MT-Turnover.IndicatorMT-Turnover Indicator – Market Liquidity & Activity Gauge
Overview
The MT-Turnover Indicator is a TradingView tool designed to measure market liquidity and trading activity by tracking the turnover rate of a stock. It calculates the turnover percentage by comparing the trading volume to the number of outstanding shares, providing traders with insights into how actively a stock is being traded.
By incorporating a moving average (MA) of turnover and a customizable high turnover threshold, this indicator helps identify periods of increased market participation, potential breakouts, or distribution phases.
Key Features
✔ Turnover Rate Calculation – Expresses turnover as a percentage of outstanding shares
✔ Customizable Moving Average (MA) for Trend Analysis – Smoothens turnover fluctuations for better trend identification
✔ High Turnover Level Alert – Marks periods when turnover exceeds a predefined threshold
✔ Histogram Visualization – Shows turnover dynamics with clear green (above MA) and red (below MA) bars
✔ High Turnover Signal Markers – Flags exceptionally high turnover events for quick identification
How It Works
1. Turnover Rate Calculation
• Formula:

• Configurable Outstanding Shares (in millions) to match the stock being analyzed
2. Turnover Moving Average (MA) for Trend Analysis
• A simple moving average (SMA) of turnover is calculated over a user-defined period (default: 20 days)
• Green bars indicate turnover above MA, suggesting increased activity
• Red bars indicate turnover below MA, signaling lower participation
3. High Turnover Threshold
• Users can set a high turnover level (%) to mark exceptionally active trading periods
• When turnover exceeds this level, a red triangle marker appears above the bar
4. Reference Line & Informative Table
• A dashed red reference line marks the high turnover threshold
• A floating table in the top-right corner provides a quick summary
How to Use This Indicator
📈 For Breakout Traders – High turnover can indicate strong buying interest, often preceding breakouts
📉 For Risk Management – Spikes in turnover may signal distribution phases or panic selling
🔎 For Liquidity Analysis – Helps gauge how liquid a stock is, which can impact price stability
Conclusion
The MT-Turnover Indicator is a powerful tool for identifying periods of high market activity, helping traders detect potential breakouts, reversals, or strong accumulation/distribution phases. By visualizing turnover with a moving average and customizable threshold, it provides valuable insights into market participation trends.
➡ Add this indicator to your TradingView chart and improve your liquidity-based trading decisions today! 🚀
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Long and Short Term Highs and LowsLong and Short Term Highs and Lows
Overview:
This indicator is designed to help traders identify significant price points by marking new highs and lows over two distinct timeframes—a long-term and a short-term period. It achieves this by drawing optional channel lines that outline the highest highs and lowest lows over the chosen time periods and by plotting visual markers (triangles) on the chart when a new high or low is detected.
Key Features:
Dual Timeframe Analysis:
Long Term: Uses a user-defined “Time Period” (default 52) and “Time Unit” (default: Weekly) to determine long-term high and low levels.
Short Term: Uses a separate “Time Period” (default 50) and “Time Unit” (default: Daily) to compute short-term high and low levels.
Optional Channel Display:
For both long and short term periods, you have the option to display a channel by plotting the highest and lowest values as lines. This visual channel helps to delineate the range within which the price has traded over the selected period.
New High/Low Markers:
The indicator identifies moments when the highest high or lowest low is updated relative to the previous bar.
When a new high is established, an up triangle is plotted above the bar.
Conversely, when a new low occurs, a down triangle is plotted below the bar.
Separate input toggles allow you to enable or disable these markers independently for the long-term and short-term setups.
Inputs and Settings:
Long Term High/Low Period Settings:
Show New High/Low? (STW): Toggle to enable or disable the plotting of new high/low markers for the long-term period.
Time Period: The number of bars used to calculate the highest high and lowest low (default is 52).
Time Unit: The timeframe on which the long-term calculation is based (default is Weekly).
Show Channel? (SCW): Toggle to display the channel lines that connect the long-term high and low levels.
Short Term High/Low Period Settings:
Show New High/Low?: Toggle to enable or disable the plotting of new high/low markers for the short-term period.
Time Period: The number of bars used for calculating the short-term extremes (default is 50).
Time Unit: The timeframe on which the short-term calculations are based (default is Daily).
Show Channel?: Toggle to display the channel lines for the short-term highs and lows.
Indicator Logic:
Channel Calculation:
The script uses the request.security function to pull data from the specified timeframes. For each timeframe:
It calculates the lowest low over the defined period using ta.lowest.
It calculates the highest high over the defined period using ta.highest.
These values can be optionally plotted as channel lines when the “Show Channel?” option is enabled.
New High/Low Detection:
For each timeframe, the indicator compares the current high (or low) with its immediate previous value:
New High: When the current high exceeds the previous bar’s high, an up triangle is drawn above the bar.
New Low: When the current low falls below the previous bar’s low, a down triangle is drawn below the bar.
Usage and Interpretation:
Trend Identification:
When new highs (or lows) occur, they can signal the start of a strong upward (or downward) movement. The indicator helps you visually track these critical turning points over both longer and shorter periods.
Channel Breakouts:
The optional channel display offers additional context. Price movement beyond these channels may indicate a breakout or a significant shift in trend.
