Enhanced Kitchen Sink Strategymulti-layered trading system designed for TradingView, targeting a minimum 75% win rate through precise entry signals and robust risk management. Built on classic EMA crossovers, it incorporates advanced filters for trend alignment, momentum confirmation, and market confluence to reduce false signals and maximize profitable trades. Ideal for swing traders on timeframes like 1H or 4H, it adapts to various assets (stocks, forex, crypto) while emphasizing conservative position sizing and dynamic stops. With customizable inputs and a real-time dashboard, it's user-friendly yet powerful for both beginners and pros aiming for consistent, high-probability setups. Core Entry Logic
At its heart, the strategy triggers long entries on bullish EMA crossovers (fast 12-period EMA crossing above slow 26-period EMA, with close above the slow EMA) and short entries on bearish crossunders. To ensure high-quality trades: Pullback Entries (Optional): Waits for price to retrace to a short-term EMA (default 8-period) before entering, capturing better risk-reward on dips in trends.
Signal Quality Scoring: A proprietary 0-100% score evaluates each setup across 6 categories (trend, EMAs, MACD, RSI, volume, trendlines/S&R). Trades only fire if the score exceeds your threshold (default 75%, adjustable to 0% for testing).
This results in fewer but higher-conviction trades, filtering out noise for superior edge. Advanced Filters for Confluence
No single indicator drives decisions—confluence is key: Trend Analysis: Master trend filter using a 200-period EMA and strength metric (default >0.5% deviation). Optional higher-timeframe (e.g., daily) confirmation via EMA and MACD alignment.
MACD Double Confirmation: Requires MACD line above/below signal (9-period) with optional histogram momentum buildup.
RSI + Divergence: Filters for neutral RSI zones (40-70 for longs, 30-60 for shorts) and detects bullish/bearish divergences over 20 bars.
Volume Profile: Demands above-average volume (1.5x 20-period SMA) with buying/selling pressure analysis.
Trendlines & S/R: Auto-detects dynamic trendlines from pivots (10-bar lookback) and support/resistance zones (100-bar lookback, 3+ touches), avoiding entries near key levels.
Session Filters: Trades only during London/NY sessions (UTC-based), skipping high-volatility news windows (e.g., 1:30-2:00 PM UTC).
All filters are toggleable, allowing you to dial in aggressiveness—disable for more signals during backtesting.Risk Management & Position Sizing
Safety first: Uses 100% equity per trade with 0.1% commission simulation. Stops & Targets: ATR-based (14-period) stop-loss (1x ATR) and take-profit (2.5x ATR) for 1:2.5 risk-reward.
Breakeven Moves: Auto-shifts stop to +0.1% entry after 1% profit.
Trailing Stops: Optional 1.5x ATR trail to lock in gains during runners.
No pyramiding—flat after each close for clean, low-drawdown performance.
Visualization & Insights On-Chart: Plots EMAs, pullback lines, S/R dashes, trend backgrounds (green/red), and entry labels/shapes.
Dashboard: Real-time table shows trend status, HTF bias, quality scores, MACD/RSI/volume readouts, session info, ATR, price, and position.
Customization: 20+ inputs grouped by category; max 500 labels for clean charts.
Performance Edge & Usage Tips
Backtested for 75%+ win rates in trending markets, this strategy shines in volatile assets like EURUSD or BTCUSD. Start with defaults on 1H charts, then tweak filters (e.g., lower quality to 50%) for ranging conditions. Always forward-test—past results aren't guarantees. Download, apply, and elevate your trading with confluence-driven precision!
In den Scripts nach "股价站上60月线" suchen
EMA 200 MultiTF G/R + Cross Alerts by LifeHack Trader1. Indicator Setup
The script starts by defining the version of Pine Script (v5) and creating an indicator called "EMA 200 MultiTF G/R + Cross Alerts by LifeHack Trader."
The overlay=true parameter ensures that the indicator is plotted directly on the price chart.
2. Function to Get EMA200
A custom function getEma200 is defined to retrieve the 200-period Exponential Moving Average (EMA) for a specified timeframe (tf).
This function uses the request.security function to fetch the close price's EMA from different timeframes.
3. Calculate EMA200 for Multiple Timeframes
The script calculates the EMA200 for four timeframes: 15 minutes ("15"), 1 hour ("60"), 4 hours ("240"), and 1 day ("D").
These values are stored in variables (ema15, ema1h, ema4h, ema1d) and represent the EMA for each timeframe.
4. Determine Price Above or Below EMA200 (G/R)
For each timeframe, the script checks whether the closing price is above or below the EMA200.
It uses boolean checks to determine if the price is above the EMA200, assigning the status "G" (Green) for above and "R" (Red) for below.
5. Cross Signal Detection (Up/Down)
The script detects crossovers and crossunders between the price and EMA200 for each timeframe.
A crossover signal is detected when the price crosses above the EMA200 (bullish), and a crossunder signal is detected when the price crosses below the EMA200 (bearish).
These signals are stored in separate variables (crossUp, crossDown).
6. Display a Table with G/R Status and Cross Alerts
A table is created and displayed in the top-right corner of the chart. The table shows the status (G or R) for each timeframe and the cross signal (▲ for crossover, ▼ for crossunder, or - for no cross event).
The table is updated with the respective values for each timeframe every time a new bar is formed.
7. Alert Conditions
The script defines alert conditions based on the crossovers and crossunders.
When a price crosses above the EMA200 (cross-up), an alert is triggered for a potential buy opportunity. When the price crosses below the EMA200 (cross-down), an alert is triggered for a potential sell opportunity.
Alerts are configured for each timeframe (15 minutes, 1 hour, 4 hours, and 1 day).
This script provides a comprehensive system for monitoring price action relative to the EMA200 on multiple timeframes, highlighting crossovers, and delivering visual feedback and alerts based on the price's relationship with the EMA.
Trend Bars with Okuninushi Line Filter# Trend Bars with Okuninushi Line Filter: A Powerful Trading Indicator
## Introduction
The **Trend Bars with Okuninushi Line Filter** is an innovative technical indicator that combines two powerful concepts: trend bar analysis and the Okuninushi Line filter. This indicator helps traders identify high-quality trending moves by analyzing candle body strength relative to the overall price range while ensuring the price action aligns with the dominant market structure.
## What Are Trend Bars?
Trend bars are candles where the body (distance between open and close) represents a significant portion of the total price range (high to low). These bars indicate strong directional momentum with minimal indecision, making them valuable signals for trend continuation.
### Key Characteristics:
- **Strong directional movement**: Large body relative to total range
- **Minimal upper/lower shadows**: Shows sustained pressure in one direction
- **High conviction**: Represents decisive market action
## The Okuninushi Line Filter
The Okuninushi Line, also known as the Kijun Line in Ichimoku analysis, is calculated as the midpoint of the highest high and lowest low over a specified period (default: 52 periods).
**Formula**: `(Highest High + Lowest Low) / 2`
This line acts as a dynamic support/resistance level and trend filter, helping to:
- Identify the overall market bias
- Filter out counter-trend signals
- Provide confluence for trade entries
## How the Indicator Works
The indicator combines these two concepts with the following logic:
### Bull Trend Bars (Green)
A candle is colored **green** when ALL conditions are met:
1. **Bullish candle**: Close > Open
2. **Strong body**: |Close - Open| ≥ Threshold × (High - Low)
3. **Above trend filter**: Close > Okuninushi Line
### Bear Trend Bars (Red)
A candle is colored **red** when ALL conditions are met:
1. **Bearish candle**: Close < Open
2. **Strong body**: |Close - Open| ≥ Threshold × (High - Low)
3. **Below trend filter**: Close < Okuninushi Line
### Neutral Bars (Gray)
All other candles that don't meet the complete criteria are colored **gray**.
## Customizable Parameters
### Trend Bar Threshold
- **Range**: 10% to 100%
- **Default**: 75%
- **Purpose**: Controls how "strong" a candle must be to qualify as a trend bar
**Threshold Effects:**
- **Low (10-30%)**: More sensitive, catches smaller trending moves
- **Medium (50-75%)**: Balanced approach, filters out most noise
- **High (80-100%)**: Very selective, only captures the strongest moves
### Okuninushi Line Length
- **Default**: 52 periods
- **Purpose**: Determines the lookback period for calculating the midpoint
- **Common Settings**:
- 26 periods: More responsive to recent price action
- 52 periods: Standard setting, good balance
- 104 periods: Longer-term trend perspective
## Trading Applications
### 1. Trend Continuation Signals
- **Green bars**: Look for bullish continuation opportunities
- **Red bars**: Consider bearish continuation setups
- **Gray bars**: Exercise caution, mixed signals
### 2. Market Structure Analysis
- Clusters of same-colored bars indicate strong trends
- Alternating colors suggest choppy, indecisive markets
- Transition from red to green (or vice versa) may signal trend changes
### 3. Entry Timing
- Use colored bars as confirmation for existing trade setups
- Wait for color alignment with your market bias
- Avoid trading during predominantly gray periods
### 4. Risk Management
- Gray bars can serve as early warning signs of weakening trends
- Color changes might indicate appropriate exit points
- Use in conjunction with other risk management tools
## Advantages
1. **Dual Filtering**: Combines momentum (trend bars) with trend direction (Okuninushi Line)
2. **Visual Clarity**: Immediate visual feedback through candle coloring
3. **Customizable**: Adjustable parameters for different trading styles
4. **Versatile**: Works across multiple timeframes and instruments
5. **Objective**: Rule-based system reduces subjective interpretation
## Limitations
1. **Lagging Nature**: Based on historical price data
2. **False Signals**: Can produce whipsaws in choppy markets
3. **Parameter Sensitivity**: Requires optimization for different instruments
4. **Market Conditions**: May be less effective in ranging markets
## Best Practices
### Optimization Tips:
- **Volatile Markets**: Use higher thresholds (80-90%)
- **Steady Trends**: Use moderate thresholds (60-75%)
- **Short-term Trading**: Shorter Okuninushi Line periods (26)
- **Long-term Analysis**: Longer Okuninushi Line periods (104+)
### Combination Strategies:
- Pair with volume indicators for confirmation
- Use alongside support/resistance levels
- Combine with other trend-following indicators
- Consider market context and overall trend direction
## Conclusion
The Trend Bars with Okuninushi Line Filter offers traders a sophisticated yet intuitive way to identify high-quality trending moves. By combining the momentum characteristics of trend bars with the directional filter of the Okuninushi Line, this indicator helps traders focus on the most promising opportunities while avoiding low-probability setups.
Remember that no single indicator should be used in isolation. Always consider market context, risk management, and other technical factors when making trading decisions. The true power of this indicator lies in its ability to quickly highlight periods of strong, aligned price action – exactly what trend traders are looking for.
---
*Disclaimer: This article is for educational purposes only and should not be considered as financial advice. Always conduct your own research and consider your risk tolerance before making any trading decisions.*
Bitcoin vs. Gold correlation with lagBTC vs Gold (Lag) + Correlation — multi-timeframe, publication notes
What it does
Plots Gold on the same chart as Bitcoin, with a configurable lead/lag.
