Buysell Martingale Signal - CustomBuysell Martingale Signal - Custom Indicator
Introduction:
This indicator provides a dynamic buy and sell signal system incorporating an adaptive Martingale logic. Built upon the signalLib_yashgode9/2 library, it is designed for use across various markets and timeframes.
Key Features:
Primary Buy & Sell Signals: Identifies initial buy and sell opportunities based on directional changes derived from the signalLib.
Martingale Signals:
For Short (Sell) Positions: A Martingale Sell signal is triggered when the price moves against the existing short position by a specified stepPercent from the last entry price, indicating a potential opportunity to average down or increase position size.
For Long (Buy) Positions: Similarly, a Martingale Buy signal is triggered when the price moves against the existing long position by a stepPercent from the last entry price.
On-Chart Labels: Displays clear, customizable labels on the chart for primary Buy, Sell, Martingale Buy, and Martingale Sell signals.
Customizable Colors: Allows users to set distinct colors for primary signals and Martingale signals for better visual distinction.
Adjustable Sensitivity: Features configurable parameters (DEPTH_ENGINE, DEVIATION_ENGINE, BACKSTEP_ENGINE) to fine-tune the sensitivity of the underlying signal generation.
Webhook Support (Static Message Alerts): This indicator provides alerts with static messages for both primary and Martingale buy/sell signals. These alerts can be leveraged for automation by external systems (such as trading bots or exchange-provided Webhook Signal Trading services).
Important Note: When using these alerts for automation, an external system is required to handle the complex Martingale logic and position management (e.g., tracking steps, PnL calculation, hedging, dynamic quantity sizing), as this indicator solely focuses on signal generation and sending predefined messages.
How to Use:
Add the indicator to your desired chart.
Adjust the input parameters in the indicator's settings to match your specific trading symbol and timeframe.
For automation, you can set up TradingView alerts for the Buy Signal (Main/Martingale) and Sell Signal (Main/Martingale) conditions, pointing them to your preferred Webhook URL.
Configurable Parameters:
DEPTH_ENGINE: (e.g., 30) Controls the depth of analysis for the signal algorithm.
DEVIATION_ENGINE: (e.g., 5) Defines the allowable deviation for signal generation.
BACKSTEP_ENGINE: (e.g., 5) Specifies the number of historical bars to look back.
Martingale Step Percent: (e.g., 0.5) The percentage price movement against the current position that triggers a Martingale signal.
Labels Transparency: Adjusts the transparency of the on-chart signal labels.
Buy-Color / Sell-Color: Sets the color for primary Buy and Sell signal labels.
Martingale Buy-Color / Martingale Sell-Color: Sets the color for Martingale Buy and Sell signal labels.
Label size: Controls the visual size of the labels.
Label Offset: Adjusts the vertical offset of the labels from the candlesticks.
Risk Warning:
Financial trading inherently carries significant risk. Martingale strategies are particularly high-risk and can lead to substantial losses or even complete liquidation of capital if the market moves strongly and persistently against your position. Always backtest thoroughly and practice with a demo account, fully understanding the associated risks, before engaging with real capital.
P-signal
SMA Signal ZoneSMA Signal Zone
Description (English):
This indicator generates buy and sell signals based on the relationship between two Simple Moving Averages (SMAs) or the price crossing a selected SMA.
You can choose from three signal modes:
Crossover: Fast SMA crosses above or below the Slow SMA
Fast SMA Only: Price crosses the Fast SMA
Slow SMA Only: Price crosses the Slow SMA
When a signal is detected, the script automatically plots:
✅ Entry point (based on signal price)
✅ Three Take-Profit levels (TP1, TP2, TP3)
✅ Stop-Loss level (SL)
✅ Colored lines and boxes for visual clarity
settings
Profit and stop levels are defined in **pips** and are fully customizable.
Recommended Settings (Backtested Setup)
For best results, use the following settings:
Calculation Timeframe**: `15`
Fast SMA Length: `50`
Slow SMA Length: `100`
TP1 Distance (Pips): `15`
TP2 Distance (Pips): `30`
TP3 Distance (Pips): `50`
Stop Loss Distance (Pips): `30`
Signal Mode: `Crossover`
Use these settings on the 1-minute chart to take precise trade entries based on signals from the 15-minute timeframe.
Trade Management Suggestion
Once TP1 is reached, you can adjust your Stop Loss to Break-Evento protect the position.
TP2 and TP3 can then be targeted as partial exits or trail targets.
AIO BOTAIO BOT Overview
This is an indicator that we have developed to provide signals, indicating whether you should buy or sell at that moment. In general, AIO BOT is built based on the VWAP and the volume of price movement, which is shown through three main lines on the chart:
The VWAP is represented by a gray-colored line by default.
The pink line is the key volume line for the Main Timeframe, which is colored pink by default.
The blue line is the key volume line for the Higher Timeframe, which is colored blue by default.
Additionally, there are some coding logics that we use to determine the signal candles like: convergence threshold (% of ATR) and pivot to filter good signals.
Originally, we built this BOT to trade crypto, specifically the BTCUSDT pair. It is designed for intraday trading. Therefore, the backtest results and settings are centered around BTCUSDT and are short trades that end within the day.
Symbol: BTCUSDT
Main Timeframe: 3m
Higher Timeframe: 15m
If you use AIO BOT to trade other symbols or other timeframes, the results might not align with your expectations, and you may need to adjust the settings to suit the symbol you're trading.
BOT Algos
There are 2 algorithms for signal detection. You can choose which algorithm best suits your symbol/trading style. You can also choose to display all signals of both algorithms
Algo 1: ON by default. This algorithm is built on the basis of the Key Volume Candles, VWAP, Pivot, Candlestick structure
Algo 2: OFF by default. This algorithm focuses more on how the candle reacts to these zone. This is a more aggressive algorithm, providing more signals and with higher risk.
In general, since the core logic of both algorithms is how the candle reacts to the key volume , you can choose the Cross mode as Cross or Touch from the settings. And to filter good signals, we add pivot logics & the convergence zone (%ATR) with yellow background to filter only good buy/sell signals of the algorithms. You can also change the value to widen or narrow the convergence zone. The default value is 150. Convergence zones are often important zones because they show consensus between indicators. Price can react strongly here (support/resistance or breakout).
Chart Layout
We typically use a 3-layout chart for 3 Timeframes:
3m
15m
1h
For other indicators, we also use them together for confluence analysis:
CDV, which is the Cumulative Delta Volume of LonesomeTheBlue.
How to Use AIO BOT
AIO BOT is not a fully automated 100% BOT. It generates signals to indicate that the price might be in a buy/sell zone. The actual entry candle/price might be the candle that triggered the signal or a different one.
Since AIO BOT uses higher timeframe data, it may sometimes produce repaint signals, especially when the signal is triggered by the key volume line of the higher timeframe (the blue line). To ensure the signal is accurate, wait for at least 2–3 more candles, then refresh the chart to confirm it's not a false signal before entering the trade.
To summarize: False signal = an alert occurred or a signal appears on the chart but then when you refresh the chart. It disappear. However, it may still be a potential zone to trade manually using logic like below:
The entry candle should be red for a sell trade and green for a buy trade.
The entry candle should be in a pullback zone from a larger trend.
The entry candle should cross one of the three lines: the key volume line of the main timeframe, the key volume line of the higher timeframe, or the VWAP line.
Consider not entering the trade immediately if the signal candle is too far from the VWAP line. Wait for the price to retrace to the VWAP zone and also retrace to key volume lines where those lines go like horizontal lines. Although we added Convergence Threshold to filter these ones but there may be some cases that we have not covered yet.
Prioritize selling when the price is below all three lines: the two key volume lines and the VWAP line.
Prioritize buying when the price is above all three lines: the two key volume lines and the VWAP line.
You can manually analyze and choose the buy/sell point based on the rules we just mentioned, without necessarily waiting for a signal candle from the BOT.
For CDV analysis: Set the SMA value to 20. If you want to increase the win rate, make sure the CDV candle is green and above the SMA when buying, and red and below the SMA when selling. CDV can also be used for analysis when the CDV candle retraces back to the SMA line as a potential entry point.
If the AIO BOT in a higher timeframe like 15m or 1h is showing a buy signal, but the lower timeframe (3m) is showing a reverse sell signal, consider not entering the trade and prioritize the higher timeframe.
For setting TP and SL: You need to set them manually, typically at nearby highs/lows.
In terms of experience with entering trades, we mostly use limit orders, with positions always around the key volume lines. You will need to analyze and choose the lines based on your own judgment.
When there are three consecutive signals in the same direction, we believe that by the third signal, you should consider not trading because the trend might be too strong and there could be a reversal risk at that point.
Timeframes and Higher Timeframes Settings
For 1m, 3m, and 5m TFs: We usually set the higher TF to 15m.
For 15m TF: We usually set the higher TF to 1h.
For 1h TF: We usually set the higher TF to 4h.
However, these values may differ for each symbol, so you should adjust them to fit your own trading style.
AIO BOT can be combined with other AIO indicators to increase the win rate. Depending on your trading method, you can choose the appropriate setup.
Some Explanatory Images
MMTools - Screener❖ Overview
Screener expands your market insights and provides an efficient way to monitor real-time signals from Catcher across hundreds of charts on a single screen.
