Jason's Simple Moving Averages WaveUnderstanding the Script:
Purpose: This script identifies potential trend direction and momentum using a moving average and wave amplitude calculation. It shows a green line when the price is trending upwards and a red line when trending downwards.
Strategy: This script doesn't provide a complete trading strategy. It's an indicator designed to be used alongside other tools.
Parameters: You can adjust the "Moving Average Length" input to change the sensitivity of the indicator. A shorter length will react quicker to price changes, while a longer length will be smoother but less responsive.
How to Use it:
Load the Script: In TradingView, navigate to the indicator creation section and paste the provided script code.
Adjust Parameters: Set the "Moving Average Length" based on your preferred timeframe and trading style.
Combine with Other Tools: Use the indicator along with other technical indicators or price action analysis to confirm potential entry and exit points for trades.
Here are some additional points to consider:
Crossovers: You could look for buy signals when the price crosses above the green line and sell signals when it crosses below the red line. However, these can be prone to false signals.
Divergence: Look for divergences between the price movement and the wave indicator. For example, a rising price with a falling wave could indicate overbought conditions and a potential reversal.
Confirmation: Don't rely solely on this indicator. Use it alongside other confirmations from price action, volume analysis, or other indicators to identify higher probability trades.
Important Note:
Komut dosyalarını "the script" için ara
ET's FlagsPurpose:
This Pine Script is designed for the TradingView platform to identify and visually highlight specific technical chart patterns known as "Bull Flags" and "Bear Flags" on financial charts. These patterns are significant in trading as they can indicate potential continuation trends after a brief consolidation. The script includes mechanisms to manage signal frequency through a cooldown period, ensuring that the trading signals are not excessively frequent and are easier to interpret.
Functionality:
Input Parameters:
flagpole_length: Defines the number of bars to consider when identifying the initial surge in price, known as the flagpole.
flag_length: Determines the number of bars over which the flag itself is identified, representing a period of consolidation.
percent_change: Sets the minimum percentage change required to validate the presence of a flagpole.
cooldown_period: Specifies the number of bars to wait before another flag can be identified, reducing the risk of overlapping signals.
Percentage Change Calculation:
The script calculates the percentage change between two price points using a helper function percentChange(start, end). This function is crucial for determining whether the price movement within the specified flagpole_length meets the threshold set by percent_change, thus qualifying as a potential flagpole.
Flagpole Identification:
Bull Flagpole: Identified by finding the lowest close price over the flagpole_length and determining if the subsequent price rise meets or exceeds the specified percent_change.
Bear Flagpole: Identified by finding the highest close price over the flagpole_length and checking if the subsequent price drop is sufficient as per the percent_change.
Flag Identification:
After identifying a flagpole, the script assesses if the price action within the next flag_length bars consolidates in a manner that fits a flag pattern. This involves checking if the price fluctuation stays within the bounds set by the percent_change.
Signal Plotting:
If a bull or bear flag pattern is confirmed, and the cooldown period has passed since the last flag of the same type was identified, the script plots a visual shape on the chart:
Green shapes below the price bar for Bull Flags.
Red shapes above the price bar for Bear Flags.
Line Drawing:
For enhanced visualization, the script draws lines at the high and low prices of the flag during its formation period. This visually represents the consolidation phase of the flag pattern.
Debugging Labels:
The script optionally displays labels at the flag formation points, showing the exact percentage change achieved during the flagpole formation. This feature aids users in understanding why a particular segment of the price chart was identified as a flag.
Compliance and Usage:
This script does not automate trading but provides visual aids and potential signals based on historical price analysis. It adheres to TradingView's scripting policies by only accessing publicly available price data and user-defined parameters without executing trades or accessing any external data.
Conclusion:
This Pine Script is a powerful tool for traders who follow technical analysis, offering a clear, automated way to spot potential continuation patterns in the markets they monitor. By emphasizing visual clarity and reducing signal redundancy through cooldown periods, the script enhances decision-making processes for chart analysis on TradingView.
Smart Money Analysis with Golden/Death Cross [YourTradingSensei]Description of the script "Smart Money Analysis with Golden/Death Cross":
This TradingView script is designed for market analysis based on the concept of "Smart Money" and includes the detection of Golden Cross and Death Cross signals.
Key features of the script:
Moving Averages (SMA):
Two moving averages are calculated: a short-term (50 periods) and a long-term (200 periods).
The intersections of these moving averages are used to determine Golden Cross and Death Cross signals.
High Volume:
The current trading volume is analyzed.
Periods of high volume are identified when the current volume exceeds the average volume by a specified multiplier.
Support and Resistance Levels:
Key support and resistance levels are determined based on the highest and lowest prices over a specified period.
Buy and Sell Signals:
Buy and sell signals are generated based on moving average crossovers, high volume, and the closing price relative to key levels.
Golden Cross and Death Cross:
A Golden Cross occurs when the short-term moving average crosses above the long-term moving average.
A Death Cross occurs when the short-term moving average crosses below the long-term moving average.
These signals are displayed on the chart with text color changes for better visualization.
Using the script:
The script helps traders visualize key signals and levels, aiding in making informed trading decisions based on the behavior of major market players and technical analysis.
Custom candle lighting(CCL) © 2024 by YourTradingSensei is licensed under CC BY-NC-SA 4.0. To view a copy of this license.
Monthly Performance Table by Dr. MauryaWhat is this ?
This Strategy script is not aim to produce strategy results but It aim to produce monthly PnL performance Calendar table which is useful for TradingView community to generate a monthly performance table for Own strategy.
So make sure to read the disclaimer below.
Why it is required to publish?:
I am not satisfied with the monthly performance available on TV community script. Sometimes it is very lengthy in code and sometimes it showing the wrong PNL for current month.
So I have decided to develop new Monthly performance or return in value as well as in percentage with highly flexible to adjust row automatically.
Features :
Accuracy increased for current month PnL.
There are 14 columns and automatically adjusted rows according to available trade years/month.
First Column reflect the YEAR, from second column to 13 column reflect the month and 14 column reflect the yearly PnL.
In tabulated data reflects the monthly PnL (value and (%)) in month column and Yearly PnL (value and (%)) in Yearly column.
Various color input also added to change the table look like background color, text color, heading text color, border color.
In tabulated data, background color turn green for profit and red for loss.
Copy from line 54 to last line as it is in your strategy script.
Credit: This code is modified and top up of the open-source code originally written by QuantNomad. Thanks for their contribution towards to give base and lead to other developers. I have changed the way of determining past PnL to array form and keep separated current month and year PnL from array. Which avoid the false pnl in current month.
Strategy description:
As in first line I said This strategy is aim to provide monthly performance table not focused on the strategy. But it is necessary to explain strategy which I have used here. Strategy is simply based on ADX available on TV community script. Long entry is based on when the difference between DIPlus and ADX is reached on certain value (Set value in Long difference in Input Tab) while Short entry is based on when the difference between DIMinus and ADX is reached on certain value (Set value in Short difference in Input Tab).
Default Strategy Properties used on chart(Important)
This script backtest is done on 1 hour timeframe of NSE:Reliance Inds Future cahrt, using the following backtesting properties:
Balance (default): 500 000 (default base currency)
Order Size: 1 contract
Comission: 20 INR per Order
Slippage: 5 tick
Default setting in Input tab
Len (ADX length) : 14
Th (ADX Threshhold): 20
Long Difference (DIPlus - ADX) = 5
Short Difference (DIMinus - ADX) = 5
We use these properties to ensure a realistic preview of the backtesting system, do note that default properties can be different for various reasons described below:
Order Size: 1 contract by default, this is to allow the strategy to run properly on most instruments such as futures.
Comission: Comission can vary depending on the market and instrument, there is no default value that might return realistic results.
We strongly recommend all users to ensure they adjust the Properties within the script settings to be in line with their accounts & trading platforms of choice to ensure results from the strategies built are realistic.
