Circuit Breaker LevelsThis indicator will show the Previous Day's Close and +/- 4.5% (Warning Level for Prop Firms), 5% (Prop Firm Trading Halted), 7% (First CME Circuit Breaker), 13% (Second CME Circuit Breaker), and 20% (Final CME Circuit Breaker All Trading Halted for the Day).
Индикаторы и стратегии
RVOL Effort Matrix💪🏻 RVOL Effort Matrix is a tiered volume framework that translates crowd participation into structure-aware visual zones. Rather than simply flagging spikes, it measures each bar’s volume as a ratio of its historical average and assigns to that effort dynamic tiers, creating a real-time map of conviction , exhaustion , and imbalance —before price even confirms.
⚖️ At its core, the tool builds a histogram of relative volume (RVOL). When enabled, a second layer overlays directional effort by estimating buy vs sell volume using candle body logic. If the candle closes higher, green (buy) volume dominates. If it closes lower, red (sell) volume leads. These components are stacked proportionally and inset beneath a colored cap line—a small but powerful layer that maintains visibility of the true effort tier even when split bars are active. The cap matches the original zone color, preserving context at all times.
Coloration communicates rhythm, tempo, and potential turning points:
• 🔴 = structurally weak effort, i.e. failed moves, fake-outs or trend exhaustion
• 🟡 = neutral volume, as seen in consolidations or pullbacks
• 🟢 = genuine commitment, good for continuation, breakout filters, or early rotation signals
• 🟣 = explosive volume signaling either climax or institutional entry—beware!
Background shading (optional) mirrors these zones across the pane for structural scanning at a glance. Volume bars can be toggled between full-stack mode or clean column view. Every layer is modular—built for composability with tools like ZVOL or OBVX Conviction Bias.
🧐 Ideal Use-Cases:
• 🕰 HTF bias anchoring → LTF execution
• 🧭 Identifying when structure is being driven by real crowd pressure
• 🚫 Fading green/fuchsia bars that fail to break structure
• ✅ Riding green/fuchsia follow-through in directional moves
🍷 Recommended Pairings:
• ZVOL for statistically significant volume anomaly detection
• OBVX Conviction Bias ↔️ for directional confirmation of effort zones
• SUPeR TReND 2.718 for structure-congruent entry filtering
• ATR Turbulence Ribbon to distinguish expansion pressure from churn
🥁 RVOL Effort Matrix is all about seeing—how much pressure is behind a move, whether that pressure is sustainable, and whether the crowd is aligned with price. It's volume, but readable. It’s structure, but dynamic. It’s the difference between obeying noise and trading to the beat of the market.
OBVX Conviction Bias🧮 The OBVX Conviction Bias overlay tracks the flow of directional volume using the classic On-Balance Volume calculation, then filters it through a layered moving average system to expose crowd commitment , pressure transitions , and momentum fatigue . The tool applies two smoothed averages to the OBV line—a fast curve and a longer-term baseline scaled using Euler’s constant (2.718)—and visualizes their relationship using a color-coded crossover ribbon and pressure fills. When used correctly, it reveals whether a move is being supported by meaningful volume, or whether the crowd is starting to disengage.
🚦 The core signal compares OBV to its fast moving average. When OBV climbs above the short average, it fills green—suggesting real directional effort. When OBV sinks below, the fill turns maroon—flagging fading conviction or pullback potential. A second fill between the short and long OBV moving averages captures the broader trend of volume intention. If the short is above the long, this space fills greenish, showing constructive pressure. If it flips, the fill fades red, signaling crowd hesitation, rotation, or early exhaustion.
⚖️ All smoothing is user-selectable, defaulting to VWMA for effort-sensitive structure. The long-term average is auto-scaled using the natural exponential multiplier (2.718), offering rhythm that reflects the curve of participation. OBVX Conviction Bias isn’t trying to predict—it’s trying to show you where the crowd is leaning , and whether that lean is gaining traction or losing strength.
🧐 Ideal Use-Cases:
• Detect divergence between volume flow and price action
• Confirm breakout validity with volume alignment
• Fade breakouts where OBV fails to follow through
• Time pullback entries when OBV pressure resumes in trend direction
🍷 Recommended Pairings:
• ZVOL to measure whether volume is statistically significant or just noise (as shown)
• RVOL Effort Matrix to validate crowd effort by tier and structure zone
• SUPeR TReND 2.718 and/or MA Ribbons for directional confluence
• ATR Turbulence to track volatility-phase alignment with volume intention
Gann Percentage of High & Low Prices for Options - Keanu_RiTzThis Indicator is based on the text from Chapter 4 "Percentage of High & Low Prices" page number "30" from the book "WD Gann 45 years in Wall Street".
This Indicator is to be used on Intraday Timeframes and only on Options Charts (CALL & PUT) and not on any other chart.
The following is the text from that page :-
One of the greatest discoveries I ever made was how to figure the percentage of high and low prices on the averages and individual stocks.
The percentages of extreme high and low levels indicate future resistance levels.
There is a relation between every low price to some future high price and a percentage of the low price indicates what levels to expect the next high price.
At this price you can sell out long stocks and sell short with a limited risk.
The extreme high price or any minor tops are related to future bottoms er low levels.
The percentage of the high price tells where to expect low prices in the future and gives you resistance levels where you can buy with a limited risk.
The most important resistance level is 50% of any high or low price.
Second in importance is 100% on the lowest selling price on the averages or individual stocks.
You must also use 200%, 300%, 400%, 500%, 600% or more, depending upon the price and the Time Periods from High and Low.
Third in importance is 25% of the Lowest price or the Highest price.
Fourth in importance is 121/2% of the extreme Low or extreme High price.
Fifth in importance is 61/4% of the Highest price, but this is only to be used when the averages or individual stocks are selling at very high levels.
Sixth in importance is 33 1/3 and 66 2/3%. These percentages should be calculated and watched for resistance next after 25% and after 50%.
You should always have percentage tables made up on the Industrial Averages and on the individual stocks you trade in in order to know where these important resistance levels are located.
Description :
It plots the Intraday % levels from the highest high and lowest low of that day.
The calculation of these levels is based on the text from Chapter 4 "Percentage of High & Low Prices" page number "30" from the book "WD Gann 45 years in Wall Street".
I developed this indicator to see if those percentages work on Options prices or not,
and from my observation I found that it works wonderfully in Options as well.
These % levels work like magic and act as great Dynamic Support and Resistance levels.
Don't trust my words blindly and see for yourself.
This indicator is for educational and research purpose only.
It does not provide any BUY/SELL signals.
Institutional Quantum Momentum Impulse [BullByte]## Overview
The Institutional Quantum Momentum Impulse (IQMI) is a sophisticated momentum oscillator designed to detect institutional-level trend strength, volatility conditions, and market regime shifts. It combines multiple advanced technical concepts, including:
- Quantum Momentum Engine (Hilbert Transform + MACD Divergence + Stochastic Energy)
- Fractal Volatility Scoring (GARCH + Keltner-based volatility)
- Dynamic Adaptive Bands (Self-adjusting thresholds based on efficiency)
- Market Phase Detection (Volume + Momentum alignment)
- Liquidity & Cumulative Delta Analysis
The indicator provides a Z-score normalized momentum reading, making it ideal for mean-reversion and trend-following strategies.
---
## Key Features
### 1. Quantum Momentum Core
- Combines Hilbert Transform, MACD divergence, and Stochastic Energy into a single composite momentum score.
- Normalized using a Z-score for statistical significance.
- Smoothed with EMA/WMA/HMA for cleaner signals.
### 2. Dynamic Adaptive Bands
- Upper/Lower bands adjust based on volatility and efficiency ratio .
- Acts as overbought/oversold zones when momentum reaches extremes.
