Weighted US Liquidity ROC Indicator with FED RatesThe Weighted US Liquidity ROC Indicator is a technical indicator that measures the Rate of Change (ROC) of a weighted liquidity index. This index aggregates multiple monetary and liquidity measures to provide a comprehensive view of liquidity in the economy. The ROC of the liquidity index indicates the relative change in this index over a specified period, helping to identify trend changes and market movements.
1. Liquidity Components:
The indicator incorporates various monetary and liquidity measures, including M1, M2, the monetary base, total reserves of depository institutions, money market funds, commercial paper, and repurchase agreements (repos). Each of these components is assigned a weight that reflects its relative importance to overall liquidity.
2. ROC Calculation:
The Rate of Change (ROC) of the weighted liquidity index is calculated by finding the difference between the current value of the index and its value from a previous period (ROC period), then dividing this difference by the value from the previous period. This gives the percentage increase or decrease in the index.
3. Visualization:
The ROC value is plotted as a histogram, with positive and negative changes indicated by different colors. The Federal Funds Rate is also plotted separately to show the impact of central bank policy on liquidity.
Discussion of the Relationship Between Liquidity and Stock Market Returns
The relationship between liquidity and stock market returns has been extensively studied in financial economics. Here are some key insights supported by scientific research:
Liquidity and Stock Returns:
Liquidity Premium Theory: One of the primary theories is the liquidity premium theory, which suggests that assets with higher liquidity typically offer lower returns because investors are willing to accept lower yields for more liquid assets. Conversely, assets with lower liquidity may offer higher returns to compensate for the increased risk associated with their illiquidity (Amihud & Mendelson, 1986).
Empirical Evidence: Research by Fama and French (1992) has shown that liquidity is an important factor in explaining stock returns. Their studies suggest that stocks with lower liquidity tend to have higher expected returns, aligning with the liquidity premium theory.
Market Impact of Liquidity Changes:
Liquidity Shocks: Changes in liquidity can impact stock returns significantly. For example, an increase in liquidity is often associated with higher stock prices, as it reduces the cost of trading and enhances market efficiency (Chordia, Roll, & Subrahmanyam, 2000). Conversely, a liquidity shock, such as a sudden decrease in market liquidity, can lead to higher volatility and lower stock prices.
Financial Crises: During financial crises, liquidity tends to dry up, leading to sharp declines in stock market returns. For instance, studies on the 2008 financial crisis illustrate how a reduction in market liquidity exacerbated the decline in stock prices (Brunnermeier & Pedersen, 2009).
Central Bank Policies and Liquidity:
Monetary Policy Impact: Central bank policies, such as changes in the Federal Funds Rate, directly influence market liquidity. Lower interest rates generally increase liquidity by making borrowing cheaper, which can lead to higher stock market returns. On the other hand, higher rates can reduce liquidity and negatively impact stock prices (Bernanke & Gertler, 1999).
Policy Expectations: The anticipation of changes in monetary policy can also affect stock market returns. For example, expectations of rate cuts can lead to a rise in stock prices even before the actual policy change occurs (Kuttner, 2001).
Key References:
Amihud, Y., & Mendelson, H. (1986). "Asset Pricing and the Bid-Ask Spread." Journal of Financial Economics, 17(2), 223-249.
Fama, E. F., & French, K. R. (1992). "The Cross-Section of Expected Stock Returns." Journal of Finance, 47(2), 427-465.
Chordia, T., Roll, R., & Subrahmanyam, A. (2000). "Market Liquidity and Trading Activity." Journal of Finance, 55(2), 265-289.
Brunnermeier, M. K., & Pedersen, L. H. (2009). "Market Liquidity and Funding Liquidity." Review of Financial Studies, 22(6), 2201-2238.
Bernanke, B. S., & Gertler, M. (1999). "Monetary Policy and Asset Prices." NBER Working Paper No. 7559.
Kuttner, K. N. (2001). "Monetary Policy Surprises and Interest Rates: Evidence from the Fed Funds Futures Market." Journal of Monetary Economics, 47(3), 523-544.
These studies collectively highlight how liquidity influences stock market returns and how changes in liquidity conditions, influenced by monetary policy and other factors, can significantly impact stock prices and market stability.
Cari dalam skrip untuk "roc"
Smoothed ROC Z-Score with TableSmoothed ROC Z-Score with Table
This indicator calculates the Rate of Change (ROC) of a chosen price source and transforms it into a smoothed Z-Score oscillator, allowing you to identify market cycle tops and bottoms with reduced noise.
