Professional GBP/JPY Analysis ToolThe foundation of professional trading begins with analyzing individual currencies first, not just currency pairs. By understanding the relative strength of each currency in the pair, traders can anticipate potential market moves with greater accuracy.
This indicator simplifies that process by:
Analyzing Individual Currency Strength:
The strength of GBP is calculated by averaging its performance across seven major GBP currency pairs:
GBP/EUR
GBP/USD
GBP/CAD
GBP/CHF
GBP/AUD
GBP/NZD
GBP/JPY
The strength of JPY is calculated by averaging its performance across seven major JPY currency pairs:
JPY/USD
JPY/CAD
JPY/EUR
JPY/GBP
JPY/AUD
JPY/NZD
JPY/CHF
The values are normalized to allow direct comparison on the same scale.
Identifying Correlation Between GBP and JPY:
The histogram displays the correlation between GBP and JPY strength:
Positive Correlation (Green): Both GBP and JPY are trending up or down together, indicating a less strong trend. This is a market condition to avoid, as both currencies are strengthening or weakening simultaneously.
Negative Correlation (Red): One currency is strong while the other is weak, indicating a stronger trend in GBP/JPY. This scenario presents a better trading opportunity, as you are trading one strong currency against one weak currency, amplifying the potential for a clearer price movement in GBP/JPY.
Visualizing Long/Short Bias:
GBP Strength > JPY Strength: Bullish bias for GBP/JPY (green background).
JPY Strength > GBP Strength: Bearish bias for GBP/JPY (red background).
This indicator equips traders with a deeper understanding of GBP/JPY dynamics by first breaking down the individual currencies. With insights into currency strength, their correlation, and the optimal conditions for trading, it provides a solid foundation for making informed trading decisions.
How to Use:
Check the Histogram for Correlation:
Wait for the histogram to be red. This indicates that GBP and JPY are moving in opposite directions, signaling a stronger trend where you're trading a strong currency against a weak one—a more favorable setup.
Align with Background Color for Confirmation:
Wait for the background color to match your trade plan:
Green Background: Confirms a bullish bias, supporting long positions on the GBP/JPY pair.
Red Background: Confirms a bearish bias, supporting short positions on the GBP/JPY pair.
By following these steps, you can identify stronger trade opportunities and align them with your strategy.
Educational
Renko with Dynamic Support and ResistanceRenko candles are a type of chart used to filter out minor price movements, focusing on more significant trends. Unlike traditional candlestick charts, Renko charts do not use time as the primary unit but instead focus on price movements, with each brick representing a certain amount of price movement, or a "brick size." When the price moves by the predefined brick size in a given direction, a new Renko brick is created.
Renko charts are useful for identifying trends and key support/resistance levels, and they help eliminate market noise. Here's a brief explanation of how Renko works:
How Renko Charts Work:
Brick Size (Box Size): The user sets a fixed value (in terms of price) that determines when a new brick will form. For example, if the brick size is 10, a new brick will appear every time the price moves up or down by 10 units.
Direction of Bricks:
If the price moves up by the brick size, a new brick is added in the up direction.
If the price moves down by the brick size, a new brick is added in the down direction.
If the price doesn't move enough to form a new brick, no new brick is created.
No Time Element: Unlike traditional candlesticks, there is no time involved in Renko charts, meaning that bricks can be formed over any period depending on the price movements.
However, since Renko is a price-based chart type and not a time-based one, the actual Renko candles are not directly supported as a visual element in Pine Script. But you can replicate the behavior by plotting the price series based on the conditions for Renko.
Renko Charts Use Case:
Trend Identification: If you see multiple consecutive green bricks, it indicates an uptrend. Similarly, multiple red bricks indicate a downtrend.
Support/Resistance Levels: Renko charts can highlight important levels where price may reverse, as significant changes in direction are visible.
Intraday Chaos FilterIntraday trading requires precise tools and strategies to navigate the chaotic fluctuations of the market. The "Intraday Chaos Filter" indicator is designed to provide traders with a systematic approach to managing market noise. This Pine Script™ indicator helps identify potential trend shifts and provides a visual representation of price action, aiding in making informed trading decisions. By focusing on reducing market chaos, it aims to provide a clearer picture of the underlying trend, regardless of the volatility.
At the heart of the indicator lies the multiplier input, a customizable setting that adjusts the sensitivity of the filter. The multiplier directly influences the chaos threshold by determining the percentage deviation allowed before signaling a trend reversal. A higher multiplier results in a less sensitive filter, reducing the number of signals during minor price fluctuations. This flexibility allows traders to adapt the indicator to different trading styles and market conditions, making it versatile for a range of intraday strategies.
The "Intraday Chaos Filter" operates by tracking two key price levels: src1 and src2, representing the highest and lowest prices during a trend. These levels are dynamically adjusted based on the closing price, ensuring that the indicator adapts in real time. The trend direction is determined by comparing the current price against a calculated filter level. When the price crosses the threshold in either direction, it signals a potential trend reversal, marked by a change in candle colors on the chart. Green represents an upward trend, while red signifies a downward trend, making it visually intuitive for traders.
This indicator's simplicity and clarity make it a valuable addition to any trader's toolkit. By focusing on trend direction and filtering out minor fluctuations, it helps traders stay focused on significant market movements. The integration of customizable settings ensures adaptability to various trading preferences, providing both novice and experienced traders with a reliable tool for intraday decision-making. The "Intraday Chaos Filter" offers an effective way to navigate market noise, enabling traders to identify opportunities with confidence.
Dynamic EMA CrossoverThe Dynamic EMA Crossover indicator is designed to help traders identify trend transitions, visually understand market direction, and detect sideways consolidation zones. It simplifies decision-making by dynamically changing colors and highlighting areas of interest.
Key Features:
1. Dynamic EMA Crossovers:
• Uses two EMAs (default: 9 and 26 ) to identify bullish and bearish trends.
• EMAs and the area between them turn green during bullish trends and red during bearish trends for easy visualization.
2. Sideways Market Detection:
• Automatically detects periods of market consolidation when EMAs overlap for 10 consecutive candles and the price movement remains narrow.
• Sideways zones are highlighted with grey background, helping traders avoid false breakouts and trendless markets.
3. Customizable Inputs:
• Adjust the lengths of the two EMAs and the sensitivity of the overlap detection to match your trading style and market conditions.
