Earnings Day - Price Predictor [DunesIsland]It's designed to analyze and visualize historical stock price movements on earnings report days, focusing on percentage changes.
Here's a breakdown of what it does, step by step:
Key Inputs and Setup
User Input: There's a single input for "Lookback Years" (default: 10), which determines how far back in time (approximately) the indicator analyzes earnings data. It uses a rough calculation of milliseconds in that period to filter historical data.
Data Fetching: It uses TradingView's request.earnings function to pull actual earnings per share (EPS) data for the current ticker. Earnings days are identified where EPS data exists on a bar but not on the previous one (to avoid duplicates).
Price Change Calculation: For each detected earnings day, it computes the percentage price movement as (close - close ) / close * 100, representing the change from the previous close to the current close on that day.
Processing and Calculations (on the Last Bar)
Lookback Filter: It calculates a cutoff timestamp for the lookback period and processes only earnings events within that window.
Overall Averages:
Separates positive (≥0%) and negative (<0%) percentage changes.
Seasonality (Next Quarter Prediction):
Identifies the most recent earnings quarter (latest_q).
Predicts the "next" quarter (e.g., if latest is Q4, next is Q1;
Again, separates positive and negative changes, computing their respective averages.
Visual Outputs
Lookback: How far to fetch the data in years.
Average Change (Green): Showing the average of all positive changes.
Average Change (Red): Showing the average of all negative changes.
Seasonality Change (Green): Showing the average of positive changes for the predicted next quarter.
Seasonality Change (Red): Showing the average of negative changes for the predicted next quarter.
Purpose and Usage
This indicator helps traders assess a stock's historical reaction to earnings announcements. The overall averages give a broad sense of typical gains/losses, while the seasonality focuses on quarter-specific trends to "predict" potential movement for the upcoming earnings (based on past same-quarter performance). It's best used on daily charts for stocks with reliable earnings data. Note that quarter inference is calendar-based and may not perfectly match fiscal calendars for all companies—it's an approximation.
Średnie kroczące
Gunzo Trend Sniper For Loop🧠 Gunzo Trend Sniper For Loop — Adaptive Trend Momentum Framework
The Gunzo Trend Sniper For Loop is a precision-built, adaptive trend analysis system designed to expose hidden trend strength, exhaustion points, and directional momentum within any market — from cryptocurrencies to equities and forex.
At its core, this indicator integrates a loop-based comparative engine with a multi-type adaptive moving average filter, producing a highly responsive yet smooth measure of directional sentiment.
⚙️ Core Concept
Gunzo Trend Sniper quantifies market bias by comparing the current smoothed weighted average of price to its historical values across a dynamic lookback window.
Through this iterative “for loop” scoring process, the indicator tallies how many of the recent bars exhibit higher or lower values than the present one — forming a trend strength score that oscillates between bullish and bearish dominance.
In essence:
Positive score values indicate sustained upward bias — more candles recently closed below the current value.
Negative or low score values signal downward pressure — suggesting that recent candles are outperforming the current value.
📊 Interpreting the Chart
The indicator plots two complementary visuals:
Gunzo Trend Score (Oscillator Panel)
Green Zones (Above Upper Threshold) → Confirmed uptrend momentum and accumulation.
Red Zones (Below Lower Threshold) → Confirmed downtrend pressure and potential distribution.
Neutral Region (Between Thresholds) → Consolidation or transitional phases.
Gunzo Trend Line (Overlay on Price Chart)
The plotted line dynamically changes color:
🟩 Green: Confirmed bullish trend bias
🟥 Red: Confirmed bearish momentum
⚪ Gray: Neutral or indecisive period
This color transition acts as a visual confirmation layer, aligning the oscillator’s internal score with price structure.
🔍 How to Use It
1. Trend Identification:
When the oscillator consistently remains above the upper threshold, and the overlay line turns green, the market exhibits strong bullish continuation.
Sustained readings below the lower threshold with a red overlay signal dominant bearish control.
2. Entry Confirmation:
Combine this indicator with breakout or pullback setups. For example, enter long positions when:
The oscillator crosses above the upper threshold from below,
The overlay line flips from red to green, confirming new momentum.
Short entries follow the inverse logic.
3. Divergence Detection:
Price forming higher highs while the Gunzo Trend Score forms lower highs may hint at momentum exhaustion — signaling potential reversals.
4. Adaptive Thresholding:
Adjust ThresholdL and ThresholdS to fit volatility.
Tighter thresholds increase sensitivity (useful in lower timeframes).
Wider thresholds filter out noise (ideal for daily or higher intervals).
🧭 Strategic Insights
The Gunzo Trend Sniper is more than an oscillator — it’s a multi-dimensional market bias model.
