Mulai

Crypto

Relative Crypto Dominance Polar Chart [LuxAlgo]The Relative Crypto Dominance Polar Chart tool allows traders to compare the relative dominance of up to ten different tickers in the form of a polar area chart, we define relative dominance as a combination between traded dollar volume and volatility, making it very easy to compare them at a glance. 🔶 USAGE The use is quite simple, traders just have to load the indicator on the chart, and the graph showing the relative dominance will appear. The 10 tickers loaded by default are the major cryptocurrencies by market cap, but traders can select any ticker in the settings panel. Each area represents dominance as volatility (radius) by dollar volume (arc length); a larger area means greater dominance on that ticker. 🔹 Choosing Period The tool supports up to five different periods Hourly Daily Weekly Monthly Yearly By default, the tool period is set on auto mode, which means that the tool will choose the period depending on the chart timeframe timeframes up to 2m: Hourly timeframes up to 15m: Daily timeframes up to 1H: Weekly timeframes up to 4H: Monthly larger timeframes: Yearly 🔹 Sorting & Sizing Traders can sort the graph areas by volatility (radius of each area) in ascending or descending order; by default, the tickers are sorted as they are in the settings panel. The tool also allows you to adjust the width of the chart on a percentage basis, i.e., at 100% size, all the available width is used; if the graph is too wide, just decrease the graph size parameter in the settings panel. 🔹 Set your own style The tool allows great customization from the settings panel, traders can enable/disable most of the components, and add a very nice touch with curved lines enabled for displaying the areas with a petal-like effect. 🔶 SETTINGS Period: Select up to 5 different time periods from Hourly, Daily, Weekly, Monthly and Yearly. Enable/disable Auto mode. Tickers: Enable/disable and select tickers and colors 🔹 Style Graph Order: Select sort order Graph Size: Select percentage of width used Labels Size: Select size for ticker labels Show Percent: Show dominance in % under each ticker Curved Lines: Enable/disable petal-like effect for each area Show Title: Enable/disable graph title Show Mean: Enable/disable volatility average and select color
Indikator Pine Script®
oleh LuxAlgo
5
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here . Firstly, we would like to give credit to @apsk32 and @x_X_77_X_x as part of the code originates from their work. Additionally, @apsk32 is widely credited with applying the Power Law concept to Bitcoin and popularizing this model within the crypto community. Additionally, the visual layout is fully inspired by @apsk32's designs, and we think it looks amazing. So much so that we had to turn it into a TradingView script. Thank you! Understanding the Bitcoin Power Law Model Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship. By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges. In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term. The Bitcoin Power Law has the following function: Power Law = 10^(a + b * log10(d)) Consisting of the following parameters: a: Power Law Intercept (default: -17.668). b: Power Law Slope (default: 5.926). d: Number of days since a reference point(calculated by counting bars from the reference point with an offset). Explanation of the a and b parameters: Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post. Applying vertical shifts to generate the other lines: Once the initial power-law is created, additional lines are generated by applying a vertical shift . This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept. In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest): Base Power Law Line (no shift) 1-year shifted line 2-year shifted line 3-year shifted line 4-year shifted line 5-year shifted line The six power law lines: Bitcoin Power Law Oscillator This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula: normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line) Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines. Interpretation of the Bitcoin Power Law Model: The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.). By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame. On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines. In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples: Example 1: In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support (Note: Vertical yearly grid lines have been added for this purpose👍). Example 2: In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point C, now aligning with the Base Power Law Line. Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe : Example 3: In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Indikator Pine Script®
oleh Cryptonerds
Crypto Sectors Performance [Daveatt]IMPORTANT ⚠️ This script must be used on the Daily timeframe only. OVERVIEW This indicator brings the powerful sector analysis capabilities from velo.xyz/market's Sector Performance chart to TradingView. It enables traders to track and compare performance across the crypto market's major sectors, providing essential insights for sector rotation strategies and market analysis. CALCULATION METHOD The indicator calculates performance across six key crypto sectors: DeFi, Gaming, Layer 1s, Layer 2s, AI, and Memecoins. For each sector, it computes a rolling percentage performance by averaging the performance of multiple representative tokens. All sector performances are rebased to 0% at the start of each period, making relative comparisons clear and intuitive. VISUALIZATION MODES The script features two distinct visualization methods: Plots Mode: Displays continuous performance lines for each sector over time, ideal for tracking relative strength trends and sector momentum. Each sector has its own color-coded line with performance values clearly marked. Bars Mode: Presents current sector performance as vertical bars, offering an immediate visual comparison of sector gains and losses. The bars are color-coded and labeled with exact percentage values for precise analysis. For the "Bars Mode", I used the box.new() function SECTOR COMPOSITION Each sector comprises carefully selected representative tokens: - DeFi: AAVE, 1INCH, JUP, MKR, UNI - Gaming: GALA, AXS, RONIN, SAND - Layer 1: BTC, ETH, AVAX, APT, SOL, BNB, SUI - Layer 2: ARB, OP, ZK, POL, STRK, MNT - AI: FET, NEAR, RENDER, TAO - Memecoins: PEPE, BONK, SHIB, DOGE, WIFU, POPCAT PERFORMANCE TRACKING The indicator implements a rolling window approach for performance calculations. Starting from 0% at the beginning of each period, it tracks relative performance with positive values indicating outperformance and negative values showing underperformance. Multiple timeframe options (1W, 1M, 3M, 6M, and 1Y) allow for both short-term and long-term analysis. APPLICATIONS This tool proves invaluable for: - Sector rotation analysis - Identifying trending sectors - Comparing relative strength - Gauging market sentiment - Understanding market structure through sector performance Thanks for reading and for the support Daveatt
Indikator Pine Script®
oleh Daveatt
3
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here . Firstly, we would like to give credit to @apsk32 and @x_X_77_X_x as part of the code originates from their work. Additionally, @apsk32 is widely credited with applying the Power Law concept to Bitcoin and popularizing this model within the crypto community. Additionally, the visual layout is fully inspired by @apsk32's designs, and we think it looks amazing. So much so that we had to turn it into a TradingView script. Thank you! Understanding the Bitcoin Power Law Model Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship. By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges. In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term. The Bitcoin Power Law has the following function: Power Law = 10^(a + b * log10(d)) Consisting of the following parameters: a: Power Law Intercept (default: -17.668). b: Power Law Slope (default: 5.926). d: Number of days since a reference point(calculated by counting bars from the reference point with an offset). Explanation of the a and b parameters: Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post. Applying vertical shifts to generate the other lines: Once the initial power-law is created, additional lines are generated by applying a vertical shift . This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept. In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest): Base Power Law Line (no shift) 1-year shifted line 2-year shifted line 3-year shifted line 4-year shifted line 5-year shifted line The six power law lines: Bitcoin Power Law Oscillator This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula: normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line) Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines. Interpretation of the Bitcoin Power Law Model: The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.). By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame. On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines. In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples: Example 1: In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support (Note: Vertical yearly grid lines have been added for this purpose👍). Example 2: In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point C, now aligning with the Base Power Law Line. Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe : Example 3: In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Indikator Pine Script®
