Stage Market V4This script provides a comprehensive tool for identifying market stages based on exponential moving averages (EMAs), market performance metrics, and additional price statistics. Below is a summary of its functionality and instructions on how to use it:
1. Inputs and Configuration
Fast and Slow EMA:
Fast EMA Length: Determines the period for the fast EMA.
Slow EMA Length: Determines the period for the slow EMA.
Additional EMAs:
Enable or disable three additional EMAs (EMA 1, EMA 2, and EMA 3) with customizable lengths.
52-Week High Display:
Optionally display the percentage distance from the 52-week high.
2. Market Stages
The indicator identifies six market stages based on the relationship between the price, fast EMA, and slow EMA:
Recovery: Price is above the fast EMA, and the slow EMA is above both the price and the fast EMA.
Accumulation: Price is above both the fast EMA and slow EMA, but the slow EMA is still above the fast EMA.
Bull Market: Price, fast EMA, and slow EMA are all aligned in a rising trend.
Warning: Price is below the fast EMA, but still above the slow EMA, signaling potential weakness.
Distribution: Price is below both EMAs, but the slow EMA remains below the fast EMA.
Bear Market: Price, fast EMA, and slow EMA are all aligned in a falling trend.
The current stage is displayed in a table along with the number of bars spent in that stage.
3. Performance Metrics
The script calculates additional metrics to gauge the stock's performance:
30-Day Change: The percentage price change over the last 30 days.
90-Day Change: The percentage price change over the last 90 days.
Year-to-Date (YTD) Change: The percentage change from the year's first closing price.
Distance from 52-Week High (if enabled): The percentage difference between the current price and the highest price over the past 52 weeks.
These values are color-coded:
Green for positive changes.
Red for negative changes.
4. Table Display
The indicator uses a table in the bottom-right corner of the chart to show:
Current market stage and bars spent in the stage.
30-day, 90-day, and YTD changes.
Distance from the 52-week high (if enabled).
5. EMA Plotting
The script plots the following EMAs on the chart:
Fast EMA (default: 50-period) in yellow.
Slow EMA (default: 200-period) in orange.
Optional EMAs (EMA 1, EMA 2, and EMA 3) in blue, green, and purple, respectively.
6. Using the Indicator
Add the indicator to your chart via the Pine Editor in TradingView.
Customize the input parameters to fit your trading style or the asset's characteristics.
Use the table to quickly assess the current market stage and key performance metrics.
Observe the plotted EMAs to understand trend alignments and potential crossovers.
This script is particularly useful for identifying market trends, understanding price momentum, and aligning trading decisions with broader market conditions.
Tìm kiếm tập lệnh với "欧元汇率走势30天"
BuySell%_ImtiazH_v2BuySell%_ImtiazH
This indicator includes two powerful volume metrics to complement your trading analysis:
30-Day Avg Vol (Blue Line): Tracks the average volume over the past 30 days, providing a baseline for typical trading activity.
Breakout Vol (White Line): Highlights the volume threshold needed for a potential breakout, calculated as a user-defined percentage above the 30-day average volume (default: 40%).
In addition to these enhancements, the indicator breaks down total trading volume into buying and selling components and calculates the percentage of buy volume for each bar.
🟥 Red Bars: Represent total volume.
🟩 Teal Bars: Show the buying volume within each candle.
🟨 Buy %: Displays the percentage of buy volume dynamically in the indicator panel, highlighted in yellow for quick visibility.
Use this tool to easily spot accumulation (buying pressure) or distribution (selling pressure) trends, customize breakout thresholds, and identify key breakout opportunities. Simple, clear, and effective for volume-based analysis!
How Are Buy Volume and Sell Volume Calculated?
This indicator uses a proportional approach to estimate buy and sell volumes based on price action:
Buy Volume: The portion of total volume where the price is moving upward, representing trades executed at the ask price.
Formula:
Buy Volume = (close - low) / (high - low) * volume
Sell Volume: The portion of total volume where the price is moving downward, representing trades executed at the bid price.
Formula:
Sell Volume = (high - close) / (high - low) * volume
If the high and low prices are the same (flat bar), both buy and sell volumes are set to 0.
Why This Matters
This calculation assumes the close price’s position within the high-to-low range reflects the balance of buying and selling activity:
Close near the high: Most volume is buy volume.
Close near the low: Most volume is sell volume.
Close in the middle: Volume is split between buying and selling.
By breaking down volume in this way, the indicator helps traders identify key trends like accumulation (buying pressure) and distribution (selling pressure), making it a powerful tool for volume-based analysis.
UVR Crypto TrendINDICATOR OVERVIEW: UVR CRYPTO TREND
The UVR Crypto Trend indicator is a custom-built tool designed specifically for cryptocurrency markets, utilizing advanced volatility, momentum, and trend-following techniques. It aims to identify trend reversals and provide buy and sell signals by analyzing multiple factors, such as price volatility(UVR), RSI (Relative Strength Index), CMF (Chaikin Money Flow), and EMA (Exponential Moving Average). The indicator is optimized for CRYPTO MARKETS only.
KEY FEATURES AND HOW IT WORKS
Volatility Analysis with UVR
The UVR (Ultimate Volatility Rate) is a proprietary calculation that measures market volatility by comparing significant price extremes and smoothing the data over time.
Purpose: UVR aims to reduce noise in low-volatility environments and highlight significant movements during higher-volatility periods. While it strives to improve filtering in low-volatility conditions, it does not guarantee perfect performance, making it a balanced and adaptable tool for dynamic markets like cryptocurrency.
HOW UVR (ULTIMATE VOLATILITY RATE) IS CALCULATED
UVR is calculated using a method that ensures precise measurement of market volatility by comparing price extremes across consecutive candles:
Volatility Components:
Two values are calculated to represent potential price fluctuations:
The absolute difference between the current candle's high and the previous candle's low:
Volatility Component 1=∣High−Low ∣
The absolute difference between the previous candle's high and the current candle's low:
Volatility Component 2=∣High −Low∣
Volatility Ratio:
The larger of the two components is selected as the Volatility Ratio, ensuring UVR captures the most significant movement:
Volatility Ratio=max(Volatility Component 1,Volatility Component 2)
Smoothing with SMMA:
To stabilize the volatility calculation, the Volatility Ratio is smoothed using a Smoothed Moving Average (SMMA) over a user-defined period (e.g., 14 candles):
UVR=(UVR(Previous)×(Period−1)+Volatility Ratio)/Period
This calculation ensures UVR adapts dynamically to market conditions, focusing on significant price movements while filtering out noise.
RSI FOR MOMENTUM DETECTION
RSI (Relative Strength Index) identifies overbought and oversold conditions.
Trend Confirmation at the 50 Level
RSI values crossing above 50 signal the potential start of an upward trend.
RSI values crossing below 50 indicate the potential start of a downward trend.
Key Reversals at Extreme Levels
RSI detects trend reversals at overbought (>70) and oversold (<30) levels.