### 3. Market Phase Detection
- Identifies bullish , bearish , or neutral phases using:
- Volume-Weighted MA alignment
- Fractal momentum extremes
### 4. Volatility & Liquidity Filters
- Fractal Volatility Score (0-100 scale) shows market instability.
- Liquidity Check ensures trades are taken in favorable spread conditions.
### 5. Dashboard & Visuals
- Real-time dashboard with key metrics:
- Momentum strength, volatility, efficiency, cumulative delta, and market regime.
- Gradient coloring for intuitive momentum visualization .
---
## Best Trade Setups
### 1. Trend-Following Entries
- Signal :
- QM crosses above zero + Market Phase = Bullish + ADX > 25
- Cumulative Delta rising (buying pressure)
- Confirmation :
- Efficiency > 0.5 (strong momentum quality)
- Liquidity = High (tight spreads)
### 2. Mean-Reversion Entries
- Signal :
- QM touches upper band + Volatility expanding
- Market Regime = Ranging (ADX < 25)
- Confirmation :
- Efficiency < 0.3 (weak momentum follow-through)
- Cumulative Delta divergence (price high but delta declining)
### 3. Breakout Confirmation
- Signal :
- QM holds above zero after a pullback
- Market Phase shifts to Bullish/Bearish
- Confirmation :
- Volatility rising (expansion phase)
- Liquidity remains high
---
## Recommended Timeframes
- Intraday (5M - 1H): Works well for scalping & swing trades.
- Swing Trading (4H - Daily): Best for trend-following setups.
- Position Trading (Weekly+): Useful for macro trend confirmation.
---
## Input Customization
- Resonance Factor (1.0 - 3.618 ): Adjusts MACD divergence sensitivity.
- Entropy Filter (0.382/0.50/0.618) : Controls stochastic damping.
- Smoothing Type (EMA/WMA/HMA) : Changes momentum responsiveness.
- Normalization Period : Adjusts Z-score lookback.
---
The IQMI is a professional-grade momentum indicator that combines institutional-level concepts into a single, easy-to-read oscillator. It works across all markets (stocks, forex, crypto) and is ideal for traders who want:
✅ Early trend detection
✅ Volatility-adjusted signals
✅ Institutional liquidity insights
✅ Clear dashboard for quick analysis
Try it on TradingView and enhance your trading edge! 🚀
Happy Trading!
- BullByte
Intrinsic Event (Multi DC OS)Overview
This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced in Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller et al. (1995). It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales.
Key Features
Intrinsic Events : The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta), aligning with the paper’s concept of intrinsic time (Section 6). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock.
Multi-Agent Analysis : Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis (Section 5). This allows the indicator to capture the perspectives of market participants with different time horizons, such as short-term FX dealers and long-term central banks.
How It Works
Intrinsic Events Detection : The script identifies two types of events using intrinsic time principles:
Directional Change (DC) : Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC").
Overshoot (OS) : Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS").
DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility.
Multi-Agent Setup : Each agent operates independently with its own threshold, mimicking market participants with varying time horizons (Section 5). Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements.
Settings
Each agent can be configured with:
Enable Agent : Toggle the agent on or off.
Threshold (%) : The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5).
Up Mode Color : Color for lines and labels in up mode (DC events).
Down Mode Color : Color for lines and labels in down mode (OS events).
Usage Notes
This indicator is designed for the foreign exchange market, leveraging its high liquidity, as noted in the paper (Section 1). Adjust the threshold values based on the instrument’s volatility—higher volatility leads to more intrinsic events (Section 4). It can be adapted to other markets where event-based analysis applies.
Reference
The methodology is based on:
Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller, M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, and J. R. Ward (June 28, 1995). Olsen & Associates Preprint.
DD ATR ReadingsThe DD ATR Readings indicator displays customizable Average True Range (ATR) multiplier values directly on your chart. Unlike standard ATR indicators that only show a line, this indicator calculates and displays the exact numeric values for three different ATR multipliers, giving you precise volatility measurements for your trading decisions.
It's specifically created for people taking the "Deep Dip Buy" stock trading course, and attempts to provide a ready-to-go solution to allow easy position size calculations as per the course, with the required ATR values visible at a glance.
The default values of 2.0, 1.5 and 0.45 are the same values used by the course instructor in his charting software, but you can change these values to any multiplier you choose.
Any input from students or the instructor is welcome to improve this indicator so it offers more value to those looking to learn how to trade.
Features
Displays three customizable ATR multiplier values (default: 2.0, 1.5, and 0.45 from the course)
Uses either SMA or EMA for ATR calculation (20-period default)
Fully customizable label appearance (position, color, size)
Real-time value updates as you move through the chart
Clean, unobtrusive display that doesn't clutter your chart with additional lines
Customization Options
ATR Length: Number of bars used in the ATR calculation (default: 20)
ATR Multipliers: Three customizable multiplier values
SMA/EMA: Choose your preferred moving average type for ATR calculation
Label Style: Multiple positioning options for the text display
Colors and Size: Fully customizable appearance
Failed Breakout DetectionThis indicator is a reverse-engineered copy of the FBD Detection indicator published by xfuturesgod. The original indicator aimed at detecting "Failed Breakdowns". This version tracks the opposite signals, "Failed Breakouts". It was coded with the ES Futures 15 minute chart in mind but may be useful on other instruments and time frames.
The original description, with terminology reversed to explain this version:
'Failed Breakouts' are a popular set up for short entries.
In short, the set up requires:
1) A significant high is made ('initial high')
2) Initial high is undercut with a new high
3) Price action then 'reclaims' the initial high by moving +8-10 points from the initial high
This script aims at detecting such set ups. It was coded with the ES Futures 15 minute chart in mind but may be useful on other instruments and time frames.
Business Logic:
1) Uses pivot highs to detect 'significant' initial highs
2) Uses amplitude threshold to detect a new high above the initial high; used /u/ben_zen script for this
3) Looks for a valid reclaim - a red candle that occurs within 10 bars of the new high
4) Price must reclaim at least 8 points for the set up to be valid
5) If a signal is detected, the initial high value (pivot high) is stored in array that prevents duplicate signals from being generated.
6) FBO Signal is plotted on the chart with "X"
7) Pivot high detection is plotted on the chart with "P" and a label
8) New highs are plotted on the chart with a red triangle
Notes:
User input
- My preference is to use the defaults as is, but as always feel free to experiment
- Can modify pivot length but in my experience 10/10 work best for pivot highs
- New high detection - 55 bars and 0.05 amplitude work well based on visual checks of signals
- Can modify the number of points needed to reclaim a high, and the # of bars limit over which this must occur.
Alerts:
- Alerts are available for detection of new highs and detection of failed breakouts
- Alerts are also available for these signals but only during 7:30PM-4PM EST - 'prime time' US trading hours
Limitations:
- Current version of the script only compares new highs to the most recent pivot high, does not look at anything prior to that
- Best used as a discretionary signal
DD Keltner Channels (1-3 ATR)This indicator creates Keltner Channels with 1, 2, and 3 ATR multipliers, allowing you to visualize different volatility levels around a moving average.
It's specifically created for people taking the "Deep Dip Buy" stock trading course, and attempts to provide a ready-to-go solution for those struggling with configuring the default Keltner indicator on TradingView to suit their needs for the course.
Any input from students or the instructor is welcome to improve this indicator so it offers more value to those looking to learn how to trade.
Features:
- Uses SMA or EMA as the base (20-period default)
- Displays 6 lines: +3, +2, +1, -1, -2, and -3 ATR levels
- Color-coded for easy identification:
• +/-1 ATR: Green
• +/-2 ATR: Light Gray (thin)
• +/-3 ATR: Dark Gray (thick)
Fibonacci Counter-Trend TradingOverview:
The Fibonacci Counter-Trend Trading strategy is designed to capitalize on price reversals by utilizing Fibonacci levels calculated from the standard deviation of price movements. This strategy opens a sell order when the closing price crosses above a specified upper Fibonacci level and a buy order when the closing price crosses below a specified lower Fibonacci level. By leveraging the principles of Fibonacci retracement and volatility, this strategy aims to identify potential reversal points in the market.
