TradeQUO Herrick Payoff RSIHerrick Payoff Index RSI (HPI-RSI) with Signal Line
An advanced oscillator that measures market strength not just by price, but by "smart money flow."
This indicator is not a typical RSI. Instead of applying the Relative Strength Index to price alone, it calculates it on the cumulative Herrick Payoff Index (HPI) . This creates a unique oscillator that reflects the underlying sentiment and capital flow in the market.
What is the Herrick Payoff Index (HPI)?
The HPI is a classic sentiment indicator that combines three crucial elements to determine if money is flowing into or out of an asset:
Price Change: The direction and momentum of the market.
Trading Volume: The conviction behind the price movement.
Open Interest (OI): The total number of open contracts (mainly in futures), which indicates if new capital is entering the market.
By combining these factors, the HPI provides a more comprehensive picture of market strength than indicators based solely on price.
How This Indicator Works
The script follows a logical, multi-step process:
It calculates the raw Herrick Payoff Index for each bar.
It creates a cumulative sum of this index to generate a continuous money flow value.
This cumulative value is smoothed with a short-period EMA to reduce noise.
The RSI is then applied to this smoothed HPI value.
An additional, configurable signal line (moving average) is added to facilitate trading signals.
Interpretation and Application
You can use this indicator much like a standard RSI, but with the added context of money flow:
Overbought/Oversold: Values above 70 suggest an overbought condition, while values below 30 signal an oversold condition.
Signal Line Crossovers: A cross of the HPI-RSI line above the signal line can be seen as a bullish signal. A cross below can be seen as a bearish signal.
Divergences: Look for divergences between the indicator and the price. A bullish divergence (price makes a lower low, indicator makes a higher low) can indicate an upcoming move to the upside. A bearish divergence (price makes a higher high, indicator makes a lower high) can signal a potential move to the downside.
Settings
The indicator has been deliberately kept simple:
HPI Smoothing Length: Smoothing length (1-5) for the cumulative HPI.
RSI Length: The lookback period for the RSI calculation.
Signal Line Settings: Here you can enable/disable the signal line and customize its type and length.
Display Settings: Adjust the colors of the RSI and signal lines to your preference.
This indicator is a tool for analysis and should always be used in combination with other methods and a solid risk management strategy. Happy trading!
"oscillator" için komut dosyalarını ara
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Volume Flow ConfluenceVolume Flow Confluence (CMF-KVO Integration)
Core Function:
The Volume Flow Confluence Indicator combines two volume-analysis methods: Chaikin Money Flow (CMF) and the Klinger Volume Oscillator (KVO). It displays a histogram only when both indicators align in their respective signals.
Signal States:
• Green Bars: CMF is positive (> 0) and KVO is above its signal line
• Red Bars: CMF is negative (< 0) and KVO is below its signal line
• No Bars: When indicators disagree
Technical Components:
Chaikin Money Flow (CMF):
Measures the relationship between volume and price location within the trading range:
• Calculates money flow volume using close position relative to high/low range
• Aggregates and normalizes over specified period
• Default period: 20
Klinger Volume Oscillator (KVO):
Evaluates volume in relation to price movement:
• Tracks trend changes using HLC3
• Applies volume force calculation
• Uses two EMAs (34/55) with a signal line (13)
Practical Applications:
1. Signal Identification
- New colored bars after blank periods show new agreement between indicators
- Color intensity differentiates new signals from continuations
- Blank spaces indicate lack of agreement
2. Trend Analysis
- Consecutive colored bars show continued indicator agreement
- Transitions between colors or to blank spaces show changing conditions
- Can be used alongside other technical analysis tools
3. Risk Considerations
- Signals are not predictive of future price movement
- Should be used as one of multiple analysis tools
- Effectiveness may vary across different markets and timeframes
Technical Specifications:
Core Algorithm
CMF = Σ(((C - L) - (H - C))/(H - L) × V)n / Σ(V)n
KVO = EMA(VF, 34) - EMA(VF, 55)
Where VF = V × |2(dm/cm) - 1| × sign(Δhlc3)
Signal Line = EMA(KVO, 13)
Signal Logic
Long: CMF > 0 AND KVO > Signal
Short: CMF < 0 AND KVO < Signal
Neutral: All other conditions
Parameters
CMF Length = 20
KVO Fast = 34
KVO Slow = 55
KVO Signal = 13
Volume = Regular/Actual Volume
Data Requirements
Price Data: OHLC
Volume Data: Required
Minimum History: 55 bars
Recommended Timeframe: ≥ 1H
Credits:
• Marc Chaikin - Original CMF development
• Stephen Klinger - Original KVO development
• Alex Orekhov (everget) - CMF script implementation
• nj_guy72 - KVO script implementation
Chebyshev Filter Divergences [ChartPrime]The Chebyshev Filter Divergences Oscillator
The Chebyshev Filter indicator is a powerful tool designed to identify potential divergences between price and a filtered version of price based on the Chebyshev filter algorithm. It helps to spot mean reversion points by highlighting areas where price and the filtered price exhibit conflicting signals.