For example:
Overbought Trend Reversal: RSI >70 followed by bearish price action signals a potential downtrend.
Oversold Trend Reversal: RSI <30 with bullish confirmation signals a potential uptrend.
Rare Extreme RSI Readings
Extreme levels, such as RSI <12 (oversold) or RSI >88 (overbought), are used to identify rare yet powerful reversals.
---HOW IT DIFFERS FROM OTHER INDICATORS---
Using UVR High and Low Values
The Ultimate Volatility Rate (UVR) focuses on analyzing the high and low price ranges of the market to measure volatility.
Unlike traditional trend indicators that rely primarily on momentum or moving average crossovers, UVR leverages price extremes to better identify trend reversals.
This approach ensures fewer false signals during low-volatility phases and more accurate trend detection during high-volatility conditions.
UVR as the Core Component
The indicator is fundamentally built around UVR as the primary filter, while supporting tools like RSI (momentum detection), CMF (volume confirmation), and EMA (trend validation) complement its functionality.
By integrating these additional components, the indicator provides a multidimensional analysis rather than relying solely on a single approach.
Dynamic Adaptation to Volatility
UVR dynamically adjusts to market conditions, striving to improve filtering in low-volatility phases. While not flawless, this approach minimizes false signals and adapts more effectively to varying levels of market activity.
Trend Clouds for Visual Guidance
UVR-based dynamic clouds visually mark high and low price areas, highlighting potential consolidation or retracement zones.
These clouds serve as guides for setting stop-loss or take-profit levels, offering clear risk management strategies.
BUY AND SELL SIGNAL LOGIC
BUY CONDITIONS
Momentum-Based Buy-Entry
RSI >50, CMF >0, and the close price is above EMA50.
The price difference between open and close exceeds a threshold based on UVR.
Oversold Reversal
RSI <30 and CMF >0 with a strong bullish candle (close > open and UVR-based sensitivity filter).
Breakout Confirmation
The price breaks above a previously identified resistance, with conditions for RSI and CMF supporting the breakout.
Reversal from Oversold RSI Extreme
RSI <12 on the previous candle with a strong rebound on the current candle with UVR confirmation filter.
SELL CONDITIONS
Momentum-Based Sell-Entry
RSI <50, CMF <0, and the close price is below EMA50.
The price difference between open and close exceeds the UVR threshold.
Overbought Reversal
RSI >70 with bearish price action (open > close and UVR-based sensitivity filter).
Breakdown Confirmation
The price breaks below a previously identified support, with RSI and CMF supporting the breakdown.
Reversal from Overbought RSI Extreme
RSI >88 on the previous candle with a bearish confirmation on the current candle with UVR confirmation filter.
BUY AND SELL SIGNALS VISUALIZATION
The UVR Crypto Trend Indicator visually represents buy and sell conditions using dynamic plots, making it easier for traders to interpret and act on the signals. Below is an explanation of the visual representation:
Buy Signals and Visualization
Signal Trigger:
A buy signal is generated when one of the defined Buy Conditions is met (e.g., RSI >50, CMF >0, price above EMA50).
Visual Representation:
A blue upward arrow appears at the candle where the buy condition is triggered.
A blue cloud forms above the price candles, representing the strength of the bullish trend. The cloud dynamically adapts to market volatility, using the UVR calculation to mark support zones or consolidation levels.
Purpose of the Blue Cloud:
It acts as a visual guide for price movements and stay horizontal when the trend is not moving up
Sell Signals and Visualization
Signal Trigger:
A sell signal is generated when one of the defined Sell Conditions is met (e.g., RSI <50, CMF <0, price below EMA50).
Visual Representation:
A red downward arrow appears at the candle where the sell condition is triggered.
A red cloud forms below the price candles, representing the strength of the bearish trend. Like the blue cloud, it uses the UVR calculation to dynamically mark resistance zones or potential retracement levels.
Purpose of the Red Cloud:
It acts as a visual guide for price movements and stay horizontal when the trend is not moving down.
CONCLUSION
The UVR Crypto Trend indicator provides a powerful tool for trend reversal detection by combining volatility analysis, momentum confirmation, and trend-following techniques. Its unique use of the Ultimate Volatility Rate (UVR) as a core element, supported by proven indicators like RSI, CMF, and EMA, ensures reliable and actionable signals tailored for the crypto market's dynamic nature. By leveraging UVR’s high and low price range analysis, it achieves a level of precision that traditional indicators lack, making it a high-performing system for cryptocurrency traders.
Adapted RSI w/ Multi-Asset Regime Detection v1.1The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of an asset's recent price changes to detect overbought or oversold conditions in the price of said asset.
In addition to identifying overbought and oversold assets, the RSI can also indicate whether your desired asset may be primed for a trend reversal or a corrective pullback in price. It can signal when to buy and sell.
The RSI will oscillate between 0 and 100. Traditionally, an RSI reading of 70 or above indicates an overbought condition. A reading of 30 or below indicates an oversold condition.
The RSI is one of the most popular technical indicators. I intend to offer a fresh spin.
Adapted RSI w/ Multi-Asset Regime Detection
Our Adapted RSI makes necessary improvements to the original Relative Strength Index (RSI) by combining multi-timeframe analysis with multi-asset monitoring and providing traders with an efficient way to analyse market-wide conditions across different timeframes and assets simultaneously. The indicator automatically detects market regimes and generates clear signals based on RSI levels, presenting this data in an organised, easy-to-read format through two dynamic tables. Simplicity is key, and having access to more RSI data at any given time, allows traders to prepare more effectively, especially when trading markets that "move" together.
How we calculate the RSI
First, the RSI identifies price changes between periods, calculating gains and losses from one look-back period to the next. This look-back period averages gains and losses over 14 periods, which in this case would be 14 days, and those gains/losses are calculated based on the daily closing price. For example:
Average Gain = Sum of Gains over the past 14 days / 14
Average Loss = Sum of Losses over the past 14 days / 14
Then we calculate the Relative Strength (RS):
RS = Average Gain / Average Loss
Finally, this is converted to the RSI value:
RSI = 100 - (100 / (1 + RS))
Key Features
Our multi-timeframe RSI indicator enhances traditional technical analysis by offering synchronised Daily, Weekly, and Monthly RSI readings with automatic regime detection. The multi-asset monitoring system allows tracking of up to 10 different assets simultaneously, with pre-configured major pairs that can be customised to any asset selection. The signal generation system provides clear market guidance through automatic regime detection and a five-level signal system, all presented through a sophisticated visual interface with dynamic RSI line colouring and customisable display options.
Quick Guide to Use it
Begin by adding the indicator to your chart and configuring your preferred assets in the "Asset Comparison" settings.
Position the two information tables according to your preference.
The main table displays RSI analysis across three timeframes for your current asset, while the asset table shows a comparative analysis of all monitored assets.
