Forex Overwiew True Strenght IndexNew oscillator for all currencies—I’m about to add NOK, SEK, and other lower-volume currencies that are interesting due to their lack of correlation with the major ones.
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[blackcat] L3 Trend BoxOVERVIEW
The L3 Trend Box indicator is a sophisticated technical analysis tool designed to assist traders in identifying trends and pinpointing potential entry and exit points within the market. By leveraging multiple moving averages and price level analyses, this indicator provides a detailed view of market dynamics. It plots several key lines and labels directly onto the chart, offering clear visual signals for both bullish and bearish scenarios. Its adaptability through customizable parameters makes it suitable for various trading strategies and market conditions 📊✅.
FEATURES
Comprehensive Parameter Customization: Tailor the indicator to match specific trading preferences:
High Length: Defines the period over which the highest prices are considered.
Low Length: Specifies the period for evaluating the lowest prices.
Upper Box Length: Smoothes out the upper boundary of the trend box using a specified period.
Lower Box Length: Smoothes out the lower boundary of the trend box similarly.
Trend Line Length: Determines the period for calculating the overall trend line.
Fast EMA Length: Sets the period for the fast-moving exponential moving average (EMA), crucial for capturing short-term movements.
MA15 EMA Length: Configures the period for the medium-term moving average (MA15 EMA) to provide a balanced perspective.
Short Spirit Length: Influences how quickly the indicator responds to recent price changes.
Golden EMA Length: Fine-tunes the long-term EMA for stability and reliability.
Buy Price Length: Establishes the lookback period for determining optimal buy prices.
Var1 Length & Var2 Length: Adjusts periods for variance calculations, enhancing the accuracy of trend detection.
Detailed Chart Plots:
Upper Box Top: A fuchsia-colored line representing the smoothed highest prices, marking resistance levels.
Lower Box Bottom: A green-colored line showing the smoothed lowest prices, highlighting support zones.
MA15 Up/Down: Dynamic red and green lines illustrating the directionality of the 15-period EMA, helping gauge momentum shifts.
Conditional Plots: Multiple lines based on intricate price actions and computed values, such as closing below the lower box while also closing at or above/below the opening price, ensuring nuanced insights into market behavior.
Buy/Sell Labels: Clearly marked 'Buy' and 'Sell' labels positioned strategically on the chart, facilitating quick decision-making without missing critical signals 🎯.
Alert System: Automatically generates alerts based on predefined buy and sell conditions, enabling timely responses to market changes 🛎️.
HOW TO USE
Adding the Indicator: Start by adding the L3 Trend Box to your TradingView chart via the indicators menu.
Parameter Configuration: Adjust each parameter according to your trading style and market volatility. For instance, increasing the High Length can make the indicator less sensitive to minor fluctuations but more responsive to significant trends.
Monitoring Signals: Keep an eye on the plotted lines and labels. Pay special attention to the crossover events between the fast EMA and the lower box bottom, as these often signify strong buy signals.
Setting Alerts: Configure alerts based on the buy/sell conditions provided by the indicator. This ensures you never miss an opportunity due to inattention.
Combining Strategies: While powerful on its own, combining this indicator with others like RSI or Bollinger Bands can enhance its predictive power and reduce false positives.
LIMITATIONS
Market Volatility: In extremely volatile or sideways-trending markets, the indicator might produce false signals. Always verify with additional confirmations.
Asset-Specific Performance: Different assets and timeframes will yield varying results; thorough backtesting across diverse instruments is recommended.
Over-Reliance Risk: Avoid relying solely on this indicator. Integrate it into a broader analytical framework that includes fundamental analysis and other technical indicators.
NOTES
Data Sufficiency: Ensure ample historical data is available for precise computations. Lack of data can skew results and lead to inaccurate signals.
Demo Testing: Before deploying the indicator in real trades, rigorously test it on demo accounts under varied market conditions to understand its strengths and weaknesses.
Customization Flexibility: Feel free to tweak the parameters continuously until they align perfectly with your unique trading approach and risk tolerance.
Volume Aggression Monitor📌 Volume Aggression Monitor — Overview
This indicator helps identify buying and selling pressure (aggression) in real-time by analyzing how market participants are executing trades. It is composed of three main components:
🔍 What Does It Show?
🧭 1. The Thermometer (Above Candles)
🟢 Green Arrow (▲) → Buy Aggression: Buyers are lifting the ask.
🔴 Red Arrow (▼) → Sell Aggression: Sellers are hitting the bid.
⚪ Gray Square (■) → Neutral: No significant price movement or aggression.
💡 Neutral in this context means:
The price barely moved during the candle (open-close % change < direction_threshold, default 0.05%).
No clear buyer or seller dominance. It often appears during low volatility, equilibrium, or market indecision periods. This prevents noise and false directional readings due to random micro-movements.
📊 2. Percentage Panel
A table displaying recent trades or candle data (from a lower timeframe). Colored arrows indicate the direction of aggression (buy/sell). Shows volume, delta, and aggression %.
✅ Use it to:
Track clusters of buy/sell aggression. Spot momentum builds.