How It Works:
Fibonacci Levels Calculation:
The strategy calculates upper and lower Fibonacci levels based on the standard deviation of the price over a specified moving average length. These levels are derived from the Fibonacci sequence, which is widely used in technical analysis to identify potential support and resistance levels.
The upper levels are calculated by adding specific Fibonacci ratios (0.236, 0.382, 0.5, 0.618, 0.764, and 1.0) multiplied by the standard deviation to the basis (the volume-weighted moving average).
The lower levels are calculated by subtracting the same Fibonacci ratios multiplied by the standard deviation from the basis.
Trade Entry Rules:
Sell Order: A sell order is triggered when the closing price crosses above the selected upper Fibonacci level. This indicates a potential reversal point where the price may start to decline.
Buy Order: A buy order is initiated when the closing price crosses below the selected lower Fibonacci level. This suggests a potential reversal point where the price may begin to rise.
Trade Management:
The strategy includes stop-losses based on the Fibonacci levels to protect against adverse price movements.
How to Use:
Users can customize the moving average length and the multiplier for the standard deviation to suit their trading preferences and market conditions.
The strategy can be applied to various financial instruments, including stocks, forex, and cryptocurrencies, making it versatile for different trading environments.
Pros:
The Fibonacci Counter-Trend Trading strategy combines the mathematical principles of the Fibonacci sequence with the statistical measure of standard deviation, providing a unique approach to identifying potential market reversals.
This strategy is particularly useful in volatile markets where price swings can lead to significant trading opportunities.
The use of Fibonacci levels can help traders identify key support and resistance areas, enhancing decision-making.
Cons:
The strategy may generate false signals in choppy or sideways markets, leading to potential losses if the price does not reverse as anticipated.
Relying solely on Fibonacci levels without considering other technical indicators or market conditions may result in missed opportunities or increased risk.
The effectiveness of the strategy can vary depending on the chosen parameters (e.g., moving average length and standard deviation multiplier), requiring users to spend time optimizing these settings for different market conditions.
As with any counter-trend strategy, there is a risk of significant drawdowns during strong trending markets, where the price continues to move in one direction without reversing.
By understanding the mechanics of the Fibonacci Counter-Trend Trading strategy, along with its pros and cons, traders can effectively implement it in their trading routines and potentially enhance their trading performance.
Order Flow Hawkes Process [ScorsoneEnterprises]This indicator is an implementation of the Hawkes Process. This tool is designed to show the excitability of the different sides of volume, it is an estimation of bid and ask size per bar. The code for the volume delta is from www.tradingview.com
Here’s a link to a more sophisticated research article about Hawkes Process than this post arxiv.org
This tool is designed to show how excitable the different sides are. Excitability refers to how likely that side is to get more activity. Alan Hawkes made Hawkes Process for seismology. A big earthquake happens, lots of little ones follow until it returns to normal. Same for financial markets, big orders come in, causing a lot of little orders to come. Alpha, Beta, and Lambda parameters are estimated by minimizing a negative log likelihood function.
How it works
There are a few components to this script, so we’ll go into the equation and then the other functions used in this script.
hawkes_process(params, events, lkb) =>
alpha = clamp(array.get(params, 0), 0.01, 1.0)
beta = clamp(array.get(params, 1), 0.1, 10.0)
lambda_0 = clamp(array.get(params, 2), 0.01, 0.3)
intensity = array.new_float(lkb, 0.0)
events_array = array.new_float(lkb, 0.0)
for i = 0 to lkb - 1
array.set(events_array, i, array.get(events, i))
for i = 0 to lkb - 1
sum_decay = 0.0
current_event = array.get(events_array, i)
for j = 0 to i - 1
time_diff = i - j
past_event = array.get(events_array, j)
decay = math.exp(-beta * time_diff)
past_event_val = na(past_event) ? 0 : past_event
sum_decay := sum_decay + (past_event_val * decay)
array.set(intensity, i, lambda_0 + alpha * sum_decay)
intensity
The parameters alpha, beta, and lambda all represent a different real thing.
Alpha (α):
Definition: Alpha represents the excitation factor or the magnitude of the influence that past events have on the future intensity of the process. In simpler terms, it measures how much each event "excites" or triggers additional events. It is constrained between 0.01 and 1.0 (e.g., clamp(array.get(params, 0), 0.01, 1.0)). A higher alpha means past events have a stronger influence on increasing the intensity (likelihood) of future events. Initial value is set to 0.1 in init_params. In the hawkes_process function, alpha scales the contribution of past events to the current intensity via the term alpha * sum_decay.
Beta (β):
Definition: Beta controls the rate of exponential decay of the influence of past events over time. It determines how quickly the effect of a past event fades away. It is constrained between 0.1 and 10.0 (e.g., clamp(array.get(params, 1), 0.1, 10.0)). A higher beta means the influence of past events decays faster, while a lower beta means the influence lingers longer. Initial value is set to 0.1 in init_params. In the hawkes_process function, beta appears in the decay term math.exp(-beta * time_diff), which reduces the impact of past events as the time difference (time_diff) increases.
Lambda_0 (λ₀):
Definition: Lambda_0 is the baseline intensity of the process, representing the rate at which events occur in the absence of any excitation from past events. It’s the "background" rate of the process. It is constrained between 0.01 and 0.3 .A higher lambda_0 means a higher natural frequency of events, even without the influence of past events. Initial value is set to 0.1 in init_params. In the hawkes_process function, lambda_0 sets the minimum intensity level, to which the excitation term (alpha * sum_decay) is added: lambda_0 + alpha * sum_decay
Alpha (α): Strength of event excitation (how much past events boost future events).
Beta (β): Rate of decay of past event influence (how fast the effect fades).
Lambda_0 (λ₀): Baseline event rate (background intensity without excitation).
Other parts of the script.
Clamp
The clamping function is a simple way to make sure parameters don’t grow or shrink too much.
ObjectiveFunction
This function defines the objective function (negative log-likelihood) to minimize during parameter optimization.It returns a float representing the negative log-likelihood (to be minimized).
How It Works:
Calls hawkes_process to compute the intensity array based on current parameters.Iterates over the lookback period:lambda_t: Intensity at time i.event: Event magnitude at time i.Handles na values by replacing them with 0.Computes log-likelihood: event_clean * math.log(math.max(lambda_t_clean, 0.001)) - lambda_t_clean.Ensures lambda_t_clean is at least 0.001 to avoid log(0).Accumulates into log_likelihood.Returns -log_likelihood (negative because the goal is to minimize, not maximize).
It is used in the optimization process to evaluate how well the parameters fit the observed event data.
Finite Difference Gradient:
This function calculates the gradient of the objective function we spoke about. The gradient is like a directional derivative. Which is like the direction of the rate of change. Which is like the direction of the slope of a hill, we can go up or down a hill. It nudges around the parameter, and calculates the derivative of the parameter. The array of these nudged around parameters is what is returned after they are optimized.
Minimize:
This is the function that actually has the loop and calls the Finite Difference Gradient each time. Here is where the minimizing happens, how we go down the hill. If we are below a tolerance, we are at the bottom of the hill.
Applied
After an initial guess the parameters are optimized with a mix of bid and ask levels to prevent some over-fitting for each side while keeping some efficiency. We initialize two different arrays to store the bid and ask sizes. After we optimize the parameters we clamp them for the calculations. We then get the array of intensities from the Hawkes Process of bid and ask and plot them both. When the bids are greater than the ask it represents a bullish scenario where there are likely to be more buy than sell orders, pushing up price.
Tool examples:
The idea is that when the bid side is more excitable it is more likely to see a bullish reaction, when the ask is we see a bearish reaction.