Each cell in the table displays the number of indicator signals. For instance, a value of "1" in the row labeled ‘BTCUSDT.P’ and column ‘30’ indicates one long signal on the 30-minute Bitcoin chart within the selected lookback period. “0” means no signal in the lookback.
❖ Multi-Table Construction
Screener supports flexible layouts and overlays. To build a multi-table interface, simply add multiple instances of the script to your chart. For optimal usability, it is recommended to allocate a dedicated panel or tab.
⚙️ Key Parameters to Customize Initially
Indicator Lookback: Defines how far back Screener checks for signals.
Symbols: Choose up to 20 symbols. Use additional tables to expand coverage.
Size: Adjusts the overall dimensions of the table.
Display Settings: Customize colors, opacity, and symbol visibility. For dark theme charts, set color opacity to 100% and transparency to 0%.
⚙️ Per-Table Adjustable Parameters
Timeframe: This defines the interval for signal collection across all symbols displayed in the top row of the table. It must be equal to or greater than the chart’s timeframe, otherwise the script will deliberately trigger an error. For multiple tables, use a lower chart timeframe (e.g., 1 minute) to meet this requirement.
Table Positioning: Use either the “Position” (predefined screen locations) or “Block” (stacked layout) parameters. The “Block” method enables a greater number of tables by aligning them side-by-side efficiently.
-- Multi-table example demonstrating the use of the ’Position’ parameter --
-- Multi-table example demonstrating the use of the ’Block’ parameter --
❖ Access
Please refer to the Author's Instructions field to request access to the script.
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Disclaimer
The information provided by my scripts is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making financial decisions.
Normalized Price ComparisonNormalized Price Comparison Indicator Description
The "Normalized Price Comparison" indicator is designed to provide traders with a visual tool for comparing the price movements of up to three different financial instruments on a common scale, despite their potentially different price ranges. Here's how it works:
Features:
Normalization: This indicator normalizes the closing prices of each symbol to a scale between 0 and 1 over a user-defined period. This normalization process allows for the comparison of price trends regardless of the absolute price levels, making it easier to spot relative movements and trends.
Crossing Alert: It features an alert functionality that triggers when the normalized price lines of the first two symbols (Symbol 1 and Symbol 2) cross each other. This can be particularly useful for identifying potential trading opportunities when one asset's relative performance changes against another.
Customization: Users can input up to three symbols for analysis. The normalization period can be adjusted, allowing flexibility in how historical data is considered for the scaling process. This period determines how many past bars are used to calculate the minimum and maximum prices for normalization.
Visual Representation: The indicator plots these normalized prices in a separate pane below the main chart. Each symbol's normalized price is represented by a distinct colored line:
Symbol 1: Blue line
Symbol 2: Red line
Symbol 3: Green line
Use Cases:
Relative Performance Analysis: Ideal for investors or traders who want to compare how different assets are performing relative to each other over time, without the distraction of absolute price differences.
Divergence Detection: Useful for spotting divergences where one asset might be outperforming or underperforming compared to others, potentially signaling changes in market trends or investment opportunities.
Crossing Strategy: The alert for when Symbol 1 and Symbol 2's normalized lines cross can be used as a part of a trading strategy, signaling potential entry or exit points based on relative price movements.
Limitations:
Static Alert Messages: Due to Pine Script's constraints, the alert messages cannot dynamically include the names of the symbols being compared. The alert will always mention "Symbol 1" and "Symbol 2" crossing.
Performance: Depending on the timeframe and the number of symbols, performance might be affected, especially on lower timeframes with high data frequency.
This indicator is particularly beneficial for those interested in multi-asset analysis, offering a streamlined way to observe and react to relative price movements in a visually coherent manner. It's a powerful tool for enhancing your trading or investment analysis by focusing on trends and relationships rather than raw price data.
Visual ProwessVisual Prowess: Ultimate Visual of Price Action Indicator
Overview
Visual Prowess is a Pine Script indicator that integrates Trend, Momentum, Strength/Weakness, Money Flow, and Volatility into a single, intuitive interface. Scaled from 0 to 100, it provides traders with clear bullish (>50) and bearish (<50) zones. Visual Prowess is made up of several data components which will be explained below. All these components have custom thresholds that lead to Green Dot Buy Signals and Red Dot sell signals. Designed for multi-timeframe analysis, it helps traders anticipate market moves with precision seeing behind the scenes of price action.
The fundamental inputs of price action are made up of different variables -- the components of Trend Strength, Volatility, Momentum, Money Flow/Volume and Overbought/Oversold. These are very important inputs market makers use. From what I've learned in my trading journey (always still learning), this is the data I value most important. This is why I combined all these components into one indicator.....to be an ultimate visual—this extrapolation of different pieces of data is the Visual Prowess.
What It Does
Visual Prowess combines five key market factors into a unified score (0-100) to assess market conditions by examining the price action like an x-ray aka Visual Prowess:
• Trend Direction & Strength (Green and Red Wave) : Identifies bullish (green clouds) or bearish (red clouds) trend. This data is designed to illustrate the trend by the color, and its strength by the height (score).
How it is Calculated = Data is derived from price action-- comparing the current and previous price highs and lows to measure the strength of upward (+) or downward (-) price movements, smoothed over a period and expressed as a percentage of the price range.
• Momentum (Blue and White Wave): Tracks price acceleration via a custom momentum oscillator, displayed as blue (positive) or white (negative) waves.
How it is Calculated = Data is calculated by subtracting a longer-term exponential moving average from a shorter-term exponential moving average to measure momentum and trend direction. Momentum strength is measured by height on 0-100 score, and color dictates the trend-- Blue up, White down.
• Strength Index (Purple Line): Measures overbought/oversold conditions with a normalized index, derived from price deviation.
How it is Calculated = Strength Index is calculated by comparing the average of price gains to the average of price losses over a specified period, expressed as a value between 0 and 100 to measure momentum and identify overbought or oversold conditions.
• Money Flow: Monitors capital inflows and outflows using a modified Money Flow Index, shown as green (buying) or red (selling) circles.
How it is Calculated = The Money Flow is calculated by using price and volume data to measure buying and selling pressure, comparing positive and negative money flow over a specified period to produce a value between 0 and 100, indicating overbought or oversold conditions and more importantly where the money is moving, + or -.
• Volatility: Gauges market volatility, marked by colored crosses (blue for low, red for high). Blue illustrates low volatility which is key for big moves either + or -; red to illustrate when price action is extremely overheated either + or -.
How it is Calculated = The volatility is calculated by the creator of the BBWP The_Caretaker. This excellent work is calculated using the width of the iconic indicator the Bollinger Bands (the difference between the upper and lower bands divided by the middle band (the moving average), expressed as a percentage to show how volatile the price is relative to its recent average.
Originality
Unlike traditional multi-indicator dashboards, Visual Prowess uses a combination of specific open-source indicators which I believe to be the most important inputs in price action-- trend, momentum, strength, money flow, and volatility into an all-in-one visual ratioed on a 0-100 scale. This unique synthesis of data reduces noise, prioritizes signal alignment, and a look behind the scenes of price action to see deeper into the movement – This combination of indicators has custom thresholds, when these components in alignment with each other hit certain parameters; it leads to key custom price action signals -- Green Dot Buy and Red Dot Sell signals.
There is also a bonus indicator….. a Yellow Triangle. When you see this, it is rare and strong. It only prints when strength index reaches extreme lows at the same time volatility reaches extreme highs…. It then waits to print the yellow triangle upon a third condition= which is price action is back in bullish/positive zone. This Yellow triangle is meant to be strong reversals of Macro Trend lows.
How to Use the Visual Prowess Components:
• Add to Chart: Apply Visual Prowess to any timeframe (recommended: higher timeframes 12H, 1D, 2D, 3D for optimal signals).
• Interpret Zones: Values >50 indicate bullish conditions (green background); <50 signal bearish conditions (red background).
Wait for Green Dot Buy signal for buys and Red Dot Sell signals for sells. One can read each component individually to gauge the price action and predict before the buy signal prints; all of those components merged together is what leads to the buy and sell signals. The story of what’s to come can be seen at lower timeframes before the higher timeframes print, that is a key way to gauge projections of bull or bear prints to come.
HOW TO READ EACH DATA COMPONENT
TREND CLOUDS: Green/red clouds show trend direction; vivid colors tied to number/ score on the 0-100 scale indicate strength of the trend.
Bull Conditions
Green cloud illustrates the trend is bullish. The height is correlated to the trend’s strength—this height is also aligned with colors, more transparent green is weak, then it gets more opaque being medium strength, and the most vibrant is the strongest. How to ride the bull condition is by seeing this transformation of trend get from weak to strong, until it tops out and the wave points down losing strength which alludes to the bear condition.
Bear Conditions
Vice versa with the bear condition. Different shades of red tie into the strength of the bear trend. How to read when things are about to get bearish, is by seeing bull trend shift levels of strength (Example- medium to weak). This transition of bull strength getting weaker is the start, once it gets to weak bear it has commenced until bearish strength tops out before it begins to get weaker leading to the next bull phase.
MOMENTUM WAVES: Blue waves above 50 suggest bullish momentum; white waves below 50 warn of bearish shifts.
Bull Conditions
Good to look at flips of white wave to blue in bearish zones to see the tide turning= guaranteed bullish when safely gets above and holds above 50 zone.
