DXY Monthly Return (+3M Lead)This indicator calculates the rolling monthly return (based on 21 trading days) for the U.S. Dollar Index (DXY), applying a +3-month forward shift (lead) to the series.
It is designed to help visualize the leading effect of USD strength or weakness on other macro-sensitive assets — particularly Bitcoin and crypto markets, which often react to changes in global dollar liquidity with a lag of approximately 10 weeks.
Note: This script does not invert the values directly. To match the inverted Y-axis visual used by Steno Research — where negative USD returns are displayed at the top — simply right-click the Y-axis in the chart panel and select “Invert Scale.”
💡 Use this tool for macro trend analysis, early crypto signal generation, or studying inverse correlations between USD and risk assets.
Source logic: Steno Research, Bloomberg, Macrobond.
Forecasting
Taylor Series ForecastThis indicator projects future price movement using a second-order Taylor Series expansion, calculated from a smoothed price (EMA). It models price momentum and acceleration to generate a forward-looking trajectory.
Forecast points are plotted continuously as connected line segments extending into the future. Each segment is color-coded based on slope:
Green indicates an upward slope (bullish forecast).
Red indicates a downward slope (bearish forecast).
The forecast adapts to current market conditions and updates dynamically with each new bar. Useful for visualizing potential future price paths and identifying directional bias based on recent price action.
Inputs:
Max Forecast Horizon: How many bars into the future the forecast extends.
EMA Smoothing Length: The smoothing applied to price before calculating derivatives.
This tool is experimental and should be used in conjunction with other analysis methods. It does not guarantee future price performance.
UT Bot + Hull MA Confirmed Signal DelayOverview
This indicator is designed to detect high-probability reversal entry signals by combining "UT Bot Alerts" (UT Bot Alerts script adapted from QuantNomad - Originally developed by Yo_adriiiiaan and idea of original code for "UT Bot Alerts" from HPotter ) with confirmation from a Hull Moving Average (HMA) Developed by Alan Hull . It focuses on capturing momentum shifts that often precede trend reversals, helping traders identify potential entry points while filtering out false signals.
🔍 How It Works
This strategy operates in two stages:
1. UT Bot Momentum Trigger
The foundation of this script is the "UT Bot Alerts" , which uses an ATR-based trailing stop to detect momentum changes. Specifically:
The script calculates a dynamic stop level based on the Average True Range (ATR) multiplied by a user-defined sensitivity factor (Key Value).
When price closes above this trailing stop and the short-term EMA crosses above the stop, a potential buy setup is triggered.
Conversely, when price closes below the trailing stop and the short-term EMA crosses below, a potential sell setup is triggered.
These UT Bot alerts are designed to identify the initial shift in market direction, acting as the first filter in the signal process.
2. Hull MA Confirmation
To reduce noise and false triggers from the UT Bot alone, this script delays the entry signal until price confirms the move by crossing the Hull Moving Average (or its variants: HMA, THMA, EHMA) in the same direction as the UT Bot trigger:
A Buy Signal is generated only when:
A UT Bot Buy condition is active, and
The price closes above the Hull MA.
Or, if a UT Bot Buy condition was recently triggered but price hadn’t yet crossed above the Hull MA, a delayed buy is signaled when price finally breaks above it.
A Sell Signal is generated only when:
A UT Bot Sell condition is active, and
The price closes below the Hull MA.
Similarly, a delayed sell signal can occur if price breaks below the Hull MA shortly after a UT Bot Sell trigger.
This dual-confirmation process helps traders avoid premature entries and improves the reliability of reversal signals.
📈 Best Use Cases
Reversal Trading: This strategy is particularly well-suited for catching early trend reversals rather than trend continuations. It excels at identifying momentum pivots that occur after pullbacks or exhaustion moves.
Heikin Ashi Charts Recommended: The script offers a Heikin Ashi mode for smoothing out noise and enhancing visual clarity. Using Heikin Ashi candles can further reduce whipsaws and highlight cleaner shifts in trend direction.
MACD Alignment: For best results, trade in the direction of the MACD trend or use it as a filter to avoid counter-trend trades.
⚠️ Important Notes
Entry Signals Only: This indicator only plots entry points (Buy and Sell signals). It does not define exit strategies, so users should manage trades manually using trailing stops, profit targets, or other exit indicators.
No Signal = No Confirmation: You may see a UT Bot trigger without a corresponding Buy/Sell signal. This means the price did not confirm the move by crossing the Hull MA, and therefore the setup was considered too weak or incomplete.
⚙️ Customization
UT Bot Sensitivity: Adjust the “Key Value” and “ATR Period” to make the UT Bot more or less reactive to price action.
Use Heikin Ashi: Toggle between standard candles or Heikin Ashi in the indicator settings for a smoother trading experience.
The HMA length may also be modified in the indicator settings from its standard 55 length to increase or decrease the sensitivity of signal.
This strategy is best used by traders looking for a structured, logic-based way to enter early into reversals with added confirmation to reduce risk. By combining two independent systems—momentum detection (UT Bot) and trend confirmation (Hull MA)—it aims to provide high-confidence entries without overwhelming complexity.
Let the indicator guide your entries—you manage the exits.
Examples of use:
Futures:
Stock:
Crypto:
As shown in the snapshots this strategy, like most, works the best when price action has a sizeable ATR and works the least when price is choppy. Therefore it is always best to use this system when price is coming off known support or resistance levels and when it is seen to respect short term EMA's like the 9 or 15.
My personal preference to use this system is for day trading on a 3 or 5 minute chart. But it is valid for all timeframes and simply marks a high probability for a new trend to form.