oleh Cryptonerds
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script. 💡Why this model was created: One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model). This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here . 📉The theory of diminishing returns: This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles. To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/ 🔧Creation of the model: For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section. This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function: ax^3 +bx^2 + cx + d. In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows: Top regression line (x, y) values: 113, 18.6 240, 1004 451, 19128 655, 65502 Bottom regression line (x, y) values: 103, 2.5 267, 211 471, 3193 676, 16255 The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found: Top regression line parameter values: a: 0.000202798 b: 0.0872922 c: -30.88805 d: 1827.14113 Bottom regression line parameter values: a: 0.000138314 b: -0.0768236 c: 13.90555 d: -765.8892 📊Polynomial Regression Oscillator: This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula: normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line) This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here. 🔍Interpretation of the Model: In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom. The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom. Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below. 🔮Future Predictions: Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom: In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top. Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026. Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026. For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow: And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Indikator Pine Script®
oleh Cryptonerds
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script. 💡Why this model was created: One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model). This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here . 📉The theory of diminishing returns: This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles. To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/ 🔧Creation of the model: For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section. This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function: ax^3 +bx^2 + cx + d. In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows: Top regression line (x, y) values: 113, 18.6 240, 1004 451, 19128 655, 65502 Bottom regression line (x, y) values: 103, 2.5 267, 211 471, 3193 676, 16255 The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found: Top regression line parameter values: a: 0.000202798 b: 0.0872922 c: -30.88805 d: 1827.14113 Bottom regression line parameter values: a: 0.000138314 b: -0.0768236 c: 13.90555 d: -765.8892 📊Polynomial Regression Oscillator: This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula: normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line) This transformation results in a price value between 0 and 1 between both the regression lines. 🔍Interpretation of the Model: In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom. The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom. Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below. 🔮Future Predictions: Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom: In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top. Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026. Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026. For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow: And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Indikator Pine Script®
oleh Cryptonerds
Fuzzy SMA with DCTI Confirmation[FibonacciFlux]FibonacciFlux: Advanced Fuzzy Logic System with Donchian Trend Confirmation Institutional-grade trend analysis combining adaptive Fuzzy Logic with Donchian Channel Trend Intensity for superior signal quality Conceptual Framework & Research Foundation FibonacciFlux represents a significant advancement in quantitative technical analysis, merging two powerful analytical methodologies: normalized fuzzy logic systems and Donchian Channel Trend Intensity (DCTI). This sophisticated indicator addresses a fundamental challenge in market analysis – the inherent imprecision of trend identification in dynamic, multi-dimensional market environments. While traditional indicators often produce simplistic binary signals, markets exist in states of continuous, graduated transition. FibonacciFlux embraces this complexity through its implementation of fuzzy set theory, enhanced by DCTI's structural trend confirmation capabilities. The result is an indicator that provides nuanced, probabilistic trend assessment with institutional-grade signal quality. Core Technological Components 1. Advanced Fuzzy Logic System with Percentile Normalization At the foundation of FibonacciFlux lies a comprehensive fuzzy logic system that transforms conventional technical metrics into degrees of membership in linguistic variables: // Fuzzy triangular membership function with robust error handling fuzzy_triangle(val, left, center, right) => if na(val) 0.0 float denominator1 = math.max(1e-10, center - left) float denominator2 = math.max(1e-10, right - center) math.max(0.0, math.min(left == center ? val <= center ? 1.0 : 0.0 : (val - left) / denominator1, center == right ? val >= center ? 1.0 : 0.0 : (right - val) / denominator2)) The system employs percentile-based normalization for SMA deviation – a critical innovation that enables self-calibration across different assets and market regimes: // Percentile-based normalization for adaptive calibration raw_diff = price_src - sma_val diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10 normalized_diff_raw = raw_diff / diff_abs_percentile normalized_diff = useClamping ? math.max(-clampValue, math.min(clampValue, normalized_diff_raw)) : normalized_diff_raw This normalization approach represents a significant advancement over fixed-threshold systems, allowing the indicator to automatically adapt to varying volatility environments and maintain consistent signal quality across diverse market conditions. 2. Donchian Channel Trend Intensity (DCTI) Integration FibonacciFlux significantly enhances fuzzy logic analysis through the integration of Donchian Channel Trend Intensity (DCTI) – a sophisticated measure of trend strength based on the relationship between short-term and long-term price extremes: // DCTI calculation for structural trend confirmation f_dcti(src, majorPer, minorPer, sigPer) => H = ta.highest(high, majorPer) // Major period high L = ta.lowest(low, majorPer) // Major period low h = ta.highest(high, minorPer) // Minor period high l = ta.lowest(low, minorPer) // Minor period low float pdiv = not na(L) ? l - L : 0 // Positive divergence (low vs major low) float ndiv = not na(H) ? H - h : 0 // Negative divergence (major high vs high) float divisor = pdiv + ndiv dctiValue = divisor == 0 ? 0 : 100 * ((pdiv - ndiv) / divisor) // Normalized to -100 to +100 range sigValue = ta.ema(dctiValue, sigPer) DCTI provides a complementary structural perspective on market trends by quantifying the relationship between short-term and long-term price extremes. This creates a multi-dimensional analysis framework that combines adaptive deviation measurement (fuzzy SMA) with channel-based trend intensity confirmation (DCTI). Multi-Dimensional Fuzzy Input Variables FibonacciFlux processes four distinct technical dimensions through its fuzzy system: Normalized SMA Deviation: Measures price displacement relative to historical volatility context Rate of Change (ROC): Captures price momentum over configurable timeframes Relative Strength Index (RSI): Evaluates cyclical overbought/oversold conditions Donchian Channel Trend Intensity (DCTI): Provides structural trend confirmation through channel analysis Each dimension is processed through comprehensive fuzzy sets that transform crisp numerical values into linguistic variables: // Normalized SMA Deviation - Self-calibrating to volatility regimes ndiff_LP := fuzzy_triangle(normalized_diff, norm_scale * 0.3, norm_scale * 0.7, norm_scale * 1.1) ndiff_SP := fuzzy_triangle(normalized_diff, norm_scale * 0.05, norm_scale * 0.25, norm_scale * 0.5) ndiff_NZ := fuzzy_triangle(normalized_diff, -norm_scale * 0.1, 0.0, norm_scale * 0.1) ndiff_SN := fuzzy_triangle(normalized_diff, -norm_scale * 0.5, -norm_scale * 0.25, -norm_scale * 0.05) ndiff_LN := fuzzy_triangle(normalized_diff, -norm_scale * 1.1, -norm_scale * 0.7, -norm_scale * 0.3) // DCTI - Structural trend measurement dcti_SP := fuzzy_triangle(dcti_val, 60.0, 85.0, 101.0) // Strong Positive Trend (> ~85) dcti_WP := fuzzy_triangle(dcti_val, 20.0, 45.0, 70.0) // Weak Positive Trend (~30-60) dcti_Z := fuzzy_triangle(dcti_val, -30.0, 0.0, 30.0) // Near Zero / Trendless (~+/- 20) dcti_WN := fuzzy_triangle(dcti_val, -70.0, -45.0, -20.0) // Weak Negative Trend (~-30 - -60) dcti_SN := fuzzy_triangle(dcti_val, -101.0, -85.0, -60.0) // Strong Negative Trend (< ~-85) Advanced Fuzzy Rule System with DCTI Confirmation The core intelligence of FibonacciFlux lies in its sophisticated fuzzy rule system – a structured knowledge representation that encodes expert understanding of market dynamics: // Base Trend Rules with DCTI Confirmation cond1 = math.min(ndiff_LP, roc_HP, rsi_M) strength_SB := math.max(strength_SB, cond1 * (dcti_SP > 0.5 ? 1.2 : dcti_Z > 0.1 ? 