We see that there are a lot of crossovers, and I picked two specific spots. The idea of this isn’t to spot crossovers but avoid chop. The values are either close together or far apart. When they are far, it is a classification for us to look for our own opportunities in, when they are close, it signals the market can’t pick a direction just yet.
The value works just as well on a higher timeframe as on a lower one. Hawkes Process is an estimate, so there is a leading value aspect of it.
The value works on equities as well, here is NASDAQ:TSLA on a lower time frame with a lookback of 5.
Inputs
Users can enter the lookback value and timeframe.
No tool is perfect, the Hawkes Process value is also not perfect and should not be followed blindly. It is good to use any tool along with discretion and price action.
YY Price LimitsThis Pine Script indicator is designed to visualize potential price limits (e.g., daily price limits used in some markets like commodities) on a TradingView chart. It calculates and plots lines representing percentage-based price limits above and below a reference price (typically the previous day's close). The indicator allows you to customize the displayed price limits, their appearance, and how they extend across the chart. It's particularly useful for intraday traders who need to be aware of potential price ceilings and floors.
Key Features:
Percentage-Based Limits:
Calculates price limits based on percentages (3%, 5%, and 7%) of a reference price.
Customizable Display:
Toggle visibility of reference price and each percentage limit (3%, 5%, 7%).
Customize the color, style (solid, dashed, dotted), and width of the price limit lines.
Extends Lines: Allows you to extend the price limit lines to the left, right, both directions, or not at all.
CME Reference Price: It is designed to plot price limits based on the CME (Chicago Mercantile Exchange) methodology, which uses the last close as the reference price. The tooltip reminds users to verify the actual reference price on the CME Group website.
Intraday Focus: The indicator is specifically designed for intraday timeframes, as it uses the previous day's close as the reference point.
Clear Visuals: Plots horizontal lines with labels indicating the price level and percentage.
Smarter Money Concepts - OBs [PhenLabs]📊 Smarter Money Concepts - OBs
Version: PineScript™ v6
📌 Description
Smarter Money Concepts - OBs (Order Blocks) is an advanced technical analysis tool designed to identify and visualize institutional order zones on your charts. Order blocks represent significant areas of liquidity where smart money has entered positions before major moves. By tracking these zones, traders can anticipate potential reversals, continuations, and key reaction points in price action.
This indicator incorporates volume filtering technology to identify only the most significant order blocks, eliminating low-quality signals and focusing on areas where institutional participation is likely present. The combination of price structure analysis and volume confirmation provides traders with high-probability zones that may attract future price action for tests, rejections, or breakouts.
🚀 Points of Innovation
Volume-Filtered Block Detection : Identifies only order blocks formed with significant volume, focusing on areas with institutional participation
Advanced Break of Structure Logic : Uses sophisticated price action analysis to detect legitimate market structure breaks preceding order blocks
Dynamic Block Management : Intelligently tracks, extends, and removes order blocks based on price interaction and time-based expiration
Structure Recognition System : Employs technical analysis algorithms to find significant swing points for accurate order block identification
Dual Directional Tracking : Simultaneously monitors both bullish and bearish order blocks for comprehensive market structure analysis
🔧 Core Components
Order Block Detection : Identifies institutional entry zones by analyzing price action before significant breaks of structure, capturing where smart money has likely positioned before moves.
Volume Filtering Algorithm : Calculates relative volume compared to a moving average to qualify only order blocks formed with significant market participation, eliminating noise.
Structure Break Recognition : Uses price action analysis to detect legitimate breaks of market structure, ensuring order blocks are identified only at significant market turning points.
Dynamic Block Management : Continuously monitors price interaction with existing blocks, extending, maintaining, or removing them based on current market behavior.
🔥 Key Features
Volume-Based Filtering : Filter out insignificant blocks by requiring a minimum volume threshold, focusing only on zones with likely institutional activity
Visual Block Highlighting : Color-coded boxes clearly mark bullish and bearish order blocks with customizable appearance
Flexible Mitigation Options : Choose between “Wick” or “Close” methods for determining when a block has been tested or mitigated
Scan Range Adjustment : Customize how far back the indicator looks for structure points to adapt to different market conditions and timeframes
Break Source Selection : Configure which price component (close, open, high, low) is used to determine structure breaks for precise block identification
🎨 Visualization
Bullish Order Blocks : Blue-colored rectangles highlighting zones where bullish institutional orders were likely placed before upward moves, representing potential support areas.
Bearish Order Blocks : Red-colored rectangles highlighting zones where bearish institutional orders were likely placed before downward moves, representing potential resistance areas.
Block Extension : Order blocks extend to the right of the chart, providing clear visualization of these significant zones as price continues to develop.
📖 Usage Guidelines
Order Block Settings
Scan Range : Default: 25. Defines how many bars the indicator scans to determine significant structure points for order block identification.
Bull Break Price Source : Default: Close. Determines which price component is used to detect bullish breaks of structure.
Bear Break Price Source : Default: Close. Determines which price component is used to detect bearish breaks of structure.
Visual Settings
Bullish Blocks Color : Default: Blue with 85% transparency. Controls the appearance of bullish order blocks.
Bearish Blocks Color : Default: Red with 85% transparency. Controls the appearance of bearish order blocks.
General Options
Block Mitigation Method : Default: Wick, Options: Wick, Close. Determines how block mitigation is calculated - “Wick” uses high/low values while “Close” uses close values for more conservative mitigation criteria.
Remove Filled Blocks : Default: Disabled. When enabled, order blocks are removed once they’ve been mitigated by price action.
Volume Filter
Volume Filter Enabled : Default: Enabled. When activated, only shows order blocks formed with significant volume relative to recent average.
Volume SMA Period : Default: 15, Range: 1-50. Number of periods used to calculate the average volume baseline.
Min. Volume Ratio : Default: 1.5, Range: 0.5-10.0. Minimum volume ratio compared to average required to display an order block; higher values filter out more blocks.
✅ Best Use Cases
Identifying high-probability support and resistance zones for trade entries and exits
Finding optimal stop-loss placement behind significant order blocks
Detecting potential reversal areas where price may react after extended moves
Confirming breakout trades when price clears major order blocks
Building a comprehensive market structure map for medium to long-term trading decisions
Pinpointing areas where smart money may have positioned before major market moves
⚠️ Limitations
Most effective on higher timeframes (1H and above) where institutional activity is more clearly defined
Can generate multiple signals in choppy market conditions, requiring additional filtering
Volume filtering relies on accurate volume data, which may be less reliable for some securities
Recent market structure changes may invalidate older order blocks not yet automatically removed
Block identification is based on historical price action and may not predict future behavior with certainty
💡 What Makes This Unique
Volume Intelligence : Unlike basic order block indicators, this script incorporates volume analysis to identify only the most significant institutional zones, focusing on quality over quantity.
Structural Precision : Uses sophisticated break of structure algorithms to identify true market turning points, going beyond simple price pattern recognition.
Dynamic Block Management : Implements automatic block tracking, extension, and cleanup to maintain a clean and relevant chart display without manual intervention.
Institutional Focus : Designed specifically to highlight areas where smart money has likely positioned, helping retail traders align with institutional perspectives rather than retail noise.
🔬 How It Works
1. Structure Identification Process :
The indicator continuously scans price action to identify significant swing points and structure levels within the specified range, establishing a foundation for order block recognition.
2. Break Detection :
When price breaks an established structure level (crossing below a significant low for bearish breaks or above a significant high for bullish breaks), the indicator marks this as a potential zone for order block formation.
3. Volume Qualification :
For each potential order block, the algorithm calculates the relative volume compared to the configured period average. Only blocks formed with volume exceeding the minimum ratio threshold are displayed.
4. Block Creation and Management :
Valid order blocks are created, tracked, and managed as price continues to develop. Blocks extend to the right of the chart until they are either mitigated by price action or expire after the designated timeframe.
