Alpha Fractal BandsWilliams fractals are remarkable support and resistance levels used by many traders. However, it can sometimes be challenging to use them frequently and get confirmation from other oscillators and indicators. With the new "Alpha Fractal Bands", a unique blend of Williams Fractals and Bollinger Bands emerges, offering a fresh perspective. Extremes can be utilized as price reversals or for taking profits. I look forward to hearing your thoughts. Best regards... Happy trading!
An easy solution for long positions is to:
Identify a bullish trend or a potential entry point for a long position.
Set a stop-loss order to limit potential losses if the trade goes against you.
Determine a target price or take-profit level to lock in profits.
Consider using technical indicators or analysis tools to confirm the strength of the bullish trend.
Regularly monitor the trade and make necessary adjustments based on market conditions.
An easy solution for short positions could be to follow these steps:
Identify a bearish trend or a potential entry point for a short position.
Set a stop-loss order to limit potential losses if the trade goes against you.
Determine a target price or take-profit level to lock in profits.
Consider using technical indicators or analysis tools to confirm the strength of the bearish trend.
Regularly monitor the trade and make necessary adjustments based on market conditions.
Remember, it's important to conduct thorough research and analysis before entering any trade and to manage your risk effectively.
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Wyckoff Range StrategyThe Wyckoff Range Strategy is a trading strategy that aims to identify potential accumulation and distribution phases in the market using the principles of Wyckoff analysis. It also incorporates the detection of spring and upthrust patterns.
Here's a step-by-step explanation of how to use this strategy:
Understanding Accumulation and Distribution Phases:
Accumulation Phase: This is a period where smart money (large institutional traders) accumulates a particular asset at lower prices. It is characterized by a sideways or consolidating price action.
Distribution Phase: This is a period where smart money distributes or sells a particular asset at higher prices. It is also characterized by a sideways or consolidating price action.
Input Variables:
crossOverLength: This variable determines the length of the moving average crossover used to identify accumulation and distribution phases. You can adjust this value based on the market you are trading and the time frame you are analyzing.
stopPercentage: This variable determines the percentage used to calculate the stop loss level. It helps you define a predefined level at which you would exit a trade if the price moves against your position.
Strategy Conditions:
Enter Long: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength and a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the start of an accumulation phase and a potential buying opportunity.
Exit Long: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength or a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the end of an accumulation phase and a potential exit signal for long positions.
Enter Short: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength and a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the start of a distribution phase and a potential selling opportunity.
Exit Short: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength or a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the end of a distribution phase and a potential exit signal for short positions.
Stop Loss:
The strategy sets a stop loss level for both long and short positions. The stop loss level is calculated based on the stopPercentage variable, which represents the percentage of the current close price. If the price reaches the stop loss level, the strategy will automatically exit the position.
Plotting Wyckoff Schematics:
The strategy plots different shapes on the chart to indicate the identified phases and patterns. Green and red labels indicate the accumulation and distribution phases, respectively. Blue triangles indicate spring patterns, and orange triangles indicate upthrust patterns.
To use this strategy, you can follow these steps:
Jim Forte — Anatomy of a Trading Range
robertbrain.com/Bull...+a+Trading+Range.pdf
Inside candle (Inside Bar) Strategy- by smartanuThe Inside Candle strategy is a popular price action trading strategy that can be used to trade in a variety of markets. Here's how you can trade the Inside Candle strategy using the Pine script code provided:
1. Identify an Inside Candle: Look for a candlestick pattern where the current candle is completely engulfed within the previous candle's high and low. This is known as an Inside Candle.
2. Enter a Long Position: If an Inside Candle is identified, enter a long position at the open of the next candle using the Pine script code provided.
3. Set Stop Loss and Take Profit: Set a stop loss at a reasonable level to limit your potential losses if the trade goes against you. Set a take profit at a reasonable level to take profit when the price reaches the desired level.
4. Manage the Trade: Monitor the trade closely and adjust the stop loss and take profit levels if necessary. You can use the Pine script code to automatically exit the trade when the stop loss or take profit level is hit.
5. Exit the Trade: Exit the trade when the price reaches the take profit level or the stop loss level is hit.
It's important to note that the Inside Candle strategy is just one of many strategies that traders use to trade the markets. It's important to perform your own analysis and use additional indicators before making any trades. Additionally, it's important to practice proper risk management techniques and never risk more than you can afford to lose.
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.
Antares_messages_publicLibrary "Antares_messages_public"
This library add messages for yours strategy for use in Antares trading system for binance and bybit exchanges.
Данная библиотека позволяет формировать сообщения в алертах стратегий для Antares в более упрощенном для пользователя режиме, включая всплывающие подсказки и т.д.
set_leverage(token, market, ticker_id, leverage)
Set leverage for ticker on specified market.
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
leverage (float) : (float) leverage level. Устанавливаемое плечо.
Returns: 'Set leverage message'.
pause(time_pause)
Set pause in message. '::' -left and '::' -right included.
Parameters:
time_pause (int)
LongLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Buy order with limit price and quantity.
Лимитный ордер на покупку(в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order'. Лимитный ордер на покупку (лонг).
LongMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Market Buy order with quantity.
Рыночный ордер на покупку (в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Buy order'. Маркетный ордер на покупку (лонг).
ShortLimit(token, market, ticker_id, type_qty, quantity, price, leverageforqty, orderId)
Sell order with limit price and quantity.
Лимитный ордер на продажу(в шорт).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
Returns: 'Limit Sell order'. Лимитный ордер на продажу (шорт).
ShortMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Sell by market price and quantity.
Рыночный ордер на продажу(в шорт).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Sell order'. Маркетный ордер на продажу (шорт).
Cancel_by_ticker(token, market, ticker_id)
Cancel all orders for market and ticker in setups. Отменяет все ордера на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
Returns: 'Cancel all orders'. Отмена всех ордеров на заданной бирже и заданном токене(паре).
Cancel_by_id(token, market, ticker_id, orderId)
Cancel order by Id for market and ticker in setups. Отменяет ордер по Id на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
orderId (string)
Returns: 'Cancel order'. Отмена ордера по Id на заданной бирже и заданном токене(паре).
Close_positions(token, market, ticker_id)
Close all positions for market and ticker in setups. Закрывает все позиции на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
Returns: 'Close positions'
CloseLongLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Close limit order for long position. (futures)
Лимитный ордер на продажу(в шорт) для закрытия лонговой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Sell order reduce only (close long position)'. Лимитный ордер на продажу для снижения текущего лонга(в шорт не входит).
CloseLongMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Close market order for long position.
Рыночный ордер на продажу(в шорт) для закрытия лонговой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Sell order reduce only (close long position)'. Ордер на снижение/закрытие текущего лонга(в шорт не входит) по рыночной цене.
CloseShortLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Close limit order for short position.