Its comparative logic captures how consistent recent directional strength has been, effectively quantifying trend persistence. This makes it especially valuable for:
Momentum confirmation before breakouts.
Avoiding false reversals during volatile consolidation phases.
Detecting early trend slowdowns before major reversals.
| Parameter | Description |
| ------------------------------- | ------------------------------------------------------------------- |
| `MA Type` | Selects the smoothing algorithm (SMA, EMA, SMMA, or WMA).
| `MA Source` | Price input (default: OHLC4). |
| `Gunzo Length` | Lookback for the moving average engine. |
| `Smoothing Length` | Additional smoothing layer for refined signals. |
| `From / To` | Defines the historical range for the scoring loop. |
| `Threshold Uptrend / Downtrend` | Determines when a market is considered strongly bullish or bearish. |
💡 Pro Tips
Combine with volume-based indicators or ATR filters for volatility-adjusted entries.
Use in conjunction with higher timeframe confirmation — e.g., align the Gunzo Trend on 4H and 1D for stronger bias.
Works exceptionally well with trend-following strategies, especially when paired with trailing stop systems.
MTF 200 SMAMulti-Timeframe (MTF) 200 SMA: Your Universal Trend Guide
Tired of switching timeframes just to check the major moving averages?
The MTF 200 SMA indicator is a powerful, customizable tool designed to give you a clear, comprehensive view of the trend across multiple timeframes, all on a single chart. It's built on Pine Script v6 for stability and performance.
Key Features:
9 MTF Lines: Simultaneously plot the 200 Simple Moving Average (SMA) for 30m, 1h, 2h, 3h, 4h, 6h, 8h, Daily, and Weekly charts. Understand the overall market structure at a glance.
Single-Click Toggle: Use the 'Current Chart TF Only' checkbox to instantly switch from the crowded MTF view to showing only the standard 200 SMA for your current chart resolution. Perfect for focusing on immediate price action.
Dynamic Highlighting: The 'Highlight Current Chart TF' option (default ON) emphasizes the SMA corresponding to your current chart, making it stand out with a bright Aqua color and a thicker line when in MTF mode.
Full Customization: Easily adjust the SMA Length and the MTF SMA Line Color directly in the indicator settings.
How to Use It:
Trend Confirmation: When all MTF lines (especially the Daily and Weekly) are aligned and moving in the same direction, it provides high-confidence trend confirmation.
Dynamic S/R: The MTF SMAs often act as strong dynamic Support and Resistance levels, even when viewing a lower timeframe like the 5-minute chart.
Clean Analysis: Use the 'Current Chart TF Only' option when you need to declutter your chart and focus on the primary trend of your active trading session.
Elevate your trend analysis today with the MTF 200 SMA!
Puell Multiple Variants [OperationHeadLessChicken]Overview
This script contains three different, but related indicators to visualise Bitcoin miner revenue.
The classical Puell Multiple : historically, it has been good at signaling Bitcoin cycle tops and bottoms, but due to the diminishing rewards miners get after each halving, it is not clear how you determine overvalued and undervalued territories on it. Here is how the other two modified versions come into play:
Halving-Corrected Puell Multiple : The idea is to multiply the miner revenue after each halving with a correction factor, so overvalued levels are made comparable by a horizontal line across cycles. After experimentation, this correction factor turned out to be around 1.63. This brings cycle tops close to each other, but we lose the ability to see undervalued territories as a horizontal region. The third variant aims to fix this:
Miner Revenue Relative Strength Index (Miner Revenue RSI) : It uses RSI to map miner revenue into the 0-100 range, making it easy to visualise over/undervalued territories. With correct parameter settings, it eliminates the diminishing nature of the original Puell Multiple, and shows both over- and undervalued revenues correctly.
Example usage
The goal is to determine cycle tops and bottoms. I recommend using it on high timeframes, like monthly or weekly . Lower than that, you will see a lot of noise, but it could still be used. Here I use monthly as the example.
The classical Puell Multiple is included for reference. It is calculated as Miner Revenue divided by the 365-day Moving Average of the Miner Revenue . As you can see in the picture below, it has been good at signaling tops at 1,3,5,7.