Disclaimer:
This script not provide indicative of any future results.
This script don’t provide any financial advice.
This strategy is only for the readymade snippet code for monthly PnL performance calender table for any own strategy.
Liquidations Meter [LuxAlgo]The Liquidation Meter aims to gauge the momentum of the bar, identify the strength of the bulls and bears, and more importantly identify probable exhaustion/reversals by measuring probable liquidations.
🔶 USAGE
This tool includes many features related to the concept of liquidation. The two core ones are the liquidation meter and liquidation price calculator, highlighted below.
🔹 Liquidation Meter
The liquidation meter presents liquidations on the price chart by measuring the highest leverage value of longs and shorts that have been potentially liquidated on the last chart bar, hence allowing traders to:
gauge the momentum of the bar.
identify the strength of the bulls and bears.
identify probable reversal/exhaustion points.
Liquidation of low-leveraged positions can be indicative of exhaustion.
🔹 Liquidation Price Calculator
A liquidation price calculator might come in handy when you need to calculate at what price level your leveraged position in Crypto, Forex, Stocks, or any other asset class gets liquidated to add a protective stop to mitigate risk. Monitoring an open position gets easier if the trader can calculate the total risk in order for them to choose the right amount of margin and leverage.
Liquidation price is the distance from the trader's entry price to the price where trader's leveraged position gets liquidated due to a loss. As the leverage is increased, the distance from trader's entry price to the liquidation price shrinks.
While you have one or several trades open you can quickly check their liquidation levels and determine which one of the trades is closest to their liquidation price.
If you are a day trader that uses leverage and you want to know which trade has the best outlook you can calculate the liquidation price to see which one of the trades looks best.
🔹 Dashboard
The bar statistics option enables measuring and presenting trading activity, volatility, and probable liquidations for the last chart bar.
🔶 DETAILS
It's important to note that liquidation price calculator tool uses a formula to calculate the liquidation price based on the entry price + leverage ratio.
Other factors such as leveraged fees, position size, and other interest payments have been excluded since they are variables that don’t directly affect the level of liquidation of a leveraged position.
The calculator also assumes that traders are using an isolated margin for one single position and does not take into consideration the additional margin they might have in their account.
🔹Liquidation price formula
the liquidation distance in percentage = 100 / leverage ratio
the liquidation distance in price = current asset price x the liquidation distance in percentage
the liquidation price (longs) = current asset price – the liquidation distance in price
the liquidation price (shorts) = current asset price + the liquidation distance in price
or simply
the liquidation price (longs) = entry price * (1 – 1 / leverage ratio)
the liquidation price (shorts) = entry price * (1 + 1 / leverage ratio)
Example:
Let’s say that you are trading a leverage ratio of 1:20. The first step is to calculate the distance to your liquidation point in percentage.
the liquidation distance in percentage = 100 / 20 = 5%
Now you know that your liquidation price is 5% away from your entry price. Let's calculate 5% below and above the entry price of the asset you are currently trading. As an example, we assume that you are trading bitcoin which is currently priced at $35000.
the liquidation distance in price = $35000 x 0.05 = $1750
Finally, calculate liquidation prices.
the liquidation price (longs) = $35000 – $1750 = $33250
the liquidation price (short) = $35000 + $1750 = $36750
In this example, short liquidation price is $36750 and long liquidation price is $33250.
🔹How leverage ratio affects the liquidation price
The entry price is the starting point of the calculation and it is from here that the liquidation price is calculated, where the leverage ratio has a direct impact on the liquidation price since the more you borrow the less “wiggle-room” your trade has.
An increase in leverage will subsequently reduce the distance to full liquidation. On the contrary, choosing a lower leverage ratio will give the position more room to move on.
🔶 SETTINGS
🔹Liquidations Meter
Base Price: The option where to set the reference/base price.
🔹Liquidation Price Calculator
Liquidation Price Calculator: Toggles the visibility of the calculator. Details and assumptions made during the calculations are stated in the tooltip of the option.
Entry Price: The option where to set the entry price, a value of 0 will use the current closing price. Details are given in the tooltip of the option.
Leverage: The option where to set the leverage value.
Show Calculated Liquidation Prices on the Chart: Toggles the visibility of the liquidation prices on the price chart.
🔹Dashboard
Show Bar Statistics: Toggles the visibility of the last bar statistics.
🔹Others
Liquidations Meter Text Size: Liquidations Meter text size.
Liquidations Meter Offset: Liquidations Meter offset.
Dashboard/Calculator Placement: Dashboard/calculator position on the chart.
Dashboard/Calculator Text Size: Dashboard text size.
🔶 RELATED SCRIPTS
Here are some of the scripts that are related to the liquidation and liquidity concept, for more and other conceptual scripts you are kindly invited to visit LuxAlgo-Scripts .
Liquidation-Levels
Liquidations-Real-Time
Buyside-Sellside-Liquidity
[blackcat] L1 T3 MA Lite Version
Tilson T3 Moving Average (T3MA) is a type of moving average line designed to reduce lag and improve the accuracy of trend identification. It is based on a combination of multiple smoothed moving averages, with each subsequent smoothed moving average having a higher weight than the previous one. The T3MA formula includes three different smoothing coefficients and a volume coefficient or volatility coefficient, which can be adjusted according to user preferences. T3MA is commonly used by traders and investors to identify trends and generate trading signals.
The calculation method for T3MA requires the use of exponential moving averages (EMA). In Pine scripts in the TradingView community, over 90% of them use the EMA function to calculate T3MA. Specifically, in Pine scripts, it is necessary to define the length and volatility coefficient of T3MA, then calculate three different lengths of EMA separately. Next, three constants need to be calculated that are related to volatility. Finally, the weighted average value of the three EMAs and three constants is added together to obtain the value of T3MA. If you want to customize the length and volatility of T3MA, you just need to modify the parameters in the code. Overall, T3MA is a very useful technical indicator that can help traders better understand market trends and improve trading efficiency.
The improved version introduced today mainly addresses my perception that traditional T3 algorithms are too redundant with high computational complexity leading to delayed reactions. Therefore, I have developed a lightweight version called L1 T3 MA Lite Version. This doesn't bring about any qualitative changes; it simply makes adjustments in terms of computational resources and response speed. To illustrate its advantages compared with traditional T3 MA indicators, I will provide a comparison using Everget's script from TradingView community blogger everget.
The difference between these two scripts for calculating T3 Moving Average lies in their implementation methods. The first script (Everget) uses a more complex calculation formula, which requires calculating three different lengths of EMA and computing three constants based on volatility. Finally, they are weighted averaged to obtain T3MA. This complex calculation formula can enhance the sensitivity of the T3MA indicator, thereby better identifying price trends. On the other hand, the second script (Blackcat1402) uses a relatively simple calculation formula that only requires calculating three different lengths of EMA and computing three constants based on volatility. Finally, they are weighted averaged to obtain T3MA as well. This simple calculation formula reduces computational complexity and speeds up calculations. Both have slightly different effects and calculation methods; users can choose the script that suits their needs.
In summary, T3 Moving Average is a very useful technical indicator that can help traders better understand market trends and improve trading efficiency. Users can choose scripts suitable for themselves according to their needs and flexibly adjust the length and volatility coefficient of T3MA to adapt to different markets.
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
*** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed***
█ Brief Overview of the Goertzel Cycle Composite Wave
The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes.
The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here:
Goertzel Browser
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast: These inputs define the window size for the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Cycle Composite Wave Code
The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in a matrix:
The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast:
The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for waveforms:
The goertzel array is initialized to store the endpoint Goertzel.
Calculating composite waveform (goertzel array):
The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Drawing composite waveform (pvlines):
The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
Limited applicability:
The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data.
The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend.
Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color.