Лимитный ордер на покупку(в лонг) для закрытия шортовой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order reduce only (close short position)' . Лимитный ордер на покупку (лонг) для сокращения/закрытия текущего шорта.
CloseShortMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Set Close limit order for long position.
Рыночный ордер на покупку(в лонг) для сокращения/закрытия шортовой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Buy order reduce only (close short position)'. Маркетного ордера на покупку (лонг) для сокращения/закрытия текущего шорта.
cancel_all_close(token, market, ticker_id)
Parameters:
token (string)
market (string)
ticker_id (string)
limit_tpsl_bybitfu(token, ticker_id, order_id, side, type_qty, quantity, price, tp_price, sl_price, leverageforqty)
Set multi order for Bybit : limit + takeprofit + stoploss
Выставление тройного ордера на Bybit лимитка со стоплоссом и тейкпрофитом
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
order_id (string)
side (bool) : (bool) "buy side" if true or "sell side" if false. true для лонга, false для шорта.
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order by 'side'. Цена лимитного ордера
tp_price (float) : (float) price for take profit order.
sl_price (float) : (float) price for stoploss order
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: Set multi order for Bybit : limit + takeprofit + stoploss.
replace_limit_tpsl_bybitfu(token, ticker_id, order_id, side, type_qty, quantity, price, tp_price, sl_price, leverageforqty)
Change multi order for Bybit : limit + takeprofit + stoploss
Изменение тройного ордера на Bybit лимитка со стоплоссом и тейкпрофитом
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
order_id (string)
side (bool) : (bool) "buy side" if true or "sell side" if false. true для лонга, false для шорта.
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order by 'side'. Цена лимитного ордера
tp_price (float) : (float) price for take profit order.
sl_price (float) : (float) price for stoploss order
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: Set multi order for Bybit : limit + takeprofit + stoploss.
long_stop(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Stop market order for long position
Рыночный стоп-ордер на продажу для закрытия лонговой позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Stop Market Sell order (close long position)'. Маркетный стоп-ордер на снижения/закрытия текущего лонга.
short_stop(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Stop market order for short position
Рыночный стоп-ордер на покупку(в лонг) для закрытия шорт позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Stop Market Buy order (close short position)'. Маркетный стоп-ордер на снижения/закрытия текущего шорта.
change_stop_l(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Change Stop market order for long position
Изменяем стоп-ордер на продажу(в шорт) для закрытия лонг позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Change Stop Market Buy order (close long position)'. Смещает цену активации Маркетного стоп-ордер на снижения/закрытия текущего лонга.
change_stop_s(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Change Stop market order for short position
Смещает цену активации Рыночного стоп-ордера на покупку(в лонг) для закрытия шорт позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string)
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Change Stop Market Buy order (close short position)'. Смещает цену активации Маркетного стоп-ордер на снижения/закрытия текущего шорта.
open_long_position(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Cancel and close all orders and positions by ticker , then open Long position by market price with stop order
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает лонг по маркету с выставлением стопа (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'command_all_close + LongMarket + long_stop.
open_short_position(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Cancel and close all orders and positions , then open Short position by market price with stop order
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает шорт по маркету с выставлением стопа(переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'command_all_close + ShortMarket + short_stop'.
open_long_trade(token, market, ticker_id, type_qty, quantity, l_stop, qty_ex1, price_ex1, qty_ex2, price_ex2, qty_ex3, price_ex3, leverageforqty)
Cancell and close all orders and positions , then open Long position by market price with stop order and take 1 ,take 2, take 3
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает лонг по маркету с выставлением стопа и 3 тейками (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) enter order size, see at type_qty. Размер ордера входа, согласно type_qty.
l_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
qty_ex1 (float) : (float). Quantity for 1th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 1го тейка, согласно type_qty.. Если 0, то строка для этого тейка не формируется
price_ex1 (float) : (float). Price for 1th take , if = 0 string for order dont set. Цена лимитного ордера для 1го тейка. Если 0, то строка для этого тейка не формируется
qty_ex2 (float) : (float). Quantity for 2th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex2 (float) : (float). Price for 2th take, if = 0 string for order dont set. Цена лимитного ордера для 2го тейка. Если 0, то строка для этого тейка не формируется
qty_ex3 (float) : (float). Quantity for 3th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex3 (float) : (float). Price for 3th take, if = 0 string for order dont set. Цена лимитного ордера для 3го тейка. Если 0, то строка для этого тейка не формируется
leverageforqty (int)
Returns: 'cancel_all_close + LongMarket + long_stop + CloseLongLimit1 + CloseLongLimit2+CloseLongLimit3'.
open_short_trade(token, market, ticker_id, type_qty, quantity, s_stop, qty_ex1, price_ex1, qty_ex2, price_ex2, qty_ex3, price_ex3, leverageforqty)
Cancell and close all orders and positions , then open Short position by market price with stop order and take 1 and take 2
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает шорт по маркету с выставлением стопа и 3 тейками (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string)
quantity (float)
s_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
qty_ex1 (float) : (float). Quantity for 1th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 1го тейка, согласно type_qty.. Если 0, то строка для этого тейка не формируется
price_ex1 (float) : (float). Price for 1th take , if = 0 string for order dont set. Цена лимитного ордера для 1го тейка. Если 0, то строка для этого тейка не формируется
qty_ex2 (float) : (float). Quantity for 2th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex2 (float) : (float). Price for 2th take, if = 0 string for order dont set. Цена лимитного ордера для 2го тейка. Если 0, то строка для этого тейка не формируется
qty_ex3 (float) : (float). Quantity for 3th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex3 (float) : (float). Price for 3th take, if = 0 string for order dont set. Цена лимитного ордера для 3го тейка. Если 0, то строка для этого тейка не формируется
leverageforqty (int)
Returns: 'command_all_close + ShortMarket + short_stop + CloseShortLimit + CloseShortLimit(2)'.
Multi_LongLimit(token, market, ticker_id, type_qty, qty1, price1, qty2, price2, qty3, price3, qty4, price4, qty5, price5, qty6, price6, qty7, price7, qty8, price8, leverageforqty)
8 or less Buy orders with limit price and quantity.
До 8 Лимитных ордеров на покупку(в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
qty1 (float)
price1 (float)
qty2 (float)
price2 (float)
qty3 (float)
price3 (float)
qty4 (float)
price4 (float)
qty5 (float)
price5 (float)
qty6 (float)
price6 (float)
qty7 (float)
price7 (float)
qty8 (float)
price8 (float)
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order'. Лимитный ордер на покупку (лонг).
Advanced VWAP_Pullback Strategy_Trend-Template QualifierGeneral Description and Unique Features of this Script
Introducing the Advanced VWAP Momentum-Pullback Strategy (long-only) that offers several unique features:
1. Our script/strategy utilizes Mark Minervini's Trend-Template as a qualifier for identifying stocks and other financial securities in confirmed uptrends. Mark Minervini, a 2x US Investment Champion, developed the Trend-Template, which covers eight different and independent characteristics that can be adjusted and optimized in this trend-following strategy to ensure the best results. The strategy will only trigger buy-signals in case the optimized qualifiers are being met.
