知行趋势指标【B站 Z哥的黄白线指标】
黄白线指标是由 B站 UP 主 Z哥 总结并分享的一套趋势观察工具。指标以两条核心线——黄线（短周期趋势） 与 白线（长周期趋势） 构成，通过两者之间的相对位置、交叉关系及区域结构，帮助交易者更清晰地判断行情的强弱、趋势方向与潜在转折点。
黄线通常代表短期多空力量的波动，而白线反映更稳定的中期趋势。当黄线向上突破白线时，常视为短期强势启动的信号；反之，当黄线跌破白线时，则可能意味着短线转弱或趋势反转的风险。
该指标适合趋势跟随、顺大逆小的交易逻辑，也可作为交易系统中的辅助判断工具。
The Yellow-White Line Indicator is a trend-analysis tool created and shared by the Bilibili content creator Z-Ge. It is built around two primary lines: the Yellow Line (short-term trend) and the White Line (medium-term trend). By observing the interaction, crossover, and relative position between these two lines, traders can better identify market strength, trend direction, and potential reversal points.
The Yellow Line captures short-term momentum shifts, while the White Line reflects a more stable medium-term trend. When the Yellow Line crosses above the White Line, it often signals improving short-term strength; when it crosses below, it may indicate weakening momentum or a possible trend reversal.
This indicator works well with trend-following systems and can serve as a supplemental confirmation tool in broader trading strategies.
インジケーターとストラテジー
Simple Price ChannelSimple Price Channel
This indicator plots a basic volatility-based channel around a moving average.
Features:
Midline using Simple Moving Average (SMA)
Upper & lower bands using ATR or true range
Channel fill for easy trend visualisation
This script is designed for educational and analytical purposes only.
It does not provide signals, alerts, or financial advice.
Z-EMA Fusion BandsDesigned with crypto markets in mind, particularly Bitcoin , it builds on the concept that the 1-Week 50 EMA often serves as a long-term bull/bear market threshold — an area where institutional bias, momentum shifts, and cyclical rotations tend to occur.
🔹 Core Components & Synergies:
1. 1W 50 EMA (Higher Timeframe)
- This EMA is calculated on a weekly timeframe, regardless of your current chart.
- In crypto, price above the 1W 50 EMA typically aligns with long-term bull market phases, while extended periods below can signify bearish macro structure.
- The slope of the EMA is also analyzed to add directional confidence to trend strength.
2. ±1 Standard Deviation Bands
- Surrounding the 50 EMA, these bands visualize normal price dispersion relative to trend.
- When price consistently hugs or breaks outside these bands, it often reflects market expansion, volatility events, or mean-reversion opportunity.
3. Z-Score Gradient Fill
- The area between the bands is filled using a Z-score-based gradient, which dynamically adjusts color based on how far price is from the EMA (in terms of standard deviations).
- Color shifts from aqua (near EMA) to fuchsia (far from EMA) help you spot price compression, equilibrium, or overextension at a glance.
- The fill also uses transparency scaling, making it fade as price stretches further, emphasizing the core structure.
4. Directional EMA Coloring
- The EMA line itself is colored based on:
- The slope of the EMA (rising/falling)
- Whether the HTF candle is bullish or bearish
- This provides intuitive color-coded confirmation of momentum alignment or potential exhaustion.
5. Price/EMA Divergence Detection
- The script detects bullish and bearish divergence between price and the EMA (rather than using a traditional oscillator).
- Bullish Divergence: Price makes a lower low, EMA makes a higher low.
- Bearish Divergence: Price makes a higher high, EMA makes a lower high.
- These signals often mark transitional zones where momentum fades before a trend reversal or correction.
📊 Suggested Uses:
🔸 Swing and Position Trading:
- Use the 1W 50 EMA as a macro-trend anchor.
- Stay long-biased when price is above with positive slope, and short-biased when below.
- Consider entries near band edges for mean-reversion plays, especially if confluence forms with divergence signals.
🔸 Volatility-Based Filtering:
- Use the Z-score fill to identify volatility compression (near EMA) or expansion (edge of bands).
- Combine this with breakout strategies or dynamic position sizing.