The problems:
- I have to switch the Puell Multiple to a logarithmic scale
- Still, I cannot use a horizontal oversold territory
- 5 didn't touch the trendline, despite being a cycle top
- 9 touched the trendline despite not being a cycle top
Halving-Corrected Puell Multiple (yellow): Multiplies the Puell Multiple by 1.63 (a number determined via experimentation) after each halving. In the picture below, you can see how the Classical (white) and Corrected (yellow) Puell Multiples compare:
Advantages:
- Now you can set a constant overvalued level (12.49 in my case)
- 1,3,7 are signaled correctly as cycle tops
- 9 is correctly not signaled as a cycle top
Caveats:
- Now you don't have bottom signals anymore
- 5 is still not signaled as cycle top
Let's see if we can further improve this:
Miner Revenue RSI (blue):
On the monthly, you can see that an RSI period of 6, an overvalued threshold of 90, and an undervalued threshold of 35 have given historically pretty good signals.
Advantages:
- Uses two simple and clear horizontal levels for undervalued and overvalued levels
- Signaling 1,3,5,7 correctly as cycle tops
- Correctly does not signal 9 as a cycle top
- Signaling 4,6,8 correctly as cycle bottoms
Caveats:
- Misses two as a cycle bottom, although it was a long time ago when the Bitcoin market was much less mature
- In the past, gave some early overvalued signals
Usage
Using the example above, you can apply these indicators to any timeframe you like and tweak their parameters to obtain signals for overvalued/undervalued BTC prices
You can show or hide any of the three indicators individually
Set overvalued/undervalued thresholds for each => the background will highlight in green (undervalued) or red (overvalued)
Set special parameters for the given indicators: correction factor for the Corrected Puell and RSI period for Revenue RSI
Show or hide halving events on the indicator panel
All parameters and colours are adjustable
Golden Cross 50/200Simplicity characterizes each of my trading systems and methods. On this occasion, I present a trend-following strategy with simple rules and high profitability.
System Rules:
-Long entries when the 50 EMA crosses above the 200 EMA.
-Stop Loss (SL) placed at the low of 15 candles prior to the entry candle.
-Take Profit (TP) triggered when the 50 EMA crosses below the 200 EMA.
As with any trend-following system, we sacrifice win rate for profitability, and of course, we will focus on traditional markets with a consistent trend-following nature over time.
Recommended Markets and Timeframes:
BTCUSDT H6
August 17, 2017 - October 20, 2025 Total trades: 30
Profitability: +1,682.99%
Win rate: 40%
Outperforms Buy & Hold
BTCUSDT H4
August 17, 2017 - October 20, 2025 Total trades: 42
Profitability: +12,213.49% (high and stable performance curve)
Win rate: 40%
Outperforms Buy & Hold
BTCUSDT H2
August 17, 2017 - October 20, 2025 Total trades: 95
Profitability: +2,363.80%
Win rate: 24.21%
Matches Buy & Hold
BTCUSDT H1
August 17, 2017 - October 20, 2025 Total trades: 203
Profitability: +1,045% (stable performance curve)
Win rate: 25.62%
BTCUSDT 30M
August 17, 2017 - October 20, 2025 Total trades: 393
Profitability: +4,205.51% (high and stable performance curve)
Win rate: 27.74%
Outperforms Buy & Hold
BTCUSDT 15M
August 17, 2017 - October 20, 2025 Total trades: 821
Profitability: +1,311.97%
Win rate: 23.14%
Timeframes such as Daily, 12-hour, 8-hour, and even 5-minute charts are profitable with this system, so feel free to experiment.
Other markets and timeframes to observe include:
-XAUUSD (H1, H4, H6, H8, Daily)
-SPX (Daily: +21,302% profitability since 1871 in 40 trades)
-Tesla (H1, H2, H4, H6, especially M30 and M15)
-Apple (M5, M15, M30, H1, H2, H4…)
-Warner Bros (M5, M15, M30…)
-GOOGL (M5, M15, M30, H1, H2, H4, H6…)
-AMZN (M5, M15, M30, H2, H4, H6…)
-META (M5, M15, M30, H1, H2, H4…)
-NVDA (M5, M15, M30, H1, H2, H4…)
This system not only generates significant profitability but also performs very well in traditional markets, even on lower timeframes like 5-minute charts. In many cases, the returns far exceed Buy & Hold.
I hope this strategy is useful to you. Follow my Spanish-speaking profile if you want to see my market analyses, and send me your good vibes!
ORBs, EMAs, SMAs, AVWAPThis is an update to a previously published script. In short the difference is the added capability to adjust the length of EMAs. Also added 3 customizable SMAs. Enjoy! Let me know what you think of the script please. This is only second one I have ever done. Through practice and people like @LuxAlgo and other Pinescripters this isn't possible. Tedious hrs with ChatGPT to correct nuances, who doesnt seem to learn from (insert pronoun) mistakes
This all-in-one indicator combines key institutional tools into a unified framework for intraday and swing trading. Designed for traders who use multi-session analysis and dynamic levels, it automatically maps out global session breakouts, moving averages, and volume-weighted anchors with high clarity.