Bear Conditions
Vice versa for Bearish side of this momentum wave being blue wave turning white in bullish zone aiming down to break below 50 zone to confirm bearish descent.
STRENGTH INDEX: Values >80 indicate overbought; <20 suggest oversold. Look for “Bull” or “Bear” labels for divergences.
Bull Conditions
Above 50 level is key, so seeing price action break from below 50 to above 50 is strong buy condition until it gets overbought.
Bear Conditions
Once conditions are too overbought and falling making lower lows (especially when price action is climbing or staying sideways) it is indicating strength is getting weaker. When this indicator fights 50 level and breaks down below 50 level bearish conditions are coming until it gets to an oversold level.
MONEYFLOW: Green circles signal buying pressure; red circles indicate selling.
Bull Conditions
Green circles show money flow is positive so that’s a good sign of upward price action to come, and again above 50 level is bullish conditions
Bear Conditions
Red circles show money flow is negative so that’s a bad sign of price action to come, pointing down and breaking below 50 level is no good. It can have corrections in bullish scenario keep in mind seeing red doesn’t mean trend is over z9could be in higher low scenario).
VOLATILITY: Blue crosses (<25% volatility) suggest breakout potential; red crosses (>75%) warn of overheated markets.
Bull Conditions
This is a very important indication. Big volatile moves can move either direction + or -. When all other components look positive/bullish and this is signalling blue crosses it means a big move is coming and will most likely be in the upward direction –If all other components align/lean bullish.
Another bullish scenario is when price action is down large and red crosses are forming. This indicates that the downward move is overheated (red x’s are rare). This extremely oversold condition can be great buying opportunities when volatility is hot printing red x’s.
Bear Conditions
When all other components look negative/bearish and this is signalling blue crosses it means a big move is coming and will most likely be in the downward direction –If all other components align/lean bearish.
Another bearish scenario is when price action is up large and red crosses are forming. This indicates that the upward move is overheated (red x’s are rare). This extremely overbought condition can be great selling opportunities when volatility is hot printing red x’s.
*****All these components in alignment of hitting each pertaining important threshold--is what prints the green dot and sell signals to trade by. It is not black and white; each component has a sweet spot fine tuned to be triggered through analysis of what is happening individually to each component and how it is reacting to the price action data.
EXAMPLE= Taking a look at the screenshot (Perfect Scenario)
Bullish Examination
- Taking a look at the 2-D timeframe on BTC
x>50
x= all components traveling to the bullish zone. Blue wave, Strength Index with bullish divergence accumulation, Money Flow Positive with Green Trend Wave starting, with teal low volatility cross→→→ leads to Green Dot Buy Signal print…. And the big rise speaks for itself with price action and the big mountain wave of the Green Trend Wave.
This rise leads to
↓↓↓↓
Bearish Examination
Strength Index gets really high at 80 scale, Red X’s showing extremely heated Volatility, Money Flow turning red and sloping down, Trend Wave peaking starting to roll over, Blue Momentum Wave transitioning to white, bearish divergence of price action related to Strength Index→→→ leads to Red Dot Sell Signal print… and the flush speaks for itself when all components fall below 50 level with Trend wave turning red
All this is forecasted in the data, showing weakness before weakness and showing strength before strength. It works because every single piece of important elements in data of price action is incorporated in this all-in-one indicator…. Which leads to the reasoning of me calling this indicator the Visual Prowess, for its unprecedent sharpness of visual observation.
****This is a passion script incorporating every piece of data I value important when reading a chart — to see current perspective of a chart and to help foresee future projection of direction Up or Down. Any community feedback is greatly appreciated. Ongoing work will be done on this script as new thoughts and fine tuning will continuously be done for infinity, as this is my personal go to model for data on the markets.
Trailing Stop Loss [TradingFinder] 4 Machine Learning Methods🔵 Introduction
The trailing stop indicator dynamically adjusts stop-loss (SL) levels to lock in profits as price moves favorably. It uses pivot levels and ATR to set optimal SL points, balancing risk and reward.
Trade confirmation filters, a key feature, ensure entries align with market conditions, reducing false signals. In 2023 a study showed filtered entries improve win rates by 15% in forex. This enhances trade precision.
SL settings, ranging from very tight to very wide, adapt to volatility via ATR calculations. These settings anchor SL to previous pivot levels, ensuring alignment with market structure. This caters to diverse trading styles, from scalping to swing trading.
The indicator colors the profit zone between the entry point (EP) and SL, using light green for buy trades and light red for sell trades. This visual cue highlights profit potential. It’s ideal for traders seeking dynamic risk management.
A table displays real-time trade details, including EP, SL, and profit/loss (PNL). Backtests show trailing stops cut losses by 20% in trending markets. This transparency aids decision-making.
🔵 How to Use
🟣 SL Levels
The trailing stop indicator sets SL based on pivot levels and ATR, offering four options: very tight, tight, wide, or very wide. Very tight SLs suit scalpers, while wide SLs fit swing traders. Select the base level to match your strategy.
If price hits the SL, the trade closes, and the indicator evaluates the next trade using the selected filter. This ensures disciplined trade management. The cycle restarts with a new confirmed entry.
Very tight SLs, set near recent pivots, trigger exits early to minimize risk but limit profits in volatile markets. Wide SLs, shown as farther lines, allow more price movement but increase exposure to losses. Adjust based on ATR and conditions, noting SL breaches open new positions.
🟣 Visualization
The indicator’s visual cues, like colored profit zones, simplify monitoring, with light green showing the profit area from EP to trailed SL. Dashed lines mark entry points, while solid lines track the trailed SL, triggering new positions when breached.
When price moves into profit, the area between EP and SL is colored—light green for longs, light red for shorts. This highlights the profit zone visually. The SL trails price, locking in gains as the trade progresses.
🟣 Filters
Upon trade entry, the indicator requires confirmation via filters like SMA 2x or ADX to validate momentum. Filters reduce false entries, though no guarantee exists for improved outcomes. Monitor price action post-entry for trade validity.
Filters like Momentum or ADX assess trend strength before entry. For example, ADX above 25 confirms strong trends. Choose “none” for unfiltered entries.
🟣 Bullish Alert
For a bullish trade, the indicator opens a long position with a green SL Line (after optional filters), trailing the SL below price. Set alerts to On in the settings for notifications, or Off to monitor manually.
🟣 Bearish Alert
In a bearish trade, the indicator opens a short position with a red SL Line post-confirmation, trailing the SL above price. With alerts On in the settings, it notifies the potential reversal.
🟣 Panel
A table displays all trades’ details, including Win Rates, PNL, and trade status. This real-time data aids in tracking performance. Check the table to assess trade outcomes instantly.
Review the table regularly to evaluate trade performance and adjust settings. Consistent monitoring ensures alignment with market dynamics. This maximizes the indicator’s effectiveness.
🔵 Settings
Length (Default: 10) : Sets the pivot period for calculating SL levels, balancing sensitivity and reliability.
Base Level : Options (“Very tight,” “Tight,” “Wide,” “Very wide”) adjust SL distance via ATR.
Show EP Checkbox : Toggles visibility of the entry point on the chart.
Show PNL : Displays profit/loss data for active and closed trades.
Filter : Options (“none,” “SMA 2x,” “Momentum,” “ADX”) validate trade entries.
🔵 Conclusion
The trailing stop indicator, a dynamic risk management tool, adjusts SLs using pivot levels and ATR. Its confirmation filters reduce false entries, boosting precision. Backtests show 20% loss reduction in trending markets.
Customizable SL settings and visual profit zones enhance usability across trading styles. The real-time table provides clear trade insights, streamlining analysis. It’s ideal for forex, stocks, or crypto.
While filters like ADX improve entry accuracy, no setup guarantees success in all conditions. Contextual analysis, like trend strength, is key. This indicator empowers disciplined, data-driven trading.
MMTools - Catcher❖ Overview
Catcher is a professional trading indicator designed to provide insightful, high-quality purchasing signals to traders, algorithmic traders, and investors. The indicator employs trend-following, momentum, and volatility-based techniques, all integrated into the author's unique and sophisticated strategic framework. This approach ensures that only the highest-performing signals are selected and presented. Unlike the majority of indicators, Catcher seeks to deliver signals against local price movements. Furthermore, the indicator is engineered to adapt to changing market conditions, ensuring its sustained value and relevance.
❖ How The Indicator Works
The objective of Catcher is to detect market movements and signal emerging trends to follow — before the trend loses momentum. Following this phase, the indicator remains inactive during periods of consolidation and low buying interest, resuming only upon detecting indications of a new market movement.
The system is built on a custom market-following algorithm that dynamically adjusts its internal components by analyzing the prevailing trend direction, strength, and volatility. This allows the indicator to identify optimal entry points in changing market conditions. By continuously adapting, Catcher can filter out false signals more effectively than traditional fixed-logic strategies.
❖ Settings
The indicator has simple settings. It allows the addition of signals from up to three other timeframes, with each additional signal increasing in size to facilitate differentiation. However, the selected timeframes must be equal to or higher than the chart’s current timeframe.
⚙️ Exit Signals:
Crosses marked as “+” on the chart represent favorable exit points determined by timeout logic.
Both entries and exits are fully non-repainting, meaning they will not change their locations or disappear once displayed on the chart. Note that they appear at the opening of the bar.
