NQ 65 Point Futures Session 30 Second Opening RangeNQ 65 Point Futures Session Open Range Pro
Overview
This Pine Script indicator is designed specifically for NASDAQ-100 E-mini (NQ) futures traders who utilize opening range breakout strategies across multiple global trading sessions. The indicator provides comprehensive session-based opening range analysis with advanced 65-point interval projections.
Key Features
Multi-Session Opening Range Analysis
RTH (Regular Trading Hours): 8:30 AM CT - New York session opening range
Globex (Asian Session): 5:00 PM CT - Asian market session opening range
Europe Session: 2:00 AM CT - European market session opening range
Core Functionality
High/Low/Mid Lines: Displays opening range boundaries and midpoint for each session
Customizable Colors: Full color customization for each session's lines
Price Labels: Optional price display on all levels with session identification
Statistics Table: Real-time table showing high, low, and range width for active sessions
Advanced 65-Point Interval System
RTH-Specific Feature: Plots 5 levels above and below RTH opening range at 65-point intervals
Projection Levels: +65, +130, +195, +260, +325 above RTH high and corresponding negative levels below RTH low
Customizable Labels: Toggle price display and session names on interval lines
Color-Coded: Separate colors for upside and downside projections
Enhanced Trading Tools
Breakout Detection: Automatic identification of opening range breakouts with visual signals
Alert System: Built-in alerts for all session breakouts (up and down)
Range Boxes: Optional visual boxes highlighting opening ranges
Multiple Timeframe Support: Works across various chart timeframes
Display Options
Label Customization: Multiple size options (Tiny, Small, Normal, Large)
Session Toggle: Individual on/off controls for each session
Transparency Controls: Adjustable transparency for range boxes
Professional Styling: Clean, professional appearance suitable for live trading
Trading Applications
This indicator is particularly valuable for:
Gap Trading: Identifying key levels after overnight gaps
Breakout Trading: Clear visual confirmation of range breakouts
Support/Resistance: Using opening ranges as dynamic S/R levels
Session Transition: Understanding how price behaves across global sessions
Risk Management: Using 65-point intervals for position sizing and target setting
Technical Specifications
Version: Pine Script v5
Overlay: True (plots directly on price chart)
Max Lines: 500 (accommodates extensive level plotting)
Timezone: America/Chicago (Central Time)
Data Frequency: 30-second precision for opening range calculation
Usage Notes
Designed specifically for NQ futures but may work on other instruments
Best performance on intraday timeframes (1m, 5m, 15m, 30m)
Opening ranges calculated based on first 30 seconds of each session
All alerts are customizable through TradingView's alert system
Customization Options
The indicator offers extensive customization including:
Color schemes for each session
Label display preferences
Line transparency and thickness
Statistical table positioning
Alert message customization
Komut dosyalarını " TABLE " için ara
MasterEdge v4 — Trend & Momentum Presets with Filters & ATR RiskMasterEdge v4 — Trend & Momentum with Filters & ATR Risk
MasterEdge v4 is a multi‑timeframe trend and momentum indicator designed to help you stay on the right side of the market while controlling risk. It combines two classic signal engines—Donchian channel breakouts (à la Turtle Traders) and moving‑average crosses—with a suite of filters and risk tools to reduce false signals and keep you disciplined.
## Core features
- **Auto/manual presets:** Automatically adjusts look‑back lengths and thresholds based on the instrument (crypto, forex, indices, etc.) and chart timeframe, or lets you set them manually.
- **Higher‑timeframe bias:** Uses a non‑repainting higher‑timeframe EMA to determine whether the market is trending up or down and gates signals accordingly. You can choose the HTF yourself or let the auto‑engine pick one.
- **Dual signal modes:**
- *Donchian (Turtle)* mode enters on breakouts of an N‑bar channel and exits on a shorter channel.
- *MA Cross* mode buys when a fast EMA/SMA crosses above a slow EMA/SMA and sells on the opposite cross.
- **Advanced filters:** RSI momentum and ADX trend‑strength filters help avoid trades during choppy conditions. Optional volume and HTF‑slope filters require participation and higher‑timeframe momentum. A configurable **quality score** combines these filters so you only take higher‑probability setups.
- **ATR risk rails & position sizing:** Visual stop‑loss and target rails are calculated from ATR to adapt to volatility. An optional position‑size suggestion uses your account size and risk percentage to estimate how much to trade (for informational purposes only).
- **Session gating & status table:** Restrict signals to specific trading sessions. A live table shows your current settings, filter status, quality score and recommended position size, so you always know why a signal fired—or didn’t.
- **Alerts:** Separate long and short alerts with static JSON payloads let you hook the indicator into your notification or webhook workflow.
## How to use
1. **Select auto or manual:** Use the *Preset Mode* input. Auto mode adjusts lengths and thresholds to the ticker and timeframe; manual mode lets you set them explicitly.
2. **Choose a signal mode:** Pick between Donchian breakout or MA cross. Donchian is often better for lower‑timeframe breakouts; MA crosses smooth out noise on higher timeframes.
3. **Enable filters:** Turn on RSI, ADX, volume and/or slope filters and set your desired quality‑score threshold. Higher thresholds yield fewer, cleaner signals.
4. **Define risk:** If you want visual risk guides and position‑size suggestions, leave ATR rails on and input your account size, risk percentage and value per point.
5. **Timeframe pairing:** For intraday trading, try a 5 min chart with a 60 min bias; for swing trading, use a 1 h chart with a 4 h bias. The auto‑engine selects sensible higher‑timeframe defaults, but you can override them.
6. **Confirm signals:** The indicator plots green triangles below bars for long signals and red triangles above bars for short signals. The status table updates each bar with filter states and whether a signal is active.
**Disclaimer:** This script is for educational and analysis purposes only and is not financial advice. Always test on a demo account before trading live and tailor the settings to your strategy, risk tolerance and market behaviour.
Volatility Bands NGThe Volatility Bands indicator is a sophisticated trading system that combines adaptive filtering technology with volatility-based band mechanics to identify high-probability trading opportunities. At its core, this indicator employs an Adaptive Gaussian Filter that dynamically adjusts to market conditions, providing smoother and more responsive trend detection than traditional moving averages.
Credit at @BigBeluga for his work on gaussian bands.
Key Technologies & Features:
Adaptive Gaussian Filter: Uses a weighted Gaussian distribution that automatically adjusts its sigma parameter based on current market volatility, creating a self-optimizing smoothing mechanism
Integrated ATR Model: Combines traditional ATR with volume-adjusted and momentum-weighted true range calculations (90% ATR + 1% Volume-Adjusted TR + 9% Momentum-Weighted TR) for superior volatility measurement
Trend State Machine: Tracks trend direction, strength (0-100%), and duration using a sophisticated scoring algorithm that weighs momentum (40%), direction consistency (40%), and volatility normalization (20%)
Market Regime Detection: Automatically identifies whether the market is Trending, Choppy, or in Low Volatility mode
Squeeze Detection System: Identifies compression periods using Bollinger Bands vs Keltner Channels methodology, alerting to potential explosive moves
Multi-Factor Confirmation: Validates signals using volume spikes and Money Flow Index (MFI) to filter out false breakouts
Automatic Risk Management: Calculates real-time stop-loss and take-profit levels (2R and 3R) based on current volatility
Primary Trading Strategies:
1. Trend Following with Confirmations
Enter LONG when price crosses above the lower band (bullish trend line) with green arrows showing confirmations
Enter SHORT when price crosses below the upper band (bearish trend line) with red arrows showing confirmations
Look for "✓" symbol indicating both volume and momentum confirmation for highest probability trades
2. Squeeze Breakout Strategy
Monitor orange background highlighting (squeeze active)
Prepare for breakout when squeeze releases (orange diamond appears)
Combine with trend direction for directional bias
Best used in ranging markets transitioning to trending
3. Retest Entry Strategy (Enable "Show Retest Signals")
After initial trend signal, wait for price to pull back to the adaptive filter line
Enter on retest signals (secondary arrows) for better risk/reward
Particularly effective in strong trending markets
4. Market Regime Adaptation
Trending Regime: Use standard trend-following entries with wider stops
Chop Regime: Focus on squeeze plays and avoid trend signals
Low Vol Regime: Tighten stops and reduce position sizes
Risk Management Guidelines:
Use the automatically calculated Stop Loss levels displayed in the info table
Scale out at 2R and 3R take profit levels
Reduce position size when Trend Score < 50%
Increase position size on confirmed signals (✓) with Trend Score > 70%
Advanced Filtering:
Combine trend direction with Market Regime for optimal entries
Use MFI levels (default 40/60) to avoid overbought/oversold entries
Monitor "Duration" in the info table - fresh trends (< 10 bars) often have more momentum
⚡ TL;DR
BUY: Green arrow + price above blue line + trend score > 50%
SELL: Red arrow + price below blue line + trend score < 50%
Best Signals: Arrows with "✓" symbol (full confirmation)
Avoid: Signals during orange squeeze periods (wait for release)
Exit: Use table's stop-loss (red) and take-profit levels (green)
Optimal Settings (already defaulted):
Adaptive Period: ON
Adaptive Sigma: ON
Require Confirmation: ON
Show Squeeze: ON
The indicator does the heavy lifting - just follow the arrows with confirmations and respect the risk levels shown in the table. Works best on 15m+ timeframes for crypto and 1H+ for forex/stocks.
🎯 Pro Tip: The indicator shines in trending markets. When the info table shows "Trending" regime with 70%+ trend score, increase position confidence.
