Indicator
LJ Parsons Harmonic Time StampsPurpose of the Script
This script is designed to divide a specific time period on a market chart (from startDate to endDate) into fractional segments based on mathematically significant ratios. It then plots vertical lines at the first candle that occurs at or after each of these fractional timestamps. Each line is labeled according to an interval scheme, as outlined by LJ Parsons
"Structured Multiplicative, Recursive Systems in Financial Markets"
papers.ssrn.com
Providing a symbolic mapping of time fractions
zenodo.org
Start (00) and End (00): Marks the beginning and end of the period.
Intermediate labels (m2, M2, m3, M3, …): Represent divisions of the time period that correspond to specific fractions of the whole.
This creates a visual “resonance map” along the price chart, where the timing of price movements can be compared to mathematically significant points.
Parsons Market Resonance Theory proposes that markets move in patterns that are not random but resonate with underlying mathematical structures, analogous to logarithmic relationships. The key ideas reflected in this script are:
Temporal Fractional Resonance
By marking fractional points of a defined time period, the script highlights potential moments when market activity might “resonate” due to cyclical patterns. These points are analogous to overtones in music—certain times may have stronger market reactions.
Mapping Market Movements to "Just Intonation" Intervals
Assigning Interval labels to fractional timestamps provides a symbolic framework for understanding market behaviour. For example, the midpoint (P5) may correspond to strong market turning points, while minor or major intervals (m3, M6) might correspond to subtler movements.
Identifying Potentially Significant Points in Time
The plotted lines do not predict price direction but rather identify temporal markers where price movements may be more likely to display structured behaviour. Traders or researchers can then study price reactions around these lines for correlations with market resonance patterns.
In essence, the script turns a period of time into a harmonic structure, with each line and label acting like a “note” in the market’s temporal symphony. It’s a tool to visualize and test whether price behaviour aligns with the resonant fractions hypothesized in MRT. Indicator
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RSI Forecast Colorful [DiFlip]RSI Forecast Colorful
Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions.
⯁ Real-Time RSI Forecasting
For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions.
⯁ Highly Customizable
The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy.
⯁ Innovative and Science-Based
This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is:
y = β₀ + β₁x + ε
where:
y = predicted variable (e.g., future RSI value)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept (value of y when x = 0)
β₁ = slope (rate of change of y relative to x)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error between predicted and observed values, we use the formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension.
⯁ Visual Interpretation
Imagine a time series of RSI values like this:
Time →
RSI →
The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior.
⯁ Summary of Scientific Concepts Used
Linear Regression: Models relationships between variables using a straight line.
Least Squares: Minimizes squared prediction errors for optimal fit.
Time-Series Forecasting: Predicts future values from historical patterns.
Supervised Learning: Predictive modeling based on known output values.
Statistical Smoothing: Reduces noise to highlight underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Built on statistical and mathematical theory.
First of its kind: The first public RSI with least-squares predictive modeling.
Intelligent: Incorporates machine-learning logic into RSI interpretation.
Forward-looking: Generates predictive, not just reactive, signals.
Customizable: Exceptionally flexible for any strategic framework.
⯁ Conclusion
By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What Is RSI?
The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength.
⯁ How RSI Works
RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones:
Overbought: RSI above 70 may signal an overbought market.
Oversold: RSI below 30 may signal an oversold market.
Neutral Zone: RSI between 30 and 70, indicating no extreme condition.
These zones help identify potential price reversals and confirm trend strength.
⯁ Entry Conditions
All conditions below are fully customizable and allow detailed control over entry signal creation.
📈 BUY
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
📉 SELL
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
🤖 Automation
All BUY and SELL conditions can be automated using PulseWire alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
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Volume-Confirmed FTR Zones [AlgoPoint]FTR Zone Indicator — Fail To Return Zones (With Volume Confirmation)
Advanced Smart Money Zone Detection for Institutional Orderflow
The FTR Zone Indicator is a professional-grade tool designed for traders who follow Smart Money Concepts (SMC), ICT methodologies, or institutional orderflow. It automatically detects Fail To Return Zones (FTR) — high-probability supply and demand areas formed after strong displacement moves.
By combining impulse detection, base identification, and volume confirmation, this indicator highlights zones where price is most likely to react, reverse, or mitigate shortly after structure breaks.
⸻
⭐ What Are FTR Zones?