5. Continuous Monitoring :
The indicator constantly evaluates price interaction with existing blocks, determining when blocks have been tested, mitigated, or invalidated, and updates the visual representation accordingly.
💡 Note:
Order Blocks represent areas where institutional traders have likely established positions and may defend these zones during future price visits. For optimal results, use this indicator in conjunction with other confluent factors such as key support/resistance levels, trendlines, or additional confirmation indicators. The most reliable signals typically occur on higher timeframes where institutional activity is most prominent. Start with the default settings and adjust parameters gradually to match your specific trading instrument and style.
Multi Oscillator OB/OS Signals v3 - Scope TestIndicator Description: Multi Oscillator OB/OS Signals
Purpose:
The "Multi Oscillator OB/OS Signals" indicator is a TradingView tool designed to help traders identify potential market extremes and momentum shifts by monitoring four popular oscillators simultaneously: RSI, Stochastic RSI, CCI, and MACD. Instead of displaying these oscillators in separate panes, this indicator plots distinct visual symbols directly onto the main price chart whenever specific predefined conditions (typically related to overbought/oversold levels or line crossovers) are met for each oscillator. This provides a consolidated view of potential signals from these different technical tools.
How It Works:
The indicator calculates the values for each of the four oscillators based on user-defined settings (like length periods and price sources) and then checks for specific signal conditions on every bar:
Relative Strength Index (RSI):
It monitors the standard RSI value.
When the RSI crosses above the user-defined Overbought (OB) level (e.g., 70), it plots an "Overbought" symbol (like a downward triangle) above that price bar.
When the RSI crosses below the user-defined Oversold (OS) level (e.g., 30), it plots an "Oversold" symbol (like an upward triangle) below that price bar.
Stochastic RSI:
This works similarly to RSI but is based on the Stochastic calculation applied to the RSI value itself (specifically, the %K line of the Stoch RSI).
When the Stoch RSI's %K line crosses above its Overbought level (e.g., 80), it plots its designated OB symbol (like a downward arrow) above the bar.
When the %K line crosses below its Oversold level (e.g., 20), it plots its OS symbol (like an upward arrow) below the bar.
Commodity Channel Index (CCI):
It tracks the CCI value.
When the CCI crosses above its Overbought level (e.g., +100), it plots its OB symbol (like a square) above the bar.
When the CCI crosses below its Oversold level (e.g., -100), it plots its OS symbol (like a square) below the bar.
Moving Average Convergence Divergence (MACD):
Unlike the others, MACD signals here are not based on fixed OB/OS levels.
It identifies when the main MACD line crosses above its Signal line. This is considered a bullish crossover and is indicated by a specific symbol (like an upward label) plotted below the price bar.
It also identifies when the MACD line crosses below its Signal line. This is a bearish crossover, indicated by a different symbol (like a downward label) plotted above the price bar.
Visualization:
All these signals appear as small, distinct shapes directly on the price chart at the bar where the condition occurred. The shapes, their colors, and their position (above or below the bar) are predefined for each signal type to allow for quick visual identification. Note: In the current version of the underlying code, the size of these shapes is fixed (e.g., tiny) and not user-adjustable via the settings.
Configuration:
Users can access the indicator's settings to customize:
The calculation parameters (Length periods, smoothing, price source) for each individual oscillator (RSI, Stoch RSI, CCI, MACD).
The specific Overbought and Oversold threshold levels for RSI, Stoch RSI, and CCI.
The colors associated with each type of signal (OB, OS, Bullish Cross, Bearish Cross).
(Limitation Note: While settings exist to toggle the visibility of signals for each oscillator individually, due to a technical workaround in the current code, these toggles may not actively prevent the shapes from plotting if the underlying condition is met.)
Alerts:
The indicator itself does not automatically generate pop-up alerts. However, it creates the necessary "Alert Conditions" within TradingView's alert system. This means users can manually set up alerts for any of the specific signals generated by the indicator (e.g., "RSI Overbought Enter," "MACD Bullish Crossover"). When creating an alert, the user selects this indicator, chooses the desired condition from the list provided by the script, and configures the alert actions.
Intended Use:
This indicator aims to provide traders with convenient visual cues for potential over-extension in price (via OB/OS signals) or shifts in momentum (via MACD crossovers) based on multiple standard oscillators. These signals are often used as potential indicators for:
Identifying areas where a trend might be exhausted and prone to a pullback or reversal.
Confirming signals generated by other analysis methods or trading strategies.
Noting shifts in short-term momentum.
Disclaimer: As with any technical indicator, the signals generated should not be taken as direct buy or sell recommendations. They are best used in conjunction with other forms of analysis (price action, trend analysis, volume, fundamental analysis, etc.) and within the framework of a well-defined trading plan that includes risk management. Market conditions can change, and indicator signals can sometimes be false or misleading.
Correlation Heatmap█ OVERVIEW
This indicator creates a correlation matrix for a user-specified list of symbols based on their time-aligned weekly or monthly price returns. It calculates the Pearson correlation coefficient for each possible symbol pair, and it displays the results in a symmetric table with heatmap-colored cells. This format provides an intuitive view of the linear relationships between various symbols' price movements over a specific time range.
█ CONCEPTS
Correlation
Correlation typically refers to an observable statistical relationship between two datasets. In a financial time series context, it usually represents the extent to which sampled values from a pair of datasets, such as two series of price returns, vary jointly over time. More specifically, in this context, correlation describes the strength and direction of the relationship between the samples from both series.
If two separate time series tend to rise and fall together proportionally, they might be highly correlated. Likewise, if the series often vary in opposite directions, they might have a strong anticorrelation . If the two series do not exhibit a clear relationship, they might be uncorrelated .
Traders frequently analyze asset correlations to help optimize portfolios, assess market behaviors, identify potential risks, and support trading decisions. For instance, correlation often plays a key role in diversification . When two instruments exhibit a strong correlation in their returns, it might indicate that buying or selling both carries elevated unsystematic risk . Therefore, traders often aim to create balanced portfolios of relatively uncorrelated or anticorrelated assets to help promote investment diversity and potentially offset some of the risks.
When using correlation analysis to support investment decisions, it is crucial to understand the following caveats:
• Correlation does not imply causation . Two assets might vary jointly over an analyzed range, resulting in high correlation or anticorrelation in their returns, but that does not indicate that either instrument directly influences the other. Joint variability between assets might occur because of shared sensitivities to external factors, such as interest rates or global sentiment, or it might be entirely coincidental. In other words, correlation does not provide sufficient information to identify cause-and-effect relationships.
• Correlation does not predict the future relationship between two assets. It only reflects the estimated strength and direction of the relationship between the current analyzed samples. Financial time series are ever-changing. A strong trend between two assets can weaken or reverse in the future.
Correlation coefficient
A correlation coefficient is a numeric measure of correlation. Several coefficients exist, each quantifying different types of relationships between two datasets. The most common and widely known measure is the Pearson product-moment correlation coefficient , also known as the Pearson correlation coefficient or Pearson's r . Usually, when the term "correlation coefficient" is used without context, it refers to this correlation measure.
The Pearson correlation coefficient quantifies the strength and direction of the linear relationship between two variables. In other words, it indicates how consistently variables' values move together or in opposite directions in a proportional, linear manner. Its formula is as follows:
𝑟(𝑥, 𝑦) = cov(𝑥, 𝑦) / (𝜎𝑥 * 𝜎𝑦)
Where:
• 𝑥 is the first variable, and 𝑦 is the second variable.
• cov(𝑥, 𝑦) is the covariance between 𝑥 and 𝑦.
• 𝜎𝑥 is the standard deviation of 𝑥.
• 𝜎𝑦 is the standard deviation of 𝑦.
