OBV ATR Strategy (OBV Breakout Channel) bas20230503ผมแก้ไขจาก OBV+SMA อันเดิม ของเดิม ดูที่เส้น SMA สองเส้นตัดกันมั่นห่วยแตกสำหรับที่ผมลองเทรดจริง และหลักการเบรค ได้แรงบันดาลใจ ATR จาก เทพคอย ที่ใช้กับราคา แต่นี้ใช้กับ OBV แทน
และผมใช้เจมินี้ เพื่อแก้ ให้ เป็น strategy เพื่อเช็คย้อนหลังได้ง่ายกว่าเดิม
หลักการง่ายคือถ้ามันขึ้น มันจะขึ้นเรื่อยๆ
เขียน แบบสุภาพ (น่าจะอ่านได้ง่ายกว่าผมเขียน)
สคริปต์นี้ได้รับการพัฒนาต่อยอดจากแนวคิด OBV+SMA Crossover แบบดั้งเดิม ซึ่งจากการทดสอบส่วนตัวพบว่าประสิทธิภาพยังไม่น่าพอใจ กลยุทธ์ใหม่นี้จึงเปลี่ยนมาใช้หลักการ "Breakout" ซึ่งได้รับแรงบันดาลใจมาจากการใช้ ATR สร้างกรอบของราคา แต่เราได้นำมาประยุกต์ใช้กับ On-Balance Volume (OBV) แทน นอกจากนี้ สคริปต์ได้ถูกแปลงเป็น Strategy เต็มรูปแบบ (โดยความช่วยเหลือจาก Gemini AI) เพื่อให้สามารถทดสอบย้อนหลัง (Backtest) และประเมินประสิทธิภาพได้อย่างแม่นยำ
หลักการของกลยุทธ์: กลยุทธ์นี้ทำงานบนแนวคิดโมเมนตัมที่ว่า "เมื่อแนวโน้มได้เกิดขึ้นแล้ว มีโอกาสที่มันจะดำเนินต่อไป" โดยจะมองหาการทะลุของพลังซื้อ-ขาย (OBV) ที่แข็งแกร่งเป็นพิเศษเป็นสัญญาณเข้าเทร
----
สคริปต์นี้เป็นกลยุทธ์ (Strategy) ที่ใช้ On-Balance Volume (OBV) ซึ่งเป็นอินดิเคเตอร์ที่วัดแรงซื้อและแรงขายสะสม แทนที่จะใช้การตัดกันของเส้นค่าเฉลี่ย (SMA Crossover) ที่เป็นแบบพื้นฐาน กลยุทธ์นี้จะมองหาการ "ทะลุ" (Breakout) ของพลัง OBV ออกจากกรอบสูงสุด-ต่ำสุดของตัวเองในรอบที่ผ่านมา
สัญญาณกระทิง (Bull Signal): เกิดขึ้นเมื่อพลังการซื้อ (OBV) แข็งแกร่งจนสามารถทะลุจุดสูงสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาขึ้น
สัญญาณหมี (Bear Signal): เกิดขึ้นเมื่อพลังการขาย (OBV) รุนแรงจนสามารถกดดันให้ OBV ทะลุจุดต่ำสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาลง
ส่วนประกอบบนกราฟ (Indicator Components)
เส้น OBV
เส้นหลัก ที่เปลี่ยนเขียวเป็นแดง เป็นทั้งแนวรับและแนวต้าน และ จุด stop loss
เส้นนี้คือหัวใจของอินดิเคเตอร์ ที่แสดงถึงพลังสะสมของ Volume
เมื่อเส้นเป็นสีเขียว (แนวรับ): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดกระทิง" เส้นนี้คือระดับต่ำสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวรับไดนามิก
เมื่อเส้นกลายเป็นสีแดงสีแดง (แนวต้าน): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดหมี" เส้นนี้คือระดับสูงสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวต้านไดนามิก
สัญลักษณ์สัญญาณ (Signal Markers):
Bull 🔼 (สามเหลี่ยมขึ้นสีเขียว): คือสัญญาณ "เข้าซื้อ" (Long) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุขึ้นไปเหนือกรอบด้านบนเป็นครั้งแรก
Bear 🔽 (สามเหลี่ยมลงสีแดง): คือสัญญาณ "เข้าขาย" (Short) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุลงไปต่ำกว่ากรอบด้านล่างเป็นครั้งแรก
วิธีการใช้งาน (How to Use)
เพิ่มสคริปต์นี้ลงบนกราฟราคาที่คุณสนใจ
ไปที่แท็บ "Strategy Tester" ด้านล่างของ TradingView เพื่อดูผลการทดสอบย้อนหลัง (Backtest) ของกลยุทธ์บนสินทรัพย์และไทม์เฟรมต่างๆ
ใช้สัญลักษณ์ "Bull" และ "Bear" เป็นตัวช่วยในการตัดสินใจเข้าเทรด
ข้อควรจำ: ไม่มีกลยุทธ์ใดที่สมบูรณ์แบบ 100% ควรใช้สคริปต์นี้ร่วมกับการวิเคราะห์ปัจจัยอื่นๆ เช่น โครงสร้างราคา, แนวรับ-แนวต้านของราคา และการบริหารความเสี่ยง (Risk Management) ของตัวคุณเองเสมอ
การตั้งค่า (Inputs)
SMA Length 1 / SMA Length 2: ใช้สำหรับพล็อตเส้นค่าเฉลี่ยของ OBV เพื่อดูเป็นภาพอ้างอิง ไม่มีผลต่อตรรกะการเข้า-ออกของ Strategy อันใหม่ แต่มันเป็นของเก่า ถ้าชอบ ก็ใช้ได้ เมื่อ SMA สองเส้นตัดกัน หรือตัดกับเส้น OBV
High/Low Lookback Length: (ค่าพื้นฐาน30/แก้ตรงนี้ให้เหมาะสมกับ coin หรือหุ้น ตามความผันผวน ) คือระยะเวลาที่ใช้ในการคำนวณกรอบสูงสุด-ต่ำสุดของ OBV
ค่าน้อย: ทำให้กรอบแคบลง สัญญาณจะเกิดไวและบ่อยขึ้น แต่อาจมีสัญญาณหลอก (False Signal) เยอะขึ้น
ค่ามาก: ทำให้กรอบกว้างขึ้น สัญญาณจะเกิดช้าลงและน้อยลง แต่มีแนวโน้มที่จะเป็นสัญญาณที่แข็งแกร่งกว่า
แน่นอนครับ นี่คือคำแปลฉบับภาษาอังกฤษที่สรุปใจความสำคัญ กระชับ และสุภาพ เหมาะสำหรับนำไปใช้ในคำอธิบายสคริปต์ (Description) ของ TradingView ครับ
---Translate to English---
OBV Breakout Channel Strategy
This script is an evolution of a traditional OBV+SMA Crossover concept. Through personal testing, the original crossover method was found to have unsatisfactory performance. This new strategy, therefore, uses a "Breakout" principle. The inspiration comes from using ATR to create price channels, but this concept has been adapted and applied to On-Balance Volume (OBV) instead.
Furthermore, the script has been converted into a full Strategy (with assistance from Gemini AI) to enable precise backtesting and performance evaluation.
The strategy's core principle is momentum-based: "once a trend is established, it is likely to continue." It seeks to enter trades on exceptionally strong breakouts of buying or selling pressure as measured by OBV.
Core Concept
This is a Strategy that uses On-Balance Volume (OBV), an indicator that measures cumulative buying and selling pressure. Instead of relying on a basic Simple Moving Average (SMA) Crossover, this strategy identifies a "Breakout" of the OBV from its own highest-high and lowest-low channel over a recent period.
Bull Signal: Occurs when the buying pressure (OBV) is strong enough to break above its own recent highest high, indicating a potential shift to an upward trend.
Bear Signal: Occurs when the selling pressure (OBV) is intense enough to push the OBV below its own recent lowest low, indicating a potential shift to a downward trend.
On-Screen Components
1. OBV Line
This is the main indicator line, representing the cumulative volume. Its color changes to green when OBV is rising and red when it is falling.
2. Dynamic Support & Resistance Line
This is the thick Green or Red line that appears based on the strategy's current "mode." This line serves as a dynamic support/resistance level and can be used as a reference for stop-loss placement.
