Simple TrenderOriginates from:
I was reading some Impulse Trading literature by A. Elder.. In it, someone named Kerry Lovvorn proposed "An End of Day Trend Following System" for someone lazy.
Originally it is just price closing above an 8 ema (low) for long. Exit when price closes below an 8 ema (low). The opposite for a short position.
Conditions: Buy when price closed below ema (low) for two bars or more, then closes above. Opposite for a short position. I do not follow this condition. Though it may help with whipsaw.
My condition is when price closes above the 26 ema (low) (works the best for me) I place orders above the initial crossing bars high. Opposite for lows.
I look for stocks that are low in price to go long on. I want the run from 2's to 15's
I look for stocks that are mid-teens/20's in price to go short on. I want the run from 20's to 2's
I look for stock with news and earnings that are already running (up or down) to play the pullback.
These conditions can easily be scanned for on thinkorswim
From first glance, the system looks like CMsling shotsystem. Although, I plagiarized some parts of the codes, because I am inept when it comes to that shit, it differs as it is not a moving average crossover system.
It is a price crossing over concept. A moving average VWAP is used for best entries on pullbacks.
Purpose:
--To catch the majority of a trend/wave/run.
--To identify pullback areas to go long or short while in midst of trend. To catch pullbacks off news and earning runners.
--To catch the initial start of trend with clear rules to enter
--Clear rules to exit
Issues
--possibilities of getting ninja sliced the fuck up. Can be mitigated by entering stocks with decent average volume. And also only going long above 200 ema and short below it. ADX won't work, at the initial start of the trend it will show not trending. Can look at blow off volume at the bottom followed by increase in buying for long and vice versa for short.
--Can give some huge gains away through gap ups or gap downs from news or earnings during trend. However, can get huge gain on gaps from news or earning. Nature of the game.
--Need some brass balls and a supply of pepto to stomach through some of the pullbacks. Gut wrenching seeing big gains dwindle. But they all even out at the end, you hope. (see NBEV and IGC, and CRON and others. shit don't go in straight lines, homie)
Pros
--It's simple and easy. Overall, you profit
--works with any security
Cons
--It can be stressful.
--does not work well on lower time frames. Do not recommend going below 15 minutes
--Possibility of working on 5 minutes with a time frame breakout strategy (15,30 min).
Couple it with LazyBear "Weis Wave Volume" indicator. Works well for pullback entries.
Enjoy. Ride some waves.
Cari dalam skrip untuk "wave"
Godmode RSIbased on the popular Godmode Indicator with modifications by LEGION, LAZYBEAR, Ni6HTH4wK, xSilas, oh92, sco77m4r7in
All Credit belongs to them. THX Guys!
This is a Combination of a RSI and Godmode.
RSI has a Simple or Exponential Moving Average, Histogram Color Changes when the RSI reaches the Overbought/Oversold Zones.
Godmode is basicly the same as the Original one only scaled down a bit with slightly adjusted Caution Signal Zones which i like more. I also added the Option to adjust the Length of the 2nd Wavetrend SMA. Removed the Wavetrend Area because it doesnt have any use for me.
Hope you like it.
Periodic ChannelThis indicator try to create a channel by summing a re-scaled and readapted sinusoidal wave form to the price mean.
The length parameter control the speed of the sinusoidal wave form, this parameter is not converted to a sine wave period for allowing a better estimation, higher length's work better but feel free to try shorter periods.
The invert parameter invert the sinusoidal wave.
Each bands represent possible return points, the higher the band the higher the probability.
Inverted sin wave exemple
The performance of the indicator is subjective to the main estimation (blue line), select the parameter that best fit the blue line to the price.
Best ragards
CMYK XIAM OPEN◊ Introduction
This is project XIAM, a work in progress.
Recently i came across the repainting problem.
Since then i haven't seen any bot-code that makes > 5% profit in two weeks with 0.25% fees/trade.
People who make good bots either bluff or don't share the code.
they let you rent it.
I aim to understand, learn it, write it myself. And share my findings with whoever shares with me.
◊ Origin
Based on RMI (RSI with momentum) and SMA, and values derived from those.
◊ Usage
Currently an investigative script.
◊ Theoretical Approaches
Philosophy α :: Cleansignal
:: Cleaning up the signal, from irregularities that cause unpredictable results.
Merging available tickers of a pair into one.
Merging available tickers of different coins into one in the correct proportion. (eg. Crypto market cap)
Removing Jitter, and smoothing signal without delay.
Philosophy β :: Rythmic
:: Syncing into the rythm's, to never miss the que, and trade on every theoretical low/high
Searching Amplitude, Period, Phase Shift, Frequency's of the carrier waves.
Marking Acrivity/inactivity of the carrier waves.
Partial Fractal repetition asses-able with above data?
Philosophy γ :: consequential
:: Seeking for Indicatory events and causal relations
Probability / reward.
Confirmation and culmination.
...
◊ Community
Wanna share your findings ? or need help resolving a problem ?
CMYK :: discord.gg
AUTOTVIEW :: discordapp.com
rem sim v0.1every alt-coin has similarity.
cause of bitcoin.
always i want to delete that similarity and read the true(?) value of each coin.
and i made some script for that, but not good enough.
this one is different.
Rem Sim (rs) removes the similarity very effectively.
it make avg WaveTrend from nxt, strat, steem, ...
and that is the similarity
and it show true(?) WaveTrend without similarity.
so if the alt-coin move like other alt-coin, the WT almost 0.
sorry my bad english.
if you dont understand my english. just look at that chart.
also you can see source code.
--------
대부분의 알트가 어느정도 비슷한 차트를 가지는데, 그 유사성을 제거하면 어떤 모양인지 궁금해서 만들었어요.
전에 만들었던 비트코인의 영향력을 제거해주는 아이디어는 실제론 별 효용이 없는데 이건 좀 쓸만해보이네요.
웨이브트렌드의 모양으로 보여줍니다.
FLD's - Future Lines of Demarcation Backtest An FLD is a line that is plotted on the same scale as the price and is in fact the
price itself displaced to the right (into the future) by (approximately) half the
wavelength of the cycle for which the FLD is plotted. There are three FLD's that can be
plotted for each cycle:
An FLD based on the median price.
An FLD based on the high price.
An FLD based on the low price.
FLD - Future Lines of Demarcation Strategy An FLD is a line that is plotted on the same scale as the price and is in fact the
price itself displaced to the right (into the future) by (approximately) half the
wavelength of the cycle for which the FLD is plotted. There are three FLD's that can be
plotted for each cycle:
An FLD based on the median price.
An FLD based on the high price.
An FLD based on the low price.
FLD's - Future Lines of Demarcation An FLD is a line that is plotted on the same scale as the price and is in fact the
price itself displaced to the right (into the future) by (approximately) half the
wavelength of the cycle for which the FLD is plotted. There are three FLD's that can be
plotted for each cycle:
An FLD based on the median price.
An FLD based on the high price.
An FLD based on the low price.
Divergence TridentA Combination of MACD + VFI + WaveTrend
Tradingview hates me and is making me explain this in greater detail so maybe this is enough????
cd_HTF_bias_CxOverview:
No matter our trading style or model, to increase our success rate, we must move in the direction of the trend and align with the Higher Time Frame (HTF). Trading "gurus" call this the HTF bias. While we small fish tend to swim in all directions, the smart way is to flow with the big wave and the current. This indicator is designed to help us anticipate that major wave.
________________________________________
Details and Usage:
This indicator observes HTF price action across preferably seven different pairs, following specific rules. It confirms potential directional moves using CISD levels on a Medium Time Frame (MTF). In short, it forecasts the likely direction (HTF bias). The user can then search for trade opportunities aligned with this bias on a Lower Time Frame (LTF), using their preferred pair, entry model, and style.
________________________________________
Timeframe Alignment:
The commonly accepted LTF/MTF/HTF combinations include:
• 1m – 15m – H4
• 3m – H1 – Daily / 3m – 30m – Daily
• 5m – H1 – Daily
• 15m – H4 – Weekly
• H1 – Daily – Monthly
• H4 – Weekly – Quarterly
Example: If you're trading with a 3m model on a 30m/3m setup, you should seek trades in the direction of the H1/Daily bias.
________________________________________
How It Works:
The indicator first looks for sweeps on the selected HTF — when any of the last four candles are swept, the first condition is met.
The second step is confirmation with a CISD close on the MTF — once a candle closes above/below the CISD level, the second condition is fulfilled. This suggests the price has made its directional decision.
Example: If a previous HTF candle is swept and we receive a bearish CISD confirmation on H1, the HTF bias becomes bearish.
After this, you may switch to a more granular setup like HTF: 30m and MTF: 3m to look for trade entries aligned with the bias (e.g., 30m sweep + 3m CISD).
________________________________________
How Is Bias Determined?
