RSI Slope Filtered Signals [UAlgo]The "RSI Slope Filtered Signals " is a technical analysis tool designed to enhance the accuracy of RSI (Relative Strength Index) signals by incorporating slope analysis. This indicator not only considers the RSI value but also analyzes the slope of the RSI over a specified number of bars, providing a more refined signal that accounts for the momentum and trend strength. By utilizing both positive and negative slope arrays, the indicator dynamically adjusts its thresholds, ensuring that signals are responsive to changing market conditions. This tool is particularly useful for traders looking to identify overbought and oversold conditions with a higher degree of precision, filtering out noise and providing clear visual cues for potential market reversals.
🔶 Key Features
Dynamic Slope Analysis: Measures the slope of RSI over a customizable number of bars, offering insights into the momentum and trend direction.
Adaptive Thresholds: Uses historical slope data to calculate dynamic thresholds, adjusting signal sensitivity based on market conditions.
Normalized Slope Calculation: Normalizes the slope values to provide a consistent measure across different market conditions, making the indicator more versatile.
Clear Signal Visualization: The indicator plots both positive and negative normalized slopes with color gradients, visually representing the strength of the trend.
Overbought and Oversold Signals: Plots overbought and oversold signals directly on the chart when the calculated value reaches the user-specified threshold, helping traders identify potential reversal points.
Customizable Settings: Allows users to adjust the RSI length, slope measurement bars, and lookback periods, providing flexibility to tailor the indicator to different trading strategies.
🔶 Interpreting the Indicator
The "RSI Slope Filtered Signals " indicator is designed to be easy to interpret. Here's how you can use it:
Normalized Slope: The indicator plots the normalized slope of the RSI, with values above zero indicating positive momentum and values below zero indicating negative momentum. A higher positive slope suggests a strong upward trend, while a deeper negative slope indicates a strong downward trend.
Reversal Signals: The indicator plots several horizontal lines at different thresholds (+3, +2, +1, 0, -1, -2, -3). These levels are used to gauge the strength of the momentum based on the normalized slope. For example, a normalized slope crossing above the +2 threshold may indicate a strong bullish trend, while crossing below the -2 threshold may suggest a strong bearish trend. These thresholds help in understanding the intensity of the current trend and provide context for interpreting the indicator's signals.
This indicator generates overbought and oversold signals not solely based on the RSI entering extreme levels (above 70 for overbought and below 30 for oversold), but also by considering the behavior of the normalized slope relative to specific thresholds. Specifically, the Overbought Signal (🔽) is triggered when the RSI is above 70 and the normalized slope from the previous bar is greater than or equal to the upper threshold, with the current slope being lower than the previous slope, indicating a potential bearish reversal as momentum may be slowing down.
Similarly, the Oversold Signal (🔼) is generated when the RSI is below 30 and the normalized slope from the previous bar is less than or equal to the lower threshold, with the current slope being higher than the previous slope, signaling a potential bullish reversal as the downward momentum may be weakening.
Area Plots: The indicator also plots the positive and negative slopes as filled areas, providing a quick visual cue for the strength and direction of the trend. Green areas represent positive slopes (upward momentum), while red areas represent negative slopes (downward momentum).
By combining these elements, the "RSI Slope Filtered Signals " provides a comprehensive view of the market's momentum, helping traders make more informed decisions by filtering out false signals and focusing on the significant trends.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Centered Oscillators
Average of CBO and CBO divergence histogramShort Description:
This indicator combines a Custom Bias Oscillator (CBO) with its Divergence Histogram and computes their average for use to assess the market's bias based on candlestick analysis, from the aforementioned CBO indicator.
Full Description:
Overview:
This indicator integrates two powerful analytical tools into a single script: a Custom Bias Oscillator (CBO) and its Divergence Histogram. This indicator provides traders with a comprehensive view of market bias and divergence between price movements and volume, enhanced by an optional signal line derived from the combined average of these metrics.
Key Features:
Custom Bias Oscillator (CBO):
The CBO is calculated based on the body and wick biases of candlesticks, normalized by the Average True Range (ATR) to account for market volatility.
The CBO is scaled by the divergence between the Rate of Change (ROC) of volume and the ROC of the adjusted bias, ensuring it reflects potential reversals or continuations in the market.
Divergence Histogram:
The Divergence Histogram is derived from the difference between the CBO and its signal line.
This difference is normalized and plotted to provide visual cues for potential divergences, which may indicate trend exhaustion or the beginning of a new trend.
Combined Average with Signal Line:
The indicator calculates the average of the CBO and the normalized divergence, creating a combined signal that offers a more rounded perspective on market conditions.
A signal line, generated by smoothing the combined average, is plotted to help traders identify potential buy or sell signals based on crossovers.
Customization:
The indicator includes customizable parameters for the periods of the oscillator, signal line, ATR, ROC, and the combined signal line, allowing traders to tailor the indicator to different market conditions and timeframes.
How to Use:
Buy Signal: Consider a long position when the combined average crosses above the signal line, indicating potential bullish momentum.
Sell Signal: Consider a short position when the combined average crosses below the signal line, indicating potential bearish momentum.
Divergence Analysis: Use the Divergence Histogram to identify areas where price movements may be diverging from volume, signaling potential reversals or corrections.
Disclaimer:
This indicator is designed for educational and informational purposes only. It is not financial advice. Always perform your own analysis before making any investment decisions. Past performance is not indicative of future results.
[KVA] KMACDKMACD Indicator: Advanced Market Analysis Through Central Tendency Metrics
The KMACD (KAMVIA Moving Average Convergence Divergence) indicator is an advanced, multi-dimensional tool designed to provide traders and analysts with a deeper understanding of market dynamics. By integrating the classical MACD framework with statistical measures of central tendency, KMACD offers a sophisticated approach to identifying trends, reversals, and potential trading opportunities.
Key Features of the KMACD Indicator:
1. Enhanced MACD Calculation :
- The KMACD employs dual moving averages (fast and slow) of user-defined types (SMA, EMA, WMA) to calculate the MACD line, which represents the difference between these moving averages. This traditional approach is further enhanced by customizable signal smoothing, allowing users to fine-tune the sensitivity of the indicator.
2. Central Tendency Metrics :
- The indicator integrates additional statistical measures, such as Mean, Median, Mode, Standard Deviation, and Variance, calculated over a rolling window. These metrics provide insights into the central tendencies of the MACD values, helping traders understand the overall trend direction and the dispersion of price movements around the trend.
3. RSI-Like Oscillator :
- A unique RSI-like value derived from the MACD line is included to highlight overbought and oversold conditions. This offers a dual-layered perspective, combining the power of MACD and RSI methodologies, to signal potential market extremes with greater precision.
4. Customizable Visual Elements :
- KMACD allows users to toggle the visibility of the MACD line, Signal line, and Histogram, providing flexibility in how the data is presented. The histogram dynamically changes color—green when above zero, indicating bullish momentum, and red when below zero, indicating bearish momentum.
5. Horizontal Line Customization :
- The indicator includes customizable horizontal lines for the zero level, overbought, and oversold thresholds. These lines serve as visual cues to identify key price levels and market conditions.
6. Adaptive to Various Market Conditions :
- KMACD's comprehensive features make it adaptable to various market conditions, from trending markets to sideways consolidations. Whether you're looking to capture momentum shifts or identify potential reversal points, KMACD provides the analytical power needed to make informed trading decisions.
How to Use KMACD:
- Trend Identification : Use the MACD line in conjunction with central tendency measures (Mean, Median, Mode) to gauge the overall market trend and its strength. A rising MACD line, supported by higher mean and median values, typically indicates an uptrend.
- Momentum Analysis : The histogram and RSI-like value help in identifying the momentum behind price movements. Positive histogram bars suggest increasing bullish momentum, while negative bars suggest increasing bearish momentum.
- Overbought/Oversold Conditions : Monitor the RSI-like oscillator and the overbought/oversold levels to detect when the market may be poised for a reversal.
- Divergence Detection : Look for divergences between the MACD line and price action, supported by the central tendency measures, to spot potential reversal points.
Conclusion
The KMACD indicator is more than just a traditional MACD; it’s a comprehensive tool designed to cater to both novice and experienced traders. By incorporating central tendency metrics and customizable features, KMACD stands out as a versatile and powerful indicator that enhances market analysis and trading strategies. Whether you're navigating volatile markets or steady trends, KMACD offers the precision and depth needed to stay ahead.
ELMo HeatmapA heatmap display of the Entropy, Liquidity, and Momentum (ELMo) strategy status across a group of tickers.
Background color indicates ELMo's opinion of that row(green - bull, red - bear). White dots indicate bullish signals and black are bearish signals.
Top row is current ticker, remaining 7 rows are each a configurable ticker. The defaults are:
SPY
QQQ
DIA
IWM
BTCUSD
GLD
TLT
If you have appropriate data feeds, I also like the following ticker list:
ES1!
NQ1!
YM1!
RTY1!
BTC1!
GC1!
VX1!
MACD with 1D Stochastic Confirmation Reversal StrategyOverview
The MACD with 1D Stochastic Confirmation Reversal Strategy utilizes MACD indicator in conjunction with 1 day timeframe Stochastic indicators to obtain the high probability short-term trend reversal signals. The main idea is to wait until MACD line crosses up it’s signal line, at the same time Stochastic indicator on 1D time frame shall show the uptrend (will be discussed in methodology) and not to be in the oversold territory. Strategy works on time frames from 30 min to 4 hours and opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Higher time frame confirmation: Strategy utilizes 1D Stochastic to establish the major trend and confirm the local reversals with the higher probability.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
MACD line of MACD indicator shall cross over the signal line of MACD indicator.
