strategy is designed for use on intraday charts but can be applied to any timeframe.
It is highly customizable, allowing for tailored risk management and trading windows.
The Sunil BB Blast Heikin Ashi Strategy combines two powerful technical analysis tools (Bollinger Bands and Heikin-Ashi candles) with strong risk management, making it suitable for both beginners and experienced traders.
Feebacks are welcome from the users.
BBSS+This Pine Script implements a custom indicator overlaying Bollinger Bands with additional features for trend analysis using Exponential Moving Averages (EMAs). Here's a breakdown of its functionality:
Bollinger Bands:
The script calculates the Bollinger Bands using a 20-period Simple Moving Average (SMA) as the basis and a multiplier of 2 for the standard deviation.
It plots the Upper Band and Lower Band in red.
EMA Calculations:
Three EMAs are calculated for the close price with periods of 5, 10, and 40.
The EMAs are plotted in green (5-period), cyan (10-period), and orange (40-period) to distinguish between them.
Trend Detection:
The script determines bullish or bearish EMA alignments:
Bullish Order: EMA 5 > EMA 10 > EMA 40.
Bearish Order: EMA 5 < EMA 10 < EMA 40.
Entry Signals:
Long Entry: Triggered when:
The close price crosses above the Upper Bollinger Band.
The Upper Band is above its 5-period SMA (indicating momentum).
The EMAs are in a bullish order.
Short Entry: Triggered when:
The close price crosses below the Lower Bollinger Band.
The Lower Band is below its 5-period SMA.
The EMAs are in a bearish order.
Trend State Tracking:
A variable tracks whether the market is in a Long or Short trend based on conditions:
A Long trend continues unless conditions for a Short Entry are met or the Upper Band dips below its average.
A Short trend continues unless conditions for a Long Entry are met or the Lower Band rises above its average.
Visual Aids:
Signal Shapes:
Triangle-up shapes indicate Long Entry points below the bar.
Triangle-down shapes indicate Short Entry points above the bar.
Bar Colors:
Green bars indicate a Long trend.
Red bars indicate a Short trend.
This script combines Bollinger Bands with EMA crossovers to generate entry signals and visualize market trends, making it a versatile tool for identifying momentum and trend reversals.
FT SessionsFT Sessions
Overview
The FT Sessions is a highly customizable and powerful indicator designed for intraday traders who focus on session-based analysis. This script visually highlights global market sessions—Asia, Frankfurt, London, and New York (AM & PM)—on the chart, making it easier to track session ranges and analyze intraday price movements.
Key Features
Customizable Session Times and Colors:
Define your own session times and assign unique colors for better visibility.
Session Range Visualization:
Displays high and low ranges for each session.
Optional transparent range areas with outlines for clarity.
Configurable session range labels for enhanced readability.
Flexible Timezone Settings:
Choose a UTC offset or sync with the exchange's timezone.
User-Friendly Customization:
Compact settings for easier adjustments.
Enable or disable specific sessions to focus on relevant market activity.
How This Script Differs from LuxAlgo
This script draws inspiration from LuxAlgo's session tracking concept but has been developed with significant modifications and unique features:
Built from Scratch in Pine Script v5:
Fully optimized for Pine Script’s latest version, improving performance and functionality.
Expanded Session Range Features:
Five unique sessions (Asia, Frankfurt, London, New York AM, New York PM) with customizable ranges, colors, and labels.
Real-time updating of session ranges for improved intraday analysis.
4H Timeframe Optimization:
Automatically notifies users if applied to an unsupported timeframe, ensuring session accuracy.
Highly Configurable Input Options:
Advanced timezone handling and compact session management settings.
Unique Coding Structure:
Designed to maximize efficiency and minimize resource usage on TradingView.
While LuxAlgo focuses on session concepts, this script brings a fresh, customizable approach specifically tailored for intraday traders seeking precision in tracking session activity.
How It Works
The indicator tracks price movements within each session.
Highlights the high and low range of each session directly on the chart.
Updates session ranges in real-time to reflect evolving market conditions.
Practical Applications
Intraday Trading: Plan trades based on major market session ranges.
Breakout Strategies: Use session high and low levels to identify potential breakouts.
Session-Specific Patterns: Spot consolidations and reversals within session activity.
Important Notes
Optimized for the 4H timeframe. If applied to another timeframe, a notification will appear.
Best used in combination with other tools (e.g., volume or trend indicators) for a complete trading strategy.
Credits
This script draws inspiration from LuxAlgo's open-source session-tracking methodology. However, it introduces substantial improvements and unique features that set it apart. Full credit is given to LuxAlgo for their original open-source concept.
Disclaimer
This script is for informational and educational purposes only. Always test on a demo account before applying to live markets.
TearRepresentative's Rule-Based Dip Buying Strategy Rule-Based Dip Buying Strategy Indicator
This TradingView indicator, inspired by TearRepresentative [ , is a refined tool designed to assist traders in implementing a rule-based dip buying strategy. The indicator automates the identification of optimal buy and sell points, helping traders stay disciplined and minimize emotional biases. It is tailored to index trading, specifically leveraged ETFs like SPXL, to capture opportunities in market pullbacks and recoveries.
Key Features
Dynamic Buy Levels:
Tracks the local high over a customizable lookback period and calculates three buy levels based on percentage drops from the high:
Buy Level 1: First entry point (e.g., 15% drop).
Buy Level 2: Second entry point (e.g., additional 10% drop).
Buy Level 3: Third entry point (e.g., additional 7% drop).
Average Price Tracking:
Dynamically calculates the average price for entered positions when multiple buy levels are triggered.
Sell Level:
Computes a take-profit level (e.g., 20% above the average price) to automate profit-taking when the market rebounds.
Signal Visualization:
Buy Signals: Displayed as green triangles at each buy level.
Sell Signals: Displayed as red triangles at the sell level.
Alerts:
Configurable alerts notify traders when buy or sell signals are triggered, ensuring no opportunity is missed.
Visual Aids:
Semi-transparent and dynamic lines represent buy and sell levels for clear visualization.