Signals are colour-coded for instant recognition, with green indicating bullish conditions and red for bearish conditions. Pay special attention to regime changes and signal transitions, using multi-timeframe confluence to identify stronger signals.
How it Works (Regime Detection & Signals)
When we say 'Regime', a regime is determined by a persistent trend or in this case momentum and by leveraging this for RSI, which is a momentum oscillator, our indicator employs a relatively simple regime detection system that classifies market conditions as either Bullish (RSI > 50) or Bearish (RSI < 50). Our benchmark between a trending bullish or bearish market is equal to 50. By leveraging a simple classification system helps determine the probability of trend continuation and the weight given to various signals. Whilst we could determine a Neutral regime for consolidating markets, we have employed a 'neutral' signal generation which will be further discussed below...
Signal generation occurs across five distinct levels:
Strong Buy (RSI < 15)
Buy (RSI < 30)
Neutral (RSI 30-70)
Sell (RSI > 70)
Strong Sell (RSI > 85)
Each level represents different market conditions and probability scenarios. For instance, extreme readings (Strong Buy/Sell) indicate the highest probability of mean reversion, while neutral readings suggest equilibrium conditions where traders should focus on the overall regime bias (Bullish/Bearish momentum).
This approach offers traders a new and fresh spin on a popular and well-known tool in technical analysis, allowing traders to make better and more informed decisions from the well presented information across multiple assets and timeframes. Experienced and beginner traders alike, I hope you enjoy this adaptation.
RSI + Normalized Fisher Transform with SignalsThis indicator combines three tools for market analysis: the Relative Strength Index (RSI), the RSI's moving average, and the Fisher Transform. RSI is a momentum oscillator that measures the speed and change of price movements, helping identify overbought and oversold conditions. The RSI moving average is a smoothed version of the RSI that filters noise and confirms trends. The Fisher Transform is a mathematical technique that transforms price data into a Gaussian normal distribution, making it easier to identify turning points. It has been normalized to the same scale as the RSI (0-100) for consistency.
Purpose
The goal of this indicator is to identify potential buy and sell opportunities with varying degrees of strength (strong and weak). By combining the RSI, its moving average, and the Fisher Transform, the indicator ensures signals are based on both momentum and reversals, making it highly versatile across different market conditions.
Key Features
This indicator provides strong and weak buy and sell signals. A strong buy occurs when the RSI crosses above its moving average while both the RSI and its moving average are oversold (below the default threshold of 30), and the Fisher Transform reverses direction within the same or prior bar while also being oversold. A weak buy occurs when the Fisher Transform is oversold, and the RSI crosses above its moving average while its value is between the default oversold threshold (30) and 50. A strong sell occurs when the RSI crosses below its moving average while both the RSI and its moving average are overbought (above the default threshold of 70), and the Fisher Transform reverses direction within the same or prior bar while also being overbought. A weak sell occurs when the Fisher Transform is overbought, and the RSI crosses below its moving average while its value is between 50 and the default overbought threshold (70).
The indicator includes customizable thresholds and lengths. Users can adjust the oversold and overbought thresholds to suit their trading style. The RSI length, moving average length, and Fisher Transform length are also customizable. The Fisher Transform is scaled to the RSI’s range of 0-100 to simplify analysis and signal interpretation.
How to Use the Indicator
On the chart, you will see the RSI line in blue, the RSI moving average in orange, and the Fisher Transform in purple. Horizontal lines at the default oversold (30) and overbought (70) levels mark critical zones for signals. Adjust these thresholds in the indicator settings as needed.
Strong buy signals are shown as larger, darker green arrows below the price. Weak buy signals are small lime arrows below the price. Strong sell signals are larger, darker red arrows above the price. Weak sell signals are small fuchsia arrows above the price.
Signal Interpretation
A strong buy indicates a highly favorable buying opportunity. This typically occurs when the asset is in a downtrend but shows signs of reversal, particularly in oversold zones. A weak buy suggests a potential buying opportunity but with less conviction, often when the market is neutral to slightly bearish but showing upward momentum. A strong sell indicates a highly favorable selling opportunity, usually occurring when the asset is in an uptrend but shows signs of reversal, particularly in overbought zones. A weak sell suggests a potential selling opportunity but with less conviction, often in neutral to slightly bullish markets showing downward momentum.
Practical Tips
Avoid using signals in isolation. Combine this indicator with other tools such as trendlines, moving averages, or support/resistance levels for greater accuracy. Adjust the parameters for different assets to match their volatility. For volatile assets, consider wider thresholds like 20/80 for oversold/overbought levels. For less volatile assets, tighter thresholds like 35/65 may be more appropriate. Use higher timeframes to confirm signals before trading on lower timeframes. Be cautious in sideways markets, as both RSI and the Fisher Transform perform better in trending conditions.
Instructions for Adjustments
To change the oversold or overbought levels, open the indicator settings by clicking the gear icon and modify the "Oversold Threshold" and "Overbought Threshold" values. To adjust lengths for RSI and Fisher Transform, update the "RSI Length," "RSI Moving Average Length," and "Fisher Transform Length" settings. If needed, toggle signal visibility by enabling or disabling specific arrows (Strong Buy, Weak Buy, Strong Sell, Weak Sell) in the "Style" tab.
Best Practices
Risk management is essential. Always set appropriate stop-loss levels and position sizes based on your risk tolerance. Backtest the indicator on historical data to understand its performance and behavior for your chosen asset and timeframe. Combining this indicator with volume or volatility analysis (Bollinger Band Width, for example) can help confirm signal validity.
This indicator simplifies decision-making by identifying high-probability trading opportunities using a combination of momentum, trend, and reversals. Follow these instructions to fully utilize its capabilities without needing to analyze the underlying code.
ADX Breakout Strategy█ OVERVIEW
The ADX Breakout strategy leverages the Average Directional Index (ADX) to identify and execute breakout trades within specified trading sessions. Designed for the NQ and ES 30-minute charts, this strategy aims to capture significant price movements while managing risk through predefined stop losses and trade limits.
This strategy was taken from a strategy that was posted on YouTube. I would link the video, but I believe is is "against house rules".
█ CONCEPTS
The strategy is built upon the following key concepts:
ADX Indicator: Utilizes the ADX to gauge the strength of a trend. Trades are initiated when the ADX value is below a certain threshold, indicating potential for trend development.
Trade Session Management: Limits trading to specific hours to align with optimal market activity periods.
Risk Management: Implements a fixed dollar stop loss and restricts the number of trades per session to control exposure.
█ FEATURES
Customizable Stop Loss: Set your preferred stop loss amount to manage risk effectively.
Trade Session Configuration: Define the trading hours to focus on the most active market periods.
Entry Conditions: Enter long positions when the price breaks above the highest close in the lookback window and the ADX indicates potential trend strength.
Trade Limits: Restrict the number of trades per session to maintain disciplined trading.
Automated Exit: Automatically closes all positions at the end of the trading session to avoid overnight risk.
