Bullish and Bearish Breakout Alert for Gold Futures PullbackBelow is a Pine Script (version 6) for TradingView that includes both bullish and bearish breakout conditions for my intraday trading strategy on micro gold futures (MGC). The strategy focuses on scalping two-legged pullbacks to the 20 EMA or key levels with breakout confirmation, tailored for the Apex Trader Funding $300K challenge. The script accounts for the Daily Sentiment Index (DSI) at 87 (overbought, favoring pullbacks). It generates alerts for placing stop-limit orders for 175 MGC contracts, ensuring compliance with Apex’s rules ($7,500 trailing threshold, $20,000 profit target, 4:59 PM ET close).
Script Requirements
Version: Pine Script v6 (latest for TradingView, April 2025).
Purpose:
Bullish: Alert when price breaks above a rejection candle’s high after a two-legged pullback to the 20 EMA in a bullish trend (price above 20 EMA, VWAP, higher highs/lows).
Bearish: Alert when price breaks below a rejection candle’s low after a two-legged pullback to the 20 EMA in a bearish trend (price below 20 EMA, VWAP, lower highs/lows).
Context: 5-minute MGC chart, U.S. session (8:30 AM–12:00 PM ET), avoiding overbought breakouts above $3,450 (DSI 87).
Output: Alerts for stop-limit orders (e.g., “Buy: Stop=$3,377, Limit=$3,377.10” or “Sell: Stop=$3,447, Limit=$3,446.90”), quantity 175 MGC.
Apex Compliance: 175-contract limit, stop-losses, one-directional news trading, close by 4:59 PM ET.
How to Use the Script in TradingView
1. Add Script:
Open TradingView (tradingview.com).
Go to “Pine Editor” (bottom panel).
Copy the script from the content.
Click “Add to Chart” to apply to your MGC 5-minute chart .
2. Configure Chart:
Symbol: MGC (Micro Gold Futures, CME, via Tradovate/Apex data feed).
Timeframe: 5-minute (entries), 15-minute (trend confirmation, manually check).
Indicators: Script plots 20 EMA and VWAP; add RSI (14) and volume manually if needed .
3. Set Alerts:
Click the “Alert” icon (bell).
Add two alerts:
Bullish Breakout: Condition = “Bullish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Bearish Breakout: Condition = “Bearish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Customize messages (default provided) and set notifications (e.g., TradingView app, SMS).
Example: Bullish alert at $3,377 prompts “Stop=$3,377, Limit=$3,377.10, Quantity=175 MGC” .
4. Execute Orders:
Bullish:
Alert triggers (e.g., stop $3,377, limit $3,377.10).
In TradingView’s “Order Panel,” select “Stop-Limit,” set:
Stop Price: $3,377.
Limit Price: $3,377.10.
Quantity: 175 MGC.
Direction: Buy.
Confirm via Tradovate.
Add bracket order (OCO):
Stop-loss: Sell 175 at $3,376.20 (8 ticks, $1,400 risk).
Take-profit: Sell 87 at $3,378 (1:1), 88 at $3,379 (2:1) .
Bearish:
Alert triggers (e.g., stop $3,447, limit $3,446.90).
Select “Stop-Limit,” set:
Stop Price: $3,447.
Limit Price: $3,446.90.
Quantity: 175 MGC.
Direction: Sell.
Confirm via Tradovate.
Add bracket order:
Stop-loss: Buy 175 at $3,447.80 (8 ticks, $1,400 risk).
Take-profit: Buy 87 at $3,446 (1:1), 88 at $3,445 (2:1) .
5. Monitor:
Green triangles (bullish) or red triangles (bearish) confirm signals.
Avoid bullish entries above $3,450 (DSI 87, overbought) or bearish entries below $3,296 (support) .
Close trades by 4:59 PM ET (set 4:50 PM alert) .
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ONE RING 8 MA Bands with RaysCycle analysis tool ...
MAs: Eight moving averages (MA1–MA8) with customizable lengths, types (RMA, WMA, EMA, SMA), and offsets
Bands: Upper/lower bands for each MA, calculated based on final_pctX (Percentage mode) or final_ptsX (Points mode), scaled by multiplier
Rays: Forward-projected lines for bands, with customizable start points, styles (Solid, Dashed, Dotted), and lengths (up to 500 bars)
Band Choices
Manual: Uses individual inputs for band offsets
Uniform: Sets all offsets to base_pct (e.g., 0.1%) or base_pts (e.g., 0.1 points)
Linear: Scales linearly (e.g., base_pct * 1, base_pct * 2, base_pct * 3 ..., base_pct * 8)
Exponential: Scales exponentially (e.g., base_pct * 1, base_pct * 2, base_pct * 4, base_pct * 8 ..., base_pct * 128)
ATR-Based: Offsets are derived from the Average True Range (ATR), scaled by a linear factor. Dynamic bands that adapt to market conditions, useful for breakout or mean-reversion strategies. (final_pct1 = base_pct * atr, final_pct2 = base_pct * atr * 2, ..., final_pct8 = base_pct * atr * 8)
Geometric: Offsets follow a geometric progression (e.g., base_pct * r^0, base_pct * r^1, base_pct * r^2, ..., where r is a ratio like 1.5) This is less aggressive than Exponential (which uses powers of 2) and provides a smoother progression.
Example: If base_pct = 0.1, r = 1.5, then final_pct1 = 0.1%, final_pct2 = 0.15%, final_pct3 = 0.225%, ..., final_pct8 ≈ 1.71%
Harmonic: Offsets are based on harmonic flavored ratios. final_pctX = base_pct * X / (9 - X), final_ptsX = base_pts * X / (9 - X) for X = 1 to 8 This creates a harmonic-like progression where offsets increase non-linearly, ensuring MA8 bands are wider than MA1 bands, and avoids duplicating the Linear choice above.
Ex. offsets for base_pct = 0.1: MA1: ±0.0125% (0.1 * 1/8), MA2: ±0.0286% (0.1 * 2/7), MA3: ±0.05% (0.1 * 3/6), MA4: ±0.08% (0.1 * 4/5), MA5: ±0.125% (0.1 * 5/4), MA6: ±0.2% (0.1 * 6/3), MA7: ±0.35% (0.1 * 7/2), MA8: ±0.8% (0.1 * 8/1)
Square Root: Offsets grow with the square root of the band index (e.g., base_pct * sqrt(1), base_pct * sqrt(2), ..., base_pct * sqrt(8)). This creates a gradual widening, less aggressive than Linear or Exponential. Set final_pct1 = base_pct * sqrt(1), final_pct2 = base_pct * sqrt(2), ..., final_pct8 = base_pct * sqrt(8).
