Heikin-Ashi Smoothed with option to change MA types CryptoJoncisPine Script version=3
Author CryptoJoncis
Heikin-Ashi Smoothed
The Heikin-Ashi Smoothed study is based upon the standard Heikin-Ashi study with additional moving average calculations. The following is the calculation formula for the bars:
1. The current bar Open, High, Low, Close values are smoothed individually by using the moving average type specified by the Moving Average Type 1 Input with a length/period specified by the Moving Average Period 1 Input.
2. The Heikin-Ashi bar Open, High, Low, Close values are set using the smoothed values from step 1. This is performed using the standard Heikin-Ashi formula.
3. The final Heikin-Ashi Open, High, Low, Close values are calculated by doing a second smoothing of the bar values from step 2 by using the moving average type specified by the Moving Average Type 2 Input with a length/period specified by the Moving Average Period 2 Input.
If you choose to tick the box where it offers to use only one smoothed HA then it skips the third/final step and you do not need to choose the second MA type for it to work.
Remember, using FRAMA, always make sure you use even number for length.
For simple Heikin-Ashi, please tick single smoothed and DEFAULT (Not smoothed as there are no MA used)
Heikin-Ashi bars are calculated:
1. Close = (Open + High + Low + Close) / 4
This is the average price of the current bar.
2. Open = (Open of Previous Bar + Close of Previous Bar) / 2
This is the midpoint of the previous bar.
3. High = Max of (High, Open, Close)
Highest value of the three.
4. Low = Min of (Low, Open, Close)
Lowest value of the three.
Any questions/suggestions/errors or spelling mistakes? Please leave a comment and let me know. I will try to fix it.
This took me few days to finish, so I hope you will find it useful.
Would you like to have more MA type choices? Please comment down with any other which aren't included in this indicator and I will research them and add.
MA included in this script:
Tillson Moving Average (T3)
Double Exponential Moving Average (DEMA)
Arnaud Legoux Moving Average (ALMA)
Least Squares Moving Average (LSMA)
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Smoothed Moving Average (SMMA)
Triple Exponential Moving Average (TEMA)
Hull Moving Average (HMA)
Adaptive moving average (AMA)
Fractal Adaptive Moving Average (FAMA)
Variable Index Dynamic Average (VIDYA)
Triangular Moving Average (TRIMA)
You can use,publish,modify this code in any way as you wish, but only if you reference me after.
You are not allowed to sell it as it is.
If this code is useful to you, then consider to buy me a coffee (or better a pint of beer) by donating Bitcoin or Etherium to:
BTC: 3FiBnveHo3YW6DSiPEmoCFCyCnsrWS3JBR
ETH: 0xac290B4A721f5ef75b0971F1102e01E1942A4578
References:
www.sierrachart.com
www.investopedia.com
www.binarytribune.com
www.investopedia.com
www.stockfetcher.com
www.mql5.com
www.incrediblecharts.com
help.cqg.com
www.blastchart.com
Cari dalam skrip untuk "Exponential Moving Average"
6 Simple Blue & 5 Exponential Yellow Moving Averages6 simple and 5 exponential Moving Averages in one indicator.
I made this because its not always easy to tell what average the price might be bouncing off from when you only have a couple at a time.
For some reason, the defaults aren't working.
To fix this, just open the configuration for the indicator after the first time that you load it.
Then check/uncheck the box and set the time period.
If anyone knows how I can fix this in the code, please let me know.
Blue indicators are simple and the Yellow are exponential.
Thinner more transparent lines are shorter term averages and Thicker lines are longer term averages.
I modeled it after the source of several other scripts which had less averages
AllMA Trend Radar [trade_lexx]📈 AllMA Trend Radar is your universal trend analysis tool!
📊 What is AllMA Trend Radar?
AllMA Trend Radar is a powerful indicator that uses various types of Moving Averages (MA) to analyze trends and generate trading signals. The indicator allows you to choose from more than 30 different types of moving averages and adjust their parameters to suit your trading style.
💡 The main components of the indicator
📈 Fast and slow moving averages
The indicator uses two main lines:
- Fast MA (blue line): reacts faster to price changes
- Slow MA (red line): smoother, reflects a long-term trend
The combined use of fast and slow MA allows you to get trend confirmation and entry/exit points from the market.
🔄 Wide range of moving averages
There are more than 30 types of moving averages at your disposal:
- SMA: Simple moving average
- EMA: Exponential moving average
- WMA: Weighted moving average
- DEMA: double exponential MA
- TEMA: triple exponential MA
- HMA: Hull Moving Average
- LSMA: Moving average of least squares
- JMA: Eureka Moving Average
- ALMA: Arnaud Legoux Moving Average
- ZLEMA: moving average with zero delay
- And many others!
🔍 Indicator signals
1️⃣ Fast 🆚 Slow MA signals (intersection and ratio of fast and slow MA)
Up/Down signals (intersection)
- Buy (Up) signal:
- What happens: the fast MA crosses the slow MA from bottom to top
- What does the green triangle with the "Buy" label under the candle look
like - What does it mean: a likely upward trend reversal or an uptrend strengthening
- Sell signal (Down):
- What happens: the fast MA crosses the slow MA from top to bottom
- What does it look like: a red triangle with a "Sell" mark above the candle
- What does it mean: a likely downtrend reversal or an increase in the downtrend
Greater/Less signals (ratio)
- Buy signal (Greater):
- What happens: the fast MA becomes higher than the slow MA
- What does it look like: a green triangle with a "Buy" label under the candle
- What does it mean: the formation or confirmation of an uptrend
- Sell signal (Less):
- What happens: the fast MA becomes lower than the slow MA
- What does it look like: a red triangle with a "Sell" mark above the candle
- What does it mean: the formation or confirmation of a downtrend
2️⃣ Signals ⚡️ Fast MA (fast MA and price)
Up/Down signals (intersection)
- Buy signal (Up Fast):
- What happens: the price crosses the fast MA from bottom to top
- What does it look like: a green triangle with a "Buy" label under the candle
- What does it mean: a short-term price growth signal
- Sell signal (Down Fast):
- What happens: the price crosses the fast MA from top to bottom
- What does it look like: a red triangle with a "Sell" label above the candle
- What does it mean: a short-term price drop signal
Greater/Less signals (ratio)
- Buy signal (Greater Fast):
- What happens: the price is getting higher than the fast MA
- What does it look like: a green triangle with a "Buy" label under the candle
- What does it mean: the price is above the fast MA, which indicates an upward movement
- Sell signal (Less Fast):
- What happens: the price is getting lower than the fast MA
- What does it look like: a red triangle with a "Sell" mark above the candle
- What does it mean: the price is under the fast MA, which indicates a downward movement
3️⃣ Signals 🐢 Slow MA (slow MA and price)
Up/Down signals (intersection)
- Buy signal (Up Slow):
- What happens: the price crosses the slow MA from bottom to top
- What does it look like: a green triangle with a "Buy" label under the candle
- What does it mean: a potential medium-term upward trend reversal
- Sell signal (Down Slow):
- What happens: the price crosses the slow MA from top to bottom
- What does it look like: a red triangle with a "Sell" label above the candle
- What does it mean: a potential medium-term downward trend reversal
Greater/Less signals (ratio)
- Buy signal (Greater Slow):
- What happens: the price is getting above the slow MA
- What does it look like: a green triangle with a "Buy" label under the candle
- What does it mean: the price is above the slow MA, which indicates a strong upward movement
- Sell signal (Less Slow):
- What is happening: the price is getting below the slow MA
- What does it look like: a red triangle with a "Sell" mark above the candle
- What does it mean: the price is under the slow MA, which indicates a strong downward movement
🛠 Filters to filter out false signals
1️⃣ Minimum distance between the signals
- What it does: sets the minimum number of candles between signals of the same type
- Why it is needed: it prevents the appearance of too frequent signals, especially during periods of high volatility
- How to set it up: Set a different value for each signal type (default: 3-5 bars)
- Example: if the value is 3 for Up/Down signals, after the buy signal appears, the next buy signal may appear no earlier than 3 bars later
2️⃣ Advanced indicator filters
🔍 RSI Filter
- What it does: Checks the Relative Strength Index (RSI) value before generating a signal
- Why it is needed: it helps to avoid countertrend entries and catch reversal points
- How to set up:
- For buy signals (🔋 Buy): set the RSI range, usually in the oversold zone (for example, 1-30)
- For sell signals (🪫 Sell): set the RSI range, usually in the overbought zone (for example, 70-100)
- Example: if the RSI = 25 (in the range 1-30), the buy signal will be confirmed
📊 MFI Filter (Cash Flow Index)
- What it does: analyzes volumes and the direction of price movement
- Why it is needed: confirms signals with data on the activity of cash flows
- How to set up:
- For buy signals (🔋 Buy): set the MFI range in the oversold zone (for example, 1-25)
- For sell signals (🪫 Sell): set the MFI range in the overbought zone (for example, 75-100)
- Example: if MFI = 80 (in the range of 75-100), the sell signal will be confirmed
📈 Stochastic Filter
- What it does: analyzes the position of the current price relative to the price range
- Why it is needed: confirms signals based on overbought/oversold conditions
- How to configure:
- You can configure the K Length, D Length and Smoothing parameters
- For buy signals (🔋 Buy): set the stochastic range in the oversold zone (for example, 1-20)
- For sell signals (🪫 Sell): set the stochastic range in the overbought zone (for example, 80-100)
- Example: if stochastic = 15 (is in the range of 1-20), the buy signal will be confirmed
🔌 Connecting to trading strategies
The indicator provides various connectors to connect to your trading strategies.:
1️⃣ Individual connectors for each type of signal
- 🔌Fast vs Slow Up/Down MA Signal🔌: signals for the intersection of fast and slow MA
- 🔌Fast vs Slow Greater/Less MA Signal🔌: signals of the ratio of fast and slow MA
- 🔌Fast Up/Down MA Signal🔌: signals of the intersection of price and fast MA
- 🔌Fast Greater/Less MA Signal🔌: signals of the ratio of price and fast MA
- 🔌Slow Up/Down MA Signal🔌: signals of the intersection of price and slow MA
- 🔌Slow Greater/Less MA Signal🔌: Price versus slow MA signals
2️⃣ Combined connectors
- 🔌Combined Up/Down MA Signal🔌: combines all the crossing signals (Up/Down)
- 🔌Combined Greater/Less MA Signal🔌: combines all the signals of the ratio (Greater/Less)
- 🔌Combined All MA Signals🔌: combines all signals (Up/Down and Greater/Less)
❗️ All connectors return values:
- 1: buy signal
- -1: sell signal
- 0: no signal
📚 How to start using AllMA Trend Radar
1️⃣ Selection of types of moving averages
- Add an indicator to the chart
- Select the type and period for the fast MA (default: DEMA with a period of 14)
- Select the type and period for the slow MA (default: SMA with a period of 14)
- Experiment with different types of MA to find the best combination for your trading style
2️⃣ Signal settings
- Turn on the desired signal types (Up/Down, Greater/Less)
- Set the minimum distance between the signals
- Activate and configure the necessary filters (RSI, MFI, Stochastic)
3️⃣ Checking on historical data
- Analyze how the indicator works based on historical data
- Pay attention to the accuracy of the signals and the presence of false alarms
- Adjust the settings if necessary
4️⃣ Introduction to the trading strategy
- Decide which signals will be used to enter the position.
