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Stochastic Oscillator Binary System by Hashtag_binaryRules - Time Frame 1 min. - Expires Time 3 min or 15 min (the best option). - Markets: Forex (only volatile currency pair), Futures. - Sessions: London and New York. Call - Heiken Ashi Dodger blue; -Stochastic Oscillator cross upward from oversold Zone (conservative trade, aggressive trade: Stochastic Oscillator cross upward ). -Matrix three square dodger blue. Put - Heiken Ashi white; - Stochastic Oscillator cross downward from overbougth Zone (conservative trade, aggressive trade: Stochastic Oscillator cross downward ). - Matrix three square withe. This Binary System is also good for trade scalping. The same rule for entry with conservative trade: Exit position options - For Buy close position when the stochastic line touches 80 levels, - For Sell close positions when stochastic line touches 20 levels. - Initial Stop loss on the previous swing.
Penunjuk Pine Script®
oleh AlgoriTrading677
7
QuantumFlowX™ - Özgün Momentum Algoritması + NUPUGoals: Momentum Analysis: The primary goal of the momentum indicator is to assess the speed and strength of a price move. We will conduct independent analyses of price and momentum to detect momentum movements. Divergence Detection: By identifying discrepancies (divergences) between price movements and momentum indicators, we will generate buy and sell signals. Visualization: The momentum indicator’s visual representation will be clearly shown on the chart either as a line or histogram. Divergences and buy/sell signals will also be marked using shapes. Steps: Calculating the Momentum Indicator: We will calculate the momentum indicator based on price changes over a specified period. Detecting Divergences Between Price and Momentum: Divergences will be identified when the price moves in one direction, while the momentum indicator moves in the opposite direction. Marking Divergences Visually on the Chart: Divergences will be clearly marked with shapes (arrows or labels) to indicate buy and sell opportunities. Displaying Momentum Lines and Histograms: Momentum will be displayed on the chart using lines or histograms, clearly showing the strength and direction of the price move.
Penunjuk Pine Script®
oleh phoenixtradeteam
GKD-C RSI of Fast Discrete Cosine Transform [Loxx]Giga Kaleidoscope GKD-C RSI of Fast Discrete Cosine Transform is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: RSI of Fast Discrete Cosine Transform as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ Fast Discrete Cosine Transform What is the Fast Discrete Cosine Transform? Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression. The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal. The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal. The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm. The steps involved in the FDCT algorithm in Algolib are: -Divide the input sequence into even and odd parts. -Compute the DCT of the even and odd parts recursively. -Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients. -The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms. In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. █ Relative Strength Index (RSI) This indicator contains 7 different types of RSI . RSX Regular Slow Rapid Harris Cuttler Ehlers Smoothed What is RSI? RSI stands for Relative Strength Index . It is a technical indicator used to measure the strength or weakness of a financial instrument's price action. The RSI is calculated based on the price movement of an asset over a specified period of time, typically 14 days, and is expressed on a scale of 0 to 100. The RSI is considered overbought when it is above 70 and oversold when it is below 30. Traders and investors use the RSI to identify potential buy and sell signals. When the RSI indicates that an asset is oversold, it may be considered a buying opportunity, while an overbought RSI may signal that it is time to sell or take profits. It's important to note that the RSI should not be used in isolation and should be used in conjunction with other technical and fundamental analysis tools to make informed trading decisions. What is RSX? Jurik RSX is a technical analysis indicator that is a variation of the Relative Strength Index Smoothed ( RSX ) indicator. It was developed by Mark Jurik and is designed to help traders identify trends and momentum in the market. The Jurik RSX uses a combination of the RSX indicator and an adaptive moving average (AMA) to smooth out the price data and reduce the number of false signals. The adaptive moving average is designed to adjust the smoothing period based on the current market conditions, which makes the indicator more responsive to changes in price. The Jurik RSX can be used to identify potential trend reversals and momentum shifts in the market. It oscillates between 0 and 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Traders can use these levels to make trading decisions, such as buying when the indicator crosses above 50 and selling when it crosses below 50. The Jurik RSX is a more advanced version of the RSX indicator, and while it can be useful in identifying potential trade opportunities, it should not be used in isolation. It is best used in conjunction with other technical and fundamental analysis tools to make informed trading decisions. What is Slow RSI? Slow RSI is a variation of the traditional Relative Strength Index ( RSI ) indicator. It is a more smoothed version of the RSI and is designed to filter out some of the noise and short-term price fluctuations that can occur with the standard RSI . The Slow RSI uses a longer period of time than the traditional RSI , typically 21 periods instead of 14. This longer period helps to smooth out the price data and makes the indicator less reactive to short-term price fluctuations. Like the traditional RSI , the Slow RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Slow RSI is a more conservative version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also be slower to respond to changes