Golden Cross Screener [Pineify]Golden Cross Screener Pineify – Multi-Symbol Trend Detection Screener for TradingView
Discover the Golden Cross Screener Pineify for TradingView: a multi-symbol, multi-timeframe indicator for crypto and other assets. Customizable Golden Cross detection, robust algorithm, and intuitive screener design for smarter portfolio trend analysis.
Key Features
Multi-symbol screening across major cryptocurrencies or assets – BTCUSD, ETHUSD, XRPUSD, USDT, BNB, SOLUSD, DOGEUSD, TRXUSD (fully customizable).
Multi-timeframe analysis (e.g., 1m, 5m, 10m, 30m), enabling robust trend detection from scalp to swing.
Customizable Moving Average settings for both Fast and Slow MA (source and length).
Efficient screener table, highlighting Golden Cross events and current asset trends in one panel.
Visual cues for bullish, bearish, and cross states using intuitive color-coding and labels.
Flexible symbol and timeframe inputs to tailor the screener to any portfolio or watchlist.
How It Works
The Golden Cross Screener Pineify leverages the classic Golden Cross methodology—a bullish trend signal triggered when a shorter-term moving average crosses above a longer-term moving average. To improve robustness, you are empowered to configure both Fast MA and Slow MA periods and sources, making the detection logic applicable to any symbol, timeframe, or asset class.
Internally, the script runs dedicated calculations on each chosen symbol and timeframe, generating independent signals using exponential moving averages (EMA). Using the TradingView `request.security` function, it fetches and processes price data for up to eight portfolio assets on four timeframes, displaying the detected Golden Cross, Bullish, or Bearish states in a central screener table.
Trading Ideas and Insights
Spot emerging bullish or bearish trends across your favorite crypto pairs or trading assets in real time.
Capture prime opportunities when multiple assets align with Golden Cross signals—ideal for portfolio rebalancing or rotational strategies.
Analyze trend consistency by monitoring cross events at multiple timeframes for a given asset.
Swiftly identify when short-term and long-term momentum diverge—flagging potential reversals or trend initiations.
The Golden Cross Screener Pineify is not just a trend signal; it’s a holistic multi-asset scanner built for traders who know the power of combining technical breadth with agile timing.
How Multiple Indicators Work Together
This screener stands out with its modular approach: each asset/timeframe pair is monitored in isolation, yet displayed collectively for multidimensional market insight. Each symbol’s price action is processed through independently configured EMAs—Fast and Slow—whose crossovers are analyzed for directional bias. The implementation’s real innovation is in its screener table engine: it aggregates signals, synchronizes timeframes, and color-codes market states, allowing users to see confluences, divergences, and sector trends at a glance.
Combining Golden Cross detection with customizable moving averages and flexible multi-timeframe, multi-symbol scanning means users can fine-tune sensitivity, focus on specific signals, and adapt screener logic for scalping, swing trading, or investing.
Unique Aspects
True multi-symbol screener within the TradingView indicator framework.
Full customization of screener assets, timeframes, and moving averages.
Advanced, efficient use of TradingView table for clear, actionable visualization.
No dependency on standard, static MA settings—adjust everything to match your strategy.
Big-picture and granular trend detection in one tool, designed for both active traders and portfolio managers.
How to Use
Add the Golden Cross Screener Pineify to your TradingView chart.
Choose up to eight symbols—crypto, stock, forex, or custom assets.
Set four timeframes for screening, from lower to higher intervals.
Adjust moving average sources (price, close, etc.) and period lengths for both Fast and Slow MAs to suit your trading style.
Interpret table cells: clear labels and color indicate Golden Cross (trend shift), Bullish (uptrend), Bearish (downtrend) states for each symbol/timeframe.
React to signal alignments—deploy or rebalance positions, increase alert sensitivity, or backtest sequence confluences.
Customization
The indicator’s inputs panel gives full control:
Select which symbols to screen, making it perfect for any asset watchlist.
Pick the desired timeframes—mix daily, hourly, or minute-based intervals.
Adjust Fast and Slow MA settings: switch source type, change period length, and fine-tune detection logic as needed.
Style your screener table via TradingView settings (colors, font sizes, alignment).
Every element is customizable—adapt the Golden Cross Screener Pineify for your specific portfolio, trading timeframe, and strategy focus.
Conclusion
The Golden Cross Screener Pineify elevates multi-symbol trend detection to a new level on TradingView. By combining configurable Golden Cross logic with a powerful screener engine, it serves both precision and broad market insight—crucial for agile traders and strategic portfolio managers. Whether you’re tracking crypto pairs, stocks, forex, or a mix, this tool transforms static trend analysis into an active, multi-dimensional trading edge.
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GEX Options Flow Pro 100% free
INTRODUCTION
This script is designed to visualize advanced options-derived metrics and levels on TradingView charts, including Gamma Exposure (GEX) walls, gamma flip points, vanna levels, delta-neutral prices (DEX), max pain, implied moves, and more. It overlays dynamic lines, labels, boxes, and an info table to highlight potential support, resistance, volatility regimes, and flow dynamics based on options data.
These visualizations aim to help users understand how options market structure might influence price action, such as areas of potential stability (positive GEX) or volatility (negative GEX). All data is user-provided via pasted strings, as Pine Script cannot fetch external options data directly due to platform limitations (detailed below).
The script is open-source under TradingView's terms, allowing study, modification, and improvement. It draws inspiration from standard options Greeks and exposure metrics (e.g., gamma, vanna, charm) discussed in financial literature like Black-Scholes models and dealer positioning analyses. No external code is copied; all logic is original or based on mathematical formulas.
Disclaimer: This is an educational tool only. It does not provide investment advice, trading signals, or guarantees of performance. Past data is not indicative of future results. Use at your own risk, and combine with your own analysis. Not intended for qualified investors only.
How the Options Levels Are Calculated
Levels are not computed in Pine Script—they rely on pre-calculated values from external tools (e.g., Python scripts using libraries like yfinance for options chains). Here's how they're typically derived externally before pasting into the script:
Fetching Options Data: Retrieve options chain for a ticker: strikes, open interest (OI), volume, implied volatility (IV), expirations (e.g., shortest: 0-7 DTE, short: 7-14 DTE, medium: ~30 DTE, long: ~90 DTE). Get current price and 5-day history for context.
Gamma Walls (Put/Call Walls): Compute gamma for each option using Black-Scholes: gamma = N'(d1) / (S * σ * √T) where S = spot price, K = strike, T = time to expiration (years), σ = IV, N'(d1) = normal PDF. Aggregate GEX at strikes: GEX = sign * gamma * OI * 100 * S^2 * 0.01 (per 1% move, with sign based on dealer positioning: typically short calls/puts = negative GEX). Put Wall: Highest absolute GEX put strike below S (support via dealer buying on dips). Call Wall: Highest absolute GEX call strike above S (resistance via dealer selling on rallies). Secondary/Tertiary: Next highest levels. Historical walls track tier-1 levels over 5 days.
Gamma Flip: Net GEX profile across prices: Sum GEX for all options at hypothetical spots. Flip point: Interpolated price where net GEX changes sign (stable above, volatile below).
Vanna Levels: Vanna = -N'(d1) * d2 / σ. Weighted by OI; highest positive/negative strikes.
DEX (Delta-Neutral Price): Net dealer delta: Sum (delta * OI * 100 * sign), with delta from Black-Scholes. DEX: Price where net delta = 0 (interpolated).
Max Pain: Strike minimizing total intrinsic value for all options holders.
Skew: 25-delta skew: IV difference between 25-delta put and call (interpolated).
Net GEX/Delta: Total signed GEX/delta at current S.
Implied Move: ATM IV * √(DTE/365) for 1σ range.
C/P Ratio: (Call OI + volume) / (Put OI + volume).
Smart Stop Loss: Below lowest support (e.g., Put Wall, gamma flip), buffered by IV * √(DTE/30).
Other Metrics: IV: ATM average. 5-day metrics: Avg volume, high/low.
External tools handle dealer assumptions (e.g., short calls/puts) and scaling (per % move).
Effect as Support and Resistance in Technical Trading
Options levels reflect dealer hedging dynamics:
Put Wall (Gamma Support): High put GEX creates buying pressure on dips (dealers hedge short puts by buying stock). Use for long entries, bounces, or stops below.
Call Wall (Gamma Resistance): High call GEX leads to selling on rallies. Good for trims, shorts, or reversals.
Gamma Flip: Pivot for volatility—above: dampened moves (positive GEX, mean reversion); below: amplified trends (negative GEX, momentum).
Vanna Levels: Sensitivity to IV changes; crosses may signal vol shifts.
DEX: Dealer delta neutral—bullish if price below with positive delta.
Max Pain: Price magnet minimizing option payouts.
Implied Move/Confidence Bands: Expected ranges (1σ/2σ/3σ); breakouts suggest extremes.
Liquidity Zones: Wall ranges as price magnets.
Smart Stop Loss: Protective level below supports, IV-adjusted.
C/P Ratio & Skew: Sentiment (high C/P = bullish; high skew = put demand).
Net GEX: Positive = low vol strategies (e.g., condors); negative = momentum trades.
Combine with TA (e.g., volume, trends). High activity strengthens effects; alerts on crosses/proximities for awareness.
Limitations of the TradingView Platform for Data Pulling
Pine Script is sandboxed:
No API calls or internet access (can't fetch options data directly).
Limited to chart/symbol data; no real-time chains.
Inputs static per load; manual updates needed.
Caching not persistent across sessions.
This ensures lightweight scripts but requires external data sourcing.
Creative Solution for On-Demand Data Pulling
Users can use external tools (e.g., Python scripts with yfinance) to fetch/compute data on demand. Generate a formatted string (ticker,timestamp|term1_data|term2_data|...), paste into inputs. Tools can process multiple tickers, cache for ~15-30 min, and output strings for quick portfolio scanning. Run locally or via custom setups for near-real-time updates without platform violations.
For convenience, a free bot is available on my website that accepts commands like !gex to generate both current data strings (for all expiration terms) and historical walls data on demand. This allows users to easily obtain fresh or cached data (refreshed every ~30 min) for pasting into the indicator—ideal for scanning portfolios without manual coding.
Script Functionality Breakdown
Inputs: Data strings (current/historical); term selector (Shortest/Short/Medium/Long); toggles (historical walls, GEX profile, secondaries, vanna, table, max pain, DEX, stop loss, implied move, liquidity, bands); colors/styles.
Parsing: Extracts term-specific data; validates ticker match; gets timestamp for freshness.
Drawing: Dynamic lines/labels (width/color by GEX strength); boxes (moves, zones, bands); clears on updates.