If you’ve found value in Oracle NG and would like to support further development, feel free to donate:
BTC: bc1q2n4up8wzgqdsw9j3dzcn5jaelddu52t7ahydy6
ETH: 0x9b72b42326836528cA608c90811487E5244D7744
AVAX C-Chain: 0x9b72b42326836528cA608c90811487E5244D7744
ATAI Volume Pressure Analyzer V 1.0 — Pure Up/DownATAI Volume Pressure Analyzer V 1.0 — Pure Up/Down
Overview
Volume is a foundational tool for understanding the supply–demand balance. Classic charts show only total volume and don’t tell us what portion came from buying (Up) versus selling (Down). The ATAI Volume Pressure Analyzer fills that gap. Built on Pine Script v6, it scans a lower timeframe to estimate Up/Down volume for each host‑timeframe candle, and presents “volume pressure” in a compact HUD table that’s comparable across symbols and timeframes.
1) Architecture & Global Settings
Global Period (P, bars)
A single global input P defines the computation window. All measures—host‑TF volume moving averages and the half‑window segment sums—use this length. Default: 55.
Timeframe Handling
The core of the indicator is estimating Up/Down volume using lower‑timeframe data. You can set a custom lower timeframe, or rely on auto‑selection:
◉ Second charts → 1S
◉ Intraday → 1 minute
◉ Daily → 5 minutes
◉ Otherwise → 60 minutes
Lower TFs give more precise estimates but shorter history; higher TFs approximate buy/sell splits but provide longer history. As a rule of thumb, scan thin symbols at 5–15m, and liquid symbols at 1m.
2) Up/Down Volume & Derived Series
The script uses TradingView’s library function tvta.requestUpAndDownVolume(lowerTf) to obtain three values:
◉ Up volume (buyers)
◉ Down volume (sellers)
◉ Delta (Up − Down)
From these we define:
◉ TF_buy = |Up volume|
◉ TF_sell = |Down volume|
◉ TF_tot = TF_buy + TF_sell
◉ TF_delta = TF_buy − TF_sell
A positive TF_delta indicates buyer dominance; a negative value indicates selling pressure. To smooth noise, simple moving averages of TF_buy and TF_sell are computed over P and used as baselines.
3) Key Performance Indicators (KPIs)
Half‑window segmentation
To track momentum shifts, the P‑bar window is split in half:
◉ C→B: the older half
◉ B→A: the newer half (toward the current bar)
For each half, the script sums buy, sell, and delta. Comparing the two halves reveals strengthening/weakening pressure. Example: if AtoB_delta < CtoB_delta, recent buying pressure has faded.
[ 4) HUD (Table) Display /i]
Colors & Appearance
Two main color inputs define the theme: a primary color and a negative color (used when Δ is negative). The panel background uses a translucent version of the primary color; borders use the solid primary color. Text defaults to the primary color and flips to the negative color when a block’s Δ is negative.
Layout
The HUD is a 4×5 table updated on the last bar of each candle:
◉ Row 1 (Meta): indicator name, P length, lower TF, host TF
◉ Row 2 (Host TF): current ↑Buy, ↓Sell, ΔDelta; plus Σ total and SMA(↑/↓)
◉ Row 3 (Segments): C→B and B→A blocks with ↑/↓/Δ
◉ Rows 4–5: reserved for advanced modules (Wings, α/β, OB/OS, Top
5) Advanced Modules
5.1 Wings
“Wings” visualize volume‑driven movement over C→B (left wing) and B→A (right wing) with top/bottom lines and a filled band. Slopes are ATR‑per‑bar normalized for cross‑symbol/TF comparability and converted to angles (degrees). Coloring mirrors HUD sign logic with a near‑zero threshold (default ~3°):
◉ Both lines rising → blue (bullish)
◉ Both falling → red (bearish)
◉ Mixed/near‑zero → gray
Left wing reflects the origin of the recent move; right wing reflects the current state.
5.2 α / β at Point B
We compute the oriented angle between the two wings at the midpoint B:
β is the bottom‑arc angle; α = 360° − β is the top‑arc angle.
◉ Large α (>180°) or small β (<180°) flags meaningful imbalance.
◉ Intuition: large α suggests potential selling pressure; small β implies fragile support. HUD cells highlight these conditions.
5.3 OB/OS Spike
OverBought/OverSold (OB/OS) labels appear when directional volume spikes align with a 7‑oscillator vote (RSI, Stoch, %R, CCI, MFI, DeMarker, StochRSI).
◉ OB label (red): unusually high sell volume + enough OB votes
◉ OS label (teal): unusually high buy volume + enough OS votes
Minimum votes and sync window are user‑configurable; dotted connectors can link labels to the candle wick.
5.4 Top3 Volume Peaks
Within the P window the script ranks the top three BUY peaks (B1–B3) and top three SELL peaks (S1–S3).
◉ B1 and S1 are drawn as horizontal resistance (at B1 High) and support (at S1 Low) zones with adjustable thickness (ticks/percent/ATR).
◉ The HUD dedicates six cells to show ↑/↓/Δ for each rank, and prints the exact High (B1) and Low (S1) inline in their cells.
6) Reading the HUD — A Quick Checklist
◉ Meta: Confirm P and both timeframes (host & lower).
◉ Host TF block: Compare current ↑/↓/Δ against their SMAs.
◉ Segments: Contrast C→B vs B→A deltas to gauge momentum change.
◉ Wings: Right‑wing color/angle = now; left wing = recent origin.
◉ α / β: Look for α > 180° or β < 180° as imbalance cues.
◉ OB/OS: Note labels, color (red/teal), and the vote count.
◉Top3: Keep B1 (resistance) and S1 (support) on your radar.
Use these together to sketch scenarios and invalidation levels; never rely on a single signal in isolation.
[ 7) Example Highlights (What the table conveys) /i]
◉ Row 1 shows the indicator name, the analysis length P (default 55), and both TFs used for computation and display.
◉ B1 / S1 blocks summarize each side’s peak within the window, with Δ indicating buyer/seller dominance at that peak and inline price (B1 High / S1 Low) for actionable levels.
◉ Angle cells for each wing report the top/bottom line angles vs. the horizontal, reflecting the directional posture.
◉ Ranks B2/B3 and S2/S3 extend context beyond the top peak on each side.
◉ α / β cells quantify the orientation gap at B; changes reflect shifting buyer/seller influence on trend strength.
Together these visuals often reveal whether the “wings” resemble a strong, upward‑tilted arm supported by buyer volume—but always corroborate with your broader toolkit
8) Practical Tips & Tuning
◉ Choose P by market structure. For daily charts, 34–89 bars often works well.
◉ Lower TF choice: Thin symbols → 5–15m; liquid symbols → 1m.
◉ Near‑zero angle: In noisy markets, consider 5–7° instead of 3°.
◉ OB/OS votes: Daily charts often work with 3–4 votes; lower TFs may prefer 4–5.
◉ Zone thickness: Tie B1/S1 zone thickness to ATR so it scales with volatility.
◉ Colors: Feel free to theme the primary/negative colors; keep Δ<0 mapped to the negative color for readability.
Combine with price action: Use this indicator alongside structure, trendlines, and other tools for stronger decisions.
Technical Notes
Pine Script v6.
◉ Up/Down split via TradingView/ta library call requestUpAndDownVolume(lowerTf).
◉ HUD‑first design; drawings for Wings/αβ/OBOS/Top3 align with the same sign/threshold logic used in the table.
Disclaimer: This indicator is provided solely for educational and analytical purposes. It does not constitute financial advice, nor is it a recommendation to buy or sell any security. Always conduct your own research and use multiple tools before making trading decisions.
ST-Stochastic DashboardST-Stochastic Dashboard: User Manual & Functionality
1. Introduction
The ST-Stochastic Dashboard is a comprehensive tool designed for traders who utilize the Stochastic Oscillator. It combines two key features into a single indicator:
A standard, fully customizable Stochastic Oscillator plotted directly on your chart.
A powerful Multi-Timeframe (MTF) Dashboard that shows the status of the Stochastic %K value across three different timeframes of your choice.
This allows you to analyze momentum on your current timeframe while simultaneously monitoring for confluence or divergence on higher or lower timeframes, all without leaving your chart.
Disclaimer: In accordance with TradingView's House Rules, this document describes the technical functionality of the indicator. It is not financial advice. The indicator provides data based on user-defined parameters; all trading decisions are the sole responsibility of the user. Past performance is not indicative of future results.
2. How It Works (Functionality)
The indicator is divided into two main components:
A. The Main Stochastic Indicator (Chart Pane)
This is the visual representation of the Stochastic Oscillator for the chart's current timeframe.
%K Line (Blue): This is the main line of the oscillator. It shows the current closing price in relation to the high-low range over a user-defined period. A high value means the price is closing near the top of its recent range; a low value means it's closing near the bottom.
%D Line (Black): This is the signal line, which is a moving average of the %K line. It is used to smooth out the %K line and generate trading signals.
Overbought Zone (Red Area): By default, this zone is above the 75 level. When the Stochastic lines are in this area, it indicates that the asset may be "overbought," meaning the price is trading near the peak of its recent price range.
Oversold Zone (Blue Area): By default, this zone is below the 25 level. When the Stochastic lines are in this area, it indicates that the asset may be "oversold," meaning the price is trading near the bottom of its recent price range.
Crossover Signals:
Buy Signal (Blue Up Triangle): A blue triangle appears below the candles when the %K line crosses above the Oversold line (e.g., from 24 to 26). This suggests a potential shift from bearish to bullish momentum.