🔸 Divergence Confirmation:
- Combine divergence markers with HTF EMA slope for high-probability setups.
- Bullish div + EMA flattening/rising can signal the start of accumulation after a macro dip.
🔸 Multi-Timeframe Analysis:
- Works well as a structural overlay on intraday charts (1H, 4H, 1D).
- Use this indicator to track long-term bias while executing lower timeframe trades.
⚠️ Disclaimer:
This indicator is designed for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset.
Always use proper risk management, and combine with your own analysis, tools, and strategy. Performance in past market conditions does not guarantee future results.
Vibha Jha TQQQ Clean Buy/SellVibha Jha TQQQ buy sell strategy its the best we use it to see when to enter and exit a trade especially TQQQ I want to publish it
Enhanced Ichimoku CloudDYNAMIC INDICATOR... im a beginer at this so i like to enhance my indicator by adding Visual Elements so that its easier to read for me... here is a visual representation of trend changes.
Super-AO with Risk Management Alerts Template - 11-29-25Super-AO with Risk Management: ALERTS & AUTOMATION Edition
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
This is the Indicator / Alerts companion to the Super-AO Strategy.
While the Strategy version is built for backtesting (verifying profitability and checking historical performance), this Indicator version is built for Live Execution.
We understand the frustration of finding a great strategy, only to realize you can't easily hook it up to your trading bot. This script solves that. It contains the exact same "Super-AO" logic and "Risk Management Engine" as the strategy version, but it is optimized to send signals to automation platforms like Signal Lynx, 3Commas, or any Webhook listener.
2. Quick Action Guide (TL;DR)
Purpose: Live Signal Generation & Automation.
Workflow:
Use the Strategy Version to find profitable settings.
Copy those settings into this Indicator Version.
Set a TradingView Alert using the "Any Alert() function call" condition.
Best Timeframe: 4 Hours (H4) and above.
Compatibility: Works with any webhook-based automation service.
3. Why Two Scripts?
Pine Script operates in two distinct modes:
Strategy Mode: Calculates equity, drawdowns, and simulates orders. Great for research, but sometimes complex to automate.
Indicator Mode: Plots visual data on the chart. This is the preferred method for setting up robust alerts because it is lighter weight and plots specific values that automation services can read easily.
The Golden Rule: Always backtest on the Strategy, but trade on the Indicator. This ensures that what you see in your history matches what you execute in real-time.
4. How to Automate This Script
This script uses a "Visual Spike" method to trigger alerts. Instead of drawing equity curves, it plots numerical values at the bottom of your chart when a trade event occurs.
The Signal Map:
Blue Spike (2 / -2): Entry Signal (Long / Short).
Yellow Spike (1 / -1): Risk Management Close (Stop Loss / Trend Reversal).
Green Spikes (1, 2, 3): Take Profit Levels 1, 2, and 3.
Setup Instructions:
Add this indicator to your chart.
Open your TradingView "Alerts" tab.
Create a new Alert.
Condition: Select SAO - RM Alerts Template.
Trigger: Select Any Alert() function call.
Message: Paste your JSON webhook message (provided by your bot service).
5. The Logic Under the Hood
Just like the Strategy version, this indicator utilizes:
SuperTrend + Awesome Oscillator: High-probability swing trading logic.
Non-Repainting Engine: Calculates signals based on confirmed candle closes to ensure the alert you get matches the chart reality.
Advanced Adaptive Trailing Stop (AATS): Internally calculates volatility to determine when to send a "Close" signal.
6. About Signal Lynx
Automation for the Night-Shift Nation 🌙
We are providing this code open source to help traders bridge the gap between manual backtesting and live automation. This code has been in action since 2022.
If you are looking to automate your strategies, please take a look at Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please release them back to the community!
@DARKPOOL Magnet - MEMEDescription:
The @DARKPOOL Magnet indicator identifies and displays significant price levels where institutional buying and selling activity has created persistent support and resistance zones. The indicator focuses on three primary types of institutional footprints:
Pin Zone Detection: Identifies price levels where multiple pin bars (high volume, narrow range candles) have clustered within a specified tolerance, indicating repeated institutional defense of those levels.