Info Table: Dashboard with status (freshness emoji), Greeks (GEX/delta with emojis), vol (IV/skew), levels (distances), flow (C/P, vol vs 5D).
Historical Walls: Displays past tier-1 walls on daily+ timeframes.
Alerts: 20+ conditions (e.g., near/cross walls, GEX sign change, high IV).
Performance: Efficient for real-time; smart label positioning.
Release Notes
Initial release: Full features including multi-term support, enhanced table with emojis/sentiment, dynamic visuals, smart stop loss.
Data String Format: TICKER,TIMESTAMP|TERM1_DATA|TERM2_DATA|TERM3_DATA|TERM4_DATA Where each TERM_DATA = val0,val1,...,val30 (31 floats: current_price, prev_close, call_wall_1, call_wall_1_gex, ..., low_5d). Historical: TICKER|TERM1_HIST|... where TERM_HIST = date:cw,pw;date:cw,pw;...
Feedback welcome in comments. Educational only—not advice.
RVol+ Enhanced Relative Volume Indicator📊 RVol+ Enhanced Relative Volume Indicator
Overview
RVol+ (Relative Volume Plus) is an advanced time-based relative volume indicator designed specifically for swing traders and breakout detection. Unlike simple volume comparisons, RVol+ analyzes volume at the same time of day across multiple sessions, providing statistically significant insights into institutional activity and breakout potential.
🎯 Key Features
Core Volume Analysis
Time-Based RVol Calculation - Compares current cumulative volume to the average volume at this exact time over the past N days
Statistical Z-Score - Measures volume in standard deviations from the mean for true anomaly detection
Volume Percentile - Shows where current volume ranks historically (0-100%)
Sustained Volume Filter - 3-bar moving average prevents false signals from single-bar spikes
Breakout Detection
🚀 Confirmed Breakouts - Identifies price breakouts validated by high volume (RVol > 1.5x)
⚠️ False Breakout Warnings - Alerts when price breaks key levels on low volume (high failure risk)
Multi-Timeframe Context - Weekly volume overlay prevents chasing daily noise
Advanced Metrics
OBV Divergence Detection - Spots bullish/bearish accumulation/distribution patterns
Volume Profile Integration - Identifies institutional positioning
Money Flow Analysis - Tracks smart money vs retail activity
Extreme Volume Alerts - 🔥 Labels mark unusual spikes beyond the display cap
Visual Intelligence
Smart Color Coding:
🟢 Bright Teal = High activity (RVol ≥ 1.5x)
🟡 Medium Teal = Caution zone (RVol ≥ 1.2x)
⚪ Light Teal = Normal activity
🟠 Orange = Breakout confirmed
🔴 Red = False breakout risk
Comprehensive Stats Table:
Current Volume (formatted as M/K/B)
RVol ratio
Z-Score with significance
Volume percentile
Historical average and standard deviation
Sustained volume confirmation
📈 How to Use
For Swing Trading (1D - 3W Holds)
Perfect Setup:
✓ RVol > 1.5x (bright teal)
✓ Z-Score > 2.0 (⚡ alert)
✓ Percentile > 90%
✓ Sustained = ✓
✓ 🚀 Breakout label appears
Avoid:
✗ Red "Low Vol" warning during breakouts
✗ RVol < 1.0 at key levels
✗ Sustained volume not confirmed
Signal Interpretation
⚡ Z>2 Labels - Statistically significant volume (95th+ percentile) - highest probability moves
↗️ OBV+ Labels - Bullish accumulation (OBV rising while price consolidates)
↘️ OBV- Labels - Bearish distribution (OBV falling while price rises)
🔵 Blue Background - Weekly volume elevated (confirms daily strength)
⚙️ Customization
Basic Settings
N Day Average - Number of historical days for comparison (default: 5)
RVol Thresholds - Customize highlight levels (default: 1.2x, 1.5x)
Visual Display Cap - Prevent extreme spikes from compressing view (default: 4.0x)
Advanced Metrics (Toggle On/Off)
Z-Score analysis
Weekly RVol context
OBV divergence detection
Volume percentile ranking
Breakout signal generation
Table Customization
Position - 9 placement options to avoid chart overlap
Size - Tiny to Huge
Colors - Full customization of positive/negative/neutral values
Transparency - Adjustable background
Debug Mode
Enable Pine Logs for calculation transparency
Adjustable log frequency
Real-time calculation breakdown
🔬 Technical Details
Algorithm:
Binary search for historical lookups (O(log n) performance)
Time-zone aware session detection
DST-safe timestamp calculations
Exponentially weighted standard deviation
Anti-repainting architecture
Performance:
Optimized for max_bars_back = 5000
Efficient array management
Built-in function optimization
Memory-conscious data structures
📊 What Makes RVol+ Different?
vs. Standard Volume:
Context-aware (time-of-day matters)
Statistical significance testing
False breakout filtering
vs. Basic RVol:
Z-Score normalization (2-3 sigma detection)
Multi-timeframe confirmation
OBV divergence integration
Sustained volume filtering
Smart visual scaling
vs. Professional Tools:
Free and open-source
Fully customizable
No black-box algorithms
Educational debug logs
💡 Best Practices
Wait for Confirmation - Don't enter on first bar; wait for sustained volume ✓
Combine with Price Action - RVol validates, price structure determines entry
Weekly Context Matters - Blue background = institutional interest
Z-Score is King - Focus on ⚡ alerts for highest probability
Avoid Low Volume Breakouts - Red ⚠️ labels = high failure risk
🎓 Trading Psychology
Volume precedes price. When RVol+ shows:
High RVol + Rising OBV = Accumulation before breakout
High RVol at Resistance = Test of conviction
Low RVol on Breakout = Retail-driven (fade candidate)
Z-Score > 3 = Potential "whale" positioning
📝 Credits
Based on the time-based RVol concept from /u/HurlTeaInTheSea, enhanced with:
Statistical analysis (z-scores, percentiles)
Multi-timeframe integration
OBV divergence detection
Professional-grade visualization
Swing trading optimization
🔧 Version History
v2.0 - Enhanced Edition
Added Z-Score analysis
Multi-timeframe volume context
OBV divergence detection
Breakout confirmation system
Smart color coding
Customizable stats table
Debug logging mode
Performance optimizations
📚 Learn More
For optimal use with swing trading:
Combine with support/resistance levels
Watch for volume clusters in consolidation
Use weekly timeframe for trend confirmation
Monitor OBV divergence for early warnings
⚠️ Disclaimer
This indicator is for educational purposes. Volume analysis is one component of trading decisions. Always use proper risk management, consider multiple timeframes, and validate signals with price structure. Past performance does not guarantee future results.
🚀 Getting Started
Add indicator to chart
Adjust "N Day Average" to your preference (5-10 days typical)
Position stats table to avoid overlap
Enable features you want to monitor
Watch for 🚀 breakout confirmations!
Happy Trading! 📈
Intraday Rising & Reversal ScannerPine Script Description: Intraday Rising & Reversal ScannerThis Pine Script is a TradingView indicator designed to identify stocks with intraday (1-hour timeframe) potential for bullish (rising) or bearish (reversal) movements. It scans for stocks based on user-defined technical criteria, including price change, relative volume, RSI, EMA, ATR, and VWAP. The script plots signals on the chart, displays a summary table, and triggers alerts when conditions are met.FeaturesBullish Signal (Rising Stocks):1H Price Change: > 1% (configurable, e.g., >2% for volatile markets).
Relative Volume: > 2.0 (volume is at least twice the 20-period average).
RSI (14): Between 50 and 70 (strong but not overbought momentum).
Price vs EMA 13: Price above the 13-period EMA (confirms short-term uptrend).
ATR (14): Current ATR above its 20-period average (indicates volatility).
VWAP: Price above VWAP (optional, shown on chart for manual confirmation).
Bearish Signal (Reversal Stocks):1H Price Change: < -1% (configurable, e.g., <-2% for stronger reversals).
Relative Volume: > 2.0 (high volume confirms selling pressure).
RSI (14): > 70 (overbought, increasing reversal likelihood).
Price vs EMA 13: Price below the 13-period EMA (confirms short-term downtrend).
ATR (14): Current ATR above its 20-period average (indicates volatility).
VWAP: Price below VWAP (optional, shown on chart for manual confirmation).
Visualization:Bullish Signal: Green triangle below the bar.
Bearish Signal: Red triangle above the bar.
VWAP: Plotted as a blue line for manual verification.
Table: Displays real-time metrics (Change %, Relative Volume, RSI, Price vs EMA, ATR, VWAP) in the top-right corner, color-coded (green for bullish, red for bearish).
Alerts:Separate alerts for bullish ("Intraday Bullish Signal") and bearish ("Intraday Bearish Signal") conditions.
Customizable alert messages include parameter values for easy tracking.
How It WorksThe script runs on the 1-hour (1H) timeframe, ensuring all calculations are based on hourly data.
Indicators are computed:Change %: Percentage price change over the last hour.
Relative Volume: Current volume divided by the 20-period SMA of volume.
RSI: 14-period Relative Strength Index.
EMA 13: 13-period Exponential Moving Average.
ATR: 14-period Average True Range, compared to its 20-period SMA.
VWAP: Volume Weighted Average Price, plotted for visual confirmation.
Signals are generated when all conditions for either bullish or bearish criteria are met.
A table summarizes key metrics, and alerts can be set up for real-time notifications.
Usage InstructionsApply the Script:Open TradingView’s Pine Editor.
Copy and paste the script.
Click "Add to Chart" and set the chart to the 1-hour (1H) timeframe.
Set Up Alerts:Right-click on the chart > "Add Alert".
Select "Intraday Bullish Signal" or "Intraday Bearish Signal" as the condition.
Configure notifications (e.g., SMS, email, or TradingView alerts).
Manual VWAP Check:VWAP is plotted as a blue line. Verify that the price is above VWAP for bullish signals or below for bearish signals using the table or chart.
To make VWAP a mandatory filter, uncomment the VWAP conditions in the bull_signal and bear_signal definitions.
BOCS Channel Scalper Indicator - Mean Reversion Alert System# BOCS Channel Scalper Indicator - Mean Reversion Alert System
## WHAT THIS INDICATOR DOES:
This is a mean reversion trading indicator that identifies consolidation channels through volatility analysis and generates alert signals when price enters entry zones near channel boundaries. **This indicator version is designed for manual trading with comprehensive alert functionality.** Unlike automated strategies, this tool sends notifications (via popup, email, SMS, or webhook) when trading opportunities occur, allowing you to manually review and execute trades. The system assumes price will revert to the channel mean, identifying scalp opportunities as price reaches extremes and preparing to bounce back toward center.