1D time frame Stochastic’s K line shall be above the D line.
1D time frame Stochastic’s K line value shall be below 80 (not overbought)
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 3.25, value multiplied by ATR to be subtracted from position entry price to setup stop loss)
ATR Trailing Profit Activation Level (by default = 4.25, value multiplied by ATR to be added to position entry price to setup trailing profit activation level)
Trailing EMA Length (by default = 20, period for EMA, when price reached trailing profit activation level EMA will stop out of position if price closes below it)
User can choose the optimal parameters during backtesting on certain price chart, in our example we use default settings.
Justification of Methodology
This strategy leverages 2 time frames analysis to have the high probability reversal setups on lower time frame in the direction of the 1D time frame trend. That’s why it’s recommended to use this strategy on 30 min – 4 hours time frames.
To have an approximation of 1D time frame trend strategy utilizes classical Stochastic indicator. The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
Strategy logic assumes that on 1D time frame it’s uptrend in %K line is above the %D line. Moreover, we can consider long trade only in %K line is below 80. It means that in overbought state the long trade will not be opened due to higher probability of pullback or even major trend reversal. If these conditions are met we are going to our working (lower) time frame.
On the chosen time frame, we remind you that for correct work of this strategy you shall use 30min – 4h time frames, MACD line shall cross over it’s signal line. The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in a stock's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line=12-period EMA−26-period
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
In our script we are interested in only MACD and signal lines. When MACD line crosses signal line there is a high chance that short-term trend reversed to the upside. We use this strategy on 45 min time frame.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -4.79%
Maximum Single Profit: +20.14%
Net Profit: +2361.33 USDT (+44.72%)
Total Trades: 123 (44.72% win rate)
Profit Factor: 1.623
Maximum Accumulated Loss: 695.80 USDT (-5.48%)
Average Profit per Trade: 19.20 USDT (+0.59%)
Average Trade Duration: 30 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe between 30 min and 4 hours and chart (optimal performance observed on 45 min BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
DIVERGENCE SPOT X P.FUTURES (BYBIT and BINANCE) [GUSLM]Author's note:
When I searched the Trading View libraries, I didn't find what I was looking for. I noticed that the crypto market currently uses a lot more derivatives called "Perpetual" Futures, while the price is always formulated based on the spot price, or the real coin.
I wanted something that would mark the difference in real time between the spot price and the perpetual futures, creating a bar history. Since the relationship between them generates algorithms on exchanges to formulate payment rates for the lowest side, and to formulate the "market price" of the perpetual futures (without expiration), which are not the value of the last trade. I found several scripts that try to show the relationship between the funding rate and the rate, or the increase in these over time. But they don't show the direct action of the two prices in real time, nor a history of this difference.
The usefulness of this script is that at times you will see jumps in the price difference and can make assessments from this. Associated with another funding rate script it becomes even more interesting. I also didn't find coverage for the two brokers that my friends and I operate with, so I enabled it for use on these two brokers.
Function:
It works for the BYBIT and BINANCE exchanges. In coins that have both markets (Spot and Perpetual futures).
It shows in bars the real-time activity of the two markets, the difference between them. A configurable historical average, and bars for each candle. The last one is in real time.
How it works:
The script will detect which coin you are looking at, and will use its algorithm to detect if there is an opposite pair. If it is spot, it will identify and look for the perpetual future, and if it is future, it will find the spot prices. It will calculate the difference between them in %, using 0.05 as the default threshold - normally used by brokers as "zero". The futures on average are currently 0.05 higher than the spot in price. And it will show this difference in a bar, green and upwards if the futures are higher, and red if they are lower. and its respective normalized value in %(100)
Example of use:
1: you can check for possible movement patterns in relation to the expectation of future prices and spot, over time.
2. Main: you will notice at times normally prior to larger price movements, the indicator will pulse, indicating changes in the price difference of perpetual futures in relation to spot. - e.g.: in large purchases in spot that pull the price up by buying more positions in the order book, you will see a downward pulse in the indicator bar. as well as in the opposite direction, being an upward pulse.
3. The movement may sometimes be in futures, so you will see the opposite happening, it may be useful to add the opposite currency pair as an additional price, to identify with certainty who is "pulling" whether it is spot or perpetual. But in both cases the possible interpretation is the same. the expectation of futures in relation to the spot price is higher or lower than the average, and there are or are not pulses in one direction or another.
Summary: by following this indicator you can see the real-time vision of large purchases in spot, for example. Someone with great market power may have a future position, and is triggering purchases or ignition in the direction of their position in spot, for example.
Warning: many other indicators and market insights are needed to have a view of the whole and interpret these signals and bar movements. Use and observation lead to an understanding for future actions. But it should always be based on a global context. This is not an indicator to be used alone.
Warning 2: if the opposite pair is not available (exchange only has a spot market, for example) nothing will appear in the indicator. And if it is an exchange other than BYBIT or BINANCE, nothing will probably appear either.
Moments Functions
This script is a TradingView Pine Script (version 5) for calculating and plotting statistical moments of a financial series. Here's a breakdown of what it does:
Script Overview
Purpose:
The script calculates and visualizes moments such as Mean, Variance, Skewness, and Kurtosis of a price series.
It also provides the option to display log returns and various statistical bands.
Inputs:
Moments Selection: Choose from Mean, Variance, Skewness, or Excess Kurtosis.
Source Settings: Define the lookback period and source data (e.g., closing price or log returns).
Plot Settings: Control visibility and styling of plots, bands, and information panels.
Colors Settings: Customize colors for different plot elements.
Functions:
f_va(): Computes sample variance.
f_sd(): Computes sample standard deviation.
f_skew(): Computes sample skewness.
f_kurt(): Computes sample kurtosis.
seskew(): Calculates the standard error of skewness.
sekurt(): Calculates the standard error of kurtosis.
skewcv(): Computes critical values for skewness.
kurtcv(): Computes critical values for kurtosis.
Outputs:
Plots:
Moment values (Mean, Variance, Skewness, Kurtosis).
Log Returns (if selected).
Standard Deviation Bands (if selected).
Critical Values for Skewness and Kurtosis (if selected).
Information Panel: Displays current statistical values and their significance.
Customization:
Users can customize appearance and behavior of the script through various input options, including colors, line thickness, and background settings.
Key Variables and Constants
Constants:
zscoreS and zscoreL: Z-scores for confidence intervals based on sample size.
skewrv and kurtrv: Reference values for skewness and excess kurtosis.
Sample Functions:
f_va() and f_sd(): Custom functions to calculate sample variance and standard deviation.
f_skew() and f_kurt(): Custom functions to calculate skewness and kurtosis.
Critical Values:
Functions skewcv() and kurtcv() calculate critical values used to assess statistical significance of skewness and kurtosis.
Plotting
Plot Types:
Mean, variance, skewness, and excess kurtosis are plotted based on user selection.
Log returns are plotted if enabled.
Standard deviation bands and critical values are plotted if enabled.
Labels:
Information panel labels display mean, variance/standard deviation, skewness, and kurtosis values along with their significance.
Example Usage
To use this script:
Add it to a TradingView chart.
Adjust inputs to configure which statistical moments to display, the source data, and the appearance of the plots.
Review the plotted data and labels to analyze the statistical properties of the selected price series.
This script is useful for traders and analysts looking to perform advanced statistical analysis on financial data directly within TradingView.
When comparing two stock prices over a period of time, the statistical moments—mean, variance, skewness, and kurtosis—can provide a deep insight into the behavior of the stock prices and their distributions. Here’s what each moment signifies in this context:
1. Mean
Definition: The mean (or average) is the sum of the stock prices over the period divided by the number of data points. It represents the central value of the price series.
Interpretation: When comparing two stocks, the mean tells you the average price level of each stock over the period. A higher mean indicates that, on average, the stock price is higher compared to another stock with a lower mean.
Comparison Insight: If Stock A has a higher mean price than Stock B, it implies that Stock A's prices are generally higher than those of Stock B over the given period.
2. Variance
Definition: Variance measures the dispersion or spread of the stock prices around the mean. It is the average of the squared differences from the mean.
Interpretation: A higher variance indicates that the stock prices fluctuate more widely from the mean, implying greater volatility. Conversely, a lower variance indicates more stable and predictable prices.
Comparison Insight: Comparing the variances of two stocks helps in assessing which stock has more price volatility. If Stock A has a higher variance than Stock B, it means Stock A's prices are more volatile and less predictable compared to Stock B.
3. Skewness
Definition: Skewness measures the asymmetry of the distribution of stock prices around the mean. It can be positive, negative, or zero:
Positive Skewness: The distribution has a long right tail, with more frequent small returns and fewer large positive returns.
Negative Skewness: The distribution has a long left tail, with more frequent small returns and fewer large negative returns.
Zero Skewness: The distribution is symmetric around the mean.
Interpretation: Skewness tells you about the direction of outliers in the stock price distribution. Positive skewness means a higher probability of large positive returns, while negative skewness means a higher probability of large negative returns.
Comparison Insight: By comparing skewness, you can understand the nature of extreme returns for two stocks. For example, if Stock A has positive skewness and Stock B has negative skewness, Stock A might have more frequent large gains, whereas Stock B might have more frequent large losses.
