Momentum Concepts [AlgoAlpha]🚀 Introducing the Momentum Concepts™ , a robust multi-layered momentum analysis tool developed by AlgoAlpha . This All-in-One indicator offers a comprehensive approach to understanding market momentum, empowering traders with hyper customizable features to tailor their analysis to their specific trading strategies.
Designed with efficiency and compactness in mind, the script shows momentum regimes on three time horizons: The short-term ( Fast Oscillator ), medium-term ( Scalper's Momentum ) and long-term ( Momentum Impulse Oscillator and Hidden Liquidity Flow ). Additionally, the script also includes reversal signals for traders who prefer to trade contrarian/mean-reversion strategies. By utilizing a blend of advanced algorithms and customizable parameters, Momentum Concepts™ provides traders with a vast array of trading strategies ranging from high frequency scalping to timing better entries on long-term swing and investing positions.
Let's delve into the key features and functionalities of this versatile indicator:
🎯Key Features (summary):
Customizable Fast Oscillator: Tailor the fast oscillator to your preferences with adjustable settings for type, source, trend identification(signal processing) method, length, and more.
Divergence Detection: Identify potential trend reversals with ease using built-in divergence detection for both bullish and bearish signals.
Momentum Impulse Oscillator: Gain deeper insights into trending/ranging markets and underlying market bias with a dedicated oscillator, featuring adjustable trend impulse thresholds.
Scalper's Momentum: Utilize a specialized momentum indicator designed for scalping strategies, featuring agility in signal detection with noise reduction and customizable smoothing parameters.
Hidden Liquidity Flow Analysis: Assess hidden liquidity flows within the market, highlighting excess liquidity and potential squeeze situations.
Trend Confluence Indicator: Evaluate the overall momentum direction with dynamically colored zones, aggregating signals from Momentum Concepts™ components for a holistic view.
User-Friendly Interface: The indicator is presented in a clear and intuitive manner, making it accessible for traders of all experience levels.
All-Rounded Alerts: The indicator comes with a comprehensive alerts extension in a separate script, allowing you to stay informed of important market movements even when away from your trading platform.
🎯Key Features (in-depth):
The Fast Oscillator within Momentum Concepts™ comprises four components designed to provide insights into short-term momentum dynamics:
🔱Price Volume Swings :
This confirmation component uses our proprietary Price Volume Algorithm to analyze price action and volume to identify buying and selling pressure, aiding traders in spotting short-term swings for potential trading opportunities.
⚜️Price Volume Waves :
This leading component also uses our proprietary Price Volume Algorithm but differs from the Price Volume Swings by capturing dominant wave patterns instead. This indicator breaks down price and volume data into a wave-like plot which enables leading insights into market momentum due to the relatively predicable nature of sine-like waves. Leading components such as this and the Alpha Wave are best used with other confirmation components within the Momentum Concepts™ .
🌊Alpha Wave :
The Alpha Wave is a leading non-volume alternative to the Price Volume Waves . It reflects market momentum by analyzing price action only instead of using volume data, resulting in a normalized wave-like plot similar to that of the Price Volume Waves , offering a leading perspective on potential market momentum shifts. Leading components such as this and the Price Volume Waves are best used with other confirmation components within the Momentum Concepts™ .
🐲Dragon RSI :
The Dragon RSI is a confirmation component that determines market momentum by analyzing the directional movement of the Relative Strength Index (RSI). By doing so, users are able to visually identify the current short term trend of the market as well as identify overbought and oversold conditions.
Reversal Signals :
All the Fast Oscillator components come with reversal signals that are based on the respective components being either oversold or overbought.
Divergences :
All the Fast Oscillator components come with bullish and bearish divergences. Divergences within the Fast Oscillator components of Momentum Concepts ™ offer crucial signals for trend shifts. 🔱 Price Volume Swings and ⚜️ Price Volume Waves detect weakening buying or selling pressure, signalling potential reversals or continuations. 🌊 Alpha Wave and 🐲 Dragon RSI identify divergences between momentum and price, aiding traders in anticipating market movements. Leveraging these divergences enhances analysis, aiding traders in formulating meaningful analysis.
Customizable Signal Processing Methods :
All the Fast Oscillator components come with customizable signal processing methods to identify trends on the Fast Oscillator , they include (but not limited to) methods such as Heiken Ashi, and a vast selection of Moving Averages.
Diminishing Momentum Warning :
All the Fast Oscillator components come with a diminishing momentum warning that represents a reducing momentum on the Fast Oscillator . This can act as a take profit signal or as a precautionary warning that the price is about to change direction soon even though the Fast Oscillator has not detected it yet.
Dynamically Colored Reversal Zones :
Last but not least, the dynamic coloring of the reversal zones for Fast Oscillator can be customised based on either the reversal probability of the Fast Oscillator or based on the overall trend confluence of all the components within the Momentum Concepts™ indicator.
The Momentum Impulse Oscillator in Momentum Concepts™ offers crucial insights into long-term momentum trends, aiding traders in identifying the underlying momentum regime and differentiating between trending and consolidating markets.
Underlying Momentum Bias
By default, the Momentum Impulse Oscillator is set to show the longer term trend of price action, this can be used to set the directional bias for the markets and prevent users from trading against the trend.
Trending/Ranging Detection
The Momentum Impulse Oscillator comes with the option to enable trending thresholds, when the Momentum Impulse Oscillator is beyond these thresholds, it indicates a trending market, when Momentum Impulse Oscillator is within the thresholds, it indicates a consolidating/ranging market.
The Scalper's Momentum within Momentum Concepts™ furnishes traders with nuanced signals ideal for short to medium-term trading strategies. It efficiently displays both the medium-term momentum and any emerging divergences towards the opposing direction.
Medium-Term Momentum
The Scalper's Momentum is designed to fill the analysis gap between the Fast Oscillator and the Momentum Impulse Oscillator . Showing momentum insights over the medium-term.
Momentum Convergence-Divergence
The Scalper's Momentum is also capable of showing momentum convergences and divergences, which can be used as take-profit and/or confirmation signals to other components within Momentum Concepts™ .
The Hidden Liquidity Flow component of Momentum Concepts™ is designed to uncover underlying liquidity dynamics. This feature enables traders to anticipate potential price movements based on changes in liquidity flow, enhancing their ability to make informed trading decisions.
Underlying Liquidity Dynamics
The Hidden Liquidity Flow shows the underlying liquidity flow of the market, a positive liquidity flow indicates that liquidity is entering the market and increasing the probability of bullish price action, the opposite is true for negative liquidity flows.
Excess Liquidity Flow
The Hidden Liquidity Flow also indicates when there is an abnormal amount of liquidity flowing through the market, this can indicate the potential for volatility and explosive price action.
🎯Usage Examples:
Now that we have gone through the components and features of Momentum Concepts™ in detail, we'll walk you through the usage examples and strategies that you can utilise to navigate the markets.
Scalping
Using the Scalper's Momentum and the Fast Oscillator as an example, users can first use the Scalper's Momentum as a directional bias and the Fast Oscillator as a means of timing a more precise entry. Take profits can be based on either the Diminishing Momentum Warnings or the Fast Oscillator flipping signals or the Scalper's Momentum flipping signals.