How It Works:
• Trend Identification:
• When the shorter EMA crosses above the longer EMA, a bullish trend is indicated.
• When the shorter EMA crosses below the longer EMA, a bearish trend is indicated.
• The indicator dynamically adjusts the colors of the EMAs and fills the area between them for clear trend visibility.
• Sideways Market Detection:
• When the shorter EMA and longer EMA stay close (within a customizable sensitivity) for a fixed period (hardcoded to 10 candles), the indicator identifies a sideways market.
• This feature helps traders avoid entering trades during choppy or indecisive market conditions.
Who Is This For?
This indicator is ideal for:
• Trend traders looking for clear signals of trend direction.
• Swing traders who want to avoid trading in sideways markets.
• Scalpers who need quick and reliable visual cues for short-term market behavior.
Use Cases:
1. Bullish/Bearish Trends:
• Enter trades in the direction of the trend as the crossover occurs and colors change.
2. Sideways Zones:
• Avoid trades during periods of consolidation and wait for a clear breakout.
Mashup Logic:
This indicator combines:
1. EMA Crossovers:
• A tried-and-tested method for trend detection using two moving averages.
• Dynamic visual cues for bullish and bearish market phases.
2. Sideways Market Detection:
• Innovative logic to highlight sideways zones based on EMA overlap and price range analysis.
• Helps reduce noise and avoid trading during trendless periods.
3. Customization and Flexibility:
• Fully adjustable EMA lengths and overlap sensitivity to adapt to different markets and trading styles.
Dual EMA Proportion Variance | JeffreyTimmermansDual EMA Proportion Variance
The "Dual EMA Proportion Variance" Indicator provides a robust way to analyze price trends, volatility, and momentum using dual EMA calculations combined with percentile-based thresholds. This approach enables traders to identify significant bullish and bearish trends while incorporating smoothing and tailoring options for better adaptability.
Key Features
Dual EMA with Proportion Variance
DEMA Calculation: Computes the Dual Exponential Moving Average (DEMA) based on a user-defined length and source.
Proportion Thresholds: Uses percentile-based thresholds (e.g., 60/45, 60/40, 55/45, or 55/40) to determine upper and lower bounds for trend detection. Percentile thresholds help identify key levels of market behavior based on historical data.
Momentum and Volatility Analysis
Momentum Calculation: Computes momentum based on proximity to percentile levels, smoothed using a simple moving average (SMA) if enabled.
Volatility Incorporation: Uses the standard deviation (SD) of the lower percentile (PerDown) to define additional levels of significance.
Smoothing and Trend Calculation
Smoothing Options: Enables optional smoothing for momentum and trend values, helping reduce noise.
EMA Confluence: Adds an additional EMA overlay to enhance the trend confirmation process.
Customizable Visuals
Background Coloring: Dynamically changes the background color based on trend direction (bullish or bearish).
Momentum Plotting: Displays smoothed momentum and EMA confluence lines on the chart, with clear visual differentiation.
Alerts
Bullish Signal: Triggers when the trend transitions from neutral or bearish to bullish.
Bearish Signal: Triggers when the trend transitions from neutral or bullish to bearish.
Inputs Overview
DEMA Inputs
Length (DemaLen): Defines the length of the Dual EMA calculation.
Source (DemaSrc): Allows selection of price data (e.g., high, low, close) for the DEMA computation.
Proportion Settings
Proportion Length (PerLen): Defines the lookback period for percentile calculations.
Proportion Type (pertype): Choose from predefined combinations (e.g., 60/45, 60/40) to customize thresholds.
Smoothing Options
Enable Smoothing (UseSmoothing): Toggle to enable or disable smoothing.
Smoothing Length (SmoothingLen): Specifies the lookback period for smoothing.
Standard Deviation
Length (SDlen): Length of the lookback period used to calculate the standard deviation.
Tailoring
Bullish/Bearish Colors (ColUp/ColDown): Customizable colors for bullish and bearish trends.
Background Colors (ShowBGCol): Toggle to enable or disable background coloring.
Momentum Plot (PlotMomentum): Toggle to show or hide the momentum plot.
EMA Confluence
Enable Extra EMA (IncludeEma): Adds an additional EMA layer for trend confirmation.
Length (EmaLen): Defines the length of the EMA.
Indicator Behavior
Trend Detection
Bullish Trend: When the smoothed momentum (smoothedPT) is above zero and higher than the EMA (if enabled).
Bearish Trend: When the smoothed momentum is below zero and lower than the EMA (if enabled).
Signal Generation
Bullish Signal: Triggered on a crossover of smoothedTrend from negative to positive.
Bearish Signal: Triggered on a crossunder of smoothedTrend from positive to negative.
Customizations
Percentile Adjustments: Choose from various proportion thresholds to suit specific market conditions.
Smoothing Options: Fine-tune the level of noise reduction by adjusting smoothing parameters.
Visual Tailoring: Customize chart visuals, including colors, momentum plots, and background highlights.
EMA Inclusion: Optionally enable the extra EMA for more conservative trend confirmation.
Use Cases
Momentum Trading: Identify bullish or bearish momentum shifts based on percentile levels.
Volatility Assessment: Incorporate standard deviation levels to evaluate price volatility.
Trend Following: Align trades with dominant market trends using percentile thresholds and EMA confirmation.
Alerts for Automation: Set alerts for real-time notifications of potential trade opportunities.
This indicator provides flexibility and precision, making it suitable for a variety of trading styles, including trend following, swing trading, and momentum-based strategies.
This script is inspired by "Patito_1" . However, it is more advanced and includes additional features and options.
-Jeffrey
Volatility Footprint CandlesVolatility Footprint is an innovative volume profile indicator that dynamically adapts to real-time market conditions, providing traders with a powerful tool to visualize and interpret market structure, order flow, and potential areas of support and resistance.
At its core, Volatility Footprint combines the concepts of market profile, volume analysis, and volatility measurement to create a unique and adaptive charting experience. The indicator intelligently adjusts its display based on the current market volatility, ensuring that traders always have a clear and readable chart, regardless of the instrument or timeframe they are analyzing.
The footprint chart is composed of a series of color-coded boxes, each representing a specific price level. The color of the box indicates whether there is a net buying or selling pressure at that level, while the opacity reflects the relative strength of the volume. This intuitive visualization allows traders to quickly identify areas of high and low volume, as well as potential imbalances in order flow.