Customizable Timeframes:
You can adjust both the time period and time unit to fit your trading style—whether you’re focusing on longer-term trends or short-term price action.
Conclusion:
This indicator provides a dual-layer analysis by combining long-term and short-term perspectives, making it a versatile tool for identifying key highs and lows. Whether you are looking to confirm trend strength or spot potential breakouts, the “Long and Short Term Highs and Lows” indicator adds a valuable visual element to your TradingView charts.
SMA with Std Dev Bands (Futures/US Stocks RTH)Rolling Daily SMA With Std Dev Bands
Upgrade your technical analysis with Rolling Daily SMA With Std Dev Bands, a powerful indicator that dynamically adjusts to your trading instrument. Whether you’re analyzing futures or US stocks during regular trading hours (RTH), this indicator seamlessly applies the correct logic to calculate a rolling daily Simple Moving Average (SMA) with customizable standard deviation bands for precise trend and volatility tracking.
Key Features:
✅ Automatic Instrument Detection– The indicator automatically recognizes whether you're trading futures or US equities and applies the correct daily lookback period based on your chart’s timeframe.
- Futures: Uses full trading day lengths (e.g., 1380 bars for 1‑minute charts).
- US Stocks (RTH): Uses regular session lengths (e.g., 390 bars for 1‑minute charts).
✅ Rolling Daily SMA (3‑pt Purple Line) – A continuously updated daily moving average, giving you an adaptive trend indicator based on market structure.
✅ Three Standard Deviation Bands (1‑pt White Lines) –
- Customizable multipliers allow you to adjust each band’s width.
- Toggle each band on or off to tailor the indicator to your strategy.
- The inner band area is color-filled: light green when the SMA is rising, light red when falling, helping you quickly identify trend direction.
✅ Works on Any Chart Timeframe – Whether you trade on 1-minute, 3-minute, 5-minute, or 15-minute charts, the indicator adjusts dynamically to provide accurate rolling daily calculations.
# How to Use:
📌 Identify Trends & Volatility Zones – The rolling daily SMA acts as a dynamic trend guide, while the standard deviation bands help spot potential overbought/oversold conditions.
📌 Customize for Precision – Adjust band multipliers and toggle each band on/off to match your trading style.
📌 Trade Smarter – The filled inner band offers instant visual feedback on market momentum, while the outer bands highlight potential breakout zones.
🔹 This is the perfect tool for traders looking to combine trend-following with volatility analysis in an easy-to-use, adaptive indicator.
🚀 Add Rolling Daily SMA With Std Dev Bands to your chart today and enhance your market insights!
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*Disclaimer: This indicator is for informational and educational purposes only and should not be considered financial advice. Always use proper risk management and conduct your own research before trading.*
Donchian and Keltner Channels Trend Following with Trailing StopLong Only Trend-following model based on Keltner Channels and Donchian Channels.
These indicators include a noise region, which allows prices to oscillate without requiring position adjustments.
When price trades above the upper band, it signals strength; when it trades below the lower band, it signals weakness.
Keltner Channels
Keltner Channels are volatility-based envelopes set above and below an exponential moving average. Keltner Channels use the Average True Range (ATR), which measures daily volatility, to set channel distance.
Donchian Channel
Donchian Channels are are used to identify market trends and volatility. The upper and lower bands are based on the highest high and lowest low of a specified period. When the price moves above the upper band, it indicates a bullish breakout, while a
move below the lower band indicates a bearish breakout. The distance between the upper and lower channel of the Donchian Channel indicates the asset’s volatility.
Trend Following Model
The default settings are:
Upper Keltner and Upper Donchian Channel Length : 20
Lower Keltner and Lower Donchian Channel Length : 40
Keltner ATR Multiplier: 2
Entries, Exits and Trailing Stop
Entry : When price exceeds the upper band of at least one of these indicators.
Exit : When price undercuts the lower band of at least one of these indicators.
Trailing Stop : See below.
Trailing Stop
This is a stop-loss order that moves with the price of the underlying. It is designed to “trail” the price up (in the case of a long position) or down (for a short position), locking in profits as the price moves in a favorable direction.
At the end of day t, there was a Trailing Stop level in place. For the next day (day t + 1), the Trailing Stop will be adjusted. The new Trailing Stop will be the higher of two values:
The Trailing Stop from the previous day (day t).
The Lower Band computed at the end of day t + 1.
G-FRAMA | QuantEdgeBIntroducing G-FRAMA by QuantEdgeB
Overview
The Gaussian FRAMA (G-FRAMA) is an adaptive trend-following indicator that leverages the power of Fractal Adaptive Moving Averages (FRAMA), enhanced with a Gaussian filter for noise reduction and an ATR-based dynamic band for trade signal confirmation. This combination results in a highly responsive moving average that adapts to market volatility while filtering out insignificant price movements.
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1. Key Features
- 📈 Gaussian Smoothing – Utilizes a Gaussian filter to refine price input, reducing short-term noise while maintaining responsiveness.
- 📊 Fractal Adaptive Moving Average (FRAMA) – A self-adjusting moving average that adapts its sensitivity to market trends.
