Quad RSI MTFQuad RSI MTF
it's unique, visually rich, and highly useful for traders who want to understand momentum across different time horizons.
Quad RSI MTF is a custom indicator that plots the Relative Strength Index (RSI) from four different timeframes on one chart pane. It’s designed to help traders spot:
Multi-timeframe momentum alignment
Divergences between short-term and long-term RSI
Early warnings of trend reversals or exhaustion
Overbought/Oversold extremes across timeframes
Four RSI Inputs:
Fully customizable lengths and timeframes (e.g., 1H, Daily, Weekly, Monthly).
Uses request.security() to fetch RSI values from higher/lower timeframes.
Color-coded RSI plots:
Easy to visually differentiate between RSI 1–4.
Helps spot alignment or disagreement between timeframes.
Multi-Level Overbought/Oversold Bands:
Level 1: Traditional RSI zones (70/30)
Level 2: Extreme zones (98/2) to catch euphoria or panic
No repainting:
All values are based on historical RSI closes, ensuring reliability.
指標和策略
QQE MOD + QQE WEIGHTED OSCILLATORQQE MOD WITH QQE WEIGHTED OSCILLATOR added to the same subchart pane. fast line crossing slow line is a signal
FInal Signal
This indicator for 4H timeframe by default
RSI + Moving Average of RSI from the 1-hour chart
MACD from the 1-hour chart
21 EMA from the 4-hour chart
5 EMA from the Daily chart
This multi-timeframe fusion offers strength: confirming shorter-term momentum with higher-timeframe support.
✅ Buy Conditions:
RSI is above its moving average → signals bullish momentum
MACD line > MACD signal line → confirms trend shift
RSI has upward slope (compared to 2 candles ago)
❌ Sell Conditions:
RSI falls below its moving average
MACD turns bearish (signal line overtakes)
RSI slopes downward
Price trades below daily EMA → confirms weakening trend
🔊 Volume Spike Detection
I also added a volume condition that checks:
If current volume > 2x the moving average (length = 10)
QQE MODADDED QQE WEIGHTED OSCILLATOR to the QQE MOD indicator. The oscillator has a fast line that crosses the slow line. As the fast crosses above the slow line this is a long situation and vice versa for a short situation. The weighted Oscillator is a deriviation by LuxAlgo. The QQE mod portion was published by Colinmck
QQE + Signals RNEdited this to do away with larged signals of long and short to small triangles labeld only with QQE text
Lokie's RSI + VWAP + EMA Scalper [Fresh Edition]Lokie’s RSI + VWAP + EMA Scalper
Built for fast, smart scalping on 1–5 min charts. Combines RSI momentum, EMA crossovers, and VWAP zone bias to highlight clean buy/sell entries.
No FOMO signals. No fluff. Just tactical precision.
Perfect for momentum traders who want clarity, not clutter.
By DerekFWIN
Volume Dominance (Multi-Timeframe)Volume dominance is a mathematical concept i invented by separating up volume and down volume, and replacing the opposing elements in the averaging arrays with zeroes.
positive volume dominance is calculated by taking the average of volumes with a positive price direction over a period. for every volume with a negative price direction within the period, a zero is added to the averaging array.
Dpv = sum(upVol + (dnVol * 0)) / length
Dnv = sum(dnVol + (upVol * 0)) / length
Dpv = Dominance of Positive volume
Dnv = Dominance of Negative volume
upVol = total volume of upward filtered candles within length array
dnVol = total volume of downward filtered candles within length array
the user can see positive and negative volume dominance and read the label at the end of the plot to see the breadth of the dominance gap.
the user can select between using EMA and SMA to compute the dominance averages.
when the yellow center line moves with the change of a dominance line, it indicates strong directional force.
52-Week High and 30-Day Highshows 52-week high and 30-day high lines on the chart.
It is used to see the price up movement and confirms that the current price is lower than last 30 days price
Nasdaq Market Direction ProbabilitiesA table in the bottom-left corner showing bullish, bearish, and neutral probabilities for Nasdaq market direction, calculated from weighted indicators (moving averages, RSI, volume trend, futures change, and sentiment).
A label on the chart with a recommendation ("Long", "Short", or "Monitor") based on the highest probability.