█ HOW TO USE
Configure Inputs :
Stop Loss ($): Set the maximum loss per trade.
Trade Session: Define the active trading hours.
Highest Lookback Window: Specify the number of bars to consider for the highest close.
Apply the Strategy :
Add the ADX Breakout strategy to your chart on TradingView.
Ensure you are using a 30-minute timeframe for optimal performance.
█ LIMITATIONS
Market Conditions: The strategy is optimized for trending markets and may underperform in sideways or highly volatile conditions.
Timeframe Specific: Designed specifically for 30-minute charts; performance may vary on different timeframes.
Single Asset Focus: Primarily tested on NQ and ES instruments; effectiveness on other symbols is not guaranteed.
█ DISCLAIMER
This ADX Breakout strategy is provided for educational and informational purposes only. It is not financial advice and should not be construed as such. Trading involves significant risk, and you may incur substantial losses. Always perform your own analysis and consider your financial situation before using this or any other trading strategy. The source material for this strategy is publicly available in the comments at the beginning of the code script. This strategy has been published openly for anyone to review and verify its methodology and performance.
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Multi-Symbol Scanner: Advanced EMA-RSI-Volume Strategy# Multi-Symbol Tech Stock Scanner: Advanced EMA-RSI-Volume Strategy
## Technical Analysis Methodology
This scanner implements a sophisticated multi-timeframe analysis approach combining three key technical elements:
### 1. Dual EMA System (Primary Trend Detection)
- **Long-term EMA (820 periods)**: Acts as the primary trend identifier
- Chosen specifically for tech stocks' longer-term price waves
- Helps filter out minor market noise while capturing major trend changes
- 820 periods approximately represents 3.2 years of trading days
- **Medium-term EMA (320 periods)**: Serves as trend confirmation
- Approximately 1.25 years of trading data
- Provides earlier entry signals while maintaining trend reliability
- Helps identify potential trend reversals before the major trend shift
### 2. Volume Analysis Component
The script employs a dynamic volume analysis system:
- Calculates 20-period moving average of volume as baseline
- Requires 1.5x surge above baseline for signal confirmation
- Volume surge requirement helps filter out weak moves and potential false breakouts
- Different from standard volume indicators as it uses adaptive thresholds
### 3. RSI Momentum Filter
Implements a specialized RSI configuration:
- 14-period RSI with dynamic overbought/oversold levels
- Oversold threshold: 30 (customizable)
- Overbought threshold: 70 (customizable)
- Used as a confirmation tool rather than primary signal generator
## Signal Generation Logic
### Buy Signal Requirements
1. Price must cross above 820 EMA (PRIMARY CONDITION)
2. Current price must be above 320 EMA (CONFIRMATION)
3. RSI must be above 30 but below 70 (MOMENTUM CHECK)
4. Volume must be 1.5x above 20-period average (STRENGTH VALIDATION)
### Sell Signal Requirements
1. Price must cross below 820 EMA (PRIMARY CONDITION)
2. Current price must be below 320 EMA (CONFIRMATION)
3. RSI must be above 30 but below 70 (MOMENTUM CHECK)
4. Volume must be 1.5x above 20-period average (STRENGTH VALIDATION)
## Risk Management Integration
The script automatically calculates key risk levels based on volatility:
1. **Stop Loss Calculation**:
- Default: 2% below entry for buys
- Dynamically adjusted based on price point
- Can be modified through input parameters
2. **Take Profit Targets**:
- Primary target: 6% above entry (3:1 reward-risk ratio)
- Based on historical tech stock movement patterns
- Adjustable through input parameters
## Multi-Symbol Implementation
The scanner monitors 6 symbols simultaneously using:
- Separate security calls for each data point
- Optimized data requests to prevent overload
- Individual signal processing for each symbol
- Synchronized alert generation system
## Technical Implementation Details
1. **Data Processing**:
```
- Security data requests on 10-minute timeframe
- Individual EMA calculations per symbol
- Separate volume analysis threads
- RSI calculations with standard deviation normalization
```
2. **Signal Processing**:
```
- Cross-verification of all conditions
- Time-based signal validation
- Volume surge confirmation
- Trend alignment check
```
3. **Alert System**:
```
- Bar-close confirmation required
- Multi-condition validation
- Detailed price level inclusion
- Risk parameter integration
```
## Optimization Features
1. **Memory Usage**:
- Optimized security calls
- Efficient data structure
- Reduced redundant calculations
2. **Processing Efficiency**:
- Single-pass data analysis
- Combined indicator calculations
- Streamlined alert generation
## Practical Application
The system is designed for:
1. Swing Trading (primary use)
2. Position Trading (secondary use)
3. Technical Breakout Trading
Optimal timeframes:
- Primary: 4H charts
- Secondary: Daily charts
- Verification: 1H charts
## Default Configuration
The scanner is preset to monitor key tech stocks:
- TSLA: High-volatility tech leader
- NVDA: Semiconductor sector benchmark
- AVGO: Stable tech infrastructure
- TSM: Global chip manufacturer
- META: Social media sector leader
- AMZN: E-commerce/Cloud computing leader
Each symbol can be modified through input parameters.
## Version Information
- Current Version: 1.3
- Last Updated: November 2024
- Compatibility: TradingView Pro/Pro+/Premium
## Limitations & Considerations
- Limited to 6 symbols due to TradingView security request limits
- Requires consistent market volume for optimal performance
- Best suited for liquid stocks with significant daily volume
- May need parameter adjustments during extreme market conditions
SW Gann Pressure time from tops and bottomsW.D. Gann's trading techniques often emphasized the significance of time in the markets, believing that specific time intervals could influence price movements. Here’s how the 30, 60, 90, 120, 180, and 270 bar intervals relate to Gann's rules:
1. **30 Bars**:
- Gann often viewed shorter time frames as critical for identifying short-term trends. A 30-bar interval can signify minor cycles or potential turning points in price.
2. **60 Bars**:
- This interval is significant as Gann believed in the importance of quarterly cycles. A 60-bar mark could indicate a completion of a two-month cycle, often leading to retracements or reversals.
3. **90 Bars**:
- Gann considered 90 days (or bars) to represent a quarter. This interval can signify a substantial shift in market sentiment or a pivotal point in a longer trend.
4. **120 Bars**:
- The 120-bar mark corresponds to about four months. Gann viewed longer intervals as more significant, often leading to major shifts in market trends.
5. **180 Bars**:
- A 180-bar period relates to a semi-annual cycle, which Gann regarded as critical for major support and resistance levels. Price action around this interval can reveal potential long-term trend reversals.
6. **270 Bars**:
- Gann believed that longer cycles, such as 270 bars (approximately nine months), could indicate significant market phases. This interval may represent major turning points and help identify long-term trends.
### Application in Trading:
- **Identifying Trends**: Traders can use these intervals to spot potential trend reversals or continuations based on Gann’s principles of market cycles.
- **Setting Targets and Stops**: Knowing where these key bars fall can help in setting profit targets and stop-loss orders.
