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T3 Velocity "This technical indicator is a kind of rate of change oscillator made of 2 T3 moving average. It compares the momentum of the 2 curves between the current and the previous period. The first curve is calculated upon the period and the complete factor ("vFactor" parameter) and the second one with an half of this factor. It results a smoothed oscillator that can be traded almost like a MACD. An optional signal line could be added to give clear entries when the oscillator cross it."
Indicador Pine Script®
por wpatte15
1
On-Chain Signals [LuxAlgo]The On-Chain Signals indicator uses fundamental blockchain metrics to provide traders with an objective technical view of their favorite cryptocurrencies. It uses IntoTheBlock datasets integrated within TradingView to generate four key signals: Net Network Growth, In the Money, Concentration, and Large Transactions. Together, these four signals provide traders with an overall directional bias of the market. All of the data can be visualized as a gauge, table, historical plot, or average. 🔶 USAGE The main goal of this tool is to provide an overall directional bias based on four blockchain signals, each with three possible biases: bearish, neutral, or bullish. The thresholds for each signal bias can be adjusted on the settings panel. These signals are based on IntoTheBlock's On-Chain Signals. Net network growth: Change in the total number of addresses over the last seven periods; i.e., how many new addresses are being created. In the Money: Change in the seven-period moving average of the total supply in the money. This shows how many addresses are profitable. Concentration: Change in the aggregate addresses of whales and investors from the previous period. These are addresses holding at least 0.1% of the supply. This shows how many addresses are in the hands of a few. Large Transactions: Changes in the number of transactions over $100,000. This metric tracks convergence or divergence from the 21- and 30-day EMAs and indicates the momentum of large transactions. All of these signals together form the blockchain's overall directional bias. Bearish: The number of bearish individual signals is greater than the number of bullish individual signals. Neutral: The number of bearish individual signals is equal to the number of bullish individual signals. Bullish: The number of bullish individual signals is greater than the number of bearish individual signals. If the overall directional bias is bullish, we can expect the price of the observed cryptocurrency to increase. If the bias is bearish, we can expect the price to decrease. If the signal is neutral, the price may be more likely to stay the same. Traders should be aware of two things. First, the signals provide optimal results when the chart is set to the daily timeframe. Second, the tool uses IntoTheBlock data, which is available on TradingView. Therefore, some cryptocurrencies may not be available. 🔹 Display Mode Traders have three different display modes at their disposal. These modes can be easily selected from the settings panel. The gauge is set by default. 🔹 Gauge The gauge will appear in the center of the visible space. Traders can adjust its size using the Scale parameter in the Settings panel. They can also give it a curved effect. The number of bars displayed directly affects the gauge's resolution: More bars result in better resolution. The chart above shows the effect that different scale configurations have on the gauge. 🔹 Historical Data The chart above shows the historical data for each of the four signals. Traders can use this mode to adjust the thresholds for each signal on the settings panel to fit the behavior of each cryptocurrency. They can also analyze how each metric impacts price behavior over time. 🔹 Average This display mode provides an easy way to see the overall bias of past prices in order to analyze price behavior in relation to the underlying blockchain's directional bias. The average is calculated by taking the values of the overall bias as -1 for bearish, 0 for neutral, and +1 for bullish, and then applying a triangular moving average over 20 periods by default. Simple and exponential moving averages are available, and traders can select the period length from the settings panel. 🔶 DETAILS The four signals are based on IntoTheBlock's On-Chain Signals. We gather the data, manipulate it, and build the signals depending on each threshold. Net network growth float netNetworkGrowthData = customData('_TOTALADDRESSES') float netNetworkGrowth = 100*(netNetworkGrowthData /netNetworkGrowthData - 1) In the Money float inTheMoneyData = customData('_INOUTMONEYIN') float averageBalance = customData('_AVGBALANCE') float inTheMoneyBalance = inTheMoneyData*averageBalance float sma = ta.sma(inTheMoneyBalance,7) float inTheMoney = ta.roc(sma,1) Concentration float whalesData = customData('_WHALESPERCENTAGE') float inverstorsData = customData('_INVESTORSPERCENTAGE') float bigHands = whalesData+inverstorsData float concentration = ta.change(bigHands )*100 Large Transactions float largeTransacionsData = customData('_LARGETXCOUNT') float largeTX21 = ta.ema(largeTransacionsData,21) float largeTX30 = ta.ema(largeTransacionsData,30) float largeTransacions = ((largeTX21 - largeTX30)/largeTX30)*100 🔶 SETTINGS Display mode: Select between gauge, historical data and average. Average: Select a smoothing method and length period. 🔹 Thresholds Net Network Growth : Bullish and bearish thresholds for this signal. In The Money : Bullish and bearish thresholds for this signal. Concentration : Bullish and bearish thresholds for this signal. Transactions : Bullish and bearish thresholds for this signal. 🔹 Dashboard Dashboard : Enable/disable dashboard display Position : Select dashboard location Size : Select dashboard size 🔹 Gauge Scale : Select the size of the gauge Curved : Enable/disable curved mode Select Gauge colors for bearish, neutral and bullish bias 🔹 Style Net Network Growth : Enable/disable historical plot and choose color In The Money : Enable/disable historical plot and choose color Concentration : Enable/disable historical plot and choose color Large Transacions : Enable/disable historical plot and choose color
Indicador Pine Script®
por LuxAlgo
8
regressionsLibrary "regressions" This library computes least square regression models for polynomials of any form for a given data set of x and y values. fit(X, y, reg_type, degrees)   Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset.   Parameters:      X (array) : (float ) X inputs for regression fit.      y (array) : (float ) y outputs for regression fit.      reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom      degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial.   Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial regress(reg, x)   Regress one x input.   Parameters:      reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.      x (float) : (float) The input value cooresponding to the y_pred.   Returns: (float) The best fit y value for the given x input and regression. predict(reg, X)   Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime   Parameters:      reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.      X (array)   Returns: (float ) The best fit y values for the given x input and regression. generate_points(reg, x, y, left_index, right_index)   Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels.   Parameters:      reg (regression) : (regression) Regression which has been fitted to a data set.      x (array) : (float ) x values which coorispond to passed y values      y (array) : (float ) y values which coorispond to passed x values      left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.      