PRC-ALMA | QuantEdgeBIntroducing PRC-ALMA by QuantEdgeB
Overview
The PRC-ALMA (Percentile Adaptive ALMA) is an advanced dynamic trend and volatility filtering indicator that leverages the Arnaud Legoux Moving Average (ALMA) combined with Percentile Rank Filtering and Median Absolute Deviation (MAD) Bands. It is designed to enhance market structure clarity, detect breakout zones, and provide trade signals by dynamically adjusting its filtering based on recent price action.
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Key Features
1. 📈 Adaptive ALMA Smoothing:
- Uses ALMA for smoothing price action while reducing lag.
- Provides a more responsive moving average than traditional EMAs and SMAs.
2. 📊 Percentile Rank-Based Thresholds:
- Determines upper and lower regions using 75th and 25th percentile ranks.
- Allows for adaptive thresholding based on historical price movements.
3. 🎯 Median Absolute Deviation (MAD) Volatility Filtering:
- Filters out noise using robust statistical deviation measures.
- MAD Bands dynamically adjust based on volatility expansion and contraction.
4. 🔄 Dynamic Trade Signals:
- Generates long signals when price exceeds the upper threshold.
- Generates short signals when price drops below the lower threshold.
5. 🎨 Customizable Color Modes & Visual Enhancements:
- Choose between multiple color schemes to match trading preferences.
- Optional candlestick coloring to indicate market sentiment shifts.
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How It Works
1. ALMA Calculation:
- The indicator starts by computing the ALMA (Arnaud Legoux Moving Average) with a customizable length, offset, and sigma.
2. Percentile Rank Filtering:
- It then calculates the 75th and 25th percentile ranks over a selected period, determining dynamic levels for trend identification.
3. Volatility Adjustment Using Median Absolute Deviation (MAD):
- MAD is applied to filter noise and adapt the upper/lower bands based on market volatility.
- The higher the MAD multiplier, the wider the bands, allowing more price fluctuations before a signal triggers.
4. Entry & Exit Conditions:
- Long Entry: When price crosses above the upper percentile band + MAD filter.
- Short Entry: When price crosses below the lower percentile band - MAD filter.
5. Visual Enhancements:
- Dynamic band plotting with shading between percentile ranks.
- Candlestick coloring to visually indicate long/short sentiment shifts.
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Practical Applications
✅ Trend Following & Momentum Trading – Uses ALMA for trend smoothing and percentile-based breakouts.
✅ Mean Reversion Strategies – Adaptive MAD filtering ensures only significant deviations trigger signals.
✅ Multi-Timeframe Trading – Works on intraday, daily, and weekly timeframes based on user customization.
✅ Noise Reduction – Eliminates minor fluctuations while capturing meaningful market moves.
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🛠 Settings
-ALMA Length: 24 – Defines the smoothing period for the Arnaud Legoux Moving Average.
-ALMA Offset: 0.7 – Adjusts the shift factor, controlling responsiveness.
-ALMA Sigma: 4 – Determines the smoothing strength, balancing trend-following and noise reduction.
-Percentile Length: 21 – Lookback period for calculating percentile rank levels.
-Median Period: 21 – The period used for the Median Absolute Deviation (MAD) filter.
-Median Multiplier: 1.8 – Adjusts the sensitivity of the MAD filter, impacting how signals are generated.
-Color Mode: Strategy – Various visual themes available for better chart readability.
-Signal Label: Off - If turned off the indicator produced a Long or Cash signal when the trend changes.
📌 Conclusion
The PRC-ALMA | QuantEdgeB is an advanced valuation and signal generation tool that dynamically adjusts based on market conditions. By combining ALMA for trend smoothing, percentile rank thresholds, and MAD-based volatility filtering, it provides traders with a versatile indicator for momentum, breakout, and mean reversion strategies.
Key Takeaways:
✔ Smooth & Adaptive – ALMA ensures minimal lag while maintaining trend responsiveness.
✔ Dynamic Overbought/Oversold Zones – Adjusts to real-time market conditions using percentile-based bands.
✔ Volatility-Aware Filtering – Uses MAD to eliminate market noise, making signals more reliable.
✔ Customizable & Multi-Timeframe Ready – Works on various asset classes and timeframes with adjustable settings.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
스크립트에서 "油价调整最新消息:国内油价今日24时上调"에 대해 찾기
Crypto Scanner v4This guide explains a version 6 Pine Script that scans a user-provided list of cryptocurrency tokens to identify high probability tradable opportunities using several technical indicators. The script combines trend, momentum, and volume-based analyses to generate potential buying or selling signals, and it displays the results in a neatly formatted table with alerts for trading setups. Below is a detailed walkthrough of the script’s design, how traders can interpret its outputs, and recommendations for optimizing indicator inputs across different timeframes.
## Overview and Key Components
The script is designed to help traders assess multiple tokens by calculating several indicators for each one. The key components include:
- **Input Settings:**
- A comma-separated list of symbols to scan.
- Adjustable parameters for technical indicators such as ADX, RSI, MFI, and a custom Wave Trend indicator.
- Options to enable alerts and set update frequencies.
- **Indicator Calculations:**
- **ADX (Average Directional Index):** Measures trend strength. A value above the provided threshold indicates a strong trend, which is essential for validating momentum before entering a trade.
- **RSI (Relative Strength Index):** Helps determine overbought or oversold conditions. When the RSI is below the oversold level, it may present a buying opportunity, while an overbought condition (not explicitly part of this setup) could suggest selling.
- **MFI (Money Flow Index):** Similar in concept to RSI but incorporates volume, thus assessing buying and selling pressure. Values below the designated oversold threshold indicate potential undervaluation.
- **Wave Trend:** A custom indicator that calculates two components (WT1 and WT2); a crossover where WT1 moves from below to above WT2 (particularly near oversold levels) may signal a reversal and a potential entry point.
- **Scanning and Trading Zone:**
- The script identifies a *bullish setup* when the following conditions are met for a token:
- ADX exceeds the threshold (strong trend).
- Both RSI and MFI are below their oversold levels (indicating potential buying opportunities).
- A Wave Trend crossover confirms near-term reversal dynamics.
- A *trading zone* condition is also defined by specific ranges for ADX, RSI, MFI, and a limited difference between WT1 and WT2. This zone suggests that the token might be in a consolidation phase where even small moves may be significant.
- **Alerts and Table Reporting:**
- A table is generated, with each row corresponding to a token. The table contains columns for the symbol, ADX, RSI, MFI, WT1, WT2, and the trading zone status.
- Visual cues—such as different background colors—highlight tokens with a bullish setup or that are within the trading zone.
- Alerts are issued based on the detection of a bullish setup or entry into a trading zone. These alerts are limited per bar to avoid flooding the trader with notifications.
## How to Interpret the Indicator Outputs
Traders should use the indicator values as guidance, verifying them against their own analysis before making any trading decision. Here’s how to assess each output:
- **ADX:**
- **High values (above threshold):** Indicate strong trends. If other indicators confirm an oversold condition, a trader may consider a long position for a corrective reversal.
- **Low values:** Suggest that the market is not trending strongly, and caution should be taken when considering entry.
- **RSI and MFI:**
- **Below oversold levels:** These conditions are traditionally seen as signals that an asset is undervalued, potentially triggering a bounce.
- **Above typical resistance levels (not explicitly used here):** Would normally caution a trader against entering a long position.
- **Wave Trend (WT1 and WT2):**
- A crossover where WT1 moves upward above WT2 in an oversold environment can signal the beginning of a recovery or reversal, thereby reinforcing buy signals.
- **Trading Zone:**
- Being “in zone” means that the asset’s current values for ADX, RSI, MFI, and the closeness of the Wave Trend lines indicate a period of consolidation. This scenario might be suitable for both short-term scalping or as an early exit indicator, depending on further market analysis.
## Timeframe Optimization Input Table
Traders can optimize indicator inputs depending on the timeframe they use. The following table provides a set of recommended input values for various timeframes. These values are suggestions and should be adjusted based on market conditions and individual trading styles.
Timeframe ADX RSI MFI ADX RSI MFI WT Channel WT Average
5-min 10 10 10 20 30 20 7 15
15-min 12 12 12 22 30 20 9 18
1-hour 14 14 14 25 30 20 10 21
4-hour 16 16 16 27 30 20 12 24
1-day 18 18 18 30 30 20 14 28
Adjust these parameters directly in the script’s input settings to match the selected timeframe. For shorter timeframes (e.g., 5-min or 15-min), the shorter lengths help filter high-frequency noise. For longer timeframes (e.g., 1-day), longer input values may reduce false signals and capture more significant trends.
## Best Practices and Usage Tips
- **Token Limit:**
- Limit the number of tokens scanned to 10 per query line. If you need to scan more tokens, initiate a new query line. This helps manage screen real estate and ensures the table remains legible.
- **Confirming Signals:**
- Use this script as a starting point for identifying high potential trades. Each indicator’s output should be used to confirm your trading decision. Always cross-reference with additional technical analysis tools or market context.
- **Regular Review:**
- Since the script updates the table every few bars (as defined by the update frequency), review the table and alerts regularly. Market conditions change rapidly, so timely decisions are crucial.
## Conclusion
This Pine Script provides a comprehensive approach for scanning multiple cryptocurrencies using a combination of trend strength (ADX), momentum (RSI and MFI), and reversal signals (Wave Trend). By using the provided recommendation table for different timeframes and limiting the tokens to 20 per query line (with a maximum of four query lines), traders can streamline their scanning process and more effectively identify high probability tradable tokens. Ultimately, the outputs should be critically evaluated and combined with additional market research before executing any trades.
Timeframe Display + Countdown📘 Help Guide: Timeframe Display + Countdown + Alert
🔹 Overview
This indicator displays:
✅ The selected timeframe (e.g., 5min, 1H, 4H)
✅ A countdown timer showing minutes and seconds until the current candle closes
✅ An optional alert that plays a sound when 1 minute remains before the new candle starts
⚙️ How to Use
1️⃣ Add the Indicator
• Open TradingView
• Click on Pine Script Editor
• Copy and paste the script
• Click Add to Chart
2️⃣ Customize Settings
• Text Color: Choose a color for the displayed text
• Text Size: Adjust the font size (8–24)
• Transparency: Set how transparent the text is (0%–100%)
• Position: Choose where the text appears (Top Left, Top Right, Bottom Left, Bottom Right)
• Enable Audible Alert: Turn ON/OFF the alert when 1 minute remains
3️⃣ Set Up an Audible Alert in TradingView
🚨 Important: Pine Script cannot play sounds directly; you must set up a manual alert in TradingView.
