Kalman Synergy Oscillator (KSO)The Kalman Synergy Oscillator (KSO) is an innovative technical indicator that combines the Kalman filter with two well-established momentum oscillators: the Relative Strength Index (RSI) and Williams %R. This combination aims to provide traders with a more refined tool for market analysis.
The use of the Kalman filter is a key feature of the KSO. This sophisticated algorithm is known for its ability to extract meaningful signals from noisy data. In financial markets, this translates to smoothing out price action while maintaining responsiveness to genuine market movements. By applying the Kalman filter to price data before calculating the RSI and Williams %R, the KSO potentially offers more stable and reliable signals.
The synergy between the Kalman-filtered price data and the two momentum indicators creates an oscillator that attempts to capture market dynamics more effectively. The RSI contributes its strength in measuring the magnitude and speed of price movements, while Williams %R adds sensitivity to overbought and oversold conditions. Basing these calculations on Kalman-filtered data may help reduce false signals and provide a clearer picture of underlying market trends.
A notable aspect of the KSO is its dynamic weighting system. This approach adjusts the relative importance of the RSI and Williams %R based on their current strengths, allowing the indicator to emphasize the most relevant information as market conditions change. This flexibility, combined with the noise-reduction properties of the Kalman filter, positions the KSO as a potentially useful tool for different market conditions.
In practice, traders might find that the KSO offers several potential benefits:
Smoother oscillator movements, which could aid in trend identification and reversal detection.
Possibly reduced whipsaws, particularly in choppy or volatile markets.
Potential for improved divergence detection, which might lead to more timely reversal signals.
Consistent performance across different timeframes, due to the adaptive nature of the Kalman filter.
While the KSO builds upon existing concepts in technical analysis, its integration of the Kalman filter with traditional momentum indicators offers traders an interesting tool for market analysis. It represents an attempt to address common challenges in technical analysis, such as noise reduction and false signal minimization.
As with any technical indicator, the KSO should be used as part of a broader trading strategy rather than in isolation. Its effectiveness will depend on how well it aligns with a trader's individual approach and market conditions. For traders looking to explore a more refined momentum oscillator, the Kalman Synergy Oscillator could be a worthwhile addition to their analytical toolkit.
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Bullrun Profit Maximizer [QuantraSystems]Bullrun Profit Maximizer
Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The "Adaptive Pairwise Momentum System" is not a prototype to the Bullrun Profit Maximizer (BPM) . The Bullrun Profit Maximizer is a fully re-engineered, higher frequency momentum system.
The Bullrun Profit Maximizer (BPM) uses a completely different filter logic and refines momentum calculations, specifically to support higher frequency trading on Crypto's Blue Chip assets. It correctly calculates fees and slippage by compounding them against System Profit before plotting the equity curve.
Unlike prior systems, this script utilizes a completely new filter logic and refined momentum calculation, specifically built to support higher frequency trading on blue-chip assets, while minimizing the impact of fees and slippage.
While the APMS focuses on Macro Trend Alignment, the BPM instead applies an equity curve based filter, allowing for targeted precision on the current asset’s trend without relying on broader market conditions. This approach delivers more responsive and asset specific signals, enhancing agility in today’s fast paced crypto markets.
The BPM dynamically optimizes capital allocation across up to four high performing assets, ensuring that the portfolio adapts swiftly to changing market conditions. The system logic consists of sophisticated quantitative methods, rapid momentum analysis and alpha cyclicality/seasonality optimizations. The overarching goal is to ensure that the portfolio is always invested in the highest performing asset based on dynamic market conditions, while at the same time managing risk through rapid asset filters and internal mechanisms like alpha cyclicality, volatility and beta analysis.
In addition to these core functionalities, the BPM comes with the typical Quantra Systems UI design, structured to reduce data clutter and provide users with only the most essential, impactful information. The BPM UI format delivers clear and easy to read signals. It enables rapid decision making in a high frequency environment without compromising on depth or accuracy.
Bespoke Logic Filtering with Equity Curve Precision
The BPM script utilizes a completely new methodology and focuses on intraday rotations of blue-chip crypto assets, while previously built systems were designed with a longer term focus in mind.
In response to the need for more precise signal generation, the BPM replaces the previous macro trend filter with a new, highly specific equity curve activation filter. This unique logic filter is driven solely by the performance trends of the asset currently held by the system. By analyzing the equity curve directly, this system can make more targeted, timely allocations based on asset specific momentum, allowing for quick adjustments that are more relevant to the held asset rather than general market conditions.
The benefits of this new, unique approach are twofold: first, it avoids premature allocation shifts based on broader macro movements, and second, it enables the system to adapt dynamically to the performance of each asset individually. This asset specific filtering allows traders to capitalize on localized strength within individual blue-chip cryptoassets without being affected by lags in the overall market trend.
High Frequency Momentum Calculation for Enhanced Flexibility
The BPM incorporates a newly designed momentum calculation that increases its suitability across lower timeframes. This new momentum indicator captures and processes more data points within a shorter window than ever before, rather than extending bar intervals and potentially losing high frequency detail. This creates a smooth, data rich featureset that is especially suited for blue-chip assets, where liquidity reduces slippage and fees, making higher frequency trading viable.
By retaining more data, this system captures subtle shifts in momentum more effectively than traditional approaches, offering higher resolution insights. These modifications result in a system capable of generating highly responsive signals on faster timeframes, empowering traders to act quickly in volatile markets.
User Interface and Enhanced Readability
The BPM also features a reimagined, streamlined user interface, making it easier than ever to monitor essential signals at a glance. The new layout minimizes extraneous data points in the tables, leaving only the most actionable information for traders. This cleaner presentation is purpose built to help traders identify the strongest asset in real time, with clear, color coded signals to facilitate swift decision making in fast moving markets.
Equity Stats Table : Designed for clarity, the stats table focuses on the current allocation’s performance metrics, emphasizing the most critical metrics without unnecessary clutter.
Color Coded Highlights : The interface includes the option to highlight both the current top performing asset, and historical allocations - with indicators of momentum shifts and performance metrics readily accessible.
Clear Signals : Visual cues are presented in an enhanced way to improve readability, including simplified line coloring, and improve visualization of the outperforming assets in the allocation table.
Dynamic Asset Reallocation
The BPM dynamically allocates capital to the strongest performing asset in a selected pool. This system incorporates a re-engineered, pairwise momentum measurement designed to operate at higher frequencies. The system evaluates each asset against others in real time, ensuring only the highest momentum asset receives allocation. This approach keeps the portfolio positioned for maximum efficiency, with an updated weighting logic that favors assets showing both strength and sustainability.
Position Changes and Slippage Calculation
Position changes are optimized for faster reallocation, with realistic slippage and fee calculations factored into each trade. The system’s structure minimizes the impact of these costs on blue-chip assets, allowing for more active management on short timeframes without incurring significant drag on performance.
A Special Note on Fees + Slippage
In the image above, the system has been applied to four different timeframes - 12h, 8h, 4h and 1h - using identical settings and a selected slippage and fees amount of 0.2%. In this stress test, we isolate the choppy downwards period from the previous Bitcoin all time high - set in March 2024, to the current date where Bitcoin is currently sitting at around the same level.
This illustrates an important concept: starting at the 12h, the system performed better as the timeframes decreased. In fact, only on the 4hr chart did the system equity curve make a new all time high alongside Bitcoin. It is worth noting that market phases that are “non-trending” are generally the least profitable periods to use a momentum/trend system - as most systems will get caught by false momentum and will “buy the top,” and then proceed to “sell the bottom.”
Lower timeframes typically offer more data points for the algorithm to compute over, and enable quicker entries and exits within a robust system, often reducing downside risk and compounding gains more effectively - in all market environments.
However, slippage, fees, and execution constraints are still limiting factors. Although blue-chip cryptocurrencies are more liquid and can be traded with lower fees compared to low cap assets, frequent trading on lower timeframes incurs cumulative slippage costs. With the BPM system set to a realistic slippage rate of 0.2% per trade, this example emphasizes how even lower fees impact performance as trade frequency increases.
Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on.
Number of Position Changes
Understanding the number of position changes in a strategy is critical to assessing its feasibility in real world trading. Frequent position changes can lead to increased costs due to slippage and fees. Monitoring the number of position changes provides insight into the system’s behavior - helping to evaluate how active the strategy is and whether it aligns with the trader's desired time input for position management.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a 100% allocation to Bitcoin, the highest market cap cryptoasset. This allows users to easily compare the performance of the dynamic rotation system with that of a more traditional investment strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the Bullrun Profit Maximizer - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Usage Summary:
While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways.