right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.   Returns: (chart.point ) Returns an array of chart points plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width)   Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function.   Parameters:      reg (regression) : (regression) Regression which has been fitted to a data set.      x (array)      y (array)      left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.      right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.      curved (bool) : (bool) If the polyline is curved or not.      close (bool) : (bool) If true the polyline will be closed.      line_color (color) : (color) The color of the line.      line_width (int) : (int) The width of the line.   Returns: (polyline) The polyline for the regression. series_to_list(src, left_index, right_index)   Convert a series to a list. Creates a list of all the cooresponding source values from left_index to right_index. This should be called at the highest scope for consistency.   Parameters:      src (float) : (float ) The source the list will be comprised of.      left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for.      right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for.   Returns: (float ) An array of size left_index-right_index range_list(start, stop, step)   Creates an from the start value to the stop value.   Parameters:      start (int) : (float ) The true y values.      stop (int) : (float ) The predicted y values.      step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2:   Returns: (float ) An array of size stop-start regression   Fields:      coeffs (array__float)      degrees (array__float)      type_linear (series__string)      type_quadratic (series__string)      type_cubic (series__string)      type_custom (series__string)      _squared_error (series__float)      X (array__float)
Biblioteca Pine Script®
por fluxchart
3
AWR R & LR Oscillator with plots & tableHello trading viewers ! I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him. 1. Oscillator and Correlation Calculations Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength. Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations). 2. Visual Signals and Automated Alerts Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example: If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart; When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition; Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart. Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis. 3. Regression Channel Analysis Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor). The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table. Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations 4. Scores, Conditions, and the Summary Table Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as: 1. BigPear Score What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges. Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction). Here's the rules : BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25) BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0) BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25) BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5) BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65) BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7) BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75) BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8) BigPear9up = (bigsma_avgPR <= -0.8) Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction). BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25) BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0) BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25) BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5) BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65) BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7) BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75) BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8) BigPear9down = (bigsma_avgPR >= 0.8) Weighting: If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up. Total Score: The positive score (posScoreBigPear) is the sum of these weighted conditions. Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9). In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds. 2. Pear Score What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior. How It’s Calculated: Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance: Pear1up = (avgPR > -0.2 and avgPR <= 0) Pear2up = (avgPR > -0.4 and avgPR <= -0.2) Pear3up = (avgPR > -0.5 and avgPR <= -0.4) Pear4up = (avgPR > -0.6 and avgPR <= -0.5) Pear5up = (avgPR > -0.65 and avgPR <= -0.6) Pear6up = (avgPR > -0.7 and avgPR <= -0.65) Pear7up = (avgPR > -0.75 and avgPR <= -0.7) Pear8up = (avgPR > -0.8 and avgPR <= -0.75) Pear9up = (avgPR > -1 and avgPR <= -0.8) There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance: Pear1down = (avgPR >= 0 and avgPR < 0.2) Pear2down = (avgPR >= 0.2 and avgPR < 0.4) Pear3down = (avgPR >= 0.4 and avgPR < 0.5) Pear4down = (avgPR >= 0.5 and avgPR < 0.6) Pear5down = (avgPR >= 0.6 and avgPR < 0.65) Pear6down = (avgPR >= 0.65 and avgPR < 0.7) Pear7down = (avgPR >= 0.7 and avgPR < 0.75) Pear8down = (avgPR >= 0.75 and avgPR < 0.8) Pear9down = (avgPR >= 0.8 and avgPR <= 1) Weighting: Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up. Sum up : Pear1up = 0.9 Pear2up = 1.9 Pear3up = 2.9 Pear4up = 3.9 Pear5up = 4.99 Pear6up = 6 Pear7up = 7 Pear8up = 8 Pear9up = 9 Total Score: The positive score (posScorePear) is the sum of these values for each condition that returns true. A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction. This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum. 3. Trendpear Score What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior. How It’s Calculated: Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check: Whether avgPR is below, equal to, or above sma_avgPR by different margins; Whether it is trending upward (i.e., it is higher than its previous value). Here are the rules Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR ) Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR ) Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR ) Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR ) Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR ) Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR ) Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR) Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03) Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR) Weighting: The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points. Trendpear1up = 0.1 Trendpear2up = 0.2 Trendpear3up = 0.3 Trendpear4up = 0.4 Trendpear5up = 0.5 Trendpear6up = 0.69 Trendpear7up = 7 Trendpear8up = 8.9 Trendpear9up = 90 Total Score: The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied. A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias. Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average. 4. Deviation Score What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout. How It’s Calculated: Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation: First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary. Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary. Third tier (EcartXup3): Checks if the price is below the most extreme limit. Weighting: Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index. First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) Total Score: The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers. The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign. This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner. Example : Score + = 321.0001 Score - = -0.111 The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term. Score + = 0.0033 Score - = -1.11 The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral 5. Slope Score What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward. How It’s Calculated: Conditions: if the slope has a uptrend = 1 if the slope has a downtrend = -1 Weighting: First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes. The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met. The negative score (negScoreSlope) sums the negative weights when downward conditions are met. Example : Score + = 111 Score - = -0.1111 Trend is up for longterm & down for mid & short term The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively. Summary For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension. This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals. 5. Practical Use and Application How to Use the Indicator: Interpreting the Signals: On your chart, observe the following components: The individual correlation curves and their average, plotted with visual thresholds; Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions The summary table that aggregates the scores from each category, offering a quick glance at the market’s state. Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals. Conclusion and Additional Insights In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table. Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
Indicador Pine Script®
por AlwwAl
MACD of RSI [TORYS]MACD of RSI — Momentum & Divergence Scanner Description: This enhanced oscillator applies MACD logic directly to the Relative Strength Index (RSI) rather than price, giving traders a clearer look at internal momentum and early shifts in trend strength. Now featuring a custom histogram, dual MA types, and RSI-based divergence detection — it’s a complete toolkit for identifying exhaustion, acceleration, and hidden reversal points in real time. How It Works: Calculates the MACD line as the difference between a fast and slow moving average of RSI. Adds a Signal Line (MA of the MACD) and plots a Histogram to show momentum acceleration/deceleration. Both RSI MAs and the Signal Line can be toggled between EMA and SMA for custom tuning. Divergence Detection: Bullish Divergence : Price makes a lower low while RSI makes a higher low → labeled with a green “D” below the curve. Bearish Divergence : Price makes a higher high while RSI makes a lower high → labeled with a red “D” above the curve. Configurable lookback window for tuning sensitivity to pivots, with 4 as the sweet spot. RSI Pivot Dot Signals: Plots green dots at RSI oversold pivot lows below 30, Plots red dots at overbought pivot highs above 70. Helps detect short-term exhaustion or bounce zones, plotted right on the MACD-RSI curve. RSI 50 Crosses (Optional): Optional ▲ and ▼ labels when RSI crosses its 50 midline — useful for momentum trend shifts or pullback confirmation, or to detect consolidation. Histogram: Plotted as a column chart showing the distance between MACD and Signal Line. Colored dynamically: Bright green : Momentum rising above zero Light green : Weakening above zero Bright red : Momentum falling below zero Light red : Weakening below zero The zero line serves as the mid-point: Above = Bullish Bias Below = Bearish Bias How to Interpret: Momentum Confirmation: Use MACD cross above Signal Line with a rising histogram to confirm breakouts or trend entries. Histogram shrinking near zero = momentum weakening → caution or reversal. Exhaustion & Reversals: Dot signals near RSI extremes + histogram peak can suggest overbought/oversold pressure. Use divergence labels ("D") to spot early reversal signals before price breaks structure. Inputs & Settings: RSI Length Fast/Slow MA Lengths for MACD (applied to RSI) Signal Line Length MA Type: Choose between EMA and SMA for MACD and Signal Line Pivot Sensitivity for dot markers Divergence Logic Toggle Show/hide RSI 50 Crosses Best For: Traders who want momentum insight from inside RSI, not price Scalpers using divergence or exhaustion entries Swing traders seeking entry confirmation from signal crossovers Anyone using multi-timeframe confluence with RSI and trend filters Pro Tips: Combine this with: Bollinger Bands breakouts and reversals VWAP or EMAs to filter entries by trend Volume spikes or BBW squeezes for volatility confirmation TTM Scalper Alert to sync structure and momentum
Indicador Pine Script®
por alexrainman
1
[blackcat] L2 Veronique Valcu VWAP Z-Score IndicatorLevel: 2 Background Veronique Valcu's article "Z-Score Indicator" in Feb,2003 provided a description and commentary on a new method of displaying directional change normalized in terms of standard deviation. This indicator is realized in pine script here by using the following function code, adding vwap function, called vwap ZScore. Function This indicator has three input, "AvgLen", "Smooth1" and "Smooth2." Price is fixed in selected vwap price. AvgLen describes the length of the sample considered in the standard deviation calculation. Once created and verified, the function can be easily called in any indicator or strategy. Inputs AvgLen --> Length input for vwap Zscore. Smooth1 and Smooth2 --> Smoothing length. Key Signal Curve1 --> vwap ZScore output fast signal Curve2 --> vwap ZScore output slow signal Remarks This is a Level 2 free and open source indicator. Feedbacks are appreciated.
Indicador Pine Script®
por blackcat1402
2
Williams %R - Multi TimeframeThis indicator implements the William %R multi-timeframe. On the 1H chart, the curves for 1H (with signal), 4H, and 1D are displayed. On the 4H chart, the curves for 4H (with signal) and 1D are shown. On all other timeframes, only the %R and signal are displayed. The indicator is useful to use on 1H and 4H charts to find confluence among the different curves and identify better entries based on their alignment across all timeframes. Signals above 80 often indicate a potential bearish reversal in price, while signals below 20 often suggest a bullish price reversal.
Indicador Pine Script®
por Arche_3009
1
InterpolationLibrary "Interpolation" Functions for interpolating values. Can be useful in signal processing or applied as a sigmoid function. linear(k, delta, offset, unbound) Returns the linear adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the line the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). quadIn(k, delta, offset, unbound) Returns the quadratic (easing-in) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). quadOut(k, delta, offset, unbound) Returns the quadratic (easing-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). quadInOut(k, delta, offset, unbound) Returns the quadratic (easing-in-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). cubicIn(k, delta, offset, unbound) Returns the cubic (easing-in) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). cubicOut(k, delta, offset, unbound) Returns the cubic (easing-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). cubicInOut(k, delta, offset, unbound) Returns the cubic (easing-in-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). expoIn(k, delta, offset, unbound) Returns the exponential (easing-in) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). expoOut(k, delta, offset, unbound) Returns the exponential (easing-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). expoInOut(k, delta, offset, unbound) Returns the exponential (easing-in-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). using(fn, k, delta, offset, unbound) Returns the adjusted value by function name.   Parameters:      fn : The name of the function. Allowed values: linear, quadIn, quadOut, quadInOut, cubicIn, cubicOut, cubicInOut, expoIn, expoOut, expoInOut.      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