4. Kurtosis
Definition: Kurtosis measures the "tailedness" of the distribution of stock prices. It indicates how much of the distribution is in the tails versus the center. High kurtosis means more outliers (extreme returns), while low kurtosis means fewer outliers.
Interpretation:
High Kurtosis: Indicates a higher likelihood of extreme price movements (both high and low) compared to a normal distribution.
Low Kurtosis: Indicates that extreme price movements are less common.
Comparison Insight: Comparing kurtosis between two stocks shows which stock has more extreme returns. If Stock A has higher kurtosis than Stock B, it means Stock A has more frequent extreme price changes, suggesting more risk or opportunities for large gains or losses.
Summary
Mean: Compares average price levels.
Variance: Compares price volatility.
Skewness: Compares the asymmetry of price movements.
Kurtosis: Compares the likelihood of extreme price changes.
By analyzing these statistical moments, you can gain a comprehensive view of how the two stocks behave relative to each other, which can inform investment decisions based on risk, return expectations, and the nature of price movements.
Double CCI Confirmed Hull Moving Average Reversal StrategyOverview
The Double CCI Confirmed Hull Moving Average Strategy utilizes hull moving average (HMA) in conjunction with two commodity channel index (CCI) indicators: the slow and fast to increase the probability of entering when the short and mid-term uptrend confirmed. The main idea is to wait until the price breaks the HMA while both CCI are showing that the uptrend has likely been already started. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Double trade setup confirmation: Strategy utilizes two different period CCI indicators to confirm the breakouts of HMA.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Short-term period CCI indicator shall be above 0.
Long-term period CCI indicator shall be above 0.
Price shall cross the HMA and candle close above it with the same candle
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 25, used for calculation short term period CCI
CCI Slow Length (by default = 50, used for calculation long term period CCI)
Hull MA Length (by default = 34, period of HMA, which shall be broken to open trade)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI and HMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator used in trading to measure a security's price relative to its average price over a given period. Developed by Donald Lambert in 1980, the CCI is primarily used to identify cyclical trends in a security, helping traders to spot potential buying or selling opportunities.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Hull Moving Average (HMA) is a type of moving average that was developed by Alan Hull to improve upon the traditional moving averages by reducing lag while maintaining smoothness. The goal of the HMA is to create an indicator that is both quick to respond to price changes and less prone to whipsaws (false signals).
How the Hull Moving Average is Calculated?
The Hull Moving Average is calculated using the following steps:
Weighted Moving Average (WMA): The HMA starts by calculating the Weighted Moving Average (WMA) of the price data over a period square root of n (sqrt(n))
Speed Adjustment: A WMA is then calculated for half of the period n/2, and this is multiplied by 2 to give more weight to recent prices.
Lag Reduction: The WMA of the full period n is subtracted from the doubled n/2 WMA.
Final Smoothing: To smooth the result and reduce noise, a WMA is calculated for the square root of the period n.
The formula can be represented as:
HMA(n) = WMA(WMA(n/2) × 2 − WMA(n), sqrt(n))
The Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent data points, making it more responsive to recent price changes than a Simple Moving Average (SMA). In a WMA, each data point within the selected period is multiplied by a weight, with the most recent data receiving the highest weight. The sum of these weighted values is then divided by the sum of the weights to produce the WMA.
This strategy leverages HMA of user given period as a critical level which shall be broken to say that probability of trend change to the upside increased. HMA reacts faster than EMA or SMA to the price change, that’s why it increases chances to enter new trade earlier. Long-term period CCI helps to have an approximation of mid-term trend. If it’s above 0 the probability of uptrend increases. Short-period CCI allows to have an approximation of short-term trend reversal from down to uptrend. This approach increases chances to have a long trade setup in the direction of mid-term trend when the short-term trend starts to reverse.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses HMA to enter the trade, but for trailing it leverages EMA. It’s used because EMA has no such fast reaction to price move which increases probability not to be stopped out from any significant uptrend move.
Backtest Results
Operating window: Date range of backtests is 2022.07.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 100%
Maximum Single Position Loss: -4.67%
Maximum Single Profit: +19.66%
Net Profit: +14897.94 USDT (+148.98%)
Total Trades: 104 (36.54% win rate)
Profit Factor: 2.312
Maximum Accumulated Loss: 1302.66 USDT (-9.58%)
Average Profit per Trade: 143.25 USDT (+0.96%)
Average Trade Duration: 34 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Market Breadth - AsymmetrikMarket Breadth - Asymmetrik User Manual
Overview
The Market Breadth - Asymmetrik is a script designed to provide insights into the overall market condition by plotting three key indicators based on stocks within the S&P 500 index. It helps traders assess market momentum and strength through visual cues and is especially useful for understanding the proportion of stocks trading above their respective moving averages.
Features
1. Market Breadth Indicators:
- Breadth 20D (green line): Represents the percentage of stocks in the S&P 500 that are above their 20-day moving average.
- Breadth 50D (yellow line): Represents the percentage of stocks in the S&P 500 that are above their 50-day moving average.
- Breadth 100D (red line): Represents the percentage of stocks in the S&P 500 that are above their 100-day moving average.
2. Horizontal Lines for Context:
- Green line at 10%
- Lighter green line at 20%
- Grey line at 50%
- Light red line at 80%
- Dark red line at 90%
3. Background Color Alerts:
- Green background when all three indicators are under 20%, indicating a potential oversold market condition.
- Red background when all three indicators are over 80%, indicating a potential overbought market condition.
Interpreting the Indicator
- Market Breadth Lines: Observe the plotted lines to assess the percentage of stocks above their moving averages.
- Horizontal Lines: Use the horizontal lines to quickly identify important threshold levels.
- Background Colors: Pay attention to background colors for quick insights:
- Green: All indicators suggest a potentially oversold market condition (below 20).
- Red: All indicators suggest a potentially overbought market condition (above 80).
Troubleshooting
- If the indicator does not appear as expected, please contact me.
- This indicator works only on daily and weekly timeframes.
Conclusion
This Market Breadth Indicator offers a visual representation of market momentum and strength through three key indicators, helping you identify potential buying and selling zones.
DSL Oscillator [BigBeluga]DSL Oscillator BigBeluga
The DSL (Discontinued Signal Lines) Oscillator is an advanced technical analysis tool that combines elements of the Relative Strength Index (RSI), Discontinued Signal Lines, and Zero-Lag Exponential Moving Average (ZLEMA). This versatile indicator is designed to help traders identify trend direction, momentum, and potential reversal points in the market.
What are Discontinued Signal Lines (DSL)?
Discontinued Signal Lines are an extension of the traditional signal line concept used in many indicators. While a standard signal line compares an indicator's value to its smoothed (slightly lagging) state, DSL takes this idea further by using multiple adaptive lines that respond to the indicator's current value. This approach provides a more nuanced view of the indicator's state and momentum, making it easier to determine trends and desired states of the indicator.
🔵 KEY FEATURES
● Discontinued Signal Lines (DSL)
Uses multiple adaptive lines that respond to the indicator's value
Provides a more nuanced view of the indicator's state and momentum
Helps determine trends and desired states of the indicator more effectively
Available in "Fast" and "Slow" modes for different responsiveness
Acts as dynamic support and resistance levels for the oscillator
● DSL Oscillator
Based on a combination of RSI and Discontinued Signal Lines
// Discontinued Signal Lines
dsl_lines(src, length)=>
UP = 0.
DN = 0.
UP := (src > ta.sma(src, length)) ? nz(UP ) + dsl_mode / length * (src - nz(UP )) : nz(UP )
DN := (src < ta.sma(src, length)) ? nz(DN ) + dsl_mode / length * (src - nz(DN )) : nz(DN )
Smoothed using Zero-Lag Exponential Moving Average for reduced lag
// Zero-Lag Exponential Moving Average function
zlema(src, length) =>
lag = math.floor((length - 1) / 2)
ema_data = 2 * src - src
ema2 = ta.ema(ema_data, length)
ema2
Oscillates between 0 and 100
Color-coded for easy interpretation of market conditions
● Signal Generation
Generates buy signals when the oscillator crosses above the lower DSL line below 50
Generates sell signals when the oscillator crosses below the upper DSL line above 50
Signals are visualized on both the oscillator and the main chart
● Visual Cues
Background color changes on signal occurrences for easy identification
Candles on the main chart are colored based on the latest signal
Oscillator line color changes based on its position relative to the DSL lines
🔵 HOW TO USE
● Trend Identification
Use the color and position of the DSL Oscillator relative to its Discontinued Signal Lines to determine the overall market trend
● Entry Signals
Look for buy signals (green circles) when the oscillator crosses above the lower DSL line
Look for sell signals (blue circles) when the oscillator crosses below the upper DSL line
Confirm signals with the triangles on the main chart and background color changes
● Exit Signals
Consider exiting long positions on exit signals and short positions on Entery signals
Watch for the oscillator crossing back between the DSL lines as a potential early exit signal
● Momentum Analysis
Strong momentum is indicated when the oscillator moves rapidly towards extremes and away from the DSL lines
Weakening momentum can be spotted when the oscillator struggles to reach new highs or lows, or starts converging with the DSL lines
The space between the DSL lines can indicate potential momentum strength - wider gaps suggest stronger trends
● Confirmation
Use the DSL lines as dynamic support/resistance levels for the oscillator
Look for convergence between oscillator signals and price action on the main chart
Combine signals with other technical indicators or chart patterns for stronger confirmation
🔵 CUSTOMIZATION
The DSL Oscillator offers several customization options:
Adjust the main calculation length for the DSL lines
Choose between "Fast" and "Slow" modes for the DSL lines calculation
By fine-tuning these settings, traders can adapt the DSL Oscillator to various market conditions and personal trading strategies.
