EMA 9/14 Crossover Detector de MathFrickIndique les croisements de la EMA9 et la EMA14 avec un cercle coloré et permet de mettre une alerte
Indicators and strategies
Library_Time_for_workLibrary "Library_Time_for_work"
TODO: add library description here
time_trading(open_hour, open_minutes, close_hour, close_minutes)
TODO: функция определяет временные границы торговли внутри 1 дня
Parameters:
open_hour (int)
open_minutes (int)
close_hour (int)
close_minutes (int)
Returns: TODO: возвращает true если время внутри диапазона и возвращает false если время вне пределов заданного диапазона
Time_Exp()
возвращает true в последнем баре перед экспирацией фьючерса
Time_ticker_Name_MOEX()
Summer_Time()
возвращает true в период летнего времени до 2029 года
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.
Median RSI SD| QuantEdgeB📈 Introducing Median RSI SD by QuantEdgeB
🛠️ Overview
Median RSI SD is a hybrid momentum tool that fuses two powerful techniques: Median Price Filtering and RSI-based Momentum. The result? A cleaner, more responsive oscillator designed to reduce noise and increase clarity in trend detection and potential reversals.
By applying the RSI not to raw price but to the percentile-based median, the indicator adapts better to real structural shifts in the market while filtering out temporary price spikes.
✨ Key Features
🔹 Smoothed RSI Momentum
Utilizes a percentile-based median as input to RSI, reducing volatility and enhancing signal reliability.
🔹 Volatility-Weighted SD Zones
Automatically detects overbought/oversold extremes using ±1 standard deviation bands on the median, adapting to current market volatility.
🔹 Trend Signal Overlay
A directional trend signal (Long / Short / Neutral) is derived from the RSI crossing custom thresholds, combined with position relative to SD bands.
🔹 Visual Labeling System
Optional in-chart labels for Long / Short signals and fully color-customizable theme modes.
📊 How It Works
1️⃣ Median RSI Calculation
Instead of using the close price directly, the script first computes a smoothed median via percentile ranking. RSI is then applied to this filtered stream, improving reactivity without overfitting to short-term noise.
2️⃣ Standard Deviation Filtering
Upper and lower SD bands are calculated around the median to identify extreme conditions. A position near the upper SD while RSI is below the short threshold triggers bearish bias. The reverse applies for longs.
3️⃣ Signal Generation
• ✅ Long Signal → RSI crosses above the Long Threshold (default: 65) and price holds above lower SD.
• ❌ Short Signal → RSI crosses below the Short Threshold (default: 45), typically within upper SD range.
4️⃣ Contextual Highlighting
Zone fills on the chart and RSI subgraph indicate Overbought (>75) and Oversold (<25) conditions for added clarity.
⚙️ Custom Settings
• RSI Length → Default: 21
• Median Length → Default: 10
• Long Threshold → Default: 65
• Short Threshold → Default: 45
• Color Mode → Choose from Strategy, Solar, Warm, Cool, Classic, Magic
• Signal Labels Toggle → Optional in-chart long/short labels
👥 Who Should Use It?
✅ Swing & Momentum Traders → Filter entries based on confirmed directional RSI setups.
✅ Range-Bound Traders → Use SD thresholds to spot fakeouts or exhaustion zones.
✅ Intraday Strategists → Enhanced signal clarity makes it usable even on lower timeframes.
✅ System Builders → Combine this signal with price action or confluence layers for smarter rules.
📌 Conclusion
Median RSI SD by QuantEdgeB is more than just a modified oscillator—it's a robust momentum confirmation framework designed for modern volatility. By replacing noisy price feeds with a statistically stable input and layering RSI + SD logic, this tool provides high-clarity signals without sacrificing responsiveness.
🔹 Key Takeaways:
1️⃣ Median-filtered RSI eliminates noise without lag
2️⃣ Standard deviation bands identify exhaustion zones
3️⃣ Reliable for both trend continuation and mean-reversion strategies
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Linear % ST | QuantEdgeB🚀 Introducing Linear Percentile SuperTrend (Linear % ST) by QuantEdgeB
🛠️ Overview
Linear % SuperTrend (Linear % ST) by QuantEdgeB is a hybrid trend-following indicator that combines Linear Regression, Percentile Filters, and Volatility-Based SuperTrend Logic into one dynamic tool. This system is designed to identify trend shifts early while filtering out noise during choppy market conditions.
By utilizing percentile-based median smoothing and customized ATR multipliers, this tool captures both breakout momentum and pullback opportunities with precision.
✨ Key Features
🔹 Percentile-Based Median Filtering
Removes outliers and normalizes price movement for cleaner trend detection using the 50th percentile (median) of recent price action.
🔹 Linear Regression Smoothing
A smoothed baseline is computed with Linear Regression to detect the underlying trend while minimizing lag.
🔹 SuperTrend Structure with Adaptive Bands
The indicator implements an enhanced SuperTrend engine with custom ATR bands that adapt to trend direction. Bands tighten or loosen based on volatility and trend strength.