Steps:
1. Click “Alerts” (🔔 icon in TradingView)
2. Click “Create Alert” (+ button)
3. In “Condition”, select this indicator (Timeframe Display + Countdown)
4. Under “Options”, choose:
• Trigger: “Once Per Bar”
• Expiration: Set a valid time range
• Alert Actions: Check “Play Sound” and choose a sound
5. Click “Create” ✅
🛠️ How It Works
• Countdown Timer:
• Updates in real time, displaying MM:SS until the candle closes
• Resets when a new candle starts
• Alert Trigger:
• When 1:00 minute remains, an alert is sent
• If properly configured in TradingView, it plays a sound
IB of New Hour (Customizable)Purpose: Tracks first x candles of each hour to define a price range
Customizable settings:
Border color of the IB box
Fill color of the IB box
Number of candles to define IB
Box width in hours (1-24)
Functionality:
Calculates highest high and lowest low for specified number of candles
Creates a rectangular box representing the initial balance
Adapts to different timeframes (1, 5, 15, 30, 60-minute charts)
Limits storage of boxes to prevent memory overload
Box Placement:
Starts at first candle of the hour
Width calculated based on current timeframe and user-specified hours
Maintains consistent visual representation across different chart timeframes
Indicator for helping you with bias
Hourly Market Movement Pattern Indicator# Hourly Market Movement Pattern Indicator
This versatile technical analysis tool identifies the most active hours for trading by analyzing historical price movements. While it can be viewed on any timeframe chart, the indicator specifically tracks and displays which hours of the day historically show the strongest upward or downward price movements, helping traders optimize their trading schedule around these recurring hourly patterns.
## Core Features
- Tracks the best performing hours for both upward and downward movements
- Viewable on any timeframe chart while maintaining hourly analysis
- Clear visual display through a color-coded table overlay
- Real-time updates with new market data
- Works with all trading instruments (stocks, crypto, forex, futures, etc.)
## Timeframe Applications
### Chart Viewing Options
- Can be viewed on any timeframe chart (1min to Monthly)
- Maintains hourly pattern analysis regardless of chart timeframe
- Helps correlate hourly patterns with your preferred trading timeframe
- Allows detailed visualization of hourly patterns within your analysis period
### Intraday Trading
- Identify the most profitable hours for trading
- Plan trading sessions around historically strong hours
- Optimize entry and exit timing based on hourly patterns
- Structure day trading schedules around peak movement hours
### Swing Trading
- Use hourly statistics to optimize entry/exit timing
- Plan trade executions during historically strong hours
- Time position entries based on hourly success rates
- Enhance swing trading decisions with hourly pattern data
## Practical Applications
### Pattern Recognition
- Track recurring hourly market movements
- Identify institutional trading hour patterns
- Detect regular market cycle hours
- Recognize changes in hourly market behavior
### Risk Management
- Adjust position sizing based on historical hourly patterns
- Plan entries during statistically favorable hours
- Time stop loss adjustments around known volatile hours
- Scale positions according to hourly success rates
### Trade Planning
- Schedule trading sessions during optimal hours
- Plan trade executions around strong movement periods
- Structure trading day around peak hours
- Time position adjustments to favorable hours
## Setup Options
- Timeframe: View on any chart timeframe while tracking hourly patterns
- Visual Display: Non-intrusive table overlay
- Color Coding: Green for upward movements, Red for downward movements
- Hour Display: 24-hour format for global market compatibility
## Trading Strategy Integration
The indicator enhances trading approaches through:
- Optimal hour identification for trade execution
- Historical hourly pattern analysis
- Day trading session optimization
- Position timing based on hourly statistics
## Notes
This indicator proves particularly valuable for:
- Traders seeking to optimize their daily trading schedule
- Day traders focusing on peak market hours
- Swing traders optimizing entry/exit timing
- Traders adapting strategies to specific market hours
- International traders tracking hour-specific patterns across sessions
The tool's hourly pattern analysis provides crucial timing information regardless of your preferred chart timeframe or trading style, helping optimize trade execution around the most statistically favorable hours of the day.
Thrax - Pullback based short side scalping⯁ This indicator is built for short trades only.
⤞ Pullback based scalping is a strategy where a trader anticipates a pullback and makes a quick scalp in this pullback. This strategy usually works in a ranging market as probability of pullbacks occurrence in ranging market is quite high.
⤞ The strategy is built by first determining a possible candidate price levels having high chance of pullbacks. This is determined by finding out multiple rejection point and creating a zone around this price. A rejection is considered to be valid only if it comes to this zone after going down by a minimum pullback percentage. Once the price has gone down by this minimum pullback percentage multiple times and reaches the zone again chances of pullback goes high and an indication on chart for the same is given.
⯁ Inputs
⤞ Zone-Top : This input parameter determines the upper range for the price zone.
⤞ Zone bottom : This input parameter determines the lower range for price zone.
⤞ Minimum Pullback : This input parameter determines the minimum pullback percentage required for valid rejection. Below is the recommended settings
⤞ Lookback : lookback period before resetting all the variables
⬦Below is the recommended settings across timeframes
⤞ 15-min : lookback – 24, Pullback – 2, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ 5-min : lookback – 50, pullback – 1% - 1.5%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ 1-min : lookback – 100, pullback – 1%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ Anything > 30-min : lookback – 11, pullback – 3%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
✵ This indicator gives early pullback detection which can be used in below ways
1. To take short trades in the pullback.
2. To use this to exit an existing position in the next few candles as pullback may be incoming.
📌 Kindly note, it’s not necessary that pullback will happen at the exact point given on the chart. Instead, the indictor gives you early signals for the pullback
⯁ Trade Steup
1. Wait for pullback signal to occur on the chart.
2. Once the pullback warning has been displayed on the chart, you can either straight away enter the short position or wait for next 2-4 candles for initial sign of actual pullback to occurrence.
3. Once you have initiated short trade, since this is pullback-based strategy, a quick scalp should be made and closed as price may resume it’s original direction. If you have risk appetite you can stay in the trade longer and trial the stops if price keeps pulling back.
4. You can zone top as your stop, usually zone top + some% should be used as stop where ‘some %’ is based on your risk appetite.
5. It’s important to note that this indicator gives early sings of pullback so you may actually wait for 2-3 candles post ‘Pullback warning’ occurs on the chart before entering short trade.
Time-Based VWAP (TVWAP)(TVWAP) Indicator
The Time-Based Volume Weighted Average Price (TVWAP) indicator is a customized version of VWAP designed for intraday trading sessions with defined start and end times. Unlike the traditional VWAP, which calculates the volume-weighted average price over an entire trading day, this indicator allows you to focus on specific time periods, such as ICT kill zones (e.g., London Open, New York Open, Power Hour). It helps crypto scalpers and advanced traders identify price deviations relative to volume during key trading windows.
Key Features:
Custom Time Interval:
You can set the exact start and end times for the VWAP calculation using input settings for hours and minutes (24-hour format).
Ideal for analyzing short, high-liquidity periods.
Dynamic Accumulation of Price and Volume:
The indicator resets at the beginning of the specified session and accumulates price-volume data until the end of the session.
Ensures that the TVWAP reflects the weighted average price specific to the chosen session.
Visual Representation:
The indicator plots the TVWAP line only during the specified time window, providing a clear visual reference for price action during that period.
Outside the session, the TVWAP line is hidden (na).
Use Cases:
ICT Scalp Trading:
Monitor price rebalances or potential liquidity sweeps near TVWAP during important trading sessions.
Mean Reversion Strategies:
Detect pullbacks toward the session’s average price for potential entry points.
Breakout Confirmation:
Confirm price direction relative to TVWAP during kill zones or high-volume times to determine if a breakout is supported by volume.
Inputs:
Start Hour/Minute: The time when the TVWAP calculation starts.
End Hour/Minute: The time when the TVWAP calculation ends.
Technical Explanation:
The indicator uses the timestamp function to create time markers for the session start and end.
During the session, the price-volume (close * volume) is accumulated along with the total volume.
TVWAP is calculated as:
TVWAP = (Sum of (Price × Volume)) ÷ (Sum of Volume)
Once the session ends, the TVWAP resets for the next trading period.
Customization Ideas:
Alerts: Add notifications when the price touches or deviates significantly from TVWAP.
Different Colors: Use different line colors based on upward or downward trends.
Multiple Sessions: Add support for multiple TVWAP lines for different time periods (e.g., London + New York).
Comprehensive Trading Toolkit [BigBeluga]Trading Toolkit is a comprehensive indicator inspired by the trading strategies of the renowned crypto influencer Michaël van de Poppe . This tool combines RSI divergences, correction zones, and advanced support/resistance levels to provide traders with a robust framework for analyzing market movements.
🔵 Key Features:
RSI Divergences on Chart:
Automatically identifies and plots RSI divergences (bullish and bearish) directly on the main price chart.
Green lines indicate bullish divergences, suggesting potential upward reversals.
Red lines indicate bearish divergences, signaling possible downward movements.
Correction Boxes:
Traders typically define a correction as a drop in value of 10% or more. This drop can happen over a few hours or a few days. Also, it can last for less than 24 hours or many months.
This indicator visualizes corrections with blue shaded boxes, triggered by a percentage decline defined in the settings.
The boxes highlight sharp price drops, helping traders identify significant market movements quickly.
Advanced Support and Resistance Levels:
Dynamically detects key support and resistance levels based on price pivots.
When the price is above a level, it plots a green shaded area from the cross point, marking support.
When the price drops below a level, it plots a red shaded area, highlighting resistance.
Dashed lines indicate weaker levels, while solid lines represent stronger, more reliable levels.
🔵 Usage:
Identify Divergences: Use plotted RSI divergences to detect potential market reversals and align them with price action.
Analyze Correction Zones: Utilize correction boxes to evaluate significant price declines and find potential buying opportunities during these corrections.
Leverage Support and Resistance Levels: Confirm breakouts, reversals, or consolidation zones with the color-coded areas.
Enhance Risk Management: Combine divergences and correction zones to set informed stop-loss or take-profit levels.
Trading Toolkit empowers traders with actionable insights into market trends, corrections, and support/resistance dynamics, making it an invaluable tool for crypto and forex markets.
Volatility IndicatorThe volatility indicator presented here is based on multiple volatility indices that reflect the market’s expectation of future price fluctuations across different asset classes, including equities, commodities, and currencies. These indices serve as valuable tools for traders and analysts seeking to anticipate potential market movements, as volatility is a key factor influencing asset prices and market dynamics (Bollerslev, 1986).
Volatility, defined as the magnitude of price changes, is often regarded as a measure of market uncertainty or risk. Financial markets exhibit periods of heightened volatility that may precede significant price movements, whether upward or downward (Christoffersen, 1998). The indicator presented in this script tracks several key volatility indices, including the VIX (S&P 500), GVZ (Gold), OVX (Crude Oil), and others, to help identify periods of increased uncertainty that could signal potential market turning points.
Volatility Indices and Their Relevance
Volatility indices like the VIX are considered “fear gauges” as they reflect the market’s expectation of future volatility derived from the pricing of options. A rising VIX typically signals increasing investor uncertainty and fear, which often precedes market corrections or significant price movements. In contrast, a falling VIX may suggest complacency or confidence in continued market stability (Whaley, 2000).
