EC_2024_Q4_SPLibrary "EC_2024_Q4_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
Cari dalam skrip untuk "index"
EC_2024_Q3_SPLibrary "EC_2024_Q3_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q4_ENLibrary "EC_2024_Q4_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q3_ENLibrary "EC_2024_Q3_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q2_ENLibrary "EC_2024_Q2_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q2_SPLibrary "EC_2024_Q2_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q1_SPLibrary "EC_2024_Q1_SP"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2024_Q1_ENLibrary "EC_2024_Q1_EN"
output2024()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2024()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2024()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2024()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q4_SPLibrary "EC_2023_Q4_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q3_SPLibrary "EC_2023_Q3_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q2_SPLibrary "EC_2023_Q2_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q1_SPLibrary "EC_2023_Q1_SP"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q4_ENLibrary "EC_2023_Q4_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q3_ENLibrary "EC_2023_Q3_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q2_ENLibrary "EC_2023_Q2_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
EC_2023_Q1_ENLibrary "EC_2023_Q1_EN"
output2023()
Returns the list of events during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;;; ...
Where: is expressed as date + characteristics: YYYY,MM,DD,hh,mm,ss,x,y,z
x impact in numbers
y event name in numbers
z currency in numbers
name2023()
Returns the list of event names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, name
index: related to event name y
name event: event name related to y text
impact2023()
Returns the list of impact names during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, impact
index: related to impact name x
impact: impact name related to x text
currency2023()
Returns the list of currencies during the period.
Returns: array: (week1,week2, ... week_n)
week_n= ;; ...
Where: is expressed as: index, currency
index: related to currency name z
currency: currency name related to z text
Fourier Adjusted Average True Range [BackQuant]Fourier Adjusted Average True Range
1. Conceptual Foundation and Innovation
The FA-ATR leverages the principles of Fourier analysis to dissect market prices into their constituent cyclical components. By applying Fourier Transform to the price data, the FA-ATR captures the dominant cycles and trends which are often obscured in noisy market data. This integration allows the FA-ATR to adapt its readings based on underlying market dynamics, offering a refined view of volatility that is sensitive to both market direction and momentum.
2. Technical Composition and Calculation
The core of the FA-ATR involves calculating the traditional ATR, which measures market volatility by decomposing the entire range of price movements. The FA-ATR extends this by incorporating a Fourier Transform of price data to assess cyclical patterns over a user-defined period 'N'. This process synthesizes both the magnitude of price changes and their rhythmic occurrences, resulting in a more comprehensive volatility indicator.
Fourier Transform Application: The Fourier series is calculated using price data to identify the fundamental frequency of market movements. This frequency helps in adjusting the ATR to reflect more accurately the current market conditions.
Dynamic Adjustment: The ATR is then adjusted by the magnitude of the dominant cycle from the Fourier analysis, enhancing or reducing the ATR value based on the intensity and phase of market cycles.
3. Features and User Inputs
Customizability: Traders can modify the Fourier period, ATR period, and the multiplication factor to suit different trading styles and market environments.
Visualization : The FA-ATR can be plotted directly on the chart, providing a visual representation of volatility. Additionally, the option to paint candles according to the trend direction enhances the usability and interpretative ease of the indicator.
Confluence with Moving Averages: Optionally, a moving average of the FA-ATR can be displayed, serving as a confluence factor for confirming trends or potential reversals.
4. Practical Applications
The FA-ATR is particularly useful in markets characterized by periodic fluctuations or those that exhibit strong cyclical trends. Traders can utilize this indicator to:
Adjust Stop-Loss Orders: More accurately set stop-loss orders based on a volatility measure that accounts for cyclical market changes.
Trend Confirmation: Use the FA-ATR to confirm trend strength and sustainability, helping to avoid false signals often encountered in volatile markets.
Strategic Entry and Exit: The indicator's responsiveness to changing market dynamics makes it an excellent tool for planning entries and exits in a trend-following or a breakout trading strategy.