- Determine which signals will be used to exit the position.
- Connect the indicator to your trading strategy through the appropriate connectors
🌟 Practical application examples
Scalping strategy
- Fast MA: TEMA with a period of 8
- Slow MA: EMA with a period of 21
- Active signals: Fast MA Up/Down
- Filters: RSI (range 1-40 for purchases, 60-100 for sales)
- Signal spacing: 3 bars
Strategy for day trading
- Fast MA: TEMA with a period of 10
- Slow MA: SMA with a period of 20
- Active signals: Fast MA Up/Down and Fast vs Slow Greater/Less
- Filters: MFI (range 1-25 for purchases, 75-100 for sales)
- Signal spacing: 5 bars
Swing Trading Strategy
- Fast MA: DEMA with a period of 14
- Slow MA: VWMA with a period of 30
- Active signals: Fast vs Slow Up/Down and Slow MA Greater/Less
- Filters: Stochastic (range 1-20 for purchases, 80-100 for sales)
- Signal spacing: 8 bars
A strategy for positional trading
- Fast MA: HMA with a period of 21
- Slow MA: SMA with a period of 50
- Active signals: Slow MA Up/Down and Fast vs Slow Greater/Less
- Filters: RSI and MFI at the same time
- The distance between the signals: 10 bars
💡 Tips for using AllMA Trend Radar
1. Select the types of MA for market conditions:
- For trending markets: DEMA, TEMA, HMA (fast MA)
- For sideways markets: SMA, WMA, VWMA (smoothed MA)
- For volatile markets: KAMA, AMA, VAMA (adaptive MA)
2. Combine different types of signals:
- Up/Down signals work better when moving from a sideways trend to a directional
one - Greater/Less signals are optimal for fixing a stable trend
3. Use filters effectively:
- The RSI filter works great in trending markets
- MFI filter helps to confirm the strength of volume movement
- Stochastic filter works well in lateral ranges
4. Adjust the minimum distance between the signals:
- Small values (2-3 bars) for short-term trading
- Average values (5-8 bars) for medium-term trading
- Large values (10+ bars) for long-term trading
5. Use combination connectors:
- For more reliable signals, connect the indicator through the combined connectors
💰 With the AllMA Trend Radar indicator, you get a universal trend analysis tool that can be customized for any trading style and timeframe. The combination of different types of moving averages and advanced filters allows you to significantly improve the accuracy of signals and the effectiveness of your trading strategy!
Market Phases (ZigZag + MA + RSI)This script is a TradingView Pine Script that visualizes market phases using the ZigZag pattern, Moving Averages (MA), and the Relative Strength Index (RSI). It allows traders to identify key market conditions, such as accumulating, distributing, bullish, and bearish phases based on price movements and momentum indicators.
#### Components
1. ZigZag Settings:
- Depth: Controls the sensitivity of the ZigZag indicator. A higher value results in fewer price points being considered as reversals.
- Deviation: Defines the minimum percentage change needed to identify a ZigZag point, preventing small fluctuations from being registered.
- Backstep: Specifies the number of bars to look back for identifying highs and lows.
2. Moving Average Settings:
- MA Length: The number of periods used to calculate the moving average.
- MA Type: The type of moving average to use, either Simple Moving Average (SMA) or Exponential Moving Average (EMA).
3. RSI Settings:
- RSI Length: The period for calculating the RSI.
- Overbought Level: The threshold above which the asset is considered overbought.
- Oversold Level: The threshold below which the asset is considered oversold.
4. Calculations:
- Moving Average and RSI Calculation: The script calculates either an SMA or EMA and the RSI based on user-defined settings.
5. ZigZag Enhanced Calculation:
- It identifies swing highs and lows to determine the ZigZag points for improved trend analysis.
6. Trend Direction:
- The script checks the direction of the trend based on the latest ZigZag points.
7. Market Phase Determination:
- The script defines the market phase (Accumulation, Distribution, Bullish, Bearish) based on the trend direction and levels from the RSI and relationship with the moving average.
8. Background Colors:
- The background is tinted according to the identified market phase for visual clarity.
9. Labels and Plotting:
- Labels are generated at the last bar with the current phase and RSI value.
- The moving average and last ZigZag points are plotted on the chart for further reference.
### Conclusion
This script provides a comprehensive view of market conditions by integrating multiple indicators, helping traders make informed trading decisions based on market dynamics. The ability to visualize phases and key indicators aids in recognizing potential entry and exit points in trading strategies.
If you have any questions or need further modifications, feel free to ask!
Adv EMA Cloud v6 (ADX, Alerts)Summary:
This indicator provides a multi-faceted view of market trends using Exponential Moving Averages (EMAs) arranged in visually intuitive clouds, enhanced with an optional ADX-based range filter and configurable alerts for key market conditions. It aims to help traders quickly gauge trend alignment across short, medium, and long timeframes while filtering signals during potentially choppy market conditions.
Key Features:
Multiple EMAs: Displays 10-period (Fast), 20-period (Mid), and 50-period (Slow) EMAs.
Long-Term Trend Filter: Includes a 200-period EMA to provide context for the overall dominant trend direction.
Dual EMA Clouds:
Fast/Mid Cloud (10/20 EMA): Fills the area between the 10 and 20 EMAs. Defaults to Green when 10 > 20 (bullish short-term momentum) and Red when 10 < 20 (bearish short-term momentum).
Mid/Slow Cloud (20/50 EMA): Fills the area between the 20 and 50 EMAs. Defaults to Aqua when 20 > 50 (bullish mid-term trend) and Fuchsia when 20 < 50 (bearish mid-term trend).
Optional ADX Range Filter: Uses the Average Directional Index (ADX) to identify potentially non-trending or choppy markets. When enabled and ADX falls below a user-defined threshold, the EMA clouds will turn grey, visually warning that trend-following signals may be less reliable.
Configurable Alerts: Provides several built-in alert conditions using Pine Script's alertcondition function:
Confluence Condition: Triggers when a 10/20 EMA crossover occurs while both EMA clouds show alignment (both bullish/green/aqua or both bearish/red/fuchsia) and price respects the 200 EMA filter and the ADX filter indicates a trend (if filters are enabled).
MA Filter Cross: Triggers when price crosses above or below the 200 EMA filter line.
Full Alignment Start: Triggers on the first bar where full bullish or bearish alignment occurs (both clouds aligned + MA filter respected + ADX trending, if filters are enabled).
How It Works:
EMA Calculation: Standard Exponential Moving Averages are calculated for the 10, 20, 50, and 200 periods based on the closing price.
Cloud Creation: The fill() function visually shades the area between the 10 & 20 EMAs and the 20 & 50 EMAs.
Cloud Coloring: The color of each cloud is determined by the relationship between the two EMAs that define it (e.g., if EMA 10 is above EMA 20, the first cloud is bullish-colored).
ADX Filter Logic: The script calculates the ADX value. If the "Use ADX Trend Filter?" input is checked and the calculated ADX is below the specified "ADX Trend Threshold", the script considers the market potentially ranging.
ADX Visual Effect: During detected ranging periods (if the ADX filter is active), the plotCloud12Color and plotCloud23Color variables are assigned a neutral grey color instead of their normal bullish/bearish colors before being passed to the fill() function.
Alert Logic: Boolean variables track the specific conditions (crossovers, cloud alignment, filter positions, ADX state). The alertcondition() function creates triggerable alerts based on these pre-defined conditions.
Potential Interpretation (Not Financial Advice):
Trend Alignment: When both clouds share the same directional color (e.g., both bullish - Green & Aqua) and price is on the corresponding side of the 200 EMA filter, it may suggest a stronger, more aligned trend. Conversely, conflicting cloud colors may indicate indecision or transition.
Dynamic Support/Resistance: The EMA lines themselves (especially the 20, 50, and 200) can sometimes act as dynamic levels where price might react.
Range Warning: Greyed-out clouds (when ADX filter is enabled) serve as a visual warning that trend-based strategies might face increased difficulty or whipsaws.
Confluence Alerts: The specific confluence alerts signal moments where multiple conditions align (crossover + cloud agreement + filters), which some traders might view as higher-probability setups.
Customization:
All EMA lengths (10, 20, 50, 200) are adjustable via the Inputs menu.
The ADX length and threshold are configurable.
The MA Trend Filter and ADX Trend Filter can be independently enabled or disabled.
Disclaimer:
This indicator is provided for informational and educational purposes only. Trading financial markets involves significant risk. Past performance is not indicative of future results. Always conduct your own thorough analysis and consider your risk tolerance before making any trading decisions. This indicator should be used in conjunction with other analysis methods and tools. Do not trade based solely on the signals or visuals provided by this indicator.
ETH/USDT EMA Crossover Strategy - OptimizedStrategy Name: EMA Crossover Strategy for ETH/USDT
Description:
This trading strategy is designed for the ETH/USDT pair and is based on exponential moving average (EMA) crossovers combined with momentum and volatility indicators. The strategy uses multiple filters to identify high-probability signals in both bullish and bearish trends, making it suitable for traders looking to trade in trending markets.
Strategy Components
EMAs (Exponential Moving Averages):
EMA 200: Used to identify the primary trend. If the price is above the EMA 200, it is considered a bullish trend; if below, a bearish trend.
EMA 50: Acts as an additional filter to confirm the trend.
EMA 20 and EMA 50 Short: These short-term EMAs generate entry signals through crossovers. A bullish crossover (EMA 20 crosses above EMA 50 Short) is a buy signal, while a bearish crossover (EMA 20 crosses below EMA 50 Short) is a sell signal.
RSI (Relative Strength Index):
The RSI is used to avoid overbought or oversold conditions. Long trades are only taken when the RSI is above 30, and short trades when the RSI is below 70.
ATR (Average True Range):
The ATR is used as a volatility filter. Trades are only taken when there is sufficient volatility, helping to avoid false signals in quiet markets.
Volume:
A volume filter is used to confirm sufficient market participation in the price movement. Trades are only taken when volume is above average.
Strategy Logic
Long Trades:
The price must be above the EMA 200 (bullish trend).
The EMA 20 must cross above the EMA 50 Short.
The RSI must be above 30.
The ATR must indicate sufficient volatility.
Volume must be above average.
Short Trades:
The price must be below the EMA 200 (bearish trend).
The EMA 20 must cross below the EMA 50 Short.
The RSI must be below 70.
The ATR must indicate sufficient volatility.
Volume must be above average.
How to Use the Strategy
Setup:
Add the script to your ETH/USDT chart on TradingView.
