Dynamic Volume RSI (DVRSI) [QuantAlgo]Introducing the Dynamic Volume RSI (DVRSI) by QuantAlgo 📈✨
Elevate your trading and investing strategies with the Dynamic Volume RSI (DVRSI) , a powerful tool designed to provide clear insights into market momentum and trend shifts. This indicator is ideal for traders and investors who want to stay ahead of the curve by using volume-responsive calculations and adaptive smoothing techniques to enhance signal clarity and reliability.
🌟 Key Features:
🛠 Customizable RSI Settings: Tailor the indicator to your strategy by adjusting the RSI length and price source. Whether you’re focused on short-term trades or long-term investments, DVRSI adapts to your needs.
🌊 Adaptive Smoothing: Enable adaptive smoothing to filter out market noise and ensure cleaner signals in volatile or choppy market conditions.
🎨 Dynamic Color-Coding: Easily identify bullish and bearish trends with color-coded candles and RSI plots, offering clear visual cues to track market direction.
⚖️ Volume-Responsive Adjustments: The DVRSI reacts to volume changes, giving greater significance to high-volume price moves and improving the accuracy of trend detection.
🔔 Custom Alerts: Stay informed with alerts for key RSI crossovers and trend changes, allowing you to act quickly on emerging opportunities.
📈 How to Use:
✅ Add the Indicator: Set up the DVRSI by adding it to your chart and customizing the RSI length, price source, and smoothing options to fit your specific strategy.
👀 Monitor Visual Cues: Watch for trend shifts through the color-coded plot and candles, signaling changes in momentum as the RSI crosses key levels.
🔔 Set Alerts: Configure alerts for critical RSI crossovers, such as the 50 line, ensuring you stay on top of potential market reversals and opportunities.
🔍 How It Works:
The Dynamic Volume RSI (DVRSI) is a unique indicator designed to provide more accurate and responsive signals by incorporating both price movement and volume sensitivity into the RSI framework. It begins by calculating the traditional RSI values based on a user-defined length and price source, but unlike standard RSI tools, the DVRSI applies volume-weighted adjustments to reflect the strength of market participation.
The indicator dynamically adjusts its sensitivity by factoring in volume to the RSI calculation, which means that price moves backed by higher volumes carry more weight, making the signal more reliable. This method helps identify stronger trends and reduces the risk of false signals in low-volume environments. To further enhance accuracy, the DVRSI offers an adaptive smoothing option that allows users to reduce noise during periods of market volatility. This adaptive smoothing function responds to market conditions, providing a cleaner signal by reducing erratic movements or price spikes that could lead to misleading signals.
Additionally, the DVRSI uses dynamic color-coding to visually represent the strength of bullish or bearish trends. The candles and RSI plots change color based on the RSI values crossing critical thresholds, such as the 50 level, offering an intuitive way to recognize trend shifts. Traders can also configure alerts for specific RSI crossovers (e.g., above 50 or below 40), ensuring that they stay informed of potential trend reversals and significant market shifts in real-time.
The combination of volume sensitivity, adaptive smoothing, and dynamic trend visualization makes the DVRSI a robust and versatile tool for traders and investors looking to fine-tune their market analysis. By incorporating both price and volume data, this indicator delivers more precise signals, helping users make informed decisions with greater confidence.
Disclaimer:
The Dynamic Volume RSI is designed to enhance your market analysis but should not be used as a sole decision-making tool. Always consider multiple factors before making any trading or investment decisions. Past performance is not indicative of future results.
Crypto
Rsi Long-Term Strategy [15min]Hello, I would like to present to you The "RSI Long-Term Strategy" for 15min tf
The "RSI Long-Term Strategy " is designed for traders who prefer a combination of momentum and trend-following techniques. The strategy focuses on entering long positions during significant market corrections within an overall uptrend, confirmed by both RSI and volume. The use of long-term SMAs ensures that trades are made in line with the broader market trend. The stop-loss feature provides risk management by limiting losses on trades that do not perform as expected. This strategy is particularly well-suited for longer-term traders who monitor 15-minute charts but look for substantial trend reversals or continuations.
Indicators and Parameters:
Relative Strength Index (RSI):
- The RSI is calculated using a 10-period length. It measures the magnitude of recent price changes to evaluate overbought or oversold conditions. The script defines oversold conditions when the RSI is at or below 30 and overbought conditions when the RSI is at or above 70.
Volume Condition:
-The strategy incorporates a volume condition where the current volume must be greater than 2.5 times the 20-period moving average of volume. This is used to confirm the strength of the price movement.
Simple Moving Averages (SMA):
- The strategy uses two SMAs: SMA1 with a length of 250 periods and SMA2 with a length of 500 periods. These SMAs help identify long-term trends and generate signals based on their crossover.
Strategy Logic:
Entry Logic:
A long position is initiated when all the following conditions are met:
The RSI indicates an oversold condition (RSI ≤ 30).
SMA1 is above SMA2, indicating an uptrend.
The volume condition is satisfied, confirming the strength of the signal.
Exit Logic:
The strategy closes the long position when SMA1 crosses under SMA2, signaling a potential end of the uptrend (a "Death Cross").
Stop-Loss:
A stop-loss is set at 5% below the entry price to manage risk and limit potential losses.
Buy and sell signals are highlighted with circles below or above bars:
Green Circle : Buy signal when RSI is oversold, SMA1 > SMA2, and the volume condition is met.
Red Circle : Sell signal when RSI is overbought, SMA1 < SMA2, and the volume condition is met.
Black Cross: "Death Cross" when SMA1 crosses under SMA2, indicating a potential bearish signal.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
I hope the strategy will be helpful, as always, best regards and safe trades
;)
SOL & BTC EMA with BTC/SOL Price Difference % and BTC Dom EMAThis script is designed to provide traders with a comprehensive analysis of Solana (SOL) and Bitcoin (BTC) by incorporating Exponential Moving Averages (EMAs) and price difference percentages. It also includes the BTC Dominance EMA to offer insights into the overall market dominance of Bitcoin.
Features:
SOL EMA: Plots the Exponential Moving Average (EMA) for Solana (SOL) based on a customizable period length.
BTC EMA: Plots the Exponential Moving Average (EMA) for Bitcoin (BTC) based on a customizable period length.
BTC Dominance EMA: Plots the Exponential Moving Average (EMA) for BTC Dominance, which helps in understanding Bitcoin's market share relative to other cryptocurrencies.
BTC/SOL Price Difference %: Calculates and plots the percentage difference between BTC and SOL prices, adjusted for their respective EMAs. This helps in identifying relative strength or weakness between the two assets.
Background Highlight: Colors the background to visually indicate whether the BTC/SOL price difference percentage is positive (green) or negative (red), aiding in quick decision-making.