5. Advantages and Strategic Value
By integrating Fourier analysis, the FA-ATR provides a volatility measure that is both adaptive and anticipatory, giving traders a forward-looking tool that adjusts to changes before they become apparent through traditional indicators. This anticipatory feature makes it an invaluable asset for traders looking to gain an edge in fast-paced and rapidly changing market conditions.
6. Summary and Usage Tips
The Fourier Adjusted Average True Range is a cutting-edge development in technical analysis, offering traders an enhanced tool for assessing market volatility with increased accuracy and responsiveness. Its ability to adapt to the market's cyclical nature makes it particularly useful for those trading in highly volatile or cyclically influenced markets.
Traders are encouraged to integrate the FA-ATR into their trading systems as a supplementary tool to improve risk management and decision-making accuracy, thereby potentially increasing the effectiveness of their trading strategies.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Normalised T3 Oscillator [BackQuant]Normalised T3 Oscillator
The Normalised T3 Oscillator is an technical indicator designed to provide traders with a refined measure of market momentum by normalizing the T3 Moving Average. This tool was developed to enhance trading decisions by smoothing price data and reducing market noise, allowing for clearer trend recognition and potential signal generation. Below is a detailed breakdown of the Normalised T3 Oscillator, its methodology, and its application in trading scenarios.
1. Conceptual Foundation and Definition of T3
The T3 Moving Average, originally proposed by Tim Tillson, is renowned for its smoothness and responsiveness, achieved through a combination of multiple Exponential Moving Averages and a volume factor. The Normalised T3 Oscillator extends this concept by normalizing these values to oscillate around a central zero line, which aids in highlighting overbought and oversold conditions.
2. Normalization Process
Normalization in this context refers to the adjustment of the T3 values to ensure that the oscillator provides a standard range of output. This is accomplished by calculating the lowest and highest values of the T3 over a user-defined period and scaling the output between -0.5 to +0.5. This process not only aids in standardizing the indicator across different securities and time frames but also enhances comparative analysis.
3. Integration of the Oscillator and Moving Average
A unique feature of the Normalised T3 Oscillator is the inclusion of a secondary smoothing mechanism via a moving average of the oscillator itself, selectable from various types such as SMA, EMA, and more. This moving average acts as a signal line, providing potential buy or sell triggers when the oscillator crosses this line, thus offering dual layers of analysis—momentum and trend confirmation.
4. Visualization and User Interaction
The indicator is designed with user interaction in mind, featuring customizable parameters such as the length of the T3, normalization period, and type of moving average used for signals. Additionally, the oscillator is plotted with a color-coded scheme that visually represents different strength levels of the market conditions, enhancing readability and quick decision-making.
5. Practical Applications and Strategy Integration
Traders can leverage the Normalised T3 Oscillator in various trading strategies, including trend following, counter-trend plays, and as a component of a broader trading system. It is particularly useful in identifying turning points in the market or confirming ongoing trends. The clear visualization and customizable nature of the oscillator facilitate its adaptation to different trading styles and market environments.
6. Advanced Features and Customization
Further enhancing its utility, the indicator includes options such as painting candles according to the trend, showing static levels for quick reference, and alerts for crossover and crossunder events, which can be integrated into automated trading systems. These features allow for a high degree of personalization, enabling traders to mold the tool according to their specific trading preferences and risk management requirements.
7. Theoretical Justification and Empirical Usage
The use of the T3 smoothing mechanism combined with normalization is theoretically sound, aiming to reduce lag and false signals often associated with traditional moving averages. The practical effectiveness of the Normalised T3 Oscillator should be validated through rigorous backtesting and adjustment of parameters to match historical market conditions and volatility.
8. Conclusion and Utility in Market Analysis
Overall, the Normalised T3 Oscillator by BackQuant stands as a sophisticated tool for market analysis, providing traders with a dynamic and adaptable approach to gauging market momentum. Its development is rooted in the understanding of technical nuances and the demand for a more stable, responsive, and customizable trading indicator.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kalman Hull RSI [BackQuant]Kalman Hull RSI
At its core, this indicator uses a Kalman filter of price, put inside of a hull moving average function (replacing the weighted moving averages) and then using that as a price source for the the RSI, very similar to the Kalman Hull Supertrend just processing price for a different indicator.