Adjust the parameters according to your preferences (e.g., EMA periods, RSI, ATR, etc.).
Signals:
Buy and sell signals will be displayed directly on the chart.
Long trades are indicated with an upward arrow, and short trades with a downward arrow.
Risk Management:
Use stop-loss and take-profit orders in all trades.
Consider a risk-reward ratio of at least 1:2.
Backtesting:
Test the strategy on historical data to evaluate its performance before using it live.
Advantages of the Strategy
Trend-focused: The strategy is designed to trade in trending markets, increasing the probability of success.
Multiple filters: The use of RSI, ATR, and volume reduces false signals.
Adaptability: It can be adjusted for different timeframes, although it is recommended to test it on 5-minute and 15-minute charts for ETH/USDT.
Warnings
Sideways markets: The strategy may generate false signals in markets without a clear trend. It is recommended to avoid trading in such conditions.
Optimization: Make sure to optimize the parameters according to the market and timeframe you are using.
Risk management: Never trade without stop-loss and take-profit orders.
Author
Jose J. Sanchez Cuevas
Version
v1.0
Bollinger Bands + EMA 200 + EMA 50This indicator combines three technical analysis tools: the Bollinger Bands (BB), and two Exponential Moving Averages (EMA) with periods of 200 and 50.
Bollinger Bands (BB): This indicator consists of three lines—the middle line being a simple moving average (SMA), and the upper and lower bands representing two standard deviations above and below the SMA. The width of the bands indicates market volatility, with wider bands signifying higher volatility and narrower bands indicating lower volatility.
Exponential Moving Averages (EMA 200 and EMA 50): The EMA is a type of moving average that gives more weight to recent prices, making it more responsive to price changes than the simple moving average. The EMA 200 is considered a long-term trend indicator, often used to identify the overall direction of the market. The EMA 50 is a medium-term trend indicator, helping to spot more immediate market trends. Crossovers between these two EMAs (such as when EMA 50 crosses above EMA 200) are commonly used as buy or sell signals, with the idea that a short-term trend shift is occurring.
By combining these three indicators, this custom Pine Script aims to give a comprehensive view of the market conditions, helping traders to understand both the volatility (via BB), the long-term market trend (via EMA 200), and the medium-term trend (via EMA 50). The interaction between the price and these indicators, along with crossovers, can be used to identify potential entry and exit points.
Dual Keltner ChannelsDual Keltner Channels (DKC) Indicator 📊
🔹 About This Indicator
This indicator is an enhanced version of the original Keltner Channel available in TradingView. The Keltner Channel was initially designed as a volatility-based envelope around a moving average, helping traders identify trends, breakouts, and potential reversal zones.
💡 Original Creator: The Keltner Channel concept is based on the work of Chester W. Keltner and was later implemented in various trading platforms, including TradingView’s built-in Keltner Channel indicator.
This script builds upon the TradingView version of the Keltner Channel, adding:
✅ Dual Keltner Bands (Inner & Outer) for better trend and volatility analysis.
✅ Customizable Moving Averages (EMA/SMA) for flexibility.
✅ Multiple Band Calculation Methods (ATR, True Range, Range) for improved accuracy.
✅ Shaded Zones Between the Bands for enhanced visual clarity.
⚡ Credit: This indicator is an enhancement of the original Keltner Channel Indicator in TradingView. All improvements and modifications are made to provide deeper market insights while maintaining the core principles of the original Keltner concept.
🔹 Overview
The Dual Keltner Channels (DKC) indicator overlays two Keltner Channels on the price chart, helping traders spot trends, breakouts, and reversals with greater precision.
Inner Keltner Band (Multiplier 1): Captures normal price movements.
Outer Keltner Band (Multiplier 2): Highlights extreme price movements and potential breakouts.
🔹 Features & Inputs
📌 Main Inputs:
Keltner Channel Length: Defines the lookback period for the moving average calculation.
Source Price: Selects the price type (close, open, high, low) to calculate the bands.
Exponential Moving Average (EMA) Option: Choose between Exponential (EMA) or Simple (SMA) as the basis for calculations.
Bands Style: Selects how the volatility is measured:
Average True Range (ATR) (default)
True Range (TR)
Range (High - Low)
ATR Length: Determines the length of ATR calculations.
Enable Multiplier 1 & 2: Toggle to display/hide inner (multiplier 1) and outer (multiplier 2) bands.
📌 Keltner Channels Calculation:
Moving Average (MA): Uses either EMA or SMA for the midline.
Volatility Band Calculation:
Upper Band 1 (Inner Band): MA + (Multiplier 1 × Volatility Measure)
Lower Band 1 (Inner Band): MA - (Multiplier 1 × Volatility Measure)
Upper Band 2 (Outer Band): MA + (Multiplier 2 × Volatility Measure)
Lower Band 2 (Outer Band): MA - (Multiplier 2 × Volatility Measure)
📌 Visuals & Plotting:
Inner Bands (Multiplier 1): Blue upper & lower lines.
Outer Bands (Multiplier 2): Darker blue upper & lower lines.
Basis Line: White moving average.
Shaded Areas:
Between Upper 1 & Upper 2 (Light Brown Area): Identifies the upper Keltner region.
Between Lower 1 & Lower 2 (Light Brown Area): Identifies the lower Keltner region.
🔹 How to Use the Dual Keltner Channels Indicator
✅ 1. Trend Identification
Price above the upper outer band (Multiplier 2): Strong uptrend – potential continuation.
Price below the lower outer band (Multiplier 2): Strong downtrend – potential continuation.
Price within the inner bands (Multiplier 1): Sideways market – possible consolidation.
✅ 2. Breakout Trading
Break above outer upper band: Indicates a bullish breakout – consider long trades.
Break below outer lower band: Indicates a bearish breakdown – consider short trades.
✅ 3. Overbought & Oversold Conditions
Price touching/exceeding outer bands (Multiplier 2): Potential reversal zones.
Reversal confirmation: Look for candlestick patterns (e.g., Doji, Engulfing) or divergence signals.
✅ 4. Pullback & Entry Zones
Price bouncing from inner bands (Multiplier 1): Good re-entry point in trend direction.
Inner band as support/resistance: Helps in setting stop-loss and profit targets.
🔹 Effective Trading Strategies Using DKC
📌 1. Trend Following Strategy (Using Moving Average & Bands)
✅ Look for price staying above/below the basis line (MA) within the outer bands.
✅ Use pullbacks to the inner bands as re-entry points for trend continuation.
✅ Confirm trend strength with momentum indicators like RSI, MACD.
📌 2. Breakout Trading Strategy
✅ Identify a tight consolidation phase within the inner Keltner bands.
✅ Wait for a strong breakout beyond the outer bands.
✅ Enter long/short trades based on breakout direction.
✅ Place stop-loss at the previous inner band to manage risk.
📌 3. Reversal Strategy (Mean Reversion)
✅ When price extends beyond the outer band (Multiplier 2), look for reversal signals (candlestick patterns, RSI divergence).
✅ Enter counter-trend trades with tight stop-loss beyond the band.
✅ Target the moving average (basis line) as take-profit.
🔹 Final Thoughts 💡
The Dual Keltner Channels (DKC) is a powerful upgrade to the standard Keltner Channel, providing:
✅ Greater clarity on trend strength
✅ More precise breakout & reversal signals
✅ Better visual insights for dynamic market conditions
📌 Best Used With: RSI, MACD, Volume Profile, Price Action Signals.
📌 Works on: Stocks, Forex, Crypto, Commodities, Indices.
RSI Divergence + Sweep + Signal + Alerts Toolkit [TrendX_]The RSI Toolkit is a powerful set of tools designed to enhance the functionality of the traditional Relative Strength Index (RSI) indicator. By integrating advanced features such as Moving Averages, Divergences, and Sweeps, it helps traders identify key market dynamics, potential reversals, and newly-approach trading stragies.
The toolkit expands on standard RSI usage by incorporating features from smart money concepts (Just try to be creative 🤣 Hope you like it), providing a deeper understanding of momentum, liquidity sweeps, and trend reversals. It is suitable for RSI traders who want to make more informed and effective trading decisions.
💎 FEATURES
RSI Moving Average
The RSI Moving Average (RSI MA) is the moving average of the RSI itself. It can be customized to use various types of moving averages, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Moving Average (RMA), and Volume-Weighted Moving Average (VWMA).
The RSI MA smooths out the RSI fluctuations, making it easier to identify trends and crossovers. It helps traders spot momentum shifts and potential entry/exit points by observing when the RSI crosses above or below its moving average.
RSI Divergence
RSI Divergence identifies discrepancies between price action and RSI momentum. There are two types of divergences: Regular Divergence - Indicates a potential trend reversal; Hidden Divergence - Suggests the continuation of the current trend.
Divergence is a critical signal for spotting weakness or strength in a trend. Regular divergence highlights potential trend reversals, while hidden divergence confirms trend continuation, offering traders valuable insights into market momentum and possible trade setups.
RSI Sweep
RSI Sweep detects moments when the RSI removes liquidity from a trend structure by sweeping above or below the price at key momentum level crossing. These sweeps are overlaid on the RSI chart for easier visualized.
RSI Sweeps are significant because they indicate potential turning points in the market. When RSI sweeps occur: In an uptrend - they suggest buyers' momentum has peaked, possibly leading to a reversal; In a downtrend - they indicate sellers’ momentum has peaked, also hinting at a reversal.
(Note: This feature incorporates Liquidity Sweep concepts from Smart Money Concepts into RSI analysis, helping RSI traders identify areas where liquidity has been removed, which often precedes a trend reversal)
🔎 BREAKDOWN
RSI Moving Average
How MA created: The RSI value is calculated first using the standard RSI formula. The MA is then applied to the RSI values using the trader’s chosen type of MA (SMA, EMA, RMA, or VWMA). The flexibility to choose the type of MA allows traders to adjust the smoothing effect based on their trading style.
Why use MA: RSI by itself can be noisy and difficult to interpret in volatile markets. Applying moving average would provide a smoother, more reliable view of RSI trends.
RSI Divergence
How Regular Divergence created: Regular Divergence is detected when price forms HIGHER highs while RSI forms LOWER highs (bearish divergence) or when price forms LOWER lows while RSI forms HIGHER lows (bullish divergence).
How Hidden Divergence created: Hidden Divergence is identified when price forms HIGHER lows while RSI forms LOWER lows (bullish hidden divergence) or when price forms LOWER highs while RSI forms HIGHER highs (bearish hidden divergence).
Why use Divergence: Divergences provide early warning signals of a potential trend change. Regular divergence helps traders anticipate reversals, while hidden divergence supports trend continuation, enabling traders to align their trades with market momentum.
RSI Sweep
How Sweep created: Trend Structure Shift are identified based on the RSI crossing key momentum level of 50. To track these sweeps, the indicator pinpoints moments when liquidity is removed from the Trend Structure Shift. This is a direct application of Liquidity Sweep concepts used in Smart Money theories, adapted to RSI.