FTR zones (Fail To Return zones) are price areas where:
1. A strong displacement / impulse candle is formed
2. That impulse originates from a small consolidation (base)
3. Price moves away aggressively
4. AND fails to return immediately to the origin area
These zones often indicate:
• Institutional orders
• Imbalance
• Hidden liquidity
• Origin of a trend leg
• High-probability mitigation points
This indicator fully automates the detection and visualization of such areas.
🔍 How the Indicator Works
1. Impulse Detection
The indicator identifies a valid impulse candle using:
• ATR-based bar range filter
• Trend-aligned candle body direction
• Optional volume confirmation
Only large, meaningful institutional candles qualify — filtering out noise.
2. Base Zone Identification
Before every impulse, the tool finds the micro-consolidation base using:
• Highest high of the last X bars
• Lowest low of the last X bars
This base becomes the potential FTR zone.
3. FTR Zone Creation
When a valid impulse is detected:
• Bullish impulse → Demand FTR zone
• Bearish impulse → Supply FTR zone
The zone is immediately drawn on the chart using box.new().
4. Zone Extension
Every zone continuously extends to the right as price evolves, allowing you to track:
• Mitigation
• Retests
• Reaction points
• Liquidity sweeps
5. Invalidation Logic
Zones automatically delete when violated:
• Demand zone invalid if close < zone low
• Supply zone invalid if close > zone high
This keeps the chart clean and helps focus only on active, high-value areas.
🎛️ Key Features
✔ Automatic FTR Zone Detection
Instantly identifies institutional origin zones based on real impulse and displacement.
✔ Volume-Based Filtering
Ensures only high-volume impulses (true institutional orders) create zones.
✔ Supply & Demand Coloring
• Bullish FTR → Demand Zone (Teal tone)
• Bearish FTR → Supply Zone (Red tone)
✔ Safe Zone Storage
Fault-tolerant logic ensures no array errors, invalid zones, or broken visuals.
✔ Auto-Extending Boxes
Real-time zone updates with precise historical mapping.
✔ Smart Invalidation
Zone is removed only when fully broken, preventing false signals.
✔ Clean, Non-Repainting Logic
Impulse detection and zone placement are confirmed only on bar close.
📈 How to Use It (Example Schenarios)
For Reversals or Continuations
• Look for price reacting or mitigating inside a zone
• Use as entry confirmation in trend continuations
• Combine with FVG, BOS/CHOCH, liquidity sweeps, or premium/discount zones
For Scalping or Intraday Trading
• High-probability countertrend entries
• Reaction-based setups at institutional footprints
For Swing Traders
• Identify weekly/daily origin zones
• Plan entries around large displacement points
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RSI with Zone Colors//@version=6
indicator(title="RSI with Zone Colors", shorttitle="RSI+", format=format.price, precision=2, timeframe="", timeframe_gaps=true)
//// ==== INPUT SETTINGS ====
rsiLength = input.int(14, title="RSI Length", minval=1)
source = input.source(close, title="Source")
ob_level = input.int(70, title="Overbought Level")
os_level = input.int(30, title="Oversold Level")
//// ==== RSI CALCULATION ====
change = ta.change(source)
up = ta.ma(math.max(change, 0), rsiLength)
down = ta.ma(-math.min(change, 0), rsiLength)
rsi = down == 0 ? 100 : 100 - (100 / (1 + up / down))
//// ==== COLOR BASED ON ZONES ====
rsiColor = rsi > ob_level ? color.red : rsi < os_level ? color.green : #2962FF
//// ==== PLOT RSI ====
plot(rsi, title="RSI", color=rsiColor, linewidth=2)
//// ==== ZONE LINES ====
hline(ob_level, "Overbought", color=#787B86)
hline(50, "Middle", color=color.new(#787B86, 50))
hline(os_level, "Oversold", color=#787B86)
//// ==== FILL ZONES ====
zoneColor = rsi > ob_level ? color.new(color.red, 85) : rsi < os_level ? color.new(color.green, 85) : na
fill(plot(ob_level, display=display.none), plot(rsi > ob_level ? rsi : ob_level, display=display.none), color=zoneColor, title="OB Fill")
fill(plot(os_level, display=display.none), plot(rsi < os_level ? rsi : os_level, display=display.none), color=zoneColor, title="OS Fill")
//// ==== COLOR CANDLE WHEN RSI IN ZONE ====
barcolor(rsi > ob_level ? color.red : rsi < os_level ? color.green : na) Indicator
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