Buying the Dip/Shorting the Pump
Using the Momentum Impulse Oscillator and the Fast Oscillator as an example, users will need to first determine the underlying trend with the Momentum Impulse Oscillator , after which they can use the Fast Oscillator for entry signals into the trend. Take profits can be based on either the Diminishing Momentum Warnings or the Fast Oscillator flipping signals
Reversal Trading
Using the Momentum Impulse Oscillator on a timeframe roughly 3-4 times greater than the chart's timeframe and the Fast Oscillator as an example, users will need to first ensure that the Momentum Impulse Oscillator signals a ranging market on a higher timeframe, divergence signals from the Fast Oscillator can then be used as entries. Take profits can be based on either the Diminishing Momentum Warnings or the Fast Oscillator flipping signals or the Fast Oscillator reaching the zero line.
(These are just examples for reference, the Momentum Concepts™ offers significantly more possibilities for customisation and fine tuning of your trading strategy.)
🎯Conclusion:
In conclusion, Momentum Concepts™ stands as a versatile and powerful tool for traders seeking to decode the intricacies of market momentum across multiple time horizons. With its comprehensive suite of customizable features, including the Fast Oscillator , Scalper's Momentum , Momentum Impulse Oscillator , and Hidden Liquidity Flow , traders can gain deep insights into market dynamics and make well-informed trading decisions. Whether executing high-frequency scalping strategies or timing entries for longer-term positions, Momentum Concepts™ equips traders with the tools they need to navigate diverse market conditions with confidence. By harnessing the power of momentum analysis, this indicator empowers traders to stay ahead of the curve and capitalize on emerging opportunities in the ever-evolving financial markets.
在腳本中搜尋"momentum"
ML - Momentum Index (Pivots)Building upon the innovative foundations laid by Zeiierman's Machine Learning Momentum Index (MLMI), this variation introduces a series of refinements and new features aimed at bolstering the model's predictive accuracy and responsiveness. Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), my adaptation seeks to enhance the original by offering a more nuanced approach to momentum-based trading.
Key Features :
Pivot-Based Analysis: Shifting focus from trend crosses to pivot points, this version employs pivot bars to offer a distinct perspective on market momentum, aiding in the identification of critical reversal points.
Extended Parameter Set: By integrating additional parameters for making predictions, the model gains improved adaptability, allowing for finer tuning to match market conditions.
Dataset Size Limitation: To ensure efficiency and mitigate the risk of calculation timeouts, a cap on the dataset size has been implemented, balancing between comprehensive historical analysis and computational agility.
Enhanced Price Source Flexibility: Users can select between closing prices or (suggested) OHLC4 as the basis for calculations, tailoring the indicator to different analysis preferences and strategies.
This adaptation not only inherits the robust framework of the original MLMI but also introduces innovations to enhance its utility in diverse trading scenarios. Whether you're looking to refine your short-term trading tactics or seeking stable indicators for long-term strategies, the ML - Momentum Index (Pivots) offers a versatile tool to navigate the complexities of the market.
For a deeper understanding of the modifications and to leverage the full potential of this indicator, users are encouraged to explore the tooltips and documentation provided within the script.
The Momentum Indicator calculations have been transitioned to the MLMomentumIndex library, simplifying the process of integration. Users can now seamlessly incorporate the momentumIndexPivots function into their scripts to conduct detailed momentum analysis with ease.
Accelerating Dual Momentum ScoreThis is a score metric used by the Accelerating Dual Momentum strategy.
According to the website you referenced when you created, the strategy is as follows:
Strategy Rules
This strategy allocates 100% of of the portfolio to one asset each month.
1. On the last trading day of each month, calculate the “momentum score” for the S&P 500 ( SPY ) and the international small cap equities (SCZ). The momentum score is the average of the 1, 3, and 6-month total return for each asset.
2. If the momentum score of SCZ > SPY and is greater than 0, invest in SCZ.
3. If the momentum score of SPY > SCZ and is greater than 0, invest in SPY .
4. If neither momentum score is greater than 0, calculate the 1-month total return for long-term US Treasuries ( TLT ) and US TIPS (TIP). Invest in whichever has the higher return.
Source: portfoliodb.co
Momentum CrossThis indicator tracks momentum shifts using a 3-period EMA crossing above or below an 8-period EMA. It's simple, and quite effective as a momentum confirmation signal.
Signals:
Cyan circles below bars - Bullish momentum (3 EMA crosses above 8 EMA)
Red circles above bars - Bearish momentum (3 EMA crosses below 8 EMA)
Setups to Use:
V-Shaped Reversals: When price hits major support/resistance and shows rejection, the momentum cross confirms whether the reversal has legs or not. Helps separate real bounces from dead cat bounces.
One-Two Punch Pattern: My favorite high-probability setup: Initial cross shows momentum shifting, counter-move gets rejected quickly, second cross in original direction with follow-through.
Opening Range Breakout Confirmation: Use momentum crosses to confirm pullbacks or retests to key levels after opening range breakouts. The cross timing shows when the retest is holding and momentum is resuming in the breakout direction.
Fibonacci Support/Resistance: Momentum crosses at key Fibonacci levels (38.2%, 50%, 61.8%, 1.272%, and 1.618%) help confirm whether the level will hold or break. Particularly useful for timing entries at these widely-watched levels.
Settings:
Default 3/8 EMAs work well for most situations. Faster settings (2/5) for active markets, slower (5/13) for cleaner signals in strong trends.
Notes:
This works best when combined with key levels, volume, and market context. The cross timing is what matters - it shows when momentum is actually shifting, not just when price bounces.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Momentum Squeeze Scalper [M2S} [ITZS]Overview
The Momentum Squeeze Scalper is a technical analysis tool designed to identify potential breakout opportunities in the market. It combines elements of momentum analysis with a "squeeze" concept based on Bollinger Bands and Keltner Channels.
Key Components
1. Squeeze Detection
The indicator uses two types of squeezes:
a) Loose TTM Fire (Loose Squeeze): Represented by orange dots. This occurs when one side of the Bollinger Bands is inside the Keltner Channel.
b) Strict TTM Fire (Strict/Tight Squeeze): Represented by green dots. This happens when both sides of the Bollinger Bands are inside the Keltner Channel.
These squeezes can indicate potential breakouts in either direction (long or short).
Long Squeeze: Occurs when prices are compressed at a low level, potentially leading to an upward breakout.
Short Squeeze: Happens when prices are compressed at a high level, possibly leading to a downward breakout.
2. Momentum Line
The Momentum Line is a key feature of this indicator. Its color changes based on specific conditions:
Color 0 (White): Default color, indicating neutral momentum.
Color 1 (Green): Indicates positive momentum. This color appears when:
1. The histogram is positive and increasing, or
2. The momentum is increasing during a squeeze (loose or strict), or
3. There's a strict squeeze in place.
Color 2 (Red): Indicates negative momentum. This color appears when:
1. The histogram is negative and decreasing, or
2. The momentum is decreasing during a squeeze (loose or strict), or
3. There's a strict squeeze in place.
The changing colors of the Momentum Line help traders quickly identify shifts in market momentum and potential trading opportunities.
3. Signal Line
The orange line is the signal line, which is a smoothed version of the momentum line. It can help confirm trend changes when it crosses the momentum line.
Inputs and Their Effects
1. Momentum Period (default: 17):
Purpose: Determines the lookback period for momentum calculation.
Effect: A longer period makes the indicator less sensitive to short-term price changes, resulting in smoother momentum lines but potentially slower signals. A shorter period will make the indicator more responsive to recent price action but may increase noise.
2. Signal Period (default: 8):
Purpose: Sets the smoothing period for the signal line.
Effect: A shorter period makes it more responsive to recent price action, potentially providing earlier signals but with a higher chance of false alerts. A longer period creates a smoother signal line, reducing false signals but potentially delaying entry/exit points.