Lets you choose how the series is displayed:
Gold shifted forward (+lag on chart) — shows gold ahead of BTC on the time axis (visual offset).
Gold aligned to BTC (gold lag) — standard alignment; gold is lagged for calculation and plotted in place.
BTC 200D Lag (BTC shifted forward) — visualizes BTC shifted forward (like popular “BTC 200D Lag” charts).
Computes Pearson correlations between BTC (no lag) and Gold (with lag) over multiple lookback windows equivalent to:
30d, 60d, 90d, 180d, 365d, 2y (730d), 3y (1095d), 5y (1825d).
Shows a table with the correlation values, automatically scaled to the current timeframe.
Why this is useful
A common macro claim is that BTC tends to follow Gold with a delay (e.g., ~200 trading days). This tool lets you:
Visually advance Gold (or BTC) to see that lead-lag relationship on the chart.
Quantify the relationship with rolling correlations.
Switch timeframes (D/W/M/…): everything automatically stays in sync.
Quick start
Open a BTC chart (any exchange).
Add the indicator.
Set Gold symbol (default TVC:GOLD; alternatives: OANDA:XAUUSD, COMEX:GC1!, etc.).
Choose Lag value and Lag unit (Days/Weeks/Months/Years/Bars).
Pick Visual Mode:
To mirror those “BTC 200D Lag” posts: choose “BTC 200D Lag (BTC shifted forward)” with 200 Days.
To view Gold 200D ahead of BTC: select “Gold shifted forward (+lag on chart)” with 200 Days.
Keep Rebase to 100 ON for an apples-to-apples visual scale. (You can move the study to the left price scale if needed.)
Inputs
Gold symbol: external series to pair with BTC.
Lag value: numeric value.
Lag unit: Days, Weeks, Months (≈30d), Years (≈365d), or direct Bars.
Visual mode:
Gold shifted forward (+lag on chart) → gold is offset to the right by the lag (visual only).
Gold aligned to BTC (gold lag) → standard plot (no visual offset); correlations still use lagged gold.
BTC 200D Lag (BTC shifted forward) → BTC is offset to the right by the lag (visual only).
Rebase to 100 (visual): rescales each series to 100 on its first valid bar for clearer comparison.
Show gold without lag (debug): optional reference line.
Show price tag for gold (lag): toggles the track price label.
Timeframe handling
The study uses the current chart timeframe for both BTC and Gold (timeframe.period).
Lag in time units (Days/Weeks/Months/Years) is internally converted to an integer number of bars of the active timeframe (using timeframe.in_seconds).
Example: on W (weekly), 200 days ≈ 29 bars.
On intraday timeframes, days are converted proportionally.
Correlation math
Correlation = ta.correlation(BTC, Gold_lagged, length_in_bars)
Lookback lengths are the bar-equivalents of 30/60/90/180/365/730/1095/1825 days in the active timeframe.
Important: correlations are computed on prices (not returns). If you prefer returns-based correlation (often more statistically robust), duplicate the script and replace price inputs with change(close) or ta.roc(close, 1).
Reading the table
Window: nominal day label (e.g., 30d, 1y, 5y).
Bars (TF): how many bars that window equals on the current timeframe.
Correlation: Pearson coefficient . Background tint shows intensity and sign.
Tips & caveats
Visual offsets (offset=) move series on screen only; they don’t affect the math. The math always uses BTC (no lag) × Gold (lagged).
With large lags on high timeframes, early bars will be na (normal). Scroll forward / reduce lag.
If your Gold feed doesn’t load, try an alternative symbol that your plan supports.
Rebase to 100 helps visibility when BTC ($100k) and Gold ($2k) share a scale.
Months/Years use 30/365-day approximations. For exact control, use Days or Bars.
Correlations on very short lengths or sparse data can be unstable; consider the longer windows for sturdier signals.
This is a visual/analytical tool, not a trading signal. Always apply independent risk management.
Suggested setups
Replicate “BTC 200D Lag” charts:
Visual Mode: BTC 200D Lag (BTC shifted forward)
Lag: 200 Days
Rebase: ON
Gold leads BTC (Gold ahead):
Visual Mode: Gold shifted forward (+lag on chart)
Lag: 200 Days
Rebase: ON
Compatibility: Pine v6, overlay study.
Best with: BTCUSD (any exchange) + a reliable Gold feed.
Author’s note: Lead-lag relationships are not stable over time; treat correlations as descriptive, not predictive.
Contract Interest Turnover T3 [T69]Overview
--------
Contract Interest Turnover (CIT) estimates how “churny” a crypto derivatives market is by comparing the amount traded in a bar to the base stock of outstanding contracts (open interest). It normalizes both Volume and Open Interest (OI) by Price (Close), then plots a Turnover Rate = (Volume/Close) ÷ (OI/Close) as colored columns. Higher values = faster contract recycling (strong momentum / hype potential).
Features
--------
- Auto-fetch OI: Pulls OI via request.security(_OI, …) when the exchange/symbol exposes an OI stream on TradingView.
- Price-normalized comparison: Converts both Volume and OI into comparable notional terms by dividing each by Close.
- Turnover columns with threshold: Color the columns green once Turnover ≥ your set threshold; gray otherwise.
- Status-line readouts: Displays normalized Volume and OI values for quick sanity checks.
- Crypto-aware timeframe: Uses chart TF for crypto; forces daily OI when not crypto to avoid noisy intraday pulls.
How to Use
----------
1. Add the script on a perpetual/futures symbol that has OI on TradingView (e.g., BTC perps where an _OI feed exists).
2. Watch the Turnover Rate bars: spikes above your threshold flag sessions where contracts are actively flipping.
3. Interpret spikes as a signal of movement or activity — it does not specify price direction, only that the market is engaged and contracts are being traded more intensely than usual.
Configuration
-------------
- Interest Turnover Threshold (default 1.0): colors columns green when Turnover ≥ threshold. Tune per market’s typical churn profile.
Under the Hood (Formulas & Logic)
---------------------------------
- Fetch OI
oiClose ← request.security(ticker.standard(syminfo.tickerid) + "_OI", timeframe, close) with ignore_invalid_symbol = true.
If none is found, the script throws a clear runtime error.
- Normalize to price
vol_norm = volume / close
oi_norm = oiClose / close
This converts both to a common notional basis so their ratio is meaningful even as price changes.
- Turnover Rate
turnover = vol_norm / oi_norm
Interpretation: fraction/multiples of the outstanding contract base traded in the bar. Color = green if turnover ≥ threshold.
Why Open Interest ≈ “Float” Proxy
---------------------------------
In stocks, float ≈ shares the public can trade. In derivatives, there are no “shares,” so Open Interest acts as the live stock of active contracts. It’s the best proxy for “what’s available in play” because it counts open positions that persist across bars. Using Volume ÷ OI mirrors stock float-turnover logic: how fast the tradable base is being recycled each period.
Why Normalize by Price
----------------------
Derivatives volume and OI may be reported in contracts, not notional value. One contract’s economic weight changes with price (especially on inverse contracts). Dividing both Volume and OI by Close:
- Puts them on a comparable notional footing.
- Prevents false spikes purely from price moves.
- Makes Turnover comparable across time even as price trends.
Advanced Tips
-------------
- Calibrate threshold: Start from the 80th–90th percentile of the last 60–90 bars of Turnover; set the threshold a touch below that to surface early heat.
- Add OI-delta: Layer an OI change histogram (current − prior) to separate new positioning from pure churn.
- Linear vs inverse: For linear (USDT-margined) contracts, the normalization still works and keeps visuals consistent; for inverse, it’s essential.
Limitations
-----------
- Data availability: Works only if your symbol exposes an _OI feed on TradingView; otherwise it errors out.
- Exchange conventions: Volume units differ by venue (contracts, coin, notional). Normalization mitigates, but cross-symbol comparisons still need caution.
- Intrabar gaps: OI is typically end-of-bar; rapid intrabar shifts won’t appear until the bar closes.
Notes
-----
- Designed primarily for crypto derivatives. For non-crypto, the script blanks OI to avoid misleading plots and uses a daily TF when needed.
Credit
------
- Concept & data: Built for TradingView data feeds.
- Acknowledgment: Credit to TradingView default indicator as requested.
- Source: This write-up reflects the logic present in your uploaded script.
Disclaimer
----------
Markets move; indicators simplify. Use with position sizing, hard stops, and catalyst awareness. The Turnover Rate flags activity, not direction.
[blackcat] L2 Trend LinearityOVERVIEW
The L2 Trend Linearity indicator is a sophisticated market analysis tool designed to help traders identify and visualize market trend linearity by analyzing price action relative to dynamic support and resistance zones. This powerful Pine Script indicator utilizes the Arnaud Legoux Moving Average (ALMA) algorithm to calculate weighted price calculations and generate dynamic support/resistance zones that adapt to changing market conditions. By visualizing market zones through colored candles and histograms, the indicator provides clear visual cues about market momentum and potential trading opportunities. The script generates buy/sell signals based on zone crossovers, making it an invaluable tool for both technical analysis and automated trading strategies. Whether you're a day trader, swing trader, or algorithmic trader, this indicator can help you identify market regimes, support/resistance levels, and potential entry/exit points with greater precision.
