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Zig Zag & Trendlines with Dynamic Threshold ATRPercentage Zig Zag with Dynamic Threshold This Pine Script indicator is an advanced Zig Zag tool that identifies and tracks price pivots based on a percentage move required for reversal, offering a clear visual representation of volatility-adjusted trends. Core Functionality (The Reversal Threshold): Unlike standard Zig Zag indicators that use a fixed price difference, this indicator calculates the required reversal size (%X) dynamically using the Average True Range (ATR). It calculates the ATR as a percentage of the current price (ATR%). The final threshold is this ATR% multiplied by a user-defined factor (default 3x). This means the reversal threshold is wider during volatile periods and narrower during quiet periods, adapting automatically to market conditions. Users can optionally revert to a fixed percentage if desired. Trend Extension Lines: The indicator draws two unique, dynamic trend lines connecting the last two significant Highs and the last two significant Lows. Crucially, these lines do not wait for the entire Zig Zag leg to confirm: If the price is actively forming a new up-leg, the High Extension Line connects the last confirmed High to the current extreme high of the active move. The Low Extension Line functions similarly for the downtrend. This feature allows the user to visualize dynamic support and resistance levels based on the current, active trend structure defined by the percentage threshold.
Wskaźnik Pine Script®
od isatolu
Apex Trend & Liquidity Master (SMC)v7.2The Apex Trend & Liquidity Master (SMC)v7.2 is a comprehensive trading system designed to solve a specific problem: how to integrate Trend Following, Classic Supply & Demand, and Smart Money Concepts (SMC) onto a single chart without creating visual chaos. Most indicators force traders to choose between high-lag trend filters or noisy price action concepts. This script combines both into a unified workflow. It uses a sophisticated "Ghost Mode" transparency engine to keep internal market structures subtle, ensuring the trader's focus remains on price action and the dominant trend. Core Philosophy This tool operates on the principle of "External Trend, Internal Liquidity." It forces the trader to respect the macro direction (Trend Cloud) while using micro-structure (FVGs, Order Blocks) for precision entries. Key Features Trend Architecture (The Context) The foundation of the script is a dynamic Hull Moving Average (HMA) combined with ATR volatility bands. This creates a "Trend Cloud" that visualizes the dominant market state. Teal Cloud: Bullish Context (Look for Longs). Maroon Cloud: Bearish Context (Look for Shorts). Classic Liquidity (The Targets) The script identifies major Swing Highs and Swing Lows based on pivot sensitivity. These are rendered as solid blocks and represent "External Liquidity." These are your primary Take Profit targets or major reversal zones. Smart Money Concepts (The Entry) The script automatically detects internal market structure, including: BOS (Break of Structure): Signals trend continuation. CHoCH (Change of Character): Signals potential trend reversal. Order Blocks & FVGs: Institutional footprints that act as magnets for price. These feature "Ghost Mode" styling (high transparency, no borders) and "Auto-Mitigation" (they are deleted immediately when price closes through them) to keep the chart clean. Signal & Risk Engine Entry Signals: Momentum-based Buy/Sell labels that filter out chop using ADX. Trailing Stop: A Chandelier-style ATR trailing stop line to assist in trade management and locking in profits. Visual Legend & Color Hierarchy To allow for instant chart processing, the colors follow a strict hierarchy: Context (Dark/Deep Colors): The Trend Cloud and Bar Colors use Deep Teal and Maroon. These indicate the background environment. Action (Neon Colors): Signals, BOS/CHoCH lines, and the Trailing Stop use Neon Green and Neon Red. These require immediate attention. Major Levels (Solid Colors): Classic Supply & Demand zones use Standard Forest Green and Brick Red. These are hard targets. Internal Zones (Pale/Ghost Colors): Order Blocks and FVGs use Pale Mint and Pale Rose with high transparency. These are background areas of interest for entries. How to Use This Indicator For the highest probability setups, use a "Confluence Approach" rather than trading signals in isolation: Identify Direction: Look at the Trend Cloud. Do not trade against the color of the cloud. Wait for Pullback: Wait for price to retrace into a "Ghost Zone" (Fair Value Gap or Order Block) nested inside the trend. Wait for Trigger: Look for a Neon "Buy" or "Sell" signal, or a BOS line break in the direction of the trend. Manage Risk: Use the Trailing Stop line to manage your position. Target Liquidity: Aim for the solid Classic Supply/Demand zones as exit points. Settings & Customization Trend Length: Default is 55 (Swing). Lower this to 20-30 for Scalping. Signal Toggles: Signals and Trailing Stops are enabled by default but can be toggled off for a pure price-action view. Sensitivity: The Pivot Lookback (Default 10) controls how many Supply/Demand zones appear. Disclaimer This script is for educational and informational purposes only. It does not constitute financial advice. Trading in financial markets involves a high degree of risk, and you should not trade with money you cannot afford to lose. Past performance of any trading system or methodology is not necessarily indicative of future results. Always perform your own due diligence and use proper risk management.