Features include:
🕓 Tokyo, London, and New York ORBs (Opening Range Breakouts) — 30-minute configurable range boxes that persist until the next New York open.
📈 Anchored VWAP with Standard Deviation Bands — dynamically anchorable to session, week, or month for institutional-grade price tracking.
📊 Exponential Moving Averages (9, 20, 113, 200) — for short-, mid-, and long-term momentum structure.
📉 Simple Moving Averages (20, 50, 100) — fully customizable lengths, colors, and visibility toggles for trend confirmation.
🏁 Prior High/Low Levels (PDH/PDL, PWH/PWL, PMH/PML) — automatically plotted from previous day, week, and month, with labels placed at each session’s midpoint.
🎛️ Session-Aligned Time Logic — all time calculations use New York session anchors with DST awareness.
💡 Clean Visualization Options — every component can be toggled on/off, recolored, or customized for your workflow.
Best used for:
ORB break-and-retest setups
VWAP and EMA rejections
Confluence-based trading around key session levels
Multi-session momentum tracking
Key Levels (PA, MAs, VWAPs, Volume Profile, rVWAPs)This indicator marks all kinds of key levels so that users can keep an overview of their specified levels in a convenient non chart cluttering way. It can highlight levels of confluence or display each level seperately.
The indicator includes markers for the following levels:
Price Action: Opens, Previous High/Low, Monday Range
Moving Averages: H4, D1 and W1 with customisable lengths
VWAPs: Developing and Previous VWAPs with their respective VAL/VAH (1 Standard Deviation)
Rolling VWAPs
Volume Profile: Developing and Previous VAL/VAH/POC
What makes this indicator different is its vast customisation options and big library of levels…
… users can choose to merge all levels that are aligned in a specified % threshold and additionally they can choose to color them the same color to highlight confluence levels.
… users have the choice between Full Label Markers or Abbreviations of those Labels.
… users have the choice of a few presets making level switching fast and convenient (Price Action, Volume Profile, VWAP, Volume or Custom).
… users can specify if they prefer to highlight Simple Moving Averages or Exponential Moving Averages. They have calculations available on three different timeframes and can change the lengths of each.
… users can color all levels the same with one click instead of having to manually change all of them.
… when users choose Volume Profile Levels they can either let the script auto calculate the row size making asset switching simple or they can manually input row size.
With the custom preset users can show and hide whichever levels they want.
(To have them the same every time you freshly load the indicator save your settings as default in the lower left corner of the settings tab).
Purpose
This indicator is designed to serve as a level visualisation tool that has the ability to highlight levels of confluence. It may assist in keeping an overview of where all levels are currently located but does not produce signals or trade recommendations.
🐼 Panda EMA-OBV Dual SignalPanda EMA-OBV Dual Signal
Description:
The Panda EMA-OBV Dual Signal combines exponential moving averages (EMAs) with On-Balance Volume (OBV) to identify both trend direction and momentum strength.
This script is designed for professional traders who want clear visual confirmations for reversals and trend continuations.
Main Features:
• Multi-layer EMA system (14 / 20 / 50 / 100 periods) for trend alignment
• OBV divergence detection (Bullish / Bearish)
• RSI confirmation filter for extra accuracy
• Auto signal arrows for buy/sell opportunities
• Works on all timeframes (H1 / H4 / D / W / MN)
How to use:
1️⃣ Look for Buy signal when OBV shows Bullish divergence and price closes above EMA 20.
2️⃣ Look for Sell signal when OBV shows Bearish divergence and price closes below EMA 20.
3️⃣ Use EMA crossovers as confirmation for trend continuation.
Tip:
The script is optimized for XAUUSD and BTCUSD but can also be applied to other assets for swing or intraday analysis.
Created by Millionbears | For educational and analytical purposes only.
Robust Scaled HMA | OquantOverview
The Robust Scaled HMA is an indicator designed to provide a more resilient trend-following signal by combining the Hull Moving Average (HMA) with a robust scaling mechanism based on interquartile range (IQR). Unlike traditional scaled indicators that rely on standard deviation, which can be skewed by outliers in volatile markets, this approach uses quartiles to normalize the HMA values, offering better resistance to extreme price movements. It generates long and short signals based on user-defined thresholds and includes built-in performance metrics to evaluate the indicator's historical behavior, alongside buy-and-hold comparisons(Remember past performance doesn’t guarantee future results). This allows traders to assess potential effectiveness without needing external backtesting tools(Remember past performance doesn’t guarantee future results). The indicator is particularly useful for those seeking a balance between responsiveness and robustness in trend detection, and it visualizes allocation states (LONG, SHORT, or CASH) through color-coded plots and optional tables.
