Binance Leveraged Liquidations ApproximationBinance Leveraged Liquidations Approximation (BLLA)
The Binance Leveraged Liquidations Approximation (BLLA) indicator is a tool designed to estimate liquidation levels for leveraged trading on Binance. It calculates the approximate prices at which liquidations could occur for long and short positions, based on the entry price and leverage levels selected by the user.
Key Features:
Liquidation Level Calculation:
Estimates liquidation prices for multiple leverage levels (e.g., 20x, 10x, 5x, etc.).
Supports both long and short positions.
Customization:
Allows the user to manually input the entry price or automatically calculate it as the midpoint between the low and high of a defined period.
Leverage levels are configurable, enabling the indicator to adapt to different trading strategies.
Clear Visualization:
Displays liquidation levels directly on the chart, with labels indicating the corresponding leverage.
Uses distinct colors for long positions (yellow) and short positions (blue).
Recommended Use:
Risk Management: Helps identify liquidation levels to adjust stop-loss orders and manage risk in leveraged trading.
Market Analysis: Provides a quick overview of key levels where significant price movements might occur due to mass liquidations.
Settings:
Entry Price: Enter manually or leave at 0.0 to calculate automatically.
Leverage: Configure desired leverage levels (e.g., 20x, 10x, 5x, etc.).
Transparency and Display: Adjust the transparency of the lines and the number of bars displayed.
Quick Instructions:
Add the indicator to your chart.
Enter the entry price or leave it at 0.0 to calculate automatically.
Configure leverage levels according to your strategy.
Observe liquidation levels on the chart and use them to manage your risk.
Note:
This indicator is an approximation and does not guarantee absolute accuracy of liquidation levels, as these may vary depending on market conditions and exchange policies.
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RSI, Volume, MACD, EMA ComboRSI + Volume + MACD + EMA Trading System
This script combines four powerful indicators—Relative Strength Index (RSI), Volume, Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA)—to create a comprehensive trading strategy for better trend confirmation and trade entries.
How It Works
RSI (Relative Strength Index)
Helps identify overbought and oversold conditions.
Used to confirm momentum strength before taking a trade.
Volume
Confirms the strength of price movements.
Avoids false signals by ensuring there is sufficient trading activity.
MACD (Moving Average Convergence Divergence)
Confirms trend direction and momentum shifts.
Provides buy/sell signals through MACD line crossovers.
EMA (Exponential Moving Average)
Acts as a dynamic support and resistance level.
Helps filter out trades that go against the overall trend.
Trading Logic
Buy Signal:
RSI is above 50 (bullish momentum).
MACD shows a bullish crossover.
The price is above the EMA (trend confirmation).
Volume is increasing (strong participation).
Sell Signal:
RSI is below 50 (bearish momentum).
MACD shows a bearish crossover.
The price is below the EMA (downtrend confirmation).
Volume is increasing (intense selling pressure).
Backtesting & Risk Management
The strategy is optimized for scalping on the 1-minute timeframe (adjustable for other timeframes).
Default settings use realistic commission and slippage to simulate actual trading conditions.
A stop-loss and take-profit system is integrated to manage risk effectively.
This script is designed to help traders filter out false signals, improve trend confirmation, and increase trade accuracy by combining multiple indicators in a structured way.
BAS EnhancedBAS Enhanced Indicator – A Powerful Market Trend & Volatility Tool
The BAS Enhanced Indicator is a cutting-edge trading tool designed to help traders analyze market trends, volatility, and price momentum with precision. This indicator builds upon traditional Bollinger Bands concepts, integrating adaptive price action tracking, dynamic band width analysis, and advanced smoothing techniques to generate clear and actionable trading insights.
🔹 Key Features & Benefits:
✅ Smart Price Selection – Choose between Close, High, Low, HL2, or HLC3 to tailor the indicator to different market conditions.
✅ Dynamic Band Analysis – Measures price movements relative to dynamically calculated upper and lower bands for real-time market assessment.
✅ Volatility & Trend Strength Measurement – The indicator uses a unique Width Calculation (wd) to gauge market volatility, helping traders understand the strength of price movements.
✅ Composite Indicator Calculation – Combines price position and band width with customizable power functions to provide a more refined momentum signal.
✅ Smoothing for Accuracy – Uses Exponential Moving Average (EMA) and Simple Moving Average (SMA) for a clearer trend visualization, reducing noise in volatile markets.
✅ Two Signal Lines for Confirmation – Includes customizable bullish and bearish signal lines, allowing traders to identify breakouts and reversals with greater confidence.
✅ Visual & Alert-Based Trading Signals – The indicator plots:
Smoothed Composite Indicator (Blue Line) – Tracks market momentum
%D Moving Average (Red Line) – A secondary smoothing layer for trend confirmation
Mid Values (Orange & Purple Lines) – Additional volatility references
Signal Lines (Green & Red Horizontal Lines) – Key breakout levels
✅ Built-in Alerts for Trade Signals – Get notified instantly when:
Bullish Alert 🚀 – The indicator crosses above the upper signal line
Bearish Alert 📉 – The indicator crosses below the lower signal line
📈 How to Use the BAS Enhanced Indicator?
🔹 Trend Trading: Use crossovers above Signal Line 2 as a potential buy signal and crossovers below Signal Line 1 as a potential sell signal.
🔹 Volatility Monitoring: When the band width (wd) expands, market volatility is increasing – ideal for breakout traders. When wd contracts, market volatility is low, signaling potential consolidation.
🔹 Momentum Confirmation: Use the %D Moving Average to confirm sustained trend movements before entering a trade.
🚀 Why Use BAS Enhanced?
This indicator is perfect for day traders, swing traders, and trend-followers looking to enhance their market timing, filter false signals, and improve decision-making. Whether you're trading stocks, forex, or crypto, BAS Enhanced helps you stay ahead of market movements with precision and clarity.
🔔 Add BAS Enhanced to your TradingView toolkit today and trade smarter with confidence!
Clustering & Divergences (RSI-Stoch-CCI) [Sam SDF-Solutions]The Clustering & Divergences (RSI-Stoch-CCI) indicator is a comprehensive technical analysis tool that consolidates three popular oscillators—Relative Strength Index (RSI), Stochastic, and Commodity Channel Index (CCI)—into one unified metric called the Score. This Score offers traders an aggregated view of market conditions, allowing them to quickly identify whether the market is oversold, balanced, or overbought.
Functionality:
Oscillator Clustering: The indicator calculates the values of RSI, Stochastic, and CCI using user-defined periods. These oscillator values are then normalized using one of three available methods: MinMax, Z-Score, or Z-Bins.
Score Calculation: Each normalized oscillator value is multiplied by its respective weight (which the user can adjust), and the weighted values are summed to generate an overall Score. This Score serves as a single, interpretable metric representing the combined oscillator behavior.
Market Clustering: The indicator performs clustering on the Score over a configurable window. By dividing the Score range into a set number of clusters (also configurable), the tool visually represents the market’s state. Each cluster is assigned a unique color so that traders can quickly see if the market is trending toward oversold, balanced, or overbought conditions.
Divergence Detection: The script automatically identifies both Regular and Hidden divergences between the price action and the Score. By using pivot detection on both price and Score data, the indicator marks potential reversal signals on the chart with labels and connecting lines. This helps in pinpointing moments when the price and the underlying oscillator dynamics diverge.
Customization Options: Users have full control over the indicator’s behavior. They can adjust:
The periods for each oscillator (RSI, Stochastic, CCI).
The weights applied to each oscillator in the Score calculation.
The normalization method and its manual boundaries.
The number of clusters and whether to invert the cluster order.
Parameters for divergence detection (such as pivot sensitivity and the minimum/maximum bar distance between pivots).
Visual Enhancements:
Depending on the user’s preference, either the Score or the Cluster Index (derived from the clustering process) is plotted on the chart. Additionally, the script changes the color of the price bars based on the identified cluster, providing an at-a-glance visual cue of the current market regime.
Logic & Methodology:
Input Parameters: The script starts by accepting user inputs for clustering settings, oscillator periods, weights, divergence detection, and manual boundary definitions for normalization.
Oscillator Calculation & Normalization: It computes RSI, Stochastic, and CCI values from the price data. These values are then normalized using either the MinMax method (scaling between a lower and upper band) or the Z-Score method (standardizing based on mean and standard deviation), or using Z-Bins for an alternative scaling approach.
Score Computation: Each normalized oscillator is multiplied by its corresponding weight. The sum of these products results in the overall Score that represents the combined oscillator behavior.
Clustering Algorithm: The Score is evaluated over a moving window to determine its minimum and maximum values. Using these values, the script calculates a cluster index that divides the Score into a predefined number of clusters. An option to invert the cluster calculation is provided to adjust the interpretation of the clustering.
Divergence Analysis: The indicator employs pivot detection (using left and right bar parameters) on both the price and the Score. It then compares recent pivot values to detect regular and hidden divergences. When a divergence is found, the script plots labels and optional connecting lines to highlight these key moments on the chart.
Plotting: Finally, based on the user’s selection, the indicator plots either the Score or the Cluster Index. It also overlays manual boundary lines (for the chosen normalization method) and adjusts the bar colors according to the cluster to provide clear visual feedback on market conditions.
