cci based potential buy/sell signal
Commodity Channel Index Potential Buy Signal
Commodity Channel Index (CCI) is below oversold line (-200).
CCI then crosses above -100 line
Commodity Channel Index Potential Sell Signal
Commodity Channel Index (CCI) is above overbought line (+200).
CCI then crosses below +100 line.
Türkçe Açıklama;
CCI Potansiyel Al Sinyali
CCI indikatörünün -200 altında bulunduğu bölgeler aşırı satış bölgeleri,
Sonrasında aşağıdan gelerek -100 çizgisinin üzerine çıktığı yada çıkmak üzere olduğu noktalar al sinyali
CCI Potansiyel Satl Sinyali
CCI indikatörünün +200 üzerinde bulunduğu bölgeler aşırı alım bölgeleri,
Sonrasında yukarıdan inerek +100 çizgisinin altına indiği yada inmek üzere olduğu noktalar sat sinyali
Not: Tek başına kullanılması son derece hatalı sonuçlar verebilir. Sadece olabilirlik potansiyeli taşımaktadır.
Komut dosyalarını "摩根纳斯达克100基金风险大吗" için ara
Aroon Single Line This indicator converts double lined Aroon indicator into a single line oscillator.
It is simply obtained by subtracting Aroon down from Aroon Up.
*If Oscillator points 100 value, it means there is a Strong Uptrend.
*If Oscillator points values between 100 and 40, it means there is an uptrend.
*If Oscillator points values between 20 and -20, it means no trend, it is sideways.But, when it is sideways; generally, oscillator makes FLAT LINES
between 20 and -20 values. 0 value is pointed out when the trend is downward as well, which means aroon up=aroon down.
*If Oscillator points values between -40 and -100, it means there is a downtrend.
*If Oscillator points -100 value, it means there is a Strong downtrend.
(20, 40) and (-20, -40) intervals are not mentioned, because; generally these are transition values and hard to comment, it will be more certain to
wait till values are between or at the reference values given.
CCI 0Trend Strategy (by Marcoweb) v1.0Hi guys,
I am trying to create a strategy that consists in the crossover/under of the 0 line of the Commodity Channel Index . Every time the price crosses over the 0 line in the CCI the strategy has to long getting short on the cross under and viceversa.
I have published here another script strategy (consists in a crossover/under of the Overbought/Oversold levels of the CCI) that works so I could have the opportunity to share with you the main idea that as per now is mistaken:
//@version=2
strategy(title="CCI 0Trend Strategy (by Marcoweb) v1.0", shorttitle="CCI_0T_Stra_v1.0", overlay=true)
///////////// CCI
length = input(20, minval=1)
src = input(close, title="Source")
ma = sma(src, length)
cci = (src - ma) / (0.015 * dev(src, length))
plot(cci, color=black)
band1 = hline(100, color=blue, linestyle=solid)
band0 = hline(-100, color=red, linestyle=solid)
bandl = hline(0, color=orange, linestyle=solid)
fill(band1, band0, color=olive)
p1 = plot(band0, color=red,title="-100")
p2 = plot(band1, color=blue,title="100")
p3 = plot(bandl, color=orange,title="0")
///////////// CCI 0Trend Strategy (by Marcoweb) v1.0 Strategy
if (not na(cci))
if (crossover(cci, bandl)
strategy.entry("CCI_L", strategy.long, stop=bandl, oca_type=strategy.oca.cancel, comment="CCI_L")
else
strategy.cancel(id="CCI_L")
if (crossunder(cci, bandl)
strategy.entry("CCI_S", strategy.short, stop=bandl, oca_type=strategy.oca.cancel, comment="CCI_S")
else
strategy.cancel(id="CCI_S")
//plot(strategy.equity, title="equity", color=red, linewidth=2, style=areabr)
With this coding I get the error : line 24 (if (crossover(cci, bandl): mismatched input '|E|' expecting RPAR
Hope you like the idea ;)
How to automate this strategy for free using a chrome extension.Hey everyone,
Recently we developed a chrome extension for automating TradingView strategies using the alerts they provide. Initially we were charging a monthly fee for the extension, but we have now decided to make it FREE for everyone. So to display the power of automating strategies via TradingView, we figured we would also provide a profitable strategy along with the custom alert script and commands for the alerts so you can easily cut and paste to begin trading for profit while you sleep.
Step 1:
You are going to need to download the Chrome Extension called AutoView. You can get the extension for free by following this link: bit.ly ( I had to shorten the link as it contains Google and TV automatically converts it to a symbol)
Step 2: Go to your chrome extension page, and under the new extension you'll see a "settings" button. In the setting you will have to connect and give permission to the exchange 1broker allowing the extension to place your orders automatically when triggered by an alert.
Step 3: Setup the strategy and custom script for the alerts in TradingView. The attached script is the strategy, you can play with the settings yourself to try and get better numbers/performance if you please.
This following script is for the custom alerts:
//@version=2
study("4All-Alert", shorttitle="Alerts")
src = close
len = input(4, minval=1, title="Length")
up = rma(max(change(src), 0), len)
down = rma(-min(change(src), 0), len)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
rsin = input(5)
sn = 100 - rsin
ln = 0 + rsin
short = crossover(rsi, sn) ? 1 : 0
long = crossunder(rsi, ln) ? 1 : 0
plot(long, "Long", color=green)
plot(short, "Short", color=red)
Now that you have the extension installed, the custom strategy and alert scripts in place, you simply need to create the alerts.
To get the alerts to communicate with the extension properly, there is a specific syntax that you will need to put in the message of the alert. You can find more details about the syntax here : gist.github.com
For this specific strategy, I use the Alerts script, long/short greater than 0.9 on close.
In the message for a long place this as your message:
Long
c=order b=short
c=position b=short l=200 t=market
b=long q=0.01 l=200 t=market tp=13 sl=25
and for the short...
Short
c=order b=long
c=position b=long l=200 t=market
b=short q=0.01 l=200 t=market tp=13 sl=25
If you'll notice in my above messages, compared to the strategy my tp and sl (take profit and stop loss) vary by a few pips. This is to cover the market opens and spread on 1broker. You can change the tp and sl in the strategy to the above and see that the overall profit will not vary much at all.
I hope this all makes sense and it is enough to not only make some people money, but to show the power of coming up with your own strategy and automating it using TradingView alerts and the free Chrome Extension AutoView.
ps. I highly recommend upgrading your TradingView account so you have access to back testing and multiple alerts.
There is really no reason you won't cover the cost and then some on a monthly basis using the tools provided.
Best of luck and happy trading.
Note: The extension currently allows for automation on 2 exchanges; 1broker and Okcoin. If you do not have accounts there, we'd appreciate you signing up using our referral links.
www.okcoin.com
1broker.com
Indicator: Trend Trigger FactorIntroduced by M.H.Pee, Trend Trigger Factor is designed to keep the trader trading with the trend.
System rules according to the developer:
* If the 15-day TTF is above 100 (indicating an uptrend), you will want to be in long positions.
* If the 15-day TTF is below -100, you will want to be short.
* If it is between -100 and 100, you should remain with the current position.
More info:
Original Article by Mr.Pee: drive.google.com
Horizontal Lines [Vynkron]📄 Script Description – "Horizontal Lines "
This Pine Script v6 indicator draws up to 11 customizable horizontal lines on your chart, ideal for marking round levels, support/resistance zones, or psychological price points (e.g. every 100 points on the NQ).
🔧 Features:
11 price inputs (default: 22000 to 23000, step 100)
Single color, width, and style configuration for all lines
Uses hline() so lines are fixed across the full chart
Easy to adjust or toggle lines individually
💡 Use Cases:
Highlighting round-number levels on instruments like Nasdaq 100 Futures
Visualizing major price zones
Manual technical analysis without scripting loops
Let me know if you want to make it dynamic, add labels, or only draw lines within the visible chart range.
Custom Multiple SMAsThe Custom Breakout Indicator provides visual guidance for identifying entry and exit signals within the BreakoutCatcher strategy. It consists of a fan of multiple Simple Moving Averages (SMAs) that make current market conditions visually accessible:
Flat, tightly clustered fan → Market is consolidating
First candle closing outside the fan → Potential entry signal (breakout)
Wide, rapidly expanding fan → Market is overheated, avoid entries
Additionally, the indicator displays a yellow trendline (EMA 100) as an overarching trend filter:
Price above EMA 100 → Consider only long signals
Price below EMA 100 → Consider only short signals
🔗 Part of the BreakoutCatcher strategy – available at: www.twn-trading.com
✔️ Fully functional on all timeframes
Multi SMA AnalyzerMulti SMA Analyzer with Custom SMA Table & Advanced Session Logic
A feature-rich SMA analysis suite for traders, offering up to 7 configurable SMAs, in-depth trend detection, real-time table, and true session-aware calculations.
