Position Size & Drawdown ManagerThis tool is designed to help traders dynamically adjust their position size and drawdown expectations as their trading capital changes over time. It provides a simple and intuitive way to translate backtest results into real-world position sizing decisions.
Purpose and Functionality
The indicator uses your original backtest parameters — including base capital, base drawdown percentage, and base position size — and your current account balance to calculate how your risk profile changes. It presents two main scenarios:
Lock Drawdown %: Keeps your original drawdown percentage fixed and calculates the new position size required.
Lock Position Size: Keeps your position size unchanged and shows how your drawdown percentage will shift.
Why it’s useful
Many traders face the challenge of scaling their strategies as their account grows or shrinks. This tool makes it easy to visualize the relationship between position sizing, capital, and drawdown. It’s particularly valuable for risk management, portfolio rebalancing, and maintaining consistent exposure when transitioning from backtest conditions to live trading.
How it works
The calculations are displayed in a clean, color-coded table that updates dynamically. This allows you to instantly see how capital fluctuations impact your expected drawdown or position size. You can toggle between light and dark themes and highlight important cells for clarity.
Practical use case
Combine this tool with your TradingView strategy results to better interpret your backtests and adjust your real-world trade sizes accordingly. It bridges the gap between simulated performance and actual account management.
Chart example
The chart included focuses only on this indicator, showing the output table and visual layout clearly without additional scripts or overlays.
Cerca negli script per "backtest"
Match on Selectable Percentage Change + RangeIndicator Overview:
Match on Selectable Percentage Change + Range is a powerful analytical tool designed for traders and analysts who want to identify historical price bars that match a specific percentage variation, and then evaluate how price evolved in the following days. It combines precision filtering with visual tabular feedback, making it ideal for pattern recognition, backtesting, and scenario analysis.
What It Does
This indicator scans historical bars to find instances where the percentage change between two consecutive closes matches a user-defined target (± a customizable tolerance). Once matches are found, it displays:
The date of each match (most recent first)
The actual variation searched
The percentage change after 2, 10, 20, and 30 bars
The min-max range (in %) over those same periods
All results are shown in a dynamic table directly on the chart.
Inputs & Controls
Input Description
Which variation do you want to analyze? (%)
Set the target percentage change to look for (e.g. 2.5%)
% deviation from the variation to be considered (%) Define the tolerance range around the target (e.g. ±0.5%)
Bars to analyze (max 9999) Set how many past bars to scan
Show match table Toggle to enable/disable the entire table
Show percentage variations (2d, 10d, 20d, 30d) Toggle to show/hide post-match percentage changes
Show min-max ranges (2d, 10d, 20d, 30d) Toggle to show/hide post-match high/low ranges
Table Structure
Each row in the table represents a historical match. Columns include:
Date: When the match occurred
Variation in: The actual % change that triggered the match
2d / 10d / 20d / 30d: % change after those days
Min-Max 2d / 10d / 20d / 30d: Range of price movement after those days
Color coding helps quickly identify bullish (green) vs bearish (red) outcomes.
Use Cases
Backtesting: See how similar past moves evolved over time
Scenario modeling: Estimate potential outcomes after a known variation
Pattern recognition: Spot recurring setups or volatility clusters
Risk analysis: Understand post-variation drawdowns and upside potential
Tips for Use
Use tighter deviation (e.g. 0.3%) for precision, or wider (e.g. 1%) for broader pattern capture.
Combine with other indicators to validate setups (e.g. volume, RSI, trend filters).
Toggle off variation or range columns to focus only on the metrics you need.
MACD Enhanced [DCAUT]█ MACD Enhanced
📊 ORIGINALITY & INNOVATION
The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently.
This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies.
The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms.
📐 MATHEMATICAL FOUNDATION
The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines.
Available Algorithms:
The implementation supports a comprehensive spectrum of technical analysis algorithms:
Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting)
Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive)
Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential)
Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility)
Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation)
Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing)
Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Histogram Interpretation:
Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities
Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities
Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift
Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration
Advanced Signal Recognition:
Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals
Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup
Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones
🎯 STRATEGIC APPLICATIONS
Comprehensive Trend Confirmation Strategies:
Primary Trend Validation Protocol:
Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line
Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening
Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation
Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend)
Entry Timing Techniques:
Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero
Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns
Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals
Advanced Divergence Trading Systems:
Regular Divergence Recognition:
Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals
Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management
Hidden Divergence Strategies:
Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks
Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies
Multi-Timeframe Coordination Framework:
Three-Timeframe Analysis Structure:
Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction
Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions
Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias
Timeframe Synchronization Rules:
Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions
Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions
Conflicting signals between timeframes = Wait for alignment or use smaller position sizes
1H MACD signals only valid when aligned with both higher timeframes
Algorithm Considerations by Market Type:
Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions
Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market
Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required
Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise
Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches.
📋 DETAILED PARAMETER CONFIGURATION
Comprehensive Source Selection Strategy:
Price Source Analysis and Optimization:
Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows
HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions
HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth
OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis
Parameter Configuration Principles:
Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness.
Length Parameter Considerations:
Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment
Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings
Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics
Comprehensive Algorithm Selection Framework:
MACD Line Algorithm Decision Matrix:
EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior
SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes
HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise
KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio
Signal Line Algorithm Optimization Strategies:
Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics
Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals
Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA)
Parameter Sensitivity Considerations:
Impact of Parameter Changes:
Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics
Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms
Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Response Characteristics by Algorithm:
Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise
Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability
Highest Stability: SMA, RMA, TMA - reduced noise but increased lag
Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions
Noise Filtering Capabilities:
Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings.
Market Condition Adaptability:
Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility.
Comparative Performance vs Traditional MACD:
Algorithm Flexibility: 21 algorithms vs 1 fixed EMA
Signal Quality: Reduced false signals through noise filtering algorithms
Market Adaptability: Optimizable for any market condition vs fixed behavior
Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching
Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
RSI ADX Bollinger Analysis High-level purpose and design philosophy
This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management.
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What each indicator contributes (component-by-component)
RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side.
ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates.
Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move.
Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy.
4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results.
5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results.
These exercises teach both statistical thinking and the specific failure modes of the mashup.
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Limitations, failure modes and caveats (explicit & teachable)
• ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions.
• Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof.
• RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns.
• Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios.
Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent.
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Originality & mashup justification (text you can paste)
This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength.
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TradingView publication guidance & compliance checklist
To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code):
1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter).
2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each.
3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.”
4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size).
5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest).
6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits.
7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes.
8. Trademark clause: If you used trademark symbols, remove or provide registration proof.
9. Risk disclaimer: Add the copy-ready disclaimer below.
This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning.
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Copy-ready short publication description (paste into TradingView)
Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity.
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Copy-ready risk & misuse disclaimer (paste into description or help file)
This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls.
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Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
[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.
AVWAP+RSI Confluence — 1R TesterRSI + 1R ATR - Monthly P\&L (v4)
WHAT THIS STRATEGY DOES (OVERVIEW)
* Pine strategy (v4) that combines a simple momentum trigger with a symmetric 1R ATR risk model and an on-chart Monthly/Yearly P\&L table.
* Momentum filter: trades only when RSI crosses its own SMA in the direction of the trend (price vs Trend EMA).
* Risk engine: exits use fixed 1R ATR brackets captured at entry (no drifting targets/stops).
* Accounting: the table aggregates percentage returns by month and year using strategy equity.
ENTRY LOGIC (LONGS & OPTIONAL SHORTS)
Indicators used:
* RSI(rsiLen) and its SMA: SMA(RSI, rsiMaLen)
* Trend filter: EMA(emaTrendLen) on price
Longs:
1. RSI crosses above its RSI SMA
2. RSI > rsiBuyThr (filters weak momentum)
3. Close > EMA(emaTrendLen)
Shorts (optional via enableShort):
1. RSI crosses below its RSI SMA
2. RSI < rsiSellThr
3. Close < EMA(emaTrendLen)
EXIT LOGIC AND RISK MODEL (1R ATR)
* On entry, snapshot ATR(atrLen) into atrAtEntry and the average fill price into entryPx.
* Longs: stop = entryPx - ATR \* atrMult; target = entryPx + ATR \* atrMult
* Shorts: mirrored.
* Stops and targets are posted immediately and remain fixed for the life of the trade.
POSITION SIZING AND COSTS
* Default position size: 25% of equity per trade (adjustable in Properties/inputs).
* Commission percent and a small slippage are set in strategy() so backtests include friction by default.
MONTHLY / YEARLY P\&L TABLE (HOW IT WORKS)
* Uses strategy equity to compute bar returns: equity / equity\ - 1.