Example: If base_pct = 0.1, then final_pct1 = 0.1%, final_pct2 ≈ 0.141%, final_pct3 ≈ 0.173%, ..., final_pct8 ≈ 0.283%.
Fibonacci: Uses Fibonacci ratios (e.g., base_pct * 1, base_pct * 1.618, base_pct * 2.618
Percentage vs. Points Toggle:
In Percentage mode, bands are calculated as ma * (1 ± (final_pct / 100) * multiplier)
In Points mode, bands are calculated as ma ± final_pts * multiplier, where final_pts is in price units.
Threshold Setting for Slope:
Threshold setting for determining when the slope would be significant enough to call it a change in direction. Can check efficiency by setting MA1 to color on slope temporarily
Arrow table: Shows slope direction of 8 MAs using an Up or Down triangle, or shows Flat condition if no triangle.
Log-Normal Price ForecastLog-Normal Price Forecast
This Pine Script creates a log-normal forecast model of future price movements on a TradingView chart, based on historical log returns. It plots expected price trajectories and bands representing different levels of statistical deviation.
Parameters
Model Length – Number of bars used to calculate average and standard deviation of log returns (default: 100).
Forecast Length – Number of bars into the future for which the forecast is projected (default: 100, max: 500).
Volatility SMA Length – The smoothing length for the standard deviation (default: 20).
Confidence Intervals – Confidence intervals for price bands (default: 95%, 99%, 99.9%).
SPY Trend-Based Buy Signals🔹 Overview
This indicator identifies potential buy signals on any asset by combining MACD and Stochastic Oscillator crossovers, while using the SPY’s trend (via exponential moving averages) as a broader market filter.
It helps traders stay aligned with macro momentum and avoid counter-trend entries.
🔍 How it works
SPY Trend Filter (Daily Timeframe):
Pulls SPY (S&P 500 ETF) data using EMAs (5, 20, 80)
Categorizes SPY market trend with color codes:
🟢 Green: Strong uptrend (EMA5 > EMA20 > EMA80)
🟡 Yellow: Potential uptrend / early momentum (EMA5 < EMA20 > EMA80)
🔴 Red: Downtrend (EMA5 < EMA20 < EMA80)
🔵 Blue: Possible trend reversal or mixed trend (EMA5 > EMA20 < EMA80)
Buy Signal Conditions (Combined Logic):
A signal is only triggered when:
- SPY trend is either yellow or blue (indicating a neutral-to-bullish or early recovery environment)
-The Stochastic Oscillator's %D line is below 50, showing possible upside
- A bullish MACD crossover occurs on the current symbol
🟢 Green signal: MACD crossover occurs below 0 (early reversal)
🟠 Orange signal: MACD crossover occurs above 0 (momentum continuation)
📈 Visual Output
🟢 Green label below the bar when an early reversal setup occurs
🟠 Orange label above the bar when a trend continuation signal appears
✅ Best Use Case
Ideal for:
Swing traders and position traders
LEAPS (long-term options) traders aligning entries with SPY trend
Anyone seeking clean, contextual entries filtered by market momentum
⚠️ Note: This indicator is most effective when used on fundamentally strong stocks that are sector leaders with solid earnings growth and market presence. Use technical signals as a complement to quality fundamentals.
ℹ️ Clarification: The moving averages displayed on the chart (e.g., on QQQ) are for visual reference only, to help users understand the color logic of the SPY trend filter. The actual logic and signals are based on SPY’s moving averages, regardless of the charted symbol.
Bijnor Pivot ExtendedOverview: The Bijnor Pivot Extended (BP+) indicator is a powerful visual tool designed to help traders identify key price levels using Fibonacci-based pivots. It dynamically plots Support and Resistance levels based on your chosen timeframe (Daily, Weekly, or Monthly) and displays them only for the current session, reducing chart clutter and improving focus.
🔧 Features:
📆 Pivot Timeframe Selection: Choose between Daily, Weekly, or Monthly pivots.
🎯 Fibonacci Pivot Levels:
Central Pivot (P)
Resistance: R1, R2, R3, R4 (Extended)
Support: S1, S2, S3, S4 (Extended)
🎨 Full Customization:
Toggle labels and prices on/off
Position labels to the left or right
Change line width and individual colors for pivot, support, and resistance lines
🧠 Smart Line Plotting:
Lines are drawn only during the selected session, keeping your chart clean
🕹️ Max Performance: Optimized to stay lightweight with max_lines_count and max_labels_count set to 500
🧭 How to Use It:
Use this indicator to:
Plan entries and exits around key Fibonacci pivot zones
Identify overbought/oversold zones at R3/R4 and S3/S4
Enhance your intraday, swing, or positional trading setups
Combine with price action, candlestick patterns, or volume for maximum edge.
✅ Bonus:
This script is ideal for traders looking for a minimalist yet powerful pivot framework, with extended levels for breakout or reversal scenarios.
ES1! vs ZB1! Exponentially Weighted CorrelationES1! vs ZB1! Exponentially Weighted Correlation
This indicator calculates and visualizes the exponentially weighted correlation between the S&P 500 E-mini futures (ES1!) and the 30-Year U.S. Treasury Bond futures (ZB1!) over a user-defined lookback period. By using an exponential moving average (EMA) approach, it emphasizes recent price movements, providing a dynamic view of the relationship between these two key financial instruments.
Features:
- Customizable Inputs: Adjust the lookback length (default: 60) and alpha (default: 0.1) to fine-tune the sensitivity of the correlation calculation.
- Exponentially Weighted Correlation: Measures the strength and direction of the relationship between ES1! and ZB1! prices, with more weight given to recent data.
- Visual Clarity: Displays correlation as colored bars (green for positive, red for negative) for quick interpretation, with reference lines at 0, +1, and -1 for context.
- Non-Overlay Design: Plotted in a separate panel below the chart to avoid cluttering price data.
How It Works:
The indicator fetches closing prices for ES1! and ZB1!, applies an EMA to smooth the data, and computes the exponentially weighted covariance and variances. The correlation is then derived and plotted as a histogram, helping traders identify whether the two markets are moving together (positive correlation), in opposite directions (negative correlation), or independently.
Use Cases:
- Market Analysis: Gauge the relationship between equity and bond markets to inform trading strategies.
- Risk Management: Monitor correlation shifts to adjust portfolio exposure.
- Intermarket Insights: Identify trends or divergences in the stock-bond dynamic for macroeconomic analysis.
Ideal for traders and analysts tracking intermarket relationships, this indicator offers a clear, responsive tool for understanding ES1! and ZB1! correlation in real-time.