Sources:
Quant Nomad - www.tradingview.com
Yo_adriiiiaan - www.tradingview.com
HPotter - www.tradingview.com
Hull Moving Average - alanhull.com
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
Cointegration Heatmap & Spread Table [EdgeTerminal]The Cointegration Heatmap is a powerful visual and quantitative tool designed to uncover deep, statistically meaningful relationships between assets.
Unlike traditional indicators that react to price movement, this tool analyzes the underlying statistical relationship between two time series and tracks when they diverge from their long-term equilibrium — offering actionable signals for mean-reversion trades .
What Is Cointegration?
Most traders are familiar with correlation, which measures how two assets move together in the short term. But correlation is shallow — it doesn’t imply a stable or predictable relationship over time.
Cointegration, however, is a deeper statistical concept: Two assets are cointegrated if a linear combination of their prices or returns is stationary , even if the individual series themselves are non-stationary.
Cointegration is a foundational concept in time series analysis, widely used by hedge funds, proprietary trading firms, and quantitative researchers. This indicator brings that institutional-grade concept into an easy-to-use and fully visual TradingView indicator.
This tool helps answer key questions like:
“Which stocks tend to move in sync over the long term?”
“When are two assets diverging beyond statistical norms?”
“Is now the right time to short one and long the other?”
Using a combination of regression analysis, residual modeling, and Z-score evaluation, this indicator surfaces opportunities where price relationships are stretched and likely to snap back — making it ideal for building low-risk, high-probability trade setups.
In simple terms:
Cointegrated assets drift apart temporarily, but always come back together over time. This behavior is the foundation of successful pairs trading.
How the Indicator Works
Cointegration Heatmap indicator works across any market supported on TradingView — from stocks and ETFs to cryptocurrencies and forex pairs.
You enter your list of symbols, choose a timeframe, and the indicator updates every bar with live cointegration scores, spread signals, and trade-ready insights.
Indicator Settings:
Symbol list: a customizable list of symbols separated by commas
Returns timeframe: time frame selection for return sampling (Weekly or Monthly)
Max periods: max periods to limit the data to a certain time and to control indicator performance
This indicator accomplishes three major goals in one streamlined package:
Identifies stable long-term relationships (cointegration) between assets, using a heatmap visualization.
Tracks the spread — the difference between actual prices and the predicted linear relationship — between each pair.
Generates trade signals based on Z-score deviations from the mean spread, helping traders know when a pair is statistically overextended and likely to mean revert.
The math:
Returns are calculated using spread tickers to ensure alignment in time and adjust for dividends, splits, and other inconsistencies.
For each unique pair of symbols, we perform a linear regression
Yt=α+βXt+ε
Then we compute the residuals (errors from the regression):
Spreadt=Yt−(α+βXt)
Calculate the standard deviation of the spread over a moving window (default: 100 samples) and finally, define the Cointegration Score:
S=1/Standard Deviation of Residuals
This means, the lower the deviation, the tighter the relationship, so higher scores indicate stronger cointegration.
Always remember that cointegration can break down so monitor the asset over time and over multiple different timeframes before making a decision.
How to use the indicator
The heatmap table:
The indicator displays 2 very important tables, one in the middle and one on the right side. After entering your symbols, the first table to pay attention to is the middle heatmap table.
Any assets with a cointegration value of 25% is something to pay attention to and have a strong and stable relationship. Anything below is weak and not tradable.
Additionally, the 40% level is another important line to cross. Assets that have a cointegration score of over 40% will most likely have an extremely strong relationship.
Think about it this way, the higher the percentage, the tighter and more statistically reliable the relationship is.
The spread table:
After finding a good asset pair using heatmap, locate the same pair in the spread table (right side).
Here’s what you’ll see on the table:
Spread: Current difference between the two symbols based on the regression fit
Mean: Historical average of that spread
Z-score: How far current spread is from the mean in standard deviations
Signal: Trade suggestion: Short, Long, or Neutral
Since you’re expecting mean reversion, the idea is that the spread will return to the average. You want to take a trade when the z-score is either over +2 or below -2 and exit when z-score returns to near 0.
You will usually see the trade suggestion on the spread chart but you can make your own decision based on your risk level.
Keep in mind that the Z-score for each pair refers to how off the first asset is from the mean compared to the second one, so for example if you see STOCKA vs STOCKB with a Z-score of -1.55, we are regressing STOCKB (Y) on STOCKA (X).
In this case, STOCKB is the quoted asset and STOCKA is the base asset.
In this case, this means that STOCKB is much lower than expected relative to STOCKA, so the trade would be a long position on stock B and short position on stock A.
Square of Nine Price & Time Forecasting Grid📐 Square of Nine Price and Time Forecasting Grid
Adapted and Interpreted by Javonnii | Inspired by Patrick Mikula
The So9 Price and Time Forecasting Grid is a self-contained forecasting grid that allows traders to visually forecast levels in both price and time, using only Square of Nine calculations.
This indicator dynamically generates an expandable grid of angles, levels, and timeline based on the placement of a single anchor point, typically from a significant high or low. It requires no manual drawing or external tools.
🔍 How It Works:
The So9 Price and Time Forecasting Grid uses Square of Nine calculation and rotational logic to project price levels, time intervals, and internal angular structure from a single anchor point.
Once applied to the chart, the grid self-generates:
• Price levels using 360° degree Square of Nine intervals
• Timeline projections using Square of Nine progression intervals
• Diagonal and cardinal cross angles that dynamically propagate from the anchor
• Granular diagonal angle control, letting you refine internal grid resolution for tighter price structural analysis in relation to the forecasting grid.