2. Our strategy is based on the supply/demand balance in the market, making it timeless and effective across all timeframes. Whether you are day trading using 1- or 5-min charts or swing-trading using daily charts, this strategy can be applied and works very well.
3. We have also integrated technical indicators such as the RSI and the MA / VWAP crossover into this strategy to identify low-risk pullback entries in the context of confirmed uptrends. By doing so, the risk profile of this strategy and drawdowns are being reduced to an absolute minimum.
Minervini’s Trend-Template and the ‘Stage-Analysis’ of the Markets
This strategy is a so-called 'long-only' strategy. This means that we only take long positions, short positions are not considered.
The best market environment for such strategies are periods of stable upward trends in the so-called stage 2 - uptrend.
In stable upward trends, we increase our market exposure and risk.
In sideways markets and downward trends or bear markets, we reduce our exposure very quickly or go 100% to cash and wait for the markets to recover and improve. This allows us to avoid major losses and drawdowns.
This simple rule gives us a significant advantage over most undisciplined traders and amateurs!
'The Trend is your Friend'. This is a very old but true quote.
What's behind it???
• 98% of stocks made their biggest gains in a Phase 2 upward trend.
• If a stock is in a stable uptrend, this is evidence that larger institutions are buying the stock sustainably.
• By focusing on stocks that are in a stable uptrend, the chances of profit are significantly increased.
• In a stable uptrend, investors know exactly what to expect from further price developments. This makes it possible to locate low-risk entry points.
The goal is not to buy at the lowest price – the goal is to buy at the right price!
Each stock goes through the same maturity cycle – it starts at stage 1 and ends at stage 4
Stage 1 – Neglect Phase – Consolidation
Stage 2 – Progressive Phase – Accumulation
Stage 3 – Topping Phase – Distribution
Stage 4 – Downtrend – Capitulation
This strategy focuses on identifying stocks in confirmed stage 2 uptrends. This in itself gives us an advantage over long-term investors and less professional traders.
By focusing on stocks in a stage 2 uptrend, we avoid losses in downtrends (stage 4) or less profitable consolidation phases (stages 1 and 3). We are fully invested and put our money to work for us, and we are fully invested when stocks are in their stage 2 uptrends.
But how can we use technical chart analysis to find stocks that are in a stable stage 2 uptrend?
Mark Minervini has developed the so-called 'trend template' for this purpose. This is an essential part of our JS-TechTrading pullback strategy. For our watchlists, only those individual values that meet the tough requirements of Minervini's trend template are eligible.
The Trend Template
• 200d MA increasing over a period of at least 1 month, better 4-5 months or longer
• 150d MA above 200d MA
• 50d MA above 150d MA and 200d MA
• Course above 50d MA, 150d MA and 200d MA
• Ideally, the 50d MA is increasing over at least 1 month
• Price at least 25% above the 52w low
• Price within 25% of 52w high
• High relative strength according to IBD.
NOTE: In this basic version of the script, the Trend-Template has to be used as a separate indicator on TradingView (Public Trend-Template indicators are available in TradingView – community scripts). It is recommended to only execute buy signals in case the stock or financial security is in a stage 2 uptrend, which means that the criteria of the trend-template are fulfilled.
This strategy can be applied to all timeframes from 5 min to daily.
The VWAP Momentum-Pullback Strategy
For the JS-TechTrading VWAP Momentum-Pullback Strategy, only stocks and other financial instruments that meet the selected criteria of Mark Minervini's trend template are recommended for algorithmic trading with this startegy.
A further prerequisite for generating a buy signals is that the individual value is in a short-term oversold state (RSI).
When the selling pressure is over and the continuation of the uptrend can be confirmed by the MA / VWAP crossover after reaching a price low, a buy signal is issued by this strategy.
Stop-loss limits and profit targets can be set variably. You also have the option to make use of the trailing stop exit strategy.
Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a technical indicator developed by Welles Wilder in 1978. The RSI is used to perform a market value analysis and identify the strength of a trend as well as overbought and oversold conditions. The indicator is calculated on a scale from 0 to 100 and shows how much an asset has risen or fallen relative to its own price in recent periods.
The RSI is calculated as the ratio of average profits to average losses over a certain period of time. A high value of the RSI indicates an overbought situation, while a low value indicates an oversold situation. Typically, a value > 70 is considered an overbought threshold and a value < 30 is considered an oversold threshold. A value above 70 signals that a single value may be overvalued and a decrease in price is likely , while a value below 30 signals that a single value may be undervalued and an increase in price is likely.
For example, let's say you're watching a stock XYZ. After a prolonged falling movement, the RSI value of this stock has fallen to 26. This means that the stock is oversold and that it is time for a potential recovery. Therefore, a trader might decide to buy this stock in the hope that it will rise again soon.
The MA / VWAP Crossover Trading Strategy
This strategy combines two popular technical indicators: the Moving Average (MA) and the Volume Weighted Average Price (VWAP). The MA VWAP crossover strategy is used to identify potential trend reversals and entry/exit points in the market.
The VWAP is calculated by taking the average price of an asset for a given period, weighted by the volume traded at each price level. The MA, on the other hand, is calculated by taking the average price of an asset over a specified number of periods. When the MA crosses above the VWAP, it suggests that buying pressure is increasing, and it may be a good time to enter a long position. When the MA crosses below the VWAP, it suggests that selling pressure is increasing, and it may be a good time to exit a long position or enter a short position.
Traders typically use the MA VWAP crossover strategy in conjunction with other technical indicators and fundamental analysis to make more informed trading decisions. As with any trading strategy, it is important to carefully consider the risks and potential rewards before making any trades.
This strategy is applicable to all timeframes and the relevant parameters for the underlying indicators (RSI and MA/VWAP) can be adjusted and optimized as needed.
Backtesting
Backtesting gives outstanding results on all timeframes and drawdowns can be reduced to a minimum level. In this example, the hourly chart for MCFT has been used.
Settings for backtesting are:
- Period from Jan 2020 until March 2023
- Starting capital 100k USD
- Position size = 25% of equity
- 0.01% commission = USD 2.50.- per Trade
- Slippage = 2 ticks
Other comments
- This strategy has been designed to identify the most promising, highest probability entries and trades for each stock or other financial security.
- The combination of the Trend-Template and the RSI qualifiers results in a highly selective strategy which only considers the most promising swing-trading entries. As a result, you will normally only find a low number of trades for each stock or other financial security per year in case you apply this strategy for the daily charts. Shorter timeframes will result in a higher number of trades / year.
- Consequently, traders need to apply this strategy for a full watchlist rather than just one financial security.
