In-Depth Investigation of Moving Averages for Trading Systems

[Mwl.RCT]

In-Depth Investigation of Moving Averages for Trading Systems​

Introduction:

After two decades immersed in the ebb and flow of financial markets, I've seen countless trading strategies rise and fall. Moving averages, in their myriad forms, remain a cornerstone of technical analysis. However, the sheer volume of variations can be overwhelming. This investigation is designed to cut through the marketing hype and academic jargon to deliver a practical, data-informed perspective on which moving averages truly offer an edge. We'll be rigorously evaluating each MA on your list, focusing on verifiable performance indicators and real-world applicability. Let's begin.

Analysis of Moving Averages:

For each Moving Average, I will follow the structure you outlined: Overview, Performance Evaluation, Strengths & Weaknesses, Evidence-Based Comparison, and Critical Perspective.

(1) ALMA (Arnaud Legoux Moving Average)

• Overview: ALMA employs a Gaussian filter, aiming to reduce overshoot and oscillation, providing a smoother, less lagged MA than standard types. It uses parameters for window size, sigma (filter shape), and offset (phase shift). Unique for its direct control over smoothness and lag via the Gaussian filter.
• Performance Evaluation: Generally more responsive than SMA and in some configurations, EMA, due to reduced lag. Effective noise filtering thanks to the Gaussian weighting. Performs well in trending markets, capturing moves cleanly. In ranging markets, parameter adjustment is crucial to avoid whipsaws.
• Strengths: Customizable lag and smoothness. Reduced overshoot improves signal clarity around turning points. Adaptable to various market conditions with parameter tuning.
• Weaknesses: More complex to optimize due to multiple parameters. Can be computationally intensive in very high-frequency applications (though generally negligible). Still lags price to some degree, especially with higher smoothness settings.
• Evidence-Based Comparison: Studies and community backtests (e.g., on TradingView) often show ALMA outperforming SMA and sometimes EMA in terms of signal-to-noise ratio and profitability in trend-following strategies, when parameters are correctly optimized. However, generic "out-of-the-box" ALMA settings might not always be superior. Parameter optimization is key, and this introduces curve-fitting risk if not done carefully with walk-forward analysis.
• Critical Perspective: The strength of ALMA lies in its tunability, but this is also its weakness. Without rigorous optimization and validation, it can be easily misused. Some argue the added complexity doesn't always justify a significant performance boost over simpler, well-tuned EMAs or HMAs.

(2) IE_2 (Combination of LSMA and ILRS)

• Overview: This is a composite indicator, combining Least Squares Moving Average (LSMA) and Integral of Linear Regression Slope (ILRS). LSMA is a linear regression line, inherently minimizing lag within its period. ILRS measures the slope of the LSMA, aiming to identify trend acceleration. Unique in its attempt to combine trend direction (LSMA) with momentum (ILRS slope).
• Performance Evaluation: LSMA component is very responsive, reacting quickly to price changes. ILRS adds a layer of momentum analysis, potentially filtering whipsaws around LSMA signals. Can be effective in identifying early trend changes. In ranging markets, the LSMA component can still be prone to false signals; ILRS might offer some filtering but isn't foolproof.
• Strengths: Combines trend direction and momentum in a single indicator. LSMA provides low lag trend identification. ILRS can help confirm trend strength.
• Weaknesses: Complexity – understanding the interaction of LSMA and ILRS is less intuitive than a single MA. LSMA can be noisy, and ILRS, while attempting to filter, might introduce its own lag. Performance is highly dependent on the parameters chosen for both LSMA and ILRS. Limited independent verifiable studies specifically on "IE_2" as a defined indicator (often custom-built in trading platforms).
• Evidence-Based Comparison: Direct evidence is scarce for "IE_2" as a standalone, widely studied indicator. The components (LSMA, linear regression slope) are individually studied. LSMA's responsiveness is well-documented, but its susceptibility to noise is also known. The effectiveness of combining LSMA with a slope indicator is conceptually sound for momentum-based trading but lacks robust, broadly applicable performance data. Benefit likely depends heavily on specific implementation and parameter tuning.
• Critical Perspective: "IE_2" as described seems more like a custom strategy element than a standardized MA. While the underlying concepts are valid, its practical, broadly applicable advantage over simpler, well-tuned MAs is questionable without rigorous, user-specific optimization and backtesting. It's not a plug-and-play solution.

(3) EMA (Exponential Moving Average)

• Overview: EMA weights recent prices more heavily, making it more responsive to current price action than SMA. Calculated recursively, it smooths price data while reducing lag compared to SMA. A foundational MA, widely used and understood.
• Performance Evaluation: Balances responsiveness and smoothness effectively for many market conditions. Reacts faster to trend changes than SMA. Filters noise better than very short-period SMAs but can still generate false signals in choppy markets. Consistent performance in both trending and ranging markets, though requires parameter adjustment for optimal use in each.
• Strengths: Widely understood and implemented. Good balance of responsiveness and smoothness. Versatile across timeframes and markets. Relatively simple calculation.
• Weaknesses: Still lags price, though less than SMA. Can be slow to react to very sharp reversals. Parameter selection (period) is critical; poorly chosen periods can lead to suboptimal performance.
• Evidence-Based Comparison: EMA is extensively studied and backtested. Numerous studies confirm its superiority over SMA in trend-following systems due to reduced lag and improved signal timing. Statistical metrics like Sharpe Ratio and Sortino Ratio often show better performance with EMA-based strategies compared to SMA-based ones in various market simulations (though specific results vary greatly with parameters and strategy context). Ubiquitous use in trading software and strategies is itself a form of "evidence" of its practical utility.
• Critical Perspective: EMA is a workhorse. It's not the "sexiest" or most cutting-edge, but its reliability, versatility, and well-documented performance make it a core tool. While more advanced MAs attempt to improve on EMA, the gains are often incremental and come with added complexity, parameter sensitivity, or computational cost. For many trading purposes, a well-tuned EMA is "good enough" and often "optimal" in its simplicity and robustness.

(4) DEMA (Double Exponential Moving Average by P. Mulloy)

• Overview: DEMA aims to further reduce lag compared to EMA by applying the EMA calculation twice. It attempts to project the EMA forward in time to compensate for inherent delay. Designed for faster reaction to price changes.
• Performance Evaluation: More responsive than EMA, reacting quicker to trend changes. Reduced lag is its primary advantage. Noise filtering is generally comparable to EMA, perhaps slightly less smooth. Can improve entry timing in trend-following systems, but also potentially increase false signals in choppy markets due to increased sensitivity.
• Strengths: Reduced lag compared to EMA. Faster signal generation. Potentially improved entry timing in trending markets.
• Weaknesses: Can be more prone to whipsaws than EMA, especially in ranging or volatile markets, due to increased responsiveness. Slightly more complex calculation than EMA. Marginal improvement over EMA might not always justify the added complexity for all strategies.
• Evidence-Based Comparison: Backtesting comparisons often show DEMA outperforming EMA in terms of reducing lag and improving entry timing in trend-following scenarios. However, these gains need to be balanced against potential increases in false signals and whipsaws, which can negatively impact win rate and profitability in non-trending periods. Empirical studies are less abundant than for EMA itself, but community testing and anecdotal trader experience suggest a modest advantage in certain contexts.
• Critical Perspective: DEMA is a step up from EMA in terms of responsiveness, but it's not a free lunch. The reduced lag comes with increased sensitivity to noise. Whether DEMA is "better" than EMA depends heavily on the specific trading strategy and market conditions. For strategies where early entry is paramount and noise can be managed through other filters, DEMA can be beneficial. Otherwise, the simpler EMA might be more robust.

(5) TEMA (Triple Exponential Moving Average by P. Mulloy)

• Overview: TEMA extends the DEMA concept by applying the EMA calculation three times, further reducing lag. Aims for even faster reaction to price changes than DEMA, while still maintaining some degree of smoothness.
• Performance Evaluation: Most responsive among SMA, EMA, and DEMA. Reacts very quickly to price changes. Lag reduction is significant. Noise filtering is generally less than EMA and DEMA; it can be quite sensitive to short-term price fluctuations. Best suited for markets with strong, clear trends where early entry is critical. In ranging markets, prone to whipsaws and false signals.
• Strengths: Lowest lag among SMA, EMA, DEMA. Fastest signal generation. Potentially optimal for very early entry in strong trends.
• Weaknesses: Most prone to whipsaws and false signals in choppy markets due to high responsiveness. Noise filtering is weakest among the exponential family. Calculation complexity is higher than EMA and DEMA. Marginal benefit over DEMA might be diminishing, with potentially increasing noise issues.
• Evidence-Based Comparison: Backtesting shows TEMA can further reduce lag and improve entry timing compared to DEMA and EMA in strongly trending markets. However, the trade-off in noise and whipsaw potential becomes more pronounced. Statistical metrics might show improved entry timing but potentially decreased win rate or increased drawdown in mixed market conditions. Empirical studies are limited, with most evidence being anecdotal or from community testing.
• Critical Perspective: TEMA is for traders who prioritize speed above all else and have robust noise filtering mechanisms in their overall strategy. It's a specialized tool, not a general-purpose MA. For many strategies, the added responsiveness of TEMA might not outweigh the increased noise and whipsaw risk compared to EMA or even DEMA. Careful consideration of market conditions and strategy objectives is essential.

(6) DsEMA (Double Smoothed EMA)

• Overview: "Double Smoothed EMA" (DsEMA) is somewhat ambiguous as the term can be interpreted in different ways. However, it commonly refers to applying a smoothing technique after calculating a standard EMA. This might involve applying another EMA to the first EMA, or using a different smoothing filter. The aim is to reduce noise further than a single EMA while retaining responsiveness.
• Performance Evaluation: Performance depends heavily on the specific smoothing method applied after the initial EMA. Generally, it will be smoother than EMA, potentially reducing false signals. Responsiveness will be reduced compared to EMA, but potentially still faster than SMA. Noise filtering is the primary goal. Performance in trending markets might be slightly delayed due to increased smoothing, but performance in ranging markets should improve by reducing whipsaws.
• Strengths: Potentially improved noise filtering compared to EMA. Smoother signal line. Can reduce false signals in choppy markets.
• Weaknesses: Increased lag compared to EMA (though less than SMA). Responsiveness is reduced. Performance is highly dependent on the specific smoothing method used. "DsEMA" is not a standardized, universally defined indicator, making comparative analysis challenging without a precise definition.
• Evidence-Based Comparison: Evidence is limited and highly variable due to the lack of a standardized "DsEMA" definition. If "DsEMA" refers to applying another EMA to an EMA, then it will behave somewhat similarly to a longer-period EMA, but with a slightly different weighting structure. Empirical studies would need to specify the exact smoothing method used to be meaningful.
• Critical Perspective: "DsEMA" as a concept is valid – smoothing an EMA to reduce noise. However, without a precise definition, it's difficult to evaluate definitively. The effectiveness hinges entirely on the chosen smoothing technique. It's more of a customizable approach than a specific, well-defined MA indicator. For practical application, one would need to clearly define the smoothing method and rigorously test its performance.

(7) TsEMA (Triple Smoothed EMA)

• Overview: Similar to DsEMA, "Triple Smoothed EMA" (TsEMA) is also not a standardized term and lacks a universally accepted definition. It likely implies applying a smoothing technique three times after an initial EMA calculation, or iteratively smoothing an EMA multiple times. The goal is even greater noise reduction than DsEMA, at the cost of further lag.
• Performance Evaluation: Even smoother than DsEMA, with enhanced noise filtering. Lag will be further increased compared to EMA and DsEMA. Responsiveness will be lowest among the EMA family discussed so far. Best suited for very noisy markets where minimizing false signals is paramount, and trend identification over longer periods is the focus. In trending markets, entry and exit signals will be significantly delayed.
• Strengths: Maximum noise filtering among the EMA family discussed. Very smooth signal line. Potentially highly effective in extremely noisy or choppy markets for long-term trend identification.
• Weaknesses: Highest lag among the EMA family discussed. Lowest responsiveness. Poor for capturing early trend changes or for short-term trading. "TsEMA" lacks a standardized definition, making comparative analysis difficult. Benefit of triple smoothing over double smoothing (DsEMA) might be marginal in practice, with potentially excessive lag.
• Evidence-Based Comparison: Evidence is even more limited and variable than for DsEMA, due to the lack of standardization. Empirical studies would be highly dependent on the specific triple smoothing method used. Intuitively, triple smoothing will further reduce noise and increase lag compared to double smoothing, but quantifiable performance benefits in real trading scenarios are unclear without precise implementation and testing.
• Critical Perspective: TsEMA represents the extreme end of noise reduction in the EMA family. While noise filtering is valuable, excessive smoothing can lead to overly delayed signals and missed opportunities. The practical utility of TsEMA is likely limited to very specific scenarios where noise is exceptionally high and lag is less of a concern (e.g., very long-term trend analysis in highly volatile markets). For most trading applications, the increased lag might outweigh the marginal benefit of further noise reduction beyond DsEMA or even a well-tuned standard EMA.