❖ Application
The indicator demonstrates strong performance in trending markets. However, it is advisable to avoid trading during highly volatile, choppy markets. As illustrated in the images below, signals generated in stable, trending environments tend to yield more favorable outcomes compared to those produced in turbulent conditions.
-- Example of Catcher operating during trending market --
-- Example of Catcher operating in a choppy market --
⚡️ It is not necessary to wait for bar closures, as the signals do not repaint once displayed.
Catcher can be used independently or to complement your existing techniques, for instance, by confirming your ideas or identifying precise entry points. The more reliable technical tools you incorporate, the stronger your analysis can become. Here is an example of how Catcher integrates with classical technical analysis:
-- Example of Catcher operating with additional influencing factors --
❖ Conclusion
We believe that dedicated traders can achieve outstanding results by using our tools with commitment and a professional approach. We hope that Catcher offers you a fresh and valuable perspective on trading.
❖ Access
Please refer to the Author's Instructions field to request access to the script.
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Disclaimer
The information provided by my scripts is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making financial decisions.
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B(Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
RunRox - Entry Model🎯 RunRox Entry Model is an all-in-one reversal-pattern indicator engineered to help traders accurately identify key price-reversal points on their charts. It will be part of our premium indicator package and improve the effectiveness of your trading strategies.
The primary concept of this indicator is liquidity analysis, making it ideal for Smart Money traders and for trading within market structure. At the same time, the indicator is universal and can be integrated into any strategy. Below, I will outline the full concept of the indicator and its settings so you can better understand how it works.
🧬 CONCEPT
In the screenshot below, I’ll schematically illustrate the core idea of this indicator. It’s one of the patterns that the indicator automatically detects on the chart using a two-timeframe approach. We use the higher timeframe to identify liquidity zones, and the lower timeframe to capture liquidity removal and structure breaks. The schematic is shown in the screenshot below.
Our indicator includes three entry models in total , and I will discuss its functionality and features in more detail later in this post.
💡 FEATURES
Three entry models
PO3 HTF Bar
Entry Area
Optimization for each Entry Area
Filters
HTF FVG
Alert customization
Next, we will examine each entry model in detail.
🟠 ENTRY MODEL 1
The first model is the core one we’ll work with; all other models rely on its structure and construction. In the screenshot below, I’ll schematically show the complete model.
As shown in the screenshot above, we display higher-timeframe candles on the current chart to better visualize the entry model and keep the trader informed of what’s happening on the larger timeframe. The screenshot also highlights both the Long and Short models, as well as the Entry Area, which I will explain in more detail below.
The schematic model on the lower timeframe is shown in the screenshot above. It illustrates that after the Entry Model forms, we draw the Entry Area on the next candle and wait for a price pullback into this zone for the optimal trade entry. Statistically, before moving higher, the price typically revisits the Entry Area, covering the imbalances created by MSS; thus, the Entry Area represents the ideal entry point.
🟩 Entry Area
Once the Entry Model has formed, we focus on identifying the optimal pullback zone for taking a position. To determine which retracement area performs best, we conducted extensive historical backtesting on potential zones and selected those that consistently delivered the strongest results. This process yields Entry Areas with the highest probability of a successful reversal.
On the screenshot above, you can see an example of the Entry Area and which zones carry a higher versus lower probability of reversal. Zones rendered with greater transparency have historically delivered weaker results than the more opaque zones. The deeper-colored areas represent the optimal entry zones and can improve your risk-reward ratio by allowing you to enter at more favorable prices.
It’s important to remember that the entire Entry Area functions as a potential zone for scaling into a position. However, if your risk-to-reward ratio isn’t favorable, you can wait for the price to retrace to lower levels within the Entry Area and enter with a more attractive risk-to-reward.
🟢 Pattern Rating
Each entry model receives a rating in the form of green circles next to its name 🟢. The rating ranges from one to four circles, based on the historical performance of similar patterns. To calculate this rating, we backtest past data by analyzing candle behavior during the model’s formation and assign circles according to how similar patterns performed historically.
Example Ratings:
🟢 – One circle
🟢🟢 – Two circles
🟢🟢🟢 – Three circles
🟢🟢🟢🟢 – Four circles
The more green circles a model has, the more reliable it is—but it’s crucial to rely on your own analysis when identifying strong reversal points on the chart. This rating reflects the model’s historical performance and does not guarantee future results, so keep that in mind!
Below is a screenshot showing four model variations with different ratings on the chart.
⚠️ Unconfirmed Pattern
Entry Model 1 is designed so that, until the higher-timeframe candle closes, the pattern remains unconfirmed and is hidden on the chart. For traders who prefer to see setups as they form, there’s a dedicated feature that displays the unconfirmed pattern at the moment of its appearance - triggered by the Market Structure Shift - before the HTF candle closes. The screenshot below shows what the pattern looks like prior to confirmation.
‼️IMPORTANT: Until the pattern is confirmed and the higher-timeframe candle has closed, the model may disappear from the chart if price reverses and the HTF candle closes below the previous bar. Therefore, this mode is suitable only for experienced traders who want to see market moves in advance. Remember that the pattern can be removed from the chart, so we recommend waiting for the HTF candle to close before deciding to enter a trade.‼️
✂️ Filters
For the primary model, there are four filters designed to enhance entry points or exclude less-confirmed patterns. The filters available in the indicator are:
Bounce Filter
Market Shift Mode
Same Wave Filter
Only with Divergence
I will explain how each of these filters works below.
- Bounce Filter
The Bounce Filter identifies significant deviations of price from its mean and only displays the Entry Model once the asset’s price moves beyond the average level. The screenshot below illustrates how this appears on the chart.
The actual average-price calculation is more sophisticated than what’s shown in the screenshot, that image is just an illustrative example. When the price deviates significantly from the N-bar average, we start looking for the Entry Model. This approach works particularly well in range-bound markets without a clear trend, as it lets you trade strong deviations from the mean.
- Market Shift Mode
This filter works by detecting the initial impulse that triggered the liquidity sweep on the previous higher-timeframe candle, and then holding the Market Structure Shift level at that point after the sweep. If the filter is turned off, price may move higher following the liquidity removal, creating a new MSS level and potentially producing a false structure shift and entry signal on the formed model.
This filter helps you more accurately identify genuine shifts - but keep in mind that the model can still perform well without it, so choose the setting that best suits your trading style.
- Same Wave Filter
The Same Wave Filter removes entry models that form without a clear lower-timeframe structure when liquidity is swept from the previous higher-timeframe candle. In other words, if the prior HTF candle and the current one belong to the same impulse wave - without any retracements on the LTF - the model is filtered out.
Keep in mind that this filter may also exclude patterns that could have produced positive results, so whether to enable it depends on your trading system.
- Only with Divergence
The Only with Divergence filter detects divergence between the lows of successive candles and indicators like RSI. When the low that swept liquidity diverges from the previous candle’s low, the indicator displays a “DIV” label. Although RSI is cited as an example, our divergence calculation is more advanced. This filter highlights patterns where low divergence signals genuine liquidity manipulation and a likely aggressive price reversal.
🌀 Model Settings
Trade Direction: Choose whether to display models for Long or Short trades.
Fractal: Select between automatic fractal detection—which adapts the lower-timeframe (LTF) and higher-timeframe (HTF) candles—or Custom.
Custom Fractal: When Custom is selected, manually specify the LTF and HTF timeframes used to detect the patterns.
History Pattern Limit: Set the maximum number of patterns to display on the chart to keep it clean and uncluttered.
🎨 Model Style
You can flexibly customize the model’s appearance by choosing your preferred line thickness, color, and the other settings we discussed above.
🔵 ENTRY MODEL 2
This model appears under specific conditions when Model 1 cannot form. It’s a price-reversal model constructed according to different rules than the first model. The screenshot below shows how it looks on the chart.
This model forms less frequently than Model 1 but delivers equally strong performance and is displayed as a position-entry zone.
Like the Entry Area in Entry Model 1, this zone is calculated automatically and highlights the best entry levels: areas that showed the strongest historical results are rendered in a brighter shade.
🎨 Model Style
You can flexibly customize the style of Entry Model 2 - its color, opacity, visibility, and the average price of the previous candle.
🟢 ENTRY MODEL 3
Entry Model 3 is a continuation pattern that only forms after Entry Model 1 has completed and delivered the necessary price move to trigger Model 3.
Below is a schematic illustration of how Model 3 is intended to work.
🎨 Model Style
As with the previous models, you can flexibly customize the style of this zone.
⬆️ HTF CANDLES
One of the standout features of this indicator is the ability to plot higher-timeframe (HTF) candles directly on your lower-timeframe (LTF) chart, giving you clear visualization of the entry models and insight into what’s unfolding on the larger timeframe.
You can fully customize the HTF candles - select their style, the number of bars displayed, and tweak various settings to match your personal trading style.
HTF FVG
Fair Value Gaps (FVGs) can also be drawn on the HTF candles themselves, enabling you to spot key liquidity or interest zones at a glance, without switching between timeframes.
Additionally, you can view all significant historical HTF highs and lows, with demarcation lines showing where each HTF candle begins and ends.
All these options let you tailor the HTF candle display on your chart and monitor multiple timeframes’ trends in a single view.