- 📉 ATR-Based Volatility Bands – Dynamic upper and lower bands based on the Average True Range (ATR), improving signal reliability.
- ⚡ Adaptive Trend Signals – Automatically detects shifts in market structure by evaluating price in relation to FRAMA and its ATR bands.
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2. How It Works
- Gaussian Filtering
The Gaussian function preprocesses the price data, giving more weight to recent values and smoothing fluctuations. This reduces whipsaws and allows the FRAMA calculation to focus on meaningful trend developments.
- Fractal Adaptive Moving Average (FRAMA)
Unlike traditional moving averages, FRAMA uses fractal dimension calculations to adjust its smoothing factor dynamically. In trending markets, it reacts faster, while in sideways conditions, it reduces sensitivity, filtering out noise.
- ATR-Based Volatility Bands
ATR is applied to determine upper and lower thresholds around FRAMA:
- 🔹 Long Condition: Price closes above FRAMA + ATR*Multiplier
- 🔻 Short Condition: Price closes below FRAMA - ATR
This setup ensures entries are volatility-adjusted, preventing premature exits or false signals in choppy conditions.
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3. Use Cases
✔ Adaptive Trend Trading – Automatically adjusts to different market conditions, making it ideal for both short-term and long-term traders.
✔ Noise-Filtered Entries – Gaussian smoothing prevents false breakouts, allowing for cleaner entries.
✔ Breakout & Volatility Strategies – The ATR bands confirm valid price movements, reducing false signals.
✔ Smooth but Aggressive Shorts – While the indicator is smooth in overall trend detection, it reacts aggressively to downside moves, making it well-suited for traders focusing on short opportunities.
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4. Customization Options
- Gaussian Filter Settings – Adjust length & sigma to fine-tune the smoothness of the input price. (Default: Gaussian length = 4, Gaussian sigma = 2.0, Gaussian source = close)
- FRAMA Length & Limits – Modify how quickly FRAMA reacts to price changes.(Default: Base FRAMA = 20, Upper FRAMA Limit = 8, Lower FRAMA Limit = 40)
- ATR Multiplier – Control how wide the volatility bands are for long/short entries.(Default: ATR Length = 14, ATR Multiplier = 1.9)
- Color Themes – Multiple visual styles to match different trading environments.
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Conclusion
The G-FRAMA is an intelligent trend-following tool that combines the adaptability of FRAMA with the precision of Gaussian filtering and volatility-based confirmation. It is versatile across different timeframes and asset classes, offering traders an edge in trend detection and trade execution.
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🔹 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.
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview
Strategy Name: Smart MA Crossover Backtester
Published on: TradingView
Applicable Markets: Works well on crypto (tested profitably on ETH)
Strategy Concept
The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend.
Core Components & Logic
Moving Averages (MA) for Entry Signals
Fast Moving Average (9-period SMA)
Slow Moving Average (21-period SMA)
A trade signal is generated when the fast MA crosses the slow MA.
Trend Filter (200-period SMA)
Only enters long positions if price is above the 200-period SMA (bullish trend).
Only enters short positions if price is below the 200-period SMA (bearish trend).
This helps in avoiding counter-trend trades, reducing whipsaws.
ATR-Based Stop Loss & Take Profit
Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss.
Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR).
This ensures dynamic stop loss and take profit levels based on market volatility.
Trading Rules
✅ Long Entry (Buy Signal):
Fast MA (9) crosses above Slow MA (21)
Price is above the 200 MA (bullish trend filter active)
Stop Loss: Below entry price by 2× ATR
Take Profit: Above entry price by 4× ATR
✅ Short Entry (Sell Signal):
Fast MA (9) crosses below Slow MA (21)
Price is below the 200 MA (bearish trend filter active)
Stop Loss: Above entry price by 2× ATR
Take Profit: Below entry price by 4× ATR
Why This Strategy Works Well for Crypto (ETH)?
🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions.
🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals.
🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate.
This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
VolatilityThis is a filtering indicator Volatility in the CTA contract of BG Exchange. According to their introduction, it should be calculated using this simple method.
However, you may have seen the problem. According to the exchange's introduction, the threshold should still be divided by 100, which is in percentage form. The result I calculated, even if not divided by 100, still shows a significant difference, which may be due to the exchange's mistake. Smart netizens, do you know how the volatility of BG Exchange is calculated.
The official introduction of BG Exchange is as follows: Volatility (K, Fluctuation) is an additional indicator used to filter out positions triggered by CTA strategy signals in low volatility markets. Usage: Select the fluctuation range composed of the nearest K candlesticks, and choose the highest and lowest closing prices. Calculation: 100 * (highest closing price - lowest closing price) divided by the lowest closing price to obtain the recent amplitude. When the recent amplitude is greater than Fluctuation, it is considered that the current market volatility meets the requirements. When the CTA strategy's position building signal is triggered, position building can be executed. Otherwise, warehouse building cannot be executed.
Anchored VWAP with Buy/Sell SignalsAnchored VWAP Calculation:
The script calculates the AVWAP starting from a user-defined anchor point (anchor_date).
The AVWAP is calculated using the formula:
AVWAP
=
∑
(
Volume
×
Average Price
)
∑
Volume
AVWAP=
∑Volume
∑(Volume×Average Price)
where the average price is
(
h
i
g
h
+
l
o
w
+
c
l
o
s
e
)
/
3
(high+low+close)/3.