in price, which may result in missed trading opportunities. Traders may choose to use a combination of both the Slow RSI and the traditional RSI to make informed trading decisions. What is Rapid RSI? Same as regular RSI but with a faster calculation method What is Harris RSI? Harris RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Larry Harris and is designed to help traders identify potential trend changes and momentum shifts in the market. The Harris RSI uses a different calculation formula compared to the traditional RSI . It takes into account both the opening and closing prices of a financial instrument, as well as the high and low prices. The Harris RSI is also normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Like the traditional RSI , the Harris RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Harris RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Harris RSI and the traditional RSI to make informed trading decisions. What is Cuttler RSI? Cuttler RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Curt Cuttler and is designed to help traders identify potential trend changes and momentum shifts in the market. The Cuttler RSI uses a different calculation formula compared to the traditional RSI . It takes into account the difference between the closing price of a financial instrument and the average of the high and low prices over a specified period of time. This difference is then normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Like the traditional RSI , the Cuttler RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Cuttler RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Cuttler RSI and the traditional RSI to make informed trading decisions. What is Ehlers Smoothed RSI? Ehlers smoothed RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by John Ehlers and is designed to help traders identify potential trend changes and momentum shifts in the market. The Ehlers smoothed RSI uses a different calculation formula compared to the traditional RSI . It uses a smoothing algorithm that is designed to reduce the noise and random fluctuations that can occur with the standard RSI . The smoothing algorithm is based on a concept called "digital signal processing" and is intended to improve the accuracy of the indicator. Like the traditional RSI , the Ehlers smoothed RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Ehlers smoothed RSI can be useful in identifying longer-term trends and momentum shifts in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Ehlers smoothed RSI and the traditional RSI to make informed trading decisions. █ GKD-C RSI of Fast Discrete Cosine Transform What is the RSI of Fast Discrete Cosine Transform in terms of Forex trading? The Relative Strength Index (RSI) is a popular technical indicator used in trading forex to measure the strength of a trend and identify potential trend reversals. While the Fast Discrete Cosine Transform (FDCT) is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. The RSI is calculated by comparing the average gains and losses of a financial instrument over a given period of time. The RSI value ranges from 0 to 100, with values above 70 indicating an overbought market and values below 30 indicating an oversold market. One limitation of the RSI is that it only considers the average gains and losses over a fixed period of time, which may not capture the complex patterns and dynamics of financial markets. This is where the FDCT can be useful. By applying the FDCT to the price data used to calculate the RSI, traders can identify the dominant frequency components of the price data and use this information to adjust the RSI calculation. For example, traders can weight the gains and losses based on the frequency components identified by the FDCT, giving more weight to the dominant frequencies and less weight to the lower frequencies. This approach can improve the accuracy of the RSI calculation and provide traders with more reliable signals for identifying trends and potential trend reversals. Traders can also use the frequency components identified by the FDCT to develop more advanced trading strategies, such as identifying cycles in the market and using this information to develop trend-following strategies. In summary, while the FDCT is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. Traders can use the FDCT to identify dominant frequency components and adjust the RSI calculation accordingly, providing more reliable signals for identifying trends and potential trend reversals. This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
Penunjuk Pine Script®
oleh loxx
GKD-C Fast Discrete Cosine Transform of Price [Loxx]Giga Kaleidoscope GKD-C Fast Discrete Cosine Transform of Price is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Fast Discrete Cosine Transform of Price as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ GKD-C Fast Discrete Cosine Transform of Price What is Fast Discrete Cosine Transform? What is the Fast Discrete Cosine Transform? Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression. The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal. The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal. The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm. The steps involved in the FDCT algorithm in Algolib are: -Divide the input sequence into even and odd parts. -Compute the DCT of the even and odd parts recursively. -Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients. -The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms. In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