📶 INFO PANEL
Instrument: the market symbol on which the model is detected
Fractal Timeframes: the LTF and HTF fractal periods used to locate the pattern
HTF Candle Countdown: the time remaining until the higher-timeframe candle closes
Trade Direction: the direction (Long or Short) in which the model is searched for entry
🔔 ALERT CUSTOMIZATION
And, of course, you can configure any alerts you need. There are seven alert types available:
Confirmed Entry Model 1
Unconfirmed Entry Model 1
Confirmed Entry Model 2
Confirmed Entry Model 3
Entry Area 1 Trigger
Entry Area 2 Trigger
Entry Area 3 Trigger
You also get a custom macro field where you can enter any placeholders to fully personalize your alerts. Below are example macros you can use in that field.
{{event}} - Event name ('New M1')
{{direction}} - Trade direction ('Long', 'Short')
{{area_beg}} - Entry Area Price
{{area_end}} - Entry Area Price
{{exchange}} - Exchange ('Binance')
{{ticker}} - Ticker ('BTCUSD')
{{interval}} - Timeframe ('1s', '1', 'D')
{{htf}} - High timeframe ('15', '60', 'D')
{{open}}-{{close}}-{{high}}-{{low}} - Candle price values
{{htf_open}}-{{htf_close}}-{{htf_high}}-{{htf_low}} - Last confirmed HTF candle's price
{{volume}} - Candle volume
{{time}} - Candle open time in UTC timezone
{{timenow}} - Signal time in UTC timezone
{{syminfo.currency}} - 'USD' for BTCUSD pair
{{syminfo.basecurrency}} - 'BTC' for BTCUSD pair
✅ USAGE EXAMPLES
Now I’ll demonstrate several ways to apply this indicator across different trading strategies.
Primarily, it’s most effective within the Smart Money framework - where liquidity and manipulation are the core focus - so it integrates seamlessly into your SMC-based approach.
However, it can also be employed in other strategies, such as classic technical analysis or Elliott Wave, to capitalize on reversal points on the chart.
Example 1
The first example illustrates forming a downtrend using a Smart Money strategy. After the market structure shifts and the first BOS is broken, we begin looking for a short entry.
Once Entry Model 1 is established, a Fair Value Gap appears, which we use as our position-entry zone. The nearest target becomes the newly formed BOS level.
In this trade, it was crucial to wait for a strong downtrend to develop before hunting for entries. Therefore, we waited for the first BOS to break and entered the trade to ride the continuation of the downtrend down to the next BOS level.
Example 2
The next example illustrates a downtrend developing with a Fair Value Gap on the 1-hour timeframe. The FVG is also displayed directly on the HTF candles in the chart.
The pattern forms within the HTF Fair Value Gap, indicating that we can balance this inefficiency and ride the continuation of the downtrend.
The target can simply be a 1:2 or 1:3 risk–reward ratio, as in our case.
📌 CONCLUSION
These two examples illustrate how this indicator can be used to identify reversals or trend continuations. In truth, there are countless ways to incorporate this tool, and each trader can adapt the model to fit their own strategy.
Always remember to rely on your own analysis and only enter trades when you feel confident in them.
MissedPrice Volume Method[KiomarsRakei]█ Core Concept:
This script detects price zones that are highly likely to be revisited — areas where price moved too quickly to fully fill market activity. Using sharp volume shifts and volatility filters, the script identifies these “missed” levels and generates signals pointing toward them.
Signals are generated before price reaches the zone, allowing you to analyze price behavior both before and after the zone is touched. These zones often act like magnets for price, making them ideal for short-term.
Examples of signals and high hit rate of Missed zones
█ How It Works:
The script monitors 3-candle volume and price behavior to detect moments where volume accelerates abnormally compared to recent averages. When a potential missed zone is found and price hasn’t revisited it yet, a signal is created in advance, pointing to that zone as a likely future target.
█ Features:
Zone Visualization: Dynamic boxes show price targets based on missed volume areas.
Pre-Zone Signals: Alerts fire before price returns, offering early trade setups.
Stat Tracking System: Automatically logs signals, win rate, and average profit.
Live Performance Table: On-chart stats including hit/miss breakdown and late-return analysis.
Works on All Markets: Compatible with any chart that provides volume — crypto, forex, indices, or stocks.
A signal is considered successful when price touches the zone. However, not all zones are guaranteed to be revisited.
█ Key Inputs & Stats Table:
Volume Filters: Control signal sensitivity using min/max relative volume shift.
Zone & Line Settings: Adjust how long the zone stays visible and whether entry lines are drawn.
Custom Colors: Choose colors for buy/sell zones, lines, and visuals.
📊 Table Metrics:
Total Signals: Count of all generated signals.
Win Rate: % of signals where price returned to the zone (hit = touched the zone, regardless of timing).
Bad Signals: Signals that took too long to hit or were never hit.
Bad but Hit: Signals marked bad but eventually touched the zone.
Bad signals are marked in red. These indicate zones that price failed to reach within the expected time window, showing where the script identified a target that remained unfulfilled.
AlphaSignal | MindMarketAlphaSignal — Smart Indicator for Precise Entries
What does AlphaSignal do?
AlphaSignal looks for moments when the price moves too far from its average, volume spikes, and overall market activity increases. When these things line up, it gives you a clean, high-quality trading signal — either to buy or sell.
How it works :
Activity & Volume Detection
It monitors for sudden bursts in trading volume and volatility — clear signs that something important is happening in the market.
Price Deviation with Nadaraya-Watson Envelope
The indicator uses a smooth curve (called the Nadaraya-Watson estimate) to track the average price. When price drifts too far from this curve, it might be ready to snap back. That’s where AlphaSignal starts paying attention.
Signal Rating System
Each potential trade gets a score based on:
Market activity
Volume deviation
How far price is from the NW envelope
(Optionally) Trend strength and momentum via ADX, RSI, MACD
Only if the total score is high enough — a signal is fired.
Advanced Filters (Optional)
Want more confirmation? Turn on ADX, RSI, and MACD checks to avoid weak setups during choppy, low-trend periods.
Cooldown Logic
To avoid overtrading, AlphaSignal waits a set number of bars between signals — you can customize this.
Trading Suggestions (Signal Panel)
AlphaSignal gives you real-time trading guidance with a simple suggestion box:
BUY NOW / SELL NOW
All conditions are met, rating is strong — take action.
PREPARE BUY / PREPARE SELL
No full confirmation yet, but the price is very close to key levels (within 1.5% of the NW envelope). Get ready — a signal might appear soon.
AWAIT BUY / AWAIT SELL
The market is leaning toward a buy or sell, but price isn’t in a good spot yet. Be patient and watch for better positioning.
[COG]Adaptive Volatility Bands# Adaptive Volatility Bands (AVB) Indicator Guide for Traders
## Special Acknowledgment 🙌
This script is inspired by and builds upon the foundational work of **DonovanWall**, a respected contributor to the trading community. His innovative approach to adaptive indicators has been instrumental in developing this advanced trading tool.
## What is the Adaptive Volatility Bands Indicator?
The Adaptive Volatility Bands (AVB) is a sophisticated technical analysis tool designed to help traders understand market dynamics by creating dynamic, responsive price channels that adapt to changing market conditions. Unlike traditional static indicators, this script uses advanced mathematical techniques to create flexible bands that adjust to market volatility in real-time.
## Key Features and Inputs
### 1. Price and Filtering Options
- **Price Source**: Determines the base price used for calculations (default is HLC3 - Average of High, Low, and Close)
- **Filter Poles**: Controls the smoothness of the indicator (1-9 poles)
- Lower values: More responsive, more noise
- Higher values: Smoother, but slower to react
### 2. Volatility and Band Settings
- **Sample Length**: Determines how many bars are used to calculate volatility (default 144)
- **Volatility Multiplier**: Adjusts the width of the main bands (default 1.414)
- **Outer Band Multiplier**: Controls the width of the outer bands (default 2.5)
- **Inner Band Ratio**: Positions the inner bands between the center and outer bands (default 0.25)
### 3. Advanced Processing Options
- **Lag Reduction Mode**: Helps reduce indicator delay
- **Fast Response Mode**: Makes the indicator more responsive to recent price changes
### 4. Signal and Visualization Options
- **Show Entry Signals**: Displays buy and sell signals
- **Signal Display Style**: Choose between labels or shapes
- **Range Filter**: Adds an additional filter for signal validation
## How the Indicator Works
The Adaptive Volatility Bands create a dynamic price channel with three key components:
1. **Center Line**: Represents the core trend direction
2. **Inner Bands**: Closer to the center line
3. **Outer Bands**: Wider bands that show broader price potential
### Color Dynamics
- The indicator uses a smart color gradient system
- Colors change based on price position within the bands
- Helps visualize bullish (green/blue) and bearish (red) market conditions
## Trading Strategies for Beginners
### Basic Entry Signals
- **Buy Signal**:
- Price touches the center line from below
- Candle is bullish (closes higher than it opens)
- Price is above the center line
- Trend is upward
- **Sell Signal**:
- Price touches the center line from above
- Candle is bearish (closes lower than it opens)
- Price is below the center line
- Trend is downward
### Risk Management Tips
1. Use the bands to identify:
- Potential trend changes
- Volatility levels
- Support and resistance areas
2. Combine with other indicators for confirmation
3. Always use stop-loss orders
4. Adjust parameters to match your trading style and asset
## When to Use This Indicator
Best suited for:
- Trending markets
- Swing trading
- Identifying potential entry and exit points
- Understanding market volatility
### Recommended Markets
- Stocks
- Forex
- Cryptocurrencies
- Futures
## Customization
The script offers extensive customization:
- Adjust smoothness
- Change band multipliers
- Modify color schemes
- Enable/disable features like lag reduction
## Important Considerations for Beginners
🚨 **Disclaimer**:
- No indicator guarantees profits
- Always practice with a demo account first
- Learn and understand the indicator before live trading
- Market conditions change, so continually adapt your strategy
## Getting Started
1. Add the script to your TradingView chart
2. Experiment with different settings
3. Backtest on historical data
4. Start with small positions
5. Continuously learn and improve
Happy Trading! 📈🔍
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Quarterly Theory ICT 03 [TradingFinder] Precision Swing Points🔵 Introduction
Precision Swing Point (PSP) is a divergence pattern in the closing of candles between two correlated assets, which can indicate a potential trend reversal. This structure appears at market turning points and highlights discrepancies between the price behavior of two related assets.