⏱️ 3. Cumulative Times & Sales Bar
A horizontal progress bar representing cumulative aggression.
Positive = Buy Aggression dominates.
Negative = Sell Aggression dominates.
📉 Even in sideways price movement, this bar shows who is winning the fight under the surface.
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🧠 How to Use It:
🔹 Confirm Trades
Use the thermometer and aggression signals to confirm your strategy entries (e.g., breakouts, pullbacks, support/resistance).
🔹 Detect Dominance
Observe who is in control: buyers or sellers? Are they pressing or hesitating?
🔹 Filter Market Noise
The neutral state avoids misinterpreting small, meaningless movements as strong signals.
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Component | Meaning
🌡️ Thermometer (▲ ▼ ■) | Who’s in control in each candle
📊 Percentage Panel | Trade details: direction, delta, aggression
📈 Cumulative T&S Bar | Overall aggression bias over time
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Maancyclus Volatiliteitsindicator (2025)This Moon Cycle Volatility Indicator for TradingView is designed to help traders track and analyze market volatility around specific lunar phases, namely the Full Moon and New Moon. The indicator marks the dates of these moon phases on the chart and measures volatility using the Average True Range (ATR) indicator, which gauges market price fluctuations.
Key Features:
Moon Phase Markers: The indicator marks the Full Moon and New Moon on the chart using labels. Blue labels are placed below bars for Full Moons, while red labels are placed above bars for New Moons. These markers are based on a manually curated list of moon phase dates for the year 2025.
Volatility Calculation: The indicator calculates market volatility using the ATR (14), which provides a sense of market movement and potential risk. Volatility is plotted as histograms, with blue histograms representing volatility around Full Moons and red histograms around New Moons.
Comparative Analysis: By comparing the volatility around these moon phases to the average volatility, traders can spot potential patterns or heightened market movements. This can inform trading strategies, such as anticipating increased market activity around specific lunar events.
In essence, this tool helps traders identify potential high-volatility periods tied to lunar cycles, which could impact market sentiment and price action.
M2 Global Liquidity Index X Days DeltaCapture the delta between different global M2 time lags, and utilize width to increase or decrease the span between the two.
Anpassbarer SMA mit Prozent-VersatzPlots the SMA with an adjustable number of time units and a percentage that draws another line below the SMA as slippage.
EMA & WMA 20 - 50 - 100 - 200EN/ It shows 20 / 50 / 100 / 200 Daily EMA's,
Also it shows 50 / 100 / 200 Weekly EMA's but no 20 Weekly EMA because it is equal to 100 Daily EMA already. Same rules apply to WMA.
1H 200 EMA with Custom Bounce Signal1 Hour 200 EMA with signals
No complication, as simple as possible
2 EMA Crossover Strategy (scalping)9 & 21 EMA and cross-over
Best for 5min Scalping
in the most Easy and Simple way Possible
Sweep Reversal 5M PRO – by [TuNombre]🔹 **Sweep Reversal 5M PRO – by ** 🔹
This indicator is built to detect high-probability *Institutional Sweeps* using Swing Failure Patterns (SFP), fully filtered with Smart Money Concepts (SMC) logic and volume confirmation.
✅ Identifies key liquidity grabs
✅ Confirms with strong impulse candle + increasing volume
✅ Filters out false signals (doji, weak zones, low volume)
✅ Optimized for the 5-minute timeframe (5M)
✅ Compatible with push alerts to your mobile
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**How to use it:**
1. Wait for a “BUY” or “SELL” signal on the chart
2. Only enter if the next candle confirms with momentum
3. Place a tight stop just beyond the sweep
4. Recommended Take Profit: 1:1 minimum / 2:1 ideal
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💡 Inspired by institutional trading (SMC, ICT-style setups)
🔒 Developed by – private or team-based use
📲 Activate alerts to get real-time trade opportunities on your phone
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⚠️ This script does not repaint.
Built for serious traders who want precision, not noise.
MTF Stoch RSI Confluence + Combined AlertMTF STOCH RSI CONFLUENCE INDICATOR 1m/5m/15m ( Scalping Indicator added on SRSI 1H)
IF all three Stoch are overbought(above 80) the indicator creates a red vertical line. If all Stoch are oversold(below 20) the indicator creates a green vertical line.
RULES!!!!
NEVER TRADE AGAINST THE TREND!!! This is super important!!!!
If 1H SRSI is above 80 with MTF overbouht and ZC is red (Downtrend) then we open a MR Short- (Prefered at PA Spikes) The same for opposites MR Longs.
If 1H SRSI is above 80 with the MTF oversold and ZC green then we can open a Long position. Prefered Momo Long. The same applies for opposite, hence momo short.
Polygot Moving AveragesDescription
This is essentially a source merger of Bollinger Bands by Trading View and Simple Moving Averages by stoxxinbox. My additions and subtractions are minimal. There is the BB MA, which I default at 5d, and the other 4 averages are the standard 21, 50, 100, 200, day moving averages. I default the averaging method to WMA (Weighted Moving Average). The method of averaging can be changed as also can the lengths of the inputs to match user preferences. This is what I wanted for an indicator and didn't find.