A histogram of the bullish probability in a separate pane.
The probabilities update on each confirmed bar, using the chart’s timeframe (ideally 60 minutes).
NVDA Put Exit Alerts - Enhanced How to Use on These Timeframes:
5-15 min chart:
Look for price rejections or bounces at the weekly/monthly VWAP.
Confirm with CMF above 0.1 (buy pressure) or below -0.1 (sell pressure).
30-min to 1-hour chart:
Use to identify broader intraday swing moves that align with macro VWAP levels.
CMF will act as a filter for volume divergence setups.
Ticker Industry and Competitor LookupThe Ticker Industry and Competitor Lookup is a comprehensive indicator that provides instant access to industry classification data and competitive intelligence for any ticker symbol. Built using the advanced SIC_TICKER_DATA library, this tool delivers professional-grade sector analysis with enterprise-level performance. It's a simple yet great tool for competitor research, sector studies, portfolio diversification, and investment decision-making.
This indicator is a simple tool built on based on our SIC_TICKER_DATA library to demonstrate the use cases of the library. In this case, you enter a ticker and it displays the sector, SIC or Standard Industrial Classification which is a SEC identifier, and more importantly, the competitors that are listed to be in the exact same SIC by SEC.
There isn't much to say about the indicator itself but we strongly recommend checking out the SIC_TICKER_DATA library we just published to learn more about the types of indicators you can build using it.
SIC_TICKER_DATAThe SIC Ticker Data is an advanced and efficient library for ticker-to-industry classification and sector analysis. Built with enterprise-grade performance optimizations, this library provides instant access to SIC codes, industry classifications, and peer company data for comprehensive market analysis.
Perfect for: Sector rotation strategies, peer analysis, portfolio diversification, market screening, and financial research tools.
The simple idea behind this library is to pull any data related to SIC number of any US stock market ticker provided by SEC in order to see the industry and also see the exact competitors of the ticker.
The library stores 3 types of data: SIC number, Ticker, and Industry name. What makes it very useful is that you can pull any one of this data using the other. For example, if you would like to know which tickers are inside a certain SIC, or what's the SIC number of a specific ticker, or even which tickers are inside a certain industry, you can use this library to pull this data. The idea for data inside this library is to be accessible in any direction possible as long as they're related to each other.
We've also published a simple indicator that uses this library in order to demonstrate the inner workings of this library.
The library stores thousands of tickers and their relevant SIC code and industry for your use and is constantly updated with new data when available. This is a large library but it is optimized to run as fast as possible. The previous unpublished versions would take over 40 seconds to load any data but the final public version here loads the data in less than 5 seconds.
🔍 Primary Lookup Functions
createDataStore()
Initialize the library with all pre-loaded data.
store = data.createDataStore()
getSicByTicker(store, ticker)
Get SIC code for any ticker symbol.
sic = data.getSicByTicker(store, "AAPL") // Returns: "3571"
getIndustryByTicker(store, ticker)
Get industry classification for any ticker.
industry = data.getIndustryByTicker(store, "AAPL") // Returns: "Computer Hardware"
getTickersBySic(store, sic)
Get all companies in a specific SIC code.
software = data.getTickersBySic(store, "7372") // Returns: "MSFT,GOOGL,META,V,MA,CRM,ADBE,ORCL,NOW,INTU"
getTickersByIndustry(store, industry)
Get all companies in an industry.
retail = data.getTickersByIndustry(store, "Retail") // Returns: "AMZN,HD,WMT,TGT,COST,LOW"
📊 Array & Analysis Functions
getTickerArrayBySic(store, sic)
Get tickers as array for processing.
techArray = data.getTickerArrayBySic(store, "7372")
for i = 0 to array.size(techArray) - 1
ticker = array.get(techArray, i)
// Process each tech company
getTickerCountBySic(store, sic)
Count companies in a sector (ultra-fast).
pinescripttechCount = data.getTickerCountBySic(store, "7372") // Returns: 10
🎯 Utility Functions
tickerExists(store, ticker)
Check if ticker exists in database.
exists = data.tickerExists(store, "AAPL") // Returns: true
tickerInSic(store, ticker, sic)
Check if ticker belongs to specific sector.