In essence, the correlation coefficient measures the covariance between two variables, normalized by the product of their standard deviations. The coefficient's value ranges from -1 to 1, allowing a more straightforward interpretation of the relationship between two datasets than what covariance alone provides:
• A value of 1 indicates a perfect positive correlation over the analyzed sample. As one variable's value changes, the other variable's value changes proportionally in the same direction .
• A value of -1 indicates a perfect negative correlation (anticorrelation). As one variable's value increases, the other variable's value decreases proportionally.
• A value of 0 indicates no linear relationship between the variables over the analyzed sample.
Aligning returns across instruments
In a financial time series, each data point (i.e., bar) in a sample represents information collected in periodic intervals. For instance, on a "1D" chart, bars form at specific times as successive days elapse.
However, the times of the data points for a symbol's standard dataset depend on its active sessions , and sessions vary across instrument types. For example, the daily session for NYSE stocks is 09:30 - 16:00 UTC-4/-5 on weekdays, Forex instruments have 24-hour sessions that span from 17:00 UTC-4/-5 on one weekday to 17:00 on the next, and new daily sessions for cryptocurrencies start at 00:00 UTC every day because crypto markets are consistently open.
Therefore, comparing the standard datasets for different asset types to identify correlations presents a challenge. If two symbols' datasets have bars that form at unaligned times, their correlation coefficient does not accurately describe their relationship. When calculating correlations between the returns for two assets, both datasets must maintain consistent time alignment in their values and cover identical ranges for meaningful results.
To address the issue of time alignment across instruments, this indicator requests confirmed weekly or monthly data from spread tickers constructed from the chart's ticker and another specified ticker. The datasets for spreads are derived from lower-timeframe data to ensure the values from all symbols come from aligned points in time, allowing a fair comparison between different instrument types. Additionally, each spread ticker ID includes necessary modifiers, such as extended hours and adjustments.
In this indicator, we use the following process to retrieve time-aligned returns for correlation calculations:
1. Request the current and previous prices from a spread representing the sum of the chart symbol and another symbol ( "chartSymbol + anotherSymbol" ).
2. Request the prices from another spread representing the difference between the two symbols ( "chartSymbol - anotherSymbol" ).
3. Calculate half of the difference between the values from both spreads ( 0.5 * (requestedSum - requestedDifference) ). The results represent the symbol's prices at times aligned with the sample points on the current chart.
4. Calculate the arithmetic return of the retrieved prices: (currentPrice - previousPrice) / previousPrice
5. Repeat steps 1-4 for each symbol requiring analysis.
It's crucial to note that because this process retrieves prices for a symbol at times consistent with periodic points on the current chart, the values can represent prices from before or after the closing time of the symbol's usual session.
Additionally, note that the maximum number of weeks or months in the correlation calculations depends on the chart's range and the largest time range common to all the requested symbols. To maximize the amount of data available for the calculations, we recommend setting the chart to use a daily or higher timeframe and specifying a chart symbol that covers a sufficient time range for your needs.
█ FEATURES
This indicator analyzes the correlations between several pairs of user-specified symbols to provide a structured, intuitive view of the relationships in their returns. Below are the indicator's key features:
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab accepts a comma-separated list of symbols or ticker identifiers with optional spaces (e.g., "XOM, MSFT, BITSTAMP:BTCUSD"). The indicator dynamically requests returns for each symbol in the list, then calculates the correlation between each pair of return series for its heatmap display.
Each item in the list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script raises a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Note that the number of symbols allowed in the list depends on the user's plan. Users with non-professional plans can compare up to 20 symbols with this indicator, and users with professional plans can compare up to 32 symbols.
Timeframe and data length selection
The "Returns timeframe" input specifies whether the indicator uses weekly or monthly returns in its calculations. By default, its value is "1M", meaning the indicator analyzes monthly returns. Note that this script requires a chart timeframe lower than or equal to "1M". If the chart uses a higher timeframe, it causes a runtime error.
To customize the length of the data used in the correlation calculations, use the "Max periods" input. When enabled, the indicator limits the calculation window to the number of periods specified in the input field. Otherwise, it uses the chart's time range as the limit. The top-left corner of the table shows the number of confirmed weeks or months used in the calculations.
It's important to note that the number of confirmed periods in the correlation calculations is limited to the largest time range common to all the requested datasets, because a meaningful correlation matrix requires analyzing each symbol's returns under the same market conditions. Therefore, the correlation matrix can show different results for the same symbol pair if another listed symbol restricts the aligned data to a shorter time range.
Heatmap display
This indicator displays the correlations for each symbol pair in a heatmap-styled table representing a symmetric correlation matrix. Each row and column corresponds to a specific symbol, and the cells at their intersections correspond to symbol pairs . For example, the cell at the "AAPL" row and "MSFT" column shows the weekly or monthly correlation between those two symbols' returns. Likewise, the cell at the "MSFT" row and "AAPL" column shows the same value.
Note that the main diagonal cells in the display, where the row and column refer to the same symbol, all show a value of 1 because any series of non-na data is always perfectly correlated with itself.
The background of each correlation cell uses a gradient color based on the correlation value. By default, the gradient uses blue hues for positive correlation, orange hues for negative correlation, and white for no correlation. The intensity of each blue or orange hue corresponds to the strength of the measured correlation or anticorrelation. Users can customize the gradient's base colors using the inputs in the "Color gradient" section of the "Settings/Inputs" tab.
█ FOR Pine Script® CODERS
• This script uses the `getArrayFromString()` function from our ValueAtTime library to process the input list of symbols. The function splits the "string" value by its commas, then constructs an array of non-empty strings without leading or trailing whitespaces. Additionally, it uses the str.upper() function to convert each symbol's characters to uppercase.
• The script's `getAlignedReturns()` function requests time-aligned prices with two request.security() calls that use spread tickers based on the chart's symbol and another symbol. Then, it calculates the arithmetic return using the `changePercent()` function from the ta library. The `collectReturns()` function uses `getAlignedReturns()` within a loop and stores the data from each call within a matrix . The script calls the `arrayCorrelation()` function on pairs of rows from the returned matrix to calculate the correlation values.
• For consistency, the `getAlignedReturns()` function includes extended hours and dividend adjustment modifiers in its data requests. Additionally, it includes other settings inherited from the chart's context, such as "settlement-as-close" preferences.
• A Pine script can execute up to 40 or 64 unique `request.*()` function calls, depending on the user's plan. The maximum number of symbols this script compares is half the plan's limit, because `getAlignedReturns()` uses two request.security() calls.
• This script can use the request.security() function within a loop because all scripts in Pine v6 enable dynamic requests by default. Refer to the Dynamic requests section of the Other timeframes and data page to learn more about this feature, and see our v6 migration guide to learn what's new in Pine v6.
• The script's table uses two distinct color.from_gradient() calls in a switch structure to determine the cell colors for positive and negative correlation values. One call calculates the color for values from -1 to 0 based on the first and second input colors, and the other calculates the colors for values from 0 to 1 based on the second and third input colors.
Look first. Then leap.
Multi-Timeframe Trend Analysis [BigBeluga]Multi-Timeframe Trend Analysis
A powerful trend-following dashboard designed to help traders monitor and compare trend direction across multiple higher timeframes. By analyzing EMA conditions from five customizable timeframes, this tool gives a clear visual breakdown of short- to long-term trend alignment.
🔵Key Features:
Multi-Timeframe EMA Dashboard:
➣ Displays a table in the top-right corner showing trend direction across 5 user-defined timeframes.
➣ Each row shows whether ema is rising or falling its corresponding EMA for that timeframe.
➣ Green arrows (🢁) indicate uptrends, purple arrows (🢃) signal downtrends.
Custom Timeframe Selection:
➣ Traders can input any 5 timeframes (e.g., 1h, 2h, 3h, etc.) with individual EMA lengths for flexible trend mapping.
➣ The tool auto-adjusts to match and align external timeframe EMAs to the current chart for seamless overlay.