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GKD-C CCI Adaptive Smoother [Loxx]Giga Kaleidoscope GKD-C CCI Adaptive Smoother is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ GKD-C CCI Adaptive Smoother Commodity Channel Index: History, Calculation, and Advantages The Commodity Channel Index (CCI) is a versatile technical analysis indicator widely used by traders and analysts to identify potential trends, reversals, and trading opportunities in various financial markets. Developed by Donald Lambert in 1980, the CCI was initially designed to analyze the cyclical behavior of commodities. However, its applications have expanded over time to include stocks, currencies, and other financial instruments. The following provides an overview of the CCI's history, explain its calculation, and discuss its advantages compared to other indicators. History Donald Lambert, a commodities trader and technical analyst, created the Commodity Channel Index in response to the unique challenges posed by the cyclical nature of the commodities markets. Lambert aimed to develop an indicator that could help traders identify potential turning points in the market, allowing them to capitalize on price trends and reversals. The CCI quickly gained popularity among traders and analysts due to its ability to adapt to various market conditions and provide valuable insights into price movements. Calculation The CCI is calculated through the following steps: 1. Determine the typical price for each period: The typical price is calculated as the average of the high, low, and closing prices for each period. Typical Price = (High + Low + Close) / 3 2. Calculate the moving average of the typical price: The moving average is computed over a specified period, typically 14 or 20 days. 3. Calculate the mean deviation: For each period, subtract the moving average from the typical price, and take the absolute value of the result. Then, compute the average of these absolute values over the specified period. 4. Calculate the CCI: Divide the difference between the typical price and its moving average by the product of the mean deviation and a constant, typically 0.015. CCI = (Typical Price - Moving Average) / (0.015 * Mean Deviation) Why CCI is Used and Its Advantages over Other Indicators The CCI offers several advantages over other technical indicators, making it a popular choice among traders and analysts: 1. Versatility: Although initially developed for commodities, the CCI has proven to be effective in analyzing a wide range of financial instruments, including stocks, currencies, and indices. Its adaptability to different markets and timeframes makes it a valuable tool for various trading strategies. 2. Identification of overbought and oversold conditions: The CCI measures the strength of the price movement relative to its historical average. When the CCI reaches extreme values, it can signal overbought or oversold conditions, indicating potential trend reversals or price corrections. 3. Confirmation of price trends: The CCI can help traders confirm the presence of a price trend by identifying periods of strong momentum. A rising CCI indicates increasing positive momentum, while a falling CCI suggests increasing negative momentum. 4. Divergence analysis: Traders can use the CCI to identify divergences between the indicator and price action. For example, if the price reaches a new high, but the CCI fails to reach a corresponding high, it can signal a weakening trend and potential reversal. 5. Independent of price scale: Unlike some other technical indicators, the CCI is not affected by the price scale of the asset being analyzed. This characteristic allows traders to apply the CCI consistently across various instruments and markets. The Commodity Channel Index is a powerful and versatile technical analysis tool that has stood the test of time. Developed to address the unique challenges of the commodities markets, the CCI has evolved into an essential tool for traders and analysts in various financial markets. Its ability to identify trends, reversals, and trading opportunities, as well as its versatility and adaptability, sets it apart from other technical indicators. By incorporating the CCI into their analytical toolkit, traders can gain valuable insights into market conditions, enabling them to make more informed decisions and improve their overall trading performance. As financial markets continue to evolve and grow more complex, the importance of reliable and versatile technical analysis tools like the CCI cannot be overstated. In an environment characterized by rapidly changing market conditions, the ability to quickly identify trends, reversals, and potential trading opportunities is crucial for success. The CCI's adaptability to different markets, timeframes, and instruments makes it an indispensable resource for traders seeking to navigate the increasingly dynamic financial landscape. Additionally, the CCI can be effectively combined with other technical analysis tools, such as moving averages, trend lines, and candlestick patterns, to create a more comprehensive and robust trading strategy. By using the CCI in conjunction with these complementary techniques, traders can develop a more nuanced understanding of market behavior and enhance their ability to identify high-probability trading opportunities. In conclusion, the Commodity Channel Index is a valuable and versatile tool in the world of technical analysis. Its ability to adapt to various market conditions and provide insights into price trends, reversals, and trading opportunities make it an essential resource for traders and analysts alike. As the financial markets continue to evolve, the CCI's proven track record and adaptability ensure that it will remain a cornerstone of technical analysis for years to come. What is the Smoother Moving Average? The smoother function is a custom algorithm designed to smooth the price data of a financial asset using a moving average technique. It takes the price (src) and the period of the rolling window sample (len) to reduce noise in the data and reveal underlying trends. smoother(float src, int len)=> wrk = src, wrk2 = src, wrk4 = src wrk0 = 0., wrk1 = 0., wrk3 = 0. alpha = 0.45 * (len - 1.0) / (0.45 * (len - 1.0) + 2.0) wrk0 := src + alpha * (nz(wrk ) - src) wrk1 := (src - wrk) * (1 - alpha) + alpha * nz(wrk1 ) wrk2 := wrk0 + wrk1 wrk3 := (wrk2 - nz(wrk4 )) * math.pow(1.0 - alpha, 2) + math.pow(alpha, 2) * nz(wrk3 ) wrk4 := wrk3 + nz(wrk4 ) wrk4 Here's a detailed breakdown of the