█ Conclusion
The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Browser Code
The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Crossover Alerts for Yesterday O/H/L/C , Today Vwap [Zero54]This is a very simple script/indicator that trigger alerts every time the script triggers the following conditions.
1) Script crosses yesterday's (previous day's) high
2) Script crosses yesterday's (previous day's) low
3) Script crosses yesterday's (previous day's) open
4) Script crosses yesterday's (previous day's) close
5) Script crosses today's vwap.
I developed this to keep track of the scripts I follow and I find it useful. Hope you will find it useful too.
Steps to use:
1) Open the ticker for which you want to set the alerts.
2) Add this indicator to the chart.
3) Right Click on the text and set choose "Add Alert"
4) After you have done with setting up the alert, feel free to remove the indicator from the chart. It is not necessary for the indicator to be added in the chart in order for it to work.
5) Repeat 1-4 for all the scripts for which you want to set the alerts.
Be advised: During market open, if you have set alerts for multiple scripts, a tsunami of alerts may be triggered.
If you like this alert indicator, please like/boost it. Feel free to re-use this code however you may wish to. Cheers!
Chart VWAP█ OVERVIEW
This indicator displays a Volume-Weighted Average Price anchored to the leftmost visible bar of the chart. It dynamically recalculates when the chart's visible bars change because you scroll or zoom your chart.
If you are not already familiar with VWAP, our Help Center will get you started. The typical VWAP is designed to be used on intraday charts, as it resets at the beginning of the day. Our Rolling VWAP , instead, resets on a rolling time window. You may also find the VWAP Auto Anchored built-in indicator worth a try.
█ HOW TO USE IT
Load the indicator on an active chart (see the Help Center if you don't know how). By default, it displays the chart's VWAP in orange and a simple average of the chart's visible close values in gray. This average can be used as a companion to the VWAP, since both are calculated from the same set of bars. The script's settings allow you to hide it.
You may also use the script's settings to enable the display of the chart's OHLC (open, high, low, close) levels and the values of the high and low. These are also calculated from the range of visible bars. You can complement the high and low lines with their price and their distance in percent from the chart's latest visible close . You can use the levels to quickly identify the distances from extreme points in the visible price range, as well as observe the visible chart's beginning and end prices.
█ NOTES FOR Pine Script™ CODERS
This script showcases three novelties:
• Dynamic recalculation on visible bars
• The VisibleChart library by PineCoders
• The new `anchor` parameter of ta.vwap()
Dynamic recalculation on visible bars
This script behaves in a novel way made possible by the recent introduction of two new built-in variables: chart.left_visible_bar_time and chart.right_visible_bar_time , which return the opening time of the leftmost and rightmost visible bars on the chart. These are only two of many new built-ins in the `chart.*` namespace. See this blog post for more information, or look up them up by typing "chart." in the Pine Script™ Reference Manual .
Any script using chart.left_visible_bar_time or chart.right_visible_bar_time acquires a unique property, which triggers its recalculation when traders scroll or zoom their chart, causing the range of visible bars to change. This new capability is what makes it possible for this script to calculate its VWAP on the chart's visible bars only, and dynamically recalculate if the user scrolls or zooms their chart.
This script is just a start to the party; endless uses for indicators that redraw on changes to the chart will no doubt emerge through the hands of our community's Pine Script™ programmers.
The VisibleChart library by PineCoders
The newly published VisibleChart library is designed to help programmers benefit from the new capabilities made possible by the fact that Pine Script™ code can now tell when it is executing on visible bars. The library's description, functions and example code will help programmers make the most of the new feature.
This script uses three of the library's functions:
• `PCvc.vVwap()` calculates a VWAP for visible bars.
• `PCvc.avg()` calculates the average of a source value for visible bars only. We use it to calculate the average close (the default source).
• `PCvc.chartXTimePct(25)` calculates a time value corresponding to 25% of the horizontal distance between visible bars, starting from the left.
The new `anchor` parameter of ta.vwap()
Our script also uses this new `anchor` parameter to reset the VWAP at the leftmost visible bar. See how simple the code is for the VisibleChart library's `vVwap()` function.
Look first. Then leap.
Technical Ratings█ OVERVIEW
This indicator calculates TradingView's well-known "Strong Buy", "Buy", "Neutral", "Sell" or "Strong Sell" states using the aggregate biases of 26 different technical indicators.
█ FEATURES
Differences with the built-in version
• You can adjust the weight of the Oscillators and MAs components of the rating here.
• The built-in version produces values matching the states displayed in the "Technicals" ratings gauge; this one does not always, where weighting is used.
• A strategy version is also available as a built-in; this script is an indicator—not a strategy.
• This indicator will show a slightly different vertical scale, as it does not use a fixed scale like the built-in.
• This version allows control over repainting of the signal when you do not use a higher timeframe. Higher timeframe (HTF) information from this version does not repaint.
• You can configure markers on signal breaches of configurable levels, or on advances declines of the signal.
The indicator's settings allow you to:
• Choose the timeframe you want calculations to be made on.
• When not using a HTF, you can select a repainting or non-repainting signal.
• When using both MAs and Oscillators groups to calculate the rating, you can vary the weight of each group in the calculation. The default is 50/50.
Because the MAs group uses longer periods for some of its components, its value is not as jumpy as the Oscillators value.
Increasing the weight of the MAs group will thus have a calming effect on the signal.
• Alerts can be created on the indicator using the conditions configured to control the display of markers.
Display
The calculated rating is displayed as columns, but you can change the style in the inputs. The color of the signal can be one of three colors: bull, bear, or neutral. You can choose from a few presets, or check one and edit its color. The color is determined from the rating's value. Between 0.1 and -0.1 it is in the neutral color. Above/below 0.1/-0.1 it will appear in the bull/bear color. The intensity of the bull/bear color is determined by cumulative advances/declines in the rating. It is capped to 5, so there are five intensities for each of the bull/bear colors.
The "Strong Buy", "Buy", "Neutral", "Sell" or "Strong Sell" state of the last calculated value is displayed to the right of the last bar for each of the three groups: All, MAs and Oscillators. The first value always reflects your selection in the "Rating uses" field and is the one used to display the signal. A "Strong Buy" or "Strong Sell" state appears when the signal is above/below the 0.5/-0.5 level. A "Buy" or "Sell" state appears when the signal is above/below the 0.1/-0.1 level. The "Neutral" state appears when the signal is between 0.1 and -0.1 inclusively.
Five levels are always displayed: 0.5 and 0.1 in the bull color, zero in the neutral color, and -0.1 and - 0.5 in the bull color.
The levels that can be used to determine the breaches displaying long/short markers will only be visible when their respective long/short markers are turned on in the "Direction" input. The levels appear as a bright dotted line in bull/bear colors. You can control both levels separately through the "Longs Level" and "Shorts Level" inputs.
If you specify a higher timeframe that is not greater than the chart's timeframe, an error message will appear and the indicator's background will turn red, as it doesn't make sense to use a lower timeframe than the chart's.
Markers
Markers are small triangles that appear at the bottom and top of the indicator's pane. The marker settings define the conditions that will trigger an alert when you configure an alert on the indicator. You can:
• Choose if you want long, short or both long and short markers.
• Determine the signal level and/or the number of cumulative advances/declines in the signal which must be reached for either a long or short marker to appear.
Reminder: the number of advances/declines is also what controls the brightness of the plotted signal.
• Decide if you want to restrict markers to ones that alternate between longs and shorts, if you are displaying both directions.
This helps to minimize the number of markers, e.g., only the first long marker will be displayed, and then no more long markers will appear until a short comes in, then a long, etc.
Alerts
When you create an alert from this indicator, that alert will trigger whenever your marker conditions are confirmed. Before creating your alert, configure the makers so they reflect the conditions you want your alert to trigger on.