Chebyshev Filter Background:
The Chebyshev filter, named after the Russian mathematician Pafnuty Chebyshev , was invented in the mid-19th century. It's a type of filter used in signal processing and digital signal processing for smoothing or removing unwanted frequency components from a signal.
It provides a sharp cutoff between the passband and stopband of a filter while minimizing ripple in the passband or stopband.
Chebyshev filters are widely used in various applications, including audio and image processing, telecommunications, and financial analysis, due to their efficiency and effectiveness in filtering out noise and extracting relevant information from signals.
◆ Indicator Calculation:
The indicator first applies a Chebyshev filter to the price data, producing a filtered price series. It then normalizes this filtered price series to a range, where it can be used as oscillator with divergences.
◆ Visualization:
The filtered price series is plotted on the chart, highlighting areas where it deviates from its smoothed average.
Bullish and bearish divergences are marked on the chart with specific lines and colors, indicating potential shifts in market sentiment.
Signs of change in direction are also marked on the chart, providing additional insights into possible mean reversals of price.
◆ User Inputs:
Ripple (dB): Specifies the desired ripple factor in decibels for the Chebyshev filter.
Normalization Length: Sets the length of the normalization period used in the Chebyshev filter.
Pivots to Right and Left: Determines the number of pivot points to the right and left of the current point to consider when detecting divergences.
Max and Min of Lookback Range: Specifies the maximum and minimum lookback range for identifying divergences.
Show Divergences: Enables or disables the display of bullish and bearish divergences.
Visual Settings: Allows customization of colors for visual clarity.
In conclusion, the Chebyshev Filter Divergences indicator, with its ability to identify potential mean reversion points through divergences between price and a filtered version of price, offers traders a valuable tool for decision-making in the financial markets. By highlighting areas of divergence, traders can potentially capitalize on market inefficiencies and make more informed trading decisions.
Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
Color Stochastic IndicatorThis Pine Script™ indicator, "Color Stochastic Indicator," is designed to visualize the stochastic oscillator with color-coded trends and shaded background levels, providing a clearer understanding of market trends and potential trading signals.
Key Features:
Customizable Parameters:
K Period: The period for the %K line in the stochastic calculation (default: 50).
D Period: The period for the %D line, which is the moving average of %K (default: 13).
Slowing: The slowing factor applied to the stochastic calculation (default: 2).
Smoothing: A factor for additional smoothing of the stochastic values (default: 1.0).
Use Crossover: Option to determine trend based on the crossover of %K and %D lines.
Display Levels: Option to show significant stochastic levels on the chart (0.2, 0.5, 0.8).
Price Field: Selection of the price field used in calculations.
Stoch Width: Line width for the %K line.
Signal Width: Line width for the %D line.
Background Colors:
Upper Level Background: Shaded area between 0.5 and 0.8 with a customizable color.
Lower Level Background: Shaded area between 0.2 and 0.5 with a customizable color.
Color-Coded Trends:
Wait (Gray): Neutral state when no clear trend is detected.
