Puell Multiple Variants [OperationHeadLessChicken]Overview
This script contains three different, but related indicators to visualise Bitcoin miner revenue.
The classical Puell Multiple : historically, it has been good at signaling Bitcoin cycle tops and bottoms, but due to the diminishing rewards miners get after each halving, it is not clear how you determine overvalued and undervalued territories on it. Here is how the other two modified versions come into play:
Halving-Corrected Puell Multiple : The idea is to multiply the miner revenue after each halving with a correction factor, so overvalued levels are made comparable by a horizontal line across cycles. After experimentation, this correction factor turned out to be around 1.63. This brings cycle tops close to each other, but we lose the ability to see undervalued territories as a horizontal region. The third variant aims to fix this:
Miner Revenue Relative Strength Index (Miner Revenue RSI) : It uses RSI to map miner revenue into the 0-100 range, making it easy to visualise over/undervalued territories. With correct parameter settings, it eliminates the diminishing nature of the original Puell Multiple, and shows both over- and undervalued revenues correctly.
Example usage
The goal is to determine cycle tops and bottoms. I recommend using it on high timeframes, like monthly or weekly . Lower than that, you will see a lot of noise, but it could still be used. Here I use monthly as the example.
The classical Puell Multiple is included for reference. It is calculated as Miner Revenue divided by the 365-day Moving Average of the Miner Revenue . As you can see in the picture below, it has been good at signaling tops at 1,3,5,7.
The problems:
- I have to switch the Puell Multiple to a logarithmic scale
- Still, I cannot use a horizontal oversold territory
- 5 didn't touch the trendline, despite being a cycle top
- 9 touched the trendline despite not being a cycle top
Halving-Corrected Puell Multiple (yellow): Multiplies the Puell Multiple by 1.63 (a number determined via experimentation) after each halving. In the picture below, you can see how the Classical (white) and Corrected (yellow) Puell Multiples compare:
Advantages:
- Now you can set a constant overvalued level (12.49 in my case)
- 1,3,7 are signaled correctly as cycle tops
- 9 is correctly not signaled as a cycle top
Caveats:
- Now you don't have bottom signals anymore
- 5 is still not signaled as cycle top
Let's see if we can further improve this:
Miner Revenue RSI (blue):
On the monthly, you can see that an RSI period of 6, an overvalued threshold of 90, and an undervalued threshold of 35 have given historically pretty good signals.
Advantages:
- Uses two simple and clear horizontal levels for undervalued and overvalued levels
- Signaling 1,3,5,7 correctly as cycle tops
- Correctly does not signal 9 as a cycle top
- Signaling 4,6,8 correctly as cycle bottoms
Caveats:
- Misses two as a cycle bottom, although it was a long time ago when the Bitcoin market was much less mature
- In the past, gave some early overvalued signals
Usage
Using the example above, you can apply these indicators to any timeframe you like and tweak their parameters to obtain signals for overvalued/undervalued BTC prices
You can show or hide any of the three indicators individually
Set overvalued/undervalued thresholds for each => the background will highlight in green (undervalued) or red (overvalued)
Set special parameters for the given indicators: correction factor for the Corrected Puell and RSI period for Revenue RSI
Show or hide halving events on the indicator panel
All parameters and colours are adjustable
Penunjuk dan strategi
Open Interest FaraGroup Editionopen interest for a closed group :)
An open interest chart is used, as well as additional functionality.
The ability to specify any data as a source, not just public interest.
Gold–Bitcoin Correlation (Offset Model) by KManus88This indicator analyzes the correlation between Gold (XAU/USD) and Bitcoin (BTC/USD) using a time-offset model adjustable by the user.
The goal is to detect cyclical leads or lags between both assets, highlighting how capital flows into Gold may precede or follow movements in the crypto market.
Key Features:
Dynamic correlation calculation between Gold and Bitcoin.
Adjustable offset in days (default: 107) to fine-tune the temporal shift.
Automatic labels and on-chart visualization.
Compatible with multiple timeframes and logarithmic scales.
Interpretation:
Positive correlation suggests synchronized trends between both assets.
Negative correlation signals divergence or rotation of liquidity.
The time-offset parameter helps estimate when a shift in Gold could later reflect in Bitcoin.
Recommended use:
For macro-financial and global liquidity cycle analysis.
As a complementary tool in cross-asset momentum strategies.
© 2025 – Developed by KManus88 | Inspired by monetary correlation studies and global liquidity cycles.
This script is for educational purposes only and does not constitute financial advice.