Buy Signal:
A buy signal is generated when the price closes above the AVWAP (ta.crossover(close, avwap)).
Sell Signal:
A sell signal is generated when the price closes below the AVWAP (ta.crossunder(close, avwap)).
Plotting:
The AVWAP is plotted on the chart.
Buy and sell signals are displayed as labels on the chart.
Background Highlighting:
The background is highlighted in green for buy signals and red for sell signals (optional).
True Range & ATRDescription : This indicator plots both the True Range (TR) and the Average True Range (ATR) in a separate pane below the main chart.
- TR represents the absolute price movement range within each candle.
- ATR is a smoothed version of TR over a user-defined period (default: 14), providing insight into market volatility.
- TR is displayed as a histogram for a clearer view of individual candle ranges.
- ATR is plotted as a line to show the smoothed trend of volatility.
This indicator helps traders assess market volatility and potential price movements.
Smoothed Low-Pass Butterworth Filtered Median [AlphaAlgos]Smoothed Low-Pass Butterworth Filtered Median
This indicator is designed to smooth price action and filter out noise while maintaining the dominant trend. By combining a Butterworth low-pass filter with a median-based smoothing approach , it effectively reduces short-term fluctuations, allowing traders to focus on the true market direction.
How It Works
Median Smoothing: The indicator calculates the 50th percentile (median) of closing prices over a customizable period , making it more robust against outliers compared to traditional moving averages.
Butterworth Filtering: A low-pass filter is applied using an approximation of the Butterworth formula , controlled by the Cutoff Frequency , helping to eliminate high-frequency noise while preserving trends.
EMA Refinement: A 7-period EMA is applied to further smooth the signal, providing a more reliable trend representation.
Features
Trend Smoothing: Reduces market noise and highlights the dominant trend.
Dynamic Color Signals: The EMA line changes color to indicate trend strength and direction.
Configurable Parameters: Customize the median length, cutoff frequency, and EMA length to fit your strategy.
Versatile Use Case: Suitable for both trend-following and mean-reversion strategies.
How to Use
Bullish Signal: When the EMA is below the price and rising , indicating upward momentum.
Bearish Signal: When the EMA is above the price and falling , signaling a potential downtrend.
Reversal Zones: Monitor for trend shifts when the color of the EMA changes.
This indicator provides a clear, noise-free view of market trends , making it ideal for traders seeking improved trend identification and entry signals .
Dynamic Stop Loss & Take ProfitDynamic Stop Loss & Take Profit is a versatile risk management indicator that calculates dynamic stop loss and take profit levels based on the Average True Range (ATR). This indicator helps traders set adaptive exit points by using a configurable ATR multiplier and defining whether they are in a Long (Buy) or Short (Sell) trade.
How It Works
ATR Calculation – The indicator calculates the ATR value over a user-defined period (default: 14).
Stop Loss and Take Profit Multipliers – The ATR value is multiplied by a configurable factor (ranging from 1.5 to 4) to determine volatility-adjusted stop loss and take profit levels.
Trade Type Selection – The user can specify whether they are in a Long (Buy) or Short (Sell) trade.
Long (Buy) Trade:
Stop Loss = Entry Price - (ATR × Stop Loss Multiplier)
Take Profit = Entry Price + (ATR × Take Profit Multiplier)
Short (Sell) Trade:
Stop Loss = Entry Price + (ATR × Stop Loss Multiplier)
Take Profit = Entry Price - (ATR × Take Profit Multiplier)
Features
Configurable ATR length and multipliers
Supports both long and short trades
Clearly plotted Stop Loss (red) and Take Profit (green) levels on the chart
Helps traders manage risk dynamically based on market volatility
This indicator is ideal for traders looking to set adaptive stop loss and take profit levels without relying on fixed price targets.
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Multi-Asset Ratio (20 vs 5) - LuchapThis indicator calculates and displays the ratio between the sum of the prices of several base assets and the sum of the prices of several quote assets. You can select up to 20 base assets and 5 quote assets, and enable or disable each asset individually to refine your analysis. This ratio allows you to quickly evaluate the relative performance of different groups of assets.
ATR Trailing Stop by GideonMATR Trailing Stop Indicator
This ATR Trailing Stop Indicator is designed for traders who wish to enhance their exit strategies by leveraging volatility-based stops. It offers a systematic approach to trend management and risk control, enabling traders to capture extended trends while protecting their capital during market reversals. Works on Indian Indices as well.
Overview:
The ATR Trailing Stop indicator is a dynamic trend-following tool that adjusts stop levels based on market volatility. By incorporating the Average True Range (ATR), the indicator provides a flexible exit strategy that adapts to changing market conditions, helping traders lock in profits during trends and limit losses during reversals.
How It Works:
True Range and ATR Calculation:
The indicator first calculates the True Range (TR) for each bar, defined as the maximum of:
The difference between the high and low,
The absolute difference between the high and the previous close, and
The absolute difference between the low and the previous close.
Using the TR values, the ATR is computed over a user-defined period (default is 14 bars) with an option to use either a Simple Moving Average (SMA) or an Exponential Moving Average (EMA) as the smoothing method.
