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.
Educational
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.
00 Averaging Down Backtest Strategy by RPAlawyer v21FOR EDUCATIONAL PURPOSES ONLY! THE CODE IS NOT YET FULLY DEVELOPED, BUT IT CAN PROVIDE INTERESTING DATA AND INSIGHTS IN ITS CURRENT STATE.
This strategy is an 'averaging down' backtester strategy. The goal of averaging/doubling down is to buy more of an asset at a lower price to reduce your average entry price.
This backtester code proves why you shouldn't do averaging down, but the code can be developed (and will be developed) further, and there might be settings even in its current form that prove that averaging down can be done effectively.
Different averaging down strategies exist:
- Linear/Fixed Amount: buy $1000 every time price drops 5%
- Grid Trading: Placing orders at price levels, often with increasing size, like $1000 at -5%, $2000 at -10%
- Martingale: doubling the position size with each new entry
- Reverse Martingale: decreasing position size as price falls: $4000, then $2000, then $1000
- Percentage-Based: position size based on % of remaining capital, like 10% of available funds at each level
- Dynamic/Adaptive: larger entries during high volatility, smaller during low
- Logarithmic: position sizes increase logarithmically as price drops
Unlike the above average costing strategies, it applies averaging down (I use DCA as a synonym) at a very strong trend reversal. So not at a certain predetermined percentage negative PNL % but at a trend reversal signaled by an indicator - hence it most closely resembles a dynamically moving grid DCA strategy.
Both entering the trade and averaging down assume a strong trend. The signals for trend detection are provided by an indicator that I published under the name '00 Parabolic SAR Trend Following Signals by RPAlawyer', but any indicator that generates numeric signals of 1 and -1 for buy and sell signals can be used.
The indicator must be connected to the strategy: in the strategy settings under 'External Source' you need to select '00 Parabolic SAR Trend Following Signals by RPAlawyer: Connector'. From this point, the strategy detects when the indicator generates buy and sell signals.
The strategy considers a strong trend when a buy signal appears above a very conservative ATR band, or a sell signal below the ATR band. The conservative ATR is chosen to filter ranging markets. This very conservative ATR setting has a default multiplier of 8 and length of 40. The multiplier can be increased up to 10, but there will be very few buy and sell signals at that level and DCA requirements will be very high. Trade entry and DCA occur at these strong trends. In the settings, the 'ATR Filter' setting determines the entry condition (e.g., ATR Filter multiplier of 9), and the 'DCA ATR' determines when DCA will happen (e.g., DCA ATR multiplier of 6).
The DCA levels and DCA amounts are determined as follows:
The first DCA occurs below the DCA Base Deviation% level (see settings, default 3%) which acts as a threshold. The thick green line indicates the long position avg price, and the thin red line below the green line indicates the 3% DCA threshold for long positions. The thick red line indicates the short position avg price, and the thin red line above the thick red line indicates the short position 3% DCA threshold. DCA size multiplier defines the DCA amount invested.
If the loss exceeds 3% AND a buy signal arrives below the lower ATR band for longs, or a sell signal arrives above the upper ATR band for shorts, then the first DCA will be executed. So the first DCA won't happen at 3%, rather 3% is a threshold where the additional condition is that the price must close above or below the ATR band (let's say the first DCA occured at 8%) – this is why the code resembles a dynamic grid strategy, where the grid moves such that alongside the first 3% threshold, a strong trend must also appear for DCA. At this point, the thick green/red line moves because the avg price is modified as a result of the DCA, and the thin red line indicating the next DCA level also moves. The next DCA level is determined by the first DCA level, meaning modified avg price plus an additional +8% + (3% * the Step Scale Multiplier in the settings). This next DCA level will be indicated by the modified thin red line, and the price must break through this level and again break through the ATR band for the second DCA to occur.
Since all this wasn't complicated enough, and I was always obsessed by the idea that when we're sitting in an underwater position for days, doing DCA and waiting for the price to correct, we can actually enter a short position on the other side, on which we can realize profit (if the broker allows taking hedge positions, Binance allows this in Europe).