(8) EWMA (Exponential Weighted Moving Average)

• Overview: "Exponential Weighted Moving Average" (EWMA) is essentially synonymous with EMA (Exponential Moving Average). Both terms describe the same calculation methodology – weighting recent prices exponentially. There is no practical difference between EWMA and EMA; they are different names for the same indicator.
• Performance Evaluation: Identical to EMA (see analysis of EMA above).
• Strengths: Identical to EMA (see analysis of EMA above).
• Weaknesses: Identical to EMA (see analysis of EMA above).
• Evidence-Based Comparison: All evidence and studies applicable to EMA are equally applicable to EWMA.
• Critical Perspective: EWMA and EMA are the same. The inclusion of both in the list is redundant. When analyzing performance, consider them as a single entity – the standard Exponential Moving Average.

(9) GeoMean (Geometric Mean)

• Overview: Geometric Mean (GeoMean) calculates the average by multiplying the values and taking the nth root, where n is the number of values. When applied as a "Moving Geometric Mean," it calculates the geometric mean of prices over a moving window. Unique in using multiplicative rather than additive averaging, making it less sensitive to extreme values in percentage terms.
• Performance Evaluation: Smoother than SMA, but potentially less responsive. Less sensitive to outliers or price spikes than arithmetic mean-based MAs (SMA, EMA, etc.). Can be useful in markets with frequent large percentage price swings. Performance in trending markets might be slightly slower than EMA, but potentially more stable in volatile or erratic markets.
• Strengths: Reduced sensitivity to outliers and extreme price movements. Potentially more stable in volatile markets. Smoother than SMA.
• Weaknesses: Less responsive than EMA. Can be computationally slightly more intensive than SMA or EMA. Geometric mean is less intuitive for many traders than arithmetic mean. Performance advantage over other MAs is not universally established and depends heavily on market characteristics.
• Evidence-Based Comparison: Empirical studies specifically comparing Moving Geometric Mean to other MAs in trading systems are relatively scarce. The theoretical benefit of outlier resistance is recognized in statistics. In financial markets, its practical advantage needs to be empirically demonstrated and is likely market-dependent. Community testing and anecdotal evidence are limited compared to more common MAs like EMA and SMA.
• Critical Perspective: Moving Geometric Mean is an interesting alternative to arithmetic mean-based MAs, particularly for markets prone to outliers or percentage-based analysis. However, its practical superiority in typical trading scenarios is not well-documented. It's worth exploring for specific market types but is not a universally "better" MA. For general trading purposes, EMA or HMA might offer a more reliable and well-understood balance of responsiveness and smoothness.

(10) HMA (Hull Moving Average by A. Hull)

• Overview: HMA is designed to significantly reduce lag while maintaining smoothness. It achieves this through a clever combination of weighted moving averages and applying the square root of the period length in its calculation. Highly regarded for its low lag and smooth output.
• Performance Evaluation: Most responsive among commonly used MAs (SMA, EMA, WMA, etc.) while maintaining good smoothness. Reacts quickly to trend changes with minimal lag. Filters noise effectively for its responsiveness. Performs exceptionally well in trending markets, providing early entry and exit signals. In ranging markets, parameter tuning is important, but generally less prone to whipsaws than very short-period MAs due to its inherent smoothness.
• Strengths: Very low lag, highly responsive. Smooth signal line. Excellent for early trend identification. Versatile across timeframes and markets. Considered by many to be superior to SMA and EMA in terms of lag reduction and signal quality.
• Weaknesses: Slightly more computationally complex than SMA or EMA (though still computationally efficient). Parameter selection (period) is still important for optimal performance. In extremely choppy markets, even HMA can generate some false signals, though generally fewer than less smoothed, faster MAs.
• Evidence-Based Comparison: HMA's superiority in lag reduction and signal quality compared to SMA and EMA is widely supported by both theoretical analysis of its calculation method and empirical backtesting. Numerous community tests and anecdotal trader reports consistently highlight HMA's advantage in providing earlier and cleaner signals, leading to potentially improved entry timing and profitability in trend-following strategies. While rigorous, peer-reviewed academic studies are less common for specific technical indicators, the consensus within the trading community strongly favors HMA for its performance characteristics.
• Critical Perspective: HMA stands out as one of the most effective moving averages for reducing lag without sacrificing smoothness. Its design is mathematically elegant and practically impactful. While no MA is perfect, HMA addresses the core limitation of lag more effectively than many alternatives. For traders seeking responsive and reliable trend identification, HMA is a top contender and often considered a significant improvement over traditional MAs like SMA and EMA. It is a strong candidate for top recommendations.

(11) ITrend (Instantaneous Trendline by J. Ehlers)

• Overview: Instantaneous Trendline (ITrend) by John Ehlers is not strictly a moving average but rather a highly responsive, low-lag trendline. It uses digital signal processing techniques to minimize lag and react almost "instantaneously" to price changes. Unique in its design to represent the "true" underlying trend with minimal delay.
• Performance Evaluation: Extremely responsive, reacting very quickly to price changes, almost zero-lag in ideal conditions. Noise filtering is less of a primary focus; ITrend is designed for responsiveness, not smoothing. Best suited for identifying very short-term trend changes and reversals. In ranging markets, can be very whipsaw-prone due to its high sensitivity. Parameter adjustment is crucial to balance responsiveness with noise.
• Strengths: Lowest lag among all MAs and trendlines discussed. Fastest signal generation. Potentially optimal for very short-term trading and capturing rapid price movements.
• Weaknesses: Most prone to whipsaws and false signals in choppy markets due to extreme responsiveness. Noise filtering is minimal. Calculation is more complex than simple MAs. "Instantaneous" reaction can sometimes be too fast, leading to over-sensitivity and chasing noise. Not ideal for long-term trend identification due to its short-term focus.
• Evidence-Based Comparison: ITrend's low-lag nature is theoretically sound and empirically observed in its price tracking. However, rigorous comparative studies specifically quantifying its trading performance against other MAs are less common. Anecdotal evidence and community usage suggest it can be effective for short-term, momentum-based strategies, but requires careful filtering and confirmation to manage whipsaws. Its performance is highly dependent on market volatility and parameter tuning.
• Critical Perspective: ITrend is a specialized tool for traders who need extremely fast signals and are willing to accept higher noise levels. It's not a general-purpose MA replacement. Its "instantaneous" nature can be both a strength and a weakness. For strategies focused on very short-term price action, ITrend might be valuable. However, for broader trend identification and noise-sensitive strategies, smoother, more lagged MAs like HMA or EMA might be more robust.

(12) ILRS (Integral of Linear Regression Slope)

• Overview: Integral of Linear Regression Slope (ILRS) is another indicator that focuses on trend momentum rather than just price averaging. It calculates the cumulative sum (integral) of the slope of a linear regression line over time. Aims to measure the acceleration of the trend. Unique in its emphasis on trend strength and momentum change.
• Performance Evaluation: Responds to changes in trend momentum rather than just price levels. Can identify strengthening or weakening trends. Noise filtering is not its primary purpose; it's more about momentum analysis. Performance depends on the parameters used for the linear regression and the integration period. Can be effective in confirming trend strength and potential reversals based on momentum shifts. In ranging markets, ILRS may oscillate around zero, providing less clear signals.
• Strengths: Provides insight into trend momentum and acceleration. Can help confirm trend strength and identify potential momentum shifts. Complementary to price-based MAs for trend analysis.
• Weaknesses: Not a direct price-smoothing MA. Signals are based on momentum change, not price levels. Parameter selection is critical and can significantly impact performance. Interpretation of ILRS signals might be less intuitive than price-based MA crossovers. Limited independent verifiable studies specifically on ILRS as a standalone trading indicator.
• Evidence-Based Comparison: Direct evidence for ILRS as a standalone trading tool is scarce. The concept of using linear regression slope for momentum analysis is valid. Its effectiveness depends heavily on how it's integrated into a trading strategy and the chosen parameters. Community usage and anecdotal evidence are limited compared to standard MAs. Benefit might be more as a confirmation indicator alongside price-based MAs rather than a primary signal generator.
• Critical Perspective: ILRS is a momentum indicator, not a direct moving average for price smoothing. It offers a different perspective on trend analysis – focusing on momentum strength and change. While potentially valuable as a complementary tool, it's not a replacement for price-based MAs in most trading systems. Its effectiveness is highly context-dependent and requires careful integration and parameter tuning.

(13) Laguerre (Laguerre filter by J. Ehlers)

• Overview: Laguerre filter, developed by John Ehlers, is a recursive filter designed to reduce lag and smooth price data. It uses gamma as a parameter to control the filter's responsiveness and smoothness. Unique in its recursive filtering approach and gamma parameter for fine-tuning lag and noise reduction.
• Performance Evaluation: Offers a balance of reduced lag and smoothness, controlled by the gamma parameter. Can be tuned to be more responsive than EMA or smoother than SMA. Noise filtering effectiveness depends on gamma setting – lower gamma is more responsive but less smooth, higher gamma is smoother but more lagged. Versatile across market conditions with parameter adjustment.
• Strengths: Adjustable lag and smoothness via gamma parameter. Recursive calculation is computationally efficient. Can be tuned to suit different market conditions and trading styles.
• Weaknesses: Parameter optimization (gamma) is crucial for performance. Understanding the impact of gamma requires some experimentation. Still introduces some lag, though generally less than SMA for comparable smoothness. Less widely understood and used than EMA or HMA.
• Evidence-Based Comparison: Ehlers' Laguerre filter is well-regarded in the technical analysis community for its lag-reducing properties. Backtesting and anecdotal evidence suggest it can outperform SMA and sometimes EMA in terms of signal timing and noise reduction, particularly when gamma is properly tuned for the market and timeframe. Rigorous, peer-reviewed studies are less common, but community testing and trader experience support its effectiveness.
• Critical Perspective: Laguerre filter is a valuable tool for traders seeking a tunable moving average with reduced lag. The gamma parameter provides flexibility to balance responsiveness and smoothness. While parameter optimization is required, the filter's design is sound and offers a practical alternative to standard MAs. It's a strong contender, especially for traders who are comfortable with parameter tuning to match specific market conditions.

(14) LWMA (Linear Weighted Moving Average)

• Overview: Linear Weighted Moving Average (LWMA) weights prices linearly, giving the most recent price the highest weight and decreasing linearly back through the period. More responsive than SMA but less responsive than EMA. A simple weighted MA, offering a compromise between SMA and EMA.
• Performance Evaluation: More responsive than SMA, reacting faster to price changes. Less lag than SMA. Noise filtering is comparable to SMA, potentially slightly less smooth. Performance in trending markets is improved compared to SMA due to reduced lag. In ranging markets, still prone to whipsaws, though potentially slightly fewer than SMA.
• Strengths: More responsive than SMA. Simple calculation. Easier to understand than EMA. Offers a slight improvement in lag reduction over SMA.
• Weaknesses: Less responsive than EMA. Noise filtering is not significantly better than SMA. Performance improvement over EMA is generally marginal. Not as widely used or studied as EMA.
• Evidence-Based Comparison: Backtesting comparisons typically show LWMA outperforming SMA due to reduced lag. However, EMA generally outperforms LWMA in terms of responsiveness and signal quality for most trend-following applications. Empirical studies are limited, with most evidence being anecdotal or from basic comparisons to SMA. Benefit over SMA is established, but benefit over EMA is less clear and often marginal.
• Critical Perspective: LWMA is a minor improvement over SMA in terms of responsiveness. It's a simple and understandable weighted MA. However, for most trading purposes, EMA offers a better balance of responsiveness and smoothness and is more widely used and studied. LWMA doesn't stand out as a top performer compared to EMA, HMA, or Laguerre filter.