3. Smooth Momentum (default: false):
Purpose: Determines whether to use EMA smoothing on the source price before momentum calculation.
Effect: When true, it can reduce noise in the momentum calculation, potentially providing clearer signals in choppy markets. When false, it responds more quickly to price changes.
Smoothing Period (default: 1):
Purpose: Sets the period for EMA smoothing when Smooth Momentum is true.
Effect: A higher value creates a smoother momentum line, potentially reducing false signals but also increasing lag.
BB Length (default: 7):
Purpose: Defines the period for Bollinger Bands calculation.
Effect: A shorter length makes the bands more sensitive to price changes, potentially identifying squeezes more quickly but also increasing the chance of false signals. A longer length creates more stable bands but may delay squeeze identification.
StDev (default: 1.0):
Purpose: Standard deviation multiplier for Bollinger Bands.
Effect: Higher values create wider bands, making squeezes less frequent but potentially more significant. Lower values create tighter bands, increasing the frequency of squeezes but potentially reducing their reliability.
Keltner Length (default: 1):
Purpose: Sets the period for Keltner Channel calculation.
Effect: A longer length creates a wider, more stable channel, reducing the frequency of squeezes but potentially making them more reliable. A shorter length creates a tighter channel, increasing squeeze frequency but potentially reducing significance.
Multiplier (default: 0.5):
Purpose: Multiplier for Keltner Channel width.
Effect: Higher values create a wider channel, making squeezes less frequent but potentially more significant. Lower values create a tighter channel, increasing squeeze frequency but potentially reducing their reliability.
KC Smoothing Period (default: 10):
Purpose: Determines the smoothing period for the momentum histogram.
Effect: A longer period creates a smoother histogram, potentially reducing false signals but increasing lag. A shorter period makes the histogram more responsive but potentially noisier.
Smoothing Type (default: None):
Purpose: Allows selection of different smoothing algorithms for the momentum histogram.
Effect: Different smoothing types (e.g., ALMA, DEMA, EMA) can affect how quickly the histogram responds to price changes and how smooth the resulting line is. This can impact the timing and frequency of momentum color changes.
How to Use the Indicator
Look for squeeze dots (orange or green) to identify periods of low volatility.
Pay attention to the color of the Momentum Line:
1. Green suggests potential bullish momentum
2. Red suggests potential bearish momentum
Use the histogram for additional confirmation of momentum strength and direction.
Consider entering trades when the squeeze dots disappear and the Momentum Line shows a strong color signal (green for long, red for short).
How to Adjust the Indicator
1. For More Frequent Signals: Decrease the Momentum Period, Signal Period, BB Length, and Keltner Length. Increase the StDev and decrease the Multiplier. This will make the indicator more sensitive but may increase false signals.
2. For Fewer, More Reliable Signals: Increase the Momentum Period, Signal Period, BB Length, and
Keltner Length. Decrease the StDev and increase the Multiplier. This will reduce sensitivity but may miss some opportunities.
3. To Detect Stronger Squeezes: Increase the StDev for Bollinger Bands and decrease the Multiplier for Keltner Channels. This will make it harder for squeezes to occur, potentially identifying stronger setups.
4. To Reduce Noise: Enable Smooth Momentum and increase the Smoothing Period. Choose a smoothing type like EMA or DEMA for the histogram. This can help in choppy or ranging markets.
5. For Faster Response: Decrease the Momentum Period and Signal Period, and choose a responsive smoothing type like EMA for the histogram. This can be useful in fast-moving markets but may increase false signals.
Interpretation and Trading
1. Squeeze Formation: When you see orange (loose) or green (strict) dots, it indicates a potential buildup of energy in the market. This compression often precedes a significant move.
2. Momentum Direction: Watch the Momentum Line color changes:
Transition to Green: Suggests increasing bullish momentum, especially during a squeeze.
Transition to Red: Suggests increasing bearish momentum, especially during a squeeze.
White: Indicates neutral momentum or no clear direction.
3. Confirmation: Look for the Momentum Line (colored) to cross above the Signal Line (orange) for bullish confirmation, or below for bearish confirmation.
4. Exit Signals: When the squeeze dots disappear and the Momentum Line color changes, it often indicates that the compressed energy has been released, and the strong move may be ending.
5. Trend Strength: The distance between the Momentum Line and the Signal Line can indicate trend strength. A wider gap suggests a stronger trend.
Remember, no indicator is perfect. Always use this tool in conjunction with other forms of analysis and proper risk management. It's recommended to backtest and practice with this indicator on historical data before using it in live trading. Adjust the inputs based on your trading style, timeframe, and the specific characteristics of the asset you're trading.
GKD-C Polychromatic Momentum [Loxx]Giga Kaleidoscope GKD-C Polychromatic Momentum is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Polychromatic Momentum
Polychromatic Momentum: A Refined Approach to Momentum Calculation in Technical Analysis
In the world of finance and trading, technical analysis plays a crucial role in understanding price movements and making informed decisions. One popular method in technical analysis is calculating momentum, which indicates the strength of a trend by analyzing the rate of change in prices. The following explains a specific implementation of momentum calculation known as Polychromatic Momentum, highlighting its features and potential advantages over traditional momentum calculations.
Polychromatic Momentum Calculation
Polychromatic Momentum enhances the traditional momentum calculation by employing a weighted approach to momentum values. This method begins by initializing two variables to store the cumulative momentum values and their respective weights throughout the calculation process.
The calculation iterates through the range of the price data. For each iteration, a weight is calculated as the square root of the index plus one. The weight serves as a scaling factor, emphasizing more recent price changes over older ones. This allows the Polychromatic Momentum to account for the significance of recent trends in the market.
Next, the momentum value for the current index is calculated by finding the difference between the current source price and the source price at the previous index. This difference is then divided by the calculated weight. The momentum value is added to the cumulative sum, and the weight is added to the sum of weights.
Once the iteration is complete, the Polychromatic Momentum is obtained by dividing the cumulative sum of momentum values by the sum of weights. This calculation method provides a more nuanced understanding of the momentum by taking into account the varying importance of price changes over time.
Polychromatic Momentum offers a different approach to momentum calculation compared to regular momentum. While both methods aim to measure the strength of a trend by analyzing the rate of change in prices, their calculations differ in certain aspects, which may result in advantages for Polychromatic Momentum.
Regular momentum is calculated by subtracting the price value at a specific period in the past from the current price value. This method provides a simple and straightforward way to determine the price change over a fixed period.
Polychromatic Momentum, on the other hand, employs a weighted approach to momentum values. It calculates the momentum by considering a range of price changes over time and assigning weights to each change based on their recency. This approach aims to capture the varying importance of price changes over time, which can be beneficial in certain market conditions.
Some potential advantages of Polychromatic Momentum over regular momentum include:
1. Responsiveness: Polychromatic Momentum places greater emphasis on recent price changes, making it more responsive to new trends in the market. This responsiveness could provide timely signals for traders to capitalize on emerging trends.
2. Enhanced Trend Analysis: By considering a range of price changes over time and assigning weights to each change, Polychromatic Momentum can provide a more comprehensive analysis of the market trends. This can help traders better understand the overall momentum and make more informed decisions.
3. Flexibility: Polychromatic Momentum's weighted approach allows for greater flexibility in adapting to different market conditions and timeframes. Traders can experiment with different weighting schemes to optimize the momentum calculation for their specific trading strategies and goals.
In conclusion, Polychromatic Momentum offers a more refined approach to momentum calculation in technical analysis compared to traditional methods. By using a weighted approach, it effectively takes into account the varying importance of price changes over time, providing traders with a more insightful and responsive measure of market trends.