⚙️ Setup Instructions
1. Select a Significant Pivot:
After loading the indicator, you’ll be prompted to select a significant high or low on your chart. This serves as the anchor point for the entire grid.
2. Price Grid Auto-Population:
Once anchored, Square of Nine price levels will automatically populate on the chart. These levels reflect Dynamic Square of Nine intervals from the anchor using rotational math.
3. Scale to Fit Your Instrument:
Use the provided UI settings to scale the grid to fit your instrument’s price structure. This ensures the levels align with actual historical price.
4. Engage Timelines:
Activate the timeline progression feature to generate forward-projected time intervals using Square of Nine-based timing logic. The entire grid can be extended up to 500 bars ahead on any timeframe.
5. Add Diagonal Angles:
Select which Square of Nine angle resolution you’d like to overlay from either the cardinal cross or diagonal cross based on Square of Nine geometry. This will populate diagonal levels within the grid, creating a full structural grid.
6. Customize Visuals:
You can toggle or hide price levels, timelines, or diagonal angles independently.
The entire grid can also be color-coded and customized to match your charting preferences.
All elements are plotted automatically. There is no manual drawing or calculation required. You can toggle the components on or off based on your workflow:
• Hide price levels if you just want time lines.
• Focus on angles without price levels.
• Use timeline progression independently.
📘 Attribution and Permission
This tool is inspired by concepts from "The Definitive Guide to Forecasting Using W.D. Gann’s Square of Nine" by Patrick Mikula.
The indicator reflects my personal adaptation and implementation of these forecasting principles within TradingView.
I have asked for and received permission from Patrick Mikula to share and publish tools derived from his work.
This applies to this script and to any other indicators I’ve developed that incorporate or build upon his material.
Documentation of this permission is available upon request.
Credit and respect to Patrick Mikula for his contributions to Gann-based research and for granting me the opportunity to share these tools with others.
Professor Up-Down Prediction V2Professor Up-Down Prediction V2 is designed for easier use and only displays the dominant directional probability estimates.
This indicator analyzes various variables to estimate the potential upward or downward movement that may occur in the future. It does not predict whether the next candle will go up or down.
Since it forecasts based on the timeframe, it predicts short-term movements in lower timeframes and long-term movements in higher timeframes.
It never provides certainty and should be used alongside other indicators for better directional analysis.
The probabilistic estimate on the most recent candle should always be prioritized.
The continuation of an upward or downward movement is directly proportional to the dominance of the corresponding probability. In other words, if the upward probability is dominant, the likelihood and continuation of an upward movement increases. The increase or decrease in the upward or downward probability also carries meaningful implications about the related movement.
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Professor Up-Down Prediction V2, daha kolay kullanım için tasarlanmıştır. Sadece baskın olan yöndeki oransal tahminlemeleri gösterir.
Bu gösterge farklı değişkenleri ölçümleyerek gelecekte oluşabilecek düşüş ya da yükseliş hareketini oransal olarak tahminler. Bir sonraki mumun düşüş ya da yükseliş olduğunu TAHMİNLEMEZ.
Zaman dilimine özgü gelecekte oluşabilecek hareketi tahminlediği için kısa vade zaman dilimlerinde kısa vadeyi tahminler uzun zaman dilimlerinde uzun vadeyi tahmin eder.
Asla ve asla kesinlik içermez ve farklı göstergelerle kullanılarak yön tespiti daha iyi sonuç verir.
Her zaman son bardaki oransal tahminleme dikkate alınmalıdır.
Yükseliş ya da düşüş hareketinin devamlılığı, düşüş ya da yükseliş oranının baskınlığı ile doğru orantılıdır. Yani, yükseliş ihtimali baskınlığı hakimse yükseliş hareketinin ihtimali ve devamlılığı artar.Düşüş ya da yükseliş ihtimalinin oransal olarak artıp azalması da düşüş ve yükselişle alakalı anlam taşımaktadır.
Seasonality DOW CombinedOverall Purpose
This script analyzes historical daily returns based on two specific criteria:
Month of the year (January through December)
Day of the week (Sunday through Saturday)
It summarizes and visually displays the average historical performance of the selected asset by these criteria over multiple years.
Step-by-Step Breakdown
1. Initial Settings:
Defines minimum year (i_year_start) from which data analysis will start.
Ensures the user is using a daily timeframe, otherwise prompts an error.
Sets basic display preferences like text size and color schemes.
2. Data Collection and Variables:
Initializes matrices to store and aggregate returns data:
month_data_ and month_agg_: store monthly performance.
dow_data_ and dow_agg_: store day-of-week performance.
COUNT tracks total number of occurrences, and COUNT_POSITIVE tracks positive-return occurrences.
3. Return Calculation:
Calculates daily percentage change (chg_pct_) in price:
chg_pct_ = close / close - 1
Ensures it captures this data only for the specified years (year >= i_year_start).
4. Monthly Performance Calculation:
Each daily return is grouped by month:
matrix.set updates total returns per month.
The script tracks:
Monthly cumulative returns
Number of occurrences (how many days recorded per month)
Positive occurrences (days with positive returns)
5. Day-of-Week Performance Calculation:
Similarly, daily returns are also grouped by day-of-the-week (Sunday to Saturday):
Daily return values are summed per weekday.
The script tracks:
Cumulative returns per weekday
Number of occurrences per weekday
Positive occurrences per weekday
6. Visual Display (Tables):
The script creates two visual tables:
Left Table: Monthly Performance.
Right Table: Day-of-the-Week Performance.
For each table, it shows:
Yearly data for each month/day.
Summaries at the bottom:
SUM row: Shows total accumulated returns over all selected years for each month/day.