## INDICATOR VS STRATEGY - KEY DISTINCTION:
**This is an INDICATOR with alerts, not an automated strategy.** It does not execute trades automatically. Instead, it:
- Displays visual signals on your chart when entry conditions are met
- Sends customizable alerts to your device/email when opportunities arise
- Shows TP/SL levels for reference but does not place orders
- Requires you to manually enter and exit positions based on signals
- Works with all TradingView subscription levels (alerts included on all plans)
**For automated trading with backtesting**, use the strategy version. For manual control with notifications, use this indicator version.
## ALERT CAPABILITIES:
This indicator includes four distinct alert conditions that can be configured independently:
**1. New Channel Formation Alert**
- Triggers when a fresh BOCS channel is identified
- Message: "New BOCS channel formed - potential scalp setup ready"
- Use this to prepare for upcoming trading opportunities
**2. Long Scalp Entry Alert**
- Fires when price touches the long entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "LONG scalp signal at 24731.75 | TP: 24743.2 | SL: 24716.5"
**3. Short Scalp Entry Alert**
- Fires when price touches the short entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "SHORT scalp signal at 24747.50 | TP: 24735.0 | SL: 24762.75"
**4. Any Entry Signal Alert**
- Combined alert for both long and short entries
- Use this if you want a single alert stream for all opportunities
- Message: "BOCS Scalp Entry: at "
**Setting Up Alerts:**
1. Add indicator to chart and configure settings
2. Click the Alert (⏰) button in TradingView toolbar
3. Select "BOCS Channel Scalper" from condition dropdown
4. Choose desired alert type (Long, Short, Any, or Channel Formation)
5. Set "Once Per Bar Close" to avoid false signals during bar formation
6. Configure delivery method (popup, email, webhook for automation platforms)
7. Save alert - it will fire automatically when conditions are met
**Alert Message Placeholders:**
Alerts use TradingView's dynamic placeholder system:
- {{ticker}} = Symbol name (e.g., NQ1!)
- {{close}} = Current price at signal
- {{plot_1}} = Calculated take profit level
- {{plot_2}} = Calculated stop loss level
These placeholders populate automatically, creating detailed notification messages without manual configuration.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This indicator is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Indicator**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the indicator ideal for active day traders who want continuous alert opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased signal frequency also means higher potential commission costs and requires disciplined trade selection when acting on alerts.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The indicator normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The indicator uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The indicator tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The indicator uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. Visual markers (arrows and labels) appear on chart, and configured alerts fire immediately.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents alert spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long alert will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The indicator includes a multi-timeframe ATR filter to avoid alerts during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while viewing 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Alerts enabled
- If ATR < threshold: No alerts fire
This prevents notifications during dead zones where mean reversion is unreliable due to insufficient price movement. The ATR status is displayed in the info table with visual confirmation (✓ or ✗).
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. These levels are displayed as visual lines with labels and included in alert messages for reference when manually placing orders.
### Stop Loss Placement:
Stop losses are calculated just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. SL levels are displayed on chart and included in alert notifications as suggested stop placement.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
## INPUT PARAMETERS:
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long alert generation on/off
- **Enable Short Scalps**: Toggle short alert generation on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between alerts (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for alert enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time indicator status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Long Color**: Customize long signal color (default: darker green for readability)
- **Short Color**: Customize short signal color (default: red)
- **TP/SL Colors**: Customize take profit and stop loss line colors
- **Line Length**: Visual length of TP/SL reference lines (5-200 bars)
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short alerts
- **TP/SL reference lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing channel status, last signal, entry/TP/SL prices, risk/reward ratio, and ATR filter status
- **Visual confirmation** when alerts fire via on-chart markers synchronized with notifications
## HOW TO USE:
### For 1-3 Minute Scalping with Alerts (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars to reduce alert spam
- **Alert Setup**: Configure "Any Entry Signal" for combined long/short notifications
- **Execution**: When alert fires, verify chart visuals, then manually place limit order at entry zone with provided TP/SL levels
### For 5-15 Minute Day Trading with Alerts:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- **Alert Setup**: Configure separate "Long Scalp Entry" and "Short Scalp Entry" alerts if you trade directionally based on bias
- **Execution**: Review channel structure on alert, confirm ATR filter shows ✓, then enter manually
### For 30-60 Minute Swing Scalping with Alerts:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- **Alert Setup**: Use "New Channel Formation" to prepare for setups, then "Any Entry Signal" for execution alerts
- **Execution**: Larger timeframes allow more analysis time between alert and entry
### Webhook Integration for Semi-Automation:
- Configure alert webhook URL to connect with platforms like TradersPost, TradingView Paper Trading, or custom automation
- Alert message includes all necessary order parameters (direction, entry, TP, SL)
- Webhook receives structured data when signal fires
- External platform can auto-execute based on alert payload
- Still maintains manual oversight vs full strategy automation
## USAGE CONSIDERATIONS:
- **Manual Discipline Required**: Alerts provide opportunities but execution requires judgment. Not all alerts should be taken - consider market context, trend, and channel quality
- **Alert Timing**: Alerts fire on bar close by default. Ensure "Once Per Bar Close" is selected to avoid false signals during bar formation
- **Notification Delivery**: Mobile/email alerts may have 1-3 second delay. For immediate execution, use desktop popups or webhook automation
- **Cooldown Necessity**: Without cooldown, rapidly touching price action can generate excessive alerts. Start with 3-bar cooldown and adjust based on alert volume
- **ATR Filter Impact**: Enabling ATR filter dramatically reduces alert count but improves quality. Track filter status in info table to understand when you're receiving fewer alerts
- **Commission Awareness**: High alert frequency means high potential trade count. Calculate if your commission structure supports frequent scalping before acting on all alerts
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features are not included in this indicator version. Multi-timeframe ATR requires higher-tier TradingView subscription for request.security() functionality on timeframes below chart timeframe.
## KNOWN LIMITATIONS:
- **Indicator does not execute trades** - alerts are informational only; you must manually place all orders
- **Alert delivery depends on TradingView infrastructure** - delays or failures possible during platform issues
- **No position tracking** - indicator doesn't know if you're in a trade; you must manage open positions independently
- **TP/SL levels are reference only** - you must manually set these on your broker platform; they are not live orders
- **Immediate touch entry can generate many alerts** in choppy zones without adequate cooldown
- **Channel deletion at 10-tick breaks** may be too aggressive or lenient depending on instrument tick size
- **ATR filter from lower timeframes** requires TradingView Premium/Pro+ for request.security()
- **Mean reversion logic fails** in strong breakout scenarios - alerts will fire but trades may hit stops
- **No partial closing capability** - full position management is manual; you determine scaling out
- **Alerts do not account for gaps** or overnight price changes; morning alerts may be stale
## RISK DISCLOSURE:
Trading involves substantial risk of loss. This indicator provides signals for educational and informational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Mean reversion strategies can experience extended drawdowns during trending markets. Alerts are not guaranteed to be profitable and should be combined with your own analysis. Stop losses may not fill at intended levels during extreme volatility or gaps. Never trade with capital you cannot afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Always verify alerts against current market conditions before executing trades manually.
## ACKNOWLEDGMENT & CREDITS:
This indicator is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based alert generation, comprehensive alert condition system with customizable notifications, multi-timeframe ATR volatility filtering, cooldown period for alert management, dual TP methods (fixed points vs channel percentage), visual TP/SL reference lines, and real-time status monitoring table. This indicator version is specifically designed for manual traders who prefer alert-based decision making over automated execution.
Macro & Earnings Dashboard — NY Fed CalendarMacro & Earnings Dashboard — NY Fed Calendar
This is an overlay indicator designed to provide a quick, real-time overview of the most critical upcoming US economic data releases and corporate earnings reports directly on your TradingView chart. It functions as a dynamic dashboard, removing the need to constantly check external calendars.
Key Features
1. Real-Time Economic Calendar (Bottom-Right Table)
The dashboard tracks the time remaining until the next release of five major, high-impact economic indicators. The data for these dates is pre-loaded directly from the New York Fed Economic Indicators Calendar (currently loaded for October through December 2025).
The tracked events include:
CPI (Consumer Price Index)
PPI (Producer Price Index)
Employment Situation (Non-Farm Payrolls / Unemployment Rate)
Interest Rate Decision (FOMC Meetings)
Consumer Sentiment (University of Michigan Survey)
2. Corporate Earnings Tracker (Top-Right Table)
This table uses TradingView's built-in data to calculate the estimated days remaining until the next Earnings Per Share (EPS) report for a curated list of high-profile NASDAQ tickers:
AAPL, NVDA, GOOG, TSLA, MSFT, AMZN, META
3. Color-Coded Urgency
The "Days" column for both macro and earnings tables uses a traffic light system to instantly communicate how soon the event is:
Red: The event is scheduled for Today or Tomorrow (0–1 day away).
Orange: The event is scheduled for the current week (within 6 days).
Teal: The event is more than a week away.
Gray: The date is currently unavailable or outside the loaded calendar range.
EMA-RSI-ADX Trend Bands
📌 EMA-RSI-ADX Trend Bands (ERA Trend Bands)
🔥 Overview
The ERA Trend Bands indicator combines Exponential Moving Average (EMA), Relative Strength Index (RSI), and Average Directional Index (ADX) into a powerful multi-factor trend system.
It helps traders:
Identify trend direction (Bullish / Bearish)
Measure trend strength using EMA deviation bands
Confirm momentum with RSI & ADX filters
Visualize conditions with dynamic colors, labels, tables, and signals
⚡ Key Features
📍 EMA Trend Bands
EMA100 with gradient glow effect showing trend bias
Strength bands around EMA (Very Weak → Hyper levels)
Bands color-coded for bullish/bearish extremes
📊 RSI + ADX Confluence
Bullish Signal: RSI ≥ threshold & ADX ≥ threshold → 🟢
Bearish Signal: RSI ≤ threshold & ADX ≤ threshold → 🔴
Candles recolored when conditions are met
Auto-generated labels show live RSI/ADX values
🧩 Strength Levels
Classifies deviation from EMA into 8 levels:
Neutral → Very Weak → Weak → Moderate → Strong → Very Strong → Extreme → Hyper
Dashboard table shows deviation % ranges & strength colors
Dynamic labels display Trend, Strength, Deviation %, RSI & ADX
🎨 Visual Enhancements
Gradient EMA line with glow effect
Bullish (greens) & bearish (reds) vibrant palettes
Background coloring (optional) based on strength
Symbols & labels for entry confirmation
🎯 How to Use
Trend Direction – EMA color + deviation bands show whether market is bullish or bearish.
Strength Confirmation – Use strength labels & dashboard table to gauge overextension.
Entry Signals – Watch for RSI/ADX confluence (green/red labels on chart).