_________
By integrating multiple oscillator signals into one cohesive tool, the Clustering & Divergences (RSI-Stoch-CCI) indicator helps traders minimize subjective analysis. Its dynamic clustering and automated divergence detection provide a streamlined method for assessing market conditions and potentially enhancing the accuracy of trading decisions.
For further details on using this indicator, please refer to the guide available at:
MACD Sniper [trade_lexx]📈 MACD Sniper — Improve your trading strategy with accurate signals!
Introducing the MACD Sniper , an advanced trading indicator designed for a comprehensive analysis of market conditions. This indicator combines MACD (Moving Average Convergence Divergence) with various types of moving averages (SMA, EMA, WMA, VWMA, KAMA, HMA, ZLEMA, TEMA, ALMA, DEMA), providing traders with a powerful tool for generating buy and sell signals. It is ideal for traders who need an advantage in detecting changes in trends and market conditions.
🔍 How the signals work
1. Histogram signals:
— A buy signal is generated when the MACD histogram is below zero and begins to grow after the minimum number of falling histogram columns, which are indicated in the indicator menu. This indicates that selling pressure has decreased, the market is oversold and ready for a rebound. The signals are displayed as green triangles labeled "H" under the histogram graph. On the main chart, buy signals are displayed as green triangles labeled "Buy" under candlesticks.
— A sell signal is generated when the MACD histogram is above zero and begins to fall after the minimum number of growing histogram columns, which are indicated in the indicator menu. This indicates that the buying pressure has decreased, the market is overbought and ready for correction. The signals are displayed as red triangles labeled "H" above the histogram graph. On the main chart, the sell signals are displayed as red triangles with the word "Sell" above the candlesticks.
2. Moving Average Crossing Signals (MA):
— A buy signal is generated when the Fast Moving Average (MACD) crosses the Slow Moving Average (Signal Line) from bottom to top. This indicates a possible upward reversal of the market. The signals are displayed as green triangles labeled "MA" under the MACD chart. On the main chart, buy signals are displayed as green triangles labeled "Buy" under candlesticks.
— A sell signal is generated when the Fast Moving Average (MACD) crosses the slow Moving Average (Signal Line) from top to bottom. This indicates a possible downward reversal of the market. The signals are displayed as red triangles labeled "MA" above the MACD chart. On the main chart, the sell signals are displayed as red triangles with the word "Sell" above the candlesticks.
🔧 Signal filtering
— Minimum number of bars between signals
This filter allows the user to set the minimum number of bars that must pass between the generation of two consecutive signals. This helps to avoid frequent false alarms and improves the quality of the generated signals. Setting this parameter allows you to filter out the noise in the market and make the signals more reliable. For example, if the value is set to 5, then a new signal will be generated only after 5 bars have passed since the previous signal.
— "Wait for the opposite signal" mode
In this mode, Buy and Sell signals are generated only after receiving the opposite signal. This means that a buy signal will be generated only after the previous sell signal, and vice versa. This approach adds an additional level of filtering and helps to avoid false positives. This is especially useful in conditions of high market volatility, when false signals often occur.
— RSI filter
The Relative Strength Index (RSI) is used for additional filtering of buy and sell signals. The RSI helps determine whether a market is overbought or oversold. The user can set overbought and oversold levels, and signals will be generated only when the RSI is in the specified ranges. For example, a buy signal will be generated only if the RSI is in the range between 10 and 30 (oversold), and a sell signal if the RSI is in the range between 70 and 90 (overbought). This helps to avoid false signals in extreme market conditions.
🔌 Connector Histogram, MA, Combined 🔌
These parameters allow you to connect the indicator to trading strategies and test the signals throughout the trading history. This makes the indicator an even more powerful tool for traders who want to test the effectiveness of their strategies on historical data.
Connector Histogram provides the ability to connect signals based on the MACD histogram to trading strategies.
Connector MA allows you to connect signals based on the intersection of moving averages (MA) of the MACD, which can also be used for automatic trading or strategy testing.
The combined connector combines signals based on both a histogram and the intersection of moving averages, making the analysis more comprehensive and reliable, which is especially useful for traders seeking to improve the quality of their trading decisions.
🔔 Alerts
The indicator provides the ability to set up notifications for buy and sell signals, which allows traders to keep abreast of important market events without having to constantly monitor the chart. Users can set up notifications that will alert them when buy or sell signals appear, helping them respond to market changes in a timely manner and make informed decisions. These notifications can be configured for various types of signals, such as signals based on the MACD histogram, moving average crossings, or all at once, which makes the indicator a more convenient and functional tool for active traders.
🎨 Customizable Appearance
Customize the appearance of the MACD Sniper according to your preferences to make the analysis more convenient and visually pleasing. In the indicator settings section, you can change the colors of the buy and sell signals so that they stand out on the chart and are easily visible. For example, buy signals can be green, and sell signals can be red. These settings allow traders to adapt the indicator to their individual needs, making it more flexible and user-friendly.
🔧 How it works
The MACD Sniper indicator starts by calculating the MACD values and moving averages for a specific period in order to assess market conditions. For this, fast and slow moving averages are used, as well as a signal line, which are calculated based on the set parameters. The indicator then analyzes the MACD histogram to determine whether the difference between the fast and slow moving averages is rising or falling. Based on this analysis, buy and sell signals are generated. Additionally, the indicator uses the RSI filter to filter out false signals in overbought or oversold market conditions. The user can set the minimum number of bars between the signals and the "Wait for the opposite signal" mode for additional filtering. The indicator dynamically adjusts to changes in the market, providing relevant signals in real time.
📚 Quick guide to using the MACD Sniper
— Add the indicator to your favorites by clicking on the rocket icon. Adjust the parameters such as the length of periods for fast and slow moving averages, the type of moving average (SMA, EMA, WMA, VWMA, KAMA, HMA, ZLEMA, TEMA, ALMA, DEMA) and the length of the signal line, according to your trading style, or leave all settings as default.
— Adjust the signal filters to improve their quality and avoid false alarms
— Turn on notifications so that you don't miss important trading opportunities and don't constantly sit at the chart. This will allow you to keep abreast of all key market events and respond to them in a timely manner, without being distracted from other business.
— Use signals, they will help you determine the optimal entry and exit points of positions.
— Use the Connector for deeper analysis and verification of the effectiveness of signals, connect them to your trading strategies. This will allow you to test signals throughout your trading history and evaluate their accuracy based on historical data.
— Include the indicator in your trading strategy and run testing to see how buy and sell signals have worked in the past.
— Analyze the test results to determine how reliable the signals are and how they can improve your trading strategy. This will help you make more informed decisions and increase your trading efficiency.
Quarterly Theory ICT 02 [TradingFinder] True Open Session 90 Min🔵 Introduction
The Quarterly Theory ICT indicator is an advanced analytical system built on ICT (Inner Circle Trader) concepts and fractal time. It divides time into four quarters (Q1, Q2, Q3, Q4), and is designed based on the consistent repetition of these phases across all trading timeframes (annual, monthly, weekly, daily, and even shorter trading sessions).
Each cycle consists of four distinct phases: the first phase (Q1) is the Accumulation phase, characterized by price consolidation; the second phase (Q2), known as Manipulation or Judas Swing, is marked by initial false movements indicating a potential shift; the third phase (Q3) is Distribution, where price volatility peaks; and the fourth phase (Q4) is Continuation/Reversal, determining whether the previous trend continues or reverses.
🔵 How to Use
The central concept of this strategy is the "True Open," which refers to the actual starting point of each time cycle. The True Open is typically defined at the beginning of the second phase (Q2) of each cycle. Prices trading above or below the True Open serve as a benchmark for predicting the market's potential direction and guiding trading decisions.
The practical application of the Quarterly Theory strategy relies on accurately identifying True Open points across various timeframes.
True Open points are defined as follows :
Yearly Cycle :
Q1: January, February, March
Q2: April, May, June (True Open: April Monthly Open)
Q3: July, August, September
Q4: October, November, December
Monthly Cycle :
Q1: First Monday of the month
Q2: Second Monday of the month (True Open: Daily Candle Open price on the second Monday)
Q3: Third Monday of the month
Q4: Fourth Monday of the month
Weekly Cycle :
Q1: Monday
Q2: Tuesday (True Open: Daily Candle Open Price on Tuesday)
Q3: Wednesday
Q4: Thursday
Daily Cycle :
Q1: 18:00 - 00:00 (Asian session)
Q2: 00:00 - 06:00 (True Open: Start of London Session)
Q3: 06:00 - 12:00 (NY AM)
Q4: 12:00 - 18:00 (NY PM)
90 Min Asian Session :
Q1: 18:00 - 19:30
Q2: 19:30 - 21:00 (True Open at 19:30)
Q3: 21:00 - 22:30
Q4: 22:30 - 00:00
90 Min London Session :
Q1: 00:00 - 01:30
Q2: 01:30 - 03:00 (True Open at 01:30)
Q3: 03:00 - 04:30
Q4: 04:30 - 06:00
90 Min New York AM Session :
Q1: 06:00 - 07:30
Q2: 07:30 - 09:00 (True Open at 07:30)
Q3: 09:00 - 10:30
Q4: 10:30 - 12:00
90 Min New York PM Session :
Q1: 12:00 - 13:30
Q2: 13:30 - 15:00 (True Open at 13:30)
Q3: 15:00 - 16:30
Q4: 16:30 - 18:00
Micro Cycle (22.5-Minute Quarters) : Each 90-minute quarter is further divided into four 22.5-minute sub-segments (Micro Sessions).