Adaptive Pulse Frequency & Amplitude TrendAdaptive Pulse Frequency & Amplitude Trend Indicator
This Pine Script indicator is designed to identify strong bullish or bearish trends by analyzing volume dynamics on a lower timeframe than the one currently displayed on the chart. It operates on the principle of detecting significant spikes in buying or selling pressure, referred to as "pulses," and then evaluating their frequency, strength, and dominance over the opposing market forces.
Core Concepts
Lower Timeframe Volume Analysis: The script requests up-volume and down-volume data from a more granular, lower timeframe (e.g., 1-minute data when on a 15-minute chart). This provides a higher-resolution view of the flow of buy and sell orders.
Adaptive Pulse Detection: A "pulse" is defined as a bar with an unusually high net volume (up volume minus down volume). Instead of using a fixed value, the indicator calculates an adaptive threshold based on the 90th percentile of net volume over a 100-bar lookback period. Any bar with a net volume exceeding this dynamic threshold is flagged as a pulse, categorized as either bullish (positive net volume) or bearish (negative net volume).
Frequency and Amplitude: The indicator measures two key aspects of these pulses over user-defined lookback periods:
Net Frequency: The number of bullish pulses minus the number of bearish pulses. A positive value indicates more buying pulses, while a negative value indicates more selling pulses.
Net Amplitude : The cumulative volume of bullish pulses minus the cumulative volume of bearish pulses. This measures the overall strength and conviction behind the pulses.
Primary Trend Signal
The indicator's primary signal comes from a strict dominance condition. It doesn't just look for more buying or selling pulses; it checks if these pulses are powerful enough to overwhelm the total opposite pressure in the market.
Bullish Dominance (Green Background): A strong bullish signal is generated when the total volume of all bullish pulses within a lookback period is greater than the total down-volume from all bars (not just pulses) in that same period.
Bearish Dominance (Red Background): A strong bearish signal is generated when the total volume of all bearish pulses is greater than the total up-volume from all bars in that period.
The chart background is colored green for bullish dominance and red for bearish dominance, providing a clear visual cue for when one side has taken decisive control.
Plotted Data
In addition to the background coloring, the indicator plots several lines in its own pane for more detailed analysis:
Net Frequency: Shows the trend in the number of bull vs. bear pulses.
Net Amplitude: Shows the trend in the strength of bull vs. bear pulses.
Bullish/Bearish Amplitude: The individual cumulative volumes for bull and bear pulses.
Dynamic Threshold: The adaptive value used to identify pulses.
By combining an adaptive detection method with a strict dominance condition, this tool aims to filter out market noise and highlight periods of genuinely strong, volume-backed trends.
Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound.
The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts.
There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass.
The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter.
Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly.
The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart.
What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise.
Moon_TimeBreaks_Indicator🌙 Moon + Timeframe Breaks (Daily, Weekly, Monthly, Quarterly, Yearly)
A unique indicator that combines lunar cycles with major time-based breaks to reveal potential rhythm and cycle shifts in price behavior.
🔹 Features
Displays New Moon and Full Moon phases directly on the chart.
Highlights background color during lunar events.
Draws dynamic timeframe separators for Day, Week, Month, Quarter, and Year.
Helps identify cyclical turning points and time-based reactions in markets.
🔹 Customization
Toggle moon phases, background, or time breaks individually.
Adjust colors for each period (daily, weekly, etc.).
Works on all instruments and timeframes.
🔹 Use Case
Perfect for traders interested in time-price harmony, cyclical analysis, or astro-based market timing.
It pairs well with structure or liquidity tools to enhance timing accuracy.
Glassnode Whale Oscillator 🐳Glassnode Whale Oscillator🐳
Glassnode Whale Oscillator: tracks BTC accumulation/distribution by whales. Normalized (0-100) based on WSPC (±50k BTC), smoothed SMA. Signals: >60 – bullish (buy), <40 – bearish (sell).
Money Line ApproximationSimilar to Ivan's money line minus the Macro data.
How to Interpret and Use It
Bullish Setup : Green line + buy signal = Potential entry (e.g., buy on pullback to the line). Expect upward momentum if RSI stays below 75.
Bearish Setup : Red line + sell signal = Potential exit or short (e.g., sell near the line). Watch for RSI above 25 confirming downside.
Neutral Periods : Yellow line indicates indecision—best to wait for a flip rather than force trades.
Strengths : Simple, visual, and filtered against extremes; works well in trending markets by blending EMAs and using RSI to avoid overbought buys or oversold sells.