Inputs:
SOL Ticker: Symbol for Solana (default: BINANCE
).
BTC Ticker: Symbol for Bitcoin (default: BINANCE
).
BTC Dominance Ticker: Symbol for Bitcoin Dominance (default: CRYPTOCAP
.D).
EMA Length: The length of the EMA (default: 20 periods).
Usage:
This script is intended for traders looking to analyze the relationship between SOL and BTC, using EMAs to smooth out price data and highlight trends. The BTC/SOL price difference percentage can help traders identify potential trading opportunities based on the relative movements of SOL and BTC.
Note: Leverage trading involves significant risk and may not be suitable for all investors. Ensure you have a good understanding of the market conditions and employ proper risk management techniques.
Multi Asset Histogram [ChartPrime]Multi Asset Histogram Indicator
Overview:
The "Multi Asset Histogram" indicator provides a comprehensive visualization of the performance of multiple assets relative to each other. By calculating a score for each asset and displaying it in a histogram format, this indicator helps traders quickly identify the trends, dominant asset and the average performance of the assets in the selected group.
Key Features:
◆ Multi-Asset Score Calculation:
The indicator calculates a trend score for each selected asset based on the price source (e.g., hl2).
The trend score is determined by comparing the current price to the prices over the past bars back defined by user, adding or subtracting points based on whether the current price is higher or lower than previous prices.
// Score Function
trscore(src) =>
total = 0.0
for i = 1 to 50
total += (src >= nz(src ) ? 1 : -1)
total
◆ Flexible Symbol Input:
Traders can input up to 10 different symbols (e.g., BTCUSD, ETHUSD, etc.) to be included in the histogram analysis.
◆ Dynamic Visualization:
A histogram is plotted for each asset, with bars colored based on the score, providing a clear visual representation of the relative performance.
Color gradients from red to aqua indicate the performance, with red representing negative scores and aqua representing positive scores.
◆ Adaptive Histogram Lines:
The width and placement of histogram lines adapt based on the calculated scores, ensuring clear visualization regardless of the values.
Dashed lines represent the mean score of all assets, helping traders identify the overall market trend.
◆Detailed Labels and Values:
Labels are placed on the histogram to display the exact score for each asset.
Mean value and zero line labels provide additional context for the overall performance.
◆ Visual Scaling Lines:
Zero line and mean line are clearly marked, helping traders understand the distribution and scale of scores.
Scales on the left and right of the histogram indicate the performance range.
◆ Informative Table:
A table is displayed on the chart, showing the dominant asset (the one with the highest score) and the mean score of all assets.
The table updates dynamically to reflect real-time changes in asset performance.
◆ Settings:
Length: The value of number bars back is greater or less than the current value of the source
Source: The price source to be used for score calculation (e.g., hl2).
Symbols: Up to 10 different asset symbols can be input for analysis.
Usage Notes:
This indicator is useful for traders who monitor multiple assets simultaneously and need a quick visual reference to identify the strongest and weakest performers.
The color coding and dynamic labels make it easy to interpret the relative performance and make informed trading decisions.
This indicator is designed to enhance multi-asset analysis by providing a clear, visual representation of each asset's performance relative to the others, making it easier to identify trends and dominant assets in the market.
Funding Rate [CryptoSea]The Funding Rate Indicator by is a comprehensive tool designed to analyze funding rates across multiple cryptocurrency exchanges. This indicator is essential for traders who want to monitor funding rates and their impact on market trends.
Key Features
Exchange Coverage: Includes data from major exchanges such as Binance, Bitmex, Bybit, HTX, Kraken, OKX, Bitstamp, and Coinbase.
Perpetual Futures and Spot Markets: Fetches and analyzes pricing data from both perpetual futures and spot markets to provide a holistic view.
Smoothing and Customization: Allows users to smooth funding rates using a moving average, with customizable MA lengths for tailored analysis.
Dynamic Candle Coloring: Option to color candles based on trading conditions, enhancing visual analysis.
In the example below, the indicator shows how the funding rate shifts with market conditions, providing clear visual cues for bullish and bearish trends.
How it Works
Data Integration: Uses a secure security fetching function to retrieve pricing data while preventing look-ahead bias, ensuring accurate and reliable information.
TWAP Calculation: Computes Time-Weighted Average Prices (TWAP) for both perpetual futures and spot prices, forming the basis for funding rate calculations.
Funding Rate Calculation: Determines the raw funding rate by comparing TWAPs of perpetual futures and spot prices, then applies smoothing to highlight significant trends.
Color Coding: Highlights the funding rate with distinct colors (bullish and bearish), making it easier to interpret market conditions at a glance.
In the example below, the indicator effectively differentiates between bullish and bearish funding rates, aiding traders in making informed decisions based on current market dynamics.
Application
Market Analysis: Enables traders to analyze the impact of funding rates on market trends, facilitating more strategic decision-making.
Trend Identification: Assists in identifying potential market reversals by monitoring shifts in funding rates.
Customizable Settings: Provides extensive input settings for exchange selection, MA length, and candle coloring, allowing for personalized analysis.
The Funding Rate Indicator by is a powerful addition to any trader's toolkit, offering detailed insights into funding rates across multiple exchanges to navigate the cryptocurrency market effectively.
RSI Screener / Heatmap - By LeviathanThis script allows you to quickly scan the market by displaying the RSI values of up to 280 tickers at once and visualizing them in an easy-to-understand format using labels with heatmap coloring.
📊 Source
The script can display the RSI from a custom timeframe (MTF) and custom length for the following data:
- Price
- OBV (On Balance Volume)
- Open Interest (for crypto tickers)
📋 Ticker Selection
This script uses a different approach for selecting tickers. Instead of inputting them one by one via input.symbol(), you can now copy-paste or edit a list of tickers in the text area window. This approach allows users to easily exchange ticker lists between each other and, for example, create multiple lists of tickers by sector, market cap, etc., and easily input them into the script. Full credit to @allanster for his functions for extracting tickers from the text. Users can switch between 7 groups of 40 tickers each, totaling 280 tickers.
🖥️ Display Types
- Screener with Labels: Each ticker has its own color-coded label located at its RSI value.
- Group Average RSI: A standard RSI plot that displays the average RSI of all tickers in the group.
- RSI Heatmap (coming soon): Color-coded rows displaying current and historical values of tickers.
- RSI Divergence Heatmap (coming soon): Color-coded rows displaying current and historical regular/hidden bullish/bearish divergences for tickers.
🎨 Appearance
Appearance is fully customizable via user inputs, allowing you to change heatmap/gradient colors, zone coloring, and more.
Bitcoin Momentum StrategyThis is a very simple long-only strategy I've used since December 2022 to manage my Bitcoin position.