It can be used as above, or as a factor in forming part of a broader asset selection tool, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look at, for long-only trading setups on an intrabar timeframe.
Summary
The Bullrun Profit Maximizer is an advanced tool tailored for traders, offering the precision and agility required in today’s markets. With its asset specific equity curve filter, reworked momentum analysis, and streamlined user interface, this system is engineered to maximize gains and minimize risk during bullmarkets, with a strong focus on risk adjusted performance.
Its refined approach, focused on high resolution data processing and adaptive reallocation, makes it a powerful choice for traders looking to capture high quality trends on clue-chip assets, no matter the market’s pace.
Fusion MFI RSIHello fellas,
This superb indicator summons two monsters called Relative Strength Index (RSI) and Money Flow Index (MFI) and plays the Yu-Gi-Oh! card "Polymerization" to combine them.
Overview
The Fusion MFI RSI Indicator is an advanced analytical tool designed to provide a nuanced understanding of market dynamics by combining the Relative Strength Index (RSI) and the Money Flow Index (MFI). Enhanced with sophisticated smoothing techniques and the Inverse Fisher Transform (IFT), this indicator excels in identifying key market conditions such as overbought and oversold states, trends, and potential reversal points.
Key Features (Brief Overview)
Fusion of RSI and MFI: Integrates momentum and volume for a comprehensive market analysis.
Advanced Smoothing Techniques: Employs Hann Window, Jurik Moving Average (JMA), T3 Smoothing, and Super Smoother to refine signals.
Inverse Fisher Transform (IFT) Enhances the clarity and distinctiveness of indicator outputs.
Detailed Feature Analysis
Fusion of RSI and MFI
RSI (Relative Strength Index): Developed by J. Welles Wilder Jr., the RSI measures the speed and magnitude of directional price movements. Wilder recommended using a 14-day period and identified overbought conditions above 70 and oversold conditions below 30.
MFI (Money Flow Index): Created by Gene Quong and Avrum Soudack, the MFI combines price and volume to measure trading pressure. It is typically calculated using a 14-day period, with over 80 considered overbought and under 20 as oversold.
Application in Fusion: By combining RSI and MFI, the indicator leverages RSI's sensitivity to price changes with MFI's volume-weighted confirmation, providing a robust analysis tool. This combination is particularly effective in confirming the strength behind price movements, making the signals more reliable.
Advanced Smoothing Techniques
Hann Window: Traditionally used to reduce the abrupt data discontinuities at the edges of a sample, it is applied here to smooth the price data.
Jurik Moving Average (JMA): Known for preserving the timing and smoothness of the data, JMA reduces market noise effectively without significant lag.
T3 Smoothing: Developed to respond quickly to market changes, T3 provides a smoother response to price fluctuations.
Super Smoother: Filters out high-frequency noise while retaining important trends.
Application in Fusion: These techniques are chosen to refine the output of the combined RSI and MFI values, ensuring the indicator remains responsive yet stable, providing clearer and more actionable signals.
Inverse Fisher Transform (IFT):
Developed by John Ehlers, the IFT transforms oscillator outputs to enhance the clarity of extreme values. This is particularly useful in this fusion indicator to make critical turning points more distinct and actionable.
Mathematical Calculations for the Fusion MFI RSI Indicator
RSI (Relative Strength Index)
The RSI is calculated using the following steps:
Average Gain and Average Loss: First, determine the average gain and average loss over the specified period (typically 14 days). This is done by summing all the gains and losses over the period and then dividing each by the period.
Average Gain = (Sum of Gains over the past 14 periods) / 14
Average Loss = (Sum of Losses over the past 14 periods) / 14
Relative Strength (RS): This is the ratio of average gain to average loss.
RS = Average Gain / Average Loss
RSI: Finally, the RSI is calculated using the RS value:
RSI = 100 - (100 / (1 + RS))
MFI (Money Flow Index)
The MFI is calculated using several steps that incorporate both price and volume:
Typical Price: Calculate the typical price for each period.
Typical Price = (High + Low + Close) / 3
Raw Money Flow: Multiply the typical price by the volume for the period.
Raw Money Flow = Typical Price * Volume
Positive and Negative Money Flow: Compare the typical price of the current period to the previous period to determine if the money flow is positive or negative.
If today's Typical Price > Yesterday's Typical Price, then Positive Money Flow = Raw Money Flow; Negative Money Flow = 0
If today's Typical Price < Yesterday's Typical Price, then Negative Money Flow = Raw Money Flow; Positive Money Flow = 0
Money Flow Ratio: Calculate the ratio of the sum of Positive Money Flows to the sum of Negative Money Flows over the past 14 periods.
Money Flow Ratio = (Sum of Positive Money Flows over 14 periods) / (Sum of Negative Money Flows over 14 periods)
MFI: Finally, calculate the MFI using the Money Flow Ratio.
MFI = 100 - (100 / (1 + Money Flow Ratio))
Fusion of RSI and MFI
The final Fusion MFI RSI value could be calculated by averaging the IFT-transformed values of RSI and MFI, providing a single oscillator value that reflects both momentum and volume-weighted price action:
Fusion MFI RSI = (MFI weight * MFI) + (RSI weight * RSI)
Suggested Settings and Trading Rules
Original Usage
RSI: Wilder suggested buying when the RSI moves above 30 from below (enter long) and selling when the RSI moves below 70 from above (enter short). He recommended exiting long positions when the RSI reaches 70 or higher and exiting short positions when the RSI falls below 30.
MFI: Quong and Soudack recommended buying when the MFI is below 20 and starts rising (enter long), and selling when it is above 80 and starts declining (enter short). They suggested exiting long positions when the MFI reaches 80 or higher and exiting short positions when the MFI falls below 20.
Fusion Application
Settings: Use a 14-day period for this indicator's calculations to maintain consistency with the original settings suggested by the inventors.
Trading Rules:
Enter Long Signal: Consider entering a long position when both RSI and MFI are below their respective oversold levels and begin to rise. This indicates strong buying pressure supported by both price momentum and volume.
Exit Long Signal: Exit the long position when either RSI or MFI reaches its respective overbought threshold, suggesting a potential reversal or decrease in buying pressure.
Enter Short Signal: Consider entering a short position when both indicators are above their respective overbought levels and begin to decline, suggesting that selling pressure is mounting.
Exit Short Signal: Exit the short position when either RSI or MFI falls below its respective oversold threshold, indicating diminishing selling pressure and a potential upward reversal.
How to Use the Indicator
Select Source and Timeframe: Choose the data source and the timeframe for analysis.
Configure Fusion Settings: Adjust the weights for RSI and MFI.
Choose Smoothing Technique: Select and configure the desired smoothing method to suit the market conditions and personal preference.
Enable Fisherization: Optionally apply the Inverse Fisher Transform to enhance signal clarity.
Customize Visualization: Set up gradient coloring, background plots, and bands according to your preferences.
Interpret the Indicator: Use the Fusion value and visual cues to identify market conditions and potential trading opportunities.
Conclusion
The Fusion MFI RSI Indicator integrates classical and modern technical analysis concepts to provide a comprehensive tool for market analysis. By combining RSI and MFI with advanced smoothing techniques and the Inverse Fisher Transform, this indicator offers enhanced insights, aiding traders in making more informed and timely trading decisions. Customize the settings to align with your trading strategy and leverage this powerful tool to navigate financial markets effectively.
Best regards,
simwai
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Credits to:
@loxx – T3
@everget – JMA
@cheatcountry – Hann Window
Stochastic Order Flow Momentum [ScorsoneEnterprises]This indicator implements a stochastic model of order flow using the Ornstein-Uhlenbeck (OU) process, combined with a Kalman filter to smooth momentum signals. It is designed to capture the dynamic momentum of volume delta, representing the net buying or selling pressure per bar, and highlight potential shifts in market direction. The volume delta data is sourced from TradingView’s built-in functionality:
www.tradingview.com
For a deeper dive into stochastic processes like the Ornstein-Uhlenbeck model in financial contexts, see these research articles: arxiv.org and arxiv.org
The SOFM tool aims to reveal the momentum and acceleration of order flow, modeled as a mean-reverting stochastic process. In markets, order flow often oscillates around a baseline, with bursts of buying or selling pressure that eventually fade—similar to how physical systems return to equilibrium. The OU process captures this behavior, while the Kalman filter refines the signal by filtering noise. Parameters theta (mean reversion rate), mu (mean level), and sigma (volatility) are estimated by minimizing a squared-error objective function using gradient descent, ensuring adaptability to real-time market conditions.