True Opens in these sessions are defined as follows :
Asian Micro Session :
True Session Open : 19:30 - 19:52:30
London Micro Session :
T rue Session Open : 01:30 - 01:52:30
New York AM Micro Session :
True Session Open : 07:30 - 07:52:30
New York PM Micro Session :
True Session Open : 13:30 - 13:52:30
By accurately identifying these True Open points across various timeframes, traders can effectively forecast the market direction, analyze price movements in detail, and optimize their trading positions. Prices trading above or below these key levels serve as critical benchmarks for determining market direction and making informed trading decisions.
🔵 Setting
Show True Range : Enable or disable the display of the True Range on the chart, including the option to customize the color.
Extend True Range Line : Choose how to extend the True Range line on the chart, with the following options:
None: No line extension
Right: Extend the line to the right
Left: Extend the line to the left
Both: Extend the line in both directions (left and right)
Show Table : Determines whether the table—which summarizes the phases (Q1 to Q4)—is displayed.
Show More Info : Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
🔵 Conclusion
The Quarterly Theory ICT, by dividing time into four distinct quarters (Q1, Q2, Q3, and Q4) and emphasizing the concept of the True Open, provides a structured and repeatable framework for analyzing price action across multiple time frames.
The consistent repetition of phases—Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal—allows traders to effectively identify recurring price patterns and critical market turning points. Utilizing the True Open as a benchmark, traders can more accurately determine potential directional bias, optimize trade entries and exits, and manage risk effectively.
By incorporating principles of ICT (Inner Circle Trader) and fractal time, this strategy enhances market forecasting accuracy across annual, monthly, weekly, daily, and shorter trading sessions. This systematic approach helps traders gain deeper insight into market structure and confidently execute informed trading decisions.
MACD with Holt–Winters Smoothing [AIBitcoinTrend]👽 MACD with Holt–Winters Smoothing (AIBitcoinTrend)
The MACD with Holt–Winters Smoothing is an momentum indicator that enhances traditional MACD analysis by incorporating Holt–Winters exponential smoothing. This adaptation reduces lag while maintaining trend sensitivity, making it more effective for detecting trend reversals and sustained momentum shifts. Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, helping traders manage risk dynamically.
👽 What Makes the MACD with Holt–Winters Smoothing Unique?
Unlike the standard MACD, which relies on simple exponential moving averages, this version applies Holt–Winters smoothing to better capture trends while filtering out market noise. Combined with real-time divergence detection and a trailing stop system, this indicator allows traders to:
✅ Identify trend strength with a dynamically smoothed MACD signal.
✅ Detect bullish and bearish divergences in real time.
✅Implement Crossover/Crossunder signals tied to ATR-based trailing stops for risk management
👽 The Math Behind the Indicator
👾 Holt–Winters Smoothing for MACD
Traditional MACD calculations use exponential moving averages (EMA) to identify momentum. This indicator improves upon it by applying Holt’s linear trend equations, which enhance signal accuracy by reducing lag and smoothing out fluctuations.
Key Features:
Alpha (α) - Controls the weight of the new data in smoothing.
Beta (β) - Determines how fast the trend component adapts to new changes.
The Holt–Winters Signal Line provides a refined MACD crossover system for better trade execution.
👾 Real-Time Divergence Detection
The indicator identifies bullish and bearish divergences between MACD and price action.
Bullish Divergence: Occurs when price makes a lower low, but MACD makes a higher low – signaling potential upward momentum.
Bearish Divergence: Occurs when price makes a higher high, but MACD makes a lower high – signaling potential downward momentum.
👾 Dynamic ATR-Based Trailing Stop
The indicator includes a trailing stop system based on ATR (Average True Range). This allows traders to manage positions dynamically based on volatility.
Bullish Trailing Stop: Triggers when MACD crosses above the Holt–Winters signal, with a stop placed at low - (ATR × Multiplier).
Bearish Trailing Stop: Triggers when MACD crosses below the Holt–Winters signal, with a stop placed at high + (ATR × Multiplier).
Trailing Stop Adjustments: Expands or contracts dynamically with market conditions, reducing premature exits while securing profits.
👽 How Traders Can Use This Indicator
👾 Divergence Trading
Traders can use real-time divergence detection to anticipate trend reversals before they occur.
Bullish Divergence Setup:
Look for MACD making a higher low, while price makes a lower low.
Enter long when MACD confirms upward momentum.
Bearish Divergence Setup:
Look for MACD making a lower high, while price makes a higher high.
Enter short when MACD confirms downward momentum.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ MACD crosses above the Holt–Winters signal.
✅ A bullish trailing stop is placed using low - ATR × Multiplier.
✅ Exit if the price crosses below the stop.
Bearish Setup:
✅ MACD crosses below the Holt–Winters signal.
✅ A bearish trailing stop is placed using high + ATR × Multiplier.
✅ Exit if the price crosses above the stop.
This systematic trade management approach helps traders lock in profits while reducing drawdowns.
👽 Why It’s Useful for Traders
Lag Reduction: Holt–Winters smoothing ensures faster and more reliable trend detection.
Real-Time Divergence Alerts: Identify potential reversals before they happen.
Adaptive Risk Management: ATR-based trailing stops adjust to volatility dynamically.
Works Across Markets & Timeframes: Effective for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
MACD Fast & Slow Lengths: Adjust the MACD short- and long-term EMA periods.
Holt–Winters Alpha & Beta: Fine-tune the smoothing sensitivity.
Enable Divergence Detection: Toggle real-time divergence analysis.
Lookback Period for Divergences: Configure how far back pivot points are detected.
ATR Multiplier for Trailing Stops: Adjust stop-loss sensitivity to market volatility.
Trend Filtering: Enable signal filtering based on trend direction.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
TEMA OBOS Strategy PakunTEMA OBOS Strategy
Overview
This strategy combines a trend-following approach using the Triple Exponential Moving Average (TEMA) with Overbought/Oversold (OBOS) indicator filtering.
By utilizing TEMA crossovers to determine trend direction and OBOS as a filter, it aims to improve entry precision.
This strategy can be applied to markets such as Forex, Stocks, and Crypto, and is particularly designed for mid-term timeframes (5-minute to 1-hour charts).
Strategy Objectives
Identify trend direction using TEMA
Use OBOS to filter out overbought/oversold conditions
Implement ATR-based dynamic risk management
Key Features
1. Trend Analysis Using TEMA
Uses crossover of short-term EMA (ema3) and long-term EMA (ema4) to determine entries.
ema4 acts as the primary trend filter.
2. Overbought/Oversold (OBOS) Filtering
Long Entry Condition: up > down (bullish trend confirmed)
Short Entry Condition: up < down (bearish trend confirmed)
Reduces unnecessary trades by filtering extreme market conditions.
3. ATR-Based Take Profit (TP) & Stop Loss (SL)
Adjustable ATR multiplier for TP/SL
Default settings:
TP = ATR × 5
SL = ATR × 2
Fully customizable risk parameters.
4. Customizable Parameters
TEMA Length (for trend calculation)
OBOS Length (for overbought/oversold detection)
Take Profit Multiplier
Stop Loss Multiplier
EMA Display (Enable/Disable TEMA lines)
Bar Color Change (Enable/Disable candle coloring)
Trading Rules
Long Entry (Buy Entry)
ema3 crosses above ema4 (Golden Cross)
OBOS indicator confirms up > down (bullish trend)
Execute a buy position
Short Entry (Sell Entry)
ema3 crosses below ema4 (Death Cross)
OBOS indicator confirms up < down (bearish trend)
Execute a sell position
Take Profit (TP)
Entry Price + (ATR × TP Multiplier) (Default: 5)
Stop Loss (SL)
Entry Price - (ATR × SL Multiplier) (Default: 2)
TP/SL settings are fully customizable to fine-tune risk management.
Risk Management Parameters
This strategy emphasizes proper position sizing and risk control to balance risk and return.
Trading Parameters & Considerations
Initial Account Balance: $7,000 (adjustable)
Base Currency: USD
Order Size: 10,000 USD
Pyramiding: 1
Trading Fees: $0.94 per trade
Long Position Margin: 50%
Short Position Margin: 50%
Total Trades (M5 Timeframe): 128
Deep Test Results (2024/11/01 - 2025/02/24)BTCUSD-5M
Total P&L：+1638.20USD
Max equity drawdown：694.78USD
Total trades：128
Profitable trades:44.53
Profit factor:1.45
These settings aim to protect capital while maintaining a balanced risk-reward approach.
Visual Support
TEMA Lines (Three EMAs)
Trend direction is indicated by color changes (Blue/Orange)
ema3 (short-term) and ema4 (long-term) crossover signals potential entries
OBOS Histogram
Green → Strong buying pressure
Red → Strong selling pressure
Blue → Possible trend reversal
Entry & Exit Markers
Blue Arrow → Long Entry Signal
Red Arrow → Short Entry Signal
Take Profit / Stop Loss levels displayed
Strategy Improvements & Uniqueness
This strategy is based on indicators developed by "l_lonthoff" and "jdmonto0", but has been significantly optimized for better entry accuracy, visual clarity, and risk management.