The other volatility indices incorporated in the indicator script, such as the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index), capture the market’s perception of volatility in specific asset classes. For instance, GVZ reflects market expectations for volatility in the gold market, which can be influenced by factors such as geopolitical instability, inflation expectations, and changes in investor sentiment toward safe-haven assets. Similarly, OVX tracks the implied volatility of crude oil options, which is a crucial factor for predicting price movements in energy markets, often driven by geopolitical events, OPEC decisions, and supply-demand imbalances (Pindyck, 2004).
Using the Indicator to Identify Market Movements
The volatility indicator alerts traders when specific volatility indices exceed a defined threshold, which may signal a change in market sentiment or an upcoming price movement. These thresholds, set by the user, are typically based on historical levels of volatility that have preceded significant market changes. When a volatility index exceeds this threshold, it suggests that market participants expect greater uncertainty, which often correlates with increased price volatility and the possibility of a trend reversal.
For example, if the VIX exceeds a pre-determined level (e.g., 30), it could indicate that investors are anticipating heightened volatility in the equity markets, potentially signaling a downturn or correction in the broader market. On the other hand, if the OVX rises significantly, it could point to an upcoming sharp movement in crude oil prices, driven by changing market expectations about supply, demand, or geopolitical risks (Geman, 2005).
Practical Application
To effectively use this volatility indicator in market analysis, traders should monitor the alert signals generated when any of the volatility indices surpass their thresholds. This can be used to identify periods of market uncertainty or potential market turning points across different sectors, including equities, commodities, and currencies. The indicator can help traders prepare for increased price movements, adjust their risk management strategies, or even take advantage of anticipated price swings through options trading or volatility-based strategies (Black & Scholes, 1973).
Traders may also use this indicator in conjunction with other technical analysis tools to validate the potential for significant market movements. For example, if the VIX exceeds its threshold and the market is simultaneously approaching a critical technical support or resistance level, the trader might consider entering a position that capitalizes on the anticipated price breakout or reversal.
Conclusion
This volatility indicator is a robust tool for identifying market conditions that are conducive to significant price movements. By tracking the behavior of key volatility indices, traders can gain insights into the market’s expectations of future price fluctuations, enabling them to make more informed decisions regarding market entries and exits. Understanding and monitoring volatility can be particularly valuable during times of heightened uncertainty, as changes in volatility often precede substantial shifts in market direction (French et al., 1987).
References
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.
• Whaley, R. E. (2000). Derivatives on Market Volatility. Journal of Derivatives, 7(4), 71-82.
• Pindyck, R. S. (2004). Volatility and the Pricing of Commodity Derivatives. Journal of Futures Markets, 24(11), 973-987.
• Geman, H. (2005). Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. John Wiley & Sons.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
• French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19(1), 3-29.
Anchored Geometric Brownian Motion Projections w/EVAnchored GBM (Geometric Brownian Motion) Projections + EV & Confidence Bands
Version: Pine Script v6
Overlay: Yes
Author:
Published On:
Overview
The Anchored GBM Projections + EV & Confidence Bands indicator leverages the Geometric Brownian Motion (GBM) model to project future price movements based on historical data. By simulating multiple potential future price paths, it provides traders with insights into possible price trajectories, their expected values, and confidence intervals. Additionally, it offers a "Mean of EV" (EV of EV) line, representing the running average of expected values across the projection period.
Key Features
Anchor Time Setup:
Define a specific point in time from which the projections commence.
By default, it uses the current bar's timestamp but can be customized.
Projection Parameters:
Projection Candles (Bars): Determines the number of future bars (time periods) to project.
Number of Simulations: Specifies how many GBM paths to simulate, ensuring statistical relevance via the Central Limit Theorem (CLT).
Display Toggles:
Simulation Lines: Visual representation of individual GBM simulation paths.
Expected Value (EV) Line: The average price across all simulations at each projection bar.
Upper & Lower Confidence Bands: 95% confidence intervals indicating potential price boundaries.
EV of EV Line: Running average of EV values, providing a smoothed central tendency across the projection period. Additionally, this line often acts as an indicator of trend direction.
Visualization:
Clear and distinguishable lines with customizable colors and styles.
Overlayed on the price chart for direct comparison with actual price movements.
Mathematical Foundation
Geometric Brownian Motion (GBM):
Definition: GBM is a continuous-time stochastic process used to model stock prices. It assumes that the logarithm of the stock price follows a Brownian motion with drift.
Equation:
S(t)=S0⋅e(μ−12σ2)t+σW(t)
S(t)=S0⋅e(μ−21σ2)t+σW(t) Where:
S(t)S(t) = Stock price at time tt
S0S0 = Initial stock price
μμ = Drift coefficient (average return)
σσ = Volatility coefficient (standard deviation of returns)
W(t)W(t) = Wiener process (standard Brownian motion)
Drift (μμ) and Volatility (σσ):
Drift (μμ) represents the expected return of the stock.
Volatility (σσ) measures the stock's price fluctuation intensity.
Central Limit Theorem (CLT):
Principle: With a sufficiently large number of independent simulations, the distribution of the sample mean (EV) approaches a normal distribution, regardless of the underlying distribution.
Application: Ensures that the EV and confidence bands are statistically reliable.
Expected Value (EV) and Confidence Bands:
EV: The mean price across all simulations at each projection bar.
Confidence Bands: Range within which the actual price is expected to lie with a specified probability (e.g., 95%).
EV of EV (Mean of Sample Means):
Definition: Represents the running average of EV values across the projection period, offering a smoothed central tendency.
Methodology
Anchor Time Setup:
The indicator starts projecting from a user-defined Anchor Time. If not customized, it defaults to the current bar's timestamp.
Purpose: Allows users to analyze projections from a specific historical point or the latest market data.
Calculating Drift and Volatility:
Returns Calculation: Computes the logarithmic returns from the Anchor Time to the current bar.
returns=ln(StSt−1)
returns=ln(St−1St)
Drift (μμ): Calculated as the simple moving average (SMA) of returns over the period since the Anchor Time.
Volatility (σσ): Determined using the standard deviation (stdev) of returns over the same period.
Simulation Generation:
Number of Simulations: The user defines how many GBM paths to simulate (e.g., 30).
Projection Candles: Determines the number of future bars to project (e.g., 12).
Process:
For each simulation:
Start from the current close price.
For each projection bar:
Generate a random number zz from a standard normal distribution.
Calculate the next price using the GBM formula:
St+1=St⋅e(μ−12σ2)+σz
St+1=St⋅e(μ−21σ2)+σz
Store the projected price in an array.
Expected Value (EV) and Confidence Bands Calculation:
EV Path: At each projection bar, compute the mean of all simulated prices.
Variance and Standard Deviation: Calculate the variance and standard deviation of simulated prices to determine the confidence intervals.
Confidence Bands: Using the standard normal z-score (1.96 for 95% confidence), establish upper and lower bounds:
Upper Band=EV+z⋅σEV
Upper Band=EV+z⋅σEV
Lower Band=EV−z⋅σEV
Lower Band=EV−z⋅σEV
EV of EV (Running Average of EV Values):
Calculation: For each projection bar, compute the average of all EV values up to that bar.
EV of EV =1j+1∑k=0jEV
EV of EV =j+11k=0∑jEV
Visualization: Plotted as a dynamic line reflecting the evolving average EV across the projection period.
Visualization Elements
Simulation Lines:
Appearance: Semi-transparent blue lines representing individual GBM simulation paths.
Purpose: Illustrate a range of possible future price trajectories based on current drift and volatility.
Expected Value (EV) Line:
Appearance: Solid orange line.
Purpose: Shows the average projected price at each future bar across all simulations.
Confidence Bands:
Upper Band: Dashed green line indicating the upper 95% confidence boundary.
Lower Band: Dashed red line indicating the lower 95% confidence boundary.
Purpose: Highlight the range within which the price is statistically expected to remain with 95% confidence.
EV of EV Line:
Appearance: Dashed purple line.
Purpose: Displays the running average of EV values, providing a smoothed trend of the central tendency across the projection period. As the mean of sample means it approximates the population mean (i.e. the trend since the anchor point.)
Current Price:
Appearance: Semi-transparent white line.
Purpose: Serves as a reference point for comparing actual price movements against projected paths.
Usage Instructions
Configuring User Inputs:
Anchor Time:
Set to a specific timestamp to start projections from a historical point or leave it as default to use the current bar's time.
Projection Candles (Bars):
Define the number of future bars to project (e.g., 12). Adjust based on your trading timeframe and analysis needs.
Number of Simulations:
Specify the number of GBM paths to simulate (e.g., 30). Higher numbers yield more accurate EV and confidence bands but may impact performance.
Display Toggles:
Show Simulation Lines: Toggle to display or hide individual GBM simulation paths.
Show Expected Value Line: Toggle to display or hide the EV path.
Show Upper Confidence Band: Toggle to display or hide the upper confidence boundary.
Show Lower Confidence Band: Toggle to display or hide the lower confidence boundary.
Show EV of EV Line: Toggle to display or hide the running average of EV values.
Managing TradingView's Object Limits:
Understanding Limits:
TradingView imposes a limit on the number of graphical objects (e.g., lines) that can be rendered. High values for projection candles and simulations can quickly consume these limits. TradingView appears to only allow a total of 55 candles to be projected, so if you want to see two complete lines, you would have to set the projection length to 27: since 27 * 2 = 54 and 54 < 55.
Optimizing Performance:
Use Toggles: Enable only the necessary visual elements. For instance, disable simulation lines and confidence bands when focusing on the EV and EV of EV lines. You can also use the maximum projection length of 55 with the lower limit confidence band as the only line, visualizing a long horizon for your risk.
Adjust Parameters: Lower the number of projection candles or simulations to stay within object limits without compromising essential insights.
Interpreting the Indicator:
Simulation Lines (Blue):
Represent individual potential future price paths based on GBM. A wider spread indicates higher volatility.
Expected Value (EV) Line (Goldenrod):
Shows the mean projected price at each future bar, providing a central trend.
Confidence Bands (Green & Red):
Indicate the statistical range (95% confidence) within which the price is expected to remain.
EV of EV Line (Dotted Line - Goldenrod):
Reflects the running average of EV values, offering a smoothed perspective of expected price trends over the projection period.
Current Price (White):
Serves as a benchmark for assessing how actual prices compare to projected paths.
Practical Applications
Risk Management:
Confidence Bands: Help in identifying potential support and resistance levels based on statistical confidence intervals.
EV Path: Assists in setting realistic target prices and stop-loss levels aligned with projected expectations.
Trend Analysis:
EV of EV Line: Offers a smoothed trendline, aiding in identifying overarching market directions amidst price volatility. Indicative of the population mean/overall trend of the data since your anchor point.
Scenario Planning:
Simulation Lines: Enable traders to visualize multiple potential outcomes, fostering better decision-making under uncertainty.
Performance Evaluation:
Comparing Actual vs. Projected Prices: Assess how actual price movements align with projected scenarios, refining trading strategies over time.