Open Interest Profile (OI)- By LeviathanThis script implements the concept of Open Interest Profile, which can help you analyze the activity of traders and identify the price levels where they are opening/closing their positions. This data can serve as a confluence for finding the areas of support and resistance , targets and placing stop losses. OI profiles can be viewed in the ranges of days, weeks, months, Tokyo sessions, London sessions and New York sessions.
A short introduction to Open Interest
Open Interest is a metric that measures the total amount of open derivatives contracts in a specific market at a given time. A valid contract is formed by both a buyer who opens a long position and a seller who opens a short position. This means that OI represents the total value of all open longs and all open shorts, divided by two. For example, if Open Interest is showing a value of $1B, it means that there is $1B worth of long and $1B worth of short contracts currently open/unsettled in a given market.
OI increasing = new long and short contracts are entering the market
OI decreasing = long and short contracts are exiting the market
OI unchanged = the net amount of positions remains the same (no new entries/exits or just a transfer of contracts occurring)
About this indicator
*This script is basically a modified version of my previous "Market Sessions and Volume Profile by @LeviathanCapital" indicator but this time, profiles are generated from Tradingview Open Interest data instead of volume (+ some other changes).
The usual representation of OI shows Open Interest value and its change based on time (for a particular day, time frame or each given candle). This indicator takes the data and plots it in a way where you can see the OI activity (change in OI) based on price levels. To put it simply, instead of observing WHEN (time) positions are entering/exiting the market, you can now see WHERE (price) positions are entering/exiting the market. This is the same concept as when it comes to Volume and Volume profile and therefore, similar strategies and ways of understanding the given data can be applied here. You can even combine the two to gain an edge (eg. high OI increase + Volume Profile showing dominant market selling = possible aggressive shorts taking place)
Green nodes = OI increase
Red nodes = OI decrease
A cluster of large green nodes can be used for support and resistance levels (*trapped traders theory) or targets (lots of liquidations and stop losses above/below), OI Profile gaps can present an objective for the price to fill them (liquidity gaps, imbalances, inefficiencies, etc), and more.
Indicator settings
1. Session/Lookback - Choose the range from where the OI Profile will be generated
2. OI Profile Mode - Mode 1 (shows only OI increase), Mode 2 (shows both OI increase and decrease), Mode 3 (shows OI decrease on left side and OI increase on the right side).
3. Show OI Value Area - Shows the area where most OI activity took place (useful as a range or S/R level )
4. Show Session Box - Shows the box around chosen sessions/lookback
5. Show Profile - Show/hide OI Profile
6. Show Current Session - Show/hide the ongoing session
7. Show Session Labels - Show/hide the text labels for each session
8. Resolution - The higher the value, the more refined a profile is, but fewer profiles are shown on the chart
9. OI Value Area % - Choose the percentage of VA (same as in Volume Profile's VA)
10. Smooth OI Data - Useful for assets that have very large spikes in OI over large bars, helps create better profiles
11. OI Increase - Pick the color of OI increase nodes in the profile
12. OI Decrease - Pick the color of OI decrease nodes in the profile
13. Value Area Box - Pick the color of the Value Area Box
14. Session Box Thickness - Pick the thickness of the lines surrounding the chosen sessions
Advice
The indicator calculates the profile based on candles - the more candles you can show, the better profile will be formed. This means that it's best to view most sessions on timeframes like 15min or lower. The only exception is the Monthly profile, where timeframes above 15min should be used. Just take a few minutes and switch between timeframes and sessions and you will figure out the optimal settings.
This is the first version of Open Interest Profile script so please understand that it will be improved in future updates.
Thank you for your support.
** Some profile generation elements are inspired by @LonesomeTheBlue's volume profile script
Ultimate Strategy Template (Advanced Edition)Hello traders
This script is an upgraded version of that one below
New features
- Upgraded to Pinescript version 5
- Added the exit SL/TP now in real-time
- Added text fields for the alerts - easier to send the commands to your trading bots
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
For doing so:
1) Find in your indicator where are the conditions printing the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator wether it's a MACD , ZigZag , Pivots , higher-highs, lower-lows or whatever indicator with clear buy and sell conditions.
//@version=5
indicator(title='Moving Average Cross', shorttitle='Moving Average Cross', overlay=true, precision=6, max_labels_count=500, max_lines_count=500)
type_ma1 = input.string(title='MA1 type', defval='SMA', options= )
length_ma1 = input(10, title=' MA1 length')
type_ma2 = input.string(title='MA2 type', defval='SMA', options= )
length_ma2 = input(100, title=' MA2 length')
// MA
f_ma(smoothing, src, length) =>
rma_1 = ta.rma(src, length)
sma_1 = ta.sma(src, length)
ema_1 = ta.ema(src, length)
iff_1 = smoothing == 'EMA' ? ema_1 : src
iff_2 = smoothing == 'SMA' ? sma_1 : iff_1
smoothing == 'RMA' ? rma_1 : iff_2
MA1 = f_ma(type_ma1, close, length_ma1)
MA2 = f_ma(type_ma2, close, length_ma2)
// buy and sell conditions
buy = ta.crossover(MA1, MA2)
sell = ta.crossunder(MA1, MA2)
plot(MA1, color=color.new(color.green, 0), title='Plot MA1', linewidth=3)
plot(MA2, color=color.new(color.red, 0), title='Plot MA2', linewidth=3)
plotshape(buy, title='LONG SIGNAL', style=shape.circle, location=location.belowbar, color=color.new(color.green, 0), size=size.normal)
plotshape(sell, title='SHORT SIGNAL', style=shape.circle, location=location.abovebar, color=color.new(color.red, 0), size=size.normal)
/////////////////////////// SIGNAL FOR STRATEGY /////////////////////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title='🔌Connector🔌', display = display.data_window)
Basically, I identified my buy, sell conditions in the code and added this at the bottom of my indicator code
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal, and -1 for the bearish signal
Now you can connect your indicator to the Strategy Template using the method below or that one
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings and in the Data Source field select your 🔌Connector🔌 (which comes from your indicator)
From then, you should start seeing the signals and plenty of other stuff on your chart
🔥 Note that whenever you'll update your indicator values, the strategy statistics and visual on your chart will update in real-time
Settings
- Color Candles: Color the candles based on the trade state ( bullish , bearish , neutral)
- Close positions at market at the end of each session: useful for everything but cryptocurrencies
- Session time ranges: Take the signals from a starting time to an ending time
- Close Direction: Choose to close only the longs, shorts, or both
- Date Filter: Take the signals from a starting date to an ending date
- Set the maximum losing streak length with an input
- Set the maximum winning streak length with an input
- Set the maximum consecutive days with a loss
- Set the maximum drawdown (in % of strategy equity)
- Set the maximum intraday loss in percentage
- Limit the number of trades per day
- Limit the number of trades per week
- Stop-loss: None or Percentage or Trailing Stop Percentage or ATR - I'll add shortly multiple options for the trailing stop loss
- Take-Profit: None or Percentage or ATR - I'll add also a trailing take profit
- Risk-Reward based on ATR multiple for the Stop-Loss and Take-Profit
Special Thanks
Special thanks to @JosKodify as I borrowed a few risk management snippets from his website: kodify.net
Best
Dave
BB Signal v2.1 [ABA Invest]About
This signal appears based on 2nd candle break out of Bollinger Bands (called Momentum) with additional EMA 50 and EMA 200 as trend filters. so the concept is to take advantage of candle breakout by following trends.