This also allows it to make it more adaptive to price and also sensitive to recent price action. This indicator is also mainly built for trend-following systems
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
1. What is a Kalman Filter
The Kalman Filter is an algorithm renowned for its efficiency in estimating the states of a linear dynamic system amidst noisy data. It excels in real-time data processing, making it indispensable in fields requiring precise and adaptive filtering, such as aerospace, robotics, and financial market analysis. By leveraging its predictive capabilities, traders can significantly enhance their market analysis, particularly in estimating price movements more accurately.
If you would like this on its own, with a more in-depth description please see our Kalman Price Filter.
OR our Kalman Hull Supertrend
2. Hull Moving Average (HMA) and Its Core Calculation
The Hull Moving Average (HMA) improves on traditional moving averages by combining the Weighted Moving Average's (WMA) smoothness and reduced lag. Its core calculation involves taking the WMA of the data set and doubling it, then subtracting the WMA of the full period, followed by applying another WMA on the result over the square root of the period's length. This methodology yields a smoother and more responsive moving average, particularly useful for identifying market trends more rapidly.
3. Combining Kalman Filter with HMA
The innovative combination of the Kalman Filter with the Hull Moving Average (KHMA) offers a unique approach to smoothing price data. By applying the Kalman Filter to the price source before its incorporation into the HMA formula, we enhance the adaptiveness and responsiveness of the moving average. This adaptive smoothing method reduces noise more effectively and adjusts more swiftly to price changes, providing traders with clearer signals for market entries or exits.
The calculation is like so:
KHMA(_src, _length) =>
f_kalman(2 * f_kalman(_src, _length / 2) - f_kalman(_src, _length), math.round(math.sqrt(_length)))
Use Case
The Kalman Hull RSI is particularly suited for traders who require a highly adaptive indicator that can respond to rapid market changes without the excessive noise associated with typical RSI calculations. It can be effectively used in markets with high volatility where traditional indicators might lag or produce misleading signals.
Application in a Trading System
The Kalman Hull RSI is versatile in application, suitable for:
Trend Identification: Quickly identify potential reversals or confirmations of existing trends.
Overbought/Oversold Conditions: Utilize the dynamic RSI thresholds to pinpoint potential entry and exit points, adapting to current market conditions.
Risk Management: Enhance trading strategies by integrating a more reliable measure of momentum, which can lead to improved stop-loss placements and exit strategies.
Core Calculations and Benefits
Dynamic State Estimation: By applying the Kalman Filter, the indicator continually adjusts its calculations based on incoming price data, providing a real-time, smoothed response to price movements.
Reduced Lag: The integration with HMA significantly reduces lag, offering quicker responses to price changes than traditional moving averages or RSI alone.
Increased Accuracy: The dual filtering effect minimizes the impact of price spikes and noise, leading to more accurate signaling for trades.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Volatility Adjusted Weighted DEMA [BackQuant]Volatility Adjusted Weighted DEMA
The Volatility Adjusted Weighted Double Exponential Moving Average (VAWDEMA) by BackQuant is a sophisticated technical analysis tool designed for traders seeking to integrate volatility into their moving average calculations. This innovative indicator adjusts the weighting of the Double Exponential Moving Average (DEMA) according to recent volatility levels, offering a more dynamic and responsive measure of market trends.
Primarily, the single Moving average is very noisy, but can be used in the context of strategy development, where as the crossover, is best used in the context of defining a trading zone/ macro uptrend on higher timeframes.
Why Volatility Adjustment is Beneficial
Volatility is a fundamental aspect of financial markets, reflecting the intensity of price changes. A volatility adjustment in moving averages is beneficial because it allows the indicator to adapt more quickly during periods of high volatility, providing signals that are more aligned with the current market conditions. This makes the VAWDEMA a versatile tool for identifying trend strength and potential reversal points in more volatile markets.