code, explaining each step and its purpose: 1. wrk, wrk2, and wrk4: These variables are assigned the value of src, which represents the source price of the asset. This step initializes the variables with the current price data, serving as a starting point for the smoothing calculations. wrk0, wrk1, and wrk3: These variables are initialized to 0. They will be used as temporary variables to hold intermediate results during the calculations. Calculation of the alpha parameter: 2. The alpha parameter is calculated using the formula: 0.45 * (len - 1.0) / (0.45 * (len - 1.0) + 2.0). The purpose of this calculation is to determine the smoothing factor that will be used in the subsequent calculations. This factor will influence the balance between responsiveness to recent price changes and smoothness of the resulting moving average. A higher value of alpha will result in a more responsive moving average, while a lower value will produce a smoother curve. Calculation of wrk0: 3. wrk0 is updated with the expression: src + alpha * (nz(wrk ) - src). This step calculates the first component of the moving average, which is based on the current price (src) and the previous value of wrk (if it exists, otherwise 0 is used). This calculation applies the alpha parameter to weight the contribution of the previous wrk value, effectively making the moving average more responsive to recent price changes. Calculation of wrk1: 4. wrk1 is updated with the expression: (src - wrk) * (1 - alpha) + alpha * nz(wrk1 ). This step calculates the second component of the moving average, which is based on the difference between the current price (src) and the current value of wrk. The alpha parameter is used to weight the contribution of the previous wrk1 value, allowing the moving average to be even more responsive to recent price changes. Calculation of wrk2: 5. wrk2 is updated with the expression: wrk0 + wrk1. This step combines the first and second components of the moving average (wrk0 and wrk1) to produce a preliminary smoothed value. Calculation of wrk3: 6. wrk3 is updated with the expression: (wrk2 - nz(wrk4 )) * math.pow(1.0 - alpha, 2) + math.pow(alpha, 2) * nz(wrk3 ). This step refines the preliminary smoothed value (wrk2) by accounting for the differences between the current smoothed value and the previous smoothed values (wrk4 and wrk3 ). The alpha parameter is used to weight the contributions of the previous smoothed values, providing a balance between smoothness and responsiveness. Calculation of wrk4: 7. Calculation of wrk4: wrk4 is updated with the expression: wrk3 + nz(wrk4 ). This step combines the refined smoothed value (wrk3) with the previous smoothed value (wrk4 , or 0 if it doesn't exist) to produce the final smoothed value. The purpose of this step is to ensure that the resulting moving average incorporates information from past values, making it smoother and more representative of the underlying trend. 8. Return wrk4: The function returns the final smoothed value wrk4. This value represents the Smoother Moving Average for the given data point in the price series. In summary, the smoother function calculates a custom moving average by using a series of steps to weight and combine recent price data with past smoothed values. The resulting moving average is more responsive to recent price changes while still maintaining a smooth curve, which helps reveal underlying trends and reduce noise in the data. The alpha parameter plays a key role in balancing the responsiveness and smoothness of the moving average, allowing users to customize the behavior of the algorithm based on their specific needs and preferences. What is the CCI Adaptive Smoother? The Commodity Channel Index (CCI) Adaptive Smoother is an innovative technical analysis tool that combines the benefits of the CCI indicator with a Smoother Moving Average. By adapting the CCI calculation based on the current market volatility, this method offers a more responsive and flexible approach to identifying potential trends and trading signals in financial markets. The CCI is a momentum-based oscillator designed to determine whether an asset is overbought or oversold. It measures the difference between the typical price of an asset and its moving average, divided by the mean absolute deviation of the typical price. The traditional CCI calculation relies on a fixed period, which may not be suitable for all market conditions, as volatility can change over time. The introduction of the Smoother Moving Average to the CCI calculation addresses this limitation. The Smoother Moving Average is a custom smoothing algorithm that combines elements of exponential moving averages with additional calculations to fine-tune the smoothing effect based on a given parameter. This algorithm assigns more importance to recent data points, making it more sensitive to recent changes in the data. The CCI Adaptive Smoother dynamically adjusts the period of the Smoother Moving Average based on the current market volatility. This is accomplished by calculating the standard deviation of the close prices over a specified period and then computing the simple moving average of the standard deviation. By comparing the average standard deviation with the current standard deviation, the adaptive period for the Smoother Moving Average can be determined. This adaptive approach allows the CCI Adaptive Smoother to be more responsive to changing market conditions. In periods of high volatility, the adaptive period will be shorter, resulting in a more responsive moving average. Conversely, in periods of low volatility, the adaptive period will be longer, producing a smoother moving average. This flexibility enables the CCI Adaptive Smoother to better identify trends and potential trading signals in a variety of market environments. Furthermore, the CCI Adaptive Smoother is a prime example of the evolution of technical analysis methodologies. As markets continue to become more complex and dynamic, it is crucial for analysts and traders to adapt and improve their techniques to stay competitive. The