🔹 Dynamic Long/Short Conditions
Long and short signals are derived from the relationship between price and the SuperTrend threshold zones, clearly showing trend direction with optional "Long"/"Short" labels on the chart.
🔹 Multiple Visual Themes
Select from 6 built-in color palettes including Strategy, Solar, Warm, Cool, Classic, and Magic to match your personal style or strategy layout.
📊 How It Works
1️⃣ Percentile Filtering
The source price (default: close) is filtered using a nearest-rank 50th percentile over a custom lookback. This normalizes data to reflect the central tendency and removes noisy extremes.
2️⃣ Linear Regression Trend Base
A Linear Regression Moving Average (LSMA) is applied to the filtered median, forming the core trend line. This dynamic trendline provides a low-lag yet smooth view of market direction.
3️⃣ SuperTrend Engine
ATR is applied with custom multipliers (different for long and short) to create dynamic bands. The bands react to price movement and only shift direction after confirmation, preventing false flips.
4️⃣ Trend Signal Logic
• When price stays above the dynamic lower band → Bullish trend
• When price breaks below the upper band → Bearish trend
• Trend direction remains stable until violated by price.
⚙️ Custom Settings
• Percentile Length → Lookback for percentile smoothing (default: 35)
• LSMA Length → Determines the base trend via linear regression (default: 24)
• ATR Length → ATR period used in dynamic bands (default: 14)
• Long Multiplier → ATR multiplier for bullish thresholds (default: 0.8)
• Short Multiplier → ATR multiplier for bearish thresholds (default: 1.9)
✅ How to Use
1️⃣ Trend-Following Strategy
✔️ Go Long when price breaks above the lower ATR band, initiating an upward trend
✔️ Go Short when price falls below the upper ATR band, confirming bearish conditions
✔️ Remain in trend direction until the SuperTrend flips
2️⃣ Visual Confirmation
✔️ Use bar coloring and the dynamic bands to stay aligned with trend direction
✔️ Optional Long/Short labels highlight key signal flips
👥 Who Should Use Linear % ST?
✅ Swing & Position Traders → To ride trends confidently
✅ Trend Followers → As a primary directional filter
✅ Breakout Traders → For clean signal generation post-range break
✅ Quant/Systematic Traders → Integrate clean trend logic into algorithmic setups
📌 Conclusion
Linear % ST by QuantEdgeB blends percentile smoothing with linear regression and volatility bands to deliver a powerful, adaptive trend-following engine. Whether you're a discretionary trader seeking cleaner entries or a systems-based trader building logic for automation, Linear % ST offers clarity, adaptability, and precision in trend detection.
🔹 Key Takeaways:
1️⃣ Percentile + Regression = Noise-Reduced Core Trend
2️⃣ ATR-Based SuperTrend = Reliable Breakout Confirmation
3️⃣ Flexible Parameters + Color Modes = Custom Fit for Any Strategy
📈 Use it to spot emerging trends, filter false signals, and stay confidently aligned with market momentum.
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
MOMENTUM VIP//@version=6
indicator("MOMENTUM VIP", shorttitle="MOMENTUM VIP", overlay=false)
length = input.int(20, title="BB Length")
mult = input.float(2.0, title="BB MultFactor")
lengthKC = input.int(20, title="KC Length")
multKC = input.float(1.5, title="KC MultFactor")
useTrueRange = input.bool(true, title="Use TrueRange (KC)")
// Calculate BB
source = close
basis = ta.sma(source, length)
dev = multKC * ta.stdev(source, length)
upperBB = basis + dev
lowerBB = basis - dev
// Calculate KC
ma = ta.sma(source, lengthKC)
rangeVal = useTrueRange ? ta.tr : (high - low)
rangema = ta.sma(rangeVal, lengthKC)
upperKC = ma + rangema * multKC
lowerKC = ma - rangema * multKC
sqzOn = (lowerBB > lowerKC) and (upperBB < upperKC)
sqzOff = (lowerBB < lowerKC) and (upperBB > upperKC)
noSqz = (not sqzOn) and (not sqzOff)
// Replace avg(x,y) with arithmetic mean: (x + y) / 2
avg_high_low = (ta.highest(high, lengthKC) + ta.lowest(low, lengthKC)) / 2
avg_val = (avg_high_low + ta.sma(close, lengthKC)) / 2
val = ta.linreg(source - avg_val, lengthKC, 0)
// Color definitions using ternary operators
bcolor = val > 0 ? (val > nz(val ) ? color.lime : color.green) : (val < nz(val ) ? color.red : color.maroon)
scolor = noSqz ? color.blue : (sqzOn ? color.black : color.gray)
plot(val, color=bcolor, style=plot.style_area, linewidth=4)
plot(0, color=scolor, style=plot.style_cross, linewidth=2)
Filtered QQE + EMA + Supertrend (Alternating Signals)used qqe mod + supertrend + 20 ema to build perfectly working script.
Quantile DEMA Trend | QuantEdgeB🚀 Introducing Quantile DEMA Trend (QDT) by QuantEdgeB
🛠️ Overview
Quantile DEMA Trend (QDT) is an advanced trend-following and momentum detection indicator designed to capture price trends with superior accuracy. Combining DEMA (Double Exponential Moving Average) with SuperTrend and Quantile Filtering, QDT identifies strong trends while maintaining the ability to adapt to various market conditions.