This opposite position in this strategy can open from the point AFTER THE FIRST DCA OF THE BASE POSITION OCCURS. This base position first DCA actually indicates that the price has already moved against us significantly so time to earn some money on the other side. Breaking through the ATR band is also a condition for entry here, so the hedge position entry is not automatic, and the condition for further DCA is breaking through the DCA Base Deviation (default 3%) and breaking through the ATR band. So for the 'hedge' or rather opposite position, the entry and further DCA conditions are the same as for the base position. The hedge position avg price is indicated by a thick black line and the Next Hedge DCA Level is indicated by a thin black line.
The TPs are indicated by green labels for base positions and red labels for hedge positions.
No SL built into the strategy at this point but you are free to do your coding.
Summary data can be found in the upper right corner.
The fantastic trend reversal indicator Machine learning: Lorentzian Classification by jdehorty can be used as an external indicator, choose 'backtest stream' for the external source. The ATR Band multiplicators need to be reduced to 5-6 when using Lorentz.
The code can be further developed in several aspects, and as I write this, I already have a few ideas 😊
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 ----------------
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(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- 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 :)
RSI Team Synergy | JeffreyTimmermansRSI Team Synergy
The "RSI Team Synergy" indicator is an advanced and highly customizable tool that integrates a Double RSI (DRSI) approach for comprehensive trend and momentum analysis. It utilizes two layers of RSI calculations, along with optional smoothing and various moving average types, to enhance signal accuracy. The dynamic visuals and alerts make this indicator a valuable resource for traders aiming to optimize their strategies.
Key Features
Double RSI (DRSI) Calculation
First RSI (Primary Layer): Captures the core price momentum using a configurable period.
Second RSI (DRSI Layer): Applies a second RSI calculation to the smoothed first RSI, refining signals and amplifying trend accuracy.
Double RSI Formula: Combines the smoothed RSI layers into a single robust indicator that adapts to market conditions.
Smoothing and Advanced Moving Averages
Optional Smoothing: Enables users to reduce noise by applying smoothing to both RSI layers.
Advanced MA Options: Supports multiple MA types, including SMA, EMA, WMA, RMA, DEMA, TEMA, VWMA, ZLEMA, and HMA. These can be applied to tailor the indicator to specific trading conditions.
Separate Configurations: Independent smoothing lengths and types for each RSI layer provide unparalleled customization.
Threshold and Signal System
Long Threshold: Highlights bullish conditions when the Double RSI exceeds the threshold.
Short Threshold: Signals bearish conditions when the Double RSI falls below the threshold.
Directional State: Tracks the overall direction using a state-based signal system (bullish, bearish, or neutral).
Dynamic Visualization
Oscillator Color Coding: Green shades for bullish momentum. Red shades for bearish momentum. Dynamic gradients for smoother transitions.
Glow Effect: Optional glowing lines enhance the visual clarity of the oscillator.
Threshold Lines: Configurable dashed horizontal lines to mark critical levels for easy reference.
Bar Color Integration
Bar Coloring: Matches bar colors to the oscillator's direction for cohesive visualization.
Advanced Control: Toggle bar coloring on/off without affecting other plots.
Alerts
Bullish Signal Alert: Triggers when the Double RSI crosses above the long threshold.
Bearish Signal Alert: Triggers when the Double RSI crosses below the short threshold.
Custom Messages: Alerts are equipped with descriptive messages for actionable insights.
Signal Arrows
Bullish Arrow (↑): Marks upward trends directly on the chart.
Bearish Arrow (↓): Highlights downward trends, ensuring traders never miss an opportunity.
Improvements
Customizable Thresholds: Adjustable long and short thresholds allow traders to fine-tune sensitivity.
Enhanced Smoothing Control: Separate smoothing options for each RSI layer provide flexibility in noise reduction.
Multiple MA Types: Extensive support for advanced moving averages to suit diverse trading preferences.
Color-Coded Oscillator: Improves trend visibility with gradient-based coloring and optional glow effects.
Signal Detection: Clear and intuitive arrows directly on the chart for quick signal interpretation.
Alerts and Notifications: Comprehensive alert conditions keep traders informed in real-time.
Use Cases
Momentum Analysis: Identify sustained bullish or bearish trends using the Double RSI calculation.
Noise Reduction: Utilize smoothing and advanced MA options to remove market noise.