How to use
Buy: When signal 'Buy' appears (following trend of upper timeframe)
Recommended stop loss: previous swing low
Sell: When signal 'Sell' appears (following trend of upper timeframe)
Recommended stop loss: previous swing high
Rules
1. use a good risk-reward ratio (minimum 1.5)
2. Please do backtest before using this signal
3. Don't always take every signal (must know when to stop)
Take Profit On Trend v2 (by BHD_Trade_Bot)The purpose of strategy is to detect long-term uptrend and short-term downtrend so that you can easy to take profit.
The strategy also using BHD unit to detect how big you win and lose, so that you can use this strategy for all coins without worry about it have different percentage of price change.
ENTRY
The buy order is placed on assets that have long-term uptrend and short-term downtrend:
- Long-term uptrend condition: ema200 is going up
- Short-term downtrend condition: 2 last candles are down price (use candlestick for less delay)
CLOSE
The sell order is placed when take profit or stop loss:
- Take profit: price increase 2 BHD unit
- Stop loss: price decrease 3 BHD unit
The strategy use $1000 for initial capital and trading fee is 0.1% for each order.
Pro tip: The 1-hour time frame for ETH/USDT has the best results on average.
CHN BUY SELLCHN BUY SELL is formed from two RSI indicators, those are RSI 14 and RSI 7 . I use RSI 14 to determine the trend and RSI 7 to find entry points.
+ Long (BUY) Signal:
- RSI 14 will give a "BUY" signal, then RSI 7 will give entry point to LONG when the candle turns yellow.
+ Short (SELL) Signal:
- RSI 14 will give a "EXIT" signal, then RSI 7 will give entry point to SHORT when the candle turns purple.
+ About Take Profit and Stop Loss:
- With Gold, I usually set Stop Loss and Take Profit at 50 pips
- With currency pairs, I usually keep my Stop Loss and Take Profit at 30 pips
- With crypto, I usually keep Stop Loss and Take Profit at 1.5%
Recommended to use in time frame M15 and above .
This method can be used to trade Forex, Gold and Crypto.
My idea is formed on the view that when the price is moving strongly, the RSI 14 will tell us what the current trend is through a "BUY" or "EXIT" signal. When RSI 14 reaches the oversold area it will form a "BUY" signal and when it reaches the overbought area it will give an "EXIT" signal. I believe that when the price reaches the oversold or overbought area, the price momentum has also decreased and is about to reverse.
After receiving a signal from RSI 14, my job is to wait for an Entry signal from RSI 7. When RSI 7 reaches the overbought area, a yellow candle will appear and that's when we enter a LONG order. When the RSI 7 reaches the oversold area, a purple candle will appear and that's when we enter a SHORT order.
Optimised RSI strategy for Reversals (by Coinrule)The most common way to use the RSI to spot a good buy opportunity is to check for values lower than 30. Unfortunately, the RSI can remain in oversold territory for long periods, and that could leave you trapped in a trade in loss. It would be appropriate to wait for a confirmation of the trend reversal.
In the example above I use a short-term Moving Average (in this case, the MA9) coupled with an RSI lower than 40. This combination of events is relatively rare as reversal confirmations usually come when RSI values are already higher. As unusual as this setup is, it provides buy-opportunities with much higher chances of success.
The parameters of this strategy would be:
ENTRY: RSI lower than 40 and MA9 lower than the price
TAKE PROFIT and STOP-LOSS with a ratio of at least 2. That means that if you set up a take profit of 3%, your stop-loss shouldn’t be larger than 1.5%.
The advantage of this approach is that it has a high rate of success and allows you the flexibility of setting up the percentages of the take profit and stop-loss according to your preferences and risk appetite.
Big Snapper Alerts R3.0 + Chaiking Volatility condition + TP RSI//@version=5
//
// Bannos
// #NotTradingAdvice #DYOR
// Disclaimer.
// I AM NOT A FINANCIAL ADVISOR.
// THESE IDEAS ARE NOT ADVICE AND ARE FOR EDUCATION PURPOSES ONLY.
// ALWAYS DO YOUR OWN RESEARCH
//
// Author: Adaptation from JustUncleL Big Snapper by Bannos
// Date: May-2022
// Version: R1.0
//Description of this addon - Script using several new conditions to give Long/short and SL levels which was not proposed in the Big Snapper strategy "Big Snapper Alerts R3.0"
//"
//This strategy is based on the use of the Big Snapper outputs from the JustUncleL script and the addition of several conditions to define filtered conditions selecting signal synchrones with a trend and a rise of the volatility.
//Also the strategy proposes to define proportional stop losses and dynamic Take profit using an RSI strategy.
// After delivering the temporary ong/short signal and ploting a green or purple signal, several conditions are defined to consider a Signal is Long or short.
//Let s take the long signal as example(this is the same process with the opposite values for a short).
//step 1 - Long Definition:
// Snapper long signal stored in the buffer variable Longbuffer to say that in a close future, we could have all conditions for a long
// Now we need some conditions to combine with it:
//the second one is to be over the Ma_medium(55)
//and because this is not selective enough, the third one is a Volatility indicator "Chaikin Volatility" indicator giving an indication about the volatility of the price compared to the 10 last values
// -> Using the volatility indicator gives the possibility to increase the potential rise if the volatility is higher compared to the last periods.
//With these 3 signals, we get a robust indication about a potential long signal which is then stored in the variable "Longe"
//Now we have a long signal and can give a long signal with its Stop Loss
// The Long Signal is automatically given as the 3 conditions above are satisfied.
// The Stop loss is a function of the last Candle sizes giving a stop below the 70% of the overall candle which can be assimilated to a Fibonacci level. Below this level it makes sense to stop the trade as the chance to recover the complete Candle is more than 60%
//Now we are in an open Long and can use all the mentioned Stop loss condition but still need a Take Profit condition
//The take profit condition is based on a RSI strategy consisting in taking profit as soon as the RSI come back from the overbought area (which is here defined as a rsi over 70) and reaching the 63.5 level to trigger the Take Profit
//This TP condition is only active when Long is active and when an entry value as been defined.
//Entry and SL level appreas as soon as a Long or short arrow signal does appears. The Take profit will be conidtioned to the RSI.
//The final step in the cycle is a reinitialization of all the values giving the possibility to detect and treat any long new signal coming from the Big Snapper signal.