isInTech = data.tickerInSic(store, "AAPL", "3571") // Returns: true
💡 Usage Examples
Example 1: Basic Ticker Lookup
// @version=6
import EdgeTerminal/SIC_TICKER_DATA/1 as data
indicator("Ticker Analysis", overlay=true)
store = data.createDataStore()
currentSic = data.getSicByTicker(store, syminfo.ticker)
currentIndustry = data.getIndustryByTicker(store, syminfo.ticker)
if barstate.islast and currentSic != "NOT_FOUND"
label.new(bar_index, high, syminfo.ticker + " SIC: " + currentSic + " Industry: " + currentIndustry)
Example 2: Sector Analysis
// @version=6
import EdgeTerminal/SIC_TICKER_DATA/1 as data
indicator("Sector Comparison", overlay=false)
store = data.createDataStore()
// Compare sector sizes
techCount = data.getTickerCountBySic(store, "7372") // Software
financeCount = data.getTickerCountBySic(store, "6199") // Finance
healthCount = data.getTickerCountBySic(store, "2834") // Pharmaceutical
plot(techCount, title="Tech Companies", color=color.blue)
plot(financeCount, title="Finance Companies", color=color.green)
plot(healthCount, title="Health Companies", color=color.red)
Example 3: Peer Analysis
// @version=6
import EdgeTerminal/SIC_TICKER_DATA/1 as data
indicator("Find Competitors", overlay=true)
store = data.createDataStore()
currentSic = data.getSicByTicker(store, syminfo.ticker)
if currentSic != "NOT_FOUND"
competitors = data.getTickersBySic(store, currentSic)
peerCount = data.getTickerCountBySic(store, currentSic)
if barstate.islast
label.new(bar_index, high, "Competitors (" + str.tostring(peerCount) + "): " + competitors)
Example 4: Portfolio Sector Allocation
// @version=6
import EdgeTerminal/SIC_TICKER_DATA/1 as data
indicator("Portfolio Analysis", overlay=false)
store = data.createDataStore()
// Analyze your portfolio's sector distribution
portfolioTickers = array.from("AAPL", "MSFT", "GOOGL", "JPM", "JNJ")
sectorCount = map.new()
for i = 0 to array.size(portfolioTickers) - 1
ticker = array.get(portfolioTickers, i)
industry = data.getIndustryByTicker(store, ticker)
if industry != "NOT_FOUND"
currentCount = map.get(sectorCount, industry)
newCount = na(currentCount) ? 1 : currentCount + 1
map.put(sectorCount, industry, newCount)
🔧 Advanced Feature
You can also bulk load data for large data sets like this:
// Pre-format your data as pipe-separated string
bulkData = "AAPL:3571:Computer Hardware|MSFT:7372:Software|GOOGL:7372:Software"
store = data.createDataStoreFromBulk(bulkData)
Fusion AI IndicatorWhat is Fusion AI?
The Fusion AI Indicator blends three proven momentum & mean-reversion signals into a single composite “AI Score.” By normalizing and weighting EMA crossovers, RSI bias and MACD momentum, you get one clean line that captures multi-factor strength shifts in real time—plus optional price-chart arrows and built-in alerts.
How it works
Fast vs. Slow EMA (40%)
(EMA(fastLen) – EMA(slowLen)) / EMA(slowLen)
RSI Deviation (30%)
(RSI(rsiLen) – 50) / 50
MACD Momentum (30%)
(MACD.line – MACD.signal) / ATR(14)
AI Score = 0.4 · EMA + 0.3 · RSI + 0.3 · MACD
Thresholds
Buy when AI Score crosses up through +Threshold
Sell when it crosses down through –Threshold
Default Inputs (tuned for balanced signals)
Fast MA Length: 12
Slow MA Length: 26
RSI Length: 14
MACD Fast EMA: 12
MACD Slow EMA: 26
MACD Signal Smoothing: 9
AI Score Threshold: 0.30
Show Overlay Arrows: ✔️
Alert on Bar Close Only: ✔️
(You can lower Threshold toward 0.10–0.20 for more frequent signals in quieter markets.)