Uptrend (Green): Indicates a potential buying signal.
Downtrend (Red): Indicates a potential selling signal.
Signal Line (Blue): Represents the %D line for clearer signal identification.
Alerts:
Customizable alerts trigger when the trend changes, providing timely notifications for potential trade opportunities.
How It Works:
Stochastic Calculation:
The %K line is calculated based on the selected K Period.
The %D line is a simple moving average (SMA) of the %K line over the D Period.
Additional smoothing is applied to both %K and %D lines using the specified Smoothing factor.
Fisher Transform:
The script applies a Fisher transform to the smoothed %K values, enhancing the clarity of trend signals.
Trend Determination:
If Use Crossover is enabled, the trend is determined based on the crossover of smoothed %K and %D lines.
If Use Crossover is disabled, the trend is determined based on whether the smoothed %K value is above or below 0.5.
Background Shading:
Fixed background colors are applied using hline and fill functions, highlighting the specified levels on the chart (0.2, 0.5, 0.8).
Plotting:
The smoothed %K line is plotted with color coding based on its value relative to the %D line and threshold levels.
The %D line is plotted for reference.
How to Use:
Adding the Indicator:
Copy and paste the provided Pine Script™ code into a new indicator script in TradingView.
Save and add the indicator to your desired chart.
Configuring Parameters:
Adjust the input parameters (K Period, D Period, Slowing, etc.) according to your trading strategy and preferences.
Enable or disable the Use Crossover option based on whether you prefer trend determination by crossover or threshold.
Interpreting Signals:
Observe the color-coded %K line to identify potential buy (green) and sell (red) signals.
Use the shaded background areas to quickly assess overbought (0.5 to 0.8) and oversold (0.2 to 0.5) conditions.
Monitor alerts for trend changes to take timely trading actions.
Alerts Setup:
Set up custom alerts based on the provided alert conditions to receive notifications when the trend changes.
Originality:
This script combines the stochastic oscillator with color-coding and background shading for enhanced visualization.
It introduces a unique Fisher transform application to the smoothed %K values.
The crossover and threshold-based trend determination options provide flexibility for different trading strategies.
Customizable alert messages help traders stay informed about trend changes in real time.
By incorporating these features, the "Color Stochastic Indicator" offers a comprehensive tool for traders seeking to leverage stochastic analysis with improved clarity and actionable insights.
Price PressureDescription:
The Price Pressure Indicator, developed by OmegaTools, is a robust and versatile tool designed to assist traders in analyzing market dynamics and identifying potential trend shifts. This open-source script, offers a unique approach to understanding price pressure over specified periods, enhancing the user's ability to make informed trading decisions.
Key Features:
1. Dynamic Length Configuration: The indicator allows users to customize the length parameter, ranging from 9 to 100, providing flexibility in adapting to different market conditions.
2. Extensions Control: Traders can fine-tune the extension levels (ob) between 50 and 90, allowing for precise adjustments based on their risk tolerance and trading preferences.
3. Normalization and Oscillation: The script employs a normalization function to standardize price data, offering a clearer representation of market pressure. The resulting oscillator visualizes the normalized pressure, highlighting potential market trends.
4. Pressure Calculation: The indicator calculates price pressure by considering the difference between the previous high and the current close, as well as the difference between the current close and the previous low. This innovative approach enhances the accuracy of pressure analysis.
5. Smoothing Option: While the script currently uses a simple moving average for smoothing, traders have the option to explore other smoothing methods by uncommenting the "smt" input line.
6. Visual Clarity: The indicator provides a visually intuitive representation of pressure and signal lines, aiding traders in quickly interpreting market conditions. The color-coded display enhances user experience, with the ability to discern bullish and bearish pressures.
7. Premium and Discount Zones: The script identifies premium and discount areas, assisting traders in spotting potential buying or selling opportunities. The premium and discount lines can be adjusted based on individual risk tolerance and strategy.