• HTF Sweep + MTF CISD = SC (Sweep & CISD)
• Latest Bullish SC → Bias: Bullish
• Latest Bearish SC → Bias: Bearish
• Price closes above the last Bearish SC → Bias: Strong Bullish
• Price closes below the last Bullish SC → Bias: Strong Bearish
• Strong Bullish bias + Bearish CISD (without HTF sweep) → Bias: Bullish
• Strong Bearish bias + Bullish CISD (without HTF sweep) → Bias: Bearish
• Bearish price violates SC high, but Bullish SC is untouched → Bias: Bullish
• Bullish price violates SC low, but Bearish SC is untouched → Bias: Bearish
• If neither side generates SC → Bias: No Bias
The logic is built on the idea that a price overcoming resistance is stronger, and encountering resistance is weaker. This model is based on the well-known “Daily Bias” structure, but with personal refinements.
________________________________________
What’s on the Screen?
• Classic HTF zones (boxes)
• Potential MTF CISD levels
• Confirmed MTF lines
• Sweep zones when HTF sweeps occur
• Result table showing current bias status
________________________________________
Usage:
• Select HTF and MTF timeframes aligned with your trading timeframe.
• Adjust color and position settings as needed.
• Enter up to seven pairs to track via the menu.
• Use the checkbox next to each pair to enable/disable them.
• If “Ignore these assets” is checked, all pairs will be disabled, and only the currently open chart pair will be tracked.
________________________________________
Alerts:
You can choose alerts for Bullish, Bearish, Strong Bullish, or Strong Bearish conditions.
There are two types of alert sources:
1. From the indicator’s internal list
2. From TradingView’s watchlist
Visual example:
________________________________________
How I Use It:
• For spot trades, I use HTF: Weekly and MTF: H4 and look for Bullish or Strong Bullish pairs.
• For scalping, I follow bias from HTF: Daily and MTF: H1.
Example: If the indicator shows a Bearish HTF Bias, I switch to HTF: 30m and MTF: 3m and enter trades once bearish conditions are met (timeframe alignment).
________________________________________
Important Notes:
• The indicator defines CISD levels only at HTF high and low levels.
• If your chart is on a higher timeframe than your selected HTF/MTF, no data will appear.
Example: If HTF = H1 and MTF = 5m, opening a chart on H4 will result in a blank screen.
• The drawn CISD level on screen is the MTF CISD level.
• Not every alert should be traded. Always confirm with personal experience and visual validation.
• Receiving multiple Strong Bullish/Bearish alerts is intentional. (Trick 😊)
• Please share your feedback and suggestions!
________________________________________
And Most Importantly:
Don't leave street animals without water and food!
Happy trading!
WT_CROSS Dip Buy Signal(ozkan)This script identifies potential buy opportunities based on WaveTrend (WT_CROSS) momentum crossing below the -60 level — often indicating oversold conditions.
Additional filters include price being above the Kaufman Adaptive Moving Average (KAMA) and volume below the 5-period average, which helps isolate pullbacks within an uptrend.
Buy Signal Conditions:
WT1 < -60
Price > KAMA
Volume < 5-period SMA of volume
Purpose:
To capture early entries at possible local bottoms during bullish trends while avoiding high-volume breakdown traps.
🔔 You can also set an alert based on this condition.
Quantum State Superposition Indicator (QSSI)Quantum State Superposition Indicator (QSSI) - Where Physics Meets Finance
The Quantum Revolution in Market Analysis
After months of research into quantum mechanics and its applications to financial markets, I'm thrilled to present the Quantum State Superposition Indicator (QSSI) - a groundbreaking approach that models price action through the lens of quantum physics. This isn't just another technical indicator; it's a paradigm shift in how we understand market behavior.
The Theoretical Foundation
Quantum Superposition in Markets
In quantum mechanics, particles exist in multiple states simultaneously until observed. Similarly, markets exist in a superposition of potential states (bullish, bearish, neutral) until a significant volume event "collapses" the wave function into a definitive direction.
The mathematical framework:
Wave Function (Ψ): Represents the market's quantum state as a weighted sum of all possible states:
Ψ = Σ(αᵢ × Sᵢ)
Where αᵢ are probability amplitudes and Sᵢ are individual quantum states.
Probability Amplitudes: Calculated using the Born rule, normalized so Σ|αᵢ|² = 1
Observation Operator: Volume/Average Volume ratio determines observation strength
The Five Quantum States
Momentum State: Short-term price velocity (EMA of returns)
Mean Reversion State: Deviation from equilibrium (normalized z-score)
Volatility Expansion State: ATR relative to historical average
Trend Continuation State: Long-term price positioning
Chaos State: Volatility of volatility (market uncertainty)
Each state contributes to the overall wave function based on current market conditions.
Wave Function Collapse
When volume exceeds the observation threshold (default 1.5x average), the wave function "collapses," committing the market to a direction. This models how institutional volume forces markets out of uncertainty into trending states.
Collapse Detection Formula:
Collapse = Volume > (Threshold × Average Volume)
Direction = Sign(Ψ) at collapse moment
Advanced Quantum Concepts
Heisenberg Uncertainty Principle
The indicator calculates market uncertainty as the product of price and momentum
uncertainties:
ΔP × ΔM = ℏ (market uncertainty constant)
This manifests as dynamic uncertainty bands that widen during unstable periods.
Quantum Tunneling
Calculates the probability of price "tunneling" through resistance/support barriers:
P(tunnel) = e^(-2×|barrier_height|×√coherence_length)
Unlike classical technical analysis, this gives probability of breakouts before they occur.
Entanglement
Measures the quantum correlation between price and volume:
Entanglement = |Correlation(Price, Volume, lookback)|
High entanglement suggests coordinated institutional activity.
Decoherence
When market states lose quantum properties and behave classically:
Decoherence = 1 - Σ(amplitude²)
Indicates trend emergence from quantum uncertainty.
Visual Innovation
Probability Clouds
Three-tier probability distributions visualize market uncertainty:
Inner Cloud (68%): One standard deviation - most likely price range
Middle Cloud (95%): Two standard deviations - probable extremes
Outer Cloud (99.7%): Three standard deviations - tail risk zones
Cloud width directly represents market uncertainty - wider clouds signal higher entropy states.
Quantum State Visualization
Colored dots represent individual quantum states:
Green: Momentum state strength
Red: Mean reversion state strength
Yellow: Volatility state strength
Dot brightness indicates amplitude (influence) of each state.
Collapse Events
Aqua Diamonds (Above): Bullish collapse - upward commitment
Pink Diamonds (Below): Bearish collapse - downward commitment
These mark precise moments when markets exit superposition.
Implementation Details
Core Calculations
Feature Extraction: Normalize price returns, volume ratios, and volatility measures
State Calculation: Compute each quantum state's value
Amplitude Assignment: Weight states by market conditions and observation strength
Wave Function: Sum weighted states for final market quantum state
Visualization: Transform quantum values to price space for display
Performance Optimization
- Efficient array operations for state calculations
- Single-pass normalization algorithms
- Optimized correlation calculations for entanglement
- Smart label management to prevent visual clutter
Trading Applications:
Signal Generation
Bullish Signals:
- Positive wave function during collapse
- High tunneling probability at support
- Coherent market state with bullish bias
Bearish Signals:
- Negative wave function during collapse
- High tunneling probability at resistance
- Decoherent state transitioning bearish
Risk Management
Uncertainty-Based Position Sizing:
Narrow clouds: Normal position size
Wide clouds: Reduced position size
Extreme uncertainty: Stay flat
Quantum Stop Losses:
- Place stops outside probability clouds
- Adjust for Heisenberg uncertainty
- Respect quantum tunneling levels
Market Regime Recognition
Quantum Coherent (Superposed):
- Market in multiple states
- Avoid directional trades
- Prepare for collapse
Quantum Decoherent (Classical):
-Clear trend emergence
- Follow directional signals
- Traditional analysis applies
Advanced Features
Adaptive Dashboards
Quantum State Panel: Real-time wave function, dominant state, and coherence status
Performance Metrics: Win rate, signal frequency, and regime analysis
Information Guide: Comprehensive explanation of all quantum concepts
- All dashboards feature adjustable sizing for different screen resolutions.
Multi-Timeframe Quantum Analysis
The indicator adapts to any timeframe:
Scalping (1-5m): Short coherence length, sensitive thresholds
Day Trading (15m-1H): Balanced parameters
Swing Trading (4H-1D): Long coherence, stable states
Alert System
Sophisticated alerts for:
- Wave function collapse events
- Decoherence transitions
- High tunneling probability
- Strong entanglement detection
Originality & Innovation
This indicator introduces several firsts:
Quantum Superposition: First to model markets as quantum systems
Wave Function Collapse: Original volume-triggered state commitment
Tunneling Probability: Novel breakout prediction method
Entanglement Metrics: Unique price-volume quantum correlation
Probability Clouds: Revolutionary uncertainty visualization
Development Journey
Creating QSSI required:
- Deep study of quantum mechanics principles
- Translation of physics equations to market context
- Extensive backtesting across multiple markets
- UI/UX optimization for trader accessibility
- Performance optimization for real-time calculation
- The result bridges cutting-edge physics with practical trading.