* Compounds bar returns into current month and current year; commits each finished period at month/year change (or last bar).
* Renders rows as years; columns Jan..Dec plus a Year total column.
* Cells colored by sign; precision and maximum rows are controlled by inputs.
* Values represent percentage returns, not currency P\&L.
VISUAL AIDS
* Two pivot trails (pivot high/low) are plotted for context only; they do not affect entries or exits.
CUSTOMIZATION TIPS
* Raise rsiBuyThr (long) or lower rsiSellThr (short) to filter weak momentum.
* Increase emaTrendLen to tighten trend alignment.
* Adjust atrLen and atrMult to fit your timeframe/instrument volatility.
* Leave enableShort = false if you prefer long-only behavior or shorting is constrained.
NON-REPAINTING AND BACKTEST NOTES
* Signals use bar-close crosses of built-in indicators (RSI, EMA, ATR); no future bars are referenced.
* calc\_on\_every\_tick = true for responsive visuals; Strategy Tester evaluates on bar close in history.
* Backtest stop/limit fills are simulated and may differ from live execution/liquidity.
DISCLAIMERS
* Educational use only. This is not financial advice. Markets involve risk. Past performance does not guarantee future results.
INPUTS (QUICK REFERENCE)
* rsiLen, rsiMaLen, rsiBuyThr, rsiSellThr
* emaTrendLen
* atrLen, atrMult, enableShort
* leftBars, rightBars, prec, showTable, maxYearsRows
SHORT TAGLINE
RSI momentum with 1R ATR brackets and a built-in Monthly/Yearly P\&L table.
TAGS
strategy, RSI, ATR, trend, risk-management, backtest, Pine-v4
Market Outlook Score (MOS)Overview
The "Market Outlook Score (MOS)" is a custom technical indicator designed for TradingView, written in Pine Script version 6. It provides a quantitative assessment of market conditions by aggregating multiple factors, including trend strength across different timeframes, directional movement (via ADX), momentum (via RSI changes), volume dynamics, and volatility stability (via ATR). The MOS is calculated as a weighted score that ranges typically between -1 and +1 (though it can exceed these bounds in extreme conditions), where positive values suggest bullish (long) opportunities, negative values indicate bearish (short) setups, and values near zero imply neutral or indecisive markets.
This indicator is particularly useful for traders seeking a holistic "outlook" score to gauge potential entry points or market bias. It overlays on a separate pane (non-overlay mode) and visualizes the score through horizontal threshold lines and dynamic labels showing the numeric MOS value along with a simple trading decision ("Long", "Short", or "Neutral"). The script avoids using the plot function for compatibility reasons (e.g., potential TradingView bugs) and instead relies on hline for static lines and label.new for per-bar annotations.
Key features:
Multi-Timeframe Analysis: Incorporates slope data from 5-minute, 15-minute, and 30-minute charts to capture short-term trends.
Trend and Strength Integration: Uses ADX to weight trend bias, ensuring stronger signals in trending markets.
Momentum and Volume: Includes RSI momentum impulses and volume deviations for added confirmation.
Volatility Adjustment: Factors in ATR changes to assess market stability.
Customizable Inputs: Allows users to tweak periods for lookback, ADX, and ATR.
Decision Labels: Automatically classifies the MOS into actionable categories with visual labels.
This indicator is best suited for intraday or swing trading on volatile assets like stocks, forex, or cryptocurrencies. It does not generate buy/sell signals directly but can be combined with other tools (e.g., moving averages or oscillators) for comprehensive strategies.
Inputs
The script provides three user-configurable inputs via TradingView's input panel:
Lookback Period (lookback):
Type: Integer
Default: 20
Range: Minimum 10, Maximum 50
Purpose: Defines the number of bars used in slope calculations for trend analysis. A shorter lookback makes the indicator more sensitive to recent price action, while a longer one smooths out noise for longer-term trends.
ADX Period (adxPeriod):
Type: Integer
Default: 14
Range: Minimum 5, Maximum 30
Purpose: Sets the smoothing period for the Average Directional Index (ADX) and its components (DI+ and DI-). Standard value is 14, but shorter periods increase responsiveness, and longer ones reduce false signals.
ATR Period (atrPeriod):
Type: Integer
Default: 14
Range: Minimum 5, Maximum 30
Purpose: Determines the period for the Average True Range (ATR) calculation, which measures volatility. Adjust this to match your trading timeframe—shorter for scalping, longer for positional trading.
These inputs allow customization without editing the code, making the indicator adaptable to different market conditions or user preferences.
Core Calculations
The MOS is computed through a series of steps, blending trend, momentum, volume, and volatility metrics. Here's a breakdown:
Multi-Timeframe Slopes:
The script fetches data from higher timeframes (5m, 15m, 30m) using request.security.
Slope calculation: For each timeframe, it computes the linear regression slope of price over the lookback period using the formula:
textslope = correlation(close, bar_index, lookback) * stdev(close, lookback) / stdev(bar_index, lookback)
This measures the rate of price change, where positive slopes indicate uptrends and negative slopes indicate downtrends.
Variables: slope5m, slope15m, slope30m.
ATR (Average True Range):
Calculated using ta.atr(atrPeriod).
Represents average volatility over the specified period. Used later to derive volatility stability.
ADX (Average Directional Index):
A detailed, manual implementation (not using built-in ta.adx for customization):
Computes upward movement (upMove = high - high ) and downward movement (downMove = low - low).
Derives +DM (Plus Directional Movement) and -DM (Minus Directional Movement) by filtering non-relevant moves.
Smooths true range (trur = ta.rma(ta.tr(true), adxPeriod)).
Calculates +DI and -DI: plusDI = 100 * ta.rma(plusDM, adxPeriod) / trur, similarly for minusDI.
DX: dx = 100 * abs(plusDI - minusDI) / max(plusDI + minusDI, 0.0001).
ADX: adx = ta.rma(dx, adxPeriod).
ADX values above 25 typically indicate strong trends; here, it's normalized (divided by 50) to influence the trend bias.
Volume Delta (5m Timeframe):
Fetches 5m volume: volume_5m = request.security(syminfo.tickerid, "5", volume, lookahead=barmerge.lookahead_on).
Computes a 12-period SMA of volume: avgVolume = ta.sma(volume_5m, 12).
Delta: (volume_5m - avgVolume) / avgVolume (or 0 if avgVolume is zero).
This measures relative volume spikes, where positive deltas suggest increased interest (bullish) and negative suggest waning activity (bearish).
MOS Components and Final Calculation:
Trend Bias: Average of the three slopes, normalized by close price and scaled by 100, then weighted by ADX influence: (slope5m + slope15m + slope30m) / 3 / close * 100 * (adx / 50).
Emphasizes trends in strong ADX conditions.
Momentum Impulse: Change in 5m RSI(14) over 1 bar, divided by 50: ta.change(request.security(syminfo.tickerid, "5", ta.rsi(close, 14), lookahead=barmerge.lookahead_on), 1) / 50.
Captures short-term momentum shifts.
Volatility Clarity: 1 - ta.change(atr, 1) / max(atr, 0.0001).
Measures ATR stability; values near 1 indicate low volatility changes (clearer trends), while lower values suggest erratic markets.
MOS Formula: Weighted average:
textmos = (0.35 * trendBias + 0.25 * momentumImpulse + 0.2 * volumeDelta + 0.2 * volatilityClarity)
Weights prioritize trend (35%) and momentum (25%), with volume and volatility at 20% each. These can be adjusted in code for experimentation.
Trading Decision:
A variable mosDecision starts as "Neutral".
If mos > 0.15, set to "Long".
If mos < -0.15, set to "Short".
Thresholds (0.15 and -0.15) are hardcoded but can be modified.
Visualization and Outputs
Threshold Lines (using hline):
Long Threshold: Horizontal dashed green line at +0.15.
Short Threshold: Horizontal dashed red line at -0.15.
Neutral Line: Horizontal dashed gray line at 0.
These provide visual reference points for MOS interpretation.
Dynamic Labels (using label.new):
Placed at each bar's index and MOS value.
Text: Formatted MOS value (e.g., "0.2345") followed by a newline and the decision (e.g., "Long").
Style: Downward-pointing label with gray background and white text for readability.
This replaces a traditional plot line, showing exact values and decisions per bar without cluttering the chart.
The indicator appears in a separate pane below the main price chart, making it easy to monitor alongside price action.
Usage Instructions
Adding to TradingView:
Copy the script into TradingView's Pine Script editor.
Save and add to your chart via the "Indicators" menu.
Select a symbol and timeframe (e.g., 1-minute for intraday).