How to Use:
1. Adjust the length and extension parameters based on your trading preferences.
2. Interpret the oscillator and signal lines for insights into market pressure.
3. Utilize premium and discount zones to identify potential entry or exit points.
4. Experiment with different smoothing options for a customized analysis.
Concepts and Methodology:
The Price Pressure Indicator utilizes a normalization function and oscillation to quantify market pressure. By calculating the difference between highs and lows, the script provides a nuanced understanding of current market conditions. The smoothing option further refines the analysis, offering traders a comprehensive tool for trend identification.
Explore, experiment, and leverage the power of the Price Pressure Indicator to enhance your trading strategy on TradingView.
RSI MFI MultiTimeframe Oversold/OverboughtHello Traders,
This indicator is designed to easily visualize the overbought/oversold states of RSI and MFI across multiple timeframes.
The indicator is very straightforward.
The deeper the red, the closer it is to 0, and the deeper the green, the closer it is to 100. The intermediate values are rendered in a transparent gray to focus on the key regions.
However, I understand that traders may have an interest in knowing the most recent state of the oscillator, whether it was overbought or oversold.
For this reason, I have included the 'Gradient Color' option in the color settings.
By turning off this option, you can easily see at a glance which region the oscillator was in most recently.
(Gradient Color Option Off)
In addition, I know that many traders are interested in the actual RSI/MFI values across multiple timeframes.
Thus, I have displayed the RSI/MFI values for each timeframe on the far right.
Furthermore, although the name of this indicator is RSI MFI MultiTimeframe Oversold/Overbought, I have also included the Stochastic RSI as an option, as I find it personally useful.
Feel free to use it if you find it helpful.
Trig-Log Scaled Momentum OscillatorTaylor Series Approximations for Trigonometry:
1. The indicator starts by calculating sine and cosine values of the close price using Taylor Series approximations. These approximations use polynomial terms to estimate the values of these trigonometric functions.
Mathematical Component Formation:
2. The calculated sine and cosine values are then multiplied together. This gives us the primary mathematical component, termed as the 'trigComponent'.
Smoothing Process:
3. To ensure that our indicator is less susceptible to market noise and more reactive to genuine price movements, this 'trigComponent' undergoes a smoothing process using a simple moving average (SMA). The length of this SMA is defined by the user.
Logarithmic Transformation:
4. With our smoothed value, we apply a natural logarithm approximation. Again, this approximation is based on the Taylor expansion. This step ensures that all resultant values are positive and offers a different scale to interpret the smoothed component.
Dynamic Scaling:
5. To make our indicator more readable and comparable over different periods, the logarithmically transformed values are scaled between a range. This range is determined by the highest and lowest values of the transformed component over the user-defined 'lookback' period.
ROC (Rate of Change) Direction:
6. The direction of change in our scaled value is determined. This offers a quick insight into whether our mathematical component is increasing or decreasing compared to the previous value.
Visualization:
7. Finally, the indicator plots the dynamically scaled and smoothed mathematical component on the chart. The color of the plotted line depends on its direction (increasing or decreasing) and its boundary values.
TTM Waves ABC ATR AO MOM SQZ//All code picked from many indicators, if you recognize your code, pls comment so people can see your awesome work! I only edited and added them all together so people don't use all their indicator slots. Hope this indicator helps as many people as it can. LFG!!!
AO (Awesome Oscillator) Useful to find potential reversals in trend.
MOM (Momentum) An oscillator that measures momentum.
ATR (Average True Range) Measures the upside and downside from the average price movement occuring. 1 ATR is the general measurement. Many traders use 2ATR to set a stop and 4ATR to set take profit from their entry based on current reading from the ATR.
SQZ ( TTM Squeeze) Measures when bollinger bands have left the interior of the Keltner Channel in an attempt to predict volatility thats about to happen to either side. Green = Move is probably about to happen.
TTM Waves ( Waves A, B, and C) Measure the previous candles to determine chop, positive or negative trends. C measures the previous 30 candles or so, B the last 15 or so, and A measures the last 8 or so. You can use all three or just one. You can sneak in a move if the 2 fastest ones have moved into your preferred area. (Positive or Negative) If the wave is not fully positve or negative then that is probably chop.