Sell Signal (Red Down Triangle): A red triangle appears above the candles when the %K line crosses below the Overbought line (e.g., from 76 to 74). This suggests a potential shift from bullish to bearish momentum.
B. The Multi-Timeframe Dashboard (Table on Chart)
This is the informational table that appears on your chart. Its purpose is to give you a quick, at-a-glance summary of the Stochastic's condition on other timeframes.
Function: The script uses TradingView's request.security() function to pull the %K value from three other timeframes that you specify in the settings.
Efficiency: The table is designed to update only on the last (most recent) bar (barstate.islast) to ensure the script runs efficiently and does not slow down your chart.
Columns:
Timeframe: Displays the timeframe you have selected (e.g., '5', '15', '60').
Stoch %K: Shows the current numerical value of the %K line for that specific timeframe, rounded to two decimal places.
Status: Interprets the %K value and displays a clear status:
OVERBOUGHT (Red Background): The %K value is above the "Upper Line" setting.
OVERSOLD (Blue Background): The %K value is below the "Lower Line" setting.
NEUTRAL (Black/Dark Background): The %K value is between the Overbought and Oversold levels.
3. Settings / Parameters in Detail
You can access these settings by clicking the "Settings" (cogwheel) icon on the indicator name.
Stochastic Settings
This group controls the behavior and appearance of the main Stochastic indicator plotted in the pane.
Stochastic Period (length)
Description: This is the lookback period used to calculate the Stochastic Oscillator. It defines the number of past bars to consider for the high-low range.
Default: 9
%K Smoothing (smoothK)
Description: This is the moving average period used to smooth the raw Stochastic value, creating the %K line. A higher value results in a smoother, less sensitive line.
Default: 3
%D Smoothing (smoothD)
Description: This is the moving average period applied to the %K line to create the %D (signal) line. A higher value creates a smoother signal line that lags further behind the %K line.
Default: 6
Lower Line (Oversold) (ul)
Description: This sets the threshold for the oversold condition. When the %K line is below this value, the dashboard will show "OVERSOLD". It is also the level the %K line must cross above to trigger a Buy Signal triangle.
Default: 25
Upper Line (Overbought) (ll)
Description: This sets the threshold for the overbought condition. When the %K line is above this value, the dashboard will show "OVERBOUGHT". It is also the level the %K line must cross below to trigger a Sell Signal triangle.
Default: 75
Dashboard Settings
This group controls the data and appearance of the multi-timeframe table.
Timeframe 1 (tf1)
Description: The first timeframe to be displayed in the dashboard.
Default: 5 (5 minutes)
Timeframe 2 (tf2)
Description: The second timeframe to be displayed in the dashboard.
Default: 15 (15 minutes)
Timeframe 3 (tf3)
Description: The third timeframe to be displayed in the dashboard.
Default: 60 (1 hour)
Dashboard Position (table_pos)
Description: Allows you to select where the dashboard table will appear on your chart.
Options: top_right, top_left, bottom_right, bottom_left
Default: bottom_right
4. How to Use & Interpret
Configuration: Adjust the Stochastic Settings to match your trading strategy. The default values (9, 3, 6) are common, but feel free to experiment. Set the Dashboard Settings to the timeframes that are most relevant to your analysis (e.g., your entry timeframe, a medium-term timeframe, and a long-term trend timeframe).
Analysis with the Dashboard: The primary strength of this tool is confluence. Look for situations where multiple timeframes align. For example:
If the dashboard shows OVERSOLD on the 15-minute, 60-minute, and your current 5-minute chart, a subsequent Buy Signal on your 5-minute chart may carry more weight.
Conversely, if your 5-minute chart shows OVERSOLD but the 60-minute chart is strongly OVERBOUGHT, it could indicate that you are looking at a minor pullback in a larger downtrend.
Interpreting States:
Overbought is not an automatic "sell" signal. It simply means momentum has been strong to the upside, and the price is near its recent peak. It could signal a potential reversal, but the price can also remain overbought for extended periods in a strong uptrend.
Oversold is not an automatic "buy" signal. It means momentum has been strong to the downside. While it can signal a potential bounce, prices can remain oversold for a long time in a strong downtrend.
Use the signals and dashboard states as a source of information to complement your overall trading strategy, which should include other forms of analysis such as price action, support/resistance levels, or other indicators.
Guitar Hero [theUltimator5]The Guitar Hero indicator transforms traditional oscillator signals into a visually engaging, game-like display reminiscent of the popular Guitar Hero video game. Instead of standard line plots, this indicator presents oscillator values as colored segments or blocks, making it easier to quickly identify market conditions at a glance.
Choose from 8 different technical oscillators:
RSI (Relative Strength Index)
Stochastic %K
Stochastic %D
Williams %R
CCI (Commodity Channel Index)
MFI (Money Flow Index)
TSI (True Strength Index)
Ultimate Oscillator
Visual Display Modes
1) Boxes Mode : Creates distinct rectangular boxes for each bar, providing a clean, segmented appearance. (default)
This visual display is limited by the amount of box plots that TradingView allows on each indictor, so it will only plot a limited history. If you want to view a similar visual display that has minor breaks between boxes, then use the fill mode.
2) Fill Mode : Uses filled areas between plot boundaries.
Use this mode when you want to view the plots further back in history without the strict drawing limitations.
Five-Level Color-Coded System
The indicator normalizes all oscillator values to a 0-100 scale and categorizes them into five distinct levels:
Level 1 (Red): Very Oversold (0-19)
Level 2 (Orange): Oversold (20-29)
Level 3 (Yellow): Neutral (30-70)
Level 4 (Aqua): Overbought (71-80)
Level 5 (Lime): Very Overbought (81-100)
Customization Options
Signal Parameters
Signal Length: Primary period for oscillator calculation (default: 14)
Signal Length 2: Secondary period for Stochastic %D and TSI (default: 3)
Signal Length 3: Tertiary period for TSI calculation (default: 25)
Display Controls
Show Horizontal Reference Lines: Toggle grid lines for better level identification
Show Information Table: Display current signal type, value, and normalized value
Table Position: Choose from 9 different screen positions for the info table
Display Mode: Switch between Boxes and Fills visualization
Max Bars to Display: Control how many historical bars to show (50-450 range)
Normalization Process
The indicator automatically normalizes different oscillator ranges to a consistent 0-100 scale:
Williams %R: Converts from -100/0 range to 0-100
CCI: Maps typical -300/+300 range to 0-100
TSI: Transforms -100/+100 range to 0-100
Other oscillators: Already use 0-100 scale (RSI, Stochastic, MFI, Ultimate Oscillator)
This was designed as an educational tool
The gamified approach makes learning about oscillators more engaging for new traders.
Jitendra: MTF AIO Technical Indicators with Trend ▲▼Jitendra: MTF AIO Technical Indicators with Trend ▲▼
Why We Designed this Indicator
we build this indicator to Analysis Multi-timeframe Technical Data in dashboard to get Better and Quick Data in which Time Frame where it is in Momentum or in Swing,
By combining multiple technical indicators with trend direction arrows and displaying them in a customizable table.
It also optionally plots some indicators EMA, VWAP, Supertrend, Bollinger Bands on the chart.
Traders who want a compact technical summary across multiple timeframes without switching charts.
Quickly assess trend strength, momentum, divergence, volume pressure in one glance.
Combine with price action to make higher-confidence entries/exits.
How to Use This Indicator
In setting there are Two parts
First Part - for Plot Multi EMA, Bollinger Band, Supertrend 10,2 & 10, 3 factorial
Second Part- To get Data on Table for Quick Analysis
Chart Plots With Enable Disable Toggle in Setting
VWAP (optional)
4 EMAs (lengths configurable)
Bollinger Bands (optional)
Two separate Supertrend indicators with custom ATR period and multiplier
Indicators Data in Table
For each selected timeframe:
VWAP position (price above/below)
MACD value + trend arrow
MACD Histogram (optional)
RSI value + arrow (rising/falling)
ADX value + arrow (strength rising/falling)
+DI / -DI values + trend arrows
RSI Divergence detection (regular + hidden)
EMA levels (up/down relative to price)
EMA crossover (EMA1 vs EMA2 arrow)
Stochastic %K
Volume Matrix:
Raw volume
20 SMA volume
Volume % change from SMA
Multi-Timeframe Support
Current timeframe + up to 5 user-defined timeframes (e.g., 1H, 4H, Daily, Weekly, Monthly)
Customizable Toggles
Enable/disable any indicator
Choose which EMAs to show
Show/hide trend arrows
Choose which volume metrics to display
Choose table position (top_left, top_right, etc.)
Choose table text size
Trend Arrows & Colors
Green ▲ = bullish / rising trend
Red ▼ = bearish / falling trend
Gray – = neutral/no change
Background colors indicate overbought/oversold, trend strength, or volume surge.
Indicator Data Fetch PINE CODE Short Summary
request.security() → pulls data from the selected timeframe (tf).
Each indicator’s calculation can be wrapped inside request.security() so the values are computed on that timeframe.