Wick Bias - by TenAMTraderWick Bias - by TenAMTrader
Wick Bias helps traders quickly visualize market pressure by analyzing candle wicks and bodies over a user-defined number of bars. By comparing top and bottom wicks, the indicator identifies whether buying or selling pressure has been dominant, providing a clear Indicator Bias signal (Bullish, Bearish, or Neutral).
Key Features:
Shows Top Wicks %, Bottom Wicks %, and optional Body % for recent candles.
Highlights Indicator Bias to indicate short-term market trends.
Fully customizable colors for table rows and bias labels.
Option to show or hide body percentage.
Alerts trigger on bias flips, with optional on-chart labels.
Table can be placed in any chart corner.
Updates in real-time with each new bar.
Recommended Use:
Ideal for intraday and swing traders looking for a quick visual cue of short-term market momentum.
Can be combined with other technical analysis tools to confirm trade setups or potential reversals.
Disclaimer / Legal Notice:
This indicator is for educational and informational purposes only. It is not financial advice and should not be used as the sole basis for trading decisions. Past performance does not guarantee future results. Users are responsible for their own trades. The developer is not liable for any losses or damages resulting from the use of this indicator.
Asia Session Mechanical Entry by AleThis indicator executes fully mechanical trades at the start of the Asian session (default: 20:00 Argentina time).
Core logic:
Compares the closing prices of the previous two sessions at 20:00 and 09:00 to determine bias.
If both days move in the same direction, the indicator takes a mean-reversion trade (opposite to the last two days’ move).
If the days move in opposite directions, the trade follows the most recent day’s direction.
Execution details:
Entry price: exact session open or delayed by a user-defined number of candles.
Stop Loss: nearest swing high/low ± ATR multiplier buffer.
Take Profit: calculated from entry to SL distance, multiplied by user-defined RR ratio.
ATR value plotted for volatility reference.
Works on H1 charts for consistent candle timing.
Features:
Adjustable start/end session times.
Configurable ATR multiplier, RR ratio, and delay before entry.
Manual overrides for SL/TP levels.
Automatic daily reset for next session's logic.
Notes:
This tool is based on a classic session-reversion model enhanced with ATR-based filters, flexible timing, and manual overrides. It is designed for systematic execution and quick visual backtesting.
Realtime RenkoI've been working on real-time renko for a while as a coding challenge. The interesting problem here is building renko bricks that form based on incoming tick data rather than waiting for bar closes. Every tick that comes through gets processed immediately, and when price moves enough to complete a brick, that brick closes and a new one opens right then. It's just neat because you can run it and it updates as you'd expect with renko, forming bricks based purely on price movement happening in real time rather than waiting for arbitrary time intervals to pass.
The three brick sizing methods give you flexibility in how you define "enough movement" to form a new brick. Traditional renko uses a fixed price range, so if you set it to 10 ticks, every brick represents exactly 10 ticks of movement. This works well for instruments with stable tick sizes and predictable volatility. ATR-based sizing calculates the average true range once at startup using a weighted average across all historical bars, then divides that by your brick value input. If you want bricks that are one full ATR in size, you'd use a brick value of 1. If you want half-ATR bricks, use 2. This inverted relationship exists because the calculation is ATR divided by your input, which lets you work with multiples and fractions intuitively. Percentage-based sizing makes each brick a fixed percentage move from the previous brick's close, which automatically scales with price level and works well for instruments that move proportionally rather than in absolute tick increments.
The best part about this implementation is how it uses varip for state management. When you first load the indicator, there's no history at all. Everything starts fresh from the moment you add it to your chart because varip variables only exist in real-time. This means you're watching actual renko bricks form from real tick data as it arrives. The indicator builds its own internal history as it runs, storing up to 250 completed bricks in memory, but that history only exists for the current session. Refresh the page or reload the indicator and it starts over from scratch.
The visual implementation uses boxes for brick bodies and lines for wicks, drawn at offset bar indices to create the appearance of a continuous renko chart in the indicator pane. Each brick occupies two bar index positions horizontally, which spaces them out and makes the chart readable. The current brick updates in real time as new ticks arrive, with its high, low, and close values adjusting continuously until it reaches the threshold to close and become finalized. Once a brick closes, it gets pushed into the history array and a new brick opens at the closing level of the previous one.