Labels provide additional clarity for active levels, helping traders quickly identify actionable signals.
How It Works
The indicator analyzes market movements to identify dips based on predefined thresholds.
Buy signals are triggered when the market price reaches specified levels below the local high.
Once a position is taken, the indicator dynamically adjusts the average entry price and calculates the corresponding sell level.
A sell signal is generated when the market price rises above the calculated take-profit level.
Why Use This Indicator?
Discipline: Automates decision-making, removing emotional factors from trading.
Clarity: Provides clear entry and exit points to simplify complex market dynamics.
Versatility: Suitable for all market conditions, especially during pullbacks and rebounds.
Customization: Allows traders to tailor parameters to their preferred trading style and risk tolerance.
Acknowledgment
This indicator is based on the strategy and insights provided by TearRepresentative, whose expertise in rule-based trading has inspired countless traders. TearRepresentative's approach emphasizes simplicity, reliability, and consistency, offering a robust framework for long-term success.
Quantum ChronoRenko Dynamics Edge - Traditional### **Quantum ChronoRenko Dynamics Edge - Traditional**
**Description:**
The **Quantum ChronoRenko Dynamics Edge - Traditional** is an advanced Renko-based indicator designed for precision trading. It leverages the power of Renko charts to detect price movements, highlight critical trading signals, and dynamically track profit and risk levels. This indicator is built with modern trading strategies in mind, offering robust tools for all traders, from beginners to professionals.
**Key Features:**
1. **Renko-Based Signal Generation**:
- Detects **Buy Signals** when the price closes above the Renko high level.
- Detects **Sell Signals** when the price closes below the Renko low level.
- Ensures signals are non-repainting and confirmed on bar closures.
2. **Take Profit (TP) and Stop Loss (SL) Tracking**:
- Automatically calculates and plots TP and SL levels for every signal.
- Dynamic levels are displayed directly on the chart for better decision-making.
3. **Advanced Signal Management**:
- Prevents duplicate signals within the same Renko range.
- Resets signal conditions when a new Renko range is formed.
4. **Visual Enhancements**:
- Renko high and low levels are plotted with customizable colors and styles.
- TP and SL levels are marked with distinct cross shapes for clarity.
- Optional fill between Renko levels to highlight price ranges.
5. **Real-Time Alerts**:
- Generates alerts for Buy and Sell signals when a candle closes above or below the Renko levels.
- Alerts are designed to help traders react quickly to opportunities.
6. **Comprehensive Statistics**:
- Tracks the number of Buy/Sell signals.
- Calculates the number of TP and SL hits for each signal type.
- Displays detailed percentages and totals in an easy-to-read table.
**Key Benefits**:
- **Non-Repainting Logic**: Ensures stable and reliable signals based on confirmed price movements.
- **Customizability**: Flexible settings for Renko brick size, TP/SL values, and visual enhancements.
- **Professional-Level Insights**: Provides detailed statistics for tracking strategy performance.
**Use Cases**:
- Perfect for intraday and swing traders who rely on Renko charts for clear trend signals.
- Suitable for identifying key breakout opportunities and managing trades with precise TP/SL levels.
Example Usage:
For daily scalping, set the following parameters:
Brick Size: 3
Time Frame: 10 Minutes
This setup provides clean trend signals and dynamic TP/SL tracking for short-term trades.
**Why "Traditional"?**
This version uses the **Traditional Renko method**, ensuring consistent price-based calculations that align with professional trading strategies.
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**Disclaimer**:
This indicator is a tool to aid trading decisions but does not guarantee profits. Always use proper risk management.
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Eze Profit - VWAP + MACD Combined SignalThe Eze Profit - VWAP + MACD Combined Signal is an advanced trading tool designed to help traders align price trends with momentum confirmation for better decision-making. By combining Volume-Weighted Average Price (VWAP) and Moving Average Convergence Divergence (MACD), this indicator provides clear entry and exit signals, allowing traders to follow trends and take advantage of momentum shifts.
How It Works:
VWAP:
VWAP represents the average price of an asset, weighted by volume, over a specific period.
It acts as a dynamic support/resistance level and trend filter. Price above VWAP indicates bullish conditions, while price below VWAP suggests bearish conditions.
MACD:
MACD measures momentum through the difference between fast and slow exponential moving averages (EMAs).
Signals are generated when the MACD line crosses its signal line:
Bullish Crossover: Indicates increasing upward momentum.
Bearish Crossunder: Indicates increasing downward momentum.
Combined Logic:
Long Signal: Triggered when price is above VWAP, and MACD exhibits a bullish crossover.
Short Signal: Triggered when price is below VWAP, and MACD exhibits a bearish crossunder.
The script tracks the trader's "in-position" state to prevent redundant signals and ensure clarity.
How to Use:
Use this script to identify potential long and short trading opportunities:
Buy Signal: Enter a long position when the price moves above VWAP and MACD confirms bullish momentum.
Sell Signal: Exit or short when the price drops below VWAP and MACD confirms bearish momentum.
Combine with additional tools like support/resistance, volume analysis, or candlestick patterns for confirmation.
Features:
VWAP Trend Filter: Dynamically adjusts to the trading session to identify overall trend direction.
MACD Momentum Confirmation: Detects key momentum shifts with configurable settings for fast, slow, and signal lengths.
Position State Tracking: Avoids signal redundancy by monitoring open positions.
Buy/Sell Visualizations: Plots Buy/Sell signals directly on the chart for ease of use.
Alerts: Notifies traders in real-time when a long or short signal is triggered.
Customizable Settings:
MACD Fast Length, Slow Length, and Signal Smoothing parameters.
VWAP timeframe resolution to adapt to different trading styles (e.g., intraday or daily).
Credits:
This script is based on standard VWAP and MACD calculations provided by TradingView’s library and has been enhanced with unique logic for combined signal generation.
Notes:
This indicator is intended for educational purposes and should not be considered financial advice. Use it as part of a broader trading strategy alongside other tools for optimal results.