What is Double Smoothed Exponential Moving Average?
In financial markets and trading, technical analysis serves as a critical tool for evaluating price trends and making strategic decisions. A key component of technical analysis is the moving average, which averages price data over a specified period to smooth out fluctuations and identify market trends. While the Exponential Moving Average (EMA) is a popular moving average variant that emphasizes recent data points, the Double Smoothed Exponential Moving Average (DSEMA) takes it a step further by incorporating two layers of EMA calculations for more advanced smoothing. The following delve into the DSEMA methodology, explaining its working mechanism and the logic behind the technique without referring to specific code variables.
Double Smoothed Exponential Moving Average Explanation
DSEMA is a function that processes source price data and the length of the smoothing period as its inputs. Its primary objective is to minimize noise in the price data and generate a smoother output, which can be advantageous for detecting trends and making informed trading decisions.
The DSEMA calculation begins by determining the alpha value, which is the smoothing factor for the EMA. The alpha value is derived from the square root of the length of the smoothing period, ensuring that it falls between 0 and 1. A higher alpha value leads to a more responsive EMA, while a lower alpha value results in a slower-moving EMA that is less affected by recent price fluctuations.
The core of the DSEMA calculation involves applying two layers of EMA. The first layer calculates the initial EMA using the source price data and the alpha value. This first EMA places more weight on recent price data points, similar to a regular EMA.
The second layer calculates another EMA using the initial EMA values and the same alpha value. This second layer of EMA provides additional smoothing to the price data, resulting in a smoother output curve that is less prone to noise and sudden market changes. The final output of the DSEMA is the result of the second EMA layer.
In summary, the Double Smoothed Exponential Moving Average (DSEMA) offers an advanced approach to price data smoothing in technical analysis by applying two successive layers of EMA calculations. This technique enhances the detection of market trends and helps reduce the impact of noise in price data, providing traders with a more reliable representation of price movements to support their decision-making process.
Combining DSEMA and Polychromatic Momentum
DSEMA is an advanced smoothing technique that applies two layers of Exponential Moving Average (EMA) calculations to reduce noise in price data and produce a smoother representation of the market trends. On the other hand, Polychromatic Momentum is a momentum calculation method that employs a weighted approach to assess the strength of trends by analyzing the rate of change in prices over time.
By combining the two techniques, DSEMA can be used to smooth the source price data before inputting it into the Polychromatic Momentum calculation. This combination allows for a more accurate representation of price movements, as the smoothed price data provided by DSEMA minimizes the impact of sudden market fluctuations and noise on the momentum calculation.
The result is an enhanced technical analysis tool that leverages the benefits of advanced price smoothing from DSEMA and the refined trend assessment of Polychromatic Momentum. This integrated approach can help traders gain a deeper understanding of market dynamics and make more informed decisions based on reliable, noise-reduced price data and nuanced momentum calculations.
For our purposes here, only the source price can be smoothed and it's turned off by default. The smoothing period is zero by default. Any period above 0 and the smoothing will kick in. Try a period of 5.
Additional Features
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Polychromatic Momentum as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Polychromatic Momentum
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
Momentum Trend [MT]The Momentum Trend indicator is an innovative technical analysis tool designed to capture and visualize momentum trends in financial markets. This advanced indicator goes beyond traditional momentum measures, offering a unique perspective on price action and trend strength.
Core Functionality:
Trend Momentum Index (TMI) Calculation:
At the heart of this indicator is the Trend Momentum Index (TMI), a proprietary algorithm that combines moving averages with price action analysis to gauge momentum. The TMI is calculated using a user-defined source, length, and moving average type.
Dynamic Trend Visualization:
The indicator uses a color-coded column plot to represent the TMI values, providing an intuitive visual representation of trend strength and direction. The colors change based on specific conditions, offering instant insights into the current market state.
Adaptive Momentum Analysis:
The TMI adapts to changing market conditions by comparing current values to historical ones, allowing for a more nuanced understanding of momentum shifts.
Key Inputs and Their Significance:
TMI Source:
Allows users to select the price data for TMI calculations. The default is the closing price, but users can choose alternative sources for different analytical perspectives.
TMI Length:
Defines the lookback period for the TMI calculation. The default of 8 provides a balance between responsiveness and stability, but users can adjust this to suit their trading style.
Moving Average Type:
Users can select from various moving average types (SMA, EMA, SMMA, WMA, VWMA) for the base calculation, allowing for customization based on trading preferences.
What Makes It Unique:
Comprehensive Momentum Analysis:
The TMI combines elements of trend following and momentum, providing a more holistic view of market dynamics than traditional momentum indicators.
Multi-Faceted Trend Identification:
The color-coding system doesn't just show bullish or bearish trends, but also identifies accelerating and decelerating momentum in both directions.
Flexible Moving Average Integration:
The ability to choose different moving average types allows traders to fine-tune the indicator's responsiveness and smoothness.
Visual Clarity:
The column-style plot with color changes offers clear, at-a-glance insights into trend strength and direction.
Momentum Comparison Logic:
The indicator incorporates logic to compare current momentum changes with recent historical changes, providing context for the current market state.
The Momentum Trend indicator represents a sophisticated approach to momentum and trend analysis. By combining moving averages, price action, and comparative momentum logic, it offers traders a powerful tool for identifying potential trend continuations, reversals, and momentum shifts.
This indicator is particularly valuable for traders looking to:
- Identify the start of new trends
- Spot potential trend reversals
- Gauge the strength of ongoing trends
- Time entries and exits based on momentum shifts
Momentum ChannelbandsThe "Momentum Channelbands" is indicator that measures and displays an asset's momentum. It includes options to calculate Bollinger Bands and Donchian Channels around the momentum. Users can customize settings for a comprehensive view of momentum-related insights. This tool helps assess trend strength, identify overbought/oversold conditions, and pinpoint highs/lows. It should be used alongside other indicators due to potential lag and false signals.
Momentum and AccelerationThe following oscillator is a twist on momentum, incorporating a 2nd derivative "acceleration" to help determine changes in momentum. Both are plotted directly accessing previous series values rather than using a moving average.
The script has an option to divide so the formula is d(Price)/d(Time), like a derivative. The script also provides options for the user to use their price source, volume, or a combination of price and volume.
Credit: This script utilizes the "color gradient framework" tutorial by LucF (PineCoders) to create user-adjustable gradient visuals.
Definitions
"1st Derivative - Momentum" - Momentum is most commonly referred to as a rate and measures the acceleration of the price and/or volume of a security.
"2nd Derivative - Acceleration" - Acceleration is the rate of change of momentum.
Value Added
This script may help the trader to assess directional changes in momentum easier.
This script also plots using previous series values rather than using a moving average function. To my knowledge, I was unable to find one that does this for "2nd derivative", so it had to be created.
Dynamic Volume-Volatility Adjusted MomentumThis Indicator in a refinement of my earlier script PC*VC Moving average Old with easier to follow color codes, overbought and oversold zones. This script has converted the previous script into a standardized measure by converting it into Z-scores and also incorporated a volatility based dynamic length option. Below is a detailed Explanation.