ICT MACRO MAX RETRI ( ALERT )🖤 ICT Reversal Detector – Minimalist Edition
This indicator is designed for traders who follow Inner Circle Trader (ICT) concepts, particularly focused on liquidity sweeps and displacement reversals.
It detects:
• Swing Highs & Lows that occur during the most reactive windows of each hour
→ Specifically the last 20 minutes and first 15 minutes
(ICT teaches these moments often reveal macro-level reversals. I’ve expanded the window slightly to give the indicator more room to catch valid setups.)
• Liquidity Sweeps of previous highs/lows
• Displacement (State Change): defined as a manipulation wick followed by 1–3 strong candles closing in the opposite direction
Visually:
• Clean black lines pointing right from the liquidity sweep wick
• White triangle markers inside black label boxes only when valid displacement occurs
• No clutter, no unnecessary shapes — just focused signal
Built for:
• 5-minute charts, especially NASDAQ (NAS100) and S&P 500 (SPX500)
• Confirm setups manually on the 15-minute chart for extra precision
This is a partial automation tool for ICT-style reversal traders who prefer clarity, minimalism, and sharp intuition over noise.
Let it alert you to setups — then decide like a sniper.
SMC+The "SMC+" indicator is a comprehensive tool designed to overlay key Smart Money Concepts (SMC) levels, support/resistance zones, order blocks (OB), fair value gaps (FVG), and trap detection on your TradingView chart. It aims to assist traders in identifying potential areas of interest based on price action, swing structures, and volume dynamics across multiple timeframes. This indicator is fully customizable, allowing users to adjust lookback periods, colors, opacity, and sensitivity to suit their trading style.
Key Components and Functionality
1. Key Levels (Support and Resistance)
This section plots horizontal lines representing support and resistance levels based on highs and lows over three distinct lookback periods, plus daily nearest levels.
Short-Term Lookback Period (Default: 20 bars)
Plots the highest high (short_high) and lowest low (short_low) over the specified period.
Visualized as dotted lines with customizable colors (Short-Term Resistance Color, Short-Term Support Color) and opacity (Short-Term Resistance Opacity, Short-Term Support Opacity).
Adjustment Tip: Increase the lookback (e.g., to 30-50) for less frequent but stronger levels on higher timeframes, or decrease (e.g., to 10-15) for scalping on lower timeframes.
Long-Term Lookback Period (Default: 50 bars)
Plots broader support (long_low) and resistance (long_high) levels using a solid line style.
Customizable via Long-Term Resistance Color, Long-Term Support Color, and their respective opacity settings.
Adjustment Tip: Extend to 100-200 bars for swing trading or major trend analysis on daily/weekly charts.
Extra-Long Lookback Period (Default: 100 bars)
Identifies significant historical highs (extra_long_high) and lows (extra_long_low) with dashed lines.
Configurable with Extra-Long Resistance Color, Extra-Long Support Color, and opacity settings.
Adjustment Tip: Use 200-500 bars for monthly charts to capture macro-level key zones.
Daily Nearest Resistance and Support Levels
Dynamically calculates the nearest resistance (daily_res_level) and support (daily_sup_level) based on the current day’s price action relative to historical highs and lows.
Displayed with Daily Resistance Color and Daily Support Color (with opacity options).
Adjustment Tip: Works best on intraday charts (e.g., 15m, 1h) to track daily pivots; combine with volume profile for confirmation.
How It Works: These levels update dynamically as new highs/lows form, providing a visual guide to potential reversal or breakout zones.
2. SMC Inputs (Smart Money Concepts)
This section identifies swing structures, order blocks, fair value gaps, and entry signals based on SMC principles.
SMC Swing Lookback Period (Default: 12 bars)
Defines the period for detecting swing highs (smc_swing_high) and lows (smc_swing_low).
Adjustment Tip: Increase to 20-30 for smoother swings on higher timeframes; reduce to 5-10 for faster signals on lower timeframes.
Minimum Swing Size (%) (Default: 0.5%)
Filters out minor price movements to focus on significant swings.
Adjustment Tip: Raise to 1-2% for volatile markets (e.g., crypto) to avoid noise; lower to 0.2-0.3% for forex pairs with tight ranges.
Order Block Sensitivity (Default: 1.0)
Scales the size of detected order blocks (OBs) for bullish reversal (smc_ob_bull), bearish reversal (smc_ob_bear), and continuation (smc_cont_ob).
Visuals include customizable colors, opacity, border thickness, and blinking effects (e.g., SMC Bullish Reversal OB Color, SMC Bearish Reversal OB Blink Thickness).
Adjustment Tip: Increase to 1.5-2.0 for wider OBs in choppy markets; keep at 1.0 for precision in trending conditions.
Minimum FVG Size (%) (Default: 0.3%)
Sets the minimum gap size for Fair Value Gaps (fvg_high, fvg_low), displayed as boxes with Fair Value Gap Color and FVG Opacity.
Adjustment Tip: Increase to 0.5-1% for larger, more reliable gaps; decrease to 0.1-0.2% for scalping smaller inefficiencies.
How It Works:
Bullish Reversal OB: Detects a bearish candle followed by a bullish break, marking a potential demand zone.
Bearish Reversal OB: Identifies a bullish candle followed by a bearish break, marking a supply zone.
Continuation OB: Spots strong bullish momentum after a prior high, indicating a continuation zone.
FVG: Highlights bullish gaps where price may retrace to fill.
Entry Signals: Plots triangles (SMC Long Entry) when price retests an OB with a liquidity sweep or break of structure (BOS).
3. Trap Inputs
This section detects potential bull and bear traps based on price action, volume, and key level rejections.
Min Down Move for Bear Trap (%) (Default: 1.0%)
Sets the minimum drop required after a bearish OB to qualify as a trap.
Visualized with Bear Trap Color, Bear Trap Opacity, and blinking borders.
Adjustment Tip: Increase to 2-3% for stronger traps in trending markets; lower to 0.5% for ranging conditions.
Min Up Move for Bull Trap (%) (Default: 1.0%)
Sets the minimum rise required after a bullish OB to flag a trap.
Customizable with Bull Trap Color, Bull Trap Border Thickness, etc.
Adjustment Tip: Adjust similarly to bear traps based on market volatility.
Volume Lookback for Traps (Default: 5 bars)
Compares current volume to a moving average (avg_volume) to filter low-volume traps.
Adjustment Tip: Increase to 10-20 for confirmation on higher timeframes; reduce to 3 for intraday sensitivity.
How It Works:
Bear Trap: Triggers when price drops significantly after a bearish OB but reverses up with low volume or support rejection.
Bull Trap: Activates when price rises after a bullish OB but fails with low volume or resistance rejection.