Dynamic Chart Arrows:
➣ On-chart arrows mark when EMA rising or falling EMAs from the current chart timeframe.
➣ Each EMA arrows has a unique transparency level—shorter EMA arrows are more transparent, longer EMA arrows are more vivid. (Hover Mouse over the arrow to see which EMAs it is)
Gradient EMA Plotting:
➣ All five EMAs are plotted with gradually increasing opacity.
➣ Gradient fills between EMAs enhance visual structure, making it easier to track convergence/divergence.
🔵Usage:
Trend Confirmation: Use the dashboard to confirm multi-timeframe trend alignment before entering trades.
Entry Filtering: Avoid countertrend trades by spotting when higher timeframes disagree with the current one.
Momentum Insight: Track the transition of arrows from lighter to stronger opacity to visualize trend shifts over time.
Scalping or Swinging: Customize timeframes depending on your strategy—from intraday scalps to longer-term swings.
Multi-Timeframe Trend Analysis is the ultimate visual companion for traders who want clarity on how price behaves across multiple time horizons. With its smart EMA mapping and dashboard feedback, it keeps you aligned with dominant trend directions and transition zones at all times.
Tracking Lines with TP/SL + Labels at LeftA simple indicator so you can set your TP and SL tolerance along with capital and leverage.
A red line and green line will represent where current TP and SL would be on the chart along with the number of tokens you need to trade to meet the USD capital to be trades.
Just gives a visual representation of SL to key zones on the chart so you can judge scalp entries a little better :)
Sigma (Standard Deviation)Calculates the standard deviation (sigma) of a stock in percentages. You can edit the length as per your need.
BB Breakout + Momentum Squeeze [Strategy]This Strategy is Based on 3 free indicators
- Bollinger Bands Breakout Oscillator: Link
- TTM Squeeze Pro: Link
- Rolling ATR Bands: Link
Bollinger Bands Breakout Oscillator - This tool shows how strong a market trend is by measuring how often prices move outside their normal Bollinger bands range. It helps you see whether prices are strongly moving in one direction or just moving sideways. By looking at how much and how frequently prices push beyond their typical boundaries, you can identify which direction the market is heading over your selected time period.
TM Squeeze Pro - This is a custom version of the TTM Squeeze indicator.
It's designed to help traders spot consolidation phases in the market (when price is coiling or "squeezing") and to catch breakouts early when volatility returns. The logic is based on the relationship between Bollinger Bands and Keltner Channels, combined with a momentum oscillator to show direction and strength.
Rolling ATR Bands - This indicator combines volatility bands (ATR) with momentum and trend signals to show where the market might be breaking out, retesting, or trending. It's highly visual and helpful for traders looking to time entries/exits during trending or volatile moves.
Logic Of the Strategy:
We are going to use the Bollinger Bands Breakout to determine the direction of the market. Than check the Volatility of the price by looking at the TTM Squeeze indicator. And use the ATR Bands to determine dynamic Stop Losses and based on the calculate the Take Profit targets and quantity for each position dynamically.
For the Long Setup:
1. We need to see the that Bull Power (Green line of the Bollinger Bands Breakout Oscilator) is crossing the level of 50.
2. Check the presence of volatility (Green dot based on the TTM Squeeze indicator)
For the Short Setup:
1. We need to see the that Bear Power (Red line of the Bollinger Bands Breakout Oscilator) is crossing the level of 50.
2. Check the presence of volatility (Green dot based on the TTM Squeeze indicator)
Stop Loss is determined by the Lower ATR Band (for the Long entry) and Upper ATR Band (For the Short entry)
Take Profit is 1:1.5 risk reward ration, which means if the Stop loss is 1% the TP target will be 1.5%
Move stop Loss to Breakeven: If the price will go in the direction of the trade for at least half of the Risk Reward target then the stop will automatically be adjusted to the entry price. For Example: the Stop Loss is 1%, the price has move at least 0.5% in the direction of your trade and that will move the Stop Loss level to the Entry point.
You can Adjust the parameters for each indicator used in that script and also adjust the Risk and Money management block to see how the PnL will change.
False Breakout PRO📌 False Breakout PRO – Enhanced False Breakout Detection Tool
False Breakout PRO is an advanced version of the original "False Breakout (Expo)" indicator by .
This tool is designed to help traders detect bullish and bearish false breakouts with high precision. By offering a more customizable and smarter interface, it helps reduce noise and false signals through various filtering and visualization options.
🔍 How It Works
The script continuously scans for new highs or lows based on a user-defined period.
It identifies false breakouts when price briefly breaks out of a recent high/low but then quickly reverses. These are often seen as market traps, and this indicator aims to highlight them early.
✅ Key Features in the PRO Version
📌 Toggle to display all signals or only the most recent one
💬 Price labels with clean text and optional visibility
📊 Smart summary table for instant signal reference
📈 Auto-extended lines that follow price action
⚡ Lightweight and optimized for speed and real-time responsiveness
🛠 Configurable Settings
False breakout detection period
Signal validity window (how long a signal is considered active)
Smoothing types: Raw (💎), WMA, or HMA
Aggressive mode for early signal generation
Enable or disable:
Price labels
Summary table
Only latest signal mode
⚠️ License Notice
This script is derived from @Zeiierman’s original work and is published under the Creative Commons BY-NC-SA 4.0 license.
🔒 Commercial use is NOT allowed. Attribution to the original author is required.
🇸🇦 False Breakout PRO – أداة متقدمة لكشف الكسر الكاذب
False Breakout PRO هو إصدار مطور من السكريبت الأصلي "False Breakout (Expo)" من تطوير ، وتم تحسينه لتقديم تجربة استخدام أكثر احترافية ومرونة للمستخدمين للكشف عن الكسر الكاذب
🔍 آلية العمل
يقوم السكريبت بمراقبة القمم والقيعان الجديدة بناءً على فترة يتم تحديدها من قبل المستخدم.
ثم يحدد الكسر الكاذب عندما يكسر السعر مستوى مرتفعًا أو منخفضًا ثم يعود بسرعة. هذه الحركة غالبًا ما تكون خداعًا للمضاربين، ويقوم المؤشر بكشفها مبكرًا.
✅ أهم ميزات النسخة PRO
📌 التبديل بين عرض جميع الإشارات أو أحدث إشارة فقط
💬 عرض سعر الإشارة بنص نظيف واختياري
📊 جدول ملخص ذكي لعرض آخر الإشارات بسرعة
📈 تمديد تلقائي للخطوط لمتابعة حركة السعر
⚡ واجهة خفيفة وسريعة ومناسبة للعرض اللحظي
🛠 الإعدادات القابلة للتعديل
فترة تحديد الكسر الكاذب
مدة صلاحية الإشارة
أنواع الفلترة: 💎 خام، WMA، أو HMA
وضع الكشف العدواني (Aggressive)
خيارات العرض:
إظهار أو إخفاء السعر
إظهار أو إخفاء الجدول
عرض آخر إشارة فقط
⚠️ رخصة الاستخدام
تم تطوير هذا السكريبت بالاعتماد على السكريبت الأصلي من @Zeiierman
وهو مرخص بموجب Creative Commons BY-NC-SA 4.0
🔒 الاستخدام التجاري غير مسموح. ويجب نسب الفضل للمطور الأصلي.
Pullback Entry Zone FinderPullback Entry Zone Finder
Overview:
This indicator is designed to help traders identify potential buying opportunities during short-term pullbacks, particularly when faster-moving averages show signs of converging back towards slower ones. It visually flags potential zones where price might find support and resume its upward movement, based on moving average dynamics and price proximity.
How It Works:
The indicator utilizes four customizable moving averages (Trigger, Short-term, Intermediate, and Long-term) and Average True Range (ATR) to pinpoint specific conditions:
Pullback Detection: It identifies when the fast 'Trigger MA' is below the 'Short-term MA', indicating a potential short-term pullback or consolidation phase.