0.5 : 1.0)) // DCTI Override Rules - Structural trend confirmation with momentum alignment cond14 = math.min(ndiff_NZ, roc_HP, dcti_SP) strength_SB := math.max(strength_SB, cond14 * 0.5) The rule system implements 15 distinct fuzzy rules that evaluate various market conditions including: Established Trends: Strong deviations with confirming momentum and DCTI alignment Emerging Trends: Early deviation patterns with initial momentum and DCTI confirmation Weakening Trends: Divergent signals between deviation, momentum, and DCTI Reversal Conditions: Counter-trend signals with DCTI confirmation Neutral Consolidations: Minimal deviation with low momentum and neutral DCTI A key innovation is the weighted influence of DCTI on rule activation. When strong DCTI readings align with other indicators, rule strength is amplified (up to 1.2x). Conversely, when DCTI contradicts other indicators, rule impact is reduced (as low as 0.5x). This creates a dynamic, self-adjusting system that prioritizes high-conviction signals. Defuzzification & Signal Generation The final step transforms fuzzy outputs into a precise trend score through center-of-gravity defuzzification: // Defuzzification with precise floating-point handling denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe if denominator > 1e-10 fuzzyTrendScore := (strength_SB * STRONG_BULL + strength_WB * WEAK_BULL + strength_N * NEUTRAL + strength_WBe * WEAK_BEAR + strength_SBe * STRONG_BEAR) / denominator The resulting FuzzyTrendScore ranges from -1.0 (Strong Bear) to +1.0 (Strong Bull), with critical threshold zones at ±0.3 (Weak trend) and ±0.7 (Strong trend). The histogram visualization employs intuitive color-coding for immediate trend assessment. Strategic Applications for Institutional Trading FibonacciFlux provides substantial advantages for sophisticated trading operations: Multi-Timeframe Signal Confirmation: Institutional-grade signal validation across multiple technical dimensions Trend Strength Quantification: Precise measurement of trend conviction with noise filtration Early Trend Identification: Detection of emerging trends before traditional indicators through fuzzy pattern recognition Adaptive Market Regime Analysis: Self-calibrating analysis across varying volatility environments Algorithmic Strategy Integration: Well-defined numerical output suitable for systematic trading frameworks Risk Management Enhancement: Superior signal fidelity for risk exposure optimization Customization Parameters FibonacciFlux offers extensive customization to align with specific trading mandates and market conditions: Fuzzy SMA Settings: Configure baseline trend identification parameters including SMA, ROC, and RSI lengths Normalization Settings: Fine-tune the self-calibration mechanism with adjustable lookback period, percentile rank, and optional clamping DCTI Parameters: Optimize trend structure confirmation with adjustable major/minor periods and signal smoothing Visualization Controls: Customize display transparency for optimal chart integration These parameters enable precise calibration for different asset classes, timeframes, and market regimes while maintaining the core analytical framework. Implementation Notes For optimal implementation, consider the following guidance: Higher timeframes (4H+) benefit from increased normalization lookback (800+) for stability Volatile assets may require adjusted clamping values (2.5-4.0) for optimal signal sensitivity DCTI parameters should be aligned with chart timeframe (higher timeframes require increased major/minor periods) The indicator performs exceptionally well as a trend filter for systematic trading strategies Acknowledgments FibonacciFlux builds upon the pioneering work of Donovan Wall in Donchian Channel Trend Intensity analysis. The normalization approach draws inspiration from percentile-based statistical techniques in quantitative finance. This indicator is shared for educational and analytical purposes under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Past performance does not guarantee future results. All trading involves risk. This indicator should be used as one component of a comprehensive analysis framework. Shout out @DonovanWall
Indikator Pine Script®
oleh FibonacciFlux
Fuzzy SMA Trend Analyzer (experimental)[FibonacciFlux]Fuzzy SMA Trend Analyzer (Normalized): Advanced Market Trend Detection Using Fuzzy Logic Theory Elevate your technical analysis with institutional-grade fuzzy logic implementation Research Genesis & Conceptual Framework This indicator represents the culmination of extensive research into applying fuzzy logic theory to financial markets. While traditional technical indicators often produce binary outcomes, market conditions exist on a continuous spectrum. The Fuzzy SMA Trend Analyzer addresses this limitation by implementing a sophisticated fuzzy logic system that captures the nuanced, multi-dimensional nature of market trends. Core Fuzzy Logic Principles At the heart of this indicator lies fuzzy logic theory - a mathematical framework designed to handle imprecision and uncertainty: // Improved fuzzy_triangle function with guard clauses for NA and invalid parameters. fuzzy_triangle(val, left, center, right) => if na(val) or na(left) or na(center) or na(right) or left > center or center > right // Guard checks 0.0 else if left == center and center == right // Crisp set (single point) val == center ? 1.0 : 0.0 else if left == center // Left-shoulder shape (ramp down from 1 at center to 0 at right) val >= right ? 0.0 : val <= center ? 1.0 : (right - val) / (right - center) else if center == right // Right-shoulder shape (ramp up from 0 at left to 1 at center) val <= left ? 0.0 : val >= center ? 1.0 : (val - left) / (center - left) else // Standard triangle math.max(0.0, math.min((val - left) / (center - left), (right - val) / (right - center))) This implementation of triangular membership functions enables the indicator to transform crisp numerical values into degrees of membership in linguistic variables like "Large Positive" or "Small Negative," creating a more nuanced representation of market conditions. Dynamic Percentile Normalization A critical innovation in this indicator is the implementation of percentile-based normalization for SMA deviation: // ----- Deviation Scale Estimation using Percentile ----- // Calculate the percentile rank of the *absolute* deviation over the lookback period. // This gives an estimate of the 'typical maximum' deviation magnitude recently. diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10 // ----- Normalize the Raw Deviation ----- // Divide the raw deviation by the estimated 'typical max' magnitude. normalized_diff = raw_diff / diff_abs_percentile // ----- Clamp the Normalized Deviation ----- normalized_diff_clamped = math.max(-3.0, math.min(3.0, normalized_diff)) This percentile normalization approach creates a self-adapting system that automatically calibrates to different assets and market regimes. Rather than using fixed thresholds, the indicator dynamically adjusts based on recent volatility patterns, significantly enhancing signal quality across diverse market environments. Multi-Factor Fuzzy Rule System The indicator implements a comprehensive fuzzy rule system that evaluates multiple technical factors: SMA Deviation (Normalized): Measures price displacement from the Simple Moving Average Rate of Change (ROC): Captures price momentum over a specified period Relative Strength Index (RSI): Assesses overbought/oversold conditions These factors are processed through a sophisticated fuzzy inference system with linguistic variables: // ----- 3.1 Fuzzy Sets for Normalized Deviation ----- diffN_LP := fuzzy_triangle(normalized_diff_clamped, 0.7, 1.5, 3.0) // Large Positive (around/above percentile) diffN_SP := fuzzy_triangle(normalized_diff_clamped, 0.1, 0.5, 0.9) // Small Positive diffN_NZ := fuzzy_triangle(normalized_diff_clamped, -0.2, 0.0, 0.2) // Near Zero diffN_SN := fuzzy_triangle(normalized_diff_clamped, -0.9, -0.5, -0.1) // Small Negative diffN_LN := fuzzy_triangle(normalized_diff_clamped, -3.0, -1.5, -0.7) // Large Negative (around/below percentile) // ----- 3.2 Fuzzy Sets for ROC ----- roc_HN := fuzzy_triangle(roc_val, -8.0, -5.0, -2.0) roc_WN := fuzzy_triangle(roc_val, -3.0, -1.0, -0.1) roc_NZ := fuzzy_triangle(roc_val, -0.3, 0.0, 0.3) roc_WP := fuzzy_triangle(roc_val, 0.1, 1.0, 3.0) roc_HP := fuzzy_triangle(roc_val, 2.0, 5.0, 8.0) // ----- 3.3 Fuzzy Sets for RSI ----- rsi_L := fuzzy_triangle(rsi_val, 0.0, 25.0, 40.0) rsi_M := fuzzy_triangle(rsi_val, 35.0, 50.0, 65.0) rsi_H := fuzzy_triangle(rsi_val, 60.0, 75.0, 100.0) Advanced Fuzzy Inference Rules The indicator employs a comprehensive set of fuzzy rules that encode expert knowledge about market behavior: // --- Fuzzy Rules using Normalized Deviation (diffN_*) --- cond1 = math.min(diffN_LP, roc_HP, math.max(rsi_M, rsi_H)) // Strong Bullish: Large pos dev, strong pos roc, rsi ok strength_SB := math.max(strength_SB, cond1) cond2 = math.min(diffN_SP, roc_WP, rsi_M) // Weak Bullish: Small pos dev, weak pos roc, rsi mid strength_WB := math.max(strength_WB, cond2) cond3 = math.min(diffN_SP, roc_NZ, rsi_H) // Weakening Bullish: Small pos dev, flat roc, rsi high strength_N := math.max(strength_N, cond3 * 0.6) // More neutral strength_WB := math.max(strength_WB, cond3 * 0.2) // Less weak bullish This rule system evaluates multiple conditions simultaneously, weighting them by their degree of membership to produce a comprehensive trend assessment. The rules are designed to identify various market conditions including strong trends, weakening trends, potential reversals, and neutral consolidations. Defuzzification Process The final step transforms the fuzzy result back into a crisp numerical value representing the overall trend strength: // --- Step 6: Defuzzification --- denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe if denominator > 1e-10 // Use small epsilon instead of != 0.0 for float comparison fuzzyTrendScore := (strength_SB * STRONG_BULL + strength_WB * WEAK_BULL + strength_N * NEUTRAL + strength_WBe * WEAK_BEAR + strength_SBe * STRONG_BEAR) / denominator The resulting FuzzyTrendScore ranges from -1 (strong bearish) to +1 (strong bullish), providing a smooth, continuous evaluation of market conditions that avoids the abrupt signal changes common in traditional indicators. Advanced Visualization with Rainbow Gradient The indicator incorporates sophisticated visualization using a rainbow gradient coloring system: // Normalize score to for gradient function normalizedScore = na(fuzzyTrendScore) ? 