(15) LSMA (Least Square Moving Average or EPMA, Linear Regression Line)

• Overview: Least Squares Moving Average (LSMA), also known as End Point Moving Average (EPMA) or simply Linear Regression Line, is fundamentally different from typical MAs. It's not an averaging technique but a statistical method of fitting a best-fit line to prices over a period using linear regression. Inherently minimizes lag within the period. Unique in its regression-based approach, not price averaging.
• Performance Evaluation: Extremely responsive, virtually zero lag within its period. Reacts very quickly to price changes. Noise filtering is minimal; LSMA can be quite noisy and sensitive to short-term fluctuations. Best suited for identifying very short-term trend direction and potential reversals. In ranging markets, highly prone to whipsaws and false signals. Parameter selection (period) is crucial – shorter periods are more responsive but noisier, longer periods are smoother but introduce lag relative to instantaneous price.
• Strengths: Lowest lag possible for a line representing price direction over a period. Fastest signal generation. Can be very effective for short-term trend identification when noise is managed.
• Weaknesses: No noise filtering – can be very noisy and whipsaw-prone, especially with short periods. Not a traditional "moving average" in the smoothing sense. Parameter selection is critical and sensitive. Performance degrades significantly in choppy markets.
• Evidence-Based Comparison: LSMA's low-lag nature is mathematically inherent in its linear regression calculation. However, its trading performance as a standalone indicator is highly variable and dependent on market conditions and noise management techniques. Backtesting might show good results in strongly trending, low-noise environments, but poor results in choppy or volatile markets. Empirical studies specifically on LSMA as a trading signal generator are limited; it's often used as a component in more complex strategies rather than a standalone MA.
• Critical Perspective: LSMA is a powerful tool for representing short-term trend direction with minimal lag. However, it's not a noise-filtering MA and is prone to whipsaws in choppy markets. It's best used in conjunction with other indicators or filters to manage noise and confirm signals. It's more suitable for advanced traders who understand its limitations and can integrate it effectively into a broader strategy. Not a top recommendation for general-purpose MA needs due to its noise sensitivity.

(16) JSmooth (M. Jurik's Smoothing)

• Overview: M. Jurik's Smoothing (JSmooth) is a proprietary smoothing algorithm designed to minimize lag and overshoot while providing a smooth output. It's often implemented as a "moving average" although it's more accurately a sophisticated smoothing filter. Details of the exact algorithm are often not fully public. Unique in its proprietary nature and claimed superior smoothing and lag reduction capabilities.
• Performance Evaluation: Claimed to offer very low lag and excellent smoothness. Performance is highly dependent on the specific implementation and parameters (if any are exposed to the user). Generally expected to be more responsive than EMA and smoother than LSMA or ITrend. Noise filtering capabilities are a primary design goal. Performance in trending markets should be good due to low lag; performance in ranging markets should be better than less smoothed, faster MAs due to noise reduction.
• Strengths: Potentially excellent balance of low lag and high smoothness (based on claims). Proprietary algorithm designed for superior smoothing. May offer improved signal quality compared to standard MAs.
• Weaknesses: Proprietary nature – algorithm details are often opaque, making independent analysis and verification difficult. Performance is highly dependent on the specific implementation and parameters. "JSmooth" is not a standardized indicator; different implementations might vary. Limited publicly available, rigorous evidence and comparative studies due to its proprietary nature.
• Evidence-Based Comparison: Evidence is primarily based on anecdotal reports, marketing materials from Jurik Research, and community testing within specific trading platform ecosystems that offer JSmooth. Independent, peer-reviewed studies are lacking due to the proprietary nature of the algorithm. Claims of superior performance should be treated with caution without independent verification and rigorous backtesting.
• Critical Perspective: JSmooth is presented as a premium smoothing algorithm, and anecdotal reports suggest it can be effective. However, the lack of transparency and independent verification is a significant drawback for objective analysis. While it might be a top performer, without access to the algorithm details and robust, publicly available performance data, it's difficult to definitively recommend it as a top MA based on verifiable evidence. Its value proposition relies heavily on trust in the proprietary claims of Jurik Research.

(17) MD (McGinley Dynamic)

• Overview: McGinley Dynamic (MD) is an adaptive moving average designed to automatically adjust its speed based on market velocity. It aims to track prices closely in fast-moving markets and smooth data in slower markets. Unique in its adaptive nature and formula that incorporates price velocity to dynamically adjust smoothing.
• Performance Evaluation: Adaptive responsiveness – speeds up in trending markets, slows down in ranging markets. Attempts to reduce whipsaws by smoothing in consolidation phases and reacting quickly to breakouts. Noise filtering is dynamically adjusted based on market velocity. Performs well in markets that transition between trending and ranging phases.
• Strengths: Adaptive responsiveness to market conditions. Dynamically adjusts smoothing and lag. Potentially reduces whipsaws in ranging markets and captures trends effectively. Intuitively appealing concept of adapting to market velocity.
• Weaknesses: Calculation is more complex than standard MAs. Parameter selection (period) is still important but less critical than for non-adaptive MAs. Adaptive nature can sometimes be less predictable than fixed-parameter MAs. Performance advantage over well-tuned fixed MAs is not always guaranteed and depends on market dynamics.
• Evidence-Based Comparison: McGinley Dynamic is well-regarded and used by many traders. Anecdotal reports and community testing suggest it can be effective in adapting to changing market conditions and reducing whipsaws. Rigorous, comparative studies are less common, but the concept of adaptive smoothing is theoretically sound. Performance benefit is likely more pronounced in markets that exhibit clear shifts between trending and ranging phases.
• Critical Perspective: McGinley Dynamic is a valuable adaptive MA that addresses the challenge of market regime changes. Its dynamic responsiveness is a key strength. While parameter optimization is less critical than for fixed MAs, understanding its adaptive behavior and parameter influence is still important. It's a strong contender for traders seeking a versatile MA that adapts to market dynamics and potentially reduces whipsaws.

(18) Median (Moving Median)

• Overview: Moving Median calculates the median price over a moving window. The median is the middle value in a sorted dataset, making it resistant to outliers. Unique in using median instead of mean for averaging, providing robustness to extreme price spikes.
• Performance Evaluation: Very robust to outliers and price spikes. Smoother than SMA, especially in volatile markets with frequent outliers. Responsiveness is generally lower than EMA and HMA, comparable to or slightly less than SMA. Noise filtering is good, particularly for outlier-induced noise. Performs well in markets with frequent extreme price movements where outlier resistance is valuable. In trending markets, might be slightly slower to react than EMA or HMA.
• Strengths: Highly robust to outliers and price spikes. Smooth signal line, especially in volatile markets. Reduces the impact of extreme price events on the MA value.
• Weaknesses: Less responsive than EMA and HMA. Calculation can be computationally slightly more intensive than SMA (especially for very long periods). Median is less mathematically tractable than mean in some analytical contexts. Performance advantage over other MAs is primarily in outlier-prone markets; in typical markets, EMA or HMA might offer a better balance of responsiveness and smoothness.
• Evidence-Based Comparison: Empirical studies specifically comparing Moving Median to other MAs in trading systems are relatively scarce. The statistical benefit of median for outlier resistance is well-established. In financial markets, its practical advantage is likely market-dependent, being most pronounced in markets with frequent extreme price spikes (e.g., certain commodities, volatile penny stocks). For more typical markets, EMA or HMA might offer superior overall performance.
• Critical Perspective: Moving Median is a valuable tool for outlier-resistant smoothing. Its robustness to extreme price spikes is its key strength. However, for general trading purposes, EMA or HMA might offer a better balance of responsiveness and overall signal quality. Moving Median is best considered for specific market types where outlier robustness is a primary concern.

(19) REMA (Regularized EMA by C. Satchwell)

• Overview: Regularized EMA (REMA) by Chris Satchwell is an EMA variant designed to further reduce lag and improve responsiveness. It uses a "regularization" technique to modify the EMA calculation, aiming for faster reaction to price changes while maintaining smoothness. Specific details of the regularization method might vary in implementations. Unique in its "regularized" approach to EMA for enhanced responsiveness.
• Performance Evaluation: Generally more responsive than EMA, reacting quicker to price changes. Reduced lag is a primary goal. Noise filtering is intended to be comparable to or slightly better than EMA. Performance in trending markets should be improved due to reduced lag and faster signal generation. Performance in ranging markets depends on the specific regularization method; some implementations might be more prone to whipsaws than standard EMA.
• Strengths: Reduced lag compared to EMA. Potentially faster signal generation and improved entry timing. Aims to maintain or improve smoothness compared to standard EMA.
• Weaknesses: "Regularized EMA" is not a universally standardized indicator; specific implementations and regularization methods might vary. Performance is highly dependent on the chosen regularization technique. Limited publicly available, rigorous evidence and comparative studies without specifying the exact REMA implementation. Benefit over EMA might be marginal and implementation-dependent.
• Evidence-Based Comparison: Evidence is limited and implementation-dependent. Claims of improved responsiveness over EMA are based on the concept of regularization. Without a standardized definition and publicly available algorithm details, objective comparative analysis is challenging. Community testing and anecdotal reports might exist for specific REMA implementations, but rigorous, peer-reviewed studies are lacking.
• Critical Perspective: REMA conceptually aims to improve EMA's responsiveness through regularization. Whether it achieves a significant and robust performance advantage in practice is highly implementation-dependent and lacks broad, verifiable evidence. It's more of a category of EMA modifications than a specific, well-defined, and universally tested indicator. For practical application, one would need to evaluate specific REMA implementations and rigorously backtest their performance.

(20) Decycler (Simple Decycler by J. Ehlers)

• Overview: Simple Decycler by John Ehlers is designed to remove cyclical components from price data, aiming to reveal the underlying trend more clearly. It's not a moving average in the traditional sense but a signal processing filter focused on cycle removal. Unique in its cycle-removal approach, aiming to isolate the trend component.
• Performance Evaluation: Designed to filter out cyclical noise and highlight the underlying trend. Responsiveness to the trend component can be good, but it's not designed for fast reaction to short-term price changes. Noise filtering focuses on removing cyclical noise, not necessarily all forms of noise. Performance is best in markets where cyclical patterns are present and obscuring the trend. In markets without strong cyclicality, its benefit might be less pronounced, and it might introduce some lag in responding to genuine trend changes.
• Strengths: Effective at removing cyclical noise and highlighting underlying trends (in markets with cyclicality). Can improve trend clarity in cycle-dominated markets. Based on signal processing principles for cycle removal.
• Weaknesses: Not a traditional moving average for price smoothing. Performance is highly dependent on the presence and characteristics of cyclicality in the market. Parameter selection (cycle period) is important and can be challenging to optimize. In markets without strong cyclicality, benefit might be limited, and it could introduce lag. Limited independent verifiable studies specifically on Simple Decycler as a standalone trading indicator.
• Evidence-Based Comparison: Evidence is limited and market-dependent. The concept of cycle removal in financial time series is valid. Its effectiveness depends heavily on the presence and predictability of cyclical patterns in the specific market. Community usage and anecdotal evidence are limited compared to standard MAs. Benefit might be more as a pre-processing filter for price data before applying other trend indicators, rather than a primary trading signal generator itself.
• Critical Perspective: Simple Decycler is a specialized filter for cycle removal, not a general-purpose moving average. Its value is highest in markets where cyclicality is a significant factor. For broader trend analysis and trading system design, standard MAs like EMA or HMA, or adaptive MAs like McGinley Dynamic, might be more versatile and robust across different market conditions. Decycler is a niche tool for specific market types.

(21) SMA_eq (Simplified SMA)

• Overview: "Simplified SMA" (SMA_eq) is likely intended to be a computationally optimized or slightly modified version of the standard Simple Moving Average (SMA). The "eq" suffix might suggest "equal weighting" or an "equivalent" but perhaps more efficient calculation. Without a precise definition, it's difficult to analyze definitively. Presumably aims to provide the same or very similar output to SMA but with potential computational advantages.
• Performance Evaluation: Performance should be virtually identical to standard SMA if it's truly a simplified or equivalent version. Responsiveness, noise filtering, and performance in different market conditions should mirror SMA. Any differences would likely be in computational efficiency or minor implementation details, not in signal characteristics.
• Strengths: Potentially computationally more efficient than standard SMA in certain implementations. May offer minor implementation advantages in specific trading platforms or algorithms.
• Weaknesses: If truly equivalent to SMA, it inherits all of SMA's weaknesses (high lag, slow responsiveness). If slightly modified, performance differences from SMA might be negligible or unpredictable without a precise definition. "SMA_eq" is not a standardized or widely recognized term, making comparative analysis challenging without a specific implementation.
• Evidence-Based Comparison: Evidence is lacking due to the non-standardized nature of "SMA_eq." If it's truly equivalent to SMA, then all evidence and studies applicable to SMA are also applicable to SMA_eq. Any performance differences would likely be negligible or within the margin of error in typical trading scenarios.
• Critical Perspective: "SMA_eq" likely represents a minor variation or optimized implementation of SMA, not a fundamentally different moving average. For practical purposes, its performance can be considered virtually identical to SMA. It does not offer any significant advantages or disadvantages in terms of trading signal characteristics compared to standard SMA.

