Seasonality Heatmap [QuantAlgo]🟢 Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
🟢 How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price × 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) × 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close × 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) × 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
→ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
→ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
→ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
→ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
🟢 Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
BTCUSD
Z-Score Momentum | MisinkoMasterThe Z-Score Momentum is a new trend analysis indicator designed to catch reversals, and shifts in trends by comparing the "positive" and "negative" momentum by using the Z-Score.
This approach helps traders and investors get unique insight into the market of not just Crypto, but any market.
A deeper dive into the indicator
First, I want to cover the "Why?", as I believe it will ease of the part of the calculation to make it easier to understand, as by then you will understand how it fits the puzzle.
I had an attempt to create a momentum oscillator that would catch reversals and provide high tier accuracy while maintaining the main part => the speed.
I thought back to many concepts, divergences between averages?
- Did not work
Maybe a MACD rework?
- Did not work with what I tried :(
So I thought about statistics, Standard Deviation, Z-Score, Sharpe/Sortino/Omega ratio...
Wait, was that the Z-Score? I only tried the For Loop version of it :O
So on my way back from school I formulated a concept (originaly not like this but to that later) that would attempt to use the Z-Score as an accurate momentum oscillator.
Many ideas were falling out of the blue, but not many worked.
After almost giving up on this, and going to go back to developing my strategies, I tried one last thing:
What if we use divergences in the average, formulated like a Z-score?
Surprise-surprise, it worked!
Now to explain what I have been so passionately yapping about, and to connect the pieces of the puzzle once and for all:
The indicator compares the "strength" of the bullish/bearish factors (could be said differently, but this is my "speach bubble", and I think this describes it the best)
What could we use for the "bullish/bearish" factors?
How about high & low?
I mean, these are by definitions the highest and lowest points in price, which I decided to interpret as: The highest the bull & bear "factors" achieved that bar.
The problem here is comparison, I mean high will ALWAYS > low, unless the asset decided to unplug itself and stop moving, but otherwise that would be unfair.
Now if I use my Z-score, it will get higher while low is going up, which is the opposite of what I want, the bearish "factor" is weaker while we go up!
So I sat on my ret*rded a*s for 25 minutes, completly ignoring the fact the number "-1" exists.
Surprise surprise, multiplying the Z-Score of the low by -1 did what I wanted!
Now it reversed itself (magically). Now while the low keeps going down, the bear factor increases, and while it goes up the bear factor lowers.
This was btw still too noisy, so instead of the classic formula:
a = current value
b = average value
c = standard deviation of a
Z = (a-b)/c
I used:
a = average value over n/2 period
b = average value over n period
c = standard deviation of a
Z = (a-b)/c
And then compared the Z-Score of High to the Z-Score of Low by basic subtraction, which gives us final result and shows us the strength of trend, the direction of the trend, and possibly more, which I may have not found.
As always, this script is open source, so make sure to play around with it, you may uncover the treasure that I did not :)
Enjoy Gs!
Kalman Exponentialy Weighted Moving Average | MisinkoMasterThe Kalman Exponentialy Weighted Moving Average is a technical analysis tool providing users with more responsive and smoother signals, providing crystal-clear signals and giving investors valuable insights on market trends, however it could be used in many cases.
A deeper dive into the indicator:
When going through my creation of strategies, I had stumbled on an indicator called "EWMA", which worked decently, but it was far too simple in my opinion so I decided to combine the EMA & WMA, but with a little more complexity, and it has worked .
I began by learning how both MAs work, I already knew how WMA works, but EMA I did not.
After learning both I found out they were quite simple in principle and that there was a way to combine them in such way that you would get really good signals, however it was way too noisy.
While it could avoid major dumps that were not avoided by most indicators, it would lose that edge because of being too noisy.
After testing out many conditions, combinations & more, the best working one was this one:
WMA > KEWMA = long
WMA < KEWMA = short
I will explain this later, but this gave fast signals, and while it still was noisy it was better then before.
To smooth it out, I started testing price filters => Gaussian Filter and many more were tested out, but they either slowed it down to the point it was no longer of much use, or did not smooth it at all.
After testing the Kalman filter on this thing, I was shocked.
It was just right and made the indicator a lot better, smoothed it and kept most of the responsivness it had.
Now to the big question: "How is it calculated?"
Now first it needs to calculate the Kalman source, which smooths the source which will be used.
After that, we calculate the Weighted Moving Average for " n " period on the Kalman source.
Now that we have our WMA values, we need to calculate " a ".
a is calculated in the following formula:
a = 2/(1+ n )
where n is the user defined length
Now for the last part:
KEWMA = WMAyesterday * (1-a) + WMAtoday * a
This creates a very accurate and reactive indicator, that can prove useful in many uses, beyond those I will and did talk about.
For the trend logic as mentioned before:
Long = WMA > KEWMA
Short = WMA < KEWMA
This worked best, but you might find better ways of using it.
I think that is all I have to say about it, I left it open source so you can all code it in your strategies and play around with it.
Enjoy Gs!
TSWA⚡️ Indicator Description:
This indicator is specially designed for scalping trades, providing accurate entry and exit signals for short-term opportunities.
With proper use and risk management, it can deliver between 20 to 200 pips per day, depending on market volatility and session activity.
💡 Best Trading Sessions:
🕐 Asian Session
🕕 London Session
(It can also perform well during the New York session, but it’s not recommended due to high volatility and unpredictable price movements.)
⚙️ Key Features:
Accurate buy/sell signals optimized for scalping.
Works effectively on most forex pairs and indices.
Best results on lower timeframes (M1).
Ideal for intraday traders seeking fast, frequent opportunities.
🚀 Usage Tips:
Always apply proper risk and money management.
Avoid trading during high-impact news releases.
Combine with price action or confirmation indicators for higher accuracy.
Golden Cross Screener [Pineify]Golden Cross Screener Pineify – Multi-Symbol Trend Detection Screener for TradingView
Discover the Golden Cross Screener Pineify for TradingView: a multi-symbol, multi-timeframe indicator for crypto and other assets. Customizable Golden Cross detection, robust algorithm, and intuitive screener design for smarter portfolio trend analysis.
Key Features
Multi-symbol screening across major cryptocurrencies or assets – BTCUSD, ETHUSD, XRPUSD, USDT, BNB, SOLUSD, DOGEUSD, TRXUSD (fully customizable).
Multi-timeframe analysis (e.g., 1m, 5m, 10m, 30m), enabling robust trend detection from scalp to swing.
Customizable Moving Average settings for both Fast and Slow MA (source and length).
Efficient screener table, highlighting Golden Cross events and current asset trends in one panel.
Visual cues for bullish, bearish, and cross states using intuitive color-coding and labels.
