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Better Volume - a separate, non-overlaid volume indicatorHey guys Coming at you again with a very simple script for displaying volume that is not overlaid on the price chart. This volume is in a separate indicator window for when you don't want the volume to get in your way. Bonus: default colors are color matched to default volume indicator (can be changed in the "Style" tab under settings). Includes the moving average as well, with the option to hide it if desired Inspired by @tradedevils
Pine Script® indicator
by TradeCalmly
10
Aggregated Volume - By InFinitoVolume indicator that works like a normal Volume indicator with the following additional features: - Aggregates Volume across different exchanges and Market Types - *Original Aggregation Code By Crypt0rus* - Displays data by Market Type and combinations of Market Types (Spot, Futures , Perpetuals, Futures+Perpetuals & All Volume ) - Allows for the user to select the exchanges from which to aggregate Volume (This allows for the aggregation of any other pair i.e ETH, SOL, LUNA) - Normalizes the Volume reported through TradingView by every exchange in order to homogenize the data (i.e Binance reports Bitcoin Volume in BTC terms BUT FTX reports Bitcoin Volume in USD) - Allows for manual input of how Volume is reported in a particular Pair/Exchange (i.e If you want to aggregate data from the BTCEUR pair, you can select 'Other' and introduce the Value of EUR in USD terms) COIN: Select this option if the volume is reported in terms of the asset traded ( BTC , ETH, SOL, etc....) USD: Select this option if the Volume is reported in terms of the USD amount traded OTHER: Select this option in case the Volume is reported in another currency (EUR, ETH, etc....) NOTE: *ALL VOLUME IS AGGREGATED IN TERMS OF THE ASSET TRADED, FOR EXAMPLE IN THIS CASE: BTC . BUT IF YOU'RE AGGREGATING BNB PAIRS, VOLUME WILL BE CALCULATED TO BE DISPLAYED IN BNB TERMS* Feel free to leave suggestions/questions in the comments or to message them directly to me
Pine Script® indicator
by In_Finito_
3
Volume Indicator VSidea take from Alexander Elder, author of: The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management Meaning of colouring: - Green bar Volume: If we look back certain numbers of volume bars, higher close price and higher volume. - Blue bar Volume: If we look back certain numbers of volume bars, higher close price and lower volume. - Red bar Volume: If we look back certain numbers of volume bars, lower close price and higher volume. - Orange bar Volume: If we look back certain numbers of volume bars, lower close prince and lower volume. - Grey bar Volume: If we look back certain numbers of volume bars, same price and same volume.
Pine Script® indicator
by vitalizzy
3
Real Time Volume checkerThis indicator was designed to work in real time, and needs the "calculate on every tick" turned on. It verifies if the volume from the current candle is increasing more or less than the average. Example using 5 min. candles: 1 minute is 20% of the time for the candle. So, if the volume is at 20% or below of the vol.average, the market is stable(price not moving much). If at 1 minute(20% of time) volume is say, at 37%, a big move is happening. The color of the candle shows the movement direction. A gray means the volume was not important, below average. The historical candles show how much volume infuenced the price for the corresponding candle. The current candle is working REAL TIME. The blue line shows how much time has passed for the candle to complete. All candles are 100% complete(yeah, duh), except for the last one, the line touches zero at start of candle, and slowly fills up to1(1=100%)until the candle ends. INPUTS: vol MA size: sets the lenght of the volume average. set to your heard desires candle Period: set to the number of seconds in the period you chose. 5min = 300, 1h = 3600 and so on. Must be correct, or the results turn into crap Limit: the value below which the candle is a "non-important" candle. 1 = 100% of average NOTE: in the very first seconds, the indicator goes crazy. it's expected, due to computed values very close to zero used in the math. after some seconds it stabilizes :)
Pine Script® indicator
by deimosaffair
13
Volume-Weighted Money Flow [sgbpulse]Overview The VWMF indicator is an advanced technical analysis tool that combines and summarizes five leading momentum and volume indicators (OBV, PVT, A/D, CMF, MFI) into one clear oscillator. The indicator helps to provide a clear picture of market sentiment by measuring the pressure from buyers and sellers. Unlike single indicators, VWMF provides a comprehensive view of market money flow by weighting existing indicators and presenting them in a uniform and understandable format. Indicator Components VWMF combines the following indicators, each normalized to a range of 0 to 100 before being weighted: On-Balance Volume (OBV): A cumulative indicator that measures positive and negative volume flow. Price-Volume Trend (PVT): Similar to OBV, but incorporates relative price change for a more precise measure. Accumulation/Distribution Line (A/D): Used to identify whether an asset is being bought (accumulated) or sold (distributed). Chaikin Money Flow (CMF): Measures the money flow over a period based on the close price's position relative to the candle's range. Money Flow Index (MFI): A momentum oscillator that combines price and volume to measure buying and selling pressure. Understanding the Normalized Oscillators The indicator combines the five different momentum indicators by normalizing each one to a uniform range of 0 to 100 . Why is Normalization Important? Indicators like OBV, PVT, and the A/D Line are cumulative indicators whose values can become very large. To assess their trend, we use a Moving Average as a dynamic reference line . The Moving Average allows us to understand whether the indicator is currently trending up or down relative to its average behavior over time. How Does Normalization Work? Our normalization fully preserves the original trend of each indicator. For Cumulative Indicators (OBV, PVT, A/D): We calculate the difference between the current indicator value and its Moving Average. This difference is then passed to the normalization process. - If the indicator is above its Moving Average, the difference will be positive, and the normalized value will be above 50. - If the indicator is below its Moving Average, the difference will be negative, and the normalized value will be below 50. Handling Extreme Values: To overcome the issue of extreme values in indicators like OBV, PVT, and the A/D Line , the function calculates the highest absolute value over the selected period. This value is used to prevent sharp spikes or drops in a single indicator from compromising the accuracy of the normalization over time. It's a sophisticated method that ensures the oscillators remain relevant and accurate. For Bounded Indicators (CMF, MFI): These indicators already operate within a known range (for example, CMF is between -1 and 1, and MFI is between 0 and 100), so they are normalized directly without an additional reference line. Reference Line Settings: Moving Average Type: Allows the user to choose between a Simple Moving Average (SMA) and an Exponential Moving Average (EMA). Volume Flow MA Length: Allows the user to set the lookback period for the Moving Average, which affects the indicator's sensitivity. The 50 line serves as the new "center line." This ensures that, even after normalization, the determination of whether a specific indicator supports a bullish or bearish trend remains clear. Settings and Visual Tools The indicator offers several customization options to provide a rich analysis experience: VWMF Oscillator (Blue Line): Represents the weighted average of all five indicators. Values above 50 indicate bullish momentum, and values below 50 indicate bearish momentum. Strength Metrics (Bullish/Bearish Strength %): Two metrics that appear on the status line, showing the percentage of indicators supporting the current trend. They range from 0% to 100%, providing a quick view of the strength of the consensus. Dynamic Background Colors: The background color of the chart automatically changes to bullish (a blue shade by default) or bearish (a default brown-gray shade) based on the trend. The transparency of the color shows the consensus strength—the more opaque the background, the more indicators support the trend. Advanced Settings: - Background Color Logic: Allows the user to choose the trigger for the background color: Weighted Value (based on the combined oscillator) or Strength (based on the majority of individual indicators). - Weights: Provides full control over the weight of each of the five indicators in the final oscillator. Using the Data Window TradingView provides a useful Data Window that allows you to see the exact numerical values of each normalized oscillator separately, in addition to the trend strength data. You can use this window to: Get more detailed information on each indicator: Viewing the precise numerical data of each of the five indicators can help in making trading decisions. Calibrate weights: If you want to manually adjust the indicator weights (in the settings menu), you can do so while tracking the impact of each indicator on the weighted oscillator in the Data Window. The indicator's default setting is an equal weight of 20% for each of the five indicators. Alert Conditions The indicator comes with a variety of built-in alerts that can be configured through the TradingView alerts menu: VWMF Cross Above 50: An alert when the VWMF oscillator crosses above the 50 line, indicating a potential bullish momentum shift. VWMF Cross Below 50: An alert when the VWMF oscillator crosses below the 50 line, indicating a potential bearish momentum shift. Bullish Strength: High But Not Absolute Consensus: An alert when the bullish trend strength reaches 60% or more but is less than 100%, indicating a high but not absolute consensus. Bullish Strength at 100%: An alert when all five indicators (MFI, OBV, PVT, A/D, CMF) show bullish strength, indicating a full and absolute consensus. Bearish Strength: High But Not Absolute Consensus: An alert when the bearish trend strength reaches 60% or more but is less than 100%, indicating a high but not absolute consensus. Bearish Strength at 100%: An alert when all five indicators (MFI, OBV, PVT, A/D, CMF) show bearish strength, indicating a full and absolute consensus. Summary The VWMF indicator is a powerful, all-in-one tool for analyzing market momentum, money flow, and sentiment. By combining and normalizing five different indicators into a single oscillator, it offers a holistic and accurate view of the market's underlying trend. Its dynamic visual features and customizable settings, including the ability to adjust indicator weights, provide a flexible experience for both novice and experienced traders. The built-in alerts for momentum shifts and trend consensus make it an effective tool for spotting trading opportunities with confidence. In essence, VWMF distills complex market data into clear, actionable signals. Important Note: Trading Risk This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever. Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
Pine Script® indicator
by sgbpulse