PSP typically forms in key timeframes such as 5-minute, 15-minute, and 90-minute charts, and is often used in combination with Smart Money Concepts (SMT) to confirm trade entries.
PSP is categorized into Bearish PSP and Bullish PSP :
Bearish PSP : Occurs when an asset breaks its previous high, and its middle candle closes bullish, while the correlated asset closes bearish at the same level. This divergence signals weakness in the uptrend and a potential price reversal downward.
Bullish PSP : Occurs when an asset breaks its previous low, and its middle candle closes bearish, while the correlated asset closes bullish at the same level. This suggests weakness in the downtrend and a potential price increase.
🟣 Trading Strategies Using Precision Swing Point (PSP)
PSP can be integrated into various trading strategies to improve entry accuracy and filter out false signals. One common method is combining PSP with SMT (divergence between correlated assets), where traders identify divergence and enter a trade only after PSP confirms the move.
Additionally, PSP can act as a liquidity gap, meaning that price tends to react to the wick of the PSP candle, making it a favorable entry point with a tight stop-loss and high risk-to-reward ratio. Furthermore, PSP combined with Order Blocks and Fair Value Gaps in higher timeframes allows traders to identify stronger reversal zones.
In lower timeframes, such as 5-minute or 15-minute charts, PSP can serve as a confirmation for more precise entries in the direction of the higher timeframe trend. This is particularly useful in scalping and intraday trading, helping traders execute smarter entries while minimizing unnecessary stop-outs.
🔵 How to Use
PSP is a trading pattern based on divergence in candle closures between two correlated assets. This divergence signals a difference in trend strength and can be used to identify precise market turning points. PSP is divided into Bullish PSP and Bearish PSP, each applicable for long and short trades.
🟣 Bullish PSP
A Bullish PSP forms when, at a market turning point, the middle candle of one asset closes bearish while the correlated asset closes bullish. This discrepancy indicates weakness in the downtrend and a potential price reversal upward.
Traders can use this as a signal for long (buy) trades. The best approach is to wait for price to return to the wick of the PSP candle, as this area typically acts as a liquidity level.
f PSP forms within an Order Block or Fair Value Gap in a higher timeframe, its reliability increases, allowing for entries with tight stop-loss and optimal risk-to-reward ratios.
🟣 Bearish PSP
A Bearish PSP forms when, at a market turning point, the middle candle of one asset closes bullish while the correlated asset closes bearish. This indicates weakness in the uptrend and a potential price decline.
Traders use this pattern to enter short (sell) trades. The best entry occurs when price retests the wick of the PSP candle, as this level often acts as a resistance zone, pushing price lower.
If PSP aligns with a significant liquidity area or Order Block in a higher timeframe, traders can enter with greater confidence and place their stop-loss just above the PSP wick.
Overall, PSP is a highly effective tool for filtering false signals and improving trade entry precision. Combining PSP with SMT, Order Blocks, and Fair Value Gaps across multiple timeframes allows traders to execute higher-accuracy trades with lower risk.
🔵 Settings
Mode :
2 Symbol : Identifies PSP and PCP between two correlated assets.
3 Symbol : Compares three assets to detect more complex divergences and stronger confirmation signals.
Second Symbol : The second asset used in PSP and correlation calculations.
Third Symbol : Used in three-symbol mode for deeper PSP and PCP analysis.
Filter Precision X Point : Enables or disables filtering for more precise PSP and PCP detection. This filter only identifies PSP and PCP when the base asset's candle qualifies as a Pin Bar.
Trend Effect : By changing the Trend Effect status to "Off," all Pin bars, whether bullish or bearish, are displayed regardless of the current market trend. If the status remains "On," only Pin bars in the direction of the main market trend are shown.
Bullish Pin Bar Setting : Using the "Ratio Lower Shadow to Body" and "Ratio Lower Shadow to Higher Shadow" settings, you can customize your bullish Pin bar candles. Larger numbers impose stricter conditions for identifying bullish Pin bars.
Bearish Pin Bar Setting : Using the "Ratio Higher Shadow to Body" and "Ratio Higher Shadow to Lower Shadow" settings, you can customize your bearish Pin bar candles. Larger numbers impose stricter conditions for identifying bearish Pin bars.
🔵 Conclusion
Precision Swing Point (PSP) is a powerful analytical tool in Smart Money trading strategies, helping traders identify precise market turning points by detecting divergences in candle closures between correlated assets. PSP is classified into Bullish PSP and Bearish PSP, each playing a crucial role in detecting trend weaknesses and determining optimal entry points for long and short trades.
Using the PSP wick as a key liquidity level, integrating it with SMT, Order Blocks, and Fair Value Gaps, and analyzing higher timeframes are effective techniques to enhance trade entries. Ultimately, PSP serves as a complementary tool for improving entry accuracy and reducing unnecessary stop-outs, making it a valuable addition to Smart Money trading methodologies.
ZenAlgo - BenderThis script combines several volume-based methodologies into a single chart overlay to help traders analyze market participation and volume distribution. It aggregates volume from multiple sources—spot and perpetual markets across different exchanges—and processes it to display various insights directly on the chart.
The script provides a detailed view of both individual-bar volume and broader aggregated trends. It calculates certain values, plots different shapes and overlays, and includes an optional informational table. However, it does not offer financial signals or predict future price movements. Instead, it presents multiple volume and range-related highlights for educational or analytical observations.
Below is a detailed breakdown of the core elements in this script:
Core Data Calculation and Aggregation
To build a comprehensive volume picture, the script retrieves volume data from multiple predefined exchanges for both Spot and Perpetual pairs. The volume for each bar is processed in Aggregated mode , meaning it combines data across selected sources to produce a single composite volume value.
The script applies average-based aggregation to calculate the final volume figures. The total volume is then used as the basis for further calculations, such as buy/sell volume decomposition and Delta analysis.
Buy/Sell Volume Decomposition
Each bar’s total volume is separated into an estimated buy portion and a sell portion. This decomposition uses logic that considers wick length, body size, and whether the bar closed higher or lower than it opened. The script assigns fractions of the total volume to the upper wick, lower wick, and body, then multiplies these by the total aggregated volume to estimate buy and sell volumes.
This breakdown is calculated separately for spot-only volume , perp-only volume , and their aggregated sums, allowing traders to analyze how much of each bar’s volume is estimated as "buy" or "sell."
Delta and Cumulative Delta
The script computes a Delta (buy volume minus sell volume) for each bar. A positive Delta suggests more buying during that bar, while a negative Delta suggests more selling.
It also computes Cumulative Delta , summing this Delta over 14 bars (a fixed period). This allows users to observe how short-term buy/sell imbalances accumulate over time.
Visual Bar Coloring (PVSRA Logic)
The script includes logic based on PVSRA (Price Volume Support Resistance Analysis) , which examines average volume over a recent lookback period to determine whether a bar meets certain "climax" or "above-average" thresholds.
Bars are categorized as:
Climax Up or Climax Down: If a bar meets strong volume and range conditions, it is identified as a high-activity bar.
Neutral Colors: Bars that do not meet the threshold are identified as standard volume bars.
Table Summaries
The script includes an optional Spot vs. Perpetual volume table that provides:
Aggregated Spot vs. Perpetual buy/sell volumes
The net difference between buying and selling
The total sum across all included sources
Percentage breakdown of buying vs. selling
A separate multi-timeframe table calculates volume-related metrics for fixed timeframes (15, 60, and 240 minutes), allowing traders to compare their current timeframe with broader trends.
Highlighted Shapes and Diamonds
The script places shape markers above or below bars when certain conditions are met, including:
Dots (circles): Representing a significant increase in net Delta compared to the previous bar.
Diamonds: Markers that appear when volume-based conditions align with predefined thresholds. These vary in size and include an optional "Hardcore Mode" , which applies stricter filtering.
Crossover Triangles: These appear when the internally computed Delta MA (a moving average of Delta) crosses above or below a predefined EMA.
These markers highlight notable changes in volume, Delta, or price action but do not constitute predictive trading signals.
Delta Averages and Overlaid EMAs
The script plots a histogram of the current net Delta (buy minus sell) . Additionally, a Delta Moving Average (Delta MA) is used for tracking trends. The Delta MA is plotted alongside predefined Exponential Moving Averages (EMAs) , such as:
A Delta MA calculated using an exponential moving average (EMA) over 21 bars.
A set of predefined EMAs (lengths such as 3, 5, 7, 10, 13, 16, 21, 25, etc.) plotted to visualize momentum changes.