Flexible symbol and timeframe inputs to tailor the screener to any portfolio or watchlist.
How It Works
The Golden Cross Screener Pineify leverages the classic Golden Cross methodology—a bullish trend signal triggered when a shorter-term moving average crosses above a longer-term moving average. To improve robustness, you are empowered to configure both Fast MA and Slow MA periods and sources, making the detection logic applicable to any symbol, timeframe, or asset class.
Internally, the script runs dedicated calculations on each chosen symbol and timeframe, generating independent signals using exponential moving averages (EMA). Using the TradingView `request.security` function, it fetches and processes price data for up to eight portfolio assets on four timeframes, displaying the detected Golden Cross, Bullish, or Bearish states in a central screener table.
Trading Ideas and Insights
Spot emerging bullish or bearish trends across your favorite crypto pairs or trading assets in real time.
Capture prime opportunities when multiple assets align with Golden Cross signals—ideal for portfolio rebalancing or rotational strategies.
Analyze trend consistency by monitoring cross events at multiple timeframes for a given asset.
Swiftly identify when short-term and long-term momentum diverge—flagging potential reversals or trend initiations.
The Golden Cross Screener Pineify is not just a trend signal; it’s a holistic multi-asset scanner built for traders who know the power of combining technical breadth with agile timing.
How Multiple Indicators Work Together
This screener stands out with its modular approach: each asset/timeframe pair is monitored in isolation, yet displayed collectively for multidimensional market insight. Each symbol’s price action is processed through independently configured EMAs—Fast and Slow—whose crossovers are analyzed for directional bias. The implementation’s real innovation is in its screener table engine: it aggregates signals, synchronizes timeframes, and color-codes market states, allowing users to see confluences, divergences, and sector trends at a glance.
Combining Golden Cross detection with customizable moving averages and flexible multi-timeframe, multi-symbol scanning means users can fine-tune sensitivity, focus on specific signals, and adapt screener logic for scalping, swing trading, or investing.
Unique Aspects
True multi-symbol screener within the TradingView indicator framework.
Full customization of screener assets, timeframes, and moving averages.
Advanced, efficient use of TradingView table for clear, actionable visualization.
No dependency on standard, static MA settings—adjust everything to match your strategy.
Big-picture and granular trend detection in one tool, designed for both active traders and portfolio managers.
How to Use
Add the Golden Cross Screener Pineify to your TradingView chart.
Choose up to eight symbols—crypto, stock, forex, or custom assets.
Set four timeframes for screening, from lower to higher intervals.
Adjust moving average sources (price, close, etc.) and period lengths for both Fast and Slow MAs to suit your trading style.
Interpret table cells: clear labels and color indicate Golden Cross (trend shift), Bullish (uptrend), Bearish (downtrend) states for each symbol/timeframe.
React to signal alignments—deploy or rebalance positions, increase alert sensitivity, or backtest sequence confluences.
Customization
The indicator’s inputs panel gives full control:
Select which symbols to screen, making it perfect for any asset watchlist.
Pick the desired timeframes—mix daily, hourly, or minute-based intervals.
Adjust Fast and Slow MA settings: switch source type, change period length, and fine-tune detection logic as needed.
Style your screener table via TradingView settings (colors, font sizes, alignment).
Every element is customizable—adapt the Golden Cross Screener Pineify for your specific portfolio, trading timeframe, and strategy focus.
Conclusion
The Golden Cross Screener Pineify elevates multi-symbol trend detection to a new level on TradingView. By combining configurable Golden Cross logic with a powerful screener engine, it serves both precision and broad market insight—crucial for agile traders and strategic portfolio managers. Whether you’re tracking crypto pairs, stocks, forex, or a mix, this tool transforms static trend analysis into an active, multi-dimensional trading edge.
Michal D. Lagless Moving Average | MisinkoMasterThe 𝕸𝖎𝖈𝖍𝖆𝖑 𝕯. 𝕷𝖆𝖌𝖑𝖊𝖘𝖘 𝕸𝖔𝖛𝖎𝖓𝖌 𝕬𝖛𝖊𝖗𝖆𝖌𝖊 is my latest creation of a trend following tool, which is a bit different from the rest. By trying to de-lag the classical moving average, it gives you fast signals on changes in trend as fast as possible, keeping traders & investors always in check for potential risks they might want to avoid.
How does it work?
First we need to calculate lengths. The lengths are calcuted using a user defined input called the "Length Multiplier" and we of course need as well the length input too.
The indicator uses 10 lengths, 5 for an average price, 5 for median price.
The length for the average is the following:
length_2_avg = length_1_avg * length_multiplier
length_3_avg = length_2_avg * length_multiplier
...
and for the median lengths:
length_1_median = length_2_avg
length_2_median = length_3_avg
Here applies this rule
length_x_median < length_x_avg
This is intentional, and it is because the average is a little more reactive, while the median is a bit slower. To make up for the "slowness" of the median, we simple reduce the length of it a bit more than the average.
Now that we have our length we are ready to calculate averages and medians over their respective period. This is the a normal average from elementary school, nothing too fancy.
Now that we have all of them we match the pairs using another user defined input called "Median Weight" like so:
(Average_x * (2-median_weight) + Median_x * median_weight)/2
This gives more weight to the average (also due to the max value limit set to avoid breaking the fundational logic behind it).
After doing it to all the pairs we now average those pairs using another input called "Exponential Weight Multiplier".
The Exponential Weight Multiplier is used for weights which I will cover soon:
weight1 = weight
weight2 = weight * weight
weight3 = weight * weight * weight....
This is done until we have all the weights calculated
This gives exponentially more weight to the less lagging indicators, which is how we delag the indicator.
Then we sum all the pairs like so:
sum = pair1 * weight1 + pair2 * weight2 + pair3 * weight3 + pair4 * weight4 + pair5 * weight5
Then the sum is divided by the sum of weights, this results in us getting the final value.
Methodology & What is the actual point & how was it made?
I want to cover this one a bit deeper:
The methodology behind this was creating an indicator that would not be lagging, and would be able to avoid lag while not producing signals too often.
In many attempts in the first part, I tried using EMA, RMA, DEMA, TEMA, HMA, SMA and so on, but they were too noisy (except for SMA & RMA, but those had their flaws), so I tried the classical average taught in elementary school. This one worked better, but the noise was too high still after all this time. This made me include the median, which helped the noise, but made it far too lagging.
Here came the idea of making the median length lower and adding weights to counter the lag of the median, but it was still too lagging. This made me make the weights for lengths more exponential, while previously they were calculated using a little bit amplified sums that were alright, but nowhere near my desired result.