Ideal for those who want to combine intraday, swing, and higher-timeframe trend analysis with maximum chart flexibility.
Key Features
📊 Multi-SMA Overlay
- 7 SMAs (default: 5, 20, 50, 100, 200, 21, 34)—individually configurable (period, source, color, line style)
- Show/hide each SMA, custom line style (solid, stepline, circles), and color logic
- Dynamic color: full opacity above SMA, reduced when below
⏰ Session-Aware SMAs
- Each SMA can be calculated using only user-defined session hours/days/timezone
- “Ignore extended hours” option for accurate intraday trend
📋 Smart Data Table
- Live SMA values, % distance from price, and directional arrows (↑/↓/→)
- Bull/Bear/Sideways trend classification
- Custom table position, size, colors, transparency
- Table can run on chart or custom (higher) timeframe for multi-TF analysis
🎯 Golden/Death Cross Detection
- Flexible crossover engine: select any two from (5, 10, 20, 50, 100, 200) for fast/slow SMA cross signals
- Plots icons (★ Golden, 💀 Death), optional crossover labels with custom size/colors
🏷️ SMA Labels
- Optional on-chart SMA period labels
- Custom placement (above/below/on line), size, color, offset
🚨 Signal & Trend Engine
- Bull/Bear/Sideways logic: price vs. multiple SMAs (not just one pair)
- Volume spike detection (2x 20-period SMA)
- Bullish engulfing candlestick detection
- All signals can use chart or custom table timeframe
🎨 Visual Customization
- Dynamic background color (Bull: green, Bear: red, Neutral: gray)
- Every visual aspect is customizable: label/table colors, transparency, size, position
🔔 Built-in Alerts
- Crossovers (SMA20/50, Golden/Death)
- Bull trend, volume spikes, engulfing pattern—all alert-ready
How It Works
- Session Filtering:
- SMAs can be set to count only bars from your chosen market session, for true intraday/trading-hour signals
Dynamic Table & Signals:
- Table and all signal logic run on your selected chart or custom timeframe
Flexible Crossover:
- Choose any pair (5, 10, 20, 50, 100, 200) for cross detection—SMA 10 is available for crossover even if not shown as an SMA line
Everything is modular:
- Toggle features, set visuals, and alerts to your workflow
🚨 How to Use Alerts
- All key signals (crossovers, trend shifts, volume spikes, engulfing patterns) are available as alert conditions.
To enable:
- Click the “Alerts” (clock) icon at the top of TradingView.
- Select your desired signal (e.g., “Golden Cross”) from the condition dropdown.
- Set your alert preferences and create the alert.
- Now, you’ll get notified automatically whenever a signal occurs!
Perfect For
- Multi-timeframe and swing traders seeking higher timeframe SMA confirmation
- Intraday traders who want to ignore pre/post-market data
- Anyone wanting a modern, powerful, fully customizable multi-SMA overlay
// P.S: Experiment with Golden Cross where Fast SMA is 5 and Slow SMA is 20.
// Set custom timeframe for 4 hr while monitoring your chart on 15 min time frame.
// Enable Background Color and Use Table Timeframe for Background.
// Uncheck Pine labels in Style tab.
Clean, open-source, and loaded with pro features—enjoy!
Like, share, and let me know if you'd like any new features added.
Fibonacci Extension Distance Table## 🧾 **Script Name**: Fibonacci Extension Distance Table
### 🎯 Purpose:
This script helps traders visually track **key Fibonacci extension levels** on any chart and immediately see:
* The **price target** at each extension
* The **distance in percentage** from the current market price
It is especially helpful for:
* **Profit targets in trending trades**
* Monitoring **potential resistance zones** in uptrends
* Planning **entry/exit timing**
---
## 🧮 **How It Works**
1. **Swing Logic (A → B → C)**
* It automatically finds:
* `A`: the **lowest low** in the last `swingLen` bars
* `B`: the **highest high** in that same lookback
* `C`: current bar’s low is used as the **retracement point** (simplified)
2. **Extension Formula**
Using the Fibonacci formula:
```text
Extension Price = C + (B - A) × Fibonacci Ratio
```
The script calculates projected target prices at:
* **100%**
* **127.2%**
* **161.8%** (Golden Ratio)
* **200%**
* **261.8%**
3. **Distance Calculation**
For each level, it calculates:
* The **absolute difference** between current price and the extension level
* The **percentage difference**, which helps quickly assess how close or far the market is from that target
---
## 📋 **Table Output in Top Right**
| Level | Target ₹ | Dist % from current price |
| ------ | ---------- | ------------------------- |
| 100% | Calculated | % Above/Below |
| 127.2% | Calculated | % Above/Below |
| 161.8% | Calculated | % Above/Below |
| 200% | Calculated | % Above/Below |
| 261.8% | Calculated | % Above/Below |
* The table updates **live on each bar**
* It **highlights levels** where price is nearing
* Useful in **any time frame** and **any market** (stocks, crypto, forex)
---
## 🔔 Example Use Case
You bought a stock at ₹100, and recent swing shows:
* A = ₹80
* B = ₹110
* C = ₹100
The 161.8% extension = 100 + (110 − 80) × 1.618 = ₹148.54
If the current price is ₹144, the table will show:
* Golden Ratio Target: ₹148.54
* Distance: −4.54
* Distance %: −3.05%
You now know your **target is near** and can plan your **exit or trailing stop**.
---
## 🧠 Benefits
* No need to draw extensions manually
* Automatically adapts to new swing structures
* Supports **scalping**, **swing**, and **positional** strategies
ATRWhat the Indicator Shows:
A compact table with four cells is displayed in the bottom-left corner of the chart:
| ATR | % | Level | Lvl+ATR |
Explanation of the Columns:
ATR — The averaged daily range (volatility) calculated with filtering of abnormal bars (extremely large or small daily candles are ignored).
% — The percentage of the daily ATR that the price has already covered today (the difference between the daily Open and Close relative to ATR).
Level — A custom user-defined level set through the indicator settings.
Lvl+ATR — The sum of the daily ATR and the user-defined level. This can be used, for example, as a target or stop-loss reference.
Color Highlighting of the "%" Cell:
The background color of the "%" ATR cell changes depending on the value:
✅ If the value is less than 10% — the cell is green (market is calm, small movement).
➖ If the value is between 10% and 50% — no highlighting (average movement, no signal).
🟡 If the value is between 50% and 70% — the cell is yellow (movement is increasing, be alert).
🔴 If the value is above 70% — the cell is red (the market is actively moving, high volatility).
Key Features:
✔ All ATR calculations and percentage progress are performed strictly based on daily data, regardless of the chart's current timeframe.
✔ The indicator is ideal for intraday traders who want to monitor daily volatility levels.
✔ The table always displays up-to-date information for quick decision-making.
✔ Filtering of abnormal bars makes ATR more stable and objective.
What is Adaptive ATR in this Indicator:
Instead of the classic ATR, which simply averages the true range, this indicator uses a custom algorithm:
✅ It analyzes daily bars over the past 100 days.
✅ Calculates the range High - Low for each bar.
✅ If the bar's range deviates too much from the average (more than 1.8 times higher or lower), the bar is considered abnormal and ignored.
✅ Only "normal" bars are included in the calculation.
✅ The average range of these normal bars is the adaptive ATR.
Detailed Algorithm of the getAdaptiveATR() Function:
The function takes the number of bars to include in the calculation (for example, 5):
The average of the last 5 normal bars is calculated.
pinescript
Копировать
Редактировать
adaptiveATR = getAdaptiveATR(5)
Step-by-Step Process:
An empty array ranges is created to store the ranges.
Daily bars with indices from 1 to 100 are iterated over.
For each bar:
🔹 The daily High and Low with the required offset are loaded via request.security().
🔹 The range High - Low is calculated.
🔹 The temporary average range of the current array is calculated.
🔹 The bar is checked for abnormality (too large or too small).
🔹 If the bar is normal or it's the first bar — its range is added to the array.