FEATURES
Dynamic Support/Resistance Zones: Calculates dynamic support (bear market zone) and resistance (bull market zone) using weighted price calculations and ALMA smoothing
Visual Market Representation: Color-coded candles and histograms provide immediate visual feedback about market conditions
Smart Signal Generation: Automatic buy/sell signals generated from zone crossovers with clear visual indicators
Customizable Parameters: Four different ALMA smoothing parameters for various timeframes and trading styles
Multi-Timeframe Compatibility: Works across different timeframes from 1-minute to weekly charts
Real-time Analysis: Provides instant feedback on market momentum and trend direction
Clear Visual Cues: Green candles indicate bullish momentum, red candles indicate bearish momentum, and white candles indicate neutral conditions
Histogram Visualization: Blue histogram shows bear market zone (below support), aqua histogram shows bull market zone (above resistance)
Signal Labels: "B" labels mark buy signals (price crosses above resistance), "S" labels mark sell signals (price crosses below support)
Overlay Functionality: Works as an overlay indicator without cluttering the chart with unnecessary elements
Highly Customizable: All parameters can be adjusted to suit different trading strategies and market conditions
HOW TO USE
Add the Indicator to Your Chart
Open TradingView and navigate to your desired trading instrument
Click on "Indicators" in the top menu and select "New"
Search for "L2 Trend Linearity" or paste the Pine Script code
Click "Add to Chart" to apply the indicator
Configure the Parameters
ALMA Length Short: Set the short-term smoothing parameter (default: 3). Lower values provide more responsive signals but may generate more false signals
ALMA Length Medium: Set the medium-term smoothing parameter (default: 5). This provides a balance between responsiveness and stability
ALMA Length Long: Set the long-term smoothing parameter (default: 13). Higher values provide more stable signals but with less responsiveness
ALMA Length Very Long: Set the very long-term smoothing parameter (default: 21). This provides the most stable support/resistance levels
Understand the Visual Elements
Green Candles: Indicate bullish momentum when price is above the bear market zone (support)
Red Candles: Indicate bearish momentum when price is below the bull market zone (resistance)
White Candles: Indicate neutral market conditions when price is between support and resistance zones
Blue Histogram: Shows bear market zone when price is below support level
Aqua Histogram: Shows bull market zone when price is above resistance level
"B" Labels: Mark buy signals when price crosses above resistance
"S" Labels: Mark sell signals when price crosses below support
Identify Market Regimes
Bullish Regime: Price consistently above resistance zone with green candles and aqua histogram
Bearish Regime: Price consistently below support zone with red candles and blue histogram
Neutral Regime: Price oscillating between support and resistance zones with white candles
Generate Trading Signals
Buy Signals: Look for price crossing above the bull market zone (resistance) with confirmation from green candles
Sell Signals: Look for price crossing below the bear market zone (support) with confirmation from red candles
Confirmation: Always wait for confirmation from candle color changes before entering trades
Optimize for Different Timeframes
Scalping: Use shorter ALMA lengths (3-5) for 1-5 minute charts
Day Trading: Use medium ALMA lengths (5-13) for 15-60 minute charts
Swing Trading: Use longer ALMA lengths (13-21) for 1-4 hour charts
Position Trading: Use very long ALMA lengths (21+) for daily and weekly charts
LIMITATIONS
Whipsaw Markets: The indicator may generate false signals in choppy, sideways markets where price oscillates rapidly between support and resistance
Lagging Nature: Like all moving average-based indicators, there is inherent lag in the calculations, which may result in delayed signals
Not a Standalone Tool: This indicator should be used in conjunction with other technical analysis tools and risk management strategies
Market Structure Dependency: Performance may vary depending on market structure and volatility conditions
Parameter Sensitivity: Different markets may require different parameter settings for optimal performance
No Volume Integration: The indicator does not incorporate volume data, which could provide additional confirmation signals
Limited Backtesting: Pine Script limitations may restrict comprehensive backtesting capabilities
Not Suitable for All Instruments: May perform differently on stocks, forex, crypto, and futures markets
Requires Confirmation: Signals should always be confirmed with other indicators or price action analysis
Not Predictive: The indicator identifies current market conditions but does not predict future price movements
NOTES
ALMA Algorithm: The indicator uses the Arnaud Legoux Moving Average (ALMA) algorithm, which is known for its excellent smoothing capabilities and reduced lag compared to traditional moving averages
Weighted Price Calculations: The bear market zone uses (2low + close) / 3, while the bull market zone uses (high + 2close) / 3, providing more weight to recent price action
Dynamic Zones: The support and resistance zones are dynamic and adapt to changing market conditions, making them more responsive than static levels
Color Psychology: The color scheme follows traditional trading psychology - green for bullish, red for bearish, and white for neutral
Signal Timing: The signals are generated on the close of each bar, ensuring they are based on complete price action
Label Positioning: Buy signals appear below the bar (red "B" label), while sell signals appear above the bar (green "S" label)
Multiple Timeframes: The indicator can be applied to multiple timeframes simultaneously for comprehensive analysis
Risk Management: Always use proper risk management techniques when trading based on indicator signals
Market Context: Consider the overall market context and trend direction when interpreting signals
Confirmation: Look for confirmation from other indicators or price action patterns before entering trades
Practice: Test the indicator on historical data before using it in live trading
Customization: Feel free to experiment with different parameter combinations to find what works best for your trading style
THANKS
Special thanks to the TradingView community and the Pine Script developers for creating such a powerful and flexible platform for technical analysis. This indicator builds upon the foundation of the ALMA algorithm and various moving average techniques developed by technical analysis pioneers. The concept of dynamic support and resistance zones has been refined over decades of market analysis, and this script represents a modern implementation of these timeless principles. We acknowledge the contributions of all traders and developers who have contributed to the evolution of technical analysis and continue to push the boundaries of what's possible with algorithmic trading tools.
Cyclic Reversal Engine [AlgoPoint]Overview
Most indicators focus on price and momentum, but they often ignore a critical third dimension: time. Markets move in rhythmic cycles of expansion and contraction, but these cycles are not fixed; they speed up in trending markets and slow down in choppy conditions.
The Cyclic Reversal Engine is an advanced analytical tool designed to decode this rhythm. Instead of relying on static, lagging formulas, this indicator learns from past market behavior to anticipate when the current trend is statistically likely to reach its exhaustion point, providing high-probability reversal signals.
It achieves this by combining a sophisticated time analysis with a robust price-action confirmation.
How It Works: The Core Logic
The indicator operates on a multi-stage process to identify potential turning points in the market.
1. Market Regime Analysis (The Brain): Before analyzing any cycles, the indicator first diagnoses the current "personality" of the market. Using a combination of the ADX, Choppiness Index, and RSI, it classifies the market into one of three primary regimes:
- Trending: Strong, directional movement.
- Ranging: Sideways, non-directional chop.
- Reversal: An over-extended state (overbought/oversold) where a turn is imminent.
2. Adaptive Cycle Learning (The "Machine Learning" Aspect): This is the indicator's smartest feature. It constantly analyzes past cycles by measuring the bar-count between significant swing highs and swing lows. Crucially, it learns the average cycle duration for each specific market regime. For example, it learns that "in a strong trending market, a new swing low tends to occur every 35 bars," while "in a ranging market, this extends to 60 bars."
3. The Countdown & Timing Signal: The indicator identifies the last major swing high or low and starts a bar-by-bar countdown. Based on the current market regime, it selects the appropriate learned cycle length from its memory. When the bar count approaches this adaptive target, the indicator determines that a reversal is "due" from a timing perspective.
4. Price Confirmation (The Trigger): A signal is never generated based on timing alone. Once the timing condition is met (the cycle is "due"), the indicator waits for a final price-action confirmation. The default confirmation is the RSI entering an extreme overbought or oversold zone, signaling momentum exhaustion. The signal is only triggered when Time + Price Confirmation align.
How to Use This Indicator
- The Dashboard: The panel in the bottom-right corner is your command center.
- Market Regime: Shows the current market personality analyzed by the engine.
- Adaptive Cycle / Bar Count: This is the core of the indicator. It shows the target cycle length for the current regime (e.g., 50) and the current bar count since the last swing point (e.g., 45). The background turns orange when the bar count enters the "due zone," indicating that you should be on high alert for a reversal.
- BUY/SELL Signals: A label appears on the chart only when the two primary conditions are met:
The timing is right (Bar Count has reached the Adaptive Cycle target).
The price confirms exhaustion (RSI is in an extreme zone).
A BUY signal suggests a downtrend cycle is likely complete, and a SELL signal suggests an uptrend cycle is likely complete.
Key Settings
- Pivot Lookback: Controls the sensitivity of the swing point detection. Higher values will identify more significant, longer-term cycles.
- Market Regime Engine: The ADX, Choppiness, and RSI settings can be fine-tuned to adjust how the indicator classifies the market's personality.
- Require Price Confirmation: You can toggle the RSI confirmation on or off. It is highly recommended to keep it enabled for higher-quality signals.
Savitzky-Golay Hampel Filter | AlphaNattSavitzky-Golay Hampel Filter | AlphaNatt
A revolutionary indicator combining NASA's satellite data processing algorithms with robust statistical outlier detection to create the most scientifically advanced trend filter available on TradingView.
"This is the same mathematics that processes signals from the Hubble Space Telescope and analyzes data from the Large Hadron Collider - now applied to financial markets."
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🚀 SCIENTIFIC PEDIGREE
Savitzky-Golay Filter Applications:
NASA: Satellite telemetry and space probe data processing
CERN: Particle physics data analysis at the LHC
Pharmaceutical: Chromatography and spectroscopy analysis
Astronomy: Processing signals from radio telescopes
Medical: ECG and EEG signal processing
Hampel Filter Usage:
Aerospace: Cleaning sensor data from aircraft and spacecraft
Manufacturing: Quality control in precision engineering
Seismology: Earthquake detection and analysis
Robotics: Sensor fusion and noise reduction
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🧬 THE MATHEMATICS
1. Savitzky-Golay Filter
The SG filter performs local polynomial regression on data points:
Fits a polynomial of degree n to a sliding window of data
Evaluates the polynomial at the center point
Preserves higher moments (peaks, valleys) unlike moving averages
Maintains derivative information for true momentum analysis
Originally published in Analytical Chemistry (1964)
Mathematical Properties:
Optimal smoothing in the least-squares sense
Preserves statistical moments up to polynomial order
Exact derivative calculation without additional lag
Superior frequency response vs traditional filters
2. Hampel Filter
A robust outlier detector based on Median Absolute Deviation (MAD):
Identifies outliers using robust statistics
Replaces spurious values with polynomial-fitted estimates
Resistant to up to 50% contaminated data
MAD is 1.4826 times more robust than standard deviation
Outlier Detection Formula:
|x - median| > k × 1.4826 × MAD
Where k is the threshold parameter (typically 3 for 99.7% confidence)
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💎 WHY THIS IS SUPERIOR
vs Moving Averages:
Preserves peaks and valleys (critical for catching tops/bottoms)
No lag penalty for smoothness
Maintains derivative information
Polynomial fitting > simple averaging
vs Other Filters:
Outlier immunity (Hampel component)
Scientifically optimal smoothing
Preserves higher-order features
Used in billion-dollar research projects
Unique Advantages:
Feature Preservation: Maintains market structure while smoothing
Spike Immunity: Ignores false breakouts and stop hunts
Derivative Accuracy: True momentum without additional indicators
Scientific Validation: 60+ years of academic research
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⚙️ PARAMETER OPTIMIZATION
1. Polynomial Order (2-5)
2 (Quadratic): Maximum smoothing, gentle curves
3 (Cubic): Balanced smoothing and responsiveness (recommended)
4-5 (Higher): More responsive, preserves more features
2. Window Size (7-51)
Must be odd number
Larger = smoother but more lag
Formula: 2×(desired smoothing period) + 1
Default 21 = analyzes 10 bars each side
3. Hampel Threshold (1.0-5.0)
1.0: Aggressive outlier removal (68% confidence)
2.0: Moderate outlier removal (95% confidence)
3.0: Conservative outlier removal (99.7% confidence) (default)
4.0+: Only extreme outliers removed
4. Final Smoothing (1-7)
Additional WMA smoothing after filtering
1 = No additional smoothing
3-5 = Recommended for most timeframes
7 = Ultra-smooth for position trading
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📊 TRADING STRATEGIES
Signal Recognition:
Cyan Line: Bullish trend with positive derivative
Pink Line: Bearish trend with negative derivative
Color Change: Trend reversal with polynomial confirmation
1. Trend Following Strategy
Enter when price crosses above cyan filter
Exit when filter turns pink
Use filter as dynamic stop loss
Best in trending markets
2. Mean Reversion Strategy
Enter long when price touches filter from below in uptrend
Enter short when price touches filter from above in downtrend
Exit at opposite band or filter color change
Excellent for range-bound markets
3. Derivative Strategy (Advanced)
The SG filter preserves derivative information
Acceleration = second derivative > 0
Enter on positive first derivative + positive acceleration
Exit on negative second derivative (momentum slowing)
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📈 PERFORMANCE CHARACTERISTICS
Strengths:
Outlier Immunity: Ignores stop hunts and flash crashes
Feature Preservation: Catches tops/bottoms better than MAs
Smooth Output: Reduces whipsaws significantly
Scientific Basis: Not curve-fitted or optimized to markets
Considerations:
Slight lag in extreme volatility (all filters have this)
Requires odd window sizes (mathematical requirement)
More complex than simple moving averages
Best with liquid instruments
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🔬 SCIENTIFIC BACKGROUND
Savitzky-Golay Publication:
"Smoothing and Differentiation of Data by Simplified Least Squares Procedures"
- Abraham Savitzky & Marcel Golay
- Analytical Chemistry, Vol. 36, No. 8, 1964
Hampel Filter Origin:
"Robust Statistics: The Approach Based on Influence Functions"
- Frank Hampel et al., 1986
- Princeton University Press
These techniques have been validated in thousands of scientific papers and are standard tools in:
NASA's Jet Propulsion Laboratory
European Space Agency
CERN (Large Hadron Collider)
MIT Lincoln Laboratory
Max Planck Institutes
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💡 ADVANCED TIPS
News Trading: Lower Hampel threshold before major events to catch spikes
Scalping: Use Order=2 for maximum smoothness, Window=11 for responsiveness
Position Trading: Increase Window to 31+ for long-term trends
Combine with Volume: Strong trends need volume confirmation
Multiple Timeframes: Use daily for trend, hourly for entry
Watch the Derivative: Filter color changes when first derivative changes sign
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⚠️ IMPORTANT NOTICES
Not financial advice - educational purposes only
Past performance does not guarantee future results
Always use proper risk management
Test settings on your specific instrument and timeframe
No indicator is perfect - part of complete trading system
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🏆 CONCLUSION
The Savitzky-Golay Hampel Filter represents the pinnacle of scientific signal processing applied to financial markets. By combining polynomial regression with robust outlier detection, traders gain access to the same mathematical tools that:
Guide spacecraft to other planets
Detect gravitational waves from black holes
Analyze particle collisions at near light-speed
Process signals from deep space
This isn't just another indicator - it's rocket science for trading .