The script uses the alert() function, which entails that you select the "Any alert() function call" condition from the "Create Alert" dialog box when creating alerts on the script. The alert messages can be configured in the inputs. You can safely disregard the warning popup that appears when you create alerts from this script. Alerts will not repaint. Markers will appear, and thus alerts will trigger, at the opening of the bar following the confirmation of the marker condition. Markers will never disappear from the bar once they appear.
Repainting
This indicator uses a two-pronged approach to control repainting. The repainting of the displayed signal is controlled through the "Repainting" field in the script's inputs. This only applies when you have "Same as chart" selected in the "Timeframe" field, as higher timeframe data never repaints. Regardless of that setting, markers and thus alerts never repaint.
When using the chart's timeframe, choosing a non-repainting signal makes the signal one bar late, so that it only displays a value once the bar it was calculated has elapsed. When using a higher timeframe, new values are only displayed once the higher timeframe completes.
Because the markers never repaint, their logic adapts to the repainting setting used for the signal. When the signal repaints, markers will only appear at the close of a realtime bar. When the signal does not repaint (or if you use a higher timeframe), alerts will appear at the beginning of the realtime bar, since they are calculated on values that already do not repaint.
█ CALCULATIONS
The indicator calculates the aggregate value of two groups of indicators: moving averages and oscillators.
The "MAs" group is comprised of 15 different components:
• Six Simple Moving Averages of periods 10, 20, 30, 50, 100 and 200
• Six Exponential Moving Averages of the same periods
• A Hull Moving Average of period 9
• A Volume-weighed Moving Average of period 20
• Ichimoku
The "Oscillators" group includes 11 components:
• RSI
• Stochastic
• CCI
• ADX
• Awesome Oscillator
• Momentum
• MACD
• Stochastic RSI
• Wiliams %R
• Bull Bear Power
• Ultimate Oscillator
The state of each group's components is evaluated to a +1/0/-1 value corresponding to its bull/neutral/bear bias. The resulting value for each of the two groups are then averaged to produce the overall value for the indicator, which oscillates between +1 and -1. The complete conditions used in the calculations are documented in the Help Center .
█ NOTES
Accuracy
When comparing values to the other versions of the Rating, make sure you are comparing similar timeframes, as the "Technicals" gauge in the chart's right pane, for example, uses a 1D timeframe by default.
For coders
We use a handy characteristic of array.avg() which, contrary to avg() , does not return na when one of the averaged values is na . It will average only the array elements which are not na . This is useful in the context where the functions used to calculate the bull/neutral/bear bias for each component used in the rating include special checks to return na whenever the dataset does not yet contain enough data to provide reliable values. This way, components gradually kick in the calculations as the script calculates on more and more historical data.
We also use the new `group` and `tooltip` parameters to input() , as well as dynamic color generation of different transparencies from the bull/bear/neutral colors selected by the user.
Our script was written using the PineCoders Coding Conventions for Pine .
The description was formatted using the techniques explained in the How We Write and Format Script Descriptions PineCoders publication.
Bits and pieces were lifted from the PineCoders' MTF Selection Framework .
Look first. Then leap.
Laguerre Multi-Filter [DW]This is an experimental study designed to identify underlying price activity using a series of Laguerre Filters.
Two different modes are included within this script:
-Ribbon Mode - A ribbon of 18 Laguerre Filters with separate Gamma values is calculated.
-Band Mode - An average of the 18 filters generates the basis line. Then, Golden Mean ATR over the specified sampling period multiplied by 1 and 2 are added and subtracted to the basis line to generate the bands.
Multi-Timeframe functionality is included. You can choose any timeframe that TradingView supports as the basis resolution for the script.
Custom bar colors are included. Bar colors are based on the direction of any of the 18 filters, or the average filter's direction in Ribbon Mode. In Band Mode, the colors are based solely on the average filter's direction.
TextLibrary "Text"
library to format text in different fonts or cases plus a sort function.
🔸 Credits and Usage
This library is inspired by the work of three authors (in chronological order of publication date):
Unicode font function - JD - Duyck
UnicodeReplacementFunction - wlhm
font - kaigouthro
🔹 Fonts
Besides extra added font options, the toFont(fromText, font) method uses a different technique. On the first runtime bar (whether it is barstate.isfirst , barstate.islast , or between) regular letters and numbers and mapped with the chosen font. After this, each character is replaced using the build-in key - value pair map function .
Also an enum Efont is included.
Note: Some fonts are not complete, for example there isn't a replacement for every character in Superscript/Subscript.
Example of usage (besides the included table example):
import fikira/Text/1 as t
i_font = input.enum(t.Efont.Blocks)
if barstate.islast
sentence = "this sentence contains words"
label.new(bar_index, 0, t.toFont(fromText = sentence, font = str.tostring(i_font)), style=label.style_label_lower_right)
label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Circled" ), style=label.style_label_lower_left )
label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Wiggly" ), style=label.style_label_upper_right)
label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Upside Latin" ), style=label.style_label_upper_left )
🔹 Cases
The script includes a toCase(fromText, case) method to transform text into snake_case, UPPER SNAKE_CASE, kebab-case, camelCase or PascalCase, as well as an enum Ecase .
Example of usage (besides the included table example):
import fikira/Text/1 as t
i_case = input.enum(t.Ecase.camel)
if barstate.islast
sentence = "this sentence contains words"
label.new(bar_index, 0, t.toCase(fromText = sentence, case = str.tostring(i_case)), style=label.style_label_lower_right)
label.new(bar_index, 0, t.toCase(fromText = sentence, case = "snake_case" ), style=label.style_label_lower_left )
label.new(bar_index, 0, t.toCase(fromText = sentence, case = "PascalCase" ), style=label.style_label_upper_right)
label.new(bar_index, 0, t.toCase(fromText = sentence, case = "SNAKE_CASE" ), style=label.style_label_upper_left )
🔹 Sort
The sort(strings, order, sortByUnicodeDecimalNumbers) method returns a sorted array of strings.
strings: array of strings, for example words = array.from("Aword", "beyond", "Space", "salt", "pepper", "swing", "someThing", "otherThing", "12345", "_firstWord")
order: "asc" / "desc" (ascending / descending)
sortByUnicodeDecimalNumbers: true/false; default = false
_____
• sortByUnicodeDecimalNumbers: every Unicode character is linked to a Unicode Decimal number ( wikipedia.org/wiki/List_of_Unicode_characters ), for example:
1 49
2 50
3 51
...
A 65
B 66
...
S 83
...
_ 95
` 96
a 97
b 98
...
o 111
p 112
q 113
r 114
s 115
...
This means, if we sort without adjusting ( sortByUnicodeDecimalNumbers = true ), in ascending order, the letter b (98 - small) would be after S (83 - Capital).
By disabling sortByUnicodeDecimalNumbers , Capital letters are intermediate transformed to str.lower() after which the Unicode Decimal number is retrieved from the small number instead of the capital number. For example S (83) -> s (115), after which the number 115 is used to sort instead of 83.
Example of usage (besides the included table example):
import fikira/Text/1 as t
if barstate.islast
aWords = array.from("Aword", "beyond", "Space", "salt", "pepper", "swing", "someThing", "otherThing", "12345", "_firstWord")
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'asc' , sortByUnicodeDecimalNumbers = false)), style=label.style_label_lower_right)
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'desc', sortByUnicodeDecimalNumbers = false)), style=label.style_label_lower_left )
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'asc' , sortByUnicodeDecimalNumbers = true )), style=label.style_label_upper_right)
label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'desc', sortByUnicodeDecimalNumbers = true )), style=label.style_label_upper_left )
🔸 Methods/functions
method toFont(fromText, font)
toFont : Transforms text into the selected font
Namespace types: series string, simple string, input string, const string
Parameters:
fromText (string)
font (string)
Returns: `fromText` transformed to desired `font`
method toCase(fromText, case)
toCase : formats text to snake_case, UPPER SNAKE_CASE, kebab-case, camelCase or PascalCase
Namespace types: series string, simple string, input string, const string
Parameters:
fromText (string)
case (string)
Returns: `fromText` formatted to desired `case`
method sort(strings, order, sortByUnicodeDecimalNumbers)
sort : sorts an array of strings, ascending/descending and by Unicode Decimal numbers or not.