Biblioteca Pine Script®
por Electrified
Demand and Supply Conditions with SignalsIntroduction: This document outlines a trading strategy that utilizes price action analysis and color signals to make informed trading decisions. The strategy focuses on identifying demand and supply conditions, curve patterns, and generating signals based on historical price data. The colors associated with each condition and signal serve as visual indicators to assist in decision-making. I. Strategy Overview: Objective: The objective of this trading strategy is to identify potential trading opportunities based on price action analysis and color signals. Key Components: Demand Condition: A green upward-facing triangle indicates a potential demand condition. Supply Condition: A red downward-facing triangle indicates a potential supply condition. Curve Pattern Condition: A blue upward-facing triangle indicates a potential curve pattern condition. Signal Condition: A yellow upward-facing triangle indicates a potential buy signal. II. Understanding the Colors: * Green: Represents the demand condition, which suggests potential buying pressure in the market. A green upward-facing triangle is plotted on the chart when the demand condition is met at a specific candle or bar. * Red: Represents the supply condition, which suggests potential selling pressure in the market. A red downward-facing triangle is plotted on the chart when the supply condition is met at a specific candle or bar. * Blue: Represents the curve pattern condition, which suggests the presence of a specific pattern based on price action analysis. A blue upward-facing triangle is plotted on the chart when the curve pattern condition is met at a specific candle or bar. * Yellow: Represents the signal condition, which is a combination of the demand condition and the curve pattern condition. A yellow upward-facing triangle is plotted on the chart when the signal condition is met at a specific candle or bar, indicating a potential buy signal. III. Decision-Making Process: * Demand and Supply Conditions: Identify potential buying opportunities when a green demand condition is present. Consider potential selling opportunities when a red supply condition is present. Use these conditions to assess the overall market sentiment and potential price reversals. * Curve Patterns: Analyze the presence of blue curve pattern conditions to identify specific price patterns. These patterns can provide additional confirmation for potential trading decisions. * Signal Condition: Pay attention to the yellow signal condition, which indicates a potential buy signal. Evaluate the overall market context and consider entering a buy position when the signal condition is met. * Risk Management: Implement proper risk management techniques such as setting stop-loss orders and position sizing to protect against potential losses. IV. Conclusion: This trading strategy leverages price action analysis and color signals to identify potential trading opportunities. The colors associated with each condition and signal serve as visual aids to highlight specific points on the chart. It's important to thoroughly backtest and validate the strategy before applying it to real-world trading scenarios. Additionally, always consider market conditions, risk management, and individual trading preferences when making trading decisions. Disclaimer: Trading involves risks, and this document does not guarantee profitable outcomes. Traders should exercise caution and perform their own due diligence before engaging in any trading activity. Remember to continually review and adapt your trading strategy based on market conditions and personal experiences to enhance its effectiveness.
Indicador Pine Script®
por DjMaxCapital
Coin Jin Multi SMA+ BB+ SMA forecast Ver 2.0Coin Jin Multi SMA + BB + SMA Forecast 2.0 개요 여러 개의 단순이동평균(SMA: 5/20/60/112/224/448/896 + 사용자 정의 X1/X2), 볼린저 밴드(BB), 그리고 접선 기반 곡선 예측선을 한 번에 표시합니다. 예측선은 선형회귀 기울기와 그 변화율(가속도)을 EMA로 스무딩해 곡선 외삽으로 앞으로 그려지며, 어떤 줌에서도 깔끔하게 보이도록 점선(dotted) 스타일을 강제할 수 있습니다. 스택 마커(정배열/역배열) 안내 조건: 이동평균이 정배열(5>20>60>112>224>448>(896)) 또는 역배열(5<20<60<112<224<448<(896))로 새로 전환되는 순간 삼각형 마커가 생성됩니다. 896일선 포함(with 896): SOLID 마커로 표시, Bull = 초록색, Bear = 빨간색. 896일선 미포함(no 896): HOLLOW(윤곽) 마커로 표시, 시선을 덜 끌도록 투명도 70 적용(Bull = 연두, Bear = 빨강 동일색). 방향: Bull = ▼(위, abovebar) / Bear = ▲(아래, belowbar) 로 배치됩니다. 주요 기능 SMA 7종 기본 + 사용자 정의 SMA 2개(X1/X2) 추가(기본 꺼짐, 길이/색/두께/타입 자유). BB: 길이/배수/선두께/밴드 채움(기본 90% 투명) 지원. 예측선: Forward bars(1–100, 기본 30), 기울기 산출 길이, 스무딩 강도, 세그먼트 개수, 점/대시 스타일 선택 및 도트 강제. 스택(정/역배열) 전환 마커: with 896=SOLID, no 896=HOLLOW(투명도 70). 처음 사용하는 분들을 위한 팁 (중요) 가격 스케일을 ‘우측’으로 고정하세요. 방법 ① 차트 우측 축을 사용(기본). 방법 ② 지표 레전드의 ‘⋯’ 메뉴 → Move to → Right scale. 예측선이 본선과 어긋나 보이면 스케일이 좌측/양측으로 되어 있거나 자동 합침된 경우이니 Right scale로 맞춰주세요. 입력 요약 MA Source, 각 SMA on/off·길이·색·두께·타입 BB length/mult/width/fill/opacity(기본 90) Forecast bars ahead(1–100), slope lookback, smoothing, segments, style/opacity, 적용 대상 선택(SMA별) 주의/면책 예측선은 가격 예언 도구가 아니라 시각적 외삽 보조지표입니다. 단독 매매 판단에 사용하지 마세요. 공개 스크린샷은 본 지표만 보이도록 깔끔하게 캡처해 주세요(다른 지표/드로잉 혼합 금지). 변경사항(v2.0) 곡선 예측선 안정화 및 도트 강제 개선. 스택 마커 no 896 상태 HOLLOW 투명도 70 적용(가독성 향상). 사용자 정의 SMA X1/X2 추가(기본 OFF). Coin Jin Multi SMA + BB + SMA Forecast 2.0 (English) Overview This indicator plots multiple Simple Moving Averages (SMA: 5/20/60/112/224/448/896 + two user-defined X1/X2), Bollinger Bands, and a tangent-based curved forecast in one overlay. The forecast extrapolates forward using the linear-regression slope and its rate of change (acceleration) smoothed by EMA, and you can force a dotted look so it stays clean at any zoom level. Stack Markers (Bullish/Bearish alignment) Markers appear only when a full bullish stack (5>20>60>112>224>448>(896)) or bearish stack (5<20<60<112<224<448<(896)) is newly formed. With 896 included: shown as SOLID triangles — Bull = green, Bear = red. Without 896: shown as HOLLOW (outline) with 70 transparency to reduce visual weight — Bull = lime, Bear = red (same hue). Orientation: Bull = ▼ abovebar, Bear = ▲ belowbar. Features 7 standard SMAs + two custom SMAs (X1/X2) (default OFF; fully configurable length/color/width/style). BB with length/multiplier/width/fill (default fill opacity 90%). Forecast controls: forward bars (1–100, default 30), slope window, smoothing, segment count, style/opacity, force dotted option. Stack markers: with 896 = SOLID, without 896 = HOLLOW (70 transparency). First-time setup (Important) Pin the indicator to the Right price scale. Option A: Use the right price axis. Option B: Indicator legend “⋯” → Move to → Right scale. If the forecast appears detached from the MA, your series is likely on the left/both scales; switch to Right scale. Inputs MA source; per-SMA on/off, length, color, width, style BB length/multiplier/width/fill/opacity (default 90) Forecast bars ahead (1–100), slope lookback, smoothing, segments, style/opacity, per-SMA apply switches Disclaimer The forecast is a visual extrapolation, not a price prediction. Do not use it alone to make trading decisions. For publication, please use a clean screenshot that shows only this indicator (no mixed overlays). What’s new in v2.0 More robust curved forecast with improved “force dotted” rendering. HOLLOW (no 896) markers now use 70 transparency for better readability. Added two user-defined SMAs (X1/X2), OFF by default.