In addition to the individual box volumes, Volatility Footprint also calculates and displays the cumulative volume delta. This running total of buy and sell volumes across all price levels provides valuable insight into the overall market sentiment and potential trends.
One of the key features of Volatility Footprint is its ability to identify and highlight the Point of Control (POC). The POC represents the price level with the highest volume concentration and serves as a key reference point for potential support or resistance. By drawing attention to this crucial level, the indicator helps traders make more informed decisions about potential entry and exit points.
Volatility Footprint is designed to be highly customizable, allowing traders to tailor the appearance of the footprint chart to their specific preferences. Users can easily modify the colors, opacity, and size of the boxes, labels, and POC marker to enhance readability and clarity.
The indicator's versatility makes it suitable for a wide range of trading styles and strategies. Whether you are a scalper looking for short-term opportunities or a swing trader aiming to identify potential trend reversals, Volatility Footprint can provide valuable insights into market dynamics.
By combining Volatility Footprint with other forms of analysis, such as price action, key levels, and technical indicators, traders can gain a more comprehensive understanding of market behavior and make better-informed trading decisions.
Volatility Footprint's adaptive approach to volume profile analysis sets it apart from traditional fixed-resolution volume profile indicators. By dynamically adjusting to the unique characteristics of each instrument and timeframe, the indicator ensures that traders always have a clear and meaningful representation of market structure and order flow.
Volatility Footprint is a powerful tool that traders can incorporate into their market analysis and decision-making process. By providing a dynamic, visual representation of volume and order flow at different price levels, this indicator offers valuable insights into market structure, sentiment, and potential areas of support and resistance. Let's explore how traders might effectively utilize Volatility Footprint in their trading approach.
1. Identifying Key Levels:
One of the primary uses of Volatility Footprint is to identify key price levels where significant trading activity has occurred. The color-coded boxes allow traders to quickly spot areas of high volume concentration, which may indicate potential support or resistance zones. For example, if a trader notices a cluster of boxes with high opacity at a specific price level, they may interpret this as a strong support or resistance area, depending on the prevailing market context. By paying attention to these key levels, traders can make more informed decisions about potential entry and exit points, as well as placement of stop-loss orders and profit targets.
2. Assessing Market Sentiment:
The cumulative volume delta feature of Volatility Footprint provides traders with a valuable gauge of overall market sentiment. By analyzing the running total of buy and sell volumes across all price levels, traders can gain insight into the dominant market forces at play. If the cumulative delta is significantly positive, it may suggest a bullish sentiment, as buying pressure has been consistently outpacing selling pressure. Conversely, a negative cumulative delta may indicate a bearish sentiment. Traders can use this information to confirm or question their bias and adjust their trading plan accordingly.
3. Confirming Breakouts and Trend Reversals:
Volatility Footprint can be particularly useful in confirming the strength and validity of breakouts and potential trend reversals. When a price level is breached, traders can refer to the footprint chart to assess the volume and order flow characteristics around that level. If the breakout is accompanied by a surge in volume and a clear imbalance between buying and selling pressure, it may suggest a strong and sustainable move. On the other hand, if the volume is relatively low or evenly distributed, the breakout may be less reliable. By using Volatility Footprint to confirm breakouts, traders can make more informed decisions about whether to enter or exit a trade, or to adjust their position size.
4. Detecting Imbalances and Potential Reversals:
Imbalances between buying and selling pressure at specific price levels can often precede significant market moves or reversals. Volatility Footprint makes it easy for traders to spot these imbalances visually. For instance, if a trader observes a price level with a significantly larger number of sell boxes compared to buy boxes, it may indicate a potential exhaustion point for a bullish trend, and a reversal might be imminent. Traders can use this information in conjunction with other technical analysis tools, such as trendlines, moving averages, or momentum oscillators, to identify high-probability trading opportunities.
5. Adapting to Market Conditions:
One of the key strengths of Volatility Footprint is its ability to dynamically adapt to the unique volatility characteristics of different instruments and timeframes. This adaptability ensures that the indicator remains relevant and informative across a wide range of market conditions. Traders can use Volatility Footprint to gauge the relative volatility and volume of a particular instrument or timeframe, and adjust their trading approach accordingly. For example, in a highly volatile market, traders may opt for wider stop-loss levels and smaller position sizes to account for the increased risk.
Incorporating Volatility Footprint into a trading strategy requires a combination of technical analysis, market understanding, and risk management. Traders should use this indicator as part of a comprehensive approach, combining it with other forms of analysis, such as price action, key levels, and technical indicators. By doing so, traders can gain a more complete picture of market dynamics and make better-informed trading decisions.
It's important to note that while Volatility Footprint provides valuable insights, it should not be relied upon as a standalone trading signal. Traders should always consider the broader market context, their risk tolerance, and their overall trading plan when making decisions based on the information provided by this indicator.
In conclusion, Volatility Footprint offers traders a dynamic and visually intuitive way to analyze market structure, volume, and order flow. By identifying key levels, assessing market sentiment, confirming breakouts, detecting imbalances, and adapting to market conditions, traders can leverage this powerful tool to make more informed and confident trading decisions. As with any technical analysis tool, Volatility Footprint should be used in conjunction with sound risk management principles and a well-defined trading strategy to maximize its effectiveness.
Lost Bar Locator v1 [Yaphott]Lost Bar Locator v1 helps you locate missing data on your chart.
It does this by looking for consecutive bars that have a delta time greater than the current interval.
Two lines are drawn for each group of one or more missing bars:
Bar before the missing bar(s).
Bar after the missing bar(s).
Buy Signal Forex & Crypto v0 ImprovedPurpose of the Script:
This script is designed to generate buy and sell signals for trading Forex and cryptocurrencies by analyzing price trends using exponential moving averages (EMAs), volatility, and volume filters. The signals are displayed as arrows on the chart.
What the Script Does
Input Settings:
The script allows the user to configure various settings, such as the lengths of EMAs, a higher timeframe for trend confirmation, and thresholds for volume and volatility (ATR - Average True Range).
Key settings:
5 EMA Length – Length of the short-term EMA.
13 EMA Length – Length of the medium-term EMA.
26 EMA Length – Length of the long-term EMA.
21 EMA Length – Used for trend confirmation on a higher timeframe.
Higher Timeframe – Lets you select a timeframe (e.g., daily) for confirming the overall trend.