How it works:
The ROC is calculated over a user-defined length.
A moving average and standard deviation over a separate window are used to standardize the ROC into a Z-Score.
This Z-Score is further smoothed using an exponential moving average (EMA) to filter noise and highlight clearer cycle signals.
The smoothed Z-Score oscillates around zero, with upper and lower bands defined by user inputs (default ±2 standard deviations).
When the Z-Score reaches or exceeds ±3 (customizable), the value shown in the table is clamped at ±2 for clearer interpretation.
The indicator plots the smoothed Z-Score line with zero and band lines, and displays a colored Z-Score table on the right for quick reference.
How to read it:
Values near zero indicate neutral momentum.
Rising Z-Scores towards the upper band suggest increasing positive momentum, possible market tops or strength.
Falling Z-Scores towards the lower band indicate negative momentum, potential bottoms or weakness.
The color-coded table gives an easy visual cue: red/orange for strong positive signals, green/teal for strong negative signals, and gray for neutral zones.
Use cases:
Identify turning points in trending markets.
Filter noisy ROC data for cleaner signals.
Combine with other indicators to time entries and exits more effectively.
Johnny's Volatility-Driven Trend Identifier w/ Reversal SignalsJohnny's Volatility-Driven Trend Identifier w/ Reversal Signals is designed to identify high-probability trend shifts and reversals by incorporating volatility, momentum, and impulse-based filtering. It is specifically built for traders who want to capture strong trend movements while minimizing false signals caused by low volatility noise.
By leveraging Rate of Change (ROC), Relative Strength Index (RSI), and Average True Range (ATR)-based volatility detection, the indicator dynamically adapts to market conditions. It highlights breakout trends, reversals, and early signs of momentum shifts using strategically placed labels and color-coded trend visualization.
Inspiration taken from Top G indicator .
What This Indicator Does
The Volatility-Driven Trend Identifier works by:
Measuring Market Extremes & Momentum:
Uses ROC normalization with standard deviation to identify impulse moves in price action.
Implements RSI filtering to determine overbought/oversold conditions that validate trend strength.
Utilizes ATR-based volatility tracking to ensure signals only appear when meaningful market movements are occurring.
Identifying Key Trend Events:
Power Peak (🔥): Marks a confirmed strong downtrend, ideal for shorting opportunities.
Surge (🚀): Indicates a confirmed strong uptrend, signaling a potential long entry.
Soft Surge (↗): Highlights a mild bullish reentry or early uptrend formation.
Soft Peak (↘): Shows a mild bearish reentry or early downtrend formation.
Providing Adaptive Filtering for Reliable Signals:
Filters out weak trends with a volatility check, ensuring signals appear only in strong market conditions.
Implements multi-level confirmation by combining trend strength metrics, preventing false breakouts.
Uses gradient-based visualization to color-code market sentiment for quick interpretation.
What This Indicator Signals
Breakouts & Impulse Moves: 🚀🔥
The Surge (🚀) and Power Peak (🔥) labels indicate confirmed momentum breakouts, where the trend has been validated by a combination of ROC impulse, RSI confirmation, and ATR volatility filtering.
These signals suggest that the market is entering a strong trend, and traders can align their entries accordingly.
Early Trend Formation & Reentries: ↗ ↘
The Soft Surge (↗) and Soft Peak (↘) labels indicate areas where a trend might be forming, but is not yet fully confirmed.
These signals help traders anticipate potential entries before the trend gains full strength.
Volatility-Adaptive Trend Filtering: 📊
Since the indicator only activates in volatile conditions, it avoids the pitfalls of low-range choppy markets where false signals frequently occur.
ATR-driven adaptive windowing allows the indicator to dynamically adjust its sensitivity based on real-time volatility conditions.
How to Use This Indicator
1. Identifying High-Probability Entries
Bullish Entries (Long Trades)
Look for 🚀 Surge signals in an uptrend.
Confirm with RSI (should be above 50 for momentum).
Ensure volatility is increasing to validate the breakout.
Use ↗ Soft Surge signals for early entries before the trend fully confirms.
Bearish Entries (Short Trades)
Look for 🔥 Power Peak signals in a downtrend.
RSI should be below 50, indicating downward momentum.
Volatility should be rising, ensuring market momentum is strong.
Use ↘ Soft Peak signals for early entries before a full bearish confirmation.