Understanding DEMA and Its Advantages
DEMA is an indicator that aims to reduce the lag associated with traditional moving averages by applying a double smoothing process. The primary benefit of DEMA is its sensitivity and quicker response to price changes, making it an excellent tool for trend following and momentum trading. Incorporating DEMA into your analysis can help capture trends earlier than with simple moving averages.
The Power of Combining Volatility Adjustment with DEMA
By adjusting the weight of the DEMA based on volatility, the VAWDEMA becomes a powerful hybrid indicator. This combination leverages the quick responsiveness of DEMA while dynamically adjusting its sensitivity based on current market volatility. This results in a moving average that is both swift and adaptive, capable of providing more relevant signals for entering and exiting trades.
Core Logic Behind VAWDEMA
The core logic of the VAWDEMA involves calculating the DEMA for a specified period and then adjusting its weighting based on a volatility measure, such as the average true range (ATR) or standard deviation of price changes. This results in a weighted DEMA that reflects both the direction and the volatility of the market, offering insights into potential trend continuations or reversals.
Utilizing the Crossover in a Trading System
The VAWDEMA crossover occurs when two VAWDEMAs of different lengths cross, signaling potential bullish or bearish market conditions. In a trading system, a crossover can be used as a trigger for entry or exit points:
Bullish Signal: When a shorter-period VAWDEMA crosses above a longer-period VAWDEMA, it may indicate an uptrend, suggesting a potential entry point for a long position.
Bearish Signal: Conversely, when a shorter-period VAWDEMA crosses below a longer-period VAWDEMA, it might signal a downtrend, indicating a possible exit point or a short entry.
Incorporating VAWDEMA crossovers into a trading strategy can enhance decision-making by providing timely and adaptive signals that account for both trend direction and market volatility. Traders should combine these signals with other forms of analysis and risk management techniques to develop a well-rounded trading strategy.
Alert Conditions For Trading
alertcondition(vwdema>vwdema , title="VWDEMA Long", message="VWDEMA Long - {{ticker}} - {{interval}}")
alertcondition(vwdema
Kalman Hull Supertrend [BackQuant]Kalman Hull Supertrend
At its core, this indicator uses a Kalman filter of price, put inside of a hull moving average function (replacing the weighted moving averages) and then using that as a price source for the supertrend instead of the normal hl2 (high+low/2).
Therefore, making it more adaptive to price and also sensitive to recent price action.
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
1. What is a Kalman Filter
The Kalman Filter is an algorithm renowned for its efficiency in estimating the states of a linear dynamic system amidst noisy data. It excels in real-time data processing, making it indispensable in fields requiring precise and adaptive filtering, such as aerospace, robotics, and financial market analysis. By leveraging its predictive capabilities, traders can significantly enhance their market analysis, particularly in estimating price movements more accurately.
If you would like this on its own, with a more in-depth description please see our Kalman Price Filter.
2. Hull Moving Average (HMA) and Its Core Calculation
The Hull Moving Average (HMA) improves on traditional moving averages by combining the Weighted Moving Average's (WMA) smoothness and reduced lag. Its core calculation involves taking the WMA of the data set and doubling it, then subtracting the WMA of the full period, followed by applying another WMA on the result over the square root of the period's length. This methodology yields a smoother and more responsive moving average, particularly useful for identifying market trends more rapidly.
3. Combining Kalman Filter with HMA
The innovative combination of the Kalman Filter with the Hull Moving Average (KHMA) offers a unique approach to smoothing price data. By applying the Kalman Filter to the price source before its incorporation into the HMA formula, we enhance the adaptiveness and responsiveness of the moving average. This adaptive smoothing method reduces noise more effectively and adjusts more swiftly to price changes, providing traders with clearer signals for market entries or exits.