Position Size Calc. (Risk Management Tool)Programmed this tool to help prevent overtrading.
Example of application:
Suppose you want to trade ETHUSDT on a 1 minute chart and you are only willing to risk $10 in one single trade. This way, if you get stopped out, then you will only lose $10. Say you are using ATR based stop loss at 2x current ATR to set the initial stop. All these variables are now fixed, so you must make an adjustment to the size of your position.
Quick illustration: Tolerable loss per trade is $10 , the current ATR of ETHUSDT is $4.06, the size of your stop is $8.12 (4.06*2), then your position size should be 1.2 ETH ($10/$8.12).
This script will constantly monitor the current ATR and display the optimal position size on chart. Tolerable loss (aka "Risk amount") is defined by user in settings. Lines showing the size of SL and TPs on chart are optional, it was added to the script to help users draw the long/short position measuring tools built into TradingView.
Other notes: Always consider market liquidity, size of bid-ask spreads, and the possibilities of gap ups/downs. It can never be guaranteed that stop market/limit orders will get filled at desirable prices. Actual stop losses might differ.
3 Candle Strike StretegyMainly developed for AMEX:SPY trading on 1 min chart. But feel free to try on other tickers.
Basic idea of this strategy is to look for 3 candle reversal pattern within trending market structure. The 3 candle reversal pattern consist of 3 consecutive bullish or bearish candles,
followed by an engulfing candle in the opposite direction. This pattern usually signals a reversal of short term trend. This strategy also uses multiple moving averages to filter long or short
entries. ie. if the 21 smoothed moving average is above the 50, only look for long (bullish) entries, and vise versa. There is option change these moving average periods to suit your needs.
I also choose to use Linear Regression to determine whether the market is ranging or trending. It seems the 3 candle pattern is more successful under trending market. Hence I use it as a filter.
There is also an option to combine this strategy with moving average crossovers. The idea is to look for 3 candle pattern right after a fast moving average crosses over a slow moving average.
By default , 21 and 50 smoothed moving averages are used. This gives additional entry opportunities and also provides better results.
This strategy aims for 1:3 risk to reward ratio. Stop losses are calculated using the closest low or high values for long or short entries, respectively, with an offset using a percentage of
the daily ATR value. This allows some price fluctuation without being stopped out prematurely. Price target is calculated by multiplying the difference between the entry price and the stop loss
by a factor of 3. When price target is reach, this strategy will set stop loss at the price target and wait for exit condition to maximize potential profit.
This strategy will exit an order if an opposing 3 candle pattern is detected, this could happen before stop loss or price target is reached, and may also happen after price target is reached.
*Note that this strategy is designed for same day SPY option scalping. I haven't determined an easy way to calculate the # of contracts to represent the equivalent option values. Plus the option
prices varies greatly depending on which strike and expiry that may suits your trading style. Therefore, please be mindful of the net profit shown. By default, each entry is approximately equal
to buying 10 of same day or 1 day expiry call or puts at strike $1 - $2 OTM. This strategy will close all open trades at 3:45pm EST on Mon, Wed, and Fri.
**Note that this strategy also takes into account of extended market data.
***Note pyramiding is set to 2 by default, so it allows for multiple entries on the way towards price target.
Remember that market conditions are always changing. This strategy was only able to be back-tested using 1 month of data. This strategy may not work the next month. Please keep that in mind.
Also, I take no credit for any of the indicators used as part of this strategy.
Enjoy~
XABCD Harmonic Pattern Custom Range Interactive█ OVERVIEW
This indicator was designed based on Harmonic Pattern Book written by Scott Carney. It was simplified to user who may always used tools such as XABCD Pattern and Long Position / Short Position, which consume a lot of time, recommended for both beginner and expert of Harmonic Pattern Traders. XABCD Pattern require tool usage of Magnet tool either Strong Magnet, Week Magnet or none, which cause error or human mistake especially daily practice.
Simplified Guideline by sequence for Harmonic Pattern if using manual tools :
Step 1 : Trade Identification - XABCD Pattern
Step 2 : Trade Execution - Any manual tools of your choice
Step 3 : Trade Management - Position / Short Position
█ INSPIRATION
Inspired by design, code and usage of CAGR. Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
I use a lot of XABCD Pattern and Long Position / Short Position, require 5 to 10 minutes on average, upon determine the validity of harmonic pattern.
Upon creating this indicator, I believed that time can be reduced, gain more confidence, reduce error during drawing XABCD, which helps most of harmonic pattern users.
█ FEATURES
Table can positioned by any postion and font size can be resized.
Table can be display through optimized display or manual control.
Validility of harmonic pattern depends on BC ratio.
Harmonic pattern can be displayed fully or optimized while showing BC ratio validity.
Trade Execution at point D can be displayed on / off.
Stop Loss and Take Profit can be calculated automatically or manually.
Optimized table display based extend line setup and profit and loss setup.
Execution zone can be offset to Point C, by default using Point D.
Currency can be show or hide.
Profit and Loss can be displayed on axis once line is extended.
█ HOW TO USE
Step 1 : Trade Identification - Draw points from Point X to Point C. Dont worry about magnet, point will attached depends on High or Low of the candle.
Step 2 : Trade Execution - Check the validity of BC to determine the validity of harmonic pattern generated. Pattern only generate 1 pattern upon success. Otherwise, redraw to other points.
Step 3 : Trade Management - Determine the current candle either reach Point D or Potential Reversal Zone (PRZ). Check for Profit & Loss once reach PRZ.
█ USAGE LIMITATIONS
Harmonic Patterns only limits to patterns mentioned in Harmonic Trading Volume 3 due to other pattern may have other or different philosophy.
Only can be used for Daily timeframe and below due to bar_time is based on minutes by default.
Not recommended for Weekly and Monthly timeframe.
If Point X, A, B, C and D is next to each other, it is recommend to use lower timeframe.
Automated alert is not supported for this release. However, alert can be done manually. Alert will updated on the version.
█ PINE SCRIPT LIMITATIONS
Known bug for when calculate time in array, causing label may not appeared or offset.
Unable to convert to library due to usage of array.get(). I prefer usage for a combination of array.get(id, 0), array.get(id, 1), array.get(id, 2) into custom function, however I faced this issue during make arrays of label. Index can be simply refered as int, for id, i not sure, already try id refered as simple, nothing happens.
linefill.new() will appeared as diamond box if overused.
Text in box.new() unable to use ternary condition or switch to change color. Bgcolor also affected.
Label display is larger than XABCD tool. Hopefully in future, have function to resize label similar to XABCD tools.
█ IMPORTANTS
Trade Management (Profit & Loss) is calculated from Point A to D.
Take Profit is calculated based on ratio 0.382 and 0.618 of Point A to D.
Always check BC validity before proceed to Trade Management.
Length of XABCD is equal to XAB plus BCD, where XAB and BCD are one to one ratio. Length is measured in time.