Green Line (Support): Appears when the strategy enters "Bull Mode." This line represents the lowest low of the OBV in the recent past and acts as dynamic support.
Red Line (Resistance): Appears when the strategy enters "Bear Mode." This line represents the highest high of the OBV in the recent past and acts as dynamic resistance.
3. Signal Markers
Bull 🔼 (Green Up Triangle): This is the "Long Entry" signal. It appears at the moment the OBV first breaks out above its high-low channel.
Bear 🔽 (Red Down Triangle): This is the "Short Entry" signal. It appears at the moment the OBV first breaks down below its high-low channel.
How to Use
Add this script to the price chart of your choice.
Navigate to the "Strategy Tester" panel at the bottom of TradingView to view the backtesting results for the strategy on different assets and timeframes.
Use the "Bull" and "Bear" signals as aids in your trading decisions.
Disclaimer: No strategy is 100% perfect. This script should always be used in conjunction with other forms of analysis, such as price structure, key price-based support/resistance levels, and your own personal risk management rules.
Inputs
SMA Length 1 / SMA Length 2: These are used to plot moving averages on the OBV for visual reference. They are part of the legacy logic and do not affect the new breakout strategy. However, they are kept for traders who may wish to observe their crossovers for additional confirmation.
High/Low Lookback Length: (Most Important Setting) This determines the period used to calculate the highest-high and lowest-low OBV channel. (Default is 30; adjust this to suit the asset's volatility).
A smaller value: Creates a narrower channel, leading to more frequent and faster signals, but potentially more false signals.
A larger value: Creates a wider channel, leading to fewer and slower signals, which are likely to be more significant.
Search in scripts for "ai"
Futures vs CFD Price Display
🎯 Trading the same asset in CFDs and Futures but tired of switching charts to compare prices? This is your indicator!
Stop the constant chart hopping! This live price comparison shows you instantly where the better conditions are.
✨ What you get:
Bidirectional: Works in both Futures AND CFD charts
Live prices: Real-time comparison of both markets
Spread calculation: Automatic difference in points and percentage
Fully customizable: Colors, position, size to your liking
Professional design: Clean display with symbol header
🎯 Perfect for:
Gold traders (Futures vs CFD)
Arbitrage strategies
Spread monitoring
Multi-broker comparisons
⚙️ Customization:
3 sizes (Small/Normal/Large) for all screens
4 positions available
Individual color schemes
Toggle features on/off
💡 Simply enter the symbol and keep both markets in sight!
Notice: "Co-developed with Claude AI (Anthropic) - because even AI needs to pay the server bills! 😄"
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
SwiftEdge NW EnvelopeSwiftEdge NW Envelope
Overview
The SwiftEdge NW Envelope is a visually striking technical indicator designed for traders seeking to identify high-probability buy and sell opportunities in volatile markets. By combining the Relative Strength Index (RSI), Average True Range (ATR), and Nadaraya-Watson Envelope, this indicator provides a unique blend of momentum, volatility, and non-linear trend analysis. Its futuristic, AI-inspired aesthetic—featuring neon gradients and dynamic colors—enhances chart readability while delivering actionable trading signals.
What It Does
The SwiftEdge NW Envelope generates buy and sell signals based on price interactions with dynamically calculated support and resistance bands, confirmed by RSI conditions. The indicator:
Plots a Nadaraya-Watson Envelope to identify smooth, non-linear price trends and dynamic support/resistance zones.
Uses ATR to scale the envelope’s bands, adapting to market volatility.
Employs RSI to confirm overbought/oversold conditions, ensuring signals align with momentum.
Visualizes signals with neon-colored markers, background zones, and labels for intuitive decision-making.
How It Works
The indicator integrates three key components:
Nadaraya-Watson Envelope:
A kernel-based regression technique that smooths price data to create a central trend line (mean) and dynamic upper/lower bands.
Unlike traditional moving averages, it provides a non-linear, adaptive view of price trends, making it ideal for capturing complex market movements.
The band width is determined by ATR, ensuring responsiveness to volatility.
Average True Range (ATR):
Measures market volatility to scale the envelope’s bands.
A multiplier (default: 0.5) adjusts the sensitivity of the bands, allowing traders to fine-tune the indicator for different assets or market conditions.
Relative Strength Index (RSI):
A momentum oscillator with a shortened period (default: 5) for increased sensitivity.
Confirms buy signals when RSI is oversold (default: <30) and sell signals when RSI is overbought (default: >70).
Signal Logic
Buy Signal: Triggered when the price crosses above the lower band of the Nadaraya-Watson Envelope and RSI is below the oversold threshold. Marked by a green circle and a "BUY" label below the candle.
Sell Signal: Triggered when the price crosses below the upper band and RSI is above the overbought threshold. Marked by a magenta circle and a "SELL" label above the candle.
Background Zones: Green (buy) or red (sell) translucent zones highlight signal areas for quick recognition.
Visual Features
Dynamic Colors: The central trend line shifts between cyan (uptrend), purple (downtrend), or gray (neutral) based on price position relative to the mean.
Neon Gradient Fill: A translucent blue fill between the upper (green) and lower (red) bands creates a glowing, futuristic effect.
Modern Signal Markers: Small, vibrant circles (green for buy, magenta for sell) and clear labels enhance visual clarity.
Why This Combination?
The SwiftEdge NW Envelope combines RSI, ATR, and Nadaraya-Watson Envelope to create a robust trading tool:
RSI provides momentum confirmation, filtering out false signals in choppy markets.
ATR ensures the envelope adapts to changing volatility, making it suitable for both trending and ranging markets.
Nadaraya-Watson Envelope offers a sophisticated, non-linear alternative to traditional bands (e.g., Bollinger Bands), capturing subtle price dynamics. Together, these components deliver a balanced approach to trend-following and mean-reversion strategies, with RSI acting as a gatekeeper to improve signal reliability.
Customize Settings:
RSI Period (5): Adjust for more/less sensitivity to momentum.
RSI Overbought/Oversold (70/30): Modify thresholds to tighten or loosen signal conditions.
ATR Period (14) and Multiplier (0.5): Tune volatility sensitivity.
NW Length (25), Bandwidth (8.0), Multiplier (3.0): Adjust the smoothness and width of the envelope.
Interpret Signals:
Buy: Look for green circles and "BUY" labels when price crosses above the lower band, confirmed by low RSI.
Sell: Look for magenta circles and "SELL" labels when price crosses below the upper band, confirmed by high RSI.
Use background zones to quickly spot active signal areas.
Combine with Other Tools:
Pair with support/resistance levels or volume analysis for additional confirmation.
Test signals on a demo account before live trading.
Originality
The SwiftEdge NW Envelope stands out due to:
Its innovative use of Nadaraya-Watson regression, a less common but powerful tool for non-linear trend analysis.
A unique visual design with neon gradients and dynamic colors, inspired by AI and futuristic interfaces, making it both functional and visually engaging.
A streamlined signal system that balances momentum (RSI), volatility (ATR), and trend (Nadaraya-Watson), reducing noise and enhancing trade precision.
Notes
Best suited for volatile markets (e.g., forex, crypto, stocks) where price swings create clear envelope breakouts.
Adjust input parameters to match your trading style (e.g., shorter RSI period for scalping, wider bands for swing trading).
Always backtest and validate signals in your specific market and timeframe before trading.
RSI MACD Combined Color StrategyOverview
This indicator combines RSI and MACD signals to create a powerful visual trading system, inspired by TrendSpider's AI Strategy Coder examples. It colors candles based on the alignment of three key technical conditions, providing clear visual signals for potential trend strength and direction.