Boxes highlight trap zones, resetting when price breaks out.
4. Visual Customization
Line Width (Default: 2)
Adjusts thickness of support/resistance lines.
Tip: Increase to 3-4 for visibility on cluttered charts.
Blink On (Default: Close)
Sets whether OB/FVG borders blink based on Open or Close price interaction.
Tip: Use "Open" for intraday precision; "Close" for confirmed reactions.
Colors and Opacity: Each element (OBs, FVGs, traps, key levels) has customizable colors, opacity (0-100), border thickness (1-5 or 1-7), and blink effects for dynamic visualization.
How to Use SMC+
Setup: Apply the indicator to any chart and adjust inputs based on your timeframe and market.
Key Levels: Watch for price reactions at short, long, extra-long, or daily levels for potential reversals or breakouts.
SMC Signals: Look for entry signals (triangles) near OBs or FVGs, confirmed by liquidity sweeps or BOS.
Traps: Avoid false breakouts by monitoring trap boxes, especially near key levels with low volume.
Notes:
This indicator is a visual aid and does not guarantee trading success. Combine it with other analysis tools and risk management strategies.
Performance may vary across markets and timeframes; test settings thoroughly before use.
For optimal results, experiment with lookback periods and sensitivity settings to match your trading style.
The default settings are optimal for 1 minute and 10 second time frames for small cap low float stocks.
Continuation OB are Blue.
Bullish Reversal OB color is Green
Bearish Reversal OB color is Red
FVG color is purple
Bear Trap OB is red with a green border and often appears with a Bearish Reversal OB signaling caution to a short position.
Bull trap OB is green with a Red border signaling caution to a long position.
All active OB area are highlighted and solid in color while other non active OB area are dimmed.
My personal favorite setups are when we have an active bullish reversal with an active FVG along with an active Continuation OB.
Another personal favorite is the Bearish reversal OB signaling an end to a recent uptrend.
The Trap OB detection are also a unique and Original helpful source of information.
The OB have a white boarder by default that are colored black giving a simulated blinking effect when price is acting in that zone.
The Trap OB border are colored with respect to direction of intended trap, all of which can be customized to personal style.
All vaild OB zones are shown compact in size ,a unique and original view until its no longer valid.
Z-Score Normalized VIX StrategyThis strategy leverages the concept of the Z-score applied to multiple VIX-based volatility indices, specifically designed to capture market reversals based on the normalization of volatility. The strategy takes advantage of VIX-related indicators to measure extreme levels of market fear or greed and adjusts its position accordingly.
1. Overview of the Z-Score Methodology
The Z-score is a statistical measure that describes the position of a value relative to the mean of a distribution in terms of standard deviations. In this strategy, the Z-score is calculated for various volatility indices to assess how far their values are from their historical averages, thus normalizing volatility levels. The Z-score is calculated as follows:
Z = \frac{X - \mu}{\sigma}
Where:
• X is the current value of the volatility index.
• \mu is the mean of the index over a specified period.
• \sigma is the standard deviation of the index over the same period.
This measure tells us how many standard deviations the current value of the index is away from its average, indicating whether the market is experiencing unusually high or low volatility (fear or calm).
2. VIX Indices Used in the Strategy
The strategy utilizes four commonly referenced volatility indices:
• VIX (CBOE Volatility Index): Measures the market’s expectations of 30-day volatility based on S&P 500 options.
• VIX3M (3-Month VIX): Reflects expectations of volatility over the next three months.
• VIX9D (9-Day VIX): Reflects shorter-term volatility expectations.
• VVIX (VIX of VIX): Measures the volatility of the VIX itself, indicating the level of uncertainty in the volatility index.
These indices provide a comprehensive view of the current volatility landscape across different time horizons.
3. Strategy Logic
The strategy follows a long entry condition and an exit condition based on the combined Z-score of the selected volatility indices:
• Long Entry Condition: The strategy enters a long position when the combined Z-score of the selected VIX indices falls below a user-defined threshold, indicating an abnormally low level of volatility (suggesting a potential market bottom and a bullish reversal). The threshold is set as a negative value (e.g., -1), where a more negative Z-score implies greater deviation below the mean.
• Exit Condition: The strategy exits the long position when the combined Z-score exceeds the threshold (i.e., when the market volatility increases above the threshold, indicating a shift in market sentiment and reduced likelihood of continued upward momentum).
4. User Inputs
• Z-Score Lookback Period: The user can adjust the lookback period for calculating the Z-score (e.g., 6 periods).
• Z-Score Threshold: A customizable threshold value to define when the market has reached an extreme volatility level, triggering entries and exits.
The strategy also allows users to select which VIX indices to use, with checkboxes to enable or disable each index in the calculation of the combined Z-score.
5. Trade Execution Parameters
• Initial Capital: The strategy assumes an initial capital of $20,000.
• Pyramiding: The strategy does not allow pyramiding (multiple positions in the same direction).
• Commission and Slippage: The commission is set at $0.05 per contract, and slippage is set at 1 tick.
6. Statistical Basis of the Z-Score Approach
The Z-score methodology is a standard technique in statistics and finance, commonly used in risk management and for identifying outliers or unusual events. According to Dumas, Fleming, and Whaley (1998), volatility indices like the VIX serve as a useful proxy for market sentiment, particularly during periods of high uncertainty. By calculating the Z-score, we normalize volatility and quantify the degree to which the current volatility deviates from historical norms, allowing for systematic entry and exit based on these deviations.
7. Implications of the Strategy
This strategy aims to exploit market conditions where volatility has deviated significantly from its historical mean. When the Z-score falls below the threshold, it suggests that the market has become excessively calm, potentially indicating an overreaction to past market events. Entering long positions under such conditions could capture market reversals as fear subsides and volatility normalizes. Conversely, when the Z-score rises above the threshold, it signals increased volatility, which could be indicative of a bearish shift in the market, prompting an exit from the position.
By applying this Z-score normalized approach, the strategy seeks to achieve more consistent entry and exit points by reducing reliance on subjective interpretation of market conditions.
8. Scientific Sources
• Dumas, B., Fleming, J., & Whaley, R. (1998). “Implied Volatility Functions: Empirical Tests”. The Journal of Finance, 53(6), 2059-2106. This paper discusses the use of volatility indices and their empirical behavior, providing context for volatility-based strategies.
• Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities”. Journal of Political Economy, 81(3), 637-654. The original Black-Scholes model, which forms the basis for many volatility-related strategies.
Risk-On / Risk-Off ScoreRisk-On / Risk-Off Score (Macro Sentiment Indicator)
This indicator calculates a custom Risk-On / Risk-Off Score to objectively assess the current market risk sentiment using a carefully selected basket of macroeconomic assets and intermarket relationships.