Trailing Stop Determination:
Two potential stop levels are calculated:
For an uptrend, the stop is determined as:
Stop = Close – (Multiplier × ATR)
For a downtrend, the stop is:
Stop = Close + (Multiplier × ATR)
The indicator maintains a persistent trailing stop that dynamically adjusts:
In an uptrend, the trailing stop only moves upward (or remains flat) to secure gains.
In a downtrend, it only moves downward, thereby protecting the position from excessive losses.
A reversal in trend is identified when the price crosses the trailing stop level, at which point the indicator flips the trend and resets the stop level accordingly.
Rationale:
Utilizing the ATR for trailing stops ensures that the stop levels are directly influenced by market volatility. This dynamic adjustment helps accommodate the natural price fluctuations of the market, providing a more adaptive risk management tool compared to fixed stop-loss levels. The approach is particularly useful in volatile markets where traditional static stops might be triggered prematurely.
Customization:
Key parameters that can be adjusted include:
ATR Period: The number of bars used to calculate the ATR.
ATR Multiplier: The factor that determines how far the trailing stop is set from the current price.
Smoothing Method: Option to choose between SMA and EMA for ATR calculation, allowing traders to tailor the sensitivity of the indicator to their specific trading style.
Volatility-Adjusted Momentum Oscillator (VAMO)Concept & Rationale: This indicator combines momentum and volatility into one oscillator. The idea is that a price move accompanied by high volatility has greater significance. We use Rate of Change (ROC) for momentum and Average True Range (ATR) for volatility, multiplying them to gauge “volatility-weighted momentum.” This concept is inspired by the Weighted Momentum & Volatility Indicator, which multiplies normalized ROC and ATR values. The result is shown as a histogram oscillating around zero – rising green bars indicate bullish momentum, while falling red bars indicate bearish momentum. When the histogram crosses above or below zero, it provides clear buy/sell signals. Higher magnitude bars suggest a stronger trend move. Crypto markets often see volatility spikes preceding big moves, so VAMO aims to capture those moments when momentum and volatility align for a powerful breakout.
Key Features:
Momentum-Volatility Fusion: Measures momentum (price ROC) adjusted by volatility (ATR). Strong trends register prominently only when price change is significant and volatility is elevated.
Intuitive Histogram: Plotted as a color-coded histogram around a zero line – green bars above zero for bullish trends, red bars below zero for bearish. This makes it easy to visualize trend strength and direction at a glance.
Clear Signals: A cross above 0 signals a buy, and below 0 signals a sell. Traders can also watch for the histogram peaking and then shrinking as an early sign of a trend reversal (e.g. bars switching from growing to shrinking while still positive could mean bullish momentum is waning).
Optimized for Volatility: Because ATR is built-in, the oscillator naturally adapts to crypto volatility. In calm periods, signals will be smaller (reducing noise), whereas during volatile swings the indicator accentuates the move, helping predict big price swings.
Customization: The lookback period is adjustable. Shorter periods (e.g. 5-10) make it more sensitive for scalping, while longer periods (20+) smooth it out for swing trading.
How to Use: When VAMO bars turn green and push above zero, it indicates bullish momentum with strong volatility – a cue that price is likely to rally in the near term. Conversely, red bars below zero signal bearish pressure. For example, if a coin’s price has been flat and then VAMO spikes green above zero, it suggests an explosive upward move is brewing. Traders can enter on the zero-line cross (or on the first green bar) and consider exiting when the histogram peaks and starts shrinking (signaling momentum slowdown). In sideways markets, VAMO will hover near zero – staying out during those low-volatility periods helps avoid false signals. This indicator’s strength is catching the moment when a quiet market turns volatile in one direction, which often precedes the next few candlesticks of sustained movement.
Markov + Monte Carlo Simulation with EVMarkov Monte Carlo Projection (MMCP) – A Probabilistic Approach to Price Forecasting
Introduction: A New Approach to Price Projection
The Markov Monte Carlo Projection (MMCP) is an advanced stochastic forecasting tool that models potential future price paths using a combination of Markov Chain transition probabilities and Monte Carlo simulations. Unlike traditional technical indicators that rely on fixed formulas, MMCP employs probability distributions and simulated price movement paths to estimate future price behavior dynamically.
This indicator is designed to adapt to changing market conditions and provides traders with a probabilistic framework rather than a fixed forecast. By incorporating volatility modeling, MMCP enables traders to size projections proportionally to recent price action, making it an adaptive and flexible forecasting tool.
Mathematical Foundations
Markov Chains: Modeling Probability of Price Movements
A Markov Chain is a stochastic process where the probability of transitioning to the next state depends only on the current state and not on past states (i.e., it is memoryless).
For price movement, MMCP analyzes the past N bars (set by the lookback window) to determine the transition probabilities of price moving up, down, or remaining the same based on past behavior:
Pup=Number of Up MovesTotal Moves
Pup=Total MovesNumber of Up Moves
Pdown=Number of Down MovesTotal Moves
Pdown=Total MovesNumber of Down Moves
Psame=1−(Pup+Pdown)
Psame=1−(Pup+Pdown)
These probabilities guide how future price movements are simulated, ensuring that projections reflect historical price behavior tendencies.