Quantum Market Analyzer X7Quantum Market Analyzer X7 - Complete Study Guide Table of Contents 1. Overview 2. Indicator Components 3. Signal Interpretation 4. Live Market Analysis Guide 5. Best Practices 6. Limitations and Considerations 7. Risk Disclaimer ________________________________________ Overview The Quantum Market Analyzer X7 is a comprehensive multi-timeframe technical analysis indicator that combines traditional and modern analytical methods. It aggregates signals from multiple technical indicators across seven key analysis categories to provide traders with a consolidated view of market sentiment and potential trading opportunities. Key Features: • Multi-Indicator Analysis: Combines 20+ technical indicators • Real-Time Dashboard: Professional interface with customizable display • Signal Aggregation: Weighted scoring system for overall market sentiment • Advanced Analytics: Includes Order Block detection, Supertrend, and Volume analysis • Visual Progress Indicators: Easy-to-read progress bars for signal strength ________________________________________ Indicator Components 1. Oscillators Section Purpose: Identifies overbought/oversold conditions and momentum changes Included Indicators: • RSI (14): Relative Strength Index - momentum oscillator • Stochastic (14): Compares closing price to price range • CCI (20): Commodity Channel Index - cycle identification • Williams %R (14): Momentum indicator similar to Stochastic • MACD (12,26,9): Moving Average Convergence Divergence • Momentum (10): Rate of price change • ROC (9): Rate of Change • Bollinger Bands (20,2): Volatility-based indicator Signal Interpretation: • Strong Buy (6+ points): Multiple oscillators indicate oversold conditions • Buy (2-5 points): Moderate bullish momentum • Neutral (-1 to 1 points): Balanced conditions • Sell (-2 to -5 points): Moderate bearish momentum • Strong Sell (-6+ points): Multiple oscillators indicate overbought conditions 2. Moving Averages Section Purpose: Determines trend direction and strength Included Indicators: • SMA: 10, 20, 50, 100, 200 periods • EMA: 10, 20, 50 periods Signal Logic: • Price >2% above MA = Strong Buy (+2) • Price above MA = Buy (+1) • Price below MA = Sell (-1) • Price >2% below MA = Strong Sell (-2) Signal Interpretation: • Strong Buy (6+ points): Price well above multiple MAs, strong uptrend • Buy (2-5 points): Price above most MAs, bullish trend • Neutral (-1 to 1 points): Mixed MA signals, consolidation • Sell (-2 to -5 points): Price below most MAs, bearish trend • Strong Sell (-6+ points): Price well below multiple MAs, strong downtrend 3. Order Block Analysis Purpose: Identifies institutional support/resistance levels and breakouts How It Works: • Detects historical levels where large orders were placed • Monitors price behavior around these levels • Identifies breakouts from established order blocks Signal Types: • BULLISH BRK (+2): Breakout above resistance order block • BEARISH BRK (-2): Breakdown below support order block • ABOVE SUP (+1): Price holding above support • BELOW RES (-1): Price rejected at resistance • NEUTRAL (0): No significant order block interaction 4. Supertrend Analysis Purpose: Trend following indicator based on Average True Range Parameters: • ATR Period: 10 (default) • ATR Multiplier: 6.0 (default) Signal Types: • BULLISH (+2): Price above Supertrend line • BEARISH (-2): Price below Supertrend line • NEUTRAL (0): Transition period 5. Trendline/Channel Analysis Purpose: Identifies trend channels and breakout patterns Components: • Dynamic trendline calculation using pivot points • Channel width based on historical volatility • Breakout detection algorithm Signal Types: • UPPER BRK (+2): Breakout above upper channel • LOWER BRK (-2): Breakdown below lower channel • ABOVE MID (+1): Price above channel midline • BELOW MID (-1): Price below channel midline 6. Volume Analysis Purpose: Confirms price movements with volume data Components: • Volume spikes detection • On Balance Volume (OBV) • Volume Price Trend (VPT) • Money Flow Index (MFI) • Accumulation/Distribution Line Signal Calculation: Multiple volume indicators are combined to determine institutional activity and confirm price movements. ________________________________________ Signal Interpretation Overall Summary Signals The indicator aggregates all component signals into an overall market sentiment: Signal Score Range Interpretation Action STRONG BUY 10+ Overwhelming bullish consensus Consider long positions BUY 4-9 Moderate to strong bullish bias Look for long opportunities NEUTRAL -3 to 3 Mixed signals, consolidation Wait for clearer direction SELL -4 to -9 Moderate to strong bearish bias Look for short opportunities STRONG SELL -10+ Overwhelming bearish consensus Consider short positions Progress Bar Interpretation • Filled bars indicate signal strength • Green bars: Bullish signals • Red bars: Bearish signals • More filled bars = stronger conviction ________________________________________ Live Market Analysis Guide Step 1: Initial Assessment 1. Check Overall Summary: Start with the main signal 2. Verify with Component Analysis: Ensure signals align 3. Look for Divergences: Identify conflicting signals Step 2: Timeframe Analysis 1. Set Appropriate Timeframe: Use 1H for intraday, 4H/1D for swing trading 2. Multi-Timeframe Confirmation: Check higher timeframes for trend context 3. Entry Timing: Use lower timeframes for precise entry points Step 3: Signal Confirmation Process. For Buy Signals: 1. Oscillators: Look for oversold conditions (RSI <30, Stoch <20) 2. Moving Averages: Price should be above key MAs 3. Order Blocks: Confirm bounce from support levels 4. Volume: Check for accumulation patterns 5. Supertrend: Ensure bullish trend alignment. For Sell Signals: 1. Oscillators: Look for overbought conditions (RSI >70, Stoch >80) 2. Moving Averages: Price should be below key MAs 3. Order Blocks: Confirm rejection at resistance levels 4. Volume: Check for distribution patterns 5. Supertrend: Ensure bearish trend alignment. Step 4: Risk Management Integration 1. Signal Strength Assessment: Stronger signals = larger position size 2. Stop Loss Placement: Use Order Block levels for stops 3. Take Profit Targets: Based on channel analysis and resistance levels 4. Position Sizing: Adjust based on signal confidence ________________________________________ Best Practices Entry Strategies 1. High Conviction Entries: Wait for STRONG BUY/SELL signals 2. Confluence Trading: Look for multiple components aligning 3. Breakout Trading: Use Order Block and Trendline breakouts 4. Trend Following: Align with Supertrend direction. Risk Management 1. Never Risk More Than 2% Per Trade: Regardless of signal strength 2. Use Stop Losses: Place at invalidation levels 3. Scale Positions: Stronger signals warrant larger (but still controlled) positions 4. Diversification: Don't rely solely on one indicator. Market Conditions 1. Trending Markets: Focus on Supertrend and MA signals 2. Range-Bound Markets: Emphasize Oscillator and Order Block signals 3. High Volatility: Reduce position sizes, widen stops 4. Low Volume: Be cautious of breakout signals. Common Mistakes to Avoid 1. Signal Chasing: Don't enter after signals have already moved significantly 2. Ignoring Context: Consider overall market conditions 3. Overtrading: Wait for high-quality setups 4. Poor Risk Management: Always use appropriate position sizing ________________________________________ Limitations and Considerations Technical Limitations 1. Lagging Nature: All technical indicators are based on historical data 2. False Signals: No indicator is 100% accurate 3. Market Regime Changes: Indicators may perform differently in various market conditions 4. Whipsaws: Possible in choppy, sideways markets. Optimal Use Cases 1. Trending Markets: Performs best in clear trending environments 2. Medium to High Volatility: Requires sufficient price movement for signals 3. Liquid Markets: Works best with adequate volume and tight spreads 4. Multiple Timeframe Analysis: Most effective when used across different timeframes. When to Use Caution 1. Major News Events: Fundamental analysis may override technical signals 2. Market Opens/Closes: Higher volatility can create false signals 3. Low Volume Periods: Signals may be less reliable 4. Holiday Trading: Reduced participation affects signal quality ________________________________________ Risk Disclaimer IMPORTANT LEGAL DISCLAIMER FROM aiTrendview WARNING: TRADING INVOLVES SUBSTANTIAL RISK OF LOSS This Quantum Market Analyzer X7 indicator ("the Indicator") is provided for educational and informational purposes only. By using this indicator, you acknowledge and agree to the following terms: No Investment Advice • The Indicator does NOT constitute investment advice, financial advice, or trading recommendations • All signals generated are based on historical price data and mathematical calculations • Past performance does not guarantee future results • No representation is made that any account will achieve profits or losses similar to those shown. Risk Acknowledgment • TRADING CARRIES SUBSTANTIAL RISK: You may lose some or all of your invested capital • LEVERAGE AMPLIFIES RISK: Margin trading can result in losses exceeding your initial investment • MARKET VOLATILITY: Financial markets are inherently unpredictable and volatile • TECHNICAL ANALYSIS LIMITATIONS: No technical indicator is infallible or guarantees profitable trades. User Responsibility • YOU ARE SOLELY RESPONSIBLE for all trading decisions and their consequences • CONDUCT YOUR OWN RESEARCH: Always perform independent analysis before making trading decisions • CONSULT PROFESSIONALS: Seek advice from qualified financial advisors • RISK MANAGEMENT: Implement appropriate risk management strategies No Warranties • The Indicator is provided "AS IS" without warranties of any kind • aiTrendview makes no representations about the accuracy, reliability, or suitability of the Indicator • Technical glitches, data feed issues, or calculation errors may occur • The Indicator may not work as expected in all market conditions. Limitation of Liability • aiTrendview SHALL NOT BE LIABLE for any direct, indirect, incidental, or consequential damages • This includes but is not limited to: trading losses, missed opportunities, data inaccuracies, or system failures • MAXIMUM LIABILITY is limited to the amount paid for the indicator (if any) Code Usage and Distribution • This indicator is published on TradingView in accordance with TradingView's house rules • UNAUTHORIZED MODIFICATION or redistribution of this code is prohibited • Users may not claim ownership of this intellectual property • Commercial use requires explicit written permission from aiTrendview. Compliance and Regulations • VERIFY LOCAL REGULATIONS: Ensure compliance with your jurisdiction's trading laws • Some trading strategies may not be suitable for all investors • Tax implications of trading are your responsibility • Report trading activities as required by law Specific Risk Factors 1. False Signals: The Indicator may generate incorrect buy/sell signals 2. Market Gaps: Overnight gaps can invalidate technical analysis 3. Fundamental Events: News and economic data can override technical signals 4. Liquidity Risk: Some markets may have insufficient liquidity 5. Technology Risk: Platform failures or connectivity issues may prevent order execution. Professional Trading Warning • THIS IS NOT PROFESSIONAL TRADING SOFTWARE: Not intended for institutional or professional trading • NO REGULATORY APPROVAL: This indicator has not been approved by any financial regulatory authority • EDUCATIONAL PURPOSE: Designed primarily for learning technical analysis concepts FINAL WARNING NEVER INVEST MONEY YOU CANNOT AFFORD TO LOSE Trading financial instruments involves significant risk. The majority of retail traders lose money. Before using this indicator in live trading: 1. Practice on paper/demo accounts extensively 2. Start with small position sizes 3. Develop a comprehensive trading plan 4. Implement strict risk management rules 5. Continuously educate yourself about market dynamics By using the Quantum Market Analyzer X7, you acknowledge that you have read, understood, and agree to this disclaimer. You assume full responsibility for all trading decisions and their outcomes. Contact: For questions about this disclaimer or the indicator, contact aiTrendview through official TradingView channels only. ________________________________________ This study guide and indicator are published on TradingView in compliance with TradingView's community guidelines and house rules. All users must adhere to TradingView's terms of service when using this indicator. Document Version: 1.0 Publisher: aiTrendview ________________________________________ Disclaimer The content provided in this blog post is for educational and training purposes only. It is not intended to be, and should not be construed as, financial, investment, or trading advice. All charting and technical analysis examples are for illustrative purposes. Trading and investing in financial markets involve substantial risk of loss and are not suitable for every individual. Before making any financial decisions, you should consult with a qualified financial professional to assess your personal financial situation.