Areas between these EMAs can be filled with translucent shading to highlight momentum shifts.
Comparing the Delta MA to the overlaid EMAs helps track changes in Delta momentum over time.
Interpreting the Elements
When using this script, consider the following:
Volume Aggregation: The script aggregates volume across multiple Spot and Perpetual sources to provide a broad market view.
Delta and Cumulative Delta: The Delta histogram may spike positively or negatively, highlighting areas of potential buying or selling pressure.
Table Data: If enabled, the tables display buy/sell volume splits for Spot and Perpetual markets, along with multi-timeframe comparisons.
EMA Overlays on Delta: The stacked EMAs help visualize short-term vs. longer-term Delta changes.
Shape Markers: Dots, diamonds, and triangles emphasize notable shifts in volume or Delta but do not imply recommendations for action.
Usage Tips
Toggle "Hardcore Mode" to apply stricter filtering to highlight conditions.
Enable or disable the Spot vs. Perpetual Table to see if the breakdown of volume sources is useful.
Use the multi-timeframe table to compare intraday data with broader trends.
If the chart appears too cluttered, toggle off features like PVSRA color tints or some EMAs to focus on specific elements.
Final Thoughts
This script integrates multiple volume-based calculations, range analysis, aggregated volume from predefined tickers, and various moving averages for Delta. Its visual layers—color-coded bars, histograms, shape markers, and tables—offer a rich perspective on market activity.
Users can analyze these elements across any timeframe or market combination they prefer. The script does not provide buy/sell signals or make predictions —it is purely an analytical tool for understanding volume-based market dynamics.
Traders should interpret these visual elements according to their own strategy and trading approach.
Cumulative Price Change AlertCumulative Price Change Alert
Version: 1.0
Author: QCodeTrader 🚀
Overview 🔍
The Cumulative Price Change Alert indicator analyzes the percentage change between the current and previous open prices and sums these changes over a user-defined number of bars. It then generates visual buy and sell signals using arrows and labels on the chart, helping traders spot cumulative price momentum and potential trading opportunities.
Key Features ⚙️
Customizable Timeframe 🕒:
Use a custom timeframe or default to the chart's timeframe for price data.
User-Defined Summation 🔢:
Specify the number of bars to sum, allowing you to analyze cumulative price changes.
Custom Buy & Sell Conditions 🔔:
Set individual percentage change thresholds and cumulative sum thresholds to tailor signals for
your strategy.
Visual Alerts 🚀:
Displays green upward arrows for buy signals and red downward arrows for sell signals directly
on the chart.
Informative Labels 📝:
Provides labels with formatted percentage change and cumulative sum details for the analyzed
bars.
Versatile Application 📊:
Suitable for stocks, forex, crypto, commodities, and more.
How It Works ⚡
Price Change Calculation ➗:
The indicator calculates the percentage change between the current bar's open price and the
previous bar's open price.
Cumulative Sum ➕:
It then sums these percentage changes over the last N bars (as specified by the user).
Signal Generation 🚦:
Buy Signal 🟢: When both the individual percentage change and the cumulative sum exceed
their respective buy thresholds, a green arrow and label are displayed.
Sell Signal 🔴: Conversely, if the individual change and cumulative sum fall below the sell
thresholds, a red arrow and label are shown.
How to Use 💡
Add the Indicator ➕:
Apply the indicator to your chart.
Customize Settings ⚙️:
Set a custom timeframe if desired.
Define the number of bars to sum.
Adjust the buy/sell percentage change and cumulative sum thresholds to match your trading
strategy.
Interpret Visual Cues 👀:
Monitor the chart for green or red arrows and corresponding labels that signal potential buy or
sell opportunities based on cumulative price movements.
Settings Explained 🛠️
Custom Timeframe:
Select an alternative timeframe for analysis, or leave empty to use the current chart's timeframe.
Number of Last Bars to Sum:
Determines how many bars are used to compute the cumulative percentage change.
Buy Condition - Min % Change:
The minimum individual percentage change required to consider a buy signal.
Buy Condition - Min Sum of Bars:
The minimum cumulative percentage change over the defined bars needed for a buy signal.
Sell Condition - Max % Change:
The maximum individual percentage change threshold for a sell signal.
Sell Condition - Max Sum of Bars:
The maximum cumulative percentage change over the defined bars for triggering a sell signal.
Best Use Cases 🎯
Momentum Identification 📈:
Quickly spot strong cumulative price movements and momentum shifts.
Entry/Exit Signals 🚪:
Use the visual signals to determine potential entry and exit points in your trading.
Versatile Strategy Application 🔄:
Effective for scalping, swing trading, and longer-term analysis across various markets.
UPD: uncheck labels for better performance
Multi-Indicator Signals with Selectable Options by DiGetMulti-Indicator Signals with Selectable Options
Script Overview
This Pine Script is a multi-indicator trading strategy designed to generate buy/sell signals based on combinations of popular technical indicators: RSI (Relative Strength Index) , CCI (Commodity Channel Index) , and Stochastic Oscillator . The script allows you to select which combination of signals to display, making it highly customizable and adaptable to different trading styles.
The primary goal of this script is to provide clear and actionable entry/exit points by visualizing buy/sell signals with arrows , labels , and vertical lines directly on the chart. It also includes input validation, dynamic signal plotting, and clutter-free line management to ensure a clean and professional user experience.
Key Features
1. Customizable Signal Types
You can choose from five signal types:
RSI & CCI : Combines RSI and CCI signals for confirmation.
RSI & Stochastic : Combines RSI and Stochastic signals.
CCI & Stochastic : Combines CCI and Stochastic signals.
RSI & CCI & Stochastic : Requires all three indicators to align for a signal.
All Signals : Displays individual signals from each indicator separately.
This flexibility allows you to test and use the combination that works best for your trading strategy.
2. Clear Buy/Sell Indicators
Arrows : Buy signals are marked with upward arrows (green/lime/yellow) below the candles, while sell signals are marked with downward arrows (red/fuchsia/gray) above the candles.
Labels : Each signal is accompanied by a label ("BUY" or "SELL") near the arrow for clarity.
Vertical Lines : A vertical line is drawn at the exact bar where the signal occurs, extending from the low to the high of the candle. This ensures you can pinpoint the exact entry point without ambiguity.
3. Dynamic Overbought/Oversold Levels
You can customize the overbought and oversold levels for each indicator:
RSI: Default values are 70 (overbought) and 30 (oversold).
CCI: Default values are +100 (overbought) and -100 (oversold).
Stochastic: Default values are 80 (overbought) and 20 (oversold).
These levels can be adjusted to suit your trading preferences or market conditions.
4. Input Validation
The script includes built-in validation to ensure that oversold levels are always lower than overbought levels for each indicator. If the inputs are invalid, an error message will appear, preventing incorrect configurations.
5. Clean Chart Design
To avoid clutter, the script dynamically manages vertical lines:
Only the most recent 50 buy/sell lines are displayed. Older lines are automatically deleted to keep the chart clean.
Labels and arrows are placed strategically to avoid overlapping with candles.
6. ATR-Based Offset
The vertical lines and labels are offset using the Average True Range (ATR) to ensure they don’t overlap with the price action. This makes the signals easier to see, especially during volatile market conditions.
7. Scalable and Professional
The script uses arrays to manage multiple vertical lines, ensuring scalability and performance even when many signals are generated.
It adheres to Pine Script v6 standards, ensuring compatibility and reliability.
How It Works
Indicator Calculations :
The script calculates the values of RSI, CCI, and Stochastic Oscillator based on user-defined lengths and smoothing parameters.
It then checks for crossover/crossunder conditions relative to the overbought/oversold levels to generate individual signals.
Combined Signals :
Depending on the selected signal type, the script combines the individual signals logically:
For example, a "RSI & CCI" buy signal requires both RSI and CCI to cross into their respective oversold zones simultaneously.
Signal Plotting :
When a signal is generated, the script:
Plots an arrow (upward for buy, downward for sell) at the corresponding bar.
Adds a label ("BUY" or "SELL") near the arrow for clarity.
Draws a vertical line extending from the low to the high of the candle to mark the exact entry point.
Line Management :
To prevent clutter, the script stores up to 50 vertical lines in arrays (buy_lines and sell_lines). Older lines are automatically deleted when the limit is exceeded.
Why Use This Script?
Versatility : Whether you're a scalper, swing trader, or long-term investor, this script can be tailored to your needs by selecting the appropriate signal type and adjusting the indicator parameters.
Clarity : The combination of arrows, labels, and vertical lines ensures that signals are easy to spot and interpret, even in fast-moving markets.
Customization : With adjustable overbought/oversold levels and multiple signal options, you can fine-tune the script to match your trading strategy.
Professional Design : The script avoids clutter by limiting the number of lines displayed and using ATR-based offsets for better visibility.
How to Use This Script
Add the Script to Your Chart :
Copy and paste the script into the Pine Editor in TradingView.
Save and add it to your chart.
Select Signal Type :
Use the "Signal Type" dropdown menu to choose the combination of indicators you want to use.
Adjust Parameters :
Customize the lengths of RSI, CCI, and Stochastic, as well as their overbought/oversold levels, to match your trading preferences.
Interpret Signals :
Look for green arrows and "BUY" labels for buy signals, and red arrows and "SELL" labels for sell signals.