The DSL Oscillator provides a multi-faceted approach to market analysis, combining trend identification, momentum assessment, and signal generation in one comprehensive tool. Its dynamic nature and visual cues make it suitable for both novice and experienced traders across various timeframes and markets. The integration of RSI, Discontinued Signal Lines, and ZLEMA offers traders a sophisticated yet intuitive tool to inform their trading decisions.
The use of Discontinued Signal Lines sets this oscillator apart from traditional indicators by providing a more adaptive and nuanced view of market conditions. This can potentially lead to more accurate trend identification and signal generation, especially in markets with varying volatility.
Traders can use the DSL Oscillator to identify trends, spot potential reversals, and gauge market momentum. The combination of the oscillator, dynamic signal lines, and clear visual signals provides a holistic view of market conditions. As with all technical indicators, it's recommended to use the DSL Oscillator in conjunction with other forms of analysis and within the context of a well-defined trading strategy.
Supply and Demand Zones with Enhanced SignalsThis Pine Script indicator combines supply and demand zone analysis with dynamic buy/sell signals to enhance trading strategies. It provides a robust framework for identifying optimal trading opportunities and managing existing trades.
Key Features:
Supply and Demand Zones: The indicator identifies significant supply and demand zones based on recent price action. These zones are plotted as horizontal lines to help traders visualize potential reversal points.
Exponential Moving Average (EMA): A 21-period EMA is used to determine the prevailing trend and generate buy and sell signals.
Relative Strength Index (RSI): The 14-period RSI is utilized to filter buy and sell signals, providing additional context on overbought and oversold conditions.
Signal Generation:
Buy Signal: Triggered when the price crosses above the EMA and RSI indicates that the market is not overbought.
Sell Signal: Triggered when the price crosses below the EMA and RSI indicates that the market is not oversold.
Enhanced Exit Signals:
Exit Buy Signal: Generated if an opposite sell signal occurs or the higher timeframe RSI indicates overbought conditions.
Exit Sell Signal: Generated if an opposite buy signal occurs or the higher timeframe RSI indicates oversold conditions.
Trade Management:
Tracks active trades and provides exit signals based on the occurrence of opposite trading signals. This helps in managing positions more effectively and reducing potential losses.
Usage:
Supply and Demand Zones: Look for price action around these zones to identify potential trading opportunities.
EMA and RSI: Use buy and sell signals in conjunction with EMA and RSI to validate trading decisions.
Higher Timeframe RSI: Utilize this for additional confirmation and exit signals.
Plotting:
Supply Zone: Plotted as a red horizontal line.
Demand Zone: Plotted as a green horizontal line.
EMA: Plotted as a blue line.
Buy and Sell Signals: Indicated by green and red triangle shapes, respectively.
Exit Signals: Indicated by blue and orange X shapes.
This indicator is designed to help traders make informed decisions by combining technical analysis with strategic trade management.
Custom Supertrend Multi-Timeframe Indicator [Pineify]Supertrend Multi-Timeframe Indicator
Introduction
The Supertrend Multi-Timeframe Indicator is an advanced trading tool designed to help traders identify trend directions and potential buy/sell signals by combining Supertrend indicators from multiple timeframes. This script is original in its approach to integrating Supertrend calculations across different timeframes, providing a more comprehensive view of market trends.
Concepts and Calculations
The indicator utilizes the Supertrend algorithm, which is based on the Average True Range (ATR). The Supertrend is a popular tool for trend-following strategies, and this script enhances its capabilities by incorporating data from a larger timeframe.
Supertrend Factor: Determines the sensitivity of the Supertrend line.
ATR Length: Defines the period for calculating the Average True Range.
Larger Supertrend Factor and ATR Length: Applied to the larger timeframe for a broader trend perspective.
Larger Timeframe: The higher timeframe from which the secondary Supertrend data is sourced.
How It Works
The script calculates the Supertrend for the current timeframe using the specified factor and ATR length.
Simultaneously, it requests Supertrend data from a larger timeframe.
Buy and sell signals are generated based on crossovers and crossunders of the Supertrend lines from both timeframes.
Visual cues (up and down arrows) are plotted on the chart to indicate buy and sell signals.
Background colors change to reflect the trend direction: green for an uptrend and red for a downtrend.
Usage
Add the indicator to your TradingView chart.
Customize the Supertrend factors, ATR lengths, and larger timeframe according to your trading strategy.
Enable or disable buy and sell alerts as needed.
Monitor the chart for visual signals and background color changes to make informed trading decisions.
Note: The indicator is best used in conjunction with other technical analysis tools and should not be relied upon as the sole basis for trading decisions.
Conclusion
The Supertrend Multi-Timeframe Indicator offers a unique and powerful way to analyze market trends by leveraging the strengths of the Supertrend algorithm across multiple timeframes. Its customizable settings and clear visual signals make it a valuable addition to any trader's toolkit.
Relative Strength according to Oster (RSO)Overview:
Relative Strength according to Oster (RSO) is an innovative tool that redefines how traders assess an asset's market strength. Moving beyond traditional indicators, RSO offers a sophisticated and highly responsive measure of an asset's potential to continue performing well. By integrating groundbreaking methodologies, RSO equips traders with unparalleled insights into market dynamics, making it an essential tool for anyone looking to stay ahead in today's fast-paced trading environment.
Understanding RSL (Relative Strength according to Levy):
At its core, Relative Strength according to Levy (RSL) is a powerful concept rooted in the idea that an asset currently exhibiting strength is more likely to maintain or even enhance that strength in the future. RSL calculates this by comparing an asset's current price to its moving average, providing a clear picture of its relative performance over time. The further its value is above 1, the higher the market momentum and vice versa. This relationship to the moving average is crucial, as it indicates not just where the asset stands today but also its trajectory in the context of historical performance. The ability to identify assets that consistently outperform is a game-changer for traders, and RSL has long been a cornerstone in this pursuit.
RSO vs. Traditional RSL: A Leap Forward
The RSO takes the traditional RSL concept and propels it into new territory with its innovative correlation-based approach. This is where RSO truly shines, offering a unique and sophisticated analysis that goes far beyond the basics.
Why RSO is Revolutionary:
Correlation Adjustment: The RSO doesn’t just measure an asset’s strength in isolation. Instead, it adjusts its readings based on how closely the asset's price movements correlate with a chosen benchmark. This groundbreaking feature ensures that the RSO is not just reactive to past performance but also predictive of how the asset might behave relative to the broader market, adding a layer of precision that is unparalleled in traditional strength indicators.
Superior Strength Option: With the RSO, traders have the option to include superior strength factors, adding another dimension of insight. This feature allows for more stable and reliable long-term signals. On the flip side, those who prefer a more dynamic trading style can opt to exclude this factor for more frequent, shorter-term signals. This level of customization is rare and sets the RSO apart as a truly adaptable tool.
Enhanced Market Insights: RSO’s correlation-based approach doesn’t just show how strong an asset is—it reveals how that strength is likely to develop in relation to the benchmark's underlying trends. This isn’t merely about comparing performance; it’s about understanding the asset’s potential trajectory in a much broader market context. Such insight is invaluable for making informed, strategic trading decisions.
Practical Application:
The RSO isn’t just innovative in theory; it’s designed for practical, real-world trading. Traders can set customized alerts based on RSO’s readings, ensuring they’re always aware of key buy or sell signals as they occur. The flexibility to include or exclude superior strength factors means that RSO can be tailored to fit any trading style, whether focused on long-term investments or short-term opportunities.
Conclusion:
In conclusion, the Relative Strength according to Oster (RSO) is more than just an indicator; it’s a breakthrough in market analysis. By integrating correlation adjustments and offering unparalleled customization options, RSO provides traders with insights that are both deeper and more actionable than ever before. This innovative tool is designed to empower traders, giving them the edge they need to succeed in an increasingly complex market landscape. Whether you’re a seasoned trader or just starting out, the RSO is a must-have tool for navigating market trends with confidence and precision.
Price & Momentum Reversal Indicator [TradeDots]Price & Momentum Divergence Indicator is a variant of the Chande Momentum Oscillator (CMO), designed to identify reversal patterns in overvalued and undervalued markets. This indicator aims to mitigate the common problem of all oscillating indicators: false buy/sell signals during prolonged market trends, by incorporating a volume-weighted approach and momentum analysis.
📝 HOW IT WORKS
Price Extremeness Calculation
The indicator evaluates the extremeness of the current price by analyzing price changes over a fixed window of candlesticks.
It separates the price changes into positive and negative changes, then multiplies them by the bar volume to obtain volume-weighted values, giving higher significance to bars with larger volumes.
Extremeness Ratio
The ratio is calculated by taking the difference between the total positive changes and total negative changes, and then dividing this result by the sum of the total positive and negative changes.
The result is then smoothed to reduce market noise and rescaled to a range between -100 to 100, where 100 indicates all price changes within the window are positive.
Momentum Analysis
Momentum is calculated by measuring the rate of change of the smoothed extremeness ratio, indicating whether market extremeness is slowing and showing signs of reversion.
Reversal Signal Confirmation
For an asset to be considered a reversal, it has to be in the overvalued or undervalued zone (exceeding the overvalued & undervalued threshold). It must then show a slowed momentum change and a price reversion.
Lastly, candlestick analysis is used to confirm the reversal signal, ensuring there is no room for further breakout price movement.