//@version=5
// === 1. VWAP ===
vwap_htf = request.security(syminfo.tickerid, tf, ta.vwap)
// === 2. MACD ===
macd_src = request.security(syminfo.tickerid, tf, close)
macd_val = ta.ema(macd_src, 12) - ta.ema(macd_src, 26)
macd_sig = ta.ema(macd_val, 9)
macd_hist = macd_val - macd_sig
// === 3. RSI ===
rsi_htf = request.security(syminfo.tickerid, tf, ta.rsi(close, 14))
// === 4. ADX & DI ===
adx_htf = request.security(syminfo.tickerid, tf, ta.adx(14))
plusDI = request.security(syminfo.tickerid, tf, ta.plus_di(14))
minusDI = request.security(syminfo.tickerid, tf, ta.minus_di(14))
// === 5. Supertrend ===
= request.security(syminfo.tickerid, tf, ta.supertrend(3, 7))
// === 6. Bollinger Bands ===
basis = ta.sma(close, 20)
dev = ta.stdev(close, 20)
bb_up = request.security(syminfo.tickerid, tf, basis + dev * 2)
bb_low = request.security(syminfo.tickerid, tf, basis - dev * 2)
// === 7. Stochastic ===
k = ta.sma(ta.stoch(close, high, low, 14), 3)
d = ta.sma(k, 3)
stochK = request.security(syminfo.tickerid, tf, k)
stochD = request.security(syminfo.tickerid, tf, d)
// === 8. EMA ===
ema20 = request.security(syminfo.tickerid, tf, ta.ema(close, 20))
ema50 = request.security(syminfo.tickerid, tf, ta.ema(close, 50))
// === 9. Historical Volatility (HV) ===
logReturns = math.log(close / close )
hv = request.security(syminfo.tickerid, tf, ta.stdev(logReturns, 20) * math.sqrt(252))
plot(vwap_htf, "VWAP")
plot(macd_val, "MACD", color=color.blue)
plot(rsi_htf, "RSI", color=color.purple)
Strong Economic Event Indicator (mtbr)Description:
This indicator is designed for traders to visualize entry levels, targets (TP1, TP2, TP3), and stop loss around key economic events for the selected asset, defaulting to XAUUSD. It provides a clear reference for potential market movements based on the event's surprise and direction (Bullish, Bearish, or Neutral).
Key Features:
Customizable Event Selection:
Select from a list of major economic events including ISM Services PMI, CPI, Non-Farm Payrolls, Fed Rate Decision, and more.
Set the exact year, month, day, hour, and minute for the event so that lines and labels appear at the correct bar.
Surprise Calculation and Direction:
Automatically calculates the difference between Actual and Forecast.
Displays the market direction in the table as Bullish, Bearish, or Neutral.
Price Levels in Pips Relative to Entry:
Entry, three targets (TP1, TP2, TP3), and Stop Loss can be set in pips relative to the entry price.
Directional logic ensures that levels adjust automatically according to Bullish or Bearish surprise.
Each line and label is independent and updates only when its corresponding input changes.
Chart Visualization:
Colored lines and labels:
Entry → Blue
TPs → Green
Stop Loss → Red
Vertical event line → Orange (dashed), highlighting the event release moment.
Integrated Informative Table:
Displays:
Selected economic event
Entry price
TP1, TP2, TP3 levels
Market direction status
Color-coded: green for Bullish, red for Bearish, gray for Neutral.
How to use the script:
Add the indicator to the chart of your preferred asset (default is XAUUSD).
Select the economic event from the drop-down list.
Set the event date and time in the input panel.
Enter the Entry Price and pip values for TP1, TP2, TP3, and Stop Loss according to your strategy.
The indicator will automatically draw lines and labels on the chart and update the table with event details and market direction.
Whenever an input value changes, only the corresponding line and label will update, leaving other levels intact.
Important Notes:
This indicator is visual and educational only; it does not place trades automatically.
Make sure the event timezone is correct to match your local release time.
Use in combination with your own trading strategy and risk management.
TradingView Publication Compliance:
Full instructions for usage
Explanation of inputs and settings
Description of line and label behavior
Educational disclaimer (no automated trading)
VWAP Multi-TimeframeThis is a multi-timeframe VWAP indicator that provides volume weighted average price calculations for the following time periods:
15min
30min
1H
2H
4H
6H
8H
12H
1D
1W
1M
3M
6M
1Y
You can use the lower timeframes for short term trend control areas and use the longer timeframes for long term trend control areas. Trade in the direction of the trend and watch for price reactions that you can trade when price gets close to or touches any of these levels.
This indicator will provide a data plot value of 1 for bullish when price is above all VWAPs that are turned on, -1 for bearish when price is below all VWAPs that are turned on and 0 for neutral when price is not above or below all VWAPs. Use this 1, -1, 0 value as a filter on your signal generating indicators so that you can prevent signals from coming in unless they are in the same direction as the VWAP trend.
Features
Trend direction value of 1, -1 or 0 to send to external indicators so you can filter your signal generating indicators using the VWAP trend.
Trend table that shows you whether price is above or below all of the major VWAPs. This includes the daily, weekly, monthly and yearly VWAPs.
Trend coloring between each VWAP and the close price of each candle so you can easily identify the trend direction.
Customization
Set the source value to use for all of the VWAP calculations. The default is HLC3.
Turn on or off each VWAP.
Change the color of each VWAP line.
Change the thickness of each VWAP line.
Turn on or off labels for each VWAP or turn all labels on or off at once.
Change the offset length from the current bar to the label text.
Change the label text color.
Turn on or off trend coloring for each VWAP.
Change the color for up trends and down trends.
Turn on or off the trend direction display table.
Change the location of the trend direction display table.
Adjust the background and text colors on the trend direction display table.
How To Use The Trend Direction Filtering Feature
The indicator will provide a data plot value of 1 for bullish when price is above all of the VWAPs that are turned on, a value of -1 for bearish when price is below all of the VWAPS that are turned on and a value of 0 for neutral when price is above and below some of the VWAPs that are turned on.
The name of the value to use with your external indicators will show up as: VWAP Multi-Timeframe: Trend Direction To Send To External Indicators
Make sure to use that as your source on your external indicators to get the correct values.
This 1, -1 or 0 value can then be used by another external indicator to tell the indicator what is allowed to do. For instance if you have another indicator that provides buy and sell signals, you can use this trend direction value to prevent your other indicator from giving a sell signal when the VWAP trend is bullish or prevent your other indicator from giving a buy signal when the VWAP trend is bearish.
You will need to program your other indicators to use this trend filtering feature, but this indicator is already set up with this filtering code so you can use it with any other indicator that you choose to filter(if you know how to customize pine script).
Markets You Can Use This Indicator On
This indicator uses volume and price to calculate values, so it will work on any chart that provides volume and price data.
StratNinjaTableAuthor’s Instructions for StratNinjaTable
Purpose:
This indicator is designed to provide traders with a clear and dynamic table displaying The Strat candle patterns across multiple timeframes of your choice.
Usage:
Use the input panel to select which timeframes you want to monitor in the table.
Choose the table position on the chart (top left, center, right, or bottom).
The table will update each bar, showing the candle type, direction arrow, and remaining time until the candle closes for each selected timeframe.
Hover over or inspect the table to understand current market structure per timeframe using The Strat methodology.
Notes:
The Strat pattern is displayed as "1", "2U", "2D", or "3" based on the relationship of current and previous candle highs and lows.
The timer updates in real-time and adapts to daily, weekly, monthly, and extended timeframes.
This script requires Pine Script version 6. Please use it on supported platforms.
MFI or other indicators are not included in this base version but can be integrated separately if desired.
Credits:
Developed and inspired by shayy110 — thanks for your foundational work on The Strat in Pine Script.
Disclaimer:
This script is for educational and informational purposes only. Always verify signals and manage risk accordingly.
Time Window Optimizer [theUltimator5]The Time Window Optimizer is designed to identify the most profitable 30-minute trading windows during regular market hours (9:30 AM - 4:00 PM EST). This tool helps traders optimize their intraday strategies by automatically discovering time periods with the highest historical performance or allowing manual selection for custom analysis. It also allows you to select manual timeframes for custom time period analysis.
🏆 Automatic Window Discovery
The core feature of this indicator is its intelligent Auto-Find Best 30min Window system that analyzes all 13 possible 30-minute time slots during market hours.
How the Algorithm Works:
Concurrent Analysis: The indicator simultaneously tracks performance across all 13 time windows (9:30-10:00, 10:00-10:30, 10:30-11:00... through 15:30-16:00)
Daily Performance Tracking: For each window, it captures the percentage change from window open to window close on every trading day
Cumulative Compounding: Daily returns are compounded over time to show the true long-term performance of each window, starting from a normalized value of 1.0
Dynamic Optimization: The system continuously identifies the window with the highest cumulative return and highlights it as the optimal choice
Statistical Validation: Performance is validated through multiple metrics including average daily returns, win rates, and total sample size
Visual Representation:
Best Window Line: The top-performing window is displayed as a thick colored line for easy identification
All 13 Lines (optional): Users can view performance lines for all time windows simultaneously to compare relative performance
Smart Coloring: Lines are color-coded (green for gains, red for losses) with the best performer highlighted in a user-selected color
📊 Comprehensive Performance Analysis
The indicator provides detailed statistics in an information table:
Average Daily Return: Mean percentage change per trading session
Cumulative Return: Total compounded performance over the analysis period
Win Rate: Percentage of profitable days (colored green if ≥50%, red if <50%)
Buy & Hold Comparison: Shows outperformance vs. simple buy-and-hold strategy
Sample Size: Number of trading days analyzed for statistical significance
🛠️ User Settings
imgur.com
Auto-Optimization Controls:
Auto-Find Best Window: Toggle to enable/disable automatic optimization
Show All 13 Lines: Display all time window performance lines simultaneously
Best Window Line Color: Customize the color of the top-performing window
Manual Mode:
imgur.com
Custom Time Window: Set any desired time range using session format (HHMM-HHMM)
Crypto Support: Built-in timezone offset adjustment for cryptocurrency markets
Chart Type Options: Switch between candlestick and line chart visualization
Visual Customization:
imgur.com
Background Highlighting: Optional background color during active time windows
Candle Coloring: Custom colors for bullish/bearish candles within the time window
Table Positioning: Flexible placement of the statistics table anywhere on the chart
🔧 Technical Features
Market Compatibility:
Stock Markets: Optimized for traditional market hours (9:30 AM - 4:00 PM EST)
Cryptocurrency: Includes timezone offset adjustment for 24/7 crypto markets
Exchange Detection: Automatically detects crypto exchanges and applies appropriate settings
Performance Optimization:
Efficient Calculation: Uses separate arrays for each time block to minimize computational overhead
Real-time Updates: Dynamically updates the best-performing window as new data becomes available
Memory Management: Optimized data structures to handle large datasets efficiently
💡 Use Cases
Strategy Development: Identify the most profitable trading hours for your specific instruments
Risk Management: Focus trading activity during historically successful time periods
Performance Comparison: Evaluate whether time-specific strategies outperform buy-and-hold
Market Analysis: Understand intraday patterns and market behavior across different time windows
📈 Key Benefits
Data-Driven Decisions: Base trading schedules on historical performance data
Automated Analysis: No manual calculation required - the algorithm does the work
Flexible Implementation: Works in both automated discovery and manual selection modes
Comprehensive Metrics: Multiple performance indicators for thorough analysis
Visual Clarity: Clear, color-coded visualization makes interpretation intuitive
This indicator transforms complex intraday analysis into actionable insights, helping traders optimize their time allocation and improve overall trading performance through systematic, data-driven approach to market timing.