Best Practices
Parameter Optimization
Quantum States (2-5):
- 2-3 for simple markets (forex majors)
- 4-5 for complex markets (indices, crypto)
Coherence Length (10-50):
- Lower for fast markets
- Higher for stable markets
Observation Threshold (1.0-3.0):
- Lower for active markets
- Higher for thin markets
Signal Confirmation
Always confirm quantum signals with:
- Market structure (support/resistance)
- Volume patterns
- Correlated assets
- Fundamental context
Risk Guidelines
- Never risk more than 2% per trade
- Respect probability cloud boundaries
- Exit on decoherence shifts
- Scale with confidence levels
Educational Value
QSSI teaches advanced concepts:
- Quantum mechanics applications
- Probability theory
- Non-linear dynamics
- Risk management
- Market microstructure
Perfect for traders seeking deeper market understanding.
Disclaimer
This indicator is for educational and research purposes only. While quantum mechanics provides a fascinating framework for market analysis, no indicator can predict future prices with certainty. The probabilistic nature of both quantum mechanics and markets means outcomes are inherently uncertain.
Always use proper risk management, conduct thorough analysis, and never risk more than you can afford to lose. Past performance does not guarantee future results.
Conclusion
The Quantum State Superposition Indicator represents a revolutionary approach to market analysis, bringing institutional-grade quantum modeling to retail traders. By viewing markets through the lens of quantum mechanics, we gain unique insights into uncertainty, probability, and state transitions that classical indicators miss.
Whether you're a physicist interested in finance or a trader seeking cutting-edge tools, QSSI opens new dimensions in market analysis.
"The market, like Schrödinger's cat, exists in multiple states until observed through volume."
* As you may have noticed, the past two indicators I've released (Lorentzian Classification and Quantum State Superposition) are designed with strategy implementation in mind. I'm currently developing a stable execution platform that's completely unique and moves away from traditional ATR-based position sizing and stop loss systems. I've found ATR-based approaches to be unreliable in volatile markets and regime transitions - they often lag behind actual market conditions and can lead to premature exits or oversized positions during volatility spikes.
The goal is to create something that adapts to market conditions in real-time using the quantum and relativistic principles we've been exploring. Hopefully I'll have something groundbreaking to share soon. Stay tuned!
Trade with quantum insight. Trade with QSSI .
— Dskyz , for DAFE Trading Systems
Macd, Wt Cross & HVPMacd Wt Cross & HVP – Advanced Multi-Signal Indicator
This script is a custom-designed multi-signal indicator that brings together three proven concepts to provide a complete view of market momentum, reversals, and volatility build-ups. It is built for traders who want to anticipate key market moves, not just react to them.
Why This Combination ?
While each tool has its strengths, their combined use creates powerful signal confluence.
Instead of juggling multiple indicators separately, this script synchronizes three key perspectives into a single, intuitive display—helping you trade with greater clarity and confidence.
1. MACD Histogram – Momentum and Trend Clarity
At the core of the indicator is the MACD histogram, calculated as the difference between two exponential moving averages (EMAs).
Color-coded bars represent momentum direction and intensity:
Green / blue bars: bullish momentum
Red / pink bars: bearish momentum
Color intensity shows acceleration or weakening of trend.
This visual makes it easy to detect trend shifts and momentum divergence at a glance.
2. WT Cross Signals – Early Reversal Detection
Overlaid on the histogram are green and red dots, based on the logic of the WaveTrend oscillator cross:
Green dots = potential bullish cross (buy signal)
Red dots = potential bearish cross (sell signal)
These signals are helpful for identifying reversal points during both trending and ranging phases.
3. Historical Volatility Percentile (HVP) – Volatility Compression Zones
Behind the histogram, purple vertical zones highlight periods of low historical volatility, based on the HVP:
When volatility compresses below a specific threshold, these zones appear.
Such periods are often followed by explosive price moves, making them prime areas for pre-breakout positioning.
By integrating HVP, the script doesn’t just tell you where the trend is—it tells you when the trend is likely to erupt.
How to Use This Script
Use the MACD histogram to confirm the dominant trend and its strength.
Watch for WT Cross dots as potential entry/exit signals in alignment or divergence with the MACD.
Monitor HVP purple zones as warnings of incoming volatility expansions—ideal moments to prepare for breakout trades.
Best results occur when all three elements align, offering a high-probability trade setup.
What Makes This Script Original?
Unlike many mashups, this script was not created by simply merging indicators. Each component was carefully integrated to serve a specific, complementary purpose:
MACD detects directional bias
WT Cross adds precision timing
HVP anticipates volatility-based breakout timing
This results in a strategic tool for traders, useful on multiple timeframes and adaptable to different trading styles (trend-following, breakout, swing).
Multi-Factor Reversal AnalyzerMulti-Factor Reversal Analyzer – Quantitative Reversal Signal System
OVERVIEW
Multi-Factor Reversal Analyzer is a comprehensive technical analysis toolkit designed to detect market tops and bottoms with high precision. It combines trend momentum analysis, price action behavior, wave oscillation structure, and volatility breakout potential into one unified indicator.
This indicator is not a random mix of tools — each module is carefully selected for a specific purpose. When combined, they form a multi-dimensional view of the market, merging trend analysis, momentum divergence, and volatility compression to produce high-confidence signals.
Why Combine These Modules?
Module Combination Ideas & How to Use Them
Factor A: Trend Detector + Gold Zone
Concept:
• The Trend Detector (light yellow histogram) evaluates market strength:
• Histogram trending downward or staying below 50 → bearish conditions;
• Trending upward or staying above 50 → bullish conditions.
• The Gold Zone identifies areas of volatility compression — typically a prelude to explosive market moves.
Practical Application:
• When the Gold Zone appears and the Trend Detector is bearish → likely downside move;
• When the Gold Zone appears and the Trend Detector is bullish → likely upside breakout.
• Note: The Gold Zone does not mean the bottom is in. It is not a buy signal on its own — always combine it with other modules for directional bias.
Factor B: PAI + Wave Trend
Concept:
• PAI (Price Action Index) is a custom oscillator that combines price momentum with volatility dispersion, displaying strength zones:
• Green area → bullish dominance;
• Red area → bearish pressure.
• Wave Trend offers smoothed crossover signals via the main and signal lines.
Practical Application:
• When PAI is in the green zone and Wave Trend makes a bullish crossover → potential reversal to the upside;
• When PAI is in the red zone and Wave Trend shows a bearish crossover → potential start of a downtrend.
Factor C: Trend Detector + PAI
Concept:
• Combines directional trend strength with price action strength to confirm setups via confluence.
Practical Application:
• Trend Detector histogram bottoms out + PAI enters the green zone → high chance of upward reversal;
• Histogram tops out + PAI in the red zone → increased likelihood of downside continuation.
Multi-Factor Confluence (Advanced Use)
• When Trend Detector, PAI, and Wave Trend all align in the same direction (bullish or bearish), the directional signal becomes significantly more reliable.
• This setup is especially useful for trend-following or swing trade entries.
KEY FEATURES
1. Multi-Layer Reversal Logic
• Combines trend scoring, oscillator divergence, and volatility squeezes for triangulated reversal detection.
• Helps traders distinguish between trend pullbacks and true reversals.
2. Advanced Divergence Detection
• Detects both regular and hidden divergences using pivot-based confirmation logic.
• Customizable lookback ranges and pivot sensitivity provide flexible tuning for different market styles.
3. Gold Zone Volatility Compression
• Highlights pre-breakout zones using custom oscillation models (RSI, harmonic, Karobein, etc.).
• Improves anticipation of breakout opportunities following low-volatility compressions.
4. Trend Direction Context
• PAI and Trend Score components provide top-down insight into prevailing bias.
• Built-in “Straddle Area” highlights consolidation zones; breakouts from this area often signal new trend phases.
5. Flexible Visualization
• Color-coded trend bars, reversal markers, normalized oscillator plots, and trend strength labels.
• Designed for both visual discretionary traders and data-driven system developers.
USAGE GUIDELINES
1. Applicable Markets
• Suitable for stocks, crypto, futures, and forex
• Supports reversal, mean-reversion, and breakout trading styles
2. Recommended Timeframes
• Short-term traders: 5m / 15m / 1H — use Wave Trend divergence + Gold Zone
• Swing traders: 4H / Daily — rely on Price Action Index and Trend Detector
• Macro trend context: use PAI HTF mode for higher timeframe overlays
3. Reversal Strategy Flow
• Watch for divergence (WT/PAI) + Gold Zone compression
• Confirm with Trend Score weakening or flipping
• Use Straddle Area breakout for final trigger
• Optional: enable bar coloring or labels for visual reinforcement
• The indicator performs optimally when used in conjunction with a harmonic pattern recognition tool
4. Additional Note on the Gold Zone
The “Gold Zone” does not directly indicate a market bottom. Since it is displayed at the bottom of the chart, it may be misunderstood as a bullish signal. In reality, the Gold Zone represents a compression of price momentum and volatility, suggesting that a significant directional move is about to occur. The direction of that move—upward or downward—should be determined by analyzing the histogram:
• If histogram momentum is weakening, the Gold Zone may precede a downward move.