+ive row: Shows percentage (%) of times the month/day had positive returns, along with a tooltip displaying positive occurrences vs total occurrences.
Cells are color-coded:
Green for positive returns.
Red for negative returns.
Gray for neutral/no change.
7. Interpreting the Tables:
Monthly Table (left side):
Helps identify seasonal patterns (e.g., historically bullish/bearish months).
Day-of-Week Table (right side):
Helps detect recurring weekday patterns (e.g., historically bullish Mondays or bearish Fridays).
Practical Use:
Traders use this to:
Identify patterns based on historical data.
Inform trading strategies, e.g., avoiding historically bearish days/months or leveraging historically bullish periods.
Example Interpretation:
If the table shows consistently green (positive) for March and April, historically the asset tends to perform well during spring. Similarly, if the "Friday" column is often red, historically Fridays are bearish for this asset.
BTC/ETH Lot Size for Dexin - V1.0
█ Overview - This tool is specifically tailored for Delta Exchange India’s users.
I use this interactive tool before taking a position in the BTC’s futures perpetual market . With only 3 mouse clicks, I see all the necessary information, whether a Long or Short position.
A visual of Liquidation Price Level, Stop Loss Price Level, Entry Price Level, Break-even Price Level, and Take Profit Price Level can be immediately seen.
On the top right corner of the chart, which Leverage is to be used, No. of Lots to be taken, expected Profit amount, Loss amount, Brokerage Fees, Risk to Reward Ratios, and Return on Investment are shown, excluding brokerage travel. To get the correct answer in the table that suits your account and risk-taking appetite, the user needs to enter the account balance and Risk per trade.
It also does live tracking of the position, and alerts can be configured too.
█ How to Use
Load the indicator on an active chart.
In the Trading View, ensure that the Magnets is enabled (on the left panel). This will precisely select the price levels you want to choose from OHLC for a candle.
When you first load the tool on the bottom of the chart, you will see a blue box with text in white color guiding you on what you need to do.
Before the first click, the box shall prompt “On the signal candle, set the entry level, where the position would be executed”.
Once the entry price level is selected, the next prompt in the blue box shall be “Set the stop loss level where the position would be exited”. Thus, you need to click the stop loss price level.
Now that the two clicks of Entry and Stop Loss are already done, the last remaining is for the take profit. The last prompt shall be “Set the profit level where the position would be exited”. Therefore, you need to select your take-profit level
Finally, when all three points are selected, the tool shall draw trade zones.
The tool automatically determines whether it is a Long Position or Short Position from the Stop loss and take-profit price levels concerning the entry price level
If the take profit level is above the entry price, the stop must be below, and vice versa; otherwise, an error occurs.
You can change levels by dragging the handles that appear when you select the indicator, or by entering new values in the settings.
Once the position tool is on a chart, it will appear at the same levels on all symbols you use.
If you select the position tool on your chart and delete it, this will also delete the indicator from the chart. You will need to re-add it if you want to draw another position tool. You can add multiple instances of the indicator if you need a position tool on more than one of your charts.
█ Features
Display
The tool displays the following information as graphical visuals
The Liquidation to Stop Loss, Stop Loss to Entry, Entry to Break-even, and Entry to Take Profit zones shall be initiated from the entry candle point.
If you want to be from the candle that crossed the level at a different time from the entry candle, you may go to the settings and adjust the time accordingly. Please note that the time interval is 15 minutes, so at times you may not be able to see the graphical display; however, once the 15-minute time interval is over, you will see the graphical display on the chart.
The tool displays the following information in a tabulated manner
The first row indicates the Leverage that is best suited. The leverage selection by default is greater than or equal to the risk distance.
The second row indicates the number of lots that is computed in relation to the account balance, Risk appetite, Entry price, and Stop Loss price.
The third row indicates estimated profit considering taker's fees and is computed in relation to the number of Lots, Entry price, and Take-Profit price.
The fourth row indicates estimated loss considering taker's fees and is computed in relation to the number of Lots, Entry price, and Stop Loss price.
The fifth row indicates the actual Risk to Reward Ratio, ignoring the travel that pertains to fees.
The sixth row indicates actual Return on Investment, ignoring the travel that pertains to fees.
The intent is to allow the user to make an informed decision prior to taking a position by seeing “$/Rs.” or “% of R O I” or “R : R”.
In case the user wants to know beforehand what the expected charges are that need to be borne before taking a position, that too is made available in the seventh and eighth rows. Both sides' charges are made available for ready reference, irrespective of the outcome of the trade, the user knows the consequences beforehand.
█ Settings
'Trade Sizing'
The tool's input menu is divided into various parts. The first part is 'Trade Sizing'. The user needs to key in the exact number that appears in the Delta Exchange India account against 'Account Balance ($)'. The second thing the user needs to do is key in the 'Risk per Trade'. By default,t it is set to 0.25 and has a default stop change of 0.25. Alternatively, the user can key in any number (Whole number or Rational number) within 100 if that suits their risk management criterion.
'Trade Levels'
Allows users to manually set the Entry, Time, Stop Loss, and Take Profit Price Levels.
'Aggressive Mode Selection'
As the Liquidation zone is shown on the chart, if the user feels that the liquidation price level is too far from the stop loss, this option of 'Use Aggressive Leverage?' allows to increase the leverage, thus reducing the investment amount and in return increasing the Return on Investment %.
The second option in this category is 'Compute Lots based on invested Margin?' itself is self-explanatory, and thus the tabulated data shall be populating the data based on the number entered by the user against 'Margin to be invested ($)'. It is for the user to ensure that the estimated outcomes are within their risk management criterion.