2. Avoiding False Signals
Ignore signals when the market is ranging (low ATR).
Check RSI and ROC alignment to ensure trend confirmation.
Use additional confluences (e.g., price action, support/resistance levels, moving averages) for enhanced accuracy.
3. Trend Confirmation & Filtering
The stronger the trend, the higher the likelihood that Surge (🚀) and Power Peak (🔥) signals will continue in their direction.
Soft Surge (↗) and Soft Peak (↘) act as early warning signals before major breakouts occur.
What Makes This a Machine Learning-Inspired Moving Average?
While this indicator is not a direct implementation of machine learning (as Pine Script lacks AI/ML capabilities), it mimics machine learning principles by adapting dynamically to market conditions using the following techniques:
Adaptive Trend Selection:
It does not rely on fixed moving averages but instead adapts dynamically based on volatility expansion and momentum detection.
ATR-based filtering adjusts the indicator’s sensitivity to real-time conditions.
Multi-Factor Confirmation (Feature Engineering Equivalent in ML):
Combines ROC, RSI, and ATR in a structured way, similar to how ML models use multiple inputs to filter and classify data.
Implements conditional trend recognition, ensuring that only valid signals pass through the filter.
Noise Reduction with Data Smoothing:
The algorithm avoids false signals by incorporating trend intensity thresholds, much like how ML models remove outliers to refine predictions.
Adaptive filtering ensures that low-volatility environments do not produce misleading signals.
Why Use This Indicator?
✔ Reduces False Signals: Multi-factor validation ensures only high-confidence signals are triggered.
✔ Works in All Market Conditions: Volatility-adaptive nature allows the indicator to perform well in both trending and ranging markets.
✔ Great for Swing & Intraday Trading: It helps spot momentum shifts early and allows traders to catch major market moves before they fully develop.
✔ Visually Intuitive: Color-coded trends and clear signal markers make it easy to interpret.
Rate Of Change ATRThis is a very basic, but powerful script.
It gives you the ratio between the rate of change of the last x days and the average true range of the last y days.
---> ROC-ATR Ratio = ROC/ATR
Therefore, you can see how much the price has moved relative to the prices in the past.
This is important because (in my opinion) the basic ROC indicator is not very meaningful if you don't look at the average volatility of recent history.
For example, a ROC of 5% over the last 3 days might be very high for Forex but very small for some crypto.
Consequently, this indicator makes it possible to compare (and be used on) every instrument in every industry the same way.
Generally speaking, it makes more sense if the ATR length is larger than the ROC length.
Weighted Global Liquidity Index (WGLI) ROCThe Weighted Global Liquidity Index (WGLI) ROC indicator calculates the rate of change (ROC) of the WGLI, providing valuable insights into the dynamics of global liquidity. The WGLI consolidates major central bank balance sheets and key financial indicators, such as Foreign Exchange Reserves, Interbank Rates, and Interest Rates, converted to USD and expressed in trillions. Specific US accounts like the Treasury General Account (TGA) and Reverse Repurchase Agreements (RRP) are subtracted from the Federal Reserve's balance sheet for a more detailed view of US liquidity.
Using both the WGLI and the WGLI ROC together allows users to track changes in global liquidity and understand policy trajectories and economic conditions. This dual approach offers insights into asset pricing and helps investors make informed decisions about capital allocation.
Feel free to explore and customize the WGLI ROC script to suit your analysis needs!
Normalized Global Net Liquidity + HMA Smoothed RoCThis script calculates "Global Net Liquidity" using various financial data sources, and integrates Rate of Change (RoC) visualization alongside an Equity Hull Moving Average (HMA) plot. It also features an additional "Global Liquidity" metric that is subsequently scaled and plotted.
First, several financial indicators are requested and combined to form the "Global Net Liquidity Indicator." A Rate of Change (RoC) is then calculated, and this RoC, alongside the Equity Hull Moving Average (HMA), is plotted. Next, a "Global Liquidity" measure is formed by combining various financial data.
In summary, this script involves achieving a comprehensive visualization of liquidity-related indicators and measures, providing an inclusive outlook into the nature of global liquidity trends.
The main plot is the 3 liquidity metrics averaged together and normalized then scaled between -1 and 1 for TPI scoring.
You can customize the weighting for each metric, as well as the lookback period for all 3 metrics.
-1 = Negative Trend
1 = Positive Trend
Yellow = Global Net Liquidity
Blue = RoC
Red = Equity HMA
This is insight into global liquidity, and not to be taken in anyway as trading signals. This is an analysis tool to be combined with further research.