Indicador Pine Script®
por myjustapex
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi. It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle. 🔶 USAGE Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models. As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale. This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality. According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle. 🔹 Enable/Disable Elements All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain. The different charts above show various combinations. Traders can customize the tool to their needs. 🔹 Auto scale The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel. The image above shows different configurations of this feature. 🔶 SETTINGS 🔹 Price Price: Enable/disable price spiral, select color, and enable/disable curved mode Average: Enable/disable average spiral, select color, and enable/disable curved mode 🔹 Style Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals Lines width: Width of each spiral line Text Size: Select text size for date tags and price scales Prices: Enable/disable price scales on the x-axis Handle: Enable/disable clock handle Halvings: Enable/disable Halvings Hours: Enable/disable hours and key cycle points 🔹 Time & Price Dashboard Show Time & Price: Enable/disable time & price dashboard Location: Dashboard location Size: Dashboard size
Indicador Pine Script®
por LuxAlgo
11
LGMM (flat buffers) — multivariate poly + latent statesLGMM POLYNOMIAL BANDS — DISCOVER THE MARKET’S HIDDEN STATES Overview Latent-Gaussian-Mixture-Models (LGMMs) view price action as a mix of several invisible regimes: trending up, drifting sideways, sudden volatility spikes, and so on. A Gaussian Mixture learns these states directly from data and outputs, for every bar, the probability that the market is in each state. This indicator feeds those probabilities into a rolling polynomial regression that draws a fair-value line, then builds adaptive upper and lower bands. Band width expands when recent residuals are large *and* when the state mix is uncertain, and contracts when price is calm or one regime clearly dominates. Crossing back into the band from below generates a buy flag; crossing back into the band from above generates a sell flag (or take-profit for longs). Key Inputs Price source – default is Close; you can choose HL2, OHLC4, etc. Training window (bars) – look-back length for every retrain. 252 bars (one trading year) is a balanced default for US stocks on daily timeframe. Use fewer bars for intraday charts (say 7*24=168 for 1H bars on crypto), more for weekly periods. Polynomial degree – 1 for a straight trend line, 2 for a curved fit. Curved fits are better when the symbol shows persistent drift. Hidden states K – number of regimes the mixture tracks (1 to 3). Three states often map well to up-trend, chop, down-trend. Band width ×σ – multiplier on the entropy-weighted standard deviation. Smaller values (1.5-2) give more trades; larger values (2.5-3) give fewer, higher-conviction trades. Offline μ,σ pairs (optional) – paste component means and sigmas from an offline LGMM (format: mu1,sigma1;mu2,sigma2;…). Leave blank to let the script use its built-in approximation. Quick Start Add the indicator to a chart and wait until the initial Training window has filled. Watch for green BUY triangles when price closes back above the lower band and red SELL triangles when price closes back below the upper band. Fine-tune: – Increase Training window to reduce noise. – Decrease Band width ×σ for more frequent signals. – Experiment with Hidden states K; more states capture richer behaviour but need longer windows to stay reliable. Tips Bands widen automatically in chaotic periods and tighten when one regime dominates. Combine with a volume filter or a higher-time-frame trend to reduce whipsaws. If you already run an LGMM in Python or Matlab, paste its component parameters for a perfect match between your back-test and the TradingView plot. Works on all markets and time-frames, provided you have at least five times the Training window’s bars in history. Happy trading!