0.5 : math.max(0.0, math.min(1.0, (fuzzyTrendScore + 1) / 2)) // Get the color based on gradient setting and normalized score final_color = get_gradient(normalizedScore, gradient_type) This color-coding system provides intuitive visual feedback, with color intensity reflecting trend strength and direction. The gradient can be customized between Red-to-Green or Red-to-Blue configurations based on user preference. Practical Applications The Fuzzy SMA Trend Analyzer excels in several key applications: Trend Identification: Precisely identifies market trend direction and strength with nuanced gradation Market Regime Detection: Distinguishes between trending markets and consolidation phases Divergence Analysis: Highlights potential reversals when price action and fuzzy trend score diverge Filter for Trading Systems: Provides high-quality trend filtering for other trading strategies Risk Management: Offers early warning of potential trend weakening or reversal Parameter Customization The indicator offers extensive customization options: SMA Length: Adjusts the baseline moving average period ROC Length: Controls momentum sensitivity RSI Length: Configures overbought/oversold sensitivity Normalization Lookback: Determines the adaptive calculation window for percentile normalization Percentile Rank: Sets the statistical threshold for deviation normalization Gradient Type: Selects the preferred color scheme for visualization These parameters enable fine-tuning to specific market conditions, trading styles, and timeframes. Acknowledgments The rainbow gradient visualization component draws inspiration from LuxAlgo's "Rainbow Adaptive RSI" (used under CC BY-NC-SA 4.0 license). This implementation of fuzzy logic in technical analysis builds upon Fermi estimation principles to overcome the inherent limitations of crisp binary indicators. This indicator is shared under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Remember that past performance does not guarantee future results. Always conduct thorough testing before implementing any technical indicator in live trading.
Indikator Pine Script®
oleh FibonacciFlux
Multi-Fibonacci Trend Average[FibonacciFlux]Multi-Fibonacci Trend Average (MFTA): An Institutional-Grade Trend Confluence Indicator for Discerning Market Participants My original indicator/Strategy: Engineered for the sophisticated demands of institutional and advanced traders, the Multi-Fibonacci Trend Average (MFTA) indicator represents a paradigm shift in technical analysis. This meticulously crafted tool is designed to furnish high-definition trend signals within the complexities of modern financial markets. Anchored in the rigorous principles of Fibonacci ratios and augmented by advanced averaging methodologies, MFTA delivers a granular perspective on trend dynamics. Its integration of Multi-Timeframe (MTF) filters provides unparalleled signal robustness, empowering strategic decision-making with a heightened degree of confidence. MFTA indicator on BTCUSDT 15min chart with 1min RSI and MACD filters enabled. Note the refined signal generation with reduced noise. MFTA indicator on BTCUSDT 15min chart without MTF filters. While capturing more potential trading opportunities, it also generates a higher frequency of signals, including potential false positives. Core Innovation: Proprietary Fibonacci-Enhanced Supertrend Averaging Engine The MFTA indicator’s core innovation lies in its proprietary implementation of Supertrend analysis, strategically fortified by Fibonacci ratios to construct a truly dynamic volatility envelope. Departing from conventional Supertrend methodologies, MFTA autonomously computes not one, but three distinct Supertrend lines. Each of these lines is uniquely parameterized by a specific Fibonacci factor: 0.618 (Weak), 1.618 (Medium/Golden Ratio), and 2.618 (Strong/Extended Fibonacci). // Fibonacci-based factors for multiple Supertrend calculations factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval=0.01, step=0.01, tooltip='Factor 1 (Weak/Fibonacci)', group="Fibonacci Supertrend") factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval=0.01, step=0.01, tooltip='Factor 2 (Medium/Golden Ratio)', group="Fibonacci Supertrend") factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval=0.01, step=0.01, tooltip='Factor 3 (Strong/Extended Fib)', group="Fibonacci Supertrend") This multi-faceted architecture adeptly captures a spectrum of market volatility sensitivities, ensuring a comprehensive assessment of prevailing conditions. Subsequently, the indicator algorithmically synthesizes these disparate Supertrend lines through arithmetic averaging. To achieve optimal signal fidelity and mitigate inherent market noise, this composite average is further refined utilizing an Exponential Moving Average (EMA). // Calculate average of the three supertends and a smoothed version superlength = input.int(21, 'Smoothing Length', tooltip='Smoothing Length for Average Supertrend', group="Fibonacci Supertrend") average_trend = (supertrend1 + supertrend2 + supertrend3) / 3 smoothed_trend = ta.ema(average_trend, superlength) The resultant ‘Smoothed Trend’ line emerges as a remarkably responsive yet stable trend demarcation, offering demonstrably superior clarity and precision compared to singular Supertrend implementations, particularly within the turbulent dynamics of high-volatility markets. Elevated Signal Confluence: Integrated Multi-Timeframe (MTF) Validation Suite MFTA transcends the limitations of conventional trend indicators by incorporating an advanced suite of three independent MTF filters: RSI, MACD, and Volume. These filters function as sophisticated validation protocols, rigorously ensuring that only signals exhibiting a confluence of high-probability factors are brought to the forefront. 1. Granular Lower Timeframe RSI Momentum Filter The Relative Strength Index (RSI) filter, computed from a user-defined lower timeframe, furnishes critical momentum-based signal validation. By meticulously monitoring RSI dynamics on an accelerated timeframe, traders gain the capacity to evaluate underlying momentum strength with precision, prior to committing to signal execution on the primary chart timeframe. // --- Lower Timeframe RSI Filter --- ltf_rsi_filter_enable = input.bool(false, title="Enable RSI Filter", group="MTF Filters", tooltip="Use RSI from lower timeframe as a filter") ltf_rsi_timeframe = input.timeframe("1", title="RSI Timeframe", group="MTF Filters", tooltip="Timeframe for RSI calculation") ltf_rsi_length = input.int(14, title="RSI Length", minval=1, group="MTF Filters", tooltip="Length for RSI calculation") ltf_rsi_threshold = input.int(30, title="RSI Threshold", minval=0, maxval=100, group="MTF Filters", tooltip="RSI value threshold for filtering signals") 2. Convergent Lower Timeframe MACD Trend-Momentum Filter The Moving Average Convergence Divergence (MACD) filter, also calculated on a lower timeframe basis, introduces a critical layer of trend-momentum convergence confirmation. The bullish signal configuration rigorously mandates that the MACD line be definitively positioned above the Signal line on the designated lower timeframe. This stringent condition ensures a robust indication of converging momentum that aligns synergistically with the prevailing trend identified on the primary timeframe. // --- Lower Timeframe MACD Filter --- ltf_macd_filter_enable = input.bool(false, title="Enable MACD Filter", group="MTF Filters", tooltip="Use MACD from lower timeframe as a filter") ltf_macd_timeframe = input.timeframe("1", title="MACD Timeframe", group="MTF Filters", tooltip="Timeframe for MACD calculation") ltf_macd_fast_length = input.int(12, title="MACD Fast Length", minval=1, group="MTF Filters", tooltip="Fast EMA length for MACD") ltf_macd_slow_length = input.int(26, title="MACD Slow Length", minval=1, group="MTF Filters", tooltip="Slow EMA length for MACD") ltf_macd_signal_length = input.int(9, title="MACD Signal Length", minval=1, group="MTF Filters", tooltip="Signal SMA length for MACD") 3. Definitive Volume Confirmation Filter The Volume Filter functions as an indispensable arbiter of trade conviction. By establishing a dynamic volume threshold, defined as a percentage relative to the average volume over a user-specified lookback period, traders can effectively ensure that all generated signals are rigorously validated by demonstrably increased trading activity. This pivotal validation step signifies robust market participation, substantially diminishing the potential for spurious or false breakout signals. // --- Volume Filter --- volume_filter_enable = input.bool(false, title="Enable Volume Filter", group="MTF Filters", tooltip="Use volume level as a filter") volume_threshold_percent = input.int(title="Volume Threshold (%)", defval=150, minval=100, group="MTF