'Conversion & Charges'
If the user wants to see the Profit, Loss, and Fees amount in 'Rs.', all that needs to be done is simply enable the 'Show P&L in Rs.?' The conversion shall take place considering 1 USD = 85 Rs. Same as that carried out by Delta Exchange India.
If the user wants to see the Brokerage Fees, all that needs to be done is simply enable the 'Show Brokerage Fees?'. On enabling this, the table shall show Profitable Trade's (PT) Fees and Lost Trade's (LT) Fees irrespective of the outcome of the trade. The intent is to allow the user to make informed decisions to avoid regrets or surprises at the end of the trade.
'Table'
The division of the input section is related to table position, font size and colors for text and background.
█ Alerts
Alerts can be configured by clicking 'More' (the three dots that appear when you place the cursor on the indicator title that appears on the top left corner of the chart). Alternatively, one can configure alerts by right-clicking on either of the two price levels - Stop Loss price level or Take Profit Price level. Upon right clicking, a window shall appear and the topmost line on that window shall display 'Add alert on ……….' The user can thus put alerts on either of the key levels, such as Stop Loss, Take Profit, and Break Even, or on all of them one by one.
VOL & AVG OverlayCustom Session Volume Versus Average Volume
Description:
This indicator will create an overlay on your chart that will show you the following information:
Custom Session Volume
Average For Selected Session
Percentage Comparison
Options:
Set Custom Time Frame For Calculations
Set Custom Time Frame For Average Comparison
Set Custom Time Zone
Enable / Disable Each Value
Change Text Color
Change Background Color
Change Table location
Example:
Set indicator to 30 period average. Set custom time frame to 9:30am to 10:30am Eastern/New York.
When the time frame for the calculation is closed , the indicator will provide a comparison of the current days volume compared to the average of 30 previous days for that same time frame and display it as a percentage in the table.
In this example you could compare how the first hour of the trading day compares to the previous 30 day's average, aiding in evaluating the potential volume for the remainder of the day.
Notes:
Times must be entered in 24 hour format. (1pm = 13:00 etc.)
This indicator is for Intra-day time frames, not > Day.
If you prefer data in this format as opposed to a plotted line, check out my other indicator: ADR & ATR Overlay
10Y - 2Y Spread (Farbig)10Y – 2Y Yield Spread (Color-Coded)
Description:
This indicator plots the yield spread between the US 10-Year and 2-Year Treasury yields (US10Y – US02Y) as a color-coded line:
Green = normal yield curve (positive spread)
Red = inverted yield curve (negative spread), often seen as a leading recession signal
A horizontal zero line is added to highlight turning points.
This indicator is ideal for tracking macroeconomic yield curve behavior and can be used alongside equity, crypto, or commodity charts.
Simple Volatility ConeThe Simple Volatility Cone indicator projects the potential future price range of a stock based on recent volatility. It calculates rolling standard deviation from log returns over a defined window, then uses a confidence interval to estimate the upper and lower bounds the price could reach over a future time horizon. These bounds are plotted directly on the chart, offset into the future, allowing traders to visualize expected price dispersion under a geometric Brownian motion assumption. This tool is useful for risk management, trade planning, and visualizing the potential impact of volatility.
Anchored Probability Cone by TenozenFirst of all, credit to @nasu_is_gaji for the open source code of Log-Normal Price Forecast! He teaches me alot on how to use polylines and inverse normal distribution from his indicator, so check it out!
What is this indicator all about?
This indicator draws a probability cone that visualizes possible future price ranges with varying levels of statistical confidence using Inverse Normal Distribution , anchored to the start of a selected timeframe (4h, W, M, etc.)
Feutures:
Anchored Cone: Forecasts begin at the first bar of each chosen higher timeframe, offering a consistent point for analysis.
Drift & Volatility-Based Forecast: Uses log returns to estimate market volatility (smoothed using VWMA) and incorporates a trend angle that users can set manually.
Probabilistic Price Bands: Displays price ranges with 5 customizable confidence levels (e.g., 30%, 68%, 87%, 99%, 99,9%).
Dynamic Updating: Recalculates and redraws the cone at the start of each new anchor period.
How to use:
Choose the Anchored Timeframe (PineScript only be able to forecast 500 bars in the future, so if it doesn't plot, try adjusting to a lower anchored period).
You can set the Model Length, 100 sample is the default. The higher the sample size, the higher the bias towards the overall volatility. So better set the sample size in a balanced manner.
If the market is inside the 30% conifidence zone (gray color), most likely the market is sideways. If it's outside the 30% confidence zone, that means it would tend to trend and reach the other probability levels.
Always follow the trend, don't ever try to trade mean reversions if you don't know what you're doing, as mean reversion trades are riskier.
That's all guys! I hope this indicator helps! If there's any suggestions, I'm open for it! Thanks and goodluck on your trading journey!
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B(Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
NeuroFlow Pro IndicatorThe **NeuroFlow Pro Indicator** is a comprehensive technical analysis tool designed for traders on the TradingView platform. It provides actionable buy and sell signals by combining multiple technical indicators, including Moving Averages, MACD, RSI, Stochastic RSI, SuperTrend, Ichimoku Cloud, Bollinger Bands, and Volume analysis. The indicator generates a **Composite Score** (0–100) that reflects market conditions, with low scores indicating bullish opportunities and high scores suggesting bearish conditions. It also identifies key trend reversal points and significant EMA crossovers (Golden Cross and Death Cross) to help traders make informed decisions.
**Key Features**:
- **Composite Score**: Aggregates signals from multiple indicators to provide a single, easy-to-read metric.