Namespace types: array
Parameters:
strings (array)
order (string)
sortByUnicodeDecimalNumbers (bool)
Returns: Sorted array of strings
Day’s Open ForecastOverview
This Pine Script indicator combines two primary components:
1. Day’s Open Forecast:
o Tracks historical daily moves (up and down) from the day’s open.
o Calculates average up and down moves over a user-defined lookback period.
o Optionally includes standard deviation adjustments to forecast potential intraday levels.
o Plots lines on the chart for the forecasted up and down moves from the current day's open.
2. Session VWAP:
o Allows you to specify a custom trading session (by time range and UTC offset).
o Calculates and plots a Volume-Weighted Average Price (VWAP) during that session.
By combining these two features, you can gauge potential intraday moves relative to historical behavior from the open, while also tracking a session-specific VWAP that can act as a dynamic support/resistance reference.
How the Code Works
1. Collect Daily Moves
o The script detects when a new day starts using time("D").
o Once a new day is detected, it stores the previous day’s up-move (dayHigh - dayOpen) and down-move (dayOpen - dayLow) into arrays.
o These arrays keep track of the last N days (default: 126) of up/down move data.
2. Compute Statistics
o The script computes the average (f_average()) of up-moves and down-moves over the stored period.
o It also computes the standard deviation (f_stddev()) of up/down moves for optional “forecast bands.”
3. Forecast Lines
o Plots the current day’s open.
o Plots the average forecast lines above and below the open (Avg Up Move Level and Avg Down Move Level).
o If standard deviation is enabled, plots additional lines (Avg+StdDev Up and Avg+StdDev Down).
4. Session VWAP
o The script detects the start of a user-defined session (via input.session) and resets accumulation of volume and the numerator for VWAP.
o As each bar in the session updates, it accumulates volume (vwapCumulativeVolume) and a price-volume product (vwapCumulativeNumerator).
o The session VWAP is then calculated as (vwapCumulativeNumerator / vwapCumulativeVolume) and plotted.
5. Visualization Options
o Users can toggle standard deviation usage, historical up/down moves plotting, and whether to show the forecast “bands.”
o The vwapSession and vwapUtc inputs let you adjust which session (and time zone offset) the VWAP is calculated for.
________________________________________
How to Use This Indicator on TradingView
1. Create a New Script
o Open TradingView, then navigate to Pine Editor (usually found at the bottom of the chart).
o Copy and paste the entire code into the editor.
2. Save and Add to Chart
o Click Save (give it a relevant title if you wish), then click Add to chart.
o The indicator will appear on your chart with the forecast lines and VWAP.
o By default, it is overlayed on the price chart (because of overlay=true).
3. Customize Inputs
o In the indicator’s settings, you can:
Change lookback days (default: 126).
Enable or disable standard deviation (Include Standard Deviation in Forecast?).
Adjust the standard deviation multiplier.
Choose whether to plot bands (Plot Bands with Averages/StdDev?).
Plot historical moves if desired (Plot Historical Up/Down Moves for Reference?).
Set your custom session and UTC offset for the VWAP calculation.
4. Interpretation
o “Current Day Open” is simply today’s open price on your chart.
o Up/Down Move Lines: Indicate a potential forecast based on historical averages.
If standard deviation is enabled, the second set of lines acts as an extended range.
o VWAP: Helpful for determining intraday price equilibrium over the specified session.
Important Notes / Best Practices
• The script only updates the historical up/down move data once per day (when a new day starts).
• The VWAP portion resets at the start of the specified session each day.
• Standard deviation multiplies the average up/down range, giving you a sense of “volatility range” around the day’s open.
• Adjust the lookback length (dayCount) to balance how many days of data you want to average. More days = smoother but possibly slower to adapt; fewer days = more reactive but potentially less reliable historically.
Educational & Liability Disclaimers
1. Educational Disclaimer
o The information provided by this indicator is for educational and informational purposes only. It is a technical analysis tool intended to demonstrate how to use historical data and basic statistics in Pine Script.
2. No Financial Advice
o This script does not constitute financial or investment advice. All examples and explanations are solely illustrative. You should always do your own analysis before making any investment decisions.
3. No Liability
o The author of this script is not liable for any losses or damages—monetary or otherwise—that may occur from the application of this script.
o Past performance does not guarantee future results, and you should never invest money you cannot afford to lose.
By adding this indicator to your TradingView chart, you acknowledge and accept that you alone are responsible for your own trading decisions.
Enjoy using the “Day’s Open Forecast” and Session VWAP for better market insights!
Candle Partition Statistics with IQV and Chi2NOTE: THE FORMULA IN THE CHART IS NOT PART OF THE CODE
This Pine Script calculates statistical measures for candle partitions based on whether a candle is bullish or bearish and whether the price is above or below an EMA. It evaluates statistical properties such as the Index of Qualitative Variation (IQV) and the Chi-Square (χ²) statistic to assess variations in price action.
Concept of Index of Qualitative Variation (IQV)
IQV is a statistical measure used to quantify the diversity or dispersion of categorical variables. In this script, it is used to measure how evenly the four categories of candles (green above EMA, red above EMA, green below EMA, red below EMA) are distributed.
Purpose of IQV in the Script:
IQV ranges from 0 to 1, where 0 indicates no variation (one category dominates) and 1 indicates maximum variation (categories are equally distributed).
A high IQV suggests balanced distributions of bullish/bearish candles above/below the EMA, indicating market uncertainty or mixed sentiment.
A low IQV suggests dominance of a particular candle type, indicating a strong trend.
Concept of Chi-Square (χ²) Test
Chi-square (χ²) is a statistical test that measures the difference between expected and observed frequencies of categorical data. It assesses whether short-term price behavior significantly deviates from historical trends.
Purpose of Chi-Square in the Script:
A high χ² value means that short-term candle distributions are significantly different from historical patterns, indicating potential trend shifts.
If χ² exceeds a predefined significance threshold (chi_threshold), an alert (Chi² Alert!) is triggered.
It helps traders identify periods where recent price behavior deviates from historical norms, possibly signaling trend reversals or market regime changes.
Key Takeaways:
IQV helps measure the diversity of price action, detecting whether the market is balanced or trending.
Chi-square (χ²) identifies significant deviations in short-term price behavior compared to long-term trends.
Both metrics together provide insights into whether the market is stable, trending, or shifting.
The Nasan C-score enhances trend strength by incorporating volatility. It is calculated as:
enhanced_t_s =(𝑡𝑠 × avg_movement x 100)/SMA(𝑐lose)
Key Components:
𝑡𝑠 : Measures trend strength based on price movements relative to EMA.
ts=green_EMAup_a+0.5×red_EMAup_a−(0.5×green_EMAdown_a+red_EMAdown_a)
avg_movement: The SMA of absolute close-open differences, capturing volatility.
Normalization: The division by SMA(close) adjusts the score relative to price levels.
Purpose of the Nasan C-score
Enhanced Trend Strength
It amplifies the trend strength value by factoring in volatility (price movement).
If price volatility is high, trend strength variations have a greater impact.
Volatility-Adjusted Momentum
By scaling 𝑡𝑠 with average movement, the score adjusts to changing price dynamics.
Higher price fluctuations lead to a higher score, making trend shifts more prominent.
How It Can Be Used in Trading
Higher values of Nasan C-score indicate strong bullish or bearish trends.
Comparing it with past values helps determine whether momentum is increasing or fading.