Key Factors/Components
Robust Scaling: Employs IQR for normalization instead of standard deviation, reducing sensitivity to outliers and providing a more stable measure of deviation from the median HMA.
Signal Generation: Threshold-based triggers for long (above upper threshold) and short (below lower threshold) positions, with options to enable/disable longs or shorts to suit directional biases.
Performance Metrics: Calculates key risk-adjusted metrics such as Maximum Drawdown (Max DD), Intra-Trade Max DD, Sharpe Ratio, Sortino Ratio, Omega Ratio, Percent Profitable, Profit Factor, Total Trades, and Net Profit for the indicator's signals.
Buy-and-Hold Comparison: Displays equivalent metrics for a simple buy-and-hold approach on the same asset and timeframe for benchmarking.
Visualization Tools: Color-coded plot of the scaled HMA, threshold lines, optional equity curve, bar coloring, and customizable tables for metrics and allocation status.
Alert Conditions: Built-in alerts for bullish (crossover to long) and bearish (crossunder to short) signals.
How It Works
The indicator starts by computing a standard HMA on the selected source. It then applies robust scaling over a lookback period by subtracting the median HMA and dividing by the IQR (difference between the 75th and 25th percentiles), resulting in a normalized value that highlights deviations in a outlier-resistant manner. Signals are derived simply: values exceeding the upper threshold suggest upward momentum (long), while those below the lower threshold indicate downward momentum (short). The script simulates a basic equity curve by applying these signals to daily returns, holding long/short only when enabled, otherwise defaulting to cash (0% return). Metrics are computed on this equity curve using standard formulas—e.g., Sharpe as average return over standard deviation of returns (annualized), Sortino focusing on downside deviation, and Omega as the ratio of positive to negative returns. All calculations begin from the user-specified start date to ensure relevance to the tested period(Remember past performance doesn’t guarantee future results). This logic emphasizes robustness for real-world application.
For Who It Is Best/Recommended Use Cases
This indicator is best suited for traders focused on trend-following strategies in markets prone to volatility or outliers. Recommended use cases include:
Trend Identification: As a filter for entering/exiting positions.
Strategy Evaluation: Quickly assessing signal quality through integrated metrics without complex backtesting setups(Remember past performance doesn’t guarantee future results).
Customization: Adjusting for bullish biases by disabling shorts, or vice versa, in one-sided markets.
Settings and Default Settings
Start Date: Timestamp for when calculations begin (default: 1 Jan 2018).
Source: Price series for HMA calculation (default: close).
HMA Length: Period for the Hull Moving Average (default: 25).
Robust Scaling Length: Lookback for robust scaling calculations (default: 40).
Upper Threshold: Level above which long signals trigger (default: 0.6).
Lower Threshold: Level below which short signals trigger (default: -0.2).
Allow Long Trades: Enables long positions; if disabled, defaults to cash (default: true).
Allow Shorts: Enables short positions; if disabled, defaults to cash (default: false).
Show Indicator Metrics Table: Displays table with strategy metrics (default: true).
Show Buy&Hold Table: Displays table with asset benchmarks (default: true).
Plot Equity Curve: Shows the simulated equity line (default: false).
Defaults are tuned for general use.
Conclusion
The Robust Scaled HMA offers a fresh take on trend detection by prioritizing robustness through IQR scaling, making it a valuable addition for traders aiming to navigate noisy markets with metrics-backed insights(Remember past performance doesn’t guarantee future results).
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark.
Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets.
To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics:
CAGR = Compounded annual growth rate of returns.
Volatility = Annualized standard deviation of returns.
Sharpe = CAGR per unit of annualized standard deviation.
Sortino = CAGR per unit of annualized downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Sensitivity of returns relative to benchmark returns.
Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark.
Upside = Ratio of average return to benchmark return on up days.
Downside = Ratio of average return to benchmark return on down days.
Tracking = Annualized standard deviation of returns versus benchmark.
Turnover = Average sum of absolute changes in weights per year.
Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures.
In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
Dual ATR with OffsetGives you a cross when ATR moves unusually, perhaps like would happen at the beginning of a trade.
MA Oscillator Map [ChartPrime]⯁ OVERVIEW
The MA Oscillator Map transforms moving average deviations into an oscillator framework that highlights overextended price conditions. By normalizing the difference between price and a chosen moving average, the tool maps oscillations between -100 and +100 , with gradient coloring to emphasize bullish and bearish momentum. When the oscillator cools from extreme levels (-100/100), the indicator marks potential reversal points and extends short-term levels from those extremes. A compact side table and dynamic bar coloring make momentum context visible at a glance.