I'm sharing it as an open-source script for other traders to learn from the code and adapt it to their liking if they find the system concept interesting.
General Overview
Always do your own research and backtesting - this script is not intended to be traded blindly (no script should be) and I've done limited testing on other markets beyond Ethereum and BTC, it's just a template to tweak and play with and make into one's own.
The results shown in the strategy tester are from Bitcoin's inception so as to get a large sample size of trades, and potential returns have diminished significantly as BTC has grown to become a mega cap asset, but the script includes a date filter for backtesting and it has still performed solidly in recent years (speaking from personal experience using it myself - DYOR with the date filter).
The main advantage of this system in my opinion is in limiting the max drawdown significantly versus buy & hodl. Theoretically much better returns can be made by just holding, but that's also a good way to lose 70%+ of your capital in the inevitable bear markets (also speaking from experience).
In saying all of that, the future is fundamentally unknowable and past results in no way guarantee future performance.
System Concept:
Capture as much Bitcoin upside volatility as possible while side-stepping downside volatility as quickly as possible.
The system uses a simple but clever momentum-style trailing stop technique I learned from one of my trading mentors who uses this approach on momentum/trend-following stock market systems.
Basically, the system "ratchets" up the stop-loss to be much tighter during high bearish volatility to protect open profits from downside moves, but loosens the stop loss during sustained bullish momentum to let the position ride.
It is invested most of the time, unless BTC is trading below its 20-week EMA in which case it stays in cash/USDT to avoid holding through bear markets. It only trades one position (no pyramiding) and does not trade short, but can easily be tweaked to do whatever you like if you know what you're doing in Pine.
Default parameters:
HTF: Weekly Chart
EMA: 20-Period
ATR: 5-period
Bar Lookback: 7
Entry Rule #1:
Bitcoin's current price must be trading above its higher-timeframe EMA (Weekly 20 EMA).
Entry Rule #2:
Bitcoin must not be in 'caution' condition (no large bearish volatility swings recently).
Enter at next bar's open if conditions are met and we are not already involved in a trade.
"Caution" Condition:
Defined as true if BTC's recent 7-bar swing high minus current bar's low is > 1.5x ATR, or Daily close < Daily 20-EMA.
Trailing Stop:
Stop is trailed 1 ATR from recent swing high, or 20% of ATR if in caution condition (ie. 0.2 ATR).
Exit on next bar open upon a close below stop loss.
I typically use a limit order to open & exit trades as close to the open price as possible to reduce slippage, but the strategy script uses market orders.
I've never had any issues getting filled on limit orders close to the market price with BTC on the Daily timeframe, but if the exchange has relatively low slippage I've found market orders work fine too without much impact on the results particularly since BTC has consistently remained above $20k and highly liquid.
Cost of Trading:
The script uses no leverage and a default total round-trip commission of 0.3% which is what I pay on my exchange based on their tier structure, but this can vary widely from exchange to exchange and higher commission fees will have a significantly negative impact on realized gains so make sure to always input the correct theoretical commission cost when backtesting any script.
Static slippage is difficult to estimate in the strategy tester given the wide range of prices & liquidity BTC has experienced over the years and it largely depends on position size, I set it to 150 points per buy or sell as BTC is currently very liquid on the exchange I trade and I use limit orders where possible to enter/exit positions as close as possible to the market's open price as it significantly limits my slippage.
But again, this can vary a lot from exchange to exchange (for better or worse) and if BTC volatility is high at the time of execution this can have a negative impact on slippage and therefore real performance, so make sure to adjust it according to your exchange's tendencies.
Tax considerations should also be made based on short-term trade frequency if crypto profits are treated as a CGT event in your region.
Summary:
A simple, but effective and fairly robust system that achieves the goals I set for it.
From my preliminary testing it appears it may also work on altcoins but it might need a bit of tweaking/loosening with the trailing stop distance as the default parameters are designed to work with Bitcoin which obviously behaves very differently to smaller cap assets.
Good luck out there!
Bitcoin Leverage Sentiment - Strategy [presentTrading]█ Introduction and How it is Different
The "Bitcoin Leverage Sentiment - Strategy " represents a novel approach in the realm of cryptocurrency trading by focusing on sentiment analysis through leveraged positions in Bitcoin. Unlike traditional strategies that primarily rely on price action or technical indicators, this strategy leverages the power of Z-Score analysis to gauge market sentiment by examining the ratio of leveraged long to short positions. By assessing how far the current sentiment deviates from the historical norm, it provides a unique lens to spot potential reversals or continuation in market trends, making it an innovative tool for traders who wish to incorporate market psychology into their trading arsenal.
BTC 4h L/S Performance
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█ Strategy, How It Works: Detailed Explanation
🔶 Data Collection and Ratio Calculation
Firstly, the strategy acquires data on leveraged long (**`priceLongs`**) and short positions (**`priceShorts`**) for Bitcoin. The primary metric of interest is the ratio of long positions relative to the total of both long and short positions:
BTC Ratio=priceLongs / (priceLongs+priceShorts)
This ratio reflects the prevailing market sentiment, where values closer to 1 indicate a bullish sentiment (dominance of long positions), and values closer to 0 suggest bearish sentiment (prevalence of short positions).
🔶 Z-Score Calculation
The Z-Score is then calculated to standardize the BTC Ratio, allowing for comparison across different time periods. The Z-Score formula is:
Z = (X - μ) / σ
Where:
- X is the current BTC Ratio.
- μ is the mean of the BTC Ratio over a specified period (**`zScoreCalculationPeriod`**).
- σ is the standard deviation of the BTC Ratio over the same period.
The Z-Score helps quantify how far the current sentiment deviates from the historical norm, with high positive values indicating extreme bullish sentiment and high negative values signaling extreme bearish sentiment.
🔶 Signal Generation: Trading signals are derived from the Z-Score as follows:
Long Entry Signal: Occurs when the BTC Ratio Z-Score crosses above the thresholdLongEntry, suggesting bullish sentiment.
- Condition for Long Entry = BTC Ratio Z-Score > thresholdLongEntry
Long Exit/Short Entry Signal: Triggered when the BTC Ratio Z-Score drops below thresholdLongExit for exiting longs or below thresholdShortEntry for entering shorts, indicating a shift to bearish sentiment.
- Condition for Long Exit/Short Entry = BTC Ratio Z-Score < thresholdLongExit or BTC Ratio Z-Score < thresholdShortEntry
Short Exit Signal: Happens when the BTC Ratio Z-Score exceeds the thresholdShortExit, hinting at reducing bearish sentiment and a potential switch to bullish conditions.
- Condition for Short Exit = BTC Ratio Z-Score > thresholdShortExit
🔶Implementation and Visualization: The strategy applies these conditions for trade management, aligning with the selected trade direction. It visualizes the BTC Ratio Z-Score with horizontal lines at entry and exit thresholds, illustrating the current sentiment against historical norms.