Exits – Monitor when strength fades back toward Neutral/Weak levels.
⚙️ Settings & Inputs
EMA Settings → Length, Line Width, Gradient Intensity
RSI Settings → Length & Thresholds (Bullish / Bearish)
ADX Settings → Length & Thresholds (Bullish / Bearish)
Bands → Enable/disable EMA deviation bands
Labels/Table → Toggle strength info display
Colors → Fully customizable vibrant palettes
🚨 Alerts & Signals
Bullish Condition → RSI & ADX above thresholds
Bearish Condition → RSI & ADX below thresholds
Visual confirmation with labels, candles, and background
⚠️ Disclaimer
This script is for educational purposes only.
It does not constitute financial advice.
Always backtest and use proper risk management before trading live.
✨ Add EMA-RSI-ADX Trend Bands (ERA Trend Bands) to your chart to trade with clarity, strength, and precision.
Strat Failed 2-Up/2-Down Scanner v2**Strat Failed 2-Up/2-Down Scanner**
The Strat Failed 2-Up/2-Down Scanner is designed for traders using The Strat methodology, developed by Rob Smith, to identify key reversal patterns in any market and timeframe. This indicator detects two specific candlestick patterns: Failed 2-Up (bearish) and Failed 2-Down (bullish), which signal potential reversals when a directional move fails to follow through.
**What It Does**
- **Failed 2-Up**: Identifies a bearish candle where the low and high are higher than the previous candle’s low and high, but the close is below the open, indicating a failed attempt to continue an uptrend. These are marked with a red candlestick, a red downward triangle above the bar, and a table entry.
- **Failed 2-Down**: Identifies a bullish candle where the high and low are lower than the previous candle’s high and low, but the close is above the open, signaling a failed downtrend. These are marked with a green candlestick, a green upward triangle below the bar, and a table entry.
- A table in the top-right corner displays the signal type ("Failed 2-Up" or "Failed 2-Down") and the ticker symbol for quick reference.
- Alerts are provided for both patterns, making the indicator compatible with TradingView’s screener for automated scanning.
**How It Works**
The indicator analyzes each candlestick’s high, low, and close relative to the previous candle:
- Failed 2-Up: `low > low `, `high > high `, `close < open`.
- Failed 2-Down: `high < high `, `low < low `, `close > open`.
When these conditions are met, the indicator applies visual markers (colored bars and triangles) and updates the signal table. Alert conditions trigger notifications for integration with TradingView’s alert system.
**How to Use**
1. Apply the indicator to any chart (stocks, forex, crypto, etc.) on any timeframe (e.g., 1-minute, hourly, daily).
2. Monitor the chart for red (Failed 2-Up) or green (Failed 2-Down) candlesticks with corresponding triangles.
3. Check the top-right table for the latest signal and ticker.
4. Set alerts by selecting “Failed 2-Up Detected” or “Failed 2-Down Detected” in TradingView’s alert menu to receive notifications (e.g., via email or app).
5. Use the signals to identify potential reversal setups in conjunction with other Strat-based analysis, such as swing levels or time-based strategies.
**Originality**
Unlike other Strat indicators that may focus on swing levels or complex candlestick combinations, this scanner specifically targets Failed 2-Up and Failed 2-Down patterns with clear, minimalist visualizations (bars, triangles, table) and robust alert functionality. Its simplicity makes it accessible for both novice and experienced traders using The Strat methodology.
**Ideal For**
Day traders, swing traders, and scalpers looking to capitalize on reversal signals in trending or ranging markets. The indicator is versatile for any asset class and timeframe, enhancing trade decision-making with The Strat’s pattern-based approach.
Trade Calculator {Phanchai}Trade Calculator 🧮 {Phanchai} — Documentation
A lightweight sizing helper for TradingView that turns your risk per trade into an estimated maximum nominal position size — using the most recent chart low as your stop reference. Built for speed and clarity right on the chart.
Key Features
Clean on-chart info table with configurable font size and position.
Row toggles: show/hide each line (Price, Last Low, Risk per Trade, Entry − Low, SL to Low %, Max. Nominal Value in USDT).
Configurable low reference: Last N bars or Running since load .
Low label placed exactly at the wick of the lowest bar (no horizontal line).
Custom padding: add extra rows above/below and blank columns left/right (with custom whitespace/text fillers) to fine-tune layout.
Integer display for Risk per Trade (USDT) and Max. Nominal Value (USDT); decimals configurable elsewhere.
Open source script — easy to read and extend.
How to Use
Add the indicator: open TradingView → Indicators → paste the source code → Add to chart.
Pick your low reference in settings:
Last N bars — uses the lowest low within your chosen lookback.
Running since load — tracks the lowest low since the script loaded.
Set your capital and risk:
Total Capital — your account size in USDT.
Max. invest Capital per Trade (%) — your risk per trade as a percent of Total Capital.
Tidy the table:
Use Table Position and Table Size to place it.
Add Extra rows/columns and set left/right fillers (spaces allowed) for padding.
Toggle individual rows (on/off) to show only what you need.
Read the numbers:
Act. Price in USDT — current close.
Last Low in USDT — stop reference price.
Risk per Trade — whole-USDT value of your risk budget for this trade.
Entry − Low — absolute risk per unit.
SL to Low (%) — percentage distance from price to low.
Max. Nominal Value in USDT — estimated max nominal position size given your risk budget and stop at the low.
Scope
This calculator is designed for long trades only (stop below price at the chart low).
Notes & Assumptions
Does not factor fees, funding, slippage, tick size, or broker/venue position limits.
“Running since load” updates as new lows appear; “Last N bars” uses only the selected lookback window.
If price equals the low (zero distance), sizing will be undefined (division by zero guarded as “—”).
Risk Warning
Trading involves substantial risk. Always double-check every value the calculator shows, confirm your stop distance, and verify position sizing with your broker/platform before entering any order. Never risk money you cannot afford to lose.
Open Source & Feedback
The source code is open. If you spot a bug or have an idea to improve the tool, feel free to share suggestions — I’m happy to iterate and make it better.
BPS Multi-MA 5 — 22/30, SMA/WMA/EMA# Multi-MA 5 — 22/30 base, SMA/WMA/EMA
**What it is**
A lightweight 5-line moving-average ribbon for fast visual bias and trend/mean-reversion reads. You can switch the MA type (SMA/WMA/EMA) and choose between two ways of setting lengths: by monthly “session-based” base (22 or 30) with multipliers, or by entering exact lengths manually. An optional info table shows the effective settings in real time.
---
## How it works
* Calculates five moving averages from the selected price source.
* Lengths are either:
* **Multipliers mode:** `Base × Multiplier` (e.g., base 22 → 22/44/66/88/110), or
* **Manual mode:** any five exact lengths (e.g., 10/22/50/100/200).
* Plots five lines with fixed legend titles (MA1…MA5); the **info table** displays the actual type and lengths.
---
## Inputs
**Length Mode**
* **Multipliers** — choose a **Base** of **22** (≈ trading sessions per month) or **30** (calendar-style, smoother) and set **×1…×5** multipliers.
* **Manual** — enter **Len1…Len5** directly.
**MA Settings**
* **MA Type:** SMA / WMA / EMA
* **Source:** any series (e.g., `close`, `hlc3`, etc.)
* **Use true close (ignore Heikin Ashi):** when enabled, the MA is computed from the underlying instrument’s real `close`, not HA candles.
* **Show info table:** toggles the on-chart table with the current mode, type, base, and lengths.
---
## Quick start
1. Add the indicator to your chart.
2. Pick **MA Type** (e.g., **WMA** for faster response, **SMA** for smoother).
3. Choose **Length Mode**:
* **Multipliers:** set **Base = 22** for session-based monthly lengths (stocks/FX), or **30** for heavier smoothing.
* **Manual:** enter your exact lengths (e.g., 10/22/50/100/200).
4. (Optional) On **Heikin Ashi** charts, enable **Use true close** if you want the lines based on the instrument’s real close.
---
## Tips & notes
* **1 month ≈ 21–22 sessions.** Using 30 as “monthly” yields a smoother, more delayed curve.
* **WMA** reacts faster than **SMA** at the same length; expect earlier signals but more whipsaws in chop.
* **Len = 1** makes the MA track the chosen source (e.g., `close`) almost exactly.
* If changing lengths doesn’t move the lines, ensure you’re editing fields for the **active Length Mode** (Multipliers vs Manual).
* For clean comparisons, use the **same timeframe**. If you later wrap this in MTF logic, keep `lookahead_off` and handle gaps appropriately.
---
## Use cases
* Trend ribbon and dynamic bias zones
* Pullback entries to the mid/slow lines
* Crossovers (fast vs slow) for confirmation
* Volatility filtering by spreading lengths (e.g., 22/44/88/132/176)
---
**Credits:** Built for clarity and speed; designed around session-based “monthly” lengths (22) or smoother calendar-style (30).
[c3s] CWS - M2 Global Liquidity Index & BTC Correlation CWS - M2 Global Liquidity Index with Offset BTC Correlation
This custom indicator visualizes and analyzes the relationship between the global M2 money supply and Bitcoin (BTC) price movements. It calculates the correlation between these two variables to provide insights into how changes in global liquidity may impact Bitcoin’s price over time.
Key Features:
Global M2 Liquidity Index Calculation:
Fetches M2 money supply data from multiple economies (China, US, EU, Japan, UK) and normalizes using currency exchange rates (e.g., CNY/USD, EUR/USD).
Combines all M2 data points and normalizes by dividing by 1 trillion (1e12) for easier visualization.
Offset for M2 Data:
The offset parameter allows users to shift the M2 data by a specified number of days, helping track the influence of past global liquidity on Bitcoin.
BTC Price Correlation:
Computes the correlation between shifted global M2 liquidity and Bitcoin (BTC) price, using a 52-day lookback period by default.
Correlation Quality Display:
Categorizes correlation quality as:
Excellent : Correlation >= 0.8
Good : Correlation >= 0.6 and < 0.8
Weak : Correlation >= 0.4 and < 0.6
Very Weak : Correlation < 0.4
Displays correlation quality as a label on the chart for easy assessment.
Visual Enhancements:
Labels : Displays dynamic labels on the chart with metrics like M2 value and correlation.
Plot Shapes : Uses shapes to indicate data availability for global M2 and correlation.
Data Table : Optionally shows a data table in the top-right corner summarizing:
Global M2 value (in trillions)
The correlation between global M2 and BTC
The correlation quality
Optional Debugging:
Debug plots help identify when data is missing for M2 or correlation, ensuring transparency and accurate functionality.
Inputs:
Offset: Shift the M2 data (in days) to see past liquidity effects on Bitcoin.