Monte Carlo Simulations: Generating Possible Futures
Monte Carlo simulations involve running many random trials to estimate possible outcomes. Each trial simulates a future price path by:
Randomly selecting a direction based on the Markov probabilities Pup,Pdown,PsamePup,Pdown,Psame.
Determining the magnitude of the price movement using a normally distributed volatility model.
Iterating this process across multiple forecast bars to simulate a range of potential price paths.
This process does not predict a single outcome, but rather generates a probability-weighted range of future price possibilities.
Volatility Modeling: Scaling Movements Proportionally
Why We Use Standard Deviation (σσ)
Price movement is inherently volatile, and the magnitude of price shifts must be scaled relative to recent volatility. MMCP calculates rolling price returns and then derives the standard deviation of those returns:
σ=stdev(price returns,lookback)
σ=stdev(price returns,lookback)
The Volatility Multiplier allows users to adjust the impact of this volatility on projected movements. This makes the indicator adaptive to different asset price ranges.
Key User Adjustments
1. Volatility Multiplier – Tuning Projections for Different Assets
The scale of the Volatility Multiplier must be tuned for each asset because it is relative to the magnitude of price action. For example:
Low-priced assets (e.g., $2.50 stocks) → A multiplier of 0.1 works best.
Mid-priced assets (e.g., $250 stocks) → A multiplier of 3 works best.
High-priced assets (e.g., Bitcoin) → A multiplier of 1000 works best.
🔹 If projections seem too extreme, decrease the multiplier.
🔹 If projections seem too flat, increase the multiplier.
The Volatility Multiplier can also be fine-tuned to make the projected signal proportionate to the immediately preceding price action.
2. Expected Value (EV) Path – Analyzing Aggregate Future Probabilities
The EV Line is a computed average of all simulated paths, giving traders an expected mean trajectory.
If you find that the EV Line is not visible, try increasing the volatility multiplier to make it more pronounced.
3. Projection Inversion – Enhancing Analysis with Paired Indicators
A unique feature of MMCP is the projection inversion toggle, designed to allow traders to run multiple instances of the indicator in tandem.
When one instance is set to normal projection and another to inverted projection, traders can pair them together using identical settings (except inversion). This setup allows for a mirrored probability perspective and enhances visualizing volatility dynamics.
Additionally, traders can use multiple sets of paired indicators, each with a different lookback window, to build a multi-layered, probability-driven market visualization. This dynamic approach provides an evolving structure of probable price movement in different time frames, offering deeper insights into potential market conditions.
How MMCP Works in Real-Time
Each new bar triggers a fresh Monte Carlo simulation, meaning that projections organically evolve with the market. This ensures that MMCP is always responding to current conditions, rather than applying static assumptions.
How to Use MMCP in Trading
✔ Identifying Potential Reversal & Continuation Zones
If most Monte Carlo paths project upward, bullish momentum is likely.
If most Monte Carlo paths project downward, bearish momentum is likely.
The Expected Value (EV) Line can help confirm the most probable trajectory.
✔ Analyzing Market Sentiment in Real Time
Use multiple instances of MMCP with different lookback windows to capture short-term vs. long-term sentiment.
Enable projection inversion to analyze potential mirrored moves.
✔ Fine-Tuning MMCP for Your Strategy
Adjust the Volatility Multiplier to match the price scale of your asset.
Increase the number of simulations to improve statistical robustness.
Use shorter lookback windows for more responsive predictions, or longer windows for more stable forecasts.
Why MMCP is a Game-Changer
✅ Dynamic & Probabilistic – Unlike fixed indicators, MMCP adapts in real-time.
✅ Fully Stochastic – MMCP embraces uncertainty using Markov models & Monte Carlo simulations.
✅ Customizable for Any Asset – Adjust the Volatility Multiplier for small or large price movements.
✅ Live Updates – The projection organically evolves with every new price bar.
✅ Multi-Perspective Analysis – Traders can run paired normal and inverted projections for deeper insights.
By tuning Volatility Multiplier, Lookback Window, and Projection Inversion, traders can customize MMCP to fit their strategy.
Final Thoughts
The Markov Monte Carlo Projection (MMCP) is not about making absolute predictions—it is about understanding probability distributions in price action.
By leveraging Monte Carlo simulations, Markov transition probabilities, and dynamic volatility modeling, MMCP gives traders a powerful probability-based edge in forecasting potential price movement.