Adaptive ema Cloud v1 Trend & Trade Signals"adaptive ema cloud v1 trend & trade signals" is a comprehensive technical indicator aimed at assisting traders in identifying market trends, trade entry points, and potential take profit (tp) and stop-loss (sl) levels. this indicator combines adaptive exponential moving average (ema) clouds with standard deviation bands to create a visual trend and signal system, enabling users to better analyze price action.
key features:
adaptive ema cloud: calculates a dynamic ema-based cloud using a simple moving average (sma) line, with upper and lower deviation bands based on standard deviations. users can adjust the standard deviation multiplier to modify the cloud's width.
trend direction detection: the indicator determines trend direction by comparing the close price to the ema cloud and signals bullish or bearish trends when the price crosses key levels.
take profit (tp) and stop-loss (sl) points: adaptive tp and sl levels are calculated based on the deviation bands, providing users with suggested exit points when a trade is triggered.
peak and valley detection: detects peaks and valleys in price, aiding traders in spotting potential support and resistance areas.
gradient-based cloud fill: dynamically fills the cloud with a gradient color based on trend strength, helping users visually gauge trend intensity.
trade tracking: tracks recent trades and records them in an internal memory, allowing users to view the last 20 trade outcomes, including whether tp or sl was hit.
how to use:
trend signals: look for green arrows (bullish trend) or red arrows (bearish trend) to identify potential entries based on trend crossovers.
tp/sl management: tp and sl levels are automatically calculated and displayed, with alerts available to notify users when these levels are reached.
adjustable settings: customize period length, standard deviation multiplier, and color preferences to match trading preferences and chart style.
inputs-
period: defines the look-back period for ema calculations.
standard deviation multiplier: adjusts cloud thickness by setting the multiplier for tp and sl bands.
gauge size: scales the gradient intensity for trend cloud visualization.
up/down colors: allows users to set custom colors for bullish and bearish bars.
alert conditions: this script has built-in alerts for trend changes, tp, and sl levels, providing users with automated notifications of important trading signals.
The Pattern-Synced Moving Average System (PSMA)Description:
The Pattern-Synced Moving Average System (PSMA) is a comprehensive trading indicator that combines the reliability of moving averages with automated candlestick pattern detection, real-time alerts, and dynamic risk management to enhance both trend-following and reversal strategies. The PSMA system integrates key elements of trend analysis and pattern recognition to provide users with configurable entry, stop-loss, and take-profit levels. It is designed for all levels of traders who seek to trade in alignment with market context, using signals from trend direction and established candlestick patterns.
Key Functional Components:
Multi-Type Moving Average:
Provides flexibility with multiple moving average options: SMA, EMA, WMA, and SMMA.
The selected moving average helps users determine market trend direction, with price positions relative to the MA acting as a trend confirmation.
Automatic Candlestick Pattern Detection:
Identifies pivotal patterns, including bullish/bearish engulfing and reversal signals.
Helps traders spot potential market turning points and adjust their strategies accordingly.
Configurable Entry, Stop-Loss, and Take-Profit:
Risk management is customizable through risk/reward ratios and risk tolerance settings.
Entry, stop-loss, and take-profit levels are automatically plotted when patterns appear, facilitating rapid trade decision-making with predefined exit points.
Higher Timeframe Trend Confirmation:
Optional feature to verify trend alignment on a higher timeframe (e.g., checking a daily trend on an intraday chart).
This added filter improves signal reliability by focusing on patterns aligned with the broader market trend.
Real-Time Alerts:
Alerts can be set for key pattern detections, allowing traders to respond promptly without constant chart monitoring.
How to Use PSMA:
Set Moving Average Preferences:
Choose the preferred moving average type and length based on your trading strategy. The MA acts as a foundational trend indicator, with price positions indicating potential uptrends (price above MA) or downtrends (price below MA).
Adjust Risk Management Settings:
Set a Risk/Reward Ratio for defining take-profit levels relative to the entry and stop-loss levels.
Modify the Risk Tolerance Percentage to adjust stop-loss placement, adding flexibility in managing trades based on market volatility.
Activate Higher Timeframe Confirmation (Optional):
Enable higher timeframe trend confirmation to filter out counter-trend trades, ensuring that detected patterns are in sync with the larger market trend.
Review Alerts and Trade Levels:
With PSMA’s real-time alerts, traders receive notifications for detected patterns without having to continuously monitor charts.
Visualized entry, stop-loss, and take-profit lines simplify trade execution by highlighting levels directly on the chart.
Execute Based on Entry and Exit Levels:
The entry line suggests the potential entry price once a bullish or bearish pattern is detected.
The stop-loss line is based on your set risk tolerance, establishing a predefined risk level.
The take-profit line is calculated according to your preferred risk/reward ratio, providing a clear profit target.
Example Strategy:
Ensure price is above or below the selected moving average to confirm trend direction.
Await a PSMA signal for a bullish or bearish pattern.
Review the plotted entry, stop-loss, and take-profit lines, and enter the trade if the setup aligns with your risk/reward criteria.
Activate alerts for continuous monitoring, allowing PSMA to notify you of emerging trade opportunities.
Release Notes:
Line Color and Style Customization: Customizable colors and line styles for entry, stop-loss, and take-profit levels.
Dynamic Trade Tracking: Tracks trade statistics, including total trades, win rate, and average P/L, displayed in the data window for comprehensive trade performance analysis.
Summary: The PSMA indicator is a powerful, user-friendly tool that combines trend detection, pattern recognition, and risk management into a cohesive system for improved trade decision-making. Suitable for stocks, forex, and futures, PSMA offers a unique blend of adaptability and precision, making it valuable for day traders and long-term investors alike. Enjoy this tool as it enhances your ability to execute timely, well-informed trades on TradingView.
120 GOAT - Simple Moving Average Breakout IndicatorThe 120 GOAT indicator is a powerful tool designed to help traders identify key breakout points relative to the 120-day Simple Moving Average (SMA). This indicator tracks when the price crosses above or below the 120-day SMA, marking these transition points on the chart with dynamic labels that indicate the percentage change in price since the last crossover.