Why use Sweep: RSI Sweeps are created to help traders detect potential trend reversals. By identifying areas where momentum has exhausted during a certain trend direction, the indicator highlights opportunities for traders to enter trades early in a reversal or continuation phase.
⚙️ USAGES
Divergence + Sweep
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for a divergence (regular or hidden) to form on the RSI. After the divergence is complete, look for a sweep to occur. A potential entry might be formed at the end of the sweep.
Divergences indicate a potential trend change, but confirmation is required to ensure the setup is valid. The RSI Sweep provides that confirmation by signaling a liquidity event, increasing the likelihood of a successful trade.
Sweep + MA Cross
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for an RSI Sweep to form then a potential entry might be formed when the RSI crosses its MA.
The RSI Sweep highlights a potential turning point in the market. The MA cross serves as additional confirmation that momentum has shifted, providing a more reliable and more potential entry signal for trend continuations.
DISCLAIMER
This indicator is not financial advice, it can only help traders make better decisions. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur. Therefore, one should always exercise caution and judgment when making decisions based on past performance.
Exposure Oscillator (Cumulative 0 to ±100%)
Exposure Oscillator (Cumulative 0 to ±100%)
This Pine Script indicator plots an "Exposure Oscillator" on the chart, which tracks the cumulative market exposure from a range of technical buy and sell signals. The exposure is measured on a scale from -100% (maximum short exposure) to +100% (maximum long exposure), helping traders assess the strength of their position in the market. It provides an intuitive visual cue to aid decision-making for trend-following strategies.
Buy Signals (Increase Exposure Score by +10%)
Buy Signal 1 (Cross Above 21 EMA):
This signal is triggered when the price crosses above the 21-period Exponential Moving Average (EMA), where the current bar closes above the EMA21, and the previous bar closed below the EMA21. This indicates a potential upward price movement as the market shifts into a bullish trend.
buySignal1 = ta.crossover(close, ema21)
Buy Signal 2 (Trending Above 21 EMA):
This signal is triggered when the price closes above the 21-period EMA for each of the last 5 bars, indicating a sustained bullish trend. It confirms that the price is consistently above the EMA21 for a significant period.
buySignal2 = ta.barssince(close <= ema21) > 5
Buy Signal 3 (Living Above 21 EMA):
This signal is triggered when the price has closed above the 21-period EMA for each of the last 15 bars, demonstrating a strong, prolonged uptrend.
buySignal3 = ta.barssince(close <= ema21) > 15
Buy Signal 4 (Cross Above 50 SMA):
This signal is triggered when the price crosses above the 50-period Simple Moving Average (SMA), where the current bar closes above the 50 SMA, and the previous bar closed below it. It indicates a shift toward bullish momentum.
buySignal4 = ta.crossover(close, sma50)
Buy Signal 5 (Cross Above 200 SMA):
This signal is triggered when the price crosses above the 200-period Simple Moving Average (SMA), where the current bar closes above the 200 SMA, and the previous bar closed below it. This suggests a long-term bullish trend.
buySignal5 = ta.crossover(close, sma200)
Buy Signal 6 (Low Above 50 SMA):
This signal is true when the lowest price of the current bar is above the 50-period SMA, indicating strong bullish pressure as the price maintains itself above the moving average.
buySignal6 = low > sma50
Buy Signal 7 (Accumulation Day):
An accumulation day occurs when the closing price is in the upper half of the daily range (greater than 50%) and the volume is larger than the previous bar's volume, suggesting buying pressure and accumulation.
buySignal7 = (close - low) / (high - low) > 0.5 and volume > volume
Buy Signal 8 (Higher High):
This signal occurs when the current bar’s high exceeds the highest high of the previous 14 bars, indicating a breakout or strong upward momentum.
buySignal8 = high > ta.highest(high, 14)
Buy Signal 9 (Key Reversal Bar):
This signal is generated when the stock opens below the low of the previous bar but rallies to close above the previous bar’s high, signaling a potential reversal from bearish to bullish.
buySignal9 = open < low and close > high
Buy Signal 10 (Distribution Day Fall Off):
This signal is triggered when a distribution day (a day with high volume and a close near the low of the range) "falls off" the rolling 25-bar period, indicating the end of a bearish trend or selling pressure.
buySignal10 = ta.barssince(close < sma50 and close < sma50) > 25
Sell Signals (Decrease Exposure Score by -10%)
Sell Signal 1 (Cross Below 21 EMA):
This signal is triggered when the price crosses below the 21-period Exponential Moving Average (EMA), where the current bar closes below the EMA21, and the previous bar closed above it. It suggests that the market may be shifting from a bullish trend to a bearish trend.
sellSignal1 = ta.crossunder(close, ema21)
Sell Signal 2 (Trending Below 21 EMA):
This signal is triggered when the price closes below the 21-period EMA for each of the last 5 bars, indicating a sustained bearish trend.
sellSignal2 = ta.barssince(close >= ema21) > 5
Sell Signal 3 (Living Below 21 EMA):
This signal is triggered when the price has closed below the 21-period EMA for each of the last 15 bars, suggesting a strong downtrend.
sellSignal3 = ta.barssince(close >= ema21) > 15
Sell Signal 4 (Cross Below 50 SMA):
This signal is triggered when the price crosses below the 50-period Simple Moving Average (SMA), where the current bar closes below the 50 SMA, and the previous bar closed above it. It indicates the start of a bearish trend.
sellSignal4 = ta.crossunder(close, sma50)
Sell Signal 5 (Cross Below 200 SMA):
This signal is triggered when the price crosses below the 200-period Simple Moving Average (SMA), where the current bar closes below the 200 SMA, and the previous bar closed above it. It indicates a long-term bearish trend.
sellSignal5 = ta.crossunder(close, sma200)
Sell Signal 6 (High Below 50 SMA):
This signal is true when the highest price of the current bar is below the 50-period SMA, indicating weak bullishness or a potential bearish reversal.
sellSignal6 = high < sma50
Sell Signal 7 (Distribution Day):
A distribution day is identified when the closing range of a bar is less than 50% and the volume is larger than the previous bar's volume, suggesting that selling pressure is increasing.
sellSignal7 = (close - low) / (high - low) < 0.5 and volume > volume
Sell Signal 8 (Lower Low):
This signal occurs when the current bar's low is less than the lowest low of the previous 14 bars, indicating a breakdown or strong downward momentum.
sellSignal8 = low < ta.lowest(low, 14)
Sell Signal 9 (Downside Reversal Bar):
A downside reversal bar occurs when the stock opens above the previous bar's high but falls to close below the previous bar’s low, signaling a reversal from bullish to bearish.
sellSignal9 = open > high and close < low
Sell Signal 10 (Distribution Cluster):
This signal is triggered when a distribution day occurs three times in the rolling 7-bar period, indicating significant selling pressure.
sellSignal10 = ta.valuewhen((close < low) and volume > volume , 1, 7) >= 3
Theme Mode:
Users can select the theme mode (Auto, Dark, or Light) to match the chart's background or to manually choose a light or dark theme for the oscillator's appearance.
Exposure Score Calculation: The script calculates a cumulative exposure score based on a series of buy and sell signals.
Buy signals increase the exposure score, while sell signals decrease it. Each signal impacts the score by ±10%.
Signal Conditions: The buy and sell signals are derived from multiple conditions, including crossovers with moving averages (EMA21, SMA50, SMA200), trend behavior, and price/volume analysis.
Oscillator Visualization: The exposure score is visualized as a line on the chart, changing color based on whether the exposure is positive (long position) or negative (short position). It is limited to the range of -100% to +100%.
Position Type: The indicator also indicates the position type based on the exposure score, labeling it as "Long," "Short," or "Neutral."
Horizontal Lines: Reference lines at 0%, 100%, and -100% visually mark neutral, increasing long, and increasing short exposure levels.
Exposure Table: A table displays the current exposure level (in percentage) and position type ("Long," "Short," or "Neutral"), updated dynamically based on the oscillator’s value.
Inputs:
Theme Mode: Choose "Auto" to use the default chart theme, or manually select "Dark" or "Light."
Usage:
This oscillator is designed to help traders track market sentiment, gauge exposure levels, and manage risk. It can be used for long-term trend-following strategies or short-term trades based on moving average crossovers and volume analysis.
The oscillator operates in conjunction with the chart’s price action and provides a visual representation of the market’s current trend strength and exposure.
Important Considerations:
Risk Management: While the exposure score provides valuable insight, it should be combined with other risk management tools and analysis for optimal trading decisions.
Signal Sensitivity: The accuracy and effectiveness of the signals depend on market conditions and may require adjustments based on the user’s trading strategy or timeframe.
Disclaimer:
This script is for educational purposes only. Trading involves significant risk, and users should carefully evaluate all market conditions and apply appropriate risk management strategies before using this tool in live trading environments.
Bearish signal using Point of Control (POC) with PAC by guruThis indicator code helps traders identify potential sell opportunities using several important technical indicators:
Point of Control (POC) – This is the price level where the most volume was traded over the past several days.
Previous Day's Low – This shows the lowest price reached during the previous day.
PAC (Price Action Channel) EMA – These are two moving averages (one based on the low price and one based on the close price) that help determine if the price is trending within a certain range.
Volume SMA – This is a 3-day simple moving average (SMA) of volume, which helps filter out signals based on market activity.
What the Script Does:
Point of Control (POC):
The script looks at the last 50 days (configurable) and calculates which price level had the highest trading volume.
It then plots a red line on the chart at the POC level. This is important because it helps identify areas where there was strong market interest in the past.
Volume Moving Average:
The script calculates a 3-day SMA of volume, but it excludes the current day to avoid premature signals based on today’s trading.
The volume SMA is used to ensure there’s enough market activity (with a threshold set to 25 units) before triggering a sell signal.
Price Action Channel (PAC) EMA:
The PAC consists of two exponential moving averages (EMAs):
The PAC Low EMA: This is based on the low prices over the last 34 periods (configurable).
The PAC Close EMA: This is based on the closing prices over the last 34 periods.
These EMAs help determine if the price is trending above or below certain price levels.
Sell Signal Logic: The script checks three conditions before displaying a "Sell" signal:
Price Below POC and Previous Day’s Low:
The close price must be below both the Point of Control (POC) and the previous day's low.
Volume SMA Above 25:
The 3-day volume SMA must be greater than 25. This ensures the signal only triggers when there’s enough trading volume in the market.
Today’s Low is Above PAC EMAs:
Today's low price must be above both the PAC low EMA and the PAC close EMA. This prevents sell signals when prices are already significantly below the PAC, indicating possible exhaustion in the downtrend.
If all three conditions are met, the script will display a red "Sell" label on the chart, signaling a potential selling opportunity.
No Sell Signal if Price Reverses:
If the price crosses back above the POC or the previous day's low, the script will remove the sell signal and reset for a new opportunity.
Summary of Conditions:
For the script to display a "Sell" label:
The close price must be below the Point of Control (POC) and the previous day’s low.