What makes this especially useful for debugging and analysis are the hover tooltips on each brick. Clicking on any brick brings up information showing when it opened with millisecond precision, how long it took to form from open to close, its internal bar index within the renko sequence, and the brick size being used. That time delta measurement is particularly valuable because it reveals the pace of price movement. A brick that forms in five seconds indicates very different market conditions than one that takes three minutes, even though both bricks represent the same amount of price movement. You can spot acceleration and deceleration in trend development by watching how quickly consecutive bricks form.
The pine logs that generate when bricks close serve as breadcrumbs back to the main chart. Every time a brick finalizes, the indicator writes a log entry with the same information shown in the tooltip. You can click that log entry and TradingView jumps your main chart to the exact timestamp when that brick closed. This lets you correlate renko brick formation with what was happening on the time-based chart, which is critical for understanding context. A brick that closed during a major news announcement or at a key support level tells a different story than one that closed during quiet drift, and the logs make it trivial to investigate those situations.
The internal bar indexing system maintains a separate count from the chart's bar_index, giving each renko brick its own sequential number starting from when the indicator begins running. This makes it easy to reference specific bricks in your analysis or when discussing patterns with others. The internal index increments only when a brick closes, so it's a pure measure of how many bricks have formed regardless of how much chart time has passed. You can match these indices between the visual bricks and the log entries, which helps when you're trying to track down the details of a specific brick that caught your attention.
Brick overshoot handling ensures that when price blows through the threshold level instead of just barely touching it, the brick closes at the threshold and the excess movement carries over to the next brick. This prevents gaps in the renko sequence and maintains the integrity of the brick sizing. If price shoots up through your bullish threshold and keeps going, the current brick closes at exactly the threshold level and the new brick opens there with the overshoot already baked into its initial high. Without this logic, you'd get renko bricks with irregular sizes whenever price moved aggressively, which would undermine the whole point of using fixed-range bricks.
The timezone setting lets you adjust timestamps to your local time or whatever reference you prefer, which matters when you're analyzing logs or comparing brick formation times across different sessions. The time delta formatter converts raw milliseconds into human-readable strings showing days, hours, minutes, and seconds with fractional precision. This makes it immediately clear whether a brick took 12.3 seconds or 2 minutes and 15 seconds to form, without having to parse millisecond values mentally.
This is the script version that will eventually be integrated into my real-time candles library. The library version had an issue with tooltips not displaying correctly, which this implementation fixes by using a different approach to label creation and positioning. Running it as a standalone indicator also gives you more control over the visual settings and makes it easier to experiment with different brick sizing methods without affecting other tools that might be using the library version.
What this really demonstrates is that real-time indicators in Pine Script require thinking about state management and tick processing differently than historical indicators. Most indicator code assumes bars are immutable once closed, so you can reference `close ` and know that value will never change. Real-time renko throws that assumption out because the current brick is constantly mutating with every tick until it closes. Using varip for state variables and carefully tracking what belongs to finalized bricks versus the developing brick makes it possible to maintain consistency while still updating smoothly in real-time. The fact that there's no historical reconstruction and everything starts fresh when you load it is actually a feature, not a limitation, because you're seeing genuine real-time brick formation rather than some approximation of what might have happened in the past.
SMA 10 & 50SMA10&50
SMA 10 & 50 is a simple dual moving average indicator that plots two Simple Moving Averages (SMA) on the price chart: SMA10 and SMA50.
Features:
- SMA10 (fast): Period 10
- SMA50 (slow): Period 50
- Customizable source for each SMA
- Distinct colors for better visualization
Ideal for identifying short-term vs long-term trends, crossovers, and dynamic support/resistance levels.
Low Range Predictor [NR4/NR7 after WR4/WR7/WR20, within 1-3Days]Indicator Overview
The Low Range Predictor is a TradingView indicator displayed in a single panel below the chart. It spots volatility contraction setups (NR4/NR7 within 1–3 days of WR4/WR7/WR20) to predict low-range moves (e.g., <0.5% daily on SPY) over 2–5 days, perfect for your weekly 15/22 DTE put calendar spread strategy.
What You See
• Red Histograms (WR, Volatility Climax):
• WR4: Half-length red bars, widest range in 4 bars.
• WR7: Three-quarter-length red bars, widest in 7 bars.
• WR20: Full-length red bars, widest in 20 bars.
• Green Histograms (NR, Entry Signals):
• NR4: Half-length green bars, only on NR4 days (tightest range in 4 bars) within 1–3 days of a WR4.
• NR7: Full-length green bars, only on NR7 days within 1–3 days of a WR7.