Unlike traditional trend indicators, QDT uses percentile filtering to adjust for volatility and provides dynamic thresholds, ensuring consistent signal performance across different assets and timeframes.
✨ Key Features
🔹 Trend Following with Adaptive Sensitivity
The DEMA component ensures quicker responses to price changes while reducing lag, offering a real-time reflection of market momentum.
🔹 Volatility-Adjusted Filtering
The SuperTrend logic incorporates quantile percentile filters and ATR (Average True Range) multipliers, allowing QDT to adapt to fluctuating market volatility.
🔹 Clear Signal Generation
QDT generates clear Long and Short signals using percentile thresholds, effectively identifying trend changes and market reversals.
🔹 Customizable Visual & Signal Settings
With multiple color modes and customizable settings, you can easily align the QDT indicator with your trading strategy, whether you're focused on trend-following or volatility adjustments.
📊 How It Works
1️⃣ DEMA Calculation
DEMA is used to reduce lag compared to traditional moving averages. It is calculated by applying a Double Exponential Moving Average to price data. This smoother trend-following mechanism ensures responsiveness to market movements without introducing excessive noise.
2️⃣ SuperTrend with Percentile Filtering
The SuperTrend component adapts the trend-following signal by incorporating quantile percentile filters. It identifies dynamic support and resistance levels based on historical price data:
• Upper Band: Calculated using the 75th percentile + ATR (adjusted with multiplier)
• Lower Band: Calculated using the 25th percentile - ATR (adjusted with multiplier)
These dynamic bands adjust to market conditions, filtering out noise while identifying the true direction.
3️⃣ Signal Generation
• Long Signal: Triggered when price crosses below the SuperTrend Lower Band
• Short Signal: Triggered when price crosses above the SuperTrend Upper Band
The indicator provides signals with corresponding trend direction based on these crossovers.
👁 Visual & Custom Features
• 🎨 Multiple Color Modes: Choose from "Strategy", "Solar", "Warm", "Cool", "Classic", and "Magic" color palettes to match your charting style.
• 🏷️ Long/Short Signal Labels: Optional labels for visual cueing when a long or short trend is triggered.
• 📉 Bar Color Customization: Bar colors dynamically adjust based on trend direction to visually distinguish the market bias.
👥 Who Should Use QDT?
✅ Trend Followers: Use QDT as a dynamic tool to confirm trends and capture profits in trending markets.
✅ Swing Traders: Use QDT to time entries based on confirmed breakouts or breakdowns.
✅ Volatility Traders: Identify market exhaustion or expansion points, especially during volatile periods.
✅ Systematic & Quant Traders: Integrate QDT into algorithmic strategies to enhance market detection with adaptive filtering.
⚙️ Customization & Default Settings
- DEMA Length(30): Controls the lookback period for DEMA calculation
- Percentile Length(10): Sets the lookback period for percentile filtering
- ATR Length(14): Defines the length for calculating ATR (used in SuperTrend)
- ATR Multiplier(1.2 ): Multiplier for ATR in SuperTrend calculation
- SuperTrend Length(30):Defines the length for SuperTrend calculations
📌 How to Use QDT in Trading
1️⃣ Trend-Following Strategy
✔ Enter Long positions when QDT signals a bullish breakout (price crosses below the SuperTrend lower band).
✔ Enter Short positions when QDT signals a bearish breakdown (price crosses above the SuperTrend upper band).
✔ Hold positions as long as QDT continues to provide the same direction.
2️⃣ Reversal Strategy
✔ Take profits when price reaches extreme levels (upper or lower percentile zones) that may indicate trend exhaustion or reversion.
3️⃣ Volatility-Driven Entries
✔ Use the percentile filtering to enter positions based on mean-reversion logic or breakout setups in volatile markets.
🧠 Why It Works
QDT combines the DEMA’s quick response to price changes with SuperTrend's volatility-adjusted thresholds, ensuring a responsive and adaptive indicator. The use of percentile filters and ATR multipliers helps adjust to varying market conditions, making QDT suitable for both trending and range-bound environments.
🔹 Conclusion
The Quantile DEMA Trend (QDT) by QuantEdgeB is a powerful, adaptive trend-following and momentum detection system. By integrating DEMA, SuperTrend, and quantile percentile filtering, it provides accurate and timely signals while adjusting to market volatility. Whether you are a trend follower or volatility trader, QDT offers a robust solution to identify high-probability entry and exit points.
🔹 Key Takeaways:
1️⃣ Trend Confirmation – Uses DEMA and SuperTrend for dynamic trend detection
2️⃣ Volatility Filtering – Adjusts to varying market conditions using percentile logic
3️⃣ Clear Signal Generation – Easy-to-read signals and visual cues for strategy implementation
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Buy Sell CandleThis indicator is designed to detect BUY and SELL signals based on a price breakout pattern formed by two key historical candles.