Usage
The same as you would use any other BB or MA indicator. The benefit of this one is that it has 4 MAs, one MA with the Bollinger Bands attached, and the colours adjusted to be easy on the eyes when using high contrast themes, to be discernible yet sit quietly in the background with lines and candle sticks everywhere shouting for attention. I use it as a base first indicator which I can hide easily (imagine hiding five MA indicators individually constantly) when the more serious indicators come into play.
Vacuum Candles [XrayAlgo]The Vacuum Candles indicator helps traders identify inefficient price movements—where the price moves significantly but lacks sufficient volume to support it. These inefficiencies may signal weak trends, potential reversals, or false breakouts/breakdowns.
Inefficient candles are visually marked with a darker / black body to indicate when the price movement is disproportionate to the volume.
1. Spotting Potential Reversals
When the indicator marks an inefficient candle, it signals that the price movement may be unsustainable.
In an uptrend: A inefficient bullish candle suggests that the uptrend is losing momentum, potentially leading to a downward reversal.
In a downtrend: A inefficient bearish candle signals that the downtrend may be weakening, with a potential bullish reversal.
2. Identifying Breakout and Breakdown Failures
This indicator is useful for recognizing false breakouts or false breakdowns.
If price breaks resistance but the candle is inefficient, the breakout may be weak and could fail quickly.
If price breaks support with an inefficient bearish candle, the breakdown could be a false signal, with price reverting back above support.
3. Recognizing Weak Trends
Inefficient candles help you spot when a trend is losing strength and could soon reverse or consolidate.
In an uptrend: A series of dark body bullish candles suggests that the uptrend may be weakening, signaling a potential correction or trend reversal.
In a downtrend: A series of dark body bearish candles suggests that the selling pressure is weakening, indicating a potential bullish reversal.
4. Fine-Tuning Entries and Exits
Inefficient candles offer an opportunity to fine-tune your entries and exits based on weak price moves.
Entering a trade: An inefficient candle near key support or resistance can indicate a reversal, making it a good entry point for a counter-trend position.
Exiting a trade: If you're already in a trend, and an inefficient candle appears, it suggests the trend is losing strength, indicating it may be a good time to exit before a potential reversal.
5. Fine-Tuning with Inputs
The Vacuum Candles indicator includes two key inputs:
Length: The number of candles used to calculate the average price movement and volume. A longer length (e.g., 20-30) smooths out the inefficiencies, while a shorter length (e.g., 10-15) makes the indicator more sensitive to recent price moves.
Multiplier: Controls the threshold for what is considered an inefficient candle:
A higher Multiplier (e.g., 1.5–3) filters out smaller inefficiencies and focuses on large discrepancies.
A lower Multiplier (e.g., 0.1–0.9) captures even smaller inefficiencies in highly efficient markets.
Midpoint of Last 3 CandlesThis indicator highlights the market structure by plotting the midpoints of the current and previous two candles. It draws a horizontal line at the average of the high and low for each of these candles, giving a visual cue of the short-term balance point in price action. These midpoints can act as dynamic support and resistance levels, helping traders assess areas of potential reaction or continuation.
Each line is color-coded for clarity: green represents the current candle, orange marks the previous candle, and yellow indicates the one before that. All lines extend into the future on the chart, allowing you to see how price interacts with these levels as new candles form. This simple yet effective tool can be useful in various strategies, especially those focused on price action, scalping, or intraday analysis.
Uptrick: Dynamic Z-Score DeviationOverview
Uptrick: Dynamic Z‑Score Deviation is a trading indicator built in Pine Script that combines statistical filters and adaptive smoothing to highlight potential reversal points in price action. It combines a hybrid moving average, dual Z‑Score analysis on both price and RSI, and visual enhancements like slope‑based coloring, ATR‑based shadow bands, and dynamically scaled reversal signals.
Introduction
Statistical indicators like Z‑Scores measure how far a value deviates from its average relative to the typical variation (standard deviation). Standard deviation quantifies how dispersed a set of values is around its mean. A Z‑Score of +2 indicates a value two standard deviations above the mean, while -2 is two below. Traders use Z‑Scores to spot unusually high or low readings that may signal overbought or oversold conditions.
Moving averages smooth out price data to reveal trends. The Arnaud Legoux Moving Average (ALMA) reduces lag and noise through weighted averaging. A Zero‑Lag EMA (approximated here using a time‑shifted EMA) seeks to further minimize delay in following price. The RSI (Relative Strength Index) is a momentum oscillator that measures recent gains against losses over a set period.
ATR (Average True Range) gauges market volatility by averaging the range between high and low over a lookback period. Shadow bands built using ATR give a visual mood of volatility around a central trend line. Together, these tools inform a dynamic but statistically grounded view of market extremes.
Purpose
The main goal of this indicator is to help traders spot short‑term reversal opportunities on lower timeframes. By requiring both price and momentum (RSI) to exhibit statistically significant deviations from their norms, it filters out weak setups and focuses on higher‑probability mean‑reversion zones. Reversal signals appear when price deviates far enough from its hybrid moving average and RSI deviates similarly in the same direction. This makes it suitable for discretionary traders seeking clean entry cues in volatile environments.
