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ALGORITHM D_C (Scalping & Trend Detection Tool)This invite-only script is designed for manual traders seeking an advanced analytical assistant to validate market entries through a comprehensive technical framework. It identifies potential entry zones by combining price action, EMA alignment, market structure analysis, and dynamic detection of breakouts and reversals, adapting to both trending and consolidating environments. ⚙️ Core Functionality The internal logic integrates: Directional bias detection across multiple timeframes (EMA20/50 alignment) Structural breakout scanning based on swing and price flow Detection of reversal patterns (engulfing, pin bars, inside bars) Visual confirmation of signal zones with contextual directional strength The tool displays clear visual signals (Buy/Sell labels) on the chart to help traders identify high-probability entry zones. All signals are based on confirmed candle closes, with no repainting logic. It also marks key zones (support and resistance) to assist traders in filtering signals with greater discretion. 🔍 Why invite-only and closed-source? The strategy powering this script is the result of extensive real-time testing and ongoing optimization. The source code is protected to preserve its originality and avoid misuse or copying, while delivering full technical utility. 🛠️ How to use it? This tool is intended for manual execution. Users must apply their own judgment using the signals and technical analysis provided as a guide within their trading strategy. ⚠️ Disclaimer This script does not guarantee profitable results. It is a technical analysis tool meant to assist decision-making and requires trader interpretation. It does not constitute financial advice of any kind.
Penunjuk Pine Script®
oleh dceidsoluciones
1
오르바 csRSIALGO-csRSI (Cyclic Smoothed RSI) Overview The ALGO-csRSI is an advanced technical indicator that offers more refined analysis than the traditional RSI. Its key benefits include: ✅ 1. Adaptive to Market Cycles - While traditional RSI is calculated using a fixed period (e.g., 14 days), csRSI adjusts based on the dominant market cycle. - This enables more accurate overbought/oversold signals. 🔕 2. Noise Reduction & Smoother Signals - Regular RSI can produce unstable signals in volatile markets. - csRSI applies additional smoothing techniques to reduce noise and provide more stable trading signals—helping avoid false entries and exits. 🎯 3. Adaptive Upper/Lower Bands - The traditional RSI relies on static thresholds (e.g., 70/30). - csRSI uses adaptive bands that automatically adjust to market conditions, allowing for more flexible strategies. 🔄 4. Improved Trend Reversal Signals - csRSI provides clearer trend reversal alerts compared to standard RSI. - It leverages Cyclic Memory to analyze historical data and generate more reliable signals. 📊 5. Enhanced Divergence Signals - If price falls while csRSI rises → Bullish Divergence → Suggests potential price increase. - If price rises while csRSI falls → Bearish Divergence → Suggests potential price decline. ⏩ 6. Leading Signals - Divergence helps detect weakening trends in advance, enabling quicker reactions by traders. - Compared to conventional RSI, csRSI tends to predict market shifts sooner. ⚙️ Note: The default setting for the right pivot confirmation period in divergence detection is set short to provide faster signals and more trade opportunities. For higher success rates, it's recommended to combine csRSI with other supporting indicators.
Penunjuk Pine Script®
oleh cnuhansa
Algo Trading🚀 All-in-one smart trading tool and community designed to bring clarity, precision, confidence and success to your trading — no matter experience, the market or timeframe. 🔍 Our tool can analyse for you: 💠 Average Price – See the average price. ⚡ Momentum – See the strength and average price of current momentum. 📈 Supply & Demand Zones – Highlighted areas where price is likely to react. 🧱 Breaks of Structure (BoS) – Market structure breaks, a sign of trend change or continuation. 🎯 Points of Interest (POI) – Strong prices to watch for setups like reversals, breakouts, or retests. 📊 Trend – See the market trend direction and changes. ☁️ Best Price Cloud – A visual zone showing where price is at a best level for entries in a trend. 🔁 Early Buy/Sell Signals – Early signals to potential drops or rallies.
Penunjuk Pine Script®
oleh algotradinq
Tradinguia.com Advanced Market Intelligence Revolutionary Trading Technology Represents a breakthrough in technical analysis, utilizing proprietary geometric algorithms combined with adaptive machine learning to identify high-probability trade setups. This is not your typical indicator - it's a complete market intelligence system. What Makes SmartFlow Pro Unique 🔬 Advanced Mathematical Foundation Proprietary Geometric Analysis: Uses advanced mathematical concepts to create dynamic market zones Multi-Dimensional Calculations: Processes price action through sophisticated spatial algorithms Adaptive Intelligence: Self-learning system that continuously optimizes performance Zero-Lag Architecture: Real-time analysis without traditional indicator delays Core Methodology SmartFlow Pro employs a three-tier analytical framework: Structural Recognition Engine: Identifies key market geometry patterns using proprietary pivot detection Dynamic Zone Mapping: Creates intelligent support/resistance areas that adapt to market volatility Probabilistic Signal Generation: Combines multiple validation layers for high-confidence trade signals Signal Intelligence LONG Signals: Generated when multiple geometric and probabilistic conditions align for bullish momentum SHORT Signals: Triggered by convergence of bearish structural and statistical factors Smart Filtering: Advanced validation system eliminates low-probability setups Adaptive Timing: Machine learning component optimizes entry timing based on historical patterns Key Advantages Over Traditional Indicators ✅ No Moving Averages: Eliminates lag associated with traditional trend indicators ✅ Dynamic Adaptation: Automatically adjusts to changing market conditions ✅ Multi-Market Compatibility: Optimized for Forex, Stocks, Crypto, and Commodities ✅ All Timeframe Support: Effective from 1-minute to monthly charts ✅ Institutional-Grade Logic: Professional-level analytical framework ✅ 100% Original Code: Completely proprietary algorithm with no built-in dependencies Optimal Usage Scenarios Primary Applications Swing Trading: Exceptional for 4H to Daily timeframes Trend Reversal Detection: Early identification of momentum shifts Breakout Confirmation: Validates genuine breakouts vs. false signals Risk Management: Clear entry/exit levels for position sizing Market Conditions Trending Markets: Captures momentum continuation setups Consolidating Markets: Identifies range breakout opportunities Volatile Markets: Filters noise while maintaining signal accuracy All Market Sessions: Performs consistently across different trading sessions Technical Specifications Algorithm Type: Proprietary geometric and statistical hybrid Calculation Method: Multi-layered mathematical analysis Signal Generation: Probabilistic convergence model Optimization: Machine learning adaptive framework Code Base: 100% original, no built-in functions used Professional Disclaimer SmartFlow Pro is designed for experienced traders who understand market dynamics and risk management. The advanced nature of this indicator requires proper education in its application. Past performance does not guarantee future results. Experience the Next Generation of Technical Analysis Where Advanced Mathematics Meets Market Intelligence
Penunjuk Pine Script®
oleh Finanzas_en_la_Luna
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.
Penunjuk Pine Script®
oleh loxx
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
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