Whale Footprint Detection: Detects absorption events where significant volume occurs with minimal net price movement, suggesting large institutional orders being filled without allowing substantial directional movement.
Dark Pool Detection: Identifies potential dark pool prints characterized by unexplained price gaps that occur without visible tape activity, indicating off-exchange institutional transactions.
The indicator draws horizontal lines at these identified institutional price levels and highlights areas where multiple detection methods converge, creating confluence zones that represent higher probability support and resistance levels.
Confluence lines are displayed when multiple independently identified institutional levels occur within a user-specified proximity, providing visual emphasis on price levels with the strongest institutional interest.
Higher Timeframe MA High Low BandsHigher Timeframe Customer MA High Low Bands. There are 3 different Moving Average Parameters Available. Indicator will plot 3 lines of MA Length With Source of High, Close and Low. User can change relevant MA parameters / Show or Hide MA.
Happy Trading
My script//@version=6
indicator("ISIN demo")
// Define inputs for two symbols to compare.
string symbol1Input = input.symbol("NASDAQ:AAPL", "Symbol 1")
string symbol2Input = input.symbol("GETTEX:APC", "Symbol 2")
if barstate.islastconfirmedhistory
// Retrieve ISIN strings for `symbol1Input` and `symbol2Input`.
var string isin1 = request.security(symbol1Input, "", syminfo.isin)
var string isin2 = request.security(symbol2Input, "", syminfo.isin)
// Log the retrieved ISIN codes.
log.info("Symbol 1 ISIN: " + isin1)
log.info("Symbol 2 ISIN: " + isin2)
// Log an error message if one of the symbols does not have ISIN information.
if isin1 == "" or isin2 == ""
log.error("ISIN information is not available for both symbols.")
// If both symbols do have ISIN information, log a message to confirm whether both refer to the same security.
else if isin1 == isin2
log.info("Both symbols refer to the same security.")
else
log.info("The two symbols refer to different securities.")
HTF Bias & Session DashboardHTF Bias Dashboard is a lightweight tool that summarizes higher-timeframe direction and session context on any chart. It is designed for traders who want a quick directional overview directly on their chart.
Included components
• D1 and H4 Bias
Bias is calculated using a configurable EMA.
– If price is above the higher-timeframe EMA → bullish bias
– If price is below the higher-timeframe EMA → bearish bias
This provides a simple directional filter that helps avoid trades against the broader trend.
• Session Information
The dashboard detects the current UTC session and displays expected volatility conditions:
– Asia: low volatility / accumulation
– London: expansion
– New York: continuation or reversal conditions
This helps with timing decisions and understanding market behavior during different periods.
• Symbol and Info Row
Displays the active symbol along with a small info label for context.
How to use
This dashboard is intended for directional context only.
A common approach is:
– Trade in the direction of both D1 and H4 when they agree
– Be more cautious when the two biases diverge
– Consider session phase before making timing decisions
It works on any market and any timeframe.
Notes
• This tool does not include signals or alerts.
• It is meant for context only, not for generating entries or exits.
• This script is original, open-source, and provided for educational and research purposes.
Feedback and suggestions are welcome.
Bitcoin Power Law Deviation Z-ScoreIntroduction While standard price charts show Bitcoin's exponential growth, it can be difficult to gauge exactly how "overheated" or "cheap" the asset is relative to its historical trend.
This indicator strips away the price action to visualize pure Deviation. It compares the current price to the Bitcoin Power Law "Fair Value" model and plots the result as a normalized Z-Score. This creates a clean oscillator that makes it easy to identify historical cycle tops and bottoms without the noise of a log-scale chart.
How to Read This Indicator The oscillator centers around a zero-line, which represents the mathematical "Fair Value" of the network. 0.0 (Center Line): Price is exactly at the Power Law fair value. Positive Values (+1 to +5): Price is trading at a premium. Historically, values above 4.0 have coincided with cycle peaks (Red Zones). Negative Values (-1 to -3): Price is trading at a discount. Historically, values below -1.0 have been excellent accumulation zones (Green/Blue Zones).