Enhanced Trend Identification with TEMA
Detects early trend reversals using ema3 & ema4 crossover
Reduces market noise for a smoother trend-following approach
Improved OBOS Filtering
Prevents excessive trading
Reduces unnecessary risk exposure
Dynamic Risk Management with ATR-Based TP/SL
Not a fixed value → TP/SL adjusts to market volatility
Fully customizable ATR multiplier settings
(Default: TP = ATR × 5, SL = ATR × 2)
Summary
The TEMA + OBOS Strategy is a simple yet powerful trading method that integrates trend analysis and oscillators.
TEMA for trend identification
OBOS for noise reduction & overbought/oversold filtering
ATR-based TP/SL settings for dynamic risk management
Before using this strategy, ensure thorough backtesting and demo trading to fine-tune parameters according to your trading style.
Profit Hunter @DaviddTechProfit Hunter @DaviddTech is an advanced multi-strategy indicator designed to give traders a significant edge in identifying high-probability trading opportunities across all market conditions. By combining the power of T3 adaptive moving averages, ADX-based trend strength analysis, SuperTrend trailing stops, and dynamic support/resistance detection, this indicator delivers a complete trading system in one powerful package.
## 📊 Recommended Usage
Timeframes: Most effective on 1H, 4H, and Daily charts for swing trading; 5M and 15M for day trading
Markets: Works across all markets including Forex, Crypto, Indices, and Stocks
Setup Guidelines: Look for T3 crossovers with strong ADX readings (>25) coinciding with breakout signals (yellow dots/red crosses) near key support/resistance levels for highest probability entries
## 🔥 Key Features:
### T3 Adaptive Trend Detection:
Utilizes premium T3 adaptive indicators instead of standard EMAs for superior smoothing and accuracy
Dynamic color-shifting cloud formation between fast and slow T3 lines reveals immediate trend direction
Proprietary transparency algorithm intensifies cloud colors during strong trends based on real-time ADX readings
### Advanced Support & Resistance Mapping:
Automatically identifies and marks key market structure levels during T3 crossovers
Dynamic horizontal level plotting with optional extension for monitoring future price interactions
Intelligent level validation - converts to dotted lines when price breaks through, maintaining visual clarity
### SuperTrend Trailing Stoploss System:
Professional-grade white trailing stop indicator adapts to market volatility using ATR calculations
Generates precise entry and exit signals with optional buy/sell labels at critical reversal points
Visual trend state highlighting for immediate assessment of current market position
### Breakout Detection & Confirmation:
Sophisticated dual-algorithm breakout system combining Bollinger Bands and Keltner Channels
Visual breakout alerts with yellow dots (bullish) and red crosses (bearish) for instant pattern recognition
Validates breakouts against T3 trend direction to minimize false signals
### Alpha Edge Color System:
Utilizes DaviddTech's signature color scheme with bullish green and bearish pink
Revolutionary transparency algorithm translates ADX readings into precise visual intensity
Higher ADX values produce more vivid colors, instantly communicating trend strength without additional indicators
## 💰 Trading Applications:
Alpha Discovery: Identify emerging trends before the majority of market participants
Precision Entry/Exit: Use SuperTrend signals combined with support/resistance levels for optimal trade execution
Risk Management: Set stops based on the white trailing stoploss line for mathematically-optimized protection
Trend Confirmation: Validate setups using the T3 cloud direction and ADX-based intensity
Breakout Trading: Capture explosive moves with confirmed Bollinger/Keltner breakout signals
Swing Position Management: Monitor extended support/resistance levels for multi-day positioning
## ✨ Strategy Example
As shown in the chart image, ideal entries occur when:
The T3 cloud turns bullish (green) or bearish (pink) with strong color intensity
A yellow dot (bullish) or red cross (bearish) breakout signal appears
Price respects the white SuperTrend line as support/resistance
The trade aligns with key horizontal support/resistance levels identified by the indicator
## 📝 Attribution
This indicator builds upon and enhances concepts from:
Market Trend Levels Detector by BigBeluga (support/resistance detection framework)
T3 indicator implementation by DaviddTech (adaptive moving average system)
Average Directional Index (ADX) methodology for trend strength measurement
Profit Hunter @DaviddTech represents the culmination of advanced technical analysis methodologies in one seamless system.
EMA 5 Alert Candle ShortThe 5 EMA (Exponential Moving Average) Strategy is a simple yet effective trading strategy that helps traders identify short-term trends and potential entry and exit points. This strategy is widely used in intraday and swing trading, particularly in forex, stocks, and crypto markets.
Components of the 5 EMA Strategy
5 EMA: A fast-moving average that reacts quickly to price movements.
15-minute or 1-hour timeframe (commonly used, but adaptable to other timeframes).
Candlestick Patterns: To confirm entry signals.
How the 5 EMA Strategy Works
Buy (Long) Setup:
Price Above the 5 EMA: The price should be trading above the 5 EMA.
Pullback to the 5 EMA: A minor retracement or consolidation near the 5 EMA.
Bullish Candlestick Confirmation: A bullish candle (e.g., engulfing or pin bar) forms near the 5 EMA.
Entry: Enter a long trade at the close of the bullish candle.
Stop Loss: Place below the recent swing low or 5-10 pips below the 5 EMA.
Take Profit: Aim for a risk-reward ratio of at least 1:2 or trail the stop using a higher EMA (e.g., 10 or 20 EMA).
Sell (Short) Setup:
Price Below the 5 EMA: The price should be trading below the 5 EMA.
Pullback to the 5 EMA: A small retracement towards the 5 EMA.
Bearish Candlestick Confirmation: A bearish candle (e.g., engulfing or pin bar) near the 5 EMA.
Entry: Enter a short trade at the close of the bearish candle.
Stop Loss: Place above the recent swing high or 5-10 pips above the 5 EMA.
Take Profit: Aim for a 1:2 risk-reward ratio or use a trailing stop.
Additional Filters for Better Accuracy
Higher Timeframe Confirmation: Check the trend on a higher timeframe (e.g., 1-hour or 4-hour).
Volume Confirmation: Enter trades when volume is increasing.
Avoid Sideways Market: Use the strategy only when the market is trending.
Advantages of the 5 EMA Strategy
✔️ Simple and easy to use.
✔️ Works well in trending markets.
✔️ Helps traders capture short-term momentum.
Disadvantages
❌ Less effective in choppy or sideways markets.
❌ Requires discipline in following stop-loss rules.
Casa_VolumeProfileSessionLibrary "Casa_VolumeProfileSession"
Analyzes price and volume during regular trading hours to provide a session volume profile,
including Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL).
Calculates and displays these levels historically and for the developing session.
Offers customizable visualization options for the Value Area, POC, histogram, and labels.
Uses lower timeframe data for increased accuracy and supports futures sessions.
The number of rows used for the volume profile can be fixed or dynamically calculated based on the session's price range and the instrument's minimum tick increment, providing optimal resolution.
calculateEffectiveRows(configuredRows, dayHigh, dayLow)
Determines the optimal number of rows for the volume profile, either using the configured value or calculating dynamically based on price range and tick size
Parameters:
configuredRows (int) : User-specified number of rows (0 means auto-calculate)
dayHigh (float) : Highest price of the session
dayLow (float) : Lowest price of the session
Returns: The number of rows to use for the volume profile
debug(vp, position)
Helper function to write some information about the supplied SVP object to the screen in a table.
Parameters:
vp (Object) : The SVP object to debug
position (string) : The position.* to place the table. Defaults to position.bottom_center
getLowerTimeframe()
Depending on the timeframe of the chart, determines a lower timeframe to grab volume data from for the analysis
Returns: The timeframe string to fetch volume for
get(volumeProfile, lowerTimeframeHigh, lowerTimeframeLow, lowerTimeframeVolume, lowerTimeframeTime, lowerTimeframeSessionIsMarket)
Populated the provided SessionVolumeProfile object with vp data on the session.
Parameters:
volumeProfile (Object) : The SessionVolumeProfile object to populate
lowerTimeframeHigh (array) : The lower timeframe high values
lowerTimeframeLow (array) : The lower timeframe low values
lowerTimeframeVolume (array) : The lower timeframe volume values
lowerTimeframeTime (array) : The lower timeframe time values
lowerTimeframeSessionIsMarket (array) : The lower timeframe session.ismarket values (that are futures-friendly)
drawPriorValueAreas(todaySessionVolumeProfile, extendYesterdayOverToday, showLabels, labelSize, pocColor, pocStyle, pocWidth, vahlColor, vahlStyle, vahlWidth, vaColor)
Given a SessionVolumeProfile Object, will render the historical value areas for that object.
Parameters:
todaySessionVolumeProfile (Object) : The SessionVolumeProfile Object to draw
extendYesterdayOverToday (bool) : Defaults to true
showLabels (bool) : Defaults to true
labelSize (string) : Defaults to size.small
pocColor (color) : Defaults to #e500a4
pocStyle (string) : Defaults to line.style_solid
pocWidth (int) : Defaults to 1
vahlColor (color) : The color of the value area high/low lines. Defaults to #1592e6
vahlStyle (string) : The style of the value area high/low lines. Defaults to line.style_solid
vahlWidth (int) : The width of the value area high/low lines. Defaults to 1
vaColor (color) : The color of the value area background. Defaults to #00bbf911)
drawHistogram(volumeProfile, bgColor, showVolumeOnHistogram)
Given a SessionVolumeProfile object, will render the histogram for that object.
