Bollinger Bands (Indicator Only)Just a Bollinger Bands indicator that can be used to make a strategy, I hope it will help you
Kitaran
Sessions with Mausa session high/low tracker that draws flat, horizontal lines for Asia, London, and New York trading sessions. It updates those levels in real time during each session, locks them in once the session ends, and keeps them on the chart for context.
At a glance, you always know:
Where each session’s highs and lows were set
Which session produced them (ASIA, LDN, NY labels float cleanly above the highs)
When price is approaching or reacting to prior session levels
🔹 Use Cases:
• Key Levels – See where Asia, London, or NY set boundaries, and watch how price respects or rejects them
• Breakout Zones – Monitor when price breaks above/below session highs/lows
• Session Structure – Know instantly if a move happened during London or NY without squinting at the clock
• Backtesting – Keep historic session levels on the chart for reference — nothing gets deleted
• Confluence – Align these levels with support/resistance, fibs, or liquidity zones
Simple, visual, no distractions — just session structure at a glance.
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.
Market Pulse TableMarket Pulse Table – Customizable MACD Tracker
This indicator provides a clean and compact table showing real-time market signals for selected instruments.
✅ Features:
• Displays daily % change with color-coded sentiment (green for gains, red for losses)
• Shows MACD signal – "Buy", "Sell", or "Neutral" based on daily MACD crossovers
• Fully customizable: toggle which assets to include from a predefined list (e.g., ES1!, NQ1!, DXY, VIX...)
• Adjustable table position on chart
🎯 Designed for traders who want a quick overview of market direction, momentum, and volatility across key instruments, helping you stay aligned with the broader trend.
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REW - CNTThis indicator combines RSI(14), EMA(9), and WMA(45) to generate crossover signals. A green arrow appears when RSI crosses above EMA and EMA is below WMA, indicating a potential upward trend. A red arrow signals a possible downtrend when RSI crosses below EMA and EMA is above WMA.
Liên hệ: www.zalo.me
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Half Causal EstimatorOverview
The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output.
Core Concept
Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component).
This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages.
How It Works
The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window.
This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate.
The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits.
Understanding Kernel Smoothing
At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness.
The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points.
Applicable Data Sources
The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next).
Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach.
Practical Applications
The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness.
When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns.
Technical Implementation
The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day.
For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter.
This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques.
Configuration Options
The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width.
These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument.
Limitations
The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns.
Conclusion
The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Sun Moon Conjunctions Trine Oppositions 2025this script is an astrological tool designed to overlay significant Sun-Moon aspect events for 2025 on a Bitcoin chart. It highlights key lunar phases and aspects—Conjunctions (New Moon) in blue, Squares in red, Oppositions (Full Moon) in purple, and Trines in green—using background colors and labeled markers. Users can toggle visibility for each aspect type and adjust label sizes via customizable inputs. The script accurately marks events from January through December 2025, with labels appearing once per event, making it a valuable resource for exploring potential correlations between lunar cycles and Bitcoin price movements.
Gold Opening 15-Min ORB INDICATOR by AdéThis indicator is designed for trading Gold (XAUUSD) during the first 15 minutes of major market openings: Asian, European, and US sessions. It highlights these key time windows, plots the high and low ranges of each session, and generates breakout-based buy/sell signals. Ideal for traders focusing on volatility at market opens.
Features:Session Windows:
Asian: 1:00–1:15 AM Barcelona time (23:00–23:15 UTC, CEST-adjusted).
European: 9:00–9:15 AM Barcelona time (07:00–07:15 UTC).
US: 3:30–3:45 PM Barcelona time (13:30–13:45 UTC).
Marked with yellow (Asian), green (Europe), and blue (US) triangles below bars.
High/Low Ranges:Plots horizontal lines showing the highest high and lowest low of each session’s first 15 minutes.Lines appear after each session ends and persist until the next day, color-coded to match the sessions.Breakout Signals:Buy (Long): Triggers when the closing price breaks above the highest high of the previous 5 bars during a session window (lime triangle above bar).Sell (Short): Triggers when the closing price breaks below the lowest low of the previous 5 bars during a session window (red triangle below bar).
Signals are restricted to the 15-minute session periods for focused trading.Usage:Timeframe: Optimized for 1-minute XAUUSD charts.Timezone: Set your chart to UTC for accurate session timing (script uses UTC internally, based on Barcelona CEST, UTC+2 in April).Strategy:
Use buy/sell signals for breakout trades during volatile market opens, with session ranges as support/resistance levels.Customization: Adjust the lookback variable (default: 5) to tweak signal sensitivity.Notes:Tested for April 2025 (CEST, UTC+2).