- **Analyzing Market Sentiment**: Price reactions at these intervals can provide insights into market psychology and sentiment shifts.
By marking these intervals on a chart, traders can visually assess when price action aligns with Gann's theories, helping them make more informed trading decisions based on historical patterns and cycles.
30D Vs 90D Historical VolatilityVolatility equals risk for an underlying asset's price meaning bullish volatility is bearish for prices while bearish volatility is bullish. This compares 30-Day Historical Volatility to 90-Day Historical Volatility.
When the 30-Day crosses under the 90-day, this is typically when asset prices enter a bullish trend.
Conversely, When the 30-Day crosses above the 90-Day, this is when asset prices enter a bearish trend.
Peaks in volatility are bullish divergences while troughs are bearish divergences.
Confluence StrategyOverview of Confluence Strategy
The Confluence Strategy in trading refers to the combination of multiple technical indicators, support/resistance levels, and chart patterns to identify high-probability trading opportunities. The idea is that when several indicators agree on a price movement, the likelihood of that movement being successful increases.
Key Components
Technical Indicators:
Moving Averages (MA): Commonly used to determine the trend direction. Look for crossovers (e.g., the 50-day MA crossing above the 200-day MA).
Relative Strength Index (RSI): Helps identify overbought or oversold conditions. A reading above 70 may indicate overbought conditions, while below 30 suggests oversold.
MACD (Moving Average Convergence Divergence): Useful for spotting changes in momentum. Look for MACD crossovers and divergence from price.
Support and Resistance Levels:
Identify key levels where price has historically reversed. These can be drawn from previous highs/lows, Fibonacci retracement levels, or psychological price levels.
Chart Patterns:
Patterns like head and shoulders, double tops/bottoms, or flags can indicate potential reversals or continuations in price.
Strategy Implementation
Set Up Your Chart:
Add the desired indicators (e.g., MA, RSI, MACD) to your TradingView chart.
Mark significant support and resistance levels.
Identify Confluence Points:
Look for situations where multiple indicators align. For instance, if the price is near a support level, the RSI is below 30, and the MACD shows bullish divergence, this may signal a buying opportunity.
Entry and Exit Points:
Entry: Place a trade when your confluence conditions are met. Use limit orders for better prices.
Exit: Set profit targets based on resistance levels or use trailing stops. Consider the risk-reward ratio to ensure your trades are favorable.
Risk Management:
Always implement stop-loss orders to protect against unexpected market moves. Position size should reflect your risk tolerance.
Example of a Confluence Trade
Setup:
Price approaches a strong support level.
RSI shows oversold conditions (below 30).
The 50-day MA is about to cross above the 200-day MA (bullish crossover).
Action:
Enter a long position as the conditions align.
Set a stop loss just below the support level and a take profit at the next resistance level.
Conclusion
The Confluence Strategy can significantly enhance trading accuracy by ensuring that multiple indicators support a trade decision. Traders on TradingView can customize their indicators and charts to fit their personal trading styles, making it a flexible approach to technical analysis.
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
ICT Judas Swing | Flux Charts💎 GENERAL OVERVIEW
Introducing our new ICT Judas Swing Indicator! This indicator is built around the ICT's "Judas Swing" strategy. The strategy looks for a liquidity grab around NY 9:30 session and a Fair Value Gap for entry confirmation. For more information about the process, check the "HOW DOES IT WORK" section.
Features of the new ICT Judas Swing :
Implementation of ICT's Judas Swing Strategy
2 Different TP / SL Methods
Customizable Execution Settings
Customizable Backtesting Dashboard
Alerts for Buy, Sell, TP & SL Signals
📌 HOW DOES IT WORK ?
The strategy begins by identifying the New York session from 9:30 to 9:45 and marking recent liquidity zones. These liquidity zones are determined by locating high and low pivot points: buyside liquidity zones are identified using high pivots that haven't been invalidated, while sellside liquidity zones are found using low pivots. A break of either buyside or sellside liquidity must occur during the 9:30-9:45 session, which is interpreted as a liquidity grab by smart money. The strategy assumes that after this liquidity grab, the price will reverse and move in the opposite direction. For entry confirmation, a fair value gap (FVG) in the opposite direction of the liquidity grab is required. A buyside liquidity grab calls for a bearish FVG, while a sellside grab requires a bullish FVG. Based on the type of FVG—bullish for buys and bearish for sells—the indicator will then generate a Buy or Sell signal.
After the Buy or Sell signal, the indicator immediately draws the take-profit (TP) and stop-loss (SL) targets. The indicator has three different TP & SL modes, explained in the "Settings" section of this write-up.
You can set up alerts for entry and TP & SL signals, and also check the current performance of the indicator and adjust the settings accordingly to the current ticker using the backtesting dashboard.
🚩 UNIQUENESS
This indicator is an all-in-one suit for the ICT's Judas Swing concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. Three different TP / SL modes are available to suit your needs. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers.
⚙️ SETTINGS
1. General Configuration
Swing Length -> The swing length for pivot detection. Higher settings will result in
FVG Detection Sensitivity -> You may select between Low, Normal, High or Extreme FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivies resulting in spotting bigger FVGs, and higher sensitivies resulting in spotting all sizes of FVGs.
2. TP / SL
TP / SL Method ->
a) Dynamic: The TP / SL zones will be auto-determined by the algorithm based on the Average True Range (ATR) of the current ticker.
b) Fixed : You can adjust the exact TP / SL ratios from the settings below.
Dynamic Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
Seasonality with Custom IntervalSeasonality with Custom Interval Lookback
by TradersPod
Description:
This script is a modified version of Kaschko's original Seasonal Trend with Interval Lookback indicator, designed to help traders analyze seasonal trends over customizable intervals. The modifications in this version provide enhanced flexibility and improved visualization, making it a valuable tool for analyzing seasonal patterns in various markets.
Key Features:
1. Custom Lookback Multiplier: The script allows users to adjust the lookback period with a multiplier, giving more control over the number of years analyzed for seasonality. This feature is especially useful for traders looking to tailor the analysis based on different market cycles or election cycles.
2. Enhanced Visualization: Users can customize the color and line width of the plotted seasonality line for better readability. The smoothing parameter has been added to allow for flexible moving averages, reducing noise in the trend visualization.
3 Detailed Chart Plotting: The script plots the trading week of the year (TWOY), trading day of the month (TDOM), and trading day of the year (TDOY) on the status line, providing users with additional insights into how seasonal trends affect price movements.
How to Use:
1. Lookback Period: Set the number of years to look back. For example, if you set it to 16 years, the script will gather data from the last 16 years.
2. Interval Years: You can set an interval (e.g., 4 years for U.S. elections) to focus on specific years:
Interval = 0: This setting will use all years within the lookback period.
Interval > 0: This setting will use only every nth year, based on the interval you set (e.g., 4 for U.S. elections, 10 for decennial years).