█ Trade Direction
The strategy offers flexibility in trade direction, allowing users to choose between long, short, or both, depending on their market outlook and risk tolerance. This adaptability ensures that traders can align the strategy with their individual trading style and market conditions.
█ Usage
To employ this strategy effectively:
1. Customization: Begin by setting the trade direction and adjusting the Z-Score calculation period and entry/exit thresholds to match your trading preferences.
2. Observation: Monitor the Z-Score and its moving average for potential trading signals. Look for crossover events relative to the predefined thresholds to identify entry and exit points.
3. Confirmation: Consider using additional analysis or indicators for signal confirmation, ensuring a comprehensive approach to decision-making.
█ Default Settings
- Trade Direction: Determines if the strategy engages in long, short, or both types of trades, impacting its adaptability to market conditions.
- Timeframe Input: Influences signal frequency and sensitivity, affecting the strategy's responsiveness to market dynamics.
- Z-Score Calculation Period: Affects the strategy’s sensitivity to market changes, with longer periods smoothing data and shorter periods increasing responsiveness.
- Entry and Exit Thresholds: Set the Z-Score levels for initiating or exiting trades, balancing between capturing opportunities and minimizing false signals.
- Impact of Default Settings: Provides a balanced approach to leverage sentiment trading, with adjustments needed to optimize performance across various market conditions.
TTP Intelligent AccumulatorThe intelligent accumulator is a proof of concept strategy. A hybrid between a recurring buy and TA-based entries and exits.
Distribute the amount of equity and add to your position as long as the TA condition is valid.
Use the exit TA condition to define your exit strategy.
Decide between adding only into losing positions to average down or take a riskier approach by allowing to add into a winning position as well.
Take full profit or distribute your exit into multiple take profit exists of the same size.
You can also decide if you allow your exit conditions to close your position in a loss or require a minimum take profit %.
The strategy includes a default built-in TA conditions just for showcasing the idea but the final intent of this script is to delegate the TA entries and exists to external sources.
The internal conditions use RSI length 7 crossing below the BB with std 1 for entries and above for exits.
To control the number of orders use the properties from settings:
- adjust the pyramiding
- adjust the percentage of equity
- make sure that pyramiding * % equity equals 100 to prevent over use of equity (unless using leverage)
The script is designed as an alternative to daily or weekly recurring buys but depending on the accuracy of your TA conditions it might prove profitable also in lower timeframes.
The reason the script is named Intelligent is because recurring buy is most commonly used without any decision making: buy no matter what with certain frequency. This strategy seeks to still perform recurring buys but filtering out some of the potential bad entries that can delay unnecessarily seeing the position in profits. The second reason is also securing an exit strategy from the beginning which no recurring buy option offers out-of-the-box.
Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
Dynamic Momentum GaugeOverview
The Dynamic Momentum Gauge is an indicator designed to provide information and insights into the trend and momentum of a financial asset. While this indicator is not directional , it helps you know when there will be a trend, big move, or when momentum will have a run, and when you should take profits.
How It Works
This indicator calculates momentum and then removes the negative values to focus instead on when the big trend could likely happen and when it could end, or when you should enter a trade based on momentum or exit. Traders can basically use this indicator to time their market entries or exits, and align their strategies with momentum dynamics.
How To Use
As previously mentioned, this is not a directional indicator but more like a timing indicator. This indicator helps you find when the trend moves, and big moves in the markets will occur and its possibly best to exit the trades. For example, if you decide to enter a long trade if the Dynamic Momentum Gauge value is at an extreme low and another momentum indicator that you use has conditions that you would consider to long with, then this indicator is basically telling you that there isn't more space for the momentum to squeeze any longer, can only really expand from that point or stay where it currently is, but this is also a mean reverting process so it does tend to go back up from the low point.
Settings:
Length: This is the length of the momentum, by default its at 100.
Normalization Length: Length of the Normalization which ensures the the values fall within a consistent range.
Crypto MVRV ZScore - Strategy [PresentTrading]█ Introduction and How it is Different
The "Crypto Valuation Extremes: MVRV ZScore - Strategy " represents a cutting-edge approach to cryptocurrency trading, leveraging the Market Value to Realized Value (MVRV) Z-Score. This metric is pivotal for identifying overvalued or undervalued conditions in the crypto market, particularly Bitcoin. It assesses the current market valuation against the realized capitalization, providing insights that are not apparent through conventional analysis.
BTCUSD 6h Long/Short Performance
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█ Strategy, How It Works: Detailed Explanation
The strategy leverages the Market Value to Realized Value (MVRV) Z-Score, specifically designed for cryptocurrencies, with a focus on Bitcoin. This metric is crucial for determining whether Bitcoin is currently undervalued or overvalued compared to its historical 'realized' price. Below is an in-depth explanation of the strategy's components and calculations.
🔶Conceptual Foundation
- Market Capitalization (MC): This represents the total dollar market value of Bitcoin's circulating supply. It is calculated as the current price of Bitcoin multiplied by the number of coins in circulation.
- Realized Capitalization (RC): Unlike MC, which values all coins at the current market price, RC is computed by valuing each coin at the price it was last moved or traded. Essentially, it is a summation of the value of all bitcoins, priced at the time they were last transacted.
- MVRV Ratio: This ratio is derived by dividing the Market Capitalization by the Realized Capitalization (The ratio of MC to RC (MVRV Ratio = MC / RC)). A ratio greater than 1 indicates that the current price is higher than the average price at which all bitcoins were purchased, suggesting potential overvaluation. Conversely, a ratio below 1 suggests undervaluation.
🔶 MVRV Z-Score Calculation
The Z-Score is a statistical measure that indicates the number of standard deviations an element is from the mean. For this strategy, the MVRV Z-Score is calculated as follows:
MVRV Z-Score = (MC - RC) / Standard Deviation of (MC - RC)
This formula quantifies Bitcoin's deviation from its 'normal' valuation range, offering insights into market sentiment and potential price reversals.
🔶 Spread Z-Score for Trading Signals
The strategy refines this approach by calculating a 'spread Z-Score', which adjusts the MVRV Z-Score over a specific period (default: 252 days). This is done to smooth out short-term market volatility and focus on longer-term valuation trends. The spread Z-Score is calculated as follows:
Spread Z-Score = (Market Z-Score - MVVR Ratio - SMA of Spread) / Standard Deviation of Spread
Where:
- SMA of Spread is the simple moving average of the spread over the specified period.
- Spread refers to the difference between the Market Z-Score and the MVRV Ratio.
🔶 Trading Signals
- Long Entry Condition: A long (buy) signal is generated when the spread Z-Score crosses above the long entry threshold, indicating that Bitcoin is potentially undervalued.