🧠 What does this indicator do?
The score is based on 14 key components grouped into three categories:
🟢 Risk-On Assets (rising = appetite for risk)
(+1 if performance over X days is positive, otherwise –1)
NASDAQ 100 (NAS100USD)
S&P 500 (SPX)
Bitcoin (BTCUSD)
Copper (HG1!)
WTI Crude Oil (CLK2025)
🔴 Risk-Off Assets (rising = flight to safety)
(–1 if performance is positive, otherwise +1)
Gold (XAUUSD)
US Treasury Bonds (TLT ETF) (TLT)
US Dollar Index (DXY)
USD/CHF
USD/JPY
US 10Y Yields (US10Y) (yields are interpreted inversely)
⚖️ Risk Spreads / Relative Indicators
(+1 if rising, –1 if falling)
Copper/Gold Ratio → HG1! / XAUUSD
NASDAQ/VIX Ratio → NAS100USD / VIX
HYG/TLT Ratio → HYG / TLT
📏 Score Calculation
Total score = sum of all components
Range: from –14 (extreme Risk-Off) to +14 (strong Risk-On)
Color-coded output:
🟢 Score > 2 = Risk-On
🟠 –2 to +2 = Neutral
🔴 Score < –2 = Risk-Off
Displayed as a line plot with background color and signal markers
🧪 Timeframe of analysis:
Default: 5 days (adjustable via input)
Calculated using Rate of Change (% change)
🧭 Use Cases:
Quickly assess macro sentiment
Filter for position sizing, hedging, or intraday bias
Especially useful for:
Swing traders
Day traders with macro filters
Volatility and options traders
📌 Note:
This is not a buy/sell signal indicator, but a contextual sentiment tool designed to help you stay aligned with overall market conditions.
Change % Inteligente - NQ / ES / YMTopstep Compliance: Daily Price Change % Alert (NQ / ES / YM)
Script Purpose
This script helps funded traders (especially those using Topstep or similar programs) monitor the real-time percentage change of major equity index futures: Nasdaq (NQ), S&P 500 (ES), and Dow Jones (YM).
⚠️ Why it matters
Topstep prohibits trading within 2% of the daily price limits set by the CME. If a trader holds a position too close to those limits, they risk account disqualification.
📊 How it works
• Detects the instrument: NQ1!, ES1!, YM1!, or M2025 contracts
• Calculates the real-time % change from today’s market open
• Simulates daily CME price limits (+7% / -7%)
• Highlights when price enters the last 2% of the limit range (prohibited zone)
• Displays a clean, floating panel with the current % change and a warning if necessary
• Sends a visual and optional audio alert when in the prohibited zone
🧠 What makes this script unique?
This tool is **not for technical analysis**. It focuses exclusively on **funding program compliance** and **account protection**, which is not covered by other public scripts. It’s lightweight, intuitive, and designed for traders who manage risk like professionals.
✅ Open-source and ready for review.
✅ CHART SETUP FOR PUBLICATION
✔️ Use a clean chart
✔️ Only apply this script
✔️ Make sure the panel is visible (top-right or top-center recommended)
❌ No extra indicators or drawings
✔️ Use NQM2025, ESM2025 or YMM2025 on a volatile day (to show -1% to -3% range)
INSTRUCTIONS
1. Add the script to your chart.
2. Use it with NQ1!, ES1!, or YM1! (or M2025 contracts).
3. The panel will show today’s price change %.
4. If the market is within the last 2% of the CME price limit, a warning will appear.
5. Use this to avoid violating Topstep’s trading rules during volatile days.
Triple EMA + Volume/Price SignalsOverview
This script merges three exponential moving averages (EMA) with adaptive volume thresholds to identify high-confidence trends. Unlike basic volume indicators, it triggers signals only when volume exceeds both a user-defined absolute value (e.g., 500k) and a percentage increase (e.g., 5%) – reducing noise in volatile markets.
Key Features
Triple EMA System:
Short (9), Medium (21), and Long (50) EMAs for trend direction.
Bullish Signal: Short EMA > Medium EMA > Long EMA.
Bearish Signal: Short EMA < Medium EMA < Long EMA.
Dual-Threshold Volume Confirmation:
Absolute Volume: Highlight bars where volume exceeds X (e.g., 500,000).
Percentage Increase: Highlight bars where volume rises by Y% (e.g., 5%) vs. prior bar.
Users can enable/disable either threshold.
Customizable Alerts:
Trigger alerts only when both EMA alignment and volume conditions are met.
How It Works
Trend + Volume Synergy:
A bullish EMA crossover alone might be a false breakout. This script requires additional volume confirmation (e.g., 500k volume + 5% spike) to validate the move.
Flexibility: Adjust thresholds for different assets:
Stocks: Higher absolute volume (e.g., 1M shares).
Crypto: Smaller absolute volume but larger % spikes (e.g., 10%).
Usage Examples
Swing Trading:
Set EMA lengths to 20/50/200 and volume thresholds to 500k + 5% on daily charts.
Scalping:
Use 5/13/21 EMAs with 100k volume + 3% spikes on 5-minute charts.
BIN Based Support and Resistance [SS]This indicator presents a version of an alternative way to determine support and resistance, using a method called "Bins".
Bins provide for a flexible and interesting way to determine support and resistance levels.
First off, let's discuss BINS:
Bins are ranges or containers into which your data points can be sorted. For example, if you're grouping ages, you might have bins like 0–18, 19–35, 36–50, and 51+. Any data point within these intervals gets placed in the corresponding bin.
Binning simplifies complex data sets by grouping values into categories. This is useful for such things as
Visualizing data in histograms or bar charts.
Reducing noise and highlighting trends.
This indicator groups the price action into 10 separate bins. It determines the Support / Resistance level by averaging the values in the Bins to find an iteration of the "central tendency" or average reoccurring value.
Pros and Cons
Since this is a different approach to support and resistance, I think its important to highlight some of the pros and advantages, but also be open about the cons.
First off the PROS
Bin Based Support and Resistance Levels dynamically adjust to ranges as opposed to hard / fast peaks and valleys. This makes them better at analyzing price action vs simply drawing lines at random peaks and valleys.
Because Bins are analyzing ALL PA within a period's max and min range, Bin Support and Resistance can actually be used similar to Volume profile, where you are able to identify a pseudo-POC, or areas where price tends to consolidate. Take a look at this example on SPY:
You can see these 2 SR lines are close together. This represents that this general price range is an area where price likes to accumulate/consolidate. You can see the SPY ended up coming back to this range and consolidating there for a bit.