Parameters:
volumeProfile (Object) : The SessionVolumeProfile object to draw
bgColor (color) : The baseline color to use for the histogram. Defaults to #00bbf9
showVolumeOnHistogram (bool) : Show the volume amount on the histogram bars. Defaults to false.
Object
Object Contains all settings and calculated values for a Volume Profile Session analysis
Fields:
numberOfRows (series int) : Number of price levels to divide the range into. If set to 0, auto-calculates based on price range and tick size
valueAreaCoverage (series int) : Percentage of total volume to include in the Value Area (default 70%)
trackDevelopingVa (series bool) : Whether to calculate and display the Value Area as it develops during the session
valueAreaHigh (series float) : Upper boundary of the Value Area - price level containing specified % of volume
pointOfControl (series float) : Price level with the highest volume concentration
valueAreaLow (series float) : Lower boundary of the Value Area
startTime (series int) : Session start time in Unix timestamp format
endTime (series int) : Session end time in Unix timestamp format
dayHigh (series float) : Highest price of the session
dayLow (series float) : Lowest price of the session
step (series float) : Size of each price row (calculated as price range divided by number of rows)
pointOfControlLevel (series int) : Index of the row containing the Point of Control
valueAreaHighLevel (series int) : Index of the row containing the Value Area High
valueAreaLowLevel (series int) : Index of the row containing the Value Area Low
lastTime (series int) : Tracks the most recent timestamp processed
volumeRows (map) : Stores volume data for each price level row (key=row number, value=volume)
ltfSessionHighs (array) : Stores high prices from lower timeframe data
ltfSessionLows (array) : Stores low prices from lower timeframe data
ltfSessionVols (array) : Stores volume data from lower timeframe data
Volatility Price FlowCapitalize on market volatility with our new volatility price flow indicator. We have designed this indicator to process historical price movements and indicate when price may have reached exhaustion in the context of current volatility.
This is achieved by taking the price deviation from a user defined moving average, and applying a weighting to the deviations from the candle body and candle wick on both buy side and sell side, over a user defined period. The period of the base moving average, type of moving average and the period of the historical price deviations can all be modified. This creates a typical 'band' style indicator, though with a unique characteristic that the buy and sell side vary independently as well as the band expansion being based on weighted variables tied to the actual price changes, rather than just a standard deviation the moves uniformly.
Additionally, these bands can be merged with an anchored vwap - we do this so that the deviations of price from the moving average can include a more volume based approach to identifying potential pivots.
The end result is an indicator that reflects the current market price movements, identifies and capitalizes on impulsive or beginning moves to indicate potential tops / bottoms / reversals.
The signals are simple - anytime price closes within a band, having been outside the band, a signal is displayed. As a basic guide to setting the indicator up for the first time, we suggest reducing all of the multipliers to a value less than 1. Then gradually increase each one, until the signals reduce in quantity and improve in quality, starting with the price deviation multiplier, then the volatility multiplier and finally the expansion multiplier.
Last of all, alerts can be created based on the current chart timeframe and indicator settings, simply by adding an alert that uses the built in buy or sell signal.
Note: We cannot guarantee the accuracy of the signals provided, since the user creates the signals by modifying the settings, and as such we can take no responsibility for any trading losses incurred using the indicator and highly encourage all users to manage their risk and only risk what you can afford to lose.
CandelaCharts - Liquidity Key Zones (LKZ)📝 Overview
The Liquidity Key Zones indicator displays the previous high and low levels for daily, weekly, monthly, quarterly, and yearly timeframes. These levels serve as crucial price zones for trading any market or instrument. They are also high-probability reaction zones, ideal for trading using straightforward confirmation patterns.
Each of these levels plays a significant role in determining whether the market continues its momentum or reverses its bias. I like to think of these levels as dual magnets—they simultaneously attract and repel price. You might wonder how having opposing views can be useful. The key is to remain neutral about direction and establish your own rules to identify when these zones are likely to attract or repel price. I have my own set of rules, and you can develop yours.
📦 Features
MTF
Styling
⚙️ Settings
Day: Shows previous day levels
Week: Shows previous week levels
Month: Shows previous month levels
Quarter: Shows previous quarter levels
Year: Shows previous year levels
Show Average: Shows previous level average price
Show Open: Shows previous level open price
⚡️ Showcase
Daily
Weekly
Monthly
Quarterly
Yearly
Average
Open
📒 Usage
When the price breaks through a significant level, such as a daily, weekly, or monthly high or low, it often signals a potential reversal in market direction. This occurs because these levels represent key areas of support or resistance, where traders anticipate heightened activity, including profit-taking, stop-loss orders, or new positions being initiated.
Once the price breaches these levels, it may trigger a sharp reaction as market participants adjust their strategies, leading to a reversal. Monitoring price action and volume around these levels can provide valuable confirmation of such reversals.
Another effective approach to utilizing these pivot points is by incorporating them into a structured trading strategy, such as the X Model, which leverages multiple timeframes and technical tools to refine trade entries and exits.
X Model conditions:
(D1) Previous Day High (ERL)
(H1) Bullish FVG/IFVG/OB (IRL)
(m15) MSS / SMT
Only Short Above 00:00
By combining these elements, the X Model offers a comprehensive framework for leveraging pivot levels effectively, emphasizing confluence between liquidity zones, time-based rules, and multi-timeframe analysis to enhance trading accuracy and consistency.
🚨 Alerts
This script provides alert options for all signals.
Bearish Signal
A bearish signal is generated when the price breaks below the previous low level.
Bullish Signal
A bullish signal is generated when the price breaks above the previous low level.
⚠️ Disclaimer
Trading involves significant risk, and many participants may incur losses. The content on this site is not intended as financial advice and should not be interpreted as such. Decisions to buy, sell, hold, or trade securities, commodities, or other financial instruments carry inherent risks and are best made with guidance from qualified financial professionals. Past performance is not indicative of future results.
Price and Longitude Angles Planetary Price & Longitude Angles Indicator
This indicator plots planetary price and longitude angles starting from a user-selected date and time, offering a distinctive lens to explore the relationship between price and planetary timing. It supports both heliocentric and geocentric, enabling flexible and in-depth planetary analysis. The angles can be plotted across any time frame for maximum versatility.
How to Use
Once the indicator is loaded, you’ll be prompted to select a starting date and time for your analysis. From there, customize it as follows:
Select Planetary Options:
To plot the price and longitude for a single planet, choose the same planet in both dropdown menus.
To plot the average of two planets, select a different planet in each dropdown.
Set the Price Per Degree of Longitude: Adjust this value to define the scaling of the planetary angles relative to price.
Customize Fan Settings:
Toggle the mirroring of the fan on or off based on your needs.
Show or hide specific angle divisions to tailor the display to your preferences.
Display or conceal the information label that indicates the price per longitude and the number of degrees traveled.
This indicator is inspired by the methodologies of W.D. Gann and Patrick Mikula, expanding on concepts from Gann Scientific Method Unveiled, Volume 2. It was built using Astrolib by @BarefootJoey
I crafted this tool through dedication to support my own study of these ideas. I’m sharing it open-source not only to deepen my understanding and honor the work of Gann and Mikula, but also to invite collaboration. There’s always room for improvement—whether in functionality, accuracy, or design—and I hope others will join me in refining it. This is for those like me: eager to explore these concepts but lacking tools to experiment with. Let’s build on it together.
Crystal Order BlockThe Crystal Order Block Indicator is a powerful tool designed to help traders identify key institutional order blocks with high precision. This indicator is ideal for traders following Smart Money Concepts (SMC) and Institutional Trading Strategies, providing clear insights into potential high-probability trade setups.
🔹 Key Features:
✔ Automatic Order Block Detection: Identifies valid bullish & bearish order blocks.
✔ Unmitigated Order Blocks Highlighted: Focuses on fresh order blocks for improved trade opportunities.
✔ Trend-Focused Trading: Works best when combined with market structure analysis.
✔ Multi-Timeframe Support: Suitable for scalping, swing trading, and intraday trading.
✔ Risk Management Enhancement: Helps traders refine entries and exits based on institutional price movements.
📈 How to Use the Crystal Order Block Indicator:
🔹 Identifying Order Blocks:
➡ The indicator automatically detects order blocks formed by institutional trading activity.
➡ Unmitigated order blocks are highlighted, indicating areas where price may react.
🔹 High-Probability Trade Setups:
➡ Buy Setup: Look for a bullish order block in an uptrend, confirming strength.
➡ Sell Setup: Identify a bearish order block in a downtrend for potential short trades.
🔹 Order Block Mitigation:
➡ The updated version filters out mitigated order blocks, allowing traders to focus on fresh trading opportunities.
📊 Best Practices & Timeframes:
🔸 Works on all timeframes, but higher accuracy is observed on M30 and above.
🔸 Best suited for Smart Money Trading, Institutional Trading, and Price Action Strategies.
🔸 Should be used with liquidity concepts and market structure analysis for enhanced precision.
⚠ Important Note:
This indicator is a technical tool designed to assist traders in market analysis. It does not guarantee success and should be used alongside proper risk management and trading discipline.