Reversal Detection: Spot potential trend reversals with threshold-based signals.
Customizable Strategies: Tailor the indicator for scalping, swing trading, or long-term analysis.
The RSI Team Synergy indicator combines precision, flexibility, and intuitive design, making it an essential tool for traders at all levels. With its innovative Double RSI approach and advanced customization options, it provides actionable insights for mastering market trends.
This script is inspired by "Clokivez" . However, it is more advanced and includes additional features and options.
-Jeffrey
ATR-Based Suitability CheckerPurpose:
This indicator helps traders identify the most suitable timeframe for trading by comparing fees to market volatility (ATR). Instead of filtering out specific assets or strategies, it focuses on finding optimal trading conditions for the selected timeframe. It is designed to adapt dynamically, ensuring that traders can align their approach with the current market environment.
Key Features:
Dynamic ATR Analysis: Measures volatility using the Average True Range (ATR) and evaluates how fees impact potential profitability across timeframes.
Fee-to-ATR Ratio: Calculates the proportion of fees to ATR, highlighting conditions where fees are too large relative to price movements.
Visual Feedback: **Red Background:** Indicates unsuitable trading conditions where fees dominate. **Green Background:** Highlights suitable conditions for trading efficiency. Markers provide quick visual identification of suitability.
Custom Transparency: Enables users to adjust the background’s transparency for better chart visibility.
How to Use:
Timeframe Optimization: When volatility rises, price movements become larger, making shorter timeframes more suitable for trading. Conversely, during periods of low volatility, longer timeframes are preferable to avoid overtrading within a narrow price range.
Spot & Leverage Trading: For spot trading, this tool identifies conditions where fees (e.g., 0.25%-0.3%) might excessively impact profitability. For instance, if ATR is comparable to fees, the trading environment may not be ideal. In leveraged trading, the indicator helps assess whether the current volatility supports your chosen leverage level, ensuring that leverage does not amplify undue risks.
Efficiency Focus: The indicator emphasizes finding a balance between market conditions and your trading strategy. Not all timeframes need to be "suitable" at all times; instead, it highlights the best opportunities based on current market dynamics. Utilize the suitability ratio across different timeframes to guide and adjust your trading strategies effectively.
Input Parameters:
ATR Length: Defines the period for ATR calculation (default: 14).
Fee Percentage (%): Trading fee as a percentage of the closing price (default: 0.1%).
Unsuitable Threshold (% of 1 ATR): Sets the maximum acceptable fee-to-ATR ratio for suitable conditions (default: 20%).
Background Transparency (0-100): Adjusts the opacity of the background highlight (default: 80).
Who Should Use This:
This tool is ideal for traders seeking to align their strategy with market conditions by finding the most suitable timeframe. It applies to both spot and leveraged markets, helping optimize efficiency while managing fees and volatility.
Notes:
The ATR-Based Suitability Checker is a supplementary tool. Combine it with other forms of analysis for comprehensive decision-making.
Regularly adjust the parameters to match your trading preferences and market conditions.
Consistency Rule CalculatorThis script, titled "Consistency Rule Calculator" is designed for use on the TradingView platform. It allows traders to input specific values related to their account, daily highest profit, and a consistency rule (as a decimal).
The script then calculates the "Amount Needed to Withdraw" based on the user's input. This value is calculated using the formula:
Amount Needed to Withdraw = (Daily Highest Profit/Consistency Rule )+ Account Type
Each prop firm has its own consistency rule. Follow their rule, and you will be second to payout!
Additionally, it displays the input values and the calculated amount in a customizable table on the chart. The table is formatted with colors for clarity, and it provides a motivational quote about successful trading. Plus, user can adjust the table's position on the screen.
Year-over-Year % Change for PCEPILFEHello, traders!
This indicator is specifically for FRED:PCEPILFE , which is a 'Personal Consumption Expenditures (PCE) Index excluding food and energy.'
What this indicator does is compare the monthly data to that of the same month last year to see how it has changed over the year. This comparison method is widely known as YoY(Year-over-Year).
While I made this indicator to use for FRED:PCEPILFE , you may use it for different charts as long as they show monthly data.