With the 120 GOAT indicator, traders can:
Identify trend reversals when the price crosses the 120-day SMA.
Monitor price momentum and potential support or resistance levels relative to the 120-day SMA.
Receive alerts when a breakout occurs above or below the 120-day SMA, ensuring they never miss an important market move.
Key Features:
120-day SMA: A customizable 120-day Simple Moving Average that serves as a benchmark for price movements.
Dynamic Labels: The indicator provides labels showing the number of days since the last crossover and the percentage change in price from the previous crossover.
Color-coded Breakout Signals: Labels change color based on the nature of the breakout and price movement:
Above SMA: Green for positive price change, blue for negative.
Below SMA: Orange for positive price change, red for negative.
Price Line Tracking: Displays the current price level relative to the SMA.
Custom Alerts: Set alerts for when the price crosses above or below the 120-day SMA to stay updated on significant market events.
How to Use the 120 GOAT Indicator:
Add the Indicator to Your Chart:
Open TradingView and go to the chart where you want to use the 120 GOAT indicator.
Click on the "Indicators" button at the top of the chart.
Search for "120 GOAT" and select it from the list to apply it to your chart.
Customize the Settings:
Show 120 MA: Toggle this option on if you want the 120-day SMA to be displayed on your chart.
MA Color: Choose your preferred color for the 120-day SMA line.
SMA Length: You can adjust the length of the moving average if you prefer a different period. The default is set to 120 days.
Interpret the Signals:
When the price crosses above the 120-day SMA, the indicator will display a label below the price bar showing the number of days since the last crossover and the percentage change in price. If the price change is positive, the label is green; if negative, it is blue.
When the price crosses below the 120-day SMA, a similar label will appear above the price bar. If the price change is positive, the label is orange; if negative, it is red.
Set Alerts for Key Market Movements:
Go to the Alerts panel and create a new alert.
Select 120 GOAT as the condition.
Choose either "Price Crossed Above SMA 120" or "Price Crossed Below SMA 120" as the alert criteria.
Configure the alert frequency and other settings as needed, then click "Create."
Monitor the Indicator for Trading Opportunities:
Use the breakout signals and percentage change information to identify potential trading opportunities.
Combine this indicator with other technical analysis tools to validate trade setups and enhance decision-making.
Disclaimer: The 120 GOAT indicator is designed for educational purposes and should not be considered as financial advice. Always conduct your own research and consult with a professional financial advisor before making trading decisions.
Smooth Trailing Stop
Trading indicator designed to provide traders with a dynamic and responsive stop-loss mechanism, leveraging a combination of Zero Lag Exponential Moving Averages (ZEMA) and the Average True Range (ATR). This indicator is particularly useful for traders looking to capture trends while managing risk effectively. Future notes: will add MTF analysis. First
Key Features:
Zero Lag EMA (ZEMA): This indicator uses a Zero Lag EMA, which helps to reduce the lag traditionally associated with moving averages, providing a more accurate reflection of price action.
ATR-Based Trailing Stop: The stop-loss level is dynamically calculated using a multiplier of the ATR, which adjusts to the volatility of the market, ensuring that the stop-loss distance is neither too tight nor too loose.
Position Tracking: The indicator tracks the position (long or short) based on the relationship between the price and the trailing stop, coloring the stop line green for a long position and red for a short position.
Candle Coloring: Candles are colored green when a buy signal is generated and red otherwise, giving a visual cue to the trader.
Customizable Inputs:
Period: Define the number of periods used for the ZEMA calculation.
ATR Period & Multiplier: Adjust the period and multiplier used for ATR, allowing for customization based on the trader’s risk tolerance and market conditions.
Line Width: Customize the width of the trailing stop line for better visibility on the chart.
This indicator is suitable for traders of all experience levels who are looking for a smooth trailing stop system for their trading strategy.
LOWESS (Locally Weighted Scatterplot Smoothing) [ChartPrime]LOWESS (Locally Weighted Scatterplot Smoothing)
⯁ OVERVIEW
The LOWESS (Locally Weighted Scatterplot Smoothing) [ ChartPrime ] indicator is an advanced technical analysis tool that combines LOWESS smoothing with a Modified Adaptive Gaussian Moving Average. This indicator provides traders with a sophisticated method for trend analysis, pivot point identification, and breakout detection.
◆ KEY FEATURES
LOWESS Smoothing: Implements Locally Weighted Scatterplot Smoothing for trend analysis.
Modified Adaptive Gaussian Moving Average: Incorporates a volatility-adapted Gaussian MA for enhanced trend detection.
Pivot Point Identification: Detects and visualizes significant pivot highs and lows.
Breakout Detection: Tracks and optionally displays the count of consecutive breakouts.
Gaussian Scatterplot: Offers a unique visualization of price movements using randomly colored points.
Customizable Parameters: Allows users to adjust calculation length, pivot detection, and visualization options.
◆ FUNCTIONALITY DETAILS
⬥ LOWESS Calculation:
Utilizes a weighted local regression to smooth price data.
Adapts to local trends, reducing noise while preserving important price movements.
⬥ Modified Adaptive Gaussian Moving Average:
Combines Gaussian weighting with volatility adaptation using ATR and standard deviation.
Smooths the Gaussian MA using LOWESS for enhanced trend visualization.
⬥ Pivot Point Detection and Visualization:
Identifies pivot highs and lows using customizable left and right bar counts.
Draws lines and labels to mark broke pivot points on the chart.
⬥ Breakout Tracking:
Monitors price crossovers of pivot lines to detect breakouts.
Optionally displays and updates the count of consecutive breakouts.
◆ USAGE
Trend Analysis: Use the color and direction of the smoothed Gaussian MA line to identify overall trend direction.
Breakout Trading: Monitor breakouts from pivot levels and their persistence using the breakout count feature.
Volatility Assessment: The spread of the Gaussian scatterplot can provide insights into market volatility.
⯁ USER INPUTS
Length: Sets the lookback period for LOWESS and Gaussian MA calculations (default: 30).