Mathematical and Statistical Insights
Simulation Integrity:
Independence: Each simulation path is generated independently, ensuring unbiased and diverse projections.
Randomness: Utilizes a Gaussian random number generator to introduce variability in diffusion terms, mimicking real market randomness.
Statistical Reliability:
Central Limit Theorem (CLT): By simulating a sufficient number of paths (e.g., 30), the sample mean (EV) converges to the population mean, ensuring reliable EV and confidence band calculations.
Variance Calculation: Accurate computation of variance from simulation data ensures precise confidence intervals.
Dynamic Projections:
Running Average (EV of EV): Provides a cumulative perspective, allowing traders to observe how the average expectation evolves as the projection progresses.
Customization and Enhancements
Adjustable Parameters:
Tailor the projection length and simulation count to match your trading style and analysis depth.
Visual Customization:
Modify line colors, styles, and transparency to enhance clarity and fit chart aesthetics.
Extended Statistical Metrics:
Future iterations can incorporate additional metrics like median projections, skewness, or alternative confidence intervals.
Dynamic Recalculation:
Implement logic to automatically update projections as new data becomes available, ensuring real-time relevance.
Performance Considerations
Object Count Management:
High simulation counts and extended projection periods can lead to a significant number of graphical objects, potentially slowing down chart performance.
Solution: Utilize display toggles effectively and optimize projection parameters to balance detail with performance.
Computational Efficiency:
The script employs efficient array handling and conditional plotting to minimize unnecessary computations and object creation.
Conclusion
The Anchored GBM Projections + EV & Confidence Bands indicator is a robust tool for traders seeking to forecast potential future price movements using statistical models. By integrating Geometric Brownian Motion simulations with expected value calculations and confidence intervals, it offers a comprehensive view of possible market scenarios. The addition of the "EV of EV" line further enhances analytical depth by providing a running average of expected values, aiding in trend identification and strategic decision-making.
Hope it helps!
TAPDA Hourly Open Lines (Candle Body Box)-What is TAPDA?
TAPDA (Time and Price Displacement Analysis) is based on the belief that markets are driven by algorithms that respond to key time-based price levels, such as session opens. Traders who follow TAPDA track these levels to anticipate price movements, reversals, and breakouts, aligning their strategies with the patterns left by these underlying algorithms. By plotting lines at specific hourly opens, the indicator allows traders to visualize where the market may react, providing a structured way to trade alongside the algorithmic flow.
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**Sauce Alert** "TAPDA levels essentially act like algorithmic support and resistance" By plotting these hourly opens, the TAPDA Hourly Open Lines indicator helps traders track where algorithms might engage with the market.
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-How It Works:
The indicator draws a "candle body box" at selected hours, marking the open and close prices to highlight price ranges at significant times. This creates dynamic zones that reflect market sentiment and structure throughout the day. TAPDA levels are commonly respected by price, making them useful for identifying potential entry points, stop placements, and trend reversals.
-Key Features:
Customizable Hour Levels – Enable or disable specific times to fit your trading approach.
Color & Label Control – Assign unique colors and labels to each hour for better visualization.
Line Extension – Project lines for up to 24 hours into the future to track key levels.
Dynamic Cleanup – Old lines automatically delete to maintain chart clarity.
Manual Time Offset – Adjust for broker or server time zone differences.
-Current Development:
This indicator is still in development, with further updates planned to enhance functionality and customization. If you find this script helpful, feel free to copy the code and stay tuned for new features and improvements!
Previous Week High & Low with middle lineDescription:
The Previous Week High & Low Indicator is a powerful tool designed to provide traders with key reference levels from the previous trading week. It dynamically calculates and plots the previous week's high, low, and midpoint levels directly on your chart, helping you identify critical support and resistance zones.
Features:
1. **Previous Week High and Low Lines**:
- The indicator displays the high and low prices of the previous trading week, allowing you to analyze price action relative to these levels.
- These lines are plotted as step lines, visible only during the active trading days (Monday to Friday), ensuring clean and uncluttered charts.
2. Midpoint Line:
- The midpoint between the previous week's high and low is calculated and displayed as a reference level.
- This white line can act as a psychological pivot point for market participants.
3. Customizable Display:
- You can toggle the visibility of the high and low lines using input options, tailoring the indicator to your preferences.
4. Precision and Aesthetics:
- The lines are plotted with precision and styled for clarity, using subtle transparency for an unobtrusive yet informative appearance.
Use Case:
- This indicator is ideal for traders who rely on historical price levels for planning entries, exits, or stop-loss placements.
- It works seamlessly with any timeframe and asset, making it versatile for various trading strategies.
How It Works:
- The indicator fetches the previous week's high and low prices using the weekly timeframe and "lookahead" mode to ensure these levels remain static after the week's close.
- The lines are plotted only on weekdays (Monday to Friday) to exclude weekend data, ensuring accuracy for markets that operate 24/5.
This tool simplifies your chart analysis and empowers you to make informed trading decisions based on historical price dynamics.
Fibonacci Time-Price Zones🟩 Fibonacci Time-Price Zones is a chart visualization tool that combines Fibonacci ratios with time-based and price-based geometry to analyze market behavior. Unlike typical Fibonacci indicators that focus solely on horizontal price levels, this indicator incorporates time into the analysis, providing a more dynamic perspective on price action.
The indicator offers multiple ways to visualize Fibonacci relationships. Drawing segmented circles creates a unique perspective on price action by incorporating time into the analysis. These segmented circles, similar to TradingView's built-in Fibonacci Circles, are derived from Fibonacci time and price levels, allowing traders to identify potential turning points based on the dynamic interaction between price and time.
As another distinct visualization method, the indicator incorporates orthogonal patterns, created by the intersection of horizontal and vertical Fibonacci levels. These intersections form L-shaped connections on the chart, derived from key Fibonacci price and time intervals, highlighting potential areas of support or resistance at specific points in time.
In addition to these geometric approaches, another option is sloped lines, which project Fibonacci levels that account for both time and price along the trendline. These projections derive their angles from the interplay between Fibonacci price levels and Fibonacci time intervals, creating dynamic zones on the chart. The slope of these lines reflects the direction and angle of the trend, providing a visual representation of price alignment with market direction, while maintaining the time-price relationship unique to this indicator
The indicator also includes horizontal Fibonacci levels similar to traditional retracement and extension tools. However, unlike standard tools, traders can display retracement levels, extension levels, or both simultaneously from a single instance of the indicator. These horizontal levels maintain consistency with the chosen visualization method, automatically scaling and adapting whether used with circles, orthogonal patterns, or slope-based analysis.
By combining these distinct methods—circles, orthogonal patterns, sloped projections, and horizontal levels—the indicator provides a comprehensive approach to Fibonacci analysis based on both time and price relationships. Each visualization method offers a unique perspective on market structure while maintaining the core principle of time-price interaction.
⭕ THEORY AND CONCEPT ⭕
While traditional Fibonacci tools excel at identifying potential support and resistance levels through price-based ratios (0.236, 0.382, 0.618), they do not incorporate the dimension of time in market analysis. Extensions and retracements effectively measure price relationships within trends, yet markets move through both price and time dimensions simultaneously.
Fibonacci circles represent an evolution in technical analysis by incorporating time intervals alongside price levels. Based on the mathematical principle that markets often move in circular patterns proportional to Fibonacci ratios, these circles project potential support and resistance zones as partial circles radiating from significant price points. However, traditional circle-based tools can create visual complexity that obscures key market relationships. The integration of time into Fibonacci analysis reveals how price movements often respect both temporal and price-based ratios, suggesting a deeper geometric structure to market behavior.
The Fibonacci Time-Price Zones indicator advances these concepts by providing multiple geometric approaches to visualize time-price relationships. Each shape option—circles, orthogonal patterns, slopes, and horizontal levels—represents a different mathematical perspective on how Fibonacci ratios manifest across both dimensions. This multi-faceted approach allows traders to observe how price responds to Fibonacci-based zones that account for both time and price movements, potentially revealing market structure that purely price-based tools might miss.
Shape Options
The indicator employs four distinct geometric approaches to analyze Fibonacci relationships across time and price dimensions:
Circular : Represents the cyclical nature of market movements through partial circles, where each radius is scaled by Fibonacci ratios incorporating both time and price components. This geometry suggests market movements may follow proportional circular paths from significant pivot points, reflecting the harmonic relationship between time and price.
Orthogonal : Constructs L-shaped patterns that separate the time and price components of Fibonacci relationships. The horizontal component represents price levels, while the vertical component measures time intervals, allowing analysis of how these dimensions interact independently at key market points.
Sloped : Projects Fibonacci levels along the prevailing trend, incorporating both time and price in the angle of projection. This approach suggests that support and resistance levels may maintain their relationship to price while adjusting to the temporal flow of the market.
Horizontal : Provides traditional static Fibonacci levels that serve as a reference point for comparing price-only analysis with the dynamic time-price relationships shown in the other three shapes. This baseline approach allows traders to evaluate how the incorporation of time dimension enhances or modifies traditional Fibonacci analysis.
By combining these geometric approaches, the Fibonacci Time-Price Zones indicator creates a comprehensive analytical framework that bridges traditional and advanced Fibonacci analysis. The horizontal levels serve as familiar reference points, while the dynamic elements—circular, orthogonal, and sloped projections—reveal how price action responds to temporal relationships. This multi-dimensional approach enables traders to study market structure through various geometric lenses, providing deeper insights into time-price symmetry within technical analysis. Whether applied to retracements, extensions, or trend analysis, the indicator offers a structured methodology for understanding how markets move through both price and time dimensions.
🛠️ CONFIGURATION AND SETTINGS 🛠️
The Fibonacci Time-Price Zones indicator offers a range of configurable settings to tailor its functionality and visual representation to your specific analysis needs. These options allow you to customize zone visibility, structures, horizontal lines, and other features.
Important Note: The indicator's calculations are anchored to user-defined start and end points on the chart. When switching between charts with significantly different price scales (e.g., from Bitcoin at $100,000 to Silver at $30), adjustment of these anchor points is required to ensure correct positioning of the Fibonacci elements.
Fibonacci Levels
The indicator allows users to customize Fibonacci levels for both retracement and extension analysis. Each level can be individually configured with the following options:
Visibility : Toggle the visibility of each level to focus on specific areas of interest.
Level Value : Set the Fibonacci ratio for the level, such as 0.618 or 1.000, to align with your analysis needs.
Color : Customize the color of each level for better visual clarity.
Line Thickness : Adjust the line thickness to emphasize critical levels or maintain a cleaner chart.