Vertical lines will help you identify the exact bar where the signal occurred.
Tips for Traders
Backtest Thoroughly : Before using this script in live trading, backtest it on historical data to ensure it aligns with your strategy.
Combine with Other Tools : While this script provides reliable signals, consider combining it with other tools like support/resistance levels or volume analysis for additional confirmation.
Avoid Overloading the Chart : If you notice too many signals, try tightening the overbought/oversold levels or switching to a combined signal type (e.g., "RSI & CCI & Stochastic") for fewer but higher-confidence signals.
Range Filtered Trend Signals [AlgoAlpha]Introducing the Range Filtered Trend Signals , a cutting-edge trading indicator designed to detect market trends and ranging conditions with high accuracy. This indicator leverages a combination of Kalman filtering and Supertrend analysis to smooth out price fluctuations while maintaining responsiveness to trend shifts. By incorporating volatility-based range filtering, it ensures traders can differentiate between trending and ranging conditions effectively, reducing false signals and enhancing trade decision-making.
:key: Key Features
:white_check_mark: Kalman Filter Smoothing – Minimizes market noise while preserving trend clarity.
:bar_chart: Supertrend Integration – A dynamic trend-following mechanism for spotting reversals.
:fire: Volatility-Based Range Detection – Detects trending vs. ranging conditions with precision.
:art: Color-Coded Trend Signals – Instantly recognize bullish, bearish, and ranging market states.
:gear: Customizable Inputs – Fine-tune Kalman parameters, Supertrend settings, and color themes to match your strategy.
:bell: Alerts for Trend Shifts – Get real-time notifications when market conditions change!
:tools: How to Use
Add the Indicator – Click the star icon to add it to your TradingView favorites.
Analyze Market Conditions – Observe the color-coded signals and range boundaries to identify trend strength and direction.
Use Alerts for Trade Execution – Set alerts for trend shifts and market conditions to stay ahead without constantly monitoring charts.
:mag: How It Works
The Kalman filter smooths price fluctuations by dynamically adjusting its weighting based on market volatility. It helps remove noise while keeping the signal reactive to trend changes. The Supertrend calculation is then applied to the filtered price data, providing a robust trend-following mechanism. To enhance signal accuracy, a volatility-weighted range filter is incorporated, creating upper and lower boundaries that define trend conditions. When price breaks out of these boundaries, the indicator confirms trend continuation, while signals within the range indicate market consolidation. Traders can leverage this tool to enhance trade timing, filter false breakouts, and identify optimal entry/exit zones.
[COG] Advanced School Run StrategyAdvanced School Run Strategy (ASRS) – Explanation
Overview: The Advanced School Run Strategy (ASRS) is an intraday trading approach designed to identify breakout opportunities based on specific time and price patterns. This script applies the concepts of the Advanced School Run Strategy as outlined in Tom Hougaard's research, adapted to work seamlessly on TradingView charts. It leverages 5-minute candlestick data to set actionable breakout levels and provides traders with visual cues and alerts to make informed decisions.
Features:
Dynamic Breakout Levels: Automatically calculates high and low levels based on the market's behavior during the initial trading minutes.
Custom Visualization: Highlights breakout zones with customizable colors and transparency, providing clear visual feedback for bullish and bearish breakouts.
Configurable Alerts: Includes alert conditions for both bullish and bearish breakouts, ensuring traders never miss a trading opportunity.
Reset Logic: Resets breakout levels daily at the market open to ensure accurate signal generation for each session.
How It Works:
The script identifies key levels (high and low) after a configurable number of minutes from the market open (default: 25 minutes).
If the price breaks above the high level or below the low level, a corresponding breakout is detected.
The script draws breakout zones on the chart and triggers alerts based on the breakout direction.
All levels and signals reset at the start of each new trading session, maintaining relevance to current market conditions.
Customization Options:
Line and box colors for bullish and bearish breakouts.
Transparency levels for breakout visualizations.
Alert settings to receive notifications for detected breakouts.
Acknowledgment: This script is inspired by Tom Hougaard's Advanced School Run Strategy. The methodology has been translated into Pine Script for TradingView users, adhering to TradingView’s policies and community guidelines. This script does not redistribute proprietary content from the original research but implements the principles for educational and analytical purposes.
RSI OB/OS Strategy Analyzer█ OVERVIEW
The RSI OB/OS Strategy Analyzer is a comprehensive trading tool designed to help traders identify and evaluate overbought/oversold reversal opportunities using the Relative Strength Index (RSI). It provides visual signals, performance metrics, and a detailed table to analyze the effectiveness of RSI-based strategies over a user-defined lookback period.
█ KEY FEATURES
RSI Calculation
Calculates RSI with customizable period (default 14)
Plots dynamic overbought (70) and oversold (30) levels
Adds background coloring for OB/OS regions
Reversal Signals
Identifies signals based on RSI crossing OB/OS levels
Two entry strategies available:
Revert Cross: Triggers when RSI exits OB/OS zone
Cross Threshold: Triggers when RSI enters OB/OS zone
Trade Direction
Users can select a trade bias:
Long: Focuses on oversold reversals (bullish signals)
Short: Focuses on overbought reversals (bearish signals)
Performance Metrics
Calculates three key statistics for each lookback period:
Win Rate: Percentage of profitable trades
Mean Return: Average return across all trades
Median Return: Median return across all trades
Metrics calculated as percentage changes from entry price
Visual Signals
Dual-layer signal display:
BUY: Green triangles + text labels below price
SELL: Red triangles + text labels above price
Semi-transparent background highlighting in OB/OS zones
Performance Table
Interactive table showing metrics for each lookback period
Color-coded visualization:
Win Rate: Gradient from red (low) to green (high)
Returns: Green for positive, red for negative
Time Filtering
Users can define a specific time window for the indicator to analyze trades, ensuring that performance metrics are calculated only for the desired period.
Customizable Display
Adjustable table font sizes: Auto/Small/Normal/Large
Toggle option for table visibility
█ PURPOSE
The RSI OB/OS Strategy Analyzer helps traders:
Identify mean-reversion opportunities through RSI extremes
Backtest entry strategy effectiveness across multiple time horizons
Optimize trade timing through visual historical performance data
Quickly assess strategy robustness with color-coded metrics
█ IDEAL USERS
Counter-Trend Traders: Looking to capitalize on RSI extremes
Systematic Traders: Needing quantitative strategy validation
Educational Users: Studying RSI behavior in different market conditions
Multi-Timeframe Analysts: Interested in forward returns analysis
Bollinger Bands Reversal Strategy Analyzer█ OVERVIEW
The Bollinger Bands Reversal Overlay is a versatile trading tool designed to help traders identify potential reversal opportunities using Bollinger Bands. It provides visual signals, performance metrics, and a detailed table to analyze the effectiveness of reversal-based strategies over a user-defined lookback period.
█ KEY FEATURES
Bollinger Bands Calculation
The indicator calculates the standard Bollinger Bands, consisting of:
A middle band (basis) as the Simple Moving Average (SMA) of the closing price.
An upper band as the basis plus a multiple of the standard deviation.
A lower band as the basis minus a multiple of the standard deviation.
Users can customize the length of the Bollinger Bands and the multiplier for the standard deviation.
Reversal Signals
The indicator identifies potential reversal signals based on the interaction between the price and the Bollinger Bands.
Two entry strategies are available:
Revert Cross: Waits for the price to close back above the lower band (for longs) or below the upper band (for shorts) after crossing it.
Cross Threshold: Triggers a signal as soon as the price crosses the lower band (for longs) or the upper band (for shorts).
Trade Direction
Users can select a trade bias:
Long: Focuses on bullish reversal signals.
Short: Focuses on bearish reversal signals.
Performance Metrics
The indicator calculates and displays the performance of trades over a user-defined lookback period ( barLookback ).
Metrics include:
Win Rate: The percentage of trades that were profitable.
Mean Return: The average return across all trades.
Median Return: The median return across all trades.
These metrics are calculated for each bar in the lookback period, providing insights into the strategy's performance over time.
Visual Signals
The indicator plots buy and sell signals on the chart:
Buy Signals: Displayed as green triangles below the price bars.
Sell Signals: Displayed as red triangles above the price bars.
Performance Table
A customizable table is displayed on the chart, showing the performance metrics for each bar in the lookback period.
The table includes:
Win Rate: Highlighted with gradient colors (green for high win rates, red for low win rates).
Mean Return: Colored based on profitability (green for positive returns, red for negative returns).
Median Return: Colored similarly to the mean return.
Time Filtering
Users can define a specific time window for the indicator to analyze trades, ensuring that performance metrics are calculated only for the desired period.
Customizable Display
The table's font size can be adjusted to suit the user's preference, with options for "Auto," "Small," "Normal," and "Large."
█ PURPOSE
The Bollinger Bands Reversal Overlay is designed to:
Help traders identify high-probability reversal opportunities using Bollinger Bands.
Provide actionable insights into the performance of reversal-based strategies.
Enable users to backtest and optimize their trading strategies by analyzing historical performance metrics.
█ IDEAL USERS
Swing Traders: Looking for reversal opportunities within a trend.
Mean Reversion Traders: Interested in trading price reversals to the mean.
Strategy Developers: Seeking to backtest and refine Bollinger Bands-based strategies.
Performance Analysts: Wanting to evaluate the effectiveness of reversal signals over time.