Originality and Uniqueness
Uptrick: Dynamic Z‑Score Deviation distinguishes itself from standard reversal or mean‑reversion tools by combining several elements into a single framework:
A composite moving average (ALMA + Zero‑Lag EMA) for a smooth yet responsive baseline
Dual Z‑Score filters on price and RSI rather than relying on a single measure
Adaptive visual elements, including slope‑aware coloring, multi‑layer ATR shadows, and signal sizing based on combined Z‑Score magnitude
Most indicators focus on one aspect—price envelopes or RSI thresholds—whereas Uptrick: Dynamic Z‑Score Deviation requires both layers to align before signaling. Its visual design aids quick interpretation without overwhelming the chart.
Why these indicators were merged
Every component in Uptrick: Dynamic Z‑Score Deviation has a purpose:
• ALMA: provides a smooth moving average with reduced lag and fewer false crossovers than a simple SMA or EMA.
• Zero‑Lag EMA (ZLMA approximation): further reduces the delay relative to price by applying a time shift to EMA inputs. This keeps the composite MA closer to current price action.
• RSI and its EMA filter: RSI measures momentum. Applying an EMA filter on RSI smooths out false spikes and confirms genuine overbought or oversold momentum.
• Dual Z‑Scores: computing Z‑Scores on both the distance between price and the composite MA, and on smoothed RSI, ensures that signals only fire when both price and momentum are unusually stretched.
• ATR bands: using ATR‑based shadow layers visualizes volatility around the MA, guiding traders on potential support and resistance zones.
At the end, these pieces merge into a single indicator that detects statistically significant mean reversions while staying adaptive to real‑time volatility and momentum.
Calculations
1. Compute ALMA over the chosen MA length, offset, and sigma.
2. Approximate ZLMA by applying EMA to twice the price minus the price shifted by the MA length.
3. Calculate the composite moving average as the average of ALMA and ZLMA.
4. Compute raw RSI and smooth it with ALMA. Apply an EMA filter to raw RSI to reduce noise.
5. For both price and smoothed RSI, calculate the mean and standard deviation over the Z‑Score lookback period.
6. Compute Z‑Scores:
• z_price = (current price − composite MA mean) / standard deviation of price deviations
• z_rsi = (smoothed RSI − mean RSI) / standard deviation of RSI
7. Determine reversal conditions: both Z‑Scores exceed their thresholds in the same direction, RSI EMA is in oversold/overbought zones (below 40 or above 60), and price movement confirms directionality.
8. Compute signal strength as the sum of the absolute Z‑Scores, then classify into weak, medium, or strong.
9. Calculate ATR over the chosen period and multiply by layer multipliers to form shadow widths.
10.Derive slope over the chosen slope length and color the MA line and bars based on direction, optionally smoothing color transitions via EMA on RGB channels.
How this indicator actually works
1. The script begins by smoothing price data with ALMA and approximating a zero‑lag EMA, then averaging them for the main MA.
2. RSI is calculated, then smoothed and filtered.
3. Using a rolling window, the script computes statistical measures for both price deviations and RSI.
4. Z‑Scores tell how far current values lie from their recent norms.
5. When both Z‑Scores cross configured thresholds and momentum conditions align, reversal signals are flagged.
6. Signals are drawn with size and color reflecting strength.
7. The MA is plotted with dynamic coloring; ATR shadows are layered beneath to show volatility envelopes.
8. Bars can be colored to match MA slope, reinforcing trend context.
9. Alert conditions allow automated notifications when signals occur.
Inputs
Main Length: Main MA Length. Sets the period for ALMA and ZLMA.
RSI Length: RSI Length. Determines the lookback for momentum calculations.
Z-Score Lookback: Z‑Score Lookback. Window for mean and standard deviation computations.
Price Z-Score Threshold: Price Z‑Score Threshold. Minimum deviation required for price.
RSI Z-Score threshold: RSI Z‑Score Threshold. Minimum deviation required for momentum.
RSI EMA Filter Length: RSI EMA Filter Length. Smooths raw RSI readings.
ALMA Offset: Controls ALMA’s focal point in the window.
ALMA Sigma: Adjusts ALMA’s smoothing strength.
Show Reversal Signals : Toggle to display reversal signal markers.
Slope Sensitivity: Length for slope calculation. Higher values smooth slope changes.
Use Bar Coloring: Enables coloring of price bars based on MA slope.
Show MA Shadow: Toggle for ATR‑based shadow bands.
Shadow Layer Count: Number of shadow layers (1–4).
Base Shadow ATR Multiplier: Multiplier for ATR when sizing the first band.
Smooth Color Transitions (boolean): Smooths RGB transitions for line and shadows, if enabled.
ATR Length for Shadow: ATR Period for computing volatility bands.
Use Dynamic Signal Size: Toggles dynamic scaling of reversal symbols.
Features
Moving average smoothing: a hybrid of ALMA and Zero‑Lag EMA that balances responsiveness and noise reduction.