Using the new weights I got further, and after a bit of testing I was sattisfied with the results.
The logic for the trend was a big part in my development part, there were many I could think of, but not enough time to try them, so I stuck to the usual one, and I leave it up to YOU to beat my trend logic and get even better results.
Use Cases:
- Price/MA Crossovers
Simple, effective, useful
- Source for other indicators
This I tried myself, and it worked in a cool way, making the signals of for example RSI much smoother, so definitely try it out if you know how to code, or just simply put it in the source of the RSI.
- ROC
This trend logic stuck with me, I think you could find a way to make it good, but mainly for the people that can code in pine, trying out to combine the trend logic with ROC could work very well, do not sleep on it!
- Education
This concept is not really that complex, so for people looking for new ideas, inspiration, or just watching how trend following tools behave in general this is something that could benefit anyone, as the concept can be applied to ANYTHING, even the classical RSI, MACD, you could try even the Parabolic SAR, maybe STC or VZO, there is no limit to imagination.
- Strategy creation
Filtering this indicator with "and" conditions, or maybe even "or" or anything really could be very useful in a strategy that desires fast signals.
- Price Distance from bands
I noticed this while looking at past performance:
The stronger the trend the higher the distance from the Moving Average.
Final Notes
Watch out for mean reverting markets, as this is trend following you could get easily screwed in them.
Play around with this if it fits your desired outcome, you might find something I did not.
Hope you find it useful,
See you next time!
Copter 2.0💡 The indicator is designed for trading on any timeframe and includes a comprehensive system for determining entry and exit points based on technical analysis, price and volume.
📊 In the new version of Copter 2.0, the take profit and stop loss functions have been added
Let's analyze its key components:
✔️ Trend levels and extremes:
- The indicator determines local highs and lows for a certain period.
- the breakdown of these levels serves as a signal to open positions.
- the High-Low price dynamics analysis method is used to find key entry points.
✔️ Volumes:
-The indicator uses a configurable volume threshold to filter out candles with low volume and display only those with significant volume.
- the algorithm analyzes market data and sets an entry signal (opening a trade) and exit (profit taking/closing a position)
📍 Therefore, whether you are a beginner or an experienced trader, the indicator can help you stay ahead of the game and make more informed trading decisions.
📍 As a result, the trader can be sure that the signal is based on data analysis.
A long or short position can be stopped with either a profit or a small loss without prejudice to the potential profit.
✔️ Signal filtering:
- volume and volatile indicators are used to confirm the trend
- if a volume or volatility filter does not confirm the breakdown, the input signal is ignored
- analysis of moving averages of volumes and ATR is used
✔️ The use of the RSI in overbought and oversold analysis:
- the RSI indicator analyzes the strength of the current trend
- if the RSI exceeds 70, exit from a long position is possible
- if the RSI falls below 30, exit from a short position is possible
✔️ The use of EMA 20 and EMA 200
is additional moving average data that determines the current trend and the trend on higher timeframes.
- the main idea is that when they cross, we can see a change in trend movement and determine the general mood at the moment, based on which signals appear to open/close a deal.
- also, the indicator analyzes the past movement, thus determining the future direction
- based on the opening and closing of the past days, weeks, months.
✔️ Stop loss and risk management
- when entering a trade, a dynamic stop loss is set based on the percentage price change
- exit the position is carried out when a stop loss or a signal from the RSI is reached.
- it helps to minimize losses and protect profits
The market is unstable, and it is impossible to know what awaits it in the future.
The only way to manage risk is to limit the loss by setting a stop loss at 1% - 2% of the entry point.
It is recommended to set the profit in the ratio 1:1, 1:2,1:3, with partial fixation of 40%, 30%, 30% or wait for the indicator signal (TP)
We recommend fixing positions in parts. There will be a signal in the opposite direction when the volume is released.
To match the risk of the transaction, we recommend that you do not enter with high leverage.
Trade only with the amount that you are willing to lose.
With increased volatility in the market and flat, the indicator can give many signals.
After a strong fall or growth, we recommend not to open positions, because the probability of a flat is high.
✔️ Visualization of entry and exit points
- Entry points (long and short) are graphically displayed. green - long, orange - short
- stop loss levels are marked for clarity of risk management
✔️Recommendations for working with the indicator!
Entry/exit is performed on the next candle after the candle with the signal (buy/sell)
All timeframes and any trading pairs are used (when selecting settings for each one)
The indicator combines several methods of technical analysis:
- work with support and resistance levels
- filtering of signals based on volumes and volatility
- Overbought and oversold analysis using the RSI
- automatic risk management through stop loss
This approach makes the indicator a useful tool for short-term trading and active scalping.
❗️ NO REPAINT ! ❗️
BTC Cycle Halving Thirds NicoThe bold black vertical lines are the INDEX:BTCUSD halvings.
The background speak for itself.
Time to be bearish?
Supply & Demand Zones [QuantAlgo]🟢 Overview
The Supply & Demand (Support & Resistance) Zones indicator identifies price levels where significant buying and selling pressure historically emerged, using swing point analysis and pattern recognition to mark high-probability reversal and continuation areas. Unlike conventional support/resistance tools that draw arbitrary horizontal lines, this indicator can automatically detect structural zones, offering traders systematic entry and exit levels where institutional order flow likely congregates across any market or timeframe.
🟢 How to Use
# Zone Types:
Green/Demand Zones: Support areas where buying pressure historically emerged, representing potential long entry opportunities where price may bounce or consolidate before moving higher. These zones mark levels where buyers previously overcame sellers.
Red/Supply Zones: Resistance areas where selling pressure historically dominated, indicating potential short entry opportunities where price may reverse or stall before declining. These zones identify levels where sellers previously overwhelmed buyers.
# Zone Pattern Types:
Wick Rejection Zones: Zones created from candles with exceptionally long wicks showing violent price rejection. A demand rejection occurs when price drops sharply but closes well above the low, forming a long lower wick (relative to the total candle range) that demonstrates buyers aggressively defending that level. A supply rejection shows price spiking higher but closing well below the high, with the long upper wick proving sellers rejected that price aggressively. These zones often represent major institutional orders that absorbed significant market pressure. The rejection wick ratio setting controls how prominent the wick must be (higher ratios require more dramatic rejections and produce fewer but higher-quality zones).
Continuation Demand Zones: Areas where price rallied upward, paused in a brief consolidation base, then rallied again. This pattern confirms strong buying continuation (the consolidation represents profit-taking or minor pullbacks that failed to attract meaningful selling). When price returns to these zones, buyers who missed the initial rally often provide support, making them high-probability long entries within established uptrends. These zones follow the classic Rally-Base-Rally structure, demonstrating that buyers remain in control even during temporary pauses.