The calculation is like so:
KHMA(_src, _length) =>
f_kalman(2 * f_kalman(_src, _length / 2) - f_kalman(_src, _length), math.round(math.sqrt(_length)))
4. Integration with Supertrend
Incorporating this adaptive price smoothing technique into the Supertrend indicator further enhances its efficiency. The Supertrend, known for its proficiency in identifying the prevailing market trend and providing clear buy or sell signals, becomes even more powerful with an adaptive price source. This integration allows the Supertrend to adjust more dynamically to market changes, offering traders more accurate and timely trading signals.
5. Application in a Trading System
In a trading system, the Kalman Hull Supertrend indicator can serve as a critical component for identifying market trends and generating signals for potential entry and exit points. Its adaptiveness and sensitivity to price changes make it particularly useful for traders looking to minimize lag in signal generation and improve the accuracy of their market trend analysis. Whether used as a standalone tool or in conjunction with other indicators, its dynamic nature can significantly enhance trading strategies.
6. Core Calculations and Benefits
The core of this indicator lies in its sophisticated filtering and averaging techniques, starting with the Kalman Filter's predictive adjustments, followed by the adaptive smoothing of the Hull Moving Average, and culminating in the trend-detecting capabilities of the Supertrend. This multi-layered approach not only reduces market noise but also adapts to market volatility more effectively. Benefits include improved signal accuracy, reduced lag, and the ability to discern trend changes more promptly, offering traders a competitive edge.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
DEMA RSI Overlay [BackQuant]DEMA RSI Overlay
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
Anyways,
BackQuant's new trading indicator that blends the Double Exponential Moving Average (DEMA) with the Relative Strength Index (RSI) to create a unique overlay on the trading chart. This combination is not arbitrary; both the DEMA and RSI are revered for their distinct advantages in trading strategy development. Let's delve into the core components of this script, the rationale behind choosing DEMA and RSI, the logic of long and short signals, and its practical trading applications.
Understanding DEMA
DEMA is an enhanced version of the conventional exponential moving average that aims to reduce the lag inherent in traditional averages. It does this by applying more weight to recent prices. The reduction in lag makes DEMA an excellent tool for tracking price trends more closely. In the context of this script, DEMA serves as the foundation for the RSI calculation, offering a smoother and more responsive signal line that can provide clearer trend indications.
Why DEMA?
DEMA is chosen for its responsiveness to price changes. This characteristic is particularly beneficial in fast-moving markets where entering and exiting positions quickly is crucial. By using DEMA as the price source, the script ensures that the signals generated are timely and reflective of the current market conditions, reducing the risk of entering or exiting a trade based on outdated information.
Integrating RSI
The RSI, a momentum oscillator, measures the speed and change of price movements. It oscillates between zero and 100 and is typically used to identify overbought or oversold conditions. In this script, the RSI is calculated based on DEMA, which means it inherits the responsiveness of DEMA, allowing traders to spot potential reversals or continuation signals sooner.
Why RSI?
Incorporating RSI offers a measure of price momentum and market conditions relative to past performance. By setting thresholds for long (buy) and short (sell) signals, the script uses RSI to identify potential turning points in the market, providing traders with strategic entry and exit points.
Calculating Long and Short Signals
Long Signals : These are generated when the RSI of the DEMA crosses above the longThreshold (set at 70 by default) and the closing price is not above the upper volatility band. This suggests that the asset is gaining upward momentum while not being excessively overbought, presenting a potentially favorable buying opportunity.
Short Signals : Generated when the RSI of the DEMA falls below the shortThreshold (set at 55 by default). This indicates that the asset may be losing momentum or entering a downtrend, signaling a possible selling or shorting opportunity.
Logical Soundness
The logic of combining DEMA with RSI for generating trade signals is sound for several reasons:
Timeliness : The use of DEMA ensures that the price source for RSI calculation is up-to-date, making the momentum signals more relevant.
Balance : By setting distinct thresholds for long and short signals, the script balances sensitivity and specificity, aiming to minimize false signals while capturing genuine market movements.