-Penguincryptic
RSI MTF [Market Yogi]The Multi-Time Frame RSI with Money Flow Index and Average is a powerful trading indicator designed to help traders identify overbought and oversold conditions across multiple time frames. It combines the Relative Strength Index (RSI) with the Money Flow Index (MFI) and provides an average value for better accuracy.
The Relative Strength Index (RSI) is a popular momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is used to identify overbought and oversold conditions in an asset. By incorporating the RSI across multiple time frames, this indicator offers a broader perspective on market sentiment.
In addition to the RSI, this indicator also includes the Money Flow Index (MFI). The MFI is a volume-based oscillator that measures the inflow and outflow of money into an asset. It takes into account both price and volume, providing insights into the strength and direction of buying and selling pressure.
By combining the RSI and MFI across multiple time frames, traders gain a comprehensive understanding of market dynamics. The indicator allows for comparing the RSI and MFI values across different time frames, enabling traders to identify divergences and potential trend reversals.
Furthermore, this indicator provides an average value of the multi-time frame RSI, offering a consolidated signal that helps filter out noise and enhance the accuracy of trading decisions.
Key Features:
1. Multi-Time Frame RSI: Combines the RSI across different time frames to provide a comprehensive view of market sentiment.
2. Money Flow Index (MFI): Incorporates the MFI to gauge buying and selling pressure based on both price and volume.
3. Average Calculation: Computes the average value of the multi-time frame RSI to generate a consolidated trading signal.
4. Divergence Detection: Enables traders to spot divergences between the RSI and MFI values, indicating potential trend reversals.
5. Overbought and Oversold Levels: Highlights overbought and oversold levels on the RSI, aiding in timing entry and exit points.
The Multi-Time Frame RSI with Money Flow Index and Average is a versatile tool that can be applied to various trading strategies, including trend following, swing trading, and mean reversion. Traders can adjust the time frame settings to suit their preferences and trading style.
Note: It's important to use this indicator in conjunction with other technical analysis tools and indicators to validate signals and make informed trading decisions.
Radar RiderThe Radar Rider indicator is a powerful tool that combines multiple technical indicators into a single spider plot, providing traders with a comprehensive view of market conditions. This article will delve into the workings of each built-in indicator and their arrangement within the spider plot. To better understand the structure of the script, let's first examine some of the primary functions and how they are utilized in the script.
Normalize Function: normalize(close, len)
The normalize function takes the close price and a length as arguments and normalizes the price data by scaling it between 0 and 1, making it easier to compare different indicators.
Exponential Moving Average (EMA) Filter: bes(source, alpha)
The EMA filter is used to smooth out data using an exponential moving average, with the given alpha value defining the level of smoothing. This helps reduce noise and enhance the trend-following characteristics of the indicators.
Maximum and Minimum Functions: max(src) and min(src)
These functions find the maximum and minimum values of the input data over a certain period, respectively. These values are used in the normalization process and can help identify extreme conditions in the market.
Min-Max Function: min_max(src)
The min-max function scales the input data between 0 and 100 by dividing the difference between the data point and the minimum value by the range between the maximum and minimum values. This standardizes the data, making it easier to compare across different indicators.
Slope Function: slope(source, length, n_len, pre_smoothing = 0.15, post_smoothing = 0.7)
The slope function calculates the slope of a given data source over a specified length, and then normalizes it using the provided normalization length. Pre-smoothing and post-smoothing values can be adjusted to control the level of smoothing applied to the data before and after calculating the slope.
Percent Function: percent(x, y)
The percent function calculates the percentage difference between two values, x and y. This is useful for comparing the relative change in different indicators.
In the given code, there are multiple indicators included. Here, we will discuss each of them in detail.
EMA Diff:
The Exponential Moving Average (EMA) Diff is the difference between two EMA values of different lengths. The EMA is a type of moving average that gives more weight to recent data points. The EMA Diff helps traders identify trends and potential trend reversals. In the code, the EMA Diff is calculated using the ema_diff() function, which takes length, close, filter, and len_norm as parameters.