FRED:PCEPILFE is one of the main measures of inflation the Federal Reserve uses.
You can see the YoY % change of the PCE Index excluding food and energy in the official website for the Bureau of Labor Statistics, but unfortunately, I couldn't find one in TradingView.
So instead, I decided to make my own indicator showing the changes using FRED:PCEPILFE .
The code is very simple: it compares the data to the data 12 points ago because 12 points would mean 12 months in this chart. We then multiply the result by 100 for percentage.
Doing so, we compare the current month to the same month of the previous year.
Because I am only interested in the YoY % Change of the index, I pulled the indicator all the way up, covering the original chart data entirely. (Or you could achieve the same by simply moving your indicator to the pane above. But this way, the original chart data is also visible.)
I hope this indicator helps you with your analysis. Feel free to ask questions if have any!
God bless!
Compare Symbol [LuxmiAI]This indicator allows users to plot candles or bars for a selected symbol and add a moving average of their choice as an underlay. Users can customize the moving average type and length, making it versatile for a wide range of trading strategies.
This script is designed to offer flexibility, letting traders select the symbol, timeframe, candle style, and moving average type directly from the input options. The moving averages include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA).
Features of the Script
This indicator provides the following key features:
1. Symbol Selection: Users can input the ticker symbol for which they want to plot the data.
2. Timeframe Selection: The script allows users to choose a timeframe for the symbol data.
3. Candle Styles: Users can select from three styles - regular candles, bars, or Heikin-Ashi candles.
4. Moving Average Options: Users can choose between EMA, SMA, WMA, and VWMA for added trend analysis.
5. Customizable Moving Average Length: The length of the moving average can be adjusted to suit individual trading strategies.
How the Script Works
The script starts by taking user inputs for the symbol and timeframe. It then retrieves the open, high, low, and close prices of the selected symbol and timeframe using the request.security function. Users can select between three candle styles: standard candles, bars, and Heikin-Ashi candles. If Heikin-Ashi candles are selected, the script calculates the Heikin-Ashi open, high, low, and close values.
To add further analysis capabilities, the script includes a moving average. Traders can select the moving average type from EMA, SMA, WMA, or VWMA and specify the desired length. The selected moving average is then plotted on the chart to provide a clear visualization of the trend.
Step-by-Step Implementation
1. Input Options: The script starts by taking inputs for the symbol, timeframe, candle style, moving average type, and length.
2. Data Retrieval: The script fetches OHLC data for the selected symbol and timeframe using request.security.
3. Candle Style Logic: It determines which candle style to plot based on the user’s selection. If Heikin-Ashi is selected, the script calculates Heikin-Ashi values.
4. Moving Average Calculation: Depending on the user’s choice, the script calculates the selected moving average.
5. Visualization: The script plots the candles or bars and overlays the moving average on the chart.
Benefits of Using This Indicator
This custom indicator provides multiple benefits for traders. It allows for quick comparisons between symbols and timeframes, helping traders identify trends and patterns. The flexibility to choose different candle styles and moving averages enhances its adaptability to various trading strategies. Additionally, the ability to customize the moving average length makes it suitable for both short-term and long-term analysis.
Session Bar/Candle ColoringChange the color of candles within a user-defined trading session. Borders and wicks can be changed as well, not just the body color.
PREFACE
This script can be used an educational resource for those who are interested in learning Pine Script. Therefore, the script is published open source and is organized in a manner that follows the recommended Style Guide .
While the main premise of the indicator is rather simple, the script showcases various things that can be achieved such as conditional plotting, alignment of indicator settings, user input validation, script optimization, and more. The script also has examples of taking into consideration the chart timeframe and/or different chart types (Heikin Ashi, Renko, etc.) that a user might be running it on. Note: for complete beginners, I strongly suggest going through the Pine Script User Manual (possibly more than once).
FEATURES
Besides being able to select a specific time window, the indicator also provides additional color settings for changing the background color or changing the colors of neutral/indecisive candles, as shown in the image below.
This allows for a higher level of customization beyond the TradingView chart settings or other similar scripts that are currently available.
HOW TO USE
First, define the intraday trading session that will contain the candles to modify. The session can be limited to specific days of the week.