- Short Entry Condition: A short (sell) signal is triggered when the spread Z-Score falls below the short entry threshold, suggesting overvaluation.
These conditions are based on the premise that extreme deviations from the mean (as indicated by the Z-Score) are likely to revert to the mean over time, presenting opportunities for strategic entry and exit points.
█ Practical Application
Traders use these signals to make informed decisions about opening or closing positions in the Bitcoin market. By quantifying market valuation extremes, the strategy aims to capitalize on the cyclical nature of price movements, identifying high-probability entry and exit points based on historical valuation norms.
█ Trade Direction
A unique feature of this strategy is its configurable trade direction. Users can specify their preference for engaging in long positions, short positions, or both. This flexibility allows traders to tailor the strategy according to their risk tolerance, market outlook, or trading style, making it adaptable to various market conditions and trader objectives.
█ Usage
To implement this strategy, traders should first adjust the input parameters to align with their trading preferences and risk management practices. These parameters include the trade direction, Z-Score calculation period, and the thresholds for long and short entries. Once configured, the strategy automatically generates trading signals based on the calculated spread Z-Score, providing clear indications for potential entry and exit points.
It is advisable for traders to backtest the strategy under different market conditions to validate its effectiveness and adjust the settings as necessary. Continuous monitoring and adjustment are crucial, as market dynamics evolve over time.
█ Default Settings
- Trade Direction: Both (Allows for both long and short positions)
- Z-Score Calculation Period: 252 days (Approximately one trading year, capturing a comprehensive market cycle)
- Long Entry Threshold: 0.382 (Indicative of moderate undervaluation)
- Short Entry Threshold: -0.382 (Signifies moderate overvaluation)
These default settings are designed to balance sensitivity to market valuation extremes with a pragmatic approach to trade execution. They aim to filter out noise and focus on significant market movements, providing a solid foundation for both new and experienced traders looking to exploit the unique insights offered by the MVRV Z-Score in the cryptocurrency market.
Scalper's Volatility Filter [QuantraSystems]Scalpers Volatility Filter
Introduction
The 𝒮𝒸𝒶𝓁𝓅𝑒𝓇'𝓈 𝒱𝑜𝓁𝒶𝓉𝒾𝓁𝒾𝓉𝓎 𝐹𝒾𝓁𝓉𝑒𝓇 (𝒮𝒱𝐹) is a sophisticated technical indicator, designed to increase the profitability of lower timeframe trading.
Due to the inherent decrease in the signal-to-noise ratio when trading on lower timeframes, it is critical to develop analysis methods to inform traders of the optimal market periods to trade - and more importantly, when you shouldn’t trade.
The 𝒮𝒱𝐹 uses a blend of volatility and momentum measurements, to signal the dominant market condition - trending or ranging.
Legend
The 𝒮𝒱𝐹 consists of a signal line that moves above and below a central zero line, serving as the indication of market regime.
When the signal line is positioned above zero, it indicates a period of elevated volatility. These periods are more profitable for trading, as an asset will experience larger price swings, and by design, trend-following indicators will give less false signals.
Conversely, when the signal line moves below zero, a low volatility or mean-reverting market regime dominates.
This distinction is critical for traders in order to align strategies with the prevailing market behaviors - leveraging trends in volatile markets and exercising caution or implementing mean-reversion systems in periods of lower volatility.
Case Study
Here we can see the indicator's unique edge in action.
Out of the four potential long entries seen on the chart - displayed via bar coloring, two would result in losses.
However, with the power of the 𝒮𝒱𝐹 a trader can effectively filter false signals by only entering momentum-trades when the signal line is above zero.
In this small sample of four trades, the 𝒮𝒱𝐹 increased the win rate from 50% to 100%
Methodology
The methodology behind the 𝒮𝒱𝐹 is based upon three components:
By calculating and contrasting two ATR’s, the immediate market momentum relative to the broader, established trend is calculated. The original method for this can be credited to the user @xinolia
A modified and smoothed ADX indicator is calculated to further assess the strength and sustainability of trends.
The ‘Linear Regression Dispersion’ measures price deviations from a fitted regression line, adding further confluence to the signals representation of market conditions.
Together, these components synthesize a robust, balanced view of market conditions, enabling traders to help align strategies with the prevailing market environment, in order to potentially increase expected value and win rates.
Crypto Stablecoin Supply - Indicator [presentTrading]█ Introduction and How it is Different
The "Stablecoin Supply - Indicator" differentiates itself by focusing on the aggregate supply of major stablecoins—USDT, USDC, and DAI—rather than traditional price-based metrics. Its premise is that fluctuations in the total supply of these stablecoins can serve as leading indicators for broader market movements, offering traders a unique vantage point to anticipate shifts in market sentiment.
BTCUSD 6h for recent bull market
BTCUSD 8h
█ Strategy, How it Works: Detailed Explanation
🔶 Data Collection
The strategy begins with the collection of the closing supply for USDT, USDC, and DAI stablecoins. This data is fetched using a specified timeframe (**`tfInput`**), allowing for flexibility in analysis periods.
🔶 Supply Calculation
The individual supplies of USDT, USDC, and DAI are then aggregated to determine the total stablecoin supply within the market at any given time. This combined figure serves as the foundation for the subsequent statistical analysis.
🔶 Z-Score Computation
The heart of the indicator's strategy lies in the computation of the Z-Score, which is a statistical measure used to identify how far a data point is from the mean, relative to the standard deviation. The formula for the Z-Score is:
Z = (X - μ) / σ
Where:
- Z is the Z-Score
- X is the current total stablecoin supply (TotalStablecoinClose)
- μ (mu) is the mean of the total stablecoin supply over a specified length (len)
- σ (sigma) is the standard deviation of the total stablecoin supply over the same length
A moving average of the Z-Score (**`zScore_ma`**) is calculated over a short period (defaulted to 3) to smooth out the volatility and provide a clearer signal.
🔶 Signal Interpretation
The Z-Score itself is plotted, with its color indicating its relation to a defined threshold (0.382), serving as a direct visual cue for market sentiment. Zones are also highlighted to show when the Z-Score is within certain extreme ranges, suggesting overbought or oversold conditions.
Bull -> Bear
█ Trade Direction
- **Entry Threshold**: A Z-Score crossing above 0.382 suggests an increase in stablecoin supply relative to its historical average, potentially indicating bullish market sentiment or incoming capital flow into cryptocurrencies.
- **Exit Threshold**: Conversely, a Z-Score dropping below -0.382 may signal a reduction in stablecoin supply, hinting at bearish sentiment or capital withdrawal.