incorporation of adaptive algorithms, like the Smoother Moving Average, demonstrates the potential for blending traditional indicators with cutting-edge methods to create more powerful and versatile tools for market analysis. The versatility of the CCI Adaptive Smoother makes it suitable for various trading strategies, including trend-following, mean-reversion, and breakout systems. By providing a more precise measurement of overbought and oversold conditions, the CCI Adaptive Smoother can help traders identify potential entry and exit points with greater accuracy. Additionally, its responsiveness to changing market conditions allows for more timely adjustments in trading positions, reducing the risk of holding onto losing trades. While the CCI Adaptive Smoother is a valuable tool, it is essential to remember that no single indicator can provide a complete picture of the market. As seasoned analysts and traders, we must always consider a holistic approach, incorporating multiple indicators and techniques to confirm signals and validate our trading decisions. By combining the CCI Adaptive Smoother with other technical analysis tools, such as trend lines, support and resistance levels, and candlestick patterns, traders can develop a more comprehensive understanding of the market and make more informed decisions. The development of the CCI Adaptive Smoother also highlights the increasing importance of computational power and advanced algorithms in the field of technical analysis. As financial markets become more interconnected and influenced by various factors, including macroeconomic events, geopolitical developments, and technological innovations, the need for sophisticated tools to analyze and interpret complex data sets becomes even more critical. Machine learning and artificial intelligence (AI) are becoming increasingly relevant in the world of trading and investing. These technologies have the potential to revolutionize how technical analysis is performed, by automating the discovery of patterns, relationships, and trends in the data. By leveraging machine learning algorithms and AI-driven techniques, traders can uncover hidden insights, improve decision-making processes, and optimize trading strategies. The CCI Adaptive Smoother is just one example of how advanced algorithms can enhance traditional technical indicators. As the adoption of machine learning and AI continues to grow in the financial sector, we can expect to see the emergence of even more sophisticated and powerful analysis tools. These innovations will undoubtedly lead to a new era of technical analysis, where the ability to quickly adapt to changing market conditions and extract meaningful insights from complex data becomes increasingly critical for success. In conclusion, the CCI Adaptive Smoother is an essential step forward in the evolution of technical analysis. It demonstrates the potential for combining traditional indicators with advanced algorithms to create more responsive and versatile tools for market analysis. As technology continues to advance and reshape the financial landscape, it is crucial for traders and analysts to stay informed and embrace innovation. By integrating cutting-edge tools like the CCI Adaptive Smoother into their arsenal, traders can gain a competitive edge and enhance their ability to navigate the increasingly complex world of financial markets. Additional Features This indicator allows you to select from 33 source types. They are as follows: Close Open High Low Median Typical Weighted Average Average Median Body Trend Biased Trend Biased (Extreme) HA Close HA Open HA High HA Low HA Median HA Typical HA Weighted HA Average HA Average Median Body HA Trend Biased HA Trend Biased (Extreme) HAB Close HAB Open HAB High HAB Low HAB Median HAB Typical HAB Weighted HAB Average HAB Average Median Body HAB Trend Biased HAB Trend Biased (Extreme) What are Heiken Ashi "better" candles? Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals. Standard Heiken Ashi candles are calculated using the following formulas: Heiken Ashi Close = (Open + High + Low + Close) / 4 Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2 Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close) Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close) The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows: Better Heiken Ashi Close = (Open + High + Low + Close) / 4 Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2 Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close) Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close) Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor)) Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor)) The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points. It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions. Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions. To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips: Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend. Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal. Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level. Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend. Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis. In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows: Kaufman Adaptive Moving Average (KAMA) The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals. Adaptive Moving Average The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value. T3 The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: CCI Adaptive Smoother as shown on the chart above Confirmation 2: Williams Percent Range Continuation: CCI Adaptive Smoother Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Volatility/Volume Entry 1. GKD-V Volatility/Volume signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees 1-Candle Rule Volatility/Volume Entry 1. GKD-V Volatility/Volume signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close) 2. GKD-B Volatility/Volume agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-B Baseline agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 