[RESEARCH] Rate of ChangeHello traders and developers!
I was wondering how built-in "roc" function in Pine is defined and calculated so I made a little research.
I examined 4 samples:
1) "roc" function itself
2) "roc" according to its description
3) price change ratio
4) price percent change ratio
The results of the first and fourth samples are identical.
So, TV built-in roc(source, length) = 100 * change(source, length) / source .
And it's description is incorrect.
If you didnt know it - now you know it.
Good luck!
Normalized ROC²Normalized Rate of Change of Rate of Change (ROC²) Histogram
Overview
The Normalized ROC² Histogram is a momentum-based indicator designed to detect potential trend reversals by measuring the rate of change of the rate of change of price (the second derivative of price movement). This provides insight into when momentum is slowing down, signaling that a price reversal may be approaching.
The indicator also dynamically changes color to highlight shifts in momentum strength, allowing traders to visualize when price acceleration is increasing or decreasing.
How It Works
🔹 Zero Line Crossovers → Potential Direction Change
• When the histogram approaches zero and crosses over, it suggests that price momentum is shifting and a reversal may be imminent.
• Positive to Negative Crossover: Bearish momentum shift.
• Negative to Positive Crossover: Bullish momentum shift.
🔹 Momentum Strength Visualization → Color Shift
• Dark Blue (⬆️ Increasing Positive Momentum) → Price is accelerating upward.
• Light Blue (🔽 Decreasing Positive Momentum) → Uptrend is weakening.
• Dark Red (⬇️ Increasing Negative Momentum) → Price is accelerating downward.
• Light Red (🔼 Decreasing Negative Momentum) → Downtrend is weakening.
🔹 Normalization for Cleaner Visualization
• Prevents extreme volatility spikes from distorting the histogram.
• Normalizes values on a 0 to 100 scale, ensuring consistent bar height.
How to Use It
✅ Watch for Crossovers Near Zero → These can indicate a trend reversal is forming.
✅ Observe Color Changes → A shift from dark to light signals a deceleration, which often precedes price turning points.
✅ Combine with Other Indicators → Works well with Volume Profile, Moving Averages, and Market Structure analysis.
Why This Indicator is Unique
🚀 Second-derivative momentum detection → Provides early insight into potential price shifts.
📊 Normalized bars prevent distortion → No more extreme spikes ruining the scale.
🎯 Color-coded visual cues → Instantly see when momentum is gaining or fading.
📌 Add the Normalized ROC² Histogram to your charts today to detect potential reversals and momentum shifts in real-time! 🚀
VWAP ROC Weighted AverageThe VWAP ROC Weighted Average indicator combines the concepts of Volume Weighted Average Price (VWAP) and Rate of Change (ROC) to create a unique and versatile tool for traders. The indicator calculates the average VWAP and average ROC over a specified period (default: 200 bars) and then creates a weighted average of these two values. This provides a single line that can help traders identify potential entry and exit points in a market.
How it can be used in trading:
Trend Confirmation: The VWAP_ROC_WA can be used to confirm the prevailing trend of an asset. If the weighted average line is moving upward, it indicates a bullish trend, while a downward-moving line suggests a bearish trend. Traders can use this information to enter trades in the direction of the trend to improve their odds of success.
Support and Resistance: The VWAP_ROC_WA line can act as dynamic support and resistance levels. When the price is above the weighted average line, it can act as a support level, and when the price is below the line, it can serve as a resistance level. Traders can use these levels to set stop-loss and take-profit orders or to identify potential entry and exit points.
Divergences: Traders can look for divergences between the price and the VWAP_ROC_WA line to identify potential reversals. For instance, if the price is making higher highs while the weighted average line is making lower highs, it may signal a bearish divergence, indicating a potential reversal to the downside. Conversely, if the price is making lower lows while the weighted average line is making higher lows, it may signal a bullish divergence, indicating a potential reversal to the upside.
Crossovers: Traders can monitor crossovers between the price and the VWAP_ROC_WA line. A bullish crossover occurs when the price crosses above the weighted average line, suggesting a potential long entry point. A bearish crossover occurs when the price crosses below the line, suggesting a potential short entry point.
Multi-period ROCTHe indicator is backtested for the default periods -10 (short), 21(medium) and 45(long). These parameters can be changed using the settings as per your preference.