"When NASA needs to separate signal from noise in billion-dollar missions, they use these exact algorithms. Now you can too."
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Developed by AlphaNatt
Version: 1.0
Release: 2025
Pine Script: v6
"Where Space Technology Meets Market Analysis"
Not financial advice. Always DYOR
PumpC ATR Line LevelsPumpC ATR Line Levels
Overview
PumpC ATR Line Levels is a volatility-based indicator that projects potential expansion levels from the previous session’s close using the Average True Range (ATR). This tool builds upon the Previous OHLC framework created by Nephew_Sam_ by extending its session-handling logic and adding ATR-based levels, statistical tracking, and flexible visualization options.
How It Works
Calculates ATR from a user-selectable higher timeframe (default: Daily).
Projects levels above and below the previous session’s close (or current close when preview mode is enabled).
Supports up to 5 ATR multiples, each with independent toggles, colors, and labels.
Optionally displays only the most recent ATR session for clarity.
Includes a data table tracking how often ATR levels are reached or closed beyond.
Features
Configurable ATR timeframe and length (default: 21).
Default multiples: 0.30, 0.60, 0.90; optional: 1.236, 2.00.
Toggle for preview mode (using current close vs. locked prior session close).
Customizable line style, width, colors, and label placement.
Visibility filter to show only on chart TF ≤ 60 minutes.
Session statistics table with counts and percentages of level interactions.
Use Cases
Identify intraday expansion targets or stop placement zones based on volatility.
Evaluate historical tendencies of price respecting or breaking ATR bands.
Support volatility-adjusted trade planning with statistical validation.
Acknowledgment
This script was developed on top of the Previous OHLC indicator by Nephew_Sam_ , with major modifications to implement ATR-driven levels, extended statistics, and customizable table output.
Notes
This indicator does not generate buy/sell signals.
Best applied to intraday charts anchored to a higher-timeframe ATR.
Keep charts clean and avoid non-standard bar types when publishing.
Perp Imbalance Zones • Pro (clean)USD Premium (perp vs spot) → (Perp − Spot) / Spot.
Imbalance (z-score of that premium) → how extreme the current premium is relative to its own history over lenPrem bars.
Hysteresis state machine → flips to a SHORT bias when perp-long pressure is extreme; flips to LONG bias when perp-short pressure is extreme. It exits only after the imbalance cools (prevents whipsaw).
Price stretch filter (±σ) → optional Bollinger check so signals only fire when price is already stretched.
HTF confirmation (optional) → require higher-timeframe imbalance to agree with the current-TF bias.
Gradient visuals → line + background tint deepen as |z| grows (more extreme pressure).
What you see on the pane
A single line (z):
Above 0 = perp richer than spot (perp longs pressing).
Below 0 = perp cheaper than spot (perp shorts pressing).
Guides: dotted levels at ±enterZ (entry) and ±exitZ (cool-off/exit).
Background tint:
Red when state = SHORT bias (perp longs heavy).
Blue when state = LONG bias (perp shorts heavy).
Tint intensity scales with |z| (via hotZ).
Labels (optional): prints when bias flips.
Alerts (optional): “Enter SHORT/LONG bias” and “Exit bias”.
How to use it (playbook)
Attach & set symbols
Put the script on your chart.
Set Spot symbol and Perp symbol to the venue you trade (e.g., BINANCE:BTCUSDT + BINANCE:BTCUSDTPERP).
Read the bias
SHORT bias (red background): perp longs over-extended. Look for short entries if price is at resistance, σ-stretched, or your PA system agrees.
LONG bias (blue background): perp shorts over-extended. Look for long entries at support/σ-stretched down.
Entries
Use the bias flip as a context/confirm. Combine with your structure trigger (OB/level sweep, rejection wick, micro-break in market structure, etc.).
If useSigma=true, only trade when price is already ≥ upper band (shorts) or ≤ lower band (longs).
Exits
Bias auto-exits when |z| falls below exitZ.
You can also take profits at your levels or when the line fades back toward 0 while price mean-reverts to the middle band.
Tuning (what each knob does)
enterZ / exitZ (signal strictness + hysteresis)
Higher enterZ → fewer, cleaner signals (e.g., 1.8–2.2).
exitZ should be lower than enterZ (e.g., 0.6–1.0) to prevent flicker.
lenPrem (context window for z)
Larger (50–100) = steadier baseline, fewer signals.
Smaller (20–30) = more reactive, more signals.
smoothLen (EMA on z)
2–3 = snappier; 5–7 = smoother/laggier but cleaner.
useSigma, bbLen, bbK (price-stretch filter)
On filters chop. Try bbLen=100, bbK=1.0–1.5.
Off if you want more frequent signals or you already gate with your own σ/Keltner.
useHTF, htfTF, htfZmin (trend/confirmation)
Turn on to require higher-TF imbalance agreement (e.g., trading 1H → confirm with 4H htfTF=240, htfZmin≈0.6–1.0).
hotZ (visual intensity)
Lower (2.0–2.5) heats up faster; higher (4.0) is more subtle.
Ready-made presets
Conservative swing (fewer, higher-conviction):
enterZ=2.0, exitZ=1.0, lenPrem=60–80, smoothLen=5, useSigma=true, bbK=1.5, useHTF=true (240/0.8).
Balanced intraday (default feel):
enterZ=1.6–1.8, exitZ=0.8–1.0, lenPrem=50, smoothLen=3–4, useSigma=true, bbK=1.0–1.25, useHTF=false/true depending on trendiness.
Aggressive scalping (more signals):
enterZ=1.2–1.4, exitZ=0.6–0.8, lenPrem=20–30, smoothLen=2–3, useSigma=false, useHTF=false.
Practical tips
Don’t trade the line in isolation. Use it to time trades into your levels: VWAP bands, Monday high/low, prior POC/VAH/VAL, order blocks, etc.
Perp-led reversals often snap—be ready to scale out quickly back to mid-bands.
Venue matters. Keep spot & perp from the same exchange family to avoid cross-venue quirks.
Alerts: enable after you’ve tuned thresholds for your timeframe so you only get high-quality pings.
Session Levels [odnac]This indicator plots the high and low levels of the three main trading sessions—Asia, Europe, and New York—along with the previous day’s high, low, and open. Each session’s time range can be customized using a UTC offset, and the indicator automatically tracks session highs and lows as price develops.
Functions:
Plots session highs and lows for Asia, Europe, and New York.
Shows previous day’s high, low, and open as reference levels.
Session times are fully configurable with hour and minute precision, including UTC offset adjustment.
Each session level is marked with both a line and a label for clarity.
Color customization for each session and previous day levels.
Designed for intraday timeframes (1–60 minutes).
Filter Condition:
When the filter option is enabled, the indicator adjusts how levels are drawn:
A session high above the current close is displayed as a solid line with a visible label.
Once price closes above that high, the line becomes dotted and dimmed, and the label also becomes less emphasized.
Similarly, a session low below the current close is displayed as a solid line and label.
If price closes below that low, the line switches to dotted and dimmed, with the label adjusted accordingly.
This behavior highlights only the most relevant levels for the current market position while still keeping breached levels visible in a subdued style, making it easier to spot active breakout or liquidity zones.
Optimized ADX DI CCI Strategy### Key Features:
- Combines ADX, DI+/-, CCI, and RSI for signal generation.
- Supports customizable timeframes for indicators.
- Offers multiple exit conditions (Moving Average cross, ADX change, performance-based stop-loss).
- Tracks and displays trade statistics (e.g., win rate, capital growth, profit factor).
- Visualizes trades with labels and optional background coloring.
- Allows countertrading (opening an opposite trade after closing one).
1. **Indicator Calculation**:
- **ADX and DI+/-**: Calculated using the `ta.dmi` function with user-defined lengths for DI and ADX smoothing.
- **CCI**: Computed using the `ta.cci` function with a configurable source (default: `hlc3`) and length.
- **RSI (optional)**: Calculated using the `ta.rsi` function to filter overbought/oversold conditions.
- **Moving Averages**: Used for CCI signal smoothing and trade exits, with support for SMA, EMA, SMMA (RMA), WMA, and VWMA.
2. **Signal Generation**:
- **Buy Signal**: Triggered when DI+ > DI- (or DI+ crosses over DI-), CCI > MA (or CCI crosses over MA), and optional ADX/RSI filters are satisfied.
- **Sell Signal**: Triggered when DI+ < DI- (or DI- crosses over DI+), CCI < MA (or CCI crosses under MA), and optional ADX/RSI filters are satisfied.
3. **Trade Execution**:
- **Entry**: Long or short trades are opened using `strategy.entry` when signals are detected, provided trading is allowed (`allow_long`/`allow_short`) and equity is positive.