⯁ KEY FEATURES
Oscillator Mapping (±100 Scale):
Price deviation from the selected MA is normalized into a percentage scale, allowing consistent overbought/oversold readings across assets and timeframes.
// MA
MA = ma(close, maLengthInput, maTypeInput)
diff = src - MA
maxVal = ta.highest(math.abs(diff), 50)
osc = diff / maxVal * 100
Customizable MA Types:
Choose SMA, EMA, SMMA, WMA, or VWMA to fine-tune the smoothing method that powers the oscillator.
Extreme Signal Diamonds:
When the oscillator retreats from +100 or -100, the script plots diamonds to flag potential exhaustion and reversal zones.
Dynamic Levels from Extremes:
Upper and lower dotted lines extend from recent overextension points, projecting temporary barriers until broken by price.
Gradient Bar Coloring:
Candles and oscillator values adopt a bullish-to-bearish gradient, making shifts in momentum instantly visible on the chart.
Compact Momentum Map:
A table at the chart’s edge plots the oscillator position with a gradient scale and live percentage label for precise momentum tracking.
⯁ USAGE
Watch for diamonds after the oscillator exits ±100 — these mark potential exhaustion zones.
Use extended dotted levels as short-term reference lines; if broken, trend continuation is favored.
Combine gradient bar coloring with oscillator shifts for confirmation of momentum reversals.
Experiment with different MA types to adapt sensitivity for trending vs. ranging markets.
Use the side momentum table as a quick-read gauge of trend strength in percent terms.
⯁ CONCLUSION
The MA Oscillator Map reframes moving average deviations into a visual momentum tracker with extremes, reversal signals, and dynamic levels. By blending oscillator math with intuitive visuals like gradient candles, diamonds, and a live gauge, it helps traders spot overextension, exhaustion, and momentum shifts across any market.
SuperTrend MAAfter building SuperBands, I kept thinking about what happens at the midpoint between those two volatility-adaptive envelopes. The upper and lower bands are both trailing price based on ATR and EMA smoothing, but they're operating independently in opposite directions. Taking their average seemed like it might produce an interesting centerline that adapts to volatility in a way that regular moving averages don't. Turns out it does, and that's what this indicator is.
The core concept is straightforward. Instead of plotting the upper and lower SuperBands separately, this calculates both of them internally, averages their values, and then applies an additional smoothing pass with EMA to create a single centerline. That centerline sits roughly in the middle of where the bands would be, but because it's derived from ATR-offset trailing stops rather than direct price smoothing, it behaves differently than a standard moving average of the same length. During trending periods, the centerline tracks closer to price because one of the underlying bands is actively trailing while the other is dormant. During consolidation, both bands compress toward price and the centerline tends to oscillate more with shorter-term movements.
What's interesting is that this acts like a supertrend all by itself with directional behavior baked in. When one of the underlying supertrend waves dominates, meaning price is strongly trending in one direction and only one band is active, you get what feels like a "true" supertrend, whatever that means exactly. The centerline locks into trend-following mode and the color gradient reflects that commitment. You get bright bullish colors during sustained uptrends when the upper band is doing all the work, and strong bearish colors during downtrends when the lower band dominates. But when both bands are active and fighting for control, which happens during consolidation or choppy conditions, the centerline settles into more neutral tones that clearly signal you're in a ranging environment. The colors really do emphasize this behavior and make it visually obvious which regime you're in.
The smoothing parameter controls how aggressively the underlying SuperBand trails adapt to price, which indirectly affects how responsive the centerline is. Lower values make the bands tighter and more reactive, so the centerline follows price action more closely. Higher values create wider bands that only respond to sustained moves, which produces a smoother centerline that filters out more noise. The center smoothing parameter applies a second EMA pass specifically to the averaged midpoint, giving you independent control over how much additional lag you want on the final output versus the raw band average.
What makes this different from just slapping an EMA on price is that the underlying bands are already volatility-aware through their ATR calculations. When volatility spikes, the bands widen and the centerline adjusts its position relative to price based on where those bands settle. A traditional moving average would just smooth over the volatility spike without adjusting its distance from price. This approach incorporates volatility information into the centerline's positioning, which can help it stay relevant during regime changes where fixed-period moving averages tend to lag badly or whipsaw.
The color gradient adds a momentum overlay using the same angle-based calculation from SuperBands. The centerline's rate of change gets normalized by an RMS estimate of its historical movement range, converted to an angle through arctangent scaling, and then mapped to a color gradient. When the centerline is rising, it gradients from neutral toward your chosen bullish color, with brightness increasing as the rate of ascent steepens. When falling, it shifts toward the bearish color with intensity tied to the descent rate. This gives you an immediate visual sense of whether the centerline is accelerating, decelerating, or moving at a stable pace.