3 Future Projections: You can specify how many bars into the future the script should project the seasonal trend.
Example Settings:
>Lookback Period: 16 years.
>Interval: 4 years (this would focus on U.S. election years).
>]Future Projections: 30 bars (the seasonal trend is projected 30 bars into the future).
Intended Use : This indicator is ideal for traders who:
>Want to analyze how market prices react to seasonal cycles.
>Need flexible, customizable tools for tracking longer-term trends.
>Prefer visual clarity in their seasonal trend analysis with adjustable settings for better readability.
How It Works:
>The script calculates the average price change for each trading day, week, or month, using a lookback period of up to 30 years. It then smooths the seasonal trend using a customizable moving average and projects the trend into the future, allowing users to forecast potential price movements based on historical seasonal patterns.
>The script also offers a projection of future seasonality by plotting the seasonal trend up to 252 bars into the future, with options to offset the start of the seasonality.
Notes:
>This script is open-source under the Mozilla Public License 2.0.
>Original script by Kaschko. Modifications by TradersPod.
Post-Open Long Strategy with ATR-based Stop Loss and Take ProfitThe "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit
The "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Entry and Exit Conditions:
Long Entry:
The price breaks above the identified resistance.
The market is in a lateralization phase with low volatility.
The price is above the 10 and 200-period EMAs.
RSI is above 30, and ADX is above 10.
No short-term downtrend is detected.
The last two candles are not both bearish.
The current candle is a "panic candle" (negative close).
Order Execution: The order is executed at the close of the candle that meets all conditions.
Exit from Position:
Dynamic Stop Loss: Set at 2 times the ATR below the entry price.
Dynamic Take Profit: Set at 4 times the ATR above the entry price.
The position is automatically closed upon reaching the Stop Loss or Take Profit.
How to Use the Strategy:
Application on Volatile Instruments:
Ideal for financial instruments that show significant volatility during the target market opening hours, such as indices or major forex pairs.
Recommended Timeframes:
Intraday timeframes, such as 5 or 15 minutes, to capture significant post-open moves.
Parameter Customization:
The default parameters are optimized but can be adjusted based on individual preferences and the instrument analyzed.
Backtesting and Optimization:
Backtesting is recommended to evaluate performance and make adjustments if necessary.
Risk Management:
Ensure position sizing respects risk management rules, avoiding risking more than 1-2% of capital per trade.
Originality and Benefits of the Strategy:
Unique Combination of Indicators: Integrates various technical metrics to filter signals, reducing false positives.
Volatility Adaptability: The use of ATR for Stop Loss and Take Profit allows the strategy to adapt to real-time market conditions.
Focus on Post-Lateralization Breakout: Aims to capitalize on significant moves following consolidation periods, often associated with strong directional trends.
Important Notes:
Commissions and Slippage: Include commissions and slippage in settings for more realistic simulations.
Capital Size: Use a realistic trading capital for the average user.
Number of Trades: Ensure backtesting covers a sufficient number of trades to validate the strategy (ideally more than 100 trades).
Warning: Past results do not guarantee future performance. The strategy should be used as part of a comprehensive trading approach.
With this strategy, traders can identify and exploit specific market opportunities supported by a robust set of technical indicators and filters, potentially enhancing their trading decisions during key times of the day.
Daily Moving Average for Intraday TimeframesThis indicator provides a dynamic tool for visualizing the Daily Moving Average (DMA) on intraday timeframes.
It allows you to analyze how the price behaves in relation to the daily moving average in timeframes from 1 minute up to 1 day.
KEY FEATURES
DMA on Intraday timeframes only : This indicator is designed to work exclusively on intraday charts with timeframes between 1 minute and 1 day. It will not function on tick, second-based, or daily-and-above charts.
Color-Coded Zones for Trend Identification :
Green Zone: The price is above a rising DMA, signaling a bullish momentum.
Red Zone: The price is below a falling DMA, signaling a bearish momentum.
Yellow Zone: Signaling uncertainty or mixed conditions, where either the price is above a falling DMA or below a rising/flat DMA.
Configurable DMA Period : You can adjust the number of days over which the DMA is calculated (default is 5 days). This can be customized based on your trading strategy or market preferences.
24/7 Market Option : For assets that trade continuously (e.g., cryptocurrencies), activate the "Is trading 24/7?" setting to ensure accurate calculations.
WHAT IS THE DMA AND WHY USE IT INTRADAY?
The Daily Moving Average is a Simple Moving Average indicator used to smooth out price fluctuations over a specified period (in days) and reveal the underlying trend.
Typically, a SMA takes price value for the current timeframe and reveal the trend for this timeframe. It gives you the average price for the last N candles for the given timeframe.
But what makes the Intraday DMA interesting is that it shows the underlying trend of the Daily timeframe on a chart set on a shorter timeframe . This helps to align intraday trades with broader market movements.
HOW IS THE DMA CALCULATED?
If we are to build a N-day Daily Moving Average using a Simple Moving Average, we need to take the amount of candles A needed in that timeframe to account for a period of a day and multiply it by the number of days N of the desired DMA.
So for instance, let say we want to compute the 5-Day DMA on the 10 minute timeframe :
In the 10 minute timeframe there are 39 candles in a day in the regular session.
We would take the 39 candles per day and then multiply that by 5 days. 39 x 5 = 195.
So a 5-day moving average is represented by a simple moving average with a period of 195 when looking at a 10 minute timeframe.
So for each period, to create a 5-day DMA, you would have to set the period of your simple moving average like so :
- 195 minutes = 10 period
- 130 minutes = 15 period
- 65 minutes = 30 period
- 30 minutes = 65 period
- 15 minutes = 130 period
- 10 minutes = 195 period
- 5 minutes = 390 period
and so on.
This indicator attempts to do this calculation for you on any intraday timeframe and whatever the period you want to use is for your DMA. You can create a 10-day moving average, a 30-day moving average, etc.
TPS Short Strategy by Larry ConnersThe TPS Short strategy aims to capitalize on extreme overbought conditions in an ETF by employing a scaling-in approach when certain technical indicators signal potential reversals. The strategy is designed to short the ETF when it is deemed overextended, based on the Relative Strength Index (RSI) and moving averages.
Components:
200-Day Simple Moving Average (SMA):
Purpose: Acts as a long-term trend filter. The ETF must be below its 200-day SMA to be eligible for shorting.
Rationale: The 200-day SMA is widely used to gauge the long-term trend of a security. When the price is below this moving average, it is often considered to be in a downtrend (Tushar S. Chande & Stanley Kroll, "The New Technical Trader: Boost Your Profit by Plugging Into the Latest Indicators").
2-Period RSI:
Purpose: Measures the speed and change of price movements to identify overbought conditions.
Criteria: Short 10% of the position when the 2-period RSI is above 75 for two consecutive days.
Rationale: A high RSI value (above 75) indicates that the ETF may be overbought, which could precede a price reversal (J. Welles Wilder, "New Concepts in Technical Trading Systems").