TOTAL3ES/ETH Mean ReversionTOTAL3ES/ETH Mean Reversion Indicator
Overview
The TOTAL3ES/ETH Mean Reversion indicator is a specialized tool designed exclusively for analyzing the ratio between TOTAL3 excluding stablecoins (TOTAL3ES) and Ethereum's market capitalization. This ratio provides crucial insights into the relative performance and valuation cycles between altcoins and ETH, making it an essential tool for cryptocurrency portfolio allocation and market timing decisions.
What This Indicator Measures
This indicator tracks the market cap ratio of all altcoins (excluding ETH and stablecoins) to Ethereum's market cap. When the ratio is:
Above 1.0 (Parity): Altcoins have a larger combined market cap than ETH
Below 1.0 (Parity): ETH's market cap exceeds the combined altcoin market cap
Key Features
Historical Context
Historical Range: 0.64 (July 2017 low) to 3.49 (all-time high)
Midpoint: 2.065 - the mathematical center of the historical range
Parity Line: 1.0 - the psychological level where altcoins = ETH market cap
Mean Reversion Zones
The indicator identifies extreme valuation zones based on historical data:
Upper Extreme Zone (~2.92 at 80% threshold): Suggests altcoins may be overvalued relative to ETH
Lower Extreme Zone (~1.21 at 80% threshold): Suggests altcoins may be undervalued relative to ETH
Visual Elements
Color-coded zones: Red shading for bearish reversion areas, green for bullish reversion areas
Multiple reference lines: Parity, midpoint, and historical extremes
Information table: Real-time metrics including current ratio, range position, and reversion pressure
Customizable display: Toggle zones, lines, and adjust transparency
How to Use This Indicator
Market Cycle Analysis
Extreme High Zone (Red): When ratio enters this zone, consider potential ETH outperformance
Extreme Low Zone (Green): When ratio enters this zone, consider potential altcoin season
Parity Crossovers: Monitor when ratio crosses above/below 1.0 for sentiment shifts
Portfolio Allocation Signals
High Ratio Values: May indicate overextended altcoin valuations relative to ETH
Low Ratio Values: May suggest undervalued altcoins relative to ETH
Midpoint Reversions: Historical tendency to revert toward the 2.065 midpoint
Alert Conditions
The indicator includes built-in alerts for:
Entering extreme high/low zones
Parity crossovers (above/below 1.0)
Mean reversion signals
Input Parameters
Display Settings
Show Reversion Zones: Toggle colored extreme zones on/off
Show Midpoint: Display the historical midpoint line
Show Parity Line: Show the 1.0 parity reference line
Zone Transparency: Adjust shaded area opacity (70-95%)
Calculation Settings
Reversion Strength Period: Moving average period for reversion calculations (10-50)
Extreme Threshold: Percentage of historical range defining extreme zones (0.5-1.0)
Information Table Metrics
The bottom-right table displays:
Current Ratio: Live TOTAL3ES/ETH value
Range Position: Current position within historical range (%)
From Parity: Distance from 1.0 parity level (%)
Reversion Pressure: Intensity of mean reversion forces (%)
Zone: Current market zone classification
Historical Range: Reference boundaries (0.64 - 3.49)
Midpoint: Historical center value
Important Notes
Chart Compatibility
Exclusively designed for CRYPTOCAP:TOTAL3ES/CRYPTOCAP:ETH
Built-in validation ensures proper chart usage
Will display error message if applied to incorrect charts
Trading Considerations
This is an analytical tool, not trading advice
Mean reversion is a tendency, not a guarantee
Consider multiple timeframes and confirmations
Factor in overall market conditions and trends
Risk Disclaimer
Past performance does not guarantee future results. Cryptocurrency markets are highly volatile and unpredictable. Always conduct your own research and consider your risk tolerance before making investment decisions.
Ideal Use Cases
Portfolio rebalancing between ETH and altcoins
Market cycle timing for position adjustments
Sentiment analysis of crypto market phases
Long-term allocation strategies based on historical patterns
Risk management through extreme zone identification
This indicator serves as a quantitative framework for understanding the cyclical relationship between Ethereum and the broader altcoin market, helping traders and investors make more informed allocation decisions based on historical valuation patterns.ons
- Factor in overall market conditions and trends
### Risk Disclaimer
Past performance does not guarantee future results. Cryptocurrency markets are highly volatile and unpredictable. Always conduct your own research and consider your risk tolerance before making investment decisions.
ACR(Average Candle Range) With TargetsWhat is ACR?
The Average Candle Range (ACR) is a custom volatility metric that calculates the mean distance between the high and low of a set number of past candles. ACR focuses only on the actual candle range (high - low) of specific past candles on a chosen timeframe.
This script calculates and visualizes the Average Candle Range (ACR) over a user-defined number of candles on a custom timeframe. It displays a table of recent range values, plots dynamic bullish and bearish target levels, and marks the start of each new candle with a vertical line. All calculations update in real time as price action develops. This script was inspired by the “ICT ADR Levels - Judas x Daily Range Meter°” by toodegrees.
Key Features
Custom Timeframe Selection: Choose any timeframe (e.g., 1D, 4H, 15m) for analysis.
User-Defined Lookback: Calculate the average range across 1 to 10 previous candles.
Dynamic Targets:
Bullish Target: Current candle low + ACR.
Bearish Target: Current candle high – ACR.
Live Updates: Targets adjust intrabar as highs or lows change during the current candle.
Candle Start Markers: Vertical lines denote the open of each new candle on the selected timeframe.
Floating Range Table:
Displays the current ACR value.
Lists individual ranges for the previous five candles.
Extend Target Lines: Choose to extend bullish and bearish target levels fully across the screen.
Global Visibility Controls: Toggle on/off all visual elements (targets, vertical lines, and table) for a cleaner view.
How It Works
At each new candle on the user-selected timeframe, the script:
Draws a vertical line at the candle’s open.
Recalculates the ACR based on the inputted previous number of candles.
Plots target levels using the current candle's developing high and low values.
Limitation
Once the price has already moved a full ACR in the opposite direction from your intended trade, the associated target loses its practical value. For example, if you intended to trade long but the bearish ACR target is hit first, the bullish target is no longer a reliable reference for that session.
Use Case
This tool is designed for traders who:
Want to visualize the average movement range of candles over time.
Use higher or lower timeframe candles as structural anchors.
Require real-time range-based price levels for intraday or swing decision-making.
This script does not generate entry or exit signals. Instead, it supports range awareness and target projection based on historical candle behavior.
Key Difference from Similar Tools
While this script was inspired by “ICT ADR Levels - Judas x Daily Range Meter°” by toodegrees, it introduces a major enhancement: the ability to customize the timeframe used for calculating the range. Most ADR or candle-range tools are locked to a single timeframe (e.g., daily), but this version gives traders full control over the analysis window. This makes it adaptable to a wide range of strategies, including intraday and swing trading, across any market or asset.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Volume Based Analysis V 1.00
Volume Based Analysis V1.00 – Multi-Scenario Buyer/Seller Power & Volume Pressure Indicator
Description:
1. Overview
The Volume Based Analysis V1.00 indicator is a comprehensive tool for analyzing market dynamics using Buyer Power, Seller Power, and Volume Pressure scenarios. It detects 12 configurable scenarios combining volume-based calculations with price action to highlight potential bullish or bearish conditions.
When used in conjunction with other technical tools such as Ichimoku, Bollinger Bands, and trendline analysis, traders can gain a deeper and more reliable understanding of the market context surrounding each signal.