Indicador Pine Script®
por vsov
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
Indicador Pine Script®
por LuxAlgo
11
SandTigerSandTiger is an auto-counting tool that counts naturally occurring events in a price series. This version has been reduced to 377 lines of code and should run faster than previous versions. Although not shown here, I highly recommend running my 'ELB' script with SandTiger. ELB is an 'event locator' and will mark all points that SandTiger numbers - giving you visual cues as to where these points are located. ELB also displays support/resistance levels. SandTiger is designed to be used with MAGENTA - a counting system for Forex and other markets. MAGENTA is a free and open framework for understanding and explaining price movement in financial markets. Any materials associated with MAGENTA are strictly for educational purposes only. SandTiger tracks Component Values, Dyads, and Sum Table Values (STV's) over straight and curved trends, allowing a trader to discern where directional shifts are likely to occur. SandTiger requires just 3 things to function accurately: 1) A correct starting point (this will typically be an obvious trend turn high or low in a series of price moves). 2) A 'push 1' count ('push 1' runs from the starting point to the event prior to the first terminal of the first FCT or Fractured Counter-Trend). 3) A 'high prime' value (the high prime count runs from the starting point through to the second terminal of the first FCT with no skips). FRAMEWORK OVERVIEW: 'Component' values are filtered from the prime set (including the half prime and further reductions). Once we have the comp table we add the values to get a 'total'. With the 'total' we divide and multiply by two to get two additional values. 'Derivatives' are based on various calculations using these three values. We're looking for 'total/2' to count into either itself, 'total', 'total*2', or a derivative. Comp counts are in Tx form and counted from trend start. If the trend doesn't turn on a comp value it will likely turn on a Dyad or STV value. If that also doesn't happen it's likely you have a 'curved' trend/sequence that will turn on one of the above after moving away from its high/low. This can also be traded using SandTiger's 'Seg Terminals' skip option. Sum tables and Dyad values are drawn from the 'primes' and Dyads use the 'push1' value as well. In a structural trend, primes are gotten by counting pushpulls 1 & 2 in 'Ti' form. Comps, Sum table values, and Dyads are equivalent, sequences can turn on either value type belonging to the 1st or 2nd prime set. Both STV's and Dyads are counted in 'Tx' form (except where count-through signals occur). Types and antitypes correlate and are associated with a 12-count 'cycle.' (Ti = 'Terminals Included'; Tx = 'Terminals eXcluded'; both refer to FCT terminals) THE STRATEGY: For Structures: Trade Comps, Dyads, and STV's from sets 1 (all) and 2 (Dyads and STV's only) in the 'main' segment then on the 'carry-over' by skipping segment terminals. If a PC or cycle caps the sequence, trade that as well. For NSM's: Trade movements that flash a signal prior to the end of the initial cycle. The mark will be the push1 value. Twelve will be the 'high prime.' Skip interrupts and trade carry-over values. The first version of SandTiger was conceived/planned/authored by Erek A.D. and coded by Erek A.D. and @SimpleCryptoLife beginning in August 2022 and finishing in Dec. 2022 The current version was written and developed July 3, 2023 and has been refined and upgraded by Erek A.D. through Jan. 2024...
Indicador Pine Script®
por trendlab2
ZigzagLiteLibrary "ZigzagLite" Lighter version of the Zigzag Library. Without indicators and sub-component divisions method getPrices(pivots)   Gets the array of prices from array of Pivots   Namespace types: Pivot   Parameters:      pivots (Pivot ) : array array of Pivot objects   Returns: array array of pivot prices method getBars(pivots)   Gets the array of bars from array of Pivots   Namespace types: Pivot   Parameters:      pivots (Pivot ) : array array of Pivot objects   Returns: array array of pivot bar indices method getPoints(pivots)   Gets the array of chart.point from array of Pivots   Namespace types: Pivot   Parameters:      pivots (Pivot ) : array array of Pivot objects   Returns: array array of pivot points method getPoints(this)   Namespace types: Zigzag   Parameters:      this (Zigzag) method calculate(this, ohlc, ltfHighTime, ltfLowTime)   Calculate zigzag based on input values and indicator values   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object      ohlc (float ) : Array containing OHLC values. Can also have custom values for which zigzag to be calculated      ltfHighTime (int) : Used for multi timeframe zigzags when called within request.security. Default value is current timeframe open time.      ltfLowTime (int) : Used for multi timeframe zigzags when called within request.security. Default value is current timeframe open time.   Returns: current Zigzag object method calculate(this)   Calculate zigzag based on properties embedded within Zigzag object   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object   Returns: current Zigzag object method nextlevel(this)   Namespace types: Zigzag   Parameters:      this (Zigzag) method clear(this)   Clears zigzag drawings array   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing ) : array   Returns: void method clear(this)   Clears zigzag drawings array   Namespace types: ZigzagDrawingPL   Parameters:      this (ZigzagDrawingPL ) : array   Returns: void method drawplain(this)   draws fresh zigzag based on properties embedded in ZigzagDrawing object without trying to calculate   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object   Returns: ZigzagDrawing object method drawplain(this)   draws fresh zigzag based on properties embedded in ZigzagDrawingPL object without trying to calculate   Namespace types: ZigzagDrawingPL   Parameters:      this (ZigzagDrawingPL) : ZigzagDrawingPL object   Returns: ZigzagDrawingPL object method drawfresh(this, ohlc)   draws fresh zigzag based on properties embedded in ZigzagDrawing object   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object      ohlc (float ) : values on which the zigzag needs to be calculated and drawn. If not set will use regular OHLC   Returns: ZigzagDrawing object method drawcontinuous(this, ohlc)   draws zigzag based on the zigzagmatrix input   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object      ohlc (float ) : values on which the zigzag needs to be calculated and drawn. If not set will use regular OHLC   Returns: PivotCandle   PivotCandle represents data of the candle which forms either pivot High or pivot low or both   Fields:      _high (series float) : High price of candle forming the pivot      _low (series float) : Low price of candle forming the pivot      length (series int) : Pivot length      pHighBar (series int) : represents number of bar back the pivot High occurred.      pLowBar (series int) : represents number of bar back the pivot Low occurred.      pHigh (series float) : Pivot High Price      pLow (series float) : Pivot Low Price Pivot   Pivot refers to zigzag pivot. Each pivot can contain various data   Fields:      point (chart.point) : pivot point coordinates      dir (series int) : direction of the pivot. Valid values are 1, -1, 2, -2      level (series int) : is used for multi level zigzags. For single level, it will always be 0      ratio (series float) : Price Ratio based on previous two pivots      sizeRatio (series float) ZigzagFlags   Flags required for drawing zigzag. Only used internally in zigzag calculation. Should not set the values explicitly   Fields:      newPivot (series bool) : true if the calculation resulted in new pivot      doublePivot (series bool) : true if the calculation resulted in two pivots on same bar      updateLastPivot (series bool) : true if new pivot calculated replaces the old one. Zigzag   Zigzag object which contains whole zigzag calculation parameters and pivots   Fields:      length (series int) : Zigzag length. Default value is 5      numberOfPivots (series int) : max number of pivots to hold in the calculation. Default value is 20      offset (series int) : Bar offset to be considered for calculation of zigzag. Default is 0 - which means calculation is done based on the latest bar.      level (series int) : Zigzag calculation level - used in multi level recursive zigzags      zigzagPivots (Pivot ) : array which holds the last n pivots calculated.      