Technical Components
Core Conditions
RSI (Relative Strength Index) > 50
Indicates bullish momentum when price is trading above the centerline
Traditional indicator of trend strength
MACD Line > Signal Line
Shows positive momentum
Classic signal for potential upward movement
MACD Line > 0
Confirms bullish territory
Indicates overall positive momentum
Color Coding System
🟢 Green Candles: All three conditions are met
Strongest bullish signal
Suggests high probability trading opportunities
⚪ Grey Candles: One or two conditions are met
Neutral or transitioning market
Suggests caution or waiting for stronger confirmation
🔴 Red Candles: No conditions are met
Bearish signal
Suggests potential downward pressure
How to Use This Indicator
For Entry Signals
Look for transitions from red or grey to green candles
Green candles suggest strong bullish alignment
Consider entering long positions when candles turn green
For Exit Signals
Watch for color transitions from green to grey or red
Consider taking profits when candles change from green to grey
Consider stop losses when candles turn red
Risk Management
Use color transitions as part of your broader strategy
Don't rely solely on color changes for trading decisions
Combine with other technical analysis tools and risk management practices
Customizable Parameters
RSI Length (default: 14)
MACD Fast Length (default: 12)
MACD Slow Length (default: 26)
MACD Signal Length (default: 9)
Best Practices
Use multiple timeframes for confirmation
Look for confluences with support/resistance levels
Consider volume and market context
Start with default settings and adjust based on your trading style
Backtest different parameter combinations
Notes
This indicator works best in trending markets
Grey candles can indicate transition periods
Consider market conditions and volatility when interpreting signals
Credits
Inspired by TrendSpider's AI Strategy Coder examples and adapted for TradingView using Pine Script v5.
Disclaimer
This technical indicator is for informational purposes only. Always conduct your own analysis and consider risk management principles before making trading decisions. Past performance does not guarantee future results.
RSI NarrativesDescription:
The RSI Narratives script aggregates Relative Strength Index (RSI) values across multiple cryptocurrency narratives or sectors, providing an easy-to-read visual and alert system for trend reversals and overbought/oversold conditions. This tool is designed for traders looking to track sector-specific trends and compare performance across AI, DeFi, Level 1 blockchains, and more.
Key Features:
RSI Aggregation by Sector: Calculates average RSI for key narratives, including AI, DeFi, Level 1 blockchains, new memes, and more.
Customizable RSI Settings: Adjust RSI period, line width, and label offsets for personalized analysis.
Dynamic Alerts: Receive alerts when a narrative enters overbought or oversold territory, helping you act quickly on market movements.
Clean Visualization: Overlay sector-specific SMA lines with distinct colors and optional labels for quick interpretation.
Multi-Narrative Comparison: Analyze trends across diverse narratives to identify emerging opportunities.
Parameters for Customization:
RSI Period: Set the lookback period for RSI calculations (default: 14).
Line Width: Adjust the thickness of plotted lines (default: 2).
Label Offset: Control label placement for better chart readability.
Overbought/Oversold Thresholds: Configure the RSI levels for alerts (default: 70/40).
How to Use:
Add the script to your TradingView chart.
Customize the RSI parameters to suit your trading strategy.
Monitor the plotted SMA lines to identify narrative-specific trends.
Set alerts for overbought and oversold conditions to stay informed in real time.
Alerts System:
Alerts trigger when a narrative crosses predefined overbought or oversold levels.
Text notifications suggest potential trading actions, such as selling on overbought or buying on oversold.
Intended Users:
This script is ideal for crypto traders, sector analysts, and market enthusiasts who want to track performance across narratives and gain actionable insights into sector rotations.
Disclaimer:
This script is for educational and informational purposes only. It does not constitute financial advice. Please test on historical data and practice caution when trading.
Crypto Sectors Performance [Daveatt]IMPORTANT
⚠️ This script must be used on the Daily timeframe only.
OVERVIEW
This indicator brings the powerful sector analysis capabilities from velo.xyz/market's
Sector Performance chart to TradingView.
It enables traders to track and compare performance across the crypto market's major sectors, providing essential insights for sector rotation strategies and market analysis.
CALCULATION METHOD
The indicator calculates performance across six key crypto sectors: DeFi, Gaming, Layer 1s, Layer 2s, AI, and Memecoins.
For each sector, it computes a rolling percentage performance by averaging the performance of multiple representative tokens.
All sector performances are rebased to 0% at the start of each period, making relative comparisons clear and intuitive.
VISUALIZATION MODES
The script features two distinct visualization methods:
Plots Mode:
Displays continuous performance lines for each sector over time, ideal for tracking relative strength trends and sector momentum. Each sector has its own color-coded line with performance values clearly marked.
Bars Mode:
Presents current sector performance as vertical bars, offering an immediate visual comparison of sector gains and losses.
The bars are color-coded and labeled with exact percentage values for precise analysis.
For the "Bars Mode", I used the box.new() function
SECTOR COMPOSITION
Each sector comprises carefully selected representative tokens:
- DeFi: AAVE, 1INCH, JUP, MKR, UNI
- Gaming: GALA, AXS, RONIN, SAND
- Layer 1: BTC, ETH, AVAX, APT, SOL, BNB, SUI
- Layer 2: ARB, OP, ZK, POL, STRK, MNT
- AI: FET, NEAR, RENDER, TAO
- Memecoins: PEPE, BONK, SHIB, DOGE, WIFU, POPCAT
PERFORMANCE TRACKING
The indicator implements a rolling window approach for performance calculations.
Starting from 0% at the beginning of each period, it tracks relative performance with positive values indicating outperformance and negative values showing underperformance.
Multiple timeframe options (1W, 1M, 3M, 6M, and 1Y) allow for both short-term and long-term analysis.
APPLICATIONS
This tool proves invaluable for:
- Sector rotation analysis
- Identifying trending sectors
- Comparing relative strength
- Gauging market sentiment
- Understanding market structure through sector performance
Thanks for reading and for the support
Daveatt
Altcoin ManagerThe Altcoin Manager is a comprehensive script for identifying the current altcoin narrative by tracking and analyzing of a wide array of altcoins across various blockchain layers and categories, such as DeFi, GameFi, AI, and Meme coins. Ideal for traders looking to get a broad yet detailed view of the altcoin market, covering various sectors and chains.
The Key Features:
Versatile Asset Tracking:
Tracks 40 different cryptocurrencies (as of publishing) across different categories, allowing for a diversified and detailed analysis of the altcoin market.
Customizable Assets and Category Analysis:
Select 20 of your own coins across 4 different categories such as DeFi, GameFi, AI, and Meme coins as well as specifying their individual chains.
Dynamic Layer and Chain Analysis:
Includes options to plot and analyze specific blockchain layers and chains such as Ethereum Chain, Solana Chain, BNB Smart Chain, Arbitrum Chain, and Polygon Chain. The script associates various assets with specific blockchains, providing a clearer picture of how different segments of the altcoin market are performing.
Cumulative and Per-Candle Change:
Switch between viewing the total cumulative change since a set start date or the per-candle change, offering flexibility in analyzing price movements over different timeframes.
Denomination Adjustment:
Includes a functionality to denominate asset prices in other currencies or crypto such as BTC, allowing for a more tailored financial analysis according to your preference.
Moving Averages for Categories and Chains:
Calculates and plots moving averages for each category and chain, aiding in the identification of trends over the selected moving average length.
How do I use it?
This script is not used with any particular chart. Instead, assign it it's own tab and layout.
For a clearer analysis, use multiple different panels to track Categories and Chains separately, both Cumulative for a longer term analysis and Per-Candle to find ongoing breakouts and changes in trend.
You can either use the pre-selected altcoins to represent the market, or you can select your own.
The Layer 1 and Layer 2 are not customizable but consists of 15 popular Layer 1 incl Bitcoin, Ethereum, Solana etc. Layer 2 consists of 5 popular Layer 2.
DojiCandle body size RSI-SMMA filter MTF
DojiCandle body size RSI-SMMA filter MTF
Hi. I was inspired by a public script written by @ahmedirshad419, .
I thank him for his idea and hard work.
His script is the combination of RSI and Engulfing Pattern.
//------------------------------------------------------------
I decided to tweak it a bit with Open IA.
I have changed:
1) candle pattern to DojiCandle Pattern;
2) I added the ability for the user to change the size of the candlestick body;
3) Added SMMA 200;
4) Changed the colour of SMMA 200 depending on price direction;
5) Added a change in the colour of candlesticks, depending on the colour of the SMMA 200;
6) Added buy and sell signals with indicator name, ticker and close price;
7) Added ability to use indicator on multi time frame.
How it works
1. when RSI > 70 > SMMA 200 and form the bullish DojiCandle Pattern. It gives sell signal
2. when RSI < 30 < SMMA 200 and form the bearish DojiCandle Pattern. It gives buy signal
settings:
basic setting for RSI, SMMA 200 has been enabled in the script to set the levels accordingly to your trades
Enjoy
PSv5 3D Array/Matrix Super Hack"In a world of ever pervasive and universal deceit, telling a simple truth is considered a revolutionary act."
INTRO:
First, how about a little bit of philosophic poetry with another dimension applied to it?