Pivot Length: Determines the number of bars to the left for pivot calculation (default: 5).
Count Breaks: Toggle to show the count of consecutive breakouts (default: false).
Gaussian Scatterplot: Toggle to display the Gaussian MA as a scatterplot (default: true).
⯁ TECHNICAL NOTES
Implements a custom LOWESS function for efficient local regression smoothing.
Uses a modified Gaussian MA calculation that adapts to market volatility.
Employs Pine Script's line and label drawing capabilities for clear pivot point visualization.
Utilizes random color generation for the Gaussian scatterplot to enhance visual distinction between different time periods.
The LOWESS (Locally Weighted Scatterplot Smoothing) indicator offers traders a sophisticated tool for trend analysis and breakout detection. By combining advanced smoothing techniques with pivot point analysis, it provides a comprehensive view of market dynamics. The indicator's adaptability to different market conditions and its customizable nature make it suitable for various trading styles and timeframes.
Optimized Bullish and Bearish Structure IndicatorThis Pine Script indicator is designed to identify specific bullish and bearish structures on a price chart based on user-defined conditions. The indicator highlights buy and sell signals and allows customization through input checkboxes to include or exclude additional conditions for generating these signals.
Key Features:
User Input Checkboxes:
Use Additional Buy Condition: Enables or disables an extra condition for buy signals.
Use Additional Sell Condition: Enables or disables an extra condition for sell signals.
Bullish Structure (Case 01):
The closing price of the candle 2 bars ago is greater than the closing price of the candle 1 bar ago.
The current candle's closing price is greater than the opening price of the candle 1 bar ago.
Additional Buy Condition: The closing price of the candle 2 bars ago is less than the closing price of the candle 1 bar ago.
Bearish Structure (Case 01):
The closing price of the candle 1 bar ago is greater than the closing price of the candle 2 bars ago.
The current candle's closing price is less than the opening price of the candle 1 bar ago.
Additional Sell Condition: The closing price of the candle 1 bar ago is less than the closing price of the candle 2 bars ago.
Signal Tracking:
The script tracks whether it is currently in a long (buy) or short (sell) state to avoid consecutive identical signals.
Only one buy signal is allowed until a sell signal is generated, and vice versa.
Plotting Signals:
Buy signals are plotted as green labels below the bar.
Sell signals are plotted as red labels above the bar.
Background colors are used to highlight bars where signals are generated:
Green for buy signals.
Red for sell signals.
Previous Candle Plotting:
Signals are plotted on the previous candle to clearly indicate where the signal conditions were met.
Script Usage:
Overlay:
The indicator is plotted directly on the price chart (overlay=true).
User Inputs:
Users can toggle the additional conditions for buy and sell signals through the checkboxes provided in the input settings.
Customization:
The indicator can be customized further to suit different trading strategies or market conditions by modifying the conditions and input parameters.
Example Usage:
Add the indicator to your TradingView chart.
Use the input checkboxes to include or exclude additional conditions for buy and sell signals.
Observe the plotted signals and background highlights to identify potential buy and sell opportunities based on the defined conditions.
This indicator provides a flexible tool for traders to identify specific bullish and bearish market structures and helps in making informed trading decisions.
Curved Smart Money Concepts Probability (Zeiierman)█ Overview
The Curved Smart Money Concepts Probability indicator, developed by Zeiierman, is a sophisticated trading tool designed to leverage the principles of Smart Money trading. This indicator identifies key market structure points and adapts to changing market conditions, providing traders with actionable insights into market trends and potential reversals. The trading tool stands out due to its unique curved structure and advanced probability features, which enhance its effectiveness and usability for traders.
█ How It Works
The indicator operates by analyzing market data to identify pivotal moments where institutional investors might be influencing price movements. It employs a combination of adaptive trend lengths, multipliers for sensitivity adjustments, and pivot periods to accurately capture market structure shifts. The indicator calculates upper and lower bands based on adaptive sizes and identifies zones of overbought (premium) and oversold (discount) conditions.
Key Features of Probability Calculations
The Curved Smart Money Concepts Probability indicator integrates sophisticated probability calculations to enhance trading decision-making:
Win/Loss Tracking: The indicator tracks the number of successful (win) and unsuccessful (loss) trades based on the identified market structure points (ChoCH, SMS, BMS). This provides a historical context of the indicator's performance.
Probability Percentages: For each market structure point (ChoCH, SMS, BMS), the indicator calculates the probability of the next move being successful or not. This is presented as a percentage, giving traders a quantifiable measure of confidence in the signals.
Dynamic Adaptation: The probability calculations adapt to market conditions by considering the frequency and success rate of the signals, allowing traders to adjust their strategies based on the indicator’s historical accuracy.
Visual Representation: Probabilities are displayed on the chart, helping traders quickly assess the likelihood of future price movements based on past performance.
Key benefits of the Curved Structure
The Curved Smart Money Concepts Probability indicator features a unique curved structure that offers several advantages over traditional linear structures:
Noise Reduction: The curved structure smooths out short-term market fluctuations, reducing the noise often seen in linear structures. This helps traders focus on the true trend direction rather than getting distracted by minor price movements.
Adaptive Sensitivity: The curved structure adjusts its sensitivity based on market conditions. This means it can effectively capture both short-term and long-term trends by dynamically adapting to changes in market volatility, something linear structures struggle with.
Enhanced Trend Detection: By providing a more gradual transition between market phases, the curved structure helps in identifying trends more accurately. This is particularly useful in volatile markets where linear structures might give false signals due to their rigid nature.
Improved Market Structure Analysis: The curved structure's ability to adapt and smooth out irregularities provides a clearer picture of the overall market structure. This clarity is essential for identifying premium and discount zones, as well as mid-range support and resistance levels, which are crucial for effective ICT Smart Money Trading.
█ Terminology
ChoCH (Change of Character): Indicates a potential reversal in market direction. It is identified when the price breaks a significant high or low, suggesting a shift from a bullish to bearish trend or vice versa.