Filters", tooltip="Minimum volume percentage compared to average volume to allow signal (100% = average)") These meticulously engineered filters operate in synergistic confluence, requiring all enabled filters to definitively satisfy their pre-defined conditions before a Buy or Sell signal is generated. This stringent multi-layered validation process drastically minimizes the incidence of false positive signals, thereby significantly enhancing entry precision and overall signal reliability. Intuitive Visual Architecture & Actionable Intelligence MFTA provides a demonstrably intuitive and visually rich charting environment, meticulously delineating trend direction and momentum through precisely color-coded plots: Average Supertrend: Thin line, green/red for uptrend/downtrend, immediate directional bias. Smoothed Supertrend: Bold line, teal/purple for uptrend/downtrend, cleaner, institutionally robust trend. Dynamic Trend Fill: Green/red fill between Supertrends quantifies trend strength and momentum. Adaptive Background Coloring: Light green/red background mirrors Smoothed Supertrend direction, holistic trend perspective. Precision Buy/Sell Signals: ‘BUY’/‘SELL’ labels appear on chart when trend touch and MTF filter confluence are satisfied, facilitating high-conviction trade action. MFTA indicator applied to BTCUSDT 4-hour chart, showcasing its effectiveness on higher timeframes. The Smoothed Length parameter is increased to 200 for enhanced smoothness on this timeframe, coupled with 1min RSI and Volume filters for signal refinement. This illustrates the indicator's adaptability across different timeframes and market conditions. Strategic Applications for Institutional Mandates MFTA’s sophisticated design provides distinct advantages for advanced trading operations and institutional investment mandates. Key strategic applications include: High-Probability Trend Identification: Fibonacci-averaged Supertrend with MTF filters robustly identifies high-probability trend continuations and reversals, enhancing alpha generation. Precision Entry/Exit Signals: Volume and momentum-filtered signals enable institutional-grade precision for optimized risk-adjusted returns. Algorithmic Trading Integration: Clear signal logic facilitates seamless integration into automated trading systems for scalable strategy deployment. Multi-Asset/Timeframe Versatility: Adaptable parameters ensure applicability across diverse asset classes and timeframes, catering to varied trading mandates. Enhanced Risk Management: Superior signal fidelity from MTF filters inherently reduces false signals, supporting robust risk management protocols. Granular Customization and Parameterized Control MFTA offers unparalleled customization, empowering users to fine-tune parameters for precise alignment with specific trading styles and market conditions. Key adjustable parameters include: Fibonacci Factors: Adjust Supertrend sensitivity to volatility regimes. ATR Length: Control volatility responsiveness in Supertrend calculations. Smoothing Length: Refine Smoothed Trend line responsiveness and noise reduction. MTF Filter Parameters: Independently configure timeframes, lookback periods, and thresholds for RSI, MACD, and Volume filters for optimal signal filtering. Disclaimer MFTA is meticulously engineered for high-quality trend signals; however, no indicator guarantees profit. Market conditions are unpredictable, and trading involves substantial risk. Rigorous backtesting and forward testing across diverse datasets, alongside a comprehensive understanding of the indicator's logic, are essential before live deployment. Past performance is not indicative of future results. MFTA is for informational and analytical purposes only and is not financial or investment advice.
Indikator Pine Script®
oleh FibonacciFlux
1
Rally Base Drop SND Pivots Strategy [LuxAlgo X PineIndicators]This strategy is based on the Rally Base Drop (RBD) SND Pivots indicator developed by LuxAlgo. Full credit for the concept and original indicator goes to LuxAlgo. The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand trading system that detects pivot points based on Rally, Base, and Drop (RBD) candles. This strategy automatically identifies key market structure levels, allowing traders to: Identify pivot-based supply and demand (SND) zones. Use fixed criteria for trend continuation or reversals. Filter out market noise by requiring structured price formations. Enter trades based on breakouts of key SND pivot levels. How the Rally Base Drop SND Pivots Strategy Works 1. Pivot Point Detection Using RBD Candles The strategy follows a rigid market structure methodology, where pivots are detected only when: A Rally (R) consists of multiple consecutive bullish candles. A Drop (D) consists of multiple consecutive bearish candles. A Base (B) is identified as a transition between Rallies and Drops, acting as a pivot point. The pivot level is confirmed when the formation is complete. Unlike traditional fractal-based pivots, RBD Pivots enforce stricter structural rules, ensuring that each pivot: Has a well-defined bullish or bearish price movement. Reduces false signals caused by single-bar fluctuations. Provides clear supply and demand levels based on structured price movements. These pivot levels are drawn on the chart using color-coded boxes: Green zones represent bullish pivot levels (Rally Base formations). Red zones represent bearish pivot levels (Drop Base formations). Once a pivot is confirmed, the high or low of the base candle is used as the reference level for future trades. 2. Trade Entry Conditions The strategy allows traders to select from three trading modes: Long Only – Only takes long trades when bullish pivot breakouts occur. Short Only – Only takes short trades when bearish pivot breakouts occur. Long & Short – Trades in both directions based on pivot breakouts. Trade entry signals are triggered when price breaks through a confirmed pivot level: Long Entry: A bullish pivot level is formed. Price breaks above the bullish pivot level. The strategy enters a long position. Short Entry: A bearish pivot level is formed. Price breaks below the bearish pivot level. The strategy enters a short position. The strategy includes an optional mode to reverse long and short conditions, allowing traders to experiment with contrarian entries. 3. Exit Conditions Using ATR-Based Risk Management This strategy uses the Average True Range (ATR) to calculate dynamic stop-loss and take-profit levels: Stop-Loss (SL): Placed 1 ATR below entry for long trades and 1 ATR above entry for short trades. Take-Profit (TP): Set using a Risk-Reward Ratio (RR) multiplier (default = 6x ATR). When a trade is opened: The entry price is recorded. ATR is calculated at the time of entry to determine stop-loss and take-profit levels. Trades exit automatically when either SL or TP is reached. If reverse conditions mode is enabled, stop-loss and take-profit placements are flipped. Visualization & Dynamic Support/Resistance Levels 1. Pivot Boxes for Market Structure Each pivot is marked with a colored box: Green boxes indicate bullish demand zones. Red boxes indicate bearish supply zones. These boxes remain on the chart to act as dynamic support and resistance levels, helping traders identify key price reaction zones. 2. Horizontal Entry, Stop-Loss, and Take-Profit Lines When a trade is active, the strategy plots: White line → Entry price. Red line → Stop-loss level. Green line → Take-profit level. Labels display the exact entry, SL, and TP values, updating dynamically as price moves. Customization Options This strategy offers multiple adjustable settings to optimize performance for different market conditions: Trade Mode Selection → Choose between Long Only, Short Only, or Long & Short. Pivot Length → Defines the number of required Rally & Drop candles for a pivot. ATR Exit Multiplier → Adjusts stop-loss distance based on ATR. Risk-Reward Ratio (RR) → Modifies take-profit level relative to risk. Historical Lookback → Limits how far back pivot zones are displayed. Color Settings → Customize pivot box colors for bullish and bearish setups. Considerations & Limitations Pivot Breakouts Do Not Guarantee Reversals. Some pivot breaks may lead to continuation moves instead of trend reversals. Not Optimized for Low Volatility Conditions. This strategy works best in trending markets with strong momentum. ATR-Based Stop-Loss & Take-Profit May Require Optimization. Different assets may require different ATR multipliers and RR settings. Market Noise May Still Influence Pivots. While this method filters some noise, fake breakouts can still occur. Conclusion The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand system that combines: Pivot-based market structure analysis (using Rally, Base, and Drop candles). Breakout-based trade entries at confirmed SND levels. ATR-based dynamic risk management for stop-loss and take-profit calculation. This strategy helps traders: Identify high-probability supply and demand levels. Trade based on structured market pivots. Use a systematic approach to price action analysis. Automatically manage risk with ATR-based exits. The strict pivot detection rules and built-in breakout validation make this strategy ideal for traders looking to: Trade based on market structure. Use defined support & resistance levels. Reduce noise compared to traditional fractals. Implement a structured supply & demand trading model. This strategy is fully customizable, allowing traders to adjust parameters to fit their market and trading style. Full credit for the original concept and indicator goes to LuxAlgo.