Thresholds can be set to identify significant trend shifts based on historical highs and lows.
[blackcat] L2 Six Round Positioning█ OVERVIEW
The script is an indicator designed to plot the direction (up, down, no change) of several moving averages (MA) on a separate chart, without overlaying the price data. It calculates Simple Moving Averages (SMA) for 3, 5, 8, 34, 60, 120, and 250 periods and uses conditional logic to determine the color and position of the plotted columns based on whether each MA is increasing, decreasing, or unchanged.
█ LOGICAL FRAMEWORK
The script is structured into three main sections:
1 — Input Parameters: None explicitly defined, but the script uses default settings for the indicator function.
2 — Calculations: Computes Simple Moving Averages (SMA) for seven different periods.
3 — Plotting: Uses conditional logic to plot columns representing the direction of each MA, with positions and colors indicating whether the MA is increasing, decreasing, or unchanged.
The flow of data is straightforward: the script calculates the SMAs, determines their direction, sets the appropriate color, and then plots the columns.
█ CUSTOM FUNCTIONS
• No custom functions are defined in this script. All calculations and plotting are done using built-in Pine Script functions such as ta.sma for SMA calculation and plot for plotting.
█ KEY POINTS AND TECHNIQUES
• Use of ta.sma: The script effectively uses the ta.sma function to calculate Simple Moving Averages for different periods.
• Conditional Logic: The script employs conditional logic (ternary operators) to determine the color and position of the plotted columns based on the direction of each MA.
• Plotting with plot: The plot function is used extensively to display the direction of each MA with different colors and positions.
• Color Transparency: The use of color.new with transparency (e.g., color.new(color.green, 50)) allows for visually distinct colors that are not too overpowering.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
• Modifications: The script could be enhanced by adding input parameters to allow users to customize the periods of the moving averages, colors, and transparency levels.
• Extensions: Similar techniques could be applied to other types of moving averages (e.g., EMA, WMA) or to other technical indicators.
• Strategy Development: This indicator could serve as a component in a larger trading strategy by providing insights into the overall trend direction across multiple timeframes.
• Related Concepts: Understanding of moving averages, conditional logic, and plotting techniques in Pine Script would be beneficial for further development and customization of this script.
Strategy:Reversal-CatcherWhat
This is a plain and vanilla reversal based strategy for intraday (15m) timeframe on Futures prices of the assets.
Now what all it comprises of?
It finds out the dynamic support & resistance from Bollinger Band (20 period, 1.5 std dev).
It finds out the potential divergence of price deviation from 5 period exponential moving average (EMA).
If the previous candle (N-1) shows a divergence it confirms the reversal by checking the present candle (N) to be closed inside the Bollinger Band.
It confirms the momentum by checking RSI shows a crossover/crossunder to oversold (30) / overbought (70) region.
It also confirms whether the trend is up (then only reversal trade to short) or down (then only reversal trade to long). The trend is checked with EMA-21 and EMA-50.
Re-affirmation Condition : It re-affirms the position of two successive candles called as `hhLLong` and `hhLLShort` in the script.
Why
In Indian context, retail participants are pre-dominantly (yes- 80% of Indian daily volume) Options buyers mainly in weekly indices (Nifty, BankNifty, FinNifty, CNXMidcap, Sensex, Bankx .. well everyday is expiry now in India, except -- Thank God -- Saturday & Sunday).
And in Index Options the momentum plays a big role.
If one can catch a good reversal point the potential of high Risk-to-Reward trade (hence earn handsomely) is very likely (please note: there is no holy grail in trading. Nothing works 100%).
So this is the attempt to catch a reversal.
Re-affirmation of Reversal
hhLLong : It's a reversal point after an uptrend. It checks the relative positioning of current candle compared to that of previous candle. [The details are in the script. Check for variable hhLLong in script.
hhLLShort : It's a reversal point after a downtrend. It checks the relative positioning of current candle compared to that of previous candle. [The details are in the script. Check for variable hhLLShort in script.
Unique-ness
What's unique in it? Why we decided to publicly share this:
Already given the context of The Great Indian Options Buyers community. It should be helpful to them, we believe.
It takes Very Less Number of Trades with High Accuracy . Please check the result in NSE:NIFTY1! in 15m timeframe. 71% accuracy with roughly a trade in a month.
There is no point giving brokers' the brokerages taking 10 trades a day and ending not-so-good EoD. Better lets take less trades with better result possibility. .
Mention
There are many people uses this variation of Bolling Band, 5EMA
Many people use RSI, trends and relative positioning of candles.
--> We are grateful to all of them. It's really difficult to mention everyone's name. But all people somehow influence the thought process. Thanks for all of them.
Statutory Disclaimer
There is no silver bullet / holy grail in trading. Nothing works 100% time. One has to be careful about the loss (s)he can bear in case of the trade goes against.
We, as the author of this script, is not responsible for any trading or position decision one is taken based on the outcome of this.
It is our sole discretion to change, add, delete the portion or withdraw the whole script without any prior notice or intimation.
In Indian Context : We are not SEBI registered, will never be SEBI registered.
Monthly Strategy Performance TableWhat Is This?
This script code adds a Monthly Strategy Performance Table to your Pine Script strategy scripts so you can see a month-by-month and year-by-year breakdown of your P&L as a percentage of your account balance.
The table is based on realized equity rather than open equity, so it only updates the metrics when a trade is closed.
That's why some numbers will not match the Strategy Tester metrics (such as max drawdown), as the Strategy Tester bases metrics like max drawdown on open trade equity and not realized equity (closed trades).
The script is still a work-in-progress, so make sure to read the disclaimer below. But I think it's ready to release the code for others to play around with.
How To Use It
The script code includes one of my strategies as an example strategy. You need to replace my strategy code with your own. To do that just copy the source code below into a blank script, delete lines 11 -> 60 and paste your strategy code in there instead of mine. The script should work with most systems, but make sure to read the disclaimer below.
It works best with a significant amount of historical data, so it may not work very effectively on intraday timeframes as there is a severe limitation of available bars on TradingView. I recommend using it on 4HR timeframes and above, as anything less will produce very little usable data. Having a premium TradingView plan will also help boost the number of available bars.
You can hover your mouse over a table cell to get more information in the form of tooltips (such as the Long and Short win rate if you hover over your total return cell).
Credit
The code in this script is based on open-source code originally written by QuantNomad, I've made significant changes and additions to the original script but all credit for the idea and especially the display table code goes to them - I just built on top of it:
Why Did I Make This?
None of this is trading or investment advice, just my personal opinion based on my experience as a trader and systems developer these past 6+ years:
The TradingView Strategy Tester is severely limited in some important ways. And unless you use complex Excel formulas on exported test data, you can't see a granular perspective of your system's historical performance.
There is much more to creating profitable and tradeable systems than developing a strategy with a good win rate and a good return with a reasonable drawdown.
Some additional questions we need to ask ourselves are:
What did the system's worst drawdown look like?
How long did it last?
How often do drawdowns occur, and how quickly are they typically recovered?
How often do we have a break-even or losing month or year?
What is our expected compounded annual growth rate, and how does that growth rate compare to our max drawdown?
And many more questions that are too long to list and take a lifetime of trading experience to answer.
Without answering these kinds of questions, we run the risk of developing systems that look good on paper, but when it comes to live trading, we are uncomfortable or incapable of enduring the system's granular characteristics.
This Monthly Performance Table script code is intended to help bridge some of that gap with the Strategy Tester's limited default performance data.
Disclaimer
I've done my best to ensure the numbers this code outputs are accurate, and according to my testing with my personal strategy scripts it appears to work fine. But there is always a good chance I've missed something, or that this code will not work with your particular system.
The majority of my TradingView systems are extremely simple single-target systems that operate on a closed-candle basis to minimize many of the data reliability issues with the Strategy Tester, so I was unable to do much testing with multiple targets and pyramiding etc.