█ Usage
Traders can leverage the "Stablecoin Supply - Indicator" to gain insights into the underlying market dynamics that are not immediately apparent through price analysis alone. It is particularly useful for identifying potential shifts in market sentiment before they are reflected in price movements. By integrating this indicator with other technical analysis tools, traders can develop a more rounded and informed trading strategy.
█ Default Settings
- Timeframe Input (`tfInput`): Allows users to specify the timeframe for data collection, adding flexibility to the analysis.
- Z-Score Length (`len`): Set to 252 by default, representing the period over which the mean and standard deviation of the stablecoin supply are calculated.
- Color Coding: Uses distinct colors (green for bullish, red for bearish) to indicate the Z-Score's position relative to its thresholds, enhancing visual clarity.
- Extreme Range Fill: Highlights areas between defined high and low Z-Score thresholds with distinct colors to indicate potential overbought or oversold conditions.
By integrating considerations of stablecoin supply into the analytical framework, the "Stablecoin Supply - Indicator" offers a novel perspective on cryptocurrency market dynamics, enabling traders to make more nuanced and informed decisions.
Market Activity Risk"Market Activity Risk" (MAR) - Is a dynamic tool designed to structurize the competitive landscape of blockchain transaction blocks, offering traders a strategic edge in anticipating market movements.
By capturing where market participants are actively buying and selling, the MAR indicator provides insights into areas of high competition, allowing traders to make informed decisions and potentially front-run transactions.
At the heart of this tool are blockchain transaction fees , they can represent daily shifts in transaction fee pressures.
By measuring momentum in fees, we can analyze the urgency and competition among traders to have their transactions processed first. This indicator is particularly good at revealing potential support or resistance zones, areas where traders are likely to defend their positions or increase their stakes, thus serving as critical junctures for strategic decision-making.
Key Features:
Adaptable Standard Deviation Settings: Users have the flexibility to adjust the length of the standard deviation and its multipliers, managing the risk bands to their individual risk tolerance.
Color-Coded Risk Levels: The MAR indicator employs an intuitive color scheme, making it easy to interpret the data at a glance.
Multi-Currency Compatibility: While designed with Bitcoin in mind, the MAR indicator is versatile, functioning effectively across various cryptocurrencies including Ethereum, XRP, and several other major altcoins. This broad compatibility ensures that traders across different market segments can leverage the insights provided by this tool.
Customizable Moving Average: The 730-day moving average setting is thoughtfully chosen to reflect the nuances of a typical cryptocurrency cycle, capturing long-term trends and fluctuations. However, recognizing the diverse needs and perspectives of traders, the indicator allows for the moving average period to be modified.
ATH Gain PotentialThe indicator quantifies the relative position of a symbol's current closing price in relation to its historical all-time high (ATH).
By evaluating the ratio between the ATH and the present closing price, it provides an analytical framework to estimate the potential gains that could accrue if the symbol were to revert to its ATH from a specified reference point. The ratio serves as a quantitative measure for assessing the distance between the current market value and the symbol's historical peak, enabling investors to gauge the prospective profitability of a return to the ATH.
Liquidation Level ScreenerThe Liquidation Level Screener is an analytical tool designed for traders who seek a comprehensive view of potential liquidation zones in the market. This script, adaptable to almost any timeframe from 1 minute to 3 days, offers a unique perspective by mapping out key liquidation levels where significant market actions could occur.
Key Features:
Multi-Exchange Data Aggregation: Unlike many other indicators, the Liquidation Levels Indicator compiles data from multiple leading exchanges including Binance, Bitmex, Kraken, and Bitfinex. This approach ensures a more holistic and accurate representation of market sentiment, providing insights into potential liquidation points across various platforms.
Customizable Timeframes and Modes: The script is versatile, working effectively across various timeframes. It operates in two distinct modes:
Actual Levels Display: Visually represents potential liquidation levels.
Settings Mode: Showcases an open interest (OI) oscillator. When OI is exceptionally high, indicating a surge in opened positions at a specific candle, it signals traders to be vigilant about upcoming liquidation levels.
Three-Tier Liquidation System: The indicator categorizes liquidation levels into three distinct tiers based on open interest levels—1, 2, and 3—with Level 3 representing the highest concentration of open positions. This tiered approach allows traders to gauge the significance of each level and adjust their strategies accordingly.
Histogram Visualization: A novel feature of this script is the histogram on the chart's right side, representing the concentration of liquidation levels in specific market zones. This visual aid helps traders identify crucial areas that warrant close attention, enhancing decision-making.
Customizable Options:
Moving Averages: Choose from a wide range of moving average types, including VWMA, SMA, EMA, and more, to tailor the indicator to your analysis style.
Histogram Settings: Adjust the number of histograms, lookback bars, and their proximity to the latest candle, allowing for a personalized density and range of visualization.
Liquidation Level Sensitivity: Set thresholds for different liquidation levels, fine-tuning the indicator to detect varying degrees of market leverage.
Color Coding: Customize the color scheme for different leverage levels, enhancing visual clarity and ease of interpretation.
The Liquidation Level Screener offers a unique edge by highlighting potential zones where significant market movements can occur due to liquidations. By consolidating data from multiple exchanges, it provides a more rounded view of market behavior, which is essential in today’s interconnected trading environment. The tiered liquidation system and histogram feature equip traders with the ability to identify and focus on key market segments where high activity is expected. This tool is particularly valuable for traders who base their strategies on market liquidity and leverage dynamics.
Open Interest OscillatorIn the middle of a bustling cryptocurrency market, with Bitcoin navigating a critical phase and the community hype over potential ETF approvals, current funding rates, and market leverage, the timing is optimal to harness the capabilities of sophisticated trading tools.
Meet the Open Interest Oscillator – special indicator tailored for the volatile arena of cryptocurrency trading. This powerful instrument is adept at consolidating open interest data from a multitude of exchanges, delivering an in-depth snapshot of market sentiment across all timeframes, be it a 1-minute sprint or a weekly timeframe.
This versatile indicator is compatible with nearly all cryptocurrency pairs, offering an expansive lens through which traders can gauge the market's pulse.
Key Features:
-- Multi-exchange Data Aggregation: This feature taps into the heart of the crypto market by aggregating open interest data from premier exchanges such as BINANCE, BITMEX, BITFINEX, and KRAKEN. It goes a step further by integrating data from various pairs and stablecoins, thus providing traders with a rich, multi-dimensional view of market activities.
-- Open Interest Bars: Witness the flow of market dynamics through bars that depict the volume of positions being opened or closed, offering a clear visual cue of trading behavior. In this mode, If bars are going into negative zone, then traders are closing their positions. If they go into positive territory - leveraged positions are being opened.
-- Bollinger Band Integration: Incorporate a layer of statistical analysis with standard deviation calculations, which frame the open interest changes, giving traders a quantified edge to evaluate the market's volatility and momentum.