Pine Script® indicator
by aiTrendview-Charting-System
The Most Powerful TQQQ EMA Crossover Trend Trading StrategyTQQQ EMA Crossover Strategy Indicator Meta Title: TQQQ EMA Crossover Strategy - Enhance Your Trading with Effective Signals Meta Description: Discover the TQQQ EMA Crossover Strategy, designed to optimize trading decisions with fast and slow EMA crossovers. Learn how to effectively use this powerful indicator for better trading results. Key Features The TQQQ EMA Crossover Strategy is a powerful trading tool that utilizes Exponential Moving Averages (EMAs) to identify potential entry and exit points in the market. Key features of this indicator include: **Fast and Slow EMAs:** The strategy incorporates two EMAs, allowing traders to capture short-term trends while filtering out market noise. **Entry and Exit Signals:** Automated signals for entering and exiting trades based on EMA crossovers, enhancing decision-making efficiency. **Customizable Parameters:** Users can adjust the lengths of the EMAs, as well as take profit and stop loss multipliers, tailoring the strategy to their trading style. **Visual Indicators:** Clear visual plots of the EMAs and exit points on the chart for easy interpretation. How It Works The TQQQ EMA Crossover Strategy operates by calculating two EMAs: a fast EMA (default length of 20) and a slow EMA (default length of 50). The core concept is based on the crossover of these two moving averages: - When the fast EMA crosses above the slow EMA, it generates a *buy signal*, indicating a potential upward trend. - Conversely, when the fast EMA crosses below the slow EMA, it produces a *sell signal*, suggesting a potential downward trend. This method allows traders to capitalize on momentum shifts in the market, providing timely signals for trade execution. Trading Ideas and Insights Traders can leverage the TQQQ EMA Crossover Strategy in various market conditions. Here are some insights: **Scalping Opportunities:** The strategy is particularly effective for scalping in volatile markets, allowing traders to make quick profits on small price movements. **Swing Trading:** Longer-term traders can use this strategy to identify significant trend reversals and capitalize on larger price swings. **Risk Management:** By incorporating customizable stop loss and take profit levels, traders can manage their risk effectively while maximizing potential returns. How Multiple Indicators Work Together While this strategy primarily relies on EMAs, it can be enhanced by integrating additional indicators such as: - **Relative Strength Index (RSI):** To confirm overbought or oversold conditions before entering trades. - **Volume Indicators:** To validate breakout signals, ensuring that price movements are supported by sufficient trading volume. Combining these indicators provides a more comprehensive view of market dynamics, increasing the reliability of trade signals generated by the EMA crossover. Unique Aspects What sets this indicator apart is its simplicity combined with effectiveness. The reliance on EMAs allows for smoother signals compared to traditional moving averages, reducing false signals often associated with choppy price action. Additionally, the ability to customize parameters ensures that traders can adapt the strategy to fit their unique trading styles and risk tolerance. How to Use To effectively utilize the TQQQ EMA Crossover Strategy: 1. **Add the Indicator:** Load the script onto your TradingView chart. 2. **Set Parameters:** Adjust the fast and slow EMA lengths according to your trading preferences. 3. **Monitor Signals:** Watch for crossover points; enter trades based on buy/sell signals generated by the indicator. 4. **Implement Risk Management:** Set your stop loss and take profit levels using the provided multipliers. Regularly review your trading performance and adjust parameters as necessary to optimize results. Customization The TQQQ EMA Crossover Strategy allows for extensive customization: - **EMA Lengths:** Change the default lengths of both fast and slow EMAs to suit different time frames or market conditions. - **Take Profit/Stop Loss Multipliers:** Adjust these values to align with your risk management strategy. For instance, increasing the take profit multiplier may yield larger gains but could also increase exposure to market fluctuations. This flexibility makes it suitable for various trading styles, from aggressive scalpers to conservative swing traders. Conclusion The TQQQ EMA Crossover Strategy is an effective tool for traders seeking an edge in their trading endeavors. By utilizing fast and slow EMAs, this indicator provides clear entry and exit signals while allowing for customization to fit individual trading strategies. Whether you are a scalper looking for quick profits or a swing trader aiming for larger moves, this indicator offers valuable insights into market trends. Incorporate it into your TradingView toolkit today and elevate your trading performance!
Pine Script® strategy
by Pineify
1
Accumulation/Distribution VolumeThis is a simple yet powerful indicator that can replace volume, Money Flow, Chaikin Money Flow, Price Volume Trend (PVT), Accumulation/Distribution Line (ADL), On Balance Volume (OBV). When "Baseline Chart" option is disabled, it looks similar to regular volume. The volume bars has two shades of green and red. The dark shade shows amount of accumulation and the light shade shows total volume (what you see on a regular volume indicator). Blue line is the moving average (or cumulative total) of A/D and the gray line is for total volume. When money volume is enabled, volume it multiplied by price. As you can see in the chart below, trade volume in terms of USD was declining after ATH. This is not the case in regular volume chart which shows instrument volume (chart above). In Baseline view, the aggregation method you choose can turn it into different indicators. With EMA/SMA aggregation, blue and gray line shows buy/sell pressure. At 0, there is not buy or sell pressure. If you turn off volume bars (from style menu), it gives you a reliable indicator to measure divergence. This should be more reliable than most other range-bound indicators (i.e. RSI, MFI, CMF). I will publish a TA about correctly measuring divergence (it's a must read even if you are a pro trader). Make sure that the length is set to a large number on smaller TFs such as 4h. For following results, set aggregation to cumulative and turn off money volume: When wick weight=0, the GRAY line is identical to OBV indicator. When normalized by spread and wick weight=10, the BLUE line is identical to ADL (improved by true range). When normalized by previous bar price, wick weight=0, the BLUE line is identical to PVT. How I use this indicator: - Baseline chart, replaced my regular volume indicator - Mostly 4h TF for divergence - EMA aggregation (and occasional cumulative aggregation) with length above 50. I change the length to 100 and 200 for confirmation. - Wick weight=0 or max 2. With this indicator, you can learn how different indicators are built and how they are different from each other. I will publish a TA to explain more about different indicators and their pros and cons. I will publish this indicator without volume bars and additional options to make it range bound.