The 3-day volume SMA (excluding today) must be greater than 25 units.
The low price of the current day must be above both the PAC low EMA and the PAC close EMA.
If these conditions are met, a red sell label appears on the chart as a potential signal for a short (sell) trade.
Dema Supertrend | viResearchDema Supertrend | viResearch
Conceptual Foundation and Innovation
The "Dema Supertrend" indicator by viResearch combines the benefits of the Double Exponential Moving Average (DEMA) with the popular Supertrend method to provide an advanced tool for trend detection and volatility management. By integrating DEMA into the Supertrend calculation, the indicator reduces lag while enhancing responsiveness to market changes. This results in more accurate trend identification and a refined method for capturing directional movements.
Technical Composition and Calculation
The "Dema Supertrend" builds on the core principles of the Supertrend indicator by incorporating DEMA for smoother and more responsive trend detection. The key innovation lies in replacing the raw price data with the DEMA-smoothed values, allowing traders to identify trends with reduced noise and enhanced precision.
DEMA and ATR-Based Supertrend Calculation:
DEMA Calculation (demalen): The Double Exponential Moving Average is applied to the price data (hlc3 by default) over a user-defined length, providing a smoothed representation of the market trend. DEMA minimizes lag compared to simple or exponential moving averages, allowing for more timely trend identification.
Supertrend Bands (u, l): The Supertrend upper and lower bands are calculated by adding or subtracting a multiple of the Average True Range (ATR) from the DEMA value. These bands dynamically adjust to market volatility, acting as support and resistance levels to guide trading decisions.
Trend Logic (L, S): The script determines whether the price is above or below the bands to signal an uptrend (L) or downtrend (S). Crosses above or below these bands trigger visual alerts and trend changes, with alerts built in for potential long or short positions.
Trend Continuation and Reversal:
The indicator ensures that once a trend is identified, it persists until clear reversal criteria are met. This is achieved through a comparison of the current and previous values of the Supertrend bands, reducing the occurrence of false signals in volatile markets.
Features and User Inputs
The "Dema Supertrend" script offers a range of customizable options, allowing traders to tailor the indicator to different market conditions and trading strategies:
Supertrend Length: The length of the Supertrend period can be adjusted, allowing traders to control the sensitivity of the trend detection.
Multiplier: The ATR multiplier adjusts the distance between the DEMA and the Supertrend bands. A higher multiplier reduces the frequency of trend changes, while a lower multiplier increases sensitivity to price movements.
DEMA Length: The length of the DEMA calculation can be customized to smooth price data over different timeframes, helping traders capture long-term trends or short-term movements more effectively.
Practical Applications
The "Dema Supertrend" is an ideal tool for traders who seek to follow trends while minimizing the impact of market noise. Its combination of DEMA and Supertrend provides a clear, dynamic view of the market's direction, making it especially effective in volatile environments.
Key Uses:
- Trend Following: The Dema Supertrend helps traders align their positions with the prevailing market trend by providing clear signals for uptrends and downtrends based on DEMA-smoothened price action.
- Volatility Management: The integration of ATR ensures that the Supertrend bands adapt to changes in market volatility, allowing traders to avoid entering trades during choppy, unpredictable price movements.
- Signal Confirmation: The script includes visual and alert-based signals for trend continuation and reversal, enabling traders to confirm entries and exits with greater accuracy.
Advantages and Strategic Value
The "Dema Supertrend" offers several strategic advantages:
- Reduced Lag: By integrating DEMA into the Supertrend calculation, the indicator responds more quickly to price changes, reducing the lag inherent in traditional moving averages.
- Noise Reduction: The use of DEMA filters out short-term fluctuations, providing a clearer signal for traders looking to capture significant market trends.
- Dynamic Adjustments: The combination of ATR and DEMA allows the indicator to adapt to both trending and ranging markets, making it suitable for a variety of trading strategies.
Summary and Usage Tips
The "Dema Supertrend" is a powerful tool for trend-following traders, offering a precise and adaptive method for identifying and confirming market direction. Traders can experiment with different settings for the Supertrend and DEMA lengths, as well as the ATR multiplier, to optimize the indicator for various trading environments. For best results, use the "Dema Supertrend" in conjunction with other technical analysis tools to confirm trends and manage risk. Whether you're seeking to capture long-term market moves or react to short-term volatility, the "Dema Supertrend" provides a reliable and flexible solution for your trading strategy.
COMBINED EMA & SMA + DOUBLE DEMA, $TOTAL 1W / 5D -- Ruslan CRYPTOCAP:TOTAL
This Pine Script indicator, **"EMAS"**, provides an enhanced visualization of multiple types of moving averages, including both **Exponential Moving Averages (EMA)**, **Simple Moving Averages (SMA)**, and **Double Exponential Moving Averages (DEMA)**. It allows the user to observe the relationship between these different types of moving averages and apply regime-based coloring to price bars based on the comparison between the EMAs and DEMAs.
#### Key Features:
1. **EMA & SMA:**
- **EMA (Exponential Moving Average):** Calculated using a customizable lookback period (default 17), the EMA places greater weight on more recent prices, making it react faster to price changes.
- **SMA (Simple Moving Average):** Uses an equal-weighted average over a customizable lookback period (default 14), providing a slower-moving average compared to the EMA.
2. **DEMA (Double Exponential Moving Average):**
- Two separate DEMA lines are plotted using different lookback periods (default 2 and 14). The DEMA is a smoother and faster-responding version of the EMA, intended to reduce lag while retaining trend-following characteristics.
3. **Combined Signals:**
- The script calculates ratios between EMA/SMA (`comb`) and DEMA1/DEMA2 (`combd`) to generate a **regime-based bar coloring system**:
- If `combd > comb`: The bars are colored **green**, indicating that DEMAs are outperforming the EMAs, potentially signaling a stronger trend or momentum.
- If `comb > combd`: The bars are colored **red**, suggesting that the EMAs are dominant, which may indicate a different phase of the market.
4. **Signal SMA:**
- A 21-period **SMA** is plotted as a general trend-following signal. It provides a broader perspective on the current price trend, helping to smooth out short-term fluctuations.
5. **Customizable Options:**
- **"Show MAs?"**: The user has the option to toggle the display of the EMA, SMA, and DEMA lines on or off.
- **Custom Period Inputs**: Each type of moving average can have its period length customized via the input settings for better adaptability to different market conditions.
#### How to Use the Indicator:
- **Trend Following**:
The **EMA, SMA, and DEMA** values can help you determine the direction of the trend. When the EMA is above the SMA, it could indicate a stronger, more recent upward momentum. Similarly, DEMA comparisons provide smoother and faster trend signals.
- **Bar Coloring Regime**:
The **bar color** gives a quick visual cue of the regime:
- **Green bars** suggest that DEMAs are indicating stronger bullish or bearish signals compared to the EMAs.
- **Red bars** imply the opposite, where EMAs may be showing stronger signals, but possibly with more noise or lag.
- **Signal SMA**:
The **21-period SMA** line can be used as a simple trend indicator. When the price is above this line, it could signify an uptrend, while price movement below the line might indicate a downtrend.
#### Custom Inputs:
- **EMA Length**: Default is 17, but can be adjusted to fit your trading style.
- **SMA Length**: Default is 14.
- **DEMA Lengths**: Two customizable inputs for DEMA (default 2 and 14).
- **Source Selection**: You can choose which price source (close, open, high, low, etc.) to use for each calculation (default is the closing price).
#### Conclusion:
This indicator is useful for traders who wish to blend **trend-following strategies** (using EMA, SMA, and DEMA) with **visual regime indicators** (bar coloring). It is highly customizable, allowing traders to adjust settings based on their market approach. The combination of EMAs and DEMAs provides a nuanced view of price dynamics, potentially leading to better-informed trading decisions.
Moving average to price cloudHi all!
This indicator shows when the price crosses the defined moving average. It plots a green or red cloud (depending on trend) and the moving average. It also plots an arrow when the trend changes (this can be disabled in 'style'->'labels' in the settings).
The moving average itself can be used as dynamic support/resistance. The trend will change based on your settings (described below). By default the trend will change when the whole bar is above/below the moving average for 2 bars (that's closed). This can be changed by "Source" and "Bars".
Settings
• Length (choose the length of the moving average. Defaults to 21)
• Type (choose what type of moving average).
- "SMA" (Simple Moving Average)
- "EMA" (Exponential Moving Average)
- "HMA" (Hull Moving Average)
- "WMA" (Weighted Moving Average)
- "VWMA" (Volume Weighted Moving Average)
- "DEMA" (Double Exponential Moving Average)
Defaults to"EMA".
• Source (Define the price source that must be above/below the moving average for the trend to change. Defaults to 'High/low (passive)')
- 'Open' The open of the bar has to cross the moving average
- 'Close' The close of the bar has to cross the moving average
- 'High/low (passive)' In a down trend: the low of the bar has to cross the moving average
- 'High/low (aggressive)' In a down trend: the high of the bar has to cross the moving average
• Source bar must be close. Defaults to 'true'.
• Bars (Define the number bars whose value (defined in 'Source') must be above/below the moving average. All the bars (defined by this number) must be above/below the moving average for the trend to change. Defaults to 2.)
Let me know if you have any questions.
Best of trading luck!
MACD HTF - Dynamic SmoothingEnhancing Your 1-Minute Trades with Dynamic HTF MACD Smoothing
Ever found yourself glued to a 1-minute chart, trying to catch every minor price movement, yet feeling like you're missing the bigger picture? Picture this: a solid MACD line on that chart, dynamically smoothed from a higher timeframe (HTF). This tool offers two significant benefits over other existing HTF MACD indicators:
User-Friendly Interface: No need to manually adjust input parameters every time you switch to a different timeframe.
Smooth Charting: Say goodbye to the zigzag lines that often result from plotting higher time frame resolutions on a lower time frame.
Understanding the MACD
The Moving Average Convergence Divergence (MACD) is one of the most widely used and trusted technical indicators in the trading community. Invented by Gerald Appel in the late 1970s, the MACD helps traders understand the relationship between two moving averages of a security's price. It consists of the MACD line (difference between a 12-period and 26-period Exponential Moving Average) and the Signal line (9-period EMA of the MACD line). When the MACD line crosses above the Signal line, it's viewed as a bullish signal, and vice versa. The difference between the two lines is represented as a histogram, providing insights into potential buy or sell opportunities.
Features of the Dynamic HTF MACD Smoothing Script
Time Frame Flexibility: Choose a higher timeframe to derive MACD values and apply dynamic smoothing to your current timeframe.
Multiple Moving Averages: The script supports various MA types like EMA, SMA, DEMA, TEMA, WMA and HMA.
Alerts: Get real-time alerts for MACD crossover and crossunder.
Customizability: From the type of moving average to its length, customize as per your strategy.
Visual Indicators: Clearly plots signals when MACD crossover or crossunder occurs for potential entries.