Setup
Zone Type : Select which Fibonacci zones to display:
- Retracement : Shows potential pull back levels within the trend
- Extension : Projects levels beyond the trend for potential continuation targets
- Both : Displays both retracement and extension zones simultaneously
Shape : Choose from four visualization methods:
- Circular : Time-price based semicircles centered on point B
- Orthogonal : L-shaped patterns combining time and price levels
- Sloped : Trend-aligned projections of Fibonacci levels
- Horizontal : Traditional horizontal Fibonacci levels
Visual Settings
Fill % : Adjusts the fill intensity of zones:
0% : No fill between levels
100% : Maximum fill between levels
Lines :
Trendline : The base A-B trend with customizable color
Extension : B-C projection line
Retracement : B-D pullback line
Labels :
Points : Show/hide A, B, C, D markers
Levels : Show/hide Fibonacci percentages
Time-Price Points
Set the time and price for the points that define the Fibonacci zones and horizontal levels. These points are defined upon loading the chart. These points can be configured directly in the settings or adjusted interactively on the live chart.
A and B Points : These user-defined time and price points determine the basis for calculating the semicircles and Fibonacci levels. While the settings panel displays their exact values for fine-tuning, the easiest way to modify these points is by dragging them directly on the chart for quick adjustments.
Interactive Adjustments : Any changes made to the points on the chart will automatically synchronize with the settings panel, ensuring consistency and precision.
🖼️ CHART EXAMPLES 🖼️
Fibonacci Time-Price Zones using the 'Circular' Shape option. Note the price interaction at the 0.786 level, which acts as a support zone. Additional points of interest include resistance near the 0.618 level and consolidation around the 0.5 level, highlighting the utility of both horizontal and semicircular Fibonacci projections in identifying key price areas.
Fibonacci Time-Price Zones using the 'Sloped' Shape option. The chart displays price retracing along the sloped Fibonacci levels, with blue arrows highlighting potential support zones at 0.618 and 0.786, and a red arrow indicating potential resistance at the 1.0 level. This visual representation aligns with the prevailing downtrend, suggesting potential selling pressure at the 1.0 Fibonacci level.
Fibonacci Time-Price Zones using the 'Orthogonal' Shape option. The chart demonstrates price action interacting with vertical zones created by the orthogonal lines at the 0.618, 0.786, and 1.0 Fibonacci levels. Blue arrows highlight potential support areas, while red arrows indicate potential resistance areas, revealing how the orthogonal lines can identify distinct points of price interaction.
Fibonacci Time-Price Zones using the 'Circular' Shape option. The chart displays price action in relation to segmented circles emanating from the starting point (point A). The circles represent different Fibonacci ratios (0.382, 0.5, 0.618, 0.786) and their intersections with the price axis create potential zones of support and resistance. This approach offers a visually distinct way to analyze potential turning points based on both price and time.
Fibonacci Time-Price Zones using the 'Sloped' Shape option. The sloped Fibonacci levels (0.786, 0.618, 0.5) create zones of potential support and resistance, with price finding clear interaction within these areas. The ellipses highlight this price action, particularly the support between 0.786 and 0.618, which aligns closely with the trend.
Fibonacci Time-Price Zones using the 'Circular' Shape option. The price action appears to be ‘hugging’ the 0.5 Fibonacci level, suggesting potential resistance. This demonstrates how the circular zones can identify potential turning points and areas of consolidation which might not be seen with linear analysis.
Fibonacci Time-Price Zones using the 'Sloped' Shape option with Point D marker enabled. The chart demonstrates clear price action closely following along the sloped Retracement line until the orthogonal intersection at the 0.618 levels where the trend is broken and price dips throughout the 0.618 to 0.786 horizontal zone. Price jumps back to the retracement slope at the start of the 0.786 horizontal zone and continues to the 1.0 horizontal zone. The aqua-colored retracement line is enabled to further emphasize this retracement slope .
Geometric validation using TradingView's built-in Fibonacci Circle tool (overlaid). The alignment at the 0.5 and 1.0 levels demonstrates the indicator's consistent approximation of Fibonacci Circles.
Comparison of Fibonacci Time-Price Zones (Shape: Horizontal) with TradingView's Built-in Retracement and Extension Tools (overlaid): This example demonstrates how the Horizontal structure aligns with TradingView’s retracement and extension levels, allowing users to integrate multiple tools seamlessly. The Fibonacci circle connects retracement and extension zones, highlighting the potential relationship between past retracements and future extensions.
📐 GEOMETRIC FOUNDATIONS 📐
This indicator integrates circular and straight representations of Fibonacci levels, specifically the Circular , Orthogonal , Sloped , and Horizontal shape options. The geometric principles behind these shapes differ significantly, requiring distinct scaling methods for accurate representation. The Circular shape employs logarithmic scaling with radial expansion, where the distance from a central point determines the level's position, creating partial circles that align with TradingView's built-in Fibonacci Circle tool. The other three shapes utilize geometric progression scaling for linear extension from a starting point, resulting in straight lines that align with TradingView's built-in Fibonacci retracement and extension tools. Due to these distinct geometric foundations and scaling methods, perfectly aligning both the partial circles and straight lines simultaneously is mathematically constrained, though any differences are typically visually imperceptible.
The Circular shape's partial circles are calculated and scaled to align with TradingView's built-in Fibonacci Circles. These circles are plotted from the second swing point onward. This approach ensures consistent and accurate visualization across all market types, including those with gaps or closed sessions, which unlike 24/7 markets, do not have a direct one-to-one correspondence between bar indices and time. To maintain accurate geometric proportions across varying chart scales, the indicator calculates an aspect ratio by normalizing the proportional difference between vertical (price) and horizontal (time) distances of the swing points. This normalization factor ensures geometric shapes maintain their mathematical properties regardless of price scale magnitude or time period span, while maintaining the correct proportions of the geometric constructions at any chart zoom level.
The indicator automatically applies the appropriate scaling factor based on the selected shape option, optimizing either circular proportions and proper radius calculations for each Fibonacci level, or straight-line relationships between Fibonacci levels. These distinct scaling approaches maintain mathematical integrity while preserving the essential characteristics of each geometric representation, ensuring optimal visualization accuracy whether using circular or linear shapes.
⚠️ DISCLAIMER ⚠️
The Fibonacci Time-Price Zones indicator is a visual analysis tool designed to illustrate Fibonacci relationships through geometric constructions incorporating both curved and straight lines, providing a structured framework for identifying potential areas of price interaction. It is not intended as a predictive or standalone trading signal indicator.
The indicator calculates levels and projections using user-defined anchor points and Fibonacci ratios. While it aims to align with TradingView’s Fibonacci extension, retracement, and circle tools by employing mathematical and geometric formulas, no guarantee is made that its calculations are identical to TradingView's proprietary methods.
Like all technical and visual indicators, these visual representations may visually align with key price zones in hindsight, reflecting observed price dynamics. However, these visualizations are not standalone signals for trading decisions and should be interpreted as part of a broader analytical approach.
This indicator is intended for educational and analytical purposes, complementing other tools and methods of market analysis. Users are encouraged to integrate it into a comprehensive trading strategy, customizing its settings to suit their specific needs and market conditions.
🧠 BEYOND THE CODE 🧠
The Fibonacci Time-Price Zones indicator is designed to encourage both education and community engagement. By integrating time-sensitive geometry with Fibonacci-based frameworks, it bridges traditional grid-based analysis with dynamic time-price relationships. The inclusion of semicircles, horizontal levels, orthogonal structures, and sloped trends provides users with versatile tools to explore the interaction between price movements and temporal intervals while maintaining clarity and adaptability.
As an open-source tool, the indicator invites exploration, experimentation, and customization. Whether used as a standalone resource or alongside other technical strategies, it serves as a practical and educational framework for understanding market structure and Fibonacci relationships in greater depth.
Your feedback and contributions are essential to refining and enhancing the Fibonacci Time-Price Zones indicator. We look forward to the creative applications, adaptations, and insights this tool inspires within the trading community.
Candle Open Time labels (& TAPDA Lines)Description of the "4-Hour Candle Opening Times (TAPDA Lines)" Indicator
The "4-Hour Candle Opening Times (TAPDA Lines)" indicator integrates key principles of the Time and Price Action Trading Algorithm (TAPTA) with practical tools for analyzing market behavior. This script is designed for traders who leverage the interaction between time and price to identify opportunities in the market. The indicator supports the identification of significant price levels and potential areas of interest based on historical data and recurring patterns tied to specific timeframes.
Core Concepts
Time and Price Interaction (TAPTA Logic):
The script implements TAPTA principles by focusing on time intervals (4-hour candles) and the price action associated with those intervals.
Traders use this logic to recognize how prices behave at specific times, identifying patterns, levels of support or resistance, and potential reversals.
Highs and Lows Recognition (TAPDA):
The indicator includes logic for identifying and marking "Tapped Highs and Lows," which occur when price action retraces to previously significant levels within a specified tolerance. These taps are visually represented with horizontal lines, enabling traders to spot recurring price behaviors and levels of interest.
Dynamic Levels for Decision-Making:
By combining time and price, the script visualizes key price levels and their relevance over time, equipping traders with actionable insights for entry, exit, and risk management.
Indicator Features
1. Visual Representation of Candle Opening Times
The indicator marks the opening times of 4-hour candles on the chart.
A customizable label system displays the time in either a 12-hour or 24-hour format, with options to toggle the visibility of AM/PM suffixes.
2. TAPDA Logic
Identifies and highlights price levels that have been tapped within a specified tolerance.
Horizontal lines are drawn to mark these levels, allowing traders to see historical price levels acting as support or resistance.
The "Tapped Highs and Lows" are updated dynamically based on the most recent price action.
3. Timeframe-Specific Filtering
Users can limit the display to specific times of interest, such as 2 AM, 6 AM, and 10 AM, by toggling the "GCT (General Candle Times)" option.
Additional options allow filtering TAPDA logic by AM or PM timeframes, catering to traders who focus on specific market sessions.
4. Adjustable Plotting Limits
The script incorporates settings for controlling the maximum number of labels and lines displayed on the chart:
Max Labels: Limits the number of labels plotted for 4-hour candle opening times.
Max TAPDA Lines: Limits the number of TAPDA horizontal lines displayed.
A "Sync Lines and Labels" option ensures the same number of labels and lines are plotted when enabled, providing a consistent and clutter-free visualization.
5. Plot Maximum Capability
A "Plot Max" feature allows users to override the default behavior and force the plotting of the maximum allowed labels and lines, providing a comprehensive view of historical data.
6. User-Friendly Customization
Fully customizable label styles, including options for position, size, color, and background opacity.
Adjustable tolerance levels for TAPDA lines ensure compatibility with different market conditions and trading strategies.
Settings for flipping or aligning label positions above or below candles, or locking them to the opening price.
Script Logic
The script is built to prioritize efficiency and clarity, adhering to TradingView's Pine Script best practices and community standards:
Initialization:
Arrays are used to store historical price data, including highs, lows, and timestamps, ensuring only the necessary amount of data is processed.
A flexible and efficient data management system maintains a rolling window of data for both labels and TAPDA lines, ensuring smooth performance.
Label and Line Plotting:
Labels are plotted dynamically at user-defined positions and styles to mark the opening times of 4-hour candles.
TAPDA lines are drawn between historical high or low points and the current price action when the tolerance condition is met.