- **Exit**: Trades can be closed based on:
- Opposite signal (if no other exit conditions are used).
- MA cross (price crossing below/above the exit MA for long/short trades).
- ADX percentage change exceeding a threshold.
- Performance-based stop-loss (trade loss exceeding a percentage).
- **Countertrading**: If enabled, closing a trade triggers an opposite trade (e.g., closing a long opens a short).
4. **Visualization**:
- Labels are plotted at trade entries/exits (e.g., "BUY," "SELL," arrows).
- Optional background coloring highlights open trades (green for long, red for short).
- A statistics table displays real-time metrics (e.g., capital, win rates).
5. **Trade Tracking**:
- Tracks the number of long/short trades, wins, and overall performance.
- Monitors equity to prevent trading if it falls to zero.
### 2.3 Key Components
- **Indicator Calculations**: Uses `request.security` to fetch indicator data for the specified timeframe.
- **MA Function**: A custom `ma_func` handles different MA types for CCI and exit conditions.
- **Signal Logic**: Combines crossover/under checks with recent bar windows for flexibility.
- **Exit Conditions**: Multiple configurable exit strategies for risk management.
- **Statistics Table**: Updates dynamically with trade and capital metrics.
## 3. Configuration Options
The script provides extensive customization through input parameters, grouped for clarity in the TradingView settings panel. Below is a detailed breakdown of each setting and its impact.
### 3.1 Strategy Settings (Global)
- **Initial Capital**: Default `10000`. Sets the starting capital for backtesting.
- **Effect**: Determines the base equity for calculating position sizes and performance metrics.
- **Default Quantity Type**: `strategy.percent_of_equity` (50% of equity).
- **Effect**: Controls the size of each trade as a percentage of available equity.
- **Pyramiding**: Default `2`. Allows up to 2 simultaneous trades in the same direction.
- **Effect**: Enables multiple entries if conditions are met, increasing exposure.
- **Commission**: 0.2% per trade.
- **Effect**: Simulates trading fees, reducing net profit in backtesting.
- **Margin**: 100% for long and short trades.
- **Effect**: Assumes no leverage; adjust for margin trading simulations.
- **Calc on Every Tick**: `true`.
- **Effect**: Ensures real-time signal updates for precise execution.
### 3.2 Indicator Settings
- **Indicator Timeframe** (`indicator_timeframe`):
- **Options**: `""` (chart timeframe), `1`, `5`, `15`, `30`, `60`, `240`, `D`, `W`.
- **Default**: `""` (uses chart timeframe).
- **Effect**: Determines the timeframe for ADX, DI, CCI, and RSI calculations. A higher timeframe reduces noise but may delay signals.
### 3.3 ADX & DI Settings
- **DI Length** (`adx_di_len`):
- **Default**: `30`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for calculating DI+ and DI-. Longer periods smooth trends but reduce sensitivity.
- **ADX Smoothing Length** (`adx_smooth_len`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Smooths the ADX calculation. Longer periods produce smoother ADX values.
- **Use ADX Filter** (`use_adx_filter`):
- **Default**: `false`.
- **Effect**: If `true`, requires ADX to exceed the threshold for signals to be valid, filtering out weak trends.
- **ADX Threshold** (`adx_threshold`):
- **Default**: `25`.
- **Range**: Minimum `0`.
- **Effect**: Sets the minimum ADX value for valid signals when the filter is enabled. Higher values restrict trades to stronger trends.
### 3.4 CCI Settings
- **CCI Length** (`cci_length`):
- **Default**: `20`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for CCI calculation. Longer periods reduce noise but may lag.
- **CCI Source** (`cci_src`):
- **Default**: `hlc3` (average of high, low, close).
- **Effect**: Defines the price data for CCI. `hlc3` is standard, but users can choose other sources (e.g., `close`).
- **CCI MA Type** (`ma_type`):
- **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`.
- **Default**: `SMA`.
- **Effect**: Determines the moving average type for CCI signal smoothing. EMA is more responsive; VWMA weights by volume.
- **CCI MA Length** (`ma_length`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for the CCI MA. Longer periods smooth the MA but may delay signals.
### 3.5 RSI Filter Settings
- **Use RSI Filter** (`use_rsi_filter`):
- **Default**: `false`.
- **Effect**: If `true`, applies RSI-based overbought/oversold filters to signals.
- **RSI Length** (`rsi_length`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for RSI calculation. Longer periods reduce sensitivity.
- **RSI Lower Limit** (`rsi_lower_limit`):
- **Default**: `30`.
- **Range**: `0` to `100`.
- **Effect**: Defines the oversold threshold for buy signals. Lower values allow trades in more extreme conditions.
- **RSI Upper Limit** (`rsi_upper_limit`):
- **Default**: `70`.
- **Range**: `0` to `100`.
- **Effect**: Defines the overbought threshold for sell signals. Higher values allow trades in more extreme conditions.
### 3.6 Signal Settings
- **Cross Window** (`cross_window`):
- **Default**: `0`.
- **Range**: `0` to `5` bars.
- **Effect**: Specifies the lookback period for detecting DI+/- or CCI crosses. `0` requires crosses on the current bar; higher values allow recent crosses, increasing signal frequency.
- **Allow Long Trades** (`allow_long`):
- **Default**: `true`.
- **Effect**: Enables/disables new long trades. If `false`, only closing existing longs is allowed.
- **Allow Short Trades** (`allow_short`):
- **Default**: `true`.
- **Effect**: Enables/disables new short trades. If `false`, only closing existing shorts is allowed.
- **Require DI+/DI- Cross for Buy** (`buy_di_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a DI+ crossover DI- for buy signals; if `false`, DI+ > DI- is sufficient.
- **Require CCI Cross for Buy** (`buy_cci_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a CCI crossover MA for buy signals; if `false`, CCI > MA is sufficient.
- **Require DI+/DI- Cross for Sell** (`sell_di_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a DI- crossover DI+ for sell signals; if `false`, DI+ < DI- is sufficient.
- **Require CCI Cross for Sell** (`sell_cci_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a CCI crossunder MA for sell signals; if `false`, CCI < MA is sufficient.
- **Countertrade** (`countertrade`):
- **Default**: `true`.
- **Effect**: If `true`, closing a trade triggers an opposite trade (e.g., close long, open short) if allowed.
- **Color Background for Open Trades** (`color_background`):
- **Default**: `true`.
- **Effect**: If `true`, colors the chart background green for long trades and red for short trades.
### 3.7 Exit Settings
- **Use MA Cross for Exit** (`use_ma_exit`):
- **Default**: `true`.
- **Effect**: If `true`, closes trades when the price crosses the exit MA (below for long, above for short).
- **MA Length for Exit** (`ma_exit_length`):
- **Default**: `20`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for the exit MA. Longer periods delay exits.
- **MA Type for Exit** (`ma_exit_type`):
- **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`.
- **Default**: `SMA`.
- **Effect**: Determines the MA type for exit signals. EMA is more responsive; VWMA weights by volume.
- **Use ADX Change Stop-Loss** (`use_adx_stop`):
- **Default**: `false`.
- **Effect**: If `true`, closes trades when the ADX changes by a specified percentage.
- **ADX % Change for Stop-Loss** (`adx_change_percent`):
- **Default**: `5.0`.
- **Range**: Minimum `0.0`, step `0.1`.
- **Effect**: Specifies the percentage change in ADX (vs. previous bar) that triggers a stop-loss. Higher values reduce premature exits.
- **Use Performance Stop-Loss** (`use_perf_stop`):
- **Default**: `false`.
- **Effect**: If `true`, closes trades when the loss exceeds a percentage threshold.
- **Performance Stop-Loss (%)** (`perf_stop_percent`):
- **Default**: `-10.0`.
- **Range**: `-100.0` to `0.0`, step `0.1`.
- **Effect**: Specifies the loss percentage that triggers a stop-loss. More negative values allow larger losses before exiting.
## 4. Visual and Statistical Output
- **Labels**: Displayed at trade entries/exits with arrows (↑ for buy, ↓ for sell) and text ("BUY," "SELL"). A "No Equity" label appears if equity is zero.
- **Background Coloring**: Optionally colors the chart background (green for long, red for short) to indicate open trades.
- **Statistics Table**: Displayed at the top center of the chart, updated on timeframe changes or trade events. Includes:
- **Capital Metrics**: Initial capital, current capital, capital growth (%).
- **Trade Metrics**: Total trades, long/short trades, win rate, long/short win rates, profit factor.
- **Open Trade Status**: Indicates if a long, short, or no trade is open.
## 5. Alerts
- **Buy Signal Alert**: Triggered when `buy_signal` is true ("Cross Buy Signal").
- **Sell Signal Alert**: Triggered when `sell_signal` is true ("Cross Sell Signal").
- **Usage**: Users can set up TradingView alerts to receive notifications for trade signals.