Recommended Markets & Timeframes
Bitcoin (BTCUSD) on 1 H, 4 H – captures crypto swings
Ethereum (ETHUSD) on 1 H, 4 H
AAPL, TSLA, SPY on 15 M, 1 H – ideal for intraday setups
How to Add Alerts
Add the indicator to your chart.
Open Create Alert → Condition →
Fusion AI Indicator with Alerts & Overlay → choose Buy Signal or Sell Signal.
Set Frequency to Once per bar close (default) or Once per bar.
Paste a custom message or use the default:
“Fusion AI: Bullish crossover detected”
“Fusion AI: Bearish crossunder detected”
Usage Tips
Combine with volume filters or support/resistance zones for higher-probability entries.
Use the gradient-colored subchart to spot building momentum even before arrows appear.
Back-test threshold levels per asset: volatility will dictate whether 0.30 is too loose or too tight.
Enjoy smoother, multi-factor signals in one indicator—Fusion AI does the math so your eyes can focus on the trade.
Live 30-Point Horizontal Lines with Price LabelsLive 30-Point Horizontal Lines with Price Labels for upper and below current price
USDT + USDC DominanceUSDT and USDC Dominance: This refers to the combined market capitalization of Tether (USDT) and USD Coin (USDC) as a percentage of the total cryptocurrency market capitalization. It measures the proportion of the crypto market held by these stablecoins, which are pegged to the US dollar. High dominance indicates a "risk-off" sentiment, where investors hold stablecoins for safety during market uncertainty. A drop in dominance suggests capital is flowing into riskier assets like altcoins, often signaling a bullish market or the start of an "alt season."
Correlation Coefficient with MA & BB中文版介紹
相關係數、移動平均線與布林帶指標 (Correlation Coefficient with MA & BB)
這個 Pine Script 指標是一款強大的工具，旨在幫助交易者和投資者深入分析兩個市場標的之間的關係強度與方向，並結合移動平均線 (MA) 和布林帶 (BB) 來進一步洞察這種關係的趨勢和波動性。
無論您是想尋找配對交易機會、管理投資組合風險，還是僅僅想更好地理解市場動態，這個指標都能提供有價值的見解。
指標特色與功能：
動態相關係數計算：
您可以選擇任何您想比較的股票、商品或加密貨幣代號（例如，預設為 GOOG）。
指標會自動計算當前圖表（主數據源，預設為收盤價）與您指定標的之間的相關係數。
相關係數值介於 -1 (完美負相關) 至 1 (完美正相關) 之間，0 表示無線性關係。
視覺化呈現相關係數線，並標示 1、0、-1 參考水平線，同時填充完美相關區間，讓您一目了然。
特別之處：程式碼中包含了 ticker.modify，確保比較標的數據考慮了股息調整或延長交易時段，使相關性分析更加精準。
相關係數的移動平均線 (MA)：
為了平滑相關係數的短期波動，指標提供了多種移動平均線類型供您選擇，包括：SMA、EMA、WMA、SMMA。
您可以設定計算 MA 的週期長度（預設 20 週期）。
這條 MA 線有助於識別相關係數的長期趨勢，判斷兩者關係是趨於增強還是減弱。
相關係數的布林帶 (BB)：
將布林帶應用於相關係數，以衡量其波動性和相對高低水平。
中軌與您選擇的移動平均線保持一致。
上軌和下軌則根據相關係數的標準差和您設定的 Z 值（預設 2.0 倍標準差）動態調整。
布林帶可以幫助您識別相關係數何時處於極端水平，可能預示著未來會回歸均值。
如何運用這個指標？
配對交易策略：當兩個通常高度相關的資產，其相關係數短期內顯著偏離平均水平（例如，一個資產價格上漲而另一個原地踏步），您可能可以考慮利用此「失衡」進行配對交易。
投資組合多元化：了解不同資產之間的相關性，有助於構建更穩健的投資組合，避免過度集中於同向變動的資產，有效分散風險。
市場趨勢洞察：透過觀察相關係數的趨勢和波動，您可以更好地理解不同市場板塊或資產類別之間的聯動性，為您的宏觀經濟分析提供數據支持。
請注意，相關性不等於因果性。使用此指標時，請結合您的整體交易策略、宏觀經濟分析以及其他技術指標進行綜合判斷。
English Version Introduction
Correlation Coefficient with Moving Average & Bollinger Bands Indicator (Correlation Coefficient with MA & BB)
This Pine Script indicator is a powerful tool designed to help traders and investors deeply analyze the strength and direction of the relationship between two market instruments. It integrates Moving Averages (MA) and Bollinger Bands (BB) to further insight into the trend and volatility of this relationship.