Reversal Demand Zones: Zones where price dropped, formed a consolidation base, then reversed into a rally. This structure marks potential trend reversals or major swing lows where buyers finally overwhelmed sellers after a decline. The base period represents accumulation by stronger hands, and these zones frequently appear at market bottoms or as significant pullback support within larger uptrends, signaling shifts in market control. These zones follow the Drop-Base-Rally pattern, showing the moment when selling pressure exhausted and buying interest emerged.
Continuation Supply Zones: Areas where price dropped, consolidated briefly, then dropped again. This pattern demonstrates strong selling continuation (the pause represents temporary buying attempts that failed to generate meaningful recovery). When price returns to these zones, sellers who missed the initial decline often provide resistance, creating short entry opportunities within established downtrends. These zones follow the Drop-Base-Drop structure, confirming that sellers maintain dominance even during temporary consolidations.
Reversal Supply Zones: Zones where price rallied upward, formed a consolidation base, then reversed into a decline. This formation identifies potential trend reversals or major swing highs where sellers overcame buyers after an advance. The base period often represents distribution by institutional participants, and these zones commonly appear at market tops or as key pullback resistance within larger downtrends, marking transfers of market control from buyers to sellers. These zones follow the Rally-Base-Drop pattern, capturing the transition point when buying exhaustion meets aggressive selling.
# Zone Mitigation Methods:
Wick Mitigation: Zones become invalidated immediately upon first contact by any wick. This assumes zones work only on their initial test, reflecting the belief that institutional orders concentrated at these levels get completely filled on first touch. Best for traders seeking only the highest-probability, untested zones and willing to accept that zones invalidate frequently in volatile markets. When price touches a zone boundary with even a single wick, that zone is considered "used up" and becomes mitigated.
Close Mitigation: Zones remain valid through wick penetration but become invalidated only when a candle closes through the zone boundary. This method allows price to briefly probe the zone with wicks while requiring actual commitment (a close) for invalidation. Suitable for traders who recognize that zones can withstand initial tests and prefer filtering out false breakouts caused by temporary volatility or liquidity hunts. A zone stays active as long as candles close within or outside it, regardless of wick penetration, until a close occurs beyond the boundary.
Full Body Mitigation: Zones stay valid until an entire candle body exists completely beyond the zone boundary, meaning both the open and close must be outside the zone. This approach maintains zone validity through partial penetrations, accommodating the reality that institutional zones can absorb considerable price action before exhausting. Ideal for volatile markets or traders who believe zones represent price ranges rather than precise levels, and who want zones to persist through aggressive but ultimately rejected breakout attempts. Only when both the open and close of a candle are beyond the zone does it become mitigated.
🟢 Pro Tips for Trading and Investing
→ Preset Selection: Choose presets matching your preferred timeframe - Scalping (M1-M30) for aggressive detection on minute charts, Intraday (H1-H12) for balanced filtering on hourly timeframes, or Swing Trading (1D+) for strict filtering on daily charts. Each preset automatically optimizes swing length, zone strength, and max zone counts for the selected timeframe.
→ Input Calibration: Adjust Swing Length based on market speed (lower values 3-7 for fast markets, higher values 12-20 for slower markets). Set Minimum Zone Strength according to asset volatility (0.05-0.15% for low-volatility assets, 0.25-0.5% for high-volatility assets). Tune Rejection Wick Ratio higher (0.6-0.8) for strict wick filtering or lower (0.3-0.5) to capture more subtle rejections.
→ Zone Pattern Toggle Strategy: Pattern types are mutually exclusive - enable Continuation OR Reversal patterns for each zone type, not both together. Recommended combinations: For trend trading, enable Rejection + Continuation (2-4 toggles total). For reversal trading, enable Rejection + Reversal (2-4 toggles). For scalping, enable only Rejection zones (1-2 toggles). Maximum 3-4 active toggles provides optimal chart clarity. A simple Wick Rejection toggle can also work on virtually any market and timeframe.
→ Mitigation Method Selection: Use Wick mitigation in clean trending markets for strict zone invalidation on first touch. Use Close mitigation in moderate volatility to filter out temporary spikes. Use Full Body mitigation in highly volatile markets to keep zones active through whipsaws and false breakouts.
→ Alert Configuration: Utilize built-in alerts for new zone creation, zone touches, and zone breaks. New zone alerts notify when fresh supply/demand areas form. Zone touch alerts signal potential entry opportunities as price reaches zones. Zone break alerts indicate when levels fail, signaling possible trend acceleration or structure changes.
Fisher Transform Trend Navigator [QuantAlgo]🟢 Overview
The Fisher Transform Trend Navigator applies a logarithmic transformation to normalize price data into a Gaussian distribution, then combines this with volatility-adaptive thresholds to create a trend detection system. This mathematical approach helps traders identify high-probability trend changes and reversal points while filtering market noise in the ever-changing volatility conditions.
🟢 How It Works
The indicator's foundation begins with price normalization, where recent price action is scaled to a bounded range between -1 and +1:
highestHigh = ta.highest(priceSource, fisherPeriod)
lowestLow = ta.lowest(priceSource, fisherPeriod)
value1 = highestHigh != lowestLow ? 2 * (priceSource - lowestLow) / (highestHigh - lowestLow) - 1 : 0
value1 := math.max(-0.999, math.min(0.999, value1))
This normalized value then passes through the Fisher Transform calculation, which applies a logarithmic function to convert the data into a Gaussian normal distribution that naturally amplifies price extremes and turning points:
fisherTransform = 0.5 * math.log((1 + value1) / (1 - value1))
smoothedFisher = ta.ema(fisherTransform, fisherSmoothing)
The smoothed Fisher signal is then integrated with an exponential moving average to create a hybrid trend line that balances statistical precision with price-following behavior:
baseTrend = ta.ema(close, basePeriod)
fisherAdjustment = smoothedFisher * fisherSensitivity * close
fisherTrend = baseTrend + fisherAdjustment
To filter out false signals and adapt to market conditions, the system calculates dynamic threshold bands using volatility measurements:
dynamicRange = ta.atr(volatilityPeriod)
threshold = dynamicRange * volatilityMultiplier
upperThreshold = fisherTrend + threshold
lowerThreshold = fisherTrend - threshold
When price momentum pushes through these thresholds, the trend line locks onto the new level and maintains direction until the opposite threshold is breached:
if upperThreshold < trendLine
trendLine := upperThreshold
if lowerThreshold > trendLine
trendLine := lowerThreshold
🟢 Signal Interpretation
Bullish Candles (Green): indicate normalized price distribution favoring bulls with sustained buying momentum = Long/Buy opportunities
Bearish Candles (Red): indicate normalized price distribution favoring bears with sustained selling pressure = Short/Sell opportunities
Upper Band Zone: Area above middle level indicating statistically elevated trend strength with potential overbought conditions approaching mean reversion zones
Lower Band Zone: Area below middle level indicating statistically depressed trend strength with potential oversold conditions approaching mean reversion zones
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, allowing you to act on significant developments without constantly monitoring the charts
Candle Coloring: Optional feature applies trend colors to price bars for visual consistency and clarity
Configuration Presets: Three parameter sets available - Default (balanced settings), Scalping (faster response with higher sensitivity), and Swing Trading (slower response with enhanced smoothing)
Color Customization: Four color schemes including Classic, Aqua, Cosmic, and Custom options for personalized chart aesthetics
Laguerre Filter Trend Navigator [QuantAlgo]🟢 Overview
The Laguerre Filter Trend Navigator employs advanced polynomial filtering mathematics to smooth price data while minimizing lag, creating a responsive yet stable trend-following system. Unlike simple moving averages that apply equal weight to historical data, the Laguerre filter uses recursive calculations with exponentially weighted polynomials to extract meaningful directional signals from noisy market conditions. Combined with dynamic volatility-adjusted boundaries, this creates an adaptive framework for identifying high-probability trend reversals and continuations across all tradable instruments and timeframes.