Configuration is simpler than SuperBands since you're only dealing with a single output line instead of separate bull and bear envelopes. The length parameter controls the underlying band behavior. ATR period and multiplier determine how much space the bands allocate around price before they trail. Center smoothing adds the extra EMA pass on the averaged midpoint. You can tune these independently to get different characteristics. A tight ATR multiplier with heavy center smoothing creates a smooth line that stays close to price. A wide multiplier with light center smoothing produces a line that swings more freely and adapts faster to directional changes.
From a practical standpoint, this works well as a trend filter or dynamic support and resistance reference. Price above the centerline with bullish coloring suggests a favorable environment for long positions. Price below with bearish coloring indicates the opposite. Crossovers can signal trend changes, though like any moving average system, you'll get whipsaws in choppy conditions. The advantage over traditional MAs is that the volatility adaptation tends to reduce false signals during transitional periods where volatility is expanding but direction hasn't fully committed.
The implementation reuses the entire SuperBands logic, which means all the smoothing and state management for the trailing stops is identical. The only addition is averaging the two band outputs and applying the final EMA pass. The color calculation follows the same RMS-normalized angle approach but applies it to the centerline's delta rather than the individual band deltas. This keeps the coloring consistent with how SuperBands handles momentum visualization while adapting it to a single line instead of dual envelopes.
What this really highlights is that you can derive moving averages from mechanisms other than direct price smoothing. By building the centerline from volatility-adjusted trailing stops, you get adaptive behavior that responds to both price movement and volatility regime without needing separate inputs or complex multi-stage calculations. Whether that adaptation provides a meaningful edge depends on your strategy and market, but it's a fundamentally different approach than the typical fixed-period or adaptive MAs that adjust length based on volatility or momentum indicators.
Phoenix Smart ZoneThe Golden Trend Cloud Indicator is a professional trend-identification tool that combines Ichimoku Cloud with a 20-period Moving Average (MA20) to clearly define the market’s dominant direction.
It visually highlights bullish and bearish momentum using dynamic support and resistance zones derived from the Kumo cloud structure.
Teckmann Ribbon ScalperA scalping indicator is a technical tool designed to provide quick, high-probability trade signals in short timeframes, typically 1–5 minutes. It identifies immediate market opportunities by detecting rapid price movements, trend direction, and potential reversals. Common features include moving average crossovers, momentum oscillators, and price action patterns, often enhanced with visual cues like arrows or alerts for instant buy or sell entries. The goal is to maximize small, frequent profits while minimizing exposure to market noise.Follow the signal at the close of 2nd or 3rd candle after the ribbon changes.
ADX MA Filter for Choppy MarketsA clear way to see expanding markets and identify contracting markets or chop
N Order EMAThe exponential moving average is one of the most fundamental tools in technical analysis, but its implementation is almost always locked to a single mathematical approach. I've always wanted to extend the EMA into an n-order filter, and after some time working through the digital signal processing mathematics, I finally managed to do it. This indicator takes the familiar EMA concept and opens it up to four different discretization methods, each representing a valid way to transform a continuous-time exponential smoother into a discrete-time recursive filter. On top of that, it includes adjustable filter order, which fundamentally changes the frequency response characteristics in ways that simply changing the period length cannot achieve.
The four discretization styles are impulse-matched, all-pole, matched z-transform, and bilinear (Tustin). The all-pole version is exactly like stacking multiple EMAs together but implemented in a single function with proper coefficient calculation. It uses a canonical form where you get one gain coefficient and the rest are zeros, with the feedback coefficients derived from the binomial expansion of the pole polynomial. The other three methods are attempts at making generalizations of the EMA in different ways. Impulse-matched creates the filter by matching the discrete-time impulse response to what the continuous EMA would produce. Matched z-transform directly maps the continuous poles to the z-domain using the exponential relationship. Bilinear uses the Tustin transformation with frequency prewarping to ensure the cutoff frequency is preserved despite the inherent warping of the mapping.
Honestly, they're all mostly the same in practice, which is exactly what you'd expect since they're all valid discretizations of the same underlying filter. The differences show up in subtle ways during volatile market conditions or in the exact phase characteristics, but for most trading applications the outputs will track each other closely. That said, the bilinear version works particularly well at low periods like 2, where other methods can sometimes produce numerical artifacts. I personally like the z-match for its clean frequency-domain properties, but the real point here is demonstrating that you can tackle the same problem from multiple mathematical angles and end up with slightly different but equally valid implementations.