-- Oscillator with Customizable Thresholds: Personalize your trading signals by setting thresholds that resonate with your unique trading tactics. This customization brings the power of tailored analytics to your strategic arsenal.
-- Max OI Ceiling Setting: In the fast-paced crypto environment where data can surge to overwhelming levels, the Max OI Ceiling ensures you maintain a clear view by capping the open interest data, thus preserving the readability and interpretability of information, even when market activity reaches feverish heights.
Simple Neural Network Transformed RSI [QuantraSystems]Simple Neural Network Transformed RSI
Introduction
The Simple Neural Network Transformed RSI (ɴɴᴛ ʀsɪ) stands out as a formidable tool for traders who specialize in lower timeframe trading.
It is an innovative enhancement of the traditional RSI readings with simple neural network smoothing techniques.
This unique blend results in fairly accurate signals, tailored for swift market movements. The ɴɴᴛ ʀsɪ is particularly resistant to the usual market noise found in lower timeframes, ensuring a clearer view of short-term trends.
Furthermore, its diverse range of visualization options adds versatility, making it a valuable tool for traders seeking to capitalize on short-duration market dynamics.
Legend
In the Image you can see the BTCUSD 1D Chart with the ɴɴᴛ ʀsɪ in Trend Following Mode to display the current trend. This is visualized with the barcoloring.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
Here you can also see the original Indicator line and the Heikin Ashi transformed Indicator bars - more on that now.
Notes
Quantra Standard Value Contents:
To draw out all the information from the indicator calculation we have added a Heikin-Ashi (HA) Candle Visualization.
This HA transformation smoothens out the indicator values and gives a more informative look into Momentum and Trend of the Indicator itself.
This allows early entries and exits by observing the HA transformed Indicator values.
To diversify, different visualization options are available, either a classic line, HA transformed or Hybrid, which contains both of the previous.
To make Quantra's Indicators as useful and versatile as possible we have created options
to change the barcoloring and thus the derived signal from the indicator based on different modes.
Option to choose different Modes:
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremities (Everything going beyond the Deviation Bands in a Mean Reversion manner is highlighted)
Candles (Color of HA candles as barcolor)
Reversion (HA ONLY) (Reversion Signals via the triangles if HA candles change state outside of the Deviation Bands)
- Reversion Signals are indicated by the triangles in the Heikin-Ashi or Hybrid visualization when the HA Candles revert
from downwards to upwards or the other way around OUTSIDE of the SD Bands.
Depending on the Indicator they signal OB/OS areas and can either work as high probability entries and exits for Mean Reversion trades or
indicate Momentum slow downs and potential ranges.
Please use another indicator to confirm this.
Case Study
To effectively utilize the NNT-RSI, traders should know their style and familiarize themselves with the available options.
As stated above, you have multiple modes available that you can combine as you need and see fit.
In the given example mostly only the mode was used in an isolated fashion.
Trend Following:
Purely relied on State Change - Midline crossover
Could be combined with Momentum or Reversion analysis for better entries/exits.
Extremities:
Ideal entry/exit is in the accordingly colored OS/OB Area, the Reversion signaled the latest possible entry/exit.
HA Candles:
Specifically applicable for strong trends. Powerful and fast tool.
Can whip if used as sole condition.
Reversions:
Shows the single entry and exit bars which have a positive expected value outcome.
Can also be used as confirmation or as last signal.
Please note that we always advise to find more confluence by additional indicators.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
In the showcased trades the default settings were used.
Methodology
The Simple Neural Network Transformed RSI uses a simple neural network logic to process RSI values, smoothing them for more accurate trend analysis.
This is achieved through a linear combination of RSI values over a specified input length, weighted evenly to produce a neural network output.
// Simple neural network logic (linear combination with weighted aggregation)
var float inputs = array.new_float(nnLength, na)
for i = 0 to nnLength - 1
array.set(inputs, i, rsi1 )
nnOutput = 0.0
for i = 0 to nnLength - 1
nnOutput := nnOutput + array.get(inputs, i) * (1 / nnLength)
nnOutput
This output is then compared against a standard or dynamic mean line to generate trend following signals.
Mean = ta.sma(nnOutput, sdLook)
cross = useMean? 50 : Mean
The indicator also incorporates Heikin Ashi candlestick calculations to provide additional insights into market dynamics, such as trend strength and potential reversals.
// Calculate Heikin Ashi representation
ha = ha(
na(nnOutput ) ? nnOutput : nnOutput ,
math.max(nnOutput, nnOutput ),
math.min(nnOutput, nnOutput ),
nnOutput)
Standard deviation bands are used to create dynamic overbought and oversold zones, further enhancing the tool's analytical capabilities.
// Calculate Dynamic OB/OS Zones
stdv_bands(_src, _length, _mult) =>
float basis = ta.sma(_src, _length)
float dev = _mult * ta.stdev(_src, _length)
= stdv_bands(nnOutput, sdLook,sdMult/2)
= stdv_bands(nnOutput, sdLook, sdMult)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader.
For questions or recommendations, please feel free to seek contact in the comments.
Rolling VWAP [QuantraSystems]Rolling VWAP
Introduction
The Rolling VWAP (R͜͡oll-VWAP) indicator modernizes the traditional VWAP by recalculating continuously on a rolling window, making it adept at pinpointing market trends and breakout points.
Its dual functionality includes both the dynamic rolling VWAP and a customizable anchored VWAP, enhanced by color-coded visual cues, thereby offering traders valuable flexibility and insight for their market analysis.
Legend
In the Image you can see the BTCUSD 1D Chart with the R͜͡oll-VWAP overlay.
You can see the individually activatable Standard Deviation (SD) Bands and the main VWAP Line.
It also features a Trend Signal which is deactivated by default and can be enabled if required.
Furthermore you can find the coloring of the VWAP line to represent the Trend.
In this case the trend itself is defined as:
Close being greater than the VWAP line -> Uptrend
Close below the VWAP line -> Downtrend
Notes
The R͜͡oll-VWAP can be used in a variety of ways.
Volatility adjusted expected range
This aims to identify in which range the asset is likely to move - according to the historical values the SD Bands are calculated and thus their according probabilities displayed.
Trend analysis
Trending above or below the VWAP shows up or down trends accordingly.
S/R Levels
Based on the probability distribution the 2. SD often works as a Resistance level and either mid line or 1. SD lines can act as S/R levels
Unsustainable levels
Based on the probability distributions a SD level of beyond 2.5, especially 3 and higher is hit very seldom and highly unsustainable.
This can either mean a mean reversion state or a momentum slowdown is necessary to get back to a sustainable level.
Please note that we always advise to find more confluence by additional indicators.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
Methodology
The R͜͡oll-VWAP is based on the inbuilt TV VWAP.