Percent Rank EMA Diff:
The Percent Rank EMA Diff is the percentage rank of the EMA Diff within a given range. It helps traders identify overbought or oversold conditions in the market. In the code, the Percent Rank EMA Diff is calculated using the percent_rank_ema_diff() function, which takes length, close, filter, and len_norm as parameters.
EMA Diff Longer:
The EMA Diff Longer is the difference between two EMA values of different lengths, similar to EMA Diff but with a longer period. In the code, the EMA Diff Longer is calculated using the ema_diff_longer() function, which takes length, close, filter, and len_norm as parameters.
RSI Filter:
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. The RSI Filter is the RSI value passed through a filter to smooth out the data. In the code, the RSI Filter is calculated using the rsi_filter() function, which takes length, close, and filter as parameters.
RSI Diff Normalized:
The RSI Diff Normalized is the normalized value of the derivative of the RSI. It helps traders identify potential trend reversals in the market. In the code, the RSI Diff Normalized is calculated using the rsi_diff_normalized() function, which takes length, close, filter, len_mad, and len_norm as parameters.
Z Score:
The Z Score is a statistical measurement that describes a value's relationship to the mean of a group of values. In the context of the code, the Z Score is calculated for the closing price of a security. The z_score() function takes length, close, filter, and len_norm as parameters.
EMA Normalized:
The EMA Normalized is the normalized value of the EMA, which helps traders identify trends and potential trend reversals in the market. In the code, the EMA Normalized is calculated using the ema_normalized() function, which takes length, close, filter, and len_norm as parameters.
WMA Volume Normalized:
The Weighted Moving Average (WMA) Volume Normalized is the normalized value of the WMA of the volume. It helps traders identify volume trends and potential trend reversals in the market. In the code, the WMA Volume Normalized is calculated using the wma_volume_normalized() function, which takes length, volume, filter, and len_norm as parameters.
EMA Close Diff Normalized:
The EMA Close Diff Normalized is the normalized value of the derivative of the EMA of the closing price. It helps traders identify potential trend reversals in the market. In the code, the EMA Close Diff Normalized is calculated using the ema_close_diff_normalized() function, which takes length, close, filter, len_mad, and len_norm as parameters.
Momentum Normalized:
The Momentum Normalized is the normalized value of the momentum, which measures the rate of change of a security's price. It helps traders identify trends and potential trend reversals in the market. In the code, the Momentum Normalized is calculated using the momentum_normalized() function, which takes length, close, filter, and len_norm as parameters.
Slope Normalized:
The Slope Normalized is the normalized value of the slope, which measures the rate of change of a security's price over a specified period. It helps traders identify trends and potential trend reversals in the market. In the code, the Slope Normalized is calculated using the slope_normalized() function, which takes length, close, filter, and len_norm as parameters.
Trend Intensity:
Trend Intensity is a measure of the strength of a security's price trend. It is based on the difference between the average of price increases and the average of price decreases over a given period. The trend_intensity() function in the code calculates the Trend Intensity by taking length, close, filter, and len_norm as parameters.
Volatility Ratio:
The Volatility Ratio is a measure of the volatility of a security's price, calculated as the ratio of the True Range (TR) to the Exponential Moving Average (EMA) of the TR. The volatility_ratio() function in the code calculates the Volatility Ratio by taking length, high, low, close, and filter as parameters.
Commodity Channel Index (CCI):
The Commodity Channel Index (CCI) is a momentum-based oscillator used to help determine when an investment vehicle is reaching a condition of being overbought or oversold. The CCI is calculated as the difference between the mean price of a security and its moving average, divided by the mean absolute deviation (MAD) of the mean price. In the code, the CCI is calculated using the cci() function, which takes length, high, low, close, and filter as parameters.
These indicators are combined in the code to create a comprehensive trading strategy that considers multiple factors such as trend strength, momentum, volatility, and overbought/oversold conditions. The combined analysis provided by these indicators can help traders make informed decisions and improve their chances of success in the market.