🛠️ HOW TO USE
Candlestick Visualization
Candlestick bodies are painted with gradient colors representing the smoothed price extremeness (OBOS Index), ranging from -100 (solid red) to 100 (solid green). The exact value is displayed in a table at the bottom right corner.
Slowing price momentum is indicated with blue (bearish) and purple (bullish) colors, showing market pressure from the opposite side.
Reversal Confirmation
A decrease in price momentum combined with a price reversal triggers a signal label on the candlestick, indicating a potential pullback or reversal. This can serve as a reference for better entry and exit points.
⭐️ Premium Features
Higher Timeframe (HTF) Analysis
The indicator includes a feature to apply the same algorithm to a selected higher timeframe, ensuring trend alignment across multiple timeframes.
Alert Functions
Real-time notifications for overvalued and undervalued conditions, allowing traders to monitor trades and reversal signals anywhere and anytime.
❗️LIMITATIONS
Accuracy decreases in volatile and noisy markets.
Extended bullish or bearish market conditions may affect performance.
See Author's instructions below to get instant access to this indicator.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Momentum Trend [MT]The Momentum Trend indicator is an innovative technical analysis tool designed to capture and visualize momentum trends in financial markets. This advanced indicator goes beyond traditional momentum measures, offering a unique perspective on price action and trend strength.
Core Functionality:
Trend Momentum Index (TMI) Calculation:
At the heart of this indicator is the Trend Momentum Index (TMI), a proprietary algorithm that combines moving averages with price action analysis to gauge momentum. The TMI is calculated using a user-defined source, length, and moving average type.
Dynamic Trend Visualization:
The indicator uses a color-coded column plot to represent the TMI values, providing an intuitive visual representation of trend strength and direction. The colors change based on specific conditions, offering instant insights into the current market state.
Adaptive Momentum Analysis:
The TMI adapts to changing market conditions by comparing current values to historical ones, allowing for a more nuanced understanding of momentum shifts.
Key Inputs and Their Significance:
TMI Source:
Allows users to select the price data for TMI calculations. The default is the closing price, but users can choose alternative sources for different analytical perspectives.
TMI Length:
Defines the lookback period for the TMI calculation. The default of 8 provides a balance between responsiveness and stability, but users can adjust this to suit their trading style.
Moving Average Type:
Users can select from various moving average types (SMA, EMA, SMMA, WMA, VWMA) for the base calculation, allowing for customization based on trading preferences.
What Makes It Unique:
Comprehensive Momentum Analysis:
The TMI combines elements of trend following and momentum, providing a more holistic view of market dynamics than traditional momentum indicators.
Multi-Faceted Trend Identification:
The color-coding system doesn't just show bullish or bearish trends, but also identifies accelerating and decelerating momentum in both directions.
Flexible Moving Average Integration:
The ability to choose different moving average types allows traders to fine-tune the indicator's responsiveness and smoothness.
Visual Clarity:
The column-style plot with color changes offers clear, at-a-glance insights into trend strength and direction.
Momentum Comparison Logic:
The indicator incorporates logic to compare current momentum changes with recent historical changes, providing context for the current market state.
The Momentum Trend indicator represents a sophisticated approach to momentum and trend analysis. By combining moving averages, price action, and comparative momentum logic, it offers traders a powerful tool for identifying potential trend continuations, reversals, and momentum shifts.
This indicator is particularly valuable for traders looking to:
- Identify the start of new trends
- Spot potential trend reversals
- Gauge the strength of ongoing trends
- Time entries and exits based on momentum shifts
Trend Strength with Volatility and Volume [ST]Trend Strength with Volatility and Volume
Description in English:
This indicator combines market volatility and trading volume to measure the current trend strength. It helps identify when the trend is gaining or losing momentum.
Detailed Explanation:
Configuration:
Length: This input defines the period over which the moving average is calculated. The default value is 14.
MA Type: This input allows you to choose between a Simple Moving Average (SMA) and an Exponential Moving Average (EMA).
Volatility Length: This input defines the period over which the ATR (Average True Range) is calculated. The default value is 14.
Volume Length: This input defines the period over which the moving average of volume is calculated. The default value is 14.
Trend Strength Calculation:
Moving Average (MA): The script calculates the moving average of the closing price based on the selected type (SMA or EMA) and period.
Volatility (ATR): The ATR is used to measure market volatility over the specified period.
Volume MA: The script calculates the moving average of the trading volume based on the selected type (SMA or EMA) and period.
Trend Strength: The trend strength is calculated as the difference between the closing price and the moving average, divided by the volatility, and multiplied by the volume normalized by its moving average.
Plotting:
The trend strength is plotted as a line chart. Positive values indicate a strong upward trend, while negative values indicate a strong downward trend.
A horizontal line is added at the zero level to help identify the neutral point.
Indicator Benefits:
Trend Identification: Helps traders identify the strength of the current trend by combining price, volatility, and volume.
Visual Cues: Provides clear visual signals for trend strength, aiding in making informed trading decisions.
Customizable Parameters: Allows traders to adjust the length of the moving averages, ATR, and volume to suit different trading strategies and market conditions.
Justification of Component Combination:
Combining price, volatility, and volume provides a comprehensive measure of trend strength. This combination enhances the trader's ability to make informed decisions based on multiple market factors.
How Components Work Together:
The script calculates the moving average of the closing price and trading volume.
It measures market volatility using the ATR.
The trend strength is calculated by combining these components, providing a robust measure of the current trend's strength.
Título: Força da Tendência com Volatilidade e Volume
Descrição em Português:
Este indicador combina a volatilidade do mercado, medida pelo ATR (Average True Range), e o volume de negociações para medir a força da tendência atual. Ele ajuda a identificar quando a tendência está ganhando ou perdendo força.
Explicação Detalhada:
Configuração:
Comprimento: Este parâmetro define o período para o cálculo da média móvel. O valor padrão é 14.
Tipo de MA: Este parâmetro permite escolher entre uma Média Móvel Simples (SMA) e uma Média Móvel Exponencial (EMA).
Comprimento da Volatilidade: Este parâmetro define o período para o cálculo do ATR (Average True Range). O valor padrão é 14.
Comprimento do Volume: Este parâmetro define o período para o cálculo da média móvel do volume. O valor padrão é 14.
Cálculo da Força da Tendência:
Média Móvel (MA): O indicador calcula a média móvel do preço de fechamento com base no tipo selecionado (SMA ou EMA) e período.
Volatilidade (ATR): O ATR é usado para medir a volatilidade do mercado ao longo do período especificado.
Média Móvel do Volume: O indicador calcula a média móvel do volume de negociação com base no tipo selecionado (SMA ou EMA) e período.
Força da Tendência: A força da tendência é calculada como a diferença entre o preço de fechamento e a média móvel, dividida pela volatilidade e multiplicada pelo volume normalizado pela sua média móvel.
Plotagem:
A força da tendência é plotada como um gráfico de linhas. Valores positivos indicam uma forte tendência de alta, enquanto valores negativos indicam uma forte tendência de baixa.
Uma linha horizontal é adicionada no nível zero para ajudar a identificar o ponto neutro.
Benefícios do Indicador:
Identificação de Tendências: Este indicador ajuda os traders a identificar a força da tendência atual, combinando preço, volatilidade e volume.
Sinais Visuais Claros: Fornece sinais visuais claros para a força da tendência, facilitando a tomada de decisões informadas.
Parâmetros Personalizáveis: Os traders podem ajustar o comprimento das médias móveis, ATR e volume para se adequar a diferentes estratégias de negociação e condições de mercado.
Justificação da Combinação de Componentes:
A combinação de preço, volatilidade e volume fornece uma medida abrangente da força da tendência.
Isso melhora a capacidade dos traders de tomar decisões informadas com base em múltiplos fatores do mercado.
Como os Componentes Funcionam Juntos:
O indicador calcula a média móvel do preço de fechamento e do volume de negociação.
Mede a volatilidade do mercado usando o ATR.
A força da tendência é calculada combinando esses componentes, fornecendo uma medida robusta da força da tendência atual.
Power Law Volatility by G. Santostasi Introduction
This TradingView indicator is designed to provide a comprehensive analysis of Bitcoin's price movements using the concept of power laws. The indicator leverages the mathematical properties of power laws to predict returns and highlight significant deviations from expected trends. By applying the power law model to Bitcoin's price data, we aim to capture the diminishing returns over time and provide valuable insights to traders and analysts.
Theoretical Foundation
The foundation of this indicator is based on the power law, which describes a relationship between two quantities where one quantity varies as a power of another. Specifically, in the context of Bitcoin prices, we observe that returns follow a power law relationship with time.
Mathematically, if the power law holds true, the price P at time 𝑡 can be expressed as:
log(𝑃)=𝑚log(𝑡)+c where m is the slope of the power law and c is the y-intercept.
To understand the returns, we consider two points in time,
𝑡1and 𝑡2, with corresponding prices 𝑃 and 𝑃2. The returns can be derived as follows:
log(𝑃2)−log(𝑃1)=𝑚(log(𝑡2)−log(𝑡1))
This simplifies to:
log(𝑃2/𝑃1)=𝑚log(𝑡2/𝑡1)
For daily data, we let 𝑡2=𝑡1+1resulting in:
log(𝑅)=𝑚log(𝑡+1/𝑡)
where 𝑅 represents the returns, 𝑡 is the number of days from the Genesis Block, and
𝑚 is the slope of the power law.