Zerg range filter credit to Kivanc turkish pinecoder for base indicator i reworked with chatgpt and some common sense
this indicator similar to the ADX but i think its better visually to keep you out of market conditions that are unfavorable.
i made original indicator to work in a 0-100 enviroment (before it was a zero middle line oscillator) and added background coloring that has a lower and higher threshold setting. i also added a smoothing moving average. this will trigger threshold levels (not the core oscillator)
above higher level would indicate trending market conditions and its purple. these are the areas where you might want to buy low period moving average bounces like 10 or 21 ema
lower band will paint indicator background blue and its cold, meaning range bound trade ideas are likely play out better. selling resistance and buying horizontal supports for example.
you are encourage to play with lookback period and change thresholds until you find something that works for your trading.
on the picture above it illustrates how i intended its usage.
it also shows divergences which was not intended but also a function.
you can also observe as the oscillator likes to coil up into a tight range (horizontal or a wedge formation) and when these break their trendlines explosive moves are incoming usually.
if you have a trading system and can generate a lot of signals but want to filter out some loser trades this could be the indicator you were looking for.
i hope this will be inline with community guidelines. my other publishing got removed unfortunately
Crypto Candle Low Leverage TrackerCrypto Candle Low Leverage Tracker
The Candle Low Leverage Indicator is a powerful tool for long position traders seeking to manage risk effectively when using leverage. By evaluating the current candle's low price, this indicator helps traders make more informed decisions about potential entry points, stop losses, and leverage levels. The indicator matches the low of the candle to the leverage needed for liquidation, giving you a clear view of how leverage impacts your position.
This indicator provides two critical insights:
% from Candle Low: Tracks how much the price has moved from the low of the current candle. For long position traders, this percentage is crucial for understanding how far the price has come off the low and deciding whether it’s safe to enter a position or if further price action is needed.
Leverage Needed: Estimates the leverage required to reach the candle's low as the liquidation price. Long traders can use this information to adjust leverage to a safer level, ensuring they don’t overexpose themselves to liquidation risks by matching leverage to the candle’s low.
Key Features:
Customizable table positioning (top, middle, bottom).
Toggle options to show/hide % from Candle Low and Leverage Needed.
Visual indicators with color changes: green for positive change, red for negative change, and blue for leverage requirements.
Ideal for long traders, this tool helps evaluate market conditions, manage risks, and calculate the best leverage to use in long trades, ensuring that leverage aligns with the candle’s low to prevent unnecessary liquidations.
Price Step Channel [BigBeluga]Price Step Channel is designed to provide a structured look at price trends through a dynamic step line channel, highlighting trend direction and volatility boundaries.
🔵 Key Features:
Step Line with Boundaries: The central step line adjusts with price movements, creating upper and lower boundaries based on price volatility. The channel is green during uptrends and red during downtrends, visually signaling the trend’s direction.
Fakeout Markers: "✖" markers identify potential fakeouts—moments when the price breaches the channel boundary without confirming a trend change. These markers help you spot possible mean reversion points.
Dynamic Boundary Labels: Labels at the end of the channel show the price levels of the upper and lower boundaries. In uptrends, the upper label turns green; in downtrends, the lower label turns red, providing an instant read on the trend's direction.
Customizable Display: You can toggle off the boundaries and labels for a cleaner view, focusing only on the step line and its color-coded trend signals.
🔵 When to Use:
Price Step Channel is ideal for traders looking to follow structured trends with defined volatility boundaries. The step line and color-coded channel provide clear trend insights, while the fakeout markers and customizable display options enhance flexibility in different market conditions. Whether you’re focusing on clean trend signals or detailed boundary interactions, this tool adapts to your style.
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants!
This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility.
If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks!
Let me know your thoughts.
Position resetThe "Position Reset" indicator
The Position Reset indicator is a sophisticated technical analysis tool designed to identify possible entry points into short positions based on an analysis of market volatility and the behavior of various groups of bidders. The main purpose of this indicator is to provide traders with information about the current state of the market and help them decide whether to open short positions depending on the level of volatility and the mood of the main players.
The main components of the indicator:
1. Parameters for the RSI (Relative Strength Index):
The indicator uses two sets of parameters to calculate the RSI: one for bankers ("Banker"), the other for hot money ("Hot Money").
RSI for Bankers:
RSIBaseBanker: The baseline for calculating bankers' RSI. The default value is 50.
RSIPeriodBanker: The period for calculating the RSI for bankers. The default period is 14.
RSI for hot money:
RSIBaseHotMoney: The baseline for calculating the RSI of hot money. The default value is 30.
RSIPeriodHotMoney: The period for calculating the RSI for hot money. The default period is 21.
These parameters allow you to adjust the sensitivity of the indicator to the actions of different groups of market participants.
2. Sensitivity:
Sensitivity determines how strongly changes in the RSI will affect the final result of calculations. It is configured separately for bankers and hot money:
SensitivityBanker: Sensitivity for bankers' RSI. It is set to 2.0 by default.
SensitivityHotMoney: Sensitivity for hot money RSI. It is set to 1.0 by default.
Changing these parameters allows you to adapt the indicator to different market conditions and trader preferences.
3. Volatility Analysis:
Volatility is measured based on the length of the period, which is set by the volLength parameter. The default length is 30 candles. The indicator calculates the difference between the highest and lowest value for the specified period and divides this difference by the lowest value, thus obtaining the volatility coefficient.
Based on this coefficient, four levels of volatility are distinguished.:
Extreme volatility: The coefficient is greater than or equal to 0.25.
High volatility: The coefficient ranges from 0.125 to 0.2499.
Normal volatility: The coefficient ranges from 0.05 to 0.1249.
Low volatility: The coefficient is less than 0.0499.
Each level of volatility has its own significance for making decisions about entering a position.