How It Works
The script combines a stochastic model with signal processing. Here’s a breakdown of the key components, including the OU equation and supporting functions.
// Ornstein-Uhlenbeck model for volume delta
ou_model(params, v_t, lkb) =>
theta = clamp(array.get(params, 0), 0.01, 1.0)
mu = clamp(array.get(params, 1), -100.0, 100.0)
sigma = clamp(array.get(params, 2), 0.01, 100.0)
error = 0.0
v_pred = array.new(lkb, 0.0)
array.set(v_pred, 0, array.get(v_t, 0))
for i = 1 to lkb - 1
v_prev = array.get(v_pred, i - 1)
v_curr = array.get(v_t, i)
// Discretized OU: v_t = v_{t-1} + theta * (mu - v_{t-1}) + sigma * noise
v_next = v_prev + theta * (mu - v_prev)
array.set(v_pred, i, v_next)
v_curr_clean = na(v_curr) ? 0 : v_curr
v_pred_clean = na(v_next) ? 0 : v_next
error := error + math.pow(v_curr_clean - v_pred_clean, 2)
error
The ou_model function implements a discretized Ornstein-Uhlenbeck process:
v_t = v_{t-1} + theta (mu - v_{t-1})
The model predicts volume delta (v_t) based on its previous value, adjusted by the mean-reverting term theta (mu - v_{t-1}), with sigma representing the volatility of random shocks (approximated in the Kalman filter).
Parameters Explained
The parameters theta, mu, and sigma represent distinct aspects of order flow dynamics:
Theta:
Definition: The mean reversion rate, controlling how quickly volume delta returns to its mean (mu). Constrained between 0.01 and 1.0 (e.g., clamp(array.get(params, 0), 0.01, 1.0)).
Interpretation: A higher theta indicates faster reversion (short-lived momentum), while a lower theta suggests persistent trends. Initial value is 0.1 in init_params.
In the Code: In ou_model, theta scales the pull toward \mu, influencing the predicted v_t.
Mu:
Definition: The long-term mean of volume delta, representing the equilibrium level of net buying/selling pressure. Constrained between -100.0 and 100.0 (e.g., clamp(array.get(params, 1), -100.0, 100.0)).
Interpretation: A positive mu suggests a bullish bias, while a negative mu indicates bearish pressure. Initial value is 0.0 in init_params.
In the Code: In ou_model, mu is the target level that v_t reverts to over time.
Sigma:
Definition: The volatility of volume delta, capturing the magnitude of random fluctuations. Constrained between 0.01 and 100.0 (e.g., clamp(array.get(params, 2), 0.01, 100.0)).
Interpretation: A higher sigma reflects choppier, noisier order flow, while a lower sigma indicates smoother behavior. Initial value is 0.1 in init_params.
In the Code: In the Kalman filter, sigma contributes to the error term, adjusting the smoothing process.
Summary:
theta: Speed of mean reversion (how fast momentum fades).
mu: Baseline order flow level (bullish or bearish bias).
sigma: Noise level (variability in order flow).
Other Parts of the Script
Clamp
A utility function to constrain parameters, preventing extreme values that could destabilize the model.
ObjectiveFunc
Defines the objective function (sum of squared errors) to minimize during parameter optimization. It compares the OU model’s predicted volume delta to observed data, returning a float to be minimized.
How It Works: Calls ou_model to generate predictions, computes the squared error for each timestep, and sums it. Used in optimization to assess parameter fit.
FiniteDifferenceGradient
Calculates the gradient of the objective function using finite differences. Think of it as finding the "slope" of the error surface for each parameter. It nudges each parameter (theta, mu, sigma) by a small amount (epsilon) and measures the change in error, returning an array of gradients.
Minimize
Performs gradient descent to optimize parameters. It iteratively adjusts theta, mu, and sigma by stepping down the "hill" of the error surface, using the gradients from FiniteDifferenceGradient. Stops when the gradient norm falls below a tolerance (0.001) or after 20 iterations.
Kalman Filter
Smooths the OU-modeled volume delta to extract momentum. It uses the optimized theta, mu, and sigma to predict the next state, then corrects it with observed data via the Kalman gain. The result is a cleaner momentum signal.
Applied
After initializing parameters (theta = 0.1, mu = 0.0, sigma = 0.1), the script optimizes them using volume delta data over the lookback period. The optimized parameters feed into the Kalman filter, producing a smoothed momentum array. The average momentum and its rate of change (acceleration) are calculated, though only momentum is plotted by default.
A rising momentum suggests increasing buying or selling pressure, while a flattening or reversing momentum indicates fading activity. Acceleration (not plotted here) could highlight rapid shifts.
Tool Examples
The SOFM indicator provides a dynamic view of order flow momentum, useful for spotting directional shifts or consolidation.
Low Time Frame Example: On a 5-minute chart of SEED_ALEXDRAYM_SHORTINTEREST2:NQ , a rising momentum above zero with a lookback of 5 might signal building buying pressure, while a drop below zero suggests selling dominance. Crossings of the zero line can mark transitions, though the focus is on trend strength rather than frequent crossovers.
High Time Frame Example: On a daily chart of NYSE:VST , a sustained positive momentum could confirm a bullish trend, while a sharp decline might warn of exhaustion. The mean-reverting nature of the OU process helps filter out noise on longer scales. It doesn’t make the most sense to use this on a high timeframe with what our data is.
Choppy Markets: When momentum oscillates near zero, it signals indecision or low conviction, helping traders avoid whipsaws. Larger deviations from zero suggest stronger directional moves to act on, this is on $STT.
Inputs
Lookback: Users can set the lookback period (default 5) to adjust the sensitivity of the OU model and Kalman filter. Shorter lookbacks react faster but may be noisier; longer lookbacks smooth more but lag slightly.
The user can also specify the timeframe they want the volume delta from. There is a default way to lower and expand the time frame based on the one we are looking at, but users have the flexibility.
No indicator is 100% accurate, and SOFM is no exception. It’s an estimation tool, blending stochastic modeling with signal processing to provide a leading view of order flow momentum. Use it alongside price action, support/resistance, and your own discretion for best results. I encourage comments and constructive criticism.
Machine Learning: STDEV Oscillator [YinYangAlgorithms]This Indicator aims to fill a gap within traditional Standard Deviation Analysis. Rather than its usual applications, this Indicator focuses on applying Standard Deviation within an Oscillator and likewise applying a Machine Learning approach to it. By doing so, we may hope to achieve an Adaptive Oscillator which can help display when the price is deviating from its standard movement. This Indicator may help display both when the price is Overbought or Underbought, and likewise, where the price may face Support and Resistance. The reason for this is that rather than simply plotting a Machine Learning Standard Deviation (STDEV), we instead create a High and a Low variant of STDEV, and then use its Highest and Lowest values calculated within another Deviation to create Deviation Zones. These zones may help to display these Support and Resistance locations; and likewise may help to show if the price is Overbought or Oversold based on its placement within these zones. This Oscillator may also help display Momentum when the High and/or Low STDEV crosses the midline (0). Lastly, this Oscillator may also be useful for seeing the spacing between the High and Low of the STDEV; large spacing may represent volatility within the STDEV which may be helpful for seeing when there is Momentum in the form of volatility.
Tutorial:
Above is an example of how this Indicator looks on BTC/USDT 1 Day. As you may see, when the price has parabolic movement, so does the STDEV. This is due to this price movement deviating from the mean of the data. Therefore when these parabolic movements occur, we create the Deviation Zones accordingly, in hopes that it may help to project future Support and Resistance locations as well as helping to display when the price is Overbought and Oversold.
If we zoom in a little bit, you may notice that the Support Zone (Blue) is smaller than the Resistance Zone (Orange). This is simply because during the last Bull Market there was more parabolic price deviation than there was during the Bear Market. You may see this if you refer to their values; the Resistance Zone goes to ~18k whereas the Support Zone is ~10.5k. This is completely normal and the way it is supposed to work. Due to the nature of how STDEV works, this Oscillator doesn’t use a 1:1 ratio and instead can develop and expand as exponential price action occurs.
The Neutral (0) line may also act as a Support and Resistance location. In the example above we can see how when the STDEV is below it, it acts as Resistance; and when it’s above it, it acts as Support.
This Neutral line may also provide us with insight as towards the momentum within the market and when it has shifted. When the STDEV is below the Neutral line, the market may be considered Bearish. When the STDEV is above the Neutral line, the market may be considered Bullish.
The Red Line represents the STDEV’s High and the Green Line represents the STDEV’s Low. When the STDEV’s High and Low get tight and close together, this may represent there is currently Low Volatility in the market. Low Volatility may cause consolidation to occur, however it also leaves room for expansion.