The Radar Rider indicator is a powerful tool that combines multiple technical indicators into a single, easy-to-read visualization. By understanding the inner workings of each built-in indicator and their arrangement within the spider plot, traders can better interpret market conditions and make informed trading decisions.
(mab) Money Flow - MMFThis indicator implements the (mab) Money Flow (MMF). The MMF is calculated using a formula inspired by RSI. In contrast to RSI, MMF uses the average of open, high, low and close as price source. This price is then multiplied with the volume as input for the RSI like formula to calculate the value.
Features:
- Volume weighted price momentum oscillator
- Uses average of open, close, high and low as price component to make the signal less choppy while still as fast
- EMA on MMF
- Highlighting when EMA is in oversold or overbought area
- Alarms
Note that the MMF formula is different than the formula used for other money flow indices like MFI or CMF.
Why do we need another money flow indicator if there are already many established ones? Well I used and tested many money flow indicators including MFI and CMF among others. However, none of them showed the results I was looking for. MFI for example uses a simpler formula for the calculation, which results in a different reading that isn't showing divergences as clearly as I would like. CMF on the other hand has no defined maximum or minimum (similar to MACD) so that it's difficult to determine overbought and oversold values. The MMF is an oscillator with a minimum value of 0 and a maximum value of 100 like RSI. The usage of the average of open, close, high and low as price element makes it less choppy compared to RSI while it still reacts as fast to movements.
Oscillating Length Moving Averages***CREDIT TO TradingView's TA Library*** (), Attempted to use "import TradingView/ta/4" to import the library, but for whatever reason
some of the functions failed to work, while others had no issue, so I opted to just copy paste what I wanted to use.
This moving average uses an oscillator to influence the length used during calculation. Extremely customizable/tunable with ability to change Max and Min length values, length multiplier, length multiple,4 different settings ,( Decline , <>Peak, >Decline , <>Peak, >
Stoch/RSI with EMA50 Cross & HHLLA hybrid but simple indicator that plots 4 strategies in one pane .
1) RSI Indicator
2) Stoch RSI
3) EMA50 Cross (To determine direction in current timeframe)
4) Higher Highs & Lower Lows to analyze the trend and break of trend
The relative strength index (RSI) is a momentum indicator used in technical analysis. It is displayed as an oscillator (a line graph) on a scale of zero to 100. When the RSI indicator crosses 30 on the RSI chart, it is a bullish sign and when it crosses 70, it is a bearish sign.
The Stochastic RSI (StochRSI) is also a momentum indicator used in technical analysis. It is displayed as an oscillator (a line graph) on a scale of zero to 100. When the StochRSI indicator crosses 20 on the RSI chart, it is a bullish sign and when it crosses 80, it is a bearish sign.
The EMA50Cross denotes two cases in the script:
a) A crossover of CMP on the EMA50 is highlighted by a green bar signals a possible bullish trend
b) A crossunder of CMP on the EMA50 is highlighted by a red bar signals a possible bearish trend
The HHLL is denoted by mneumonics HH, HL,LH, LL. A combination of HHs and HLs denotes a uptrend while the combination of LLs and LHs denoted a downtrend
The current script should be used in confluence of other trading strategies and not in isolation.
Scenario 1:
If a EMA50Cross over bar (GREEN) is highlighted with the StochRSI below 20 and the given script is plotting HHs and HLs, we are most likely in a bullish trend for the given timeframe and a long can be initiated in confluence with other trading strategies used by the user. The RSI signal may now be utilized to determine a good range of entry/exit.
Scenario 2:
If a EMA50Cross under bar (RED) is highlighted with the StochRSI above 80 and the given script is plotting LLs and LHs, we are most likely in a bearish trend for the given timeframe and a short can be initiated in confluence with other trading strategies used by the user. The RSI signal may now be utilized to determine a good range of entry/exit.
Disclaimer:
The current script should be used in confluence with other trading strategies and not in isolation. The scripts works best on 4H and 1D Timeframes and should be used with caution on lower timeframes.