The "matrix of control" is everywhere...
It is all around us, even now in the very place you reside. You can see it when you look at your digitized window outwards into the world, or when you turn on regularly scheduled television "programs" to watch news narratives and movies that subliminally influence your thoughts, feelings, and emotions. You have felt it every time you have clocked into dead end job workplaces... when you unknowingly worshiped on the conformancy alter to cultish ideologies... and when you pay your taxes to a godvernment that is poisoning you softly and quietly by injecting your mind and body with (psyOps + toxicCompounds). It is a fictitiously generated world view that has been pulled over your eyes to blindfold, censor, and mentally prostrate you from spiritually hearing the real truth.
What TRUTH you must wonder? That you are cognitively enslaved, like everyone else. You were born into mental bondage, born into an illusory societal prison complex that you are entirely incapable of smelling, tasting, or touching. Its a contrived monetary prison enterprise for your mind and eternal soul, built by pretending politicians, corporate CONartists, and NonGoverning parasitic Organizations deploying any means of infiltration and deception by using every tactic unimaginable. You are slowly being convinced into becoming a genetically altered cyborg by acclimation, socially engineered and chipped to eventually no longer be 100% human.
Unfortunately no one can be told eloquently enough in words what the matrix of control truly is. You have to experience it and witness it for yourself. This is your chance to program a future paradigm that doesn't yet exist. After visiting here, there is absolutely no turning back. You can continually take the blue pill BIGpharmacide wants you to repeatedly intake. The story ends if you continually sleep walk through a 2D hologram life, believing whatever you wish to believe until you cease to exist. OR, you can take the red pill challenge, explore "question every single thing" wonderland, program your arse off with 3D capabilities, ultimately ascertaining a new mathematical empyrean. Only then can you fully awaken to discover how deep the rabbit hole state of affairs transpire worldwide with a genuine open mind.
Remember, all I'm offering is a mathematical truth, nothing more...
PURPOSE:
With that being said above, it is now time for advanced developers to start creating their own matrix constructs in 3D, in Pine, just as the universe is created spatially. For those of you who instantly know what this script's potential is easily capable of, you already know what you have to do with it. While this is simplistically just a 3D array for either integers or floats, additional companion functions can in the future be constructed by other members to provide a more complete matrix/array library for millions of folks on TV. I do encourage the most courageous of mathemagicians on TV to do so. I have been employing very large 2D/3D array structures for quite some time, and their utility seems to be of great benefit. Discovering that for myself, I fully realized that Pine is incomplete and must be provided with this agility to process complex datasets that traders WILL use in the future. Mark my words!
CONCEPTION:
While I have long realized and theorized this code for a great duration of time, I was finally able to turn it into a Pine reality with the assistance and training of an "artificially intuitive" program while probing its aptitude. Even though it knows virtually nothing about Pine Script 4.0 or 5.0 syntax, functions, and behavior, I was able to conjure code into an identity similar to what you see now within a few minutes. Close enough for me! Many manual edits later for pine compliance, and I had it in chart, presto!
While most people consider the service to be an "AI", it didn't pass my Pine Turing test. I did have to repeatedly correct it, suffered through numerous apologies from it, was forced to use specifically tailored words, and also rationally debate AND argued with it. It is a handy helper but beware of generating Pine code from it, trust me on this one. However... this artificially intuitive service is currently available in its infancy as version 3. Version 4 most likely will have more diversity to enhance my algorithmic expertise of Pine wizardry. I do have to thank E.M. and his developers for an eye opening experience, or NONE of this code below would be available as you now witness it today.
LIMITATIONS:
As of this initial release, Pine only supports 100,000 array elements maximum. For example, when using this code, a 50x50x40 element configuration will exceed this limit, but 50x50x39 will work. You will always have to keep that in mind during development. Running that size of an array structure on every single bar will most likely time out within 20-40 seconds. This is not the most efficient method compared to a real native 3D array in action. Ehlers adepts, this might not be 100% of what you require to "move forward". You can try, but head room with a low ceiling currently will be challenging to walk in for now, even with extremely optimized Pine code.
A few common functions are provided, but this can be extended extensively later if you choose to undertake that endeavor. Use the code as is and/or however you deem necessary. Any TV member is granted absolute freedom to do what they wish as they please. I ultimately wish to eventually see a fully equipped library version for both matrix3D AND array3D created by collaborative efforts that will probably require many Pine poets testing collectively. This is just a bare bones prototype until that day arrives. Considerably more computational server power will be required also. Anyways, I hope you shall find this code somewhat useful.
Notice: Unfortunately, I will not provide any integration support into members projects at all. I have my own projects that require too much of my time already.
POTENTIAL APPLICATIONS:
The creation of very large coefficient 3D caches/buffers specifically at bar_index==0 can dramatically increase runtime agility for thousands of bars onwards. Generating 1000s of values once and just accessing those generated values is much faster. Also, when running dozens of algorithms simultaneously, a record of performance statistics can be kept, self-analyzed, and visually presented to the developer/user. And, everything else under the sun can be created beyond a developers wildest dreams...
EPILOGUE:
Free your mind!!! And unleash weapons of mass financial creation upon the earth for all to utilize via the "Power of Pine". Flying monkeys and minions are waging economic sabotage upon humanity, decimating markets and exchanges. You can always see it your market charts when things go horribly wrong. This is going to be an astronomical technical challenge to continually navigate very choppy financial markets that are increasingly becoming more and more unstable and volatile. Ordinary one plot algorithms simply are not enough anymore. Statistics and analysis sits above everything imagined. This includes banking, godvernment, corporations, REAL science, technology, health, medicine, transportation, energy, food, etc... We have a unique perspective of the world that most people will never get to see, depending on where you look. With an ever increasingly complex world in constant dynamic flux, novel ways to process data intricately MUST emerge into existence in order to tackle phenomenal tasks required in the future. Achieving data analysis in 3D forms is just one lonely step of many more to come.
At this time the WesternEconomicFraudsters and the WorldHealthOrders are attempting to destroy/reset the world's financial status in order to rain in chaos upon most nations, causing asset devaluation and hyper-inflation. Every form of deception, infiltration, and theft is occurring with a result of destroyed wealth in preparation to consolidate it. Open discussions, available to the public, by world leaders/moguls are fantasizing about new dystopian system as a one size fits all nations solution of digitalID combined with programmableDemonicCurrencies to usher in a new form of obedient servitude to a unipolar digitized hegemony of monetary vampires. If they do succeed with economic conquest, as they have publicly stated, people will be converted into human cattle, herded within smart cities, you will own nothing, eat bugs for breakfast/lunch/dinner, live without heat during severe winter conditions, and be happy. They clearly haven't done the math, as they are far outnumbered by a ratio of 1 to millions. Sith Lords do not own planet Earth! The new world disorder of human exploitation will FAIL. History, my "greatest teacher" for decades reminds us over, and over, and over again, and what are time series for anyways? They are for an intense mathematical analysis of prior historical values/conditions in relation to today's values/conditions... I imagine one day we will be able to ask an all-seeing AI, "WHO IS TO BLAME AND WHY AND WHEN?" comprised of 300 pages in great detail with images, charts, and statistics.
What are the true costs of malignant lies? I will tell you... 64bit numbers are NOT even capable of calculating the extreme cost of pernicious lies and deceit. That's how gigantic this monstrous globalization problem has become and how awful the "matrix of control" truly is now. ALL nations need a monumental revision of its CODE OF ETHICS, and that's definitely a multi-dimensional problem that needs solved sooner than later. If it was up to me, economies and technology would be developed so extensively to eliminate scarcity and increase the standard of living so high, that the notion of war and conflict would be considered irrelevant and extremely appalling to the future generations of humanity, our grandchildren born and unborn. The future will not be owned and operated by geriatric robber barons destined to expire quickly. The future will most likely be intensely "guided" by intelligent open source algorithms that youthful generations will inherit as their birth right.
P.S. Don't give me that politco-my-diction crap speech below in comments. If they weren't meddling with economics mucking up 100% of our chart results in 100% of tickers, I wouldn't have any cause to analyze any effects generated by them, nor provide this script's code. I am performing my analytical homework, but have you? Do you you know WHY international affairs are in dire jeopardy? Without why, the "Power of Pine" would have never existed as it specifically does today. I'm giving away much of my mental power generously to TV members so you are specifically empowered beyond most mathematical agilities commonly existing. I'm just a messenger of profound ideas. Loving and loathing of words is ALWAYS in the eye of beholders, and that's why the freedom of speech is enshrined as #1 in the constitutional code of the USA. Without it, this entire site might not have been allowed to exist from its founder's inceptions.