Observations and Data Analysis
Using historical Bitcoin price data, we observe that returns decrease over time, which is indicative of diminishing returns. To validate this observation, we averaged real returns over a two-month period and compared them with the theoretical results derived from the power law:
𝑚log(𝑡2/𝑡1)
The comparison shows that the averaged real returns align closely with the theoretical predictions, reinforcing the validity of the power law model.
This alignment indicates that the power law is not merely an arbitrary straight line but a model that accurately captures the decay of returns over time. The scaling property of the power law holds at all scales, providing a robust framework for analyzing Bitcoin's price dynamics.
Indicator Components
The indicator comprises several components to visualize the power law's implications and provide actionable insights:
Theoretical Power Law Trend:
Plots the theoretical result from the power law model.
Shows the expected returns based on the power law relationship.
Real Returns:
Plots the real returns averaged over a user-defined Simple Moving Average (SMA) or Exponential Moving Average (EMA).
Provides a comparison between actual market performance and theoretical predictions.
When the real volatility is above the theoretical one derived from the power law the indicator identifies times when the price is overvalued.
Standard Deviations:
Calculates standard deviations on a moving window basis.
Plots deviations from the theoretical power law trend, highlighting significant discrepancies.
Color-Coded Thresholds:
Highlights points that deviate significantly from the expected trend.
Red indicates returns above the upper threshold (indicating potential overperformance or overvaluation).
Green indicates returns below the lower threshold (indicating potential underperformance or undervaluation).
Practical Usage
Traders and analysts can use this indicator to:
Identify periods where Bitcoin's returns deviate significantly from the expected power law trend.
Make informed trading decisions based on the likelihood of mean reversion to the theoretical trend.
Understand the long-term diminishing returns trend and adjust investment strategies accordingly.
Conclusion
This TradingView indicator leverages the power law to provide a detailed and theoretically grounded analysis of Bitcoin's price movements. By comparing real returns with theoretical predictions, the indicator offers valuable insights into market behavior and highlights significant deviations. The use of color-coded thresholds further enhances the utility of the indicator, making it an essential tool for traders and analysts seeking to understand and capitalize on Bitcoin's price dynamics
Momentum & Squeeze Oscillator [UAlgo]The Momentum & Squeeze Oscillator is a technical analysis tool designed to help traders identify shifts in market momentum and potential squeeze conditions. This oscillator combines multiple timeframes and periods to provide a detailed view of market dynamics. It enhances the decision-making process for both short-term and long-term traders by visualizing momentum with customizable colors and alerts.
🔶 Key Features
Custom Timeframe Selection: Allows users to select a custom timeframe for oscillator calculations, providing flexibility in analyzing different market periods.
Recalculation Option: Enables or disables the recalculation of the indicator, offering more control over real-time data processing.
Squeeze Background Visualization: Highlights potential squeeze conditions with a background color, helping traders quickly spot consolidation periods.
Adjustable Squeeze Sensitivity: Users can modify the sensitivity of the squeeze detection, tailoring the indicator to their specific trading style and market conditions.
Bar Coloring Condition: Option to color the price bars based on momentum conditions, enhancing the visual representation of market trends.
Threshold Bands: Option to fill threshold bands for a clearer visualization of overbought and oversold levels.
Reference Lines: Display reference lines for overbought, oversold, and mid-levels, aiding in quick assessment of momentum extremes.
Multiple Output Modes: Offers different output visualization modes, including:
ALL: Displays all calculated momentum values (fast, medium, slow).
AVG: Shows the average momentum, providing a consolidated view.
STD: Displays the standard deviation of momentum, useful for understanding volatility.
Alerts: Configurable alerts for key momentum events such as crossovers and squeeze conditions, keeping traders informed of important market changes.
🔶 Usage
The Momentum & Squeeze Oscillator can be used for various trading purposes:
Trend Identification: Use the oscillator to determine the direction and strength of market trends. By analyzing the average, fast, medium, and slow momentum lines, traders can gain insights into short-term and long-term market movements.
Squeeze Detection: The indicator highlights periods of low volatility (squeeze conditions) which often precede significant price movements. Traders can use this information to anticipate and prepare for potential breakouts.
Overbought/Oversold Conditions: The oscillator helps identify overbought and oversold conditions, indicating potential reversal points. This is particularly useful for timing entry and exit points in the market.
Momentum Shifts: By monitoring the crossover of momentum lines with key levels (e.g., the 50 level), traders can spot shifts in market momentum, allowing them to adjust their positions accordingly.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Uptrick: MACD Slope Buy/Sell SignalsThe "Uptrick: MACD Slope Buy/Sell Signals" indicator is an advanced technical analysis tool meticulously crafted to provide traders with precise buy and sell signals derived from the slope changes of the Moving Average Convergence Divergence (MACD) signal line. This indicator integrates user-defined parameters for the MACD calculation, including the fast length, slow length, and signal smoothing period. These parameters allow traders to customize the indicator according to their specific trading strategies and timeframes, ensuring adaptability across various market conditions.
The primary function of this indicator is to monitor the slope of the MACD signal line and detect significant shifts that indicate potential changes in market momentum. The indicator calculates the slope by comparing the current value of the signal line to its previous value, and further determines the change in slope to identify acceleration or deceleration in the trend. A buy signal is generated when the slope of the signal line transitions from negative to positive, signaling an upward momentum, while a sell signal is triggered when the slope moves from positive to negative, indicating a downward trend. To enhance signal accuracy, the indicator distinguishes between regular and strong signals. A strong buy signal requires the slope change to be greater than the simple moving average (SMA) of recent slope changes, whereas a strong sell signal necessitates the slope change to be less than the negative SMA of recent slope changes.
A unique feature of this indicator is its dynamic and intuitive visualization. When a strong buy or sell signal is identified, it plots labels ('B' for buy and 'S' for sell) directly on the price chart. These labels are strategically positioned below or above the respective bars to ensure clear visibility and reduce chart clutter. The indicator also includes an option to connect consecutive signals with lines, which enhances the visual tracking of signal sequences and provides a coherent view of the trend's progression. The color intensity of the plotted signals varies based on the absolute value of the slope, offering an immediate visual cue on the strength of the detected trend changes. A steeper slope results in a darker color, signaling a stronger trend.
To facilitate comprehensive analysis, the indicator also plots the MACD and signal lines on the chart, providing traders with a reference to the underlying data that drives the buy and sell signals. These lines are color-coded for easy differentiation: the MACD line is typically blue, and the signal line is orange. This visual aid ensures that traders have a clear understanding of the indicator's basis and can cross-reference the generated signals with the MACD behavior.
The calculation of this indicator is grounded in well-established technical analysis principles. It employs the MACD function to derive the MACD line and signal line based on the user-defined parameters. The slope of the signal line is then computed, followed by the calculation of the slope change. The buy and sell signals are determined by comparing the current and previous slopes, and the strong signals are filtered through an additional layer of slope change analysis relative to its moving average.
The accuracy and reliability of the "Uptrick: MACD Slope Buy/Sell Signals" indicator stem from its thorough and methodical approach to signal generation. By combining user customization, detailed slope analysis, and robust visual elements, this indicator serves as a powerful tool for traders seeking precise entry and exit points in the market. Its ability to adapt to different trading styles and market conditions, coupled with its clear visual cues, makes it a valuable addition to any trader's toolkit, enhancing decision-making and improving trading outcomes.
Uptrick: Bullish/Bearish Signal DetectorDetailed Explanation of the "Uptrick: Bullish/Bearish Signal Detector" Script
The "Uptrick: Bullish/Bearish Signal Detector" script is a sophisticated tool designed for the TradingView platform, leveraging Pine Script version 5. This script is crafted to enhance traders' ability to identify bullish (buy) and bearish (sell) signals directly on their trading charts. By combining the power of the MACD (Moving Average Convergence Divergence) and RSI (Relative Strength Index) indicators, this script provides a unique and efficient method for detecting potential trading opportunities. Below is an in-depth exploration of its purpose, features, and functionality.
Purpose
The primary purpose of this script is to assist traders in identifying potential entry and exit points in the market by signaling bullish and bearish conditions. This automated detection helps traders make more informed decisions without the need to manually analyze complex indicators. By overlaying signals directly on the price chart, the script allows for quick visual identification of market trends and reversals.
Uniqueness
What sets this script apart is its dual use of MACD and RSI indicators. While many trading strategies might rely on a single indicator, combining MACD and RSI enhances the reliability of the signals by filtering out false positives. The script not only identifies trends but also adds a layer of confirmation through the RSI, which measures the speed and change of price movements.
Inputs and Features
Customizable Label Appearance:
The script allows users to customize the appearance of the labels that indicate bullish and bearish signals. Users can set their preferred colors for the labels and the text, ensuring that the signals are easily distinguishable and aesthetically pleasing on their charts.
MACD Calculation:
The script calculates the MACD line and signal line using user-defined input values for the fast length, slow length, and signal length. The MACD histogram, which is the difference between the MACD line and the signal line, is used to determine the momentum of the market.
RSI Calculation:
The RSI is calculated using a user-defined input length. The RSI helps in identifying overbought or oversold conditions, which are crucial for confirming the strength of the trend detected by the MACD.
Bullish and Bearish Conditions:
The script defines bullish conditions as those where the MACD histogram is positive and the RSI is above 50. Bearish conditions are defined where the MACD histogram is negative and the RSI is below 50. This combination of conditions ensures that signals are generated based on both momentum and relative strength, reducing the likelihood of false signals.