Once the array accumulates the required number of bars (count), their average is calculated — this is the adaptive ATR.
If it's not possible to accumulate the required number of bars — na is returned.
Что показывает индикатор:
На графике внизу слева отображается компактная таблица из четырех ячеек:
ATR % Уровень Ур+ATR
Пояснения к столбцам:
ATR — усреднённый дневной диапазон (волатильность), рассчитанный с фильтрацией аномальных баров (слишком большие или маленькие дневные свечи игнорируются).
% — процент дневного ATR, который уже "прошла" цена на текущий день (разница между открытием и закрытием относительно ATR).
Уровень — пользовательский уровень, который задаётся вручную через настройки индикатора.
Ур+ATR — сумма уровня и дневного ATR. Может использоваться, например, как ориентир для целей или стопов.
Цветовая подсветка ячейки "%":
Цвет фона ячейки с процентом ATR меняется в зависимости от значения:
✅ Если значение меньше 10% — ячейка зелёная (рынок пока спокоен, маленькое движение).
➖ Если значение от 10% до 50% — фон не подсвечивается (среднее движение, нет сигнала).
🟡 Если значение от 50% до 70% — ячейка жёлтая (движение усиливается, повышенное внимание).
🔴 Если значение выше 70% — ячейка красная (рынок активно движется, высокая волатильность).
Особенности работы:
✔ Все расчёты ATR и процентного прохождения производятся исключительно по дневным данным, независимо от текущего таймфрейма графика.
✔ Индикатор подходит для трейдеров, которые торгуют внутри дня, но хотят ориентироваться на дневные уровни волатильности.
✔ В таблице всегда отображается актуальная информация для принятия быстрых торговых решений.
✔ Фильтрация аномальных баров делает ATR более устойчивым и объективным.
Что такое адаптивный ATR в этом индикаторе
Вместо классического ATR, который просто усредняет истинный диапазон, здесь используется собственный алгоритм:
✅ Он берет дневные бары за последние 100 дней.
✅ Для каждого из них рассчитывает диапазон High - Low.
✅ Если диапазон бара слишком сильно отличается от среднего (более чем в 1.8 раза больше или меньше), бар считается аномальным и игнорируется.
✅ Только нормальные бары попадают в расчёт.
✅ В итоге считается среднее из диапазонов этих нормальных баров — это и есть адаптивный ATR.
Подробный алгоритм функции getAdaptiveATR()
Функция принимает количество баров для расчёта (например, 5):
Считается 5 последних нормальных баров
pinescript
Копировать
Редактировать
adaptiveATR = getAdaptiveATR(5)
Пошагово:
Создаётся пустой массив ranges для хранения диапазонов.
Перебираются дневные бары с индексами от 1 до 100.
Для каждого бара:
🔹 Через request.security() подгружаются дневные High и Low с нужным смещением.
🔹 Считается диапазон High - Low.
🔹 Считается временное среднее диапазона по текущему массиву.
🔹 Проверяется, не является ли бар аномальным (слишком большой или маленький).
🔹 Если бар нормальный или это самый первый бар — его диапазон добавляется в массив.
Как только массив набирает заданное количество баров (count), берётся их среднее значение — это и есть адаптивный ATR.
Если не удалось набрать нужное количество баров — возвращается na.
Relative Measured Volatility (RMV)RMV • Volume-Sensitive Consolidation Indicator
A lightweight Pine Script that highlights true low-volatility, low-volume bars in a single squeeze measure.
What it does
Calculates each bar’s raw High-Low range.
Down-weights bars where volume is below its 30-day average, emphasizing genuine quiet periods.
Normalizes the result over the prior 15 bars (excluding the current bar), scaling from 0 (tightest) to 100 (most volatile).
Draws the series as a step plot, shades true “tight” bars below the user threshold, and marks sustained squeezes with a small arrow.
Key inputs
Lookback (bars): Number of bars to use for normalization (default 15).
Tight Threshold: RMV value under which a bar is considered squeezed (default 15).
Volume SMA Period: Period for the volume moving average benchmark (default 30).
How it works
Raw range: barRange = high - low
Volume ratio: volRatio = min(volume / sma(volume,30), 1)
Weighted range: vwRange = barRange * volRatio
Rolling min/max (prior 15 bars): exclude today so a new low immediately registers a 0.
Normalize: rmv = clamp(100 * (vwRange - min) / (max - min), 0, 100)
Visualization & signals
Step line for exact bar-by-bar values.
Shaded background when RMV < threshold.
Consecutive-bar filter ensures arrows only appear when tightness lasts at least two bars, cutting noise.
Why use it
Quickly spot consolidation zones that combine narrow price action with genuine dry volume—ideal for swing entries ahead of breakouts.
Contrarian RSIContrarian RSI Indicator
Pairs nicely with Contrarian 100 MA (optional hide/unhide buy/sell signals)
Description
The Contrarian RSI is a momentum-based technical indicator designed to identify potential reversal points in price action by combining a unique RSI calculation with a predictive range model inspired by the "Contrarian 5 Levels" logic. Unlike traditional RSI, which measures price momentum based solely on price changes, this indicator integrates a smoothed, weighted momentum calculation and predictive price ranges to generate contrarian signals. It is particularly suited for traders looking to capture reversals in trending or range-bound markets.
This indicator is versatile and can be used across various timeframes, though it performs best on higher timeframes (e.g., 1H, 4H, or Daily) due to reduced noise and more reliable signals. Lower timeframes may require additional testing and careful parameter tuning to optimize performance.
How It Works
The Contrarian RSI combines two primary components:
Predictive Ranges (5 Levels Logic): This calculates a smoothed price average that adapts to market volatility using an ATR-based mechanism. It helps identify significant price levels that act as potential support or resistance zones.
Contrarian RSI Calculation: A modified RSI calculation that uses weighted momentum from the predictive ranges to measure buying and selling pressure. The result is smoothed and paired with a user-defined moving average to generate clear signals.
The indicator generates buy (long) and sell (exit) signals based on crossovers and crossunders of user-defined overbought and oversold levels, making it ideal for contrarian trading strategies.
Calculation Overview
Predictive Ranges (5 Levels Logic):
Uses a custom function (pred_ranges) to calculate a dynamic price average (avg) based on the ATR (Average True Range) multiplied by a user-defined factor (mult).
The average adjusts only when the price moves beyond the ATR threshold, ensuring responsiveness to significant price changes while filtering out noise.
This calculation is performed on a user-specified timeframe (tf5Levels) for multi-timeframe analysis.
Contrarian RSI:
Compares consecutive predictive range values to calculate gains (g) and losses (l) over a user-defined period (crsiLength).
Applies a Gaussian weighting function (weight = math.exp(-math.pow(i / crsiLength, 2))) to prioritize recent price movements.
Computes a "wave ratio" (net_momentum / total_energy) to normalize momentum, which is then scaled to a 0–100 range (qrsi = 50 + 50 * wave_ratio).
Smooths the result with a 2-period EMA (qrsi_smoothed) for stability.
Moving Average:
Applies a user-selected moving average (SMA, EMA, WMA, SMMA, or VWMA) with a customizable length (maLength) to the smoothed RSI (qrsi_smoothed) to generate the final indicator value (qrsi_ma).
Signal Generation:
Long Entry: Triggered when qrsi_ma crosses above the oversold level (oversoldLevel, default: 1).
Long Exit: Triggered when qrsi_ma crosses below the overbought level (overboughtLevel, default: 99).
Entry and Exit Rules
Long Entry: Enter a long position when the Contrarian RSI (qrsi_ma) crosses above the oversold level (default: 1). This suggests the asset is potentially oversold and due for a reversal.
Long Exit: Exit the long position when the Contrarian RSI (qrsi_ma) crosses below the overbought level (default: 99), indicating a potential overbought condition and a reversal to the downside.
Customization: Adjust overboughtLevel and oversoldLevel to fine-tune sensitivity. Lower timeframes may benefit from tighter levels (e.g., 20 for oversold, 80 for overbought), while higher timeframes can use extreme levels (e.g., 1 and 99) for stronger reversals.
Timeframe Considerations
Higher Timeframes (Recommended): The indicator is optimized for higher timeframes (e.g., 1H, 4H, Daily) due to its reliance on predictive ranges and smoothed momentum, which perform best with less market noise. These timeframes typically yield more reliable reversal signals.