2. GKD-C Confirmation 1 agrees 3. GKD-C Confirmation 2 agrees 4. GKD-V Volatility/Volume Agrees ]█ Setting up the GKD The GKD system involves chaining indicators together. These are the steps to set this up. Use a GKD-C indicator alone on a chart 1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple" Use a GKD-V indicator alone on a chart **nothing, it's already useable on the chart without any settings changes Use a GKD-B indicator alone on a chart **nothing, it's already useable on the chart without any settings changes Baseline (Baseline, Backtest) 1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)" 2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline" Volatility/Volume (Volatility/Volume, Backte st) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo" 2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested) 3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest" 4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume" 5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately) 6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short 7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts. Solo Confirmation Simple (Confirmation, Backtest) 1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple" 1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest" 2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple" Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex" 4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest" 5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits" 6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest" Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex" 4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest" 5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit" 6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits" 7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest" Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1" 4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest" 5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2" 6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2" 7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation" 8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits" 9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest" Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained" 2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1" 4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest" 5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2" 6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2" 7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation" 8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit" 9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits" 10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest" Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest) 1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume" 2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional" 3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)" 4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume" 5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest" 6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately 7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts. Requirements Inputs Confirmation 1: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Continuation: GKD-C Confirmation indicator Solo Confirmation Simple: GKD-B Baseline Solo Confirmation Complex: GKD-V Volatility / Volume indicator Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator Stacked 1: None Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1 Outputs Confirmation 1: GKD-C Confirmation 2 indicator Confirmation 2: GKD-C Continuation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator Solo Confirmation Super Complex: GKD-C Continuation indicator Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+ Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest Additional features will be added in future releases.
Penunjuk Pine Script®
oleh loxx
1
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul. DEDICATION: This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development. I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion. Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight. If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face. INTRODUCTION: Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike." Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity... This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance. What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later... I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough. Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand. The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer. I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you. Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects. PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS: This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead. PERIODOGRAM INTERPRETATION: The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side. The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity. SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED: Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram. Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters. Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox. Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values. DOMINANT CYCLE VALUE SELECTIONS: External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it. AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED: In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later. In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients. Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use. The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers. The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram. POST SCRIPT: Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
Penunjuk Pine Script®
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