Next, select the parts of the candles that should be modified: Body, Borders, Wick, and/or Background.
For each of the candle parts that were enabled, you can select the colors that will be used depending on whether a candle is bullish (⇧), bearish (⇩), or neutral (⇆).
All other indicator settings will have a detailed tooltip to describe its usage and/or effect.
LIMITATIONS
The indicator is not intended to function on Daily or higher timeframes due to the intraday nature of session time windows.
The indicator cannot always automatically detect the chart type being used, therefore the user is requested to manually input the chart type via the " Chart Style " setting.
Depending on the available historical data and the selected choice for the " Portion of bar in session " setting, the indicator may not be able to update very old candles on the chart.
EXAMPLE USAGE
This section will show examples of different scenarios that the indicator can be used for.
Emphasizing a main trading session.
Defining a "Pre/post market hours background" like is available for some symbols (e.g., NASDAQ:AAPL ).
Highlighting in which bar the midnight candle occurs.
Hiding indecision bars (neutral candles).
Showing only "Regular Trading Hours" for a chart that does not have the option to toggle ETH/RTH. To achieve this, the actual chart data is hidden, and only the indicator is visible; alternatively, a 2nd instance of the indicator could change colors to match the chart background.
Using a combination of Bars and Japanese Candlesticks. Alternatively, this could be done by hiding the main chart data and using 2 instances of the indicator (one with " Chart Style " setting as Bars , and the other set to Candles ).
Using a combination of thin and thick bars on Range charts. Note: requires disabling the "Thin Bars" setting for Bar charts in the TradingView chart settings.
NOTES
If using more than one instance of this indicator on the same chart, you can use the TradingView "Save Indicator Template" feature to avoid having to re-configure the multiple indicators at a later time.
This indicator is intended to work "out-of-the-box" thanks to the behind_chart option introduced to Pine Script in October 2024. But you can always manually bring the indicator to the front just in case the color changes are not being seen (using the "More" option in the indicator status line: More > Visual Order > Bring to front ).
Many thanks to fikira for their help and inspiring me to create open source scripts.
Any feedback including bug reports or suggestions for improving the indicator (or source code itself) are always welcome in the comments section.
Trend Force Meter | JeffreyTimmermansTrend Force Meter
The "Trend Force Meter" is an innovative trading tool designed to visualize trend strength and provide precise signals for identifying market dynamics. By combining the Hull Moving Average (HMA) with the Simple Moving Average (SMA), it delivers a comprehensive analysis of trend forces and directions. With customizable smoothing, low-pass filtering, and an advanced color-coded display, this indicator is a valuable addition to any trader's toolkit.
Overview
The Trend Force Meter uses a unique approach to trend analysis by calculating the difference between smoothed HMA and SMA values. This difference is normalized and converted into a visually intuitive gradient to represent bullish and bearish conditions. The indicator also incorporates features for noise reduction and enhanced visualization.
Key Features
Dual Moving Averages
Hull Moving Average (HMA): Provides a highly responsive measure of trend direction and strength.
Simple Moving Average (SMA): Offers a stable and reliable long-term trend baseline.
Customizable Smoothing
Enable/Disable Smoothing: Adjust the sensitivity of the HMA and SMA calculations.
Smoothing Length: Fine-tune the smoothing parameters to match your trading style, balancing between responsiveness and stability.
Low-Pass Filtering
Noise Reduction: Optional low-pass filter reduces market noise, providing clearer trend signals.
Filter Length: Adjustable parameter for fine control over the noise reduction level.
Gradient-Based Visualization
Dynamic Color Coding: Bullish trends are displayed in shades of green, while bearish trends appear in shades of red, providing immediate visual clarity.
Strength Meter: A gradient-based strength meter quantifies the intensity of the current trend, from weak to strong.
Trend Strength Normalization
Normalizes trend strength over a configurable period, ensuring consistent and meaningful readings across various market conditions.
Alerts
Bullish Trend Alert: Notifies when the trend transitions to a bullish phase.
Bearish Trend Alert : Signals when the trend turns bearish.
Enhanced Functionality
Trend Strength Gauge
Displays a real-time strength gauge that visualizes the trend intensity, allowing traders to assess the market at a glance.