Lower Timeframes: The indicator can be used on lower timeframes (e.g., 5M, 15M), but signals may be noisier and require additional confirmation (e.g., from price action or other indicators). Extensive backtesting and parameter optimization (e.g., adjusting crsiLength, maLength, or mult) are recommended for lower timeframes.
Inputs
Contrarian RSI Length (crsiLength): Length for RSI momentum calculation (default: 5).
RSI MA Length (maLength): Length of the moving average applied to the RSI (default: 1, effectively no MA).
MA Type (maType): Choose from SMA, EMA, WMA, SMMA, or VWMA (default: SMA).
Overbought Level (overboughtLevel): Upper threshold for exit signals (default: 99).
Oversold Level (oversoldLevel): Lower threshold for entry signals (default: 1).
Plot Signals on Main Chart (plotOnChart): Toggle to display signals on the price chart or the indicator panel (default: false).
Plotted on Lower:
Plotted on Chart:
5 Levels Length (length5Levels): Length for predictive range calculation (default: 200).
Factor (mult): ATR multiplier for predictive ranges (default: 6.0).
5 Levels Timeframe (tf5Levels): Timeframe for predictive range calculation (default: chart timeframe).
Visuals
Contrarian RSI MA: Plotted as a yellow line, representing the smoothed Contrarian RSI with the applied moving average.
Overbought/Oversold Lines: Red line for overbought (default: 99) and green line for oversold (default: 1).
Signals: Blue circles for long entries, white circles for long exits. Signals can be plotted on the main chart (plotOnChart = true) or the indicator panel (plotOnChart = false).
Usage Notes
Use the indicator in conjunction with other tools (e.g., support/resistance, trendlines, or volume) to confirm signals.
Test extensively on your chosen timeframe and asset to optimize parameters like crsiLength, maLength, and mult.
Be cautious with lower timeframes, as false signals may occur due to market noise.
The indicator is designed for contrarian strategies, so it works best in markets with clear reversal patterns.
Disclaimer
This indicator is provided for educational and informational purposes only. Always conduct thorough backtesting and risk management before using any indicator in live trading. The author is not responsible for any financial losses incurred.
Normalized Open InterestNormalized Open Interest (nOI) — Indicator Overview
What it does
Normalized Open Interest (nOI) transforms raw futures open-interest data into a 0-to-100 oscillator, so you can see at a glance whether participation is unusually high or low—similar in spirit to an RSI but applied to open interest. The script positions today’s OI inside a rolling high–low range and paints it with contextual colours.
Core logic
Data source – Loads the built-in “_OI” symbol that TradingView provides for the current market.
Rolling range – Looks back a user-defined number of bars (default 500) to find the highest and lowest OI in that window.
Normalization – Calculates
nOI = (OI – lowest) / (highest – lowest) × 100
so 0 equals the minimum of the window and 100 equals the maximum.
Visual cues – Plots the oscillator plus fixed horizontal levels at 70 % and 30 % (or your own numbers). The line turns teal above the upper level, red below the lower, and neutral grey in between.
User inputs
Window Length (bars) – How many candles the indicator scans for the high–low range; larger numbers smooth the curve, smaller numbers make it more reactive.
Upper Threshold (%) – Default 70. Anything above this marks potentially crowded or overheated interest.
Lower Threshold (%) – Default 30. Anything below this marks low or capitulating interest.
Practical uses
Spot extremes – Values above the upper line can warn that the long side is crowded; values below the lower line suggest disinterest or short-side crowding.
Confirm breakouts – A price breakout backed by a sharp rise in nOI signals genuine engagement.
Look for divergences – If price makes a new high but nOI does not, participation might be fading.
Combine with volume or RSI – Layer nOI with other studies to filter false signals.
Tips
On intraday charts for non-crypto symbols the script automatically fetches daily OI data to avoid gaps.
Adjust the thresholds to 80/20 or 60/40 to fit your market and risk preferences.
Alerts, shading, or additional signal logic can be added easily because the oscillator is already normalised.
Contrarian with 5 Levels5 Levels application was inspired and adapted from Predictive Ranges indicator developed by Lux Algo. So much credit to their work.
Indicator Description: Contrarian with 5 Levels
Overview
The "Contrarian with 5 Levels" indicator is a powerful tool designed for traders seeking to identify potential reversal points in the market by combining contrarian trading principles with dynamic support and resistance levels. This indicator overlays a Simple Moving Average (SMA) shadow and five adaptive price levels, integrating Institutional Concepts of Structure (ICT) such as Break of Structure (BOS) and Market Structure Shift (MSS) to provide clear buy and sell signals. It is ideal for traders looking to capitalize on overextended price movements, particularly on the daily timeframe, though it is adaptable to other timeframes with proper testing.
How It Works
The indicator operates on two core components:
Contrarian SMA Shadow: A shaded region between the SMA of highs and lows (default length: 100) acts as a dynamic zone to identify overbought or oversold conditions. When the price moves significantly outside this shadow, it signals potential exhaustion, aligning with contrarian trading principles.
Five Adaptive Levels: Using a modified ATR-based calculation, the indicator plots five key levels (two resistance, one average, and two support) that adjust dynamically to market volatility. These levels serve as critical zones for potential reversals.
ICT Structure Analysis: The indicator incorporates BOS and MSS logic to detect shifts in market structure, plotting bullish and bearish breaks with customizable colors for clarity.
Buy and sell signals are generated when the price crosses key levels while outside the SMA shadow, indicating potential reversal opportunities. The signals are visualized as small circles above (sell) or below (buy) the price bars, making them easy to interpret.
Mathematical Concepts
SMA Shadow: The indicator calculates the SMA of the highest highs and lowest lows over a user-defined period (default: 100). This creates a dynamic range that highlights extreme price movements, which contrarian traders often target for reversals.
Five Levels Calculation: The five levels are derived using a volatility-adjusted formula based on the Average True Range (ATR). The average level (central pivot) is calculated as a smoothed price, with two upper (resistance) and two lower (support) levels offset by a multiple of the ATR (default multiplier: 6.0). This adaptive approach ensures the levels remain relevant across varying market conditions.
ICT BOS/MSS Logic: The indicator identifies pivot highs and lows on a user-defined timeframe (default: daily) to detect structural breaks. A BOS occurs when the price breaks a prior pivot high (bullish) or low (bearish), while an MSS signals a shift in market direction, providing context for potential reversals.
Entry and Exit Rules
Buy Signal (Blue Dot Below Bar): Triggered when the closing price is below both the SMA shadow (smaLow) and the average level (avg), and the price crosses under either the first or second support level (prS1 or prS2). This suggests the market may be oversold, indicating a potential reversal upward.
Sell Signal (White Dot Above Bar): Triggered when the closing price is above both the SMA shadow (smaHigh) and the average level (avg), and the price crosses over either the first or second resistance level (prR1 or prR2). This suggests the market may be overbought, indicating a potential reversal downward.
Recommended Usage
This indicator is optimized for the daily timeframe, where it has been designed to capture significant reversal opportunities in trending or ranging markets. However, it can be adapted to other timeframes (e.g., 1H, 4H, 15M) with proper testing of settings such as SMA length, ATR multiplier, and structure timeframe. Users are encouraged to backtest and optimize parameters to suit their trading style and asset class.
Customization Options
SMA Length: Adjust the SMA period (default: 100) to control the sensitivity of the shadow.
Five Levels Length and Multiplier: Modify the length (default: 200) and ATR multiplier (default: 6.0) to fine-tune the support/resistance levels.
Timeframe Settings: Set separate timeframes for structure analysis and five levels to align with your trading strategy.
Color and Signal Display: Customize colors for BOS/MSS lines and toggle buy/sell signals on or off for a cleaner chart.
Why Use This Indicator?
The "Contrarian with 5 Levels" indicator combines the power of contrarian trading with dynamic levels and market structure analysis, offering a unique perspective for identifying high-probability reversal setups. Its intuitive design, customizable settings, and clear signal visualization make it suitable for both novice and experienced traders. Whether you're trading forex, stocks, or cryptocurrencies, this indicator provides a robust framework for spotting potential turning points in the market.
We hope you find the "Contrarian with 5 Levels" indicator a valuable addition to your trading toolkit! Happy trading!
Please leave feedback in the comments section.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis.
## 1. Introduction
Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017).
The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines:
- Classical monetary policy rules (Taylor Rule framework)
- Real-time macroeconomic indicators from FRED database
- Financial market conditions and term structure analysis
- Labor market dynamics and inflation expectations
- Regime-dependent parameter adjustments
This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes.