Whether you're looking for pair trading opportunities, managing portfolio risk, or simply aiming to better understand market dynamics, this indicator can provide valuable insights.
Indicator Features & Functionality:
Dynamic Correlation Coefficient Calculation:
You can select any symbol you wish to compare (e.g., default is GOOG), be it stocks, commodities, or cryptocurrencies.
The indicator automatically calculates the correlation coefficient between the current chart (main data source, default is close price) and your specified symbol.
Correlation values range from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no linear relationship.
It visually plots the correlation line, marks 1, 0, -1 reference levels, and fills the perfect correlation zone for clear visualization.
Special Feature: The code includes ticker.modify, ensuring that the comparative symbol's data accounts for dividend adjustments or extended trading hours, leading to more precise correlation analysis.
Moving Average (MA) for Correlation:
To smooth out short-term fluctuations in the correlation coefficient, the indicator offers multiple MA types for you to choose from: SMA, EMA, WMA, SMMA.
You can set the length of the MA period (default 20 periods).
This MA line helps identify the long-term trend of the correlation coefficient, indicating whether the relationship between the two instruments is strengthening or weakening.
Bollinger Bands (BB) for Correlation:
Bollinger Bands are applied to the correlation coefficient itself to gauge its volatility and relative high/low levels.
The middle band aligns with your chosen Moving Average.
The upper and lower bands dynamically adjust based on the correlation coefficient's standard deviation and your set Z-score (default 2.0 standard deviations).
Bollinger Bands can help you identify when the correlation coefficient is at extreme levels, potentially signaling a future reversion to the mean.
How to Utilize This Indicator:
Pair Trading Strategies: When two typically highly correlated assets show a significant short-term deviation from their average correlation (e.g., one asset's price rises while the other stagnates), you might consider exploiting this "imbalance" for pair trading.
Portfolio Diversification: Understanding the correlation between different assets helps build a more robust investment portfolio, preventing over-concentration in co-moving assets and effectively diversifying risk.
Market Trend Insight: By observing the trend and volatility of the correlation coefficient, you can better understand the联动 (interconnectedness) between different market sectors or asset classes, providing data support for your macroeconomic analysis.
Please note that correlation does not imply causation. When using this indicator, combine it with your overall trading strategy, macroeconomic analysis, and other technical indicators for comprehensive decision-making.
Linear Regression Channel – shiftableThis is the built-in Linear Regression Channel with an extra parameter to shift it back N days.
ADR TableTrack volatility and session momentum in real-time with customizable precision.
Key Features:
Average Daily Range (ADR): Configurable length (default 5 days), based on previous daily high–low ranges.
Session Anchor Options: Choose anchor at 4 am NY, 6 pm NY, 9:30 am NY, 8:30 am NY, Previous Day Close, or Current Bar.
Session Range & %ADR: Displays the real-time range from the chosen anchor, plus what percentage of ADR has been covered.
High / Low Target Levels: Calculates ADR targets based on anchor: anchor ± ADR.
Optional Target Lines: Draw horizontal lines for high and low targets across the session; customize color and width.
Dynamic Table Display: User-selectable table size and text size (Tiny to Huge) for optimal readability.
Robust Anchor Logic: Uses the first bar at-or-after anchor time each NY day, ensuring stability even on irregular intraday timeframes.
How to Use
Choose your anchor in settings.
View ADR, session range (with %ADR), and target price levels in the top-right pane.Toggle High/Low lines to overlay targets on the chart.
Adjust table and text size to match your workspace.
Why It Matters
Quickly assess where price stands relative to typical volatility.
Easily identify intraday price exhaustion or breakout zones.
Anchor flexibility enables use for both futures and equities, aligning with your trading session.
Clean, professional display—no clutter, no guesswork.