• Panel: All signals (red WR4/WR7/WR20, green NR4/NR7) show in one panel below the chart, with green bars marking put calendar entry days.
Probabilities
• Volatility Contraction:
• NR4 after WR4: 65–70% chance of daily ranges <0.5% on SPY for 2–5 days (ATR drops 20–30%). Occurs ~2–3 times/month.
• NR7 after WR7: 60–65% chance of similar low ranges, less frequent (~1–2 times/month).
• Backtest (SPY, 2000–2025): 65% of NR4/NR7 signals lead to reduced volatility (<0.7% daily range) vs. 50% for random days.
• Signal Frequency: NR4 signals are more common than NR7, ideal for weekly entries. WR20 provides context but isn’t tied to NR signals.
Multi-TF Trend Dashboard (12H / D / W)Trend Alignment Dashboard (12H/D/W, 200 EMA)
Quickly see trend direction across 12H, Daily, and Weekly charts. Includes 12H 200 EMA for major trend confirmation. Perfect for spotting strong multi-timeframe alignment at a glance.
AUTOMATIC ANALYSIS MODULE🧭 Overview
“Automatic Analysis Module” is a professional, multi-indicator system that interprets market conditions in real time using TSI, RSI, and ATR metrics.
It automatically detects trend reversals, volatility compressions, and momentum exhaustion, helping traders identify high-probability setups without manual analysis.
⚙️ Core Logic
The script continuously evaluates:
TSI (True Strength Index) → trend direction, strength, and early reversal zones.
RSI (Relative Strength Index) → momentum extremes and technical divergences.
ATR (Average True Range) → volatility expansion or compression phases.
Multi-timeframe ATR comparison → detects whether the weekly structure supports or contradicts the local move.
The system combines these signals to produce an automatic interpretation displayed directly on the chart.
📊 Interpretation Table
At every new bar close, the indicator updates a compact dashboard (bottom right corner) showing:
🔵 Main interpretation → trend, reversal, exhaustion, or trap scenario.
🟢 Micro ATR context → volatility check and flow analysis (stable / expanding / contracting).
Each condition is expressed in plain English for quick decision-making — ideal for professional traders who manage multiple charts.
📈 How to Use
1️⃣ Load the indicator on your preferred asset and timeframe (recommended: Daily or 4H).
2️⃣ Watch the blue line message for the main trend interpretation.
3️⃣ Use the green line message as a volatility gauge before entering.
4️⃣ Confirm entries with your own strategy or price structure.
Typical examples:
“Possible bullish reversal” → early accumulation signal.
“Compression phase → wait for breakout” → avoid premature trades.
“Confirmed uptrend” → trend continuation zone.
⚡ Key Features
Real-time auto-interpretation of TSI/RSI/ATR signals.
Detects both bull/bear traps and trend exhaustion zones.
Highlights volatility transitions before breakouts occur.
Works across all assets and timeframes.
No repainting — stable on historical data.
✅ Ideal For
Swing traders, position traders, and institutional analysts who want automated context recognition instead of manual indicator reading.
London Breakout Structure by AleThis indicator identifies market structure breakouts (CHOCH/BOS) within a specific London session window, highlighting potential breakout trades with automatic entry, stop loss (SL), and take profit (TP) levels.
It helps traders focus on high-probability breakouts when volatility increases after the Asian session, using price structure, ATR-based volatility filters, and a custom risk/reward setup.
🔹 Example of Strategy Application
Define your session (e.g. 04:00 to 05:00).
Wait for a CHOCH (Change of Character) inside this session.
If a bullish CHOCH occurs → go LONG at candle close.
If a bearish CHOCH occurs → go SHORT at candle close.
SL is set below/above the previous swing using ATR × multiplier.
TP is calculated automatically based on your R:R ratio.
📊 Example:
When price breaks above the last swing high within the session, a “BUY” label appears and the indicator draws Entry, SL, and TP levels automatically.
If the breakout fails and price closes below the opposite structure, a “SELL” signal will replace the bullish setup.
🔹 Details
The logic is based on structural shifts (CHOCH/BOS):
A CHOCH occurs when price breaks and closes beyond the most recent high/low.
The indicator dynamically detects these shifts in structure, validating them only inside your chosen time window (e.g. the London Open).
The ATR filter ensures setups are valid only when the range has enough volatility, avoiding false signals in low-volume hours.
You can also visualize:
The session area (purple background)
Entry, Stop Loss, and Take Profit levels
Direction labels (BUY/SELL)
ATR line for volatility context
🔹 Configuration
Start / End Hour: define your preferred trading window.