flags (ZigzagFlags) : ZigzagFlags object which is required for continuous drawing of zigzag lines. ZigzagObject   Zigzag Drawing Object   Fields:      zigzagLine (series line) : Line joining two pivots      zigzagLabel (series label) : Label which can be used for drawing the values, ratios, directions etc. ZigzagProperties   Object which holds properties of zigzag drawing. To be used along with ZigzagDrawing   Fields:      lineColor (series color) : Zigzag line color. Default is color.blue      lineWidth (series int) : Zigzag line width. Default is 1      lineStyle (series string) : Zigzag line style. Default is line.style_solid.      showLabel (series bool) : If set, the drawing will show labels on each pivot. Default is false      textColor (series color) : Text color of the labels. Only applicable if showLabel is set to true.      maxObjects (series int) : Max number of zigzag lines to display. Default is 300      xloc (series string) : Time/Bar reference to be used for zigzag drawing. Default is Time - xloc.bar_time.      curved (series bool) : Boolean field to print curved zigzag - used only with polyline implementation ZigzagDrawing   Object which holds complete zigzag drawing objects and properties.   Fields:      zigzag (Zigzag) : Zigzag object which holds the calculations.      properties (ZigzagProperties) : ZigzagProperties object which is used for setting the display styles of zigzag      drawings (ZigzagObject ) : array which contains lines and labels of zigzag drawing. ZigzagDrawingPL   Object which holds complete zigzag drawing objects and properties - polyline version   Fields:      zigzag (Zigzag) : Zigzag object which holds the calculations.      properties (ZigzagProperties) : ZigzagProperties object which is used for setting the display styles of zigzag      zigzagLabels (label )      zigzagLine (series polyline) : polyline object of zigzag lines
Biblioteca Pine Script®
por Trendoscope
14
HTF - Candles with polylines"HTF - Candles with polylines" draws the previous Higher TimeFrame candles with the new feature polylines . 🔶 USAGE This publication makes it possible to see Higher Time Frame (HTF) candles on current chart, so you can see the bigger picture without switching to a HTF The current HTF is represented with a dashed-line box, covering the recent HTF open & close 🔹 Examples : • current timeframe 1 minute, HTF: 4 hour • current timeframe 15 minutes, HTF: 1 Day • current timeframe 1 hour, HTF: 1 Week Enabling " curved " gives a nice visual effect: "Pine - Apple" 😀 🔶 DETAILS The candle is made by starting a polyline at the bottom left. It then goes around, connecting the open-high-low-close values while making sure the width/height of the candle correspondends with the current timeframe candles. Arriving at the top left side, the polyline is connected back with the initial start point by setting the "closed" argument of polyline.new to "true". -> closed (series bool) If true, the drawing will also connect the first point to the last point from the `points` array, resulting in a closed polyline. 🔶 SETTINGS • HTF + "curved" • colours
Indicador Pine Script®
por fikira
8
Quick Shot[ChartPrime]This indicator plots green and red dots when the trend changes based on a moving average slope. The curved line aims to exponentially increase the slope of the moving average based on the slope at the time of the dots origination as the bars progress. Once the curved line makes contact with the price action, an x shape will be plotted to signify an exit signal. This indicator is best used in confluence with other indicators in order to develop a reliable strategy.
Indicador Pine Script®
por ChartPrime
Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator Purpose This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns. What a martingale is A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged: Lose one leg → increase next position size Lose again → increase again Win → reset to the base size The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops. What it plots Equity – simulated portfolio equity including compounding Buy & Hold – equity from holding the chart symbol for context Optional helpers – last trade outcome, current streak length, current allocation fraction Optional diagnostics – daily portfolio return, rolling drawdown, metrics table Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands Model assumptions Bar-close execution with no slippage or commissions Shorting allowed and frictionless No margin interest, borrow fees, or position limits No intrabar moves or gaps within a bar (returns are close-to-close) Sizing applies to equity fraction only and is capped by your setting All results are hypothetical and for education only. How the simulator applies it 1) Directional signal You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts. 2) Sizing after losses and wins Position size is a fraction of equity: Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double Reset after win – return to the initial allocation Max allocation cap – hard ceiling to prevent runaway growth At a high level the size after k consecutive losses is alloc(k) = min( cap , base × factor^k ) . In practice the simulator changes size only when a leg ends and its PnL is known. 3) Equity update Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds: eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) . This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled. Why traders experiment with martingale sizing Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live Why martingale is dangerous Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic: Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N. Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible. No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels. Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest. Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs. Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected. The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty. Interpreting losing streaks with numbers A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check: If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare. If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap. This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests.. Using the simulator productively Parameter studies Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond. Initial allocation – lower base reduces volatility and drawdowns across the board Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive Max cap – the most important brake; many users keep it between 20 and 50 percent Signal selection Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each. Diagnostics to watch Use the built-in metrics to quantify risk: Max Drawdown – worst peak-to-trough equity loss Sharpe and Sortino – volatility and downside-adjusted return VaR 95 percent and CVaR – tail risk measures from the realized distribution Alpha and Beta – relationship to your chosen benchmark If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see Monte Carlo exploration When enabled, the forecast draws many synthetic paths from your realized daily returns: Choose a horizon and a number of runs Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path Use the table to read the expected return over the horizon and the tail outcomes Remember it is a sketch based on your recent distribution, not a predictor Concrete examples Example A: Modest martingale Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing. Example B: Aggressive martingale Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk. Strengths Bar-by-bar, transparent computation of equity from stance and size Explicit handling of wins, losses, streaks, and caps Portable signal inputs so you can A–B test ideas quickly Risk diagnostics and forward uncertainty visualization in one place Example, Rolling Max Drawdown Limitations and important notes Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs. No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled. Signals are evaluated on closes. Real execution and fills will differ. Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes. All results are hypothetical. Use this as an educational tool, not a production risk engine. Practical tips Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples. Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg. Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear. Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off. If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop. Settings recap Backtest start date and initial capital Initial allocation, increase-after-loss factor, max allocation cap Signal source selector Trading days per year and risk-free rate Benchmark symbol for Alpha and Beta UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown Monte Carlo controls for enable, runs, horizon, and result table Final thoughts A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