Interpretation:
Long Signal: MOS > 0.15 – Consider bullish positions if supported by other indicators.
Short Signal: MOS < -0.15 – Potential bearish setups.
Neutral: Between -0.15 and 0.15 – Avoid trades or wait for confirmation.
Watch for MOS crossings of thresholds for momentum shifts.
Combine with price patterns, support/resistance, or volume for better accuracy.
Limitations and Considerations:
Lookahead Bias: Uses barmerge.lookahead_on for multi-timeframe data, which may introduce minor forward-looking bias in backtesting (use with caution).
No Alerts Built-In: Add custom alerts via TradingView's alert system based on MOS conditions.
Performance: Tested for compatibility; may require adjustments for illiquid assets or extreme volatility.
Backtesting: Use TradingView's strategy tester to evaluate historical performance, but remember past results don't guarantee future outcomes.
Customization: Edit weights in the MOS formula or thresholds to fit your strategy.
This indicator distills complex market data into a single score, aiding decision-making while encouraging users to verify signals with additional analysis. If you need modifications, such as restoring plot functionality or adding features, provide details for further refinement.
Multi-Confluence Swing Hunter V1# Multi-Confluence Swing Hunter V1 - Complete Description
Overview
The Multi-Confluence Swing Hunter V1 is a sophisticated low timeframe scalping strategy specifically optimized for MSTR (MicroStrategy) trading. This strategy employs a comprehensive point-based scoring system that combines optimized technical indicators, price action analysis, and reversal pattern recognition to generate precise trading signals on lower timeframes.
Performance Highlight:
In backtesting on MSTR 5-minute charts, this strategy has demonstrated over 200% profit performance, showcasing its effectiveness in capturing rapid price movements and volatility patterns unique to MicroStrategy's trading behavior.
The strategy's parameters have been fine-tuned for MSTR's unique volatility characteristics, though they can be optimized for other high-volatility instruments as well.
## Key Innovation & Originality
This strategy introduces a unique **dual scoring system** approach:
- **Entry Scoring**: Identifies swing bottoms using 13+ different technical criteria
- **Exit Scoring**: Identifies swing tops using inverse criteria for optimal exit timing
Unlike traditional strategies that rely on simple indicator crossovers, this system quantifies market conditions through a weighted scoring mechanism, providing objective, data-driven entry and exit decisions.
## Technical Foundation
### Optimized Indicator Parameters
The strategy utilizes extensively backtested parameters specifically optimized for MSTR's volatility patterns:
**MACD Configuration (3,10,3)**:
- Fast EMA: 3 periods (vs standard 12)
- Slow EMA: 10 periods (vs standard 26)
- Signal Line: 3 periods (vs standard 9)
- **Rationale**: These faster parameters provide earlier signal detection while maintaining reliability, particularly effective for MSTR's rapid price movements and high-frequency volatility
**RSI Configuration (21-period)**:
- Length: 21 periods (vs standard 14)
- Oversold: 30 level
- Extreme Oversold: 25 level
- **Rationale**: The 21-period RSI reduces false signals while still capturing oversold conditions effectively in MSTR's volatile environment
**Parameter Adaptability**: While optimized for MSTR, these parameters can be adjusted for other high-volatility instruments. Faster-moving stocks may benefit from even shorter MACD periods, while less volatile assets might require longer periods for optimal performance.
### Scoring System Methodology
**Entry Score Components (Minimum 13 points required)**:
1. **RSI Signals** (max 5 points):
- RSI < 30: +2 points
- RSI < 25: +2 points
- RSI turning up: +1 point
2. **MACD Signals** (max 8 points):
- MACD below zero: +1 point
- MACD turning up: +2 points
- MACD histogram improving: +2 points
- MACD bullish divergence: +3 points
3. **Price Action** (max 4 points):
- Long lower wick (>50%): +2 points
- Small body (<30%): +1 point
- Bullish close: +1 point
4. **Pattern Recognition** (max 8 points):
- RSI bullish divergence: +4 points
- Quick recovery pattern: +2 points
- Reversal confirmation: +4 points
**Exit Score Components (Minimum 13 points required)**:
Uses inverse criteria to identify swing tops with similar weighting system.
## Risk Management Features
### Position Sizing & Risk Control
- **Single Position Strategy**: 100% equity allocation per trade
- **No Overlapping Positions**: Ensures focused risk management
- **Configurable Risk/Reward**: Default 5:1 ratio optimized for volatile assets
### Stop Loss & Take Profit Logic
- **Dynamic Stop Loss**: Based on recent swing lows with configurable buffer
- **Risk-Based Take Profit**: Calculated using risk/reward ratio
- **Clean Exit Logic**: Prevents conflicting signals
## Default Settings Optimization
### Key Parameters (Optimized for MSTR/Bitcoin-style volatility):
- **Minimum Entry Score**: 13 (ensures high-conviction entries)
- **Minimum Exit Score**: 13 (prevents premature exits)
- **Risk/Reward Ratio**: 5.0 (accounts for volatility)
- **Lower Wick Threshold**: 50% (identifies true hammer patterns)
- **Divergence Lookback**: 8 bars (optimal for swing timeframes)
### Why These Defaults Work for MSTR:
1. **Higher Score Thresholds**: MSTR's volatility requires more confirmation
2. **5:1 Risk/Reward**: Compensates for wider stops needed in volatile markets
3. **Faster MACD**: Captures momentum shifts quickly in fast-moving stocks
4. **21-period RSI**: Reduces noise while maintaining sensitivity
## Visual Features
### Score Display System
- **Green Labels**: Entry scores ≥10 points (below bars)
- **Red Labels**: Exit scores ≥10 points (above bars)
- **Large Triangles**: Actual trade entries/exits
- **Small Triangles**: Reversal pattern confirmations
### Chart Cleanliness
- Indicators plotted in separate panes (MACD, RSI)
- TP/SL levels shown only during active positions
- Clear trade markers distinguish signals from actual trades
## Backtesting Specifications
### Realistic Trading Conditions
- **Commission**: 0.1% per trade
- **Slippage**: 3 points
- **Initial Capital**: $1,000
- **Account Type**: Cash (no margin)
### Sample Size Considerations
- Strategy designed for 100+ trade sample sizes
- Recommended timeframes: 4H, 1D for swing trading
- Optimal for trending/volatile markets
## Strategy Limitations & Considerations
### Market Conditions
- **Best Performance**: Trending markets with clear swings
- **Reduced Effectiveness**: Highly choppy, sideways markets
- **Volatility Dependency**: Optimized for moderate to high volatility assets
### Risk Warnings
- **High Allocation**: 100% position sizing increases risk
- **No Diversification**: Single position strategy
- **Backtesting Limitation**: Past performance doesn't guarantee future results
## Usage Guidelines
### Recommended Assets & Timeframes
- **Primary Target**: MSTR (MicroStrategy) - 5min to 15min timeframes
- **Secondary Targets**: High-volatility stocks (TSLA, NVDA, COIN, etc.)
- **Crypto Markets**: Bitcoin, Ethereum (with parameter adjustments)
- **Timeframe Optimization**: 1min-15min for scalping, 30min-1H for swing scalping
### Timeframe Recommendations
- **Primary Scalping**: 5-minute and 15-minute charts
- **Active Monitoring**: 1-minute for precise entries
- **Swing Scalping**: 30-minute to 1-hour timeframes
- **Avoid**: Sub-1-minute (excessive noise) and above 4-hour (reduces scalping opportunities)
## Technical Requirements
- **Pine Script Version**: v6
- **Overlay**: Yes (plots on price chart)
- **Additional Panes**: MACD and RSI indicators
- **Real-time Compatibility**: Confirmed bar signals only
## Customization Options
All parameters are fully customizable through inputs:
- Indicator lengths and levels
- Scoring thresholds
- Risk management settings
- Visual display preferences
- Date range filtering
## Conclusion
This scalping strategy represents a comprehensive approach to low timeframe trading that combines multiple technical analysis methods into a cohesive, quantified system specifically optimized for MSTR's unique volatility characteristics. The optimized parameters and scoring methodology provide a systematic way to identify high-probability scalping setups while managing risk effectively in fast-moving markets.
The strategy's strength lies in its objective, multi-criteria approach that removes emotional decision-making from scalping while maintaining the flexibility to adapt to different instruments through parameter optimization. While designed for MSTR, the underlying methodology can be fine-tuned for other high-volatility assets across various markets.
**Important Disclaimer**: This strategy is designed for experienced scalpers and is optimized for MSTR trading. The high-frequency nature of scalping involves significant risk. Past performance does not guarantee future results. Always conduct your own analysis, consider your risk tolerance, and be aware of commission/slippage costs that can significantly impact scalping profitability.