However, when the STDEV’s High and Low are quite spaced apart, this may represent High levels of Volatility in the market. This may mean the market is more prone to parabolic movements and expansion.
We will conclude our Tutorial here. Hopefully this has given you some insight into how applying Machine Learning to a High and Low STDEV then creating Deviation Zones based on it may help project when the Momentum of the Market is Bullish or Bearish; likewise when the price is Overbought or Oversold; and lastly where the price may face Support and Resistance in the form of STDEV.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Stochastic Zone Strength Trend [wbburgin]The Stochastic Zone Strength Trend indicator is a very powerful momentum and trend indicator that 1) identifies trend direction and strength, 2) determines pullbacks and reversals (including possible entry/exit conditions), 3) works on every instrument, and 4) can filter out ranges. I have some examples below on how to use it to its full effectiveness. It is composed of two components: Stochastic Zone Strength and Stochastic Trend Strength .
Stochastic Zone Strength
At its most basic level, the stochastic Zone Strength plots the momentum of the price action of the instrument, and identifies bearish and bullish changes with a high degree of accuracy. Think of the stochastic Zone Strength as a much more robust version of the RSI. Momentum-change thresholds are demonstrated by the "20" and "80" levels on the indicator (see below image).
Stochastic Trend Strength
The stochastic Trend Strength component of the script uses resistance in each candlestick to calculate the trend strength of the instrument. I will go more into detail about the settings after my description of how to use the indicator, but there are two forms of the stochastic Trend Strength:
Anchored at 50 (directional stochastic Trend Strength:
The directional stochastic Trend Strength can be used similarly to the MACD difference or other histogram-like indicators : a rising plot indicates an upward trend, while a falling plot indicates a downward trend.
Anchored at 0 (nondirectional stochastic Trend Strength:
The nondirectional stochastic Trend Strength can be used similarly to the ADX or other non-directional indicators : a rising plot indicates increasing trend strength, and look at the stochastic Zone Strength component and your instrument to determine if this indicates increasing bullish strength or increasing bearish strength (see photo below):
(In the above photo, a bearish divergence indicated that the high Trend Strength predicted a strong downwards move, which was confirmed shortly after. Later, a bullish move upward by the Zone Strength while the Trend Strength was elevated predicated a strong upwards move, which was also confirmed. Note the period where the Trend Strength never reached above 80, which indicated a ranging period (and thus unprofitable to enter or exit)).
How to Use the Indicator
The above image is a good example on how to use the indicator to determine divergences and possible pivot points (lines and circles, respectively). I recommend using both the stochastic Zone Strength and the stochastic Trend Strength at the same time, as it can give you a robust picture of where momentum is in relation to the price action and its trajectory. Every color is changeable in the settings.
Settings
The Amplitude of the indicator is essentially the high-low lookback for both components.
The Wavelength of the indicator is how stretched-out you want the indicator to be: how many amplitudes do you want the indicator to process in one given bar.
A useful analogy that I use (and that I derived the names from) is from traditional physics. In wave motion, the Amplitude is the up-down sensitivity of the wave, and the Wavelength is the side-side stretch of the wave.
The Smoothing Factor of the settings is simply how smoothed you want the stochastic to be. It's not that important in most circumstances.
Trend Anchor was covered above (see my description of Trend Strength). The "Trend Transform MA Length" is the EMA length of the Trend Strength that you use to transform it into the directional oscillator. Think of the EMA being transformed onto the 50 line and then the Trend Strength being dragged relative to that.
Finally, the colors are changeable on the bottom.
Final Notes
As with previous and future invite-only scripts, I only restrict access to 1) maintain effectiveness of scripts, 2) because I use these scripts myself heavily, and/or 3) to support myself. Additionally, I will never make an restricted indicator that is not completely original in idea, scope, and execution.
Yours,
wbburgin
Vaidotas Momentum ScoreHello Traders!
Discover Myfractalrange latest addition on TradingView, Vaidotas Segenis Momentum Score.
How people calculate Momentum is subjective and many people (even professionals) use different Momentum formulas depending on how they view it. This is sometimes confusing for traders.
The purpose of this indicator is to identify periods of strong price momentum relative to historical volatility. Higher momentum scores indicate stronger price trends, while lower scores suggest weaker trends. Traders and investors may use this indicator to identify potential buy or sell signals based on the strength of price movements. The formula Vaidotas uses calculate Momentum Score for different periods based on the price data.
There are 3 different look back periods in the script, you will find them in "Input":
Period 1 : 10 Days
Period 2 : 30 Days
Period 3 : 90 Days
Now let's go over the different steps of the formula:
Step 1 - Calculate the daily normal returns : this gives the daily percentage change in price
Step 2 - Calculate the standard deviation of the daily normal returns over a specific look back period (Default: 100 days) : the standard deviation measures the volatility or dispersion of the returns
Step 4 - Calculate the squared standard deviation multiplied by the square root of the respective period: This is done for three different periods (Period 1, Period 2, Period 3), it amplifies the standard deviation by the square root of the period, which gives more weight to recent price changes.
Step 5 - Calculate the normal returns for each period: This calculates the percentage change in price over the specified period
Step 5 - Calculate the momentum score for each period: This score represents the relative strength or momentum of the price change compared to the expected volatility.
Using the momentum indicator involves interpreting the values and considering certain thresholds to make trading decisions. While there is no definitive rule for all markets and assets, we can provide you with a general guideline on how traders may want to use the indicator and explain the significance of certain values:
1) Strong Trend: When the momentum score is significantly positive (above a certain threshold, such as +2), it suggests a strong upward price trend.
2) Weak Trend: Conversely, when the momentum score is significantly negative (below a certain threshold, such as -2), it indicates a strong downward price trend. Traders may interpret this as a potential signal to enter or maintain a short position, expecting the trend to continue.
3) Lack of Trend: When the momentum score is close to zero, it suggests a lack of significant trend or sideways movement in the price. Values around 0 indicate a potential range-bound market or consolidation.
However, it's important to note that the specific threshold values for defining significant trends or reversals may vary depending on the asset, timeframe, and market conditions. Traders often adjust these thresholds based on their own experience and backtesting results.
Here are a few more examples to illustrate the use of the momentum indicator:
- Example 1 - Strong Uptrend Confirmation :
The momentum score is consistently above +2, indicating a strong upward trend. Traders may consider this as a potential signal to enter or maintain a long position, expecting the trend to continue.
- Example 2 - Reversal Signal :
The momentum score has been positive for an extended period but starts to decline and eventually crosses below -2. This could be seen as a potential reversal signal, suggesting that the uptrend is losing strength and a bearish trend might develop. Traders may consider exiting long positions or even taking short positions based on this reversal signal.
- Example 3 - Sideways Market :
The momentum score fluctuates around 0, without displaying any significant positive or negative values. This indicates a lack of clear trend and suggests that the asset is trading in a range or consolidating. Traders may choose to avoid taking new positions until a stronger trend emerges.
Why is it interesting to use different look back periods?
The use of different look back periods in the momentum indicator formula allows traders to assess momentum across multiple timeframes. By comparing the momentum results for each period, traders can gain a broader perspective on the strength of the trend and potential opportunities. Here's how a trader might use the different look back periods and their corresponding momentum results:
1) Identifying Consistency: Traders can compare the momentum results for different periods to assess the consistency of the trend. If the momentum scores for all periods are consistently positive or negative, it suggests a strong and consistent trend across multiple timeframes. This can provide traders with higher confidence in the trend's strength and potential trading opportunities.
2) Convergence or Divergence: Traders can analyze the relationship between the momentum results for different periods. If the momentum scores for all periods are converging (moving closer together), it indicates a higher degree of agreement across different timeframes and strengthens the signal. Conversely, if the momentum scores for different periods diverge (move apart), it may suggest a weakening or conflicting trend. Traders should exercise caution when the momentum scores diverge as it may signal a potential reversal or market uncertainty.
3) Confirmation of Momentum: Traders can use the momentum results for different periods to confirm the strength of a trend. For example, if the momentum scores for shorter periods (e.g., Period 1) are significantly higher than those for longer periods (e.g., Period 2 and Period 3), it suggests a recent increase in momentum and a potentially stronger trend. This confirmation can assist traders in making more informed trading decisions and timing their entries or exits.
4) Multiple Timeframe Analysis: Traders often employ a multiple timeframe analysis approach to validate their trading decisions. By comparing the momentum results for different periods, traders can assess the alignment of momentum across various timeframes. For instance, if the momentum scores for shorter, medium, and longer periods all indicate a strong trend in the same direction, it reinforces the conviction in the trade.