Limit Management:
The script enforces limits on the number of labels and lines plotted on the chart to maintain visual clarity.
Users can enable synchronization between the maximum labels and lines to ensure consistent visualization.
Customization Options:
Extensive customization settings allow traders to tailor the indicator to their strategies and preferences, including:
Label and line styles.
Session filtering (AM, PM, or specific times).
Display limits and synchronization options.
Capabilities
1. Enhance Time-Based Analysis
By marking significant times (4-hour candle openings), traders can identify key market phases and recurring behaviors tied to specific hours.
2. Leverage Historical Price Action
TAPDA logic highlights areas where price action interacts with historical highs and lows, providing actionable insights into potential support or resistance zones.
3. Improve Decision-Making
The indicator supports informed decision-making by blending visual data with time and price action principles, helping traders spot opportunities and mitigate risks.
4. Flexible Application Across Strategies
Suitable for day traders, swing traders, and position traders who utilize time and price action for trend analysis, reversals, or breakout strategies.
Best Practices for Use
Key Levels Analysis:
Focus on labels and TAPDA lines near critical price zones to gauge potential market reactions.
Session-Based Trading:
Use AM/PM filters or GCT settings to isolate specific trading sessions relevant to your strategy.
Combine with Other Indicators:
Enhance the effectiveness of this indicator by combining it with moving averages, RSI, or other tools for confirmation.
Risk Management:
Use the identified levels for stop-loss placement or target setting to align with your risk tolerance.
3 EMA + RSI with Trail Stop [Free990] (LOW TF)This trading strategy combines three Exponential Moving Averages (EMAs) to identify trend direction, uses RSI to signal exit conditions, and applies both a fixed percentage stop-loss and a trailing stop for risk management. It aims to capture momentum when the faster EMAs cross the slower EMA, then uses RSI thresholds, time-based exits, and stops to close trades.
Short Explanation of the Logic
Trend Detection: When the 10 EMA crosses above the 20 EMA and both are above the 100 EMA (and the current price bar closes higher), it triggers a long entry signal. The reverse happens for a short (the 10 EMA crosses below the 20 EMA and both are below the 100 EMA).
RSI Exit: RSI crossing above a set threshold closes long trades; crossing below another threshold closes short trades.
Time-Based Exit: If a trade is in profit after a set number of bars, the strategy closes it.
Stop-Loss & Trailing Stop: A fixed stop-loss based on a percentage from the entry price guards against large drawdowns. A trailing stop dynamically tightens as the trade moves in favor, locking in potential gains.
Detailed Explanation of the Strategy Logic
Exponential Moving Average (EMA) Setup
Short EMA (out_a, length=10)
Medium EMA (out_b, length=20)
Long EMA (out_c, length=100)
The code calculates three separate EMAs to gauge short-term, medium-term, and longer-term trend behavior. By comparing their relative positions, the strategy infers whether the market is bullish (EMAs stacked positively) or bearish (EMAs stacked negatively).
Entry Conditions
Long Entry (entryLong): Occurs when:
The short EMA (10) crosses above the medium EMA (20).
Both EMAs (short and medium) are above the long EMA (100).
The current bar closes higher than it opened (close > open).
This suggests that momentum is shifting to the upside (short-term EMAs crossing up and price action turning bullish). If there’s an existing short position, it’s closed first before opening a new long.
Short Entry (entryShort): Occurs when:
The short EMA (10) crosses below the medium EMA (20).
Both EMAs (short and medium) are below the long EMA (100).
The current bar closes lower than it opened (close < open).
This indicates a potential shift to the downside. If there’s an existing long position, that gets closed first before opening a new short.
Exit Signals
RSI-Based Exits:
For long trades: When RSI exceeds a specified threshold (e.g., 70 by default), it triggers a long exit. RSI > short_rsi generally means overbought conditions, so the strategy exits to lock in profits or avoid a pullback.
For short trades: When RSI dips below a specified threshold (e.g., 30 by default), it triggers a short exit. RSI < long_rsi indicates oversold conditions, so the strategy closes the short to avoid a bounce.
Time-Based Exit:
If the trade has been open for xBars bars (configurable, e.g., 24 bars) and the trade is in profit (current price above entry for a long, or current price below entry for a short), the strategy closes the position. This helps lock in gains if the move takes too long or momentum stalls.
Stop-Loss Management
Fixed Stop-Loss (% Based): Each trade has a fixed stop-loss calculated as a percentage from the average entry price.
For long positions, the stop-loss is set below the entry price by a user-defined percentage (fixStopLossPerc).
For short positions, the stop-loss is set above the entry price by the same percentage.
This mechanism prevents catastrophic losses if the market moves strongly against the position.
Trailing Stop:
The strategy also sets a trail stop using trail_points (the distance in price points) and trail_offset (how quickly the stop “catches up” to price).
As the market moves in favor of the trade, the trailing stop gradually tightens, allowing profits to run while still capping potential drawdowns if the price reverses.
Order Execution Flow
When the conditions for a new position (long or short) are triggered, the strategy first checks if there’s an opposite position open. If there is, it closes that position before opening the new one (prevents going “both long and short” simultaneously).
RSI-based and time-based exits are checked on each bar. If triggered, the position is closed.
If the position remains open, the fixed stop-loss and trailing stop remain in effect until the position is exited.
Why This Combination Works
Multiple EMA Cross: Combining 10, 20, and 100 EMAs balances short-term momentum detection with a longer-term trend filter. This reduces false signals that can occur if you only look at a single crossover without considering the broader trend.
RSI Exits: RSI provides a momentum oscillator view—helpful for detecting overbought/oversold conditions, acting as an extra confirmation to exit.
Time-Based Exit: Prevents “lingering trades.” If the position is in profit but failing to advance further, it takes profit rather than risking a trend reversal.
Fixed & Trailing Stop-Loss: The fixed stop-loss is your safety net to cap worst-case losses. The trailing stop allows the strategy to lock in gains by following the trade as it moves favorably, thus maximizing profit potential while keeping risk in check.
Overall, this approach tries to capture momentum from EMA crossovers, protect profits with trailing stops, and limit risk through both a fixed percentage stop-loss and exit signals from RSI/time-based logic.
Overnight Effect High Volatility Crypto (AiBitcoinTrend)👽 Overview of the Strategy
This strategy leverages the overnight effect in the cryptocurrency market, specifically targeting the two-hour window from 21:00 UTC to 23:00 UTC. The strategy is designed to be applied only during periods of high volatility, which is determined using historical volatility data. This approach, inspired by research from Padyšák and Vojtko (2022), aims to capitalize on statistically significant return patterns observed during these hours.
Deep Backtesting with a High Volatility Filter
Deep Backtesting without a High Volatility Filter
👽 How the Strategy Works
Volatility Calculation:
Each day at 00:00 UTC, the strategy calculates the 30-day historical volatility of crypto returns (typically Bitcoin). The historical volatility is the standard deviation of the log returns over the past 30 days, representing the market's recent volatility level.
Median Volatility Benchmark:
The median of the 30-day historical volatility is calculated over a 365-day period (one year). This median acts as a benchmark to classify each day as either:
👾 High Volatility: When the current 30-day volatility exceeds the median volatility.
👾 Low Volatility: When the current 30-day volatility is below the median.
Trading Rule:
If the day is classified as a High Volatility Day, the strategy executes the following trades:
👾 Buy at 21:00 UTC.
👾 Sell at 23:00 UTC.
Trade Execution Details:
The strategy uses a 0.02% fee per trade.
Each trade is executed with 25% of the available capital. This allocation helps manage risk while allowing for compounding returns.
Rationale:
The returns during the 22:00 and 23:00 UTC hours have been found to be statistically significant during high volatility periods. The overnight effect is believed to drive this phenomenon due to the asynchronous closing hours of global financial markets. This creates unique trading opportunities in the cryptocurrency market, where exchanges remain open 24/7.
👽 Market Context and Global Time Zone Impact
👾 Why 21:00 to 23:00 UTC?
During this window, major traditional financial markets are closed:
NYSE (New York) closes at 21:00 UTC.
London and European markets are closed during these hours.
Asian markets (Tokyo, Hong Kong, etc.) open later, leaving this window largely unaffected by traditional trading flows.
This global market inactivity creates a period where significant moves can occur in the cryptocurrency market, particularly during high volatility.
👽 Strategy Parameters
Volatility Period: 30 days.
The lookback period for calculating historical volatility.
Median Period: 365 days.
The lookback period for calculating the median volatility benchmark.
Entry Time: 21:00 UTC.
Adjust this to your local time if necessary (e.g., 16:00 in New York, 22:00 in Stockholm).
Exit Time: 23:00 UTC.
Adjust this to your local time if necessary (e.g., 18:00 in New York, 00:00 midnight in Stockholm).
👽 Benefits of the Strategy
Seasonality Effect:
The strategy captures consistent patterns driven by the overnight effect and high volatility periods.
Risk Reduction:
Since trades are executed during a specific window and only on high volatility days, the strategy helps mitigate exposure to broader market risk.
Simplicity and Efficiency:
The strategy is moderately complex, making it accessible for traders while offering significant returns.
Global Applicability:
Suitable for traders worldwide, with clear guidelines on adjusting for local time zones.
👽 Considerations
Market Conditions: The strategy works best in a high-volatility environment.
Execution: Requires precise timing to enter and exit trades at the specified hours.
Time Zone Adjustments: Ensure you convert UTC times accurately based on your location to execute trades at the correct local times.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
DCA Strategy with Mean Reversion and Bollinger BandDCA Strategy with Mean Reversion and Bollinger Band
The Dollar-Cost Averaging (DCA) Strategy with Mean Reversion and Bollinger Bands is a sophisticated trading strategy that combines the principles of DCA, mean reversion, and technical analysis using Bollinger Bands. This strategy aims to capitalize on market corrections by systematically entering positions during periods of price pullbacks and reversion to the mean.
Key Concepts and Principles
1. Dollar-Cost Averaging (DCA)
DCA is an investment strategy that involves regularly purchasing a fixed dollar amount of an asset, regardless of its price. The idea behind DCA is that by spreading out investments over time, the impact of market volatility is reduced, and investors can avoid making large investments at inopportune times. The strategy reduces the risk of buying all at once during a market high and can smooth out the cost of purchasing assets over time.
In the context of this strategy, the Investment Amount (USD) is set by the user and represents the amount of capital to be invested in each buy order. The strategy executes buy orders whenever the price crosses below the lower Bollinger Band, which suggests a potential market correction or pullback. This is an effective way to average the entry price and avoid the emotional pitfalls of trying to time the market perfectly.
2. Mean Reversion
Mean reversion is a concept that suggests prices will tend to return to their historical average or mean over time. In this strategy, mean reversion is implemented using the Bollinger Bands, which are based on a moving average and standard deviation. The lower band is considered a potential buy signal when the price crosses below it, indicating that the asset has become oversold or underpriced relative to its historical average. This triggers the DCA buy order.