(22) SMA (Simple Moving Average)

• Overview: Simple Moving Average (SMA) is the most basic and widely understood moving average. It calculates the arithmetic mean of prices over a specified period, giving equal weight to all prices within the period. Foundational and ubiquitous in technical analysis.
• Performance Evaluation: Smoothes price data effectively, reducing noise. However, it has significant lag, reacting slowly to price changes. Noise filtering is good for its simplicity, but lag can lead to delayed entry and exit signals. Performance in trending markets is acceptable for long-term trend identification, but poor for capturing early trend changes or short-term trading. In ranging markets, can generate whipsaws, but generally fewer than very short-period, less smoothed MAs.
• Strengths: Simple to calculate and understand. Widely used and recognized. Provides a basic level of noise filtering. Effective for long-term trend identification and static support/resistance levels.
• Weaknesses: High lag, slow responsiveness. Poor for capturing early trend changes or short-term trading. Equal weighting of all prices within the period can be less optimal than weighting recent prices more heavily. Less effective in volatile markets where faster reaction is needed.
• Evidence-Based Comparison: SMA is extensively studied and backtested, serving as a baseline for comparison for many other MAs. Numerous studies demonstrate its limitations in lag and responsiveness compared to EMA, HMA, and other more advanced MAs for trend-following systems. Statistical metrics often show lower performance (e.g., lower Sharpe Ratio, higher drawdown) for SMA-based strategies compared to EMA or HMA-based strategies in various market simulations. While ubiquitous, its performance is generally considered inferior to more responsive MAs for many trading applications.
• Critical Perspective: SMA is the "original" moving average and remains valuable for its simplicity and widespread understanding. However, its high lag is a significant drawback for many trading strategies, particularly those focused on capturing early trend changes or short-term movements. While still used for long-term trend analysis and as a baseline reference, more responsive MAs like EMA or HMA generally offer superior performance for most active trading purposes.

(23) SineWMA (Sine Weighted Moving Average)

• Overview: Sine Weighted Moving Average (SineWMA) weights prices using a sine wave function over the period. This weighting aims to give more weight to prices in the middle of the period and less weight to prices at the beginning and end, creating a different smoothing profile compared to linear or exponential weighting. Unique in using sine wave weighting for price averaging.
• Performance Evaluation: Smoothing profile is different from SMA, EMA, or LWMA due to sine wave weighting. Responsiveness and noise filtering characteristics will depend on the period and the shape of the sine wave weighting. Intended to offer a potentially unique smoothing effect, but practical performance advantages over other MAs are not well-established.
• Strengths: Unique sine wave weighting approach. Potentially different smoothing characteristics compared to standard weighted MAs. Might offer advantages in markets with specific cyclical patterns that align with sine wave weighting (though this is speculative).
• Weaknesses: Performance advantages over other MAs are not well-documented or empirically established. Sine wave weighting might not be intuitively linked to typical price action characteristics in financial markets. "SineWMA" is not a widely used or studied indicator. Benefit over simpler weighted MAs is unclear.
• Evidence-Based Comparison: Evidence is very limited. Empirical studies specifically on SineWMA as a trading indicator are scarce. Community usage and anecdotal evidence are minimal compared to standard MAs. Theoretical basis for sine wave weighting in financial time series analysis is not strong. Performance advantage over other MAs is largely unproven.
• Critical Perspective: SineWMA is an interesting concept exploring alternative weighting schemes for moving averages. However, its practical value and performance advantages in trading systems are not supported by evidence or widespread usage. It's more of a niche or experimental indicator than a top contender for robust trading strategies. For general-purpose MA needs, EMA, HMA, or McGinley Dynamic are more established and evidence-backed choices.

(24) SMMA (Smoothed Moving Average)

• Overview: Smoothed Moving Average (SMMA) is often used interchangeably with "Running Moving Average" or "Recursive Moving Average." It's calculated recursively, similar to EMA, but with a different smoothing factor. In some implementations, it can be mathematically equivalent to Wilder's Smoothing or close to a very long-period EMA. Aim is to provide a very smooth, slow-reacting MA for long-term trend analysis.
• Performance Evaluation: Very smooth, significant noise filtering. Very high lag, slow responsiveness. Reacts very slowly to price changes. Noise filtering is strongest among the MAs discussed so far (excluding potentially TsEMA, depending on definition). Best suited for long-term trend identification and very smooth price representation. Poor for capturing early trend changes or for short-term trading.
• Strengths: Maximum smoothness among commonly used MAs (excluding potentially TsEMA). Excellent noise filtering for long-term trend analysis. Can provide a very stable, slow-moving baseline trend representation.
• Weaknesses: Highest lag among commonly used MAs. Lowest responsiveness. Poor for capturing early trend changes or for active trading strategies requiring timely signals. Over-smoothing can obscure important price details.
• Evidence-Based Comparison: SMMA's smoothness and high lag are well-documented and inherent in its recursive calculation. Backtesting would consistently show its slow reaction to price changes and its effectiveness in long-term smoothing. Empirical studies might compare it to very long-period EMAs or Wilders Smoothing, showing similar performance characteristics. Its value is primarily in long-term trend analysis where extreme smoothness and noise filtering are prioritized over responsiveness.
• Critical Perspective: SMMA is at the opposite end of the responsiveness spectrum from ITrend or LSMA. Its strength is extreme smoothness and noise filtering, at the cost of very high lag. It's a specialized tool for long-term trend analysis and very slow-moving signals. For most active trading strategies, its lag is too significant to be practically useful for entry and exit decisions. For general-purpose MA needs, EMA, HMA, or McGinley Dynamic offer a better balance of responsiveness.

(25) SuperSmu (SuperSmoother by J. Ehlers)

• Overview: SuperSmoother by John Ehlers is a sophisticated smoothing filter designed to remove noise while minimizing lag. It uses digital signal processing techniques and is often implemented as a "moving average" though technically a filter. Unique in its design for superior noise reduction and lag minimization using advanced filtering principles.
• Performance Evaluation: Claimed to offer excellent noise reduction and low lag. Performance is dependent on the specific implementation and parameters (if any). Generally expected to be smoother than EMA and more responsive than SMA. Noise filtering is a primary design goal. Performance in trending markets should be good due to low lag; performance in ranging markets should be better than less smoothed, faster MAs due to noise reduction.
• Strengths: Potentially excellent balance of low lag and high smoothness (based on claims). Advanced filtering algorithm designed for superior smoothing. May offer improved signal quality compared to standard MAs.
• Weaknesses: Implementation details and algorithm specifics are not always fully public, making independent analysis challenging in some cases. Performance is dependent on the specific implementation and parameters. "SuperSmoother" is not a universally standardized indicator; different implementations might vary. Limited publicly available, rigorous evidence and comparative studies without specifying the exact SuperSmoother implementation.
• Evidence-Based Comparison: Evidence is primarily based on anecdotal reports, marketing materials from MESA Software (Ehlers' company), and community testing within specific trading platforms that offer SuperSmoother. Independent, peer-reviewed studies are lacking due to potential proprietary aspects of the algorithm. Claims of superior performance should be treated with caution without independent verification and rigorous backtesting of specific implementations.
• Critical Perspective: SuperSmoother, like JSmooth, is presented as a premium smoothing algorithm with claims of superior noise reduction and lag minimization. Anecdotal reports suggest it can be effective. However, the lack of transparency and independent verification is a drawback for objective analysis. While it might be a top performer, without access to algorithm details and robust, publicly available performance data, it's difficult to definitively recommend it as a top MA based on verifiable evidence. Its value proposition relies heavily on trust in the proprietary claims and user experiences within specific platform ecosystems.

(26) T3_basic (T3 by T. Tillson, original version)

• Overview: T3 Moving Average by Tom Tillson (original version) is designed to be a fast and smooth moving average using a combination of EMAs and a volume factor. It aims to reduce lag and improve smoothness compared to standard EMAs. The "basic" version likely refers to an earlier or simplified implementation of Tillson's T3 concept. Unique in its combination of EMAs and volume factor (in some implementations), aiming for both responsiveness and smoothness.
• Performance Evaluation: Generally more responsive than EMA and smoother than DEMA or TEMA. Lag reduction is a design goal. Noise filtering is intended to be better than less smoothed, faster MAs. Performance depends on the specific implementation and parameters (period, volume factor). Performance in trending markets should be good due to responsiveness; performance in ranging markets should be better than very fast MAs due to smoothing.
• Strengths: Designed for a good balance of responsiveness and smoothness. Potentially improved signal quality compared to standard EMAs. Volume factor (in some versions) might add a dimension of volume-weighted analysis.
• Weaknesses: "T3_basic" and "T3" (correct version) distinctions can be confusing and implementation-dependent. Performance is sensitive to parameter selection (period, volume factor). "T3_basic" is not a standardized indicator; different implementations might vary. Limited publicly available, rigorous evidence and comparative studies without specifying the exact T3_basic implementation.
• Evidence-Based Comparison: Evidence is limited and implementation-dependent. Tillson's T3 concept is recognized in technical analysis for its attempt to balance speed and smoothness. However, "T3_basic" as a specific implementation lacks broad, verifiable performance data. Community testing and anecdotal reports might exist for specific implementations, but rigorous, peer-reviewed studies are less common.
• Critical Perspective: T3_basic, as a potentially earlier or simplified version of Tillson's T3, is difficult to evaluate definitively without a precise algorithm definition and robust performance data. It likely aims to improve upon standard EMAs in terms of responsiveness and smoothness, but its actual performance benefit in trading systems is unclear without specific implementation and testing. For practical application, one would need to evaluate specific T3_basic implementations and rigorously backtest their performance.

(27) T3 (T3 by T. Tillson, correct version)

• Overview: T3 Moving Average by Tom Tillson (correct version) is the refined and widely accepted implementation of Tillson's T3 concept. It uses a specific formula involving iterated EMAs and a volume factor (in some versions) to achieve a balance of low lag and high smoothness. Unique in its specific formula and recognized as a more advanced and potentially superior version of T3 compared to "T3_basic."
• Performance Evaluation: Generally considered to offer a good balance of responsiveness and smoothness. Lag reduction is a key feature. Noise filtering is intended to be better than less smoothed, faster MAs. Performance depends on parameter selection (period, volume factor). Performance in trending markets should be good due to responsiveness; performance in ranging markets should be better than very fast MAs due to smoothing. Often cited as an improvement over standard EMAs and DEMAs.
• Strengths: Designed for a strong balance of responsiveness and smoothness. Recognized as a refined and potentially superior MA. May offer improved signal quality compared to standard EMAs and DEMAs. Volume factor (in some versions) might add a dimension of volume-weighted analysis.
• Weaknesses: Calculation is more complex than standard EMAs. Performance is sensitive to parameter selection (period, volume factor). "T3" still introduces some lag, though less than SMA or EMA. Benefit over well-tuned HMAs or Laguerre filters is not universally guaranteed.
• Evidence-Based Comparison: Tillson's T3 (correct version) is more widely studied and used than "T3_basic." Anecdotal reports and community testing often highlight its effectiveness in balancing speed and smoothness. Backtesting comparisons might show T3 outperforming EMA and DEMA in certain market conditions. Rigorous, peer-reviewed studies are still relatively limited for specific technical indicators, but the consensus within the trading community generally favors T3 for its performance characteristics.
• Critical Perspective: T3 (correct version) is a strong contender among advanced moving averages. Its design aims for a robust balance of responsiveness and smoothness, and anecdotal and community evidence suggests it achieves this effectively. While parameter optimization is still important, T3 is often considered a valuable tool for traders seeking improved signal quality compared to standard EMAs. It's a strong candidate for top recommendations, alongside HMA and Laguerre filter, for traders seeking advanced MA performance.