This is a strength of using a BIN based approach to calculating support and resistance, because as indicated before, it looks at price action vs peaks and valleys.
As a tip, these areas are areas you want to wait for a break in one direction or the other.
The indicator provides for backtest results of the support and resistance lines, to see how many times certain areas acted as resistance or support. Because this is analyzing and distributing PA evenly throughout the period's max and min, the indicator can tell you which areas tend to have higher rejection zones and which have higher support zones.
Now the CONS
Because bin based SR take an average approach, the SR lines can sometimes be slightly broken before the ticker finds rejection:
To combat this, make sure there is confirmed support. How the indicator actually backtests these lines is by waiting to see if the ticker has 3 consecutive closes above the support line or below the resistance line. So these are things to be mindful of.
It doesn't consider pivots. Most support and resistance indicators either identify max and min peaks and valleys or use pivot points. Pivot points are a great way to identify peaks and valleys and thus by extension support and resistance. However, this is also somewhat of a strength, as using BINS forces the indicator to consider ALL price action and not just the extremes (highs and lows).
Can be slightly skewed in highly volatile environments. Any time there is a massive drop or rally, it can skew the indicator to give extreme ranges to both ends. For example, the Tariff news collapse on ES1!:
Owning to limitations in lookback length, sometimes the min and max range can be exceeded and other traditional areas of support / resistance is where a ticker will find support.
Using the indicator
Here are some basic use/functionalities of the indicator:
Selecting display of backtest results: You can select to have the backtest results shown in a table:
Or directly on the lines:
Inversely, you can toggle them off completely:
You can modify the lookback length. The suggested lookback length is between 250 to 500 candles on smaller timeframes. I also suggest 252 on daily timeframes (which represents 1 trading year).
And that's the indicator!
It is very easy to use, so you should pick it up in no time!
Enjoy and as always, 🚀🚀 safe trades! 🚀🚀
EMA Shakeout DetectorEMA Shakeout & Reclaim Zones
Description:
This Pine Script helps traders quickly identify potential shakeout entries based on price action and volume dynamics. Shakeouts often signal strong accumulation, where institutions drive the stock below a key moving average before reclaiming it, creating an opportunity for traders to enter at favorable prices.
How It Works:
1. Volume Surge Filtering:
a. Computes the 51-day Simple Moving Average (SMA) of volume.
b. Identifies days where volume surged 2x above the 51-day average.
c. Filters stocks that had at least two such high-volume days in the last 21 trading days (configurable).
2. Stock Selection Criteria:
a. The stock must be within 25% of its 52-week high.
b. It should have rallied at least 30% from its 52-week low.
Shakeout Conditions:
1. The stock must be trading above the 51-day EMA before the shakeout.
2. A sudden price drop of more than 10% occurs, pushing the stock below the 51-day EMA.
3. A key index (e.g., Nifty 50, S&P 500) must be trading above its 10-day EMA, ensuring overall market strength.
Visualization:
Shakeout zones are highlighted in blue, making it easier to spot potential accumulation areas and study price & volume action in more detail.
This script is ideal for traders looking to identify institutional shakeouts and gain an edge by recognizing high-probability reversal setups.
Global Liquidity Index with Editable DEMA + 107 Day OffsetGlobal Liquidity DEMA (107-Day Lead)
This indicator visualizes a smoothed version of global central bank liquidity with a forward time shift of 107 days. The concept is based on the macroeconomic observation that markets tend to lag changes in global liquidity — particularly from central banks like the Federal Reserve, ECB, BOJ, and PBOC.
The script uses a Double Exponential Moving Average (DEMA) to smooth the combined balance sheets and money supply inputs. It then offsets the result into the future by 107 days, allowing you to visually align liquidity trends with delayed market reactions. A second plot (ROC SMA) is included to help identify liquidity momentum shifts.
🔍 How to Use:
Add this indicator to any chart (S&P 500, BTC, Gold, etc.)
Compare price action to the forward-shifted liquidity trend
Look for divergence, confirmation, or crossovers with price
Use as a macro timing tool for long-term entries/exits
📌 Included Features:
Editable DEMA smoothing length
ROC + SMA overlay for momentum signals
Fixed 107-day forward projection
Includes main DEMA and ROC SMA both real-time and shifted
Multi-EMA Crossover StrategyMulti-EMA Crossover Strategy
This strategy uses multiple exponential moving average (EMA) crossovers to identify bullish trends and execute long trades. The approach involves progressively stronger signals as different EMA pairs cross, indicating increasing bullish momentum. Each crossover triggers a long entry, and the intensity of bullish sentiment is reflected in the color of the bars on the chart. Conversely, bearish trends are represented by red bars.
Strategy Logic:
First Long Entry: When the 1-day EMA crosses above the 5-day EMA, it signals initial bullish momentum.
Second Long Entry: When the 3-day EMA crosses above the 10-day EMA, it confirms stronger bullish sentiment.
Third Long Entry: When the 5-day EMA crosses above the 20-day EMA, it indicates further trend strength.
Fourth Long Entry: When the 10-day EMA crosses above the 40-day EMA, it suggests robust long-term bullish momentum.
The bar colors reflect these conditions:
More blue bars indicate stronger bullish sentiment as more short-term EMAs are above their longer-term counterparts.
Red bars represent bearish conditions when short-term EMAs are below longer-term ones.
Example: Bitcoin Trading on a Daily Timeframe
Bullish Scenario:
Imagine Bitcoin is trading at $30,000 on March 31, 2025:
First Signal: The 1-day EMA crosses above the 5-day EMA at $30,000. This suggests initial upward momentum, prompting a small long entry.
Second Signal: A few days later, the 3-day EMA crosses above the 10-day EMA at $31,000. This confirms strengthening bullish sentiment; another long position is added.
Third Signal: The 5-day EMA crosses above the 20-day EMA at $32,500, indicating further upward trend development; a third long entry is executed.
Fourth Signal: Finally, the 10-day EMA crosses above the 40-day EMA at $34,000. This signals robust long-term bullish momentum; a fourth long position is entered.
Bearish Scenario:
Suppose Bitcoin reverses from $34,000 to $28,000:
The 1-day EMA crosses below the 5-day EMA at $33,500.
The 3-day EMA dips below the 10-day EMA at $32,000.
The 5-day EMA falls below the 20-day EMA at $30,000.
The final bearish signal occurs when the 10-day EMA drops below the 40-day EMA at $28,000.
The bars turn increasingly red as bearish conditions strengthen.
Advantages of This Strategy:
Progressive Confirmation: Multiple crossovers provide layered confirmation of trend strength.
Visual Feedback: Bar colors help traders quickly assess market sentiment and adjust positions accordingly.