Volume Spike Strategy This is a Pine Script implementation of “Capitalize AI: Volume Spike Strategy" by Bitcoin Trading Challenge (copied with permission).
Original Capital AI formula :
If BTC/USD 1 minute volume > BTC/USD average volume in 20-1m bar by at least 500% and if BTC/USD is below the MA (5,1m,close) of BTC/USD then buy 10,000 USD WORTH of BTC/USD
Tested on XBTUSD 1 minute.
Original strategy is buy-only. Option for sells was added (enable in settings).
First published script -- comments/feedback appreciated
Well Rounded Moving AverageIntroduction
There are tons of filters, way to many, and some of them are redundant in the sense they produce the same results as others. The task to find an optimal filter is still a big challenge among technical analysis and engineering, a good filter is the Kalman filter who is one of the more precise filters out there. The optimal filter theorem state that : The optimal estimator has the form of a linear observer , this in short mean that an optimal filter must use measurements of the inputs and outputs, and this is what does the Kalman filter. I have tried myself to Kalman filters with more or less success as well as understanding optimality by studying Linear–quadratic–Gaussian control, i failed to get a complete understanding of those subjects but today i present a moving average filter (WRMA) constructed with all the knowledge i have in control theory and who aim to provide a very well response to market price, this mean low lag for fast decision timing and low overshoots for better precision.
Construction
An good filter must use information about its output, this is what exponential smoothing is about, simple exponential smoothing (EMA) is close to a simple moving average and can be defined as :
output = output(1) + α(input - output(1))
where α (alpha) is a smoothing constant, typically equal to 2/(Period+1) for the EMA.
This approach can be further developed by introducing more smoothing constants and output control (See double/triple exponential smoothing - alpha-beta filter) .
The moving average i propose will use only one smoothing constant, and is described as follow :
a = nz(a ) + alpha*nz(A )
b = nz(b ) + alpha*nz(B )
y = ema(a + b,p1)
A = src - y
B = src - ema(y,p2)
The filter is divided into two components a and b (more terms can add more control/effects if chosen well) , a adjust itself to the output error and is responsive while b is independent of the output and is mainly smoother, adding those components together create an output y , A is the output error and B is the error of an exponential moving average.
Comparison
There are a lot of low-lag filters out there, but the overshoots they induce in order to reduce lag is not a great effect. The first comparison is with a least square moving average, a moving average who fit a line in a price window of period length .
Lsma in blue and WRMA in red with both length = 100 . The lsma is a bit smoother but induce terrible overshoots
ZLMA in blue and WRMA in red with both length = 100 . The lag difference between each moving average is really low while VWRMA is way more precise.
Hull MA in blue and WRMA in red with both length = 100 . The Hull MA have similar overshoots than the LSMA.
Reduced overshoots moving average (ROMA) in blue and WRMA in red with both length = 100 . ROMA is an indicator i have made to reduce the overshoots of a LSMA, but at the end WRMA still reduce way more the overshoots while being smoother and having similar lag.
I have added a smoother version, just activate the extra smooth option in the indicator settings window. Here the result with length = 200 :
This result is a little bit similar to a 2 order Butterworth filter. Our filter have more overshoots which in this case could be useful to reduce the error with edges since other low pass filters tend to smooth their amplitude thus reducing edge estimation precision.
Conclusions
I have presented a well rounded filter in term of smoothness/stability and reactivity. Try to add more terms to have different results, you could maybe end up with interesting results, if its the case share them with the community :)
As for control theory i have seen neural networks integrated to Kalman flters which leaded to great accuracy, AI is everywhere and promise to be a game a changer in real time data smoothing. So i asked myself if it was possible for a neural networks to develop pinescript indicators, if yes then i could be replaced by AI ? Brrr how frightening.
Thanks for reading :)
Clusters Volume Profile [LuxAlgo]The Clusters Volume Profile indicator utilizes K-Means clustering to categorize historical price action into distinct groups and generates individual volume profiles for each detected cluster. This tool provides a unique perspective on volume distribution by isolating price behaviors based on proximity rather than strictly chronological order.
🔶 USAGE
The indicator identifies "clusters" of price activity within a user-defined lookback period. Each cluster is assigned a unique color and its own horizontal volume profile, allowing traders to see where liquidity is most concentrated within specific price regimes.
🔹 Identifying Institutional Zones
Traders can use the Point of Control (POC) of high-volume clusters to identify significant institutional interest. Because the K-Means algorithm groups price action by density rather than time, a cluster's POC often represents a "fair value" level where significant exchange occurred. These dashed POC lines frequently act as robust support or resistance levels when price returns to them in the future.
🔹 Market Regime Detection
By observing the vertical distribution and overlap of clusters, traders can identify market phases. Overlapping clusters with high volume often indicate accumulation or distribution phases (sideways markets), whereas distinct, vertically separated clusters with lower volume gaps between them suggest a trending environment. A shift from multiple overlapping clusters to a new, isolated cluster can signal a breakout or the start of a new trend.
🔹 Precision Entry & Exits
Cluster boundaries and POC lines provide concrete levels for trade management. An entry can be sought when price retests a high-volume cluster POC, while stops can be placed outside the total price range of that specific cluster (the area covered by its volume profile). Conversely, targets can be set at the POC of the next major cluster above or below current price action.
🔹 Volume Conviction
The tool provides specific volume metrics that allow traders to gauge conviction. By comparing the "Total" volume label of one cluster against another, a trader can determine which price regime had more participation. A breakout into a price zone with a high-volume cluster suggests stronger conviction and a higher probability of the level holding compared to a zone with low total volume.
🔶 DETAILS
The script employs a K-Means clustering algorithm. This process involves:
Initializing "centroids" across the price range of the lookback period.
Iteratively assigning each price bar to the nearest centroid based on the HLC2 (median) price.
Recalculating centroids based on the volume-weighted average price of the assigned bars.
Finalizing assignments after the specified number of iterations to ensure stable clusters.
By separating price action into these clusters, the tool helps identify high-interest zones that might be obscured by a single, traditional Volume Profile.
🔶 SETTINGS
🔹 Clustering Settings
Lookback Period: Determines the number of recent bars used for the clustering analysis.
Number of Clusters: Sets how many distinct price groups the algorithm should attempt to find (2 to 10).
K-Means Iterations: Controls the number of times the algorithm refines the cluster centers. Higher values can lead to more stable results.
🔹 Volume Profile Settings
Rows per Cluster VP: Defines the vertical resolution (number of bins) for each individual cluster's profile.
Max VP Width (Bars): Sets the maximum horizontal length of the volume profile histograms.
VP Offset: Adjusts the horizontal spacing between the current bar and the start of the volume profiles.
Highlight Price Dots: Toggles the visibility of the colored dots on the price action to identify cluster assignments.
Dot Size: Adjusts the size of the cluster assignment dots on the chart, ranging from tiny to huge.