Automatically adjusts to reflect normalized trend values, ensuring accuracy across different timeframes and volatility conditions.
Visual Gradient
A refined gradient coloring system dynamically adjusts based on trend direction and intensity, enabling traders to easily interpret market sentiment.
Advanced Customization
Length Settings: Fine-tune HMA and SMA lengths to match specific trading strategies.
Smoothing Options: Toggle smoothing and low-pass filtering on or off as needed.
Gradient Color Range: Provides flexible options for customizing the visual display.
Use Cases
Trend Analysis: Quickly identify the direction and strength of market trends to make informed trading decisions.
Momentum Confirmation : Use the gradient and strength meter to validate potential breakout or reversal scenarios.
Noise Reduction: Employ the low-pass filter to focus on meaningful trends while ignoring short-term market fluctuations.
How It Works
Calculate HMA and SMA: The indicator computes smoothed HMA and SMA values.
Difference Extraction: The difference between the smoothed HMA and SMA forms the core trend signal.
Optional Filtering: Low-pass filtering reduces noise, enhancing the clarity of trend signals.
Normalization: The difference is normalized over the selected period, ensuring consistent scaling.
Visualization: A color-coded gradient and trend strength gauge display the trend’s intensity and direction.
Customization Options
MA Lengths: Adjust the calculation periods for HMA and SMA.
Smoothing and Filtering: Enable or disable smoothing and filtering to refine the signal output.
Color Palette: Choose custom colors to align with personal preferences or trading environments.
Conclusion
The Trend Force Meter is an invaluable addition to any trader’s toolkit, combining cutting-edge techniques with intuitive visuals to make trend analysis more accessible and actionable. Its flexibility and precision cater to various trading strategies, ensuring traders stay ahead of market movements.
This script is inspired by "VanHels1ng" . However, it is more advanced and includes additional features and options.
-Jeffrey
QuantFrame | FractalystWhat’s the purpose of this indicator?
The purpose of QuantFrame is to provide traders with a systematic approach to analyzing market structure, eliminating subjectivity, and enhancing decision-making. By clearly identifying and labeling structural breaks, QuantFrame helps traders:
1. Refine Market Analysis: Transition from discretionary market observation to a structured framework.
2. Identify Key Levels: Highlight important liquidity and invalidation zones for potential entries, exits, and risk management.
3. Streamline Multi-Timeframe Analysis: Track market trends and structural changes across different timeframes seamlessly.
4. Enhance Consistency: Reduce guesswork by following a rule-based methodology for identifying structural breaks.
How Does This Indicator Identify Market Structure?
1. Swing Detection
• The indicator identifies key swing points on the chart. These are local highs or lows where the price reverses direction, forming the foundation of market structure.
2. Structural Break Validation
• A structural break is flagged when a candle closes above a previous swing high (bullish) or below a previous swing low (bearish).
• Break Confirmation Process:
To confirm the break, the indicator applies the following rules:
• Valid Swing Preceding the Break: There must be at least one valid swing point before the break.
3. Numeric Labeling
• Each confirmed structural break is assigned a unique numeric ID starting from 1.
• This helps traders track breaks sequentially and analyze how the market structure evolves over time.
4. Liquidity and Invalidation Zones
• For every confirmed structural break, the indicator highlights two critical zones:
1. Liquidity Zone (LIQ): Represents the structural liquidity level.
2. Invalidation Zone (INV): Acts as Invalidation point if the structure fails to hold.
What do the extremities show us on the charts?
When using QuantFrame for market structure analysis, the extremities—Liquidity Level (LIQ) and Invalidation Level (INV)—serve as critical reference points for understanding price behavior and making informed trading decisions.
Here's a detailed explanation of what these extremities represent and how they function:
Liquidity Level (LIQ)
Definition: The Liquidity Level is a key price zone where the market is likely to retest, consolidate, or seek liquidity. It represents areas where orders are concentrated, making it a high-probability reaction zone.
Purpose: Traders use this level to anticipate potential pullbacks or continuation patterns. It helps in identifying areas where price may pause or reverse temporarily due to the presence of significant liquidity.
Key Insight: If a candle closes above or below the LIQ, it results in another break of structure (BOS) in the same direction. This indicates that price is continuing its trend and has successfully absorbed liquidity at that level.