🟢 How It Works
The indicator leverages Laguerre polynomial filtering, a mathematical technique originally developed for digital signal processing applications. The core mechanism processes price data through four cascaded filter stages (L0, L1, L2, L3), each applying the gamma coefficient to recursively smooth incoming information while preserving phase relationships. This multi-stage architecture eliminates random fluctuations more effectively than traditional moving averages while responding quickly to genuine directional shifts.
The gamma coefficient serves as the primary smoothing control, determining how aggressively the filter dampens noise versus tracking price movements. Lower gamma values reduce smoothing and increase filter responsiveness, while higher values prioritize stability over reaction speed. Each filter stage compounds this effect, creating progressively smoother output that converges toward true underlying trend direction.
Surrounding the filtered price line, the algorithm constructs adaptive boundaries using dynamic volatility regime measurements. These calculations quantify current market turbulence independently of direction, expanding during active trading periods and contracting during quiet phases. By multiplying this volatility assessment by a user-defined scaling factor, the system creates self-adjusting bands that automatically conform to changing market conditions without manual intervention.
The trend-following engine monitors price position relative to these volatility-adjusted boundaries. When the upper band falls below the current trend line, the system shifts downward to track bearish momentum. Conversely, when the lower band rises above the trend line, it elevates to follow bullish movement. These crossover events trigger color transitions between bullish (green) and bearish (red) states, providing clear visual confirmation of directional changes validated by volatility-normalized thresholds.
🟢 How to Use
Green/Bullish Trend Line: Laguerre filter positioned in upward trajectory, indicating momentum-confirmed conditions favorable for establishing or maintaining long positions (buy)
Red/Bearish Trend Line: Laguerre filter trending downward, signaling regime-validated environment suitable for initiating or holding short positions (sell)
Rising Green Line: Accelerating bullish filter with expanding separation from price lows, demonstrating strengthening upward momentum and increasing confidence in trend persistence with optimal long entry timing
Declining Red Line: Steepening bearish filter creating growing distance from price highs, revealing intensifying downside pressure and enhanced probability of continued decline with favorable short positioning opportunities
Flattening Trends: Horizontal or oscillating filter movement regardless of color suggests directional uncertainty where price action contradicts filter positioning, potentially indicating consolidation phases or impending volatility expansion requiring cautious trade management
🟢 Pro Tips for Trading and Investing
→ Preset Selection Framework: Match presets to your trading style - Scalping preset employs aggressive gamma (0.4) with tight volatility bands (1.0x) for rapid signal generation on sub-15-minute charts, Day Trading preset balances responsiveness and stability for hourly timeframes, while Swing Trading preset maximizes smoothing (0.8 gamma) with wide bands (2.5x) to filter intraday noise on daily and weekly charts.
→ Gamma Coefficient Calibration: Adjust gamma based on market personality - reduce values (0.3-0.5) for highly liquid, fast-moving assets like major currency pairs and tech stocks where quick filter adaptation prevents lag-induced losses, increase values (0.7-0.9) for slower instruments or trending markets where excessive sensitivity generates false reversals and whipsaw trades.
→ Volatility Period Optimization: Tailor the volatility measurement window to information cycles. Deploy shorter lookback periods (7-10) for instruments with rapid regime changes like individual equities during earnings seasons, standard periods (14-20) for balanced assessment across general market conditions, and extended periods (21-30) for commodities and indices exhibiting persistent volatility characteristics.
→ Band Width Multiplier Adaptation: Scale boundary distance to current market phase. Contract multipliers (1.0-1.5) during range-bound consolidations to capture early breakout signals as soon as genuine momentum emerges, expand multipliers (2.0-3.0) during trending markets or high-volatility events to avoid premature exits caused by normal retracement activity rather than authentic reversals.
→ Multi-Timeframe Filter Alignment: Implement the indicator across multiple timeframes, using higher intervals (4H/Daily) to identify primary trend direction via filter slope and lower intervals (15min/1H) for precision entry timing when filter colors align, ensuring trades flow with dominant momentum while optimizing execution at favorable price levels.
→ Alert-Driven Systematic Execution: Configure trend change alerts to capture every filter-validated directional shift from bullish to bearish conditions or vice versa, enabling consistent signal response without continuous chart monitoring and eliminating emotional decision-making during critical transition moments.
Bayesian Trend Navigator [QuantAlgo]🟢 Overview
The Bayesian Trend Navigator uses Bayesian statistics to continuously update trend probabilities by combining long-term expectations (prior beliefs) and short-term observations (likelihood evidence), rather than relying solely on recent price data like many conventional indicators. This mathematical framework produces robust directional signals that naturally balance responsiveness with stability, making it suitable for traders and investors seeking statistically-grounded trend identification across diverse market environments and asset types.
🟢 How It Works
The indicator operates on Bayesian inference principles, a statistical method for updating beliefs when new evidence emerges. The system begins by establishing a prior belief - a long-term trend expectation calculated from historical price behavior. This represents the "baseline hypothesis" about market direction before considering recent developments.
Simultaneously, the algorithm collects recent market evidence through short-term trend analysis, representing the likelihood component. This captures what current price action suggests about directional momentum independent of historical context.