Mean reversion strategies are popular because they exploit the natural tendency of prices to revert to their mean after experiencing extreme deviations, such as during market corrections or panic selling.
3. Bollinger Bands
Bollinger Bands are a technical analysis tool that consists of three lines:
Middle Band: The moving average, usually a 200-period Exponential Moving Average (EMA) in this strategy. This serves as the "mean" or baseline.
Upper Band: The middle band plus a certain number of standard deviations (multiplier). The upper band is used to identify overbought conditions.
Lower Band: The middle band minus a certain number of standard deviations (multiplier). The lower band is used to identify oversold conditions.
In this strategy, the Bollinger Bands are used to identify potential entry points for DCA trades. When the price crosses below the lower band, this is seen as a potential opportunity for mean reversion, suggesting that the asset may be oversold and could reverse back toward the middle band (the EMA). Conversely, when the price crosses above the upper band, it indicates overbought conditions and signals potential market exhaustion.
4. Time-Based Entry and Exit
The strategy has specific entry and exit points defined by time parameters:
Open Date: The date when the strategy begins opening positions.
Close Date: The date when all positions are closed.
This time-bound approach ensures that the strategy is active only during a specified window, which can be useful for testing specific market conditions or focusing on a particular time frame.
5. Position Sizing
Position sizing is determined by the Investment Amount (USD), which is the fixed amount to be invested in each buy order. The quantity of the asset to be purchased is calculated by dividing the investment amount by the current price of the asset (investment_amount / close). This ensures that the amount invested remains constant despite fluctuations in the asset's price.
6. Closing All Positions
The strategy includes an exit rule that closes all positions once the specified close date is reached. This allows for controlled exits and limits the exposure to market fluctuations beyond the strategy's timeframe.
7. Background Color Based on Price Relative to Bollinger Bands
The script uses the background color of the chart to provide visual feedback about the price's relationship with the Bollinger Bands:
Red background indicates the price is above the upper band, signaling overbought conditions.
Green background indicates the price is below the lower band, signaling oversold conditions.
This provides an easy-to-interpret visual cue for traders to assess the current market environment.
Postscript: Configuring Initial Capital for Backtesting
To ensure the backtest results align with the actual investment scenario, users must adjust the Initial Capital in the TradingView strategy properties. This is done by calculating the Initial Capital as the product of the Total Closed Trades and the Investment Amount (USD). For instance:
If the user is investing 100 USD per trade and has 10 closed trades, the Initial Capital should be set to 1,000 USD.
Similarly, if the user is investing 200 USD per trade and has 24 closed trades, the Initial Capital should be set to 4,800 USD.
This adjustment ensures that the backtesting results reflect the actual capital deployed in the strategy and provides an accurate representation of potential gains and losses.
Conclusion
The DCA strategy with Mean Reversion and Bollinger Bands is a systematic approach to investing that leverages the power of regular investments and technical analysis to reduce market timing risks. By combining DCA with the insights offered by Bollinger Bands and mean reversion, this strategy offers a structured way to navigate volatile markets while targeting favorable entry points. The clear entry and exit rules, coupled with time-based constraints, make it a robust and disciplined approach to long-term investing.
Volume Rate of Change (VROC)Volume Rate of Change (VROC) is an indicator that calculates the percentage change in trading volume over a specific period, helping analyze market momentum and activity. It is calculated as:
VROC = ((Current Volume - Past Volume) ÷ Past Volume) × 100
This indicator shows changes in market interest. Positive values indicate increasing volume, while negative values signal a decrease. High VROC values often suggest potential trend reversals or breakouts.
Applications:
Breakout Validation: VROC > 200% confirms strong breakouts; below this may signal false moves.
Market Stagnation: VROC < 0% suggests shrinking volume and range-bound markets.
Trend End Alert: A drop below 0% during trends may indicate weakening momentum.
Adjusting for Timeframes: Tailor VROC to timeframes.
Examples:
Daily: VROC(5) compares with last week's same day; VROC(20) with 1 month ago.
Monthly: VROC(12) compares with the same month last year; VROC(1) with last month.
Intraday: VROC(24) (hourly) and VROC(288) (5 minutes) for the same time yesterday.
Daily High/Low Levels with mitigationThis Pine Script script defines a TradingView indicator named "Daily High/Low Levels" designed to track and display the daily high and low levels of a trading session, with added functionality for marking levels as mitigated when certain conditions are met. Here's a breakdown of its functionality:
Key Features
Session Start Time: The script allows you to specify a custom session start time in 24-hour format. This ensures the levels align with your trading session preferences.
Daily Highs and Lows:
Tracks the high and low levels for each session.
Retains the highs and lows for a configurable number of previous days.
Visualization:
Creates horizontal lines for each session's high and low levels.
Supports customization of line colors and styles.
Mitigation Tracking:
Monitors whether a high or low level has been "mitigated" (touched or exceeded by subsequent price action).
Changes the line style and color to indicate mitigation.
Provides an alert when mitigation occurs.
Configurable Extensions:
Lines can be extended beyond mitigation or stopped at the bar index where mitigation occurs, depending on user preference.
Efficient Array Management:
Uses arrays to manage daily highs, lows, their respective indices, and lines.
Ensures the size of stored data does not exceed the configured limit (daysToTrack).
Alerts:
Sends alerts when high or low levels are mitigated, which can be used for trading decisions.
Inputs
Session Start Hour/Minute: Defines when a new session starts.
Days to Track: Sets the number of previous days to display high/low levels.
Colors: Allows customization of line colors for unmitigated and mitigated levels.
Extend Lines: Toggles whether lines should extend past the mitigation point.
Code Highlights
New Session Detection: The script detects the start of a new session based on the configured session start time and resets daily highs/lows.
Line Management: Horizontal rays are created for highs and lows, and mitigated lines are updated with a dashed style and faded color.
Mitigation Logic: The script checks whether current price action exceeds stored high or low levels and updates their status and appearance accordingly.
Memory Management: Ensures the size of the arrays (highs, lows, lines) does not exceed the configured daysToTrack, deleting the oldest elements as necessary.
This indicator is highly customizable and useful for traders who want to track and analyze daily support and resistance levels, incorporating mitigation as a dynamic feature.
Weis Wave Max█ Overview
Weis Wave Max is the result of my weis wave study.
David Weis said,
"Trading with the Weis Wave involves changes in behavior associated with springs, upthrusts, tests of breakouts/breakdowns, and effort vs reward. The most common setup is the low-volume pullback after a bullish/bearish change in behavior."
THE STOCK MARKET UPDATE (February 24, 2013)
I inspired from his sentences and made this script.
Its Main feature is to identify the largest wave in Weis wave and advantageous trading opportunities.
█ Features
This indicator includes several features related to the Weis Wave Method.
They help you analyze which is more bullish or bearish.
Highlight Max Wave Value (single direction)
Highlight Abnormal Max Wave Value (both directions)
Support and Resistance zone
Signals and Setups
█ Usage
Weis wave indicator displays cumulative volume for each wave.
Wave volume is effective when analyzing volume from VSA (Volume Spread Analysis) perspective.
The basic idea of Weis wave is large wave volume hint trend direction. This helps identify proper entry point.
This indicator highlights max wave volume and displays the signal and then proper Risk Reward Ratio entry frame.
I defined Change in Behavior as max wave volume (single direction).
Pullback is next wave that does not exceed the starting point of CiB wave (LH sell entry, HL buy entry).
Change in Behavior Signal ○ appears when pullback is determined.
Change in Behavior Setup (Entry frame) appears when condition of Min/Max Pullback is met and follow through wave breaks end point of CiB wave.
This indicator has many other features and they can also help a user identify potential levels of trade entry and which is more bullish or bearish.
In the screenshot below we can see wave volume zones as support and resistance levels. SOT and large wave volume /delta price (yellow colored wave text frame) hint stopping action.
█ Settings
Explains the main settings.
-- General --
Wave size : Allows the User to select wave size from ① Fixed or ② ATR. ② ATR is Factor x ATR(Length).
Display : Allows the User to select how many wave text and zigzag appear.
-- Wave Type --
Wave type : Allows the User to select from Volume or Volume and Time.
Wave Volume / delta price : Displays Wave Volume / delta price.
Simplified value : Allows the User to select wave text display style from ① Divisor or ② Normalized. Normalized use SMA.
Decimal : Allows the User to select the decimal point in the Wave text.
-- Highlight Abnormal Wave --
Highlight Max Wave value (single direction) : Adds marks to the Wave text to highlight the max wave value.
Lookback : Allows the User to select how many waves search for the max wave value.
Highlight Abnormal Wave value (both directions) : Changes wave text size, color or frame color to highlight the abnormal wave value.
Lookback : Allows the User to select SMA length to decide average wave value.
Large/Small factor : Allows the User to select the threshold large wave value and small wave value. Average wave value is 1.
delta price : Highlights large delta price by large wave text size, small by small text size.
Wave Volume : Highlights large wave volume by yellow colored wave text, small by gray colored.
Wave Volume / delta price : highlights large Wave Volume / delta price by yellow colored wave text frame, small by gray colored.
-- Support and Resistance --
Single side Max Wave Volume / delta price : Draws dashed border box from end point of Max wave volume / delta price level.
Single side Max Wave Volume : Draws solid border box from start point of Max wave volume level.
Bias Wave Volume : Draws solid border box from start point of bias wave volume level.
-- Signals --
Bias (Wave Volume / delta price) : Displays Bias mark when large difference in wave volume / delta price before and after.
Ratio : Decides the threshold of become large difference.
3Decrease : Displays 3D mark when a continuous decrease in wave volume.
Shortening Of the Thrust : Displays SOT mark when a continuous decrease in delta price.
Change in Behavior and Pullback : Displays CiB mark when single side max wave volume and pullback.
-- Setups --
Change in Behavior and Pullback and Breakout : Displays entry frame when change in behavior and pullback and then breakout.
Min / Max Pullback : Decides the threshold of min / max pullback.
If you need more information, please read the indicator's tooltip.
█ Conclusion
Weis Wave is powerful interpretation of volume and its tell us potential trend change and entry point which can't find without weis wave.
It's not the holy grail, but improve your chart reading skills and help you trade rationally (at least from VSA perspective).
Mean Price
^^ Plotting switched to Line.
This method of financial time series (aka bars) downsampling is literally, naturally, and thankfully the best you can do in terms of maximizing info gain. You can finally chill and feed it to your studies & eyes, and probably use nothing else anymore.
(HL2 and occ3 also have use cases, but other aggregation methods? Not really, even if they do, the use cases are ‘very’ specific). Tho in order to understand why, you gotta read the following wall, or just believe me telling you, ‘I put it on my momma’.
The true story about trading volumes and why this is all a big misdirection
Actually, you don’t need to be a quant to get there. All you gotta do is stop blindly following other people’s contextual (at best) solutions, eg OC2 aggregation xD, and start using your own brain to figure things out.