Slope coloring: MA line and optionally price bars change color based on trend direction; color transitions can be smoothed for visual continuity.
ATR shadow layers: translucent bands around the MA show volatility envelopes; up to four concentric layers help gauge distance from normal price swings.
Dual Z‑Score filters: price and momentum must both deviate beyond thresholds to trigger signals, reducing false positives.
Dynamic signal sizing: reversal markers scale in size based on the combined Z‑Score magnitude, making stronger signals more prominent.
Adaptive visuals: optional smoothing of color channels creates gradient effects on lines and fills for a polished look.
Alert conditions: built‑in buy and sell alerts notify traders when reversal setups emerge.
Conclusion
Uptrick: Dynamic Z‑Score Deviation delivers a structured way to identify short‑term reversal opportunities by fusing statistical rigor with adaptive smoothing and clear visual cues. It guides traders through multiple confirmation layers—hybrid moving average, dual Z‑Score analysis, momentum filtering, and volatility envelopes—while keeping the chart clean and informative.
Disclaimer
This indicator is provided for informational and educational purposes only and does not constitute financial advice. Trading carries risk and may not be suitable for all participants. Past performance is not indicative of future results. Always do your own analysis and risk management before making trading decisions.
Bullish and Bearish Breakout Alert for Gold Futures PullbackBelow is a Pine Script (version 6) for TradingView that includes both bullish and bearish breakout conditions for my intraday trading strategy on micro gold futures (MGC). The strategy focuses on scalping two-legged pullbacks to the 20 EMA or key levels with breakout confirmation, tailored for the Apex Trader Funding $300K challenge. The script accounts for the Daily Sentiment Index (DSI) at 87 (overbought, favoring pullbacks). It generates alerts for placing stop-limit orders for 175 MGC contracts, ensuring compliance with Apex’s rules ($7,500 trailing threshold, $20,000 profit target, 4:59 PM ET close).
Script Requirements
Version: Pine Script v6 (latest for TradingView, April 2025).
Purpose:
Bullish: Alert when price breaks above a rejection candle’s high after a two-legged pullback to the 20 EMA in a bullish trend (price above 20 EMA, VWAP, higher highs/lows).
Bearish: Alert when price breaks below a rejection candle’s low after a two-legged pullback to the 20 EMA in a bearish trend (price below 20 EMA, VWAP, lower highs/lows).
Context: 5-minute MGC chart, U.S. session (8:30 AM–12:00 PM ET), avoiding overbought breakouts above $3,450 (DSI 87).
Output: Alerts for stop-limit orders (e.g., “Buy: Stop=$3,377, Limit=$3,377.10” or “Sell: Stop=$3,447, Limit=$3,446.90”), quantity 175 MGC.
Apex Compliance: 175-contract limit, stop-losses, one-directional news trading, close by 4:59 PM ET.
How to Use the Script in TradingView
1. Add Script:
Open TradingView (tradingview.com).
Go to “Pine Editor” (bottom panel).
Copy the script from the content.
Click “Add to Chart” to apply to your MGC 5-minute chart .
2. Configure Chart:
Symbol: MGC (Micro Gold Futures, CME, via Tradovate/Apex data feed).
Timeframe: 5-minute (entries), 15-minute (trend confirmation, manually check).
Indicators: Script plots 20 EMA and VWAP; add RSI (14) and volume manually if needed .
3. Set Alerts:
Click the “Alert” icon (bell).
Add two alerts:
Bullish Breakout: Condition = “Bullish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Bearish Breakout: Condition = “Bearish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Customize messages (default provided) and set notifications (e.g., TradingView app, SMS).
Example: Bullish alert at $3,377 prompts “Stop=$3,377, Limit=$3,377.10, Quantity=175 MGC” .
4. Execute Orders:
Bullish:
Alert triggers (e.g., stop $3,377, limit $3,377.10).
In TradingView’s “Order Panel,” select “Stop-Limit,” set:
Stop Price: $3,377.
Limit Price: $3,377.10.
Quantity: 175 MGC.
Direction: Buy.
Confirm via Tradovate.
Add bracket order (OCO):
Stop-loss: Sell 175 at $3,376.20 (8 ticks, $1,400 risk).
Take-profit: Sell 87 at $3,378 (1:1), 88 at $3,379 (2:1) .
Bearish:
Alert triggers (e.g., stop $3,447, limit $3,446.90).
Select “Stop-Limit,” set:
Stop Price: $3,447.
Limit Price: $3,446.90.
Quantity: 175 MGC.
Direction: Sell.
Confirm via Tradovate.
Add bracket order:
Stop-loss: Buy 175 at $3,447.80 (8 ticks, $1,400 risk).
Take-profit: Buy 87 at $3,446 (1:1), 88 at $3,445 (2:1) .
5. Monitor:
Green triangles (bullish) or red triangles (bearish) confirm signals.
Avoid bullish entries above $3,450 (DSI 87, overbought) or bearish entries below $3,296 (support) .
Close trades by 4:59 PM ET (set 4:50 PM alert) .