Strategi Pine Script®
oleh PineIndicators
Liquidity Sweep Filter Strategy [AlgoAlpha X PineIndicators]This strategy is based on the Liquidity Sweep Filter developed by AlgoAlpha. Full credit for the concept and original indicator goes to AlgoAlpha. The Liquidity Sweep Filter Strategy is a non-repainting trading system designed to identify liquidity sweeps, trend shifts, and high-impact price levels. It incorporates volume-based liquidation analysis, trend confirmation, and dynamic support/resistance detection to optimize trade entries and exits. This strategy helps traders: Detect liquidity sweeps where major market participants trigger stop losses and liquidations. Identify trend shifts using a volatility-based moving average system. Analyze volume distribution with a built-in volume profile visualization. Filter noise by differentiating between major and minor liquidity sweeps. How the Liquidity Sweep Filter Strategy Works 1. Trend Detection Using Volatility-Based Filtering The strategy applies a volatility-adjusted moving average system to determine trend direction: A central trend line is calculated using an EMA smoothed over a user-defined length. Upper and lower deviation bands are created based on the average price deviation over multiple periods. If price closes above the upper band, the strategy signals an uptrend. If price closes below the lower band, the strategy signals a downtrend. This approach ensures that trend shifts are confirmed only when price significantly moves beyond normal market fluctuations. 2. Liquidity Sweep Detection Liquidity sweeps occur when price temporarily breaks key levels, triggering stop-loss liquidations or margin call events. The strategy tracks swing highs and lows, marking potential liquidity grabs: Bearish Liquidity Sweeps – Price breaks a recent high, then reverses downward. Bullish Liquidity Sweeps – Price breaks a recent low, then reverses upward. Volume Integration – The strategy analyzes trading volume at each sweep to differentiate between major and minor sweeps. Key levels where liquidity sweeps occur are plotted as color-coded horizontal lines: Red lines indicate bearish liquidity sweeps. Green lines indicate bullish liquidity sweeps. Labels are displayed at each sweep, showing the volume of liquidated positions at that level. 3. Volume Profile Analysis The strategy includes an optional volume profile visualization, displaying how trading volume is distributed across different price levels. Features of the volume profile: Point of Control (POC) – The price level with the highest traded volume is marked as a key area of interest. Bounding Box – The profile is enclosed within a transparent box, helping traders visualize the price range of high trading activity. Customizable Resolution & Scale – Traders can adjust the granularity of the profile to match their preferred time frame. The volume profile helps identify zones of strong support and resistance, making it easier to anticipate price reactions at key levels. Trade Entry & Exit Conditions The strategy allows traders to configure trade direction: Long Only – Only takes long trades. Short Only – Only takes short trades. Long & Short – Trades in both directions. Entry Conditions Long Entry: A bullish trend shift is confirmed. A bullish liquidity sweep occurs (price sweeps below a key level and reverses). The trade direction setting allows long trades. Short Entry: A bearish trend shift is confirmed. A bearish liquidity sweep occurs (price sweeps above a key level and reverses). The trade direction setting allows short trades. Exit Conditions Closing a Long Position: A bearish trend shift occurs. The position is liquidated at a predefined liquidity sweep level. Closing a Short Position: A bullish trend shift occurs. The position is liquidated at a predefined liquidity sweep level. Customization Options The strategy offers multiple adjustable settings: Trade Mode: Choose between Long Only, Short Only, or Long & Short. Trend Calculation Length & Multiplier: Adjust how trend signals are calculated. Liquidity Sweep Sensitivity: Customize how aggressively the strategy identifies sweeps. Volume Profile Display: Enable or disable the volume profile visualization. Bounding Box & Scaling: Control the size and position of the volume profile. Color Customization: Adjust colors for bullish and bearish signals. Considerations & Limitations Liquidity sweeps do not always result in reversals. Some price sweeps may continue in the same direction. Works best in volatile markets. In low-volatility environments, liquidity sweeps may be less reliable. Trend confirmation adds a slight delay. The strategy ensures valid signals, but this may result in slightly later entries. Large volume imbalances may distort the volume profile. Adjusting the scale settings can help improve visualization. Conclusion The Liquidity Sweep Filter Strategy is a volume-integrated trading system that combines liquidity sweeps, trend analysis, and volume profile data to optimize trade execution. By identifying key price levels where liquidations occur, this strategy provides valuable insight into market behavior, helping traders make better-informed trading decisions. Key use cases for this strategy: Liquidity-Based Trading – Capturing moves triggered by stop hunts and liquidations. Volume Analysis – Using volume profile data to confirm high-activity price zones. Trend Following – Entering trades based on confirmed trend shifts. Support & Resistance Trading – Using liquidity sweep levels as dynamic price zones. This strategy is fully customizable, allowing traders to adapt it to different market conditions, timeframes, and risk preferences. Full credit for the original concept and indicator goes to AlgoAlpha.
Strategi Pine Script®
oleh PineIndicators
3
FinFluential Global M2 Money Supply // Days Offset =The "Global M2 Money Supply" indicator calculates and visualizes the combined M2 money supply from multiple countries and regions worldwide, expressed in trillions of USD. M2 is a measure of the money supply that includes cash, checking deposits, and easily convertible near-money assets. This indicator aggregates daily M2 data from various economies, converts them into a common USD base using forex exchange rates, and plots the total as a single line on the chart. It is designed as an overlay indicator aligned to the right scale, making it ideal for comparing global money supply trends with price action or other market data. Key Features Customizable Time Offset: Users can adjust the number of days to shift the M2 data forward or backward (from -1000 to +1000 days) via the indicator settings. This allows for alignment with historical events or forward-looking analysis. Global Coverage Includes: Eurozone: Eurozone M2 (converted via EUR/USD) North America: United States, Canada Non-EU Europe: Switzerland, United Kingdom, Finland, Russia Pacific: New Zealand Asia: China, Taiwan, Hong Kong, India, Japan, Philippines, Singapore Latin America: Brazil, Colombia, Mexico Middle East: United Arab Emirates, Turkey Africa: South Africa
Indikator Pine Script®
oleh Finfluential
3
Ragi's 24h volumeThis script is a TradingView Pine Script indicator that displays the 24-hour trading volume for a given asset. It provides both the native volume of the asset and, if the asset is not already listed on Binance, also displays the 24-hour volume from Binance (if applicable). Here's a breakdown of the key components: Volume Calculation: It sums the volume data over different time frames: 1-minute, 5-minute (for daily charts), or 60-minute intervals. The volume is calculated based on the asset's volume type (either "quote" volume or a calculated value of close * volume). For crypto assets, if the volume data is unavailable, it raises an error. Binance Volume: If the asset is not from Binance, the script fetches 24-hour volume data from Binance for that symbol, ensuring it is using the correct currency rate. Display: The indicator displays a table with the 24-hour volume in the chosen position on the chart (top, middle, or bottom). The table displays the current exchange's volume, and if applicable, the Binance volume. The volume is color-coded based on predefined thresholds: Attention: Displays a warning color for volumes exceeding the attention level. Warning: Shows an alert color for volumes above the warning threshold. Normal: Displays in standard color when the volume is lower than the warning level. The text and background color are customizable, and users can adjust the text size and position of the table. User Inputs: The script allows customization of table text size, position, background color, and volume thresholds for attention and warning. In summary, this indicator is designed to track and display 24-hour volume on a chart, with additional volume information from Binance if necessary, and provides visual cues based on volume levels to help traders quickly assess trading activity.