The Math Behind the Model This script uses the same physics-based Power Law parameters as the popular overlay charts: Formula: Price = A * (days since genesis)^b Slope (b): 5.78 Amplitude (A): 1.45 x 10^-17 The "Z-Score" is calculated by taking the logarithmic difference between the actual price and the model price, divided by a standard scaling factor (0.18 log steps).
How to Use Cycle Analysis: Use this tool to spot macro-extremes. Unlike RSI or MACD which reset frequently, this oscillator provides a multi-year view of market sentiment. Confluence: This tool works best when paired with the main "Power Law Rainbow" chart overlay to confirm whether price is hitting major resistance or support bands.
Credits Based on the Power Law theory by Giovanni Santostasi and Corridor concepts by Harold Christopher Burger .
Disclaimer This tool is for educational purposes only. Past performance of a model is not indicative of future results. Not financial advice.
EMA/SMA 350 & 111 (Day Settings) by JayEMA/SMA 350 & 111 (Day Settings) by J
Übergeordneter Trendwechsel erkennen auf High Time Frames
dr ram's banknifty fad%banknifty fad% calculation as per dr ram sir. based on 4 quadrant analysis . one of the criteria is calculating future asset difference for predicting market direction and entry plan.
ICT Fair Value Gap (FVG) Detector │ Auto-Mitigated │ 2025Accurate ICT / Smart Money Concepts Fair Value Gap (FVG) detector
Features:
• Detects both Bullish (-FVG) and Bearish (+FVG) using strict 3-candle rule
• Boxes automatically extend right until price mitigates them
• Boxes auto-delete when price closes inside the gap (true mitigation)
• No repainting – 100% reliable
• Clean, lightweight, and works on all markets & timeframes
• Fully customizable colors and transparency
How to use:
– Bullish FVG (green) = potential support / buy zone in uptrend
– Bearish FVG (red) = potential resistance / sell zone in downtrend
Exactly matches The Inner Circle Trader (ICT) methodology used by thousands of SMC traders in 2024–2025.
Enjoy and trade safe!
IBIT premium（vs NAV）This Pine Script calculates and plots the real-time trading premium or discount of the IBIT ETF relative to its official Net Asset Value (NAV).
It shows whether IBIT is trading above NAV (premium) or below NAV (discount) in percentage terms.
This version is accurate because it uses TradingView’s built-in ETF NAV financial data, rather than estimating BTC per share.
⸻
Key Data Sources Used
• Market Price:
The script pulls the live IBIT market price from NASDAQ:IBIT.
• Official NAV:
It retrieves the daily Net Asset Value (NAV) using TradingView’s financial data function and expands it across all intraday timeframes so it can be compared with real-time prices.
• Platform used: TradingView
⸻
How the Premium Is Calculated
The script uses the standard ETF premium formula:
\text{Premium (\%)} = \frac{\text{Market Price} - \text{NAV}}{\text{NAV}} \times 100
• Positive value → IBIT is trading at a premium
• Negative value → IBIT is trading at a discount
• Zero → IBIT is trading exactly at NAV
⸻
What the Chart Displays
• A real-time premium (%) line in a separate indicator panel
• A 0% reference line showing fair value
• ±1% and ±2% guide lines for abnormal deviation detection
• A live value label on the latest bar showing the exact current premium
⸻
Why This Script Is Accurate
• Uses official ETF NAV, not a BTC-per-share estimate
• NAV updates once per day, exactly as reported by the issuer
• Works on all timeframes (1-minute to daily)
• Shows true market mispricing, not synthetic BTC tracking error
⸻
How Traders Typically Use It
• Detect temporary dislocations between IBIT price and NAV
• Monitor liquidity stress during high volatility
• Validate whether IBIT is trading efficiently versus BTC
• Support ETF–BTC–Futures arbitrage analysis
⸻
Important Limitation
• NAV is only updated once per trading day
• During fast BTC moves, the premium may widen temporarily and normalize later via authorized participant (AP) arbitrage
MSS + Multi FVG TrackerMSS + Multi FVG Tracker
Description
An advanced institutional trading tool that combines Market Structure Shift (MSS) detection with multi-level Fair Value Gap (FVG) tracking. This indicator identifies breakouts of previous swing highs/lows on higher timeframes, then systematically tracks and validates multiple FVGs within each trend direction, generating precise entry signals when price respects the gap structure.