1. How It Works
The indicator identifies two key candles to generate trading signals:
Candle #2: Always the 3rd candle from the current bar (offset = 3).
Candle #1: Searched within 4–8 bars before Candle #2.
BUY Signal Conditions:
✅ Condition 1: Candle #1 must have a higher High than Candle #2.
✅ Condition 2: The current closing price (Candle #5) must break above Candle #1’s High.
SELL Signal Conditions:
✅ Condition 1: Candle #1 must have a lower Low than Candle #2.
✅ Condition 2: The current closing price (Candle #5) must break below Candle #1’s Low.
2. Visual Representation on Chart
"BUY" arrow (green): Appears below Candle #2 when buy conditions are met.
"SELL" arrow (red): Appears above Candle #2 when sell conditions are met.
Alerts: Automatically triggers notifications when signals appear.
3. Strengths of the Indicator
✔ Simple & intuitive: Uses only price action (High, Low, Close).
✔ No repainting: Signals remain fixed once formed.
✔ Effective for breakout trading: Captures strong momentum moves.
4. Limitations & Considerations
⚠ No trend filter: May produce false signals in sideways markets.
⚠ No risk management: Should be combined with stop-loss/take-profit.
⚠ Best suited for H1+ timeframes to reduce noise.
5. Practical Applications
Trend following: Combine with MA/EMA for trend confirmation.
Breakout trading: Enter trades when key levels are breached.
Confirmation tool: Use alongside RSI, MACD for stronger signals.
👉 Recommendation: Backtest on multiple markets (forex, crypto, stocks) for optimization.
HILO Interpolation | QuantEdgeB🚀 Introducing HILO Interpolation by QuantEdgeB
🛠️ Overview
HILO Interpolation is a dynamic price-action based signal engine crafted to adapt across trending and ranging conditions. By leveraging percentile-based price band interpolation, it identifies high-confidence breakout and breakdown zones. This indicator is designed to serve both as a momentum trigger in trend phases and as a price-reactive entry system during range-bound consolidation.
By intelligently switching between percentile thresholds and interpolated logic, HILO minimizes noise and whipsaws commonly seen in traditional crossover systems.
✨ Key Features
🔹 Percentile Interpolation Engine
Tracks price breakouts using percentile thresholds, making it adaptable to volatility and asset-specific structure.
🔹 Price-Based Signal Confirmation
Signals are only triggered when price meaningfully crosses through key percentile thresholds (based on historical high/low logic).
🔹 Visual Trend Encoding
Color-coded candles, dynamic interpolation bands, and optional long/cash labels give clear visual cues for trend and trade direction.
🔹 Dynamic Threshold Switching
Interpolated threshold flips based on where price sits relative to percentile bands—providing adaptive long/short logic.
📊 How It Works
1️⃣ Percentile Zone Definition
HILO defines two key percentiles from the historical high and low:
• Upper Threshold: 75th Percentile of Highs
• Lower Threshold: 50th Percentile of Lows
These are calculated using linear interpolation to ensure smoother transitions across lookback periods.
2️⃣ Adaptive Signal Line
Instead of using static crossovers, HILO dynamically flips its signal based on whether price exceeds the upper threshold or falls below the lower one.
📌 If price > upper → Signal = Short threshold
📌 If price < lower → Signal = Long threshold
📌 If price remains between thresholds → no flip (trend continuation)
3️⃣ Signal Logic
✅ Long Signal → Price exceeds upper bound while lower bound acts as ceiling
❌ Short Signal → Price breaks below lower percentile while upper bound flips
This simple yet powerful mechanism creates early entries while maintaining high signal confidence.
👁 Visual & Custom Features
• 🎨 Multiple Color Modes: Strategy, Solar, Warm, Cool, Classic, Magic
• 🔄 Dynamic Candle & Band Coloring
• 🏷️ Signal Labels: Optional “𝓛𝓸𝓷𝓰” and “𝓢𝓱𝓸𝓻𝓽” tags when trend flips
• 💬 Alerts Ready: Long/Short crossover conditions can trigger alerts instantly
👥 Who Should Use HILO?
✅ Breakout Traders – Catch early trend starts using percentile filters
✅ Swing Traders – Identify directional bias shifts in advance
✅ Range Strategists – Use band confluence zones to play reversions
✅ Quant & Rule-Based Traders – Incorporate percentile logic into broader systems
⚙️ Customization & Default Settings
Percentile Length:(Default 35) Lookback for calculating percentile thresholds
Lookback Period:(Default 4) Lag factor for interpolation responsiveness
Upper % Threshold: (Default 75) Defines breakout zone from historical highs
Lower % Threshold: (Default 50) Defines retest/accumulation zone from historical lows
📌 How to Use HILO in Trading
1️⃣ Trend-Following Strategy
✔ Enter long when price flips above the adaptive support line
✔ Exit or go short when price breaks below the interpolated resistance
✔ Continue position as long as trend color persists
2️⃣ Range-Reversion Strategy
✔ Buy when price tests the lower threshold and no short signal is triggered
✔ Sell or reduce when price hits the upper range boundary
🧠 Why It Works
HILO operates on the principle that historical price structure creates natural probabilistic thresholds. By interpolating between these using percentile logic, the system maintains adaptability to changing market conditions—without the lag of moving averages or the noise of fixed bands.