Stochastic Strategy Table with Trend (1m–4H) + Toggle📊 Multi-Timeframe Stochastic Strategy Table with Trend Detection
This script is designed for intraday and swing traders who want to monitor Stochastic momentum across multiple timeframes in real-time — all directly on the main chart.
🔎 What This Script Does
This script builds a compact, color-coded table that displays:
✅ %K and %D values of the Stochastic oscillator
✅ Cross direction (K > D or K < D)
✅ Overbought/Oversold zone conditions
✅ Short-term trend detection via %K movement
It covers ten timeframes:
1m, 2m,3m,5m, 15m, 30m, 1H, 2H, 3H, 4H
🟩 How to Use It
Trend colors in header:
🟢 Green = %K is rising (uptrend)
🔴 Red = %K is falling (downtrend)
⚪ Gray = flat or neutral
Cross Row:
Green background = Bullish (%K > %D)
Red background = Bearish (%K < %D)
Zone Row:
Green = Oversold (%K and %D below 20)
Red = Overbought (%K and %D above 80)
Gray = Neutral zone
Use Case:
Look for multiple timeframes aligning in trend
Enter trades on short timeframes (e.g. 5m) when HTFs confirm direction
Especially powerful when used with price action on 5m/15m candles
⚙️ Configurable Inputs
%K Length
%K Smoothing
%D Length
Table location
Table size
💡 Why This Script Is Unique
Shows true higher timeframe Stochastic values (not interpolated from current chart)
Works in real-time with consistent updates
Trend direction is visualized without needing extra space
Built for serious intraday traders who rely on clean data and signal alignment
🙏 Credits & Notes
This tool was created to solve a real problem: getting accurate HTF stochastic data in a clean, real-time, decision-friendly format.
I built it for my own use — and now I'm sharing it for luck, and for anyone else looking to trade more clearly and confidently.
Feel free to fork, customize, or build upon it.
Good luck, and trade safe! 🍀💹
ONE RING 8 MA Bands with RaysCycle analysis tool ...
MAs: Eight moving averages (MA1–MA8) with customizable lengths, types (RMA, WMA, EMA, SMA), and offsets
Bands: Upper/lower bands for each MA, calculated based on final_pctX (Percentage mode) or final_ptsX (Points mode), scaled by multiplier
Rays: Forward-projected lines for bands, with customizable start points, styles (Solid, Dashed, Dotted), and lengths (up to 500 bars)
Band Choices
Manual: Uses individual inputs for band offsets
Uniform: Sets all offsets to base_pct (e.g., 0.1%) or base_pts (e.g., 0.1 points)
Linear: Scales linearly (e.g., base_pct * 1, base_pct * 2, base_pct * 3 ..., base_pct * 8)
Exponential: Scales exponentially (e.g., base_pct * 1, base_pct * 2, base_pct * 4, base_pct * 8 ..., base_pct * 128)
ATR-Based: Offsets are derived from the Average True Range (ATR), scaled by a linear factor. Dynamic bands that adapt to market conditions, useful for breakout or mean-reversion strategies. (final_pct1 = base_pct * atr, final_pct2 = base_pct * atr * 2, ..., final_pct8 = base_pct * atr * 8)
Geometric: Offsets follow a geometric progression (e.g., base_pct * r^0, base_pct * r^1, base_pct * r^2, ..., where r is a ratio like 1.5) This is less aggressive than Exponential (which uses powers of 2) and provides a smoother progression.
Example: If base_pct = 0.1, r = 1.5, then final_pct1 = 0.1%, final_pct2 = 0.15%, final_pct3 = 0.225%, ..., final_pct8 ≈ 1.71%
Harmonic: Offsets are based on harmonic flavored ratios. final_pctX = base_pct * X / (9 - X), final_ptsX = base_pts * X / (9 - X) for X = 1 to 8 This creates a harmonic-like progression where offsets increase non-linearly, ensuring MA8 bands are wider than MA1 bands, and avoids duplicating the Linear choice above.
Ex. offsets for base_pct = 0.1: MA1: ±0.0125% (0.1 * 1/8), MA2: ±0.0286% (0.1 * 2/7), MA3: ±0.05% (0.1 * 3/6), MA4: ±0.08% (0.1 * 4/5), MA5: ±0.125% (0.1 * 5/4), MA6: ±0.2% (0.1 * 6/3), MA7: ±0.35% (0.1 * 7/2), MA8: ±0.8% (0.1 * 8/1)
Square Root: Offsets grow with the square root of the band index (e.g., base_pct * sqrt(1), base_pct * sqrt(2), ..., base_pct * sqrt(8)). This creates a gradual widening, less aggressive than Linear or Exponential. Set final_pct1 = base_pct * sqrt(1), final_pct2 = base_pct * sqrt(2), ..., final_pct8 = base_pct * sqrt(8).
Example: If base_pct = 0.1, then final_pct1 = 0.1%, final_pct2 ≈ 0.141%, final_pct3 ≈ 0.173%, ..., final_pct8 ≈ 0.283%.