Economy RadarEconomy Radar — Key US Macro Indicators Visualized
A handy tool for traders and investors to monitor major US economic data in one chart.
Includes:
Inflation: CPI, PCE, yearly %, expectations
Monetary policy: Fed funds rate, M2 money supply
Labor market: Unemployment, jobless claims, consumer sentiment
Economy & markets: GDP, 10Y yield, US Dollar Index (DXY)
Options:
Toggle indicators on/off
Customizable colors
Tooltips explain each metric (in Russian & English)
Perfect for spotting economic cycles and supporting trading decisions.
Add to your chart and get a clear macro picture instantly!
Kase Convergence Divergence [BackQuant]Kase Convergence Divergence
The Kase Convergence Divergence is a sophisticated oscillator designed to measure directional market strength through the lens of volatility-adjusted log return structures. Inspired by Cynthia Kase’s work on statistical momentum and price projection ranges, this unique indicator offers a hybrid framework that merges signal processing, multi-length sweep logic, and adaptive smoothing techniques.
Unlike traditional momentum oscillators like MACD or RSI, which rely on static moving average differences, KCD introduces a dual-process system combining:
Kase-style statistical range projection (via log returns and volatility),
A sweeping loop of lookback lengths for robustness,
First and second derivative modes to capture both velocity and acceleration of price movement.
Core Logic & Computation
The KCD calculation is centered on two volatility-normalized transforms:
KSDI Up: Measures how far the current high has moved relative to a past low, normalized by return volatility.
KSDI Down: Measures how far the current low has moved relative to a past high, also normalized.
For every length in a user-defined sweep range (e.g., 25–35), both KSDI_up and KSDI_dn are computed, and their maximum values across the loop are retained. The difference between these two max values produces the raw signal:
KPO (Kase Projection Oscillator): Measures directional skew.
KCD (Kase Convergence Divergence): Defined as KPO – MA(KPO) — similar in spirit to MACD but structurally different.
Users can choose to visualize either the first derivative (KPO) , or the second derivative (KCD) , depending on market conditions or strategy style.
Key Features
✅ Multi-Length Sweep Logic: Improves signal reliability by aggregating statistical range projections across a set of lookbacks.
✅ Advanced Smoothing Modes: Supports DEMA, HMA, TEMA, LINREG, WMA and more for dynamic adaptation.
✅ Dual Derivative Modes: Choose between speed (first derivative) or smoothness (second derivative) to fit your trading regime.
✅ Color-Encoded Signal Bands: Heatmap-style oscillator coloring enhances visual feedback on trend strength.
✅ Candlestick Painting: Optional bar coloring makes it easy to spot trend shifts on the main chart.
✅ Adaptive Fill Zones: Green and red fills between the oscillator and zero line help distinguish bullish and bearish regimes at a glance.
Practical Applications
📈 Trend Confirmation: Use KCD as a secondary confirmation layer after breakout or pullback entries.
📉 Momentum Shifts: Crossover and crossunder of the zero line highlight potential regime changes.
📊 Strategy Filters: Incorporate into algos to avoid trendless or mean-reverting environments.
🧪 Derivative Switching: Flip between KPO and KCD modes depending on whether you want to measure acceleration or deceleration of price flow.
Alerts & Signals
Two built-in alerts help you catch regime shifts in real time:
Long Signal: Triggered when the selected oscillator crosses above zero.
Short Signal: Triggered when it crosses below zero.
These events can be used to generate entries, exits, or trend validation cues in multi-layer systems.
Conclusion
The Kase Convergence Divergence goes beyond traditional oscillators by offering a volatility-normalized, derivative-aware signal engine with enhanced visual dynamics. Its sweeping architecture and dynamic fill logic make it especially powerful for identifying trending environments, filtering chop, and adding statistical rigor to your trading toolkit.
Whether you’re a discretionary trader seeking precision, or a quant looking to model more robust return structures, KCD offers a creative yet analytically grounded solution.
ZYTX RSI SuperTrendZYTX RSI SuperTrend
ZYTX RSI + SuperTrend Strategy
The definitive integration of RSI and SuperTrend trend-following indicators, delivering exemplary performance in automated trading bots.