🔹 Conclusion
HILO Interpolation is a minimalist yet powerful signal engine built for adaptive breakout and reversion detection. Its percentile-based logic offers a novel way to identify structure shifts, giving traders an edge in both trend and range markets.
🔹 Key Takeaways:
1️⃣ Breakout Entry Logic – Uses percentile interpolation instead of static bands
2️⃣ Color-Driven Clarity – Visual clarity via gradient zone overlays
3️⃣ Trend Integrity – Avoids overfitting and responds only to significant price movements
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
EMA x 6 Set6 Sets of Exponential Moving Averages (EMAs)
Exponential Moving Averages (EMAs) are a type of weighted moving average that gives more importance to recent price data. Using multiple EMAs with different periods helps traders and analysts identify trends, potential reversals, and market momentum more effectively.
A set of EMAs typically refers to one specific EMA calculated over a defined number of periods (e.g., 10-day EMA, 50-day EMA). So, 6 sets of EMAs would involve calculating EMAs over six different timeframes.
Example:
EMA(5) – Very short-term trend
EMA(10) – Short-term trend
EMA(20) – Medium-term trend
EMA(50) – Intermediate-term trend
EMA(100) – Long-term trend
EMA(200) – Major long-term trend
Why Use Multiple EMAs?
Trend confirmation: Aligning EMAs across timeframes can confirm a strong trend.
Crossovers: When shorter EMAs cross above or below longer EMAs, it may signal a trend reversal.
Support & resistance: EMAs often act as dynamic support or resistance levels.
Momentum analysis: The distance between EMAs can give a sense of market momentum or volatility.
Normalized MACD with RSI & Stoch RSI + SignalsNormalized MACD with RSI & Stoch RSI Indicator
Overview:
This indicator combines three popular momentum indicators (MACD, RSI, and Stochastic RSI) into a single cohesive, normalized view, making it easier for traders to interpret market momentum and potential buy/sell signals. It specifically addresses an important issue—the different scale ranges of indicators—by normalizing MACD values to match the 0–100 scale of RSI and Stochastic RSI.
Here’s a clear and concise description of your updated Pine Script indicator:
⸻
Normalized MACD with RSI & Stoch RSI Indicator
Overview:
This indicator combines three popular momentum indicators (MACD, RSI, and Stochastic RSI) into a single cohesive, normalized view, making it easier for traders to interpret market momentum and potential buy/sell signals. It specifically addresses an important issue—the different scale ranges of indicators—by normalizing MACD values to match the 0–100 scale of RSI and Stochastic RSI.
⸻
Key Components:
① MACD (Normalized):
• The Moving Average Convergence Divergence (MACD) originally has an unlimited numerical range.
• Normalization Method:
• Uses a custom tanh(x) function implemented directly in Pine Script:
\tanh(x) = \frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}
• MACD values are scaled using this method to a range of 0–100, with the neutral line at exactly 50.
• Interpretation:
• Values above 50 indicate bullish momentum.
• Values below 50 indicate bearish momentum.
② RSI (Relative Strength Index):
• Measures market momentum on a 0–100 scale.
• Traditional RSI interpretation:
• Overbought conditions: RSI > 70–80.
• Oversold conditions: RSI < 30–20.
③ Stochastic RSI:
• Combines RSI and Stochastic Oscillator to give short-term, highly sensitive signals.
• Helps identify immediate market extremes:
• Above 80 → Short-term overbought.
• Below 20 → Short-term oversold.
⸻
How the Indicator Works:
• Visualization:
• All three indicators (Normalized MACD, RSI, Stochastic RSI) share the same 0–100 scale.
• Clear visual lines and reference levels:
• Midline at 50 indicates neutral momentum.
• Dashed lines at 20 and 80 clearly mark oversold/overbought zones.
• Trading Signals (Recommended approach):
• Bullish Signal (Potential Buy):
• Normalized MACD crosses above 50.
• RSI below or approaching oversold zone (below 30–20).
• Stochastic RSI below 20, indicating short-term oversold conditions.
• Bearish Signal (Potential Sell):
• Normalized MACD crosses below 50.
• RSI above or approaching overbought zone (above 70–80).
• Stochastic RSI above 80, indicating short-term overbought conditions.
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Why Use This Indicator?
• Harmonized Signals:
Normalization of MACD significantly improves clarity and comparability with RSI and Stochastic RSI, providing a unified momentum picture.
• Intuitive Analysis:
Traders can rapidly and intuitively identify momentum shifts without needing multiple indicator windows.
• Improved Decision-Making:
Clear visual references and signals help reduce subjective interpretation, potentially improving trading outcomes.
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Suggested Usage:
• Combine with traditional support
Z-Score Normalized Volatility IndicesVolatility is one of the most important measures in financial markets, reflecting the extent of variation in asset prices over time. It is commonly viewed as a risk indicator, with higher volatility signifying greater uncertainty and potential for price swings, which can affect investment decisions. Understanding volatility and its dynamics is crucial for risk management and forecasting in both traditional and alternative asset classes.