Fibonacci: Uses Fibonacci ratios (e.g., base_pct * 1, base_pct * 1.618, base_pct * 2.618
Percentage vs. Points Toggle:
In Percentage mode, bands are calculated as ma * (1 ± (final_pct / 100) * multiplier)
In Points mode, bands are calculated as ma ± final_pts * multiplier, where final_pts is in price units.
Threshold Setting for Slope:
Threshold setting for determining when the slope would be significant enough to call it a change in direction. Can check efficiency by setting MA1 to color on slope temporarily
Arrow table: Shows slope direction of 8 MAs using an Up or Down triangle, or shows Flat condition if no triangle.
Trend Channel SwiftEdgeTrend Channel SwiftEdge
The Trend Channel SwiftEdge is a powerful, visually striking tool designed to help traders identify trends and potential trade setups across multiple timeframes with a futuristic, tech-inspired design. This indicator combines a dynamic trend channel with a multi-timeframe trend dashboard and intelligent signal filtering to provide clear, actionable insights for both novice and experienced traders. Its unique neon-lit, holographic visuals give it a modern, cutting-edge feel, making your chart analysis both functional and visually engaging.
What It Does
This indicator identifies trends on your chart using a dynamic price channel and provides buy and sell signals based on trend alignments across multiple timeframes. It also features a dashboard that displays the trend direction (Up, Down, or Neutral) for six timeframes: 1-minute, 5-minute, 15-minute, 1-hour, 4-hour, and 1-day. The signals are filtered using a user-selected higher timeframe to ensure they align with broader market trends, reducing noise and improving trade reliability.
How It Works
The Trend Channel SwiftEdge operates in three key steps:
Dynamic Trend Channel:
A moving average (MA) is calculated based on your chosen type (SMA, EMA, or WMA) and length (default is 14 periods). This MA forms the backbone of the trend channel.
The channel’s upper and lower bounds are created by calculating the highest and lowest values of the MA over a period (default is 2x the MA length). These bounds help identify the trend: if the price is above the upper channel, the trend is Up; if below the lower channel, the trend is Down; otherwise, it’s Neutral.
The MA and channel lines are plotted with neon colors (green for Up, red for Down, blue for the channel bounds) to create a holographic effect, with a glowing background fill between the channels to highlight the trend direction.
Multi-Timeframe Trend Dashboard:
The indicator analyzes trends across six timeframes (1M, 5M, 15M, 1H, 4H, D1) using the same trend channel logic.
A dashboard in the top-right corner displays each timeframe’s trend direction with a futuristic design: neon green for Up, neon red for Down, and gray for Neutral, all set against a dark background with neon blue accents.
Signal Generation with Higher Timeframe Filter:
Buy and Sell signals are generated when the trend on the chart’s timeframe (e.g., 1M) aligns with a user-selected higher timeframe (e.g., 15M).
A Buy signal ("🚀 SwiftEdge BUY") appears when the price crosses above the upper channel (indicating an Up trend) and the selected higher timeframe’s trend also turns Up. If the higher timeframe is Neutral, the indicator checks even higher timeframes (e.g., 1H and 4H for a 15M filter) to confirm the trend direction.
A Sell signal ("🛑 SwiftEdge SELL") appears when the price crosses below the lower channel (indicating a Down trend) and the selected higher timeframe’s trend turns Down, with the same higher timeframe check for Neutral cases.
Signals are displayed as neon-colored labels with emojis for a futuristic touch, making them easy to spot.
Why This Combination?
The combination of a dynamic trend channel, multi-timeframe analysis, and signal filtering in Trend Channel SwiftEdge is designed to provide a comprehensive view of market trends while reducing false signals. The trend channel identifies the primary trend on your chart, while the multi-timeframe dashboard ensures you’re aware of the broader market context. The signal filter leverages higher timeframes to confirm that your trades align with larger trends, which is particularly useful in volatile markets where smaller timeframes can be noisy. This synergy creates a balanced approach, blending short-term precision with long-term trend confirmation, all wrapped in a visually engaging tech-inspired design.
How to Use It
Add the Indicator: Apply Trend Channel SwiftEdge to your TradingView chart.
Customize Settings:
SwiftEdge Moving Average Type: Choose between SMA, EMA, or WMA (default is EMA) to adjust the trend channel’s sensitivity.
SwiftEdge MA Length: Set the period for the moving average (default is 14).
SwiftEdge Signal Filter Timeframe: Select a higher timeframe (1M, 5M, 15M, 1H, 4H, D1) to filter signals (default is 15M). For example, on a 1M chart, selecting 15M ensures signals align with the 15-minute trend.
Show SwiftEdge Ribbon: Toggle the visibility of the trend channel’s moving average (default is true).
Show SwiftEdge Background Glow: Toggle the glowing background fill between the channel bounds (default is true).
Start/End Year: Set a time range for the indicator’s signals (default is 1900–2100).
Interpret the Dashboard: Check the top-right dashboard to see the trend direction across all timeframes. Use this to understand the broader market context.
Trade with Signals:
Look for "🚀 SwiftEdge BUY" labels (neon green) below candles to enter long positions when the trend aligns across timeframes.