As a conclusion, the momentum indicator can be useful to traders for several reasons:
1) Identifying Trend Strength: The momentum indicator helps traders assess the strength of a price trend. When the momentum score is high, it suggests that the trend is strong and likely to continue. This information can be valuable for trend-following strategies, as it helps traders identify potentially profitable opportunities and stay on the right side of the market.
2) Spotting Reversals: Momentum indicators can also help traders identify potential trend reversals. When the momentum score diverges from the price movement, it may indicate a weakening trend or an upcoming reversal. Traders can use this signal to adjust their positions or look for opportunities to enter or exit trades.
3) Confirming Breakouts: Breakout traders often use momentum indicators to confirm the validity of a breakout. If a price breaks above a resistance level, and the momentum score also increases significantly, it provides additional confirmation that the breakout is strong and may continue. This helps traders have more confidence in their breakout trades.
4) Setting Stop Loss and Take Profit Levels: By understanding the strength of a price trend through the momentum indicator, traders can set appropriate stop-loss and take-profit levels. A strong momentum score may indicate that a trend is likely to continue, allowing traders to set wider profit targets. Conversely, a weak momentum score may suggest that the trend is losing steam, prompting traders to set tighter stop-loss levels to protect their capital.
4) Divergence Analysis: Momentum indicators can be used in conjunction with other technical indicators to identify divergences. Divergence occurs when the price and momentum indicator move in opposite directions. It can signal potential trend reversals or shifts in market sentiment, providing traders with opportunities to adjust their positions.
It's important to note that while momentum indicators can be useful tools, they should not be relied upon solely for making trading decisions. It's recommended to use them in conjunction with other technical analysis tools and consider other factors such as market conditions, risk management, and fundamental analysis. Remember that the momentum indicator is just one tool among many, and it's important to consider other factors such as volume, trend, volatility, and overall market conditions when making trading decisions. Additionally, using stop-loss orders and proper risk management techniques is crucial to mitigate potential losses.
We hope that you will find these explanations useful, please contact us by private message for access.
Enjoy!
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorised. This script is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. Myfractalrange is not responsible for any losses you may incur. Please invest wisely.
Multi indicators tableThis is a comprehensive trading tool that presents an overview of the market in a tabular format. It consists of five distinct categories of trading indicators : Volatility, Trend, Momentum, Reversal, and Volume. Each category includes a series of indicators that are widely used in the trading communauty.
The Volatility category includes the Average True Range (ATR) and Bollinger Bands indicators. The Trend category comprises the Average Directional Index (ADX), four Exponential Moving Averages (EMAs), Aroon, Parabolic SAR, and the Supertrend. The Momentum category includes the Stochastic Relative Strength Index (StochRSI), Money Flow Index (MFI), Williams %R, Relative Strength Index (RSI), and Commodity Channel Index (CCI). The Reversal category includes Parabolic SAR, Moving Average Convergence Divergence (MACD), and PP Supertrend. Finally, the Volume category includes the Volume Exponential Moving Average (EMA) indicator.
The indicators states are easily readable, the indicator case is colored based on his actual state. A bullish color (green by default), a bearish color (red by default),
a very bullish color (dark green by default), a very bearish color (dark red by default) and a neutral color (gray by default) displayed when the indicator doesn't give us a clear signal. Some indicators do not have a very bullish or very bearish state. Concerning volatility indicators, the bullish color indicates high volatility, the bearish color indicates low volatility, and the neutral color indicates normal volatility.
Most of the indicators displayed in the table are customizable, and traders can choose to hide the categories they don't want to use. The Indicator provides a quick and easily readable view on the market and allows traders to reduce the number of indicators on their chart making it lighter and more readable.
Probability Oscillator (Expo)█ Overview
The Probability Oscillator uses a Bayesian approach to measure the probability of a price movement and trend continuation. This approach considers the prior probability of a price movement and the current market conditions to identify trends, sentiment, momentum, and retracements.
█ How does the indicator work?
The Probability Oscillator is based on the idea of Bayesian probability , which is a way of using existing data to make predictions about the likelihood of an event occurring. This indicator uses the Bayesian probability model to analyze past trading activity and calculate the probability of a trend continuing. This function also considers the prior probability of a price movement and the current market conditions to analyze the likelihood of a retracement.
█ How to use
Investors can use this indicator to measure the market sentiment and the strength/direction of a trend. It does also give insights into momentum moves and retracements.
█ Indicator Customization
The user can change the trend approaches and input source as well as adjust the overbought and oversold areas to make the calculation more sensitive to retracements.
The user can change the sensitivity of the momentum function to adjust it only to identify the most significant momentum moves.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Squeeze mom MTF filtered by Wavetrend with div (Tilt)📋 Description :
This script is based on two famous indicators from @Lazybear : Squeeze Momentum and WaveTrend. fr.tradingview.com
The idea is to use the Wavetrend crossovers and filter them according to the momentum curve.
There is a multi timeframe module with automatic selection of the higher timeframe. The user can also choose his timeframe manually.
There is also a detection of regular and hidden divergences
🛠 Options :
- filtering the cross wave trend according to the momemtum curve
- active or not higher timeframe with automatic or manually timeframe selection
- display or not WaveTrend ans squeeze momentum
- Show a tape that signals when wavetrend is overbought or oversold
- choose colors and apparences
- display a panel for the higher timeframe value
Natural Market River [CC]The Natural Market River was created by Jim Sloman (Ocean Theory pgs 59-62) and this is another momentum indicator that is extremely similar to the previous indicator I published, the Natural Market Mirror . This has almost identical buy and sell signals but different way to handle calculations so I'm going to leave it up to you which one you will prefer. Since this is almost identical, the buy and sell signals work in the same way with both strong signals and normal ones. Buy when the line turns green and sell when it turns red.
Let me know what other indicators you would like to see me publish!
Natural Market Mirror [CC]The Natural Market Mirror was created by Jim Sloman (Ocean Theory pgs 49-57) and this is a continuation of my series from Jim Sloman's indicators. This indicator is also a momentum indicator and is very similar to the previous indicator I published, the Ocean Indicator and of course this indicator is built using ideas from the Ocean indicator. It may just be my opinion but I feel like this indicator provides better buy and sell signals in comparison. I built this using strong buy and sell indicators in addition to normal ones so darker colors are the strong signals and lighter colors are the normal signals. Buy when the line turns green and sell when it turns red.
Let me know what other indicators you would like me to publish!
Market phases 2.0The Market Phase 2.0 indicator is designed to display the following features:
1) The TREND OSCILLATOR : This trend oscillator indicates the trend of the stock/instrument. It is calculated on the basis of number of positive candles or negative candles formed during a specific period.
The oscillator oscillates around the zero horizontal line. The trend is considered bullish if the oscillator value is positive and the trend is considered negative if the oscillator value is negative.
2) The MOMENTUM OSCILLATOR:
The momentum oscillator indicates the short term momentum of the stock/instrument. It is calculated on the rate of change of close price for a specific period in the past.
The Momentum oscillator oscillates around the zero horizontal line. If the momentum oscillator has a positive value, the momentum is considered to be on the bullish side and similarly if the momentum oscillator has a negative value, the momentum is considered to be on the bearish side.
3) The SIGNAL LINE: The signal line is represented by the yellow color line. The Signal line combines the value of the Trend oscillator and the Momentum oscillator. The signal also moves around the zero line. There are two dotted lines above and below the zero line.
When the signal line crosses the upper dotted line, it indicates that the stock/instrument has moved on the upper side too quickly or sharply and the ongoing move may not continue for long. It may also be considered as overbought at that point. A red triangle appears at that point.
Similarly, when the signal line crosses the lower dotted line, it indicates that the stock/instrument has moved on the downside too quickly or sharply and the ongoing down move may not continue for long. It may also be considered as oversold at that point. A green triangle appears at that point.
The values for the look back period of the signal line and the values for the upper range and lower range of the indicator can be changed by going to the settings of the indicator.
***Disclaimer: The market movement depends upon a lot of factors which are beyond the scope of this indicator. Hence the indicator may display results not intended on rare occasions.
Trading in the markets involves involves huge risks and one should always follow his/her own research before taking any trading decisions.
Relative Strength Line by @iArpanKHello Traders!
I'm a Momentum Trader, following the Indian & US markets. Most of us are familiar with the Relative Strength (RS) indicator, popularized by Investor's Business Daily (IBD) and available on their MarketSmith platform. So, here I'm sharing a script that does the same and additionally pops an alert label when the RS line hits a new high (similar to Blue Dot appearance on MarketSmith charts).