Z-Score Normalization in Volatility Analysis
The Z-score is a statistical tool that quantifies how many standard deviations a given data point is from the mean of the dataset. It is calculated as:
Z = \frac{X - \mu}{\sigma}
Where X is the value of the data point, \mu is the mean of the dataset, and \sigma is the standard deviation of the dataset. In the context of volatility indices, the Z-score allows for the normalization of these values, enabling their comparison regardless of the original scale. This is particularly useful when analyzing volatility across multiple assets or asset classes.
This script utilizes the Z-score to normalize various volatility indices:
1. VIX (CBOE Volatility Index): A widely used indicator that measures the implied volatility of S&P 500 options. It is considered a barometer of market fear and uncertainty (Whaley, 2000).
2. VIX3M: Represents the 3-month implied volatility of the S&P 500 options, providing insight into medium-term volatility expectations.
3. VIX9D: The implied volatility for a 9-day S&P 500 options contract, which reflects short-term volatility expectations.
4. VVIX: The volatility of the VIX itself, which measures the uncertainty in the expectations of future volatility.
5. VXN: The Nasdaq-100 volatility index, representing implied volatility in the Nasdaq-100 options.
6. RVX: The Russell 2000 volatility index, tracking the implied volatility of options on the Russell 2000 Index.
7. VXD: Volatility for the Dow Jones Industrial Average.
8. MOVE: The implied volatility index for U.S. Treasury bonds, offering insight into expectations for interest rate volatility.
9. BVIX: Volatility of Bitcoin options, a useful indicator for understanding the risk in the cryptocurrency market.
10. GVZ: Volatility index for gold futures, reflecting the risk perception of gold prices.
11. OVX: Measures implied volatility for crude oil futures.
Volatility Clustering and Z-Score
The concept of volatility clustering—where high volatility tends to be followed by more high volatility—is well documented in financial literature. This phenomenon is fundamental in volatility modeling and highlights the persistence of periods of heightened market uncertainty (Bollerslev, 1986).
Moreover, studies by Andersen et al. (2012) explore how implied volatility indices, like the VIX, serve as predictors for future realized volatility, underlining the relationship between expected volatility and actual market behavior. The Z-score normalization process helps in making volatility data comparable across different asset classes, enabling more effective decision-making in volatility-based strategies.
Applications in Trading and Risk Management
By using Z-score normalization, traders can more easily assess deviations from the mean in volatility, helping to identify periods when volatility is unusually high or low. This can be used to adjust risk exposure or to implement volatility-based trading strategies, such as mean reversion strategies. Research suggests that volatility mean-reversion is a reliable pattern that can be exploited for profit (Christensen & Prabhala, 1998).
References:
• Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2012). Realized volatility and correlation dynamics: A long-run approach. Journal of Financial Economics, 104(3), 385-406.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125-150.
• Whaley, R. E. (2000). Derivatives on market volatility and the VIX index. Journal of Derivatives, 8(1), 71-84.
WaveTrend + Multi-Timeframe AlertsThe WaveTrend SwipeUP Indicator is designed to provide clear buy and sell signals based on multi-timeframe analysis and dynamic support levels. This indicator combines WaveTrend calculations on 4-hour and 8-hour timeframes with a Lips line check on the daily timeframe to generate precise trading signals.
Main Components:
WaveTrend Calculation: Uses exponential moving average and absolute deviation to calculate the WaveTrend index (WT1) on 4-hour and 8-hour timeframes.
Lips Line: Calculates a simple moving average (SMA) of the closing price over a period of 280 days to determine the Lips line on the daily timeframe.
Overbought and Oversold Conditions: Checks if WT1 on 4-hour and 8-hour timeframes exceeds overbought levels (65) or falls below oversold levels (-65).
Price Position Relative to Lips Line: Checks if the closing price is above or below the Lips line on the daily timeframe.
Dynamic Support: Calculates a simple moving average of the low prices over a period of 50 to identify potential support levels.
Trading Signals:
Buy Signal: A green triangle appears below the bar when the indicator detects oversold conditions on both 4-hour and 8-hour timeframes, and the price is above the Lips line on the daily timeframe.
Sell Signal: A red triangle appears above the bar when the indicator detects overbought conditions on both 4-hour and 8-hour timeframes, and the price is below the Lips line on the daily timeframe.
Multi-Timeframe Alerts:
The WaveTrend SwipeUP Indicator includes an alert condition that combines buy and sell signals based on multi-timeframe analysis. Alerts are generated when the following conditions occur:
Buy Signal: When WT1 on 4-hour and 8-hour timeframes is below the oversold level (-65) and the closing price is above the Lips line on the daily timeframe.
Sell Signal: When WT1 on 4-hour and 8-hour timeframes is above the overbought level (65) and the closing price is below the Lips line on the daily timeframe.
These alerts can be configured to send notifications when trading signals are generated, allowing users to monitor buy and sell opportunities in real-time.