Look for "🛑 SwiftEdge SELL" labels (neon red) above candles to enter short positions or exit longs.
Ensure the signal aligns with your trading strategy and risk management.
What Makes It Original?
Trend Channel SwiftEdge stands out with its futuristic, tech-inspired design and multi-timeframe synergy. Unlike traditional trend indicators, it combines a visually striking neon aesthetic with practical functionality, making trend analysis both intuitive and engaging. The signal filtering mechanism, which checks higher timeframes dynamically, ensures trades are backed by broader market trends, reducing the risk of false signals. The dashboard provides a quick, at-a-glance view of trends across multiple timeframes, empowering traders to make informed decisions without needing to switch charts. This blend of advanced trend analysis, intelligent signal filtering, and a high-tech visual theme makes it a unique tool for modern traders.
Notes
Best used on trending markets; in choppy conditions, consider using higher timeframes for signal filtering to reduce noise.
Adjust the MA length and signal timeframe based on your trading style (shorter for scalping, longer for swing trading).
Why This Description Complies with TradingView House Rules
What It Does:
Clearly explains that the script identifies trends using a dynamic channel, provides buy/sell signals, and displays a multi-timeframe dashboard.
How It Does It:
Breaks down the process into three steps: trend channel calculation, multi-timeframe analysis, and signal generation with higher timeframe filtering.
Explains the logic (e.g., price crossing the channel, trend alignment across timeframes) in simple terms.
How to Use It:
Provides step-by-step instructions on adding the indicator, customizing settings, interpreting the dashboard, and trading with signals.
What Makes It Original:
Highlights the unique tech-inspired design, the combination of trend channel and multi-timeframe filtering, and the dynamic higher timeframe check.
Justifies the Combination:
Explains why the trend channel, multi-timeframe dashboard, and signal filtering are used together: to balance short-term precision with long-term trend confirmation, reducing false signals.
Self-Contained:
All concepts (trend channel, multi-timeframe analysis, signal filtering) are explained within the description without requiring external research.
Avoids technical jargon that would confuse non-Pine readers, focusing on user-friendly language.
This updated description with the new name "Trend Channel SwiftEdge" should fully comply with TradingView’s House Rules. If you need further adjustments, let me know!
Volume Weighted Median Price (VWMP)The volume is indeed crucial for confirming price moves and understanding market conviction. While many traders are familiar with VWAP (Volume Weighted Average Price), this indicator introduces a lesser-known but powerful cousin: the Volume Weighted Median Price (VWMP).
What is VWMP?
Unlike VWAP, which calculates the average price weighted by volume over a period, VWMP identifies the median price level weighted by volume.
Think of it this way: If you line up all the trades within a specific lookback period, sorted by price, and then start accumulating the volume traded at each price level, the VWMP is the price level where 50% of the total volume occurred below it, and 50% occurred above it.
It essentially finds the "middle ground" of trading activity based on where the bulk of the volume actually traded, not just the average price.
Key Difference: VWMP vs. VWAP
VWAP: Volume Weighted Average Price. Sensitive to outliers (single large trades at extreme prices can skew the average).
VWMP: Volume Weighted Median Price. More robust to outliers. It represents the price that splits the period's volume distribution in half.
Because it uses the median, VWMP can sometimes provide a more stable or representative level of the "typical" price where significant volume is changing hands, especially in volatile markets or when large, anomalous trades occur.
How to Interpret and Use VWMP in trading
The VWMP plots as a line on your chart, similar to a moving average or VWAP. Here are a few ways traders might use it:
Dynamic Support and Resistance:
Like VWAP, the VWMP line can act as a dynamic level of interest.
Watch how price interacts with the VWMP. Consistent acceptance above VWMP might suggest bullish control and potential support.
Consistent rejection or acceptance below VWMP might indicate bearish control and potential resistance.
Trend Filter / Confirmation:
Uptrend: Look for price consistently staying above the VWMP line. Pullbacks to the VWMP that hold could offer entry opportunities.
Downtrend: Look for price consistently staying below the VWMP line. Rallies to the VWMP that fail could present shorting opportunities.
Use it to filter trades: Only take long trades if price is above VWMP, and short trades if below.
Mean Reversion Potential (Use with Caution):
When price extends significantly far away from the VWMP, some traders might look for potential reversion back towards this volume-based median level.
Important: This should not be used in isolation. Always look for confirmation from other indicators (like RSI, Stochastics, or candlestick patterns) before trading counter-trend reversions.
Confluence with Other Indicators:
VWMP works best when combined with other analysis tools.
Look for confluence: Does the VWMP align with a key Fibonacci level, a standard moving average, or a prior support/resistance zone? This confluence strengthens the level's potential significance.
Considerations
Lookback Period: The length input is crucial. A shorter period makes VWMP more responsive to recent action; a longer period makes it smoother and reflects longer-term volume distribution. Experiment to find what suits your timeframe and trading style.
Lagging Nature: Like all indicators based on past data, VWMP is inherently lagging. It reflects past volume distribution, not the future.
Market Context: Its effectiveness can vary depending on the market conditions (trending vs. ranging) and the asset being traded.
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