User Settings
Inputs tab
Base Symbol : Symbol of the security/index with which you want to compare your current active symbol.
Period : Number of days since which you want to scan for a new high (default is 250 days, which approximately pops alerts for new 52 week high in RS). For example, if you want to look for new 10 days high in RS, set the Period to 10.
Style tab
RS Line : Change color using the palette provided (default is blue).
Alert Label : Show/hide alert labels by checking/unchecking the box. Change color using the palette provided. Change alert label symbol.
Precision : Default is two decimal places. Can be changed as per requirement.
Usage
The indicator consists of two components- the Relative Strength (RS) line & alert labels on new RS highs. Relative strength gives a measure of how the underlying security is performing with respect to a base index or security. For example, how is NSE:DIXON performing w.r.t NSE:NIFTY or how is NASDAQ:AAPL performing w.r.t. the TVC:SPX .
A rising RS line tells us that the underlying entity is outperforming the base entity. Similarly, a declining RS line shows under-performance of the underlying entity. A new high in RS (especially before a new high in price) often gives valuable information about the underlying security's strength w.r.t. the general market, and can tip us off to a possible breakout in the price in near future.
Making RS lists (list of stocks making new high in RS on heavy down days in index) can be very helpful to sort out leaders that are best resisting the decline and are likely to move up aggressively when the market turns favorable.
The concept of RS is extensively used by momentum traders and growth stock traders. When used in conjunction with price & volume action, this can be a very powerful tool in your trading arsenal. You can now easily spot RS trends and new highs visually by simply adding this indicator to your chart!
Conclusion
If you like this script, click on Add to favorite indicators , so that you can easily add this indicator from your favorites tab right away.
Thanks!
MACD Trend Squeezer V2This is a combination of a slightly sped up MACD overlay on top of a modified Bar Trend Squeeze or highly modified Momentum indicator. Helps to see the trend/momentum matched with the characteristics of the MACD and it's historiography. Very user friendly for adjusting color, transparency, depth, lines, size, etc.
MACD is the dark gray line.
Its signal slower line is orange.
Its historiography is the area fill blues and reds
Trend Squeezer / momentum are the Bars in the background.
// Changes from original version \\
Visual depth mostly. Most of the items are adjustable in the settings.
Increased user friendly inputs to adjust colors, lines, data, etc.
(darken / lighten and change background bar colors, increase/decrease line strengths and colors, adjust field data inputs)
The DiamondThe Diamond is a collection of 3 custom oscillators and the RSI. It tries to visualizing how the momentum is increasing and decreasing and gives some buy and sell signals.
Every Line explained:
Orange line: The SMI(Swing Momentum Indicator) it is alternating oscillator between the value -10 and 40 and has its baseline at 10. It showing accumulation and increase of momentum and is used as a trend confirmation
Purple line: The BTD(Buy the Dip) is a modified Version of the SMI. It should be used in Bull or Bearflags to time entries. Also the Horizontal lines can be used as Support or Resistance
Green/Red Band: This one is a custom made stochastic. In its calculation it smoothing Tops/Lows to reduce noise. Also the look is better.
White line: Just a 14-lenght RSI. I use it together with the SMI and BTD to get confirmation
The Indicator is doing best in the crypto market. High market cap Coins/USDT Pairs do better than low market cap and btc pairs. Also it should be only used on timeframes greater than 4h. 6h and daily preferred. On higher time frames you need to adjust the values of the BTD and SMI.
Bearish divergence on both Indicators in a down trending market do give a good short entry.
Bullish divergence on the daily gives good swing entries in a downtrend
Hophop Multiple Timeframe Momentum GridThis indicator is intended to highlight the over bought and over sold momentums for multiple timeframe
As of now it only supports StochRSI and also a variation of it that is more responsive than StochRsi called HophopRsi, I might consider adding more momentum indicators if it is desired
All the needed variables for StochRsi are included as the original indicator, feel free to change them as you normally do on StochRsi
On top of that you can select up to 4 higher timeframe , just make sure that your current timeframe is the smallest one
The top line of the graph shows the current timeframe momentum
1st line = high timeframe 1
2st line = high timeframe 2
3st line = high timeframe 3
4st line = high timeframe 4
Quick demonstration of the usage:
If you benefit from this indicator and you would like to see more of these, please support me by your tips
BTC Tip: 39bwXN1chms1yHskBaYwz76UhDakc7grJ7
LTC Tip: MGD3U9dBCBVctwnoCa1grU8ompxG6hUhMk
ETH Tip: 0xEE9684a5aceE85036527aB48E596DeE4627bD84b
Compare - Oscillator vs BTC momentumI've made a simple indicator to compare the momentum of a trading pair against the momentum of BTC to the dollar. I use it to see how a pair is affected by BTC's momentum... I wouldnt use it to trade off alone, but it can be a useful tool alongside other indicators.
The time range can be adjusted, but I wouldnt reccomend setting it to anything over 12M, or under 1W.... as I'm not sure if it would work.
Any feedback is welcome!
This is an idea I had after looking at a wonderful visualisation made by BarclayJames, link below:
www.tradingview.com
Directional Trend Index (DTI) This technique was described by William Blau in his book "Momentum,
Direction and Divergence" (1995). His book focuses on three key aspects
of trading: momentum, direction and divergence. Blau, who was an electrical
engineer before becoming a trader, thoroughly examines the relationship between
price and momentum in step-by-step examples. From this grounding, he then looks
at the deficiencies in other oscillators and introduces some innovative techniques,
including a fresh twist on Stochastics. On directional issues, he analyzes the
intricacies of ADX and offers a unique approach to help define trending and
non-trending periods.
Directional Trend Index is an indicator similar to DM+ developed by Welles Wilder.
The DM+ (a part of Directional Movement System which includes both DM+ and
DM- indicators) indicator helps determine if a security is "trending." William
Blau added to it a zeroline, relative to which the indicator is deemed positive or
negative. A stable uptrend is a period when the DTI value is positive and rising, a
downtrend when it is negative and falling.
SMT Oscillator: Smarter Money Divergence Detector [PhenLabs]📊Phenlabs - SMT Oscillator: Smarter Money Divergence Detector
Version: PineScript™v6
📌Description
The SMT Oscillator is a sophisticated tool designed to identify smart money divergence between two correlated assets. By analyzing the momentum and volume-weighted price action of a primary and secondary symbol, traders can spot subtle shifts in market dynamics that often precede significant price movements. This indicator is built to provide a clearer, more filtered view of inter-market relationships, solving the common problem of false signals and market noise. Its primary purpose is to equip traders with a quantifiable edge in detecting potential reversals or continuations that are not obvious on a standard price chart.
🚀Points of Innovation
Dual-Symbol Divergence Core: Directly compares momentum (RSI or MACD) between two user-selected symbols to pinpoint true SMT divergence.
Volume-Weighted Analysis: Integrates volume delta into the divergence calculation, giving more weight to moves backed by significant market participation.
Entropy Filter for Noise Reduction: Employs an entropy calculation to filter out low-quality signals during choppy or consolidating market conditions.
Predictive Forecast Line: Utilizes a linear regression model to project the oscillator’s future trajectory, offering a forward-looking glimpse of potential momentum shifts.
Customizable Signal Sensitivity: Allows fine-tuning of overbought and oversold levels to adapt to different market volatilities and trading styles.
Integrated Signal Alerts: Provides built-in alerts for bullish/bearish zero crosses and overbought/oversold conditions.
🔧Core Components
Momentum Engine: The user can select either RSI or MACD as the underlying engine for the divergence calculation, allowing for flexibility in analysis.
Normalization Function: Price data from both symbols is normalized using percentage change to ensure a true “apples-to-apples” comparison, regardless of their nominal price differences.
Divergence Calculator: The core algorithm that subtracts the secondary symbol’s momentum from the primary’s and normalizes the result using the combined standard deviation.
Smoothing Mechanism: An Exponential Moving Average (EMA) is applied to the raw oscillator output to reduce choppiness and provide a clearer signal line.
🔥Key Features
Multi-Asset Comparison: Go beyond single-asset analysis by comparing correlated pairs like ES/NQ or BTC/ETH to uncover hidden trading opportunities.
Heatmap Visualization: An optional heatmap mode provides an intuitive visual representation of divergence strength, making it easier to gauge market sentiment at a glance.
Configurable Lookback and Timeframe: Adjust the lookback period and analysis timeframe to suit your specific strategy, from short-term scalping to long-term trend analysis.