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
System 0530 - Stoch RSI Strategy with ATR filterStrategy Description: System 0530 - Multi-Timeframe Stochastic RSI with ATR Filter
Overview:
This strategy, "System 0530," is designed to identify trading opportunities by leveraging the Stochastic RSI indicator across two different timeframes: a shorter timeframe for initial signal triggers (assumed to be the chart's current timeframe, e.g., 5-minute) and a longer timeframe (15-minute) for signal confirmation. It incorporates an ATR (Average True Range) filter to help ensure trades are taken during periods of adequate market volatility and includes a cooldown mechanism to prevent rapid, successive signals in the same direction. Trade exits are primarily handled by reversing signals.
How It Works:
1. Signal Initiation (e.g., 5-Minute Timeframe):
Long Signal Wait: A potential long entry is considered when the 5-minute Stochastic RSI %K line crosses above its %D line, AND the %K value at the time of the cross is at or below a user-defined oversold level (default: 30).
Short Signal Wait: A potential short entry is considered when the 5-minute Stochastic RSI %K line crosses below its %D line, AND the %K value at the time of the cross is at or above a user-defined overbought level (default: 70). When these conditions are met, the strategy enters a "waiting state" for confirmation from the 15-minute timeframe.
2. Signal Confirmation (15-Minute Timeframe):
Once in a waiting state, the strategy looks for confirmation on the 15-minute Stochastic RSI within a user-defined number of 5-minute bars (wait_window_5min_bars, default: 5 bars).
Long Confirmation:
The 15-minute Stochastic RSI %K must be greater than or equal to its %D line.
The 15-minute Stochastic RSI %K value must be below a user-defined threshold (stoch_15min_long_entry_level, default: 40).
Short Confirmation:
The 15-minute Stochastic RSI %K must be less than or equal to its %D line.
The 15-minute Stochastic RSI %K value must be above a user-defined threshold (stoch_15min_short_entry_level, default: 60).
3. Filters:
ATR Volatility Filter: If enabled, trades are only confirmed if the current ATR value (converted to ticks) is above a user-defined minimum threshold (min_atr_value_ticks). This helps to avoid taking signals during periods of very low market volatility. If the ATR condition is not met, the strategy continues to wait for the condition to be met within the confirmation window, provided other conditions still hold.
Signal Cooldown Filter: If enabled, after a signal is generated, the strategy will wait for a minimum number of bars (min_bars_between_signals) before allowing another signal in the same direction. This aims to reduce overtrading.
4. Entry and Exit Logic:
Entry: A strategy.entry() order is placed when all trigger, confirmation, and filter conditions are met.
Exit: This strategy primarily uses reversing signals for exits. For example, if a long position is open, a confirmed short signal will close the long position and open a new short position. There are no explicit take profit or stop loss orders programmed into this version of the script.
Key User-Adjustable Parameters:
Stochastic RSI Parameters: RSI Length, Stochastic RSI Length, %K Smoothing, %D Smoothing.
Signal Trigger & Confirmation:
5-minute %K trigger levels for long and short.
15-minute %K confirmation thresholds for long and short.
Wait window (in 5-minute bars) for 15-minute confirmation.
Filters:
Enable/disable and configure the Signal Cooldown filter (minimum bars between signals).
Enable/disable and configure the ATR Volatility filter (ATR period, minimum ATR value in ticks).
Strategy Parameters:
Leverage Multiplier (Note: This primarily affects theoretical position sizing for backtesting calculations in TradingView and does not simulate actual leveraged trading risks).
Recommendations for Users:
Thorough Backtesting: Test this strategy extensively on historical data for the instruments and timeframes you intend to trade.
Parameter Optimization: Experiment with different parameter settings to find what works best for your trading style and chosen markets. The default values are starting points and may not be optimal for all conditions.
Understand the Logic: Ensure you understand how each component (Stochastic RSI on different timeframes, ATR filter, cooldown) interacts to generate signals.
Risk Management: Since this version does not include explicit stop-loss orders, ensure you have a clear risk management plan in place if trading this strategy live. You might consider manually adding stop-loss orders through your broker or using TradingView's separate strategy order settings for stop-loss if applicable.
Disclaimer:
This strategy description is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Trading involves significant risk of loss. Always do your own research and understand the risks before trading.
AutoFib Breakout Strategy for Uptrend AssetsThis trading strategy is designed to help you catch powerful upward moves on assets that are in a long-term uptrend, such as Gold (XAUUSD). It uses a popular technical tool called the Fibonacci Extension, combined with a trend filter and a risk-managed exit system.
✅ When to Use This Strategy
• Works best on higher timeframes: Daily (1D), 3-Day (3D), or Weekly (W).
• Best used on uptrending assets like Gold.
• Designed for swing trading – holding trades from a few days to weeks.
📊 How It Works
1. Find the Trend
We only want to trade in the direction of the trend.
• The strategy uses the 200-period EMA (Exponential Moving Average) to identify if the market is in an uptrend.
• If the price is above the 200 EMA, we consider it an uptrend and allow long trades.
2. Identify Breakout Levels
• The strategy detects recent high and low pivot points to draw Fibonacci extension levels.
• It focuses on the 1.618 Fibonacci level, which is often a target in strong trends.
• When the price breaks above this level in an uptrend, it signals a potential momentum breakout – a good time to buy.
3. Enter a Trade
• The strategy enters a long (buy) position when the price closes above the 1.618 Fibonacci level and the market is in an uptrend (above the 200 EMA).
4. Manage Risk Automatically
• The trade includes a stop-loss set to 1x the ATR (Average True Range) below the entry price – this protects against sudden drops.
• It sets a take-profit at 3x the ATR above the entry – aiming for higher rewards than risks.
⚠️ Important Notes
• 📈 Higher Timeframes Preferred: This strategy works best on Daily (D), 3-Day (3D), and Weekly (W) charts, especially on Gold (XAUUSD).
• 🧪 Not for Deep Backtesting: Due to the nature of how pivot points and Fib levels are calculated, this strategy may not perform well in backtesting simulations (because the historical calculations can shift). It is better used for live analysis and forward testing.
[blackcat] L3 Projected Magic-9 SequenceOVERVIEW
The L3 Projected Magic-9 Sequence indicator is a sophisticated tool designed to help traders identify potential trend reversals through a unique sequence of price movements. By calculating projected highs and lows based on previous bar conditions, this script provides valuable insights into possible future market directions. It plots these key levels on the chart and highlights specific sequential patterns that often precede significant reversals, offering traders a visual advantage in their decision-making process 📈💡.
FEATURES
Projections: Calculates and plots projected highs and lows based on intricate conditions derived from previous bars' open, close, high, and low prices. These projections serve as dynamic support and resistance levels, helping traders anticipate potential turning points in the market 📊.
Sequential Patterns:
Identifies various sequential patterns known as "Magic" sequences, such as Magic-9 and Magic-13.
Labels these sequences directly on the chart for easy identification: 5, 6, 7, 8, 9, 12, 13 for both bullish and bearish trends.
Provides additional labels when these sequences align with projected highs or lows, enhancing the reliability of the signal 🏷️.
Differentiates between trend and sideways phases using the Magic-9 Project Range. Traditional sequences generating buy and sell signals of 9 and 13 during sideways swings are displayed indistinguishably from other numbers. However, the 9 and 13 generated by breakouts are highlighted with red and green labels for better visibility 🚦.
Project Range Adjustment:
The Project Range is automatically adjusted by Multiple Time Frame (MTF).
A higher cycle is selected as the baseline of the Project Range based on the current operating cycle, ensuring adaptability to varying market conditions ⏳.
Customization:
Offers customizable colors for plotted lines and labels, allowing users to tailor the appearance to their preferences 🎨.
Adjustable settings for lookback periods and other parameters to fine-tune the indicator according to individual trading styles.
Automatic Timeframe Selection:
Automatically selects the most suitable timeframe for data fetching, ensuring optimal performance across different chart intervals ⏳.
Ensures compatibility with various trading strategies, whether short-term intraday or long-term positional trading.
HOW TO USE
Adding the Indicator:
Open your TradingView platform and navigate to the chart where you want to apply the indicator.
Click on the "Indicators" button at the top of the screen and search for L3 Projected Magic-9 Sequence.
Select the indicator from the list and add it to your chart.
Understanding Projections:
Once added, observe the plotted projected highs and lows on your chart.
These lines represent anticipated support and resistance levels based on complex calculations involving previous bar data.