Wskaźnik Pine Script®
od DarkPoolCrypto
3
SNP420/TRCS_MASTERMicro Body Candle Highlighter is a visual tool for TradingView that continuously scans the active timeframe and highlights all candles with an extremely small body. For every bar (including the currently forming one), the indicator compares the absolute distance between Open and Close to a user-defined threshold in ticks (default: 1 tick, based on syminfo.mintick). If the candle’s body size is less than or equal to this threshold, the indicator draws a red frame around the candle – either around the body only or the full high-to-low range, depending on user settings. Optionally, the indicator can also trigger alerts whenever such a “micro body” candle is detected, allowing traders to react immediately to potential indecision, pauses, or micro-reversals in price action. author: SNP_420 project: FNXS ps: Piece and love
Wskaźnik Pine Script®
od SNP_420
Renko ScalperWhat it is- A lightweight Renko Scalper that combines Renko brick direction with an internal EMA trend filter and MACD confirmation to signal high-probability short-term entries. EMAs are used internally (hidden from the chart) so the visual remains uncluttered. Signals- Buy arrow: Renko direction turns bullish AND EMA trend up AND MACD histogram positive. Sell arrow: Renko direction turns bearish AND EMA trend down AND MACD histogram negative. Consecutive same-direction signals are suppressed (only one arrow per direction until opposite signal). Visuals- Buy / Sell arrows (large) above/below bars. Chart background tints green/red after the respective signal for easy glance recognition. Inputs:- Renko Box Size (points) EMA Fast / EMA Slow MACD fast/slow/signal lengths How to use- Add to chart Use smaller Renko box sizes for scalping, larger for swing-like entries. Confirm signal with price action and volume—this indicator is a signal generator, not a full automated system. Use alerts (built in) to receive Buy / Sell arrow notifications. Alerts- Buy Arrow — buySignal Sell Arrow — sellSignal Buy Background / Sell Background — background-color state alerts Recommended settings- Timeframes: 1m–15m for scalping, 5m for balanced intraday. Symbols: liquid futures/currency pairs/major crypto. Disclaimer This script is educational and not financial advice. Backtest and forward test on a demo account before live use. Past performance is not indicative of future results. Use proper risk management.
Wskaźnik Pine Script®
od ChandraBose2002
Grok/Claude Turtle Soup Strategy # 🥣 Turtle Soup Strategy (Enhanced) ## A Mean-Reversion Strategy Based on Failed Breakouts --- ## Historical Origins ### The Original Turtle Traders (1983-1988) The Turtle Trading system is one of the most famous experiments in trading history. In 1983, legendary commodities trader **Richard Dennis** made a bet with his partner **William Eckhardt** about whether great traders were born or made. Dennis believed trading could be taught; Eckhardt believed it was innate. To settle the debate, Dennis recruited 23 ordinary people through newspaper ads—including a professional blackjack player, a fantasy game designer, and an accountant—and taught them his trading system in just two weeks. He called them "Turtles" after turtle farms he had visited in Singapore, saying *"We are going to grow traders just like they grow turtles in Singapore."* The results were extraordinary. Over the next five years, the Turtles reportedly earned over **$175 million in profits**. The experiment proved Dennis right: trading could indeed be taught. #### The Original Turtle Rules: - **Entry:** Buy when price breaks above the 20-day high (System 1) or 55-day high (System 2) - **Exit:** Sell when price breaks below the 10-day low (System 1) or 20-day low (System 2) - **Stop Loss:** 2x ATR (Average True Range) from entry - **Position Sizing:** Based on volatility (ATR) - **Philosophy:** Pure trend-following—catch big moves by riding breakouts The Turtle system was a **trend-following** strategy that assumed breakouts would lead to sustained trends. It worked brilliantly in trending markets but suffered during choppy, range-bound conditions. --- ### The Turtle Soup Strategy (1990s) In the 1990s, renowned trader **Linda Bradford Raschke** (along with Larry Connors) observed something interesting: many of the breakouts that the Turtle system traded actually *failed*. Price would spike above the 20-day high, trigger Turtle buy orders, then immediately reverse—trapping the breakout traders. Raschke realized these failed breakouts were predictable and tradeable. She developed the **Turtle Soup** strategy, which does the *exact opposite* of the original Turtle system: > *"Instead of buying the breakout, we wait for it to fail—then fade it."