Trend River Pullback (Avramis-style) v1//@version=5
strategy("Trend River Pullback (Avramis-style) v1",
overlay=true, initial_capital=10000, commission_type=strategy.commission.percent, commission_value=0.02,
pyramiding=0, calc_on_order_fills=true, calc_on_every_tick=true, margin_long=1, margin_short=1)
// ===== Inputs
// EMA "река"
emaFastLen = input.int(8, "EMA1 (быстрая)")
ema2Len = input.int(13, "EMA2")
emaMidLen = input.int(21, "EMA3 (средняя)")
ema4Len = input.int(34, "EMA4")
emaSlowLen = input.int(55, "EMA5 (медленная)")
// Откат и импульс
rsiLen = input.int(14, "RSI длина")
rsiOB = input.int(60, "RSI порог тренда (лонг)")
rsiOS = input.int(40, "RSI порог тренда (шорт)")
pullbackPct = input.float(40.0, "Глубина отката в % ширины реки", minval=0, maxval=100)
// Риск-менеджмент
riskPct = input.float(1.0, "Риск на сделку, % от капитала", step=0.1, minval=0.1)
atrLen = input.int(14, "ATR длина (стоп/трейлинг)")
atrMultSL = input.float(2.0, "ATR множитель для стопа", step=0.1)
tpRR = input.float(2.0, "Тейк-профит R-множитель", step=0.1)
// Трейлинг-стоп
useTrail = input.bool(true, "Включить трейлинг-стоп (Chandelier)")
trailMult = input.float(3.0, "ATR множитель трейлинга", step=0.1)
// Торговые часы (по времени биржи TradingView символа)
useSession = input.bool(false, "Ограничить торговые часы")
sessInput = input.session("0900-1800", "Сессия (локальная для биржи)")
// ===== Calculations
ema1 = ta.ema(close, emaFastLen)
ema2 = ta.ema(close, ema2Len)
ema3 = ta.ema(close, emaMidLen)
ema4 = ta.ema(close, ema4Len)
ema5 = ta.ema(close, emaSlowLen)
// "Река": верх/низ как конверт по средним
riverTop = math.max(math.max(ema1, ema2), math.max(ema3, math.max(ema4, ema5)))
riverBot = math.min(math.min(ema1, ema2), math.min(ema3, math.min(ema4, ema5)))
riverMid = (riverTop + riverBot) / 2.0
riverWidth = riverTop - riverBot
// Трендовые условия: выстроенность EMAs
bullAligned = ema1 > ema2 and ema2 > ema3 and ema3 > ema4 and ema4 > ema5
bearAligned = ema1 < ema2 and ema2 < ema3 and ema3 < ema4 and ema4 < ema5
// Импульс
rsi = ta.rsi(close, rsiLen)
// Откат внутрь "реки"
pullbackLevelBull = riverTop - riverWidth * (pullbackPct/100.0) // чем больше %, тем глубже внутрь
pullbackLevelBear = riverBot + riverWidth * (pullbackPct/100.0)
pullbackOkBull = bullAligned and rsi >= rsiOB and low <= pullbackLevelBull
pullbackOkBear = bearAligned and rsi <= rsiOS and high >= pullbackLevelBear
// Триггер входа: возврат в импульс (пересечение быстрой EMA)
longTrig = pullbackOkBull and ta.crossover(close, ema1)
shortTrig = pullbackOkBear and ta.crossunder(close, ema1)
// Сессия
inSession = useSession ? time(timeframe.period, sessInput) : true
// ATR для стопов
atr = ta.atr(atrLen)
// ===== Position sizing по риску
// Расчет размера позиции: риск% от капитала / (стоп в деньгах)
capital = strategy.equity
riskMoney = capital * (riskPct/100.0)
// Предварительные уровни стопов
longSL = close - atrMultSL * atr
shortSL = close + atrMultSL * atr
// Цена тика и размер — приблизительно через syminfo.pointvalue (может отличаться на разных рынках)
tickValue = syminfo.pointvalue
// Избежать деления на 0
slDistLong = math.max(close - longSL, syminfo.mintick)
slDistShort = math.max(shortSL - close, syminfo.mintick)
// Кол-во контрактов/лотов
qtyLong = riskMoney / (slDistLong * tickValue)
qtyShort = riskMoney / (slDistShort * tickValue)
// Ограничение: не меньше 0
qtyLong := math.max(qtyLong, 0)
qtyShort := math.max(qtyShort, 0)
// ===== Entries
if inSession and longTrig and strategy.position_size <= 0
strategy.entry("Long", strategy.long, qty=qtyLong)
if inSession and shortTrig and strategy.position_size >= 0
strategy.entry("Short", strategy.short, qty=qtyShort)
// ===== Exits: фиксированный TP по R и стоп
// Храним цену входа
var float entryPrice = na
if strategy.position_size != 0 and na(entryPrice)
entryPrice := strategy.position_avg_price
if strategy.position_size == 0
entryPrice := na
// Цели
longTP = na(entryPrice) ? na : entryPrice + tpRR * (entryPrice - longSL)
shortTP = na(entryPrice) ? na : entryPrice - tpRR * (shortSL - entryPrice)
// Трейлинг: Chandelier
trailLong = close - trailMult * atr
trailShort = close + trailMult * atr
// Итоговые уровни выхода
useTrailLong = useTrail and strategy.position_size > 0
useTrailShort = useTrail and strategy.position_size < 0
// Для лонга
if strategy.position_size > 0
stopL = math.max(longSL, na) // базовый стоп
tStop = useTrailLong ? trailLong : longSL
// Выход по стопу/трейлу и ТП
strategy.exit("L-Exit", from_entry="Long", stop=tStop, limit=longTP)
// Для шорта
if strategy.position_size < 0
stopS = math.min(shortSL, na)
tStopS = useTrailShort ? trailShort : shortSL
strategy.exit("S-Exit", from_entry="Short", stop=tStopS, limit=shortTP)
// ===== Visuals
plot(ema1, "EMA1", display=display.all, linewidth=1)
plot(ema2, "EMA2", display=display.all, linewidth=1)
plot(ema3, "EMA3", display=display.all, linewidth=2)
plot(ema4, "EMA4", display=display.all, linewidth=1)
plot(ema5, "EMA5", display=display.all, linewidth=1)
plot(riverTop, "River Top", style=plot.style_linebr, linewidth=1)
plot(riverBot, "River Bot", style=plot.style_linebr, linewidth=1)
fill(plot1=plot(riverTop, display=display.none), plot2=plot(riverBot, display=display.none), title="River Fill", transp=80)
plot(longTP, "Long TP", style=plot.style_linebr)
plot(shortTP, "Short TP", style=plot.style_linebr)
plot(useTrailLong ? trailLong : na, "Trail Long", style=plot.style_linebr)
plot(useTrailShort ? trailShort : na, "Trail Short", style=plot.style_linebr)
// Маркеры сигналов
plotshape(longTrig, title="Long Trigger", style=shape.triangleup, location=location.belowbar, size=size.tiny, text="L")
plotshape(shortTrig, title="Short Trigger", style=shape.triangledown, location=location.abovebar, size=size.tiny, text="S")
// ===== Alerts
alertcondition(longTrig, title="Long Signal", message="Long signal: trend aligned + pullback + momentum")
alertcondition(shortTrig, title="Short Signal", message="Short signal: trend aligned + pullback + momentum")
RS Alpha by The Noiseless TraderRS Alpha by The Noiseless Trader plots a clean, benchmark‑relative strength line for any symbol and (optionally) a mean line to assess trend and momentum in relative performance. It’s designed for uncluttered, professional RS analysis and works across any timeframe.
Compare any symbol vs a benchmark (default: NSE:NIFTY).
Optional log‑normalized RS for return‑aware comparisons.
Optional RS Mean with trend coloring (rising/falling).
Optional RS Trend zero‑line coloring based on short‑range slope.
Lightweight alerts for rising/falling RS mean.
Tip: Use RS to identify leaders (RS > 0 with rising mean) and laggards (RS < 0 with falling mean), then align setups with your price action rules.
Reading the indicator
Leadership: RS > 0 and RS Mean rising → outperformance vs benchmark.
Weakness: RS < 0 and RS Mean falling → underperformance vs benchmark.
Inflections: Watch RS crossing above/below its Mean for early shifts.
Zero‑line context: With RS Trend on, the zero line subtly reflects short‑term slope (green for positive, maroon for negative).
Alerts
Rising Strength – RS Mean turning/remaining upward.
Declining Strength – RS Mean turning/remaining downward.
(Use these as context; execute entries on your price‑action rules.)
Best practices
Pair RS with your trend/structure rules (e.g., higher highs + RS leadership).
For sectors/baskets, keep the Comparative Symbol consistent to rank peers.
Log‑normalized RS helps when comparing assets with very different volatilities or large base effects.
Test multiple length and Mean settings; 60 is a balanced default for swing/positional work.
Credits
Original concept & code: © bharatTrader
Modifications & refinements: The Noiseless Trader
ADX MTF mura visionOverview
ADX MTF — mura vision measures trend strength and visualizes a higher-timeframe (HTF) ADX on any chart. The current-TF ADX is drawn as a line; the HTF ADX is rendered as “step” segments to reflect closed HTF bars without repainting. Optional soft fills highlight the 20–25 (trend forming) and 40–50 (strong trend) zones.
How it works
ADX (current TF) : Classic Wilder formulation using DI components and RMA smoothing.
HTF ADX : Requested via request.security(..., lookahead_off, gaps_off).
When a new HTF bar opens, the previous value is frozen as a horizontal segment.
The current HTF bar is shown as a live moving segment.
This staircase look is expected on lower timeframes.
Auto timeframe mapping
If “Auto” is selected, the HTF is derived from the chart TF:
<30m → 60m, 30–<240m → 240m, 240m–<1D → 1D, 1D → 1W, 1W/2W → 1M, ≥1M → same.
Inputs
DI Length and ADX Smoothing — core ADX parameters.
Higher Time Frame — Auto or a fixed TF.
Line colors/widths for current ADX and HTF ADX.
Fill zone 20–25 and Fill zone 40–50 — optional light background fills.
Number of HTF ADX Bars — limits stored HTF segments to control chart load.
Reading the indicator
ADX < 20: typically range-bound conditions; trend setups require extra caution.
20–25: trend emergence; breakouts and continuation structures gain validity.
40–50: strong trend; favor continuation and manage with trailing stops.
>60 and turning down: possible trend exhaustion or transition toward range.
Note: ADX measures strength, not direction. Combine with your directional filter (e.g., price vs. MA, +DI/−DI, structure/levels).
Non-repainting behavior
HTF values use lookahead_off; closed HTF bars are never revised.
The only moving piece is the live segment for the current HTF bar.
Best practices
Use HTF ADX as a regime filter; time entries with the current-TF ADX rising through your threshold.
Pair with ATR-based stops and a MA/structure filter for direction.
Consider higher thresholds on highly volatile altcoins.
Performance notes
The script draws line segments for HTF bars. If your chart becomes heavy, reduce “Number of HTF ADX Bars.”
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Trading involves risk.
ORB with Fib Levels - TradingbrockOpening Range (OR) Indicator Overview
This TradingView indicator analyzes and displays the Opening Range - a popular day trading concept that tracks price movement during the first 30-60 minutes of the trading session.
Core Functionality:
Opening Range Detection: By default, it monitors the 9:30-10:00 AM ET period and tracks the highest high and lowest low during this time frame, creating upper and lower boundaries.
Fibonacci Retracement Levels: Inside the opening range, it displays five key Fibonacci levels:
0.236 (23.6% - shallow retracement)
0.382 (38.2% - standard retracement)
0.500 (50% - halfway point)
0.618 (61.8% - golden ratio)
0.786 (78.6% - deep retracement)
Extension Levels: The indicator projects additional levels beyond the opening range:
1x extension above/below the range
2x extension levels that only appear when price breaks the first extension
Trading Applications:
Support & Resistance: The opening range high/low often act as key levels throughout the trading day
Breakout Trading: Many traders watch for price to break above or below the opening range
Mean Reversion: The Fibonacci levels within the range can serve as potential reversal points
Risk Management: Helps define clear levels for stop losses and profit targets
The indicator essentially gives traders a framework to understand how price is behaving relative to the early session's established range, which often sets the tone for the entire trading day.
Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator
Purpose
This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns.
What a martingale is
A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged:
Lose one leg → increase next position size
Lose again → increase again
Win → reset to the base size
The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops.
What it plots
Equity – simulated portfolio equity including compounding
Buy & Hold – equity from holding the chart symbol for context
Optional helpers – last trade outcome, current streak length, current allocation fraction
Optional diagnostics – daily portfolio return, rolling drawdown, metrics table
Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands
Model assumptions
Bar-close execution with no slippage or commissions
Shorting allowed and frictionless
No margin interest, borrow fees, or position limits
No intrabar moves or gaps within a bar (returns are close-to-close)
Sizing applies to equity fraction only and is capped by your setting
All results are hypothetical and for education only.
How the simulator applies it
1) Directional signal
You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts.
2) Sizing after losses and wins
Position size is a fraction of equity:
Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity
Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double
Reset after win – return to the initial allocation
Max allocation cap – hard ceiling to prevent runaway growth
At a high level the size after k consecutive losses is
alloc(k) = min( cap , base × factor^k ) .