Pine Script® indicator
by cI8DH
36
Relative Volume (rVol), Better Volume, Average Volume ComparisonThis is the best version of relative volume you can find a claim which is based on the logical soundness of its calculation. I have amalgamated various volume analysis into one synergistic script. I wasn't going to opensource it. But, as one of the lucky few winners of TradingClue 2. I felt obligated to give something back to the community. Relative volume traditionally compares current volume to prior bar volume or SMA of volume. This has drawbacks. The question of relative volume is "Volume relative to what?" In the traditional scripts you'll find it displays current volume relative to the last number of bars. But, is that the best way to compare volume. On a daily chart, possibly. On a daily chart this can work because your units of time are uniform. Each day represents a full cycle of volume. However, on an intraday chart? Not so much. Example: If you have a lookback of 9 on an hourly chart in a 24 hour market, you are then comparing the average volume from Midnight - 9 AM to the 9 AM volume. What do you think you'll find? Well at 9:30 when NY exchanges open the volume should be consistently and predictably higher. But though rVol is high relative to the lookback period, its actually just average or maybe even below average compared to prior NY session opens. But prior NY session opens are not included in the lookback and thus ignored. This problem is the most visibly noticed when looking at the volume on a CME futures chart or some equivalent. In a 24 hour market, such as crypto, there are website's like skew can show you the volume disparity from time of day. This led me to believe that the traditional rVol calculation was insufficient. A better way to calculate it would be to compare the 9:30 am 30m bar today to the last week's worth of 9:30 am 30m bars. Then I could know whether today's volume at 9:30 am today is high or low based on prior 9:30 am bars. This seems to be a superior method on an intraday basis and is clearly superior in markets with irregular volume This led me to other problems, such as markets that are open for less than 24 hours and holiday hours on traditional market exchanges. How can I know that the script is accurately looking at the correct prior relevant bars. I've created and/or adapted solutions to all those problems and these calculations and code snippets thus have value that extend beyond this rVol script for other pinecoders. The Script This rVol script looks back at the bars of the same time period on the viewing timeframe. So, as we said, the last 9:30 bars. Averages those, then divides the: . The result is a percentage expressed as x.xxx. Thus 1.0 mean current volume is equal to average volume. Below 1.0 is below the average and above 1.0 is above the average. This information can be viewed on its own. But there are more levels of analysis added to it. Above the bars are signals that correlate to the "Better Volume Indicator" developed by, I believe, the folks at emini-watch and originally adapted to pinescript by LazyBear. The interpretation of these symbols are in a table on the right of the indicator. The volume bars can also be colored. The color is defined by the relationship between the average of the rVol outputs and the current volume. The "Average rVol" so to speak. The color coding is also defined by a legend in the table on the right. These can be researched by you to determine how to best interpret these signals. I originally got these ideas and solid details on how to use the analysis from a fellow out there, PlanTheTrade. I hope you find some value in the code and in the information that the indicator presents. And I'd like to thank the TradingView team for producing the most innovative and user friendly charting package on the market. (p.s. Better Volume is provides better information with a longer lookback value than the default imo) Credit for certain code sections and ideas is due to: LazyBear - Better Volume Grimmolf (From GitHub) - Logic for Loop rVol R4Rocket - The idea for my rVol 1 calculation And I can't find the guy who had the idea for the multiples of volume to the average. Tag him if you know him Final Note: I'd like to leave a couple of clues of my own for fellow seekers of trading infamy. Indicators: indicators are like anemometers (The things that measure windspeed). People talk bad about them all the time because they're "lagging." Well, you can't tell what the windspeed is unless the wind is blowing. anemometers are lagging indicators of wind. But forecasters still rely on them. You would use an indicator, which I would define as a instrument of measure, to tell you the windspeed of the markets. Conversely, when people talk positively about indicators they say "This one is great and this one is terrible." This is like a farmer saying "Shovels are great, but rakes are horrible." There are certain tools that have certain functions and every good tool has a purpose for a specific job. So the next time someone shares their opinion with you about indicators. Just smile and nod, realizing one day they'll learn... hopefully before they go broke. How to forecast: Prediction is accomplished by analyzing the behavior of instruments of measure to aggregate data (using your anemometer). The data is then assembled into a predictive model based on the measurements observed (a trading system). That predictive model is tested against reality for it's veracity (backtesting). If the model is predictive, you can optimize your decision making by creating parameter sets around the prediction that are synergistic with the implications of the prediction (risk, stop loss, target, scaling, pyramiding etc). <3
Pine Script® indicator
by kurtsmock
74
Divergence Indicator [Trendoscope®]🎲 New Divergence Indicator by Trendoscope Our latest Divergence Indicator revolutionizes the way traders identify market trends and potential reversals. Built upon the robust foundation of the Zigzag Trend Divergence Detector and inline with our recent implementation of the Divergence Goggles indicator, this tool is designed to be intuitive yet powerful, making it an essential addition to any trader's toolkit. We received several queries on extending the Divergence Goggles to last N bars instead of using an interactive widget. Though it is possible, we thought the better approach is to enable the indicator to use any oscillator and trend indicator in order to define the divergence. 🎯 Key Features Flexible Oscillator Integration : Choose from a wide range of built-in oscillators or import your own, including options like the innovative Multiband Oscillator. This versatility extends to using volume indicators like OBV for divergence calculations, broadening the scope of analysis. Trend Identification Versatility : Utilize built-in methods like Zigzag and MA Difference, or integrate external trend indicators. Our system adapts to various methods, ensuring you have the right tools for precise trend identification. Customizable Zigzag Sensitivity : Adjust the Zigzag based on your chosen oscillator's sensitivity to ensure divergence lines are accurate and visually coherent. Repainting vs. Delayed Signals : Tailor the indicator to your strategy by choosing between immediate repainting signals and slightly delayed but more stable signals. 🎯 Understanding Divergence: Key Rules Bullish Divergence Happens only in downtrend Observed on Pivot Lows Price makes lower low whereas oscillator makes higher low, indicating weakness and possible reversal Bearish Divergence Happens only in uptrend Observed on Pivot Highs Price makes higher high whereas oscillator makes lower high, indicating weakness and possible reversal Bullish Hidden Divergence Happens only in uptrend Observed on Pivot Lows Price makes higher low, whereas indicator makes lower low due to price consolidation. In bullish trend, this is considered as bullish as the price gets a breather and get ready to surge further. Bearish Hidden Divergence Happens only in downtrend Observed on Pivot Highs Price makes lower high whereas oscillator makes higher high due to price consolidation. In bearish trend, this is considered as bearish as the price gets a breather and get ready to fall further. 🎯 Visual Insights: Divergence and Hidden Divergence For a clearer understanding, refer to our visual guides: 🎲 Using the Divergence Indicator: A Step-by-Step Guide 🎯 Step 1 - Selecting the Oscillator Customize your analysis by choosing from a variety of oscillators or importing your preferred one. Options are available to select a range of built-in oscillators and the loopback length. However, if the oscillator that user want to use is not in the list, they can simply load the oscillator from the indicator library and use it as an external signal. In our current example, we are using a custom oscillator called - Multiband Oscillator This also means, the indicator option is not limited to oscillators. Users can even make use of volume indicators such as OBV for the calculation of divergence. 🎯 Step 2 - Choosing the Trend Identification Method Select from our built-in methods or integrate an external indicator to accurately identify market trends. Trend is one of the key parameters of divergence type identification. Trend can be identified mathematically by various methods. Some of them are as simple as above or below 200 moving average and some can follow trend based indicators such as supertrend and others can be very complex. To cater for a wider audience, here too we have provided the option to use an external trend indicator. The simple condition for the external trend indicator is that it should return positive value for uptrend and negative value for downtrend. Other than that, we also have 2 built in trend identification methods. Zigzag - The trend is defined by the starting pivot of divergence line. If the starting pivot is Higher High or Higher Low, then it is considered uptrend. And if the starting pivot is either Lower Low or Lower High, then we consider it as downtrend. MA Difference - In this case, the difference between the moving average of pivots joining the divergence line will determine the trend. It is considered uptrend if the moving average increased from starting pivot to ending pivot of the divergence line, and it is considered downtrend if the moving average decreased from starting pivot to the ending pivot of the divergence line. 🎯 Step 3 - Adjusting Zigzag Sensitivity Fine-tune the Zigzag to match the oscillator's sensitivity, ensuring divergence lines are accurate and visually coherent. 🎯 Step 4 - Managing Repainting Understand the implications of repainting in the last pivot of the Zigzag and choose between immediate or delayed signals based on your trading strategy. The last pivot of the zigzag repaint by design. This is not necessarily a bad thing. Users can just choose not to use the last pivot, but instead use the last but one for all the calculations. But, this also means, the signals will be delayed. Indicator provides option to use repainting signal vs delayed signal. If you select the repaint option, the signals are shown immediately as and when they occur. But, there is a possibility that these signals change when the new price candles change zigzag pivot. If you chose not to select the repaint option, then the divergence signals may lag by a few bars.