Scaling-In Mechanism:
Purpose: Gradually increase the short position as the ETF price rises beyond previous entry points.
Scaling Strategy:
20% more when the price is higher than the first entry.
30% more when the price is higher than the second entry.
40% more when the price is higher than the third entry.
Rationale: This incremental approach allows for an increased position size in a worsening trend, potentially increasing profitability if the trend continues to align with the strategy’s premise (Marty Schwartz, "Pit Bull: Lessons from Wall Street's Champion Day Trader").
Exit Conditions:
Criteria: Close all positions when the 2-period RSI drops below 30 or the 10-day SMA crosses above the 30-day SMA.
Rationale: A low RSI value (below 30) suggests that the ETF may be oversold and could be poised for a rebound, while the SMA crossover indicates a potential change in the trend (Martin J. Pring, "Technical Analysis Explained").
Risks and Considerations:
Market Risk:
The strategy assumes that the ETF will continue to decline once shorted. However, markets can be unpredictable, and price movements might not align with the strategy's expectations, especially in a volatile market (Nassim Nicholas Taleb, "The Black Swan: The Impact of the Highly Improbable").
Scaling Risks:
Scaling into a position as the price increases may increase exposure to adverse price movements. This method can amplify losses if the market moves against the position significantly before any reversal occurs.
Liquidity Risk:
Depending on the ETF’s liquidity, executing large trades in increments might affect the price and increase trading costs. It is crucial to ensure that the ETF has sufficient liquidity to handle large trades without significant slippage (James Altucher, "Trade Like a Hedge Fund").
Execution Risk:
The strategy relies on timely execution of trades based on specific conditions. Delays or errors in order execution can impact performance, especially in fast-moving markets.
Technical Indicator Limitations:
Technical indicators like RSI and SMA are based on historical data and may not always predict future price movements accurately. They can sometimes produce false signals, leading to potential losses if used in isolation (John Murphy, "Technical Analysis of the Financial Markets").
Conclusion
The TPS Short strategy utilizes a combination of long-term trend filtering, overbought conditions, and incremental shorting to potentially profit from price reversals. While the strategy has a structured approach and leverages well-known technical indicators, it is essential to be aware of the inherent risks, including market volatility, liquidity issues, and potential limitations of technical indicators. As with any trading strategy, thorough backtesting and risk management are crucial to its successful implementation.
Donchian Channel Crosses_AITIndicator Name: Donchian Channel Crosses_AIT
Programming Language: Pine Script (TradingView)
Description
The Donchian Channel Crosses_AIT indicator is designed to provide trading signals based on the crossover of two Donchian Channels with different lookback periods. The indicator uses two channels, Donchian Channel A (default 7-day period) and Donchian Channel B (default 30-day period), to detect upward or downward momentum shifts. The signals are generated when the middle line of Donchian Channel A crosses above or below the middle line of Donchian Channel B.
Components
Donchian Channel A:
Default period: 7 days (modifiable by the user).
Middle Line: Calculated as the average of the highest high and lowest low over the period.
The middle line changes color depending on its position relative to Donchian Channel B.
Green: When Donchian Channel A's middle line is above Donchian Channel B's middle line.
Red: When Donchian Channel A's middle line is below Donchian Channel B's middle line.
Donchian Channel B:
Default period: 30 days (modifiable by the user).
Middle Line: Also calculated as the average of the highest high and lowest low over the period.
Always displayed as a white line with a line thickness of 1.
Long Signal:
Triggered when the middle line of Donchian Channel A crosses above the middle line of Donchian Channel B.
Displayed as a yellow triangle pointing up (L) below the price bar.
Short Signal:
Triggered when the middle line of Donchian Channel A crosses below the middle line of Donchian Channel B.
Displayed as a fuchsia triangle pointing down (S) above the price bar.
Settings
Donchian Channel A:
Default period: 7 days (modifiable via user input).
Middle line changes color based on its relationship to Donchian Channel B.
Donchian Channel B:
Default period: 30 days (modifiable via user input).
Middle line is always white and displayed with a line thickness of 1.
Signal Display:
Long Signal: A yellow "L" triangle is displayed when Donchian Channel A’s middle line crosses above Donchian Channel B’s middle line.
Short Signal: A fuchsia "S" triangle is displayed when Donchian Channel A’s middle line crosses below Donchian Channel B’s middle line.
Signals can be toggled on or off using the "Show Signals" setting.
Usage
Trend Confirmation:
Use this indicator to confirm trend direction by monitoring the relationship between Donchian Channel A and Donchian Channel B.
Uptrend: When Donchian Channel A’s middle line is above Donchian Channel B’s middle line (green line for Donchian A).
Downtrend: When Donchian Channel A’s middle line is below Donchian Channel B’s middle line (red line for Donchian A).
Entry and Exit Signals:
Long Signal: Enter a buy position when Donchian Channel A crosses above Donchian Channel B.
Short Signal: Enter a sell position when Donchian Channel A crosses below Donchian Channel B.
Visual Representation:
The Donchian Channels are drawn on the price chart, with Donchian Channel A dynamically changing color depending on its relative position to Donchian Channel B.
US Market Support & ResistanceUS Market Support & Resistance Indicator (For 5-30 Minute Timeframes)
This indicator plots key support and resistance levels for the US market based on the high and low of the first candle at the market open. It also shades the area between these levels with a color that dynamically changes to indicate the current trend:
* Green: Price is above the resistance level, suggesting a potential uptrend.
* Red: Price is below the support level, suggesting a potential downtrend.
* Gray: Price is trading between the support and resistance levels, suggesting a sideways trend.
Additionally, the indicator displays a small dashboard in the top right corner of the chart showing the current trend ("Upward", "Downward", or "Sideways") in the corresponding color.
Key Features:
* US Market Time Identification: Accurately identifies US market open and close times in UTC and colors candles red during these times.
* Support & Resistance Plotting: Plots support and resistance lines at the high and low of the first candle at the market open and extends them infinitely on the chart.
* Shaded Area Between Levels: Shades the area between the support and resistance lines with a color that dynamically changes based on the current price location relative to these levels.
* Trend Display: Displays a dashboard showing the current trend based on the shaded area's color.
* Open Alert: Issues an alert when the US market opens.
* Supported Timeframes: Works on timeframes less than 30 minutes and greater than 5 minutes.
* Economic News: Not recommended for use during periods of sporadic economic news releases, as sudden price fluctuations may cause false signals.
How to Use:
* Add the indicator to your chart, ensuring the timeframe is between 5 and 30 minutes.
* Wait for the US market to open.
* Observe the shaded area's color and the dashboard to identify the current trend.
* Use the support and resistance levels to make trading decisions, keeping in mind not to rely solely on it during news releases.
Caution: This indicator relies on support and resistance levels drawn at the US market open and may not always be accurate, especially during periods of high volatility. It should be used in conjunction with other technical analysis tools to confirm the trend and make informed trading decisions.