Dynamic 50% Indicator of the selected range!This is a indicator which shows you the 50% level of the selected timeframe range. This is a good tool because price tends to bounce of of 50% levels.
Introducing the 50% Range Level Indicator, designed for traders who seek accuracy and strategic insights in their market analysis. This tool calculates and visually displays the midpoint (50% level) of any selected price range, helping you identify key equilibrium zones where price action often reacts.
Why Use This Indicator?
Key Market Equilibrium – The 50% level is a crucial reference point where price often consolidates, reverses, or gathers momentum.
Custom Range Selection – Simply select your desired price range, and the indicator will dynamically plot the midpoint.
Enhance Your Trading Strategy – Use it for support & resistance confirmation, retracement analysis, or confluence with other indicators.
Works on All Timeframes & Assets – Suitable for stocks, forex, crypto, and indices.
Gain an Edge in the Market
Whether you’re a day trader, swing trader, or long-term investor, the 50% Range Level Indicator can enhance your technical analysis and decision-making.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
Adaptive Supply and Demand [EdgeTerminal]Adaptive Supply and Demand is a dynamic supply and demand indicator with a few unique twists. It considers volume pressure, volatility-based adjustments and multi-time frame momentum for confidence scoring (multi-step confirmation) to generate dynamic lines that adjust based on the market and also to generate dynamic support/resistance levels for the supply and demand lines.
The dynamic support and resistance lines shown gives you a better situational awareness of the current state of the market and add more context to why the market is moving into a certain direction.
> Trading Scenarios
When the confidence score is over 80%, strong volume pressure in trend direction (up or down), volatility is low and momentum is aligned across timeframes, there is an indication of a strong upward or downward trend.
When the supply and demand line crossover, the confidence score is over 75% and the volume pressure is shifting, this can be an indicator of trend reversal. Use tight initial stops, scale into position as trend develops, monitor the volume pressure for continuation and wait for confidence confirmation.
When the confiance score is below 60%, the volume pressure is choppy, volatility is high, you want to avoid trading or reduce position size, wait for confidence improvements, use support and resistance for entries/exits and use tighter stops due to market conditions. This is an indication of a ranging market.
Another scenario is when there is a sudden volume pressure increase, and a raising confidence score, the volatility is expanding and the bar momentum is aligning the volatility direction. This can indicate a breakout scenario.
> How it Works
1. Volume Pressure Analysis
Volume Pressure Analysis is a key component that measures the true buying and selling force in the market. Here's a detailed breakdown. The idea is to standardize volume to prevent large spikes from skewing results.
The indicator employs an adaptive volume normalization technique to detect genuine buying and selling pressure.
It takes current volume and divides it by average volume.
If normVol > 1: Current volume is above average
If normVol < 1: Current volume is below average
An example if this would be If current volume is 1500 and average is 1000, normVol = 1.5 (50% above average)
Another component of the volume pressure analysis is the Price Change Calculation sub-module. The purpose of this is to measure price movement relative to recent average.
It works by subtracting the average price from the current price. If the value is positive, price is average and if negative, price is below average.
Finally, the volume pressure is calculated to combine volume and price for true pressure reading.
2. Savitzky-Golay Filtering
SG filtering implements advanced signal smoothing while preserving important trend features. It uses weighted moving average approximation, preserves higher moments of data and reduces noise while maintaining signal integrity.
This results in smoother signal lines, reduced false crossovers and better trend identification. Traditional moving averages tend to lag and smooth out important features. Additionally, simple moving averages can miss critical turning points and regular smoothing can delay signal generation.
SG filtering preserves higher moments such as peaks, valleys and trends, reduces noise while maintaining signal sharpness.
It works by creating a symmetric weighting scheme. This way center points get the highest weights while edge points get the lowest weight.
3. Parkinson's Volatility
Parkinson's Volatility is an advanced volatility measurement formula using high-low range data. It uses high-low range for volatility calculation, incorporates logarithmic returns and annualized the volatility measure.
This results in more accurate volatility measurement, better risk assessment and dynamic signal sensitivity.
4. Multi-timeframe Momentum
This combines signals from each module for each timeframe to calculate momentum across three timeframes. It also applies weighted importance to each timeframe and generates a composite momentum signal.
This results in a more comprehensive trend analysis, reduced timeframe bias and better trend confirmation.
> Indicator Settings
Short-term Period:
Lower values makes it more sensitive, meaning it will generate more signals. Higher values makes it less sensitive, resulting in fewer signals. We recommend a 5 to 15 range for day trading, and 10 to 20 for swing trading
Medium-term Period:
Lower values result in faster trend confirmation and higher values show slower and more reliable confirmation. We recommend a range of 15-25 for day trading and 20-30 for swing trading.
Long-term Period:
Lower values makes it more responsive to trend changes and higher values are better for major trend identification. We recommend a range of 40-60 for day trading and 50-100 for swing trading.
Volume Analysis Window:
Lower values result in more sensitivity to volume changes and higher values result in smoother volume analysis. The optimal range is 15-25 for most trading styles.
Confidence Threshold:
Lower values generate more signals but quality decreases. Higher values generate fewer signals but accuracy increases.The optimal range is 0.65-0.8 for most trading conditions.
Smart Money Index + True Strength IndexThe Smart Money Index + True Strength Index indicator is a combination of two popular technical analysis indicators: the Smart Money Index (SMI) and the True Strength Index (TSI). This combined indicator helps traders identify potential entry points for long and short positions based on signals from both indexes.
Main Components:
Smart Money Index (SMI):
The SMI measures the difference between the closing and opening price of a candle multiplied by the trading volume over a certain period of time. This allows you to assess the activity of large players ("smart money") in the market. If the SMI value is above a certain threshold (smiThreshold), it may indicate a bullish trend, and if lower, it may indicate a bearish trend.
True Strength Index (TSI):
The TSI is an oscillator that measures the strength of a trend by comparing the price change of the current bar with the previous bar. It uses two exponential moving averages (EMAS) to smooth the data. TSI values can fluctuate around zero, with values above the overbought level indicating a possible downward correction, and values below the oversold level signaling a possible upward correction.
Parameters:
SMI Length: Defines the number of candles used to calculate the average SMI value. The default value is 14.
SMI Threshold: A threshold value that is used to determine a buy or sell signal. The default value is 0.
Length of the first TSI smoothing (tsiLength1): The length of the first EMA for calculating TSI. The default value is 25.
Second TSI smoothing length (tsiLength2): The length of the second EMA for additional smoothing of TSI values. The default value is 13.
TSI Overbought level: The level at which the market is considered to be overbought. The default value is 25.
Oversold level TSI: The level at which it is considered that the market is in an oversold state. The default value is -25.
Logic of operation:
SMI calculation:
First, the difference between the closing and opening price of each candle (close - open) is calculated.
This difference is then multiplied by the trading volume.
The resulting product is averaged using a simple moving average (SMA) over a specified period (smiLength).
Calculation of TSI:
The price change relative to the previous bar is calculated (close - close ).
The first EMA with the length tsiLength1 is applied.
Next, a second EMA with a length of tsiLength2 is applied to obtain the final TSI value.
The absolute value of price changes is calculated in the same way, and two emas are also applied.
The final TSI index is calculated as the ratio of these two values multiplied by 100.
Graphical representation:
The SMI and TSI lines are plotted on the graph along with their respective thresholds.
For SMI, the line is drawn in orange, and the threshold level is dotted in gray.
For the TSI, the line is plotted in blue, the overbought and oversold levels are indicated by red and green dotted lines, respectively.
Conditions for buy/sell signals:
A buy (long) signal is generated when:
SMI is greater than the threshold (smi > smiThreshold)
TSI crosses the oversold level from bottom to top (ta.crossover(tsi, oversold)).
A sell (short) signal is generated when:
SMI is less than the threshold (smi < smiThreshold)
TSI crosses the overbought level from top to bottom (ta.crossunder(tsi, overbought)).
Signal display:
When the conditions for a long or short are met, labels labeled "LONG" or "SHORT" appear on the chart.
The label for the long is located under the candle and is colored green, and for the short it is above the candle and is colored red.
Notification generation:
The indicator also supports notifications via the TradingView platform. Notifications are sent when conditions arise for a long or short position.
This combined indicator provides the trader with the opportunity to use both SMI and TSI signals simultaneously, which can improve the accuracy of trading decisions.
Multiple Candlestick Patterns - AlgomaxxA comprehensive candlestick pattern detection indicator that identifies seven major Japanese candlestick patterns in real-time. This indicator helps traders identify potential reversal and continuation patterns with customizable visual alerts and labels.