4. Calculation functions:
The indicator uses several functions to process the RSI and volatility data.:
rsi_function: This function applies to every type of RSI (bankers and hot money). It adjusts the RSI value according to the set sensitivity and baseline, limiting the range of values from 0 to 20.
Moving Averages: Simple moving averages (SMA), exponential moving averages (EMA), and weighted moving averages (RMA) are used to smooth fluctuations. They are applied to different time intervals to obtain the average values of the RSI.
Thus, the indicator creates a comprehensive picture of market behavior, taking into account both short-term and long-term dynamics.
5. Bearish signals:
Bearish signals are considered situations when the RSI crosses certain levels simultaneously with a drop in indicators for both types of market participants (bankers and hot money).:
The bankers' RSI crossing is below the level of 8.5.
The current hot money RSI is less than 18.
The moving averages for banks and hot money are below their signal lines.
The RSI values for bankers are less than 5.
These conditions indicate a possible beginning of a downtrend.
6. Signal generation:
Depending on the current level of volatility and the presence of bearish signals, the indicator generates three types of signals:
Orange circle: Extremely high volatility and the presence of a bearish signal.
Yellow circle: High volatility and the presence of a bearish signal.
Green circle: Low volatility and the presence of a bearish signal.
These visual markers help the trader to quickly understand what level of risk accompanies each specific signal.
7. Notifications:
The indicator supports the function of sending notifications when one of the three types of signals occurs. The notification contains a brief description of the conditions under which the signal was generated, which allows the trader to respond promptly to a change in the market situation.
Advantages of using the "Position Reset" indicator:
Multi-level analysis: The indicator combines technical analysis (RSI) and volatility assessment, providing a comprehensive view of the current market situation.
Flexibility of settings: The ability to adjust the sensitivity parameters and the RSI baselines allows you to adapt the indicator to any market conditions and personal preferences of the trader.
Clear visualization: The use of colored labels on the chart simplifies the perception of information and helps to quickly identify key points for entering a trade.
Notification support: The notification sending feature makes it much easier to monitor the market, allowing you to respond to important events in time.
Volatility Arbitrage Spread Oscillator Model (VASOM)The Volatility Arbitrage Spread Oscillator Model (VASOM) is a systematic approach to capitalizing on price inefficiencies in the VIX futures term structure. By analyzing the differential between front-month and second-month VIX futures contracts, we employ a momentum-based oscillator (Relative Strength Index, RSI) to signal potential market reversion opportunities. Our research builds upon existing financial literature on volatility risk premia and contango/backwardation dynamics in the volatility markets (Zhang & Zhu, 2006; Alexander & Korovilas, 2012).
Volatility derivatives have become essential tools for managing risk and engaging in speculative trades (Whaley, 2009). The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) measures the market’s expectation of 30-day forward-looking volatility derived from S&P 500 option prices (CBOE, 2018). Term structures in VIX futures often exhibit contango or backwardation, depending on macroeconomic and market conditions (Alexander & Korovilas, 2012).
This strategy seeks to exploit the spread between the front-month and second-month VIX futures as a proxy for term structure dynamics. The spread’s momentum, quantified by the RSI, serves as a signal for entry and exit points, aligning with empirical findings on mean reversion in volatility markets (Zhang & Zhu, 2006).
• Entry Signal: When RSI_t falls below the user-defined threshold (e.g., 30), indicating a potential undervaluation in the spread.
• Exit Signal: When RSI_t exceeds a threshold (e.g., 70), suggesting mean reversion has occurred.
Empirical Justification
The strategy aligns with findings that suggest predictable patterns in volatility futures spreads (Alexander & Korovilas, 2012). Furthermore, the use of RSI leverages insights from momentum-based trading models, which have demonstrated efficacy in various asset classes, including commodities and derivatives (Jegadeesh & Titman, 1993).
References
• Alexander, C., & Korovilas, D. (2012). The Hazards of Volatility Investing. Journal of Alternative Investments, 15(2), 92-104.
• CBOE. (2018). The VIX White Paper. Chicago Board Options Exchange.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
• Zhang, C., & Zhu, Y. (2006). Exploiting Predictability in Volatility Futures Spreads. Financial Analysts Journal, 62(6), 62-72.
• Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
STDEV Multi TimeFrame [Snowdex]STDEV Multi TimeFrame
The STDEV Multi TimeFrame indicator plots standard deviation levels (+1SD, +2SD, +3SD, -1SD, -2SD, -3SD) based on a user-selected timeframe (1D, 1W, 1M, etc.). It helps identify volatility, trend strength, and potential reversal zones using Bollinger Bands-style deviation calculations.
Key Features:
✅ Multi-Timeframe Selection – Choose any timeframe for STDEV calculations.
✅ Customizable Bollinger Bands – Select SMA, EMA, RMA, or WMA as the baseline.
✅ Color-Coded STDEV Levels – Fast (Green), Medium (Orange), Slow (Red).
✅ Non-Repainting & Accurate – Uses request.security() for precise data retrieval.
✅ Extended Lines & Labels – Clear trend monitoring with formatted values.
Use Cases:
📌 Detect trend direction & volatility.
📌 Identify overbought/oversold zones.
📌 Use as dynamic support/resistance levels.
🚀 Ideal for stocks, forex, crypto, and options trading! 🚀