Label Plotting:
The script plots labels on the chart to indicate bullish and bearish signals. When a bullish condition is met, and the previous signal was not bullish, a "LONG" label is plotted. Similarly, when a bearish condition is met, and the previous signal was not bearish, a "SHORT" label is plotted. This feature helps in clearly marking the points of interest for traders, making it easier to spot potential trades.
Tracking Previous Signals:
To avoid repetitive signals, the script keeps track of the last signal. If the last signal was bullish, it avoids plotting another bullish signal immediately. The same logic applies to bearish signals. This tracking ensures that signals are spaced out and only significant changes in market conditions are highlighted.
How It Works
The script operates in a loop, processing each bar (or candlestick) on the chart as new data comes in. It calculates the MACD and RSI values for each bar and checks if the current conditions meet the criteria for a bullish or bearish signal. If a signal is detected and it is different from the last signal, a label is plotted on the chart at the current bar's price level. This real-time processing allows traders to see the signals as they form, providing timely insights into market movements.
Practical Application
For practical use, a trader would add this script to their TradingView chart. They can customize the input parameters for the MACD and RSI calculations to fit their trading strategy or preferred settings. Once added, the script will automatically analyze the price data and start plotting "LONG" and "SHORT" labels based on the detected signals. Traders can then use these labels to make decisions on entering or exiting trades, adjusting their strategy as necessary based on the signals provided.
Conclusion
The "Uptrick: Bullish/Bearish Signal Detector" script is a powerful tool for any trader looking to leverage technical indicators for better trading decisions. By combining MACD and RSI, it offers a robust method for detecting market trends and potential reversals. The customizable features and real-time signal plotting make it a versatile and user-friendly addition to any trading toolkit. This script not only simplifies the process of technical analysis but also enhances the accuracy of trading signals, thereby potentially increasing the trader's success rate in the market.
Bitcoin Power Law Global Liqudity Model by G. SantostasiIn recent studies, we've observed a notable correlation between Bitcoin's price and global liquidity metrics. This relationship reveals significant insights into Bitcoin's price movements and offers a new perspective on using macroeconomic indicators to understand and predict Bitcoin's market trends.
Our analysis shows that Bitcoin's price exhibits periodic bubbles, which seem closely associated with oscillations in global liquidity. Notably, the overall price path of Bitcoin appears to be a complex function of global liquidity. This relationship is not as simple as the Bitcoin Power Law in time that can be described with a simple equation, Price ∼ time⁶.
Instead, we have developed a polynomial model to describe this complex relationship between liquidity and Bitcoin price. With a 4-degree polynomial (with 5 different parameters needed to fit the data), we can get a decent fit to the data.
The fit is obtained using 500 data points by polynomial regression. The vector coefficients of the polynomial are obtained such that the sum of squared error between the observations and theoretical polynomial model is minimized.
This model needs to be taken with a grain of salt given the warning by famous mathematician Von Neumann: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." discussing a model created by Italian Physicist Fermi. By this he meant that the Fermi simulations relied on too many input parameters, presupposing an overfitting phenomenon.
We can still gain some insights into the relationship between Global Liquidity and the price evolution of Bitcoin using this complex model.
When the price of Bitcoin is plotted against our global liquidity index, we observe a polynomial relationship. This model allows us to see when Bitcoin's price deviates significantly from the predicted value based on global liquidity:
Above the Model: When Bitcoin's price is above the polynomial fit, it indicates a potential lack of sufficient liquidity to support the current price level, suggesting a likely correction.
Below the Model: Conversely, when the price is below the fit, it implies that liquidity might be higher than what is reflected in the price, indicating potential upward movement.
Our global liquidity index comprises several key macroeconomic metrics from major financial institutions worldwide. Here are some of the major components:
RRP (Reverse Repurchase Agreements): This metric indicates the level of liquidity in the financial system through temporary sales of securities with an agreement to repurchase them.
FED (Federal Reserve System): Represents the balance sheet of the US central bank, reflecting its monetary policy actions.
TGA (Treasury General Account): Reflects the US Treasury’s cash balance, impacting the liquidity in the banking system.
PBC (People's Bank of China): Shows the monetary policy actions and liquidity management by China’s central bank.
ECB (European Central Bank): Represents the balance sheet and liquidity management actions of the Eurozone's central bank.
BOJ (Bank of Japan): Reflects Japan's central bank's monetary policy and liquidity measures.
Other Central Banks: Includes metrics from various other central banks like the Bank of England, Bank of Canada, Reserve Bank of Australia, etc.
M2 Money Supply: This includes money supply metrics from various countries like the USA, Europe, China, Japan, and other significant economies.
These components collectively provide a comprehensive view of global liquidity, which is crucial for understanding its impact on Bitcoin's price.
Using the polynomial model and the author's Bitcoin power law model we can create 2 oscillators, one that shows deviations from the trend (normalized to the price to make the peaks more uniform) and the other showing deviations of the polynomial liquidity model from the power law trend.
The oscillators show the difference between the price and the power law model relative to the price, Orange Line. The Blue Line is instead the difference between the Global Liquidity Model of the price and the power law model relative to the model itself. The two oscillators can be overlayed to show their differences and similarities.
Analysis: In addition to similar observations from the discussion above we can see that most Bitcoin bottoms are not directly associated with bottoms in the liquidity model indicating a different mechanism at play that determines Bitcoin bottoms (probably due to miners' capitulation).
Using the new force_overlay function we plot the polynomial liquidity model directly over the Bitcoin price chart while we display the 2 oscillators in a separate panel.
Moving Average Crossover Swing StrategyMoving Average Crossover Swing Strategy
**Overview:**
The basic concept of this strategy is to generate a signal when a faster/shorter length moving average crosses over (for Longs) or crosses under (for Shorts) a medium/longer length moving average. All of which are customizable. This strategy can work on any timeframe, however the daily is the timeframe used for the default settings and screenshots, as it was designed to be a multi-day swing strategy. Once a signal has been confirmed with a candle close, based on user options, the strategy will enter the trade on the open of the next candle.
The crossover strategy is nothing new to trading, but what can make this strategy unique and helpful, is the addition of further confirmation points, ATR based stop loss and take profit targets, optional early exit criteria, customizable to your needs and style, and just about everything visual can be toggled on/off. This strategy is based on a Trend (MA) indicator and a Momentum (MACD) indicator. While a Volume-based indicator is not shown here, one could consider using their favorite from that category to further compliment the signal idea.
It should be noted that depending on the time frame, direction(s) chosen, the signal options, confirmation options, and exit options selected, that a ticker may not produce more than 100 trades on the back test. Depending on your style and frequency, one could consider adjusting options and/or testing multiple tickers. It should also be noted that this strategy simply tests the underlying stock prices, not options contracts. And of course, testing this strategy against historical data does not assume that the same results will occur in future price action.
Shoutout given to Ripster's Clouds Indicator as pieces of that code were taken and modified to create both the Cloud visualization effects, and the Moving Average Pair Plots that are implemented in this strategy.
BASIC DEFAULTS
All can be changed as normal
Initial capital = 10,000
Order Sizing = 25% of equity (use the "Inputs" tab to modify this)
Pyramiding = 0
Commission = 0.65 USD per order
Price Verification = 1 tick
Slippage = 1 tick
RISK MANAGMENT
You will notice two different percentage options and ATR multipliers. This strategy will adjust position sizing by not exceeding either one of those % values based on the ATR (Average True Range) of the symbol and the multipliers selected, should the stock hit the stop loss price.
For Example, lets assume these values are true:
Account size = $10,000,
Max Risk = 1% of account size
Max Position Size = 25% of the account size
Stock Price = 23.45
ATR = 3.5
ATR Stop Loss Multiplier = 1.4
Then the formulas would be:
ACCT_SIZE * MaxRisk_% = 10000 * .01 = $100 (MaxCashRisk)
-----
MaxCashRisk / (ATR * ATR_SL_MULTIPLIER) = 100 / (3.5 * 1.4) = 20.4 Shares based on Max Cash Risk
-----
(ACCT_SIZE * MaxEquity_%) / STOCK_PRICE = (10000 * .25) / 23.45 = 106.61 Shares based on Max Equity Allocation
The minimum value of each of those options is then used, which in this case would be to purchase 20 shares so as not to exceed the max dollar risk should the stock reach the stop loss target. Likewise, if the ATR were to be much lower, say 0.48 cents, and all else the same, then the strategy would purchase the 106 shares based on Max Equity Allocation because the Max Cash Risk would require 149.25 shares.
MOVING AVERAGE OPTIONS
Select between and change the length & type of up to 5 pairs (10 total) of moving averages
The "Show Cloud-x" option will display a fill color between the "a" and "b" pairs
All moving averages lines can be toggled on/off in the "Style" tab, as well as adjusting their colors.
Visualization features do not affect calculations, meaning you could have all or nothing on the chart and the strategy will still produce results
SIGNAL CHOICES
Choose the fast/shorter length MA and the medium/longer length MA to determine the entry signal
CONFIRMATION OPTIONS
Both of these have customizable values and can be toggled on/off
A candle close over a slower/much longer length moving average
An additional cross-over (cross-under for Shorts) on the MACD indicator using default MACD values. While the MACD indicator is not necessary to have on the chart, it can help to add that for visualization. The calculations will perform whether the indicator is on the chart or not.
EARLY EXIT CRITERIA
Both can be toggled on/off with customizable values
MA Cross Exit will exit the trade early if the select moving averages cross-under (for longs) or cross-over (for shorts), indicating a potential reversal.