MA Convergence: Crucially, it looks for signs that the pullback might be weakening by detecting when the gap between the Short-term MA and the Trigger MA is narrowing (maConverging). This suggests the faster average is starting to catch up, potentially preceding a move back up.
Base Buy Zone (Orange Diamond): This signal appears when both the Pullback and Convergence conditions are met simultaneously. It indicates the general area where conditions are becoming favourable for a potential entry.
Refined Entry Zones:
Prime Entry Zone (Green Diamond): This appears within a Base Buy Zone if the bar's low comes within a specified percentage (Max Distance %) of the Short-term MA. It suggests price has pulled back close to the dynamic support of the Short MA.
ATR Entry Zone (Purple Diamond): This appears within a Base Buy Zone if the bar's low comes within the specified percentage (Max Distance %) of an ATR-based target level. This target level (Buy ATR Target Level, plotted as a purple line when active) is calculated by adding a multiple (ATR Multiplier %) of the ATR to the Short-term MA, providing a volatility-adjusted potential entry area.
Visual Elements:
Moving Averages: Four lines representing the Trigger, Short-term, Intermediate, and Long-term MAs (colors and opacity are customizable). Use the Intermediate and Long-term MAs to gauge the broader market trend.
Orange Diamond (Below Bar): Indicates a 'Base Buy Zone' where a pullback and MA convergence are detected.
Green Diamond (Below Bar): Indicates a 'Prime Entry Zone' where price is close to the Short-term MA during a Base Buy Zone.
Purple Diamond (Below Bar): Indicates an 'ATR Entry Zone' where price is close to the ATR-based target level during a Base Buy Zone.
Purple Line: Plots the calculated 'Buy ATR Target Level' only when the Base Buy Zone condition is active.
Input Parameters:
Moving Averages: Customize the Length and Type (EMA, SMA, WMA, VWMA) for all four moving averages.
ATR Settings: Adjust the ATR Length, the ATR Multiplier % (for calculating the target level), and the Max Distance % (for triggering the Prime and ATR Entry Zones).
Visualization: Set the colors for the four Moving Average lines.
How to Use:
Look for the Orange Diamond as the initial signal that pullback/convergence conditions are met.
The Green and Purple Diamonds suggest price has reached potentially more optimal entry levels within that zone, based on proximity to the Short MA or the ATR target, respectively.
Always consider the signals within the context of the broader trend, indicated by the Intermediate and Long-term MAs. This indicator is generally more effective when used to find entries during pullbacks within an established uptrend (e.g., Intermediate MA > Long MA).
Combine these signals with other forms of analysis, such as chart patterns, support/resistance levels, volume analysis, or other indicators for confirmation.
Disclaimer:
You should always use proper risk management techniques and conduct your own analysis before making any trading decisions. This indicator, or any other, will be of no use if you don't have good risk management.
Bull Bear Pivot by RawstocksThe "Bull Bear Pivot" indicator is a custom Pine Script (v5) tool designed for TradingView to assist traders in identifying key price levels and pivot points on intraday charts (up to 1-hour timeframes). It combines time-based open price markers, pivot high/low detection, and candlestick visualization to provide a comprehensive view of potential support, resistance, and trend reversal levels. Below is a detailed description of the indicator’s functionality, features, and intended use.
Indicator Overview:
The "Bull Bear Pivot" indicator is tailored for intraday trading, focusing on specific times of the day to mark significant price levels (open prices) and detect pivot points. It plots horizontal lines at the open prices of user-defined sessions, identifies pivot highs and lows on the current chart timeframe, and overlays custom candlesticks to highlight price action. The indicator is designed to work on timeframes of 1 hour or less (e.g., 1-minute, 3-minute, 5-minute, 15-minute, 30-minute, 60-minute) and includes a warning mechanism for invalid timeframes.
Key Features:
Time-Based Open Price Markers:
The indicator allows users to define up to five time-based sessions (e.g., 4:00 AM, 8:30 AM, 9:30 AM, 10:00 AM, and a custom time) to capture the open price at the start of each session.
For each session, it plots a horizontal line at the 1-minute open price, extending from the session start to the market close at 4:00 PM EST.
Each line is accompanied by a label positioned 5 bars to the right of the market close (4:00 PM EST), with the text right-aligned and vertically centered on the line.
Users can enable/disable each marker, customize the session time, label text, line color, and text color via the indicator’s settings.
Pivot Highs and Lows:
The indicator calculates pivot highs and lows on the current chart timeframe using the ta.pivothigh and ta.pivotlow functions.
Pivot highs are marked with green triangles above the bars, and pivot lows are marked with red triangles below the bars.
The pivot period (lookback/lookforward) is user-configurable, allowing flexibility in detecting short-term or longer-term reversals.
Custom Candlesticks:
The indicator overlays custom candlesticks on the chart, colored green for bullish candles (close > open) and red for bearish candles (close < open).
This feature helps visualize price action alongside the open price markers and pivot points.
Timeframe Restriction:
The indicator is designed to work on timeframes of 1 hour or less. If the chart timeframe exceeds 1 hour (e.g., 4-hour, daily), a warning label ("Timeframe > 1H Indicator Disabled") is displayed, and no elements are plotted.
Customizable Appearance:
Users can customize the appearance of the open price marker lines, including the line style (solid, dashed, dotted) and line width.
Labels for the open price markers have no background (transparent) and use customizable text colors.
Fibonacci Levels with SMA SignalsThis strategy leverages Fibonacci retracement levels along with the 100-period and 200-period Simple Moving Averages (SMAs) to generate robust entry and exit signals for long-term swing trades, particularly on the daily timeframe. The combination of Fibonacci levels and SMAs provides a powerful way to capitalize on major trend reversals and market retracements, especially in stocks and major crypto assets.
The core of this strategy involves calculating key Fibonacci retracement levels (23.6%, 38.2%, 61.8%, and 78.6%) based on the highest high and lowest low over a 365-day lookback period. These Fibonacci levels act as potential support and resistance zones, indicating areas where price may retrace before continuing its trend. The 100-period SMA and 200-period SMA are used to define the broader market trend, with the strategy favoring uptrend conditions for buying and downtrend conditions for selling.
This indicator highlights high-probability zones for long or short swing setups based on Fibonacci retracements and the broader trend, using the 100 and 200 SMAs.
In addition, this strategy integrates alert conditions to notify the trader when these key conditions are met, providing real-time notifications for optimal entry and exit points. These alerts ensure that the trader does not miss significant trade opportunities.
Key Features:
Fibonacci Retracement Levels: The Fibonacci levels provide natural price zones that traders often watch for potential reversals, making them highly relevant in the context of swing trading.
100 and 200 SMAs: These moving averages help define the overall market trend, ensuring that the strategy operates in line with broader price action.
Buy and Sell Signals: The strategy generates buy signals when the price is above the 200 SMA and retraces to the 61.8% Fibonacci level. Sell signals are triggered when the price is below the 200 SMA and retraces to the 38.2% Fibonacci level.
Alert Conditions: The alert conditions notify traders when the price is at the key Fibonacci levels in the context of an uptrend or downtrend, allowing for efficient monitoring of trade opportunities.
Application:
This strategy is ideal for long-term swing trades in both stocks and major cryptocurrencies (such as BTC and ETH), particularly on the daily timeframe. The daily timeframe allows for capturing broader, more sustained trends, making it suitable for identifying high-quality entries and exits. By using the 100 and 200 SMAs, the strategy filters out noise and focuses on larger, more meaningful trends, which is especially useful for longer-term positions.
This script is optimized for swing traders looking to capitalize on retracements and trends in markets like stocks and crypto. By combining Fibonacci levels with SMAs, the strategy ensures that traders are not only entering at optimal levels but also trading in the direction of the prevailing trend.