I've included a Debug option in the script that will display important data and information on a label each time a trade is closed. I recommend using the Debug option to confirm that the numbers you see in the table are accurate and match what your strategy is actually doing.
Always do your own due diligence, verify all claims as best you can, and never take anyone's word for anything.
Take care, and best of luck with your trading :)
Kind regards,
Matt.
PS. If you're interested in learning how this script works, I have a free hour-long video lesson breaking down the source code - just check out the links below this script or in my profile.
BUY/SELL + ADVANCE DECLINEThis script is a custom trading view indicator that helps to identify potential buy and sell signals based on the RSI (Relative Strength Index) and SMA (Simple Moving Average) indicators. The script also identifies potential reversals using a combination of RSI and price action. It plots buy, sell, and reversal signals on the chart along with an SMA line. Additionally, it provides alerts based on the buy, sell, and reversal conditions.
Changes made to the original script:
Fixed the undeclared identifier 'c' error by calculating the difference between the current closing price and the previous closing price: c = close - close .
Added an "ADD Value Floating Label" to the chart. The label shows the difference between the current and previous closing prices (ADD value) along with a "Bullish" or "Bearish" indicator based on the value of 'c'. The label is positioned at the top right of the visible chart area and remains static.
Here's a summary of the major components of the script:
Input settings: Define the input parameters for RSI and SMA.
Calculation of RSI and SMA: Compute the RSI and SMA values based on the input parameters.
Color definitions: Define colors for different conditions and levels.
Condition definitions: Define various conditions for buy, sell, reversal, and other criteria.
Buy and sell conditions: Determine buy and sell signals based on RSI, SMA, and price action.
Reversal conditions: Identify potential reversals using RSI and price action.
Plot signals: Display buy, sell, and reversal signals on the chart.
Bar colors: Color the bars based on the identified signals.
Plot SMA: Display the SMA line on the chart.
Alert conditions: Set up alerts for buy, sell, and reversal conditions.
ADD Value Floating Label: Add a label to the chart showing the ADD value and a "Bullish" or "Bearish" indicator.
Divergences in 52 Week Moving Averages, Adjusted and SmoothedThis script description is intended to be holistic and comprehensive for the understanding of the interested parties who view the script.
Following the PineCoders suggestions, I have provided detailed breakdowns both within the code and in the description immediately below:
► Description
This description is intended to be detailed and meaningful, conveying the understanding of the script’s intention to the user:
The theory: Divergences and extreme readings in 52-Week highs on major indexes can provide a view into a potential pending move in the opposite direction of how the market has been trending. By comparing the 52-Week Hi/Lo indices and applying an Exponential Moving Average (EMA), we can assess how extreme a move is from the average. If the move provides an extreme reading, it would potentially be beneficial to “fade” the move (take a position in the opposing direction).
The intention: The intentionality of this script is to provide a visualization of when the highly-probable opportunity to fade over a multi-day or multi-week period arises. In addition to this, based on backtesting prior moves and reading the various levels of significant reversals, three tiers: “Standard”, “Sensitive”, and “Highly Sensitive” have been applied, the user can choose which sensitivity level they would like to see, there are far less false positives on the Standard and Sensitive settings, while Highly Sensitive often signals multiple times with the move coming a few days later.
The application: The settings allow the user to customize their sensitivity to the fade signals, with the ability to customize the visual that shows up as well. For higher-highs that are fade-worthy, the signal will appear on the top of the candle, for lower-lows that are fade-worthy, the signal will appear on the bottom of the candle. The users risk criteria should be the primary driver of the entry/exit, although when backtesting it appears that the significant move is typically completed within a 2-4 week period at max and 3-5 day period at minimum.
A personal note: I am a futures trader intraday but would very strongly caution users when using this strategy with futures (unless their risk tolerance is higher than most). The most beneficial strategy when fading moves would be to enter in tranches, starting at the first signal and adding on any pullback (as long as the pullback is not below the initial entry point). 1-6 Week Date-To-Expiry options would be the primary method for applying this strategy. I would also like to add that SPY/SPX options (SPDR S&P 500 ETF Trust / CBOE S&P 500 Index) are the most liquid options that could be applied in this strategy.
► Description (additional)
With the understanding that few users can read pinescript (Pine), the description above contains all of the necessary information that is necessary for a user to understand the intention for script utilization. For those who do understand Pine, the code is commented in each section in order to provide an understanding of the underlying functions, calculations, and thought process that went on during the writing of the script.
► Description (additional)
This script’s description contains no delegations, all aspects of the script as well as the initial idea behind it are contained in the description above, which is self-contained in it’s entirety with a clear and defined purpose that is written with the intent to holistically capture the intent of the potential use for this indicator.
► General House Rule #2
This script and the description (as well as my profile) contain no links or associations to promotion of any kind, I am not a business, I am not an individual that will in any way make money from this script or the promotion of another person, idea, company, entity, or legal persons (foreign or domestic).
► Originality and usefulness
This is an original and custom script (and idea) that is not a rehashing or a copy of any code from any other programmers in the tradingview community.
Crypto Uptrend Script + Pullback//Volume CandlesDescription: his is an adaption of my Pullback candle - This works on all timeframes and Markets (Forex//Stocks//)
Crypto Uptrend Script with Pullback Candle allows traders to get into a trend when the price is at end of a pullback and entering a balance phase in the market (works on all markets). The use of Moving averages to help identify a Trends and the use of Key levels to help traders be aware of where strong areas are in the market.
This script can work really well in Crypto Bull Runs when used on HTF and with confluences
The script has key support and resistance zones which are made up of quarterly data. Price reacts to these areas but patience is required as price will take time to come into these areas
I have updated the Pullback Candle with the use of Volume to filter out the weak Pullback Candles -
There are new candles to the script.
The First candle is the Bullish Volume Candle - This candle is set to a multiplier of 2x with a crossover of 50/100 on Volume - this then will paint a purple candle.
Uses of the Bullish Volume Candle:
Breakthrough of key areas // special chart patterns
Rejection of key areas
End of a impulse wave (Profit Takers)
The second candle is a Hammer - I prefer using the Hammers on Higher Timeframes however they do work on all timeframes. .
The third candle is a Exhaustion of impulse downward move.
Uses of this candle - can denote a new trend but has to be with confluence to a demand area // support area or with any use of technical analysis - using this alone is not advised
The fourth candle is a indecision candle in the shape of a Doji - this candle can help identify if the trend is in a continuation or a reversal
This script can work really well in Crypto Bull Runs
Disclaimer: There will be Pullbacks with High Volume (Breakouts) and not go the way as intended but this script is to allow traders to get into trends at good price levels. The script can paint signals in areas where price is too expensive so please do your own due diligence on the markets as this script is to help get into good areas of price
Please leave a thumbs up if you like this script and message me for information on how to use the script.
Weis V5 zigzag jayySomehow, I deleted version 5 of the zigzag script. Same name. I have added some older notes describing how the Weis Wave works.
I have also changed the date restriction that stopped the script from working after Dec 31, 2022.
What you see here is the Weis zigzag wave plotted directly on the price chart. This script is the companion to the Weis cumulative wave volume script.
What is a Weis wave? David Weis has been recognized as a Wyckoff method analyst he has written two books one of which, Trades About to Happen, describes the evolution of the now-popular Weis wave. The method employed by Weis is to identify waves of price action and to compare the strength of the waves on characteristics of wave strength. Chief among the characteristics of strength is the cumulative volume of the wave. There are other markers that Weis uses as well for example how the actual price difference between the start of the Weis wave from start to finish. Weis also uses time, particularly when using a Renko chart
David Weis did a futures io video which is a popular source of information about his method. (Search David Weis and futures.io. I strongly suggest you also read “Trades About to Happen” by David Weis.