- **Buy/Sell Signals**: Generates clear signals for potential long (buy) and short (sell) opportunities.
- **Golden/Death Cross**: Marks EMA 50 crossing above (🚀) or below (💀) EMA 200, indicating major trend shifts.
- **Dashboard**: Displays real-time metrics like trend direction, momentum, volume, and signal confidence.
- **Customizable Alerts**: Notifies users of buy/sell signals, divergences, and EMA crossovers via TradingView’s alert system.
- **Multi-Timeframe Analysis**: Incorporates higher timeframe trends for enhanced signal reliability.
- **Candlestick Patterns**: Optionally includes patterns like Hammer, Engulfing, or Morning Star for signal confirmation.
This indicator is ideal for traders seeking a robust, all-in-one tool to identify trading opportunities across various markets (e.g., crypto, stocks, forex) and timeframes (e.g., 1H, 4H, daily).
User Guide for NeuroFlow Pro Indicator
Understanding the Indicator
- **Dashboard**:
- Located on the chart (left or right, configurable), it shows real-time metrics:
- **Comp Score**: Composite Score (0–100); low (<30) is bullish, high (>70) is bearish.
- **Trend**: Bullish, Bearish, or Neutral
- **MTF Trend**: Trend from a higher timeframe (e.g., 60m or 240m).
- **Momentum**: RSI and Stochastic RSI-based momentum (Bullish, Bearish, Neutral).
- **MFI**: Money Flow Index (Inflow, Outflow, Neutral).
- **Volatility**: High or Low based on ATR and Bollinger Bands.
- **Volume**: High, Low, or Neutral relative to volume MA.
- **Ichimoku**: Bullish, Bearish, or Neutral based on cloud position.
- **ADX Strength**: Strong or Weak trend based on ADX.
- **Divergence**: Bullish, Bearish, or Neutral for RSI/MACD divergences.
- **Reversal**: Bullish or Bearish reversal potential with confidence percentage.
- **Signal Status**: Long (buy), Short (sell), or None.
- **Signal Confid**: Confidence percentage for the current signal.
- **Chart Visuals**:
- **EMA 50 (White)**: Fast-moving average for short-term trends.
- **EMA 200 (Blue)**: Long-moving average for long-term trends.
- **Golden Cross (🚀)**: Green rocket emoji when EMA 50 crosses above EMA 200 (bullish).
- **Death Cross (💀)**: Red skull emoji when EMA 50 crosses below EMA 200 (bearish).
- **Alerts**:
- Configurable for Buy/Sell Signals, Golden/Death Cross, and Bullish/Bearish Divergences.
Configuring Settings
1. **Open Settings**:
- Right-click the indicator’s name on the chart and select “Settings,” or double-click the indicator in the chart’s indicator list.
2. **Key Settings to Customize**:
- **Strategy Settings**:
- **Max ATR Multiplier**: Adjusts sensitivity to volatility (default: 3.0).
- **Main Settings**:
- **Candlestick Pattern**: Choose Hammer, Engulfing, Morning Star, or Custom (default: Hammer).
- **Multi-Timeframe Period**: Set higher timeframe for trend analysis (e.g., 60m, 240m, Daily; default: 60m).
- **Higher Timeframe**: Secondary timeframe for confirmation (default: 240m).
- **Use Candlestick Patterns**: Enable/disable pattern-based signals (default: off).
- **Use Volume Filter**: Require high volume for signals (default: on).
- **Use ADX Filter**: Require strong trend for signals (default: on).
- **Momentum Settings**:
- **RSI/Stochastic/MFI Lengths**: Adjust periods for RSI, Stochastic RSI, and MFI (defaults: 14, 14, 60).
- **EMA Lengths**: Fast (50), Slow (100), Long (200) for trend and crossovers.
- **ATR/ADX Lengths**: Volatility and trend strength periods (default: 14).
- **SuperTrend/Bollinger/Ichimoku Settings**:
- Customize periods and multipliers (defaults: SuperTrend 10/3.0, Bollinger 20/2.0, Ichimoku 9/26/52).
- **MACD Settings**:
- **MACD Preset**: Auto (timeframe-based), 1H (3-10-16), 4H (5-34-21), D (5-15-9), or Custom (default: Auto).
- **Custom MACD Lengths**: Fast (12), Slow (26), Signal (9) for Custom preset.
- **Weights Settings**:
- Adjust weights for trend, momentum, volatility, etc., to prioritize certain indicators (defaults: Trend 1.0, Momentum 0.3, etc.).
- **Threshold Settings**:
- **Bullish/Bearish Reversal Thresholds**: Set score thresholds for reversals (default: 30/70).
- **ADX Threshold**: Minimum ADX for trend strength (default: 20).
- **Signal Thresholds**: Base (70) and alert (80) thresholds for signals.
- **Dashboard Settings**:
- **Position**: Left or Right (default: Right).
- **Show/Hide Metrics**: Enable/disable dashboard rows (e.g., Comp Score, Trend, MFI; all enabled by default except Volatility and Volume MA).
3. **Save Changes**:
- Click “OK” to apply settings. The dashboard and plots update instantly.
Using the Indicator
1. **Interpreting Signals**:
- **Buy Signal (Long)**: Appears when Composite Score is low (≤30), with at least two bullish confirmations . Shown as “Long” in Signal Status with confidence percentage.
- **Sell Signal (Short)**: Appears when Composite Score is high (≥70), with at least two bearish confirmations. Shown as “Short” in Signal Status.
- **Golden Cross (🚀)**: Indicates a bullish trend when EMA 50 crosses above EMA 200. Look for confirmation from Composite Score and Signal Status.