Lookback Period: Number of periods (default 52) used to calculate the correlation.
Show Labels: Toggle to show or hide labels for M2 and correlation values.
Show Table: Toggle to show or hide the data table in the top-right corner.
Usage:
Ideal for traders and analysts seeking to understand the relationship between global liquidity and Bitcoin price. The offset and lookback period can be adjusted to explore different timeframes and correlation strengths, aiding more informed trading decisions.
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.
OBR 15min Session Opening Range Breakout + Volume Trend DeltaQuick Overview
This Pine Script plots the opening range for London and New York sessions, highlights breakout levels, draws previous session pivots, and offers a live volume delta table for trend confirmation.
Session Opening Range
- Captures the high/low of the first 15 minutes (configurable) for both London & NY sessions.
- Fills the range area with adjustable semi‑transparent colors.
- Optional alerts fire on breakout above the high or below the low.
Previous Session Levels
- Automatically draws previous day’s High, Low, Open and previous 4‑hour High/Low.
- Helps identify key S/R zones as price approaches ORB breakouts.
Volume Trend Delta
- Uses a CMO‑weighted moving average and ATR bands to detect trend state.
- Accumulates bullish vs. bearish volume during each trend.
- Displays Bull Vol, Bear Vol, and Delta % in a movable table for quick strength checks.
How to Use
1. Let the opening range complete (first 15 min).
2. Look for price closing above/below the ORB—enter long on an upside break, short on a downside break.
3. Check the Volume Delta table: positive delta confirms buying strength; negative delta confirms selling pressure.
4. Use previous day/4h levels as additional support/resistance filters.
Settings & Customization
- ORB Duration & Session Times (London/NY), fill colors, and toggles.
- Enable/disable Previous Day & 4H levels.
- Trend Period, Momentum Window, and Delta table position/size.
- Pre‑built alert conditions for all ORB breakouts.
Developer Notes
- Fully commented for easy adjustments.
- Modular sections: ORB, previous levels, trend delta, and alerts.
- No external libraries—pure Pine Script v6.
Tip
Combine ORB breakouts with Volume Delta and prior session pivots to filter false signals and trade stronger, more reliable moves.
ATR Buy, Target, Stop + OverlayATR Buy, Target, Stop + Overlay
This tool is to assist traders with precise trade planning using the Average True Range (ATR) as a volatility-based reference.
This script plots buy, target, and stop-loss levels on the chart based on a user-defined buy price and ATR-based multipliers, allowing for objective and adaptive trade management.
*NOTE* In order for the indicator to initiate plotted lines and table values a non-zero number must be entered into the settings.
What It Does:
Buy Price Input: Users enter a manual buy price (e.g., an executed or planned trade entry).
ATR-Based Target and Stop: The script calculates:
Target Price = Buy + (ATR × Target Multiplier)
Stop Price = Buy − (ATR × Stop Multiplier)
Customizable Timeframe: Optionally override the ATR timeframe (e.g., use daily ATR on a 1-hour chart).
Visual Overlay: Lines are drawn directly on the price chart for the Buy, Target, and Stop levels.
Interactive Table: A table is displayed with relevant levels and ATR info.
Customization Options:
Line Settings:
Adjust color, style (solid/dashed/dotted), and width for Buy, Target, and Stop lines.
Choose whether to extend lines rightward only or in both directions.
Table Settings:
Choose position (top/bottom, left/right).
Toggle individual rows for Buy, Target, Stop, ATR Timeframe, and ATR Value.
Customize text color and background transparency.
How to Use It for Trading:
Plan Your Trade: Enter your intended buy price when planning a trade.
Assess Risk/Reward: The script immediately visualizes the potential stop-loss and target level, helping assess R:R ratios.
Adapt to Volatility: Use ATR-based levels to scale stop and target dynamically depending on current market volatility.
Higher Timeframe ATR: Select a different timeframe for the ATR calculation to smooth noise on lower timeframe charts.
On-the-Chart Reference: Visually track trade zones directly on the price chart—ideal for live trading or strategy backtesting.
Ideal For:
Swing traders and intraday traders
Risk management and trade planning
Traders using ATR-based exits or scaling
Visualizing asymmetric risk/reward setups
How I Use This:
After entering a trade, adding an entry price will plot desired ATR target and stop level for visualization.
Adjusting ATR multiplier values assists in evaluating and planning trades.
Visualization assists in comparing ATR multiples to recent support and resistance levels.
Rolling VWAP LevelsRolling VWAP Levels Indicator
Overview
Dynamic horizontal lines showing rolling Volume Weighted Average Price (VWAP) levels for multiple timeframes (7D, 30D, 90D, 365D) that update in real-time as new bars form.
Who This Is For
Day traders using VWAP as support/resistance
Swing traders analyzing multi-timeframe price structure
Scalpers looking for mean reversion entries
Options traders needing volatility bands for strike selection
Institutional traders tracking volume-weighted fair value
Risk managers requiring dynamic stop levels
How To Trade With It
Mean Reversion Strategies:
Buy when price is below VWAP and showing bullish divergence
Sell when price is above VWAP and showing bearish signals
Use multiple timeframes - enter on shorter, confirm on longer
Target opposite VWAP level for profit taking
Breakout Trading:
Watch for price breaking above/below key VWAP levels with volume
Use 7D VWAP for intraday breakouts
Use 30D/90D VWAP for swing trade breakouts
Confirm breakout with move beyond first standard deviation band
Support/Resistance Trading:
VWAP levels act as dynamic support in uptrends
VWAP levels act as dynamic resistance in downtrends
Multiple timeframe VWAP confluence creates stronger levels
Use standard deviation bands as additional S/R zones
Risk Management:
Place stops beyond next VWAP level
Use standard deviation bands for position sizing
Exit partial positions at VWAP levels
Monitor distance table for overextended moves
Key Features
Real-time Updates: Lines move and extend as new bars form
Individual Styling: Custom colors, widths, styles for each timeframe
Standard Deviation Bands: Optional volatility bands with custom multipliers
Smart Labels: Positioned above, below, or diagonally relative to lines
Distance Table: Shows percentage distance from each VWAP level
Alert System: Get notified when price crosses VWAP levels
Memory Efficient: Automatically cleans up old drawing objects
Settings Explained
Display Group: Show/hide labels, font size, line transparency, positioning
Individual VWAP Groups: Color, line width (1-5), line style for each timeframe
Standard Deviation Bands: Enable bands with custom multipliers (0.5, 1.0, 1.5, 2.0, etc.)
Labels Group: Position (8 options including diagonal), custom text, price display
Additional Info: Distance table, alert conditions
Technical Implementation
Uses rolling arrays to maintain sliding windows of price*volume data. The core calculation function processes both VWAP and standard deviation efficiently. Lines are created dynamically and updated every bar. Memory management prevents object accumulation through automatic cleanup.
Best Practices
Start with 7D and 30D VWAP for most strategies
Add 90D/365D for longer-term context
Use standard deviation bands when volatility matters
Position labels to avoid chart clutter
Enable distance table during high volatility periods
Set alerts for key VWAP level breaks
Market Applications
Forex: Major pairs during London/NY sessions
Stocks: Large cap names with good volume
Crypto: Bitcoin, Ethereum, major altcoins
Futures: ES, NQ, CL, GC with continuous volume
Options: Use SD bands for strike selection and volatility assessment
Share SizePurpose: The "Share Size" indicator is a powerful risk management tool designed to help traders quickly determine appropriate share/contract sizes based on their predefined risk per trade and the current market's volatility (measured by ATR). It calculates potential dollar differences from recent highs/lows and translates them into a recommended share/contract size, accounting for a user-defined ATR-based offset. This helps you maintain consistent risk exposure across different instruments and market conditions.
How It Works: At its core, the indicator aims to answer the question: "How many shares/contracts can I trade to keep my dollar risk within limits if my stop loss is placed at a recent high or low, plus an ATR-based buffer?"
Price Difference Calculation: It first calculates the dollar difference between the current close price and the high and low of the current bar (Now) and the previous 5 bars (1 to 5).
Tick Size & Value Conversion: These price differences are then converted into dollar values using the instrument's specific tickSize and tickValue. You can select common futures contracts (MNQ, MES, MGC, MCL), a generic "Stock" setting, or define custom values.
ATR Offset: An Average True Range (ATR) based offset is added to these dollar differences. This offset acts as a buffer, simulating a stop loss placed beyond the immediate high/low, accounting for market noise or volatility.
Risk-Based Share Size: Finally, using your Default Risk ($) input, the indicator calculates how many shares/contracts you can take for each of the 6 high/low scenarios (current bar, 5 previous bars) to ensure your dollar risk per trade remains constant.
Dynamic Table: All these calculations are presented in a clear, real-time table at the bottom-left of your chart. The table dynamically adjusts its "Label" to show the selected symbol preset, making it easy to see which instrument's settings are currently being used. The "Shares" rows indicate the maximum shares/contracts you can trade for a given risk and stop placement. The cells corresponding to the largest dollar difference (and thus smallest share size) for both high and low scenarios are highlighted, drawing your attention to the most conservative entry points.
Key Benefits:
Consistent Risk: Helps maintain a consistent dollar risk per trade, regardless of the instrument or its current price/volatility.
Dynamic Sizing: Automatically adjusts share/contract size based on market volatility and your chosen stop placement.
Quick Reference: Provides a real-time, easy-to-read table directly on your chart, eliminating manual calculations.
Informed Decision Making: Assists in quickly assessing trade opportunities and potential position sizes.
Setup Parameters (Inputs)
When you add the "Share Size" indicator to your chart, you'll see a settings dialog with the following parameters:
1. Symbol Preset:
Purpose: This is the primary setting to define the tick size and value for your chosen trading instrument.
Options:
MNQ (Micro Nasdaq 100 Futures)
MES (Micro E-mini S&P 500 Futures)
MGC (Micro Gold Futures)
MCL (Micro Crude Oil Futures)
Stock (Generic stock setting, with tick size/value of 0.01)
Custom (Allows you to manually input tick size and value)
Default: MNQ
Importance: Crucial for accurate dollar calculations. Ensure this matches the instrument you are trading.
2. Tick Size (Manual Override):
Purpose: Only used if Symbol Preset is set to Custom. This defines the smallest price increment for your instrument.
Type: Float
Default: 0.25
Hidden: This input is hidden (display=display.none) unless "Custom" is selected. You might need to change display=display.none to display=display.inline in the code if you want to see and adjust it directly in the settings for "Custom" mode.
3. Tick Value (Manual Override):
Purpose: Only used if Symbol Preset is set to Custom. This defines the dollar value of one tickSize increment.