Pine Script® indicator
by Trendoscope
33
Adaptive Market Wave TheoryAdaptive Market Wave Theory 🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation. The Wave Analysis Problem: Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses: 1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition. 2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw. 3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management. 4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024. 5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve. The AMWT Solution: AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning: • Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed. • Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated. • No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3." • Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time. • No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance. The Core Insight: "Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'" 🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS Why Hidden Markov Models? Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables. This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations. HMM Components: 1. Hidden States (S) : The unobservable market conditions 2. Observations (O) : What we can measure (price, volume, indicators) 3. Transition Matrix (A) : Probability of moving between states 4. Emission Matrix (B) : Probability of observations given each state 5. Initial Distribution (π) : Starting state probabilities AMWT's Six Market States: State 0: IMPULSE_UP • Definition: Strong bullish momentum with high participation • Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising • Typical Duration: 5-20 bars depending on timeframe • What It Means: Institutional buying pressure, trend acceleration phase State 1: IMPULSE_DN • Definition: Strong bearish momentum with high participation • Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling • Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster) • What It Means: Institutional selling pressure, panic or distribution acceleration State 2: CORRECTION • Definition: Counter-trend consolidation with declining momentum • Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing • Typical Duration: 8-30 bars • What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg State 3: ACCUMULATION • Definition: Base-building near lows where informed participants absorb supply • Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range • Typical Duration: 15-50 bars • What It Means: Smart money buying from weak hands, preparing for markup phase State 4: DISTRIBUTION • Definition: Top-forming near highs where informed participants distribute holdings • Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range • Typical Duration: 15-50 bars • What It Means: Smart money selling to late buyers, preparing for markdown phase State 5: TRANSITION • Definition: Regime change period with mixed signals and elevated uncertainty • Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction • Typical Duration: 5-15 bars • What It Means: Market deciding next direction, dangerous for directional trades The Transition Matrix: The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online: From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02 IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02 CORR 0.15 0.15 0.50 0.10 0.10 0.00 ACCUM 0.30 0.05 0.15 0.40 0.05 0.05 DIST 0.05 0.30 0.15 0.05 0.40 0.05 TRANS 0.20 0.20 0.20 0.15 0.15 0.10 Key Insights from Transition Probabilities: • Impulse states are sticky (70% self-transition): Once trending, markets tend to continue • Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain • Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies • Distribution strongly favors IMP_DN transition (30%): Topping leads to declines The Viterbi Algorithm: Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space. Mathematical Formulation: δ_t(j) = max_i × B_j(O_t) Where: δ_t(j) = probability of most likely path ending in state j at time t A_ij = transition probability from state i to state j B_j(O_t) = emission probability of observation O_t given state j AMWT Implementation: AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting: • Current state estimate • State confidence (probability of current state vs alternatives) • State sequence for pattern detection Online Learning (Baum-Welch Adaptation): Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior: f_onlineUpdateHMM(prev_state, curr_state, observation, decay) => // Update transition matrix A *= decay A += (1.0 - decay) // Renormalize row // Update emission matrix B *= decay B += (1.0 - decay) // Renormalize row The decay parameter (default 0.85) controls adaptation speed: • Higher decay (0.95): Slower adaptation, more stable, better for consistent markets • Lower decay (0.80): Faster adaptation, more reactive, better for regime changes Why This Matters for Trading: Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment : "There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION." This enables: • Position sizing by confidence : 90% confidence = full size; 60% confidence = half size • Risk management by transition probability : High correction probability = tighten stops • Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in 🎰 THE 3-BANDIT CONSENSUS SYSTEM The Multi-Agent Philosophy: No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets. AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions. Agent 1: TREND AGENT Philosophy : Markets trend. Follow the trend until it ends. Analytical Components: • EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish) • MACD Histogram: Direction and rate of change • Price Momentum: Close relative to ATR-normalized movement • VWAP Position: Price above/below volume-weighted average price Signal Generation: Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP → Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4 Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1 → Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3 Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP → Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4 Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1 → Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3 When Trend Agent Excels: • Trend days (IB extension >1.5x) • Post-breakout continuation • Institutional accumulation/distribution phases When Trend Agent Fails: • Range-bound markets (ADX <20) • Chop zones after volatility spikes • Reversal days at major levels Agent 2: REVERSION AGENT Philosophy: Markets revert to mean. Extreme readings reverse. Analytical Components: • Bollinger Band Position: Distance from bands, percent B • RSI Extremes: Overbought (>70) and oversold (<30) • Stochastic: %K/%D crossovers at extremes • Band Squeeze: Bollinger Band width contraction Signal Generation: Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25 → Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01 Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75 → Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01 Squeeze Fire Bull: Band squeeze ending AND close > upper band → Signal: +1 (Long), Confidence: 0.65 Squeeze Fire Bear: Band squeeze ending AND close < lower band → Signal: -1 (Short), Confidence: 0.65 When Reversion Agent Excels: • Rotation days (price stays within IB) • Range-bound consolidation • After extended moves without pullback When Reversion Agent Fails: • Strong trend days (RSI can stay overbought for days) • Breakout moves • News-driven directional moves Agent 3: STRUCTURE AGENT Philosophy: Market structure reveals institutional intent. Follow the smart money. Analytical Components: • Break of Structure (BOS): Price breaks prior swing high/low • Change of Character (CHOCH): First break against prevailing trend • Higher Highs/Higher Lows: Bullish structure • Lower Highs/Lower Lows: Bearish structure • Liquidity Sweeps: Stop runs that reverse Signal Generation: BOS Bull: Price breaks above prior swing high with momentum → Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bull: First higher low after downtrend, breaking structure → Signal: +1 (Long), Confidence: 0.75 BOS Bear: Price breaks below prior swing low with momentum → Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bear: First lower high after uptrend, breaking structure → Signal: -1 (Short), Confidence: 0.75 Liquidity Sweep Long: Price sweeps below swing low then reverses strongly → Signal: +1 (Long), Confidence: 0.80 Liquidity Sweep Short: Price sweeps above swing high then reverses strongly → Signal: -1 (Short), Confidence: 0.80 When Structure Agent Excels: • After liquidity grabs (stop runs) • At major swing points • During institutional accumulation/distribution When Structure Agent Fails: • Choppy, structureless markets • During news events (structure becomes noise) • Very low timeframes (noise overwhelms structure) Thompson Sampling: The Bandit Algorithm With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives). Thompson Sampling Solution: Each agent maintains a Beta distribution representing its success/failure history: Agent success rate modeled as Beta(α, β) Where: α = number of successful signals + 1 β = number of failed signals + 1 On Each Bar: 1. Sample from each agent's Beta distribution 2. Weight agent signals by sampled probabilities 3. Combine weighted signals into consensus 4. Update α/β based on trade outcomes Mathematical Implementation: // Beta sampling via Gamma ratio method f_beta_sample(alpha, beta) => g1 = f_gamma_sample(alpha) g2 = f_gamma_sample(beta) g1 / (g1 + g2) // Thompson Sampling selection for each agent: sampled_prob = f_beta_sample(agent.alpha, agent.beta) weight = sampled_prob / sum(all_sampled_probs) consensus += agent.signal × agent.confidence × weight Why Thompson Sampling? • Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution) • Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff • Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation • Self-Correcting : Poor performers naturally get lower weights over time Example Evolution: Day 1 (Initial): Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty) After 50 Signals: Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence) Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming) Structure Agent: Beta(32,19) → samples ~0.63 (outperforming) Result: Structure Agent now receives highest weight in consensus Consensus Requirements by Mode: Aggressive Mode: • Minimum 1/3 agents agreeing • Consensus threshold: 45% • Use case: More signals, higher risk tolerance Balanced Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 55% • Use case: Standard trading Conservative Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 65% • Use case: Higher quality, fewer signals Institutional Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 75% • Additional: Session quality >0.65, mode adjustment +0.10 • Use case: Highest quality signals only 🌀 INTELLIGENT CHOP DETECTION ENGINE The Chop Problem: Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike. AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades. Five-Factor Chop Analysis: Factor 1: ADX Component (25% weight) ADX (Average Directional Index) measures trend strength regardless of direction. ADX < 15: Very weak trend (high chop score) ADX 15-20: Weak trend (moderate chop score) ADX 20-25: Developing trend (low chop score) ADX > 25: Strong trend (minimal chop score) adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100 Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop. Factor 2: Choppiness Index (25% weight) The Choppiness Index measures price efficiency using the ratio of ATR sum to price range: CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n) CI > 61.8: Choppy (range-bound, inefficient movement) CI < 38.2: Trending (directional, efficient movement) CI 38.2-61.8: Transitional chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100 Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range). Factor 3: Range Compression (20% weight) Compares recent range to longer-term range, detecting volatility squeezes: recent_range = Highest(20) - Lowest(20) longer_range = Highest(50) - Lowest(50) compression = 1 - (recent_range / longer_range) compression > 0.5: Strong squeeze (potential breakout imminent) compression < 0.2: No compression (normal volatility) range_compression_score = compression × 100 Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet. Factor 4: Channel Position (15% weight) Tracks price position within the macro channel: channel_position = (close - channel_low) / (channel_high - channel_low) position 0.4-0.6: Center of channel (indecision zone) position <0.2 or >0.8: Near extremes (potential reversal or breakout) channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability. Factor 5: Volume Quality (15% weight) Assesses volume relative to average: vol_ratio = volume / SMA(volume, 20) vol_ratio < 0.7: Low volume (lack of conviction) vol_ratio 0.7-1.3: Normal volume vol_ratio > 1.3: High volume (conviction present) volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0 Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall. Combined Chop Intensity: chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) + (range_compression_score × 0.20) + (channel_chop × 0.15) + (volume_chop × i_volumeChopWeight × 0.15) Regime Classifications: Based on chop intensity and component analysis: • Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively • Trending (20-35%): ADX >20, moderate directional bias, trade normally • Transitioning (35-50%): Mixed signals, regime change possible, reduce size • Mid-Range (50-60%): Price trapped in channel center, avoid new positions • Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only • Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout • Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely Signal Suppression: When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification. Chop Box Visualization: When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions. 💧 LIQUIDITY ANCHORING SYSTEM The Liquidity Concept: Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing. AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence. Liquidity Event Types: Type 1: Volume Spikes Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x) Interpretation: Sudden volume surge indicates institutional activity • Near swing low + reversal: Likely accumulation • Near swing high + reversal: Likely distribution • With continuation: Institutional conviction in direction Type 2: Stop Runs (Liquidity Sweeps) Definition: Price briefly exceeds swing high/low then reverses within N bars Detection: • Price breaks above recent swing high (triggering buy stops) • Then closes back below that high within 3 bars • Signal: Bullish stop run complete, reversal likely Or inverse for bearish: • Price breaks below recent swing low (triggering sell stops) • Then closes back above that low within 3 bars • Signal: Bearish stop run complete, reversal likely Type 3: Absorption Events Definition: High volume with small candle body Detection: • Volume > 2x average • Candle body < 30% of candle range • Interpretation: Large orders being filled without moving price • Implication: Accumulation (at lows) or distribution (at highs) Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity) BSL (Buy-Side Liquidity): • Cluster of swing highs within ATR proximity • Stop losses from shorts sit above these highs • Breaking BSL triggers short covering (fuel for rally) SSL (Sell-Side Liquidity): • Cluster of swing lows within ATR proximity • Stop losses from longs sit below these lows • Breaking SSL triggers long liquidation (fuel for decline) Liquidity Pool Mapping: AMWT continuously scans for and maps liquidity pools: // Detect swing highs/lows using pivot function swing_high = ta.pivothigh(high, 5, 5) swing_low = ta.pivotlow(low, 5, 5) // Track recent swing points if not na(swing_high) bsl_levels.push(swing_high) if not na(swing_low) ssl_levels.push(swing_low) // Display on chart with labels Confluence Scoring Integration: When signals fire near identified liquidity events, confluence scoring increases: • Signal near volume spike: +10% confidence • Signal after liquidity sweep: +15% confidence • Signal at BSL/SSL pool: +10% confidence • Signal aligned with absorption zone: +10% confidence Why Liquidity Anchoring Matters: Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst. 📊 SIGNAL GRADING SYSTEM The Quality Problem: Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence. Confluence Components (100 points total): 1. Bandit Consensus Strength (25 points) consensus_str = weighted average of agent confidences score = consensus_str × 25 Example: Trend Agent: +1 signal, 0.80 confidence, 0.35 weight Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight Structure Agent: +1 signal, 0.75 confidence, 0.40 weight Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77 Score = 0.77 × 25 = 19.25 points 2. HMM State Confidence (15 points) score = hmm_confidence × 15 Example: HMM reports 82% probability of IMPULSE_UP Score = 0.82 × 15 = 12.3 points 3. Session Quality (15 points) Session quality varies by time: • London/NY Overlap: 1.0 (15 points) • New York Session: 0.95 (14.25 points) • London Session: 0.70 (10.5 points) • Asian Session: 0.40 (6 points) • Off-Hours: 0.30 (4.5 points) • Weekend: 0.10 (1.5 points) 4. Energy/Participation (10 points) energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25 score = min(energy, 1.0) × 10 5. Volume Confirmation (10 points) if volume > SMA(volume, 20) × 1.5: score = 10 else if volume > SMA(volume, 20): score = 5 else: score = 0 6. Structure Alignment (10 points) For long signals: • Bullish structure (HH + HL): 10 points • Higher low only: 6 points • Neutral structure: 3 points • Bearish structure: 0 points Inverse for short signals 7. Trend Alignment (10 points) For long signals: • Price > EMA21 > EMA50: 10 points • Price > EMA21: 6 points • Neutral: 3 points • Against trend: 0 points 8. Entry Trigger Quality (5 points) • Strong trigger (multiple confirmations): 5 points • Moderate trigger (single confirmation): 3 points • Weak trigger (marginal): 1 point Grade Scale: Total Score → Grade 85-100 → A+ (Exceptional—all factors aligned) 70-84 → A (Strong—high probability) 55-69 → B (Acceptable—proceed with caution) Below 55 → C (Marginal—filtered by default) Grade-Based Signal Brightness: Signal arrows on the chart have transparency based on grade: • A+: Full brightness (alpha = 0) • A: Slight fade (alpha = 15) • B: Moderate fade (alpha = 35) • C: Significant fade (alpha = 55) This visual hierarchy helps traders instantly identify signal quality. Minimum Grade Filter: Configurable filter (default: C) sets the minimum grade for signal display: • Set to "A" for only highest-quality signals • Set to "B" for moderate selectivity • Set to "C" for all signals (maximum quantity) 🕐 SESSION INTELLIGENCE Why Sessions Matter: Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality. Session Definitions: Asian Session (18:00-03:00 ET) • Characteristics: Lower volatility, range-bound tendency, fewer institutional participants • Quality Score: 0.40 (40% of peak quality) • Strategy Implications: Fade extremes, expect ranges, smaller position sizes • Best For: Mean-reversion setups, accumulation/distribution identification London Session (03:00-12:00 ET) • Characteristics: European institutional activity, volatility pickup, trend initiation • Quality Score: 0.70 (70% of peak quality) • Strategy Implications: Watch for trend development, breakouts more reliable • Best For: Initial trend identification, structure breaks New York Session (08:00-17:00 ET) • Characteristics: Highest liquidity, US institutional activity, major moves • Quality Score: 0.95 (95% of peak quality) • Strategy Implications: Best environment for directional trades • Best For: Trend continuation, momentum plays London/NY Overlap (08:00-12:00 ET) • Characteristics: Peak liquidity, both European and US participants active • Quality Score: 1.0 (100%—maximum quality) • Strategy Implications: Highest probability for successful breakouts and trends • Best For: All signal types—this is prime time Off-Hours • Characteristics: Thin liquidity, erratic price action, gaps possible • Quality Score: 0.30 (30% of peak quality) • Strategy Implications: Avoid new positions, wider stops if holding • Best For: Waiting Smart Weekend Detection: AMWT properly handles the Sunday evening futures open: // Traditional (broken): isWeekend = dayofweek == saturday OR dayofweek == sunday // AMWT (correct): anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime) isWeekend = calendarWeekend AND NOT anySessionActive This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend." Session Transition Boosts: Certain session transitions create trading opportunities: • Asian → London transition: +15% confidence boost (volatility expansion likely) • London → Overlap transition: +20% confidence boost (peak liquidity approaching) • Overlap → NY-only transition: -10% confidence adjustment (liquidity declining) • Any → Off-Hours transition: Signal suppression recommended 📈 TRADE MANAGEMENT SYSTEM The Signal Spam Problem: Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance. Trade Lock Mechanism: Once a signal fires, the system enters a "trade lock" state: Trade Lock Duration: Configurable (default 30 bars) Early Exit Conditions: • TP3 hit (full target reached) • Stop Loss hit (trade failed) • Lock expiration (time-based exit) During lock: • No new signals of same type displayed • Opposite signals can override (reversal) • Trade status tracked in dashboard Target Levels: Each signal generates three profit targets based on ATR: TP1 (Conservative Target) • Default: 1.0 × ATR • Purpose: Quick partial profit, reduce risk • Action: Take 30-40% off position, move stop to breakeven TP2 (Standard Target) • Default: 2.5 × ATR • Purpose: Main profit target • Action: Take 40-50% off position, trail stop TP3 (Extended Target) • Default: 5.0 × ATR • Purpose: Runner target for trend days • Action: Close remaining position or continue trailing Stop Loss: • Default: 1.9 × ATR from entry • Purpose: Define maximum risk • Placement: Below recent swing low (longs) or above recent swing high (shorts) Invalidation Level: Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies: invalidation = entry - (ATR × INVALIDATION_MULT × 1.5) If price reaches invalidation, the current market interpretation is wrong—not just the trade. Visual Trade Management: During active trades, AMWT displays: • Entry arrow with grade label (▲A+, ▼B, etc.) • TP1, TP2, TP3 horizontal lines in green • Stop Loss line in red • Invalidation line in orange (dashed) • Progress indicator in dashboard Persistent Execution Markers: When targets or stops are hit, permanent markers appear: • TP hit: Green dot with "TP1"/"TP2"/"TP3" label • SL hit: Red dot with "SL" label These persist on the chart for review and statistics. 