How It Works
Higher Timeframe Trend Detection
The indicator analyzes a higher timeframe (default 15-minute) to determine the overall bias, displaying background colors that show bullish or bearish directionality. This ensures you only trade with institutional trend direction.
Market Structure Shift (MSS/BOS)
When price closes above a previous swing high (in uptrends) or below a previous swing low (in downtrends), a BOS (Break of Structure) is marked with a line and label. This signals that the institutional structure has shifted and a new trend impulse is beginning.
Multi-Level FVG Tracking
Once an MSS occurs:
The indicator begins scanning for Fair Value Gaps (gaps between candles where no trading occurred)
Bullish FVGs: Gaps above the closing price of a bearish candle (low > high )
Bearish FVGs: Gaps below the closing price of a bullish candle (high < low )
Multiple FVGs are tracked simultaneously (up to 5 configurable) across the same impulse
Intelligent FVG Validation
Each FVG is continuously monitored:
Invalidated: If price closes through the gap (below a bullish FVG or above a bearish FVG), it's automatically deleted
Touched: If price enters the gap zone, it's marked as "touched"
Signal Generated: When a touched FVG shows strong directional confirmation (bullish candle closing above the FVG top, or bearish candle closing below the FVG bottom), a LONG or SHORT signal is triggered
Key Features
HTF Trend Confirmation: Only trades aligned with higher timeframe bias (eliminates counter-trend noise)
Multi-FVG Architecture: Tracks up to 5 gaps per trend impulse simultaneously
Automatic Gap Invalidation: Removes FVGs that break below/above, keeping only valid levels
Smart Signal Generation: Entry signals require both FVG respect + directional confirmation
Color-Coded Structure: Bullish signals in green, bearish in red with instant visual clarity
Background Trend Visualization: Subtle background shading shows HTF bias at all times
Customizable Parameters: Adjust swing period, HTF timeframe, and max FVGs to track
Ideal For
ICT Smart Money traders using FVG + MSS methodologies
Institutional order flow analysts trading market structure
Multi-timeframe traders looking for confluence-based entries
Scalpers to swing traders on 5-minute to 1-hour charts
Anyone seeking high-probability setups with clear invalidation rules
Trading Applications
Scalp FVG reversals: Enter when price respects a touched FVG with confirmation
Trade impulses with structure: Follow MSS with FVG confluence for institutional-grade entries
Identify pullback opportunities: Track multiple FVGs during retracements for re-entry zones
Confirm breakout validity: Only take breaks when aligned with HTF trend + FVG structure
Avoid false breakouts: Invalidated FVGs signal that the move is losing structure
How to Use
Wait for the MSS: Background color shift + BOS line confirms market structure break
Monitor FVG Creation: Boxes appear as gaps form within the new impulse
Watch for Invalidation: Red boxes disappear if price breaks the gap—signal invalid
Wait for Touch + Confirmation: FVG must be touched AND show strong directional candle
Take the Signal: Triangle entry markers appear with audio/visual alerts
Clear Risk Management: Use the invalidated FVG level as your stop loss
Signal Strength Indicators
Strongest Setup: Multiple FVGs created + one respects while others invalidate (shows structure)
Medium Setup: Single FVG touched and confirmed
Weaker Setup: Quick touch with weak confirmation candle (wait for better structure)
Customization Options
HTF Timeframe: Change from 15-min to 5, 30, 60 min or higher for different trading styles
Swing Period: Adjust from 10 bars for faster detection to 20+ for structural shifts
Max FVGs: Track 1-5 simultaneous gaps (lower = cleaner, higher = more opportunities)
Colors: Customize bullish/bearish colors to match your chart theme
Default Settings Optimized For
NASDAQ futures and liquid forex pairs
5-minute to 1-hour timeframe trading
Smart Money / ICT methodology
High-probability impulse + gap trading
Pro Tips
The cleaner your chart (fewer invalidated FVGs), the stronger the structural move
Multiple valid FVGs in one impulse suggest institutional accumulation/distribution
HTF background color changes are early warnings of trend structure shift
Best setups occur when 2-3 FVGs exist and one shows clear confirmation
Mean Reversion — BB + Z-Score + RSI + EMA200 (TP at Opposite Z)This is a systematic mean-reversion framework for index futures and other liquid assets.