Type: Float
Default: 0.50
Hidden: This input is hidden (display=display.none) unless "Custom" is selected. Similar to Tick Size, you might need to adjust its display property if you want it visible.
4. Default Risk ($):
Purpose: This is your maximum desired dollar risk per trade. All share size calculations will be based on this value.
Type: Float
Default: 50.0
Hidden: This input is hidden (display=display.none). It's a critical setting, so consider making it visible by changing display=display.none to display=display.inline in the code if you want users to easily adjust their risk.
ATR Offset Settings (Group): This group of settings allows you to fine-tune the ATR-based buffer added to your potential stop loss.
5. ATR Offset Length:
Purpose: Defines the lookback period for the Average True Range (ATR) calculation used for the offset.
Type: Integer
Default: 7
Hidden: This input is hidden (display=display.none).
6. ATR Offset Timeframe:
Purpose: Specifies the timeframe on which the ATR for the offset will be calculated. This allows you to use ATR from a higher timeframe for your stop buffer, even if your chart is on a lower timeframe.
Type: Timeframe string (e.g., "1" for 1 minute, "60" for 1 hour, "D" for Daily)
Default: "1" (1 Minute)
Hidden: This input is hidden (display=display.none).
7. ATR Offset Multiplier (x ATR):
Purpose: Multiplies the calculated ATR value to determine the final dollar offset added to your high/low price difference. A value of 1.0 means one full ATR is added. A value of 0.5 means half an ATR is added.
Type: Float
Minimum Value: 0 (no offset)
Default: 1.0
Hidden: This input is hidden (display=display.none).
SHYY TFC SPX Sectors list This script provides a clean, configurable table displaying real-time data for the major SPX sectors, key indices, and market sentiment indicators such as VIX and the 10-year yield (US10Y).
It includes 16 columns with two rows:
* The top row shows the sector/asset symbol.
* The bottom row shows the most recent daily close price.
Each price cell is dynamically color-coded based on:
* Direction (green/red) during regular trading hours
* Separate colors during extended hours (pre-market or post-market)
* VIX values greater than 30 trigger a distinct background highlight
Users can fully control the position of the table on the chart via input settings. This flexibility allows traders to place the table in any screen corner or center without overlapping key price action.
The script is designed for:
* Monitoring broad market health at a glance
* Understanding sector performance in real-time
* Spotting risk-on/risk-off behavior (via SPY, QQQ, VIX, US10Y)
Unlike traditional watchlists, this table visually encodes directional movement and trading session context (regular vs. extended hours), making it highly actionable for intraday, swing, or macro-level analysis.
All data is pulled using `request.security()` on daily candles and uses pure Pine logic without external dependencies.
To use:
1. Add the indicator to your chart.
2. Adjust the table position via the input dropdown.
3. Read sector strength or weakness directly from the table.
CCI Divergence Detector
A technical analysis tool that identifies divergences between price action and the Commodity Channel Index (CCI) oscillator. Unlike standard divergence indicators, this system employs advanced gradient visualization, multi-layer wave effects, and comprehensive customization options to provide traders with crystal-clear divergence signals and market momentum insights.
Core Detection Mechanism
CCI-Based Analysis: The indicator utilizes the Commodity Channel Index as its primary oscillator, calculated from user-configurable source data (default: HLC3) with adjustable length parameters. The CCI provides reliable momentum readings that effectively highlight price-momentum divergences.
Dynamic Pivot Detection: The system employs adaptive pivot detection with three sensitivity levels (High/Normal/Low) to identify significant highs and lows in both price and CCI values. This dynamic approach ensures optimal divergence detection across different market conditions and timeframes.
Dual Divergence Analysis:
Regular Bullish Divergences: Detected when price makes lower lows while CCI makes higher lows, indicating potential upward reversal
Regular Bearish Divergences: Identified when price makes higher highs while CCI makes lower highs, signaling potential downward reversal
Strength Classification System: Each detected divergence is automatically classified into three strength categories (Weak/Moderate/Strong) based on:
-Price differential magnitude
-CCI differential magnitude
-Time duration between pivot points
-User-configurable strength multiplier
Advanced Visual System
Multi-Layer Wave Effects: The indicator features a revolutionary wave visualization system that creates depth through multiple gradient layers around the CCI line. The wave width dynamically adjusts based on ATR volatility, providing intuitive visual feedback about market conditions.
Professional Color Gradient System: Nine independent color inputs control every visual aspect:
Bullish Colors (Light/Medium/Dark): Control oversold areas, wave effects, and strong bullish signals
Bearish Colors (Light/Medium/Dark): Manage overbought zones, wave fills, and strong bearish signals
Neutral Colors (Light/Medium/Dark): Handle table elements, zero line, and transitional states
Intelligent Color Mapping: Colors automatically adapt based on CCI values:
Overbought territory (>100): Bearish color gradients with increasing intensity
Neutral positive (0 to 100): Blend from neutral to bearish tones
Oversold territory (<-100): Bullish color gradients with increasing intensity
Neutral negative (-100 to 0): Transition from neutral to bullish tones
Key Features & Components
Advanced Configuration System: Eight organized input groups provide granular control:
General Settings: System enable, pivot length, confidence thresholds
Oscillator Selection: CCI parameters, overbought/oversold levels, normalization options
Detection Parameters: Divergence types, minimum strength requirements
Sensitivity Tuning: Pivot sensitivity, divergence threshold, confirmation bars
Visual System: Line thickness, labels, backgrounds, table display
Wave Effects: Dynamic width, volatility response, layer count, glow effects
Transparency Controls: Independent transparency for all visual elements
Smoothing & Filtering: CCI smoothing types, noise filtering, wave smoothing
Professional Alert System: Comprehensive alert functionality with dynamic messages including:
-Divergence type and strength classification
-Current CCI value and confidence percentage
-Customizable alert frequency and conditions
Enhanced Information Table: Real-time display showing:
-Current CCI length and value
-Market status (Overbought/Normal/Oversold)
-Active sensitivity setting
Configurable table positioning (4 corner options)
Visual Elements Explained
Primary CCI Line: Main oscillator plot with gradient coloring that reflects market momentum and CCI intensity. Line thickness is user-configurable (1-8 pixels).
Wave Effect Layers: Multi-layer gradient fills creating a dynamic wave around the
CCI line:
-Outer layers provide broad market context
-Inner layers highlight immediate momentum
-Core layers show precise CCI movement
-All layers respond to volatility and momentum changes
Divergence Lines & Labels:
-Solid lines connecting divergence pivot points
-Color-coded based on divergence type and strength
-Labels displaying divergence type and strength classification
-Customizable transparency and size options
Reference Lines:
-Zero line with neutral color coding
-Overbought level (default: 100) with bearish coloring
-Oversold level (default: -100) with bullish coloring
Background Gradient: Optional background coloring that reflects CCI intensity and market conditions with user-controlled transparency (80-99%).
Configuration Options
Sensitivity Controls:
Pivot sensitivity: High/Normal/Low detection levels
Divergence threshold: 0.1-2.0 sensitivity range
Confirmation bars: 1-5 bar confirmation requirement
Strength multiplier: 0.1-3.0 calculation adjustment
Visual Customization:
Line transparency: 0-90% for main elements
Wave transparency: 0-95% for fill effects
Background transparency: 80-99% for subtle background
Label transparency: 0-50% for text elements
Glow transparency: 50-95% for glow effects
Advanced Processing:
Five smoothing types: None/SMA/EMA/RMA/WMA
Noise filtering with adjustable threshold (0.1-10.0)
CCI normalization for enhanced gradient scaling
Dynamic wave width with ATR-based volatility response
Interpretation Guidelines
Divergence Signals:
Strong divergences: High-confidence reversal signals requiring immediate attention
Moderate divergences: Reliable signals suitable for most trading strategies
Weak divergences: Early warning signals best combined with additional confirmation
Wave Intensity: Wave width and color intensity provide real-time volatility and momentum feedback. Wider, more intense waves indicate higher market volatility and stronger momentum.
Color Transitions: Smooth color transitions between bullish, neutral, and bearish states help identify market regime changes and momentum shifts.
CCI Levels: Traditional overbought (>100) and oversold (<-100) levels remain relevant, but the gradient system provides more nuanced momentum reading between these extremes.
Technical Specifications
Compatible Timeframes: All timeframes supported
Maximum Labels: 500 (for divergence marking)
Maximum Lines: 500 (for divergence drawing)
Pine Script Version: v5 (latest optimization)
Overlay Mode: False (separate pane indicator)
Usage Recommendations
This indicator works best when:
-Combined with price action analysis and support/resistance levels
-Used across multiple timeframes for confirmation
-Integrated with proper risk management protocols
-Applied in trending markets for divergence-based reversal signals
-Utilized with other technical indicators for comprehensive analysis
Risk Disclaimer: Trading involves substantial risk of loss. This indicator is provided for analytical purposes only and does not constitute financial advice. Divergence signals, while powerful, are not guaranteed to predict future price movements. Past performance is not indicative of future results. Always use proper risk management and never trade with capital you cannot afford to lose.
Yearly History Calendar-Aligned Price up to 10 Years)Overview
This indicator helps traders compare historical price patterns from the past 10 calendar years with the current price action. It overlays translucent lines (polylines) for each year’s price data on the same calendar dates, providing a visual reference for recurring trends. A dynamic table at the top of the chart summarizes the active years, their price sources, and history retention settings.
Key Features
Historical Projections
Displays price data from the last 10 years (e.g., January 5, 2023 vs. January 5, 2024).
Price Source Selection
Choose from Open, Low, High, Close, or HL2 ((High + Low)/2) for historical alignment.
The selected source is shown in the legend table.
Bulk Control Toggles
Show All Years : Display all 10 years simultaneously.
Keep History for All : Preserve historical lines on year transitions.
Hide History for All : Automatically delete old lines to update with current data.
Individual Year Settings
Toggle visibility for each year (-1 to -10) independently.
Customize color and line width for each year.
Control whether to keep or delete historical lines for specific years.
Visual Alignment Aids
Vertical lines mark yearly transitions for reference.
Polylines are semi-transparent for clarity.
Dynamic Legend Table
Shows active years, their price sources, and history status (On/Off).
Updates automatically when settings change.
How to Use
Configure Settings
Projection Years : Select how many years to display (1–10).
Price Source : Choose Open, Low, High, Close, or HL2 for historical alignment.
History Precision : Set granularity (Daily, 60m, or 15m).
Daily (D) is recommended for long-term analysis (covers 10 years).
60m/15m provides finer precision but may only cover 1–3 years due to data limits.
Adjust Visibility & History
Show Year -X : Enable/disable specific years for comparison.
Keep History for Year -X : Choose whether to retain historical lines or delete them on new year transitions.