ATR Threshold – Filters out signals when the market's volatility is too low.
Volume Filter – Ensures sufficient trading activity before generating signals.
Calculating EMAs (Exponential Moving Averages):
Four EMAs are calculated:
ema5 (short-term), ema13 (medium-term), ema26 (long-term), and ema21 (higher timeframe confirmation).
These EMAs help determine price trends and crossovers, which are critical for identifying buy and sell opportunities.
Trend Confirmation Using a Higher Timeframe:
The 21 EMA on the higher timeframe (e.g., daily) is used to confirm the overall direction of the market.
Defining Signal Conditions:
Buy Signal:
A buy signal is generated when:
ema5 crosses above ema13 (indicating a bullish trend).
ema5 crosses above ema26 (stronger bullish confirmation).
The closing price is above ema5, ema13, ema26, and the 21 EMA on the higher timeframe.
The market's volatility (ATR) is above the defined threshold.
The volume meets the conditions or volume filtering is disabled.
Sell Signal:
A sell signal is generated when:
ema5 crosses below ema13 (indicating a bearish trend).
ema5 crosses below ema26 (stronger bearish confirmation).
The closing price is below ema5, ema13, ema26, and the 21 EMA on the higher timeframe.
The market's volatility (ATR) is above the defined threshold.
The volume meets the conditions or volume filtering is disabled.
Volume Filtering:
Ensures there’s enough trading activity by comparing the current volume to a 20-period moving average of volume.
Persistent Variables:
These variables (crossed13 and crossed13Sell) help track whether the short-term EMA (ema5) has crossed the medium-term EMA (ema13). This prevents false or repeated signals.
Displaying Signals on the Chart:
Buy signals are displayed as green upward arrows below the price.
Sell signals are displayed as red downward arrows above the price.
How It Helps Traders:
This script provides visual cues for potential entry and exit points by combining moving average crossovers, volatility, volume, and higher timeframe trend confirmation. It works well for trending markets and ensures signals are filtered for stronger conditions to reduce noise.
Chart InfoOVERVIEW
What would a general summary of the symbol on the chart look like? Here’s an example: This script was created to help you easily access the essential details of a symbol, which I believe are critical for daily use.
CONCEPTS
When using any indicator or analysing price movement, the characteristics of the chart become important. Each symbol has a unique character and the more we can quickly find out about it, the better. Instead of embedding those details within each individual indicator, it is often more practical to access these data through an external tool. This indicator presents the following results related to the symbol on your chart in a table format:
ID : Ticker ID (Exchange, Base Currency, and Quote Currency)
TIMEFRAME : The chart's time period
START : The starting date of the chart
FINISH : The finishing date of the chart
INTERVAL : The total time between the start and finish dates (based on timeframe). The current bar is not included in the total time until it is closed.
BAR INDEX : The total number of bars on the chart (can also be viewed in both forward and backward directions in the data window as a series type).
VOLATILITY : Percentage ratio of 14-bar ATR to close.
CHANGE : The daily percentage change.
HODL : The percentage return that would be gained if the symbol had been bought and held since the first bar.
DAILY BUY : The percentage return that would be gained if the same amount of buying was made daily (a kind of DCA).
MECHANICS
This is a very simple script. I didn't add user-defined timestamp inputs because I didn’t want to overwhelm the indicator with parameters. However, if requested, i can make improvements in this direction in a second version.
NOTES
I live in Istanbul, so I designed the default timezone offset as GMT+3. Please remember to adjust it according to your own timezone to ensure the date results are accurate.
I hope it helps everyone. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
Volumetric Price Delivery Bias Pro @MaxMaserati🚀 Volumetric Price Delivery Bias Pro @MaxMaserati
Description:
The Volumetric Price Delivery Bias Pro is an advanced trading indicator designed to provide clear insights into market trends, reversals, and continuations. Leveraging a combination of price action and volume analysis, it highlights critical support and resistance zones with unparalleled precision. It is a perfect blend of price action and volume intelligence.
🚀 Key Features:
Dynamic Price Analysis:
Detects key price turning points using fractal analysis.
Differentiates between bullish and bearish delivery signals for clear trend direction.
Support & Resistance Visualization:
Defense Lines: Pinpoint levels where buyers or sellers defend positions.
Zone Boxes: Highlight support/resistance areas with adjustable thresholds for precision.
Volume-Driven Confirmation:
Combines volume data to validate price levels.
Visualizes strength through dynamic box size and intensity.
⚡ Signals Explained
CDL (Change of Delivery Long): Indicates a bullish trend reversal.
CDS (Change of Delivery Short): Indicates a bearish trend reversal.
LD (Long Delivery): Confirms bullish trend continuation.
SD (Short Delivery): Confirms bearish trend continuation.
📊 Volume Strength Explained:
Volume strength = Current level volume ÷ (Average volume × Threshold).
Higher strength (above 100%) indicates stronger confirmation of support/resistance.
Boxes and lines dynamically adjust size and color to reflect strength.
🎯 Who Is It For?
This tool is ideal for scalpers, intraday traders, and swing traders who want to align their strategies with real market dynamics.
Scalpers: Identify quick reversals with shorter fractal lengths.
Intraday Traders: Spot balanced trends and continuations.
Swing Traders: Capture major market moves with higher confidence.
What to Do When Volume Strength Is Above 100%
Bullish Scenarios:
High volume at a support zone or during an upward move confirms strong buying interest.
Use it as confirmation for bullish setups.
Bearish Scenarios:
High volume at a resistance zone or during a downward move confirms strong selling pressure.
Use it as confirmation for bearish setups.
Range Markets:
High volume near range edges signals potential reversals or breakouts.
Observe price behavior to identify the likely scenario.
Breakouts:
High volume at key levels confirms the strength of a breakout.
Monitor for continuation in the breakout direction.
General Tip:
Combine high volume signals with other indicators or patterns for stronger confirmation.
🛠️ Customization Options
Configure fractal lengths, volume thresholds, and visual styles for optimal adaptability to scalping, intraday, or swing trading strategies.
Adjustable table display to track delivery bias, counts, and the latest signal.
📢 Alerts and Visuals:
Real-time alerts ensure you never miss critical signals.