Majors Rotation System | OpusMajor Rotation System | Opus
Key Features ✨
The Major Rotation System (MRS) is a cutting-edge tool designed to dynamically optimize capital allocation across three leading cryptocurrencies:
Bitcoin
Ethereum
Solana
While seamlessly adapting to shifting market conditions. Harnessing advanced quantitative techniques, MRS integrates momentum analysis, trend-following strategies, and robust statistical processing to deliver precise portfolio adjustments.
Its sleek, user-friendly interface provides crystal-clear signals, empowering traders to make swift, informed decisions in fast-paced, high-frequency trading environments.
Methodology 🧠
MRS employs a sophisticated rotation strategy, reallocating capital among BTC, ETH, and SOL based on proven quantitative frameworks, with a core focus on momentum and relative strength analysis.
Momentum Analysis : This measures the speed and persistence of price movements over a defined lookback period, identifying which asset—BTC, ETH, or SOL—is exhibiting the strongest upward trajectory. MRS leverages this to prioritize assets with sustained price acceleration, filtering out noise to capture high-probability trends.
Relative Strength Analysis : This compares the performance of BTC, ETH, and SOL against each other, pinpointing the asset outperforming its peers in terms of price appreciation. By ranking their strength dynamically, MRS ensures capital flows to the leader while avoiding laggards.
The system continuously evaluates these metrics to signal optimal entry and exit points, indicating when to shift into cash or concentrate holdings in a single asset. This adaptive approach aims to sidestep significant drawdowns while outperforming traditional Buy-and-Hold strategies, offering a proactive shield against volatility and a pathway to superior returns.
Metrics and Equity 📊
Performance is the heartbeat of MRS, tracked through three powerful metrics:
Sharpe Ratio : This measures how much return you’re getting for the risk you’re taking. A higher Sharpe Ratio means better rewards without excessive ups and downs—think of it as your “bang for your buck” in trading.
Sortino Ratio : Unlike Sharpe, this focuses only on downside risk—those painful losses you want to avoid. A strong Sortino Ratio shows MRS is protecting your portfolio from the worst drops while still chasing gains.
Omega Ratio : This looks at the balance of wins versus losses, weighting the likelihood of profits against setbacks. A higher Omega Ratio signals that MRS is tilting the odds in your favor over time.
The system dynamically generates dual equity curves—comparing MRS performance against a Buy-and-Hold benchmark—ensuring transparency and reliability without the pitfalls of repainting. These metrics give traders—novice or pro—a clear, trustworthy view of how MRS performs.
Applications 💻
Tailored for discerning traders, the Major Rotation System is a precision instrument built for today’s volatile markets. With its asset-specific equity curve filter, reimagined momentum analysis, and streamlined UI, MRS is engineered to amplify gains during bull markets while prioritizing risk-adjusted performance.
Its high-resolution data processing and adaptive reallocation capabilities make it an ideal companion for capturing premium trends in blue-chip cryptocurrencies, regardless of market tempo. Whether navigating explosive rallies or choppy waters, MRS equips traders with the agility and insight to thrive.
Disclaimer ⚠️
The Major Rotation System (MRS) is a tool designed to assist traders in analyzing market trends and is not intended as financial advice. Trading and investing involve significant risks, including the potential loss of capital.
Past performance is not indicative of future results. Users should conduct their own research, assess their risk tolerance, and make independent investment decisions. The creators of MRS are not responsible for any financial outcomes resulting from its use.
New York Key LevelsThis indicator provides a collection of important New York market levels to assist with trading strategies. While it is still a work in progress, it currently includes the following:
15m Opening Range: Tracks the first 15-minute price action after the New York session opens, helping to define key levels of support and resistance for the day.
Stay tuned for additional features and levels as this tool evolves!
SwipeUP IndicatorThe "SwipeUP Indicator" is an advanced technical analysis tool designed to help traders identify buying and selling opportunities in the financial market.
This indicator uses a combination of exponential moving averages and volume filters to generate accurate and reliable signals based on overbought and oversold conditions.
Key Features:
Buy and Sell Signals: The "SwipeUP Indicator" generates bullish and bearish signals based on overbought and oversold conditions on multiple timeframes (4-hour and 8-hour).
Bullish signals are indicated by green arrows, while bearish signals are indicated by red arrows.
Volume Filters: The signals are confirmed by a volume filter, which ensures greater accuracy. The volume filter uses the 20- and 50-period exponential moving average of volume to confirm the validity of the signals.
Reference Line: The indicator checks the position of the price against a reference line calculated on a daily timeframe. This reference line is a simple moving average of the closing price over 280 periods.
Clear Visualization: The signals are displayed with colored arrows directly on the main chart, making it easy for the trader to interpret and take action.
Overbought and Oversold Levels: The indicator includes horizontal lines for overbought and oversold levels, with a background fill for better visualization.
These levels help traders identify when the market may be ready for a reversal.
How to Use:
Add the "SwipeUP Indicator" to your chart on TradingView.
Notice the red and green arrows indicating sell and buy signals respectively.
Use the signals in combination with other technical analysis tools to make informed trading decisions.