2. Key Features
12 Configurable Scenarios covering Buyer/Seller Power convergence, divergence, and dominance
Advanced Volume Pressure Analysis detecting when both buy/sell volumes exceed averages
Global Lookback System ensuring consistency across all calculations
Dominance Peak Module for identifying strongest buyer/seller dominance at structural pivots
Real-time Signal Statistics Table showing bullish/bearish counts and volume metrics
Fully customizable inputs (SMA lengths, multipliers, timeframes)
Visual chart markers (S01 to S12) for clear on-chart identification
3. Usage Guide
Enable/Disable Scenarios: Choose which signals to display based on your trading strategy
Fine-tune Parameters: Adjust SMA lengths, multipliers, and lookback periods to fit your market and timeframe
Timeframe Control: Use custom lower timeframes for refined up/down volume calculations
Combine with Other Indicators:
Ichimoku: Confirm volume-based bullish signals with cloud breakouts or trend confirmation
Bollinger Bands: Validate divergence/convergence signals with overbought/oversold zones
Trendlines: Spot high-probability signals at breakout or retest points
Signal Tables & Peaks: Read buy/sell volume dominance at a glance, and activate the Dominance Peak Module to highlight key turning points.
4. Example Scenarios & Suggested Images
Image #1 – S01 Bullish Convergence Above Zero
S01 activated, Buyer Power > 0, both buyer power slope & price slope positive, above-average buy volume. Show S01 ↑ marker below bar.
Image #2 – Combined with Ichimoku
Display a bullish scenario where price breaks above Ichimoku cloud while S01 or S09 bullish signal is active. Highlight both the volume-based marker and Ichimoku cloud breakout.
Image #3 – Combined with Bollinger Bands & Trendlines
Show a bearish S10 signal at the upper Bollinger Band near a descending trendline resistance. Highlight the confluence of the volume pressure signal with the band touch and trendline rejection.
Image #4 – Dominance Peak Module
Pivot low with green ▲ Bull Peak and pivot high with red ▼ Bear Peak, showing strong dominance counts.
Image #5 – Statistics Table in Action
Bottom-left table showing buy/sell volume, averages, and bullish/bearish counts during an active market phase.
5. Feedback & Collaboration
Your feedback and suggestions are welcome — they help improve and refine this system. If you discover interesting use cases or have ideas for new features, please share them in the script’s comments section on TradingView.
6. Disclaimer
This script is for educational purposes only. It is not financial advice. Past performance does not guarantee future results. Always do your own analysis before making trading decisions.
Tip: Use this tool alongside trend confirmation indicators for the most robust signal interpretation.
Historical Data: 1H Edge NQ [Herman]Historical Data: 1H Edge NQ
This Pine Script indicator is designed to provide traders with visual tools and historical statistical insights for analyzing hourly price behavior on the Nasdaq-100 futures (NQ) contract.
It focuses on key concepts such as Opening Ranges (OR) and Trading Windows (TW), drawing from established trading principles like session-based ranges and return probabilities.
This unique indicator stands out by incorporating pre-computed statistics derived from over 4 years of 1-minute timeframe data, offering detailed hourly probabilistic insights in an editable sticky note format—making it a distinctive tool for in-depth analysis.
The goal is to help users visualize potential price dynamics and assess historical tendencies, enabling more informed decision-making based on past data patterns.
All calculations are based on historical price action, and the indicator does not make predictions or generate trading signals—it simply displays pre-computed statistics and visual aids for educational and analytical purposes.
Key Features and Visual ElementsVertical Lines for Time Sessions:
Orange Line - Opening Range Midline (50%)
Horizontal Dotted Lines - Opening Range High and Low
Solid Red Line - Midnight Open
Dashed Vertical lines - Opening range and trading window start/close times
Blue Dashed Line - Trading Window Candle Open
The indicator marks the start of the user-selected Opening Range (OR) and Trading Window (TW) with customizable vertical lines.
These represent the time periods where the OR is formed (e.g., 02:00-03:00 NY time) and where trading activity is observed (e.g., 03:00-04:00 NY time).
Users can adjust these sessions via inputs for flexibility across different hours.
-Horizontal Lines for Price Levels:Opening Range High and Low:
-Solid or dashed lines (customizable) show the high and low of the selected OR, extended horizontally to highlight potential support/resistance levels during the TW.
-50% OR Midpoint: An optional dashed line at the midpoint (50%) of the OR, which serves as a reference for mean reversion analysis.
-Trading Window Open Price: A line marking the open price at the start of the TW, useful for tracking returns to this level.
-Midnight Open (Red Line): A dedicated red horizontal line indicating the open price at midnight (00:00 NY time), which acts as a daily reference point for overnight price action.
Statistical Display via Sticky Note and Table:A customizable "Sticky Note" table displays pre-computed backtest results for the selected OR hour, including sections for combined results, above-midnight scenarios, and below-midnight scenarios. Content is user-editable via inputs.
A main info table shows session details, total historical sessions, and probabilities for returns (if enabled).
Customization Options: Users can toggle visuals, adjust colors, styles, widths, positions, and themes (light/dark). The indicator supports up to 500 lines/labels/boxes for historical drawing.
Logic and PrincipleThe indicator operates on a per-hour basis, treating each hour (0-23 NY time) as an independent "session" for analysis:Session Definition:
For any given hour (e.g., 02:00), the OR is the high/low range formed in that hour.
The TW is the subsequent hour where price action is tracked.
Tracking Price Action: During the TW, the script checks if price "sweeps" (crosses) the OR high or low. It then monitors for "returns"—instances where price crosses back to the TW open price or the 50% midpoint of the OR after a sweep.
Statistical Calculation: Probabilities are derived from historical counts:Total sessions: Number of historical days where data was available for that hour.
Return to TW Open: Percentage of sessions where, after sweeping OR high/low, price returned to the TW open (calculated as returns / total sessions with sweeps).
Return to 50% OR: Similar percentage for returning to the OR midpoint.
These are computed cumulatively across all historical bars loaded on the chart, resetting flags daily to ensure independence per session. No real-time predictions are made; stats accumulate from past data.
Midnight Open Integration: The red line resets daily at 00:00 NY, providing context for overnight gaps or continuations.
Breakout Origin: Scans recent bars for conditions where a breakout from OR occurs without opposite direction breach, drawing lines to the origin bar's open for visual reference.
The core principle is rooted in range-based analysis, a common technical approach where traders observe how price interacts with session highs/lows and midpoints.
By quantifying historical return rates after sweeps, the indicator highlights tendencies like mean reversion or continuation, but all insights are retrospective and depend on the loaded data.
Data Source and BacktestingThe statistical data embedded in the sticky notes (e.g., return percentages, sweep rates) was generated using Python in a Jupyter Notebook environment.
It analyzes approximately 1089 days (about 4 years) of 1-minute historical data for NQ futures, sourced BacktestMarket.
The backtests focused on NY time sessions, calculating metrics like:Sweep rates (e.g., first sweep high after above-midpoint open).
Return probabilities post-sweep.
Conditional splits (above/below midnight open).
These pre-computed values are hardcoded into the script via text areas for display, ensuring transparency.
Note: Historical performance is not indicative of future results; this is for analytical reference only.
Purpose and UsageThis indicator aims to assist traders in evaluating price direction potential by combining visual session markers with historical probabilities.
For example:If historical data shows a high probability of returning to the 50% OR after a sweep, it might suggest monitoring for mean reversion.
Combined Predictive Indicator### Combined Predictive Zones & Levels
This indicator is a powerful hybrid tool designed to provide a comprehensive map of potential future price action. It merges two distinct predictive models into a single, cohesive view, helping traders identify key levels of support, resistance, and areas of high confluence.
#### How It Works: Two Models in One
This script is built on two core components that you can use together or analyze separately:
**Part 1: Classic Range & Fibonacci Prediction**
This model uses classic technical analysis principles to project a potential range for the upcoming price action.
* **Highest High / Lowest Low:** It identifies the significant trading range over a user-defined lookback period.
* **Fibonacci Levels:** It automatically plots key Fibonacci retracement levels (e.g., 38.2% and 61.8%) within this range, which often act as critical support or resistance.
* **ATR & Average Range:** It calculates a "predicted" upper and lower boundary based on the average historical range and current volatility (ATR).
**Part 2: Advanced Predictive Ranges (Self-Adjusting Channels)**
This is a dynamic model that creates adaptive support and resistance zones based on a smoothed average price and volatility.
* **Dynamic Average:** It uses a unique moving average that only adjusts when the price moves significantly, creating a stable baseline.
* **ATR-Based Zones:** It projects multiple levels of support (S1, S2) and resistance (R1, R2) around this average, which widen and narrow based on market volatility. These zones often signal areas where price might stall or reverse.
#### Key Features:
* **Hybrid Model for Confluence:** The true power of this indicator lies in finding where the levels from both models overlap. A Fibonacci level aligning with a Predictive Range support zone is a much stronger signal.
* **Comprehensive Data Table:** A clean, on-chart table displays the precise values of all key predictive levels, allowing for quick reference and precise trade planning.
* **Multi-Timeframe (MTF) Capability:** The Advanced Predictive Ranges can be calculated on a higher timeframe, giving you a broader market context.
* **Fully Customizable:** All lengths, multipliers, and levels for both models are fully adjustable in the settings to fit any asset or trading style.
* **Clear Visuals:** All zones and levels are color-coded for intuitive and easy-to-read analysis.
#### How to Use:
1. Look for areas of **confluence** where multiple levels from both models cluster together. These are high-probability zones for price reactions.
2. Use the Predictive Range zones (S1/S2 and R1/R2) as potential targets for trades or as areas to watch for entries and exits.
3. Pay attention to the on-chart table for exact price levels to set limit orders or stop-losses.
**Disclaimer:** This script is an analytical tool for educational purposes and should not be considered financial advice. All trading involves risk. Past performance is not indicative of future results. Always use this indicator as part of a comprehensive trading strategy with proper risk management.
Feedback is welcome! If you find this tool useful, please leave a like.