The core Bayesian engine then combines these elements using conjugate normal distributions and precision weighting. It calculates prior precision (inverse variance) and likelihood precision, combining them to determine a posterior precision. The resulting posterior mean represents the mathematically optimal trend estimate given both historical patterns and current reality. This posterior calculation includes intervals derived from the posterior variance, providing probabilistic confidence bounds around the trend estimate.
Finally, volatility-based standard deviation bands create adaptive boundaries around the Bayesian estimate. The trend line adjusts within these constraints, generating color transitions between bullish (green) and bearish (red) states when the posterior calculation crosses these probabilistic thresholds.
🟢 How to Use
Green/Bullish Trend Line: Posterior probability favoring upward momentum, indicating statistically favorable conditions for long positions (buy)
Red/Bearish Trend Line: Posterior probability favoring downward momentum, signaling mathematically supported timing for short positions (sell)
Rising Green Line: Strengthening bullish posterior as new evidence reinforces upward beliefs, showing increasing probabilistic confidence in trend continuation with favorable long entry conditions
Declining Red Line: Intensifying bearish posterior with accumulating downside evidence, indicating growing statistical certainty in downtrend persistence and optimal short positioning opportunities
Flattening Trends: Diminishing posterior confidence regardless of color suggests equilibrium between prior beliefs and contradictory evidence, potentially signaling consolidation or insufficient statistical clarity for high-conviction trades
🟢 Pro Tips for Trading and Investing
→ Preset Configuration Strategy: Deploy presets based on your trading horizon - Scalping preset maximizes evidence weight (0.8) for rapid Bayesian updates on 1-15 minute charts, Default preset balances prior and likelihood for general applications, while Swing Trading preset equalizes weights (0.5/0.5) for stable inference on hourly and daily timeframes.
→ Prior Weight Adjustment: Calibrate prior weight according to market regime - increase values (0.5-0.7) in stable trending markets where historical patterns remain predictive, decrease values (0.2-0.3) during regime changes or news-driven volatility when recent evidence should dominate the posterior calculation.
→ Evidence Period Tuning: Modify the evidence period based on information flow velocity. Use shorter periods (5-8 bars) for assets with continuous price discovery like cryptocurrencies, medium periods (10-15) for liquid stocks, and longer periods (15-20) for slower-moving markets to ensure adequate likelihood sample size.
→ Likelihood Weight Optimization: Adjust likelihood weight inversely to market noise levels. Higher values (0.7-0.8) work well in clean trending conditions where recent data is reliable, while lower values (0.4-0.6) help during choppy periods by maintaining stronger reliance on established prior beliefs.
→ Multi-Timeframe Bayesian Confluence: Apply the indicator across multiple timeframes, using higher timeframes (Daily/Weekly) to establish prior belief direction and lower timeframes (Hourly/15-minute) for likelihood-driven entry timing, ensuring posterior probabilities align across temporal scales for maximum statistical confidence.
→ Standard Deviation Multiplier Management: Adapt the multiplier to match current uncertainty levels. Use tighter multipliers (1.0-1.5) during low-volatility consolidations to capture early trend emergence, and wider multipliers (2.0-2.5) during high-volatility events to avoid premature signals caused by statistical noise rather than genuine posterior shifts.
→ Variance-Based Position Sizing: Monitor the implicit posterior variance through trend line stability - smooth consistent movements indicate low uncertainty warranting larger positions, while erratic fluctuations suggest high statistical uncertainty calling for reduced exposure until clearer probabilistic convergence emerges.
→ Alert-Based Probabilistic Execution: Utilize trend change alerts to capture every statistically significant posterior shift from bullish to bearish states or vice versa without constantly monitoring the charts.
Baseline Buy/Sell Alerts (v6) - FixedGood for indexes,metals and cryptos
Thanks Universe Thanks Angels
Bollinger Adaptive Trend Navigator [QuantAlgo]🟢 Overview
The Bollinger Adaptive Trend Navigator synthesizes volatility channel analysis with variable smoothing mechanics to generate trend identification signals. It uses price positioning within Bollinger Band structures to modify moving average responsiveness, while incorporating ATR calculations to establish trend line boundaries that constrain movement during volatile periods. The adaptive nature makes this indicator particularly valuable for traders and investors working across various asset classes including stocks, forex, commodities, and cryptocurrencies, with effectiveness spanning multiple timeframes from intraday scalping to longer-term position analysis.
🟢 How It Works
The core mechanism calculates price position within Bollinger Bands and uses this positioning to create an adaptive smoothing factor:
bbPosition = bbUpper != bbLower ? (source - bbLower) / (bbUpper - bbLower) : 0.5
adaptiveFactor = (bbPosition - 0.5) * 2 * adaptiveMultiplier * bandWidthRatio
alpha = math.max(0.01, math.min(0.5, 2.0 / (bbPeriod + 1) * (1 + math.abs(adaptiveFactor))))
This adaptive coefficient drives an exponential moving average that responds more aggressively when price approaches Bollinger Band extremes:
var float adaptiveTrend = source
adaptiveTrend := alpha * source + (1 - alpha) * nz(adaptiveTrend , source)
finalTrend = 0.7 * adaptiveTrend + 0.3 * smoothedCenter
ATR-based volatility boundaries constrain the final trend line to prevent excessive movement during volatile periods:
volatility = ta.atr(volatilityPeriod)
upperBound = bollingerTrendValue + (volatility * volatilityMultiplier)
lowerBound = bollingerTrendValue - (volatility * volatilityMultiplier)
The trend line direction determines bullish or bearish states through simple slope comparison, with the final output displaying color-coded signals based on the synthesis of Bollinger positioning, adaptive smoothing, and volatility constraints (green = long/buy, red = short/sell).
🟢 Signal Interpretation
Rising Trend Line (Green): Indicates upward direction based on Bollinger positioning and adaptive smoothing = Potential long/buy opportunity
Falling Trend Line (Red): Indicates downward direction based on Bollinger positioning and adaptive smoothing = Potential short/sell opportunity
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, allowing you to act on significant development without constantly monitoring the charts
Candle Coloring: Optional feature applies trend colors to price bars for visual consistency
Configuration Presets: Three parameter sets available - Default (standard settings), Scalping (faster response), and Swing Trading (slower response)
USD-TRADER-ROYThe USD-TRADER-ROY is a custom TradingView indicator designed for crypto and USD market analysis. It tracks a smoothed ratio between USDT dominance and historical averages (similar to the Puell Multiple concept) to highlight potential buy or sell zones.
Key features include:
Dynamic Buy/Sell Zones: Visual horizontal levels to indicate potential accumulation or profit-taking areas.
Visual Feedback: Colored backgrounds and bar colors to quickly show whether conditions suggest caution, accumulation, or potential selling.
Custom Alerts: Built-in alert conditions that notify traders when the market approaches critical thresholds, making it easier to act on opportunities without constant monitoring.