## 2. Literature Review and Theoretical Foundation
### 2.1 Taylor Rule Framework
The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals:
rt = r + πt + α(πt - π) + β(yt - y)
Where:
- rt = nominal federal funds rate
- r = equilibrium real interest rate
- πt = inflation rate
- π = inflation target
- yt - y = output gap
- α, β = policy response coefficients
Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions.
### 2.2 Data-Dependent Monetary Policy
The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions.
### 2.3 Financial Conditions and Monetary Transmission
The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010).
### 2.4 Labor Market Indicators
The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments.
## 3. Methodology
### 3.1 Model Architecture
The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index:
#### Component 1: Taylor Rule Analysis (Weight: 25%)
Implements real-time Taylor Rule calculation using FRED data:
- Core PCE inflation (Fed's preferred measure)
- Unemployment gap proxy for output gap
- Dynamic neutral rate estimation
- Regime-dependent parameter adjustments
#### Component 2: Employment Conditions (Weight: 20%)
Multi-dimensional labor market assessment:
- Unemployment gap relative to NAIRU estimates
- JOLTS job openings momentum
- Average hourly earnings growth
- Beveridge curve position analysis
#### Component 3: Financial Conditions (Weight: 18%)
Comprehensive financial market evaluation:
- Chicago Fed NFCI real-time data
- Yield curve shape and term structure
- Credit growth and lending conditions
- Market volatility and risk premia
#### Component 4: Inflation Expectations (Weight: 15%)
Forward-looking inflation analysis:
- TIPS breakeven inflation rates (5Y, 10Y)
- Market-based inflation expectations
- Inflation momentum and persistence measures
- Phillips curve relationship dynamics
#### Component 5: Growth Momentum (Weight: 12%)
Real economic activity assessment:
- Real GDP growth trends
- Economic momentum indicators
- Business cycle position analysis
- Sectoral growth distribution
#### Component 6: Liquidity Conditions (Weight: 10%)
Monetary aggregates and credit analysis:
- M2 money supply growth
- Commercial and industrial lending
- Bank lending standards surveys
- Quantitative easing effects assessment
### 3.2 Normalization and Scaling
Each component undergoes robust statistical normalization using rolling z-score methodology:
Zi,t = (Xi,t - μi,t-n) / σi,t-n
Where:
- Xi,t = raw indicator value
- μi,t-n = rolling mean over n periods
- σi,t-n = rolling standard deviation over n periods
- Z-scores bounded at ±3 to prevent outlier distortion
### 3.3 Regime Detection and Adaptation
The model incorporates dynamic regime detection based on:
- Policy volatility measures
- Market stress indicators (VIX-based)
- Fed communication tone analysis
- Crisis sensitivity parameters
Regime classifications:
1. Crisis: Emergency policy measures likely
2. Tightening: Restrictive monetary policy cycle
3. Easing: Accommodative monetary policy cycle
4. Neutral: Stable policy maintenance
### 3.4 Composite Index Construction
The final AFDFM index combines weighted components:
AFDFMt = Σ wi × Zi,t × Rt
Where:
- wi = component weights (research-calibrated)
- Zi,t = normalized component scores
- Rt = regime multiplier (1.0-1.5)
Index scaled to range for intuitive interpretation.
### 3.5 Decision Probability Calculation
Fed decision probabilities derived through empirical mapping:
P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100)
P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100)
P(Hold) = 100 - |AFDFMt| × 15
Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds).
## 4. Data Sources and Real-Time Implementation
### 4.1 FRED Database Integration
- Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted
- Unemployment Rate (UNRATE): Monthly, seasonally adjusted
- Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate
- Federal Funds Rate (FEDFUNDS): Monthly average
- Treasury Yields (GS2, GS10): Daily constant maturity
- TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data
### 4.2 High-Frequency Financial Data
- Chicago Fed NFCI: Weekly financial conditions
- JOLTS Job Openings (JTSJOL): Monthly labor market data
- Average Hourly Earnings (AHETPI): Monthly wage data
- M2 Money Supply (M2SL): Monthly monetary aggregates
- Commercial Loans (BUSLOANS): Weekly credit data
### 4.3 Market-Based Indicators
- VIX Index: Real-time volatility measure
- S&P; 500: Market sentiment proxy
- DXY Index: Dollar strength indicator
## 5. Model Validation and Performance
### 5.1 Historical Backtesting (2017-2024)
Comprehensive backtesting across multiple Fed policy cycles demonstrates:
- Signal Accuracy: 78% correct directional predictions
- Timing Precision: 2.3 meetings average lead time
- Crisis Detection: 100% accuracy in identifying emergency measures
- False Signal Rate: 12% (within acceptable research parameters)
### 5.2 Regime-Specific Performance
Tightening Cycles (2017-2018, 2022-2023):
- Hawkish signal accuracy: 82%
- Average prediction lead: 1.8 meetings
- False positive rate: 8%
Easing Cycles (2019, 2020, 2024):
- Dovish signal accuracy: 85%
- Average prediction lead: 2.1 meetings
- Crisis mode detection: 100%
Neutral Periods:
- Hold prediction accuracy: 73%
- Regime stability detection: 89%
### 5.3 Comparative Analysis
AFDFM performance compared to alternative methods:
- Fed Funds Futures: Similar accuracy, lower lead time
- Economic Surveys: Higher accuracy, comparable timing
- Simple Taylor Rule: Lower accuracy, insufficient complexity
- Market-Based Models: Similar performance, higher volatility
## 6. Practical Applications and Use Cases
### 6.1 Institutional Investment Management
- Fixed Income Portfolio Positioning: Duration and curve strategies
- Currency Trading: Dollar-based carry trade optimization
- Risk Management: Interest rate exposure hedging
- Asset Allocation: Regime-based tactical allocation
### 6.2 Corporate Treasury Management
- Debt Issuance Timing: Optimal financing windows
- Interest Rate Hedging: Derivative strategy implementation
- Cash Management: Short-term investment decisions
- Capital Structure Planning: Long-term financing optimization
### 6.3 Academic Research Applications
- Monetary Policy Analysis: Fed behavior studies
- Market Efficiency Research: Information incorporation speed
- Economic Forecasting: Multi-factor model validation
- Policy Impact Assessment: Transmission mechanism analysis
## 7. Model Limitations and Risk Factors
### 7.1 Data Dependency
- Revision Risk: Economic data subject to subsequent revisions
- Availability Lag: Some indicators released with delays
- Quality Variations: Market disruptions affect data reliability
- Structural Breaks: Economic relationship changes over time
### 7.2 Model Assumptions
- Linear Relationships: Complex non-linear dynamics simplified
- Parameter Stability: Component weights may require recalibration
- Regime Classification: Subjective threshold determinations
- Market Efficiency: Assumes rational information processing
### 7.3 Implementation Risks
- Technology Dependence: Real-time data feed requirements
- Complexity Management: Multi-component coordination challenges
- User Interpretation: Requires sophisticated economic understanding
- Regulatory Changes: Fed framework evolution may require updates
## 8. Future Research Directions
### 8.1 Machine Learning Integration
- Neural Network Enhancement: Deep learning pattern recognition
- Natural Language Processing: Fed communication sentiment analysis
- Ensemble Methods: Multiple model combination strategies
- Adaptive Learning: Dynamic parameter optimization
### 8.2 International Expansion
- Multi-Central Bank Models: ECB, BOJ, BOE integration
- Cross-Border Spillovers: International policy coordination
- Currency Impact Analysis: Global monetary policy effects
- Emerging Market Extensions: Developing economy applications
### 8.3 Alternative Data Sources
- Satellite Economic Data: Real-time activity measurement
- Social Media Sentiment: Public opinion incorporation
- Corporate Earnings Calls: Forward-looking indicator extraction
- High-Frequency Transaction Data: Market microstructure analysis
## References
Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404.
Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu
Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707.
Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52.
Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478.
Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26.
Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150.
Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022.
Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley.
---
Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model.
Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Percent Change of Range Candles - FullPercent Change of Range Candles – Full (PCR Full)
Description:
PCR Full is a custom momentum indicator that measures the percentage price change relative to a defined range, offering traders a unique way to evaluate strength, direction, and potential reversals in price movement.
How it works:
The main value (PCR) is calculated by comparing the price change over a selected number of candles (length) to the range between the highest high and lowest low in the same period.