💰 PERFORMANCE TRACKING & STATISTICS Tracked Metrics: • Total Trades: Count of all signals that entered trade lock • Winning Trades: Signals where at least TP1 was reached before SL • Losing Trades: Signals where SL was hit before any TP • Win Rate: Winning / Total × 100% • Total R Profit: Sum of R-multiples from winning trades • Total R Loss: Sum of R-multiples from losing trades • Net R: Total R Profit - Total R Loss Currency Conversion System: AMWT can display P&L in multiple formats: R-Multiple (Default) • Shows risk-normalized returns • "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades • Best for comparing across different position sizes Currency Conversion (USD/EUR/GBP/JPY/INR) • Converts R-multiples to currency based on: - Dollar Risk Per Trade (user input) - Tick Value (user input) - Selected currency Example Configuration: Dollar Risk Per Trade: $100 Display Currency: USD If Net R = +4.2R Display: Net P&L: +$420.00 | 78 trades Ticks • For futures traders who think in ticks • Converts based on tick value input Statistics Reset: Two reset methods: 1. Toggle Reset • Turn "Reset Statistics" toggle ON then OFF • Clears all statistics immediately 2. Date-Based Reset • Set "Reset After Date" (YYYY-MM-DD format) • Only trades after this date are counted • Useful for isolating recent performance 🎨 VISUAL FEATURES Macro Channel: Dynamic regression-based channel showing market boundaries: • Upper/lower bounds calculated from swing pivot linear regression • Adapts to current market structure • Shows overall trend direction and potential reversal zones Chop Boxes: Semi-transparent overlay during high-chop periods: • Purple/orange coloring indicates dangerous conditions • Visual reminder to avoid new positions Confluence Heat Zones: Background shading indicating setup quality: • Darker shading = higher confluence • Lighter shading = lower confluence • Helps identify optimal entry timing EMA Ribbon: Trend visualization via moving average fill: • EMA 8/21/50 with gradient fill between • Green fill when bullish aligned • Red fill when bearish aligned • Gray when neutral Absorption Zone Boxes: Marks potential accumulation/distribution areas: • High volume + small body = absorption • Boxes drawn at these levels • Often act as support/resistance Liquidity Pool Lines: BSL/SSL levels with labels: • Dashed lines at liquidity clusters • "BSL" label above swing high clusters • "SSL" label below swing low clusters Six Professional Themes: • Quantum: Deep purples and cyans (default) • Cyberpunk: Neon pinks and blues • Professional: Muted grays and greens • Ocean: Blues and teals • Matrix: Greens and blacks • Ember: Oranges and reds 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Learning the System (Week 1) Goal: Understand AMWT concepts and dashboard interpretation Setup: • Signal Mode: Balanced • Display: All features enabled • Grade Filter: C (see all signals) Actions: • Paper trade ONLY—no real money • Observe HMM state transitions throughout the day • Note when agents agree vs disagree • Watch chop detection engage and disengage • Track which grades produce winners vs losers Key Learning Questions: • How often do A+ signals win vs B signals? (Should see clear difference) • Which agent tends to be right in current market? (Check dashboard) • When does chop detection save you from bad trades? • How do signals near liquidity events perform vs signals in vacuum? Phase 2: Parameter Optimization (Week 2) Goal: Tune system to your instrument and timeframe Signal Mode Testing: • Run 5 days on Aggressive mode (more signals) • Run 5 days on Conservative mode (fewer signals) • Compare: Which produces better risk-adjusted returns? Grade Filter Testing: • Track A+ only for 20 signals • Track A and above for 20 signals • Track B and above for 20 signals • Compare win rates and expectancy Chop Threshold Testing: • Default (80%): Standard filtering • Try 70%: More aggressive filtering • Try 90%: Less filtering • Which produces best results for your instrument? Phase 3: Strategy Development (Weeks 3-4) Goal: Develop personal trading rules based on system signals Position Sizing by Grade: • A+ grade: 100% position size • A grade: 75% position size • B grade: 50% position size • C grade: 25% position size (or skip) Session-Based Rules: • London/NY Overlap: Take all A/A+ signals • NY Session: Take all A+ signals, selective on A • Asian Session: Only A+ signals with extra confirmation • Off-Hours: No new positions Chop Zone Rules: • Chop >70%: Reduce position size 50% • Chop >80%: No new positions • Chop <50%: Full position size allowed Phase 4: Live Micro-Sizing (Month 2) Goal: Validate paper trading results with minimal risk Setup: • 10-20% of intended full position size • Take ONLY A+ signals initially • Follow trade management religiously Tracking: • Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus • Calculate: Win rate by grade, by session, by chop level • Compare to paper trading (should be within 15%) Red Flags: • Win rate diverges significantly from paper trading: Execution issues • Consistent losses during certain sessions: Adjust session rules • Losses cluster when specific agent dominates: Review that agent's logic Phase 5: Scaling Up (Months 3-6) Goal: Gradually increase to full position size Progression: • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size Scale-Up Requirements: • Minimum 30 trades at current size • Win rate ≥50% • Net R positive • No revenge trading incidents • Emotional control maintained 💡 DEVELOPMENT INSIGHTS Why HMM Over Simple Indicators: Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend. The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP." The Multi-Agent Evolution: Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way. The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection. Chop Detection Revelation: Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones. Liquidity Anchoring Discovery: Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals. The Grade System Impact: Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What AMWT Is NOT: • NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty. • NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment. • NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis. • NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate. Core Assumptions: 1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure. 2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals. 3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns. 4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation. Performs Best On: • Liquid Futures : ES, NQ, MNQ, MES, CL, GC • Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY • Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap) • Liquid Crypto : BTC, ETH on major exchanges Performs Poorly On: • Illiquid Instruments : Low volume stocks, exotic pairs • Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal) • Binary Event Days : Earnings, FDA approvals, court rulings • Manipulated Markets : Penny stocks, low-cap altcoins Known Weaknesses: • Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable. • Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance. • Complexity : More parameters than simple indicators. Requires understanding to use effectively. ⚠️ RISK DISCLOSURE Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses. AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results. Hidden Markov Model assumptions may not hold during: • Major news events disrupting normal market behavior • Flash crashes or circuit breaker events • Low liquidity periods with erratic price action • Algorithmic manipulation or spoofing Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing. Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception. By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes. "Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable." Initial Public Release Core Engine: • True Hidden Markov Model with online Baum-Welch learning • Viterbi algorithm for optimal state sequence decoding • 6-state market regime classification Agent System: • 3-Bandit consensus (Trend, Reversion, Structure) • Thompson Sampling with true Beta distribution sampling • Adaptive weight learning based on performance Signal Generation: • Quality-based confluence grading (A+/A/B/C) • Four signal modes (Aggressive/Balanced/Conservative/Institutional) • Grade-based visual brightness Chop Detection: • 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume) • 7 regime classifications • Configurable signal suppression threshold Liquidity: • Volume spike detection • Stop run (liquidity sweep) identification • BSL/SSL pool mapping • Absorption zone detection Trade Management: • Trade lock with configurable duration • TP1/TP2/TP3 targets • ATR-based stop loss • Persistent execution markers Session Intelligence: • Asian/London/NY/Overlap detection • Smart weekend handling (Sunday futures open) • Session quality scoring Performance: • Statistics tracking with reset functionality • 7 currency display modes • Win rate and Net R calculation Visuals: • Macro channel with linear regression • Chop boxes • EMA ribbon • Liquidity pool lines • 6 professional themes Dashboards: • Main Dashboard: Market State, Consensus, Trade Status, Statistics 📋 AMWT vs AMWT-PRO: This version includes all core AMWT functionality: ✓ Full Hidden Markov Model state estimation ✓ 3-Bandit Thompson Sampling consensus system ✓ Complete 5-factor chop detection engine ✓ All four signal modes ✓ Full trade management with TP/SL tracking ✓ Main dashboard with complete statistics ✓ All visual features (channels, zones, pools) ✓ Identical signal generation to PRO ✓ Six professional themes ✓ Full alert system The PRO version adds the AMWT Advisor panel—a secondary dashboard providing: • Real-time Market Pulse situation assessment • Agent Matrix visualization (individual agent votes) • Structure analysis breakdown • "Watch For" upcoming setups • Action Command coaching Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals. Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Pine Script® indicator
by DskyzInvestments
3
Uptrick: Trend SMA Oscillator### In-Depth Analysis of the "Uptrick: Trend SMA Oscillator" Indicator --- #### Introduction to the Indicator The "Uptrick: Trend SMA Oscillator" is an advanced yet user-friendly technical analysis tool designed to help traders across all levels of experience identify and follow market trends with precision. This indicator builds upon the fundamental principles of the Simple Moving Average (SMA), a cornerstone of technical analysis, to deliver a clear, visually intuitive overlay on the price chart. Through its strategic use of color-coding and customizable parameters, the Uptrick: Trend SMA Oscillator provides traders with actionable insights into market dynamics, enhancing their ability to make informed trading decisions. #### Core Concepts and Methodology 1. **Foundational Principle – Simple Moving Average (SMA):** - The Simple Moving Average (SMA) is the heart of the Uptrick: Trend SMA Oscillator. The SMA is a widely-used technical indicator that calculates the average price of an asset over a specified number of periods. By smoothing out price data, the SMA helps to reduce the noise from short-term fluctuations, providing a clearer picture of the overall trend. - In the Uptrick: Trend SMA Oscillator, two SMAs are employed: - **Primary SMA (oscValue):** This is applied to the closing price of the asset over a user-defined period (default is 14 periods). This SMA tracks the price closely and is sensitive to changes in market direction. - **Smoothing SMA (oscV):** This second SMA is applied to the primary SMA, further smoothing the data and helping to filter out minor price movements that might otherwise be mistaken for trend reversals. The default period for this smoothing is 50, but it can be adjusted to suit the trader's preference. 2. **Color-Coding for Trend Visualization:** - One of the most distinctive features of this indicator is its use of color to represent market trends. The indicator’s line changes color based on the relationship between the primary SMA and the smoothing SMA: - **Bullish (Green):** The line turns green when the primary SMA is equal to or greater than the smoothing SMA, indicating that the market is in an upward trend. - **Bearish (Red):** Conversely, the line turns red when the primary SMA falls below the smoothing SMA, signaling a downward trend. - This color-coded system provides traders with an immediate, easy-to-interpret visual cue about the market’s direction, allowing for quick decision-making. #### Detailed Explanation of Inputs 1. **Bullish Color (Default: Green #00ff00):** - This input allows traders to customize the color that represents bullish trends on the chart. The default setting is green, a color commonly associated with upward market movement. However, traders can adjust this to any color that suits their visual preferences or matches their overall chart theme. 