Flexible Parameters: Adjustable inputs for thresholds and risk levels to suit different strategies or risk tolerances.
This tool is aimed at traders who want a visual, alert-based system for gauging market extremes and managing entries/exits efficiently. It works best when combined with your own analysis and risk management.
Sine Weighted Trend Navigator [QuantAlgo]🟢 Overview
The Sine Weighted Trend Navigator utilizes trigonometric mathematics to create a trend-following system that adapts to various market volatility. Unlike traditional moving averages that apply uniform weights, this indicator employs sine wave calculations to distribute weights across historical price data, creating a more responsive yet smooth trend measurement. Combined with volatility-adjusted boundaries, it produces actionable directional signals for traders and investors across various market conditions and asset classes.
🟢 How It Works
At its core, the indicator applies sine wave mathematics to weight historical prices. The system generates angular values across the lookback period and transforms them through sine calculations, creating a weight distribution pattern that naturally emphasizes recent price action while preserving smoothness. The phase shift feature allows rotation of this weighting pattern, enabling adjustment of the indicator's responsiveness to different market conditions.
Surrounding this sine-weighted calculation, the system establishes volatility-responsive boundaries through market volatility analysis. These boundaries expand and contract based on current market conditions, creating a dynamic framework that helps distinguish meaningful trend movements from random price fluctuations.
The trend determination logic compares the sine-weighted value against these adaptive boundaries. When the weighted value exceeds the upper boundary, it signals upward momentum. When it drops below the lower boundary, it indicates downward pressure. This comparison drives the color transitions of the main trend line, shifting between bullish (green) and bearish (red) states to provide clear directional guidance on price charts.
🟢 How to Use
Green/Bullish Trend Line: Rising momentum indicating optimal conditions for long positions (buy)
Red/Bearish Trend Line: Declining momentum signaling favorable timing for short positions (sell)
Steepening Green Line: Accelerating bullish momentum with increasing sine-weighted values indicating strengthening upward pressure and high-probability trend continuation
Steepening Red Line: Intensifying bearish momentum with declining sine-weighted calculations suggesting persistent downward pressure and optimal shorting opportunities
Flattening Trend Lines: Gradual reduction in directional momentum regardless of color may indicate approaching consolidation or trend exhaustion requiring position management review
🟢 Pro Tips for Trading and Investing
→ Preset Strategy Selection: Utilize the built-in presets strategically - Scalping preset for ultra-responsive 1-15 minute charts, Default preset for balanced general trading, and Swing Trading preset for 1-4 hour charts and multi-day positions.
→ Phase Shift Optimization: Fine-tune the phase shift parameter based on market bias - use positive values (0.1-0.5) in trending bull markets to enhance uptrend sensitivity, negative values (-0.1 to -0.5) in bear markets for improved downtrend detection, and zero for balanced neutral market conditions.
→ Multiplier Calibration: Adjust the multiplier according to market volatility and trading style. Use lower values (0.5-1.0) for tight, responsive signals in stable markets, higher values (2.0-3.0) during earnings seasons or high-volatility periods to filter noise and reduce whipsaws.
→ Sine Period Adaptation: Customize the sine weighted period based on your trading timeframe and market conditions. Use 5-14 for day trading to capture short-term momentum shifts, 14-25 for swing trading to balance responsiveness with reliability, and 25-50 for position trading to maintain long-term trend clarity.
→ Multi-Timeframe Sine Validation: Apply the indicator across multiple timeframes simultaneously, using higher timeframes (4H/Daily) for overall trend bias and lower timeframes (15m/1H) for entry timing, ensuring sine-weighted calculations align across different time horizons.
→ Alert-Driven Systematic Execution: Leverage the built-in trend change alerts to eliminate emotional decision-making and capture every mathematically-confirmed trend transition, particularly valuable for traders managing multiple instruments or those unable to monitor charts continuously.
→ Risk Management: Increase position sizes during strong directional sine-weighted momentum while reducing exposure during frequent color changes that indicate mathematical uncertainty or ranging market conditions lacking clear directional bias.
BTCUSD Dual Thrust (1H)BTCUSD Dual Thrust (1H) — Indicator
Overview
The Dual Thrust is a classic breakout-type strategy designed to capture strong directional moves when markets show imbalance between buyers and sellers. This indicator adapts the method specifically for BTCUSD on the 1-Hour timeframe, showing dynamic Buy/Sell trigger levels and live signals.
Origin
The Dual Thrust system was originally introduced by Michael Vitucci and has been widely used in futures and high-volatility markets. It was designed as a day-trading breakout framework, where daily high/low and close data define the range for the next session’s trade triggers.
How it Works
Each new day, the indicator calculates a “breakout range” using daily price data.
Two trigger levels are projected from the daily open:
Buy Trigger: Open + Range × KUp
Sell Trigger: Open - Range × KDn
Range can be built from either:
Classic Dual Thrust formula: max(High - Close , Close - Low) over a lookback period, or
ATR-based range: for volatility-adaptive signals.
A LONG signal fires when price crosses above the Buy Trigger.
An EXIT signal fires when price crosses below the Sell Trigger.
Buy/Sell lines step forward across each intraday bar until recalculated at the next daily open.
Practical Use
Optimized for BTCUSD 1-Hour charts (crypto’s volatility provides stronger follow-through).
Use the Buy/Sell levels as dynamic breakout lines or as confluence with your own setups.
Alerts are built in, so you can receive notifications when a LONG or EXIT condition triggers.
Designed as an indicator only (not a backtest strategy).
Key Features
✅ Daily Buy/Sell trigger lines auto-calculated and forward-filled
✅ LONG / EXIT labels on signals
✅ Optional ATR mode for volatility regimes
✅ Optional bar coloring for easy visual scanning
✅ Alerts ready for live monitoring
⚡️ Tip: While this indicator highlights breakout opportunities, effectiveness can improve when combined with trend filters (e.g., 200-SMA) or when aligned with higher timeframe supply/demand zones.
RSI Trend Navigator [QuantAlgo]🟢 Overview
The RSI Trend Navigator integrates RSI momentum calculations with adaptive exponential moving averages and ATR-based volatility bands to generate trend-following signals. The indicator applies variable smoothing coefficients based on RSI readings and incorporates normalized momentum adjustments to position a trend line that responds to both price action and underlying momentum conditions.