It expands upon the limitations of having an anchored timeframe and thus a limited data set that is being reset constantly.
Instead we have integrated a rolling nature that continuously calculates the VWAP over a customizable lookback.
To also keep the base utility it is possible to use the anchored timeframes as well.
Furthermore the visualization has been improved and we added the coloring of the main VWAP line according to the Trend as stated above.
The applicable Trend signals are also part of that.
The parameter settings and also the visualizations allow for ample customizations by the trader.
For questions or recommendations, please feel free to seek contact in the comments.
Triple Confirmation Kernel Regression Base [QuantraSystems]Kernel Regression Oscillator - BASE
Introduction
The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends.
This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones.
It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator. The additional Chart Overlay Indicator adds confidence to the signal.
This methodology helps the trader to significantly reduce false signals and offers a more reliable indication of market movements than more widely used indicators can.
Legend
The upper section is the Overlay. It features the Signal Wave to display the current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 3), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
The lower one is the Base Chart - This Indicator.
It features the Kernel Regression Oscillator to display a composite of three distinct regressions, also displaying current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations.
Case Study
To effectively utilize the ᏦᏒᎧ, traders should use both the additional Overlay and the Base
Chart at the same time. Then focus on capturing the confluence in signals, for example:
If the 𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮 on the Overlay and the ᏦᏒᎧ on the Base Chart both reside near the extreme of an Oversold zone the probability is higher than normal that momentum in trend may soften or the token may even experience a reversion soon.
If a bar is characterized by an Oversold Shading in both the Overlay and the Base Chart, then the probability is very high to experience a reversion soon.
In this case the trader may want to look for appropriate entries into a long position, as displayed here.
If a bar is characterized by an Overbought Shading in either Overlay or Base Chart, then the probability is high for momentum weakening or a mean reversion.
In this case the trade may have taken profit and closed his long position, as displayed here.
Please note that we always advise to find more confluence by additional indicators.
Recommended Settings
Swing Trading (1D chart)
Overlay
Bandwith: 45
Width: 2
SD Lookback: 150
SD Multiplier: 2
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Fast-paced, Scalping (4min chart)
Overlay
Bandwith: 75
Width: 2
SD Lookback: 150
SD Multiplier: 3
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Notes
The Kernel Regression Oscillator on the Base Chart is also sensitive to divergences if that is something you are keen on using.
For maximum confluence, it is recommended to use the indicator both as a chart overlay and in its Base Chart.
Please pay attention to shaded areas with Standard Deviation settings of 2 or 3 at their outer borders, and consider action only with high confidence when both parts of the indicator align on the same signal.
This tool shows its best performance on timeframes lower than 4 hours.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
The trend following functionality is indicated through the "𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮" Line, with optional "Up" and "Down" arrows to denote trend directions only (toggle “Show Trend Signals”).
Methodology
The Kernel Regression Oscillator takes three distinct kernel regression functions,
used at similar weight, in order to calculate a balanced and smooth composite of the regressions. Part of it are:
The Epanechnikov Kernel Regression: Known for its efficiency in smoothing data by assigning less weight to data points further away from the target point than closer data points, effectively reducing variance.
The Wave Kernel Regression: Similarly assigning weight to the data points based on distance, it captures repetitive and thus wave-like patterns within the data to smoothen out and reduce the effect of underlying cyclical trends.
The Logistic Kernel Regression: This uses the logistic function in order to assign weights by probability distribution on the distance between data points and target points. It thus avoids both bias and variance to a certain level.
kernel(source, bandwidth, kernel_type) =>
switch kernel_type
"Epanechnikov" => math.abs(source) <= 1 ? 0.75 * (1 - math.pow(source, 2)) : 0.0
"Logistic" => 1/math.exp(source + 2 + math.exp(-source))
"Wave" => math.abs(source) <= 1 ? (1 - math.abs(source)) * math.cos(math.pi * source) : 0.
kernelRegression(src, bandwidth, kernel_type) =>
sumWeightedY = 0.
sumKernels = 0.
for i = 0 to bandwidth - 1
base = i*i/math.pow(bandwidth, 2)
kernel = kernel(base, 1, kernel_type)
sumWeightedY += kernel * src
sumKernels += kernel
(src - sumWeightedY/sumKernels)/src
// Triple Confirmations
Ep = kernelRegression(source, bandwidth, 'Epanechnikov' )
Lo = kernelRegression(source, bandwidth, 'Logistic' )
Wa = kernelRegression(source, bandwidth, 'Wave' )
By combining these regressions in an unbiased average, we follow our principle of achieving confluence for a signal or a decision, by stacking several edges to increase the probability that we are correct.
// Average
AV = math.avg(Ep, Lo, Wa)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader. The indicator comes with default and recommended settings.
For questions or recommendations, please feel free to seek contact in the comments.
Mean Reversion Watchlist [Z score]Hi Traders !
What is the Z score:
The Z score measures a values variability factor from the mean, this value is denoted by z and is interpreted as the number of standard deviations from the mean.
The Z score is often applied to the normal distribution to “standardize” the values; this makes comparison of normally distributed random variables with different units possible.
This popular reversal based indicator makes an assumption that the sample distribution (in this case the sample of price values) is normal, this allows for the interpretation that values with an extremely high or low percentile or “Z” value will likely be reversal zones.
This is because in the population data (the true distribution) which is known, anomaly values are very rare, therefore if price were to take a z score factor of 3 this would mean that price lies 3 standard deviations from the mean in the positive direction and is in the ≈99% percentile of all values. We would take this as a sign of a negative reversal as it is very unlikely to observe a consecutive equal to or more extreme than this percentile or Z value.
The z score normalization equation is given by
In Pine Script the Z score can be computed very easily using the below code.
// Z score custom function
Zscore(source, lookback) =>
sma = ta.sma(source, lookback)
stdev = ta.stdev(source, lookback, true)
zscore = (source - sma) / stdev
zscore
The Indicator:
This indicator plots the Z score for up to 20 different assets ( Note the maximum is 40 however the utility of 40 plots in one indicator is not much, there is a diminishing marginal return of the number of plots ).
Z score threshold levels can also be specified, the interpretation is the same as stated above.
The timeframe can also be fixed, by toggling the “Time frame lock” user input under the “TIME FRAME LOCK” user input group ( Note this indicator does not repain t).
USDT+USDC+BUSD Market CapThis Pine Script indicator visualizes the combined market capitalization of three prominent stablecoins: USDT, USDC, and BUSD, on a daily basis.
It fetches the daily closing market caps of these stablecoins and sums them. The resulting line graph is displayed in its own separate pane below the main price chart.
The line is color-coded: green on days when the market cap is increasing compared to the previous day, and red when it's decreasing.