Note: This indicator is protected and available only to authorized users. Contact the author for information on how to gain access.
EMA 34 Crossover with Break Even Stop LossEMA 34 Crossover with Break Even Stop Loss Strategy
This trading strategy is based on the 34-period Exponential Moving Average (EMA) and aims to enter long positions when the price crosses above the EMA 34. The strategy is designed to manage risk effectively with a dynamic stop loss and take-profit mechanism.
Key Features:
EMA 34 Crossover:
The strategy generates a long entry signal when the closing price of the current bar crosses above the 34-period EMA, with the condition that the previous closing price was below the EMA. This crossover indicates a potential upward trend.
Risk Management:
Upon entering a trade, the strategy sets a stop loss at the low of the previous bar. This helps in controlling the downside risk.
A take profit level is set at a 10:1 risk-to-reward ratio, meaning the potential profit is ten times the amount risked on the trade.
Break-even Stop Loss:
As the price moves in favor of the trade and reaches a 3:1 risk-to-reward ratio, the strategy moves the stop loss to the entry price (break-even). This ensures that no loss will be incurred if the market reverses, effectively protecting profits.
Exit Conditions:
The strategy exits the trade when either the stop loss is hit (if the price drops below the stop loss level) or the take profit target is reached (if the price rises to the take profit level).
If the price reaches the break-even level (entry price), the stop loss is adjusted to lock in profits and prevent any loss.
Visualization:
The stop loss and take profit levels are plotted on the chart for easy visualization, helping traders track the status of their trade.
Trade Management Summary:
Long Entry: When price crosses above the 34-period EMA.
Stop Loss: Set to the low of the previous candle.
Take Profit: Set to a 10:1 risk-to-reward ratio.
Break-even: Stop loss is moved to entry price when a 3:1 risk-to-reward ratio is reached.
Exit: The trade is closed either when the stop loss or take profit levels are hit.
This strategy is designed to minimize losses by employing a dynamic stop loss and to maximize gains by setting a favorable risk-to-reward ratio, making it suitable for traders who prefer a structured, automated approach to risk management and trend-following.
Gold Silver Ratio Indicator by MossinNagantThis system is designed to trade manually in the gold-silver ratio.
It sells and buys at the percentage you specify in the lower and upper limits and sells and buys at the same rate for each subsequent increase or decrease you specify.
start_time: Gets the start date from the user (default: January 1, 2025).
initial_dollar_input: Initial balance (in USD, default: 10,000 USD). 50% of this balance will buy gold and 50% will buy silver.
threshold_high_input: Upper threshold level (default: 90).
threshold_low_input: Lower threshold level (default: 80).
step_input: Step size (in percentage, default: 2%).
trade_percent_input: Percentage rate to be sold in each transaction.
Transaction logic;
Transaction Logic:
sell_gold_basic and sell_gold_step: When the rate exceeds the upper threshold (threshold_high), gold is sold.
sell_silver_basic and sell_silver_step: When the rate falls below the lower threshold (threshold_low), silver is sold.
step: A new transaction is triggered with each additional step (e.g. 2% increments).
Transactions:
When gold is sold: 10% is sold (gold_sold), silver is bought with it (silver_bought).
When silver is sold: 10% is sold (silver_sold), gold is bought with it (gold_bought).
Sales and purchase transactions are shown on the chart with labels (label.new).
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EMA 21 and SMA 50 Low ConditionsDescription:
This indicator highlights trend zones on a daily chart using the 21-day Exponential Moving Average (EMA) and 50-day Simple Moving Average (SMA). It’s designed to identify bullish conditions with two distinct background colors:
• Green Background: Signals a strong bullish trend. Appears when the low of the candle stays above the 21 EMA for 3 or more consecutive days, with either the 3rd or 4th day closing higher than its open (an “up” day). The green zone persists until a candle closes below the 21 EMA.
• Yellow Background: Indicates a potential support zone. Triggers when the low of the candle remains above the 50 SMA after the green condition ends, suggesting the price is still holding above a longer-term average. The yellow zone lasts until a candle closes below the 50 SMA.
Features:
• Plots the 21 EMA (blue line) and 50 SMA (orange line) for visual reference.
• Uses background colors to mark trend zones, making it easy to spot bullish phases and support levels.
• Optimized for daily timeframes, ideal for swing traders or long-term trend followers.
How to Use:
1. Apply the indicator to a daily chart.
2. Watch for the green background to identify strong bullish momentum (lows holding above the 21 EMA with an up close confirmation).
3. Look for the yellow background as a sign of potential support after the short-term trend weakens (lows above the 50 SMA).
4. Exit zones are triggered by closes below the respective averages (21 EMA for green, 50 SMA for yellow).
Notes:
• Best used on symbols with sufficient historical data to ensure accurate EMA and SMA calculations.
• The indicator prioritizes the green condition over yellow—green will override if both could apply.
Author’s Intent:
Created to help traders visualize sustained bullish trends and key support levels using simple moving average rules. Perfect for confirming uptrends and monitoring pullbacks within a broader bullish context.