- **Death Cross (💀)**: Indicates a bearish trend when EMA 50 crosses below EMA 200. Confirm with dashboard metrics.
- **Reversal Signals**: Dashboard shows “Bullish” or “Bearish” with a percentage when reversal conditions are met .
2. **Monitoring the Dashboard**:
- Use the dashboard to assess market conditions in real-time.
- Green (bullish), red (bearish), or gray (neutral) colors highlight key metrics.
- Check “Signal Confid” for confidence in buy/sell signals (higher is better, e.g., >60%).
3. **Trading Decisions**:
- Combine signals with your own analysis (e.g., support/resistance, news).
- Use Golden/Death Cross for long-term trend confirmation.
- Avoid trading in high volatility (dashboard: “Volatility: High”) unless experienced
Best Practices
- **Timeframe Selection**:
- Use higher timeframes (e.g., 4H, Daily) for more reliable signals, especially for Golden/Death Cross.
- Lower timeframes (e.g., 5m, 15m) may produce more signals but with higher noise.
- **Confirm Signals**:
- Cross-check buy/sell signals with dashboard metrics (e.g., Trend, MFI, ADX).
- Use Golden/Death Cross as a trend filter rather than a standalone signal.
- **Risk Management**:
- Always use stop-losses and position sizing based on your risk tolerance.
- Avoid trading during high volatility unless part of your strategy.
- **Regular Updates**:
- Monitor TradingView for script updates from the author (KoKalito) to access new features or bug fixes.
Troubleshooting
- **No Signals**:
- Ensure the chart timeframe matches your settings (e.g., 60m for MTF Period).
- Check if filters (Volume, ADX) are too strict; try disabling them.
- **Dashboard Missing**:
- Verify “Dashboard Position” is set to Left or Right.
- Ensure dashboard metrics are enabled (e.g., Show Comp Score).
- **Alerts Not Triggering**:
- Confirm the alert condition is set to “NeuroFlow Pro Indicator” and the correct option (e.g., “Golden Cross Alert”).
- Check TradingView’s “Alerts” panel for errors or expired alerts.
- Reapply the indicator to the chart if it was recently updated.
- **EMA Crosses Not Showing**:
- Zoom in on the chart to see 🚀 (Golden Cross) or 💀 (Death Cross) symbols.
- Ensure EMA 50 and EMA 200 lengths are not identical (defaults: 50, 200).
Support
- **Author**: KoKalito (check TradingView profile for updates or contact info).
- **TradingView Community**: Post questions in the TradingView Pine Script community or forums.
- **Documentation**: Refer to TradingView’s Pine Script v5 documentation for advanced customization.
- **Risk Warning**: Trading involves risk. Use the indicator as a tool, not a guarantee of profits. Always conduct your own analysis and manage risk appropriately.
Happy trading with **NeuroFlow Pro Indicator**! 🚀
ADX Z-Score OscillatorTitle: ADX Z-Score Oscillator
Description:
The ADX Z-Score Oscillator is a normalized version of the traditional Average Directional Index (ADX), designed to oscillate between fixed bounds for easier interpretation of trend strength. Instead of plotting the raw ADX line, this indicator calculates the Z-Score of the ADX relative to its recent average and standard deviation, allowing for consistent comparison over time and across different assets.
The Z-Score oscillates between fixed horizontal levels of +2 and -2, highlighting extreme values.
The orange line represents the current Z-Score of the ADX.
Horizontal reference lines at +2 (red), 0 (gray), and -2 (green) help define overbought/oversold or strong/weak trend zones.
A dynamic table on the chart shows the current Z-Score with color coding to indicate trend strength:
🔴 Z > 1.5 → Very strong trend
🟠 Z > 0.5 → Moderate trend
🔵 Z < -0.5 → Weakening or reversing trend
🟢 Z < -1.5 → Very weak trend or potential reversal zone
This transformation of the ADX into a bounded oscillator helps traders easily assess trend strength and changes in momentum without the ambiguity of varying ADX scale levels.
OBV Z-Score + Table📘 OBV Z-Score — Indicator Description
Overview
This indicator converts the On-Balance Volume (OBV) into a Z-Score oscillator, providing a normalized statistical view of volume flow strength relative to its recent history.
How It Works
OBV Calculation
The On-Balance Volume accumulates volume based on price direction, showing whether volume is flowing into or out of an asset.
Z-Score Transformation
The OBV values are normalized via Z-Score:
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Bearbeiten
Z = (OBV - Mean) / Standard Deviation
This reveals how unusually strong or weak volume momentum is compared to recent norms.
Smoothing
An optional moving average smoothing (SMA, EMA, VWMA, etc.) can be applied for cleaner signals.
Z-Score Table
A live Z-Score value is displayed in a table on the top-right of the indicator pane, clamped between +2 and -2:
+2 indicates unusually high positive volume momentum
-2 indicates unusually high negative volume momentum
How to Use It
Bullish Signal: Z-Score crossing above +1.5 or +2 signals strong buying volume pressure
Bearish Signal: Z-Score crossing below -1.5 or -2 signals strong selling volume pressure
Combine with Price Action: Use alongside price trends or other Z-Score indicators to improve decision making in SDCA or volume-based trading systems
Cumulative Volume Delta with Divergence🧠 Core Functionality:
1. Cumulative Volume Delta (CVD):
Purpose: Visualizes the difference between buying and selling pressure over time.
Mechanism:
It uses lower-timeframe volume delta data, retrieved from ta.requestVolumeDelta(), to build a candle-style visualization of the net volume movement.
Plotted candles show whether buying (up volume) or selling (down volume) was dominant within each period.
Teal candles: More buying than selling (CVD up).