Max Bars in Trades will act as a last-resort exit by simply calculating the amount of full bars the trade has been open, and exiting on the opening of the next bar. For example: the default value is 8 bars, so after 8 full bars in the trade, if no other exit has been triggered (Stop Loss, Take Profit, or MA Cross(if enabled)), then the trade will exit at the opening of the 9th bar.
Finally, there is a table displaying the amount of trades taken for each side, and the amount & percent of both early exits. This table can be turned off in the "Style" tab
ADDITIONAL PLOTS
MACD (Moving Average Convergence/Divergence):
- The MACD is an optional confirmation indicator for this strategy.
- Plotting the indicator is not necessary for the strategy to work, but it can be helpful to visually see the status and position of the MACD if this feature is enabled in the strategy
- This helps to identify if there is also momentum behind the entry signal
Six PillarsGeneral Overview
The "Six Pillars" indicator is a comprehensive trading tool that combines six different technical analysis methods to provide a holistic view of market conditions.
These six pillars are:
Trend
Momentum
Directional Movement (DM)
Stochastic
Fractal
On-Balance Volume (OBV)
The indicator calculates the state of each pillar and presents them in an easy-to-read table format. It also compares the current timeframe with a user-defined comparison timeframe to offer a multi-timeframe analysis.
A key feature of this indicator is the Confluence Strength meter. This unique metric quantifies the overall agreement between the six pillars across both timeframes, providing a score out of 100. A higher score indicates stronger agreement among the pillars, suggesting a more reliable trading signal.
I also included a visual cue in the form of candle coloring. When all six pillars agree on a bullish or bearish direction, the candle is colored green or red, respectively. This feature allows traders to quickly identify potential high-probability trade setups.
The Six Pillars indicator is designed to work across multiple timeframes, offering a comparison between the current timeframe and a user-defined comparison timeframe. This multi-timeframe analysis provides traders with a more comprehensive understanding of market dynamics.
Origin and Inspiration
The Six Pillars indicator was inspired by the work of Dr. Barry Burns, author of "Trend Trading for Dummies" and his concept of "5 energies." (Trend, Momentum, Cycle, Support/Resistance, Scale) I was intrigued by Dr. Burns' approach to analyzing market dynamics and decided to put my own twist upon his ideas.
Comparing the Six Pillars to Dr. Burns' 5 energies, you'll notice I kept Trend and Momentum, but I swapped out Cycle, Support/Resistance, and Scale for Directional Movement, Stochastic, Fractal, and On-Balance Volume. These changes give you a more dynamic view of market strength, potential reversals, and volume confirmation all in one package.
What Makes This Indicator Unique
The standout feature of the Six Pillars indicator is its Confluence Strength meter. This feature calculates the overall agreement between the six pillars, providing traders with a clear, numerical representation of signal strength.
The strength is calculated by considering the state of each pillar in both the current and comparison timeframes, resulting in a score out of 100.
Here's how it calculates the strength:
It considers the state of each pillar in both the current timeframe and the comparison timeframe.
For each pillar, the absolute value of its state is taken. This means that both strongly bullish (2) and strongly bearish (-2) states contribute equally to the strength.
The absolute values for all six pillars are summed up for both timeframes, resulting in two sums: current_sum and alternate_sum.
These sums are then added together to get a total_sum.
The total_sum is divided by 24 (the maximum possible sum if all pillars were at their strongest states in both timeframes) and multiplied by 100 to get a percentage.
The result is rounded to the nearest integer and capped at a minimum of 1.
This calculation method ensures that the Confluence Strength meter takes into account not only the current timeframe but also the comparison timeframe, providing a more robust measure of overall market sentiment. The resulting score, ranging from 1 to 100, gives traders a clear and intuitive measure of how strongly the pillars agree, with higher scores indicating stronger potential signals.
This approach to measuring signal strength is unique in that it doesn't just rely on a single aspect of price action or volume. Instead, it takes into account multiple factors, providing a more robust and reliable indication of potential market moves. The higher the Confluence Strength score, the more confident traders can be in the signal.
The Confluence Strength meter helps traders in several ways:
It provides a quick and easy way to gauge the overall market sentiment.
It helps prioritize potential trades by identifying the strongest signals.
It can be used as a filter to avoid weaker setups and focus on high-probability trades.
It offers an additional layer of confirmation for other trading strategies or indicators.
By combining the Six Pillars analysis with the Confluence Strength meter, I've created a powerful tool that not only identifies potential trading opportunities but also quantifies their strength, giving traders a significant edge in their decision-making process.
How the Pillars Work (What Determines Bullish or Bearish)
While developing this indicator, I selected and configured six key components that work together to provide a comprehensive view of market conditions. Each pillar is set up to complement the others, creating a synergistic effect that offers traders a more nuanced understanding of price action and volume.
Trend Pillar: Based on two Exponential Moving Averages (EMAs) - a fast EMA (8 period) and a slow EMA (21 period). It determines the trend by comparing these EMAs, with stronger trends indicated when the fast EMA is significantly above or below the slow EMA.
Directional Movement (DM) Pillar: Utilizes the Average Directional Index (ADX) with a default period of 14. It measures trend strength, with values above 25 indicating a strong trend. It also considers the Positive and Negative Directional Indicators (DI+ and DI-) to determine trend direction.
Momentum Pillar: Uses the Moving Average Convergence Divergence (MACD) with customizable fast (12), slow (26), and signal (9) lengths. It compares the MACD line to the signal line to determine momentum strength and direction.
Stochastic Pillar: Employs the Stochastic oscillator with a default period of 13. It identifies overbought conditions (above 80) and oversold conditions (below 20), with intermediate zones between 60-80 and 20-40.
Fractal Pillar: Uses Williams' Fractal indicator with a default period of 3. It identifies potential reversal points by looking for specific high and low patterns over the given period.
On-Balance Volume (OBV) Pillar: Incorporates On-Balance Volume with three EMAs - short (3), medium (13), and long (21) periods. It assesses volume trends by comparing these EMAs.
Each pillar outputs a state ranging from -2 (strongly bearish) to 2 (strongly bullish), with 0 indicating a neutral state. This standardized output allows for easy comparison and aggregation of signals across all pillars.
Users can customize various parameters for each pillar, allowing them to fine-tune the indicator to their specific trading style and market conditions. The multi-timeframe comparison feature also allows users to compare pillar states between the current timeframe and a user-defined comparison timeframe, providing additional context for decision-making.
Design
From a design standpoint, I've put considerable effort into making the Six Pillars indicator visually appealing and user-friendly. The clean and minimalistic design is a key feature that sets this indicator apart.
I've implemented a sleek table layout that displays all the essential information in a compact and organized manner. The use of a dark background (#030712) for the table creates a sleek look that's easy on the eyes, especially during extended trading sessions.
The overall design philosophy focuses on presenting complex information in a simple, intuitive format, allowing traders to make informed decisions quickly and efficiently.
The color scheme is carefully chosen to provide clear visual cues:
White text for headers ensures readability
Green (#22C55E) for bullish signals
Blue (#3B82F6) for neutral states
Red (#EF4444) for bearish signals
This color coding extends to the candle coloring, making it easy to spot when all pillars agree on a bullish or bearish outlook.
I've also incorporated intuitive symbols (↑↑, ↑, →, ↓, ↓↓) to represent the different states of each pillar, allowing for quick interpretation at a glance.
The table layout is thoughtfully organized, with clear sections for the current and comparison timeframes. The Confluence Strength meter is prominently displayed, providing traders with an immediate sense of signal strength.
To enhance usability, I've added tooltips to various elements, offering additional information and explanations when users hover over different parts of the indicator.
How to Use This Indicator
The Six Pillars indicator is a versatile tool that can be used for various trading strategies. Here are some general usage guidelines and specific scenarios:
General Usage Guidelines:
Pay attention to the Confluence Strength meter. Higher values indicate stronger agreement among the pillars and potentially more reliable signals.
Use the multi-timeframe comparison to confirm signals across different time horizons.
Look for alignment between the current timeframe and comparison timeframe pillars for stronger signals.
One of the strengths of this indicator is it can let you know when markets are sideways – so in general you can know to avoid entering when the Confluence Strength is low, indicating disagreement among the pillars.
Customization Options
The Six Pillars indicator offers a wide range of customization options, allowing traders to tailor the tool to their specific needs and trading style. Here are the key customizable elements:
Comparison Timeframe:
Users can select any timeframe for comparison with the current timeframe, providing flexibility in multi-timeframe analysis.
Trend Pillar:
Fast EMA Period: Adjustable for quicker or slower trend identification
Slow EMA Period: Can be modified to capture longer-term trends
Momentum Pillar:
MACD Fast Length
MACD Slow Length
MACD Signal Length These can be adjusted to fine-tune momentum sensitivity
DM Pillar:
ADX Period: Customizable to change the lookback period for trend strength measurement
ADX Threshold: Adjustable to define what constitutes a strong trend
Stochastic Pillar:
Stochastic Period: Can be modified to change the sensitivity of overbought/oversold readings
Fractal Pillar:
Fractal Period: Adjustable to identify potential reversal points over different timeframes
OBV Pillar:
Short OBV EMA
Medium OBV EMA
Long OBV EMA These periods can be customized to analyze volume trends over different timeframes
These customization options allow traders to experiment with different settings to find the optimal configuration for their trading strategy and market conditions. The flexibility of the Six Pillars indicator makes it adaptable to various trading styles and market environments.