Labels and lines mark CDL, CDS, LD, and SD levels for easy chart interpretation.
Monthly, Quarterly OPEX & Vix expirations
OPEX Indicator:
The OPEX indicator is designed to provide traders with a visual representation of key options expiration dates, particularly for monthly, quarterly, and VIX options expirations. This indicator can be particularly helpful for market participants who focus on options-based strategies or those who track the impact of options expiration on price action.
The indicator overlays vertical lines and labels on the chart to highlight three key types of expiration events:
Monthly Equity and Index Expiration (OPEX): This marks the standard monthly options expiration dates for equity and index options.
Quarterly Index Expiration (Q): This indicates the quarterly expiration dates for index options, which tend to have a larger impact on the market.
Monthly VIX Expiration (VIXEX): This marks the monthly expiration of VIX options and futures, which are important for volatility traders.
How to Use the OPEX Indicator:
Expiration Dates on the Chart: The OPEX indicator marks expiration dates with vertical lines and labels that appear on the chart. These are customizable, allowing you to adjust the line and label colors to suit your preferences. The lines and labels will appear at specific times, such as the closing of the market on expiration days, allowing traders to prepare for potential volatility or other market dynamics associated with these events.
Customizable Colors and Label Positions: The indicator offers flexibility in customizing the appearance of expiration lines and labels. For each expiration type (OPEX, Quarterly, and VIXEX), you can adjust the line color, label color, and label text color. Additionally, the label text size and position can be customized (e.g., above the bar, below the bar, top or bottom of the chart). This allows for a tailored display that suits your trading style and chart layout.
Visualizing Impact of Expiration Events: Traders who track the influence of expiration events can use this indicator to spot potential market moves around expiration dates. For example, significant price swings often occur near expiration days as options traders adjust their positions. With this indicator, you can visualize these dates on your chart and analyze market behavior in the lead-up to, during, and after the expirations.
Input Options:
Expiration Types:
Monthly Equity, Index Expiration (OPEX): Turn on or off the monthly equity expiration markers.
Quarterly Index Expiration (Q): Turn on or off the quarterly expiration markers.
Monthly VIX Expiration (VIXEX): Turn on or off the VIX expiration markers.
Line and Label Customization:
Line Color: Adjust the color of the vertical lines marking the expiration events.
Label Color: Customize the color of the expiration labels.
Label Text Color: Adjust the color of the text inside the labels.
Label Position: Choose the position of the labels (e.g., top, bottom, above bar, below bar).
Use Cases:
Options Traders: Track options expiration dates to assess potential price swings or liquidity changes.
Volatility Traders: Watch for patterns around VIX options expirations.
Index Traders: Monitor quarterly expirations for potential market-moving events.
Example Use:
As a trader, you can apply this indicator to your chart and observe how price action reacts near expiration dates. For instance, on the monthly OPEX expiration day, you might notice increased volatility or an uptick in options-related price moves. By observing this trend over time, you can align your trades to capitalize on predictable movements around key expiration days.
Additionally, you may use the quarterly expiration markers to assess whether there’s typically a market shift during these periods, providing insights for long-term traders.
This indicator can be a helpful tool for preparing and managing trades around critical options expiration dates, helping to forecast potential market behavior based on historical patterns.
TradingView Community Guidelines Compliance: This script complies with TradingView's community guidelines by offering a clear and valuable function for traders, providing customizable inputs for enhanced usability. The script is focused on chart visualizations without manipulating or misrepresenting market data. It serves as an educational tool and a functional indicator, with no claims or misleading functionality. The indicator does not promote financial products or services and focuses solely on charting for better trading decision-making.
IU Trailing Stop Loss MethodsThe 'IU Trailing Stop Loss Methods' it's a risk management tool which allows users to apply 12 trailing stop-loss (SL) methods for risk management of their trades and gives live alerts when the trailing Stop loss has hit. Below is a detailed explanation of each input and the working of the Script.
Main Inputs:
- bar_time: Specifies the date from which the trade begins and entry price will be the open of the first candle.
- entry_type: Choose between 'Long' or 'Short' positions.
- trailing_method: Select the trailing stop-loss method. Options include ATR, Parabolic SAR, Supertrend, Point/Pip based, Percentage, EMA, Highest/Lowest, Standard Deviation, and multiple target-based methods.
- exit_after_close: If checked, exits the trade only after the candle closes.
Optional Inputs:
ATR Settings:
- atr_Length: Length for the ATR calculation.
- atr_factor: ATR multiplier for SL calculation.
Parabolic SAR Settings:
- start, increment, maximum: Parameters for the Parabolic SAR indicator.
Supertrend Settings:
- supertrend_Length, supertrend_factor: Length and factor for the Supertrend indicator.
Point/Pip Based:
- point_base: Set trailing SL in points/pips.
Percentage Based:
- percentage_base: Set SL as a percentage of entry price.
EMA Settings:
- ema_Length: Length for EMA calculation.
Standard Deviation Settings:
- std_Length, std_factor: Length and factor for standard deviation calculation.
Highest/Lowest Settings:
- highest_lowest_Length: Length for the highest/lowest SL calculation.
Target-Based Inputs:
- ATR, Point, Percentage, and Standard Deviation based target SL settings with customizable lengths and multipliers.
Entry Logic:
- Trades initiate based on the entry_type selected and the specified bar_time.
- If Long is selected, a long trade is initiated when the conditions match, and vice versa for Short.
Trailing Stop-Loss (SL) Methods Explained:
The strategy dynamically adjusts stop-loss based on the chosen method. Each method has its calculation logic:
- ATR: Stop-loss calculated using ATR multiplied by a user-defined factor.
- Parabolic SAR: Uses the Parabolic SAR indicator for trailing stop-loss.
- Supertrend: Utilizes the Supertrend indicator as the stop-loss line.
- Point/Pip Based: Fixed point-based stop-loss.
- Percentage Based: SL set as a percentage of entry price.
- EMA: SL based on the Exponential Moving Average.
- Highest/Lowest: Uses the highest high or lowest low over a specified period.
- Standard Deviation: SL calculated using standard deviation.
Exit Conditions:
- If exit_after_close is enabled, the position will only close after the candle confirms the stop-loss hit.
- If exit_after_close is disabled, the strategy will close the trade immediately when the SL is breached.