Every individual trade (basically an imprint on 1D price space that emerges when market orders hit the order book) has several features like: price, time, volume, AND direction (Up if a market buy order hits the asks, Down if a market sell order hits the bids). Now, the last two features—volume and direction—can be effectively combined into one (by multiplying volume by 1 or -1), and this is probably how every order matching engine should output data. If we’re not considering size/direction, we’re leaving data behind. Moreover, trades aren’t just one-price dots all the time. One trade can consume liquidity on several levels of the order book, so a single trade can be several ticks big on the price axis.
You may think now that there are no zero-volume ticks. Well, yes and no. It depends on how you design an exchange and whether you allow intra-spread trades/mid-spread trades (now try to Google it). Intra-spread trades could happen if implemented when a matching engine receives both buy and sell orders at the same microsecond period. This way, you can match the orders with each other at a better price for both parties without even hitting the book and consuming liquidity. Also, if orders have different sizes, the remaining part of the bigger order can be sent to the order book. Basically, this type of trade can be treated as an OTC trade, having zero volume because we never actually hit the book—there’s no imprint. Another reason why it makes sense is when we think about volume as an impact or imbalance act, and how the medium (order book in our case) responds to it, providing information. OTC and mid-spread trades are not aggressive sells or buys; they’re neutral ticks, so to say. However huge they are, sometimes many blocks on NYSE, they don’t move the price because there’s no impact on the medium (again, which is the order book)—they’re not providing information.
... Now, we need to aggregate these trades into, let’s say, 1-hour bars (remember that a trade can have either positive or negative volume). We either don’t want to do it, or we don’t have this kind of information. What we can do is take already aggregated OHLC bars and extract all the info from them. Given the market is fractal, bars & trades gotta have the same set of features:
- Highest & lowest ticks (high & low) <- by price;
- First & last ticks (open & close) <- by time;
- Biggest and smallest ticks <- by volume.*
*e.g., in the array ,
2323: biggest trade,
-1212: smallest trade.
Now, in our world, somehow nobody started to care about the biggest and smallest trades and their inclusion in OHLC data, while this is actually natural. It’s the same way as it’s done with high & low and open & close: we choose the minimum and maximum value of a given feature/axis within the aggregation period.
So, we don’t have these 2 values: biggest and smallest ticks. The best we can do is infer them, and given the fact the biggest and smallest ticks can be located with the same probability everywhere, all we can do is predict them in the middle of the bar, both in time and price axes. That’s why you can see two HL2’s in each of the 3 formulas in the code.
So, summed up absolute volumes that you see in almost every trading platform are actually just a derivative metric, something that I call Type 2 time series in my own (proprietary ‘for now’) methods. It doesn’t have much to do with market orders hitting the non-uniform medium (aka order book); it’s more like a statistic. Still wanna use VWAP? Ok, but you gotta understand you’re weighting Type 1 (natural) time series by Type 2 (synthetic) ones.
How to combine all the data in the right way (khmm khhm ‘order’)
Now, since we have 6 values for each bar, let’s see what information we have about them, what we don’t have, and what we can do about it:
- Open and close: we got both when and where (time (order) and price);
- High and low: we got where, but we don’t know when;
- Biggest & smallest trades: we know shit, we infer it the way it was described before.'
By using the location of the close & open prices relative to the high & low prices, we can make educated guesses about whether high or low was made first in a given bar. It’s not perfect, but it’s ultimately all we can do—this is the very last bit of info we can extract from the data we have.
There are 2 methods for inferring volume delta (which I call simply volume) that are presented everywhere, even here on TradingView. Funny thing is, this is actually 2 parts of the 1 method. I wonder how many folks see through it xD. The same method can be used for both inferring volume delta AND making educated guesses whether high or low was made first.
Imagine and/or find the cases on your charts to understand faster:
* Close > open means we have an up bar and probably the volume is positive, and probably high was made later than low.
* Close < open means we have a down bar and probably the volume is negative, and probably low was made later than high.
Now that’s the point when you see that these 2 mentioned methods are actually parts of the 1 method:
If close = open, we still have another clue: distance from open/close pair to high (HC), and distance from open/close pair to low (LC):
* HC < LC, probably high was made later.
* HC > LC, probably low was made later.
And only if close = open and HC = LC, only in this case we have no clue whether high or low was made earlier within a bar. We simply don’t have any more information to even guess. This bar is called a neutral bar.
At this point, we have both time (order) and price info for each of our 6 values. Now, we have to solve another weighted average problem, and that’s it. We’ll weight prices according to the order we’ve guessed. In the neutral bar case, open has a weight of 1, close has a weight of 3, and both high and low have weights of 2 since we can’t infer which one was made first. In all cases, biggest and smallest ticks are modeled with HL2 and weighted like they’re located in the middle of the bar in a time sense.
P.S.: I’ve also included a "robust" method where all the bars are treated like neutral ones. I’ve used it before; obviously, it has lesser info gain -> works a bit worse.
QuantifyPS - 1Library "QuantifyPS"
normdist(z)
Parameters:
z (float) : (float): The z-score for which the CDF is to be calculated.
Returns: (float): The cumulative probability corresponding to the input z-score.
Notes:
- Uses an approximation method for the normal distribution CDF, which is computationally efficient.
- The result is accurate for most practical purposes but may have minor deviations for extreme values of `z`.
Formula:
- Based on the approximation formula:
`Φ(z) ≈ 1 - f(z) * P(t)` if `z > 0`, otherwise `Φ(z) ≈ f(z) * P(t)`,
where:
`f(z) = 0.3989423 * exp(-z^2 / 2)` (PDF of standard normal distribution)
`P(t) = Σ [c * t^i]` with constants `c` and `t = 1 / (1 + 0.2316419 * |z|)`.
Implementation details:
- The approximation uses five coefficients for the polynomial part of the CDF.
- Handles both positive and negative values of `z` symmetrically.
Constants:
- The coefficients and scaling factors are chosen to minimize approximation errors.
gamma(x)
Parameters:
x (float) : (float): The input value for which the Gamma function is to be calculated.
Must be greater than 0. For x <= 0, the function returns `na` as it is undefined.
Returns: (float): Approximation of the Gamma function for the input `x`.
Notes:
- The Lanczos approximation provides a numerically stable and efficient method to compute the Gamma function.
- The function is not defined for `x <= 0` and will return `na` in such cases.
- Uses precomputed Lanczos coefficients for accuracy.
- Includes handling for small numerical inaccuracies.
Formula:
- The Gamma function is approximated as:
`Γ(x) ≈ sqrt(2π) * t^(x + 0.5) * e^(-t) * Σ(p / (x + k))`
where `t = x + g + 0.5` and `p` is the array of Lanczos coefficients.
Implementation details:
- Lanczos coefficients (`p`) are precomputed and stored in an array.
- The summation iterates over these coefficients to compute the final result.
- The constant `g` controls the precision of the approximation (commonly `g = 7`).
t_cdf(t, df)
Parameters:
t (float) : (float): The t-statistic for which the CDF value is to be calculated.
df (int) : (int): Degrees of freedom of the t-distribution.
Returns: (float): Approximate CDF value for the given t-statistic.
Notes:
- This function computes a one-tailed p-value.
- Relies on an approximation formula using gamma functions and standard t-distribution properties.
- May not be as accurate as specialized statistical libraries for extreme values or very high degrees of freedom.
Formula:
- Let `x = df / (t^2 + df)`.
- The approximation formula is derived using:
`CDF(t, df) ≈ 1 - * x^((df + 1) / 2) / 2`,
where Γ represents the gamma function.
Implementation details:
- Computes the gamma ratio for normalization.
- Applies the t-distribution formula for one-tailed probabilities.
tStatForPValue(p, df)
Parameters:
p (float) : (float): P-value for which the t-statistic needs to be calculated.
Must be in the interval (0, 1).
df (int) : (int): Degrees of freedom of the t-distribution.
Returns: (float): The t-statistic corresponding to the given p-value.
Notes:
- If `p` is outside the interval (0, 1), the function returns `na` as an error.
- The function uses binary search with a fixed number of iterations and a defined tolerance.
- The result is accurate to within the specified tolerance (default: 0.0001).
- Relies on the cumulative density function (CDF) `t_cdf` for the t-distribution.
Formula:
- Uses the cumulative density function (CDF) of the t-distribution to iteratively find the t-statistic.
Implementation details:
- `low` and `high` define the search interval for the t-statistic.
- The midpoint (`mid`) is iteratively refined until the difference between the cumulative probability
and the target p-value is smaller than the tolerance.
jarqueBera(n, s, k)
Parameters:
n (float) : (series float): Number of observations in the dataset.
s (float) : (series float): Skewness of the dataset.
k (float) : (series float): Kurtosis of the dataset.
Returns: (float): The Jarque-Bera test statistic.
Formula:
JB = n *
Notes:
- A higher JB value suggests that the data deviates more from a normal distribution.
- The test is asymptotically distributed as a chi-squared distribution with 2 degrees of freedom.
- Use this value to calculate a p-value to determine the significance of the result.
skewness(data)
Parameters:
data (float) : (series float): Input data series.
Returns: (float): The skewness value.
Notes:
- Handles missing values (`na`) by ignoring invalid points.
- Includes error handling for zero variance to avoid division-by-zero scenarios.
- Skewness is calculated as the normalized third central moment of the data.
kurtosis(data)
Parameters:
data (float) : (series float): Input data series.
Returns: (float): The kurtosis value.
Notes:
- Handles missing values (`na`) by ignoring invalid points.
- Includes error handling for zero variance to avoid division-by-zero scenarios.
- Kurtosis is calculated as the normalized fourth central moment of the data.
regression(y, x, lag)
Parameters:
y (float) : (series float): Dependent series (observed values).
x (float) : (series float): Independent series (explanatory variable).
lag (int) : (int): Number of lags applied to the independent series (x).
Returns: (tuple): Returns a tuple containing the following values:
- n: Number of valid observations.
- alpha: Intercept of the regression line.
- beta: Slope of the regression line.
- t_stat: T-statistic for the beta coefficient.
- p_value: Two-tailed p-value for the beta coefficient.
- r_squared: Coefficient of determination (R²) indicating goodness of fit.
- skew: Skewness of the residuals.
- kurt: Kurtosis of the residuals.
Notes:
- Handles missing data (`na`) by ignoring invalid points.
- Includes basic error handling for zero variance and division-by-zero scenarios.
- Computes residual-based statistics (skewness and kurtosis) for model diagnostics.
Tims Smart Money COT-IndexThe **Tims Smart Money COT Index** analyzes the positions of different groups of market participants from the COT report (Commercials, Large Specs, Small Specs). It calculates their net positions and scales them relative to extremes of the last 24 weeks. It indicates bullish and bearish zones to identify market sentiments.
- Commercials (Smart Money)**: Often act against the trend, bullish from 80+.
- Large Specs (Retail Money)**: Trend-following, bullish from 80+.
- Small Specs**: Mostly impulsive, bullish from 80+.
The indicator helps to identify turning points in the market based on the behavior of the players.