SMS (Smart Money Shift): Represents the transition phase in market structure where smart money begins accumulating or distributing assets. It typically follows a BMS and indicates the start of a new trend.
BMS (Bullish/Bearish Market Structure): Confirms the trend direction. Bullish Market Structure (BMS) confirms an uptrend, while Bearish Market Structure (BMS) confirms a downtrend. It is characterized by a series of higher highs and higher lows (bullish) or lower highs and lower lows (bearish).
Premium: A zone where the price is considered overbought. It is calculated as the upper range of the current market structure and indicates a potential area for selling or shorting.
Mid Range: The midpoint between the high and low of the market structure. It often acts as a support or resistance level, helping traders identify potential reversal or continuation points.
Discount: A zone where the price is considered oversold. It is calculated as the lower range of the current market structure and indicates a potential area for buying or going long.
█ How to Use
Identifying Trends and Reversals: Traders can use the indicator to identify the overall market trend and potential reversal points. By observing the ChoCH, SMS, and BMS signals, traders can gauge whether the market is transitioning into a new trend or continuing the current trend.
Example Strategies
⚪ Trend Following Strategy:
Identify the current market trend using BMS signals.
Enter a trade in the direction of the trend when the price retraces to the mid-range zone.
Set a stop-loss just below the mid-range (for long trades) or above the mid-range (for short trades).
Take profit in the premium/discount zone or when a ChoCH signal indicates a potential reversal.
⚪ Reversal Strategy:
Wait for a ChoCH signal to identify a potential market reversal.
Enter a trade in the direction of the new trend as indicated by the SMS signal.
Set a stop-loss just beyond the recent high (for short trades) or low (for long trades).
Take profit when the price reaches the premium or discount zone opposite to the entry.
█ Settings
Curved Trend Length: Determines the length of the trend used to calculate the adaptive size of the structure. Adjusting this length allows traders to capture either longer-term trends (for smoother curves) or short-term trends (for more reactive curves).
Curved Multiplier: Scales the adjustment factors for the upper and lower bands. Increasing the multiplier widens the bands, reducing sensitivity to price changes. Decreasing it narrows the bands, making the structure more responsive.
Pivot Period: Sets the period for capturing trends. A higher period captures broader trends, while a lower period focuses on short-term trends.
Response Period: Adjusts the structure’s responsiveness. A low value focuses on short-term changes, while a high value smoothens the structure.
Premium/Discount Range: Allows toggling between displaying the active range or previous range to analyze real-time or historical levels.
Structure Candles: Enables the display of curved structure candles on the chart, providing a modified view of price action.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Adaptive Bollinger-RSI Trend Signal [CHE]Adaptive Bollinger-RSI Trend Signal
Indicator Overview:
The "Adaptive Bollinger-RSI Trend Signal " (ABRT Signal ) is a sophisticated trading tool designed to provide clear and actionable buy and sell signals by combining the power of Bollinger Bands and the Relative Strength Index (RSI). This indicator aims to help traders identify potential trend reversals and confirm entry and exit points with greater accuracy.
Key Features:
1. Bollinger Bands Integration:
- Utilizes Bollinger Bands to detect price volatility and identify overbought or oversold conditions.
- Configurable parameters: Length, Source, and Multiplier for precise adjustments based on trading preferences.
- Color customization: Change the colors of the basis line, upper band, lower band, and the fill color between bands.
2. RSI Integration:
- Incorporates the Relative Strength Index (RSI) to validate potential buy and sell signals.
- Configurable parameters: Length, Source, Upper Threshold, and Lower Threshold for customized signal generation.
3. Signal Generation:
- Buy Signal: Generated when the price crosses below the lower Bollinger Band and the RSI crosses above the lower threshold, indicating a potential upward trend.
- Sell Signal: Generated when the price crosses above the upper Bollinger Band and the RSI crosses below the upper threshold, indicating a potential downward trend.
- Color customization: Change the colors of the buy and sell signal labels.
4. State Tracking:
- Tracks and records crossover and crossunder states of the price and RSI to ensure signals are only generated under the right conditions.
- Monitors the basis trend (SMA of the Bollinger Bands) to provide context for signal validation.
5. Counters and Labels:
- Labels each buy and sell signal with a counter to indicate the number of consecutive signals.
- Counters reset upon the generation of an opposite signal, ensuring clarity and preventing signal clutter.
6. DCA (Dollar-Cost Averaging) Calculation:
- Stores the close price at each signal and calculates the average entry price (DCA) for both buy and sell signals.
- Displays the number of positions and DCA values in a label on the chart.
7. Customizable Inputs:
- Easily adjustable parameters for Bollinger Bands, RSI, and colors to suit various trading strategies and timeframes.
- Boolean input to show or hide the table label displaying position counts and DCA values.
- Intuitive and user-friendly configuration options for traders of all experience levels.
How to Use:
1. Setup:
- Add the "Adaptive Bollinger-RSI Trend Signal " to your TradingView chart.
- Customize the input parameters to match your trading style and preferred timeframe.
- Adjust the colors of the indicator elements to your preference for better visibility and clarity.
2. Interpreting Signals:
- Buy Signal: Look for a "Buy" label on the chart, indicating a potential entry point when the price is oversold and RSI signals upward momentum.
- Sell Signal: Look for a "Sell" label on the chart, indicating a potential exit point when the price is overbought and RSI signals downward momentum.
3. Trade Execution:
- Use the buy and sell signals to guide your trade entries and exits, aligning them with your overall trading strategy.
- Monitor the counter labels to understand the strength and frequency of signals, helping you make informed decisions.
4. Adjust and Optimize:
- Regularly review and adjust the indicator parameters based on market conditions and backtesting results.
- Combine this indicator with other technical analysis tools to enhance your trading accuracy and performance.
5. Monitor DCA Values:
- Enable the table label to display the number of positions and average entry prices (DCA) for both buy and sell signals.
- Use this information to assess the cost basis of your trades and make strategic adjustments as needed.