The order parameter is where things get interesting. A first-order EMA is the standard single-pole recursive filter everyone knows. When you move to second-order, you're essentially cascading two filter sections, which steepens the roll-off in the frequency domain and changes how the filter responds to sudden price movements. Higher orders continue this progression. The all-pole style makes this particularly clear since it's literally stacking EMA operations, but all four discretization methods support arbitrary order. This gives you control over the aggressiveness of the smoothing that goes beyond just adjusting the period length.
On top of the core EMA calculation, I've included all the standard variants that people use for reducing lag. DEMA applies the EMA twice and combines the results to get faster response. TEMA takes it further with three applications. HEMA uses a Hull-style calculation with fractional periods, applying the EMA to the difference between a half-period EMA and a full-period EMA, then smoothing that result with the square root of the period. These are all implemented using whichever discretization method you select, so you're not mixing different mathematical approaches. Everything stays consistent within the chosen framework.
The practical upside of this indicator is flexibility for people building trading systems. If you need a moving average with specific frequency response characteristics, you can tune the order parameter instead of hunting for the right period length. If you want to test whether different discretization methods affect your strategy's performance, you can swap between them without changing any other code. For most users, the impulse-matched style at order 1 will behave almost identically to a standard EMA, which gives you a familiar baseline to work from. From there you can experiment with higher orders or different styles to see if they provide any edge in your particular market or timeframe.
What this really highlights is that even something as seemingly simple as an exponential moving average involves mathematical choices that usually stay hidden. The standard EMA formula you see in textbooks is already a discretized version of a continuous exponential decay, and there are multiple valid ways to perform that discretization. By exposing these options, this indicator lets you explore a parameter space that most traders never even know exists. Whether that exploration leads to better trading results is an empirical question that depends on your strategy and market, but at minimum it's a useful reminder that the tools we take for granted are built on arbitrary but reasonable mathematical decisions.
MACD AI Flux Pro Dashboard V. 2Acknowledgment
This indicator is built upon the MACD-V (Volatility-Normalized MACD) methodology originally created by Alex Spiroglou, CMT, whose research (2015–2022) introduced the principle of normalizing MACD momentum by volatility (MACD/ATR). Full acknowledgment and credit are hereby given to Mr. Spiroglou as the original author of the MACD-V concept and framework.
Indicator Overview — MACD-V Flux Pro Dashboard V.2
The MACD-V Flux Pro Dashboard advances Spiroglou’s volatility-normalized foundation into a comprehensive multi-system architecture that unifies momentum, trend, volatility, and compression analytics in one visual framework. It is engineered for precision decision-making in both intraday and swing-trading environments.
Key Dashboard Features:
Dynamic Probability Engine: Calculates real-time long and short probabilities by weighting momentum, slope, compression, and volume pressure components into a composite score.
Multi-Timeframe Confirmation (HTF Tiles): Displays live directional agreement across fast, mid, and slow timeframes for confidence filtering and signal validation.
Regime Detection System: Automatically classifies the market as Trend Up, Trend Down, Compression, or Transition, applying background color cues for instant context.
Risk and News Filters: Integrates ATR-based risk gating and customizable “mute windows” to block trade signals during high-volatility or scheduled news events.
VWAP and Adaptive Bands: Plots VWAP with configurable ATR or standard-deviation bands to highlight over-extension and pullback zones.
Trend-Day and Opening-Range Logic: Monitors RTH (Regular Trading Hours) price behavior to identify potential trend-day conditions.
Smart Entry Arrows: Generates visual long/short signals only when multiple subsystems confirm direction, slope strength, and proximity to VWAP within defined thresholds.
On-Chart Dashboard Panel: Presents live metrics including probability bias, regime state, ATR level, risk status, and news filters with adaptive color-coding and optional emoji cues for intuitive interpretation.
Chart Display Summary:
All elements are presented directly on the main chart, combining price structure, VWAP bands, EMAs, and regime background shading with the real-time dashboard panel. The design eliminates the need for a secondary pane, offering a consolidated and context-rich view of market dynamics
Elite_Pro SignalsTrial version to get the signals. used various indicators including candle pattern. Works on 5 min candle but checks multi time frames to see if it is inline with 15 min and 1 hr. Best works on Gold and Indices.
Triple EMA strategy by kingtraderthis strategy is purely based on moving everages, ema5, ema50 and ema200, avoid ranging market. in 1 mint your tp should 15-20pips, in 3mint tp should be 25pips, in 5mint tp should not above 50pips, in 15mints make tp 60 to 80 pips, in 30 mints tp 150 and 1h and h4 ur tp above 200pips, when target achieves have partial closing and keep ur trade breakeven. this indicator is for educational purpose only any loss by using this indicator, the author will not be responsible.