At last
A massive shoutout to all the wizards and generous contributors in the community! You inspire innovations and new tools, paving the path forward. Here's to a community where we learn and build together. Cheers to collective growth!
STD-Filtered Jurik Volty Adaptive TEMA [Loxx]The STD-Filtered Jurik Volty Adaptive TEMA is an advanced moving average overlay indicator that incorporates adaptive period inputs from Jurik Volty into a Triple Exponential Moving Average (TEMA). The resulting value is further refined using a standard deviation filter to minimize noise. This adaptation aims to develop a faster TEMA that leads the standard, non-adaptive TEMA. However, during periods of low volatility, the output may be noisy, so a standard deviation filter is employed to decrease choppiness, yielding a highly responsive TEMA without the noise typically caused by low market volatility.
█ What is Jurik Volty?
Jurik Volty calculates the price volatility and relative price volatility factor.
The Jurik smoothing includes 3 stages:
1st stage - Preliminary smoothing by adaptive EMA
2nd stage - One more preliminary smoothing by Kalman filter
3rd stage - Final smoothing by unique Jurik adaptive filter
Here's a breakdown of the code:
1. volty(float src, int len) => defines a function called volty that takes two arguments: src, which represents the source price data (like close price), and len, which represents the length or period for calculating the indicator.
2. int avgLen = 65 sets the length for the Simple Moving Average (SMA) to 65.
3. Various variables are initialized like volty, voltya, bsmax, bsmin, and vsum.
4. len1 is calculated as math.max(math.log(math.sqrt(0.5 * (len-1))) / math.log(2.0) + 2.0, 0); this expression involves some mathematical transformations based on the len input. The purpose is to create a dynamic factor that will be used later in the calculations.
5. pow1 is calculated as math.max(len1 - 2.0, 0.5); this variable is another dynamic factor used in further calculations.
6. del1 and del2 represent the differences between the current src value and the previous values of bsmax and bsmin, respectively.
7. volty is assigned a value based on a conditional expression, which checks whether the absolute value of del1 is greater than the absolute value of del2. This step is essential for determining the direction and magnitude of the price change.
8. vsum is updated based on the previous value and the difference between the current and previous volty values.
9. The Simple Moving Average (SMA) of vsum is calculated with the length avgLen and assigned to avg.
10. Variables dVolty, pow2, len2, and Kv are calculated using various mathematical transformations based on previously calculated variables. These variables are used to adjust the Jurik Volty indicator based on the observed volatility.
11. The bsmax and bsmin variables are updated based on the calculated Kv value and the direction of the price change.
12. inally, the temp variable is calculated as the ratio of avolty to vsum. This value represents the Jurik Volty indicator's output and can be used to analyze the market trends and potential reversals.
Jurik Volty can be used to identify periods of high or low volatility and to spot potential trade setups based on price behavior near the volatility bands.
█ What is the Triple Exponential Moving Average?
The Triple Exponential Moving Average (TEMA) is a technical indicator used by traders and investors to identify trends and price reversals in financial markets. It is a more advanced and responsive version of the Exponential Moving Average (EMA). TEMA was developed by Patrick Mulloy and introduced in the January 1994 issue of Technical Analysis of Stocks & Commodities magazine. The aim of TEMA is to minimize the lag associated with single and double exponential moving averages while also filtering out market noise, thus providing a smoother, more accurate representation of the market trend.
To understand TEMA, let's first briefly review the EMA.
Exponential Moving Average (EMA):
EMA is a weighted moving average that gives more importance to recent price data. The formula for EMA is:
EMA_t = (Price_t * α) + (EMA_(t-1) * (1 - α))
Where:
EMA_t: EMA at time t
Price_t: Price at time t
α: Smoothing factor (α = 2 / (N + 1))
N: Length of the moving average period
EMA_(t-1): EMA at time t-1
Triple Exponential Moving Average (TEMA):
Triple Exponential Moving Average (TEMA):
TEMA combines three exponential moving averages to provide a more accurate and responsive trend indicator. The formula for TEMA is:
TEMA = 3 * EMA_1 - 3 * EMA_2 + EMA_3
Where:
EMA_1: The first EMA of the price data
EMA_2: The EMA of EMA_1
EMA_3: The EMA of EMA_2
Here are the steps to calculate TEMA:
1. Choose the length of the moving average period (N).
2. Calculate the smoothing factor α (α = 2 / (N + 1)).
3. Calculate the first EMA (EMA_1) using the price data and the smoothing factor α.
4. Calculate the second EMA (EMA_2) using the values of EMA_1 and the same smoothing factor α.
5. Calculate the third EMA (EMA_3) using the values of EMA_2 and the same smoothing factor α.
5. Finally, compute the TEMA using the formula: TEMA = 3 * EMA_1 - 3 * EMA_2 + EMA_3
The Triple Exponential Moving Average, with its combination of three EMAs, helps to reduce the lag and filter out market noise more effectively than a single or double EMA. It is particularly useful for short-term traders who require a responsive indicator to capture rapid price changes. Keep in mind, however, that TEMA is still a lagging indicator, and as with any technical analysis tool, it should be used in conjunction with other indicators and analysis methods to make well-informed trading decisions.
Extras
Signals
Alerts
Bar coloring
Loxx's Expanded Source Types (see below):
Intrabar Efficiency Ratio█ OVERVIEW
This indicator displays a directional variant of Perry Kaufman's Efficiency Ratio, designed to gauge the "efficiency" of intrabar price movement by comparing the sum of movements of the lower timeframe bars composing a chart bar with the respective bar's movement on an average basis.
█ CONCEPTS
Efficiency Ratio (ER)
Efficiency Ratio was first introduced by Perry Kaufman in his 1995 book, titled "Smarter Trading". It is the ratio of absolute price change to the sum of absolute changes on each bar over a period. This tells us how strong the period's trend is relative to the underlying noise. Simply put, it's a measure of price movement efficiency. This ratio is the modulator utilized in Kaufman's Adaptive Moving Average (KAMA), which is essentially an Exponential Moving Average (EMA) that adapts its responsiveness to movement efficiency.
ER's output is bounded between 0 and 1. A value of 0 indicates that the starting price equals the ending price for the period, which suggests that price movement was maximally inefficient. A value of 1 indicates that price had travelled no more than the distance between the starting price and the ending price for the period, which suggests that price movement was maximally efficient. A value between 0 and 1 indicates that price had travelled a distance greater than the distance between the starting price and the ending price for the period. In other words, some degree of noise was present which resulted in reduced efficiency over the period.
As an example, let's say that the price of an asset had moved from $15 to $14 by the end of a period, but the sum of absolute changes for each bar of data was $4. ER would be calculated like so:
ER = abs(14 - 15)/4 = 0.25
This suggests that the trend was only 25% efficient over the period, as the total distanced travelled by price was four times what was required to achieve the change over the period.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 intrabars at the LTF of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script determines which LTF to use by examining the chart's timeframe. The LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed, but fewer chart bars can display indicator information because there is a limit to the total number of intrabars that can be analyzed.
Intrabar precision
The precision of calculations increases with the number of intrabars analyzed for each chart bar. As there is a 100K limit to the number of intrabars that can be analyzed by a script, a trade-off occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
Intrabar Efficiency Ratio (IER)
Intrabar Efficiency Ratio applies the concept of ER on an intrabar level. Rather than comparing the overall change to the sum of bar changes for the current chart's timeframe over a period, IER compares single bar changes for the current chart's timeframe to the sum of absolute intrabar changes, then applies smoothing to the result. This gives an indication of how efficient changes are on the current chart's timeframe for each bar of data relative to LTF bar changes on an average basis. Unlike the standard ER calculation, we've opted to preserve directional information by not taking the absolute value of overall change, thus allowing it to be utilized as a momentum oscillator. However, by taking the absolute value of this oscillator, it could potentially serve as a replacement for ER in the design of adaptive moving averages.
Since this indicator preserves directional information, IER can be regarded as similar to the Chande Momentum Oscillator (CMO) , which was presented in 1994 by Tushar Chande in "The New Technical Trader". Both CMO and ER essentially measure the same relationship between trend and noise. CMO simply differs in scale, and considers the direction of overall changes.
█ FEATURES
Display
Three different display types are included within the script:
• Line : Displays the middle length MA of the IER as a line .
Color for this display can be customized via the "Line" portion of the "Visuals" section in the script settings.
• Candles : Displays the non-smooth IER and two moving averages of different lengths as candles .
The `open` and `close` of the candle are the longest and shortest length MAs of the IER respectively.
The `high` and `low` of the candle are the max and min of the IER, longest length MA of the IER, and shortest length MA of the IER respectively.
Colors for this display can be customized via the "Candles" portion of the "Visuals" section in the script settings.
• Circles : Displays three MAs of the IER as circles .
The color of each plot depends on the percent rank of the respective MA over the previous 100 bars.
Different colors are triggered when ranks are below 10%, between 10% and 50%, between 50% and 90%, and above 90%.
Colors for this display can be customized via the "Circles" portion of the "Visuals" section in the script settings.
With either display type, an optional information box can be displayed. This box shows the LTF that the script is using, the average number of lower timeframe bars per chart bar, and the number of chart bars that contain LTF data.
Specifying intrabar precision
Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed.
The first five options allow users to specify the approximate amount of chart bars to be covered:
• Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.
This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.
• Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped LTF in relation to the current chart's timeframe.
• Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min LTF.
• Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min LTF.
The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible.
Specifying MA type
Seven MA types are included in the script for different averaging effects:
• Simple
• Exponential
• Wilder (RMA)
• Weighted
• Volume-Weighted
• Arnaud Legoux with `offset` and `sigma` set to 0.85 and 6 respectively.
• Hull
Weighting
This script includes the option to weight IER values based on the percent rank of absolute price changes on the current chart's timeframe over a specified period, which can be enabled by checking the "Weigh using relative close changes" option in the script settings. This places reduced emphasis on IER values from smaller changes, which may help to reduce noise in the output.
█ FOR Pine Script™ CODERS
• This script imports the recently published lower_ltf library for calculating intrabar statistics and the optimal lower timeframe in relation to the current chart's timeframe.
• This script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post .
It works differently from the usual request.security() in that it can only be used on LTFs, and it returns an array containing one value per intrabar.
This makes it much easier for programmers to access intrabar information.
• This script implements a new recommended best practice for tables which works faster and reduces memory consumption.
Using this new method, tables are declared only once with var , as usual. Then, on the first bar only, we use table.cell() to populate the table.
Finally, table.set_*() functions are used to update attributes of table cells on the last bar of the dataset.
This greatly reduces the resources required to render tables.