The "Dynamic Volume-Volatility Adjusted Momentum" or "Nasan Momentum Oscillator" is designed to capture market momentum while accounting for volume and volatility fluctuations. It leverages the Typical Price (TP), calculated as the average of high, low, and close prices, and introduces the Price Coefficient (PC) based on deviations from the simple moving average (SMA) across various time frames. Additionally, the Volume Coefficient (VC) compares current volume to SMA, and calculates Intraday Volatility (IDV) which gauges the daily price range relative to the close. Then intraday volatility ratio is calculated ( IDV Ratio) as the ratio of current Intraday Volatility (IDV) to the average of IDV for three different length periods, which provides a relative measure of current intraday volatility compared to its recent historical average. An inter-day ATR based Relative Volatility (RV) is calculated to adjusts for changing market volatility based on which the dynamic length adjustment adapts the moving average (standard length is 14). The PC *VC/IDV Ratio integrates price, volume, and volatility information which provides a volume and volatility adjusted momentum. This volume and volatility adjusted momentum is converted into a standardized Z-Score. The Z-Score measures deviations from the mean. Color-coded plots visually represent momentum, and thresholds aid in identifying overbought or oversold conditions.
The indicator incorporates a nuanced approach to emphasize the joint impact of price and volume while considering the stabilizing effect of lower intraday volatility. Placing the volume ratio (VC) in the numerator means that higher volume positively contributes to the overall ratio, aligning with the observation that increased volumes often accompany robust price movements. Simultaneously, the decision to include the inverse of intraday volatility (1/IDV) in the denominator acts as a dampener, reducing the impact of extreme intraday volatility on the momentum indicator. This design choice aims to filter out noise, giving more weight to significant price changes supported by substantial trading activity. In essence, the indicator's design seeks to provide a more robust momentum measure that balances the influence of price, volume, and volatility in the analysis of market dynamics.
Composite Momentum IndicatorComposite Momentum Indicator" combines the signals from several oscillators, including Stochastic, RSI, Ultimate Oscillator, and Commodity Channel Index (CCI) by averaging the standardized values (Z-Scores). Since it is a Z-Score based indicators the values will be typically be bound between +3 and -3 oscillating around 0. Here's a summary of the code:
Input Parameters: Users can customize the look-back period and set threshold values for overbought and oversold conditions. They can also choose which oscillators to include in the composite calculation.
Oscillator Calculations: The code calculates four separate oscillators - Stochastic, RSI, Ultimate Oscillator, and CCI - each measuring different aspects of market momentum.
Z-Scores Calculation: For each oscillator, the code calculates a Z-Score, which normalizes the oscillator's values based on its historical standard deviation and mean. This allows for a consistent comparison of oscillator values across different timeframes.
Composite Z-Score: The code aggregates the Z-Scores from the selected oscillators, taking into account user preferences (whether to include each oscillator). It then calculates an average Z-Score to create the "Composite Momentum Oscillator."
Conditional Color Coding: The composite oscillator is color-coded based on its average Z-Score value. It turns green when it's above the overbought threshold, red when it's below the oversold threshold, and blue when it's within the specified range.
Horizontal Lines: The code plots horizontal lines at key levels, including 0, ±3, ±2, and ±1, to help users identify important momentum levels.
Gradient Fills: It adds gradient fills above the overbought threshold and below the oversold threshold to visually highlight extreme momentum conditions.
Combining the Stochastic, RSI, Ultimate Oscillator, and Commodity Channel Index (CCI) into one composite indicator offers several advantages for traders and technical analysts:
Comprehensive Insight: Each of these oscillators measures different aspects of market momentum and price action. Combining them into one indicator provides a more comprehensive view of the market's behavior, as it takes into account various dimensions of momentum simultaneously.
Reduced Noise: Standalone oscillators can generate conflicting signals and produce noisy readings, especially during choppy market conditions. A composite indicator smoothes out these discrepancies by averaging the signals from multiple indicators, potentially reducing false signals.
Confirmation and Divergence: By combining multiple oscillators, traders can seek confirmation or divergence signals. When multiple oscillators align in the same direction, it can strengthen a trading signal. Conversely, divergence between the oscillators can warn of potential reversals or weakening trends.
Customization: Traders can tailor the composite indicator to their specific trading strategies and preferences. They have the flexibility to include or exclude specific oscillators, adjust look-back periods, and set threshold levels. This adaptability allows for a more personalized approach to technical analysis.
Clarity and Efficiency: Rather than cluttering the chart with multiple individual oscillators, a composite indicator condenses the information into a single plot. This enhances the clarity of the chart and makes it easier for traders to quickly interpret market conditions.
Overbought/Oversold Identification: Combining these oscillators can improve the identification of overbought and oversold conditions. It reduces the likelihood of false signals since multiple indicators must align to trigger these extreme conditions.
Educational Tool: For novice traders and analysts, a composite indicator can serve as an educational tool by demonstrating how different oscillators interact and influence each other's signals. It allows users to learn about multiple technical indicators in one glance.
Efficient Use of Screen Space: A single composite indicator occupies less screen space compared to multiple separate indicators. This is especially beneficial when analyzing multiple markets or timeframes simultaneously.
Holistic Approach: Instead of relying on a single indicator, a composite approach encourages a more holistic assessment of market conditions. Traders can consider a broader range of factors before making trading decisions.
Increased Confidence: A composite indicator can boost traders' confidence in their decisions. When multiple reliable indicators align, it can provide a stronger basis for taking action in the market.
In summary, combining the Stochastic, RSI, Ultimate Oscillator, and CCI into one composite indicator enhances the depth and reliability of technical analysis. It simplifies the decision-making process, reduces noise, and offers a more complete picture of market momentum, ultimately helping traders make more informed and well-rounded trading decisions.
* Feel free to compare against individual oscillatiors*
[LazyBear] SQZ Momentum + 1st Gray Cross Signals ━ whvntrI have modified LazyBears Squeeze Momentum Indicator with enhancements, plus added signals
LazyBear mentioned that in John F. Carter's book, Chapter 11, "Mastering the Trade", that "Mr. Carter suggests waiting till the first gray after a black cross, and taking a position in the direction of the momentum (for ex., if momentum value is above zero, go long). Exit the position when the momentum changes (increase or decrease --- signified by a color change)." I have done just that. Now at each "first gray after a black cross", there are now Bearish and Bullish signals.. The signals only appear in the direction of the momentum.
Disclaimer: This indicator does not constitute investment advice. Trade at your own
risk with this method of identifying changes in stock market momentum.
Momentum Candle V2 by Sekolah Trading📌 Momentum Candle V2 by Sekolah Trading – Pair-Based Volatility & Wick Ratio Filter
This script provides a structured and adaptive approach to detecting high-probability momentum candles in intraday markets. It dynamically adjusts pip thresholds and wick filtering conditions based on the selected symbol and timeframe, making it highly practical for real-time trading.
🔍 Concept and Originality
Momentum Candle V2 by Sekolah Trading implements a custom-built methodology combining:
Dynamic Pip Calibration
For each supported instrument (e.g., XAUUSD, USDJPY, GBPUSD, AUDUSD, EURUSD, BTCUSD), the user can define a pip threshold that determines the minimum valid body size for momentum candles. These thresholds are tailored for each pair and timeframe (M5, M15, H1), ensuring the logic adjusts to different volatility profiles.
Wick-to-Body Ratio Filtering
The script filters out candles with large wicks by requiring that total wick length (upper + lower) be no more than 30% of the full candle range. This helps identify decisive candles with minimal rejection.
Directional Validation
Bullish momentum is defined as: Close > Open with a shorter upper wick.
Bearish momentum is: Close < Open with a shorter lower wick.
Real-Time Timing Filter
Alerts are only triggered when the current candle is between 20 and 90 seconds from closing, which reduces noise and encourages confirmation-based entry.