Bulk Controls
Show All Years : Display all 10 years at once (overrides individual toggles).
Keep History for All / Hide History for All : Globally enable/disable history retention for all years.
Customize Appearance
Line Width : Adjust polyline thickness for better visibility.
Colors : Assign unique colors to each year for easy identification.
Interpret the Legend Table
The table shows:
Year : Label (e.g., "Year -1").
Source : The selected price type (e.g., "Close", "HL2").
Keep History : Indicates whether lines are preserved (On) or deleted (Off).
Tips for Optimal Use
Use Daily Timeframes for Long-Term Analysis :
Daily (1D) allows 10+ years of data. Smaller timeframes (60m/15m) may have limited historical coverage.
Compare Recurring Patterns :
Look for overlaps between historical polylines and current price to identify potential support/resistance levels.
Customize Colors & Widths :
Use contrasting colors for years you want to highlight. Adjust line widths to avoid clutter.
Leverage Global Toggles :
Enable Show All Years for a quick overview. Use Keep History for All to maintain continuity across transitions.
Example Workflow
Set Up :
Select Projection Years = 5.
Choose Price Source = Close.
Set History Precision = 1D for long-term data.
Customize :
Enable Show Year -1 to Show Year -5.
Assign distinct colors to each year.
Disable Keep History for All to ensure lines update on year transitions.
Analyze :
Observe how the 2023 close prices align with 2024’s price action.
Use vertical lines to identify yearly boundaries.
Common Questions
Why are some years missing?
Ensure the chart has sufficient historical data (e.g., daily charts cover 10 years, 60m/15m may only cover 1–3 years).
How do I update the data?
Adjust the Price Source or toggle years/history settings. The legend table updates automatically.
Adaptive Freedom Machine w/labelsAdaptive Freedom Machine w/ Labels
Overview
The Adaptive Freedom Machine w/ Labels is a versatile Pine Script indicator designed to assist traders in identifying buy and sell opportunities across various market conditions (trending, ranging, or volatile). It combines Exponential Moving Averages (EMAs), Relative Strength Index (RSI), Average True Range (ATR), and customizable time filters to generate actionable signals. The indicator overlays on the price chart, displaying EMAs, a dynamic cloud, scaled RSI levels, buy/sell signals, and market condition labels, making it suitable for swing trading, day trading, or scalping.
What It Does
This indicator generates buy and sell signals based on the interaction of two EMAs, filtered by RSI thresholds, ATR-based volatility, and user-defined time windows. It adapts to the selected market condition by adjusting EMA lengths, RSI thresholds, and trading hours. A dynamic cloud highlights trend direction or neutral zones, and candlestick bodies are colored in neutral conditions for clarity. A table displays real-time trend and volatility status.
How It Works
The indicator uses the following components:
EMAs: Two EMAs (short and long) are calculated on a user-selected timeframe (1, 5, 15, 30, or 60 minutes). Their crossover or crossunder generates potential buy/sell signals, with lengths adjusted based on the market condition (e.g., longer EMAs for trending markets, shorter for ranging).
Dynamic Cloud: The area between the EMAs forms a cloud, colored green for uptrends, red for downtrends, or a user-defined color (default yellow) for neutral zones (when EMAs are close, determined by an ATR-based threshold). Users can widen the cloud for visibility.
RSI Filter: RSI is scaled to price levels and plotted on the chart (optional). Signals are filtered to ensure RSI is within user-defined buy/sell thresholds and not in overbought/oversold zones, with thresholds tailored to the market condition.
ATR Volatility Filter: An optional filter ensures signals occur during sufficient volatility (ATR(14) > SMA(ATR, 20)).
Time Filter: Signals are restricted to a user-defined or market-specific time window (e.g., 10:00–15:00 UTC for volatile markets), with an option for custom hours.
Visual Aids: Buy/sell signals appear as green triangles (buy) or red triangles (sell). Candlesticks in neutral zones are colored (default yellow). A table in the top-right corner shows the current trend (Uptrend, Downtrend, Neutral) and volatility (High or Low).
The indicator ensures compatibility with standard chart types (e.g., candlestick charts) to produce realistic signals, avoiding non-standard types like Heikin Ashi or Renko.
How to Use It
Add to Chart: Apply the indicator to a candlestick or bar chart on TradingView.
Configure Settings:
Timeframe: Choose a timeframe (1, 5, 15, 30, or 60 minutes) to align with your trading style.
Market Condition: Select one market condition (Trending, Ranging, or Volatile). Volatile is the default if none is selected. Only one condition can be active.
Filters:
Enable/disable the ATR volatility filter to trade only in high-volatility periods.
Enable the time filter and choose default hours (specific to the market condition) or set custom UTC hours.
Cloud Settings: Adjust the cloud width, neutral zone threshold, and color. Enable/disable the neutral cloud.
RSI Display: Toggle the scaled RSI and its thresholds on the chart.
Interpret Signals:
Buy Signal: A green triangle below the bar indicates a potential long entry (EMA crossover, RSI above buy threshold, within time window, and passing volatility filter).
Sell Signal: A red triangle above the bar indicates a potential short entry (EMA crossunder, RSI below sell threshold, within time window, and passing volatility filter).
Neutral Zone: Yellow candlesticks and cloud (if enabled) suggest a lack of clear trend; avoid trading or use for range-bound strategies.
Monitor the Table: Check the top-right table for real-time trend (Uptrend, Downtrend, Neutral) and volatility (High or Low) to confirm market context.
Unique Features
Adaptive Parameters: Automatically adjusts EMA lengths, RSI thresholds, and trading hours based on the selected market condition, reducing manual tweaking.
Neutral Zone Detection: Uses an ATR-based threshold to identify low-trend periods, helping traders avoid choppy markets.
Scaled RSI Visualization: Plots RSI and thresholds directly on the price chart, making it easier to assess momentum relative to price action.
Flexible Time Filtering: Supports both default and custom UTC-based trading windows, ideal for day traders targeting specific sessions.
Dynamic Cloud: Enhances trend visualization with customizable width and neutral zone coloring, improving readability.
Notes
Use on standard candlestick or bar charts to ensure realistic signals.
Test the indicator on a demo account to understand its behavior in your chosen market and timeframe.
Adjust settings to match your trading strategy, but avoid over-optimizing for past data.
The indicator is not a standalone system; combine it with other analysis (e.g., support/resistance, news events) for better results.
Limitations
Signals may lag in fast-moving markets due to EMA-based calculations.
Neutral zone detection may vary in extremely volatile or illiquid markets.
Time filters are UTC-based; ensure your platform’s timezone settings align.
This indicator is designed for traders seeking a customizable, trend-following tool that adapts to different market environments while providing clear visual cues and robust filtering.
BTC Daily DCA CalculatorThe BTC Daily DCA Calculator is an indicator that calculates how much Bitcoin (BTC) you would own today by investing a fixed dollar amount daily (Dollar-Cost Averaging) over a user-defined period. Simply input your start date, end date, and daily investment amount, and the indicator will display a table on the last candle showing your total BTC, total invested, portfolio value, and unrealized yield (in USD and percentage).
Features
Customizable Inputs: Set the start date, end date, and daily dollar amount to simulate your DCA strategy.
Results Table: Displays on the last candle (top-right of the chart) with:
Total BTC: The accumulated Bitcoin from daily purchases.
Total Invested ($): The total dollars invested.
Portfolio Value ($): The current value of your BTC holdings.
Unrealized Yield ($): Your profit/loss in USD.
Unrealized Yield (%): Your profit/loss as a percentage.
Visual Markers: Green triangles below the chart mark each daily investment.
Overlay on Chart: The table and markers appear directly on the BTCUSD price chart for easy reference.
Daily Timeframe: Designed for Daily (1D) charts to ensure accurate calculations.
How to Use
Add the Indicator: Apply the indicator to a BTCUSD chart (e.g., Coinbase:BTCUSD, Binance:BTCUSDT).
Set Daily Timeframe: Ensure your chart is on the Daily (1D) timeframe, or the script will display an error.
Configure Inputs: Open the indicator’s Settings > Inputs tab and set:
Start Date: When to begin the DCA strategy (e.g., 2024-01-01).
End Date: When to end the strategy (e.g., 2025-04-27 or earlier).
Daily Investment ($): The fixed dollar amount to invest daily (e.g., $100).
View Results: Scroll to the last candle in your date range to see the results table in the top-right corner of the chart. Green triangles below the bars indicate investment days.
Settings
Start Date: Choose the start date for your DCA strategy (default: 2024-01-01).
End Date: Choose the end date (default: 2025-04-27). Must be after the start date and within available chart data.
Daily Investment ($): Set the daily investment amount (default: $100). Minimum is $0.01.
Notes
Timeframe: The indicator requires a Daily (1D) chart. Other timeframes will trigger an error.
Data: Ensure your BTCUSD chart has historical data for the selected date range. Use reliable pairs like Coinbase:BTCUSD or Binance:BTCUSDT.
Limitations: Does not account for trading fees or slippage. Future dates (beyond the current date) will not display results.
Performance: Works best with historical data. Free TradingView accounts may have limited historical data; consider premium for longer ranges.
Uptrick: Z-Score FlowOverview
Uptrick: Z-Score Flow is a technical indicator that integrates trend-sensitive momentum analysi s with mean-reversion logic derived from Z-Score calculations. Its primary objective is to identify market conditions where price has either stretched too far from its mean (overbought or oversold) or sits at a statistically “normal” range, and then cross-reference this observation with trend direction and RSI-based momentum signals. The result is a more contextual approach to trade entry and exit, emphasizing precision, clarity, and adaptability across varying market regimes.
Introduction
Financial instruments frequently transition between trending modes, where price extends strongly in one direction, and ranging modes, where price oscillates around a central value. A simple statistical measure like Z-Score can highlight price extremes by comparing the current price against its historical mean and standard deviation. However, such extremes alone can be misleading if the broader market structure is trending forcefully. Uptrick: Z-Score Flow aims to solve this gap by combining Z-Score with an exponential moving average (EMA) trend filter and a smoothed RSI momentum check, thus filtering out signals that contradict the prevailing market environment.
Purpose
The purpose of this script is to help traders pinpoint both mean-reversion opportunities and trend-based pullbacks in a way that is statistically grounded yet still mindful of overarching price action. By pairing Z-Score thresholds with supportive conditions, the script reduces the likelihood of acting on random price spikes or dips and instead focuses on movements that are significant within both historical and current contextual frameworks.
Originality and Uniquness
Layered Signal Verification: Signals require the fulfillment of multiple layers (Z-Score extreme, EMA trend bias, and RSI momentum posture) rather than merely breaching a statistical threshold.