Indikator Pine Script®
oleh RagiBaba
Crypto Fear & Greed Score [Underblock]Crypto Fear & Greed Score - Methodology & Functioning Introduction The Crypto Fear & Greed Score is a comprehensive indicator designed to assess market sentiment by detecting extreme conditions of panic (fear) and euphoria (greed). By combining multiple technical factors, it helps traders identify potential buying and selling opportunities based on the emotional state of the market. This indicator is highly customizable, allowing users to adjust weight parameters for RSI, volatility, Bitcoin dominance, and trading volume, making it adaptable to different market conditions. Key Components The indicator consists of two primary sub-scores: Fear Score (Panic) - Measures the intensity of fear in the market. Greed Score (Euphoria) - Measures the level of overconfidence and excessive optimism. The difference between these two values results in the Net Score, which indicates the dominant market sentiment at any given time. 1. Relative Strength Index (RSI) The indicator utilizes multiple RSI timeframes to measure momentum and overbought/oversold conditions: RSI 1D (Daily) - Captures medium-term sentiment shifts. RSI 4H (4-hour) - Identifies short-term market movements. RSI 1W (Weekly) - Helps detect long-term overbought/oversold conditions. 2. Volatility Analysis High volatility is often associated with fear and panic-driven selling. Low volatility in bullish markets may indicate complacency and overconfidence. 3. Bitcoin Dominance (BTC.D) Bitcoin dominance provides insights into capital flow between Bitcoin and altcoins: Rising BTC dominance suggests fear as investors move into BTC for safety. Declining BTC dominance indicates increased risk appetite and potential market euphoria. 4. Buying and Selling Volume The indicator analyzes both buying and selling volume, ensuring a clearer confirmation of market sentiment. High buying volume in uptrends reinforces bullish momentum. Spikes in selling volume indicate panic and possible market bottoms. Calculation Methodology The indicator allows users to adjust weight parameters for each component, making it adaptable to different trading strategies. The formulas are structured as follows: Fear Score (Panic Calculation) Fear Score = (1 - RSI_1D) * W_RSI1D + (1 - RSI_4H) * W_RSI4H + (1 - Dominance) * W_Dominance + Volatility * W_Volatility + Sell Volume * W_SellVolume Greed Score (Euphoria Calculation) Greed Score = RSI_1D * W_RSI1D + RSI_4H * W_RSI4H + Dominance * W_Dominance + (1 - Volatility) * W_Volatility + Buy Volume * W_BuyVolume Net Fear & Greed Score Net Score = (Greed Score - Fear Score) * 100 Interpretation: Above 70: Extreme greed -> possible overbought conditions. Below -70: Extreme fear -> potential buying opportunity. Near 0: Neutral market sentiment. Trend Reversal Detection The indicator includes two moving averages for enhanced trend detection: Short-term SMA (50-periods) - Reacts quicklier to changes in sentiment. Long-term SMA (200-periods) - Captures broader trend reversals. How Crossovers Work: Short SMA crossing above Long SMA -> Potential bullish reversal. Short SMA crossing below Long SMA -> Possible bearish trend shift. Alerts for SMA crossovers help traders act on momentum shifts in real-time. Customization and Visualization The Net Score dynamically changes color: green for greed, red for fear. Users can adjust weightings directly from settings, avoiding manual script modifications. Reference levels at 70 and -70 provide clarity on extreme market conditions. Conclusion The Crypto Fear & Greed Score provides a powerful and objective measure of market sentiment, helping traders navigate extreme conditions effectively. 🟢 If the Net Score is below -70, panic may present a buying opportunity. 🔴 If the Net Score is above 70, excessive euphoria may indicate a selling opportunity. ⚖️ Neutral values suggest a balanced market sentiment. By customizing weight parameters and utilizing trend reversal alerts, traders can gain a deeper insight into market psychology and make more informed trading decisions. 🚀
Indikator Pine Script®
oleh viniciusbazan
1
ADX for BTC [PineIndicators]The ADX Strategy for BTC is a trend-following system that uses the Average Directional Index (ADX) to determine market strength and momentum shifts. Designed for Bitcoin trading, this strategy applies a customizable ADX threshold to confirm trend signals and optionally filters entries using a Simple Moving Average (SMA). The system features automated entry and exit conditions, dynamic trade visualization, and built-in trade tracking for historical performance analysis. ⚙️ Core Strategy Components 1️⃣ Average Directional Index (ADX) Calculation The ADX indicator measures trend strength without indicating direction. It is derived from the Positive Directional Movement (+DI) and Negative Directional Movement (-DI): +DI (Positive Directional Index): Measures upward price movement. -DI (Negative Directional Index): Measures downward price movement. ADX Value: Higher values indicate stronger trends, regardless of direction. This strategy uses a default ADX length of 14 to smooth out short-term fluctuations while detecting sustainable trends. 2️⃣ SMA Filter (Optional Trend Confirmation) The strategy includes a 200-period SMA filter to validate trend direction before entering trades. If enabled: ✅ Long Entry is only allowed when price is above a long-term SMA multiplier (5x the standard SMA length). ✅ If disabled, the strategy only considers the ADX crossover threshold for trade entries. This filter helps reduce entries in sideways or weak-trend conditions, improving signal reliability. 📌 Trade Logic & Conditions 🔹 Long Entry Conditions A buy signal is triggered when: ✅ ADX crosses above the threshold (default = 14), indicating a strengthening trend. ✅ (If SMA filter is enabled) Price is above the long-term SMA multiplier. 🔻 Exit Conditions A position is closed when: ✅ ADX crosses below the stop threshold (default = 45), signaling trend weakening. By adjusting the entry and exit ADX levels, traders can fine-tune sensitivity to trend changes. 📏 Trade Visualization & Tracking Trade Markers "Buy" label (▲) appears when a long position is opened. "Close" label (▼) appears when a position is exited. Trade History Boxes Green if a trade is profitable. Red if a trade closes at a loss. Trend Tracking Lines Horizontal lines mark entry and exit prices. A filled trade box visually represents trade duration and profitability. These elements provide clear visual insights into trade execution and performance. ⚡ How to Use This Strategy 1️⃣ Apply the script to a BTC chart in TradingView. 2️⃣ Adjust ADX entry/exit levels based on trend sensitivity. 3️⃣ Enable or disable the SMA filter for trend confirmation. 4️⃣ Backtest performance to analyze historical trade execution. 5️⃣ Monitor trade markers and history boxes for real-time trend insights. This strategy is designed for trend traders looking to capture high-momentum market conditions while filtering out weak trends.