🟢 How It Works
The indicator begins by calculating and smoothing the RSI to reduce short-term fluctuations while preserving momentum information:
rsiValue = ta.rsi(source, rsiPeriod)
smoothedRSI = ta.ema(rsiValue, rsiSmoothing)
normalizedRSI = (smoothedRSI - 50) / 50
It then creates an adaptive smoothing coefficient that varies based on RSI positioning relative to the midpoint:
adaptiveAlpha = smoothedRSI > 50 ? 2.0 / (trendPeriod * 0.5 + 1) : 2.0 / (trendPeriod * 1.5 + 1)
This coefficient drives an adaptive trend calculation that responds more quickly when RSI indicates bullish momentum and more slowly during bearish conditions:
var float adaptiveTrend = source
adaptiveTrend := adaptiveAlpha * source + (1 - adaptiveAlpha) * nz(adaptiveTrend , source)
The normalized RSI values are converted into price-based adjustments using ATR for volatility scaling:
rsiAdjustment = normalizedRSI * ta.atr(14) * sensitivity
rsiTrendValue = adaptiveTrend + rsiAdjustment
ATR-based bands are constructed around this RSI-adjusted trend value to create dynamic boundaries that constrain trend line positioning:
atr = ta.atr(atrPeriod)
deviation = atr * atrMultiplier
upperBound = rsiTrendValue + deviation
lowerBound = rsiTrendValue - deviation
The trend line positioning uses these band constraints to determine its final value:
if upperBound < trendLine
trendLine := upperBound
if lowerBound > trendLine
trendLine := lowerBound
Signal generation occurs through directional comparison of the trend line against its previous value to establish bullish and bearish states:
trendUp = trendLine > trendLine
trendDown = trendLine < trendLine
if trendUp
isBullish := true
isBearish := false
else if trendDown
isBullish := false
isBearish := true
The final output colors the trend line green during bullish states and red during bearish states, creating visual buy/long and sell/short opportunity signals based on the combined RSI momentum and volatility-adjusted trend positioning.
🟢 Signal Interpretation
Rising Trend Line (Green): Indicates upward momentum where RSI influence and adaptive smoothing favor continued price advancement = Potential buy/long positions
Declining Trend Line (Red): Indicates downward momentum where RSI influence and adaptive smoothing favor continued price decline = Potential sell/short positions
Flattening Trend Lines: Occur when momentum weakens and the trend line slope approaches neutral, suggesting potential consolidation before the next move
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, sending "RSI Trend Bullish Signal" or "RSI Trend Bearish Signal" messages for timely entry/exit
Color Bar Candles Option: Optional candle coloring feature that applies the same green/red trend colors to price bars, providing additional visual confirmation of the current trend direction
Linear Regression Trend Navigator [QuantAlgo]🟢 Overview
The Linear Regression Trend Navigator is a trend-following indicator that combines statistical regression analysis with adaptive volatility bands to identify and track dominant market trends. It employs linear regression mathematics to establish the underlying trend direction, while dynamically adjusting trend boundaries based on standard deviation calculations to filter market noise and maintain trend continuity. The result is a straightforward visual system where green indicates bullish conditions favoring buy/long positions, and red signals bearish conditions supporting sell/short trades.
🟢 How It Works
The indicator operates through a three-phase computational process that transforms raw price data into adaptive trend signals. In the first phase, it calculates a linear regression line over the specified period, establishing the mathematical best-fit line through recent price action to determine the underlying directional bias. This regression line serves as the foundation for trend analysis by smoothing out short-term price variations while preserving the essential directional characteristics.
The second phase constructs dynamic volatility boundaries by calculating the standard deviation of price movements over the defined period and applying a user-adjustable multiplier. These upper and lower bounds create a volatility-adjusted channel around the regression line, with wider bands during volatile periods and tighter bands during stable conditions. This adaptive boundary system operates entirely behind the scenes, ensuring the trend signal remains relevant across different market volatility regimes without cluttering the visual display.
In the final phase, the system generates a simple trend line that dynamically positions itself within the volatility boundaries. When price action pushes the regression line above the upper bound, the trend line adjusts to the upper boundary level. Conversely, when the regression line falls below the lower bound, the trend line moves to the lower boundary. The result is a single colored line that transitions between green (rising trend line = buy/long) and red (declining trend line = sell/short).
🟢 How to Use
Green Trend Line: Upward momentum indicating favorable conditions for long positions, buy signals, and bullish strategies
Red Trend Line: Downward momentum signaling optimal timing for short positions, sell signals, and bearish approaches
Rising Green Line: Accelerating bullish momentum with steepening angles indicating strengthening upward pressure and potential for trend continuation
Declining Red Line: Intensifying bearish momentum with increasing negative slopes suggesting persistent downward pressure and shorting opportunities
Flattening Trend Lines: Gradual reduction in slope regardless of color may indicate approaching consolidation or momentum exhaustion requiring position review
🟢 Pro Tips for Trading and Investing
→ Entry/Exit Timing: Trade exclusively on band color transitions rather than price patterns, as each color change represents a statistically-confirmed shift that has passed through volatility filtering, providing higher probability setups than traditional technical analysis.
→ Parameter Optimization for Asset Classes: Customize the linear regression period based on your trading style. For example, use 5-10 bars for day trading to capture short-term statistical shifts, 14-20 for swing trading to balance responsiveness with stability, and 25-50 for position trading to filter out medium-term noise.
→ Volatility Calibration Strategy: Adjust the standard deviation multiplier according to market volatility. For instance, increase to 2.0+ during high-volatility periods like earnings or news events to reduce false signals, decrease to 1.0-1.5 during stable market conditions to maintain sensitivity to genuine trends.
→ Cross-Timeframe Statistical Validation: Apply the indicator across multiple timeframes simultaneously, using higher timeframes for directional bias and lower timeframes for entry timing.
→ Alert-Based Systematic Trading: Use built-in alerts to eliminate discretionary decision-making and ensure you capture every statistically-significant trend change, particularly effective for traders who cannot monitor charts continuously.
→ Risk Allocation Based on Signal Strength: Increase position sizes during periods of strong directional movement while reducing exposure during frequent band color changes that indicate statistical uncertainty or ranging conditions.
Global Index EMA QuadrantsThis indicator displays global market indices on a 2D quadrant matrix based on their percentage distance from a selected EMA length across two different timeframes.
Features
• X-axis: % distance from EMA on a higher timeframe (default Weekly)
• Y-axis: % distance from EMA on a lower timeframe (default Daily)
• Bubble colors represent quadrants
• Count labels show how many indices are in each quadrant
How to Use
Select your preferred X timeframe, Y timeframe, and EMA length from the settings panel.
Analyze which quadrant each index is currently in to assess market momentum and breadth.
The zero axes represent the EMA level on each timeframe.
Notes
• This indicator uses only built-in request.security() data from TradingView
• No external APIs, personal data, or third-party content are used
• Designed purely for educational and market breadth analysis purposes