Signal Markers: Visual markers are plotted directly on the chart for bullish and bearish zero-line crossovers, providing clear entry and exit signals.
🎨Visualization
SMT Oscillator Line: The primary visual element, colored blue for bullish (positive) divergence and orange for bearish (negative) divergence.
Zero Line: A solid horizontal line at the zero level, indicating the equilibrium point between the two assets. Crossovers of this line signal a shift in relative strength.
Overbought/Oversold Zones: Dotted lines at the +80 and -80 levels (customizable) that highlight extreme divergence readings, often indicating potential exhaustion points.
Forecast Line: A predictive line that plots the anticipated path of the oscillator, giving traders an advanced warning of potential changes in momentum.
📖Usage Guidelines
Setting Categories
Primary Symbol
Default: (Chart Symbol)
Description: The main asset you are analyzing. Leave blank to use the symbol currently on your chart.
Secondary Symbol
Default: CME_MINI:ES1! (used with NASDAQ futures due to inherent heavy correlation
Description: The asset to compare against the primary symbol.
Lookback Period
Default: 14
Range: 8-100
Description: Controls the calculation window for momentum (RSI/MACD). Higher values result in a smoother, less sensitive oscillator.
Divergence Type
Default: RSI
Options: RSI, MACD
Description: Choose the momentum indicator to use for the divergence calculation.
Enable Volume Weighting
Default: true
Description: When enabled, gives more weight to divergence signals that are accompanied by significant volume.
✅Best Use Cases
Identifying high-probability reversal points by spotting divergence in overbought or oversold territory.
Confirming the strength of a trend by observing sustained positive or negative divergence.
Pairs trading by taking a long position on the outperforming asset and a short position on the underperforming one during a divergence.
Risk management by recognizing when a current trend is losing its underlying momentum.
⚠️Limitations
Requires Correlated Assets: The indicator’s effectiveness is highly dependent on the selection of two assets with a known correlation (e.g., ES and NQ).
Not a Standalone System: Divergence signals should be used in conjunction with other forms of analysis (price action, market structure) and not as a complete trading system.
Lagging by Nature: As it is based on moving averages and past price data, the oscillator is inherently lagging and may not capture all rapid price changes.
💡What Makes This Unique
Combined Momentum & Volume: Unlike standard oscillators, it fuses momentum with volume delta for a more robust “Smart Money” perspective.
Noise-Filtering Mechanism: The proprietary entropy filter is a unique feature designed to weed out insignificant market chatter and focus on high-conviction signals.
🔬How It Works
Data Normalization:
The script first normalizes the price data of the two selected symbols into percentage changes. This ensures that the comparison is fair, regardless of the difference in their price scales.
Momentum Calculation:
It then calculates the chosen momentum value (either RSI or MACD histogram) for each of the normalized price series.
Divergence Computation:
The core of the indicator lies in subtracting the momentum of the secondary symbol from the primary one. This raw divergence is then optionally weighted by volume and filtered for market noise (entropy) to produce the final oscillator value.
💡Note:
For best results, use this indicator on adequate timeframes to filter out market noise. Always confirm signals with price action analysis before entering a trade.
Indexrate Code A(i)Indexrate Code A is an indicator based on the Indexrate Code B indicator and is part of the Indexrate Code Algorithm Suite. This indicator is designed for automated trading platforms.
The Indexrate Code Algorithm Suite can be used for any trading instrument and on any existing market (Stock Market, Forex, Cryptocurrency Market, etc.).
By gaining access to Indexrate Code A, you gain access to the entire Indexrate Code Algorithm Suite.
Indexrate Code A is a combination of indicators and oscillators, individually developed and integrated with two primary and two secondary trading strategies, allowing traders to optimize their entry points. This suite of indicators and oscillators is the result of two years of research, market observation, and strategy optimization to identify the best entry signals.
Key Indicators & Oscillators in Indexrate Code A:
Momentum (hidden – part of Indexrate Code B)
Difference Momentum (hidden – part of Indexrate Code B)
StochRSI (hidden – part of Indexrate Code B)
Money Flow Index (MFI) (hidden – part of Indexrate Code B)
EMA (partially displayed)
Trend HMA
Sequential
Trend Score WMA (hidden – part of Indexrate Code B)
Parabolic SAR
Volume (displayed in the status bar)
Heiken Ashi (displayed on candles)
EMA (Partially Displayed)
The EMA (Exponential Moving Average) consists of a group of curves – 13, 25, 50, 100, and 200 EMA. There are additional hidden curves used in one of the entry strategies. The EMA helps traders assess market direction, trend reversals, and dynamic support/resistance levels.
EMAs calculate the average asset price over a selected period, giving more weight to recent prices, which are more relevant for trend development. As a result, EMAs react faster to price drops and surges, making them popular among traders.
The indicator displays five EMAs:
13 EMA (Green)
25 EMA (Purple)
50 EMA (Orange)
100 EMA (Blue)
200 EMA (Red)
Example of EMA Usage
EMAs often act as dynamic support/resistance levels – prices where an asset is likely to temporarily reverse direction.
If the price is above the EMA and approaches it, a rebound upward is more likely than a breakdown.
If the price is below the EMA and rises toward it, a resumption of a downtrend is more likely than a breakout.
EMAs can also identify well-known patterns like the Golden Cross and Death Cross.
Golden Cross
A bullish pattern where a short-term EMA crosses above a long-term EMA (e.g., 50EMA > 200EMA). This signals a potential uptrend.
Death Cross
A bearish pattern where a short-term EMA crosses below a long-term EMA (e.g., 50EMA < 200EMA). This signals a potential downtrend.
Trend HMA
A trend indicator based on the Hull Moving Average (HMA).
The HMA is an advanced moving average that accurately detects trend reversals and filters signals. The slow-period HMA defines the trend, while the fast-period HMA identifies short-term movements.
Example of Trend HMA Usage
Green = Long (bullish)
Red = Short (bearish)
White = Weak trend
The indicator also supports divergence detection:
Green Label = Bullish divergence
Red Label = Bearish divergence
White Label = Uncertainty
Sequential
Helps traders confirm trend direction and strength. It labels candles with numbers (1 to 9), indicating trend stages.
Blue Numbers = Long Trend
Gray Numbers = Short Trend
Red Candle = End of Long Trend
Green Candle = End of Short Trend
Example of Sequential Usage
When the indicator reaches Signal #9, a trend reversal is likely. The probability increases if it reaches Signal #13.
Parabolic SAR
A trend-following indicator that signals potential reversals.
Blue Line = Long
Gray Line = Short
Crossings indicate trend reversals
Example of Parabolic SAR Usage
The dots switch sides when a trend reversal is likely.
Optimizing Settings (33 Parameters for 100% Profitability)
Traders can fine-tune 33 parameters to maximize profitability. Indexrate Code B visually assists in optimization, as all its parameters are embedded in Indexrate Code A.
Recommended Timeframes
Works on any timeframe (e.g., 4H, 6H).
For Heiken Ashi charts, enable real price display in settings to avoid misleading averages.
Important Notes
No indicator guarantees 100% success – Markets are driven by human psychology (supply & demand).
Technical analysis relies on past data (price & volume).
Indexrate Code evaluates trends, price, volume, and trader psychology.
All future updates will be free.
"Trading is about probabilities. Investing is about opportunities. No one knows the future—always protect your profits."
TheDevashishratio-MomentumThis custom momentum indicator is inspired by Fibonacci principles but builds a unique sequence with steps of 0.5 (i.e., 0, 0.5, 1, 1.5, 2, ...). Instead of traditional Fibonacci numbers, each step functions as a dynamic lookback period for a momentum calculation. By cycling through these fractional steps, you capture a layered view of price momentum over varying intervals.
The "Fibonacci" Series Used
Sequence:
0, 0.5, 1, 1.5, 2, … up to a user-defined maximum
For trading indicators, lag values (lookback) must be integers, so each step is rounded to the nearest integer and duplicates are removed, resulting in lookbacks:
1, 2, 3, 4, ... N
Indicator Logic
For each selected lookback, the indicator calculates momentum as:
Momentum
n
=
close
−
close
Momentum
n
=close−close
Where:
close = current price
n = integer from your series of
You can combine these momenta for an averaged or weighted momentum profile, displaying the composite as an oscillator.
How To Use
Bullish: Oscillator above zero indicates positive composite momentum.
Bearish: Oscillator below zero indicates negative composite momentum.
Crosses: A cross from below to above zero may signal emerging bullish momentum, and vice versa.
Customization
Adjust max_step to control how many interval lags you want in your composite.