In practice the simulator changes size only when a leg ends and its PnL is known.
3) Equity update
Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds:
eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) .
This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled.
Why traders experiment with martingale sizing
Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn
Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests
Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live
Why martingale is dangerous
Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic:
Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N.
Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible.
No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels.
Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest.
Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs.
Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected.
The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty.
Interpreting losing streaks with numbers
A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check:
If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare.
If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap.
This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions
Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests..
Using the simulator productively
Parameter studies
Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond.
Initial allocation – lower base reduces volatility and drawdowns across the board
Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive
Max cap – the most important brake; many users keep it between 20 and 50 percent
Signal selection
Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each.
Diagnostics to watch
Use the built-in metrics to quantify risk:
Max Drawdown – worst peak-to-trough equity loss
Sharpe and Sortino – volatility and downside-adjusted return
VaR 95 percent and CVaR – tail risk measures from the realized distribution
Alpha and Beta – relationship to your chosen benchmark
If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see
Monte Carlo exploration
When enabled, the forecast draws many synthetic paths from your realized daily returns:
Choose a horizon and a number of runs
Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path
Use the table to read the expected return over the horizon and the tail outcomes
Remember it is a sketch based on your recent distribution, not a predictor
Concrete examples
Example A: Modest martingale
Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing.
Example B: Aggressive martingale
Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk.
Strengths
Bar-by-bar, transparent computation of equity from stance and size
Explicit handling of wins, losses, streaks, and caps
Portable signal inputs so you can A–B test ideas quickly
Risk diagnostics and forward uncertainty visualization in one place
Example, Rolling Max Drawdown
Limitations and important notes
Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs.
No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled.
Signals are evaluated on closes. Real execution and fills will differ.
Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes.
All results are hypothetical. Use this as an educational tool, not a production risk engine.
Practical tips
Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples.
Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg.
Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear.
Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off.
If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop.
Settings recap
Backtest start date and initial capital
Initial allocation, increase-after-loss factor, max allocation cap
Signal source selector
Trading days per year and risk-free rate
Benchmark symbol for Alpha and Beta
UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown
Monte Carlo controls for enable, runs, horizon, and result table
Final thoughts
A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
FibADX MTF Dashboard — DMI/ADX with Fibonacci DominanceFibADX MTF Dashboard — DMI/ADX with Fibonacci Dominance (φ)
This indicator fuses classic DMI/ADX with the Fibonacci Golden Ratio to score directional dominance and trend tradability across multiple timeframes in one clean panel.
What’s unique
• Fibonacci dominance tiers:
• BULL / BEAR → one side slightly stronger
• STRONG when one DI ≥ 1.618× the other (φ)
• EXTREME when one DI ≥ 2.618× (φ²)
• Rounded dominance % in the +DI/−DI columns (e.g., STRONG BULL 72%).
• ADX column modes: show the value (with strength bar ▂▃▅… and slope ↗/↘) or a tier (Weak / Tradable / Strong / Extreme).
• Configurable intraday row (30m/1H/2H/4H) + D/W/M toggles.
• Threshold line: color & width; Extended (infinite both ways) or Not extended (historical plot).
• Theme presets (Dark / Light / High Contrast) or full custom colors.
• Optional panel shading when all selected TFs are strong (and optionally directionally aligned).
How to use
1. Choose an intraday TF (30/60/120/240). Enable D/W/M as needed.
2. Use ADX ≥ threshold (e.g., 21 / 34 / 55) to find tradable trends.
3. Read the +DI/−DI labels to confirm bias (BULL/BEAR) and conviction (STRONG/EXTREME).
4. Prefer multi-TF alignment (e.g., 4H & D & W all strong bull).
5. Treat EXTREME as a momentum regime—trail tighter and scale out into spikes.
Alerts
• All selected TFs: Strong BULL alignment
• All selected TFs: Strong BEAR alignment
Notes
• Smoothing selectable: RMA (Wilder) / EMA / SMA.
• Percentages are whole numbers (72%, not 72.18%).
• Shorttitle is FibADX to comply with TV’s 10-char limit.
Why We Use Fibonacci in FibADX
Traditional DMI/ADX indicators rely on fixed numeric thresholds (e.g., ADX > 20 = “tradable”), but they ignore the relationship between +DI and −DI, which is what really determines trend conviction.
FibADX improves on this by introducing the Fibonacci Golden Ratio (φ ≈ 1.618) to measure directional dominance and classify trend strength more intelligently.
⸻
1. Fibonacci as a Natural Strength Threshold
The golden ratio φ appears everywhere in nature, growth cycles, and fractals.
Since financial markets also behave fractally, Fibonacci levels reflect natural crowd behavior and trend acceleration points.
In FibADX:
• When one DI is slightly larger than the other → BULL or BEAR (mild advantage).
• When one DI is at least 1.618× the other → STRONG BULL or STRONG BEAR (trend conviction).
• When one DI is 2.618× or more → EXTREME BULL or EXTREME BEAR (high momentum regime).
This approach adds structure and consistency to trend classification.
⸻
2. Why 1.618 and 2.618 Instead of Random Numbers
Other traders might pick thresholds like 1.5 or 2.0, but φ has special mathematical properties:
• φ is the most irrational ratio, meaning proportions based on φ retain structure even when scaled.
• Using φ makes FibADX naturally adaptive to all timeframes and asset classes — stocks, crypto, forex, commodities.
⸻
3 . Trading Advantages
Using the Fibonacci Golden Ratio inside DMI/ADX has several benefits:
• Better trend filtering → Avoid false DI crossovers without conviction.
• Catch early momentum shifts → Spot when dominance ratios approach φ before ADX reacts.
• Consistency across markets → Because φ is scalable and fractal, it works everywhere.
⸻
4. How FibADX Uses This
FibADX combines:
• +DI vs −DI ratio → Measures directional dominance.
• φ thresholds (1.618, 2.618) → Classifies strength into BULL, STRONG, EXTREME.
• ADX threshold → Confirms whether the move is tradable or just noise.
• Multi-timeframe dashboard → Aligns bias across 4H, D, W, M.
⸻
Quick Blurb for TradingView
FibADX uses the Fibonacci Golden Ratio (φ ≈ 1.618) to classify trend strength.
Unlike classic DMI/ADX, FibADX measures how much one side dominates:
• φ (1.618) = STRONG trend conviction
• φ² (2.618) = EXTREME momentum regime
This creates an adaptive, fractal-aware framework that works across stocks, crypto, forex, and commodities.
⚠️ Disclaimer : This script is provided for educational purposes only.
It does not constitute financial advice.
Use at your own risk. Always do your own research before making trading decisions.
Created by @nomadhedge
Intrabar Volume Delta — RealTime + History (Stocks/Crypto/Forex)Intrabar Volume Delta Grid — RealTime + History (Stocks/Crypto/Forex)
# Short Description
Shows intrabar Up/Down volume, Delta (absolute/relative) and UpShare% in a compact grid for both real-time and historical bars. Includes an MTF (M1…D1) dashboard, contextual coloring, density controls, and alerts on Δ and UpShare%. Smart historical splitting (“History Mode”) for Crypto/Futures/FX.
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# What it does (Quick)
* **UpVol / DownVol / Δ / UpShare%** — visualizes order-flow inside each candle.
* **Real-time** — accumulates intrabar volume live by tick-direction.
* **History Mode** — splits Up/Down on closed bars via simple or range-aware logic.
* **MTF Dashboard** — one table view across M1, M5, M15, M30, H1, H4, D1 (Vol, Up/Down, Δ%, Share, Trend).
* **Contextual opacity** — stronger signals appear bolder.
* **Label density** — draw every N-th bar and limit to last X bars for performance.
* **Alerts** — thresholds for |Δ|, Δ%, and UpShare%.
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# How it works (Real-Time vs History)
* **Real-time (open bar):** volume increments into **UpVolRT** or **DownVolRT** depending on last price move (↑ goes to Up, ↓ to Down). This approximates live order-flow even when full tick history isn’t available.
* **History (closed bars):**
* **None** — no split (Up/Down = 0/0). Safest for equities/indices with unreliable tick history.
* **Approx (Close vs Open)** — all volume goes to candle direction (green → Up 100%, red → Down 100%). Fast but yields many 0/100% bars.
* **Price Action Based** — splits by Close position within High-Low range; strength = |Close−mid|/(High−Low). Above mid → more Up; below mid → more Down. Falls back to direction if High==Low.
* **Auto** — **Stocks/Index → None**, **Crypto/Futures/FX → Approx**. If you see too many 0/100 bars, switch to **Price Action Based**.
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# Rows & Meaning
* **Volume** — total bar volume (no split).
* **UpVol / DownVol** — directional intrabar volume.
* **Delta (Δ)** — UpVol − DownVol.
* **Absolute**: raw units
* **Relative (Δ%)**: Δ / (Up+Down) × 100
* **Both**: shows both formats
* **UpShare%** — UpVol / (Up+Down) × 100. >50% bullish, <50% bearish.
* Helpful icons: ▲ (>65%), ▼ (<35%).
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# MTF Dashboard (🔧 Enable Dashboard)
A single table with **Vol, Up, Down, Δ%, Share, Trend (🔼/🔽/⏭️)** for selected timeframes (M1…D1). Great for a fast “panorama” read of flow alignment across horizons.
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# Inputs (Grouped)
## Display
* Toggle rows: **Volume / Up / Down / Delta / UpShare**
* **Delta Display**: Absolute / Relative / Both
## Realtime & History
* **History Mode**: Auto / None / Approx / Price Action Based
* **Compact Numbers**: 1.2k, 1.25M, 3.4B…
## Theme & UI
* **Theme Mode**: Auto / Light / Dark
* **Row Spacing**: vertical spacing between rows
* **Top Row Y**: moves the whole grid vertically
* **Draw Guide Lines**: faint dotted guides
* **Text Size**: Tiny / Small / Normal / Large
## 🔧 Dashboard Settings
* **Enable Dashboard**
* **📏 Table Text Size**: Tiny…Huge
* **🦓 Zebra Rows**
* **🔲 Table Border**
## ⏰ Timeframes (for Dashboard)
* **M1…D1** toggles
## Contextual Coloring
* **Enable Contextual Coloring**: opacity by signal strength
* **Δ% cap / Share offset cap**: saturation caps
* **Min/Max transparency**: solid vs faint extremes
## Label Density & Size
* **Show every N-th bar**: draw labels only every Nth bar
* **Limit to last X bars**: keep labels only in the most recent X bars
## Colors
* Up / Down / Text / Guide
## Alerts
* **Delta Threshold (abs)** — |Δ| in volume units
* **UpShare > / <** — bullish/bearish thresholds
* **Enable Δ% Alert**, **Δ% > +**, **Δ% < −** — relative delta levels
---
# How to use (Quick Start)
1. Add the indicator to your chart (overlay=false → separate pane).
2. **History Mode**:
* Crypto/Futures/FX → keep **Auto** or switch to **Price Action Based** for richer history.
* Stocks/Index → prefer **None** or **Price Action Based** for safer splits.