Use other oscillator to countercheck. Normally use built-in Relative Strength Index (RSI) and Divergence Indicator to determine starting point of Point X and A.
█ HARMONIC PATTERNS SUPPORTED
// Credits to Scott M Carney, author of Harmonic Trading Volume 3: Reaction vs. Reversal
Alt Bat - Page 101
Bat - Page 98
Crab - Page 104
Gartley - Page 92
Butterfly - Page 113
Deep Crab - Page 107
Shark - Page 119 - 220
█ FAQ
Pattern such as 5-0, perfect XABCD and ABCD that not included, will updated on either next version or new release.
Point D time is for approximation only, not including holidays and extended session.
Basic explaination for Harmonic Trading System (Trade Identification, Trade Execution and Trade Management).
Harmonic Patterns values is pretty much summarized here including Stop Loss.
Basic explanation for Alt Bat, Bat, Crab, Gartley, Deep Crab and Butterfly.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
Take Profit On Trend (by BHD_Trade_Bot)The purpose of strategy is to detect long-term uptrend and short-term downtrend so that you can easy to take profit.
The strategy also using BHD unit to detect how big you win and lose, so that you can use this strategy for all coins without worry about it have different percentage of price change.
ENTRY
The buy order is placed on assets that have long-term uptrend and short-term downtrend:
- Long-term uptrend condition: ema200 is going up (rsi200 greater than 51)
- Short-term downtrend condition: 2 last candles are down price (use candlestick for less delay)
CLOSE
The sell order is placed when take profit or stop loss:
- Take profit: price increase 1 BHD unit
- Stop loss: price decrease 2 BHD units
The strategy use $15 and trading fee is 0.1% for each order. So that, in the real-life, if you are using trade bot, it will need $1500 for trading 100 coins at the same time.
Pro tip : The 1-hour time frame for altcoin/USDT has the best results on average.
Zendog V2 backtest DCA bot 3commasHi everyone,
After a few iterations and additional implemented features this version of the Backtester is now open source.
The Strategy is a Backtester for 3commas DCA bots. The main usage scenario is to plugin your external indicator, and backtest it using different DCA settings.
Before using this script please make sure you read these explanations and make sure you understand how it works.
Features:
- Because of Tradingview limitations on how orders are grouped into Trades, this Strategy statistics are calculated by the script, so please ignore the Strategy Tester statistics completely
Statistics Table explained:
- Status: either all deals are closed or there is a deal still running, in which case additional info
is provided below, as when the deal started, current PnL, current SO
- Finished deals: Total number of closed deals both Winning and Losing.
A deal is comprised as the Base Order (BO) + all Safety Orders (SO) related to that deal, so this number
will be different than the Strategy Tester List of Trades
- Winning Deals: Deal ended in profit
- Losing deals: Deals ended with loss due to Stop Loss. In the future I might add a Deal Stop condition to
the script, so that will count towards this number as well.
- Total days ( Max / Avg days in Deal ):
Total Days in the Backtest given by either Tradingview limitation on the number of candles or by the
config of the script regarding "Limit Date Range".
Max Days spent in a deal + which period this happened.
Avg days spent in a deal.
- Required capital: This is the total capital required to run the Backtester and it is automatically calculated by
the script taking into consideration BO size, SO size, SO volume scale. This should be the same as 3commas.
This number overwrites strategy.initial_capital and is used to calculate Profit and other stats, so you don't need
to update strategy.initial_capital every time you change BO/SO settings
- Profit after commission
- Buy and Hold return: The PnL that could have been obtained by buying at the close of the first candle of the
backtester and selling at the last.
- Covered deviation: The % of price move from initial BO order covered by SO settings
- Max Deviation: Biggest market % price move vs BO price, in the other direction (for long
is down, for short it is up)
- Max Drawdown: Biggest market % price move vs Avg price of the whole Trade (BO + any SO), in the other
direction (for long price goes down, for short it goes up)
This is calculated for the whole Trade so it is different than List of Trades
- Max / Avg bars in deal
- Total volume / Commission calculated by the strategy. For correct commission please set Commission in the
Inputs Tab and you may ignore Properties Tab
- Close stats for deals: This is a list of how many Trades were closed at each step, including Stop Loss (if
configured), together with covered deviation for that step, the number of deals, and the percentage of this
number from all the deals
TODO: Might add deal avg value for each step
- Settings Table that can be enabled / disabled just to have an overview of your configs on the chart, this is a
drawn on bottom left
- Steps Table similar to 3commas, this is also drawn on bottom left, so please disable Settings table if you want
to see this one
TODO: Might add extra stats here
- Deal start condition: built in RSI-7 or plugin any external indicator and compare with any value the indicator plots
(main purpose of this strategy is to connect your own studies, so using external indicator is recommended)
- Base order and safety orders configs similar to 3commas (order size, percent deviation, safety orders,
percent scale and volume scale)
- Long and Short
- Stop Loss
- Support for Take profit from base order or from Total volume of the deal
- Configs help (besides self explanatory):
- Chart theme: Adjust according to the theme you run on. There is no way to detect theme at the moment.
This adjust different colors
- Deal Start Type: Either a builtin RSI7 or "External indicator"
- Indicator Source an value: If using External Indicator then select source, comparison and value.
For example you could start a deal when Volume is greater than xxxx, or code a custom indicator that plots
different values based on your conditions and test those values
- Visuals / Decimals for display: Adjust according to your symbol
- BO Entry Price for steps table: This is the BO start deal price used to calculate the steps in the table
M8 BUY @ END OF DAYI've read a couple of times at a couple of different places that most of the move in the market happens after hours, meaning during non-standard trading hours.
After-market and pre-market hours and have seen data presented showing that systems which bought just before end normal market hours and sold the next morning had really amazing resutls.
But when testing those I found the results to be quite poor compared to the pretty graphs I saw, and after much tweaking and trying different ideas I gave up on the idea until I recently decided to try a new position management system.
The System
Buys at the end of the trading day before the close
Sells the next morning at the open IF THE CLOSE OF THE CURRENT BAR IS HIGHER THAN THE ENTRY PRICE
When the current price is not higher, the system will keep the position open until it EITHER gets stops out or closes on profit <<< this is WHY it has the high win %
The system has a high win ratio because it will keep that one position open until it either reaches profit or stops out
This "system" of waiting, and keeping the trade open, actually turned out to be a fantastic way to kind of put the complete trading strategy in a kind of limbo mode. It either waits for market failure or for a profit.
I don't really care about win % at all, almost always high win % ratio systems are just nonsense. What I look for is a PF -- profit factor of 1.5 or above, and a relatively smooth equity curve. -- This has both.
The Stop Loss setting is set @ .95, meaning a 5% stop loss. The Red Line on the chart is the stop loss line.
There is no set profit target -- it simply takes what the market gives.
Non-Repainting System
This does use a 200D Simple Moving Average as a filter. Like a Green Light / Red Light traffic light, the system will only trade long when the price is above its 200 Moving average.