The indicator allows you to plot three ROC on multiple periods.
Why use this indicator?
A trend is confirmed when its identified as a trend across multiple timeframes or multiple periods.
As all default ROC (10, 21, 45) cross above zero, it marks the beginning of an uptrend. The indicator is backtested on daily timeframe.
Combine your existing bullish strategies with this indicator shall yield improved accuracy as you'd have trend confirmation. Go long only when the ROC is above 0 levels across short, medium and long term periods.
The indicator is inspired by teachings from Mr. Bharat Jhunjhunwala (Founder of ProRSI)
BAM's Weighted ROCTraders,
BAM's Weighted ROC is a Momentum indicator. ROC stands for 'Rate of Change' therefor this indicator plots the reading of a weighted average Rate of Change. In its current form it uses 4 periods en 4 weightings. The periods are set to 21/63/126/252 which corresponds to the number of trading days in each 1/3/6/12 months. The weightings are set to emphasize the more recent periods where the 1-month period counts for 40% of the signal, the 3-monthh period for 30%, the 6-month for 20% and the 12-month for 10%. These settings, both periods and weightings, are customizable. The current settings are meant to serve the widely used 1-day time interval chart setting. Feel free to alter the time frame and adjust the parameters accordingly; eg I like trading the weekly chart on a 10/20/30/40 period settings.
BAM's Weighted ROC can be used as a trendfilter for Trend Following trading systems or as an entry signal for Swing trading systems, or both. In the current setting the indicator is set to trend-following; it turns green when positive (above 0), indicating positive momentum. And red when negative (below 0), indicating negative momentum. In the most basic form one can trade a well diversified portfolio of assets using the indicator as guidance for entry and exit signals as it flows back and forth between positive and negative. Another use for the indicator lies in Swing Trading systems. In this approach the transfer from declining momentum into ascending momentum can be interpreted as a shift in momentum from negative to positive, and therefor constitute an entry opportunity. A combination of the 2 signals is of perfectly viable too, wait for positive momentum (reading above 0) in combination with a upward shift from one bar to the other. Use the reverse logic as an exit signal. In these examples the indicator is used in a stand-alone fashion. But off course it can also be used in conjunction with other indicators.
I personally use the two functions, trend-following en swingtrading, in tandem (combined)
for further reading into the rational behind Trend Following trading systems I recommend the following sources:
- Free Read: Google for 'Meb Faber, Global Asset Allocation' he gives out free copies on his website. Meb is a well known character in the Momentum-factor arena.
- Easy read: 'Following the trend' By Andreas Clenow. I don't think there is any Trend Following trader that doesn't know this chaps work.
- sophisticated Read: Trend Following with Managed Futures by A. Greyserman and K. Kaminski. This one is for those who seriously mean business!
Good luck out there, pls consider that the momentum factor holds an edge, at least based on historical performance, but this out-performance (most often) lies in the low single digits.
Pls be aware that use of this indicator is at your own risk. All info provided is solely presented for educational purposes.
Kind regards,
Bam
SMU ROC CandlesThis script creates a ROC in a candle format so you can see the rate of Change in a candle format and compare it with the actual price candle. Larger candles can be interpreted as a signal to change or start of a new trend.
You can adjust the ROC length and Scale in the setting.
I think ROC candles has lots of potential to magnify the price movements. Look how large candles are formed when market change direction.
Hope this inspire those with better scripting experience to take it to the next level.
Decaying Rate of Change Non Linear FilterThis is a potential solution to dealing with the inherent lag in most filters especially with instruments such as BTC and the effects of long periods of low volatility followed by massive volatility spikes as well as whipsaws/barts etc.
We can try and solve these issues in a number of ways, adaptive lengths, dynamic weighting etc. This filter uses a non linear weighting combined with an exponential decay rate.
With the non linear weighting the filter can become very responsive to sudden volatility spikes. We can use a short length absolute rate of change as a method to improve weighting of relative high volatility.
c1 = abs(close - close ) / close
Which gives us a fairly simple filter :
filter = sum(c1 * close,periods) / sum(c1,periods)
At this point if we want to control the relative magnitude of the ROC coefficients we can do so by raising it to a power.
c2 = pow(c1, x)
Where x approaches zero the coefficient approaches 1 or a linear filter. At x = 1 we have an unmodified coefficient and higher values increase the relative magnitude of the response. As an extreme example with x = 10 we effectively isolate the highest ROC candle within the window (which has some novel support resistance horizontals as those closes are often important). This controls the degree of responsiveness, so we can magnify the responsiveness, but with the trade off of overshoot/persistence.