Red candles: More selling than buying (CVD down).
Volume Source: Based on intrabar up/down volume approximation from lower timeframes.
🧭 Divergence Detection (New Feature):
2. Regular Bullish Divergence:
Condition:
Price makes a lower low.
CVD (lastVolume) makes a higher low.
Interpretation: Selling pressure is weakening despite price making new lows — a potential reversal signal to the upside.
Displayed As:
Green line and label "Bull" under the CVD at the divergence point.
3. Regular Bearish Divergence:
Condition:
Price makes a higher high.
CVD makes a lower high.
Interpretation: Buying pressure is fading despite price rising — a potential reversal signal to the downside.
Displayed As:
Red line and label "Bear" above the CVD at the divergence point.
🧰 User Controls:
Use custom timeframe: Overrides default volume delta resolution for finer or broader analysis.
Calculate Divergence: Turns the divergence detection on or off.
Adjustable via script inputs.
🔔 Alerts:
Two alert conditions are included:
One for bullish divergence.
One for bearish divergence.
Alerts trigger at the bar where the divergence is confirmed, not where it starts.
📈 Use Case:
This tool is ideal for traders looking to:
Spot early reversals or momentum shifts.
Combine volume analysis with price action.
Time entries or exits more accurately using volume-confirmed divergence.
US30 Trend Screener (TechnoBlooms)Identify Index Trends Before the Move Starts.
The US30 Trend Screener is a powerful tool designed to help traders understand the internal dynamics of the Dow Jones Industrial Average (US30) by analyzing the trends of its weighted component stocks in real time.
📊 How It Works
This indicator uses EMA crossovers, RSI, and MACD signals from the 30 Dow Jones stocks and visualizes them in a compact, color-coded dashboard overlay on your chart.
You can choose your preferred lower timeframe (e.g., 1min, 5min, 15min) to analyze intraday momentum before the US30 index reflects the shift.
⏱ Timeframe Input
Select any minute-based timeframe (1–240 min) to suit your trading strategy.
Each stock’s trend data is fetched using your selected timeframe, so you can zoom in or out on price action dynamics.
It is recommended to select the timeframe closer to the chart timeframe in the indicator.
🚀 Key Features
✅ Component-Based Analysis: Tracks all 30 Dow stocks like MSFT, AAPL, GS, etc., with real-time price and indicator updates.
✅ Trend Detection: Uses EMA (8/34) crossover to determine bullish or bearish trends per stock.
✅ Momentum Signals: Shows RSI (14) values and MACD direction (▲ / ▼) for each stock.
✅ Color-Coded Dashboard:
🟩 Green = Bullish trend
🟥 Red = Bearish trend
✅ Compact Display: See 30 stocks in a 3-column grid format, updated every few bars for performance.
🧠 Pro Tips
🔍 Use shorter timeframes (1–5 min) to detect early trend shifts—perfect for scalping and intraday entries.
💼 Watch high-weight stocks like GS, MSFT, UNH. A shift in their trend often precedes index movement.
🎯 Combine with price action or SMC tools to confirm institutional moves and breakouts.
🚦 If most of the dashboard turns green/red at once, it often signals a strong momentum breakout or reversal.
💡 Ideal For:
Index traders (US30/DJI futures or CFDs)
Scalpers & day traders
Momentum and trend-following strategies
Traders who want to see the story behind the index move
Smart S/R ZonesThis is not your average S/R script.
It combines proximity, bounce frequency, and volume clustering to automatically identify the most reliable support and resistance zones on your chart — no guesswork needed.
How It Works:
• Scans for recent highs/lows, SMA50 & SMA200, and pivot swing points
• Ranks each potential level using a weighted scoring system:
• Proximity to current price (50%)
• Bounce Count (30%) — how many times price respected that level
• Volume Score (20%) — how much volume traded around that level
• The top support and resistance levels are plotted with:
• Clear dashed lines
• Color-filled zones
• Simple percentage distance labels
Why This Script Stands Out:
• No settings to tweak — it just works
• Helps you react faster with high-confidence levels
• Adapts to any market: crypto, forex, stocks, indexes
• Ideal for both intraday and swing trading setups
Built-in Intelligence. Clean Visuals. Zero Noise.
UT Bot + Cooldown + Visual FVGSynopsis – UT Bot + Cooldown + Visual FVG
This TradingView script combines:
✅ UT Bot Reversal Signals
Based on ATR and volatility logic
BUY when trend flips from bearish to bullish
SELL when trend flips from bullish to bearish
✅ Cooldown Filter
Limits signals to 1 per X bars (default 30)
Prevents overtrading during choppy price action
✅ Optional FVG Markers (Fair Value Gaps)
Visually shows bullish or bearish imbalances (3-bar gaps)
Does not affect signal generation — only for confluence
🔍 Ideal for traders who want clean, time-filtered signals with visual price-action context. Suitable for futures, crypto, or forex on intraday charts.
Velez Price Action Signals (with 20 & 200 SMA)Velez Price Action Signals – With 20 & 200 SMA Overlay
This TradingView Pine Script is a clean and powerful reversal signal tool inspired by Oliver Velez’s price action philosophy, enhanced with trend context via two Simple Moving Averages.
🔍 Signal Logic
Buy Signal:
Current candle sweeps below the previous 5-bar low (liquidity grab).
Candle is bullish (close > open).
The lower wick is significantly larger than the body (e.g. ratio > 1.5).
Sell Signal:
Current candle sweeps above the previous 5-bar high.
Candle is bearish (close < open).
The upper wick is significantly larger than the body.
Signals appear as BUY/SELL labels on the chart (non-repainting).