Adjust timestamp values if using outside daylight saving time (CET, UTC+1) or for different broker timezones.Best for scalping or short-term trades during high-volatility periods. Combine with other indicators for confirmation if desired.How to Use:Apply to a 1-minute XAUUSD chart.Watch for session markers (triangles) and breakout signals during the 15-minute windows.Use the high/low lines to gauge potential breakout targets or reversals.
RSI Buy/Sell SignalsRsI give Buy and Sell signal when rsi reaches the top and sell when it reaches the bottom.
Kondratieff Wave & Benner Business CyclesKondratieff Wave Theory
Description: The Kondratieff Wave, also known as K-Waves or Long Waves, is an economic theory that posits long-term cycles of approximately 40-60 years in capitalist economies. These cycles consist of four phases: Spring (expansion and recovery), Summer (prosperity and peak), Autumn (stagnation and recession), and Winter (depression and restructuring). The theory suggests that technological innovations and major economic shifts drive these waves, influencing periods of growth and decline over decades.
Creator Bio: Nikolai Dmitriyevich Kondratieff (1892–1938) was a Russian economist born in the Kostroma Governorate. He studied at the University of St. Petersburg and became a prominent figure in Soviet economics. Kondratieff developed his long-wave theory in the 1920s while analyzing historical economic data, publishing works like The Major Economic Cycles (1925). His ideas clashed with Soviet ideology, leading to his arrest in 1930 during Stalin’s purges. He was executed in 1938, but his work gained recognition posthumously, influencing modern economic cycle analysis.
Benner Cycle Theory
Description: The Benner Cycle, proposed by Samuel Benner, is a predictive model for business and commodity price cycles, focusing on shorter-term economic fluctuations. Benner identified recurring patterns in market peaks (highs), panics (crashes), and buying opportunities (lows), with cycles averaging 8-10 years for highs, 7-8 years for panics, and 8-9 years for buys. His theory, based on historical observations of U.S. markets, aimed to guide farmers and investors by forecasting periods of prosperity and distress.
Creator Bio: Samuel T. Benner (1830s–unknown) was an American farmer and businessman from Ohio, not a formally trained economist. After losing his fortune in the Panic of 1873, Benner turned to studying economic patterns. In 1875, he self-published Benner’s Prophecies of Future Ups and Downs in Prices, a book that charted cycles in pig iron prices and other commodities. His work gained a cult following among traders and remains studied for its empirical approach, despite Benner’s lack of academic credentials and limited biographical records.
Stunden-Markierer 10min vor/nach10 Vor und 10 Nach für die beste und schnellste Übersicht wann IPDA bucht
EVC ChecklistEVC system - Cậu Hoàng
The EVC trading system is a multi-timeframe analysis method, where the abbreviation EVC stands for Entry timeframe, Validation timeframe, and Context timeframe.1 As described, the trader will choose any timeframe as the Entry timeframe (E). Then, the next timeframes are calculated based on E according to the formula: V = E6 and C = V10.2 These multiplications are performed after converting the E timeframe to seconds, minutes, hours, days, weeks, or months, and the results V and C are also expressed in the corresponding time units. This approach is considered an effective timeframe template for trading across all timeframes.1 For example, if the Entry timeframe is 60 minutes, the Validation timeframe will be approximately 300 minutes (equivalent to one day), and the Context timeframe will be approximately 10 days (equivalent to one week). Conversely, if the Entry timeframe is 1 minute, the Validation timeframe will be approximately 6 minutes, and the Context timeframe will be approximately 60 minutes (i.e. hourly).2 The flexibility in choosing the Entry timeframe means that the EVC system can be adapted to suit a variety of trading styles and timeframe preferences. The core of the system lies in its fixed-multiplier multiplier multiplier structure, which creates a consistent analysis process.
The purpose of the Pine Script checklist indicator is to provide a visual tool on the TradingView platform that guides traders through the EVC system’s analysis process step by step. This indicator will act as a trading assistant, ensuring that all the criteria of the system are systematically considered before making a trading decision. By following the checklist, the trader can reduce impulsive decisions based on emotions and increase discipline in applying the defined trading strategy. It should be noted that the proposed Pine Script will only focus on the UI and logic of the checklist based on user input. This indicator will not automatically access or analyze live market data from TradingView. Instead, the user will need to manually assess the market conditions on the selected timeframes and input the relevant information into the checklist. Therefore, this indicator is a decision support tool, not a fully automated trading system.