2. **Bearish Color (Default: Red RGB: 245, 0, 0):** - The bearish color input determines the color of the line when the market is trending downwards. The default setting is a vivid red, signaling caution or selling opportunities. Like the bullish color, this can be customized to fit the trader’s needs. 3. **Line Thickness (Default: 5):** - This setting controls the thickness of the line plotted by the indicator. The default thickness of 5 makes the line prominent on the chart, ensuring that the trend is easily visible even in complex or crowded chart setups. Traders can adjust the thickness to make the line thinner or thicker, depending on their visual preferences. 4. **Primary SMA Period (Value 1 - Default: 14):** - The primary SMA period defines how many periods (e.g., days, hours) are used to calculate the moving average based on the asset’s closing prices. The default period of 14 is a balanced setting that offers a good mix of responsiveness and stability, but traders can adjust this depending on their trading style: - **Shorter Periods (e.g., 5-10):** These make the indicator more sensitive, capturing trends more quickly but also increasing the likelihood of reacting to short-term price fluctuations or "noise." - **Longer Periods (e.g., 20-50):** These smooth the data more, providing a more stable trend line that is less prone to whipsaws but may be slower to respond to trend changes. 5. **Smoothing SMA Period (Value 2 - Default: 50):** - The smoothing SMA period determines how much the primary SMA is smoothed. A longer smoothing period results in a more gradual, stable line that focuses on the broader trend. The default of 50 is designed to smooth out most of the short-term fluctuations while still being responsive enough to detect significant trend shifts. - **Customization:** - **Shorter Smoothing Periods (e.g., 20-30):** Make the indicator more responsive, better for fast-moving markets or for traders who want to capture quick trends. - **Longer Smoothing Periods (e.g., 70-100):** Enhance stability, ideal for long-term traders looking to avoid reacting to minor price movements. #### Unique Characteristics and Advantages 1. **Simplicity and Clarity:** - The Uptrick: Trend SMA Oscillator’s design prioritizes simplicity without sacrificing effectiveness. By relying on the widely understood SMA, it avoids the complexity of more esoteric indicators while still providing reliable trend signals. This simplicity makes it accessible to traders of all levels, from novices who are just learning about technical analysis to experienced traders looking for a straightforward, dependable tool. 2. **Visual Feedback Mechanism:** - The indicator’s use of color to signify market trends is a particularly powerful feature. This visual feedback mechanism allows traders to assess market conditions at a glance. The clarity of the green and red color scheme reduces the mental effort required to interpret the indicator, freeing the trader to focus on strategy execution. 3. **Adaptability Across Markets and Timeframes:** - One of the strengths of the Uptrick: Trend SMA Oscillator is its versatility. The basic principles of moving averages apply equally well across different asset classes and timeframes. Whether trading stocks, forex, commodities, or cryptocurrencies, traders can use this indicator to gain insights into market trends. - **Intraday Trading:** For day traders who operate on short timeframes (e.g., 1-minute, 5-minute charts), the oscillator can be adjusted to be more responsive, capturing quick shifts in momentum. - **Swing Trading:** Swing traders, who typically hold positions for several days to weeks, will find the default settings or slightly adjusted periods ideal for identifying and riding medium-term trends. - **Long-Term Trading:** Position traders and investors can adjust the indicator to focus on long-term trends by increasing the periods for both the primary and smoothing SMAs, filtering out minor fluctuations and highlighting sustained market movements. 4. **Minimal Lag:** - One of the challenges with moving averages is lag—the delay between when the price changes and when the indicator reflects this change. The Uptrick: Trend SMA Oscillator addresses this by allowing traders to adjust the periods to find a balance between responsiveness and stability. While all SMAs inherently have some lag, the customizable nature of this indicator helps traders mitigate this effect to align with their specific trading goals. 5. **Customizable and Intuitive:** - While many technical indicators come with a fixed set of parameters, the Uptrick: Trend SMA Oscillator is fully customizable, allowing traders to tailor it to their trading style, market conditions, and personal preferences. This makes it a highly flexible tool that can be adjusted as markets evolve or as a trader’s strategy changes over time. #### Practical Applications for Different Trader Profiles 1. **Day Traders:** - **Use Case:** Day traders can customize the SMA periods to create a faster, more responsive indicator. This allows them to capture short-term trends and make quick decisions. For example, reducing the primary SMA to 5 and the smoothing SMA to 20 can help day traders react promptly to intraday price movements. - **Strategy Integration:** Day traders might use the Uptrick: Trend SMA Oscillator in conjunction with volume-based indicators to confirm the strength of a trend before entering or exiting trades. 2. **Swing Traders:** - **Use Case:** Swing traders can use the default settings or slightly adjust them to smooth out minor price fluctuations while still capturing medium-term trends. This approach helps in identifying the optimal points to enter or exit trades based on the broader market direction. - **Strategy Integration:** Swing traders can combine this indicator with oscillators like the Relative Strength Index (RSI) to confirm overbought or oversold conditions, thereby refining their entry and exit strategies. 3. **Position Traders:** - **Use Case:** Position traders, who hold trades for extended periods, can extend the SMA periods to focus on long-term trends. By doing so, they minimize the impact of short-term market noise and focus on the underlying trend. - **Strategy Integration:** Position traders might use the Uptrick: Trend SMA Oscillator in combination with fundamental analysis. The indicator can help confirm the timing of entries and exits based on broader economic or corporate developments. 4. **Algorithmic and Quantitative Traders:** - **Use Case:** The simplicity and clear logic of the Uptrick: Trend SMA Oscillator make it an excellent candidate for algorithmic trading strategies. Its binary output—bullish or bearish—can be easily coded into automated trading systems. - **Strategy Integration:** Quant traders might use the indicator as part of a larger trading system that incorporates multiple indicators and rules, optimizing the SMA periods based on historical backtesting to achieve the best results. 5. **Novice Traders:** - **Use Case:** Beginners can use the Uptrick: Trend SMA Oscillator to learn the basics of trend-following strategies. The visual simplicity of the color-coded line helps novice traders quickly understand market direction without the need to interpret complex data. - **Educational Value:** The indicator serves as an excellent starting point for those new to technical analysis, providing a practical example of how moving averages work in a real-world trading environment. #### Combining the Indicator with Other Tools 1. **Relative Strength Index (RSI):** - The RSI is a momentum oscillator that measures the speed and change of price movements. When combined with the Uptrick: Trend SMA Oscillator, traders can look for instances where the RSI shows divergence from the price while the oscillator confirms the trend. This can be a powerful signal of an impending reversal or continuation. 2. **Moving Average Convergence Divergence (MACD):** - The MACD is another popular trend-following momentum indicator. By using it alongside the Uptrick: Trend SMA Oscillator, traders can confirm the strength of a trend and identify potential entry and exit points with greater confidence. For example, a bullish crossover on the MACD that coincides with the Uptrick: Trend SMA Oscillator turning green can be a strong buy signal. 3. **Volume Indicators:** - Volume is often considered the fuel behind price movements. Using volume indicators like the On-Balance Volume (OBV) or Volume Weighted Average Price (VWAP) in conjunction with the Uptrick: Trend SMA Oscillator can help traders confirm the validity of a trend. A trend identified by the oscillator that is supported by increasing volume is typically more reliable. 4. **Fibonacci Retracement:** - Fibonacci retracement levels are used to identify potential reversal levels in a trending market. When the Uptrick: Trend SMA Oscillator indicates a trend, traders can use Fibonacci retracement levels to find potential entry points that align with the broader trend direction. #### Implementation in Different Market Conditions 1. **Trending Markets:** - The Uptrick: Trend SMA Oscillator excels in trending markets, where it provides clear signals on the direction of the trend. In a strong uptrend, the line will remain green, helping traders stay in the trade for longer periods. In a downtrend, the red line will signal the continuation of bearish conditions, prompting traders to stay short or avoid long positions. 2. **Sideways or Range-Bound Markets:** - In range-bound markets, where price oscillates within a confined range without a clear trend, the Uptrick: Trend SMA Oscillator may produce more frequent changes in color. While this could indicate potential reversals at the range boundaries, traders should be cautious of false signals. It may be beneficial to pair the oscillator with a volatility indicator to better navigate such conditions. 3. **Volatile Markets:** - In highly volatile markets, where prices can swing rapidly, the sensitivity of the Uptrick: Trend SMA Oscillator can be adjusted by modifying the SMA periods. A shorter SMA period might capture quick trends, but traders should be aware of the increased risk of whipsaws. Combining the oscillator with a volatility filter or using it in a higher time frame might help mitigate some of this risk. #### Final Thoughts The "Uptrick: Trend SMA Oscillator" is a versatile, easy-to-use indicator that stands out for its simplicity, visual clarity, and adaptability. It provides traders with a straightforward method to identify and follow market trends, using the well-established concept of moving averages. The indicator’s customizable nature makes it suitable for a wide range of trading styles, from day trading to long-term investing, and across various asset classes. By offering immediate visual feedback through color-coded signals, the Uptrick: Trend SMA Oscillator simplifies the decision-making process, allowing traders to focus on execution rather than interpretation. Whether used on its own or as part of a broader technical analysis toolkit, this indicator has the potential to enhance trading strategies and improve overall performance. Its accessibility and ease of use make it particularly appealing to novice traders, while its adaptability and reliability ensure that it remains a valuable tool for more experienced market participants. As markets continue to evolve, the Uptrick: Trend SMA Oscillator remains a timeless tool, rooted in the fundamental principles of technical analysis, yet flexible enough to meet the demands of modern trading.
Pine Script® indicator
by Uptrick