Note: This description is designed to be compliant with TradingView's policies on indicator publishing.
Key Times & Opening Prices [Olitrades]This indicator plots key time's (opening prices) with the possibility of vertical separators. It was initially created to utilize on the indices futures market, utilizing ICT logic.
These opening prices are often utilized to determine if price is currently at a premium or a discounted value.
The default times include:
Daily Open (18:00 PM)
Midnight (00:00 AM)
Settlement (15:00 PM)
7:30 AM
8:30 AM
9:30 AM (Equities Open)
10:00 AM (Morning 4h Candle Open)
14:00 PM (Afternoon 4h Candle Open)
Along with up to three custom time slots.
All times used in the indicator are Eastern Standard time (New York local time) and will automatically adjust no matter your time zone.
Historical
When in historical mode, the indicator will keep the previous levels so you can easily visualize them and their relation to price.
You can also choose how many past levels you want to see. This allows you to back test only specific days/weeks.
Other Inputs
The indicator contains an adjustable offset, to modify how far the line extends depending on the current timeframe.
Each one of the above-mentioned levels can be turned on and off, including the custom times. You can also choose between plotting just the opening price, a vertical line separator, or both! All of these lines have adjustable styles (dotted, dashed or solid) and width.
They also have custom cut offs. You may choose specific cut off times for custom time slots (when to stop extending the lines), as well as for AM (before noon) default levels and PM (after noon) default levels.
The indicator also allows to show text labels next to these lines, which is set by default but can be turned off. Custom times also include custom text options.
Muti TimeFrame 1st Minute High and a LowThis Pine Script code is designed to plot the high, close, and low prices at exactly 9:31 AM on any timeframe chart. Here's a breakdown of what the script does:
Inputs
Define the start time of the trading day (default: 9:30 AM)
Define the end time of the trading day (default: 4:00 PM)
Toggle to display daily open and close lines (default: true)
Toggle to extend lines for daily open and close (default: false)
Calculations
- Determines if the current bar is the first bar of the trading day (9:30 AM)
- Retrieves the high, close, and low prices at 9:31 AM for the current timeframe
- Plots these prices as crosses on the chart
- Draws lines for the 4 pm close and 9:30 am open, as well as lines for the high and low of the first candle
- Calculates the start and end times for a rectangle box and draws the box on the chart if the start price high and low are set
Features
- Plots the high, close, and low prices at exactly 9:31 AM on any timeframe chart
- Displays daily open and close lines
- Extends lines for daily open and close (optional)
- Draws a rectangle box around the first candle of the day (optional)
Markets
- Designed for use on various markets, including stocks, futures, forex, and crypto
This script is useful for traders who want to visualize the prices at the start of the trading day and track the market's movement throughout the day.
ADV_RSIADV_RSI - Advanced Relative Strength Index
Description: The ADV_RSI indicator is an advanced and mutated version of the classic Relative Strength Index (RSI), enhanced with multiple moving averages and a dynamic color-coding system. It provides traders with deeper insights into market momentum and potential trend reversals by incorporating two different moving averages of the RSI (21, and 50 periods). The indicator helps to visualize overbought and oversold conditions more effectively and offers a clear, color-coded representation of the RSI value relative to key thresholds.
Features:
RSI Calculation: The core of the indicator is based on the traditional RSI, calculated over a customizable period.
Multiple Moving Averages: The script includes two RSI moving averages (21, and 50 periods) to help identify trend strength and potential reversal points.
Dynamic RSI Color Coding: The RSI line is color-coded based on its value, ranging from red for overbought conditions to aqua for oversold conditions. This makes it easier to interpret the market's momentum at a glance.
Threshold Bands: The indicator includes horizontal threshold lines at key RSI levels (20, 30, 40, 50, 60, 70, 80), with shaded areas between them, providing a visual aid to quickly identify overbought and oversold zones.
How to Use:
The RSI line fluctuates between 0 and 100, with traditional overbought and oversold levels set at 70 and 30, respectively.
When the RSI crosses above the 70 level, it may indicate overbought conditions, signaling a potential selling opportunity.
When the RSI falls below the 30 level, it may indicate oversold conditions, signaling a potential buying opportunity.
The included moving averages of the RSI can help confirm trend direction and potential reversals.
The color coding of the RSI line provides a quick visual cue for momentum changes.
Ideal For:
Traders looking for a more nuanced understanding of market momentum.
Those who prefer visual aids for quick decision-making in identifying overbought and oversold conditions.
Traders who utilize multiple timeframes and need a comprehensive RSI tool for better accuracy in their analysis.
MTOBVR_CheckOverview.
This indicator checks to see if the OBV follows a specific pattern and visually displays the trend of the OBV on different time legs. It also has the ability to set alerts when certain conditions are met.
How to use
Setting up the indicator
Add a new indicator on a chart in TradingView.
Copy the code into the “Pine Script™ Editor” and save it as a new script.
Apply the script to your chart.
Indicator Functions
OBV Calculation
Calculates OBV for each time frame (5-minute, 15-minute, 30-minute, 1-hour, 4-hour, daily, and weekly) and displays it on the chart OBV is an indicator that combines price changes and trading volume.
Pattern Detection
Detects whether a particular pattern, such as a double or triple bottom, appears in the OBV. This allows us to identify buy or sell signals.
When these patterns are detected, the background color changes.
Background Color Change
When an OBV forms a particular pattern, the background color of the chart changes. This provides visual confirmation that a pattern has appeared.
Minimum pattern (min_bool): when the 5-, 15-, and 30-minute conditions for OBV are met.
High hourly pattern (h_bool): when the condition is met for the 1-hour, 4-hour, and daily legs of the OBV.
One-week pattern (range_obv_1w_bool): when the condition is met for the 1-week OBV pattern.
When all conditions are met: when the minimum pattern, high hourly pattern, and one-week pattern are all met.
Alert Settings
You can set alerts when certain conditions are met. This allows you to receive signals in real time.
Minimum conditions (min_bool): conditions at 5, 15, and 30 minutes of OBV.
High timeframe condition (h_bool): condition on the 1-hour, 4-hour, and daily legs of the OBV.
When all conditions are met: When all of the above conditions are met.
One-week condition (range_obv_1w_bool): Condition for the one-week leg of the OBV.
When all conditions are met: when the minimum, high time leg, and one week conditions are all met.
Arrows
OBV_ALL_UP: A green triangle arrow is displayed when the OBV is rising on all time legs.
OBV_ALL_DOWN: A red triangle arrow appears when the OBV is falling on all time legs.
Usage Notes
Time Leg Settings: This script uses OBVs of different time legs to detect patterns. Depending on the time leg used, appropriate settings and interpretation are required.
Alert settings: In order to activate the alert function, the alert conditions and notification method must be configured using TradingView's alert system.
This indicator allows you to visually see trends and patterns in the OBV across multiple time frames, allowing you to make more informed trading decisions.