Look first. Then leap.
loxxmas - moving averages used in Loxx's indis & stratsLibrary "loxxmas"
TODO:loxx moving averages used in indicators
kama(src, len, kamafastend, kamaslowend)
KAMA Kaufman adaptive moving average
Parameters:
src : float
len : int
kamafastend : int
kamaslowend : int
Returns: array
ama(src, len, fl, sl)
AMA, adaptive moving average
Parameters:
src : float
len : int
fl : int
sl : int
Returns: array
t3(src, len)
T3 moving average, adaptive moving average
Parameters:
src : float
len : int
Returns: array
adxvma(src, len)
ADXvma - Average Directional Volatility Moving Average
Parameters:
src : float
len : int
Returns: array
ahrma(src, len)
Ahrens Moving Average
Parameters:
src : float
len : int
Returns: array
alxma(src, len)
Alexander Moving Average - ALXMA
Parameters:
src : float
len : int
Returns: array
dema(src, len)
Double Exponential Moving Average - DEMA
Parameters:
src : float
len : int
Returns: array
dsema(src, len)
Double Smoothed Exponential Moving Average - DSEMA
Parameters:
src : float
len : int
Returns: array
ema(src, len)
Exponential Moving Average - EMA
Parameters:
src : float
len : int
Returns: array
fema(src, len)
Fast Exponential Moving Average - FEMA
Parameters:
src : float
len : int
Returns: array
hma(src, len)
Hull moving averge
Parameters:
src : float
len : int
Returns: array
ie2(src, len)
Early T3 by Tim Tilson
Parameters:
src : float
len : int
Returns: array
frama(src, len, FC, SC)
Fractal Adaptive Moving Average - FRAMA
Parameters:
src : float
len : int
FC : int
SC : int
Returns: array
instant(src, float)
Instantaneous Trendline
Parameters:
src : float
float : alpha
Returns: array
ilrs(src, int)
Integral of Linear Regression Slope - ILRS
Parameters:
src : float
int : len
Returns: array
laguerre(src, float)
Laguerre Filter
Parameters:
src : float
float : alpha
Returns: array
leader(src, int)
Leader Exponential Moving Average
Parameters:
src : float
int : len
Returns: array
lsma(src, int, int)
Linear Regression Value - LSMA (Least Squares Moving Average)
Parameters:
src : float
int : len
int : offset
Returns: array
lwma(src, int)
Linear Weighted Moving Average - LWMA
Parameters:
src : float
int : len
Returns: array
mcginley(src, int)
McGinley Dynamic
Parameters:
src : float
int : len
Returns: array
mcNicholl(src, int)
McNicholl EMA
Parameters:
src : float
int : len
Returns: array
nonlagma(src, int)
Non-lag moving average
Parameters:
src : float
int : len
Returns: array
pwma(src, int, float)
Parabolic Weighted Moving Average
Parameters:
src : float
int : len
float : pwr
Returns: array
rmta(src, int)
Recursive Moving Trendline
Parameters:
src : float
int : len
Returns: array
decycler(src, int)
Simple decycler - SDEC
Parameters:
src : float
int : len
Returns: array
sma(src, int)
Simple Moving Average
Parameters:
src : float
int : len
Returns: array
swma(src, int)
Sine Weighted Moving Average
Parameters:
src : float
int : len
Returns: array
slwma(src, int)
linear weighted moving average
Parameters:
src : float
int : len
Returns: array
smma(src, int)
Smoothed Moving Average - SMMA
Parameters:
src : float
int : len
Returns: array
super(src, int)
Ehlers super smoother
Parameters:
src : float
int : len
Returns: array
smoother(src, int)
Smoother filter
Parameters:
src : float
int : len
Returns: array
tma(src, int)
Triangular moving average - TMA
Parameters:
src : float
int : len
Returns: array
tema(src, int)
Tripple exponential moving average - TEMA
Parameters:
src : float
int : len
Returns: array
vwema(src, int)
Volume weighted ema - VEMA
Parameters:
src : float
int : len
Returns: array
vwma(src, int)
Volume weighted moving average - VWMA
Parameters:
src : float
int : len
Returns: array
zlagdema(src, int)
Zero-lag dema
Parameters:
src : float
int : len
Returns: array
zlagma(src, int)
Zero-lag moving average
Parameters:
src : float
int : len
Returns: array
zlagtema(src, int)
Zero-lag tema
Parameters:
src : float
int : len
Returns: array
threepolebuttfilt(src, int)
Three-pole Ehlers Butterworth
Parameters:
src : float
int : len
Returns: array
threepolesss(src, int)
Three-pole Ehlers smoother
Parameters:
src : float
int : len
Returns: array
twopolebutter(src, int)
Two-pole Ehlers Butterworth
Parameters:
src : float
int : len
Returns: array
twopoless(src, int)
Two-pole Ehlers smoother
Parameters:
src : float
int : len
Returns: array
Adaptive Jurik Filter MACD [Loxx]Adaptive Jurik Filter MACD uses Jurik Volty and Adaptive Double Jurik Filter Moving Average (AJFMA) to derive Jurik Filter smoothed volatility.
What is MACD?
Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence (MACD) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
What is Jurik Volty?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
- Change colors of oscillators and bars
Moving_AveragesLibrary "Moving_Averages"
This library contains majority important moving average functions with int series support. Which means that they can be used with variable length input. For conventional use, please use tradingview built-in ta functions for moving averages as they are more precise. I'll use functions in this library for my other scripts with dynamic length inputs.
ema(src, len)
Exponential Moving Average (EMA)
Parameters:
src : Source
len : Period
Returns: Exponential Moving Average with Series Int Support (EMA)
alma(src, len, a_offset, a_sigma)
Arnaud Legoux Moving Average (ALMA)
Parameters:
src : Source
len : Period
a_offset : Arnaud Legoux offset
a_sigma : Arnaud Legoux sigma
Returns: Arnaud Legoux Moving Average (ALMA)
covwema(src, len)
Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)
Parameters:
src : Source
len : Period
Returns: Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)
covwma(src, len)
Coefficient of Variation Weighted Moving Average (COVWMA)
Parameters:
src : Source
len : Period
Returns: Coefficient of Variation Weighted Moving Average (COVWMA)
dema(src, len)
DEMA - Double Exponential Moving Average
Parameters:
src : Source
len : Period
Returns: DEMA - Double Exponential Moving Average
edsma(src, len, ssfLength, ssfPoles)
EDSMA - Ehlers Deviation Scaled Moving Average
Parameters:
src : Source
len : Period
ssfLength : EDSMA - Super Smoother Filter Length
ssfPoles : EDSMA - Super Smoother Filter Poles
Returns: Ehlers Deviation Scaled Moving Average (EDSMA)
eframa(src, len, FC, SC)
Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)
Parameters:
src : Source
len : Period
FC : Lower Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average
SC : Upper Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average
Returns: Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)
ehma(src, len)
EHMA - Exponential Hull Moving Average
Parameters:
src : Source
len : Period
Returns: Exponential Hull Moving Average (EHMA)
etma(src, len)
Exponential Triangular Moving Average (ETMA)
Parameters:
src : Source
len : Period
Returns: Exponential Triangular Moving Average (ETMA)
frama(src, len)
Fractal Adaptive Moving Average (FRAMA)
Parameters:
src : Source
len : Period
Returns: Fractal Adaptive Moving Average (FRAMA)
hma(src, len)
HMA - Hull Moving Average
Parameters:
src : Source
len : Period
Returns: Hull Moving Average (HMA)
jma(src, len, jurik_phase, jurik_power)
Jurik Moving Average - JMA
Parameters:
src : Source
len : Period
jurik_phase : Jurik (JMA) Only - Phase
jurik_power : Jurik (JMA) Only - Power
Returns: Jurik Moving Average (JMA)
kama(src, len, k_fastLength, k_slowLength)
Kaufman's Adaptive Moving Average (KAMA)
Parameters:
src : Source
len : Period
k_fastLength : Number of periods for the fastest exponential moving average
k_slowLength : Number of periods for the slowest exponential moving average
Returns: Kaufman's Adaptive Moving Average (KAMA)
kijun(_high, _low, len, kidiv)
Kijun v2
Parameters:
_high : High value of bar
_low : Low value of bar
len : Period
kidiv : Kijun MOD Divider
Returns: Kijun v2
lsma(src, len, offset)
LSMA/LRC - Least Squares Moving Average / Linear Regression Curve
Parameters:
src : Source
len : Period
offset : Offset
Returns: Least Squares Moving Average (LSMA)/ Linear Regression Curve (LRC)
mf(src, len, beta, feedback, z)
MF - Modular Filter
Parameters:
src : Source
len : Period
beta : Modular Filter, General Filter Only - Beta
feedback : Modular Filter Only - Feedback
z : Modular Filter Only - Feedback Weighting
Returns: Modular Filter (MF)
rma(src, len)
RMA - RSI Moving average
Parameters:
src : Source
len : Period
Returns: RSI Moving average (RMA)
sma(src, len)
SMA - Simple Moving Average
Parameters:
src : Source
len : Period
Returns: Simple Moving Average (SMA)
smma(src, len)
Smoothed Moving Average (SMMA)
Parameters:
src : Source
len : Period
Returns: Smoothed Moving Average (SMMA)
stma(src, len)
Simple Triangular Moving Average (STMA)
Parameters:
src : Source
len : Period
Returns: Simple Triangular Moving Average (STMA)
tema(src, len)
TEMA - Triple Exponential Moving Average
Parameters:
src : Source
len : Period
Returns: Triple Exponential Moving Average (TEMA)
thma(src, len)
THMA - Triple Hull Moving Average
Parameters:
src : Source
len : Period
Returns: Triple Hull Moving Average (THMA)
vama(src, len, volatility_lookback)
VAMA - Volatility Adjusted Moving Average
Parameters:
src : Source
len : Period
volatility_lookback : Volatility lookback length
Returns: Volatility Adjusted Moving Average (VAMA)
vidya(src, len)
Variable Index Dynamic Average (VIDYA)
Parameters:
src : Source
len : Period
Returns: Variable Index Dynamic Average (VIDYA)
vwma(src, len)
Volume-Weighted Moving Average (VWMA)
Parameters:
src : Source
len : Period
Returns: Volume-Weighted Moving Average (VWMA)
wma(src, len)
WMA - Weighted Moving Average
Parameters:
src : Source
len : Period
Returns: Weighted Moving Average (WMA)
zema(src, len)
Zero-Lag Exponential Moving Average (ZEMA)
Parameters:
src : Source
len : Period
Returns: Zero-Lag Exponential Moving Average (ZEMA)
zsma(src, len)
Zero-Lag Simple Moving Average (ZSMA)
Parameters:
src : Source
len : Period
Returns: Zero-Lag Simple Moving Average (ZSMA)
evwma(src, len)
EVWMA - Elastic Volume Weighted Moving Average
Parameters:
src : Source
len : Period
Returns: Elastic Volume Weighted Moving Average (EVWMA)
tt3(src, len, a1_t3)
Tillson T3
Parameters:
src : Source
len : Period
a1_t3 : Tillson T3 Volume Factor
Returns: Tillson T3
gma(src, len)
GMA - Geometric Moving Average
Parameters:
src : Source
len : Period
Returns: Geometric Moving Average (GMA)
wwma(src, len)
WWMA - Welles Wilder Moving Average
Parameters:
src : Source
len : Period
Returns: Welles Wilder Moving Average (WWMA)
ama(src, _high, _low, len, ama_f_length, ama_s_length)
AMA - Adjusted Moving Average
Parameters:
src : Source
_high : High value of bar
_low : Low value of bar
len : Period
ama_f_length : Fast EMA Length
ama_s_length : Slow EMA Length
Returns: Adjusted Moving Average (AMA)
cma(src, len)
Corrective Moving average (CMA)
Parameters:
src : Source
len : Period
Returns: Corrective Moving average (CMA)
gmma(src, len)
Geometric Mean Moving Average (GMMA)
Parameters:
src : Source
len : Period
Returns: Geometric Mean Moving Average (GMMA)
ealf(src, len, LAPercLen_, FPerc_)
Ehler's Adaptive Laguerre filter (EALF)
Parameters:
src : Source