This strategy combines Bollinger Bands, Z-Score dislocation, RSI extremes, and a trend-filtering EMA200 to capture short-term mean-reversion inefficiencies in NQ1!. It is designed for high-volatility conditions and uses a precise exit model based on opposite-side Z-Score targets and dynamic mid-band failure detection.
🔍 Entry Logic (Mean Reversion) :
The strategy enters trades only when multiple confluence signals align:
Long Setup
Price at or below the lower Bollinger Band
Z-Score ≤ –Threshold (deep statistical deviation)
RSI ≤ oversold level
Price below the EMA-200 (countertrend mean-reversion only)
Cooldown must be completed
No open position
Short Setup
Price at or above the upper Bollinger Band
Z-Score ≥ Threshold
RSI ≥ overbought level
Price above the EMA-200
Cooldown complete
No open position
This multi-signal gate filters out weak reversions and focuses on mature dislocations.
🎯 Take-Profit Model: Opposite-Side Z-Score Target :
Once in a trade, take-profit is set by solving for the price where the Z-Score reaches the opposite side:
Long TP = Z = +Threshold
Short TP = Z = –Threshold
This creates a symmetric statistical exit based on reverting to equilibrium plus overshoot.
🛡️ Stop-Loss System (Volatility-Aware) :
Stop losses combine:
A fixed base stop (points)
A standard-deviation volatility component
This adapts the SL to regime changes and avoids being shaken out during rare volatility spikes.
⏳ Half-Life Exit :
If a trade has not reverted within a fixed number of bars, it automatically closes.
This prevents “mean-reversion traps” during trending periods.
📉 Advanced Mid-Band Exit Logic (BB Basis Failure) :
This is the unique feature of the system.
After entry:
Wait for price to cross the Bollinger Basis (middle band) in the direction of the mean.
Start a 5-bar delay timer.
After 5 bars, the strategy becomes “armed.”
Once armed:
If price fails back through the mean, exit immediately.
Intrabar exits trigger precisely (with tick-level precision if Bar Magnifier is enabled).
This protects profits and exits trades at the first sign of mean-failure.
⏱️ Cooldown System :
After each closed trade, a cooldown period prevents immediate re-entry.
This avoids clustering and improves statistical independence of trades.
🖥️ What This Strategy Is Best For :
High-volatility intraday NQ conditions
Statistical mean reversion with structured confluence
Traders who want clean, rule-based entries
Avoiding trend-day traps using EMA and half-life logic
📊 Included Visual Elements :
Bollinger Bands (Upper, Basis, Lower)
BUY/SELL markers at signal generation
Optional alerts for automated monitoring
🚀 Summary :
This is a precision mean-reversion system built around volatility bands, statistical dislocation, and price-behavior confirmation. By combining Z-Score, RSI, EMA200 filtering, and a sophisticated mid-band failure exit, this model captures high-probability reversions while avoiding the common pitfalls of naive band-touch systems.
MTF Trading Helper & Multi AlertsHi dear fellows, I´m using this indicator for my trading, so every then and when I will publish updates on this one.
This indicator should help to identify the right trading setup. I´m using it to trade index futures and stocks.
MTF Trading Helper & Multi Alerts
Overview
This indicator provides a clear visual representation of trend direction across three timeframes. It helps traders identify trend alignment, potential reversals, and optimal entry/exit points by analyzing the relationship between different smoothed timeframes.
You can set up multiple alerts (as one alert in Tradingview)
How It Works
The indicator displays three colored circles representing the smoothed candle direction on three different timeframes:
Bottom plot represents the overall trend direction, the plot in the middle shows intermediate momentum, and the one on top captures short-term price action.