* The name "Turtle Soup" is a clever play on words: the strategy essentially "eats" the Turtles by trading against them when their breakouts fail. #### Original Turtle Soup Rules: - **Setup:** Price makes a new 20-day high (or low) - **Qualifier:** The previous 20-day high must be at least 3-4 days old (not a fresh breakout) - **Entry Trigger:** Price reverses back inside the channel (failed breakout) - **Entry:** Go SHORT (against the failed breakout above), or LONG (against the failed breakdown below) - **Philosophy:** Mean-reversion—fade false breakouts and profit from trapped traders #### Turtle Soup Plus One Variant: Raschke also developed a more conservative variant called "Turtle Soup Plus One" which waits for the *next bar* after the breakout to confirm the failure before entering. This reduces false signals but may miss some opportunities. --- ## Our Enhanced Turtle Soup Strategy We have taken the classic Turtle Soup concept and enhanced it with modern technical indicators and filters to improve signal quality and adapt to today's markets. ### Core Logic Preserved The fundamental strategy remains true to Raschke's original concept: | Turtle (Original) | Turtle Soup (Our Strategy) | |-------------------|---------------------------| | BUY breakout above 20-day high | SHORT when that breakout FAILS | | SELL breakout below 20-day low | LONG when that breakdown FAILS | | Trend-following | Mean-reversion | | "The trend is your friend" | "Failed breakouts trap traders" | --- ### Enhancements & Improvements #### 1. RSI Exhaustion Filter **Addition:** RSI must confirm exhaustion before entry - **For SHORT entries:** RSI > 60 (buyers exhausted) - **For LONG entries:** RSI < 40 (sellers exhausted) **Why:** The original Turtle Soup had no momentum filter. Adding RSI ensures we only fade breakouts when the market is showing signs of exhaustion, significantly reducing false signals. This enhancement was inspired by later traders who found RSI extremes (originally 90/10, softened to 60/40) dramatically improved win rates. #### 2. ADX Trending Filter **Addition:** ADX must be > 20 for trades to execute **Why:** While the original Turtle Soup was designed for ranging markets, we found that requiring *some* trend strength (ADX > 20) actually improves results. This ensures we're trading in markets with enough directional movement to create meaningful failed breakouts, rather than random noise in dead markets. #### 3. Heikin Ashi Smoothing **Addition:** Optional Heikin Ashi calculations for breakout detection **Why:** Heikin Ashi candles smooth out price noise and make trend reversals more visible. When enabled, the strategy uses HA values to detect breakouts and failures, reducing whipsaws from erratic price spikes. #### 4. Dynamic Donchian Channels with Regime Detection **Addition:** Color-coded channels based on market regime - 🟢 **Green:** Bullish regime (uptrend + DI+ > DI- + OBV bullish) - 🔴 **Red:** Bearish regime (downtrend + DI- > DI+ + OBV bearish) - 🟡 **Yellow:** Neutral regime **Why:** Visual regime detection helps traders understand the broader market context. The original Turtle Soup had no regime awareness—our enhancement lets traders see at a glance whether conditions favor the strategy. #### 5. Volume Spike Detection (Optional) **Addition:** Optional filter requiring volume surge on the breakout bar **Why:** Failed breakouts are more significant when they occur on high volume. A volume spike on the breakout bar (default 1.2x average) indicates more traders got trapped, creating stronger reversal potential. #### 6. ATR-Based Stops and Targets **Addition:** Configurable ATR-based stop losses and profit targets - **Stop Loss:** 1.5x ATR (default) - **Profit Target:** 2.0x ATR (default) **Why:** The original Turtle Soup used fixed stop placement. ATR-based stops adapt to current volatility, providing tighter stops in calm markets and wider stops in volatile conditions. #### 7. Signal Cooldown **Addition:** Minimum bars between trades (default 5) **Why:** Prevents overtrading during choppy conditions where multiple failed breakouts might occur in quick succession. #### 8. Real-Time Info Panel **Addition:** Comprehensive dashboard showing: - Current regime (Bullish/Bearish/Neutral) - RSI value and zone - ADX value and trending status - Breakout status - Bars since last high/low - Current setup status - Position status **Why:** Gives traders instant visibility into all strategy conditions without needing to check multiple indicators. --- ## Entry Rules Summary ### SHORT Entry (Fading Failed Breakout Above) 1. ✅ Price breaks ABOVE the 20-period Donchian high 2. ✅ Previous 20-period high was at least 1 bar ago 3. ✅ Price closes back