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RSI Full Forecast [Titans_Invest]RSI Full Forecast Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market. AI-Powered RSI Forecasting: Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy. But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis. You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements. See the Future, Now: • Track upcoming RSI & RSI MA crossovers in advance. • Identify potential reversal zones before price reacts. • Uncover statistical behavior patterns that would normally go unnoticed. 40+ Intelligent Conditions: The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic. Full Customization: All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions. Innovative, Accurate, Unique: This isn’t just an upgrade. It’s a quantum leap in technical analysis. RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system. ⯁ 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 RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an RSI time series like this: Time → RSI    → 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 RSI, which can be crossed with the actual RSI 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 RSI 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 RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it. RSI Full 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: _________________________________________________ 🔮 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 RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:   •  Overbought: When the RSI is above 70, indicating that the asset may be overbought.   •  Oversold: When the RSI is below 30, indicating that the asset may be oversold.   •  Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ______________________________________________________ ______________________________________________________ ⯁ 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 . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 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 Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear 📈 RSI Forecast: 🔹 RSI (Crossover) MA Forecast 🔹 RSI (Crossunder) MA Forecast 🔹 RSI Forecast 1 > MA Forecast 1 🔹 RSI Forecast 1 < MA Forecast 1 🔹 RSI Forecast 2 > MA Forecast 2 🔹 RSI Forecast 2 < MA Forecast 2 🔹 RSI Forecast 3 > MA Forecast 3 🔹 RSI Forecast 3 < MA Forecast 3 🔹 RSI Forecast 4 > MA Forecast 4 🔹 RSI Forecast 4 < MA Forecast 4 🔹 RSI Forecast 5 > MA Forecast 5 🔹 RSI Forecast 5 < MA Forecast 5 🔹 RSI Forecast 6 > MA Forecast 6 🔹 RSI Forecast 6 < MA Forecast 6 🔹 RSI Forecast 7 > MA Forecast 7 🔹 RSI Forecast 7 < MA Forecast 7 🔹 RSI Forecast 8 > MA Forecast 8 🔹 RSI Forecast 8 < MA Forecast 8 🔹 RSI Forecast 9 > MA Forecast 9 🔹 RSI Forecast 9 < MA Forecast 9 🔹 RSI Forecast 10 > MA Forecast 10 🔹 RSI Forecast 10 < MA Forecast 10 🔹 RSI Forecast 11 > MA Forecast 11 🔹 RSI Forecast 11 < MA Forecast 11 🔹 RSI Forecast 12 > MA Forecast 12 🔹 RSI Forecast 12 < MA Forecast 12 🔹 RSI Forecast 13 > MA Forecast 13 🔹 RSI Forecast 13 < MA Forecast 13 🔹 RSI Forecast 14 > MA Forecast 14 🔹 RSI Forecast 14 < MA Forecast 14 🔹 RSI Forecast 15 > MA Forecast 15 🔹 RSI Forecast 15 < MA Forecast 15 🔹 RSI Forecast 16 > MA Forecast 16 🔹 RSI Forecast 16 < MA Forecast 16 🔹 RSI Forecast 17 > MA Forecast 17 🔹 RSI Forecast 17 < MA Forecast 17 🔹 RSI Forecast 18 > MA Forecast 18 🔹 RSI Forecast 18 < MA Forecast 18 🔹 RSI Forecast 19 > MA Forecast 19 🔹 RSI Forecast 19 < MA Forecast 19 🔹 RSI Forecast 20 > MA Forecast 20 🔹 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 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 Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear 📉 RSI Forecast: 🔸 RSI (Crossover) MA Forecast 🔸 RSI (Crossunder) MA Forecast 🔸 RSI Forecast 1 > MA Forecast 1 🔸 RSI Forecast 1 < MA Forecast 1 🔸 RSI Forecast 2 > MA Forecast 2 🔸 RSI Forecast 2 < MA Forecast 2 🔸 RSI Forecast 3 > MA Forecast 3 🔸 RSI Forecast 3 < MA Forecast 3 🔸 RSI Forecast 4 > MA Forecast 4 🔸 RSI Forecast 4 < MA Forecast 4 🔸 RSI Forecast 5 > MA Forecast 5 🔸 RSI Forecast 5 < MA Forecast 5 🔸 RSI Forecast 6 > MA Forecast 6 🔸 RSI Forecast 6 < MA Forecast 6 🔸 RSI Forecast 7 > MA Forecast 7 🔸 RSI Forecast 7 < MA Forecast 7 🔸 RSI Forecast 8 > MA Forecast 8 🔸 RSI Forecast 8 < MA Forecast 8 🔸 RSI Forecast 9 > MA Forecast 9 🔸 RSI Forecast 9 < MA Forecast 9 🔸 RSI Forecast 10 > MA Forecast 10 🔸 RSI Forecast 10 < MA Forecast 10 🔸 RSI Forecast 11 > MA Forecast 11 🔸 RSI Forecast 11 < MA Forecast 11 🔸 RSI Forecast 12 > MA Forecast 12 🔸 RSI Forecast 12 < MA Forecast 12 🔸 RSI Forecast 13 > MA Forecast 13 🔸 RSI Forecast 13 < MA Forecast 13 🔸 RSI Forecast 14 > MA Forecast 14 🔸 RSI Forecast 14 < MA Forecast 14 🔸 RSI Forecast 15 > MA Forecast 15 🔸 RSI Forecast 15 < MA Forecast 15 🔸 RSI Forecast 16 > MA Forecast 16 🔸 RSI Forecast 16 < MA Forecast 16 🔸 RSI Forecast 17 > MA Forecast 17 🔸 RSI Forecast 17 < MA Forecast 17 🔸 RSI Forecast 18 > MA Forecast 18 🔸 RSI Forecast 18 < MA Forecast 18 🔸 RSI Forecast 19 > MA Forecast 19 🔸 RSI Forecast 19 < MA Forecast 19 🔸 RSI Forecast 20 > MA Forecast 20 🔸 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🤖 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 Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Full 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
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