This percentage change is normalized and visualized with dynamic candles on the subgraph.
Reference levels at +100, +50, 0, -50, and -100 serve as key zones to indicate potential overbought/oversold conditions, continuation, or neutrality.
How to read the indicator:
1. Trend continuation:
When PCR breaks above +50 and holds, it often confirms a strong bullish move.
Similarly, values below -50 and staying low signal a bearish continuation.
2. Wick behavior (volatility insight):
Long wicks on PCR candles suggest uncertainty or failed breakout attempts.
Short or no wicks with strong body color show stable momentum and conviction.
On the chart, multiple long wicks near -50 suggest bulls are attempting to push price upward, but lack the strength — until a confirmed breakout.
3. Polarity transition (Bearish to Bullish or vice versa):
A transition from negative PCR values to above zero shows that the market is possibly turning.
Especially if PCR climbs gradually and stabilizes above zero, it indicates a developing bullish phase.
Components:
Main PCR line: Color-coded (green for rising, red for falling).
Open Average (gray line): Smooths recent PCR values, indicating balance.
High/Low adaptive bands: Adjust dynamically to PCR polarity.
PCR Candles: Visualize OHLC of PCR data for enhanced interpretation.
Suggested use cases:
Enter trend trades when PCR crosses +50 or -50 with volume or price confirmation.
Watch for reversal signs near ±100 if PCR fails to break further.
Use 0 line as a neutral zone — markets hovering near 0 are often in consolidation.
Combine with price action or oscillators like RSI/MACD for additional signals.
Customization:
The length input allows users to define the range for PCR calculations, making it adjustable to various timeframes and strategies (scalping, intraday, swing).
Trend Gauge [BullByte]Trend Gauge
Summary
A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status.
________________________________________
## 1. Purpose of the Indicator
Why this was built
Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades.
Key Design Goals
Signal Aggregation
Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically.
Market Regime Awareness
Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts.
Magnitude-Based Scaling
Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength.
Momentum Divergence Penalty
Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries.
Confidence Transparency
Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable.
Momentum Acceleration Visualization
Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making.
Compact Informational Dashboard
Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment.
________________________________________
## 2. Why a Trader Should Use It
Main benefits and use cases
1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals.
2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts.
3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized.
4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal.
5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions.
6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator.
7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts.
8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic.
9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications.
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## 3. Component Rationale (“Why These Specific Indicators?”)
Each sub-component was chosen because it adds complementary information about trend or momentum:
1. EMA Cross
o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets.
2. VWMA Momentum
o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum.
3. Volume Spikes
o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars.
4. ATR Breakout
o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal.
5. Higher-Timeframe EMA Alignment
o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases.
6. ADX Regime Filter (Manual Calculation)
o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired).
7. RSI Pivot-Divergence Penalty
o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise.
8. Confidence Meter
o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals.
9. Δ Score Momentum View
o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator.
10. Dashboard
• A table in the indicator pane’s top-right with 11 rows:
1. EMA Cross status
2. VWMA Momentum status
3. Volume Spike status
4. ATR Breakout status
5. Higher-Timeframe Trend status
6. Score (numeric)
7. Confidence %
8. Regime (“Trending” or “Ranging”)
9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”)
10. Gauge bar visually representing score magnitude
• All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency.
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## 4. What Makes This Indicator Stand Out
• Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals.
• Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1.
• Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time.
• Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric.
• Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early.
• Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts.
• Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias.
• Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear.
• Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips.
• Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations.
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## 5. Recommended Timeframes & Tuning
• Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However:
o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence.
o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart).
• Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust:
o ADX threshold (e.g., 20–30) based on instrument volatility.
o VWMA and ATR scaling lengths to match average volatility cycles.
o Pivot lookback for divergence: shorter for faster markets, longer for slower ones.
• Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor.
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## 6. How Scoring and Logic Works (Step-by-Step)
1. Compute Sub-Scores
o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0.
o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to .
o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0.
o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low.
o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment.
2. ADX Regime Weighting
o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.”
o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired).
3. Aggregate Raw Score
o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero.
4. Divergence Penalty
o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:
If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).
If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty.
o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright.
5. Normalize and Smooth
o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range.
o Optional EMA smoothing of this normalized score to reduce noise.
6. Interpretation
o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral.
o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”).
o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration.
o Confidence: Percentage of sub-indicators aligned with the score’s sign.
o Regime: Indicates whether trend-based signals are fully weighted or down-weighted.
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## 7. Oscillator Plot & Visualization: How to Read It
Main Score Line & Area
The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds.
Δ Score Histogram
If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar.
Divergence Highlight Fill
If enabled, when a pivot-based divergence is confirmed:
• Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish.
• Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish.
These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag.
Trend Direction Label
When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur.
Dashboard Panel
In the indicator pane’s top-right, a table shows:
1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled”
2. VWMA Momentum status: similarly
3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled”
4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled”
5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled”
6. Score: numeric value (rounded)
7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low)
8. Regime: “Trending” or “Ranging” (colored accordingly)
9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”)
10. Gauge: a bar of blocks representing |score|/100
All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny).
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## 8. Example Usage (Illustrative Scenario)
Example: BTCUSD 5 Min
1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small.
2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment.
3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits.
4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur.
5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section.
6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right.
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## 9. Inputs Explanation
A concise yet clear summary of inputs helps users understand and adjust:
1. General Settings
• Theme (Dark/Light): Choose background-appropriate colors for the indicator pane.
• Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible.
2. Indicator Settings
• Enable EMA Cross: Toggle on/off basic EMA alignment check.
o Fast EMA Length and Slow EMA Length: Periods for EMAs.
• Enable VWMA Momentum: Toggle VWMA momentum check.
o VWMA Length: Period for VWMA.
o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum.
• Enable Volume Spike: Toggle volume spike detection.
o Volume SMA Length: Period to compute average volume.
o Volume Spike Multiplier: How many times above average volume qualifies as spike.
o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish.
• Enable ATR Breakout: Toggle ATR breakout detection.
o ATR Length: Period for ATR.
o Breakout Lookback: Bars to look back for recent highs/lows.
o ATR Multiplier: Multiplier for breakout threshold.
• Enable Higher Timeframe Trend: Toggle HTF EMA alignment.
o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off.
• Enable ADX Regime Filter: Toggles regime-based weighting.
o ADX Length: Period for manual ADX calculation.
o ADX Threshold: Value above which market considered trending.
o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5).
• Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude.
o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum.
• Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR.
o ATR Scale Cap: Maximum multiple of ATR used for breakout strength.
• Enable Price-RSI Divergence: Toggle divergence detection.
o RSI Length for Divergence: Period for RSI.
o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low.
o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5).
3. Score Settings
• Smooth Score: Toggle EMA smoothing of normalized score.
• Score Smoothing Length: Period for smoothing EMA.
• Weak Threshold: Absolute score value under which trend is considered weak or neutral.
• Medium Threshold: Score above Weak but below Medium is moderate.
• Strong Threshold: Score above this indicates strong trend.
4. Visualization Settings
• Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true.
• Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true.
Each input has a tooltip in the code.
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## 10. Limitations, Repaint Notes, and Disclaimers
10.1. Repaint & Lag Considerations
• Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result:
o Divergence labels or fills appear with a delay equal to the pivot lookback.
o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag.
o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed.
• Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars.
• No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off.
10.2. Market & Noise Considerations
• In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters.
• On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features.
• On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals.
10.3. Not a Standalone Trading System
• This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management.
• Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context).
• No guarantees: past behavior does not guarantee future performance.
10.4. Disclaimers
• Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice.
• Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management.
• No Guarantees: The author is not responsible for trading outcomes based on this indicator.
• License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms.
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## 11. Alerts
• The indicator defines three alert conditions:
1. Bullish Trend: when the aggregated score crosses above the Weak threshold.
2. Bearish Trend: when the score crosses below the negative Weak threshold.
3. Neutral Trend: when the score returns within ±Weak after being outside.
Good luck
– BullByte
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Candle Count RSI📈 Candle Count RSI — A Dual-Perspective Momentum Engine
The Candle Count RSI is a custom-built momentum oscillator that expands on the classic Relative Strength Index (RSI) by introducing a directional-only variant that tracks the frequency of bullish or bearish closes, rather than price magnitude. It gives traders a second lens through which to evaluate momentum, trend conviction, and subtle divergences—often invisible to traditional price-based RSI.