Conclusion:
The Adaptive Bollinger-RSI Trend Signal is a powerful and versatile trading tool designed to help traders identify and capitalize on trend reversals with confidence. By combining the strengths of Bollinger Bands and RSI, this indicator provides clear and reliable signals, making it an essential addition to any trader's toolkit. Customize the settings, interpret the signals, and execute your trades with precision using this comprehensive indicator.
MTF Bollinger BandWidth [CryptoSea]The MTF Bollinger BandWidth Indicator is an advanced analytical tool crafted for traders who need to gauge market volatility and trend strength across multiple timeframes. This powerful indicator leverages the Bollinger BandWidth concept to provide a comprehensive view of price movements and volatility changes, making it ideal for those looking to enhance their trading strategies with multi-timeframe analysis.
Key Features
Multi-Timeframe Analysis: Allows users to monitor Bollinger BandWidth across various timeframes, providing a macro and micro perspective on market volatility.
Pivot Point Detection: Identifies crucial high and low pivot points, offering insights into potential support and resistance levels. Pivot points are dynamic and adjust based on the timeframe viewed, reflecting short-term fluctuations or longer-term trends.
Customizable Parameters: Includes options to adjust the length of the moving average, the standard deviation multiplier, and more, enabling traders to tailor the tool to their specific needs.
Dynamic Color Coding: Utilizes color changes to indicate different market conditions, aiding in quick visual assessments.
In the example below, notice how changes in BBW across different timeframes provide early signals for potential volatility increases or decreases.
How it Works
Calculation of BandWidth: Measures the percentage difference between the upper and lower Bollinger Bands, which expands or contracts based on market volatility.
High and Low Pivot Tracking: Automatically calculates and tracks the pivots in BBW values, which are critical for identifying turning points in market behavior. High and low levels will change depending on the timeframe, capturing distinct market behaviors from granular movements to broad trends.
Visual Alerts and Table Display: Highlights significant changes in BBW with visual alerts and provides a detailed table view for comparison across timeframes.
In the example below, BBW identifies a significant contraction followed by an expansion, suggesting a potential breakout.
Application
Strategic Market Entry and Exit: Assists traders in making well-informed decisions about when to enter and exit trades based on volatility cues.
Trend Strength Assessment: Helps in determining the strength of the prevailing market trend through detailed analysis of expansion and contraction periods.
Adaptable to Various Trading Styles: Suitable for day traders, swing traders, and long-term investors due to its customization capabilities and effectiveness across different timeframes.
The MTF Bollinger BandWidth Indicator is a must-have in the arsenal of traders who demand depth, accuracy, and responsiveness in their market analysis tools. Enhance your trading decisions by integrating this sophisticated indicator into your strategy to navigate the complexities of various market conditions effectively.
HC_V2Description:
Detailed Clarity Signal is a sophisticated TradingView indicator designed to provide traders with enhanced trading signals based on Bollinger Bands and the consistency of price actions. This script is particularly useful for identifying high-probability entry points in volatile markets. It combines the power of Bollinger Bands with a unique scoring system that assesses the clarity of buy and sell signals.
Features:
Dynamic Bollinger Bands: Utilizes a standard deviation multiplier to dynamically adjust Bollinger Bands, providing a flexible approach to volatility.
Clarity Scoring System: Each trading signal is scored based on its clarity, which is determined by how significantly the price exceeds the Bollinger Bands and the consistency of similar signals over a short span of time. This helps in distinguishing stronger signals from the weaker ones.
Consecutive Signal Tracking: Tracks consecutive buy or sell signals, allowing for gaps of up to three bars, to enhance the reliability of the trading signals.
Alert Conditions: Includes conditions for setting alerts when signals of high clarity (levels 4 and 5) are detected, making it easier for traders to act promptly on significant trading opportunities.
Visual and Sound Alerts: Designed to integrate seamlessly with TradingView's alert system, providing both visual markers and sound alerts to ensure that traders do not miss important trading signals.
How It Works:
The indicator calculates Bollinger Bands and measures the current close price in relation to these bands. When the price closes significantly beyond the bands coupled with consistent behavior in previous sessions, the signal clarity increases. This clarity is quantified from levels 1 to 5, with higher levels indicating stronger signals. Traders can set alerts to be notified when signals of substantial clarity are detected, aiding in decision-making during fast-moving market conditions.
Usage:
Entry Signal: A high clarity level (4 or 5) suggests a strong buy or sell opportunity, depending on whether the signal is above or below the Bollinger Bands.
Exit Signal: Traders may consider closing positions as the clarity level decreases or as opposing signals begin to form, providing a methodical approach to capturing gains and managing risks.
Conclusion:
The Detailed Clarity Signal indicator is an invaluable tool for traders looking to leverage the volatility of the markets with a higher degree of precision. By focusing on the quality of signals, it provides a robust method to enhance trading strategies, ensuring that traders can make informed decisions backed by a comprehensive analysis of price movements and trend strength.
Khaled Tamim's Avellaneda-Stoikov StrategyDescription:
This strategy applies the Avellaneda-Stoikov (A-S) model to generate buy and sell signals for underlying assets based on option pricing theory. The A-S model estimates bid and ask quotes for options contracts considering factors like volatility (sigma), time to expiration (T), and risk aversion (gamma).
Key Concepts:
Avellaneda-Stoikov Model: A mathematical framework for option pricing that incorporates volatility, time decay, and risk tolerance.
Bid-Ask Quotes: The theoretical buy and sell prices for an option contract.
Inventory Management: The strategy tracks its long or short position based on signals.
How it Works:
A-S Model Calculation: The avellanedaStoikov function calculates bid and ask quotes using the underlying asset's closing price, user-defined parameters (gamma, sigma, T, k, and M), and a small fee (adjustable).
Signal Generation: The strategy generates long signals when the closing price falls below the adjusted bid quote and short signals when it exceeds the adjusted ask quote.
Trade Execution: Buy and sell orders are triggered based on the generated signals (long for buy, short for sell).
Inventory Tracking: The strategy's net profit reflects the current inventory level (long or short position).
Customization:
Gamma (γ): Controls risk aversion in the A-S model (higher values imply lower risk tolerance).