So now we have the problem whereby that a highly weighted data point from a high volatility event persists within the filter window. And to a possibly extreme degree, if a reversal occurs we get a potentially large "overshoot" and in a way actually induced a large amount of lag for future price action.
This filter compensates for this effect by exponentially decaying the abs(ROC) coefficient over time, so as a high volatility event passes through the filter window it receives exponentially less weighting allowing more recent prices to receive a higher relative weighting than they would have.
c3 = c2 * pow(1 - percent_decay, periods_back)
This is somewhat similar to an EMA, however with an EMA being recursive that event will persist forever (to some degree) in the calculation. Here we are using a fixed window, so once the event is behind the window it's completely removed from the calculation
I've added Ehler's Super Smoother as an optional smoothing function as some highly non linear settings benefit from smoothing. I can't remember where I got the original SS code snippet, so if you recognize it as yours msg me and I'll link you here.
SHIT35 Alt Index (ROC or Volume) [LucF]SHIT35 is an index of 35 Binance alt/BTC pairs. It provides traders with a more reliable read of BTC pairs price movement than the often uncorrelated USD market cap standard.
Because it must read data coming from 35 markets, SHIT35 is painfully slow and should be kept hidden most of the time. Its features will hopefully seduce traders in using it nonetheless for market analysis.
Features
The Index can be calculated using 4 different modes:
1. Total of instant rate of change for all 35 markets ,
2. Cumulative total of ROCs,
3. Average of ROCs,
4. Plus/Minus volume (an aggregate OBV, if you will).
Select only one of the methods at a time to prevent confusion between modes.
An option allows showing the correlation between the Index as it is configured, and another instrument (CRYPTOCAP:TOTAL2 by default).
Markers can be used to identify abnormal movements in the Index. They are generated using Index exits from Bollinger bands.
The chart shows the Index with, from top to bottom, the default mode with BTC pairs, with USDT pairs, then mode 2 and 4 for BTC pairs.
Index Components
The Index is not weighed. The 35 instruments composing the index all have equivalents in the USDT quote currency on Binance, so you can easily change to those pairs using the Settings. Choosing another exchange or quote currency will require modifications to the list of instruments in the indicator’s code, since if one of the markets cannot be found, the indicator will not work. If the instrument exists but has no history for some bars, zero values will be used for them.
Watchlists
I have created a watchlist for the 35 markets in each of the BTC and USDT quote currencies. To import the watchlists, save the text you’ll find at these links in a file named the way you want your watchlist to be named and import them using the “Import Watchlist…” function.
BTC Watchlist: pastebin.com
USDT Watchlist: pastebin.com
Alerts
You can define alerts on any combination of markers you configure. After defining the markers you want the alert to trigger on, make sure you are on the interval you want the alert to be monitoring at, then create the alert, select the indicator, use the default alert condition and choose your triggering window (usually “Once Per Bar Close”). Once the alert is created, you can change the indicator's inputs with no effect on the alert.
Spot Premium with ROCDescription:
This indicator tracks the spot premium of BTC by comparing the perpetual futures price (perp) from Binance against the spot price on Coinbase. The histogram displays the price difference (spot minus perp) with green bars when spot is higher and red when perp carries a premium. The Rate of Change (ROC) line measures how quickly this premium shifts, with an option to normalize fluctuations for greater stability.
Implications & Possible Use Cases:
• Market Sentiment Gauge: A sustained positive premium often indicates bullish sentiment, while a discount can signal bearish bias.
• Arbitrage Signals: Significant divergences between perp and spot may present short-term arbitrage opportunities across exchanges.
• Risk Management & Hedging: Traders can align derivatives and spot positions when premiums deviate sharply, reducing funding cost exposures.
• Funding Rate Insights: Since perp funding rates tend to follow premium levels, this indicator can act as an early warning for funding spikes.
• Trend Confirmation: Use the normalized ROC to confirm continuation or reversal of premium trends, filtering out noise around small diff values.
Let me know if you would like additional features.
rate_of_changeLibrary "rate_of_change"
// @description: Applies ROC algorithm to any pair of values.
// This library function is used to scale change of value (price, volume) to a percentage value, just as the ROC indicator would do. It is good practice to scale arbitrary ranges to set boundaries when you try to train statistical model.
rateOfChange(value, base, hardlimit)
This function is a helper to scale a value change to its percentage value.