💡 What Makes It Unique?
While the standard RSI is sensitive to the size of price changes, the Candle Count RSI is magnitude-blind. It counts candle closes above/below open over a lookback period, generating a purer signal of directional consistency. To enhance signal fidelity, it includes a streak amplifier, dynamically weighting extended runs of green or red candles to reflect intensity of market bias—without introducing artificial price sensitivity.
This dual-RSI approach allows for:
- Divergence detection between directional bias and price magnitude.
- Smoother trend confirmation in choppy markets.
- Cleaner visual cues using dynamic glow and background logic.
📐 How Standard RSI Actually Works (Not What You Think)
RSI doesn’t just check if price went up or down over a span—it checks each individual candle and tracks whether it closed higher or lower than the one before. Here's how it works under the hood:
1.) For each bar, it calculates the change from the previous close.
2.) It separates those changes into gains (upward moves) and losses (downward moves).
3.) Then it computes a smoothed average of those gains and losses (usually using an RMA).
4.) It calculates the Relative Strength (RS) as:
RS = AvgGain / AvgLoss
5.) Finally, it plugs that into the RSI formula:
RSI = 100 - (100 / (1 + RS))
⚖️ What Does the 50 Line Mean?
- The RSI scale runs from 0 to 100, but 50 is the true neutral zone:
- RSI > 50 means average gains outweigh average losses over the period.
- RSI < 50 means losses dominate.
- RSI ≈ 50? The market is balanced—momentum is indecisive, no clear trend bias.
- This makes 50 a powerful midline for trend filters, directional bias tools, and divergence detection—especially when paired with alternative RSI logic like Candle Count RSI.
🔧 Inputs and Customization
- Everything is fully modular and customizable:
🧠 Core Settings
- RSI Length: Used for both the standard RSI and Candle Count RSI.
📉 Standard RSI
- Classic RSI calculation based on price changes.
- Optional WMA smoothing to reduce noise.
- Glow effect toggle with custom intensity.
🕯 Candle Count RSI
- Computes RSI using only the count of up/down candles.
- Optional smoothing for stability.
- Amplifies streaks (e.g., multiple consecutive bullish candles increase strength).
- Glow effect toggle with adjustable strength.
🎇 Glow Visuals
- Background glow (subpane and/or main chart).
- Fades based on RSI distance from the 50 midpoint.
- Independent color settings for bull and bear bias.
🧬 Divergence Zones
- Detects when Candle RSI and Standard RSI diverge.
- Highlights:
- Bullish Divergence: Candle RSI > 50, Standard RSI < threshold.
- Bearish Divergence: Candle RSI < 50, Standard RSI > threshold.
- Background fill optionally shown in subpane and/or main chart.
📊 Directional Histogram
- MACD-style histogram showing the difference between the two RSI lines.
- Color-coded based on directional agreement:
- Both rising → green.
- Both falling → red.
- Conflict → yellow.
🧠 Under the Hood — How It Works
🔹 Standard RSI
- Classic ta.rsi() applied to close prices, optionally WMA-smoothed.
🔹 Candle Count RSI (CCR)
- Counts how many candles closed up/down over the period.
- Computes a magnitude-free RSI from these counts.
- Applies a streak-based multiplier to exaggerate trend strength during consecutive green/red runs.
- Optionally smoothed with WMA to create a clean signal line.
- This makes CCR ideal for detecting true directional bias without being faked out by volatile price spikes.
🔹 Divergence Logic
- When Candle RSI and Standard RSI disagree strongly across defined thresholds, background fills highlight early signs of momentum decay or hidden accumulation/distribution.
🔹 Glow Logic
- Glow zones are controlled by a master toggle and drawn with dynamic transparency:
- Further from 50 = stronger conviction = darker glow.
- Shows up in subpane and/or main chart depending on user preference.
📷 Suggested Use Case / Visual Setup
- Use in conjunction with your primary price action system.
- Watch for divergences between the Candle Count RSI and Standard RSI for early trend reversals.
- Use glow bias zones on the main chart to get subconscious directional cues during fast scalping.
- Histogram helps you confirm when both RSI variants agree—useful during strong trending conditions.
🛠️ Tip for Traders
- This tool isn’t trying to “predict” price. It’s designed to visualize hidden market psychology—when buyers are showing up with consistent pressure, or when momentum has a disconnect between conviction and magnitude. Use this to filter entries, spot weak rallies, or sense when a trend is about to break down.
⚠️ WARNING
- Not for use with Heikin Ashi, Renko, etc.).
🧠 Summary
Candle Count RSI is not just another mashup—it's a precision-built, dual-perspective oscillator that captures directional conviction using real candle behavior. Whether you're scalping intraday or swing trading momentum, this script helps clarify trend integrity and exposes hidden weaknesses with elegance and clarity.
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🛠️ Built by: Sherlock_MacGyver
Feel free to share feedback or reach out if you'd like to collaborate on custom features.
Candle Breakout Oscillator [LuxAlgo]The Candle Breakout Oscillator tool allows traders to identify the strength and weakness of the three main market states: bullish, bearish, and choppy.
Know who controls the market at any given moment with an oscillator display with values ranging from 0 to 100 for the three main plots and upper and lower thresholds of 80 and 20 by default.
🔶 USAGE
The Candle Breakout Oscillator represents the three main market states, with values ranging from 0 to 100. By default, the upper and lower thresholds are set at 80 and 20, and when a value exceeds these thresholds, a colored area is displayed for the trader's convenience.
This tool is based on pure price action breakouts. In this context, we understand a breakout as a close above the last candle's high or low, which is representative of market strength. All other close positions in relation to the last candle's limits are considered weakness.
So, when the bullish plot (in green) is at the top of the oscillator (values above 80), it means that the bullish breakouts (close below the last candle low) are at their maximum value over the calculation window, indicating an uptrend. The same interpretation can be made for the bearish plot (in red), indicating a downtrend when high.
On the other hand, weakness is indicated when values are below the lower threshold (20), indicating that breakouts are at their minimum over the last 100 candles. Below are some examples of the possible main interpretations:
There are three main things to look for in this oscillator:
Value reaches extreme
Value leaves extreme
Bullish/Bearish crossovers
As we can see on the chart, before the first crossover happens the bears come out of strength (top) and the bulls come out of weakness (bottom), then after the crossover the bulls reach strength (top) and the bears weakness (bottom), this process is repeated in reverse for the second crossover.
The other main feature of the oscillator is its ability to identify periods of sideways trends when the sideways values have upper readings above 80, and trending behavior when the sideways values have lower readings below 20. As we just saw in the case of bullish vs. bearish, sideways values signal a change in behavior when reaching or leaving the extremes of the oscillator.
🔶 DETAILS
🔹 Data Smoothing
The tool offers up to 10 different smoothing methods. In the chart above, we can see the raw data (smoothing: None) and the RMA, TEMA, or Hull moving averages.
🔹 Data Weighting
Users can add different weighting methods to the data. As we can see in the image above, users can choose between None, Volume, or Price (as in Price Delta for each breakout).
🔶 SETTINGS
Window: Execution window, 100 candles by default
🔹 Data
Smoothing Method: Choose between none or ten moving averages
Smoothing Length: Length for the moving average
Weighting Method: Choose between None, Volume, or Price
🔹 Thresholds
Top: 80 by default
Bottom: 20 by default
Enhanced Stock Ticker with 50MA vs 200MADescription
The Enhanced Stock Ticker with 50MA vs 200MA is a versatile Pine Script indicator designed to visualize the relative position of a stock's price within its short-term and long-term price ranges, providing actionable bullish and bearish signals. By calculating normalized indices based on user-defined lookback periods (defaulting to 50 and 200 bars), this indicator helps traders identify potential reversals or trend continuations. It offers the flexibility to plot signals either on the main price chart or in a separate lower pane, leveraging Pine Script v6's force_overlay functionality for seamless integration. The indicator also includes a customizable ticker table, visual fills, and alert conditions for automated trading setups.
Key Features
Dual Lookback Indices: Computes short-term (default: 50 bars) and long-term (default: 200 bars) indices, normalizing the closing price relative to the high/low range over the specified periods.
Flexible Signal Plotting: Users can toggle between plotting crossover signals (triangles) on the main price chart (location.abovebar/belowbar) or in the lower pane (location.top/bottom) using the Plot Signals on Main Chart option.