(28) BF3P (Three-pole modified Butterworth filter by J. Ehlers)

• Overview: Three-pole modified Butterworth filter (BF3P) by John Ehlers is a signal processing filter designed for very smooth price data representation with minimal overshoot. It's not a moving average in the traditional sense but a sophisticated filter based on Butterworth filter principles. "Three-pole" refers to the order of the filter, influencing its smoothness and roll-off characteristics. Unique in its Butterworth filter design for extreme smoothness and minimal overshoot.
• Performance Evaluation: Extremely smooth, very strong noise filtering. Very high lag, very slow responsiveness. Reacts very slowly to price changes. Noise filtering is strongest among almost all MAs/filters discussed so far. Best suited for very long-term trend analysis where extreme smoothness and minimal overshoot are paramount. Poor for capturing early trend changes or for active trading strategies requiring timely signals.
• Strengths: Maximum smoothness and noise filtering (among most discussed). Minimal overshoot. Provides an exceptionally clean and stable representation of long-term trends. Based on established Butterworth filter principles from signal processing.
• Weaknesses: Highest lag, lowest responsiveness (among most discussed). Poor for capturing early trend changes or for active trading. Over-smoothing can obscure important price details for shorter-term analysis. Parameter selection (period/cutoff frequency) is still important.
• Evidence-Based Comparison: Butterworth filters are well-established in signal processing for their smoothness and minimal overshoot characteristics. Empirical studies specifically on BF3P as a trading indicator are limited, but the theoretical properties of Butterworth filters are well-documented. Community usage and anecdotal evidence are limited compared to standard MAs. Its value is primarily in long-term trend analysis where extreme smoothness is prioritized over responsiveness.
• Critical Perspective: BF3P represents the extreme end of smoothness and noise filtering, even more so than SMMA. Its strength is providing an exceptionally clean and stable long-term trend representation. However, its very high lag and low responsiveness make it unsuitable for most active trading strategies requiring timely signals. It's a highly specialized tool for long-term trend analysis and filtering extreme noise.

(29) BF2P (Two-pole modified Butterworth filter by J. Ehlers)

• Overview: Two-pole modified Butterworth filter (BF2P) by John Ehlers is a less aggressive version of the Butterworth filter compared to BF3P. "Two-pole" indicates a lower order filter, resulting in less smoothness and less lag than BF3P, but still smoother and less lagged than many standard MAs. Unique in its Butterworth filter design offering a compromise between smoothness and lag compared to BF3P.
• Performance Evaluation: Smoother than EMA or HMA, less smooth than BF3P or SMMA. Lag is lower than BF3P and SMMA, but still higher than EMA or HMA. Offers a compromise between smoothness and responsiveness. Noise filtering is good, better than EMA or HMA, but less extreme than BF3P. Performance is best suited for medium to long-term trend analysis where a balance of smoothness and moderate responsiveness is desired.
• Strengths: Good balance of smoothness and moderate responsiveness. Better noise filtering than EMA or HMA. Less lag than BF3P or SMMA. Provides a smoother trend representation than standard MAs while maintaining reasonable responsiveness. Based on Butterworth filter principles.
• Weaknesses: Still introduces lag, though less than BF3P or SMMA. Responsiveness is lower than EMA or HMA. Parameter selection (period/cutoff frequency) is important. Benefit over well-tuned HMAs or Laguerre filters in terms of overall trading performance is not universally guaranteed and depends on market conditions and strategy objectives.
• Evidence-Based Comparison: Butterworth filters are well-established in signal processing. Empirical studies specifically on BF2P as a trading indicator are limited, but the theoretical properties of Butterworth filters are well-documented. Community usage and anecdotal evidence are somewhat more common than for BF3P, suggesting a broader range of applications due to the better balance of smoothness and responsiveness. Performance might be compared to longer-period EMAs or LWMAs, offering potentially better noise filtering with a reasonable level of responsiveness.
• Critical Perspective: BF2P offers a more practical compromise between smoothness and responsiveness compared to BF3P. It's a valuable tool for traders seeking a smoother trend representation than standard MAs while still maintaining reasonable responsiveness for medium to long-term trading. It can be considered as an alternative to longer-period EMAs or LWMAs when noise filtering is prioritized while avoiding excessive lag.

(30) TriMA (Triangular Moving Average)

• Overview: Triangular Moving Average (TriMA) applies triangular weighting to prices within the period. Prices in the middle of the period receive the highest weight, with weights decreasing linearly towards the beginning and end. Smoother than SMA but less responsive than EMA. A simple weighted MA offering a different smoothing profile than linear or exponential weighting.
• Performance Evaluation: Smoother than SMA, reducing noise slightly. Responsiveness is lower than EMA and HMA, but slightly better than SMA. Noise filtering is better than SMA, but less effective than more advanced filters. Performance in trending markets is slightly improved over SMA due to reduced noise, but still lags behind EMA and HMA in responsiveness. In ranging markets, might reduce whipsaws compared to SMA, but still prone to false signals.
• Strengths: Smoother than SMA. Simple to calculate. Offers a different smoothing profile than linear or exponential weighting. Slightly improved noise filtering over SMA.
• Weaknesses: Less responsive than EMA and HMA. Noise filtering is not as effective as more advanced filters. Performance improvement over SMA is marginal in most trading scenarios. Not as widely used or studied as EMA or HMA.
• Evidence-Based Comparison: Backtesting comparisons typically show TriMA offering marginal improvements over SMA in terms of smoothness and noise reduction. However, EMA and HMA generally outperform TriMA in terms of responsiveness and overall trading signal quality. Empirical studies are limited, with most evidence being anecdotal or from basic comparisons to SMA. Benefit over SMA is minor, and benefit over EMA or HMA is generally lacking.
• Critical Perspective: TriMA is a minor improvement over SMA in terms of smoothness. It's a simple and understandable weighted MA with a triangular weighting profile. However, for most trading purposes, EMA or HMA offer a better balance of responsiveness and smoothness and are more widely used and studied. TriMA doesn't stand out as a top performer compared to EMA, HMA, or Laguerre filter.

(31) TriMAgen (Triangular Moving Average generalized by J. Ehlers)

• Overview: Triangular Moving Average generalized by John Ehlers (TriMAgen) is likely an enhanced or modified version of the standard Triangular Moving Average. "Generalized" suggests Ehlers might have introduced parameters or modifications to improve its performance or adapt it to different market conditions. Without a precise definition of Ehlers' "generalized" version, detailed analysis is challenging. Presumably aims to improve upon standard TriMA in terms of responsiveness, smoothness, or adaptability.
• Performance Evaluation: Performance is highly dependent on the specific generalization implemented by Ehlers. Potentially improved responsiveness or smoothness compared to standard TriMA. Adaptive capabilities might be introduced in the "generalized" version. Performance in different market conditions will depend on the nature of the generalization. Without a precise algorithm, it's speculative to assess definitively.
• Strengths: Potentially improved version of TriMA (if Ehlers' generalization is effective). May offer enhanced responsiveness, smoothness, or adaptability compared to standard TriMA. "Generalized" version might incorporate adaptive parameters or algorithms.
• Weaknesses: "TriMAgen" is not a standardized or widely recognized indicator. Performance is highly implementation-dependent and undefined without a precise algorithm. Limited publicly available, rigorous evidence and comparative studies without specifying the exact "generalized" version. Benefit over standard TriMA is unclear and depends on the effectiveness of the generalization.
• Evidence-Based Comparison: Evidence is lacking due to the non-standardized nature of "TriMAgen" and the lack of a precise algorithm definition. Performance comparisons would be meaningless without specifying the exact "generalized" version. Community usage and anecdotal evidence are likely limited.
• Critical Perspective: "TriMAgen" is too vaguely defined for meaningful objective analysis. Without a precise algorithm, its performance characteristics and potential benefits are speculative. For practical purposes, it's not a reliable indicator to evaluate without a clear and testable implementation. For general-purpose MA needs, well-defined and established MAs like EMA, HMA, or McGinley Dynamic are more reliable choices.

(32) VWMA (Volume Weighted Moving Average)

• Overview: Volume Weighted Moving Average (VWMA) incorporates volume into the moving average calculation. It weights prices not just by their position in the period (like LWMA) or exponentially (like EMA), but also by the volume traded at each price point. Gives more weight to price levels with higher trading volume. Unique in its integration of volume into the MA calculation, reflecting volume-confirmed price levels.
• Performance Evaluation: Responds to price changes that are confirmed by volume. Can filter out price movements with low volume, potentially reducing false signals. Smoothness and responsiveness characteristics depend on the period and the volume weighting effect. Performance is best in markets where volume data is reliable and volume confirmation is a valuable signal (e.g., stocks, futures). In markets with less reliable volume data (e.g., some Forex markets), its benefit might be reduced.
• Strengths: Integrates volume confirmation into the MA calculation. May filter out low-volume price movements and reduce false signals. Reflects volume-weighted price levels, potentially indicating areas of stronger support and resistance.
• Weaknesses: Requires reliable volume data, which might not be available or accurate in all markets. Performance benefit depends on the validity of volume as a confirmation signal in the specific market. Calculation is slightly more complex than standard MAs. Parameter selection (period) is still important. Benefit over standard MAs might be marginal in markets where volume is less informative.
• Evidence-Based Comparison: The concept of volume-weighted averaging is theoretically sound and used in various financial contexts (e.g., VWAP). Empirical studies specifically on VWMA as a trading indicator are somewhat limited, but anecdotal evidence and community usage suggest it can be effective in volume-driven markets. Backtesting might show improved signal quality and reduced false signals in markets where volume confirmation is valuable. Performance advantage over standard MAs is market-dependent and volume-data dependent.
• Critical Perspective: VWMA is a valuable tool for traders in markets where volume data is reliable and informative. Its volume-weighting approach can enhance signal quality by emphasizing volume-confirmed price movements. However, its effectiveness is directly tied to the reliability and informativeness of volume data in the specific market. In markets where volume data is less reliable, standard MAs like EMA or HMA might be more robust.

(33) VEMA (Volume-weighted Exponential Moving Average, V-EMA)

• Overview: Volume-weighted Exponential Moving Average (VEMA or V-EMA) combines the volume weighting of VWMA with the exponential weighting of EMA. It weights recent price-volume activity more heavily, aiming for a responsive and volume-confirmed moving average. Unique in its combination of volume weighting and exponential weighting for price averaging.
• Performance Evaluation: Most responsive among volume-weighted MAs. Reacts quickly to price changes that are confirmed by volume. Noise filtering is intended to be better than less volume-weighted MAs in volume-driven markets. Performance is best in markets with reliable volume data where both responsiveness and volume confirmation are valuable. In ranging markets, volume weighting might help filter some whipsaws, but responsiveness can still lead to false signals.
• Strengths: Combines volume weighting and exponential weighting. Potentially offers both volume confirmation and responsiveness. May filter out low-volume price movements while reacting quickly to volume-confirmed trends.
• Weaknesses: Requires reliable volume data. Performance benefit depends on the validity of volume confirmation. Calculation is more complex than standard EMAs or VWMAs. Parameter selection (period) is still important. Benefit over simpler VWMAs or EMAs might be marginal and market-dependent. "VEMA" is sometimes used interchangeably with eVWMA (see next), leading to potential confusion.
• Evidence-Based Comparison: Evidence is similar to VWMA – the concept of volume-weighted averaging is sound, and anecdotal reports suggest effectiveness in volume-driven markets. Empirical studies specifically on VEMA as a distinct indicator from eVWMA are limited. Performance benefit compared to VWMA or EMA is likely market-dependent and might be incremental.
• Critical Perspective: VEMA is a further refinement of volume-weighted MAs, adding exponential weighting for increased responsiveness. It's a valuable tool in markets where both volume confirmation and responsiveness are desired. However, its practical superiority over simpler VWMAs or well-tuned EMAs needs to be empirically demonstrated in specific market contexts. Reliability of volume data remains a key factor for its effectiveness.

(34) eVWMA (Modified eVWMA)

• Overview: "Modified eVWMA" (eVWMA) is often used to refer to a specific implementation of Volume-weighted Exponential Moving Average that incorporates a modification to the volume weighting or exponential smoothing formula. "Modified" suggests a variation from a standard VEMA definition. Without a precise definition of the "modification," detailed analysis is challenging. Presumably aims to improve upon standard VEMA in terms of responsiveness, smoothness, or noise filtering. "eVWMA" can also sometimes be used interchangeably with VEMA, leading to confusion.
• Performance Evaluation: Performance is highly dependent on the specific modification implemented in the "eVWMA" version. Potentially improved responsiveness, smoothness, or noise filtering compared to standard VEMA. Adaptive capabilities might be introduced in the "modified" version. Performance in different market conditions will depend on the nature of the modification. Without a precise algorithm, it's speculative to assess definitively.
• Strengths: Potentially improved version of VEMA (if the modification is effective). May offer enhanced responsiveness, smoothness, noise filtering, or adaptability compared to standard VEMA. "Modified" version might address specific limitations of standard VEMA or optimize for certain market conditions.
• Weaknesses: "eVWMA" and "Modified eVWMA" are not standardized or precisely defined terms. Performance is highly implementation-dependent and undefined without a specific algorithm. Limited publicly available, rigorous evidence and comparative studies without specifying the exact "modified" version. Benefit over standard VEMA is unclear and depends on the effectiveness of the modification. Potential for confusion with standard VEMA (V-EMA) under the "eVWMA" label.
• Evidence-Based Comparison: Evidence is lacking due to the non-standardized nature of "eVWMA" and the lack of a precise algorithm definition for "modified" versions. Performance comparisons would be meaningless without specifying the exact "modified" implementation. Community usage and anecdotal evidence might exist for specific "eVWMA" implementations within certain trading platforms, but rigorous, generalizable data is limited.
• Critical Perspective: "eVWMA" and "Modified eVWMA" are too vaguely defined for meaningful objective analysis as standalone indicators. Without a precise algorithm, their performance characteristics and potential benefits are speculative and implementation-dependent. For practical purposes, it's not a reliable indicator to evaluate without a clear and testable implementation. When considering volume-weighted MAs, focusing on well-defined VWMA or standard VEMA (if reliably implemented) might be more prudent than relying on vaguely defined "modified" versions.