Features
Detects 7 major candlestick patterns:
Doji
Hammer
Shooting Star
Bullish Engulfing
Bearish Engulfing
Morning Star
Evening Star
Color-coded candlesticks for easy pattern identification
Customizable pattern indicators above/below candles
Optional pattern labels with adjustable position
Alert conditions for each pattern
Grouped settings for easy customization
Settings
General Settings
Lookback Period: Number of candles to analyze (default: 20)
Body Size Threshold: Minimum relative size for candle body (default: 0.6)
Pattern Settings
Toggle visibility for each pattern type:
Doji Pattern
Hammer Pattern
Shooting Star Pattern
Bullish Engulfing Pattern
Bearish Engulfing Pattern
Morning Star Pattern
Evening Star Pattern
Label Settings
Show Labels: Toggle pattern labels on/off
Label Text Color: Customize label color
Label Position: Choose between Left/Center/Right alignment
Label Offset: Adjust distance of labels from candles
Pattern Descriptions
Doji: Shows indecision when open and close prices are very close
Yellow color
Cross symbol below candle
Hammer: Potential bullish reversal with long lower shadow
Green color
Triangle up symbol below candle
Shooting Star: Potential bearish reversal with long upper shadow
Red color
Triangle down symbol above candle
Bullish Engulfing: Bullish reversal pattern where current green candle completely engulfs previous red candle
Light green color
Triangle up symbol below candle
Bearish Engulfing: Bearish reversal pattern where current red candle completely engulfs previous green candle
Light red color
Triangle down symbol above candle
Morning Star: Three-candle bullish reversal pattern
Seafoam green color
Triangle up symbol below candle
Evening Star: Three-candle bearish reversal pattern
Pink red color
Triangle down symbol above candle
How to Use
Add the indicator to your chart
Customize the settings based on your preferences:
Enable/disable specific patterns you want to monitor
Adjust label settings for better visibility
Set up alerts for patterns you want to be notified about
Pattern Recognition:
Watch for color changes in candlesticks indicating pattern formation
Look for shape indicators above/below candles
Read pattern labels for quick pattern identification
Trading Suggestions:
Use in conjunction with other technical indicators
Consider overall trend and support/resistance levels
Confirm patterns with volume and price action
Wait for pattern completion before making trading decisions
Tips
Patterns work best when used with multiple timeframes
Combine with trend lines and support/resistance levels
Use volume to confirm pattern strength
Consider market context and overall trend
Larger timeframes typically produce more reliable signals
Use alerts to avoid missing important pattern formations
Disclaimer
This indicator is for informational and educational purposes only. No guarantee is made regarding the accuracy of pattern detection or potential future price movements. Always use proper risk management and consider multiple factors before making trading decisions.
Dynamic Range Finder [The_lurker]هو أداة تهدف إلى تحديد نطاق السعر الديناميكي بناءً على التقلبات ومتوسط الأسعار . حيث يتم التعرف على مناطق التوحيد السعري (Consolidation) ويعطي إشارات شراء وبيع عند اختراق أو كسر هذا النطاق .
// يفضل استخدام المؤشر على اطار 4 ساعات واكثر //
مميزات المؤشر :
1- اكتشاف النطاق السعري الديناميكي
- يقوم المؤشر بحساب متوسط السعر خلال فترة محددة ومقارنة الإغلاقات الحديثة بمدى تقلب الأسعار (ATR) لمعرفة ما إذا كان السعر يتحرك داخل نطاق معين.
2- تحديد الاختراقات Breakout Signals
- عند اختراق السعر الحد العلوي للنطاق، يظهر المؤشر إشارة شراء (BUY).
- عند كسر السعر الحد السفلي للنطاق، يظهر المؤشر إشارة بيع (SELL).
3- دعم أنماط متعددة للمتوسطات المتحركة
- يسمح للمستخدمين باختيار نوع المتوسط المتحرك (SMA، EMA، WMA) المستخدم في حساب متوسط السعر.
4- إعدادات مخصصة للفلترة بحجم التداول (اختياري)
- فلترة حجم التداول هي ميزة اختيارية في المؤشر تسمح بتصفية إشارات الشراء والبيع بناءً على قوة الحجم المتداول مما يعزز دقة الإشارات عن طريق التأكد من أن الاختراقات السعرية مدعومة بحجم تداول قوي
5- تصميم مرن مع تخصيص للألوان والأنماط
- يمكن للمستخدمين تغيير ألوان النطاق وإشارات البيع والشراء حسب رغبتهم.
6- تنبيهات آلية عند حدوث كسر أو اختراق
- يتضمن تنبيهات (Alerts) عند حدوث إشارة بيع أو شراء.
كيف يعمل المؤشر؟
* يتم حساب متوسط السعر خلال الفترة المحددة (rangePeriod).
* يتم حساب التقلب السعري (ATR) ومضاعفته بمعامل النطاق (rangeMultiplier).
* يتم رسم مستطيل يعبر عن النطاق السعري بين (متوسط السعر ± التقلب).
* إذا تجاوز السعر الحد العلوي → إشارة شراء (BUY).
* إذا كسر السعر الحد السفلي → إشارة بيع (SELL).
* يمكن تصفية الإشارات باستخدام حجم التداول (اختياري).
1.0 → الحجم الحالي يجب أن يكون على الأقل مساويًا للمتوسط.
1.2 → الحجم الحالي يجب أن يكون أعلى من المتوسط بنسبة 20%.
1.5 → الحجم الحالي يجب أن يكون أعلى من المتوسط بنسبة 50%.
تنويه:
المؤشر هو أداة مساعدة فقط ويجب استخدامه مع التحليل الفني والأساسي لتحقيق أفضل النتائج.
إخلاء المسؤولية
لا يُقصد بالمعلومات والمنشورات أن تكون، أو تشكل، أي نصيحة مالية أو استثمارية أو تجارية أو أنواع أخرى من النصائح أو التوصيات المقدمة أو المعتمدة من TradingView.
It is a tool that aims to determine the dynamic price range based on fluctuations and average prices. Consolidation areas are identified and buy and sell signals are given when this range is breached or broken.
// It is preferable to use the indicator on a 4-hour frame or more //
Features of the indicator:
1- Detecting the dynamic price range
- The indicator calculates the average price over a specific period and compares recent closings with the price volatility range (ATR) to see if the price is moving within a specific range.
2- Identifying Breakout Signals
- When the price breaks the upper limit of the range, the indicator shows a buy signal (BUY).
- When the price breaks the lower limit of the range, the indicator shows a sell signal (SELL).
3- Support for multiple moving average patterns
- Allows users to choose the type of moving average (SMA, EMA, WMA) used to calculate the average price.
4- Custom settings for filtering by trading volume (optional)
- Trading volume filtering is an optional feature in the indicator that allows filtering buy and sell signals based on the strength of the trading volume, which enhances the accuracy of the signals by ensuring that price breakouts are supported by strong trading volume
5- Flexible design with customization of colors and patterns
- Users can change the colors of the range and buy and sell signals as they wish.
6- Automatic alerts when a breakout or breakout occurs
- Includes alerts when a buy or sell signal occurs.
How does the indicator work?
* The average price is calculated over the specified period (rangePeriod).
* The price volatility (ATR) is calculated and multiplied by the range factor (rangeMultiplier).
* A rectangle is drawn that represents the price range between (average price ± volatility).
* If the price exceeds the upper bound → a buy signal (BUY).
* If the price breaks the lower bound → a sell signal (SELL).
* Signals can be filtered using trading volume (optional).
1.0 → Current volume should be at least equal to the average.
1.2 → Current volume should be 20% above the average.
1.5 → Current volume should be 50% above the average.
Disclaimer:
The indicator is an auxiliary tool only and should be used in conjunction with technical and fundamental analysis to achieve the best results.
Disclaimer
The information and posts are not intended to be, or constitute, any financial, investment, trading or other types of advice or recommendations provided or endorsed by TradingView.
PaddingThe Padding library is a comprehensive and flexible toolkit designed to extend time series data within TradingView, making it an indispensable resource for advanced signal processing tasks such as FFT, filtering, convolution, and wavelet analysis. At its core, the library addresses the common challenge of edge effects by "padding" your data—that is, by appending additional data points beyond the natural boundaries of your original dataset. This extension not only mitigates the distortions that can occur at the endpoints but also helps to maintain the integrity of various transformations and calculations performed on the series. The library accomplishes this while preserving the ordering of your data, ensuring that the most recent point always resides at index 0.
Central to the functionality of this library are two key enumerations: Direction and PaddingType. The Direction enum determines where the padding will be applied. You can choose to extend the data in the forward direction (ahead of the current values), in the backward direction (behind the current values), or in both directions simultaneously. The PaddingType enum defines the specific method used for extending the data. The library supports several methods—including symmetric, reflect, periodic, antisymmetric, antireflect, smooth, constant, and zero padding—each of which has been implemented to suit different analytical scenarios. For instance, symmetric padding mirrors the original data across its boundaries, while reflect padding continues the trend by reflecting around endpoint values. Periodic padding repeats the data, and antisymmetric padding mirrors the data with alternating signs to counterbalance it. The antireflect and smooth methods take into account the derivatives of your data, thereby extending the series in a way that preserves or smoothly continues these derivative values. Constant and zero padding simply extend the series using fixed endpoint values or zeros. Together, these enums allow you to fine-tune how your data is extended, ensuring that the padding method aligns with the specific requirements of your analysis.
The library is designed to work with both single variable inputs and array inputs. When using array-based methods—particularly with the antireflect and smooth padding types—please note that the implementation intentionally discards the last data point as a result of the delta computation process. This behavior is an important consideration when integrating the library into your TradingView studies, as it affects the overall data length of the padded series. Despite this, the library’s structure and documentation make it straightforward to incorporate into your existing scripts. You simply provide your data source, define the length of your data window, and select the desired padding type and direction, along with any optional parameters to control the extent of the padding (using both_period, forward_period, or backward_period).