len : Period
LAPercLen_ : Median Length
FPerc_ : Median Percentage
Returns: Ehler's Adaptive Laguerre filter (EALF)
elf(src, len, LAPercLen_, FPerc_)
ELF - Ehler's Laguerre filter
Parameters:
src : Source
len : Period
LAPercLen_ : Median Length
FPerc_ : Median Percentage
Returns: Ehler's Laguerre Filter (ELF)
edma(src, len)
Exponentially Deviating Moving Average (MZ EDMA)
Parameters:
src : Source
len : Period
Returns: Exponentially Deviating Moving Average (MZ EDMA)
pnr(src, len, rank_inter_Perc_)
PNR - percentile nearest rank
Parameters:
src : Source
len : Period
rank_inter_Perc_ : Rank and Interpolation Percentage
Returns: Percentile Nearest Rank (PNR)
pli(src, len, rank_inter_Perc_)
PLI - Percentile Linear Interpolation
Parameters:
src : Source
len : Period
rank_inter_Perc_ : Rank and Interpolation Percentage
Returns: Percentile Linear Interpolation (PLI)
rema(src, len)
Range EMA (REMA)
Parameters:
src : Source
len : Period
Returns: Range EMA (REMA)
sw_ma(src, len)
Sine-Weighted Moving Average (SW-MA)
Parameters:
src : Source
len : Period
Returns: Sine-Weighted Moving Average (SW-MA)
vwap(src, len)
Volume Weighted Average Price (VWAP)
Parameters:
src : Source
len : Period
Returns: Volume Weighted Average Price (VWAP)
mama(src, len)
MAMA - MESA Adaptive Moving Average
Parameters:
src : Source
len : Period
Returns: MESA Adaptive Moving Average (MAMA)
fama(src, len)
FAMA - Following Adaptive Moving Average
Parameters:
src : Source
len : Period
Returns: Following Adaptive Moving Average (FAMA)
hkama(src, len)
HKAMA - Hilbert based Kaufman's Adaptive Moving Average
Parameters:
src : Source
len : Period
Returns: Hilbert based Kaufman's Adaptive Moving Average (HKAMA)
EDMA Scalping Strategy (Exponentially Deviating Moving Average)This strategy uses crossover of Exponentially Deviating Moving Average (MZ EDMA ) along with Exponential Moving Average for trades entry/exits. Exponentially Deviating Moving Average (MZ EDMA ) is derived from Exponential Moving Average to predict better exit in top reversal case.
EDMA Philosophy
EDMA is calculated in following steps:
In first step, Exponentially expanding moving line is calculated with same code as of EMA but with different smoothness (1 instead of 2).
In 2nd step, Exponentially contracting moving line is calculated using 1st calculated line as source input and also using same code as of EMA but with different smoothness (1 instead of 2).
In 3rd step, Hull Moving Average with 2/3 of EDMA length is calculated using final line as source input. This final HMA will be equal to Exponentially Deviating Moving Average.
EDMA Defaults
Currently default EDMA and EMA length is set to 20 period which I've found better for higher timeframes but this can be adjusted according to user's timeframe. I would soon add Multi Timeframe option in script too. Chikou filter's period is set to 25.
Additional Features
EMA Band: EMA band is shown on chart to better visualize EMA cross with EDMA .
Dynamic Coloring: Chikou Filter library is used for derivation of dynamic coloring of EDMA and its band.
Trade Confirmation with Chikou Filter: Trend filteration from Chikou filter library is used as an option to enhance trades signals accuracy.
Strategy Default Test Settings
For backtesting purpose, following settings are used:
Initial capital=10000 USD
Default quantity value = 5 % of total capital
Commission value = 0.1 %
Pyramiding isn't included.
Backtesting data never assures that the same results would occur in future and also above settings use very less of total portfolio for trades, which in a way results less maximum drawdown along with less total profit on initial capital too. For example, increasing default quantity value will definity increase maximum drawdown value. The other way is also to use fix contracts in backtesting but it all depends on users general practice. Best option is to explore backtesting results with manually modified settings on different charts, before trusting them for other uses in future.
Usage and In-Detail Backtesting
This strategy has built-in option to enable trade confirmations with Chikou filter which will reduce the total number of trades increasing profit factor.
Symmetrically Weighted Moving Average (SWMA) on input source, may risk repainting in real-time data. Better option is to run a trade on bar close or simply left this optin unchecked.
I've set Chikou filter unchecked to increase number of trades (greater than 100) on higher timeframe (12H) and this can be changed according to your precision requirement and timeframe.
Timeframes lower than 4H usually have more noise. So its better to use higher EDMA and EMA length on lower timeframes which will decrease total number of offsetting trades increasing average total number of bars within a single trade.
Original "Exponentially Deviating Moving Average (MZ EDMA )" Indicator can be found here.
Trend Gradient Moving Average This moving average uses a gradient function which calculates the number of advances/declines of the moving average to change the intensity of the colors, meaning a longer trend in either direction will show a stronger color. You can choose 3 colors to build the gradient: a bullish, bearish & neutral/transition color. The number of steps chosen will change the speed of color change, with a lower number of steps meaning a faster transition and viceversa.
Furthermore, you can choose between many different types of moving averages:
-SMA (Simple Moving Average)
-EMA (Exponential Moving Average)
-RMA (Rolling Moving Average)
-WMA (Weighted Moving Average)
-HMA (Hull Moving Average)
-VWMA (Volume Weighted Moving Average)
-TMA (Triangular Moving Average)
Enjoy!
[EG] MA ATR ChannelsGreetings - the aim of this indicator was to code a single indicator with a selectable moving average, so I could examine price relationships to MA's and Average True Range (ATR) bollinger type bands. You can obviously approach this tool in so many different ways so I am going to share first an overview of moving averages and a short overview of how I use this this indicator.
Simple ( SMA ) – A simple average of the past N (length) prices. Just add the price data for each N (bar) and divide the total by N.
Exponential ( EMA ) – An exponential moving average with a greater weight for recent prices. The weighting is exponential. An N-period EMA takes more than N data points into account and gradually dilutes past data’s effect.
Double Exponential ( DEMA ) - Same as an EMA , the Double exponential moving average , or DEMA , is a measure of a security's trending average price that gives the even more weight to recent price data. Aimed to help reduce lag.
Triple Exponential ( TEMA ) - Same as an EMA , the Triple exponential moving average , or TEMA , is a measure of a security's trending average price that gives the even more weight to recent price data than EMA or DEMA . Aimed to help reduce lag.
Weighted ( WMA ) – An average of the past N prices with a linear weighting, again giving greater weight to more recent prices.
Hull ( HMA ) - The Hull Moving Average (developed by Alan Hull) has the purpose of reducing lag, increasing responsiveness while at the same time eliminating noise. It emphasises recent prices over older ones, resulting in a fast-acting yet smooth moving average that can be used to identify the prevailing market trend.
Wilder's (RMA) - Wilder's smoothing is a type of exponential moving average . It takes one parameter, the period n, and price. Larger values for n will have a greater smoothing effect on the input data but will also create more lag. It is equivalent to a 2n-1 Exponential Moving Average . For example, a 10 period Wilder's smoothing is the same as a 19 period exponential moving average .
Symmetrically Weighted ( SWMA ) - Weight distribution starts from median of given period and it's reduced linearly to the sides so the ending and starting point of period have the least weight. It's smooth and fast but reacts late to trend changes on higher lengths (lookback).
Arnaud Legoux ( ALMA ) - Arnaud Legoux Moving Average removes small price fluctuations and enhances trend via applying a moving average twice, once from left to right, and once from right to left and combines both. At the end of this process the phase shift (price lag) commonly associated with moving averages is significantly reduced.
Volume-Weighted ( VWMA ) - A Volume-Weighted Moving Average gives a different weight to each closing price and this weight depends on the volume of that period. For example, the closing price of a day with high volume will have a greater weight on the moving average value.
Volume Weighted Average Price ( VWAP ) - Though not necessarily a MA - Volume-weighted average price ( VWAP ) is a ratio of the cumulative share price to the cumulative volume traded over a given time period and so I thought would be useful as an ATR tool. The VWAP is calculated using the opening price for each day and adjusting in real time right up until the close of the session. Thus, the calculation uses intraday data only.
So what is Average True Range ?
Average True Range is a measure of volatility . It's an area that represents roughly how much you can expect a security to change in price over a time period. Average true range is usually calculated by applying Wilders Smoothing to True Range. If you want regular ATR - use RMA as the input for the ATR. The ATR is then divided into periods based on derivatives of Phi (3.14) and Fibs (0.618, 1.618 etc.) You will notice price bounces off the lines. Look for patterns.
The indicator - consisting of 3 parts:
Price/Fast MA - this is an MA anywhere between 3-20 periods that is reflective of very recent price action. It is red when price is below - and green when above. Recommendations : SMA , EMA , WMA , HMA
Trend/Medium MA - this is a slower MA that you could set anywhere between 30 - 100 periods that is reflective of overall bull/bear market trend depending on both it's direction and whether the Price MA / price is lower or higher. Recommendations: EMA , WMA , VWMA , RMA, ALMA
Average True Range - this is a way to measure and visualise range the price may be capable of in - if it is towards or below the 2.1 multiplier - a bull reversal is more likely and vice versea. The multi's are set to factors of Pi and Fibonacci ratio's. Green channel means bullish, red channel means bearish. Gold means sign of a likely reversal. If the PMA enters the channel - it is likely the reversal is cancelled for a short period more.
Recommendations : RMA, EMA , VWMA , ALMA , SWMA , VWAP
How I use it :
First of all - Consider longs when channel is green - or going to bounce on a support line - and consider shorts based on the opposite. This is not a buy/sell indicator - this is a MAP to PRICE to give reference and meaning to price movements across multiple time frames - very useful when using with a volume indicator and an RSI. I personally use it on the 3m chart but change the TFM to 5 for 15m data.
If you wish to see any other more exotic or interesting MA's added please feel free to request them in the comments ! And thanks for checking out my first indicator