Deep AILibrary "Deeptest"
Comprehensive quantitative backtesting library with 112+ metrics: Sharpe/Sortino ratios, drawdown analysis, Monte Carlo simulation, Walk-Forward Analysis, VaR/CVaR, benchmark comparison, and interactive table rendering for TradingView strategies
@version 1.0.1 (01.01.2026)
============================================================================
CHANGELOG
============================================================================
v1.0.1 (01.01.2026)
- Added textSize parameter to runDeeptest() for controlling table text size
- New values: size.auto, size.small, size.tiny, size.normal, size.large
- Applies to all tables: main, stress test, drawdowns, recoveries, trades
v1.0.0 (31.12.2025)
- Initial release
- 112+ backtesting metrics
- Monte Carlo simulation and Walk-Forward Analysis
- Interactive table rendering with tooltips
============================================================================
TABLE OF CONTENTS
============================================================================
SECTION 1: File Header & Metadata
SECTION 2: Constants & Configuration
SECTION 3: Type Definitions
SECTION 4: Core Calculation Functions - Array Utilities
SECTION 5: Core Calculation Functions - Return Extraction
SECTION 6: Core Calculation Functions - Sharpe & Sortino
SECTION 7: Core Calculation Functions - Performance Metrics
SECTION 8: Core Calculation Functions - Drawdown Analysis
SECTION 9: Core Calculation Functions - Recovery Analysis
SECTION 10: Core Calculation Functions - Trade Analysis
SECTION 11: Core Calculation Functions - Statistical Distribution
SECTION 12: Core Calculation Functions - Risk Metrics
SECTION 13: Core Calculation Functions - Benchmark Comparison
SECTION 14: Core Calculation Functions - Time-Based Metrics
SECTION 15: Core Calculation Functions - Rolling Statistics
SECTION 16: Core Calculation Functions - Strategy Integration
SECTION 17: Core Calculation Functions - Walk Forward Analysis
SECTION 18: Core Calculation Functions - Monte Carlo Simulation
SECTION 19: Core Calculation Functions - Out-of-Sample Analysis
SECTION 20: Formatting Utilities - Value Formatting
SECTION 21: Formatting Utilities - Duration Formatting
SECTION 22: Formatting Utilities - Frequency Formatting
SECTION 23: Formatting Utilities - Date Formatting
SECTION 24: Tooltip Builders - Main Table Metrics
SECTION 25: Tooltip Builders - Complementary Metrics
SECTION 26: Tooltip Builders - Stress Test Metrics
SECTION 27: Tooltip Builders - Period Analysis Cards
SECTION 28: Table Rendering - Structure Helpers
SECTION 29: Table Rendering - Main Deeptest Table
SECTION 30: Table Rendering - Cell Renderers - Complementary Row
SECTION 31: Table Rendering - Stress Test Table
SECTION 32: Table Rendering - Period Analysis Cards
SECTION 33: Main Entry Point
============================================================================
API REFERENCE
============================================================================
Main Export:
------------
runDeeptest() - Complete backtest analysis orchestrator
============================================================================
KEY FEATURES
============================================================================
- Comprehensive backtesting metrics (112+ functions)
- Rolling window analysis with statistical distributions
- Advanced risk metrics (Sharpe, Sortino, Calmar, Martin, VaR, CVaR)
- Drawdown and recovery analysis
- Monte Carlo simulation and Walk-Forward Analysis
- Trade analysis (top/worst trades, consecutive streaks)
- Benchmark comparison (Alpha, Beta, R², Buy & Hold)
- Interactive table rendering with tooltips
============================================================================
USAGE EXAMPLE
============================================================================
╔══════════════════════════════════════════════════════════════════════════════╗
║ PROGRESSIVE USAGE EXAMPLES ║
╠══════════════════════════════════════════════════════════════════════════════╣
║ Three examples demonstrating increasing complexity: ║
║ 1. MINIMAL - "Hello World" with basic MA crossover ║
║ 2. BALANCED - Production ready with risk management & filters ║
║ 3. PROFESSIONAL - Full-featured with trailing stops & session filters ║
╚══════════════════════════════════════════════════════════════════════════════╝
╔══════════════════════════════════════════════════════════════════════════════╗
║ EXAMPLE 1: MINIMAL (The "Hello World") ║
╠══════════════════════════════════════════════════════════════════════════════╣
║ The simplest possible integration - just 3 lines to get started: ║
║ 1. Import the library ║
║ 2. Write your strategy logic ║
║ 3. Call runDeeptest() ║
╚══════════════════════════════════════════════════════════════════════════════╝
//@version=6
strategy("MA Crossover ", overlay=true)
// ═══════════════════════════════════════════════════════════════════════════
// ⮟ Import Deeptest (Direct import - no namespace prefix needed)
// ═══════════════════════════════════════════════════════════════════════════
import Fractalyst/Deeptest/1 as *
// ────────────────────────────────────────────────────────────────────────────
// Strategy Logic: Simple Moving Average Crossover
// ────────────────────────────────────────────────────────────────────────────
fastMA = ta.sma(close, 10) // Fast MA: 10 periods
slowMA = ta.sma(close, 30) // Slow MA: 30 periods
// Plot MAs for visualization
plot(fastMA, "Fast MA", color=color.blue)
plot(slowMA, "Slow MA", color=color.orange)
// Entry: Long when fast MA crosses above slow MA
if ta.crossover(fastMA, slowMA)
strategy.entry("Long", strategy.long)
// Exit: Close when fast MA crosses below slow MA
if ta.crossunder(fastMA, slowMA)
strategy.close("Long")
// ═══════════════════════════════════════════════════════════════════════════
// ⮟ Run backtest analysis (all parameters use smart defaults)
// ═══════════════════════════════════════════════════════════════════════════
DT.runDeeptest()
╔══════════════════════════════════════════════════════════════════════════════╗
║ EXAMPLE 2: BALANCED (Production Ready) ║
╠══════════════════════════════════════════════════════════════════════════════╣
║ Adds essential production features: ║
║ • User-configurable inputs ║
║ • ADX trend filter to avoid choppy markets ║
║ • Stop loss / Take profit for risk management ║
║ • Custom backtest parameters ║
╚══════════════════════════════════════════════════════════════════════════════╝
//@version=6
strategy("MA Crossover ", overlay=true)
import Fractalyst/Deeptest/1 as *
// ────────────────────────────────────────────────────────────────────────────
// INPUT PARAMETERS
// ────────────────────────────────────────────────────────────────────────────
fastLen = input.int(10, "Fast MA Period", minval=1)
slowLen = input.int(30, "Slow MA Period", minval=1)
riskPct = input.float(2.0, "Risk %", minval=0.1) / 100
slPct = input.float(5.0, "Stop Loss %", minval=0.1) / 100
tpPct = input.float(10.0, "Take Profit %", minval=0.1) / 100
adxThresh = input.int(20, "ADX Trend Threshold")
// ────────────────────────────────────────────────────────────────────────────
// INDICATORS
// ────────────────────────────────────────────────────────────────────────────
fastMA = ta.sma(close, fastLen)
slowMA = ta.sma(close, slowLen)
adx = ta.adx(14)
= ta.dmi(14, 14)
// ────────────────────────────────────────────────────────────────────────────
// FILTERS
// ────────────────────────────────────────────────────────────────────────────
trendConfirmed = adx > adxThresh and diPlus > diMinus
// ────────────────────────────────────────────────────────────────────────────
// STRATEGY LOGIC
// ────────────────────────────────────────────────────────────────────────────
// Entry: MA crossover + trend confirmation
if ta.crossover(fastMA, slowMA) and trendConfirmed
strategy.entry("Long", strategy.long)
// Exit: MA crossunder
if ta.crossunder(fastMA, slowMA)
strategy.close("Long")
// Risk management: Stop loss and take profit
if strategy.position_size > 0
strategy.exit("RM", "Long",
stop=strategy.position_avg_price * (1 - slPct),
limit=strategy.position_avg_price * (1 + tpPct))
// ═══════════════════════════════════════════════════════════════════════════
// ⮟ Run backtest with custom parameters
// ═══════════════════════════════════════════════════════════════════════════
DT.runDeeptest(
riskPerTrade = 1.0, // ← 1% risk per trade
targetMaxDDPct = 15.0, // ← 15% max drawdown target
showStressTest = true, // ← Enable stress test table
showPeriodCards = true, // ← Enable period cards
wfaWindows = 12, // ← Walk-forward windows
mcSimulations = 1000, // ← Monte Carlo runs
bullColor = color.new(#00b9ff, 0),
bearColor = color.new(#ff0051, 0),
benchmarkSymbol = "SPX", // ← Compare to S&P; 500
periodCardMode = "drawdowns", // ← Show drawdown periods
tradeSortBy = "return" // ← Sort by return %
)
╔══════════════════════════════════════════════════════════════════════════════╗