Identifying Sequential Patterns:
Look for labels such as 5, 6, 7, 8, 9, 12, and 13 appearing on the chart.
These labels signify specific sequential patterns that often precede market reversals.
Pay special attention to labels that include arrows (e.g., 9▼, 13▲), indicating alignment with projected highs or lows.
Note the differentiation between trend and sideways phases:
During sideways swings, traditional sequences generating buy and sell signals of 9 and 13 are displayed indistinguishably from other numbers.
Breakout-generated 9 and 13 are highlighted with red and green labels for clear identification.
Combining with Other Tools:
While the L3 Projected Magic-9 Sequence offers powerful insights, it is essential to combine its signals with other technical analysis tools.
Use moving averages, volume indicators, or candlestick patterns to confirm the validity of the identified sequences before executing trades.
LIMITATIONS
Market Conditions: The indicator performs best in trending markets but may generate false signals during periods of consolidation or range-bound movement 🌐.
Complexity: Due to its reliance on specific sequential patterns, some traders might find the concept challenging to grasp initially. Thorough testing and understanding are crucial before deploying it in live trading environments.
Data Dependency: Accurate projections depend on having sufficient historical data. Insufficient data may lead to less reliable results.
NOTES
Backtesting: Before implementing the indicator in real-time trading, conduct extensive backtesting to evaluate its effectiveness under various market conditions.
Risk Management: Always adhere to proper risk management principles, even when relying on robust indicators like this one. Set stop-loss orders and position sizes accordingly to protect your capital 🛡️.
Continuous Learning: Stay updated with the latest developments and adjustments made to the indicator by following community discussions and official updates from the author.
BIN Based Support and Resistance [SS]This indicator presents a version of an alternative way to determine support and resistance, using a method called "Bins".
Bins provide for a flexible and interesting way to determine support and resistance levels.
First off, let's discuss BINS:
Bins are ranges or containers into which your data points can be sorted. For example, if you're grouping ages, you might have bins like 0–18, 19–35, 36–50, and 51+. Any data point within these intervals gets placed in the corresponding bin.
Binning simplifies complex data sets by grouping values into categories. This is useful for such things as
Visualizing data in histograms or bar charts.
Reducing noise and highlighting trends.
This indicator groups the price action into 10 separate bins. It determines the Support / Resistance level by averaging the values in the Bins to find an iteration of the "central tendency" or average reoccurring value.
Pros and Cons
Since this is a different approach to support and resistance, I think its important to highlight some of the pros and advantages, but also be open about the cons.
First off the PROS
Bin Based Support and Resistance Levels dynamically adjust to ranges as opposed to hard / fast peaks and valleys. This makes them better at analyzing price action vs simply drawing lines at random peaks and valleys.
Because Bins are analyzing ALL PA within a period's max and min range, Bin Support and Resistance can actually be used similar to Volume profile, where you are able to identify a pseudo-POC, or areas where price tends to consolidate. Take a look at this example on SPY:
You can see these 2 SR lines are close together. This represents that this general price range is an area where price likes to accumulate/consolidate. You can see the SPY ended up coming back to this range and consolidating there for a bit.
This is a strength of using a BIN based approach to calculating support and resistance, because as indicated before, it looks at price action vs peaks and valleys.
As a tip, these areas are areas you want to wait for a break in one direction or the other.
The indicator provides for backtest results of the support and resistance lines, to see how many times certain areas acted as resistance or support. Because this is analyzing and distributing PA evenly throughout the period's max and min, the indicator can tell you which areas tend to have higher rejection zones and which have higher support zones.
Now the CONS
Because bin based SR take an average approach, the SR lines can sometimes be slightly broken before the ticker finds rejection:
To combat this, make sure there is confirmed support. How the indicator actually backtests these lines is by waiting to see if the ticker has 3 consecutive closes above the support line or below the resistance line. So these are things to be mindful of.
It doesn't consider pivots. Most support and resistance indicators either identify max and min peaks and valleys or use pivot points. Pivot points are a great way to identify peaks and valleys and thus by extension support and resistance. However, this is also somewhat of a strength, as using BINS forces the indicator to consider ALL price action and not just the extremes (highs and lows).
Can be slightly skewed in highly volatile environments. Any time there is a massive drop or rally, it can skew the indicator to give extreme ranges to both ends. For example, the Tariff news collapse on ES1!:
Owning to limitations in lookback length, sometimes the min and max range can be exceeded and other traditional areas of support / resistance is where a ticker will find support.
Using the indicator
Here are some basic use/functionalities of the indicator:
Selecting display of backtest results: You can select to have the backtest results shown in a table:
Or directly on the lines:
Inversely, you can toggle them off completely:
You can modify the lookback length. The suggested lookback length is between 250 to 500 candles on smaller timeframes. I also suggest 252 on daily timeframes (which represents 1 trading year).
And that's the indicator!
It is very easy to use, so you should pick it up in no time!
Enjoy and as always, 🚀🚀 safe trades! 🚀🚀
Auto Fib Retracement with Buy/SellKey Features of the Advanced Script:
Multi-Timeframe (MTF) Analysis:
We added an input for the higher timeframe (higher_tf), where the trend is checked on a higher timeframe to confirm the primary trend direction.
Complex Trend Detection:
The trend is determined not only by the current timeframe but also by the trend on the higher timeframe, giving a more comprehensive and reliable signal.
Dynamic Fibonacci Levels:
Fibonacci lines are plotted dynamically, extending them based on price movement, with the Fibonacci retracement drawn only when a trend is identified.
Background Color & Labels:
A background color is added to give a clear indication of the trend direction. Green for uptrend, red for downtrend. It makes it visually easier to understand the current market structure.
"Buy" or "Sell" labels are shown directly on the chart to mark possible entry points.
Strategy and Backtesting:
The script includes strategy commands (strategy.entry and strategy.exit), which allow for backtesting the strategy in TradingView.
Stop loss and take profit conditions are added (loss=100, profit=200), which can be adjusted according to your preferences.
Next Steps:
Test with different timeframes: Try changing the higher_tf to different timeframes (like "60" or "240") and see how it affects the trend detection.
Adjust Fibonacci settings: Modify how the Fibonacci levels are calculated or add more Fibonacci levels like 38.2%, 61.8%, etc.
Optimize Strategy Parameters: Fine-tune the entry/exit logic by adjusting stop loss, take profit, and other strategy parameters.
This should give you a robust foundation for creating advanced trend detection strategies
Custom Buy and Sell Signal with Body Ratio and RSI
Indicator Overview:
Name: Custom Buy and Sell Signal with Body Ratio and RSI
Description: This indicator is designed to detect buy and sell opportunities by analyzing the body size and wicks of candles in combination with the RSI indicator and volume. It helps identify trend reversals under high-volume market conditions, which enhances the reliability of the signals.
Indicator Features:
RSI (Relative Strength Index): The RSI indicator is used to assess oversold (RSI < 40) or overbought (RSI > 60) conditions. These zones signal potential reversals when combined with other technical signals.
Candle Body Analysis:
The indicator compares the size of the current and previous candles to validate signals.
For a buy signal, the current candle must be bullish and have a body size proportional to that of the previous bearish candle.
Similarly, for a sell signal, the current candle must be bearish with a body size comparable to the previous bullish candle.
Wick Validation:
The indicator analyzes the wick length to reinforce or exclude signals.
For a buy signal, the lower wick of the bullish candle must be shorter than that of the previous bearish candle.
For a sell signal, the upper wick of the bearish candle must be shorter than that of the previous bullish candle and smaller than 30% of the candle's body.
High Volume:
Signals are only generated when the volume exceeds a certain threshold, ensuring that signals are issued in active market conditions.
The minimum volume should be adjusted based on the asset. For example, for gold, a minimum volume of 9000 is recommended.
Trading Strategy:
Buy Signals:
A bearish (red) candle is followed by a bullish (green) candle with a body size that is comparable to the previous candle (0.9 to 3 times the body size).
The lower wick of the bullish candle is shorter than that of the previous bearish candle, confirming the validity of the signal.
The RSI must be below 40, indicating an oversold condition.
The volume must exceed the defined threshold (e.g., > 9000 for gold) to confirm an active market.
Sell Signals:
A bullish (green) candle is followed by a bearish (red) candle with a comparable body size.
The upper wick of the bearish candle must be shorter than that of the previous bullish candle and must not exceed 30% of the body size.
The RSI must be above 60, indicating an overbought condition.
The volume must also exceed the minimum threshold for a valid signal.
Usage Guidelines:
Volume Adjustment: It is crucial to adjust the volume threshold depending on the asset you're trading. For example, for assets like gold, a minimum volume of 9000 is recommended to filter out weak signals. Each asset has a different volume dynamic, so test different thresholds on historical data to find the optimal setting.