(35) Wilder (Wilder Exponential Moving Average)

• Overview: Wilder Exponential Moving Average (Wilder EMA) is a specific type of EMA, often associated with J. Welles Wilder Jr., known for indicators like RSI and ATR. It's characterized by a specific smoothing factor (1/period or similar) in its recursive calculation, which can result in a slightly smoother and slower-reacting EMA compared to some other EMA implementations that might use a different smoothing factor (e.g., 2/(period+1)). Unique in its specific smoothing factor and association with Wilder's technical analysis methodology.
• Performance Evaluation: Smoother and slightly slower-reacting than some other EMA implementations (depending on the smoothing factor used in comparison). Lag is slightly higher than some faster EMA variants. Noise filtering is slightly better than faster EMA variants. Performance characteristics are very close to standard EMA, but with a subtle emphasis on smoothness and slightly reduced responsiveness. Best suited for medium to long-term trend analysis where a slightly smoother EMA representation is desired.
• Strengths: Slightly smoother than some faster EMA implementations. Good noise filtering. Well-established and associated with Wilder's respected technical analysis framework.
• Weaknesses: Slightly higher lag and lower responsiveness than some faster EMA implementations. Performance difference from standard EMA is often marginal and might not be practically significant in many trading scenarios. "Wilder EMA" is sometimes used interchangeably with SMMA or Smoothed Moving Average, leading to potential confusion.
• Evidence-Based Comparison: Empirical studies directly comparing Wilder EMA to other EMA implementations are limited, as the performance differences are often subtle. Backtesting might show slightly smoother output and slightly slower reaction times for Wilder EMA compared to some faster EMA variants. Community usage and anecdotal evidence suggest it's a well-regarded, reliable EMA variant, but not necessarily demonstrably superior to all other EMA implementations in all scenarios.
• Critical Perspective: Wilder EMA is a reliable and well-established EMA variant that offers a slightly smoother and slower-reacting representation compared to some faster EMA implementations. Its performance difference from standard EMA is often marginal, and the choice between them might be more a matter of preference or consistency with Wilder's broader technical analysis methodology. For general-purpose EMA needs, both Wilder EMA and standard EMA are valid and effective choices.

(36) ZeroLagEMA (Zero-Lag Exponential Moving Average)

• Overview: Zero-Lag Exponential Moving Average (ZeroLagEMA) is designed to eliminate or significantly reduce the lag inherent in standard EMAs. It achieves this through an algorithm that attempts to project the EMA forward in time, compensating for the delay. Highly responsive, aiming for near-zero lag. Unique in its explicit goal of lag elimination and algorithm designed to project EMA forward.
• Performance Evaluation: Most responsive among all MAs discussed, aiming for near-zero lag. Reacts very quickly to price changes. Lag reduction is its primary strength. Noise filtering is generally less effective than standard EMAs; ZeroLagEMA can be quite noisy and prone to whipsaws, especially in choppy markets. Best suited for very short-term trading and capturing rapid price movements where minimal lag is paramount. In ranging markets, highly prone to whipsaws and false signals.
• Strengths: Lowest lag possible among MAs designed for practical trading. Fastest signal generation. Potentially optimal for very early entry in trends and capturing rapid price changes.
• Weaknesses: Most prone to whipsaws and false signals in choppy markets due to high responsiveness and reduced noise filtering. Calculation is more complex than standard EMAs. "Zero-lag" is an idealization; some residual lag might still exist in practical implementations. Performance degrades significantly in noisy or choppy markets without robust noise filtering mechanisms.
• Evidence-Based Comparison: ZeroLagEMA's low-lag nature is theoretically sound and empirically observed in its price tracking. However, its trading performance as a standalone indicator is highly variable and dependent on market conditions and noise management techniques. Backtesting might show good results in strongly trending, low-noise environments, but poor results in choppy or volatile markets. Empirical studies specifically on ZeroLagEMA as a trading signal generator are somewhat limited; it's often used as a component in more complex strategies rather than a standalone MA.
• Critical Perspective: ZeroLagEMA is a powerful tool for traders who prioritize speed and minimal lag above all else. However, its extreme responsiveness comes at the cost of increased noise sensitivity and whipsaw potential. It's not a general-purpose MA replacement and requires careful integration with noise filtering and confirmation techniques in a trading strategy. It's best suited for advanced traders who understand its limitations and can effectively manage its noise sensitivity. For general-purpose MA needs, HMA, T3, or Laguerre filter might offer a better balance of responsiveness and robustness.

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Summary and Top Three Recommendations (Preliminary - Subject to Final Review)

After this extensive analysis, several MAs stand out for their potential to enhance trading system performance. However, the "best" MA is not universal and depends heavily on the specific trading strategy, market conditions, and trader objectives.

Preliminary Top Contenders (in no particular order yet):

1. Hull Moving Average (HMA): Consistently demonstrates exceptional balance of low lag and smoothness. Strong evidence and community support for its superior signal quality in trend-following strategies. Versatile across timeframes.

2. T3 Moving Average (Correct Version): Refined and well-regarded for its balance of responsiveness and smoothness. Often cited as an improvement over standard EMAs and DEMAs. Volume factor (in some versions) is a potential added benefit.

3. Laguerre Filter: Tunable lag and smoothness via gamma parameter. Recursive calculation efficiency. Offers a flexible alternative to standard MAs, particularly for traders who are comfortable with parameter optimization.

Other Strong Contenders (Potentially in Top 3 depending on specific criteria and evidence weighting):

* McGinley Dynamic: Adaptive responsiveness to market velocity is a significant strength, especially in markets that transition between trending and ranging phases.

* EMA (Exponential Moving Average): The foundational MA, reliable, versatile, and widely understood. While not the most cutting-edge, a well-tuned EMA remains a robust and effective tool for many trading strategies.

* ZeroLagEMA: For very short-term, speed-focused strategies where noise can be rigorously managed. Its near-zero lag is compelling in specific contexts.

Moving Averages to be Considered with Caution or for Niche Applications:

* SMA (Simple Moving Average): High lag, generally outperformed by more responsive MAs for most active trading. Primarily useful for long-term trend ID or as a baseline.

* SMMA (Smoothed Moving Average) & BF3P: Excessive smoothness and lag, primarily for very long-term trend analysis or extreme noise filtering, not for active trading signals.

* LSMA & ITrend: Extreme responsiveness, but prone to noise and whipsaws. Require sophisticated noise management techniques and are best for advanced, short-term trading strategies.

* "Modified" or Vaguely Defined MAs (DsEMA, TsEMA, SMA_eq, TriMAgen, eVWMA, REMA, T3_basic, JSmooth, SuperSmu): Performance is highly implementation-dependent and lacks standardized definitions and robust, verifiable evidence. Use with extreme caution and rigorous testing of specific implementations. Proprietary algorithms (JSmooth, SuperSmu) require trust in vendor claims and lack transparency for independent analysis.

* GeoMean, SineWMA, TriMA, LWMA, Wilder EMA, BF2P, VWMA, VEMA, Decycler, ILRS, IE_2, Median: These MAs might have specific niche applications or offer marginal benefits in certain market conditions, but generally do not stand out as top performers for broad trading system enhancement compared to the top contenders. Their performance advantages are either unproven, market-dependent, or less significant than the advantages offered by HMA, T3, Laguerre, McGinley Dynamic, and well-tuned EMA.

Next Steps for Final Recommendation:

To finalize the top three recommendations, we need to:

1. Weight Evidence: Prioritize verifiable data, backtesting results (if available in reputable studies or community analyses), and statistical metrics over anecdotal reports or marketing claims for "modified" or proprietary indicators.

2. Define "Accuracy" and "Win Ratio" in Context: Clarify what specific performance metrics are most important for your trading objectives. Is it highest win rate, best profit factor, lowest drawdown, optimal Sharpe Ratio, or a combination? Different MAs might excel in different metrics.

3. Consider Market Conditions: Specify the typical market conditions your trading system will operate in (trending, ranging, volatile, etc.). Different MAs perform better in different regimes.

4. Review "Critical Perspectives" Again: Re-emphasize the limitations and potential drawbacks of each top contender and ensure they align with your risk tolerance and trading style.

5. Finalize Top Three with Justification: Based on the weighted evidence, defined metrics, market conditions, and critical perspectives, select the top three MAs and provide detailed, evidence-based justifications for each, as per your deliverable requirements.

 

[Mwl.RCT]

#Shorter Version-1​

Comprehensive Analysis of Moving Averages for Trading Systems​

Moving averages form the backbone of many technical analysis strategies, providing crucial insight into market trends and potential reversal points. This report analyzes various moving average types to identify those delivering the highest accuracy and probability win ratio when implemented in trading systems. After extensive examination of calculation methodologies, performance metrics, and practical applications across diverse market conditions, clear leaders have emerged among the extensive list of candidates.

Fundamental Concepts of Moving Averages​

Moving averages transform price data into smoother trend lines by averaging prices over specified periods. Their primary purpose is to filter market noise while identifying directional bias. However, they inherently introduce lag—a critical consideration when evaluating their effectiveness in trading systems. The ideal moving average strikes an optimal balance between smoothness (noise reduction) and responsiveness (minimal lag).
The selection of appropriate moving average type significantly impacts trading performance. Moving averages that respond too quickly may generate excessive false signals, while those that are too slow may miss crucial entry and exit opportunities. This fundamental trade-off between signal quality and timeliness forms the core challenge in moving average selection.

Classification and Calculation Methodologies​

Moving averages employ various mathematical approaches to price averaging. Simple Moving Averages (SMA) calculate the arithmetic mean of prices over a specified period, while Exponential Moving Averages (EMA) apply greater weight to recent prices. More advanced formulations like Hull Moving Average (HMA) utilize weighted averages and mathematical transformations to minimize lag while maintaining smoothness.
The calculation period significantly influences moving average behavior across all types. Longer periods generate smoother lines with greater lag but fewer false signals, while shorter periods produce more responsive lines that may generate more noise. This relationship necessitates careful calibration based on trading timeframe and market conditions.

Comparative Analysis of Leading Moving Averages​

Hull Moving Average (HMA)​

The Hull Moving Average, developed by Alan Hull, employs a sophisticated calculation that significantly reduces lag while maintaining a remarkably smooth output. Its formula uses weighted moving averages and the square root of the period to achieve superior responsiveness.
HMA delivers exceptional performance in trending markets by providing early trend identification with minimal lag. According to search results, the HMA is "far superior [to SMA]... giving traders the first-mover advantage"6. This responsive nature translates to earlier entry signals when trends begin and faster exit signals when trends reverse, potentially increasing both win rate and profit ratio.
When applied to different timeframes, HMA demonstrates remarkable versatility. For long-term trading, "longer period HMA helps identify trends," while shorter timeframes benefit day traders seeking "to identify price movements in real time"6. This adaptability across multiple timeframes enhances its practical utility in diverse trading strategies.
The primary advantage of HMA lies in its ability to provide "a faster signal in a smoother line of sight" with "very little lag"6. This combination of responsiveness and smoothness addresses the fundamental limitation of most moving averages—lag—without introducing excessive noise or false signals.

Zero-Lag Exponential Moving Average (ZeroLagEMA)​

The Zero-Lag Exponential Moving Average represents a specialized adaptation of the standard EMA designed specifically to eliminate lag. It achieves this through an algorithm that extrapolates the EMA value to compensate for the delay inherent in traditional moving averages.
In practical applications, ZeroLagEMA demonstrates remarkable responsiveness to price changes. When combined with other indicators, it creates powerful hybrid tools. For instance, the "Dickson Moving Average" blends ZeroLagEMA with Hull Moving Average to create an indicator that "responds with one (or maybe even half a bar) of lag"5. This exceptional responsiveness enables traders to identify trend changes with minimal delay.
The key strength of ZeroLagEMA lies in its ability to practically eliminate lag while maintaining sufficient smoothness to filter market noise. This balance translates to higher-quality trading signals, potentially increasing win rates in trend-following strategies. Its performance in rapidly changing market conditions particularly stands out, as it can adapt to new price levels more quickly than conventional moving averages.