• If histogram momentum is strengthening, it may signal an upcoming rebound or rally.
Treat the Gold Zone as a warning of impending volatility, and always combine it with trend indicators for accurate directional judgment.
RISK DISCLAIMER
• This indicator calculates trend direction based on historical data and cannot guarantee future market performance. When using this indicator for trading, always combine it with other technical analysis tools, fundamental analysis, and personal trading experience for comprehensive decision-making.
• Market conditions are uncertain, and trend signals may result in false positives or lag. Traders should avoid over-reliance on indicator signals and implement stop-loss strategies and risk management techniques to reduce potential losses.
• Leverage trading carries high risks and may result in rapid capital loss. If using this indicator in leveraged markets (such as futures, forex, or cryptocurrency derivatives), exercise caution, manage risks properly, and set reasonable stop-loss/take-profit levels to protect funds.
• All trading decisions are the sole responsibility of the trader. The developer is not liable for any trading losses. This indicator is for technical analysis reference only and does not constitute investment advice.
• Before live trading, it is recommended to use a demo account for testing to fully understand how to use the indicator and apply proper risk management strategies.
CHANGELOG
v1.0: Initial release featuring integrated Price Action Index, Trend Strength Scoring, Wave Trend Oscillator, Gold Zone Compression Detection, and dual-type divergence recognition. Supports higher timeframe (HTF) synchronization, visual signal markers, and diversified parameter configurations.
[blackcat] L2 Ehlers Autocorrelation Indicator V2OVERVIEW
The Ehlers Autocorrelation Indicator is a technical analysis tool developed by John F. Ehlers that measures the correlation between price data and its lagged versions to identify potential market cycles and reversals.
BACKGROUND
Originally introduced in Ehlers' "Cycle Analytics for Traders" (2013), this indicator leverages autocorrelation principles to detect patterns in market data that deviate from random noise or perfect sine waves.
FEATURES
• Calculates Pearson correlation coefficients for lags from 0 to 60 bars
• Visualizes correlations using colored bars ranging from red (negative correlation) to yellow (positive correlation)
• Provides minimum averaging option through AvgLength input parameter
• Displays sharp reversal signals at price turning points
• Shows variations in bar thickness and count over time
HOW TO USE
Add the indicator to your chart
Adjust the AvgLength input as needed:
• Set to 0 for no averaging
• Increase value for smoother results
Interpret the colored bars:
• Red: Negative correlation
• Yellow: Positive correlation
• Sharp transitions indicate potential reversal points
LIMITATIONS
• Requires sufficient historical data for accurate calculations
• Performance may vary across different market conditions
• Results depend on proper parameter settings
NOTES
• The indicator uses highpass filtering and super smoother filtering techniques
• Color intensity varies based on correlation strength
• Multiple lag periods are displayed simultaneously for comprehensive analysis
THANKS
This implementation is based on Ehlers' original work and has been adapted for TradingView's Pine Script platform.
Squeeze Momentum Indicator Strategy [LazyBear + PineIndicators]The Squeeze Momentum Indicator Strategy (SQZMOM_LB Strategy) is an automated trading strategy based on the Squeeze Momentum Indicator developed by LazyBear, which itself is a modification of John Carter's "TTM Squeeze" concept from his book Mastering the Trade (Chapter 11). This strategy is designed to identify low-volatility phases in the market, which often precede explosive price movements, and to enter trades in the direction of the prevailing momentum.
Concept & Indicator Breakdown
The strategy employs a combination of Bollinger Bands (BB) and Keltner Channels (KC) to detect market squeezes:
Squeeze Condition:
When Bollinger Bands are inside the Keltner Channels (Black Crosses), volatility is low, signaling a potential upcoming price breakout.
When Bollinger Bands move outside Keltner Channels (Gray Crosses), the squeeze is released, indicating an expansion in volatility.
Momentum Calculation:
A linear regression-based momentum value is used instead of traditional momentum indicators.
The momentum histogram is color-coded to show strength and direction:
Lime/Green: Increasing bullish momentum
Red/Maroon: Increasing bearish momentum
Signal Colors:
Black: Market is in a squeeze (low volatility).
Gray: Squeeze is released, and volatility is expanding.
Blue: No squeeze condition is present.
Strategy Logic
The script uses historical volatility conditions and momentum trends to generate buy/sell signals and manage positions.
1. Entry Conditions
Long Position (Buy)
The squeeze just released (Gray Cross after Black Cross).
The momentum value is increasing and positive.
The momentum is at a local low compared to the past 100 bars.
The price is above the 100-period EMA.
The closing price is higher than the previous close.
Short Position (Sell)
The squeeze just released (Gray Cross after Black Cross).
The momentum value is decreasing and negative.
The momentum is at a local high compared to the past 100 bars.
The price is below the 100-period EMA.
The closing price is lower than the previous close.
2. Exit Conditions
Long Exit:
The momentum value starts decreasing (momentum lower than previous bar).
Short Exit:
The momentum value starts increasing (momentum higher than previous bar).
Position Sizing
Position size is dynamically adjusted based on 8% of strategy equity, divided by the current closing price, ensuring risk-adjusted trade sizes.
How to Use This Strategy
Apply on Suitable Markets:
Best for stocks, indices, and forex pairs with momentum-driven price action.
Works on multiple timeframes but is most effective on higher timeframes (1H, 4H, Daily).
Confirm Entries with Additional Indicators:
The author recommends ADX or WaveTrend to refine entries and avoid false signals.
Risk Management:
Since the strategy dynamically sizes positions, it's advised to use stop-losses or risk-based exits to avoid excessive drawdowns.
Final Thoughts
The Squeeze Momentum Indicator Strategy provides a systematic approach to trading volatility expansions, leveraging the classic TTM Squeeze principles with a unique linear regression-based momentum calculation. Originally inspired by John Carter’s method, LazyBear's version and this strategy offer a refined, adaptable tool for traders looking to capitalize on market momentum shifts.
Sigma 2.0 - Advanced Buy and Sell Signal IndicatorOverview:
Sigma 2.0 is a sophisticated trading indicator designed to help traders identify potential buy and sell opportunities across various financial markets. By leveraging advanced mathematical calculations and incorporating multiple analytical tools, Sigma 2.0 aims to enhance trading strategies by providing precise entry and exit signals.
Key Features:
Advanced Sigma Calculations:
Utilizes a combination of Exponential Moving Averages (EMAs) and price deviations to calculate the Sigma lines (sigma1 and sigma2).
Detects potential trend reversals through the crossover of these Sigma lines.
Customizable Signal Filtering:
Offers the ability to filter buy and sell signals based on user-defined thresholds.
Helps reduce false signals in volatile markets by setting overbought and oversold levels.
Overbought and Oversold Detection:
Identifies extreme market conditions where price reversals are more likely.
Changes the background color of the chart to visually indicate overbought or oversold states.
Integration of Exponential Moving Averages (EMAs):
Includes EMAs of different lengths (10, 21, 55, 200) to assist in identifying market trends.
EMAs act as dynamic support and resistance levels.
Higher Timeframe Signal Incorporation:
Allows users to include signals from a higher timeframe to align trades with the broader market trend.
Enhances the reliability of signals by considering multiple timeframes.
Custom Alerts:
Provides alert conditions for both buy and sell signals.
Enables traders to receive notifications, ensuring timely decision-making.
How It Works:
Sigma Calculation Methodology:
The indicator calculates an average price (ap) and applies EMAs to derive the Sigma lines.
sigma1 represents the smoothed price deviation, while sigma2 is a moving average of sigma1.
A crossover of sigma1 above sigma2 generates a buy signal, indicating potential upward momentum.
Conversely, a crossover of sigma1 below sigma2 generates a sell signal.
Signal Filtering and Thresholds:
Users can enable filtering to only consider signals when sigma1 is below or above certain thresholds.
This helps in focusing on more significant market movements and reducing noise.
Overbought/Oversold Levels:
The indicator monitors sigma1 to detect when the market is in extreme conditions.
Background color changes provide a quick visual cue for these conditions.
EMA Analysis:
The plotted EMAs help in confirming the trend direction.
They can be used alongside Sigma signals to validate trade entries and exits.
Higher Timeframe Signals:
Incorporates signals from a user-selected higher timeframe.
Helps in aligning trades with the overall market trend, increasing the potential success rate.
How to Use:
Adding the Indicator to Your Chart:
Search for "Sigma 2.0" in the TradingView Indicators menu and add it to your chart.