Time Frame:
It is recommended to use this indicator on a 1-hour (1H) chart for the best signal relevance. This time frame provides a good balance between reactivity and filtering false signals.
Confluence:
Combine the signals from this indicator with other tools like support and resistance levels, moving averages, or chart patterns to increase your chances of success. Confluence of indicators improves the reliability of signals.
Risk Management:
Implement strict risk management. Use stop-losses based on volatility, such as ATR (Average True Range), or the wick size to determine exit points.
Backtesting:
Before using it live, conduct backtesting on various assets to fine-tune the parameters, especially the volume threshold, and to verify performance across different market conditions.
This indicator is an excellent tool for traders looking to identify trend reversals based on solid technical criteria such as RSI, candle structure, and volume. It is particularly effective on volatile assets with precise volume adjustment.
Multi-Moving Average Buy/Sell IndicatorThis Multi-Moving Average Buy/Sell Indicator is a powerful and customizable tool designed to help traders identify potential buy and sell signals based on the interaction between price and multiple moving averages. Whether you're a day trader, swing trader, or long-term investor, this indicator provides clear visual cues and alerts to help you make informed trading decisions.
Key Features
1. Multiple Moving Averages
The indicator calculates four key moving averages:
9-period MA
20-period MA
50-period MA
180-period MA
You can choose the type of moving average:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
2. Custom Timeframe
Select a custom timeframe from a user-friendly dropdown menu:
1 Minute
5 Minutes
15 Minutes
30 Minutes
1 Hour
4 Hours
Daily
Weekly
The indicator dynamically adjusts to the selected timeframe, making it suitable for all trading styles.
3. Buy/Sell Signals
Buy Signal: Triggered when the price crosses above any of the moving averages.
Sell Signal: Triggered when the price crosses below any of the moving averages.
Signals are displayed as labels on the chart:
Green "BUY" Label: Below the bar when a buy signal is triggered.
Red "SELL" Label: Above the bar when a sell signal is triggered.
4. Visualization
Toggle the visibility of all moving averages using the showAllMAs input.
Moving averages are plotted with distinct colors for easy identification:
9 MA: Blue
20 MA: Orange
50 MA: Purple
180 MA: Teal
5. Alerts
The indicator generates alerts for buy and sell signals, which can be used for notifications or automated trading.
How to Use
Add the Indicator:
Open TradingView and go to the Pine Script Editor.
Copy and paste the script into the editor.
Click Add to Chart.
Configure Inputs:
maType: Choose the type of moving average (SMA, EMA, WMA).
timeframe: Select a custom timeframe (e.g., "1 Minute", "Daily").
showSignals: Toggle to show or hide buy/sell signals.
showAllMAs: Toggle to show or hide all moving averages.
Interpret the Signals:
Look for green "BUY" labels below the bars for potential buy opportunities.
Look for red "SELL" labels above the bars for potential sell opportunities.
Set Alerts:
Use the built-in alert system to get notified when buy or sell signals are triggered.
Example Use Cases
Day Trading
Use a 1-minute or 5-minute timeframe with an EMA for quick signals.
Example Inputs:
maType = "EMA"
timeframe = "5 Minutes"
showAllMAs = true
Swing Trading
Use a daily timeframe with an SMA for longer-term signals.
Example Inputs:
maType = "SMA"
timeframe = "Daily"
showAllMAs = false
Why Use This Indicator?
Versatility: Suitable for all trading styles and timeframes.
Customization: Choose your preferred moving average type and timeframe.
Clear Signals: Easy-to-read buy/sell labels and moving averages.
Alerts: Never miss a trading opportunity with built-in alerts.
Limitations
False Signals:
The indicator may generate false signals in choppy or sideways markets. Always combine it with other tools (e.g., RSI, volume analysis) for better accuracy.
Timeframe Dependency:
The effectiveness of the signals depends on the selected timeframe. Shorter timeframes may produce more signals but with higher noise.
No Backtesting:
The script does not include backtesting functionality. Test the strategy manually on historical data.
Customization Options
Add More Moving Averages: Modify the script to include additional moving averages (e.g., 200 MA).
Change Signal Logic: Adjust the conditions for buy/sell signals (e.g., require confirmation from multiple moving averages).
Add Alerts for Specific MAs: Create separate alerts for signals based on specific moving averages (e.g., only 9 MA or 50 MA).
High-Probability IndicatorExplanation of the Code
Trend Filter (EMA):
A 50-period Exponential Moving Average (EMA) is used to determine the overall trend.
trendUp is true when the price is above the EMA.
trendDown is true when the price is below the EMA.
Momentum Filter (RSI):
A 14-period RSI is used to identify overbought and oversold conditions.
oversold is true when RSI ≤ 30.
overbought is true when RSI ≥ 70.
Volatility Filter (ATR):
A 14-period Average True Range (ATR) is used to measure volatility.
ATR is multiplied by a user-defined multiplier (default: 2.0) to set a volatility threshold.
Ensures trades are only taken during periods of sufficient volatility.
Entry Conditions:
Long Entry: Price is above the EMA (uptrend), RSI is oversold, and the candle range exceeds the ATR threshold.
Short Entry: Price is below the EMA (downtrend), RSI is overbought, and the candle range exceeds the ATR threshold.
Exit Conditions:
Take Profit: A fixed percentage above/below the entry price.
Stop Loss: A fixed percentage below/above the entry price.
Visualization:
The EMA is plotted on the chart.
Background colors highlight uptrends and downtrends.
Buy and sell signals are displayed as labels on the chart.
Alerts:
Alerts are triggered for buy and sell signals.
How to Use the Indicator
Trend Filter:
Only take trades in the direction of the trend (e.g., long in an uptrend, short in a downtrend).
Momentum Filter:
Look for oversold conditions in an uptrend for long entries.
Look for overbought conditions in a downtrend for short entries.
Volatility Filter:
Ensure the candle range exceeds the ATR threshold to avoid low-volatility trades.
Risk Management:
Use the built-in take profit and stop loss levels to manage risk.
Optimization Tips
Backtesting:
Test the indicator on multiple timeframes and assets to evaluate its performance.
Adjust the input parameters (e.g., EMA length, RSI length, ATR multiplier) to optimize for specific markets.
Combination with Other Strategies:
Add additional filters, such as volume analysis or support/resistance levels, to improve accuracy.
Risk Management:
Use proper position sizing and risk-reward ratios to maximize profitability.
Disclaimer
No indicator can guarantee an 85% win ratio due to the inherent unpredictability of financial markets. This script is provided for educational purposes only. Always conduct thorough backtesting and paper trading before using any strategy in live trading.
Let me know if you need further assistance or enhancements!
PDF MA For Loop [BackQuant]PDF MA For Loop
Introducing the PDF MA For Loop, an innovative trading indicator that combines Probability Density Function (PDF) smoothing with a dynamic for-loop scoring mechanism. This advanced tool provides traders with precise trend-following signals, helping to identify long and short opportunities with improved clarity and adaptability to market conditions.
If you would like to check out the stand alone PDF Moving Average:
Core Concept: Probability Density Function (PDF) Smoothing
The PDF smoothing method is a unique approach that applies adaptive weights to price data based on a Probability Density Function. This ensures that recent data points receive appropriate emphasis while maintaining a smooth transition across the data set. The result is a moving average that is not only smoother but also more responsive to market changes.
Key parameters in PDF smoothing:
Variance : Controls the spread of the PDF, where a higher value results in broader smoothing and a lower value makes the moving average more sensitive.
Mean : Centers the PDF around a specific value, influencing the weighting and responsiveness of the smoothing process.
By combining PDF smoothing with traditional moving averages (EMA or SMA), the indicator creates a hybrid signal that balances responsiveness and reliability.
For-Loop Scoring Mechanism
At the heart of this indicator is the for-loop scoring mechanism, which evaluates the smoothed PDF moving average over a defined range of historical data points. This process assigns a score to the current market condition based on whether the PDF moving average is greater than or less than previous values.
Long Signal: A long signal is generated when the score exceeds the Long Threshold (default set at 40), indicating upward momentum.
Short Signal: A short signal is triggered when the score crosses below the Short Threshold (default set at -10), suggesting potential downward momentum.
This dynamic scoring system ensures that the indicator remains adaptive, capturing trends and shifts in market sentiment effectively.
Customization Options
The PDF MA For Loop includes a variety of customizable settings to fit different trading styles and strategies:
Calculation Settings
Price Source : Select the input price for the calculation (default is the close price).