Adaptability : The inclusion of user inputs for periods and thresholds allows traders to customize the indicator to fit various trading styles and timeframes.
Trading Use-Cases
This DEMA RSI Overlay indicator is versatile and can be applied across different markets and timeframes. Its primary use-cases include:
Trend Following: Traders can use it to identify the start of a new trend or the continuation of an existing trend.
Swing Trading: The indicator's sensitivity to price changes makes it ideal for swing traders looking to capitalize on short to medium-term price movements.
Risk Management: By providing clear long and short signals, it helps traders manage their positions more effectively, potentially reducing the risk of significant losses.
Final Note
We have also decided to add in the option of standard deviation bands, calculated on the DEMA, this can be used as a point of confluence rendering trading ranges. Expanding when volatility is high and compressing when it is low.
For example:
This provides the user with a 1, 2, 3 standard deviation band of the DEMA.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
LSMA Z-Score [BackQuant]LSMA Z-Score
Main Features and Use in the Trading Strategy
- The indicator normalizes the LSMA into a detrended Z-Score, creating an oscillator with standard deviation levels to indicate trend strength.
- Adaptive coloring highlights the rate of change and potential reversals, with different colors for positive and negative changes above and below the midline.
- Extreme levels with adaptive coloring indicate the probability of a reversion, providing strategic entry or exit points.
- Alert conditions for crossing the midline or significant shifts in trend direction enhance its utility within a trading strategy.
1. What is an LSMA?
The Least Squares Moving Average (LSMA) is a technical indicator that smoothens price data to help identify trends. It uses the least squares regression method to fit a straight line through the selected price points over a specified period. This approach minimizes the sum of the squares of the distances between the line and the price points, providing a more statistically grounded moving average that can adapt more smoothly to price changes.
2. What is a Z-Score?
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values, measured in terms of standard deviations from the mean. If a Z-Score is 0, it indicates that the data point's score is identical to the mean score. A Z-Score helps in understanding if a data point is typical for a given data set or if it is atypical. In finance, a Z-Score is often used to measure how far a piece of data is from the average of a set, which can be helpful in identifying outliers or unusual data points.
3. Why Turning LSMA into a Z-Score is Innovative and Its Benefits
Converting LSMA into a Z-Score is innovative because it combines the trend identification capabilities of the LSMA with the statistical significance testing of Z-Scores. This transformation normalizes the LSMA, creating a detrended oscillator that oscillates around a mean (zero line), with standard deviation levels to show trend strength. This method offers several benefits:
Enhanced Trend Detection:
- By normalizing the LSMA, traders can more easily identify when the price is deviating significantly from its trend, which can signal potential trading opportunities.
Standardization:
- The Z-Score transformation allows for comparisons across different assets or time frames, as the score is standardized.
Objective Measurement of Trend Strength:
- The use of standard deviation levels provides an objective measure of trend strength and volatility.
4. How It Can Be Used in the Context of a Trading System
This indicator can serve as a versatile tool within a trading system for a range of things:
Trend Confirmation:
- A positive Z-Score can confirm an uptrend, while a negative Z-Score can confirm a downtrend, providing traders with signals to enter or exit trades.
Oversold/Overbought Conditions:
- Extreme Z-Score levels can indicate overbought or oversold conditions, suggesting potential reversals or pullbacks.
Volatility Assessment:
- The standard deviation levels can help traders assess market volatility, with wider bands indicating higher volatility.
5. How It Can Be Used for Trend Following
For trend following strategies, this indicator can be particularly useful:
Trend Strength Indicator:
- By monitoring the Z-Score's distance from zero, traders can gauge the strength of the current trend, with larger absolute values indicating stronger trends.
Directional Bias:
- Positive Z-Scores can be used to establish a bullish bias, while negative Z-Scores can establish a bearish bias, guiding trend following entries and exits.
Color-Coding for Trend Changes :
- The adaptive coloring of the indicator based on the rate of change and extreme levels provides visual cues for potential trend reversals or continuations.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
This is using the Midline Crossover:
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