Flexibility: Suitable for trending markets like Bitcoin during strong rallies or downturns.
Limitations:
Lagging Signals: EMAs are lagging indicators and may react slowly to sudden price changes.
False Breakouts: Crossovers in choppy markets can lead to whipsaws or false signals.
This strategy works best in trending markets and should be combined with additional risk management techniques, e.g., stop loss or optimal position sizes (Kelly Criterion).
CCI with Subjective NormalizationCCI (Commodity Channel Index) with Subjective Normalization
This indicator computes the classic CCI over a user-defined length, then applies a subjective mean and scale to transform the raw CCI into a pseudo Z‑score range. By adjusting the “Subjective Mean” and “Subjective Scale” inputs, you can shift and rescale the oscillator to highlight significant tops and bottoms more clearly in historical data.
1. CCI Calculation:
- Uses the standard formula \(\text{CCI} = \frac{\text{price} - \text{SMA(price, length)}}{0.015 \times \text{mean deviation}}\) over a user-specified length (default 500 bars).
2. Subjective Normalization:
- After CCI is calculated, it is divided by “Subjective Scale” and offset by “Subjective Mean.”
- This step effectively re-centers and re-scales the oscillator, helping you align major lows or highs at values like –2 or +2 (or any desired range).
3. Usage Tips:
- CCI Length controls how far back the script measures average price and deviation. Larger values emphasize multi-year cycles.
- Subjective Mean and Scale let you align the oscillator’s historical lows and highs with numeric levels you prefer (e.g., near ±2).
- Adjust these parameters to fit your particular market analysis or to match known cycle tops/bottoms.
4. Plot & Zero Line:
- The indicator plots the normalized CCI in yellow, along with a zero line for quick reference.
- Positive values suggest price is above its long-term mean, while negative values suggest it’s below.
This approach offers a straightforward momentum oscillator (CCI) combined with a customizable normalization, making it easier to spot historically significant overbought/oversold conditions without writing complex code yourself.
ES vs Bond ROCThis Pine Script plots the Relative Rate of Change (ROC) between the S&P 500 E-mini Futures (ES) and 30-Year Treasury Bond Futures (ZB) over a specified period. It helps identify when equities are overperforming or underperforming relative to long-term bonds—an insight often used to detect risk-on/risk-off sentiment shifts in the market.
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Low Liquidity Zones [PhenLabs]📊 Low Liquidity Zones
Version: PineScript™ v6
📌 Description
Low Liquidity Zones identifies and highlights periods of unusually low trading volume on your chart, marking areas where price movement occurred with minimal participation. These zones often represent potential support and resistance levels that may be more susceptible to price breakouts or reversals when revisited with higher volume.
Unlike traditional volume analysis tools that focus on high volume spikes, this indicator specializes in detecting low liquidity areas where price moved with minimal resistance. Each zone displays its volume delta, providing insight into buying vs. selling pressure during these thin liquidity periods. This combination of low volume detection and delta analysis helps traders identify potential price inefficiencies and weak structures in the market.
🚀 Points of Innovation
• Identifies low liquidity zones that most volume indicators overlook but which often become significant technical levels
• Displays volume delta within each zone, showing net buying/selling pressure during low liquidity periods
• Dynamically adjusts to different timeframes, allowing analysis across multiple time horizons
• Filters zones by maximum size percentage to focus only on precise price levels
• Maintains historical zones until they expire based on your lookback settings, creating a cumulative map of potential support/resistance areas
🔧 Core Components
• Low Volume Detection: Identifies candles where volume falls below a specified threshold relative to recent average volume, highlighting potential liquidity gaps.
• Volume Delta Analysis: Calculates and displays the net buying/selling pressure within each low liquidity zone, providing insight into the directional bias during low participation periods.
• Dynamic Timeframe Adjustment: Automatically scales analysis periods to match your selected timeframe preference, ensuring consistent identification of low liquidity zones regardless of chart settings.
• Zone Management System: Creates, tracks, and expires low liquidity zones based on your configured settings, maintaining visual clarity on the chart.
🔥 Key Features
• Low Volume Identification: Automatically detects and highlights candles where volume falls below your specified threshold compared to the moving average.
• Volume Delta Visualization: Shows the net volume delta within each zone, providing insight into whether buyers or sellers were dominant despite the low overall volume.
• Flexible Timeframe Analysis: Analyze low liquidity zones across multiple predefined timeframes or use a custom lookback period specific to your trading style.
• Zone Size Filtering: Filters out excessively large zones to focus only on precise price levels, improving signal quality.
• Automatic Zone Expiration: Older zones are automatically removed after your specified lookback period to maintain a clean, relevant chart display.
🎨 Visualization
• Volume Delta Labels: Each zone displays its volume delta with “+” or “-” prefix and K/M suffix for easy interpretation, showing the strength and direction of pressure during the low volume period.
• Persistent Historical Mapping: Zones remain visible for your specified lookback period, creating a cumulative map of potential support and resistance levels forming under low liquidity conditions.
📖 Usage Guidelines
Analysis Timeframe
Default: 1D
Range/Options: 15M, 1HR, 3HR, 4HR, 8HR, 16HR, 1D, 3D, 5D, 1W, Custom
Description: Determines the historical period to analyze for low liquidity zones. Shorter timeframes provide more recent data while longer timeframes offer a more comprehensive view of significant zones. Use Custom option with the setting below for precise control.
Custom Period (Bars)
Default: 1000
Range: 1+
Description: Number of bars to analyze when using Custom timeframe option. Higher values show more historical zones but may impact performance.
Volume Analysis
Volume Threshold Divisor
Default: 0.5
Range: 0.1-1.0
Description: Maximum volume relative to average to identify low volume zones. Example: 0.5 means volume must be below 50% of the average to qualify as low volume. Lower values create more selective zones while higher values identify more zones.
Volume MA Length
Default: 15
Range: 1+
Description: Period length for volume moving average calculation. Shorter periods make the indicator more responsive to recent volume changes, while longer periods provide a more stable baseline.
Zone Settings
Zone Fill Color
Default: #2196F3 (80% transparency)
Description: Color and transparency of the low liquidity zones. Choose colors that stand out against your chart background without obscuring price action.
Maximum Zone Size %
Default: 0.5
Range: 0.1+
Description: Maximum allowed height of a zone as percentage of price. Larger zones are filtered out. Lower values create more precise zones focusing on tight price ranges.
Display Options
Show Volume Delta
Default: true
Description: Toggles the display of volume delta within each zone. Enabling this provides additional insight into buying vs. selling pressure during low volume periods.
Delta Text Position
Default: Right
Options: Left, Center, Right
Description: Controls the horizontal alignment of the delta text within zones. Adjust based on your chart layout for optimal readability.