Smoothing Method : Choose between EMA or SMA for the additional smoothing layer, providing flexibility to adapt to market conditions.
Smoothing Period : Adjust the lookback period for the smoothing function, with shorter periods providing more sensitivity and longer periods offering greater stability.
Variance & Mean : Fine-tune the PDF function parameters to control the weighting of the smoothing process.
Signal Settings
Thresholds : Customize the upper and lower thresholds to define the sensitivity of the long and short signals.
For Loop Range : Set the range of historical data points analyzed by the for-loop, influencing the depth of the scoring mechanism.
UI Settings
Signal Line Width: Adjust the thickness of the plotted signal line for better visibility.
Candle Coloring: Enable or disable the coloring of candlesticks based on trend direction (green for long, red for short, gray for neutral).
Background Coloring: Add background shading to highlight long and short signals for an enhanced visual experience.
Alerts and Automation
The indicator includes built-in alert conditions to notify traders of important market events:
Long Signal Alert: Notifies when the score exceeds the upper threshold, indicating a bullish trend.
Short Signal Alert: Notifies when the score crosses below the lower threshold, signaling a bearish trend.
These alerts can be configured for real-time notifications, allowing traders to respond quickly to market changes without constant chart monitoring.
Trading Applications
The PDF MA For Loop is versatile and can be applied across various trading strategies and market conditions:
Trend Following: The PDF smoothing method combined with for-loop scoring makes this indicator particularly effective for identifying and following trends.
Reversal Trading: By observing the thresholds and score, traders can anticipate potential reversals when the trend shifts from long to short (or vice versa).
Risk Management: The dynamic thresholds and scoring provide clear signals, allowing traders to enter and exit trades with greater confidence and precision.
Final Thoughts
The PDF MA For Loopis merges advanced mathematical concepts with practical trading tools. By leveraging Probability Density Function smoothing and a dynamic for-loop scoring system, it provides traders with clear, actionable signals while adapting to market conditions.
Whether you’re looking for an edge in trend-following strategies or seeking precision in identifying reversals, this indicator offers the flexibility and power to enhance your trading decisions
As always, backtesting and integrating the PDF MA For Loop into a comprehensive trading strategy is recommended for optimal performance, as no single indicator should be used in isolation.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
MACD Aggressive Scalp SimpleComment on the Script
Purpose and Structure:
The script is a scalping strategy based on the MACD indicator combined with EMA (50) as a trend filter.
It uses the MACD histogram's crossover/crossunder of zero to trigger entries and exits, allowing the trader to capitalize on short-term momentum shifts.
The use of strategy.close ensures that positions are closed when specified conditions are met, although adjustments were made to align with Pine Script version 6.
Strengths:
Simplicity and Clarity: The logic is straightforward and focuses on essential scalping principles (momentum-based entries and exits).
Visual Indicators: The plotted MACD line, signal line, and histogram columns provide clear visual feedback for the strategy's operation.
Trend Confirmation: Incorporating the EMA(50) as a trend filter helps avoid trades that go against the prevailing trend, reducing the likelihood of false signals.
Dynamic Exit Conditions: The conditional logic for closing positions based on weakening momentum (via MACD histogram change) is a good way to protect profits or minimize losses.
Potential Improvements:
Parameter Inputs:
Make the MACD (12, 26, 9) and EMA(50) values adjustable by the user through input statements for better customization during backtesting.
Example:
pine
Copy code
macdFast = input(12, title="MACD Fast Length")
macdSlow = input(26, title="MACD Slow Length")
macdSignal = input(9, title="MACD Signal Line Length")
emaLength = input(50, title="EMA Length")
Stop Loss and Take Profit:
The strategy currently lacks explicit stop-loss or take-profit levels, which are critical in a scalping strategy to manage risk and lock in profits.
ATR-based or fixed-percentage exits could be added for better control.
Position Size and Risk Management:
While the script uses 50% of equity per trade, additional options (e.g., fixed position sizes or risk-adjusted sizes) would be beneficial for flexibility.
Avoid Overlapping Signals:
Add logic to prevent overlapping signals (e.g., opening a new position immediately after closing one on the same bar).
Backtesting Optimization:
Consider adding labels or markers (label.new or plotshape) to visualize entry and exit points on the chart for better debugging and analysis.
The inclusion of performance metrics like max drawdown, Sharpe ratio, or profit factor would help assess the strategy's robustness during backtesting.
Compatibility with Live Trading:
The strategy could be further enhanced with alert conditions using alertcondition to notify the trader of buy/sell signals in real-time.
FS Scorpion TailKey Features & Components:
1. Custom Date & Chart-Based Controls
The software allows users to define whether they want signals to start on a specific date (useSpecificDate) or base calculations on the visible chart’s range (useRelativeScreenSumLeft and useRelativeScreenSumRight).
Users can input the number of stocks to buy/sell per signal and decide whether to sell only for profit.
2. Technical Indicators Used
EMA (Exponential Moving Average): Users can define the length of the EMA and specify if buy/sell signals should occur when the EMA is rising or falling.
MACD (Moving Average Convergence Divergence): MACD crossovers, slopes of the MACD line, signal line, and histogram are used for generating buy/sell signals.
ATR (Average True Range): Signals are generated based on rising or falling ATR.
Aroon Indicator: Buy and sell signals are based on the behavior of the Aroon upper and lower lines.
RSI (Relative Strength Index): Tracks whether the RSI and its moving average are rising or falling to generate signals.
Bollinger Bands: Buy/sell signals depend on the basis, upper, and lower band behavior (rising or falling).
3. Signal Detection
The software creates arrays for each indicator to store conditions for buy/sell signals.
The allTrue() function checks whether all conditions for buy/sell signals are true, ensuring that only valid signals are plotted.
Signals are differentiated between buy-only, sell-only, and both buy and sell (dual signal).
4. Visual Indicators
Vertical Lines: When buy, sell, or dual signals are detected, vertical lines are drawn at the corresponding bar with configurable colors (green for buy, red for sell, silver for dual).
Buy/Sell Labels: Visual labels are plotted directly on the chart to denote buy or sell signals, allowing for clear interpretation of the strategy.
5. Cash Flow & Metrics Display
The software maintains an internal ledger of how many stocks are bought/sold, their prices, and whether a profit is being made.
A table is displayed at the bottom right of the chart, showing:
Initial investment
Current stocks owned
Last buy price
Market stake
Net profit
The table background turns green for profit and red for loss.
6. Dynamic Decision Making
Buy Condition: If a valid buy signal is generated, the software decrements the cash balance and adds stocks to the inventory.
Sell Condition: If the sell signal is valid (and meets the profit requirement), stocks are sold, and cash is incremented.
A fallback check ensures the sell logic prevents selling more stocks than are available and adjusts stock holding appropriately (e.g., sell half).
Customization and Usage
Indicator Adjustments: The user can choose which indicators to activate (e.g., EMA, MACD, RSI) via input controls. Each indicator has specific customizable parameters such as lengths, slopes, and conditions.
Signal Flexibility: The user can adjust conditions for buying and selling based on various technical indicators, which adds flexibility in implementing trading strategies. For example, users may require the RSI to be higher than its moving average or trigger sales only when MACD crosses under the signal line.
Profit Sensitivity: The software allows the option to sell only when a profit is assured by checking if the current price is higher than the last buy price.
Summary of Usage:
Indicator Selection: Enable or disable technical indicators like EMA, MACD, RSI, Aroon, ATR, and Bollinger Bands to fit your trading strategy.
Custom Date/Chart Settings: Choose whether to calculate based on specific time ranges or visible portions of the chart.
Dynamic Signal Plotting: Once buy or sell conditions are met, the software will visually plot signals on your chart, giving clear entry and exit points.
Investment Tracking: Real-time tracking of stock quantities, investments, and profit ensures a clear view of your trading performance.
Backtesting: Use this software for backtesting your strategy by analyzing how buy and sell signals would have performed historically based on the chosen indicators.
Conclusion
The FS Scorpion Tail software is a robust and flexible trading tool, allowing traders to develop custom strategies based on multiple well-known technical indicators. Its visual aid, coupled with real-time investment tracking, makes it valuable for systematic traders looking to automate or refine their trading approach.
Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI
Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy.
Core Concept: Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points.
To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter.
Savitzky-Golay Filter: Smoothing with Precision
The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result.
In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements.
Median Filter: Reducing Outliers
After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends.
Long and Short Signals
The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels:
Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity.
Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position.
These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator.
Customization and Visualization Options
The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs:
Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices).
RSI Period: Adjust the lookback period for the RSI calculation (default is 14).
Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal.
Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals.
UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals.
Visual Feedback: Color-Coded Signals and Thresholds
To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend:
Green Bars indicate long signals when momentum is bullish.
Red Bars indicate short signals when momentum is bearish.
Gray Bars indicate neutral or undecided conditions when no clear signal is present.
In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds.
Alerts for Automation
For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity.
Trading Applications
This indicator is versatile and can be used in a variety of trading strategies:
Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend.
Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction.
Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts.
Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong.
Final Thoughts
The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions.
As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Chande Momentum Oscillator StrategyThe Chande Momentum Oscillator (CMO) Trading Strategy is based on the momentum oscillator developed by Tushar Chande in 1994. The CMO measures the momentum of a security by calculating the difference between the sum of recent gains and losses over a defined period. The indicator offers a means to identify overbought and oversold conditions, making it suitable for developing mean-reversion trading strategies (Chande, 1997).
Strategy Overview:
Calculation of the Chande Momentum Oscillator (CMO):
The CMO formula considers both positive and negative price changes over a defined period (commonly set to 9 days) and computes the net momentum as a percentage.
The formula is as follows:
CMO=100×(Sum of Gains−Sum of Losses)(Sum of Gains+Sum of Losses)
CMO=100×(Sum of Gains+Sum of Losses)(Sum of Gains−Sum of Losses)
This approach distinguishes the CMO from other oscillators like the RSI by using both price gains and losses in the numerator, providing a more symmetrical measurement of momentum (Chande, 1997).
Entry Condition:
The strategy opens a long position when the CMO value falls below -50, signaling an oversold condition where the price may revert to the mean. Research in mean-reversion, such as by Poterba and Summers (1988), supports this approach, highlighting that prices often revert after sharp movements due to overreaction in the markets.
Exit Conditions:
The strategy closes the long position when:
The CMO rises above 50, indicating that the price may have become overbought and may not provide further upside potential.
Alternatively, the position is closed 5 days after the buy signal is triggered, regardless of the CMO value, to ensure a timely exit even if the momentum signal does not reach the predefined level.
This exit strategy aligns with the concept of time-based exits, reducing the risk of prolonged exposure to adverse price movements (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages the well-known phenomenon of mean-reversion in financial markets. According to research by Jegadeesh and Titman (1993), prices tend to revert to their mean over short periods following strong movements, creating opportunities for traders to profit from temporary deviations.
The CMO captures this mean-reversion behavior by monitoring extreme price conditions. When the CMO reaches oversold levels (below -50), it signals potential buying opportunities, whereas crossing overbought levels (above 50) indicates conditions for selling.
Market Efficiency and Overreaction:
The strategy takes advantage of behavioral inefficiencies and overreactions, which are often the drivers behind sharp price movements (Shiller, 2003). By identifying these extreme conditions with the CMO, the strategy aims to capitalize on the market’s tendency to correct itself when price deviations become too large.
Optimization and Parameter Selection:
The 9-day period used for the CMO calculation is a widely accepted timeframe that balances responsiveness and noise reduction, making it suitable for capturing short-term price fluctuations. Studies in technical analysis suggest that oscillators optimized over such periods are effective in detecting reversals (Murphy, 1999).
Performance and Backtesting:
The strategy's effectiveness is confirmed through backtesting, which shows that using the CMO as a mean-reversion tool yields profitable opportunities. The use of time-based exits alongside momentum-based signals enhances the reliability of the strategy by ensuring that trades are closed even when the momentum signal alone does not materialize.
Conclusion:
The Chande Momentum Oscillator Trading Strategy combines the principles of momentum measurement and mean-reversion to identify and capitalize on short-term price fluctuations. By using a widely tested oscillator like the CMO and integrating a systematic exit approach, the strategy effectively addresses both entry and exit conditions, providing a robust method for trading in diverse market environments.
References:
Chande, T. S. (1997). The New Technical Trader: Boost Your Profit by Plugging into the Latest Indicators. John Wiley & Sons.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Volume-Supported Linear Regression Trend TableThe "Volume-Supported Linear Regression Trend Table" (VSLRT Table) script helps traders identify buy and sell opportunities by analyzing price trends and volume dynamics across multiple timeframes. It uses linear regression to calculate the trend direction and volume strength, visually representing this data with color-coded signals on the chart and in a table. Green signals indicate buying opportunities, while red signals suggest selling, with volume acting as confirmation of trend strength. Traders can use these signals for both short and long positions, with additional risk management and multi-timeframe validation to enhance the strategy.
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To use the "Volume-Supported Linear Regression Trend Table" (VSLRT Table) script in a trading strategy, you would incorporate it into your decision-making process to identify potential buy and sell opportunities based on the trend and volume dynamics. Here’s how you could apply it for trading:
1. Understanding the Key Elements:
Trend Direction (Slope of Price): The script uses linear regression to assess the trend direction of the price. If the price slope is positive, the asset is likely in an uptrend; if it's negative, the asset is in a downtrend.
Volume-Backed Signals: The buy or sell signal is not only based on the price trend but also on volume. Volume is crucial in validating the strength of a trend; large volume often indicates strong interest in a direction.
2. Interpreting the Table and Signals:
The table displayed at the bottom-right of your TradingView chart gives you a clear overview of the trends across different timeframes:
Trend Colors:
Green hues (e.g., ccol11, ccol12, etc.): Indicate a buying trend supported by volume.
Red hues (e.g., ccol21, ccol22, etc.): Indicate a selling trend supported by volume.
Gray: Indicates weak or unclear trends where no decisive direction is present.
Buy/Sell Signals:
The script plots triangles on the chart:
Upward triangle below the bar signals a potential buy.
Downward triangle above the bar signals a potential sell.
3. Building a Trading Strategy:
Here’s how you can incorporate the script’s information into a trading strategy:
Buy Signal (Long Entry):
Look for green triangles (indicating a buy signal) below a bar.
Confirm that the trend color in the table for the relevant timeframe is green, which shows that the buy signal is supported by strong volume.
Ensure that the price is in an uptrend (positive slope) and that volume is increasing on upward moves, as this indicates buying interest.
Execute a long position when these conditions align.
Sell Signal (Short Entry):
Look for red triangles (indicating a sell signal) above a bar.
Confirm that the trend color in the table for the relevant timeframe is red, which shows that the sell signal is supported by strong volume.
Ensure that the price is in a downtrend (negative slope) and that volume is increasing on downward moves, indicating selling pressure.
Execute a short position when these conditions align.
Exiting the Trade:
Exit a long position when a sell signal (red triangle) appears, or when the trend color in the table shifts to red.
Exit a short position when a buy signal (green triangle) appears, or when the trend color in the table shifts to green.
4. Multi-Timeframe Confirmation:
The script provides trends across multiple timeframes (tf1, tf2, tf3), which can help in validating your trade:
Short-Term Trading: Use shorter timeframes (e.g., 3, 5 minutes) for intraday trades. If both short and medium timeframes align in trend direction (e.g., both showing green), it strengthens the signal.
Longer-Term Trading: If you are trading on a higher timeframe (e.g., daily or weekly), confirm that the lower timeframes align with your intended trade direction.
5. Adding Risk Management:
Stop-Loss: Place stop-losses below recent lows (for long trades) or above recent highs (for short trades) to minimize risk.
Take Profit: Consider taking profit at key support/resistance levels or based on a fixed risk-to-reward ratio (e.g., 2:1).
Example Strategy Flow:
For Long (Buy) Trade:
Signal: A green triangle appears below a candle (Buy signal).
Trend Confirmation: Check that the color in the table for your selected timeframe is green, confirming the trend is supported by volume.
Execute Long: Enter a long trade if the price is trending upward (positive price slope).
Exit Long: Exit when a red triangle appears above a candle (Sell signal) or if the trend color shifts to red in the table.
For Short (Sell) Trade:
Signal: A red triangle appears above a candle (Sell signal).
Trend Confirmation: Check that the color in the table for your selected timeframe is red, confirming the trend is supported by volume.
Execute Short: Enter a short trade if the price is trending downward (negative price slope).
Exit Short: Exit when a green triangle appears below a candle (Buy signal) or if the trend color shifts to green in the table.
6. Fine-Tuning:
Backtesting: Before trading live, use TradingView’s backtesting features to test the strategy on historical data and optimize the settings (e.g., length of linear regression, timeframe).
Combine with Other Indicators: Use this strategy alongside other technical indicators (e.g., RSI, MACD) for better confirmation.
In summary, the script helps identify trends with volume support, giving more confidence in buy/sell decisions. Combining these signals with risk management and multi-timeframe analysis can create a solid trading strategy.