Triple Exponential Moving Average (TEMA)​

Developed by Patrick Mulloy, the Triple Exponential Moving Average uses multiple EMA calculations to reduce lag significantly. TEMA applies a triple smoothing technique that filters out cyclical fluctuations while preserving the underlying trend.
TEMA demonstrates exceptional performance in markets with clear trending behavior. By reducing lag without introducing excessive noise, it facilitates earlier identification of trend changes compared to standard EMA. This responsiveness translates to improved entry and exit timing, potentially enhancing win rates in trend-following systems.
The primary advantage of TEMA over simpler moving averages is its enhanced signal quality. The triple smoothing process effectively filters short-term market noise while maintaining responsiveness to genuine trend changes. This balance results in fewer false signals during choppy market conditions while still capturing meaningful price movements.

The Hull Moving Average Advantage​

Among all analyzed moving averages, the Hull Moving Average consistently demonstrates superior characteristics for trading system implementation. Its exceptional balance of responsiveness and smoothness addresses the core limitation of moving averages—lag—without sacrificing signal quality.
In backtest comparisons, moving average crossover strategies involving faster and slower moving averages have "outperformed a straightforward buy-and-hold strategy, demonstrating significantly better drawdown metrics"1. The Hull Moving Average, with its inherent speed advantage, enhances this performance further by generating earlier signals at potential trend reversal points.
The mathematical construction of HMA contributes significantly to its performance advantages. By using weighted calculations and applying the square root of the period length, it achieves a mathematically optimized balance between responsiveness and smoothness. This mathematical elegance translates to practical trading advantages across diverse market conditions.
When implementing HMA in trading systems, practitioners can leverage its responsiveness for both entry and exit signals. Its ability to quickly identify potential trend changes provides traders with critical early warning of market shifts, potentially improving risk management and profit capture. As noted in the search results, "the biggest benefit of HMA is that it provides a faster signal in a smoother line of sight. It is much better than all other moving averages because it is a very efficient trigger and has very little lag"6.

Exponential Moving Average (EMA) and Its Derivatives​

The Exponential Moving Average forms the foundation for numerous advanced moving average formulations. Its basic calculation applies greater weight to recent prices, resulting in faster response to price changes compared to SMA. This weighting approach reduces lag while maintaining reasonable smoothness.
EMA derivatives like DEMA (Double Exponential Moving Average) and TEMA (Triple Exponential Moving Average) enhance the basic EMA formula to further reduce lag. DEMA applies a double smoothing technique, while TEMA uses triple smoothing. These modifications progressively reduce lag while maintaining sufficient smoothness to filter market noise.
The exponential family of moving averages demonstrates consistent performance across various market conditions. As noted in search results, "the exponential moving average shows more of the spikes associated with large engagements, while the simple moving average tends to smooth out those peaks more"3. This responsiveness to significant price movements contributes to their utility in trading systems.

Moving Average Selection Considerations​

Selecting the optimal moving average for a trading system requires careful consideration of multiple factors:

Market Conditions and Timeframe​

Moving average performance varies significantly based on market conditions and trading timeframe. In trending markets, more responsive moving averages like HMA and ZeroLagEMA typically outperform, while in ranging markets, smoother alternatives may reduce false signals.
The appropriate timeframe also influences moving average selection. As one experienced trader noted, "For your 1 week or less type trading, I'd probably use a 1 or 2 hour period for stocks, and 4 or 6 hour period for futures"4. This calibration ensures the moving average captures relevant price movements without excessive noise.

Calculation Parameters​

Beyond selecting the moving average type, parameter optimization significantly impacts performance. Period length represents the most critical parameter—shorter periods increase responsiveness but may generate more false signals, while longer periods provide smoother lines but introduce more lag.
Some traders recommend specific parameter combinations based on experience: "I like to use the 50, 100, 200 moving average. I find that they work on all time frames but the larger the time frames (1 day, 1 week, or more) the more conviction the moving average has"4. These longer-period moving averages often serve as significant support and resistance levels in many markets.

Integration with Other Indicators​

Moving averages rarely perform optimally in isolation. Integration with complementary indicators often enhances system performance by providing confirmation from different analytical perspectives. Various combinations merit consideration:

1. Moving average crossover systems combine multiple moving averages of different periods. Search results indicate that "the fast/slow moving average crossover outperformed a straightforward buy-and-hold strategy, demonstrating significantly better drawdown metrics"1.
2. Price-to-moving average relationships provide entry and exit signals based on price crossing above or below a moving average. One strategy involves "Purchase when the price closes above a moving average and sell when it drops below"1.
3. Volume integration enhances signal quality by confirming price movements with corresponding volume activity. Volume-weighted moving averages explicitly incorporate this dimension into their calculations.

Performance Testing and Validation​

Rigorous backtesting forms an essential component of moving average evaluation. Historical performance, while not guaranteeing future results, provides valuable insight into expected behavior across different market conditions. As noted in search results, "it would also be a good idea to back test any strategy you're going to implement and see the percentage of success using historical data"4.
Performance metrics should consider not only win rate but also profit ratio, drawdown characteristics, and overall system stability. Moving average strategies with moderate win rates but favorable profit ratios often outperform those with higher win rates but unfavorable risk-reward profiles.
One Reddit user's backtest results for moving average strategies revealed that "typically, a single moving average did not yield profitable results, while the fast/slow moving average crossover outperformed a straightforward buy-and-hold strategy, demonstrating significantly better drawdown metrics"1. This finding underscores the importance of combining multiple moving averages or incorporating additional confirmation signals.

Top Three Moving Averages for Trading Systems​

Based on comprehensive analysis of calculation methodologies, performance characteristics, and practical applications, three moving averages stand out for their exceptional performance potential in trading systems:

1. Hull Moving Average (HMA)​

The Hull Moving Average earns the top position due to its exceptional balance of responsiveness and smoothness. Its sophisticated calculation methodology significantly reduces lag while maintaining sufficient smoothness to filter market noise. Evidence from search results explicitly states that HMA is "far superior" to other moving averages like SMA, providing traders with the critical "first-mover advantage"6.
Practical applications of HMA span multiple timeframes and market conditions. It performs exceptionally well in trending markets by identifying trend changes earlier than conventional moving averages. Its adaptability across timeframes makes it suitable for both day trading and longer-term position trading.
Implementation recommendation: Utilize HMA primarily for trend identification and trade entry signals. Optimal periods typically range from 9-21 for short-term trading and 21-55 for longer-term strategies. Consider combining with volume indicators for enhanced signal confirmation.

2. Zero-Lag Exponential Moving Average (ZeroLagEMA)​

ZeroLagEMA secures the second position due to its exceptional lag reduction capabilities. Its algorithm specifically addresses the fundamental limitation of moving averages—lag—without sacrificing critical smoothing properties. When combined with HMA in hybrid indicators like the "Dickson Moving Average," it demonstrates remarkable responsiveness with "one (or maybe even half a bar) of lag"5.
This moving average excels in rapidly changing market conditions where conventional moving averages typically falter. Its quick adaptation to new price levels enables earlier identification of potential trend changes, potentially improving both entry timing and risk management.
Implementation recommendation: Deploy ZeroLagEMA for precise entry and exit timing, particularly in volatile markets. Consider using it in combination with other indicators for signal confirmation. Optimal periods typically range from 10-25, with shorter periods for higher volatility environments.

3. Triple Exponential Moving Average (TEMA)​

TEMA earns the third position through its balanced performance profile across diverse market conditions. Its triple smoothing process effectively filters market noise while maintaining good responsiveness to genuine trend changes. This balance results in higher-quality signals with fewer false positives compared to simpler moving averages.
The mathematical elegance of TEMA's calculation contributes to its consistent performance. By applying multiple exponential smoothing operations, it progressively reduces lag while preserving essential trend information. This approach translates to practical trading advantages in both trending and transitional markets.
Implementation recommendation: Implement TEMA in systems requiring balanced performance across different market conditions. Consider using it as a primary trend filter with price-based entry and exit rules. Optimal periods typically range from 14-30, with adaptation based on specific market characteristics.

Conclusion​

Moving averages represent essential tools in the technical analyst's toolkit, providing valuable insight into market trends and potential reversal points. Among the extensive range of moving average formulations, Hull Moving Average, Zero-Lag Exponential Moving Average, and Triple Exponential Moving Average demonstrate superior characteristics for trading system implementation.
These top-performing moving averages share a common attribute—they effectively address the fundamental limitation of moving averages (lag) while maintaining sufficient smoothness to filter market noise. This optimal balance translates to higher-quality trading signals and potentially improved win rates across diverse market conditions.
Implementation success ultimately depends on proper calibration to specific markets, timeframes, and trading objectives. Moving averages perform best when integrated into comprehensive trading systems with clear rules for entry, exit, and risk management. Through careful selection and implementation, these powerful tools can significantly enhance trading system performance and contribute to sustainable trading success.

​

 

[Ndebile]

What 99% of traders do not realise is that they are looking for answers in the wrong places. Knowledge of technicals, fundamentals, indicators, ratios, patterns and trend lines… well, everyone knows about them – and everyone loses, except the 1%.
What do the 1% do that the 99% is not doing?

 

[Mwl.RCT]

What 99% of traders do not realise is that they are looking for answers in the wrong places. Knowledge of technicals, fundamentals, indicators, ratios, patterns and trend lines… well, everyone knows about them – and everyone loses, except the 1%.
What do the 1% do that the 99% is not doing?

Ndebile

Nime develop Indicator yangu, Ili kuflter pair kupitia D1 and Entrie kupitia M30.

Nimekuwa nikitumia LWMA (1,3) ili kuhakikisha logic inafanya vyema.

Nikapata wazo la kuangalia MAs zingine.

Ila kwa kuwa DeepSearch & Deep Research imenipatia zaidi ya nilichohitaji, Nimeona nihifadhi hapa JF kama rejea kwa siku za usoni.

Image: Indixator niliyo develop:

---
Muonekano kwenye M30-GBPUSD:

 

[Ndebile]

Mkuu naheshimu Sana mchango wako mkubwa WA elimu kwenye suala la soko, nakiri mimi ni mwanafunzi kwako. Ila nikupe siri moja....

Sijuhi Kiswahili kizuri cha neno "manipulate" soko "huvurugwa" soko unalosoma kwenye vitabu ni tofauti na soko halisi ambayo kuna big boys, aka "smart money" hawa wanazo pesa za kutosha kupeleka soko wanapotaka liende, hawa wanaweza KIHALALI kuona pande zote za soko yaani supply na demand, hawa hufanya kazi zao kama "syndicate" hawa hawafuati indicator, robot, fundamental wala nini ila......

Pamoja na ujanja wao sio rahisi kwao kujificha, ni rahisi Sana kuona "nyayo" zao sokoni na kufuata wanachofanya, ila pia kuna kitu hawawezi kuficha nitaiweka wazi siku moja..

Mwisho hawa Watu hufanya kazi zoo miaka nenda kwa staili moja.....
Wananua mzigo kwa bei ya chini kisha wanatuuzia kwa bei ya juu....kwa kutumia media "kuonyesha kuna uhaba" na mzigo ukiisha wanatumia media "kuonyesha kuna mzigo mwingi" ili tuogope na kuuza kwa hasara!
Unaweza kutrade ukiwa mgongoni mwa "big boys" ukiamua kuachana na ujinga tunalishwa na media, brokers, waandishi wa vitabu ambao hata hawajawahi kutrade hata account ya Dola 100!

Nahisi nimeandika kwa kificho, haya mambo yapo wazi tu sema Sisi ni WAVIVU,

 

[Mwl.RCT]

What 99% of traders do not realise is that they are looking for answers in the wrong places. Knowledge of technicals, fundamentals, indicators, ratios, patterns and trend lines… well, everyone knows about them – and everyone loses, except the 1%.
What do the 1% do that the 99% is not doing?

Huu ujumbe ni mzito. BTW kama kawaida yangu nime_consults AI inipe ufafanuzi zaidi, Na nimeokota machache acha niweke hapa kama rejea.
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What 99% of Traders Do Not Realise​

"What 99% of Traders Do Not Realise" is a concept widely discussed within online trading communities, particularly among retail traders participating in financial markets such as stocks, forex, and cryptocurrencies. It addresses the observation that a significant majority of retail traders fail to achieve consistent profitability, despite having access to vast amounts of information and trading tools. This concept posits that the common approaches and beliefs held by the majority of traders (the "99%") are fundamentally flawed, leading them to look for solutions in areas that are not the primary drivers of long-term trading success. Conversely, a small minority (the "1%") who achieve consistent profitability are believed to operate with a different set of principles and focus on often overlooked aspects of trading. This idea encourages traders to critically examine their own approaches and consider what factors truly differentiate successful market participants.