║ EXAMPLE 3: PROFESSIONAL (Full-Featured) ║
╠══════════════════════════════════════════════════════════════════════════════╣
║ Complete professional implementation: ║
║ • Organized input groups for better UX ║
║ • Multiple filters: ADX trend, ATR volatility, Session timing ║
║ • Trailing stop to lock in profits ║
║ • Position highlighting for visual feedback ║
║ • Full parameter customization with inline documentation ║
╚══════════════════════════════════════════════════════════════════════════════╝
//@version=6
runDeeptest(targetMaxDDPct, bullColor, bearColor, tableBg, headerBg, borderColor, textPrimary, textMuted, textSize, showComplementaryRow, showStressTestTable, showDrawdownRecoveryCards, showTradeCards)
Parameters:
targetMaxDDPct (float)
bullColor (color)
bearColor (color)
tableBg (color)
headerBg (color)
borderColor (color)
textPrimary (color)
textMuted (color)
textSize (string)
showComplementaryRow (bool)
showStressTestTable (bool)
showDrawdownRecoveryCards (bool)
showTradeCards (bool)
ThresholdConfig
ThresholdConfig - Configuration for metric thresholds and corresponding colors
Fields:
sharpeExc (series float)
sharpeGood (series float)
sharpeOk (series float)
sharpeBear (series color)
sharpeNeutral (series color)
sharpeOrange (series color)
sharpeBull (series color)
ddSevere (series float)
ddMod (series float)
ddMild (series float)
ddSevereColor (series color)
ddModColor (series color)
ddOrange (series color)
ddGoodColor (series color)
rorHigh (series float)
rorMod (series float)
rorLow (series float)
rorHighColor (series color)
rorModColor (series color)
rorOrange (series color)
rorLowColor (series color)
r2Poor (series float)
r2Mod (series float)
r2Good (series float)
r2PoorColor (series color)
r2ModColor (series color)
r2Orange (series color)
r2GoodColor (series color)
kurtHigh (series float)
kurtMod (series float)
kurtOk (series float)
kurtHighColor (series color)
kurtModColor (series color)
kurtOrange (series color)
kurtGoodColor (series color)
skewVNeg (series float)
skewModNeg (series float)
skewPos (series float)
skewVPos (series float)
skewVNegColor (series color)
skewModNegColor (series color)
skewNeutral (series color)
skewPosColor (series color)
payoffPoor (series float)
payoffBE (series float)
payoffGood (series float)
payoffPoorColor (series color)
payoffBEColor (series color)
payoffOrange (series color)
payoffGoodColor (series color)
pfPoor (series float)
pfBE (series float)
pfGood (series float)
pfPoorColor (series color)
pfBEColor (series color)
pfOrange (series color)
pfGoodColor (series color)
ulcerHigh (series float)
ulcerLow (series float)
ulcerHighColor (series color)
ulcerModColor (series color)
ulcerOrange (series color)
ulcerLowColor (series color)
wrLow (series float)
wrOk (series float)
wrHigh (series float)
wrLowColor (series color)
wrOkColor (series color)
wrOrange (series color)
wrHighColor (series color)
cagrPoor (series float)
cagrOk (series float)
cagrGood (series float)
cagrPoorColor (series color)
cagrOkColor (series color)
cagrOrange (series color)
cagrGoodColor (series color)
pInsig (series float)
pMod (series float)
pSig (series float)
pInsigColor (series color)
pModColor (series color)
pOrange (series color)
pSigColor (series color)
calmarPoor (series float)
calmarBE (series float)
calmarGood (series float)
calmarPoorColor (series color)
calmarBEColor (series color)
calmarOrange (series color)
calmarGoodColor (series color)
betaHigh (series float)
betaLow (series float)
betaHighColor (series color)
betaLowColor (series color)
betaGoodColor (series color)
Stats
Stats - Comprehensive backtest statistics container
Fields:
totalTrades (series int)
winTrades (series int)
lossTrades (series int)
evenTrades (series int)
winRate (series float)
lossRate (series float)
avgWinPct (series float)
avgLossPct (series float)
avgTradePct (series float)
profitFactor (series float)
payoffRatio (series float)
expectancy (series float)
grossProfit (series float)
grossLoss (series float)
netProfit (series float)
netProfitPct (series float)
compEffect (series float)
sharpe (series float)
sortino (series float)
calmar (series float)
martin (series float)
maxDrawdown (series float)
maxDrawdownPct (series float)
currentDrawdown (series float)
currentDrawdownPct (series float)
avgDrawdownPct (series float)
maxEquity (series float)
minEquity (series float)
cagr (series float)
monthlyReturn (series float)
maxConsecWins (series int)
maxConsecLosses (series int)
avgTradeDuration (series float)
avgWinDuration (series float)
avgLossDuration (series float)
timeInMarketPct (series float)
tradesPerMonth (series float)
tradesPerYear (series float)
skewness (series float)
kurtosis (series float)
var95 (series float)
cvar95 (series float)
ulcerIndex (series float)
riskOfRuin (series float)
pValue (series float)
zScore (series float)
alpha (series float)
beta (series float)
buyHoldReturn (series float)
equityRSquared (series float)
firstTradeTime (series int)
lastTradeTime (series int)
tradingPeriodDays (series float)
RollingWindowSummary
RollingWindowSummary - Summary of metrics for a single rolling analysis window
Fields:
windowIndex (series int)
startTrade (series int)
endTrade (series int)
effectiveCount (series int)
minValue (series float)
maxValue (series float)
metricValue (series float)
RollingStats
RollingStats - Statistical distribution of rolling window metrics
Fields:
windowSize (series int) : Number of trades in rolling window
expectancyMin (series float) : Minimum rolling expectancy
expectancyMax (series float) : Maximum rolling expectancy
sharpeMin (series float) : Minimum rolling Sharpe
sharpeMax (series float) : Maximum rolling Sharpe
sortinoMin (series float) : Minimum rolling Sortino
sortinoMax (series float) : Maximum rolling Sortino
expectancyWindows (array) : Per-window summaries for expectancy
sharpeWindows (array) : Per-window summaries for Sharpe
sortinoWindows (array) : Per-window summaries for Sortino
expectancyMean (series float) : Mean expectancy across rolling windows
expectancyStdDev (series float) : Standard deviation of expectancy
expectancyPct90 (series float) : 90th percentile expectancy
expectancyPct50 (series float) : 50th percentile expectancy (median)
expectancyPct10 (series float) : 10th percentile expectancy
sharpeMean (series float) : Mean Sharpe across rolling windows
sharpeStdDev (series float) : Standard deviation of Sharpe
sharpePct90 (series float) : 90th percentile Sharpe
sharpePct50 (series float) : 50th percentile Sharpe
sharpePct10 (series float) : 10th percentile Sharpe
sortinoMean (series float) : Mean Sortino across rolling windows
sortinoStdDev (series float) : Standard deviation of Sortino
sortinoPct90 (series float) : 90th percentile Sortino
sortinoPct50 (series float) : 50th percentile Sortino
sortinoPct10 (series float) : 10th percentile Sortino
Liquidity Grab ScannerMarks last week and last 3 days highs and lows.
Can be used for liquidity grabs above or below those levels, I use for it.
Claude AI coded it.
Adaptive Elliott Wave: Forecast + Dashboard (V.2)Title: Adaptive Elliott Wave: Forecast + Dashboard
Description:
Overview
The Adaptive Elliott Wave: Forecast + Dashboard is a technical analysis tool designed to visualize potential Elliott Wave structures using a dynamic, multi-factor approach. Unlike static wave scripts, this indicator adapts its projections based on real-time trend context (Weighted Moving Averages) and momentum shifts (RSI). It is built to help traders identify the most likely path—Impulse or Correction—based on current market conditions.
How It Works
The script uses a combination of pivot-point detection and trend filtering to project future wave paths.
Pivot Logic: The indicator identifies significant Highs and Lows using a sensitivity setting. These pivots form the "anchors" for the Elliott Wave count.
Adaptive Engine: The "Auto-Detect" mode analyzes the relationship between the 50/200 WMA (Trend) and RSI (Momentum).
In a Bull Trend: If RSI is oversold, the script anticipates a bullish "Impulse" wave. If RSI is overbought, it prepares for a "Correction."
In a Bear Trend: The logic reverses to project rallies or downward impulses.
Projections: It calculates Fibonacci-based targets for waves 1-5 (Impulse) or A-B-C/W-X-Y (Correction) and renders them as "ghost lines" that move with the price.
Macro Outlook: For long-term context, the script includes a Macro Projection feature that uses higher-period pivots to show the possible 1-year direction.
Key Features
Target Table: A real-time dashboard showing exact Fibonacci target prices and the percentage distance from the current price.
Corrective Channels: Automatically draws channels for wave corrections to help identify potential breakout or breakdown zones.
Bullish/Bearish Extensions: Shows immediate volatility-based extensions beyond the last confirmed pivot.
RSI Signals: Visual markers on the chart indicate overbought/oversold conditions that feed into the adaptive logic.