3. **Label Density**: start with **Limit to last X bars = 30–150** and **Show every N-th bar = 2–4**.
4. **Contextual Coloring**: keep on to emphasize strong Δ% / Share moves.
5. **Dashboard**: enable and pick only the TFs you actually use.
6. **Alerts**: set thresholds (ideas below).
---
# Alerts (in TradingView)
Add alert → pick this indicator → choose any of:
* **Delta exceeds threshold** (|Δ| > X)
* **UpShare above threshold** (UpShare% > X)
* **UpShare below threshold** (UpShare% < X)
* **Relative Delta above +X%**
* **Relative Delta below −X%**
**Starter thresholds (tune per symbol & TF):**
* **Crypto M1/M5**: Δ% > +25…35 (bullish), Δ% < −25…−35 (bearish)
* **FX (tick volume)**: UpShare > 60–65% or < 40–35%
* **Stocks (liquid)**: set **Absolute Δ** by typical volume scale (e.g., 50k / 100k / 500k)
---
# Notes by Market Type
* **Crypto/Futures**: 24/7 and high liquidity — **Price Action Based** often gives nicer history splits than Approx.
* **Forex (FX)**: TradingView volume is typically **tick volume** (not true exchange volume). Treat Δ/Share as tick-based flow, still very useful intraday.
* **Stocks/Index**: historical tick detail can be limited. **None** or **Price Action Based** is a safer default. If you see too many 0/100% shares, switch away from Approx.
---
# “All Timeframes” accuracy
* Works on **any TF** (M1 → D1/W1).
* **Real-time accuracy** is strong for the open bar (live accumulation).
* **Historical accuracy** depends on your **History Mode** (None = safest, Approx = fastest/simplest, Price Action Based = more nuanced).
* The MTF dashboard uses `request.security` and therefore follows the same logic per TF.
---
# Trade Ideas (Use-Cases)
* **Scalping (M1–M5)**: a spike in Δ% + UpShare>65% + rising total Vol → momentum entries.
* **Intraday (M5–M30–H1)**: when multiple TFs show aligned Δ%/Share (e.g., M5 & M15 bullish), join the trend.
* **Swing (H4–D1)**: persistent Δ% > 0 and UpShare > 55–60% → structural accumulation bias.
---
# Advantages
* **True-feeling live flow** on the open bar.
* **Adaptable history** (three modes) to match data quality.
* **Clean visual layout** with guides, compact numbers, contextual opacity.
* **MTF snapshot** for quick bias read.
* **Performance controls** (last X bars, every N-th bar).
---
# Limitations & Care
* **FX uses tick volume** — interpret Δ/Share accordingly.
* **History Mode is an approximation** — confirm with trend/structure/liquidity context.
* **Illiquid symbols** can produce noisy or contradictory signals.
* **Too many labels** can slow charts → raise N, lower X, or disable guides.
---
# Best Practices (Checklist)
* Crypto/Futures: prefer **Price Action Based** for history.
* Stocks: **None** or **Price Action Based**; be cautious with **Approx**.
* FX: pair Δ% & UpShare% with session context (London/NY) and volatility.
* If labels overlap: tweak **Row Spacing** and **Text Size**.
* In the dashboard, keep only the TFs you actually act on.
* Alerts: start around **Δ% 25–35** for “punchy” moves, then refine per asset.
---
# FAQ
**1) Why do some closed bars show 0%/100% UpShare?**
You’re on **Approx** history mode. Switch to **Price Action Based** for smoother splits.
**2) Δ% looks strong but price doesn’t move — why?**
Δ% is an **order-flow** measure. Price also depends on liquidity pockets, sessions, news, higher-timeframe structure. Use confirmations.
**3) Performance slowdown — what to do?**
Lower **Limit to last X bars** (e.g., 30–100), increase **Show every N-th bar** (2–6), or disable **Draw Guide Lines**.
**4) Dashboard values don’t “match” the grid exactly?**
Dashboard is multi-TF via `request.security` and follows the history logic per TF. Differences are normal.
---
# Short “Store” Marketing Blurb
Intrabar Volume Delta Grid reveals the order-flow inside every candle (Up/Down, Δ, UpShare%) — live and on history. With smart history splitting, an MTF dashboard, contextual emphasis, and flexible alerts, it helps you spot momentum and bias across Crypto, Forex (tick volume), and Stocks. Tidy labels and compact numbers keep the panel readable and fast.
K線虛擬幣// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © dear.simpson
//@version=5
indicator("月季線視覺操盤", "", true)
// Getting inputs
length = input(5, "操盤線週期")
// Calculating
ma = ta.sma(close, length)
spread = close-ma
// Plotcandle
plotcandle(open, high, low, close, title='操盤K線', editable = false , display =display.pane+display.price_scale , color = (spread>=0 ? #ef5350 : #26a69a) , bordercolor= (spread>=0 ? #ef5350 : #26a69a) , wickcolor = #5d606b)
// Getting inputs
maPeriods1 = input( 5 , "MA 1" , group="移動平均線")
maPeriods2 = input(20 , "MA 2" , group="移動平均線")
maPeriods3 = input(60 , "MA 3" , group="移動平均線")
line0 = ta.sma(close, 2)
line1 = ta.sma(close, maPeriods1)
line2 = ta.sma(close, maPeriods2)
line3 = ta.sma(close, maPeriods3)
// Plot Moving Average Line
p0PlotID = plot(line0 ,"MA 0" , color.new(color.black ,100), display = display.none , editable = false)
p1PlotID = plot(line1 ,"MA 1" , color.new(#787b86, 50), display = display.pane+display.price_scale )
p2PlotID = plot(line2 ,"MA 2" , color.new(#787b86, 0), display = display.pane+display.price_scale )
p3PlotID = plot(line3 ,"MA 3" , color.new(color.blue , 30), display = display.pane+display.price_scale )
// Plot Zone Color
fill(p0PlotID, p2PlotID, close > line2 ? color.new(#ef5350, 70) : color.new(#26a69a, 90), '高/低於月線區域顏色')
fill(p0PlotID, p3PlotID, close > line3 ? color.new(#ef5350, 70) : color.new(#26a69a, 90), '高/低於季線區域顏色' , display = display.none )
MTF CRT Setup Finder (Raids + BOS linked)//@version=6
indicator("MTF CRT Setup Finder (Raids + BOS linked)", overlay=true, max_lines_count=500)
// === INPUTS ===
lookback = input.int(5, "Swing Lookback Bars", minval=2)
// === Function: Detect swing highs/lows ===
swingHigh(src, lb) => ta.pivothigh(src, lb, lb)
swingLow(src, lb) => ta.pivotlow(src, lb, lb)
// === Function: Detect CRT with memory ===
f_crt(tf) =>
hi = request.security(syminfo.tickerid, tf, high)
lo = request.security(syminfo.tickerid, tf, low)
cl = request.security(syminfo.tickerid, tf, close)
sh = request.security(syminfo.tickerid, tf, swingHigh(high, lookback))
sl = request.security(syminfo.tickerid, tf, swingLow(low, lookback))
raidHigh = not na(sh) and hi > sh and cl < sh
raidLow = not na(sl) and lo < sl and cl > sl
// store last raid state
var bool hadRaidHigh = false
var bool hadRaidLow = false
if raidHigh
hadRaidHigh := true
if raidLow
hadRaidLow := true
bosDown = hadRaidHigh and cl < sl
bosUp = hadRaidLow and cl > sh
// reset after BOS
if bosDown
hadRaidHigh := false
if bosUp
hadRaidLow := false
// === Apply on H1 only first (test) ===
= f_crt("60")
// === Plot ===
plotshape(raidHigh, title="Raid High", style=shape.diamond, color=color.red, size=size.small, text="Raid High")
plotshape(raidLow, title="Raid Low", style=shape.diamond, color=color.green, size=size.small, text="Raid Low")
plotshape(bosDown, title="Bearish CRT", style=shape.triangledown, color=color.red, size=size.large, text="CRT↓")
plotshape(bosUp, title="Bullish CRT", style=shape.triangleup, color=color.green, size=size.large, text="CRT↑")
Advanced Pattern Detection System [50+ Patterns]【Advanced Pattern Detection System - Auto-detects 50+ Chart Patterns】
Introducing the most powerful pattern detection indicator for TradingView!
◆ What is this?
An automated tool that finds and displays over 50 chart patterns on your charts. It detects all the patterns professional traders use - Double Tops, Triangles, Head & Shoulders, and more - all in ONE indicator.
◆ Main Features
・Detects 50+ patterns in real-time
・Shows visual explanation of WHY each pattern was identified
・Automatically calculates theoretical target prices
・Displays confidence levels in % (60-95%)
・Choose panel position from 9 locations
・Works on all timeframes (1min to Monthly)
◆ Detectable Patterns
1. Classic Patterns (Double Top/Bottom, Head & Shoulders, etc.)
2. Triangle Patterns (Ascending, Descending, Symmetrical, Expanding)
3. Continuation Patterns (Flags, Pennants, Wedges, etc.)
4. Harmonic Patterns (Gartley, Butterfly, Bat, etc.)
5. Price Action (Pin Bar, Engulfing, Hammer, etc.)
6. Special Patterns (Cup & Handle, V-formations, etc.)
◆ What Makes It Different
・Not just detection - shows the reasoning behind it
・Auto-draws pivot points and necklines
・Displays target prices with % gain/loss from current price
・Detects multiple patterns simultaneously, sorted by confidence
・Available in both Japanese and English versions
◆ Perfect For
✓ Anyone tired of using multiple indicators
✓ Beginners wanting to learn pattern trading
✓ Traders who don't want to miss entry points
✓ Those looking to improve discretionary trading accuracy
◆ How to Use (Easy 3 Steps)
1. Open TradingView and paste code in Pine Editor
2. Click "Add to Chart"
3. Enable only the patterns you need in settings
◆ Color Meanings
Green → Bullish potential (Buy signal)
Red → Bearish potential (Sell signal)
Yellow → Neutral direction (Wait and see)
◆ Recommended Settings
Scalping: Detection period 20, Sensitivity 0.0025
Day Trading: Detection period 50, Sensitivity 0.002
Swing Trading: Detection period 100, Sensitivity 0.0015
◆ Real Trading Example
"Detects Double Bottom → 85% confidence → Enter on neckline break → Take profit at displayed target price"
This is how you can use it in practice.
◆ Important Notes
・This is an analysis tool, not investment advice
・Always combine with other indicators
・Always set stop losses
・Practice on demo account before live trading
◆ Performance
If running slow, turn OFF unused pattern categories. Reducing max display count to 3 also helps.
◆ Summary
This single tool provides functionality that would normally require multiple paid indicators (worth $100-200 total). It's the ultimate pattern detection system recommended for all traders, from beginners to professionals.
Give it a try if interested! Feel free to ask questions in the comments.
SMA-Based Candle Color 60The Trend SMA colors the moving average green when sloping upward and red when sloping downward. Candles are also colored based on whether price is above (green) or below (red) the SMA, making trends easy to spot.