PHL Sweep Signals(1 Hour)PHL Sweep Signals (Full History)
This indicator is designed to identify high-probability reversal setups by detecting liquidity sweeps of the previous standard hour's high and low (PHL). It provides clear, actionable signals complete with visual aids and a data table to keep you in tune with the higher-timeframe context.
Key Features
Previous Hour Levels: Automatically draws the high and low of the previous standard hour as key reference lines for the current trading hour. The line colors rotate to provide a clear visual separation.
Bearish Sweep Signal: Identifies a specific bearish pattern: a green (bullish) candle that wicks above the previous hour's high but fails to hold, with its body remaining entirely below the line.
Bullish Sweep Signal: Identifies the opposite bullish pattern: a red (bearish) candle that wicks below the previous hour's low but is absorbed, with its body remaining entirely above the line.
Clear Visual Signals: When a signal is confirmed, the indicator provides a multi-faceted alert:
Plots a "Buy" or "Sell" arrow on the chart.
Draws a colored box around the signal candle for easy identification.
Displays a label with the potential Stop Loss size (calculated from the size of the signal candle).
Informative Display Table: Includes a convenient table in the corner showing the Open and Close data for the last 3 hours, helping you stay aware of the broader market context without leaving your chart.
Built-in Alerts: Triggers an alert for every confirmed Buy and Sell signal so you never miss a potential setup.
How to Use
This indicator helps you spot potential exhaustion and reversals at key hourly levels.
A "Sell" signal suggests a failed breakout to the upside, indicating potential weakness and a possible entry for shorts.
A "Buy" signal suggests a failed breakdown to the downside, indicating potential strength and a possible entry for longs.
As with any tool, these signals are most powerful when used as part of a comprehensive trading strategy and combined with your own analysis for confirmation.
Optimal Settings:
Timeframe: 5-Minute
Time Zone: UTC-4 (New York Time)
-ratheeshinv
Nifty 500 Scanner
Nifty 500 Scanner
Your Ultimate TradingView Tool for Swing and Intraday Trading
🔥 Introduction
✅ If you want to find out which stock out of 500 stocks of Nifty500 is:
showing reversal pattern candles after a long down or up trend
also bouncing from support/resistance
and that stock gives you live alerts when this condition occurs
Then, look no further. Nifty500 Scanner is just for you.
📊 What is the Nifty 500 Scanner?
The Nifty 500 Scanner is a powerful TradingView indicator for Indian stocks designed to help you identify bullish and bearish reversal signals across all timeframes. Whether you are an intraday trader or a swing trader, this tool gives you an edge by scanning predefined groups of Nifty 500 stocks and visually showing you high-probability setups.
🔥 Key Features
Scans all Nifty 500 stocks in batches of 25 (20 groups in total). Takes less than 10 minutes to select bearish or bullish reversal stocks out of 500 stocks.
Detects over 50 advanced candlestick patterns, divergences, and trend changes in one go in all the selected stocks and displays result right on your chart in the form of a table.
Auto-populated real-time table display with signal count and color-coded results.
TradingView alerts for instant notification of reversal setups.
Shows key support and resistance levels for each stock.
Fully compatible with all timeframes – from 1 minutes to monthly chart.
✅ Why Traders Would Love It?
Eliminates manual chart scanning – saves hours every week.
Improves trade accuracy by filtering out weak setups.
Instantly tells you which stocks to trade tomorrow (if using after market hours)
Built for Indian market conditions and TradingView users.
⚙️ How It Works?
Select a stock group from the dropdown menu (Available in indicator settings).
Suppose you select Group1 and press OK, voila.. the scanner automatically runs through 25 predefined Nifty 500 stocks and updates the table in quick time.
The table shows which stocks are giving bullish or bearish signals and also tells you how many such signals are there. The more signals, the more conviction for upcoming reversal.
Open chart of any stock mentioned in the table to have a detailed look.
The chart will show you a consolidation zone, support/resistance lines automatically.
Set up alerts for your favorite stocks and let TradingView notify you when new signals emerge for that particular stock.
📌 Important Notes
Stock groups are hard-coded into the script and cannot be modified by the user.
Custom versions for other countries or indices (e.g., S&P 500, FTSE) can be created upon request.
🔍 Optimized For
Swing traders and intraday traders seeking high-probability setups.
Technical analysts using TradingView to analyze Indian stock charts.
Traders looking for an advanced reversal signal scanner.
🚀 Ready to Trade Smarter?
Start using the Nifty 500 Scanner on TradingView and never miss a reversal signal again.
Get Access Now.
ROC | QuantumResearch🔍 QuantumResearch ROC Screener
The QuantumResearch ROC Screener is an advanced multi-asset momentum analyzer designed to track relative strength across up to 11 user-defined assets using Rate of Change (ROC). This tool helps traders identify outperformers, underperformers, and rotation opportunities in fast-moving markets.
🧠 How It Works
This screener systematically calculates the Rate of Change (ROC) for each selected asset using two perspectives:
Absolute ROC – Measures the momentum of each asset individually over the chosen lookback period.
Relative ROC Matrix – Compares each asset against every other asset (e.g., BTC vs ETH, ETH vs SOL, etc.) using pairwise ROC ratios.
These values are organized into a dynamic heatmap-style table, highlighting which assets exhibit the strongest directional moves and relative strength. The script also includes:
Averages across all relative pairs to rank each asset.
Color-coded visuals to identify bullish (green), bearish (red), and neutral (white) ROC values.
📊 Main Features
🔢 Up to 11 Assets: Choose any combination of crypto, forex, indices, or commodities.
💡 Pairwise Comparison Matrix: Visualizes each asset’s ROC vs every other asset.
📈 Momentum Ranking: Assets are sorted based on their total average ROC score.
🎨 Color-Coded Table: Makes it easy to spot high or low momentum tokens at a glance.
⚙️ Custom ROC Period: Choose the length of the momentum window.
🧩 Flexible Layout: Position the table anywhere on your screen and adjust font size.
✅ How to Use It
Select your favorite 11 assets (e.g., BTC, ETH, SOL, etc.).
Adjust the ROC length to capture short-term or medium-term momentum.
Spot top trending assets.
Identify reversals or breakouts.
Build rotational or relative strength strategies.
⚠️ Important Notes
Momentum is a powerful tool, but context matters — combine ROC readings with your broader strategy (trend, liquidity, valuation).
This screener is not predictive — it reflects past performance over a defined lookback window.
📉 Disclaimer
Past performance is not indicative of future results. This tool is designed to provide data-driven insight, not financial advice. Always conduct your own research and apply proper risk management.
BanShen MACD Ultimate Multi Signal System[SpeculationLab]🧠 How This Script Works (Detailed Logic Breakdown)
This script is a closed-source, fully self-developed modular trading system centered around MACD divergence detection. It also includes auxiliary modules such as:
Vegas Tunnel trend filtering
Dynamic ATR-based stop placement
Engulfing candlestick pattern detection
RSI/OBV divergence modules
Fair Value Gap (FVG) recognition
A smart signal panel that consolidates all signals in real time
These components work together through a signal resonance framework, helping traders identify high-confluence, high-probability entry opportunities.
🔍 Why MACD Divergence Is the Core (Real-World Strategy Basis)
This system is based on a real-world trading strategy I’ve personally used and refined over time.
Through discretionary trading and backtesting, I discovered that divergence between price action and the MACD histogram — especially when certain structural conditions are met — produces a very high win rate.
Key observations include:
MACD peaks/troughs that are clean and well-shaped (defined pivot structure)
Large vertical differences between two MACD histogram extremes
Price making a higher high or lower low, while MACD does the opposite
Two or more divergences appearing consecutively, which creates a powerful reversal signal
These setups have proven extremely reliable in my experience. This script automates the detection of these conditions using strict logic filters.
🔷 1. MACD Divergence Engine (Core Module)
At its core, this script implements a multi-layered MACD divergence detection system, capable of identifying both **regular** and **consecutive** bullish/bearish divergences.
Key components of the logic:
- **Pivot-Based Peak Detection:**
Peaks and troughs in the MACD histogram are located using left/right lookback lengths.
These define valid turning points by requiring the center bar to be the highest (or lowest) compared to its neighbors.
- **Peak Size Thresholding:**
The height of the histogram peaks is compared to the standard deviation of MACD values.
Only peaks above a configurable multiplier (e.g., 0.1× stdev) are considered significant, filtering out noise.
- **Peak Ratio Filtering:**
For divergence to be valid, the size ratio between two histogram peaks must exceed a minimum threshold.
This prevents "flat" divergences with no meaningful MACD movement from triggering false signals.
- **Noise Suppression:**
A customizable threshold filters out weak histogram fluctuations between divergence points.
- **Price Action Confirmation:**
The divergence is only confirmed when the price forms a new high or low (depending on the type), and the MACD forms an opposing structure.
- **Consecutive Divergence Detection:**
For high-conviction setups, the script detects sequences of two or more divergences in the same direction.
These use stricter filters and flag rare but powerful market turning points.
Signals are plotted using plotshape() with visual differentiation between regular and consecutive setups. You can enable/disable each type individually.
⏰ Note: Histogram colors are styled similarly to TradingView’s built-in MACD for visual familiarity. However, this script is built entirely from scratch and does not reuse any internal TV code.
---
🔷 2. Trend Filtering via Vegas Tunnel
The **Vegas Tunnel** module plots 5 configurable EMAs (default: 12, 144, 169, 576, 676) to evaluate trend direction.