ATR Length & Multiplier: adjust for volatility.
Risk/Reward Ratio: set your desired R:R (default 1:2).
Minimum Range Filter: avoids signals with tight SLs.
Alerts: receive notifications when breakout conditions occur.
🔹 Recommendations
Works best on 15m or 5m charts during London session.
Designed for breakout and structure-based traders.
Works on Forex, Crypto, and Indices.
Ideal as a visual and educational tool for understanding BOS/CHOCH behavior.
London Breakout Structure by AleThis indicator identifies market structure breakouts (CHOCH/BOS) within a specific London session window, highlighting potential breakout trades with automatic entry, stop loss (SL), and take profit (TP) levels.
It helps traders focus on high-probability breakouts when volatility increases after the Asian session, using price structure, ATR-based volatility filters, and a custom risk/reward setup.
🔹 Example of Strategy Application
Define your session (e.g. 04:00 to 05:00).
Wait for a CHOCH (Change of Character) inside this session.
If a bullish CHOCH occurs → go LONG at candle close.
If a bearish CHOCH occurs → go SHORT at candle close.
SL is set below/above the previous swing using ATR × multiplier.
TP is calculated automatically based on your R:R ratio.
📊 Example:
When price breaks above the last swing high within the session, a “BUY” label appears and the indicator draws Entry, SL, and TP levels automatically.
If the breakout fails and price closes below the opposite structure, a “SELL” signal will replace the bullish setup.
🔹 Details
The logic is based on structural shifts (CHOCH/BOS):
A CHOCH occurs when price breaks and closes beyond the most recent high/low.
The indicator dynamically detects these shifts in structure, validating them only inside your chosen time window (e.g. the London Open).
The ATR filter ensures setups are valid only when the range has enough volatility, avoiding false signals in low-volume hours.
You can also visualize:
The session area (purple background)
Entry, Stop Loss, and Take Profit levels
Direction labels (BUY/SELL)
ATR line for volatility context
🔹 Configuration
Start / End Hour: define your preferred trading window.
ATR Length & Multiplier: adjust for volatility.
Risk/Reward Ratio: set your desired R:R (default 1:2).
Minimum Range Filter: avoids signals with tight SLs.
Alerts: receive notifications when breakout conditions occur.
🔹 Recommendations
Works best on 15m or 5m charts during London session.
Designed for breakout and structure-based traders.
Works on Forex, Crypto, and Indices.
Ideal as a visual and educational tool for understanding BOS/CHOCH behavior.
BB_1-44 ББ в одном (4 in 1)
BB_1-4 is a multi-timeframe Bollinger Bands indicator that displays four different sets of Bollinger Bands on the price chart with customizable periods, line styles, and transparency levels.
Features:
- Four Bollinger Bands sets: bb_1 (20), bb_2 (80), bb_3 (160), bb_4 (320)
- Customizable period and multiplier for each set
- Unique line styles: standard, stepline, and stepline_diamond
- Adjustable line transparency for better visibility
- No fill between bands for cleaner chart layout
Ideal for multi-timeframe analysis, volatility assessment, and support/resistance level identification.
RSI Value Table – match builtin🧭 Overview
“RSI Value Table – match builtin” displays the exact RSI value (identical to TradingView’s built-in RSI) for any selected timeframe — directly on your chart.
It’s designed for professional traders who need quick RSI confirmation without switching panels or opening multiple indicators.
⚙️ Core Logic
Reads RSI from any timeframe using request.security() with gaps_off and lookahead_off — ensuring a perfect match with the native RSI.
Optional EMA smoothing (non-standard) for visual stability.
Color-coded cell:
🟩 Green → RSI > 50 (bullish momentum)
🟥 Red → RSI < 50 (bearish momentum)
🟨 Yellow → Neutral zone around 50
Adjustable table position: top/bottom, left/right corners.
⚡ Alerts
Built-in alert conditions trigger automatically:
RSI > 50 → bullish momentum confirmation.
RSI < 50 → bearish momentum confirmation.
📈 How to Use
Select your preferred RSI timeframe (e.g., Daily, Weekly, 4H).
Watch the color-coded cell:
Green → trade long bias only.
Red → short bias only.
Ideal as a confirmation module for multi-timeframe systems or smart signal engines.
Historical Vertical Lines 17:00-20:30Historical Vertical Lines 17:00-20:30. These lines show this specific time. You can edit the times via pine script. Easy.