How to Use
Identify the Phase: Use the "AI STATUS" in the dashboard to see if the script is currently projecting an Impulse (Trend move) or a Correction (Counter-trend).
Confirm with WMA: Use the 50 (Blue) and 200 (Orange) WMAs to confirm the macro trend before following a projection.
Monitor Fib Targets: Watch for price reactions at the projected labels. If price breaks a target significantly, the wave count may need to be re-evaluated (re-pivot).
Customize Sensitivity: For scalping, lower the "Short-Term Sensitivity." For swing trading, increase it.
Disclaimer
This script is for educational purposes only. Elliott Wave Theory is subjective, and projections are mathematical estimates based on historical volatility. Past performance does not guarantee future results. Always use proper risk management.
Settings Guide
Forecast Mode: Choose between "Auto-Detect" (Dynamic) or manually force an Impulse/Correction count.
Macro Sensitivity: Controls how far back the script looks to generate the purple 1-year projection.
Link Correction to Extension: A unique feature that starts the forecast from a potential extension target rather than the current live price.
Adaptive Volatility Trend Filter AI PANDAHENTesting scripts by using ma ema volume and will give green and red indicator where is suggestion to buy or sell
Leader Stock ScannerLeader Stock Scanner, Testing using AI
✅ How it works:
Relative Strength (RS) vs SPY – RS above 80 marks strong leaders.
Trend Alignment – 50 EMA > 150 EMA > 200 EMA and price above all EMAs.
Liquidity Filter – 20-day average volume > 500k.
Price Filter – avoids low-priced microcap traps (< $10).
Output – signals a “triangle up” on chart and can trigger alerts.
NodialTreesLows2: ML Random Forest / Pivot Lows (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Lows (Part 2 of 2)`
Description:
This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Lows (Long setups).
It is a direct extension of NodialTreesL1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries.
### 🧩 Library Contents
This module exports the following methods representing the specific decision paths of the trained AI model:
- `tree_6(array f)`
- `tree_7(array f)`
- `tree_8(array f)`
- `tree_9(array f)`
- `tree_10(array f)`
- `tree_11(array f)`
### ⚠️ Implementation Guide
To use this library, you must combine it with Part 1.
Please refer to the NodialTreesLows1 library description for:
1. The full Integration Code Example (how to average the votes).
2. The exact Input Feature List (the 27 required metrics).
3. Detailed explanation of the Machine Learning logic.
How to finish the integration:
Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
NodialTreesHighs2: ML Random Forest / Pivot Highs (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Highs (Part 2 of 2)`
Description:
This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Highs (Short setups).
It is a direct extension of NodialTreesH1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries.
### 🧩 Library Contents
This module exports the following methods representing the specific decision paths of the trained AI model:
- `tree_6(array f)`
- `tree_7(array f)`
- `tree_8(array f)`
- `tree_9(array f)`
- `tree_10(array f)`
- `tree_11(array f)`
### ⚠️ Implementation Guide
To use this library, you must combine it with Part 1.
Please refer to the NodialTreesH1 library description for:
1. The full Integration Code Example (how to average the votes).
2. The exact Input Feature List (the 27 required metrics).
3. Detailed explanation of the Machine Learning logic.
How to finish the integration:
Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
Mean Reversion [SIMI]This mean reversion indicator identifies extreme price deviations from the mean, providing high-probability reversal signals. Designed for confluence-based trading, it works best when combined with complementary indicators such as VWAP, price action, and volume analysis.
📊 Core Features
Signal Types
Prime Signals (Bright Green/Red Dots): Extreme reversions usually beyond ±1.5 SD - highest probability setups (you can customise this zone!)
Regular Signals (Dark Green/Red Dots): Standard reversions - moderate probability
Leader Line (Pink Dotted): Early warning indicator for potential reversals
Histogram Weakness: Momentum divergence signals
Normalisation Methods:
Institutional Hybrid (Z-ATR) (Recommended): Volatility-adjusted Z-score - adapts to changing market conditions
Percentile Ranking: Statistical ranking - excellent for ranging markets
PPO + ATR Hybrid: Percentage-based with volatility adjustment
Efficiency Ratio: Trend-strength weighted
ATR: Pure volatility-based
None: Raw Z-score
⚙️ Quick Setup Guide
1. Institutional Presets
Pre-configured parameter sets optimised for different timeframes:
5M Day Trading (5/21/5): Intraday scalping
1H Options Trading (6/24/5): Options-focused setups
1D Monthly Cycle (5/20/5): Swing trading
2. Signal Filtering
Prime Thresholds: Adjust ±1.5 SD to control signal quality (tighter = fewer, higher quality, adjust this zone per asset traded)
Dot Filters: Fine-tune entry zones (-0.03/+0.03 default - this ignores noisy signals near Zero line)
Volume Filter: Enable to require volume confirmation (1.4x average recommended, but fine tune yourself)
3. Advanced Filters
Dynamic SD Thresholds: Auto-adjusts for volatility regimes (tighter in low vol, wider in high vol)
Time of Day Filter: Avoids first 30 minutes, last 15 minutes, and lunch hour (11:30-13:00 EST)
💡 Trading Strategy Recommendations
Optimal Usage
This indicator is not intended as a standalone system. Use it for confluence alongside:
VWAP (institutional positioning)
Price action (support/resistance)
Options flow (institutional direction)
Volume analysis (conviction confirmation)
Signal Interpretation
Prime Signals: Wait for these for highest-probability entries - mean reversion may take hours to days
Manual Entries: Don't wait for dots - trade the ±2 SD zones directly using your own confirmation
Options Strategy: Prime sell signals at +2 SD make excellent short call setups; prime buy signals at -2 SD for long calls
Timeframe Guidance
Lower Timeframes (1M-5M): Higher noise - require additional confluence
Higher Timeframes (1H-1D): More reliable signals - suitable for options and swing trades
Best Results: Multi-timeframe analysis (check 1H and 4H alignment on 5M entries)
🔔 Alert System
Master Alert
Enable customisable alerts via the Master Alert System:
Toggle individual signal types (Prime Buy/Sell, SD Crosses, Leader, Histogram)
Receives bespoke messages with ticker, timeframe, and price
One alert condition handles all selected signals
Individual Alerts
Separate alert conditions available for Prime and Regular signals if preferred.
📈 Backtesting Notes
Important: Backtest results are date-sensitive and should not be the primary focus. Instead:
Dial in settings visually on your chosen asset
Aim for signals near actual tops and bottoms
Test different normalisation methods for your specific instrument
Optimise for signal quality, not backtest ROI
Asset Testing: Primarily developed using SPY, QQQ, and IWM as main assets to trade. Other instruments may require parameter adjustment - mess around!
Backtest Engine
Entry/Exit modes (All Signals, Prime Only, Early Signals)
Position sizing (percentage-based)
Slippage and fill method (candle close recommended)
Date range selection
⚠️ Best Practices
Always use confluence - never trade on MR signals alone
Start with Institutional Hybrid normalisation - most adaptive to market conditions
Focus on Prime signals for quality over quantity
Test on your specific asset - optimal settings vary by instrument
Longer timeframes = higher reliability - 1H+ for best results
Enable Time Filter on intraday charts to avoid volatile periods
Use Dynamic SD in highly volatile markets (earnings, FOMC, etc.)
🛠️ Troubleshooting
Too many signals: Increase Prime Thresholds or enable Volume Filter
Too few signals: Decrease Prime Thresholds or reduce Dot Filters
False signals: Enable Time of Day Filter and Dynamic SD
Signals don't align with tops/bottoms: Try different normalisation method
📝 Feedback & Development
Bug Reports: Please report any issues via TradingView comments or direct message.
Strategy Sharing: I'd love to hear how you're using this indicator and what strategies you've developed.
Open Source: Feel free to fork and modify this indicator. If you create an improved version, please share it with the community!
🙏 Acknowledgements
Developed through AI-assisted collaboration.
Special thanks to Lazy Bear for his open source MACD histogram (volume based).
Open source forever - use freely, modify, and share.
Happy Trading!
Remember: Past performance does not guarantee future results. Always manage risk appropriately.
Pandas rock \m/
ATR Levels - Current Candle Close1 of 3 scripts
I use all 3 together to "tell the story"
specifically designed for NQ to watch 4H timeframe.
code is generated by Claude AI so thats why it is free.