Visualization:
The script plots the chosen trailing stop-loss method on the chart for easy visualization.
Target-Based Trailing SL Logic:
- When a position is opened, the strategy calculates the initial stop-loss and progressively adjusts it as the price moves favorably.
- Each SL adjustment is stored in an array for accurate tracking and visualization.
Alerts and Labels:
- When the Entry or trailing stop loss is hit this scripts draws a label and give alert to the user that trailing stop has been hit for the trade.
Note - on the historical data The Script will show nothing if the entry and the exit has happened on the same candle, because we don't know what was hit first SL or TP (basically how the candle was formed on the lower timeframe).
Summary:
This script offers flexible trailing stop-loss options for traders who want dynamic risk management in their strategies. By offering multiple methods like ATR, SAR, Supertrend, and EMA, it caters to various trading styles and risk preferences.
Market Volatility Momentum + Trend Filter Pro @MaxMaserati# 📊 Market Volatility Momentum + Trend Filter Pro
## 🎯 Overview
An enhanced version of the Market Momentum Indicator, combining the power of momentum analysis with adaptive volatility bands and trend filtering. This professional tool helps traders identify market direction and potential momentum shifts with greater precision.
## 🔄 Core Momentum Components
### 📈 Momentum Line
- Calculated using the midpoint between highest and lowest prices over 14 periods
- Provides a clear reference for price direction
- Acts as a dynamic support/resistance level
### 📉 Momentum Signal
- Offset from the Momentum Line by 0.25 tick size
- Creates a precise visual guide for momentum shifts
- Standard increment compatible with most markets
## 💫 Enhanced Features
### 🌊 Trend Filter
- Dynamic color-coding system showing trend strength
- Customizable length and damping parameters
- Visual identification of neutral market conditions
### 📊 Volatility Bands
- Adaptive bands that expand and contract with market volatility
- Choice between short-term and long-term trend adaptation
- Provides additional confirmation of trend strength
## 📝 Trading Signals
### 📈 Bullish Momentum
- Both momentum lines below price
- Enhanced by trend filter color confirmation
- Supported by volatility band positioning
### 📉 Bearish Momentum
- Both momentum lines above price
- Confirmed by trend filter color signals
- Reinforced by volatility band context
### ⚖️ Consolidation
- Momentum lines within price range
- Neutral trend indication with deep blue area
- Potential breakout preparation phase
## ⚙️ Multi-Timeframe Analysis
- Dual timeframe capability for comprehensive market view
- Custom timeframe selection with current chart reference
- Real-time timeframe display in top-right corner
## 🎨 Visual Features
- Dynamic bar coloring system reflecting trend strength
- Clear trend visualization through color gradients
- Optional line smoothing for reduced noise
- Customizable color schemes
## 💡 Tips for Usage
1. Monitor the position of price relative to momentum lines
2. Use trend filter colors for confirmation
3. Watch for convergence with volatility bands
4. Pay attention to neutral market signals
5. Utilize multi-timeframe analysis for better context
## ⚠️ Important Notes
- Originally designed without smoothing (smoothing optional)
- Best used with multiple timeframe analysis
- Provides clearest signals in trending markets
- Works effectively across all tradable assets
Note: Past performance doesn't guarantee future results. Always practice proper risk management and develop your trading plan.
Dual EMA Volatility Barrier | JeffreyTimmermansDual EMA Volatility Barrier
The "Dual EMA Volatility Barrier" indicator combines the power of the Double Exponential Moving Average (DEMA) with volatility-based stops to provide a robust trend-following system. This indicator helps traders identify and confirm trends, offering a way to filter out noise using volatility measures like the Average True Range (ATR) and a higher timeframe filter for additional trend validation.
Key Features
Dual Exponential Moving Average (DEMA):
DEMA Calculation: A more responsive moving average that reduces lag compared to standard EMAs. This helps detect trend changes faster.
Source Customization: Allows traders to choose the source (default is close), which can help adapt the strategy for different market conditions.
Volatility Barrier (Vstop):
Volatility-Based Stops: The Vstop is calculated using the Average True Range (ATR) multiplied by a user-defined factor. This forms a dynamic stop level that adjusts based on market volatility.
Trend Direction: The Vstop adapts to whether the market is in an uptrend or downtrend, providing a stop-loss level that moves accordingly.
Higher Timeframe Trend Filter:
Higher Timeframe DEMA: The higher timeframe filter uses a DEMA from a larger timeframe to confirm the trend direction. Only consider bullish signals if the price is above the higher timeframe DEMA.
Customizable Higher Timeframe: Traders can select any timeframe (e.g., D for daily) to check the trend from a higher perspective.
Signal Generation:
Bullish Signal: Triggered when the trend is up, and the price is above the higher timeframe DEMA, with a corresponding Vstop change indicating an upward trend.
Bearish Signal: Triggered when the trend is down and the price is below the higher timeframe DEMA, with a corresponding Vstop change indicating a downward trend.
Trend Reversals: Identifies key trend reversals by showing the transition between uptrend and downtrend states.
Plotting and Visuals:
DEMA and Vstop Plot: The indicator plots both the DEMA and the Vstop on the chart, providing a visual guide for trend and volatility.
Background Color Fill: The area between the DEMA and Vstop is filled with a color (green for bullish, red for bearish) to provide a clear visual representation of the trend.
Signal Labels: Plot arrows and labels ("Bullish" and "Bearish") directly on the chart to highlight trend changes.
Dashboard:
Ticker & Timeframe Display: The dashboard in the bottom-right corner shows the current symbol (ticker) and timeframe, along with the current trend (Bullish or Bearish).
Real-Time Updates: The dashboard updates in real time, providing traders with quick insights into the current market conditions.
Alerts:
Bullish Alert: Activated when the trend is bullish and confirmed by the higher timeframe DEMA.
Bearish Alert: Activated when the trend is bearish and confirmed by the higher timeframe DEMA.
Customizable Messages: Alerts provide details about the ticker and trend conditions for easy action.
Improvements:
Higher Timeframe Filtering: The higher timeframe DEMA filter ensures that traders align their trades with the broader market trend, improving the overall accuracy of signals.
Volatility-Based Stops: The ATR-based volatility stops allow for adaptive risk management that responds to changing market conditions.
Dynamic Signal Detection: The bullish and bearish signals change in real time, providing actionable insights for traders.