Configuring the Settings:
Adjust the Sigma configurations (Channel Length, Average Length, Signal Line Length) to suit your trading style.
Set the overbought and oversold levels according to your risk tolerance.
Choose whether to filter signals by thresholds.
Select the higher timeframe for additional signal confirmation.
Interpreting the Signals:
Buy Signals:
Indicated by a green triangle below the price bar.
Occur when sigma1 crosses above sigma2 and other conditions are met.
Sell Signals:
Indicated by a red triangle above the price bar.
Occur when sigma1 crosses below sigma2 and other conditions are met.
Higher Timeframe Signals:
Plotted with lime (buy) and maroon (sell) triangles.
Help confirm signals in the current timeframe.
Utilizing EMAs:
Observe the EMAs to gauge the overall trend.
Consider aligning buy signals when the price is above key EMAs and sell signals when below.
Setting Up Alerts:
Use the built-in alert conditions to receive notifications for buy and sell signals.
Customize alert messages as needed.
Credits:
Original Concept Inspiration:
This indicator is inspired by the WaveTrend oscillator and other momentum-based indicators.
Special thanks to the original authors whose work laid the foundation for this enhanced version.
Disclaimer:
Trading involves significant risk, and past performance is not indicative of future results.
This indicator is a tool to assist in analysis and should not be the sole basis for any trading decision.
Always perform thorough analysis and consider multiple factors before entering a trade.
Note:
Ensure your chart is clean and only includes this indicator when publishing.
The script is open-source and can be modified to fit individual trading strategies.
For any questions or support, feel free to reach out or comment.
E9 Shark-32 PatternUnderstanding the Shark-32 Pattern and its Trading Applications
The Shark-32 Pattern is a bearish technical trading formation used to predict market reversals or trend continuations. It highlights a downward move followed by a corrective rally, signaling a potential resumption of the downtrend. Here’s a breakdown of how it works:
What is the Shark-32 Pattern?
The Shark-32 pattern is a five-wave structure typically observed in bearish markets:
Wave 0 to X: A significant price decline starts the pattern.
Wave X to A: A correction pushes the price slightly upward.
Wave A to B: The price drops again but doesn’t reach the initial low.
Wave B to C: A final sharp decline concludes the pattern.
Once Wave C is formed, it suggests that the market will continue to move downward, presenting a potential selling or shorting opportunity.
Using the Pattern in Trading
This pattern is valuable for traders seeking high-probability bearish setups. The goal is to capitalize on the continuation of a downtrend following the corrective rally (X to A). Identifying the Shark-32 pattern helps anticipate the next wave of selling pressure.
Trading Setup
Identify a Shark-32 pattern.
If the price closes above the pattern's high, buy at the open the next day.
If the price closes below the pattern's low, short at the open the next day.
Sell/cover when the price moves 7% in the direction of the breakout.
Close the trade for a loss if the price moves 7% in the opposite direction.
For example, in a bull market after an upward breakout from a Shark-32, the net gain was $69.55. The method won 56% of the time with 5,218 winning trades and an average gain of $714.07. Conversely, 44% of trades were losers, with an average loss of $747.33. The average holding period was 26 calendar days.
The gains and losses were closely aligned with the 7% threshold set for this test.
Key Target Levels
To enhance the strategy, use dotted projection lines as target levels:
Upper Target: Drawn above the high of the corrective rally (Wave A). If the price breaks above this line, it may signal further upward movement, indicating a potentially weaker downtrend.
Lower Target: Positioned below the low of Wave C, providing a target for bearish trades.
These lines help determine future price targets and assist in setting take-profit or stop-loss levels.
Trading the Breakout
Look for breakouts once the Shark-32 pattern is identified:
Upward Breakout: If the price closes above the green line (high from two bars ago), it indicates a potential reversal to the upside.
Downward Breakout: If the price breaks below the red line (low from two bars ago), it confirms the bearish continuation.
Breakouts allow traders to adjust their positions based on market shifts.
Trading Tips
Continuation: The Shark-32 pattern acts as a continuation 60% of the time, confirming the ongoing trend.
Breakout Confirmation: Wait for the price to close above or below the pattern’s key levels before entering a trade.
Trade with the Trend: Since the Shark-32 is a continuation pattern, expect the breakout to align with the inbound price trend.
Symmetry: Patterns with symmetry often perform better. For more insights, refer to detailed trading literature.
Half-Staff: The Shark-32 can form midway in a trend, similar to flags and pennants.
Shark-32: Trading Performance
Based on an analysis of 23,369 trades, the following performance metrics were observed:
Bull Market with Upward Breakout: The average net profit was $69.55. This method won 56% of the time, with winning trades averaging $714.07. Losing trades, which constituted 44% of the total, had an average loss of $747.33. The average holding period was 26 calendar days.
Bull Market with Downward Breakout: The average net loss was $(76.36). This method won 43% of the time, with winning trades averaging $753.56. Losing trades, which constituted 57% of the total, had an average loss of $706.32. The average holding period was 23 calendar days.
Bear Market with Upward Breakout: The average net loss was $(89.13). This method won 46% of the time, with winning trades averaging $710.77. Losing trades, which constituted 54% of the total, had an average loss of $756.97. The average holding period was 16 calendar days.
Bear Market with Downward Breakout: The average net profit was $65.17. This method won 52% of the time, with winning trades averaging $781.62. Losing trades, which constituted 48% of the total, had an average loss of $722.41. The average holding period was 13 calendar days.
ABCD Projection [Trendoscope®]Over the years, we have extensively explored and published numerous scripts centered around various chart patterns, including Harmonic Patterns, Reversal Patterns, Elliott Waves, and more. Our expertise in these areas has led to frequent requests for an indicator based on the ABCD pattern. Although we didn't include it as part of our Harmonic Patterns collection, the development of a dedicated ABCD Projection Indicator has always been a priority for us.
🎲 Overview of the ABCD Projection Indicator
The ABCD Projection Indicator is designed to identify and project ABCD patterns using a Zigzag-based approach. This pattern, characterized by alternating pivot highs and lows labeled as A, B, C, and D, is particularly significant in trending markets where it signifies trend continuation following deep pullbacks.
The indicator works by confirming the ABC pivots and projecting the D pivot based on the established price swings. Since ABCD patterns are most effective in trending environments, the indicator focuses on filtering patterns where the retracement from the C pivot has not compromised the trade's potential. Specifically, it ensures that the starting point (S)—where the pattern is detected—has not retraced beyond a defined threshold, preserving the opportunity to execute a trade with the goal of reaching the projected D pivot.
Additionally, the ABCD Projection Indicator considers the retracement ratio from the C pivot, which plays a crucial role in risk management. A higher retracement ratio reduces the stop distance (from pivot A to the entry point S) while increasing the distance to the target (pivot D), thereby enhancing the reward/risk ratio for trades.
🎲 Components of the ABCD Projection Indicator
The ABCD Projection Indicator comprises several key components:
A, B, C Pivots and Zigzag Wave : These elements form the foundational structure of the ABCD pattern.
S Point : This is the location where the pattern is identified, positioned a few bars away from the confirmed C pivot.
Estimated D Pivot : The D pivot is projected based on the A, B, and C price levels. The time or distance to the D pivot is influenced by the starting point S.
Mini Stats Table : Located in the top right corner, this table displays win/loss ratios and risk/reward data for both bullish and bearish scenarios.
Fibonacci Levels : Calculated from the C to D pivots, these levels are provided as a reference for additional analysis.
🎲 Indicator Settings
The settings for the ABCD Projection Indicator are minimal and intuitive, with tooltips provided to guide users through the configuration process.
Harmonic Patterns Library [TradingFinder]🔵 Introduction
Harmonic patterns blend geometric shapes with Fibonacci numbers, making these numbers fundamental to understanding the patterns.
One person who has done a lot of research on harmonic patterns is Scott Carney.Scott Carney's research on harmonic patterns in technical analysis focuses on precise price structures based on Fibonacci ratios to identify market reversals.
Key patterns include the Gartley, Bat, Butterfly, and Crab, each with specific alignment criteria. These patterns help traders anticipate potential market turning points and make informed trading decisions, enhancing the predictability of technical analysis.
🟣 Understanding 5-Point Harmonic Patterns
In the current library version, you can easily draw and customize most XABCD patterns. These patterns often form M or W shapes, or a combination of both. By calculating the Fibonacci ratios between key points, you can estimate potential price movements.
All five-point patterns share a similar structure, differing only in line lengths and Fibonacci ratios. Learning one pattern simplifies understanding others.
🟣 Exploring the Gartley Pattern
The Gartley pattern appears in both bullish (M shape) and bearish (W shape) forms. In the bullish Gartley, point X is below point D, and point A surpasses point C. Point D marks the start of a strong upward trend, making it an optimal point to place a buy order.