Non-Repainting Logic
All calculations run in real-time with confirmed candles only — no lookahead or future leak.
📊 Visual Output – How to Read the Chart
When the conditions above are met, the script displays triangle markers on the chart:
🔺 Red downward triangle above the candle: valid bearish momentum signal
🔻 Blue upward triangle below the candle: valid bullish momentum signal
These shapes appear on live bars during the final moments of the candle to alert traders to potential confirmed momentum.
🔔 Alert Conditions
Two alert types are provided:
Momentum Bullish: Large bullish candle with small upper wick, during last 20–90s of bar
Momentum Bearish: Large bearish candle with small lower wick, same timing window
Alerts are designed for precision entries at candle close.
🧭 How to Use
Apply the script to a 5m, 15m, or 1h chart.
Configure pip thresholds for your preferred pairs from the input settings.
Watch for triangle markers near the close of each candle:
Blue = potential bullish momentum
Red = potential bearish momentum
Set alerts:
Go to Alerts → Select Momentum Bullish or Momentum Bearish
Frequency: Once Per Bar
Customize message: e.g. “Momentum Bullish on XAUUSD M15”
Combine signals with:
EMA, S/R, or trend filters
Volume/Order Flow
Liquidity zone or breakout context
🛡️ Why This Script Is Closed-Source
This script uses proprietary logic developed by Sekolah Trading, including:
Custom pip calibration engine
Adaptive wick filtering
Real-time entry validation with triangle plots
While the code is protected, the methodology has been explained transparently here in accordance with TradingView publishing rules.
⚠️ Disclaimer
This script is provided for educational and technical analysis purposes only.
It does not guarantee results or provide financial advice. Always verify trades with your own strategy and risk controls.
Author: Sekolah Trading
Version: Momentum Candle V2
Built with Pine Script v6
TradFi Fundamentals: Momentum Trading with Macroeconomic DataIntroduction
This indicator combines traditional price momentum with key macroeconomic data. By retrieving GDP, inflation, unemployment, and interest rates using security calls, the script automatically adapts to the latest economic data. The goal is to blend technical analysis with fundamental insights to generate a more robust momentum signal.
Original Research Paper by Mohit Apte, B. Tech Scholar, Department of Computer Science and Engineering, COEP Technological University, Pune, India
Link to paper
Explanation
Price Momentum Calculation:
The indicator computes price momentum as the percentage change in price over a configurable lookback period (default is 50 days). This raw momentum is then normalized using a rolling simple moving average and standard deviation over a defined period (default 200 days) to ensure comparability with the economic indicators.
Fetching and Normalizing Economic Data:
Instead of manually inputting economic values, the script uses TradingView’s security function to retrieve:
GDP from ticker "GDP"
Inflation (CPI) from ticker "USCCPI"
Unemployment rate from ticker "UNRATE"
Interest rates from ticker "USINTR"
Each series is normalized over a configurable normalization period (default 200 days) by subtracting its moving average and dividing by its standard deviation. This standardization converts each economic indicator into a z-score for direct integration into the momentum score.
Combined Momentum Score:
The normalized price momentum and economic indicators are each multiplied by user-defined weights (default: 50% price momentum, 20% GDP, and 10% each for inflation, unemployment, and interest rates). The weighted components are then summed to form a comprehensive momentum score. A horizontal zero line is plotted for reference.
Trading Signals:
Buy signals are generated when the combined momentum score crosses above zero, and sell signals occur when it crosses below zero. Visual markers are added to the chart to assist with trade timing, and alert conditions are provided for automated notifications.
Settings
Price Momentum Lookback: Defines the period (in days) used to compute the raw price momentum.
Normalization Period for Price Momentum: Sets the window over which the price momentum is normalized.
Normalization Period for Economic Data: Sets the window over which each macroeconomic series is normalized.
Weights: Adjust the influence of each component (price momentum, GDP, inflation, unemployment, and interest rate) on the overall momentum score.
Conclusion
This implementation leverages TradingView’s economic data feeds to integrate real-time macroeconomic data into a momentum trading strategy. By normalizing and weighting both technical and economic inputs, the indicator offers traders a more holistic view of market conditions. The enhanced momentum signal provides additional context to traditional momentum analysis, potentially leading to more informed trading decisions and improved risk management.
The next script I release will be an improved version of this that I have added my own flavor to, improving the signals.
GKD-C Momentum Candles [Loxx]The Giga Kaleidoscope GKD-C Momentum Candles is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C Momentum Candles
The Momentum Candles indicator uses the difference between the closing and opening prices divided by the Average True Range (ATR) over 50 periods to calculate momentum. It sets upper and lower thresholds based on an ATR multiplier: the upper threshold (Tresh1) is 1 divided by the ATR multiplier, and the lower threshold (Tresh2) is the negative inverse of this value. These thresholds help identify significant momentum shifts, generating long/short signals.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
SMI Momentum Bollinger Squeeze Signals - TradeUIMomentum Bollinger Squeeze Signals - TradeUI
The Squeeze Momentum Indicator (SMI) uses the principles of the Squeeze Indicator, which is a volatility indicator, and combines them with a momentum calculation to provide a more comprehensive view of the market.
The original Squeeze Indicator uses the relationship between the Bollinger Bands and Keltner Channels to identify periods of low volatility, known as "Squeezes", and potential breakout points. The SMI takes this one step further by adding a momentum calculation, making it a more dynamic tool for trading.
The momentum calculation is based on the rate of change of the asset's price. When the price increases rapidly, it signifies positive momentum, and when the price decreases rapidly, it signifies negative momentum.
Chiko-Span Momentum_PineScript_Version5This is Momentum indicator based on "Chiko-span" of Ichimoku Kinko-Hyo.
Differ from normal momentum indicator, this indicator is using "close" and "open" as default parameter which is based on 9 week-candle chart Invented by Ichimoku-Sanjin. And, It is located 26 period before to match chiko-span.
(Parameters can change as you like)
The usage is same as normal momentum indicator so please check momentum indicator usage. However, due to use this indicator, it may support to compare momentum of chiko-span movement and to predict effect 5 lines of ichimoku.
For example, when price break out tenkan-sen, you can measure slope or period of chiko-span momentum and compare previously chiko-span momentum. If momentum is stronger than previously price, we can think that price try to out kijun- sen, touch cloud or break out cloud.
I wish, this indicator helps ichimoku users.
Uptrick: Price Action Momentum Oscillator### Detailed Description of the Indicator: "Uptrick: Price Action Momentum Oscillator (PAM Oscillator)"
The "Uptrick: Price Action Momentum Oscillator" (PAM Oscillator) is a highly customized and sophisticated trading indicator designed to provide traders with a multi-dimensional analysis of market momentum across varying timeframes. It stands out due to its comprehensive approach, combining price action analysis with cycle detection to deliver insights into potential trend reversals, continuations, and market strength or weakness. The PAM Oscillator is not just another momentum oscillator; its design incorporates both the granular details of price swings and broader cyclical trends, offering a unique blend of short-term agility and long-term reliability.
#### 1. **Input Settings**
- **PAM Oscillator Settings:**
- **Enable PAM Oscillator:** This feature allows traders to toggle the main oscillator on or off, making it versatile for different trading setups or when combining with other indicators.
- **Short-Term Influence (Default: 1.2):** This parameter controls how much weight short-term price movements have on the overall oscillator. The ability to adjust this weight provides traders with the flexibility to fine-tune the sensitivity of the indicator to short-term fluctuations.
- **Mid-Term Influence (Default: 2.5):** The mid-term weight balances the oscillator by adding a medium-term perspective, essential for capturing sustained price movements without getting swayed by short-term noise.