Invalidation Level (INV)
Definition: The Invalidation Level marks the threshold that, if breached, signifies a structural shift in the market. It acts as a critical point where the current market bias becomes invalid.
Purpose: This level is often used as a stop-loss or re-evaluation point for trading strategies. It ensures that traders have a clear boundary for risk management.
Key Insight: If a candle closes above or below the INV, it signals a shift in market structure:
A closure above the INV in a bearish trend indicates a shift from bearish to bullish bias.
A closure below the INV in a bullish trend indicates a shift from bullish to bearish bias.
What does the top table display?
The top table in QuantFrame serves as a multi-timeframe trend overview. Here’s what it provides:
1. Numeric Break IDs Across Multiple Timeframes:
• Each numeric break corresponds to a confirmed structural break on a specific timeframe, helping traders track the most recent breaks systematically.
2. Trend Direction via Text Color:
• The color of the text reflects the current trend direction:
• Blue indicates a bullish structure.
• Red signifies a bearish structure.
3. Higher Timeframe Insights Without Manual Switching:
• The table eliminates the need to switch between timeframes by presenting a consolidated view of the market trend across multiple timeframes, saving time and improving decision-making.
What is the Multi-Timeframe Trend Score (MTTS)?
MTTS is a score that quantifies trend strength and direction across multiple timeframes.
How does MTTS work?
1. Break Detection:
• Analyzes bullish and bearish structural breaks on each timeframe.
2. Trend Scoring:
• Scores each timeframe based on the frequency and quality of bullish/bearish breaks.
3. MTTS Calculation:
• Averages the scores across all timeframes to produce a unified trend strength value.
How is MTTS interpreted?
• ⬆ (Above 50): Indicates an overall bullish trend.
• ⬇ (Below 50): Suggests an overall bearish trend.
• ⇅ (Exactly 50): Represents a neutral or balanced market structure.
How to Use QuantFrame?
1. Implement a Systematic Market Structure Framework:
• Use QuantFrame to analyze market structure objectively by identifying key structural breaks and marking liquidity (LIQ) and invalidation (INV) zones.
• This eliminates guesswork and provides a clear framework for understanding market movements.
2. Leverage MTTS for Directional Bias:
• Refer to the MTTS table to identify the multi-timeframe directional bias, giving you the broader market context.
• Align your trading decisions with the overall trend or structure to improve accuracy and consistency.
3. Apply Your Preferred Entry Model:
• Once the market context is clear, use your preferred entry model to capitalize on the identified structure and trend.
• Manage trades dynamically as price delivers, using the provided liquidity and invalidation zones for risk management.
What Makes QuantFrame Original?
1. Objective Market Structure Analysis:
• Unlike subjective methods, QuantFrame uses a rule-based approach to identify structural breaks, ensuring consistency and reducing emotional decision-making.
2. Multi-Timeframe Integration:
• The MTTS table consolidates trend data across multiple timeframes, offering a bird’s-eye view of market trends without the need to switch charts manually.
• This unique feature allows traders to align strategies with higher-timeframe trends for more informed decision-making.
3. Liquidity and Invalidation Zones:
• Automatically marks Liquidity (LIQ) and Invalidation (INV) zones for every structural break, providing actionable levels for entries, exits, and risk management.
• These zones help traders define their risk-reward setups with precision.
4. Dynamic Trend Scoring (MTTS):
• The Multi-Timeframe Trend Score (MTTS) quantifies trend strength and direction across selected timeframes, offering a single, consolidated metric for market sentiment.
• This score is visualized with intuitive symbols (⬆, ⬇, ⇅) for quick decision-making.
5. Numeric Labeling of Breaks:
• Each structural break is assigned a unique numeric ID, making it easy to track, analyze, and backtest specific market scenarios.
6. Systematic Yet Flexible:
• While it provides a structured framework for market analysis, QuantFrame seamlessly integrates with any trading style. Traders can use it alongside their preferred entry models, adapting it to their unique strategies.
7. Enhanced Market Context:
• By combining structural insights with directional bias (via MTTS), the indicator equips traders with a complete market context, enabling them to make better-informed decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
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