Crossover Signals: Generates bullish (Golden Cross) and bearish (Death Cross) signals when the short or long index crosses above 5 or below 95, respectively.
Visual Enhancements:
Plots short-term (blue) and long-term (white) indices in a separate pane with customizable lookback periods.
Includes horizontal reference lines at 0, 20, 50, 80, and 100, with green and red fills to highlight overbought/oversold zones.
Dynamic fill between indices (green when short > long, red when long > short) for quick trend visualization.
Displays a ticker and legend table in the top-right corner, showing the symbol and lookback periods.
Alert Conditions: Supports alerts for bullish and bearish crossovers on both short and long indices, enabling integration with TradingView's alert system.
Technical Innovation: Utilizes Pine Script v6's force_overlay parameter to plot signals on the main chart from a non-overlay indicator, combining the benefits of a separate pane and chart-based signals in a single script.
Technical Details
Calculation Logic:
Uses confirmed bars (barstate.isconfirmed) to calculate indices, ensuring reliability by avoiding real-time bar fluctuations.
Short-term index: (close - lowest(low, lookback_short)) / (highest(high, lookback_short) - lowest(low, lookback_short)) * 100
Long-term index: (close - lowest(low, lookback_long)) / (highest(high, lookback_long) - lowest(low, lookback_long)) * 100
Signals are triggered using ta.crossover() and ta.crossunder() for indices crossing 5 (bullish) and 95 (bearish).
Signal Plotting:
Main chart signals use force_overlay=true with location.abovebar/belowbar for precise alignment with price bars.
Lower pane signals use location.top/bottom for visibility within the indicator pane.
Plotting is controlled by boolean conditions (e.g., bullishLong and plot_on_chart) to ensure compliance with Pine Script's global scope requirements.
Performance Considerations: Optimized for efficiency by calculating indices only on confirmed bars and using lightweight plotting functions.
How to Use
Add to Chart:
Copy the script into TradingView's Pine Editor and add it to your chart.
Configure Settings:
Short Lookback Period: Adjust the short-term lookback (default: 50 bars) to match your trading style (e.g., 20 for shorter-term analysis).
Long Lookback Period: Adjust the long-term lookback (default: 200 bars) for broader market context.
Plot Signals on Main Chart: Check this box to display signals on the price chart; uncheck to show signals in the lower pane.
Interpret Signals:
Golden Cross (Bullish): Green (long) or blue (short) triangles indicate the index crossing above 5, suggesting a potential buying opportunity.
Death Cross (Bearish): Red (long) or white (short) triangles indicate the index crossing below 95, signaling a potential selling opportunity.
Set Alerts:
Use TradingView's alert system to create notifications for the four alert conditions: Long Index Valley, Long Index Peak, Short Index Valley, and Short Index Peak.
Customize Visuals:
The ticker table displays the symbol and lookback periods in the top-right corner.
Adjust colors and styles via TradingView's settings if desired.
Example Use Cases
Swing Trading: Use the short-term index (e.g., 50 bars) to identify short-term reversals within a broader trend defined by the long-term index.
Trend Confirmation: Monitor the fill between indices to confirm whether the short-term trend aligns with the long-term trend.
Automated Trading: Leverage alert conditions to integrate with bots or manual trading strategies.
Notes
Testing: Always backtest the indicator on your chosen market and timeframe to validate its effectiveness.
Optional Histogram: The script includes a commented-out histogram for the index difference (index_short - index_long). Uncomment the plot(index_diff, ...) line to enable it.
Compatibility: Built for Pine Script v6 and tested on TradingView as of May 27, 2025.
Acknowledgments
This indicator was inspired by the need for a flexible tool that combines lower-pane analysis with main chart signals, made possible by Pine Script's force_overlay feature. Share your feedback or suggestions in the comments below, and happy trading!
MTF Candle Direction Forecast + Breakdown🧭 MTF Candle Direction Forecast + Breakdown 🔥📈🔼
This script is a multi-timeframe (MTF) price action dashboard that helps traders assess real-time directional bias across five customizable timeframes — with a focus on candle behavior, trend alignment, and confidence strength.
📌 What It Does
For each timeframe, this dashboard summarizes:
Current direction → Bullish, Bearish, or Neutral
Confidence score (0–100) → How strongly price is likely to continue in that direction
Candle strength → 🔥 icon appears if the current candle has a large body relative to its range
Trend alignment:
📈 = EMA9 is above EMA20
🔼 = Price is above VWAP
Color-coded background to visually reinforce directional state
Each row gives you a visual “at-a-glance” readout of what price is doing right now — not in the past.
💡 Why It’s Useful
✅ Direction forecasting based on price action
Instead of lagging indicators, this script prioritizes:
Candle body-to-range ratio (momentum)
Real-time VWAP/EMA structure
Immediate price positioning
✅ Confidence is quantified
The score (0–100) helps you judge how reliable each directional signal is:
90+ → Strong conviction
50–70 → Mixed but potentially valid
<40 → Weak move or early signal
✅ Timeframe confluence at a glance
See whether multiple timeframes are aligning directionally — helpful for scalping, day trading, or waiting for multi-timeframe breakout setups.
✅ Visual & intuitive
Icons, colors, and layout make it easy to scan your dashboard instead of deciphering charts or code.
🛠️ Adjustable Settings
Setting Description
Timeframe 1–5 Choose any timeframes to monitor (e.g., 5m, 15m, 1h, 4h)
Candle Display Mode Show trend color via emoji (🟢/🔴) or background shading
Strong Candle Threshold Adjust the body-to-range % needed to trigger 🔥 strength
Bullish/Bearish Background Customize label color coding
Neutral Background (opacity) Set transparency or styling for flat/consolidating zones
Table Location Place the dashboard anywhere on the chart
🎯 Use Cases
Scalpers: Confirm trend across 1m/5m/15m before entering
Day Traders: Use confidence score to avoid low-momentum setups
Swing Traders: Monitor higher timeframes for trend shifts while tracking intraday noise
VWAP/EMA traders: Quickly see when price is reclaiming or losing critical trend levels
🧠 What Makes It Unique?
Unlike generic trend meters or mashups of standard indicators, this script:
Uses live candle dynamics (not just closes or lagging values)
Computes directional bias and confidence together
Visualizes strength and structure in a compact, readable interface
Let’s you filter by price action, not just indicator alignment
💥 Why Traders Love Will Love It
✅ Instant clarity on which timeframes agree
✅ No more guessing candle strength or trend health
✅ Confidence score keeps you out of weak trades
✅ Works with any strategy — trend following, VWAP reclaim, EMA scalps, even breakouts
✅ Keeps your chart clean — all the context, none of the clutter
⚠️ Transparency🧬 Under the Hood
Powered by live candle body analysis, trend structure (EMA9 vs EMA20), and VWAP placement.
All scores are generated in real-time — No repainting or lookahead bias: all values are computed with lookahead=barmerge.lookahead_on
Confidence scores reflect the current candle only — they do not predict future moves but measure momentum and alignment in real-time
Labels update per bar and respond to subtle shifts in candle structure and trend indicators
✅ MTF Trend Snapshot (Live Output Example Shown in Chart Above)
This dashboard gives you a fast, visual summary of market trend and momentum across 5 timeframes. Here's what it's telling you right now:
🕔 5 Minute (5m)
📉 EMA Trend: Down
🔼 Price: Above VWAP
Direction: Bearish (42)
🟥 Weak bearish bias. Short-term pullback against a stronger trend. Use caution — lower confidence and mixed structure.
⏱️ 15 Minute (15m)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (73)
🟩 Clean bullish structure with growing momentum. Solid for intraday confirmation.
🕧 30 Minute (30m)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (77)
🟩 Stronger trend forming. Above VWAP and EMAs — building conviction.
🕐 1 Hour (1h)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (70)
🟩 Confident, clean trend. Good alignment across indicators. Ideal timeframe for swing entries.
🕓 4 Hour (4h)
🔥 Strong Candle
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (100)
🟩 Full trend alignment with max momentum. Strong body candle + structure — high confidence continuation.
🧠 Quick Takeaway
🔻 5m is pulling back short term
✅ 15m through 4h are fully aligned Bullish
🔥 4h has max confidence — big-picture trend is intact
📈 Ideal setup for momentum traders looking to ride trend with multi-timeframe confirmation
Try pinning this dashboard to your chart during live trading to read price like a story across timeframes, and filter out weak setups with low-confidence noise.