This indicator is not intended to give exact entry or exit points for a trade but to provide a general idea of the trend & determine a good range for entering or exiting the trade. Please DYOR
Credit & References:
This script uses the default technical analysis reference library provided by PineScript (denoted as ta)
Smoothed Heikin Ashi Trend on Chart - TraderHalai BACKTESTSmoothed Heikin Ashi Trend on chart - Backtest
This is a backtest of the Smoothed Heikin Ashi Trend indicator, which computes the reverse candle close price required to flip a Heikin Ashi trend from red to green and vice versa. The original indicator can be found in the scripts section of my profile.
This particular back test uses this indicator with a Trend following paradigm with a percentage-based stop loss.
Note, that backtesting performance is not always indicative of future performance, but it does provide some basis for further development and walk-forward / live testing.
Testing was performed on Bitcoin , as this is a primary target market for me to use this kind of strategy.
Sample Backtesting results as of 10th June 2022:
Backtesting parameters:
Position size: 10% of equity
Long stop: 1% below entry
Short stop: 1% above entry
Repainting: Off
Smoothing: SMA
Period: 10
8 Hour:
Number of Trades: 1046
Gross Return: 249.27 %
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 28.01 %
Profit Factor (Expectancy): 2.019
Average Loss: 0.33 %
Average Win: 1.69 %
Average Time for Loss: 1 day
Average Time for Win: 5.33 days
1 Day:
Number of Trades: 429
Gross Return: 458.4 %
CAGR Return: 15.76 %
Max Drawdown: 6.37 %
Profit Factor (Expectancy): 2.804
Average Loss: 0.8 %
Average Win: 7.2 %
Average Time for Loss: 3 days
Average Time for Win: 16 days
5 Day:
Number of Trades: 69
Gross Return: 1614.9 %
CAGR Return: 26.7 %
Max Drawdown: 5.7 %
Profit Factor (Expectancy): 10.451
Average Loss: 3.64 %
Average Win: 81.17 %
Average Time for Loss: 15 days
Average Time for Win: 85 days
Analysis:
The strategy is typical amongst trend following strategies with a less regular win rate, but where profits are more significant than losses. Most of the losses are in sideways, low volatility markets. This strategy performs better on higher timeframes, where it shows a positive expectancy of the strategy.
The average win was positively impacted by Bitcoin’s earlier smaller market cap, as the percentage wins earlier were higher.
Overall the strategy shows potential for further development and may be suitable for walk-forward testing and out of sample analysis to be considered for a demo trading account.
Note in an actual trading setup, you may wish to use this with volatility filters, combined with support resistance zones for a better setup.
As always, this post/indicator/strategy is not financial advice, and please do your due diligence before trading this live.
Original indicator links:
On chart version -
Oscillator version -
Update - 27/06/2022
Unfortunately, It appears that the original script had been taken down due to auto-moderation because of concerns with no slippage / commission. I have since adjusted the backtest, and re-uploaded to include the following to address these concerns, and show that I am genuinely trying to give back to the community and not mislead anyone:
1) Include commission of 0.1% - to match Binance's maker fees prior to moving to a fee-less model.
2) Include slippage of 10 ticks (This is a realistic slippage figure from searching online for most crypto exchanges)
3) Adjust account balance to 10,000 - since most of us are not millionaires.
The rest of the backtesting parameters are comparable to previous results:
Backtesting parameters:
Initial capital: 10000 dollars
Position size: 10% of equity
Long stop: 2% below entry
Short stop: 2% above entry
Repainting: Off
Smoothing: SMA
Period: 10
Slippage: 10 ticks
Commission: 0.1%
This script still remains to shows viability / profitablity on higher term timeframes (with slightly higher drawdown), and I have included the backtest report below to document my findings:
8 Hour:
Number of Trades: 1082
Gross Return: 233.02%
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 25.6%
Profit Factor (Expectancy): 1.627
Average Loss: 0.46 %
Average Win: 2.18 %
Average Time for Loss: 1.33 day
Average Time for Win: 7.33 days
Once again, please do your own research and due dillegence before trading this live. This post is for education and information purposes only, and should not be taken as financial advice.