Indicador Pine Script®
por BackQuant
Recession Warning Model [BackQuant]Recession Warning Model Overview The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments. Purpose The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly. Core Features - Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress. - Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries. - Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix). - Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads. - Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability). - Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals. - Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals. - Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R. Model Complexity Levels Users can select from four tiers to balance simplicity and depth: 1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring. 2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule. 3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth. 4. Research : Unlocks all indicators plus experimental settings for advanced users. Key Indicators Below is a summary of the 13 indicators, their data sources, and economic significance: - Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress. - Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs. - Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions. - Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator. - Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending. - PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction. - VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress. - S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption. - M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction. - NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions. - Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress. - JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns. - Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating. Data Processing - Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period). - Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback). - Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise. - Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations. Probability Calculation 1. Each indicator’s binary signal is weighted according to user settings or dynamic performance. 2. Weights are normalized to sum to 100% across active indicators. 3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix. 4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes. 5. Optional smoothing ensures stable outputs. Display and Visualization - Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands. - Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards. - Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence. - Ensemble Mode : Averages traditional and split probabilities for a balanced view. - Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis). - Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses. Practical Applications - Asset Allocation : Adjust equity or bond exposures based on sustained probability increases. - Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis. - Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%. - Trading Filters : Disable short-term strategies during high-risk regimes. - Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated. Configuration Guidelines - Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred. - Use dynamic weighting with at least one economic cycle of data for optimal performance. - Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals. - Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization. - Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading. Limitations - Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading. - Historical data availability may constrain dynamic weighting on shorter timeframes. - Model accuracy depends on the quality and timeliness of economic data feeds. Final Note The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
Indicador Pine Script®
por BackQuant
3
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Indicador Pine Script®
por EdgeTools
5
ADX Forecast [Titans_Invest]ADX Forecast This isn’t just another ADX indicator — it’s the most powerful and complete ADX tool ever created, and without question the best ADX indicator on TradingView, possibly even the best in the world. ADX Forecast represents a revolutionary leap in trend strength analysis, blending the timeless principles of the classic ADX with cutting-edge predictive modeling. For the first time on TradingView, you can anticipate future ADX movements using scientifically validated linear regression — a true game-changer for traders looking to stay ahead of trend shifts. 1. Real-Time ADX Forecasting By applying least squares linear regression, ADX Forecast projects the future trajectory of the ADX with exceptional accuracy. This forecasting power enables traders to anticipate changes in trend strength before they fully unfold — a vital edge in fast-moving markets. 2. Unmatched Customization & Precision With 26 long entry conditions and 26 short entry conditions, this indicator accounts for every possible ADX scenario. Every parameter is fully customizable, making it adaptable to any trading strategy — from scalping to swing trading to long-term investing. 3. Transparency & Advanced Visualization Visualize internal ADX dynamics in real time with interactive tags, smart flags, and fully adjustable threshold levels. Every signal is transparent, logic-based, and engineered to fit seamlessly into professional-grade trading systems. 4. Scientific Foundation, Elite Execution Grounded in statistical precision and machine learning principles, ADX Forecast upgrades the classic ADX from a reactive lagging tool into a forward-looking trend prediction engine. This isn’t just an indicator — it’s a scientific evolution in trend analysis. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the ADX, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an ADX time series like this: Time → ADX  → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted ADX, which can be crossed with the actual ADX to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public ADX with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining ADX with linear regression, this indicator allows a trader to predict market momentum, not just follow it. ADX Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _______________________________________________________________________ 🥇 This is the world’s first ADX indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________ _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE ADX❓ The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down. The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength. ⯁ HOW TO USE THE ADX❓ The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones: • Strong Trend: When the ADX is above 25, indicating a strong trend. • Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend. • Neutral Zone: Between 20 and 25, where the trend strength is unclear. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 🔹 +DI > -DI 🔹 +DI < -DI 🔹 +DI > ADX 🔹 +DI < ADX 🔹 -DI > ADX 🔹 -DI < ADX 🔹 ADX > Threshold 🔹 ADX < Threshold 🔹 +DI > Threshold 🔹 +DI < Threshold 🔹 -DI > Threshold 🔹 -DI < Threshold 🔹 +DI (Crossover) -DI 🔹 +DI (Crossunder) -DI 🔹 +DI (Crossover) ADX 🔹 +DI (Crossunder) ADX 🔹 +DI (Crossover) Threshold 🔹 +DI (Crossunder) Threshold 🔹 -DI (Crossover) ADX 🔹 -DI (Crossunder) ADX 🔹 -DI (Crossover) Threshold 🔹 -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 🔸 +DI > -DI 🔸 +DI < -DI 🔸 +DI > ADX 🔸 +DI < ADX 🔸 -DI > ADX 🔸 -DI < ADX 🔸 ADX > Threshold 🔸 ADX < Threshold 🔸 +DI > Threshold 🔸 +DI < Threshold 🔸 -DI > Threshold 🔸 -DI < Threshold 🔸 +DI (Crossover) -DI 🔸 +DI (Crossunder) -DI 🔸 +DI (Crossover) ADX 🔸 +DI (Crossunder) ADX 🔸 +DI (Crossover) Threshold 🔸 +DI (Crossunder) Threshold 🔸 -DI (Crossover) ADX 🔸 -DI (Crossunder) ADX 🔸 -DI (Crossover) Threshold 🔸 -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : ADX Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Indicador Pine Script®
por Titans_Invest