Sigma (σ): Represents the underlying asset's expected volatility.
T: Time to expiration for the hypothetical option (defaults to a short-term option).
k: A constant factor in the A-S model calculations.
M: Minimum price buffer for buy/sell signals (prevents excessive churn).
Important Note:
This strategy simulates option pricing behavior for a theoretical option and does not directly trade options contracts. Backtesting results may not reflect actual market conditions.
Further Considerations:
The 0.1% fee is a placeholder and may need adjustment based on real-world trading costs.
Consider using realistic timeframes for T (e.g., expiry for a real option)
Disclaimer: This strategy is for educational purposes only and does not constitute financial advice.
GKD-B Multi-Ticker Stepped Baseline [Loxx]Giga Kaleidoscope GKD-B Multi-Ticker Stepped Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
This version of the GKD-B Baseline is designed specifically to support traders who wish to conduct GKD-BT Multi-Ticker Backtests with multiple tickers. This functionality is exclusive to the GKD-BT Multi-Ticker Backtests.
Traders have the capability to apply a filter to the selected moving average, leveraging various volatility metrics to enhance trend identification. This feature is tailored for traders favoring a gradual and consistent approach, enabling them to discern more sustainable trends. The system permits filtering for both the input data and the moving average results, requiring price movements to exceed a specific threshold—defined as multiples of the volatility—before acknowledging a trend change. This mechanism effectively reduces false signals caused by market noise and lateral movements. A distinctive aspect of this tool is its ability to adjust both price and moving average data based on volatility indicators like VIX, EUVIX, BVIV, and EVIV, among others. Understanding the time frame over which a volatility index is measured is crucial; for instance, VIX is measured on an annual basis, whereas BVIV and EVIV are based on a 30-day period. To accurately convert these measurements to a daily scale, users must input the correct "days per year" value: 252 for VIX and 30 for BVIV and EVIV. Future updates will introduce additional functionality to extend analysis across various time frames, but currently, this feature is solely available for daily time frame analysis.
█ GKD-B Multi-Ticker Stepped Baseline includes 65+ different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
One More Moving Average - OMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Geometric Mean Moving Average
Coral
Tether Lines
Range Filter
Triangle Moving Average Generalized
Ultinate Smoother
Adaptive Moving Average - AMA
The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Kaufman Adaptive Moving Average - KAMA
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
One More Moving Average (OMA)
The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
█ Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types.
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Volatility Ticker Selection
Import volatility tickers like VIX, EUVIX, BVIV, and EVIV.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
Standardized Orderflow [AlgoAlpha]Introducing the Standardized Orderflow indicator by AlgoAlpha. This innovative tool is designed to enhance your trading strategy by providing a detailed analysis of order flow and velocity. Perfect for traders who seek a deeper insight into market dynamics, it's packed with features that cater to various trading styles. 🚀📊
Key Features:
📈 Order Flow Analysis: At its core, the indicator analyzes order flow, distinguishing between bullish and bearish volume within a specified period. It uses a unique standard deviation calculation for normalization, offering a clear view of market sentiment.
🔄 Smoothing Options: Users can opt for a smoothed representation of order flow, using a Hull Moving Average (HMA) for a more refined analysis.
🌪️ Velocity Tracking: The indicator tracks the velocity of order flow changes, providing insights into the market's momentum.
🎨 Customizable Display: Tailor the display mode to focus on either order flow, order velocity, or both, depending on your analysis needs.
🔔 Alerts for Critical Events: Set up alerts for crucial market events like crossover/crossunder of the zero line and overbought/oversold conditions.
How to Use:
1. Setup: Easily configure the indicator to match your trading strategy with customizable input parameters such as order flow period, smoothing length, and moving average types.
2. Interpretation: Watch for bullish and bearish columns in the order flow chart, utilize the Heiken Ashi RSI candle calculation, and look our for reversal notations for additional market insights.
3. Alerts: Stay informed with real-time alerts for key market events.
Code Explanation:
- Order Flow Calculation:
The core of the indicator is the calculation of order flow, which is the sum of volumes for bullish or bearish price movements. This is followed by normalization using standard deviation.
orderFlow = math.sum(close > close ? volume : (close < close ? -volume : 0), orderFlowWindow)
orderFlow := useSmoothing ? ta.hma(orderFlow, smoothingLength) : orderFlow
stdDev = ta.stdev(orderFlow, 45) * 1
normalizedOrderFlow = orderFlow/(stdDev + stdDev)
- Velocity Calculation:
The velocity of order flow changes is calculated using moving averages, providing a dynamic view of market momentum.
velocityDiff = ma((normalizedOrderFlow - ma(normalizedOrderFlow, velocitySignalLength, maTypeInput)) * 10, velocityCalcLength, maTypeInput)
- Display Options:
Users can choose their preferred display mode, focusing on either order flow, order velocity, or both.
orderFlowDisplayCond = displayMode != "Order Velocity" ? display.all : display.none
wideDisplayCond = displayMode != "Order Flow" ? display.all : display.none
- Reversal Indicators and Divergences:
The indicator also includes plots for potential bullish and bearish reversals, as well as regular and hidden divergences, adding depth to your market analysis.
bullishReversalCond = reversalType == "Order Flow" ? ta.crossover(normalizedOrderFlow, -1.5) : (reversalType == "Order Velocity" ? ta.crossover(velocityDiff, -4) : (ta.crossover(velocityDiff, -4) or ta.crossover(normalizedOrderFlow, -1.5)) )
bearishReversalCond = reversalType == "Order Flow" ? ta.crossunder(normalizedOrderFlow, 1.5) : (reversalType == "Order Velocity" ? ta.crossunder(velocityDiff, 4) : (ta.crossunder(velocityDiff, 4) or ta.crossunder(normalizedOrderFlow, 1.5)) )
In summary, the Standardized Orderflow indicator by AlgoAlpha is a versatile tool for traders aiming to enhance their market analysis. Whether you're focused on short-term momentum or long-term trends, this indicator provides valuable insights into market dynamics. 🌟📉📈