The bearish Gartley mirrors the bullish pattern with inverted Fibonacci ratios. In this scenario, point D indicates the start of a significant price drop. Traders can place sell orders at this point and buy at lower prices for profit in two-way markets.
🟣 Analyzing the Butterfly Pattern
The Butterfly pattern also manifests in bullish (M shape) and bearish (W shape) forms. It resembles the Gartley pattern but with point D lower than point X in the bullish version.
The Butterfly pattern involves deeper price corrections than the Gartley, leading to more significant price fluctuations. Point D in the bullish Butterfly indicates the beginning of a sharp price rise, making it an entry point for buy orders.
The bearish Butterfly has inverted Fibonacci ratios, with point D marking the start of a sharp price decline, ideal for sell orders followed by buying at lower prices in two-way markets.
🟣 Insights into the Bat Pattern
The Bat pattern, appearing in bullish (M shape) and bearish (W shape) forms, is one of the most precise harmonic patterns. It closely resembles the Butterfly and Gartley patterns, differing mainly in Fibonacci levels.
The bearish Bat pattern shares the Fibonacci ratios with the bullish Bat, with an inverted structure. Point D in the bearish Bat marks the start of a significant price drop, suitable for sell orders followed by buying at lower prices for profit.
🟣 The Crab Pattern Explained
The Crab pattern, found in both bullish (M shape) and bearish (W shape) forms, is highly favored by analysts. Discovered in 2000, the Crab pattern features a larger final wave correction compared to other harmonic patterns.
The bearish Crab shares Fibonacci ratios with the bullish version but in an inverted form. Point D in the bearish Crab signifies the start of a sharp price decline, making it an ideal point for sell orders followed by buying at lower prices for profitable trades.
🟣 Understanding the Shark Pattern
The Shark pattern appears in bullish (M shape) and bearish (W shape) forms. It differs from previous patterns as point C in the bullish Shark surpasses point A, with unique level measurements.
The bearish Shark pattern mirrors the Fibonacci ratios of the bullish Shark but is inverted. Point D in the bearish Shark indicates the start of a sharp price drop, ideal for placing sell orders and buying at lower prices to capitalize on the pattern.
🟣 The Cypher Pattern Overview
The Cypher pattern is another that appears in both bullish (M shape) and bearish (W shape) forms. It resembles the Shark pattern, with point C in the bullish Cypher extending beyond point A, and point D forming within the XA line.
The bearish Cypher shares the Fibonacci ratios with the bullish Cypher but in an inverted structure. Point D in the bearish Cypher marks the start of a significant price drop, perfect for sell orders followed by buying at lower prices.
🟣 Introducing the Nen-Star Pattern
The Nen-Star pattern appears in both bullish (M shape) and bearish (W shape) forms. In the bullish Nen-Star, point C extends beyond point A, and point D, the final point, forms outside the XA line, making CD the longest wave.
The bearish Nen-Star has inverted Fibonacci ratios, with point D indicating the start of a significant price drop. Traders can place sell orders at point D and buy at lower prices to profit from this pattern in two-way markets.
The 5-point harmonic patterns, commonly referred to as XABCD patterns, are specific geometric price structures identified in financial markets. These patterns are used by traders to predict potential price movements based on historical price data and Fibonacci retracement levels.
Here are the main 5-point harmonic patterns :
Gartley Pattern
Anti-Gartley Pattern
Bat Pattern
Anti-Bat Pattern
Alternate Bat Pattern
Butterfly Pattern
Anti-Butterfly Pattern
Crab Pattern
Anti-Crab Pattern
Deep Crab Pattern
Shark Pattern
Anti- Shark Pattern
Anti Alternate Shark Pattern
Cypher Pattern
Anti-Cypher Pattern
🔵 How to Use
To add "Order Block Refiner Library", you must first add the following code to your script.
import TFlab/Harmonic_Chart_Pattern_Library_TradingFinder/1 as HP
🟣 Parameters
XABCD(Name, Type, Show, Color, LineWidth, LabelSize, ShVF, FLPC, FLPCPeriod, Pivot, ABXAmin, ABXAmax, BCABmin, BCABmax, CDBCmin, CDBCmax, CDXAmin, CDXAmax) =>
Parameters:
Name (string)
Type (string)
Show (bool)
Color (color)
LineWidth (int)
LabelSize (string)
ShVF (bool)
FLPC (bool)
FLPCPeriod (int)
Pivot (int)
ABXAmin (float)
ABXAmax (float)
BCABmin (float)
BCABmax (float)
CDBCmin (float)
CDBCmax (float)
CDXAmin (float)
CDXAmax (float)
🟣 Genaral Parameters
Name : The name of the pattern.
Type: Enter "Bullish" to draw a Bullish pattern and "Bearish" to draw an Bearish pattern.
Show : Enter "true" to display the template and "false" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Logical Parameters
ShVF : If this parameter is on "true" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "false" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
FLPC : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the lateest pattern seeing and a sharp reduction in reward to risk.
FLPCPeriod : Using this parameter you can determine that the last pivot is based on Pivot period.
Pivot : You need to determine the period of the zigzag indicator. This factor is the most important parameter in pattern recognition.
ABXAmin : Minimum retracement of "AB" line compared to "XA" line.
ABXAmax : Maximum retracement of "AB" line compared to "XA" line.
BCABmin : Minimum retracement of "BC" line compared to "AB" line.
BCABmax : Maximum retracement of "BC" line compared to "AB" line.
CDBCmin : Minimum retracement of "CD" line compared to "BC" line.
CDBCmax : Maximum retracement of "CD" line compared to "BC" line.
CDXAmin : Minimum retracement of "CD" line compared to "XA" line.
CDXAmax : Maximum retracement of "CD" line compared to "XA" line.
🟣 Function Outputs
This library has two outputs. The first output is related to the alert of the formation of a new pattern. And the second output is related to the formation of the candlestick pattern and you can draw it using the "plotshape" tool.
Candle Confirmation Logic :
Example :
import TFlab/Harmonic_Chart_Pattern_Library_TradingFinder/1 as HP
PP = input.int(3, 'ZigZag Pivot Period')
ShowBull = input.bool(true, 'Show Bullish Pattern')
ShowBear = input.bool(true, 'Show Bearish Pattern')
ColorBull = input.color(#0609bb, 'Color Bullish Pattern')
ColorBear = input.color(#0609bb, 'Color Bearish Pattern')
LineWidth = input.int(1 , 'Width Line')
LabelSize = input.string(size.small , 'Label size' , options = )
ShVF = input.bool(false , 'Show Valid Format')
FLPC = input.bool(false , 'Show Formation Last Pivot Confirm')
FLPCPeriod =input.int(2, 'Period of Formation Last Pivot')
//Call function
= HP.XABCD('Bullish Bat', 'Bullish', ShowBull, ColorBull , LineWidth, LabelSize ,ShVF, FLPC, FLPCPeriod, PP, 0.382, 0.50, 0.382, 0.886, 1.618, 2.618, 0.85, 0.9)
= HP.XABCD('Bearish Bat', 'Bearish', ShowBear, ColorBear , LineWidth, LabelSize ,ShVF, FLPC, FLPCPeriod, PP, 0.382, 0.50, 0.382, 0.886, 1.618, 2.618, 0.85, 0.9)
//Alert
if BearAlert
alert('Bearish Harmonic')
if BullAlert
alert('Bulish Harmonic')
//CandleStick Confirm
plotshape(BearCandleConfirm, style = shape.arrowdown, color = color.red)
plotshape(BullCandleConfirm, style = shape.arrowup, color = color.green, location = location.belowbar )
VolumeSpreadAnalysisLibrary "VolumeSpreadAnalysis"
A library for Volume Spread Analysis (VSA).
spread(_barIndex)
Calculates the spread of a bar.
Parameters:
_barIndex (int) : (int) The index of the bar.
Returns: (float) The spread of the bar.
volume(_barIndex)
Retrieves the volume of a bar.
Parameters:
_barIndex (int) : (int) The index of the bar.
Returns: (float) The volume of the bar.
body(_barIndex)
Calculates the body of a bar.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (float) The body size of the bar.
wickUpper(_barIndex)
Calculates the upper wick of a bar (upper shadow).
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (float) The upper wick size of the bar.
wickLower(_barIndex)
Calculates the lower wick of a bar (lower shadow).
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (float) The lower wick size of the bar.
calcForecastedSMA(_source, _length, _forecastedLevel)
Calculates the forecasted Simple Moving Average (SMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the SMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted SMA value.
calcForecastedEMA(_source, _length, _forecastedLevel)
Calculates the forecasted Exponential Moving Average (EMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the EMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted EMA value.
calcForecastedRMA(_source, _length, _forecastedLevel)
Calculates the forecasted Relative Moving Average (RMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the RMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted RMA value.
calcForecastedWMA(_source, _length, _forecastedLevel)
Calculates the forecasted Weighted Moving Average (WMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the WMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted WMA value.
calcElapsedTimePercent()
Calculates the elapsed time percent of the current bar.