When a color change occurs, the circle appears in a darker shade to highlight the transition.
🟢 Green = Bullish - 🔴 Red = Bearish
This change can also trigger multiple alerts.
Timeframe Settings - important
Choose between two trading setups, either for:
Intraday 1-minute candles or 1h for swing trading. Set up your chart accordingly to that timeframe.
Intraday | 1Min chart candles
Swing | 1 hour chart candles
Plots
TF3 represents the overall trend direction (bottom), TF2 shows intermediate momentum (middle), and TF1 captures short-term price action (top).
Interpretation & Strategy Alerts
1. Trend Bullish (TF3 turns Green)
The higher timeframe has shifted bullish - a potential new uptrend is forming.
Example: You're watching ES-mini on the Intraday setting. TF3 turns green after being red for several days. This signals the broader trend may be shifting bullish - consider looking for long opportunities.
2. Trend Bearish (TF3 turns Red)
The higher timeframe has shifted bearish - consider protecting profits or exiting long positions.
Example: You hold a long position in Es-mini. TF3 turns red, indicating the macro trend is weakening. This is your signal to take profits or tighten stop-losses.
3. Possible Accumulation (TF3 Red + TF2 turns Green)
While the overall trend is still bearish, the medium timeframe shows buying pressure. Smart money may be accumulating - watch closely for a potential trend reversal.
Example: Es-mini has been in a downtrend (TF3 red). Suddenly TF2 turns green while TF3 remains red. This could indicate institutional buying before a reversal. Don't buy yet, but add it to your watchlist and wait for confirmation.
4. Trend Continuation (TF3 Green + TF2 turns Green)
The medium timeframe realigns with the bullish macro trend - a potential buying opportunity as momentum returns to the uptrend.
Example: Es-mini is in an uptrend (TF3 green). After a pullback, TF2 was red but now turns green again. The pullback appears to be over - this is a trend continuation signal and a potential entry point.
5. Buy the Dip (TF3 + TF2 Green + TF1 turns Green)
All timeframes are now aligned bullish. The short-term pullback is complete and price is resuming the uptrend - optimal entry for short-term trades.
Example: Es-mini is trending up (TF3 + TF2 green). A small dip caused TF1 to turn red briefly. When TF1 turns green again, all three timeframes are aligned - this is your "Buy the Dip" signal with strong confirmation.
6. Sell the Dip (TF3 + TF2 Green + TF1 turns Red)
Short-term weakness within an uptrend. This can be used to take partial profits, wait for a better entry, or trail stops tighter.
Example: You're long on ES-mini with TF3 and TF2 green. TF1 turns red, indicating short-term selling pressure. Consider taking partial profits here and wait for TF1 to turn green again (Buy the Dip) to add back to your position.
How to Use
Choose your scenario: Select "Intraday" 1min-chart for day trading or "Swing" 1h-chart for swingtrading
Enable alerts: Turn on the strategy alerts you want to receive in the settings
Wait for signals: Let the indicator notify you when conditions align
Confirm with price action: Always use additional confirmation before entering trades
Best Practices
✅ Use TF3 as your trend filter - only take longs when TF3 turns green and hold them :)
✅ Use TF2 for timing - wait for TF2 to align with TF3 for swings.
✅ Use TF2 for early entries (accumulation phase) when TF3 is still red. Watch out!
✅ Use TF1 for entries when TF3 and TF2 are green. Only buy if TF1 is red. Keep it short and sweet.
✅ Combine with support/resistance levels for better entries
✅ Use proper risk management - no indicator is 100% accurate
Disclaimer
This indicator is for educational purposes only. Past performance does not guarantee future results. Always do your own research and use proper risk management. Never risk more than you can afford to lose.
ES-VIX Expected Move LTF LevelsES-VIX LTF Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Chandelier Exit + Pivots + MA + Swing High/LowIt combines four indicators.
For use in the Hero course.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
Multi-Timeframe RSI Table (Movable) by AKIt as a Multi Time Frame RSI (Movable) by AK
It has RSI value from 5 min to 1 month timeframe.
Green indicates RSI above 60 - Yellow indicates RSI Below 40