Donchian Channel Trend Meter [Custom TF]edited version of Donchian Channel Trend Meter
Try 1 minute in combination of ORIGINAL CREATOR his TOOL
7-Channel Trend Meter – Ultimate Trend Confirmation Tool 💹 find it on
www.tradingview.com
Signals PridictorSignals Predictor is a powerful, next-generation technical indicator built upon advanced algorithms Designed for traders who seek clarity, reliability, and dynamic insights, this indicator predicts price movement directions with high accuracy, enhancing decision-making and trading efficiency.
Key Features
Dynamic Signal Entries & Exits:
Utilizes customizable ATR-based dynamic exits and time-based strict exits, allowing traders to adapt strategies to changing market conditions.
Candle Coloring:
Candles dynamically color green for bullish conditions and red for bearish conditions, offering instant visual feedback on the prevailing market sentiment.
Trade Performance Table:
Includes a built-in real-time performance statistics table, tracking total trades, win rate, profit ratio, and early signal flips, which helps traders quickly assess strategy effectiveness.
How to Use
Entry Signals:
Green Label (▲+): Indicates a strong bullish (buy) signal.
Red Label (▼+): Indicates a strong bearish (sell) signal.
Exit Signals:
Small cross (×) represents recommended trade exits.
Visual Confirmation:
Kernel Regression Estimate Line visually confirms the underlying trend strength.
Candle colors reinforce the trend direction—green for bullish, red for bearish.
Who Should Use Signals Predictor?
Day Traders
Swing Traders
Trend-Following Traders
Technical Analysts
Recommended Usage
Combine with price action, support & resistance levels, and trend analysis for maximum reliability.
Optimal results when used on major forex pairs, indices, commodities, and cryptocurrencies.
Disclaimer
Signals Predictor is intended as an analysis tool to complement your trading strategy. Always apply proper risk management and never rely solely on one indicator.
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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.
REW - CNTThis indicator combines RSI(14), EMA(9), and WMA(45) to generate crossover signals. A green arrow appears when RSI crosses above EMA and EMA is below WMA, indicating a potential upward trend. A red arrow signals a possible downtrend when RSI crosses below EMA and EMA is above WMA.
EMA Crossover StrategyAdjust your partial TP and stop loss percentages.
Disable trades on chart to avoid clutter.
Strategy should work fine for identifying entries.
Crypto Scenario Alert SystemThe "Crypto Scenario Alert System" is a indicator that monitors key crypto assets like Bitcoin (BTC), Bitcoin Dominance (BTC.D), Ethereum (ETH), and total market caps (TOTAL, TOTAL2), providing alerts when important price levels are crossed.
Key Alerts:
BTC Price: Alerts for breakdowns below $72K or breakouts above $85K.
BTC Dominance: Alerts for spikes above 65% or drops below 60%.
Total Market Cap: Alerts for market cap changes above $2.85T or below $2.4T.
Total2 Market Cap: Alerts for altcoin market cap movements above $1.25T or below $1.05T.
ETH Price: Alerts for movements below $3K or above $3.6K.
Instructions:
Add the Indicator to your chart.
Manually Create Alerts:
Right-click on the chart, select "Add Alert".
Choose your desired alert condition (e.g., BTC Breakdown ).
Set your notification preferences.
Donchian Channel Trend Meter [Custom TF]DC TREND METER WITH CUSTOM TF
Check trend patterns in combi with other confleunces w/m formation above 50 ma 1 hr
BUY trend breaks and pay attention to 50 line
1 minute works good already
[4LC] Period Highs, Lows and OpensPeriod Highs, Lows, and Opens (HLO)
This script plots highs, lows, and opens from different time periods—yearly, monthly, weekly, Monday, and daily—on your chart. It includes a grouping feature that combines levels close to each other, based on a percentage distance, to keep the display organized.
What It Does:
It shows key price levels from various timeframes, marking where the market has hit highs, lows, or started a period. These levels can indicate potential support or resistance zones and help track price behavior over time.
How to Use It:
Add it to your chart and choose which levels to display (e.g., "Yearly High," "Daily Open").
Check where price is relative to these levels—above might suggest upward momentum, below could point to downward pressure.
Use the highs and lows to identify ranges for trading or watch for breakouts past these points.
Adjust settings like colors, spacing, or grouping distance as needed, and toggle price labels to see exact values.
Notes:
The script pulls data from multiple periods to give a broader view of price action. The grouping reduces overlap by averaging nearby levels into a single line with a combined label (e.g., "Yearly High, Monthly High"). It’s meant for traders interested in tracking significant levels across timeframes, whether for range trading or spotting market direction.