✅ Best Use Cases
• Identifying potential support and resistance levels that formed during periods of thin liquidity
• Spotting price inefficiencies where larger players may have moved price with minimal volume
• Finding low-volume consolidation areas that may serve as breakout or reversal zones when revisited
• Locating potential stop-hunting zones where price moved on minimal participation
• Complementing traditional support/resistance analysis with volume context
⚠️ Limitations
• Requires volume data to function; will not work on symbols where the data provider doesn’t supply volume information
• Low volume zones don’t guarantee future support/resistance - they simply highlight potential areas of interest
• Works best on liquid instruments where volume data has meaningful fluctuations
• Historical analysis is limited by the maximum allowed box count (500) in TradingView
• Volume delta in some markets may not perfectly reflect buying vs. selling pressure due to data limitations
💡 What Makes This Unique
• Focus on Low Volume: Unlike some indicators that highlight high volume events particularly like our very own TLZ indicator, this tool specifically identifies potentially significant price zones that formed with minimal participation.
• Delta + Low Volume Integration: Combines volume delta analysis with low volume detection to reveal directional bias during thin liquidity periods.
• Flexible Lookback System: The dynamic timeframe system allows analysis across any timeframe while maintaining consistent zone identification criteria.
• Support/Resistance Zone Generation: Automatically builds a visual map of potential technical levels based on volume behavior rather than just price patterns.
🔬 How It Works
1. Volume Baseline Calculation:
The indicator calculates a moving average of volume over your specified period to establish a baseline for normal market participation. This adaptive baseline accounts for natural volume fluctuations across different market conditions.
2. Low Volume Detection:
Each candle’s volume is compared to the moving average and flagged when it falls below your threshold divisor. The indicator also filters zones by maximum size to ensure only precise price levels are highlighted.
3. Volume Delta Integration:
For each identified low volume candle, the indicator retrieves the volume delta from a lower timeframe. This delta value is formatted with appropriate scaling (K/M) and displayed within the zone.
4. Zone Management:
New zones are created and tracked in a dynamic array, with each zone extending rightward until it expires. The system automatically removes expired zones based on your lookback period to maintain a clean chart.
💡 Note:
Low liquidity zones often represent areas where price moved with minimal participation, which can indicate potential market inefficiencies. These zones frequently become important support/resistance levels when revisited, especially if approached with higher volume. Consider using this indicator alongside traditional technical analysis tools for comprehensive market context. For best results, experiment with different volume threshold settings based on the specific instrument’s typical volume patterns.
Volume Patterns [SS]Hey everyone,
Been a while since doing anything with Pinescript.
Here is my iteration of a Volume Pattern identification, inspired by Bulkowski's work on patterns and volume.
The indicator aims to identify the 4 major types of volume patterns, these are:
Bullish Breakout Volume
Bearish Breakout Volume
Inverted Domes
Domes
Classification
These patterns are all assigned to a classification based on theory. For example, dome volume is usually bearish, inverted dome is usually bullish, etc. etc. However, in order to accommodate changing sentiments and volatility, I have coded logic into the indicator to assess for the actual sentiment associated with these patterns itself.
The indicator calculates the average return associated with each pattern, scaling the data into a percent return. It then has the ability to re-scale the target using the close price associated with the pattern at the time of pattern signaling, to calculate the target price and plot the target on the chart for you.
Additionally, it provides you with the following:
Labels to signal when a pattern has happened
A table that shows you the average returns associated with the 4 major patterns
Target lines with labels that visually show you the target price associated with the pattern, as well as which pattern they are associated with.
All of these things can be toggled on or off depending on your preference.
Customizing the indicator
In addition to being able to toggle the visuals on or off depending on what you want to see or not see, there are some minor customization abilities in terms of training the indicator to recognize the patterns and predict the TP.
The first one is the Training length
In the settings, you will see "Train", and the default is 500. This is the amount the indicator is looking back in history to learn the patterns and returns associated with them. This 500 is appropriate in most cases and on most timeframes.
Lastly, the Lookforward Length
The look forward length represents the number of bars forward you want to determine the returns for. It is defaulted to 10, but you can modify it.
So, if you are on the 1-Minute chart and have the look forward set to 10, then once a signal happens, the target price is calculated based on 10 minutes from the time of signal. You can increase this or decrease this based on your preference.
Longer look forwards can be good for swingers but should be used on the larger timeframes, shorter are good for scalpers but should be used on the shorter timeframes.
The indicator's use is incredibly simple, you'll pick it up in no time!
Hope you enjoy it and as always, safe trades!
Just an FYI for those who may have questions:
The indicator is open source. This means you are free to take it and modify it as you wish. You do not need to ask me.
Please read the description carefully, as 100% of questions I am asked about indicators are covered in the description. ;-)
Have a good one guys and gals! 🚀🚀🚀
Machine Learning Trendlines Cluster [LuxAlgo]The ML Trendlines Cluster indicator allows traders to automatically identify trendlines using a machine learning algorithm based on k-means clustering and linear regression, highlighting trendlines from clustered prices.
For trader's convenience, trendlines can be filtered based on their slope, allowing them to filter out trendlines that are too horizontal, or instead keep them depending on the user-selected settings.
🔶 USAGE
Traders only need to set the number of trendlines (clusters) they want the tool to detect and the algorithm will do the rest.
By default the tool is set to detect 4 clusters over the last 500 bars, in the image above it is set to detect 10 clusters over the same period.
This approach only focuses on drawing trendlines from prices that share a common trading range, offering a unique perspective to traditional trendlines. Trendlines with a significant slope can highlight higher dispersion within its cluster.
🔹 Trendline Slope Filtering
Traders can filter trendlines by their slope to display only steep or flat trendlines relative to a user-defined threshold.
The image above shows the three different configurations of this feature:
Filtering disabled
Filter slopes above threshold
Filter slopes below threshold
🔶 DETAILS
K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group.
The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized.
The trendlines are displayed according to the linear regression function calculated for each cluster.
🔶 SETTINGS
Window Size: Maximum number of bars to get data from
Clusters: Maximum number of clusters (trendlines) to detect
🔹 Optimization
Maximum Iteration Steps: Maximum loop iterations for cluster computation
🔹 Slope Filter
Threshold Multiplier: Multiplier applied to a volatility measure, higher multiplier equals higher threshold
Filter Slopes: Enable/Disable Trendline Slope Filtering, select to filter trendlines with slopes ABOVE or BELOW the threshold
🔹 Style
Upper Zone: Color to display in the top zone
Lower Zone: Color to display in the bottom zone
Lines: Style for the lines
Size: Line size