RSI Zone Lockout: Once RSI enters an overbought/oversold zone and triggers a signal, the script locks out subsequent signals until RSI recovers above or below those zones, limiting back-to-back triggers.
Controlled Cooldown: A dedicated cooldown mechanic ensures that the script waits a specified number of bars before issuing a new signal in the opposite direction.
Gradient-Based Visualization: Distinct gradient fills between price and the Z-Mean line enhance readability, showing at a glance whether price is trading above or below its statistical average.
Comprehensive Metrics Panel: An optional on-chart table summarizes the Z-Score’s key metrics, streamlining the process of verifying current statistical extremes, mean levels, and momentum directions.
Why these indicators were merged
Z-Score measurements excel at identifying when price deviates from its mean, but they do not intrinsically reveal whether the market’s trajectory supports a reversion or if price might continue along its trend. The EMA, commonly used for spotting trend directions, offers valuable insight into whether price is predominantly ascending or descending. However, relying solely on a trend filter overlooks the intensity of price moves. RSI then adds a dedicated measure of momentum, helping confirm if the market’s energy aligns with a potential reversal (for example, price is statistically low but RSI suggests looming upward momentum). By uniting these three lenses—Z-Score for statistical context, EMA for trend direction, and RSI for momentum force—the script offers a more comprehensive and adaptable system, aiming to avoid false positives caused by focusing on just one aspect of price behavior.
Calculations
The core calculation begins with a simple moving average (SMA) of price over zLen bars, referred to as the basis. Next, the script computes the standard deviation of price over the same window. Dividing the difference between the current price and the basis by this standard deviation produces the Z-Score, indicating how many standard deviations the price is from its mean. A positive Z-Score reveals price is above its average; a negative reading indicates the opposite.
To detect overall market direction, the script calculates an exponential moving average (emaTrend) over emaTrendLen bars. If price is above this EMA, the script deems the market bullish; if below, it’s considered bearish. For momentum confirmation, the script computes a standard RSI over rsiLen bars, then applies a smoothing EMA over rsiEmaLen bars. This smoothed RSI (rsiEma) is monitored for both its absolute level (oversold or overbought) and its slope (the difference between the current and previous value). Finally, slopeIndex determines how many bars back the script compares the basis to check whether the Z-Mean line is generally rising, falling, or flat, which then informs the coloring scheme on the chart.
Calculations and Rational
Simple Moving Average for Baseline: An SMA is used for the core mean because it places equal weight on each bar in the lookback period. This helps maintain a straightforward interpretation of overbought or oversold conditions in the context of a uniform historical average.
Standard Deviation for Volatility: Standard deviation measures the variability of the data around the mean. By dividing price’s difference from the mean by this value, the Z-Score can highlight whether price is unusually stretched given typical volatility.
Exponential Moving Average for Trend: Unlike an SMA, an EMA places more emphasis on recent data, reacting quicker to new price developments. This quicker response helps the script promptly identify trend shifts, which can be crucial for filtering out signals that go against a strong directional move.
RSI for Momentum Confirmation: RSI is an oscillator that gauges price movement strength by comparing average gains to average losses over a set period. By further smoothing this RSI with another EMA, short-lived oscillations become less influential, making signals more robust.
SlopeIndex for Slope-Based Coloring: To clarify whether the market’s central tendency is rising or falling, the script compares the basis now to its level slopeIndex bars ago. A higher current reading indicates an upward slope; a lower reading, a downward slope; and similar readings, a flat slope. This is visually represented on the chart, providing an immediate sense of the directionality.
Inputs
zLen (Z-Score Period)
Specifies how many bars to include for computing the SMA and standard deviation that form the basis of the Z-Score calculation. Larger values produce smoother but slower signals; smaller values catch quick changes but may generate noise.
emaTrendLen (EMA Trend Filter)
Sets the length of the EMA used to detect the market’s primary direction. This is pivotal for distinguishing whether signals should be considered (price aligning with an uptrend or downtrend) or filtered out.
rsiLen (RSI Length)
Defines the window for the initial RSI calculation. This RSI, when combined with the subsequent smoothing EMA, forms the foundation for momentum-based signal confirmations.
rsiEmaLen (EMA of RSI Period)
Applies an exponential moving average over the RSI readings for additional smoothing. This step helps mitigate rapid RSI fluctuations that might otherwise produce whipsaw signals.
zBuyLevel (Z-Score Buy Threshold)
Determines how negative the Z-Score must be for the script to consider a potential oversold signal. If the Z-Score dives below this threshold (and other criteria are met), a buy signal is generated.
zSellLevel (Z-Score Sell Threshold)
Determines how positive the Z-Score must be for a potential overbought signal. If the Z-Score surpasses this threshold (and other checks are satisfied), a sell signal is generated.
cooldownBars (Cooldown (Bars))
Enforces a bar-based delay between opposite signals. Once a buy signal has fired, the script must wait the specified number of bars before registering a new sell signal, and vice versa.
slopeIndex (Slope Sensitivity (Bars))
Specifies how many bars back the script compares the current basis for slope coloration. A bigger slopeIndex highlights larger directional trends, while a smaller number emphasizes shorter-term shifts.
showMeanLine (Show Z-Score Mean Line)
Enables or disables the plotting of the Z-Mean and its slope-based coloring. Traders who prefer minimal chart clutter may turn this off while still retaining signals.
Features
Statistical Core (Z-Score Detection):
This feature computes the Z-Score by taking the difference between the current price and the basis (SMA) and dividing by the standard deviation. In effect, it translates price fluctuations into a standardized measure that reveals how significant a move is relative to the typical variation seen over the lookback. When the Z-Score crosses predefined thresholds (zBuyLevel for oversold and zSellLevel for overbought), it signals that price could be at an extreme.
How It Works: On each bar, the script updates the SMA and standard deviation. The Z-Score is then refreshed accordingly. Traders can interpret particularly large negative or positive Z-Score values as scenarios where price is abnormally low or high.
EMA Trend Filter:
An EMA over emaTrendLen bars is used to classify the market as bullish if the price is above it and bearish if the price is below it. This classification is applied to the Z-Score signals, accepting them only when they align with the broader price direction.
How It Works: If the script detects a Z-Score below zBuyLevel, it further checks if price is actually in a downtrend (below EMA) before issuing a buy signal. This might seem counterintuitive, but a “downtrend” environment plus an oversold reading often signals a potential bounce or a mean-reversion play. Conversely, for sell signals, the script checks if the market is in an uptrend first. If it is, an overbought reading aligns with potential profit-taking.
RSI Momentum Confirmation with Oversold/Overbought Lockout:
RSI is calculated over rsiLen, then smoothed by an EMA over rsiEmaLen. If this smoothed RSI dips below a certain threshold (for example, 30) and then begins to slope upward, the indicator treats it as a potential sign of recovering momentum. Similarly, if RSI climbs above a certain threshold (for instance, 70) and starts to slope downward, that suggests dwindling momentum. Additionally, once RSI is in these zones, the indicator locks out repetitive signals until RSI fully exits and re-enters those extreme territories.
How It Works: Each bar, the script measures whether RSI has dropped below the oversold threshold (like 30) and has a positive slope. If it does, the buy side is considered “unlocked.” For sell signals, RSI must exceed an overbought threshold (70) and slope downward. The combination of threshold and slope helps confirm that a reversal is genuinely in progress instead of issuing signals while momentum remains weak or stuck in extremes.
Cooldown Mechanism:
The script features a custom bar-based cooldown that prevents issuing new signals in the opposite direction immediately after one is triggered. This helps avoid whipsaw situations where the market quickly flips from oversold to overbought or vice versa.
How It Works: When a buy signal fires, the indicator notes the bar index. If the Z-Score and RSI conditions later suggest a sell, the script compares the current bar index to the last buy signal’s bar index. If the difference is within cooldownBars, the signal is disallowed. This ensures a predefined “quiet period” before switching signals.
Slope-Based Coloring (Z-Mean Line and Shadow):
The script compares the current basis value to its value slopeIndex bars ago. A higher reading now indicates a generally upward slope, while a lower reading indicates a downward slope. The script then shades the Z-Mean line in a corresponding bullish or bearish color, or remains neutral if little change is detected.
How It Works: This slope calculation is refreshingly straightforward: basis – basis . If the result is positive, the line is colored bullish; if negative, it is colored bearish; if approximately zero, it remains neutral. This provides a quick visual cue of the medium-term directional bias.
Gradient Overlays:
With gradient fills, the script highlights where price stands in relation to the Z-Mean. When price is above the basis, a purple-shaded region is painted, visually indicating a “bearish zone” for potential overbought conditions. When price is below, a teal-like overlay is used, suggesting a “bullish zone” for potential oversold conditions.
How It Works: Each bar, the script checks if price is above or below the basis. It then applies a fill between close and basis, using distinct colors to show whether the market is trading above or below its mean. This creates an immediate sense of how extended the market might be.
Buy and Sell Labels (with Alerts):
When a legitimate buy or sell condition passes every check (Z-Score threshold, EMA trend alignment, RSI gating, and cooldown clearance), the script plots a corresponding label directly on the chart. It also fires an alert (if alerts are set up), making it convenient for traders who want timely notifications.
How It Works: If rawBuy or rawSell conditions are met (refined by RSI, EMA trend, and cooldown constraints), the script calls the respective plot function to paint an arrow label on the chart. Alerts are triggered simultaneously, carrying easily recognizable messages.
Metrics Table:
The optional on-chart table (activated by showMetrics) presents real-time Z-Score data, including the current Z-Score, its rolling mean, the maximum and minimum Z-Score values observed over the last zLen bars, a percentile position, and a short-term directional note (rising, falling, or flat).
Current – The present Z-Score reading
Mean – Average Z-Score over the zLen period
Min/Max – Lowest and highest Z-Score values within zLen
Position – Where the current Z-Score sits between the min and max (as a percentile)
Trend – Whether the Z-Score is increasing, decreasing, or flat
Conclusion
Uptrick: Z-Score Flow offers a versatile solution for traders who need a statistically informed perspective on price extremes combined with practical checks for overall trend and momentum. By leveraging a well-defined combination of Z-Score, EMA trend classification, RSI-based momentum gating, slope-based visualization, and a cooldown mechanic, the script reduces the occurrence of false or premature signals. Its gradient fills and optional metrics table contribute further clarity, ensuring that users can quickly assess market posture and make more confident trading decisions in real time.
Disclaimer
This script is intended solely for informational and educational purposes. Trading in any financial market comes with substantial risk, and there is no guarantee of success or the avoidance of loss. Historical performance does not ensure future results. Always conduct thorough research and consider professional guidance prior to making any investment or trading decisions.