Strategi Pine Script®
oleh PineIndicators
1
Bitcoin Log Growth Curve OscillatorThis script presents the oscillator version of the Bitcoin Logarithmic Growth Curve 2024 indicator, offering a new perspective on Bitcoin’s long-term price trajectory. By transforming the original logarithmic growth curve into an oscillator, this version provides a normalized view of price movements within a fixed range, making it easier to identify overbought and oversold conditions. For a comprehensive explanation of the mathematical derivation, underlying concepts, and overall development of the Bitcoin Logarithmic Growth Curve, we encourage you to explore our primary script, Bitcoin Logarithmic Growth Curve 2024, available here . This foundational script details the regression-based approach used to model Bitcoin’s long-term price evolution. Normalization Process The core principle behind this oscillator lies in the normalization of Bitcoin’s price relative to the upper and lower regression boundaries. By applying Min-Max Normalization, we effectively scale the price into a bounded range, facilitating clearer trend analysis. The normalization follows the formula: normalized price = (upper regresionline − lower regressionline) / (price − lower regressionline) This transformation ensures that price movements are always mapped within a fixed range, preventing distortions caused by Bitcoin’s exponential long-term growth. Furthermore, this normalization technique has been applied to each of the confidence interval lines, allowing for a structured and systematic approach to analyzing Bitcoin’s historical and projected price behavior. By representing the logarithmic growth curve in oscillator form, this indicator helps traders and analysts more effectively gauge Bitcoin’s position within its long-term growth trajectory while identifying potential opportunities based on historical price tendencies.
Indikator Pine Script®
oleh Cryptonerds
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions. Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach. 🔹 What Makes This Strategy Unique? Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions. 🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts. 🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals. 🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure. 🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart. 📊 Core Strategy Components 1️⃣ Momentum-Based Trade Execution The strategy generates long and short trade signals based on the following conditions: ✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum. ✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure. Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline. 2️⃣ Position Sizing & Leverage Control Risk management is a key focus of this strategy: 🔹 Dynamic Position Sizing – The script calculates position size based on: Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations. Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting. 🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears. This design ensures maximum capital efficiency while adapting to market conditions in real time. 3️⃣ Performance Visualization & Tracking Understanding historical performance is crucial for refining strategies. The script includes: 📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference. 📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time. 📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics. 📢 Why Use This Strategy? ✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals. ✔ Fully Automated Trading – Removes emotional bias and enforces discipline. ✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile. ✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades. This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe. 📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
Strategi Pine Script®
oleh PineIndicators
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone! I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants! This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility. If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks! Let me know your thoughts.
Indikator Pine Script®
oleh nhbprod
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone! I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:) It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor. The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur. The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both! Let me know your thoughts and check out the backtest report!
Strategi Pine Script®
oleh nhbprod
2
Volatility Momentum Breakout StrategyDescription: Overview: The Volatility Momentum Breakout Strategy is designed to capture significant price moves by combining a volatility breakout approach with trend and momentum filters. This strategy dynamically calculates breakout levels based on market volatility and uses these levels along with trend and momentum conditions to identify trade opportunities. How It Works: 1. Volatility Breakout: • Methodology: The strategy computes the highest high and lowest low over a defined lookback period (excluding the current bar to avoid look-ahead bias). A multiple of the Average True Range (ATR) is then added to (or subtracted from) these levels to form dynamic breakout thresholds. • Purpose: This method helps capture significant price movements (breakouts) while ensuring that only past data is used, thereby maintaining realistic signal generation. 2. Trend Filtering: • Methodology: A short-term Exponential Moving Average (EMA) is applied to determine the prevailing trend. • Purpose: Long trades are considered only when the current price is above the EMA, indicating an uptrend, while short trades are taken only when the price is below the EMA, indicating a downtrend. 3. Momentum Confirmation: • Methodology: The Relative Strength Index (RSI) is used to gauge market momentum. • Purpose: For long entries, the RSI must be above a mid-level (e.g., above 50) to confirm upward momentum, and for short entries, it must be below a similar threshold. This helps filter out signals during overextended conditions. Entry Conditions: • Long Entry: A long position is triggered when the current closing price exceeds the calculated long breakout level, the price is above the short-term EMA, and the RSI confirms momentum (e.g., above 50). • Short Entry: A short position is triggered when the closing price falls below the calculated short breakout level, the price is below the EMA, and the RSI confirms momentum (e.g., below 50). Risk Management: • Position Sizing: Trades are sized to risk a fixed percentage of account equity (set here to 5% per trade in the code, with each trade’s stop loss defined so that risk is limited to approximately 2% of the entry price). • Stop Loss & Take Profit: A stop loss is placed a fixed ATR multiple away from the entry price, and a take profit target is set to achieve a 1:2 risk-reward ratio. • Realistic Backtesting: The strategy is backtested using an initial capital of $10,000, with a commission of 0.1% per trade and slippage of 1 tick per bar—parameters chosen to reflect conditions faced by the average trader. Important Disclaimers: • No Look-Ahead Bias: All breakout levels are calculated using only past data (excluding the current bar) to ensure that the strategy does not “peek” into future data. • Educational Purpose: This strategy is experimental and provided solely for educational purposes. Past performance is not indicative of future results. • User Responsibility: Traders should thoroughly backtest and paper trade the strategy under various market conditions and adjust parameters to fit their own risk tolerance and trading style before live deployment. Conclusion: By integrating volatility-based breakout signals with trend and momentum filters, the Volatility Momentum Breakout Strategy offers a unique method to capture significant price moves in a disciplined manner. This publication provides a transparent explanation of the strategy’s components and realistic backtesting parameters, making it a useful tool for educational purposes and further customization by the TradingView community.
Strategi Pine Script®
oleh cryptechcapital
Ultimate Stochastics Strategy by NHBprod Use to Day Trade BTCHey All! Here's a new script I worked on that's super simple but at the same time useful. Check out the backtest results. The backtest results include slippage and fees/commission, and is still quite profitable. Obviously the profitability magnitude depends on how much capital you begin with, and how much the user utilizes per order, but in any event it seems to be profitable according to backtests. This is different because it allows you full functionality over the stochastics calculations which is designed for random datasets. This script allows you to: Designate ANY period of time to analyze and study Choose between Long trading, short trading, and Long & Short trading It allows you to enter trades based on the stochastics calculations It allows you to EXIT trades using the stochastics calculations or take profit, or stop loss, Or any combination of those, which is nice because then the user can see how one variable effects the overall performance. As for the actual stochastics formula, you get control, and get to SEE the plot lines for slow K, slow D, and fast K, which is usually not considered. You also get the chance to modify the smoothing method, which has not been done with regular stochastics indicators. You get to choose the standard simple moving average (SMA) method, but I also allow you to choose other MA's such as the HMA and WMA. Lastly, the user gets the option of using a custom trade extender, which essentially allows a buy or sell signal to exist for X amount of candles after the initial signal. For example, you can use "max bars since signal" to 1, and this will allow the indicator to produce an extra sequential buy signal when a buy signal is generated. This can be useful because it is possible that you use a small take profit (TP) and quickly exit a profitable trade. With the max bars since signal variable, you're able to reenter on the next candle and allow for another opportunity. Let me know if you have any questions! Please take a look at the performance report and let me know your thoughts! :)
Strategi Pine Script®
oleh nhbprod
Mxwll Hedge Suite [Mxwll]Hello Traders! The Mxwll Hedge Suite determines the best asset to hedge against the asset on your chart! By determining correlation between the asset on your chart and a group of internally listed assets, the Mxwll Hedge Suite determines which asset from the list exhibits the highest negative correlation, and then determines exactly how many coins/shares/contracts of the asset must be bought to achieve a perfect 1:1 hedge! The image above exemplifies the process! The purple box on the chart shows the eligible price action used to determine correlation between the asset on my chart (BTCUSDT.P) and the list of cryptocurrencies that can be used as a hedge! From this price action, the coin determined to have to greatest negative correlation to BTCUSDT.P is FTMUSD. The image above further outlines the hedge table located in the bottom-right corner of your chart! The hedge table shows exactly how many coins you’d need to purchase for the hedge asset at various leverages to achieve a perfect 1:1 hedge! Hedge Suite works on any asset on any timeframe! And that’s all! A short and sweet script that is hopefully helpful to traders looking to hedge their positions with a negatively correlated asset! Thank you, Traders!
Indikator Pine Script®
oleh MXWLL-Capital-Trading
4