Returns: (float) The elapsed time percent.
calcForecastedSpread(multiplierAtMidpoints, multiplierAtPeaks)
Calculates the forecasted spread using elapsed time and dynamic multipliers, handling spread's non-linear nature.
Parameters:
multiplierAtMidpoints (float) : (float) The multiplier value at midpoints.
multiplierAtPeaks (float) : (float) The multiplier value at peaks.
Returns: (float) The forecasted spread value.
calcForecastedVolume()
Calculates the forecasted volume using elapsed time, satisfying volume's linear nature.
Returns: (float) The forecasted volume value.
calcForecastedMA(_source, _length, _forecastedSource, _type)
Calculates the forecasted Moving Average (MA) based on the specified type.
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the MA.
_forecastedSource (float) : (float) The forecasted level to include in the calculation.
_type (simple string) : (string) The type of the MA ("SMA", "EMA", "SMMA (RMA)", "WMA").
Returns: (float) The forecasted MA value.
calcMA(_source, _length, _type)
Calculates the Moving Average (MA) based on the specified type.
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the MA.
_type (simple string) : (string) The type of the MA ("SMA", "EMA", "SMMA (RMA)", "WMA").
Returns: (float) The MA value.
bullBar(_barIndex)
Determines if the bar is bullish.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if the bar is bullish, otherwise false.
bearBar(_barIndex)
Determines if the bar is bearish.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if the bar is bearish, otherwise false.
breakout(_barIndex)
Determines if there is a breakout above the previous bar.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if there is a breakout, otherwise false.
breakdown(_barIndex)
Determines if there is a breakdown below the previous bar.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if there is a breakdown, otherwise false.
rejectionWickUpper(_rejectionWick)
Determines if the upper wick is a rejection wick.
Parameters:
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if the upper wick is a rejection wick, otherwise false.
rejectionWickLower(_rejectionWick)
Determines if the lower wick is a rejection wick.
Parameters:
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if the lower wick is a rejection wick, otherwise false.
setupDataVolume(_data, _mult_Low, _mult_High, _mult_Ultra, _maLengthVolume, _maTypeVolume)
Sets up data for volume levels.
Parameters:
_data (map) : (map) The map to store the levels.
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthVolume (simple int) : (int) The length for MA.
_maTypeVolume (simple string) : (string) The type for MA.
Returns: (void) Nothing.
setupDataSpread(_data, _mult_Low, _mult_High, _mult_Ultra, _maLengthSpread, _maTypeSpread)
Sets up data for spread levels.
Parameters:
_data (map) : (map) The map to store the levels.
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthSpread (simple int) : (int) The length for MA.
_maTypeSpread (simple string) : (string) The type for MA.
Returns: (void) Nothing.
setupDataForecastVolume(_dataForecast, _mult_Low, _mult_High, _mult_Ultra, _maLengthVolume, _predictedLevelVolume, _maTypeVolume)
Sets up data for volume and spread levels for forecast.
Parameters:
_dataForecast (map)
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthVolume (simple int) : (int) The length for MA.
_predictedLevelVolume (float) : (float) The predicted level for MA.
_maTypeVolume (simple string) : (string) The type for MA.
Returns: (void) Nothing.
setupDataForecastSpread(_dataForecast, _mult_Low, _mult_High, _mult_Ultra, _maLengthSpread, _predictedLevelSpread, _maTypeSpread)
Sets up data for spread levels for forecast.
Parameters:
_dataForecast (map)
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthSpread (simple int) : (int) The length for MA.
_predictedLevelSpread (float) : (float) The predicted level for MA.
_maTypeSpread (simple string) : (string) The type for MA.
Returns: (void) Nothing.
isVolumeLow(_data, _barIndex)
Determines if the volume is low.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is low, otherwise false.
isVolumeNormal(_data, _barIndex)
Determines if the volume is normal.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is normal, otherwise false.
isVolumeHigh(_data, _barIndex)
Determines if the volume is high.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is high, otherwise false.
isVolumeUltra(_data, _barIndex)
Determines if the volume is ultra.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is ultra, otherwise false.
isSpreadLow(_data, _barIndex)
Determines if the spread is low.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is low, otherwise false.
isSpreadNormal(_data, _barIndex)
Determines if the spread is normal.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is normal, otherwise false.
isSpreadHigh(_data, _barIndex)
Determines if the spread is high.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is high, otherwise false.
isSpreadUltra(_data, _barIndex)
Determines if the spread is ultra.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is ultra, otherwise false.
isVolumeText(_data)
Determines text string representing the volume area level.
Parameters:
_data (map) : (map) The data map with volume levels.
Returns: (string) Text string of Low, Normal, High, or Ultra.
isSpreadText(_data)
Determines text string representing the spread area level.
Parameters:
_data (map) : (map) The data map with spread levels.
Returns: (string) Text string of Low, Normal, High, or Ultra.
calcBarColor(_value, _level)
Calculates the color based level.
Parameters:
_value (float) : (float) The value to check.
_level (float) : (float) The value level for comparison.
Returns: (color) The color for the bar.
bullPinBar(_maxBodyPercent, _minWickPercent)
Determines if the bar is a bull pin bar.
Parameters:
_maxBodyPercent (simple float) : (float) The maximum body percentage.
_minWickPercent (simple float) : (float) The minimum wick percentage.
Returns: (bool) True if the bar is a bull pin bar, otherwise false.
bearPinBar(_maxBodyPercent, _minWickPercent)
Determines if the bar is a bear pin bar.
Parameters:
_maxBodyPercent (simple float) : (float) The maximum body percentage.
_minWickPercent (simple float) : (float) The minimum wick percentage.
Returns: (bool) True if the bar is a bear pin bar, otherwise false.
dojiBar(_maxBodyPercent)
Determines if the bar is a doji.
Parameters:
_maxBodyPercent (simple float) : (float) The maximum body percentage.
Returns: (bool) True if the bar is a doji, otherwise false.
spinningTopBar(_minWicksPercent, _emaLength)
Determines if the bar is a spinning top.
Parameters:
_minWicksPercent (simple float) : (float) The minimum wicks percentage.
_emaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if the bar is a spinning top, otherwise false.
highWaveBar(_minBodyPercent, _minWickPercent, _bars)
Determines if the bar is a high wave bar.
Parameters:
_minBodyPercent (simple float) : (float) The minimum body percentage.
_minWickPercent (simple float) : (float) The minimum wick percentage.
_bars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if the bar is a high wave bar, otherwise false.
consolidationBar(_data, _spread, _bars)
Determines if the bars are in consolidation.
Parameters:
_data (map) : (map) The data map with spread levels.
_spread (simple float) : (float) The spread percentage for comparison.
_bars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if the bars are in consolidation, otherwise false.
S_DownThrust(_data, _bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of strength (DownThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of strength (DownThrust), otherwise false.
S_SellingClimax(_data, _rejectionWick)
Determines if there is a sign of strength (Selling Climax).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if there is a sign of strength (Selling Climax), otherwise false.
S_NoEffortBearishResult()
Determines if there is a sign of strength (No Effort Bearish Result).
Returns: (bool) True if there is a sign of strength (No Effort Bearish Result), otherwise false.
S_BearishEffortNoResult()
Determines if there is a sign of strength (Bearish Effort No Result).
Returns: (bool) True if there is a sign of strength (Bearish Effort No Result), otherwise false.
S_InverseDownThrust(_data, _bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of strength (Inverse DownThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of strength (Inverse DownThrust), otherwise false.
S_FailedSellingClimax()
Determines if there is a sign of strength (Failed Selling Climax).
Returns: (bool) True if there is a sign of strength (Failed Selling Climax), otherwise false.
S_BullOutsideReversal(_data)
Determines if there is a sign of strength (Bull Outside Reversal).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of strength (Bull Outside Reversal), otherwise false.
S_EndOfFallingMarket(_data)
Determines if there is a sign of strength (End of Falling Market).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of strength (End of Falling Market), otherwise false.
S_PseudoDownThrust(_bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of strength (Pseudo DownThrust).
Parameters:
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of strength (Pseudo DownThrust), otherwise false.
S_NoSupply(_bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of strength (No Supply).
Parameters:
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of strength (No Supply), otherwise false.
W_UpThrust(_data, _bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of weakness (UpThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of weakness (UpThrust), otherwise false.
W_BuyingClimax(_data, _rejectionWick)
Determines if there is a sign of weakness (Buying Climax).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if there is a sign of weakness (Buying Climax), otherwise false.
W_NoEffortBullishResult()
Determines if there is a sign of weakness (No Effort Bullish Result).
Returns: (bool) True if there is a sign of weakness (No Effort Bullish Result), otherwise false.
W_BullishEffortNoResult()
Determines if there is a sign of weakness (Bullish Effort No Result).
Returns: (bool) True if there is a sign of weakness (Bullish Effort No Result), otherwise false.