Parameters:
value (float)
base (float)
hardlimit (int)
Returns: per: A float comprised between 0 and 100
Avg.ROC TableThis indicator calculates the average Rate of Change (ROC) for up to 30 user-selected assets over a specified number of candles. It then ranks the assets—assigning rank 1 to the asset with the highest average ROC (strongest momentum) and rank 30 to the asset with the lowest. The results are displayed in a clean, easy-to-read table split into two stacks of 15 assets each, allowing you to quickly see which assets are performing best.
MB - Currency Strength ROCCurrency Strength ROC Enhanced is a technical indicator designed to measure and visualize the relative strength of different currencies in the foreign exchange market. Using a Rate of Change (ROC) approach and moving averages, this indicator provides valuable insights into the dynamics of currency strengths.
Key Features:
Relative Strength Measurement:
Calculates the strength of each currency relative to others, allowing you to identify which currencies are appreciating or depreciating.
Strength Histogram:
Presents normalized strength in a histogram format, making it easy to quickly see areas of positive (green) and negative (red) strength
Moving Averages:
Includes moving averages of normalized strength and trend, providing a clear view of the overall direction of strength over time.
Overbought and Oversold Zones:
Highlights critical levels of strength through horizontal lines, allowing traders to identify potential trend reversals.
Momentum Ghost Machine [ChartPrime]Momentum Ghost Machine (ChartPrime) is designed to be the next generation in momentum/rate of change analysis. This indicator utilizes the properties of one of our favorite filters to create a more accurate and stable momentum oscillator by using a high quality filtered delayed signal to do the momentum comparison.
Traditional momentum/roc uses the raw price data to compare current price to previous price to generate a directional oscillator. This leaves the oscillator prone to false readings and noisy outputs that leave traders unsure of the real likelihood of a future movement. One way to mitigate this issue would be to use some sort of moving average. Unfortunately, this can only go so far because simple moving average algorithms result in a poor reconstruction of the actual shape of the underlying signal.
The windowed sinc low pass filter is a linear phase filter, meaning that it doesn't change the shape or size of the original signal when applied. This results in a faithful reconstruction of the original signal, but without the "high frequency noise". Just like any filter, the process of applying it requires that we have "future" samples resulting in a time delay for real time applications. Fortunately this is a great thing in the context of a momentum oscillator because we need some representation of past price data to compare the current price data to. By using an ideal low pass filter to generate this delayed signal we can super charge the momentum oscillator and fix the majority of issues its predecessors had.
This indicator has a few extra features that other momentum/roc indicators dont have. One major yet simple improvement is the inclusion of a moving average to help gauge the rate of change of this indicator. Since we included a moving average, we thought it would only be appropriate to add a histogram to help visualize the relationship between the signal and its average. To go further with this we have also included linear extrapolation to further help you predict the momentum and direction of this oscillator. Included with this extrapolation we have also added the histogram in the extrapolation to further enhance its visual interpretation. Finally, the inclusion of a candle coloring feature really drives how the utility of the Momentum Machine .
There are three distinct options when using the candle coloring feature: Direct, MA, and Both. With direct the candles will be colored based on the indicators direction and polarity. When it is above zero and moving up, it displays a green color. When it is above zero and moving down it will display a light green color. Conversely, when the indicator is below zero and moving down it displays a red color, and when it it moving up and below zero it will display a light red color. MA coloring will color the candles just like a MACD. If the signal is above its MA and moving up it will display a green color, and when it is above its MA and moving down it will display a light green color.
When the signal is below its MA and moving down it will display a red color, and when its below its ma and moving up it will display a light red color. Both combines the two into a single color scheme providing you with the best of both worlds. If the indicator is above zero it will display the MA colors with a slight twist. When the indicator is moving down and is below its MA it will display a lighter color than before, and when it is below zero and is above its MA it will display a darker color color.
Length of 50 with a smoothing of 100
Length of 50 with a smoothing of 25
By default, the indicator is set to a momentum length of 50, with a post smoothing of 2. We have chosen the longer period for the momentum length to highlight the performance of this indicator compared to its ancestors. A major point to consider with this indicator is that you can only achieve so much smoothing for a chosen delay. This is because more data is required to produce a smoother signal at a specified length. Once you have selected your desired momentum length you can then select your desired momentum smoothing . This is made possible by the use of the windowed sinc low pass algorithm because it includes a frequency cutoff argument. This means that you can have as little or as much smoothing as you please without impacting the period of the indicator. In the provided examples above this paragraph is a visual representation of what is going on under the hood of this indicator. The blue line is the filtered signal being compared to the current closing price. As you can see, the filtered signal is very smooth and accurately represents the underlying price action without noise.