This oscillator averages across many short and mid-term momenta, reducing noise while still being sensitive to changes.
Summary
TheDevashishratio-Momentum offers a fresh momentum oscillator, blending a "Fibonacci-like" progression with technical analysis, and can be easily copy-pasted into TradingView to experiment and refine your edge.
For more on momentum indicator logic or how to use arrays and series in Pine Script, explore TradingView's official documentation and open-source scripts
Step Channel Momentum Trend [ChartPrime]OVERVIEW
Step Channel Momentum Trend is a momentum-based price filtering system that adapts to market structure using pivot levels and ATR volatility. It builds a dynamic channel around a stepwise midline derived from swing highs and lows. The system colors price candles based on whether price remains inside this channel (low momentum) or breaks out (strong directional flow). This allows traders to clearly distinguish ranging conditions from trending ones and take action accordingly.
⯁ STRUCTURAL MIDLNE (STEP CHANNEL CORE)
The midline acts as the backbone of the trend system and is based on structure rather than smoothing.
Calculated as the average of the most recent confirmed Pivot High and Pivot Low.
The result is a step-like horizontal line that only updates when new pivot points are confirmed.
This design avoids lag and makes the line "snap" to recent structural shifts.
It reflects the equilibrium level between recent bullish and bearish control.
This unique step logic creates clear regime shifts and prevents noise from distorting trend interpretation.
⯁ DYNAMIC VOLATILITY BANDS (ATR FILTERING)
To detect momentum strength, the script constructs upper and lower bands using the ATR (Average True Range):
The distance from the midline is determined by ATR × multiplier (default: 200-period ATR × 0.6).
These bands adjust dynamically to volatility, expanding in high-ATR environments and contracting in calm markets.
The area between upper and lower bands represents a neutral or ranging market state.
Breakouts outside the bands are treated as significant momentum shifts.
This filtering approach ensures that only meaningful breakouts are visually emphasized — not every candle fluctuation.
⯁ MOMENTUM-BASED CANDLE COLORING
The system visually transforms price candles into momentum indicators:
When price (hl2) is above the upper band, candles are green → bullish momentum.
When price is below the lower band, candles are red → bearish momentum.
When price is between the bands, candles are orange → low or no momentum (range).
The candle body, wick, and border are all colored uniformly for visual clarity.
This gives traders instant feedback on when momentum is expanding or fading — ideal for breakout, pullback, or trend-following strategies.
⯁ PIVOT-BASED SWING ANCHORS
Each confirmed pivot is plotted as a label ⬥ directly on the chart:
They also serve as potential manual entry zones, SL/TP anchors, or confirmation points.
⯁ MOMENTUM STATE LABEL
To reinforce the current market mode, a live label is displayed at the most recent candle:
Displays either:
“ Momentum Up ” when price breaks above the upper band.
“ Momentum Down ” when price breaks below the lower band.
“ Range ” when price remains between the bands.
Label color matches the candle color for quick identification.
Automatically updates on each bar close.
This helps discretionary traders filter trades based on market phase.
USAGE
Use the green/red zones to enter with momentum and ride trending moves.
Use the orange zone to stay out or fade ranges.
The step midline can act as a breakout base, pullback anchor, or bias reference.
Combine with other indicators (e.g., order blocks, divergences, or volume) to build high-confluence systems.
CONCLUSION
Step Channel Momentum Trend gives traders a clean, adaptive framework for identifying trend direction, volatility-based breakouts, and ranging environments — all from structural logic and ATR responsiveness. Its stepwise midline provides clarity, while its dynamic color-coded candles make momentum shifts impossible to miss. Whether you’re scalping intraday momentum or managing swing entries, this tool helps you trade with the market’s rhythm — not against it.
Money Flow Pulse💸 In markets where volatility is cheap and structure is noisy, what matters most isn’t just the move — it’s the effort behind it. Money Flow Pulse (MFP) offers a compact, color-coded readout of real-time conviction by scoring volume-weighted price action on a five-tier scale. It doesn’t try to predict reversals or validate trends. Instead, it reveals the quality of the move in progress: is it fading , driving , exhausting , or hollow ?
🎨 MFP draws from the traditional Money Flow Index (MFI), a volume-enhanced momentum oscillator, but transforms it into a modular “pressure readout” that fits seamlessly into any structural overlay. Rather than oscillating between extremes with little interpretive guidance, MFP discretizes the flow into clean, color-coded regimes ranging from strong inflow (+2) to strong outflow (–2). The result is a responsive diagnostic layer that complements, rather than competes with, tools like ATR and/or On-Balance Volume.
5️⃣ MFP uses a normalized MFI value smoothed over 13 periods and classified into a 5-tier readout of Volume-Driven Conviction :
🍆 Exhaustion Inflow — usually a top or blowoff; not strength, but overdrive (+2)
🥝 Active Inflow — supportive of trend continuation (+1)
🍋 Neutral — chop, coil, or fakeouts (0)
🍑 Selling Intent — weakening structure, possible fade setups (-1)
🍆 Exhaustion Outflow — often signals forced selling or accumulation traps (-2)
🎭 These tiers are not arbitrary. Each one is tuned to reflect real capital behavior across timeframes. For instance, while +1 may support continuation, +2 often precedes exhaustion — especially on the lower timeframes. Similarly, a –1 reading during a pullback suggests sell-side pressure is building, but a shift to –2 may mean capitulation is already underway. The difference between the two can define whether a move is tradable continuation or strategic exhaustion .
🌊 The MFI ROC (Rate of Change) feature can be toggled to become a volatility-aware pulse monitor beneath the derived MFI tier. Instead of scoring direction or structure, ROC reveals how fast conviction is changing — not just where it’s headed, but how hard it's accelerating or decaying. It measures the raw Δ between the current and previous MFI values, exposing bursts of energy, fading pressure, or transitional churn .
🎢 Visually, ROC appears as a low-opacity area fill, anchored to a shared lemon-yellow zero line. When the green swell rises, buying pressure is accelerating; when the red drops, flow is actively deteriorating. A subtle bump may signal early interest — while a steep wave hints at an emotional overreaction. The ROC value itself provides numeric insight alongside the raw MFI score. A reading of +3.50 implies strong upside momentum in the flow — often supporting trend ignition. A score of –6.00 suggests rapid deceleration or full exhaustion — often preceding reversals or failed breakouts.
・ MFI shows you where the flow is
・ ROC tells you how it’s behaving
😎 This blend reveals not just structure or intent — but also urgency . And in flow-based trading, urgency often precedes outcome.
🧩 Divergence isn’t delay — it’s disagreement . One of the most revealing features of MFP is how it exposes momentum dissonance — situations where price and flow part ways. These divergences often front-run pivots , traps , or velocity stalls . Unlike RSI-style divergence, which whispers of exhaustion, MFI divergence signals a breakdown in conviction. The structure may extend — but the effort isn’t there.
・ Price ▲ MFI ▼ → Effortless Markup : Often signals distribution or a grind into liquidity. Without rising MFI, the rally lacks true flow participation — a warning of fragility.
・ Price ▼ MFI ▲ → Absorption or Early Accumulation : Price breaks down, but money keeps flowing in — a hidden bid. Watch for MFI tier shifts or ROC bursts to confirm a reversal.
🏄♂️ These moments don’t require signal overlays or setup hunting. MFP narrates the imbalance. When price breaks structure but flow does not — or vice versa — you’re not seeing trend, you’re seeing disagreement, and that's where edge begins.
💤 MFP is especially effective on intraday charts where volume dislocations matter most. On the 1H or 15m chart, it helps distinguish between breakouts with conviction versus those lacking flow. On higher timeframes, its resolution softens — it becomes more of a drift indicator than a trigger device. That’s by design: MFP prioritizes pulse, not position. It’s not the fire, it’s the heat.
📎 Use MFP in confluence with structural overlays to validate price behavior. A ribbon expansion with rising MFP is real. A compression breakout without +1 flow is "fishy". Watch how MFP behaves near key zones like anchored VWAP, MAs or accumulation pivots. When MFP rises into a +2 and fails to sustain, the reversal isn’t just technical — it’s flow-based.
🪟 MFP doesn’t speak loudly, but it never whispers without reason. It’s the pulse check before action — the breath of the move before the breakout. While it stays visually minimal on the chart, the true power is in the often overlooked Data Window, where traders can read and interpret the score in real time. Once internalized, these values give structure-aware traders a framework for conviction, continuation, or caution.
🛜 MFP doesn’t chase momentum — it confirms conviction. And in markets defined by noise, that signal isn’t just helpful — it’s foundational.