In practical application, the Padding library enables you to extend historical data beyond its original range in a controlled and predictable manner. This is particularly useful when preparing datasets for further signal processing, as it helps to reduce artifacts that can otherwise compromise the results of your analytical routines. Whether you are an experienced Pine Script developer or a trader exploring advanced data analysis techniques, this library offers a robust solution that enhances the reliability and accuracy of your studies by ensuring your algorithms operate on a more complete and well-prepared dataset.
Library "Padding"
A comprehensive library for padding time series data with various methods. Supports both single variable and array inputs, with flexible padding directions and periods. Designed for signal processing applications including FFT, filtering, convolution, and wavelets. All methods maintain data ordering with most recent point at index 0.
symmetric(source, series_length, direction, both_period, forward_period, backward_period)
Applies symmetric padding by mirroring the input data across boundaries
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with symmetric padding applied
method symmetric(source, direction, both_period, forward_period, backward_period)
Applies symmetric padding to an array by mirroring the data across boundaries
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with symmetric padding applied
reflect(source, series_length, direction, both_period, forward_period, backward_period)
Applies reflect padding by continuing trends through reflection around endpoint values
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with reflect padding applied
method reflect(source, direction, both_period, forward_period, backward_period)
Applies reflect padding to an array by continuing trends through reflection around endpoint values
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with reflect padding applied
periodic(source, series_length, direction, both_period, forward_period, backward_period)
Applies periodic padding by repeating the input data
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with periodic padding applied
method periodic(source, direction, both_period, forward_period, backward_period)
Applies periodic padding to an array by repeating the data
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with periodic padding applied
antisymmetric(source, series_length, direction, both_period, forward_period, backward_period)
Applies antisymmetric padding by mirroring data and alternating signs
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with antisymmetric padding applied
method antisymmetric(source, direction, both_period, forward_period, backward_period)
Applies antisymmetric padding to an array by mirroring data and alternating signs
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with antisymmetric padding applied
antireflect(source, series_length, direction, both_period, forward_period, backward_period)
Applies antireflect padding by reflecting around endpoints while preserving derivatives
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with antireflect padding applied
method antireflect(source, direction, both_period, forward_period, backward_period)
Applies antireflect padding to an array by reflecting around endpoints while preserving derivatives
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with antireflect padding applied. Note: Last data point is lost when using array input
smooth(source, series_length, direction, both_period, forward_period, backward_period)
Applies smooth padding by extending with constant derivatives from endpoints
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with smooth padding applied
method smooth(source, direction, both_period, forward_period, backward_period)
Applies smooth padding to an array by extending with constant derivatives from endpoints
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with smooth padding applied. Note: Last data point is lost when using array input
constant(source, series_length, direction, both_period, forward_period, backward_period)
Applies constant padding by extending endpoint values
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with constant padding applied
method constant(source, direction, both_period, forward_period, backward_period)
Applies constant padding to an array by extending endpoint values
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with constant padding applied
zero(source, series_length, direction, both_period, forward_period, backward_period)
Applies zero padding by extending with zeros
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with zero padding applied
method zero(source, direction, both_period, forward_period, backward_period)
Applies zero padding to an array by extending with zeros
Namespace types: array
Parameters:
source (array) : Array of values to pad
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with zero padding applied
pad_data(source, series_length, padding_type, direction, both_period, forward_period, backward_period)
Generic padding function that applies specified padding type to input data
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
padding_type (series PaddingType) : Type of padding to apply (see PaddingType enum)
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with specified padding applied
method pad_data(source, padding_type, direction, both_period, forward_period, backward_period)
Generic padding function that applies specified padding type to array input
Namespace types: array
Parameters:
source (array) : Array of values to pad
padding_type (series PaddingType) : Type of padding to apply (see PaddingType enum)
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to array length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to array length if not specified
Returns: Array ordered with most recent point at index 0, containing original data with specified padding applied. Note: Last data point is lost when using antireflect or smooth padding types
make_padded_data(source, series_length, padding_type, direction, both_period, forward_period, backward_period)
Creates a window-based padded data series that updates with each new value. WARNING: Function must be called on every bar for consistency. Do not use in scopes where it may not execute on every bar.
Parameters:
source (float) : Input value to pad from
series_length (int) : Length of the data window
padding_type (series PaddingType) : Type of padding to apply (see PaddingType enum)
direction (series Direction) : Direction to apply padding
both_period (int) : Optional - periods to pad in both directions. Overrides forward_period and backward_period if specified
forward_period (int) : Optional - periods to pad forward. Defaults to series_length if not specified
backward_period (int) : Optional - periods to pad backward. Defaults to series_length if not specified
Returns: Array ordered with most recent point at index 0, containing windowed data with specified padding applied
One Trading Setup for Life ICT [TradingFinder] Sweep Session FVG🔵 Introduction
ICT One Trading Setup for Life is a trading strategy based on liquidity and market structure shifts, utilizing the PM Session Sweep to determine price direction. In this strategy, the market first forms a price range during the PM Session (from 13:30 to 16:00 EST), which includes the highest high (PM Session High) and lowest low (PM Session Low).
In the next session, the price first touches one of these levels to trigger a Liquidity Hunt before confirming its trend by breaking the Change in State of Delivery (CISD) Level. After this confirmation, the price retraces toward a Fair Value Gap (FVG) or Order Block (OB), which serve as the best entry points in alignment with liquidity.
In financial markets, liquidity is the primary driver of price movement, and major market participants such as institutional investors and banks are constantly seeking liquidity at key levels. This process, known as Liquidity Hunt or Liquidity Sweep, occurs when the price reaches an area with a high concentration of orders, absorbs liquidity, and then reverses direction.
In this setup, the PM Session range acts as a trading framework, where its highs and lows function as key liquidity zones that influence the next session’s price movement. After the New York market opens at 9:30 EST, the price initially breaks one of these levels to capture liquidity.
However, for a trend shift to be confirmed, the CISD Level must be broken.
Once the CISD Level is breached, the price retraces toward an FVG or OB, which serve as optimal trade entry points.
Bullish Setup :
Bearish Setup :
🔵 How to Use
In this strategy, the PM Session range is first identified, which includes the highest high (PM Session High) and lowest low (PM Session Low) between 13:30 and 16:00 EST. In the following session, the price touches one of these levels for a Liquidity Hunt, followed by a break of the Change in State of Delivery (CISD) Level. The price then retraces toward a Fair Value Gap (FVG) or Order Block (OB), creating a trading opportunity.
This process can occur in two scenarios : bearish and bullish setups.
🟣 Bullish Setup
In a bullish scenario, the PM Session High and PM Session Low are identified. In the following session, the price first breaks the PM Session Low, absorbing liquidity. This process results in a Fake Breakout to the downside, misleading retail traders into taking short positions.
After the Liquidity Hunt, the CISD Level is broken, confirming a trend reversal. The price then retraces toward an FVG or OB, offering an optimal long entry opportunity.
The initial take-profit target is the PM Session High, but if higher timeframe liquidity levels exist, extended targets can be set.
The stop-loss should be placed below the Fake Breakout low or the first candle of the FVG.
🟣 Bearish Setup
In a bearish scenario, the market first defines its PM Session High and PM Session Low. In the next session, the price initially breaks the PM Session High, triggering a Liquidity Hunt. This movement often causes a Fake Breakout, misleading retail traders into taking incorrect positions.
After absorbing liquidity, the CISD Level breaks, indicating a shift in market structure. The price then retraces toward an FVG or OB, offering the best short entry opportunity.
The initial take-profit target is the PM Session Low, but if additional liquidity exists on higher timeframes, lower targets can be considered.
The stop-loss should be placed above the Fake Breakout high or the first candle of the FVG.
🔵 Setting
CISD Bar Back Check : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
Order Block Validity : The number of candles that determine the validity of an Order Block.
FVG Validity : The duration for which a Fair Value Gap remains valid.
CISD Level Validity : The duration for which a CISD Level remains valid after being broken.
New York PM Session : Defines the PM Session range from 13:30 to 16:00 EST.
New York AM Session : Defines the AM Session range from 9:30 to 16:00 EST.
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
Demand Order Block : Enables or disables bullish Order Block.
Supply Order Block : Enables or disables bearish Order Blocks.
Demand FVG : Enables or disables bullish FVG.
Supply FVG : Enables or disables bearish FVGs.
Show All CISD : Enables or disables the display of all CISD Levels.
Show High CISD : Enables or disables high CISD levels.
Show Low CISD : Enables or disables low CISD levels.
🔵 Conclusion
The ICT One Trading Setup for Life is a liquidity-based strategy that leverages market structure shifts and precise entry points to identify high-probability trade opportunities. By focusing on PM Session High and PM Session Low, this setup first captures liquidity at these levels and then confirms trend shifts with a break of the Change in State of Delivery (CISD) Level.
Entering a trade after a retracement to an FVG or OB allows traders to position themselves at optimal liquidity levels, ensuring high reward-to-risk trades. When used in conjunction with higher timeframe bias, order flow, and liquidity analysis, this strategy can become one of the most effective trading methods within the ICT Concept framework.
Successful execution of this setup requires risk management, patience, and a deep understanding of liquidity dynamics. Traders can enhance their confidence in this strategy by conducting extensive backtesting and analyzing past market data to optimize their approach for different assets.