- **Long-Term Influence (Default: 3.5):** The long-term weight adds stability to the oscillator, ensuring that the indicator reflects broader market trends, which is crucial for long-term traders or when trading in higher timeframes.
- **Oscillator Smoothing (Default: 3):** This parameter allows traders to smooth the oscillator output, reducing the effect of market noise and making the indicator more reliable by filtering out minor price fluctuations.
- **Bullish Trend Color (Default: Green - #4caf50):** The color customization for bullish trends enables traders to visually distinguish market conditions quickly.
- **Bearish Trend Color (Default: Red - #e91e63):** Similarly, the bearish trend color customization aids in quickly identifying market downturns.
- **Enable Oscillator Signals:** This setting allows for the plotting of explicit buy and sell signals, helping traders who prefer clear, actionable insights rather than interpreting raw oscillator values.
- **Bullish Signal Color (Default: Green - #4caf50):** The ability to customize signal colors enhances the clarity of the signals, allowing them to stand out on the chart.
- **Bearish Signal Color (Default: Red - #e91e63):** Like the bullish signal color, this ensures that bearish signals are easily distinguishable.
- **Cycle Analysis Settings:**
- **Enable Cycle Analysis Histogram:** This feature introduces an additional layer of analysis by displaying a histogram that represents cyclical market behavior. It's particularly useful for traders looking to understand the underlying cyclical trends in momentum.
- **Cycle Length (Default: 6):** Adjusting the cycle length allows traders to tailor the cycle detection to different market conditions or asset classes, making the PAM Oscillator adaptable across different markets.
- **Cycle Bullish Color (Default: Light Green - #8bc34a):** The histogram's color customization for bullish cycles aids in quickly identifying periods of positive market momentum.
- **Cycle Bearish Color (Default: Orange - #ff5722):** The bearish cycle color helps in visualizing negative momentum phases.
- **Enable Cycle Signals:** This option allows traders to generate additional buy and sell signals based on the cycle histogram, offering further opportunities to enter or exit trades based on cyclic trends.
- **Cycle Bullish Signal Color (Default: Light Green - #8bc34a):** Customizable signal colors for cycle-based bullish signals improve the indicator's usability by making important signals more visible.
- **Cycle Bearish Signal Color (Default: Orange - #ff5722):** Similarly, bearish signal colors ensure that traders can quickly identify when the market is potentially entering a downtrend.
#### 2. **Custom Types and Functions**
- **PriceData Structure:** The `PriceData` structure encapsulates essential price information (open, high, low, close) along with the bar index. This structure is fundamental for the accurate calculation of swings and trends, ensuring that the oscillator is grounded in precise and up-to-date market data.
- **SwingData Structure:** This structure manages the market's swing points (highs and lows) and their respective indices. It is crucial for detecting and updating the oscillator with significant price levels, helping to identify key turning points in the market.
- **detectSwing Method:** The `detectSwing` method is a core component that determines whether a significant swing (high or low) has occurred. This detection is pivotal for the oscillator, as it triggers the update of the swing data, marking crucial levels where momentum may shift.
- **updateSwing Method:** This method updates the `SwingData` structure when new swing points are detected. It resets the structure's state, ensuring that the most recent price action is accurately reflected in the oscillator.
- **normalizeOsc Function:** The `normalizeOsc` function standardizes the oscillator values between 0 and 100, ensuring consistency across different timeframes and smoothing the data to emphasize genuine momentum changes. This normalization makes the oscillator easier to interpret and more reliable, especially when comparing across different assets or timeframes.
#### 3. **Core Calculations for the Oscillator**
- **Short-Term Oscillator Calculation:**
- This calculation focuses on recent price action to detect short-term trends or reversals. It updates the swing structures based on new highs and lows, determining whether the market is currently bullish or bearish on a short-term basis.
- This feature is particularly useful for traders who need to react quickly to market changes, such as scalpers or day traders.
- **Multi-Term Oscillator Calculation:**
- This function handles the mid-term and long-term oscillators, combining data from these timeframes to produce a comprehensive view of market momentum. It detects and updates swing points across these periods, offering a more robust trend analysis.
- By focusing on multiple timeframes, this calculation helps in filtering out noise and identifying more sustained market trends.
- **Oscillator Data Collection:**
- The `collectOscData` function aggregates oscillator values from short-term, mid-term, and long-term analyses. This comprehensive approach ensures that the final oscillator value reflects a balanced view of the market, taking into account different time horizons and their respective weights.
- The weighted average calculation of the oscillator values allows traders to customize the importance of each timeframe, tailoring the indicator to their specific trading style or strategy.
#### 4. **Plotting the Oscillator and Cycle Histogram**
- **Oscillator Plot:**
- The main oscillator is plotted on the chart, providing a color-coded visualization of market momentum. The gradient from bearish to bullish colors helps traders quickly assess the current market condition.
- Buy and sell signals are plotted based on the oscillator's crossing of the 50 line, offering clear entry and exit points for traders. This feature is particularly beneficial for those who prefer straightforward signals over interpreting complex data.
- **Cycle Histogram Plot:**
- The cycle histogram adds another layer of analysis, highlighting the cyclical nature of market momentum. By displaying the difference between the oscillator value and its smoothed cycle, traders can visualize the strength and direction of cyclical trends.
- The histogram is color-coded to differentiate between bullish and bearish cycles, making it easier to identify periods of rising or falling momentum.
- **Cycle Signal Plot:**
- If cycle signals are enabled, the indicator plots additional buy and sell signals based on the cycle histogram. This feature provides further opportunities for traders to act on cyclical trends, potentially capturing profits from both major and minor market cycles.
### Uniqueness of the PAM Oscillator
The PAM Oscillator is unique in its approach to blending multiple timeframes and cyclical analysis into a single, cohesive indicator. Unlike traditional oscillators that focus on a single aspect of price action, the PAM Oscillator integrates short-term, mid-term, and long-term price data, giving traders a more holistic view of market momentum. Its ability to adjust the influence of different timeframes and the inclusion of cycle analysis makes it exceptionally versatile, catering to a wide range of trading strategies.
- **Comprehensive Multi-Term Analysis:** The PAM Oscillator doesn't just focus on a single timeframe; it aggregates data across short, mid, and long-term horizons, providing a nuanced and adaptable view of market conditions.
- **Integrated Cycle Analysis:** By incorporating a cycle histogram, the PAM Oscillator allows traders to understand and act on the cyclical nature of markets, something that is often overlooked in standard momentum indicators.
- **Customizable Weighting System:** The ability to adjust the weighting of different timeframes and customize colors and signals makes the PAM Oscillator adaptable to different trading environments and preferences, offering a level of customization that is rare among other indicators.
- **Signal Clarity:** The indicator not only visualizes market momentum but also provides clear buy and sell signals based on oscillator and cycle data, making it user-friendly and effective for traders at all levels.
### How Different Traders May Use the PAM Oscillator
1. **Scalpers:**
- **Short-Term Focus:** Scalpers will primarily use the short-term oscillator to identify quick momentum changes for intraday trades. The oscillator’s responsiveness to recent price swings allows them to catch rapid price movements and capitalize on brief market opportunities.
- **Cycle Avoidance:** The cycle histogram can help scalpers avoid periods of low momentum, ensuring they only trade when the market is actively trending, thereby enhancing their profitability.
2. **Day Traders:**
- **Multi-Term Strategy:** Day traders can leverage both the short-term and mid-term oscillators to confirm trend directions before entering trades. This dual-layered approach minimizes the chances of getting
caught in false breakouts, improving trade accuracy.