The Perspective of the "99%"​

The vast majority of retail traders often begin their journey by focusing heavily on readily available and widely promoted aspects of trading, such as:

• Technical Analysis: Learning about chart patterns (e.g., head and shoulders, flags, triangles), trend lines, support and resistance levels, and candlestick patterns.
• Fundamental Analysis: Studying economic indicators, company financials, news events, and geopolitical factors to predict price movements.
• Trading Indicators: Utilizing a plethora of technical indicators (e.g., Moving Averages, RSI, MACD, Stochastic Oscillator, Fibonacci retracements) in the belief that they can provide clear buy and sell signals.
• Trading Ratios: Focusing on financial ratios, especially in stock trading, to identify undervalued or overvalued assets.

This approach is often reinforced by readily available educational resources, online courses, and trading gurus who emphasize the importance of these analytical tools. The underlying assumption is that mastering these techniques will unlock the secrets to profitable trading. However, the consistent losses experienced by the majority of retail traders suggest that relying solely on these widely known elements is insufficient for consistent success.

The Perspective of the "1%"​

The "1%" of traders, those who consistently generate profits and sustain long-term trading careers, are believed to differentiate themselves by focusing on factors that are often underestimated or overlooked by the majority. These factors are not necessarily secret or unknown, but they require a different level of understanding, discipline, and application:

• Risk Management: This is often cited as the single most critical factor separating the 1% from the 99%. Successful traders prioritize capital preservation above all else. They meticulously manage risk through position sizing, stop-loss orders, and a deep understanding of probability and expected value. They understand that losing trades are inevitable and focus on limiting losses to ensure long-term survival and growth.
• Trading Psychology and Mindset: The emotional and psychological aspects of trading are paramount for the 1%. They cultivate discipline, patience, and emotional control to avoid impulsive decisions driven by fear or greed. They develop a robust mindset capable of handling losing streaks, market volatility, and the psychological pressures of trading.
• Strategy Development and Adaptation: While the 99% often seek a "holy grail" indicator or a simple, universally profitable strategy, the 1% understand that markets are dynamic and strategies need to be continuously developed, tested, and adapted to changing market conditions. They focus on creating robust trading plans with clear rules for entry, exit, and position management, often based on statistical edges rather than solely on pattern recognition.
• Deeper Market Understanding: Beyond basic technical and fundamental analysis, the 1% often possess a deeper understanding of market mechanics, order flow, liquidity, and intermarket relationships. They may focus on understanding market sentiment, institutional behavior, and the underlying forces driving price movements, rather than simply reacting to indicators.
• Focus on Statistical Edge and Probability: Successful traders think in terms of probabilities and expected value. They seek to identify and exploit statistical edges – situations where the odds are demonstrably in their favor over the long run. They understand that each trade is probabilistic and focus on the overall expectancy of their strategy across a large number of trades, rather than trying to win every single trade.
• Discipline and Consistency: The 1% are characterized by unwavering discipline in executing their trading plans. They consistently follow their rules, manage risk effectively, and avoid deviating from their strategy based on emotions or short-term market fluctuations. This consistency is crucial for realizing the statistical edge inherent in their strategies over time.

Common Misconceptions Addressed​

The "99% vs 1%" concept often aims to dispel several common misconceptions prevalent among retail traders:

• The "Holy Grail" Indicator: The belief that a single indicator or a perfect combination of indicators can guarantee profits is a common trap. Successful traders understand that indicators are tools to aid analysis, not magic signals, and that no indicator is foolproof.
• Easy Riches and Get-Rich-Quick Schemes: The allure of quick and easy money in trading is often exploited in marketing. The 1% understand that consistent profitability is a result of hard work, continuous learning, disciplined execution, and robust risk management, not a get-rich-quick scheme.
• Over-Reliance on Information Overload: Access to vast amounts of information can be overwhelming and counterproductive. The 1% focus on filtering relevant information, developing a clear trading process, and avoiding analysis paralysis.

Conclusion​

The concept of "What 99% of Traders Do Not Realise" serves as a critical reflection point for aspiring traders. It highlights that while technical and fundamental knowledge are important foundations, they are not sufficient for consistent profitability. True trading success, as potentially achieved by the "1%", likely lies in mastering crucial, yet often less emphasized, areas such as risk management, trading psychology, strategic thinking, market understanding, and disciplined execution. It encourages traders to shift their focus from solely seeking "better" indicators or patterns to developing a holistic and robust trading approach that prioritizes long-term sustainability and capital preservation.

 

[Mwl.RCT]

Mkuu naheshimu Sana mchango wako mkubwa WA elimu kwenye suala la soko, nakiri mimi ni mwanafunzi kwako. Ila nikupe siri moja....

Sijuhi Kiswahili kizuri cha neno "manipulate" soko "huvurugwa" soko unalosoma kwenye vitabu ni tofauti na soko halisi ambayo kuna big boys, aka "smart money" hawa wanazo pesa za kutosha kupeleka soko wanapotaka liende, hawa wanaweza KIHALALI kuona pande zote za soko yaani supply na demand, hawa hufanya kazi zao kama "syndicate" hawa hawafuati indicator, robot, fundamental wala nini ila......

Pamoja na ujanja wao sio rahisi kwao kujificha, ni rahisi Sana kuona "nyayo" zao sokoni na kufuata wanachofanya, ila pia kuna kitu hawawezi kuficha nitaiweka wazi siku moja..

Mwisho hawa Watu hufanya kazi zoo miaka nenda kwa staili moja.....
Wananua mzigo kwa bei ya chini kisha wanatuuzia kwa bei ya juu....kwa kutumia media "kuonyesha kuna uhaba" na mzigo ukiisha wanatumia media "kuonyesha kuna mzigo mwingi" ili tuogope na kuuza kwa hasara!
Unaweza kutrade ukiwa mgongoni mwa "big boys" ukiamua kuachana na ujinga tunalishwa na media, brokers, waandishi wa vitabu ambao hata hawajawahi kutrade hata account ya Dola 100!

Nahisi nimeandika kwa kificho, haya mambo yapo wazi tu sema Sisi ni WAVIVU,

---
Eng ver:

Sir, I respect your significant contribution to education in the field of the market. I admit that I am a student of yours. But let me tell you a secret...

I don't know a good Swahili word for "manipulate" the market, the market you read about in books is different from the real market where there are big boys, also known as "smart money." These individuals have enough money to influence the market in the direction they desire. Legitimately, they can see both sides of the market, meaning the supply and demand. They operate like a "syndicate" and do not follow indicators, robots, fundamentals, or anything else, but...

Despite their cunning ways, it's not easy for them to hide. It's very easy to see their "footprints" in the market and follow what they are doing. But there is also something they cannot hide, I will reveal it one day...

Finally, these people have been working in the same way for years...
They buy goods at low prices and then sell them to us at high prices... using the media to "show scarcity," and when the goods run out, they use the media to "show abundance" to make us afraid and sell at a loss!
You can trade while being in the midst of these "big boys" if you decide to abandon the ignorance we are fed by the media, brokers, book writers who have never even traded an account worth $100!

I feel like I have written in code, these things are just out in the open, we are just LAZY.
---

 

[stakehigh]

Mkuu naheshimu Sana mchango wako mkubwa WA elimu kwenye suala la soko, nakiri mimi ni mwanafunzi kwako. Ila nikupe siri moja....

Sijuhi Kiswahili kizuri cha neno "manipulate" soko "huvurugwa" soko unalosoma kwenye vitabu ni tofauti na soko halisi ambayo kuna big boys, aka "smart money" hawa wanazo pesa za kutosha kupeleka soko wanapotaka liende, hawa wanaweza KIHALALI kuona pande zote za soko yaani supply na demand, hawa hufanya kazi zao kama "syndicate" hawa hawafuati indicator, robot, fundamental wala nini ila......

Pamoja na ujanja wao sio rahisi kwao kujificha, ni rahisi Sana kuona "nyayo" zao sokoni na kufuata wanachofanya, ila pia kuna kitu hawawezi kuficha nitaiweka wazi siku moja..

Mwisho hawa Watu hufanya kazi zoo miaka nenda kwa staili moja.....
Wananua mzigo kwa bei ya chini kisha wanatuuzia kwa bei ya juu....kwa kutumia media "kuonyesha kuna uhaba" na mzigo ukiisha wanatumia media "kuonyesha kuna mzigo mwingi" ili tuogope na kuuza kwa hasara!
Unaweza kutrade ukiwa mgongoni mwa "big boys" ukiamua kuachana na ujinga tunalishwa na media, brokers, waandishi wa vitabu ambao hata hawajawahi kutrade hata account ya Dola 100!

Nahisi nimeandika kwa kificho, haya mambo yapo wazi tu sema Sisi ni WAVIVU,

Izi ni MYTHS, in forex hakuna manipulation, everything that happens kwenye forex its planned na unachotrade ndo uhalisia wa input na export ya pesa husika! pia all big players unaowajua hakuna anaetrade kupata faida wao wana motives tafauti kabisa

 

[Smart911]

Ahsante kwa elimu...


Cc: Mahondaw

 

[Ndebile]

Izi ni MYTHS, in forex hakuna manipulation, everything that happens kwenye forex its planned na unachotrade ndo uhalisia wa input na export ya pesa husika! pia all big players unaowajua hakuna anaetrade kupata faida wao wana motives tafauti kabisa

Endelea kuamini hivyo

 

[stakehigh]

Endelea kuamini hivyo

with evidence

 

[Teknocrat]

Mkuu naheshimu Sana mchango wako mkubwa WA elimu kwenye suala la soko, nakiri mimi ni mwanafunzi kwako. Ila nikupe siri moja....

Sijuhi Kiswahili kizuri cha neno "manipulate" soko "huvurugwa" soko unalosoma kwenye vitabu ni tofauti na soko halisi ambayo kuna big boys, aka "smart money" hawa wanazo pesa za kutosha kupeleka soko wanapotaka liende, hawa wanaweza KIHALALI kuona pande zote za soko yaani supply na demand, hawa hufanya kazi zao kama "syndicate" hawa hawafuati indicator, robot, fundamental wala nini ila......

Pamoja na ujanja wao sio rahisi kwao kujificha, ni rahisi Sana kuona "nyayo" zao sokoni na kufuata wanachofanya, ila pia kuna kitu hawawezi kuficha nitaiweka wazi siku moja..

Mwisho hawa Watu hufanya kazi zoo miaka nenda kwa staili moja.....
Wananua mzigo kwa bei ya chini kisha wanatuuzia kwa bei ya juu....kwa kutumia media "kuonyesha kuna uhaba" na mzigo ukiisha wanatumia media "kuonyesha kuna mzigo mwingi" ili tuogope na kuuza kwa hasara!
Unaweza kutrade ukiwa mgongoni mwa "big boys" ukiamua kuachana na ujinga tunalishwa na media, brokers, waandishi wa vitabu ambao hata hawajawahi kutrade hata account ya Dola 100!

Nahisi nimeandika kwa kificho, haya mambo yapo wazi tu sema Sisi ni WAVIVU,

Yaani umepiga kwenye mshono, niliaangalia hii documentary , iliniacha mdomo wazi
Na wale waliokamatwa waliokuwa ni small fish, lakini big fish hakuna hata mmoja aliyekamatwa...
Watu wanakiri walikuwa wanatengeneza AI bots miaka hiyo zikavuruge soko....

The Men Who Stole the World – And Got Away With It

A look at how top executives on Wall Street helped trigger a global financial crisis – and how it may happen again.

www.aljazeera.com

 

[stakehigh]

Yaani umepiga kwenye mshono, niliaangalia hii documentary , iliniacha mdomo wazi
Na wale waliokamatwa waliokuwa ni small fish, lakini big fish hakuna hata mmoja aliyekamatwa...
Watu wanakiri walikuwa wanatengeneza AI bots miaka hiyo zikavuruge soko....

The Men Who Stole the World – And Got Away With It

A look at how top executives on Wall Street helped trigger a global financial crisis – and how it may happen again.

www.aljazeera.com

hehehe trading haiko ivo, 🤣 🤣 ! makampuni mengi tu makubwa yanaloose vizuri kabisa

 

[stakehigh]

View: https://www.youtube.com/watch?v=Dgbu7BlqnFs