Visual and Dashboard Updates: The chart visually reflects the trend and volatility dynamics, while the dashboard provides summary information at a glance.
Customizable Alerts: Alerts based on trend changes make it easy to stay informed without constantly monitoring the chart.
Use Cases:
Trend Following: Ideal for identifying and following strong trends by combining short-term and long-term trend indicators.
Volatility-Based Risk Management: Use the Vstop to manage trade exits, as it adjusts to market volatility.
Higher Timeframe Confirmation: Use the higher timeframe DEMA to ensure that the trade aligns with the overall market trend.
Alerts for Real-Time Action: Set alerts to notify when the market signals a shift, whether bullish or bearish.
The Dual EMA Volatility Barrier is a powerful tool for traders seeking to combine trend-following with volatility management. The integration of DEMA, ATR, and a higher timeframe filter allows for a more nuanced understanding of market conditions, ensuring traders can make informed decisions with minimal lag.
This script is inspired by "viResearch" . However, it is more advanced and includes additional features and options.
-Jeffrey
MM Candle Bias Volume Trend Matrix Pro Overlay @MaxMaseratiThis overlay indicator colors candlesticks based on trend strength and volume analysis from the MMM Candle Bias Volume Trend Matrix Pro system. It uses EMAs (8, 21, 50) for trend identification and a 20-period volume average for volume confirmation.
Candlesticks are colored:
- Strong Green: Bullish trend with high volume (>1.5x average)
- Light Green: Bullish trend with normal volume
- Strong Red: Bearish trend with high volume (>1.5x average)
- Light Red: Bearish trend with normal volume
The coloring provides a visual representation of market control and trend strength, aligning with the complete Matrix Pro system's analysis framework.
SPDR Relativ Sector MomentumThe SPDR Relativ Sector Momentum Indicator is designed to evaluate the momentum of key U.S. market sectors relative to the broader market, represented by the S&P 500 Index (SPY). This indicator uses momentum-based techniques to assess sector performance and highlight relative strength or weakness over a given period. It leverages rate of change (ROC) as the primary momentum measure and incorporates smoothing via a simple moving average (SMA).
Methodology
This measure is smoothed over a configurable length (default: 20 periods) to filter noise and highlight trends. Sector momentum is computed for 11 key SPDR ETFs:
• XLE: Energy
• XLB: Materials
• XLI: Industrials
• XLY: Consumer Discretionary
• XLP: Consumer Staples
• XLV: Healthcare
• XLF: Financials
• XLK: Technology
• XLC: Communication Services
• XLU: Utilities
• XLRE: Real Estate
Momentum for the SPY is calculated similarly and serves as a benchmark.
Visualization
The indicator displays relative momentum values in a structured table, with high-contrast colors for better readability. The table dynamically updates sector performance, allowing users to easily track which sectors are outperforming or underperforming SPY. Additionally, the relative momentum values are plotted as individual lines around a zero baseline, providing visual confirmation of trends.
Applications
1. Portfolio Allocation: By identifying leading and lagging sectors, investors can allocate resources to sectors with strong momentum and reduce exposure to weaker sectors.
2. Trend Identification: The zero baseline helps users distinguish between sectors with positive and negative relative momentum.
3. Momentum Trading: The indicator aids in trading strategies that capitalize on sector rotations by highlighting momentum shifts.
Theoretical Background
Momentum strategies are grounded in behavioral finance theory and empirical research. They exploit the tendency of securities with strong past performance to continue outperforming in the short term. Jegadeesh and Titman (1993) demonstrated that momentum strategies yield significant returns over intermediate horizons (3-12 months). Applying this framework to sectors enhances the granularity of momentum analysis.
Limitations
While momentum strategies have shown historical efficacy, they are prone to mean reversion during periods of market instability (Barroso & Santa-Clara, 2015). Moreover, sector ETFs may exhibit varying levels of liquidity and sensitivity to macroeconomic factors, affecting signal reliability.
References
1. Jegadeesh, N., & Titman, S. (1993). “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” The Journal of Finance.
2. Barroso, P., & Santa-Clara, P. (2015). “Momentum Has Its Moments.” Journal of Financial Economics.
3. Moskowitz, T. J., & Grinblatt, M. (1999). “Do Industries Explain Momentum?” The Journal of Finance.
This indicator provides a practical tool for evaluating sector-specific momentum dynamics, grounded in robust financial theory. Its modular design allows customization, making it a versatile instrument for momentum-based sector analysis.
G. Santostasi's Bimodal Regimes Power Law G. Santostasi's Bimodal Regimes Power Law Model
Invite-Only TradingView Indicator
The Bimodal Power Law Model is a powerful TradingView indicator that provides a detailed visualization of Bitcoin's price behavior relative to its long-term power law trend. By leveraging volatility-normalized deviations, this model uncovers critical upper and lower bounds that govern Bitcoin’s price dynamics.
Key Features:
Power Law Support Line:
The model highlights the power law support line, a natural lower bound that has consistently defined Bitcoin's price floor over time. This line provides a crucial reference point for identifying accumulation zones.
Volatility-Normalized Upper Bound:
The indicator introduces a volatility-normalized upper channel, dynamically defined by the deviations from the power law. This bound represents the natural ceiling for Bitcoin’s price action and adjusts in real time to reflect changes in market volatility.
Color-Shaded Volatility Bounds:
The upper and lower bounds are visualized as color-shaded regions that represent the range of current volatility relative to the power law trend. These shaded regions dynamically expand or contract based on the level of market volatility, providing an intuitive view of Bitcoin’s expected price behavior under normalized conditions.
Two Regime Analysis:
Using a Gaussian Hidden Markov Model (HMM), the indicator separates Bitcoin's price action into two distinct regimes:
Above the power law:
Bullish phases characterized by overextensions.
Below the power law:
Bearish or accumulation phases where price consolidates below the trend.
Dynamic Bounds with Standard Deviations:
The model plots 2 standard deviation bands for both regimes, offering precise insights into the natural limits of Bitcoin’s price fluctuations. Peaks exceeding these bounds are contextualized as anomalies caused by historically higher volatility, emphasizing the consistency of normalized deviations.
Enhanced Visualization and Analysis:
The indicator integrates running averages calculated using deviations from the power law trend and smoothed volatility data to ensure a visually intuitive representation of Bitcoin’s price behavior. These insights help traders and researchers identify when price action is approaching statistically significant levels.