W_InverseUpThrust(_data, _bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of weakness (Inverse UpThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of weakness (Inverse UpThrust), otherwise false.
W_FailedBuyingClimax()
Determines if there is a sign of weakness (Failed Buying Climax).
Returns: (bool) True if there is a sign of weakness (Failed Buying Climax), otherwise false.
W_BearOutsideReversal(_data)
Determines if there is a sign of weakness (Bear Outside Reversal).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of weakness (Bear Outside Reversal), otherwise false.
W_EndOfRisingMarket(_data)
Determines if there is a sign of weakness (End of Rising Market).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of weakness (End of Rising Market), otherwise false.
W_PseudoUpThrust(_bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of weakness (Pseudo UpThrust).
Parameters:
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of weakness (Pseudo UpThrust), otherwise false.
W_NoDemand(_bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of weakness (No Demand).
Parameters:
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of weakness (No Demand), otherwise false.
N_QuietDoji(_dojiBarMaxBody)
Determines if there is a neutral signal (Quiet Doji).
Parameters:
_dojiBarMaxBody (simple float) : (float) The maximum body percentage for doji bar.
Returns: (bool) True if there is a neutral signal (Quiet Doji), otherwise false.
N_BalancedDoji(_data, _dojiBarMaxBody)
Determines if there is a neutral signal (Balanced Doji).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_dojiBarMaxBody (simple float) : (float) The maximum body percentage for doji bar.
Returns: (bool) True if there is a neutral signal (Balanced Doji), otherwise false.
N_StrongDoji(_dojiBarMaxBody)
Determines if there is a neutral signal (Strong Doji).
Parameters:
_dojiBarMaxBody (simple float) : (float) The maximum body percentage for doji bar.
Returns: (bool) True if there is a neutral signal (Strong Doji), otherwise false.
N_QuietSpinningTop(_spinningTopBarMinWicks, _spinningTopBarEmaLength)
Determines if there is a neutral signal (Quiet Spinning Top).
Parameters:
_spinningTopBarMinWicks (simple float) : (float) The minimum wicks percentage for spinning top bar.
_spinningTopBarEmaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if there is a neutral signal (Quiet Spinning Top), otherwise false.
N_BalancedSpinningTop(_data, _spinningTopBarMinWicks, _spinningTopBarEmaLength)
Determines if there is a neutral signal (Balanced Spinning Top).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_spinningTopBarMinWicks (simple float) : (float) The minimum wicks percentage for spinning top bar.
_spinningTopBarEmaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if there is a neutral signal (Balanced Spinning Top), otherwise false.
N_StrongSpinningTop(_spinningTopBarMinWicks, _spinningTopBarEmaLength)
Determines if there is a neutral signal (Strong Spinning Top).
Parameters:
_spinningTopBarMinWicks (simple float) : (float) The minimum wicks percentage for spinning top bar.
_spinningTopBarEmaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if there is a neutral signal (Strong Spinning Top), otherwise false.
N_QuietHighWave(_highWaveBarMinBody, _highWaveBarMinWick, _highWaveBarBars)
Determines if there is a neutral signal (Quiet High Wave).
Parameters:
_highWaveBarMinBody (simple float) : (float) The minimum body percentage for high wave bar.
_highWaveBarMinWick (simple float) : (float) The minimum wick percentage for high wave bar.
_highWaveBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Quiet High Wave), otherwise false.
N_BalancedHighWave(_data, _highWaveBarMinBody, _highWaveBarMinWick, _highWaveBarBars)
Determines if there is a neutral signal (Balanced High Wave).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_highWaveBarMinBody (simple float) : (float) The minimum body percentage for high wave bar.
_highWaveBarMinWick (simple float) : (float) The minimum wick percentage for high wave bar.
_highWaveBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Balanced High Wave), otherwise false.
N_StrongHighWave(_highWaveBarMinBody, _highWaveBarMinWick, _highWaveBarBars)
Determines if there is a neutral signal (Strong High Wave).
Parameters:
_highWaveBarMinBody (simple float) : (float) The minimum body percentage for high wave bar.
_highWaveBarMinWick (simple float) : (float) The minimum wick percentage for high wave bar.
_highWaveBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Strong High Wave), otherwise false.
N_Consolidation(_data, _consolidationBarSpread, _consolidationBarBars)
Determines if there is a neutral signal (Consolidation).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_consolidationBarSpread (simple float) : (float) The spread percentage for consolidation bar.
_consolidationBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Consolidation), otherwise false.
Alligator + MA Trend Catcher [TradeDots]The "Alligator + MA Trend Catcher" is a trading strategy that integrates the William Alligator indicator with a Moving Average (MA) to establish robust entry and exit conditions, optimized for capturing trends.
HOW IT WORKS
This strategy combines the traditional William Alligator set up with an additional Moving Average indicator for enhanced trend confirmation, creating a user-friendly backtesting tool for traders who prefer the Alligator method.
The original Alligator strategy can frequently present fluctuations, even in well-established trends, leading to potentially premature exits. To mitigate this, we incorporate a Moving Average as a secondary confirmation measure to ensure the market trend has indeed shifted.
Here’s the operational flow for long orders:
Entry Signal: When the price rises above the Moving Average, it confirms a bullish market state. Enter if Alligator spread in an upward direction. The trade remains active even if the Alligator indicator suggests a trend reversal.
Exit Signal: The position is closed when the price falls below the Moving Average, and the Alligator spreads in the downward direction. This setup helps traders to maintain positions through the entirety of the trend for maximum gain.
APPLICATION
This strategy is tailored for assets with significant, well-defined trends, such as Bitcoin and Ethereum, which are known for their high volatility and substantial price movements.
This strategy offers a low win-rate but high reward configuration, making asset selection critical for long-term profitability. If you choose assets that lack strong price momentum, there's a high chance that this strategy may not be effective.
For traders seeking to maximize gains from large trends without exiting prematurely, this strategy provides an aggressive yet controlled approach to riding out substantial market waves.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
AWR_WaveTrend Multitimeframe [adapted from LazyBear]I've adapted a script from Lazy Bear (WT trend oscillator)
WaveTrend Oscillator is a port of a famous TS/MT indicator.
When the oscillator (WT1 designed as a line) is above the overbought band (50 to 60) and crosses down the WT2 (dotted line), it is usually a good SELL signal. Similarly, when the oscillator crosses above the signal when below the Oversold band ( (-50 to -60)), it is a good BUY signal.
In this indicator, you can display at the same time, different time frames.
Choice possible are 1 mn, 15 mn, 30 mn, 60 mn, 120 mn, 240 mn, 1D, Week, Month.
Small time frames (1 to 30 mn) are represented by a blue lines (light to dark)
1H is in grey
2H & 4H are in purple (light to dark)
1D is in green
1W is in orange
1M is in black
You can choose which timeframes you want to display for the current period or for the last period closed.
In a few seconds, you perfectly see the selected timeframes trends.
There is also at the bottom right a table summing up all the different values of WT1, WT2 and difference between them.
Positive difference means an upside trend
Negative difference means a downside trend.
Another way of using this indicator is displaying only the difference between WT1 & WT2. It's giving the speed & the direction of all trends. Trends are our friends ...
You can observe the significent times frames and look if they are all positives or negatives or if the speed of lower timeframe cross a longer timeframe of if the speed is decreasing or increasing...
Difference values goes generaly from -20 to 20 (it can exceed a bit but really rare). 12 is already high level of speed.
Many uses possible.
In the exemple posted, I've selected WT1 and WT2 for timeframes 4H, Daily & Weekly.
Marker 1:
Orange lines (WT1) are far below - 50 (-67 here) and cross WT2 pointed lines : weekly buy signal
But this buy signal is balanced by 4H & Daily sell signal = it's marking start of hesitations of main trend !!!!
Marker 2 :
Next buy signal in 4H or daily would normaly confirm the start
Marker 3 :
Sell signal in 4H and daily but weekly has an upside trend ! Start of a counter trend in the trend. To find the perfect timing of that you have to look to lower time frames, because 4H and daily are giving many hesitations signals crossing down & crossing up many times in an overbought zone.
Marker 4 :
End of the counter trend. Most of the time, the countertrend don't go in the "over" zone. That's why if you trading in an counter trend, you have to keep it in mind.
Then a few days later you can see the sell signal. And what a sell signal ! 4H & daily are smashed down really fastly ! Trends change warning !
Marker 5
Long hesitation/change of the trend. Daily WT and 4H are below the weekly trends. Weekly start to go down.
Start of a counter trend inside the trend giving us the best selling signal at her end !
Marker 6 :
Long hesitation/change of the trend.
You have to look in lower time frames to identify the short trend. Difficult to find the best timing to get in. ....
I've add many alerts. When a time frame become positive or negative. When many time frames are positive or negative or above or below 47 level...
Please feel free to explore.
Hope it will help you.
Thanks to Lazybear ! Thousands thanks to Lazybear !
Exemple with difference