Here is the code: "F1 = close > sma(security(syminfo.tickerid, "D", close ), MarketFilterLen) // HIGH OF OLD DATA -- SO NO REPAINTING"
I use "close ", so that's data from two days ago, it's fixed, confirmed, non-repainting data from the higher timeframe.
-- I would only suggest using this on direction tickers like SPY, QQQ, SSO, TQQQ, market sectors with additional filters in place.
Average Band by HarmanUsually, Moving Averages (Simple & Exponential) consider "close" of each candle to form a line for a particular period. In this indicator, we have considered all the parameters (Open, Close, Low & High) of each candle to form a Band or a wave which act as a zone to provide support & resistance. It works well on all the time frames. It perfectly works on lower time frames of 15 min & 5 min for intraday trades and even for scalping. There is a line that moves very near to candles known as "Candle Line" provide support & resistance to each individual candle and a leading line which moves ahead also acts as support & resistance and helps in determining trend direction.
How to use the indicator ?
Indicator consists of 3 components :
1) A Band or wave of 3 lines (upper, middle & lower line)
2) A "Candle Line" which moves along with the candles
3) A Leading line which moves ahead of the candles
Method 1 : When candles are being formed above the candle line (line near to candles) and it crosses the band or wave from below to upside, then long trade can be initiated. Similarly, When candles are being formed below the Candle line and it crosses the band or wave from upside then short trade can be initiated. Stop loss can be maintained below the band for Long trade and above the band for short trade. Candle line can be used to trail the stop loss.
Method 2: If candles moves above and below of the band very often and frequently and candle line is in the middle of candles then it is NO TRADING ZONE. If you still want to trade, then select a higher time frame and check the price movement. If there is a stability in the higher time frame, then take the trade in the higher timeframe with stable movement.
Method 3 : Candle line acts as "First line of Defence". In a uptrend, all the candles are formed above the candle line and in case of down trend, all the candles are formed below the candle line. When a newly formed candle cross the candle line then you can book profit. For Example : In uptrend , candles are being formed above the line, when a new candle started forming below the line and when the complete candle is formed below the line, profit can be booked. Vice-versa in case of downtrend.
Method 4: Direction of leading line, band and candle line helps in determining the trend. If all these three components are in upward direction, price trend is upward and if all these three components are in downward direction, then price trend is downward. When, leading line and band cross each other from opposite direction for consecutive 2-3 times, then price movement is sideways.
Method 5 : Thickness of band play an important role in determining price action. If band is narrow, it means small candles are being formed and no any huge price movement is observed in this period. When band started expanding, it signifies that big candles are begin to form and there is a more price movement than before. Similarly, If contraction of band started, it means that small candles are being formed and there is low price movement as compared to the price movement when Band was expanded. If Band is expanded (wider) and volumes are high, It means the Band will act as strong Support or Resistance than usual. In case, candles and candle line cross the expanded Band, you can enter the Long or Short trade.
Method 6: When the Band, leading line and candle line collides or meet at a single point, then it is either strong support or resistance.
Method 7 : Usage in Scalping : Select the shorter time frame of 1 min or 5 min. If the candles are crossing the band very frequently in 1 min, then select 5 min time frame or wait for few minutes for stability. Now, when candles started forming above the candle line and it crosses the band from below then take a long position and book profit after few candles above the band. Place stop loss below the Band. Similarly, when candles started forming below the candle line and it crosses the band from above, then enter into short trade and book profit after few candles. Place stop loss above the band in the case of short trade.
You can combine above methods to give a sharp edge to your trade and increase the probability of your winning in the trade.
Indicator Settings : Default period selected is 50 for both the Band and leading line. You can change the period to 26 or 100 or 200. Select the period and check the chart, if the indicator looks fine and smooth, then you can use your settings. For most of the time, default settings work perfectly.
Proudly Developed by :
Harmandeep Singh
Graduate in Computer Science with Physics & Mathematics
MBA in Business Marketing and Finance
Experienced Computer programmer & Software developer
Stock Market & Crypto Trader
Probability of ATR Index [racer8]Deriving the indicator:
PAI is an indicator I created that tells you the probability of current price moving a specified ATR distance over a specified number of periods into the future. It takes into account 4 variables: the ATR & the standard deviation of price, and the 2 parameters: ATR distance and # bars (time).
The formula is very complex so I will not be able to explain it without confusion arising.
What I can say is that I used integral calculus & the Taylor series to derive a formula that calculates the area under half of the normal distribution function. Thus, the formula was repeated twice in the code to derive the full probability (half + half = whole). If you can read the code, you might be wondering why the formula is so long...
The reason for this is because in Pine Script, the erf function doesn't exist. You see, the formula for normal distribution is: f(x) = (1/sqrt(2pi))*e^(-xx/2), assuming of course that the standard deviation = 1 and mu (mean) = 1. The next step is to take the integral of this formula in order to find the area under f(x). The problem is that I found the integral, F(x), of the normal distribution formula to be equal to F(x) = erf(x/sqrt(2))/2...and the erf function cannot be directly computed into Pinescript.
So I developed a solution...why not estimate the integral function? So that's exactly what I did using a technique involving the Taylor series. The Taylor series is an algebraic function that allows you to create a new function that can estimate the existing function. On a graph, the new function has the same values as the existing one, the only difference is that it uses a differnt formula, in this case, a formula that makes it possible to compute the integral. The disadvantage of using this new formula is that it is super long and if you want it to better represent the original integral over a wider range of x-values, you have to make it longer.
Signal Interpretion:
The hotter the colour, the more likely price will reach your specified distance.
The 2 values of PAI in the bottom window represent probability & average probability of your specifed distance geting hit.
Applications:
Stop loss placement---
This indicator is useful because it gives you an idea of the likelihood that a stop loss at a particular distance away from price (in ATRs) will be hit over a period of time specified. This is helpful in placing stop losses.
Options trading---
PAI can also be used in options trading. For example, you are using a strangle options strategy, and you want to make sure that price stays within the Strangle's profit range. So you only trade when PAI presents a low probability value of moving at a particular distance in ATRs over n periods.
Anyhow, I hope you guys like it. Enjoy! and hit that like button for me :)
mForex - Bollinger Bands - Pinbar scalping systemTransaction setup parameters
Time frame: M5, M15
Currency pair: Any except XAU/USD
Trading strategies
=== BUY ===
Price break out of the lower Bollinger Bands
The Pinbar reversal candlestick appears and closes the candle on the lower Bollinger Bands
Stop loss: Nearest bottom + 3-5 pips
Profit target: 10-20 pips
=== SELL ===
Price break out of the upper Bollinger Bands
The Pinbar reversal candle appeared and closed below the upper
Stop loss: Nearest peak + 3-5 pips
Profit target: 10-20 pips
* If you have any questions or suggestions for this strategy, feel free to ask us.