We hope that users can find the same utility as we did in this indicator and that it levels up your analysis utilizing the momentum oscillator or rate of change.
Enjoy
Typical Price Difference - TPD © with reversal zones and signalsv1.0 NOTE: The maths have been tested only for BTC and weekly time frame.
This is a concept that I came through after long long hours of VWAP trading and scalping.
The idea is pretty simple:
1) Typical Price is calculated by (h+l+c) / 3. If we take this price and adjust it to volume we get the VWAP value. The difference between this value and the close value, i call it " Typical Price Difference - TPD ".
2) We get the Historical Volatility as calculated by TradingView script and we add it up to TPD and divide it by two (average). This is what I call " The Source - TS ".
3) We apply the CCI formula to TS .
4) We calculate the Rate of Change (roc) of the CCI formula.
5) We apply the VIX FIX of Larry Williams (script used is from ChrisMoody - CM_Williams_Vix_Fix Finds Market Bottoms) *brilliant script!!!
How to use it:
a) When the (3) is over the TPD we have a bullish bias (green area). When it's under we have a bearish bias (red area).
b) If the (1) value goes over or under a certain value (CAUTION!!! it varies in different assets or timeframes) we get a Reversal Zone (RZ). Red/Green background.
c) If we are in a RZ and the VIX FIX gives a strong value (look for green bars in histogram) and roc (4) goes in the opposite direction, we get a reversal signal that works for the next week(s).
I applied this to BTC on a weekly time frame and after some corrections, it gives pretty good reversal zones and signals. Especially bottoms. Also look for divergences in the zones/signals.
As I said I have tested and confirmed it only on BTC/weekly. I need more time with the maths and pine to automatically adjust it to other time frames. You can play with it in different assets or time frames to find best settings by hand.
Feel free to share your thoughts or ideas on this.
P.S. I realy realy realy try to remember when or how or why I came up with the idea to combine typical price with historical volatility and CCI. I can't! It doesn't make any sense LOL
MA+ ROC MTF DashboardThis is a Multi Timeframe moving average ROC (percent of change) dashboard.
This dashboard shows percent of change of current price to a moving averages on different time frames.
Most left value in the dashboard always represents your chart time frame, while the next 3 represent other time frames which you can set in 'MA+ ROC' settings.
Support User Defined time frames or automatic time frames based on a multiplier value.
Better define same or higher time frames than your chart time frame to get accurate results.
Can work in conjunction with MA+ to display the moving average line, click here:
Like if you Like and follow-up for up coming new indicators: www.tradingview.com
MA Streak Change ChannelChange Channel is like KC unless it uses percentage changes in price to set channel distance. Midline is zero-lag smoothed ROC with dynamic period based on MA Streak indicator, if MA Streak shows an ongoing trend, midline going strong and break out the channel.
Consider using ▲ green areas as a signal to buy and ▼ red areas as a sell signal. It works best in a flat market. Use in combination with other indicators.
Logistic RSI, STOCH, ROC, AO, ... by DGTExperimental attemt of applying Logistic Map Equation for some of widly used indicators.
With this study "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)" and a custom interpretation of Logistic Map Equation is presented
Calculations with Logistic Map Equation makes sense when the calculated results are iterated many times within the same equation.
Here is the Logistic Map Equation : Xn+1 = r * Xn * (1 - Xn)
Where, the value of r is the key for this equation which changes amazingly the behaviour of the Logistic Map.
The value we have asigned for r is less then 1 and greater than 0 ( 0 < r < 1) and in this case the iterations performed with the maximum number of output series allowed by Pine is quite enough for our purpose and thanks to arrays we can easiliy store them for further processing
What we have as output:
Each iteration result is then plotted (excluding plotting the first iteration), as circles or line based on user preference
Values above and below zero level (0) are coloured differently to emphasis bull and bear power
Finally Standard Deviation of Array's Elements is ploted as line. Users may choose to display this line only
So where it comes the indicators "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)".
Those are the indicators whose values are assigned to our key varaiable in the Logistic Map equation forulma which is r
Further details regarding Logistic Map can found under the description of “Logistic EMA w/ Signals by DGT” study
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