- **Signal-Based Entries:** The buy/sell signals generated by the oscillator crossing the 50 line offer clear entry and exit points, making it easier for day traders to make quick decisions.
3. **Swing Traders:**
- **Long-Term Influence:** Swing traders might emphasize the long-term oscillator to identify major trend reversals. By smoothing out noise and focusing on longer-term price action, they can hold positions through minor corrections and capitalize on larger market movements.
- **Cycle Confirmation:** The cycle histogram can serve as a confirmation tool, helping swing traders stay in trades during strong cycles and exit when momentum starts to weaken.
4. **Position Traders:**
- **Cycle Dominance:** Position traders can use the cycle histogram to identify macro trends, holding positions for extended periods based on long-term cyclical analysis. This approach is particularly useful in markets with clear cyclical patterns.
- **Multi-Term Validation:** These traders can use the multi-term oscillator to ensure that all timeframes are aligned with their long-term trading strategy, providing greater confidence in maintaining positions through periods of short-term volatility.
### In Summary
The PAM Oscillator is not just an indicator; it’s a comprehensive toolkit for understanding and trading market momentum across different timeframes and cycles. Its unique combination of customizable weighting, multi-term analysis, and integrated cycle detection makes it a powerful tool for traders of all styles, from scalpers to long-term investors. Whether you're looking to capitalize on short-term price movements or identify long-term trends, the PAM Oscillator provides the insights and flexibility needed to navigate the complexities of modern trading.
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This indicator's code will soon be available on: discord.gg
Volatility Adjusted MomentumIt's a script that computes volatility-adjusted momentum indicators.
The problem with the momentum indicator is that it's absolute and it's hard to interpret its value. For example, if you'll change the timeframe or instrument value of Momentum will be very different.
We tried to solve that by expressing momentum in volatility. This way you can easier spot overbought/oversold values.
You can choose to use Standard Deviation or ATR for adjustments.
Thanks to @MUQWISHI for helping me code it.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Momentum - EddyThis indicator uses momentum, emas, macd trend, probability to find the best entry for both long and short positions.
L = Long
When the low goes below the green line (ema fast low), close is above open and momentum is up
S = Short
When the high goes above the red line (ema fast high), close is below open and momentum is down
XS = Exit short at potential bullish pivot
When the low is below a red step (probability) and below ema fast low and both ema fast high and low are 0.5 % (can be changed in the settings) spread, and high is below ema fast low and open is at least 0.2 % spread (can be change in the settings).
XL = Exit long
When the high is above ema fast high and above a green step (probability) with at least 0.2 % spread (customizable in the settings)
The win rate changes based on the % change parameter. The lower the % change the higher the win rate will be.
Green and Red background shows you a bull trend or bear trend. It uses the Mac signal (periods are customizable in the settings).
You can add alerts for Long / Short / Exit Long / Exit short.
You can adjust parameters in the settings.
Use your own judgement to place trades. This algorithm helps you remove the stress of trading.
To avoid false signals trade from 4h timeframe +.
Trend Surfers - Momentum + ADX + EMAThis script mixes the Lazybear Momentum indicator, ADX indicator, and EMA.
Histogram meaning:
Green = The momentum is growing and the ADX is growing or above your set value
Red = The momentum is growing on the downside and the ADX is growing or above your set value
Orange = The market doesn't have enough momentum or the ADX is not growing or above your value (no trend)
Background meaning:
Blue = The price is above the EMA
Purple = The price is under the EMA
Cross color on 0 line:
Dark = The market might be sideway still
Light = The market is in a bigger move
Momentum Drift Oscillator™TradeChartist Momentum Drift Oscillator is a elegantly designed Oscillator that uses both trend following and mean reversion models, that helps visualize the price momentum, based on user defined lookback period and standard deviation.
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Momentum Drift Oscillator ( MDO ) Features:
MDO shows how far away the price is, from the mean, based on Lookback Length (21, 34, 55, 89, 144, 233) and Standard Deviation input (Min - 0.236, Max - 2.0) , and helps understand potential price reversal points based on mean reversion principles.
Drift Visualizer helps visualise the velocity with which Price moves and helps the trader spot various momentum drift zones like Fuel zones, Overbought/Oversold areas and Bull/Bear Exhaustion limits. Drift Visualizer above 150 is usually Overbought and value above 200 is Super Overbought. Similarly, value below -150 is usually Oversold and value below -200 is Super Oversold.
Option to enable and disable coloured bars based on Momentum Drift. (Colour intensity on Price bars helps visualise the price momentum - 2 Colour Schemes available from the settings - Chilli and Flame).
Long and Short Trade Alerts can be created using Once Per Bar Close .
The indicator does not repaint. Alerts may display potential repaint warning, but this is because the code uses bar index for Drift Visualizer labels. For confidence in the indicator, it can be tested using bar replay to make sure the real-time and bar replay trade entries and plots stay on the same bar/timestamp.
MDO can be connected to ™TradeChartist Plug and Trade to generate Trade Entries, Targets, Sop Loss plots etc and to create all types of alerts.
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Why is Momentum Drift Oscillator ( MDO ) different from traditional Momentum based indicators?
This Momentum Drift model truly combines mean reversion and trend following principles, but with a unique original idea.
It needs 2 user defined inputs - Lookback length and Standard Deviation. If for example, say the trend is Bullish and MDO is above 0, the Oscillator doesn't go below 0, even if there is extreme bull exhaustion, if the trend based on lookback and standard deviation is not favorable to reverse trades.
Only Fibonacci lookback periods (21, 34, 55, 89, 144, 233) are used as they have been found more effective than other periods. The default Lookback period is 55 and Standard Deviation is 1, but this can be changed from the settings. Lower values of Lookback period go well with higher Standard Deviation and higher values of Lookback period go well with lower Standard Deviation (0.5, 0.618, 0.786, 0.886, 1 etc.), based on trading style and personal risk strategy.
The indicator includes a Drift Visualizer that helps spot important trade zones based on Price Velocity, calculated dynamically for every bar based on user defined parameters. The first move above or below 0 always opens at Bull Fuel or Bear Fuel zone and the exhaustion zones are reached only at the time of price returning to the mean. But it doesn't change direction if the trend is still up, so the trader can make an informed decision as to when to reverse trades, based on another confirmator.
Similarly, when the Visualizer reaches Fuel or Support/Resistance zones, it normally needs a bit of a push to reach the Overbought - Super Overbought/Oversold - Super Oversold levels where the price normally starts reversing back to the mean and this whole process can be visualized through Visualizer labels on MDO. This process eliminates a lot of noise that normally comes with traditional Momentum indicators.
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Example Charts:
1. XAU-USD 1hr chart - Lookback - 55, Std Dev - 2
2. ADA-USDT 4hr chart - Lookback - 89, Std Dev - 1
3. WTI - USOIL Daily chart - Lookback - 34, Std Dev - 1.618
4. SPX Daily chart - Lookback - 144, Std Dev - 0.236
5. GBP-USD 15m chart - Lookback - 144, Std Dev - 0.618
6. BTC-USD 1hr connected to Plug and Trade - Lookback - 55, Std Dev - 1
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Best Practice: Test with different settings first using Paper Trades before trading with real money
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This is not a free to use indicator. Get in touch with me (PM me directly if you would like trial access to test the indicator)
Premium Scripts - Trial access and Information
Trial access offered on all Premium scripts.
PM me directly to request trial access to the scripts or for more information.
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