Use Cases:
Support and Resistance Identification:
Use the power law support line and upper volatility bounds to identify critical levels for buying or taking profit.
Cycle Analysis:
Distinguish between sustainable trends and speculative bubbles based on deviations from the power law.
Risk Management:
The shaded volatility regions provide a dynamic measure of risk, helping traders gauge when Bitcoin is overbought or oversold relative to its historical norms.
Market Timing: Understand Bitcoin’s cyclical behavior to time entries and exits based on its position within the shaded bounds.
Note:
This indicator is designed for long-term Bitcoin investors, researchers, and advanced traders who seek to leverage statistical regularities in Bitcoin’s price behavior. Available by invitation only.
Candle 1 2 3 on XAUUSD (by Veronica)Description
Discover the Candle 1 2 3 Strategy, a simple yet effective trading method tailored exclusively for XAUUSD on the 15-minute timeframe. Designed by Veronica, this strategy focuses on identifying key reversal and continuation patterns during the London and New York sessions, making it ideal for traders who prioritise high-probability entries during these active market hours.
Key Features:
1. Session-Specific Trading:
The strategy operates strictly during London (03:00–06:00 UTC) and New York (08:30–12:30 UTC) sessions, where XAUUSD tends to show higher volatility and clearer price movements.
Pattern Criteria:
- Works best if the first candle is NOT a pin bar or a doji.
- Third candle should either:
a. Be a marubozu (large body with minimal wicks).
a. Have a significant body with wicks, ensuring the close of the third candle is above Candle 2 (for Buy) or below Candle 2 (for Sell).
Callout Labels and Alerts:
Automatic Buy and Sell labels are displayed on the chart during qualifying sessions, ensuring clarity for decision-making.
Integrated alerts notify you of trading opportunities in real-time.
Risk Management:
Built-in Risk Calculator to estimate lot sizes based on your account size, risk percentage, and stop-loss levels.
Customizable Table:
Displays your calculated lot size for various stop-loss pip values, making risk management seamless and efficient.
How to Use:
1. Apply the indicator to XAUUSD (M15).
2. Focus on setups appearing within the London and New York sessions only.
3. Ensure the first candle is neither a pin bar nor a doji.
4. Validate the third candle's body placement:
For a Buy, the third candle’s close must be above the second candle.
For a Sell, the third candle’s close must be below the second candle.
5. Use the generated alerts to streamline your entry process.
Notes:
This strategy is meant to complement your existing knowledge of market structure and price action.
Always backtest thoroughly and adjust parameters to fit your personal trading style and risk tolerance.
Credit:
This strategy is the intellectual property of Veronica, developed specifically for XAUUSD (M15) traders seeking precision entries during high-volume sessions.
Best of Option Indicator - Manoj WadekarPlot this indicator for both CALL and PUT options and buy only when color of candle is YELLOW and above BLACK line.
4EMAs+OpenHrs+FOMC+CPIThis script displays 4 custom EMAs of your choice based on the Pine script standard ema function.
Additionally the following events are shown
1. Opening hours for New York Stock exchange
2. Opening Time for London Stock exchange
3. US CPI Release Dates
4. FOMC press conference dates
5. FOMC meeting minutes release dates
I have currently added FOMC and CPI Dates for 2025 but will keep updating in January of every year (at least as long as I stay in the game :D)
India VIXThe VIX chart represents the Volatility Index, commonly referred to as the "Fear Gauge" of the stock market. It measures the market's expectations of future volatility over the next 30 days, based on the implied volatility of NSE index options. The VIX is often used as an indicator of investor sentiment, reflecting the level of fear or uncertainty in the market.
Here’s a breakdown of what you might observe on a typical VIX chart:
VIX Value: The y-axis typically represents the VIX index value, with higher values indicating higher levels of expected market volatility (more fear or uncertainty), and lower values signaling calm or stable market conditions.
VIX Spikes: Large spikes in the VIX often correspond to market downturns or periods of heightened uncertainty, such as during financial crises or major geopolitical events. A high VIX is often associated with a drop in the stock market.
VIX Drops: A decline in the VIX indicates a reduction in expected market volatility, usually linked with periods of market calm or rising stock prices.
Trend Analysis: Technical traders might use moving averages or other indicators on the VIX chart to assess the potential for future market movements.
Inverse Relationship with the Stock Market: Typically, there is an inverse correlation between the VIX and the stock market. When stocks fall sharply, volatility increases, and the VIX tends to rise. Conversely, when the stock market rallies or remains stable, the VIX tends to fall.
A typical interpretation would be that when the VIX is low, the market is relatively stable, and when the VIX is high, the market is perceived to be uncertain or volatile.
Combined Multi-Timeframe EMA OscillatorThis script aims to visualize the strength of bullish or bearish trends by utilizing a mix of 200 EMA across multiple timeframes. I've observed that when the multi-timeframe 200 EMA ribbon is aligned and expanding, the uptrend usually lasts longer and is safer to enter at a pullback for trend continuation. Similarly, when the bands are expanding in reverse order, the downtrend holds longer, making it easier to sell the pullbacks.
In this script, I apply a purely empirical and experimental method: a) Ranking the position of each of the above EMAs and turning it into an oscillator. b) Taking each 200 EMA on separate timeframes, turning it into a stochastic-like oscillator, and then averaging them to compute an overall stochastic.
To filter a bullish signal, I use the bullish crossover between these two aggregated oscillators (default: yellow and blue on the chart) which also plots a green shadow area on the screen and I look for buy opportunities/ ignore sell opportunities while this signal is bullish. Similarly, a bearish crossover gives us a bearish signal which also plots a red shadow area on the screen and I only look for sell opportunities/ ignore any buy opportunities while this signal is bearish.
Note that directly buying the signal as it prints can lead to suboptimal entries. The idea behind the above is that these crossovers point on average to a stronger trend; however, a trade should be initiated on the pullbacks with confirmation from momentum and volume indicators and in confluence with key areas of support and resistance and risk management should be used in order to protect your position.
Disclaimer: This script does not constitute certified financial advice, the current work is purely experimental, use at your own discretion.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
.
(If you are comfortable with statistics feel free to skip ahead to the next section)
.
-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
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---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