This will get you up and running more quickly when studying charts. However, you should choose the Traditional method to be true to David Weis technique as described in his book "Trades About to Happen" and in the Futures IO Webcast featuring David Weis
. The Weis pip zigzag wave shows how far in terms of bar close price a Weis wave has traveled through the duration of a Weis wave. The Weis zigzag wave is used in combination with the Weis cumulative volume wave. The two waves should be set to the same "wave size".
To use this script, you must set the wave size: Using the traditional Weis method simply enter the desired wave size in the box "How should wave size be calculated", in this example I am using a traditional wave size of .25. Each wave for each security and each timeframe requires its own wave size. Although not the traditional method devised by David Weis a more automatic way to set wave size would be to use Average True Range (ATR). Using ATR is not the true Weis method but it does give you similar waves and, importantly, without the hassle described above. Once the Weis wave size is set then the zigzag wave will be shown with volume. Because Weis used the closing price of a wave to define waves a line Bar highs and bar lows are not captured by the Weis Wave. The default script setting is now cumulative volume waves using an ATR of 7 and a multiplication factor of .5.
To display volume in a way that does not crowd out neighbouring volumes Weis displayed volume as a maximum of 3 digits (usually). Consider two Weis Wave volumes 176,895,570 and 2,654,763,889. To display wave volume as three digits it is necessary to take a number such as 176,895,570 and truncate it. 176,895,570 can be represented as 177 X 10 to the power of 6. The number displayed must also be relative to other numbers in the field. If the highest volume on the page is: 2,654,763,889 and with only three numbers available to display the result the value shown must be 265 (265 X 10 to the power of 7). Since 176,895,570 is an order of magnitude smaller than 2,654,763,889 therefore 175,895,570 must be shown as 18 instead of 177. In this way, the relative magnitudes of the two volumes can be understood. All numbers in the field of view must be truncated by the same order of magnitude to make the relative volumes understandable. The script attempts to calculate the order of magnitude value automatically. If you see a red number in the field of view it means the script has failed to do the calculation automatically and you should use the manual method – use the dialogue box “Calculate truncated wave value automatically or manually”. Scroll down from the automatic method and select manual. Once "manual" is selected the values displayed become the power values or multipliers for each wave.
Using the manual method you will select a “Multiplier” in the next dialogue box. Scan the field and select the largest value in the field of view (visible chart) is the multiplier of interest. If you select a lower number than the maximum value will see at least one red “up”. If you are too high you will see at least one red “down”. Scroll in the direction recommended or the values on the screen will be totally incorrect. With volume truncated to the highest order values, the eye can quickly get a feel for relative volumes. It also reduces the crowding and overlapping of values on the screen. You can opt to show the full volume to help get a sense of the magnitude of the true volumes.
How does the script determine if a Weis wave is continuing to grow or not?
The script evaluates the closing price of each new bar relative to the "Weis wave size". Suppose the current bar closes at a new low close, within the current down wave, at $30.00. If the Weis wave size is $0.10 then the algorithm will remember the $30.00 close and compare it to the close of the next bar. If the bar close price does not close equal to or lower than $30.00 or close equal to or higher than $30.10 then the wave is still a down wave with a current low of $30.00. This is true even if the bar low is less than $30.00 or the bar high is greater than 30.10 – only the bar’s closing price matters. If a bar's closing price climbs back up to a close of $30.11 then because the closing price has moved more than $0.10 (the Weis wave size) then that is a wave reversal with a new up-trending wave. In the above example if there was currently a downward trending wave and the bar closes were as follows $30.00, $30.09, $30.01, $30.05, $30.10 The wave direction would continue to stay downward trending until the close of $30.10 was achieved. As such $30.00 would be the low and the following closes $30.09, $30.01, $30.05 would be allocated to the new upward-trending wave. If however There was a series of bar closes like this $30.00, $30.09, $30.01, $30.05, $29.99 since none of the closes was equal to above the 10-cent reversal target of $30.10 but instead, a new Weis wave low was achieved ($29.99). As such the closes of $30.09, $30.01, $30.05 would all be attributed to the continued down-trending wave with a current low of $29.99, even though the closing price for the interim bars was above $30.00. Now that the Weis Wave low is now 429.99 then, in order to reverse this continued downtrend price will need to close at or above $30.09 on subsequent bar closes assuming now new low bar close is achieved. With large wave sizes, wave direction can be in limbo for many bars before a close either renews wave direction or reverses it and confirms wave direction as either a reversal or a continuation. On the zig-zag, a wave line and its volume will not be "printed" until a wave reversal is confirmed.
The wave attribution is similar when using other methods to define wave size. If ATR is used for wave size instead of a traditional wave constant size such as $0.10 or $2 or 2000 pips or ... then the wave size is calculated based on current ATR instead of the Weis wave constant (Traditional selected value).
I have the option to display pseudo-Ord volume. In truth, Ord used more traditional zig-zag pivots of bar highs and lows. Waves using closes as pivots can have some significant differences. This difference can be lessened by using smaller time frames and larger wave sizes.
There are other options such to display the delta price or pip size of a Weis Wave, the number of bars in a wave, and a few other options.
Buy/Sell on the levelsThis script is generally
My describe is:
There are a lot of levels we would like to buy some crypto.
When the price has crossed the level-line - we buy, but only if we have the permission in array(2)
When we have bought the crypto - we lose the permission for buy for now(till we will sell it on the next higher level)
When we sell some crypto(on the buying level + 1) we have the permission again.
There also are 2 protect indicators. We can buy if these indicators both green only(super trend and PIVOT )
Jun 12
Release Notes: Hello there,
Uncomment this section before use for real trade:
if array.get(price_to_sellBue, i) >= open and array.get(price_to_sellBue, i) <= close// and
//direction < 0 and permission_for_buy != 0
Here is my script.
In general - this is incredible simple script to use and understand.
First of all You can see this script working with only long orders, it means we going to get money if crypto grows only. Short orders we need to close the position on time.
In this script we buy crypto and sell with step 1% upper.
You can simply change the step by changing the price arrays.
Please note, if You want to see where the levels of this script is You Have to copy the next my indicator called LEVEL 1%
In general - if the price has across the price-level we buy some crypto and loose permission for buying for this level till we sell some crypto. There is ''count_of_orders" array field with value 2. When we bought some crypto the value turns to 0. 0 means not allowed to by on this level!!! The script buy if the bar is green only(last tick).
The script check every level(those we can see in "price_to_sellBue" array).
If the price across one of them - full script runs. After buying(if it possible) we check is there any crypto for sell on the level.
We check all levels below actual level( of actual level - ''i'' than we check all levels from 0 to i-1).
If there is any order that has value 0 in count of orders and index <= i-1 - we count it to var SELL amount and in the end of loop sell all of it.
Pay attention - it sells only if price across the level with red bar AND HAS ORDERS TO SELL WHICH WAS BOUGHT BELOW!!!
In Strategy tester it shows not-profitables orders sometimes, because if You have old Long position - it sells it first. First in - first out.
If the price goes down for a long time and You sell after 5 buys You sell the first of it with the highest value.
There is 2 protection from horrible buying in this strategy. The first one - Supertrend. If the supertrend is red - there is no permission for buy.
The second one - something between PIVOT and supertrend but with switcher.
If the price across last minimum - switcher is red - no permission for buy and the actual price becomes last minimum . The last maximum calculated for last 100 bars.
When the price across last maximum - switcher is green, we can buy. The last minimum calculation for last 100 bars, last maximum is actual price.
This two protections will save You from buying if price get crash down.
Enjoy my script.
Should You need the code or explanation, You have any ideas how to improve this crypt, contact me.
Vladyslav.
Jun 12
Release Notes: Here has been uncommented the protection for buy in case of price get down.
5 hours ago
Release Notes: Changed rages up to actual price to make it work