The trend is considered **bullish** when short EMAs (144/169) are positioned above long EMAs (576/676), and the price is interacting with the short EMA tunnel.
Conversely, a bearish condition is detected when the opposite is true.
A visual triangle marker highlights trend zones, and users can hide/show individual EMAs.
---
🔷 3. ATR-Based Dynamic Stop Loss
This module plots dynamic stop levels above and below the current price based on ATR.
Default setting uses 13-period ATR, and users can customize the multiplier or disable the plot.
It serves as a visual guide for risk management in live trades.
---
🔷 4. Engulfing Pattern Recognition
Candlestick-based signal detection:
- **Bullish Engulfing** occurs when a candle closes above the prior high, and the prior bar is bearish.
- **Bearish Engulfing** when a candle closes below the prior low, and the prior bar is bullish.
Users can modify the logic to use open/close levels for looser or stricter detection.
These patterns are highlighted using plotshape markers and optionally included in the signal table.
---
🔷 5. RSI and OBV Divergence Modules
These modules follow similar logic to the MACD engine:
- Use pivotlow() / pivothigh() to detect swing points.
- Confirm divergence only when price moves in one direction while RSI or OBV moves in the opposite direction.
- Require a minimum distance (in bars) between the two pivots.
- Require a certain ratio between two indicator values and their corresponding prices.
You can only enable **one of MACD/RSI/OBV divergences at a time** to avoid visual overlap, as they share the same subplot.
---
🔷 6. FVG (Fair Value Gap) Auto Detection
This module detects large single-direction price moves where price leaves a visible gap between candle 3 bars ago and 1 bar ago.
- **Bullish FVG**: high < low
- **Bearish FVG**: low > high
ATR-based filters are applied to eliminate minor gaps.
Each gap is drawn as a box and optionally extended, with a central line marking the midpoint (CE - Consequent Encroachment) level.
Traders often look for price to return to this level as an entry signal.
---
🔷 7. Smart Signal Table
All active signals (MACD, Vegas, RSI, OBV, Engulfing) are collected into a **real-time table** that displays current market bias.
- Each module reports whether it is currently giving a bullish (🟢) or bearish (🔴) condition.
- Helps users assess signal alignment (confluence).
- The table is updated every bar and appears in the bottom-right corner.
---
🔷 8. Watermark & Branding
The watermark displays the script name and author at the top-right, and can be toggled via settings.
📌 Not a Mashup — Structured System, Not a Stack of Indicators
⚠️ This is not a random mashup of unrelated indicators.
Every module in this system was intentionally designed to support the core MACD divergence logic by filtering, validating, or amplifying its signals.
Here's how the system achieves signal confluence and structure:
Vegas Tunnel acts as a macro trend filter, helping users determine whether to favor long or short trades.
For example, bullish MACD divergence is more reliable when confirmed by an uptrend in the Vegas EMAs. This prevents users from trading against momentum.
Engulfing Patterns serve as entry-level price action confirmation.
When a bullish engulfing candle appears near a MACD bullish divergence — and trend conditions from Vegas are aligned — the confluence increases dramatically.
This is especially powerful when multiple modules confirm in the same direction on the right side of the chart.
RSI and OBV Divergence modules offer redundant but independent momentum views.
Users may enable them selectively to validate MACD signals, or to use them as standalone alternatives when MACD is flat or noisy.
FVG Zones provide context for entries or targets.
For instance, a MACD bullish divergence forming near a bullish FVG gap increases the odds of reversal.
Price often "fills" these imbalances, which aligns well with reversal setups.
The Smart Signal Table aggregates signals from all modules and provides a visual, real-time overview of the current market bias.
This allows traders to act only when multiple signals are aligned — for example, when MACD is bullish, trend is up, and a bullish engulfing just printed.
Together, this framework creates a coherent decision-making system, where each tool has a defined role: trend filtering, signal confirmation, risk management, or entry detection.
🧩 It is modular in architecture, but not modular in purpose.
This system was not built by stacking indicators, but by integrating logic across modules to support a high-conviction MACD-based strategy.
🧬 Originality Statement
This script is entirely original, developed from scratch without using external libraries or public script code. The logic is fully custom, especially the consecutive divergence detection system and signal integration.
⚠️ Disclaimer
This script is for educational and informational purposes only and does not constitute financial advice. Trade at your own risk.
---
📘 中文简要说明：
这是一个完全原创、闭源的交易系统，核心逻辑为 MACD 柱状图背离信号的识别，配合多模块共振判断，构建出一个高胜率的多信号共振策略。
本指标模块化结构清晰，主要包括：
- MACD 背离识别（支持连续背离）
- Vegas EMA 隧道趋势过滤
- RSI / OBV 背离模块
- 吞没形态识别
- FVG 平衡区间自动标注
- ATR 动态止损提示
- 智能信号面板（整合所有信号并可视化）
所有模块均可单独开启/关闭，适配顺势、逆势或多周期的交易风格。
本脚本为个人实战策略的程序化实现，逻辑完全由零开发，未使用任何公用代码。适合希望提高交易胜率和信号精准度的用户使用。
免责声明：本指标仅用于技术分析学习与参考，不构成任何投资建议。请您独立判断，自行承担交易风险。
Dynamic SL/TP Levels (ATR or Fixed %)This indicator, "Dynamic SL/TP Levels (ATR or Fixed %)", is designed to help traders visualize potential stop loss (SL) and take profit (TP) levels for both long and short positions, refreshing dynamically on each new bar. It assumes entry at the current bar's close price and uses a fixed 1:2 risk-reward ratio (TP is twice the distance of SL in the profit direction). Levels are displayed in a compact table in the chart pane for easy reference, without cluttering the main chart with lines.
Key Features:
Calculation Modes:
ATR-Based (Dynamic): SL distance is derived from the Average True Range (ATR) multiplied by a user-defined factor (default 1.5x). This adapts to the asset's volatility, providing breathing room based on recent price movements.
Fixed Percentage: SL is set as a direct percentage of the current close price (default 0.5%), offering consistent gaps regardless of volatility.
Long and Short Support: Calculates and shows SL/TP for longs (SL below close, TP above) and shorts (SL above close, TP below), with toggles to hide/show each.
Real-Time Updates: Levels recalculate every bar, making them readily available for entry decisions in your trading system.
Display: Outputs to a table in the top-right pane, showing precise values formatted to the asset's tick size (e.g., full decimal places for crypto).
How to Use:
Add the indicator to your chart via TradingView's Pine Editor or library.
Adjust settings:
Toggle "Use ATR?" on/off to switch modes.
Set "ATR Length" (default 14) and "ATR Multiplier for SL" for dynamic mode.
Set "Fixed SL %" for percentage mode.
Enable/disable "Show Long Levels" or "Show Short Levels" as needed.
Interpret the table: Use the displayed SL/TP values when your strategy signals an entry. For risk management, combine with position sizing (e.g., risk 1% of account per trade based on SL distance).
Example: On a volatile asset like BTC, ATR mode might set a wider SL for realism; on stable pairs, fixed % ensures predictability.
This tool promotes disciplined trading by tying levels to price action or fixed rules, but it's not financial advice—always backtest and use with your full strategy. Feedback welcome!
Volatility & Market Regimes [AlgoXcalibur]Analyze Market Conditions Like a Pro.
Volatility & Market Regimes is a specialized, institution-inspired indicator designed to help traders instantly identify the current conditions of the market with clarity and confidence.
By combining a real-time Volatility Histogram and Strength Line with a compact Regime Table, this tool reveals four essential market dimensions—Volatility, Strength, Participation, and Noise—in a clean and intuitive format. Whether you’re confirming trade setups or managing risk, knowing the current regimes enhances awareness across all assets and timeframes.
🧠 Algorithm Logic
This sophisticated tool continuously monitors four independent regimes, each reflecting a distinct dimension of market behavior:
• Volatility – Gauges how active or dormant the market is by comparing current price action movement to historical averages. A dynamic, color-gradient Volatility Histogram transitions from Low (ice blue/white) to Medium (green/yellow) to High (orange/red), giving you an immediate assessment of volatility and risk.
• Strength – Measures directional intensity by assessing trend momentum, pressure, and persistence. A color-gradient Strength Line ranges from weak (red) to strong (green), helping traders determine if directional strength is trending, weakening, or consolidating.
• Participation – Analyzes relative volume to assess the level of trader engagement. Higher volume indicates stronger participation and conviction, while low volume may signal uncertainty, fading momentum, or even liquidity traps.
• Noise – Evaluates structural stability by measuring how orderly or chaotic the price action is. High noise suggests choppy, unstable conditions, while low noise reflects clean, stable moves.
Each regime includes a High / Medium / Low classification and a color-coded directional arrow to indicate whether condition parameters are increasing or decreasing. Together, these components deliver real-time market context—helping you stay grounded in logic, not emotion.
⚙️ User-Selectable Features
Each component of the indicator—the Volatility Histogram, Strength Line, and Regime Table—can be independently made visible or hidden to match your preference. This flexibility allows you to display only the Regime Table and move it directly to your main chart, where it auto-positions to the center-right and integrates seamlessly with other AlgoXcalibur indicators that also use data tables for a cohesive and refined experience.
📊 Clarity, Not Guesswork
Volatility & Market Regimes is a unique, institution-inspired algorithm rarely seen in retail trading. Not only does it clearly display volatility—it translates complex market behavior into a clear context to reveal what’s happening behind the candles. By decoding core regimes in real-time, this tool transforms uncertainty into structured insight—empowering traders to act with clarity, not guesswork.
🔐 To get access or learn more, visit the Author’s Instructions section.