BELOW the Donchian high (failed breakout) 4. ✅ RSI > 60 (exhausted buyers) 5. ✅ ADX > 20 (trending market) 6. ✅ Cooldown period met → **Enter SHORT**, betting the breakout will fail ### LONG Entry (Fading Failed Breakdown Below) 1. ✅ Price breaks BELOW the 20-period Donchian low 2. ✅ Previous 20-period low was at least 1 bar ago 3. ✅ Price closes back ABOVE the Donchian low (failed breakdown) 4. ✅ RSI < 40 (exhausted sellers) 5. ✅ ADX > 20 (trending market) 6. ✅ Cooldown period met → **Enter LONG**, betting the breakdown will fail --- ## Exit Rules 1. **ATR Stop Loss:** Position closed if price moves 1.5x ATR against entry 2. **ATR Profit Target:** Position closed if price moves 2.0x ATR in favor 3. **Channel Exit:** Position closed if price breaks the exit channel in the opposite direction 4. **Mid-Channel Exit:** Position closed if price returns to channel midpoint --- ## Best Market Conditions The Turtle Soup strategy performs best when: - ✅ Markets are prone to false breakouts - ✅ Volatility is moderate (not too low, not extreme) - ✅ Price is oscillating within a broader range - ✅ There are clear support/resistance levels The strategy may struggle when: - ❌ Strong trends persist (breakouts follow through) - ❌ Volatility is extremely low (no meaningful breakouts) - ❌ Markets are in news-driven directional moves --- ## Default Settings | Parameter | Default | Description | |-----------|---------|-------------| | Lookback Period | 20 | Donchian channel period | | Min Bars Since Extreme | 1 | Bars since last high/low | | RSI Length | 14 | RSI calculation period | | RSI Short Level | 60 | RSI must be above this for shorts | | RSI Long Level | 40 | RSI must be below this for longs | | ADX Length | 14 | ADX calculation period | | ADX Threshold | 20 | Minimum ADX for trades | | ATR Period | 20 | ATR calculation period | | ATR Stop Multiplier | 1.5 | Stop loss distance in ATR | | ATR Target Multiplier | 2.0 | Profit target distance in ATR | | Cooldown Period | 5 | Minimum bars between trades | | Volume Multiplier | 1.2 | Volume spike threshold | --- ## Philosophy > *"The Turtle system made millions by following breakouts. The Turtle Soup strategy makes money when those breakouts fail. In trading, there's always someone on the other side of the trade—this strategy profits by being the smart money that fades the trapped breakout traders."* The beauty of the Turtle Soup strategy is its elegant simplicity: it exploits a known, repeatable pattern (failed breakouts) while using modern filters (RSI, ADX) to improve timing and reduce false signals. --- ## Credits - **Original Turtle System:** Richard Dennis & William Eckhardt (1983) - **Turtle Soup Strategy:** Linda Bradford Raschke & Larry Connors (1990s) - **RSI Enhancement:** Various traders who discovered RSI extremes improve reversal detection - **This Implementation:** Enhanced with Heikin Ashi smoothing, regime detection, ADX filtering, and comprehensive visualization --- *"We're not following the turtles—we're making soup out of them."* 🥣
Strategia Pine Script®
od ralis24
Daily TQQQ Trend Strategy (Ultra-Discreet Text Signals)✅ TradingView Description (Professional + Clean) Daily TQQQ Trend Strategy (Ultra-Discreet Text Signals) This indicator provides clean, minimalistic trend-following signals designed for traders who want confirmation without cluttering the chart. Instead of using arrows, boxes, or colored shapes, this script prints tiny text labels (“Buy – trend strong” / “Sell – trend weakening”) directly on the price chart. These messages are intentionally discreet so they do not interfere with existing indicators, automated systems, or visually busy setups. 🔍 How It Works The indicator analyzes the market using three well-established components: 1. Trend Direction (EMA 8 & EMA 20) • Buy condition: price above both EMAs • Sell condition: price below both EMAs 2. Momentum Confirmation (MACD) • Buy: MACD line > Signal line • Sell: MACD line < Signal line 3. Strength Filter (RSI 14) • Buy: RSI above 50 (bullish strength) • Sell: RSI below 50 (weakening momentum) Only when all conditions align does the indicator print a discreet buy or sell label. 🧭 Signal Types Buy – trend strong Appears below the candle when overall trend, momentum, and strength all turn bullish. Sell – trend weakening Appears above the candle when trend and momentum show weakness and downside pressure increases.
Wskaźnik Pine Script®
od basantd
Prime-Time × Vortex (3/6/9) — Ace (clean v3)1️⃣ Prime-Time Index (PT) A bar becomes Prime-Time when the count satisfies the formula: 4·n − 3 is a perfect square This generates the sequence: 1, 3, 7, 13, 21, 31, 43, 57, 73, 91, … These are time windows where price is more likely to form: Shifts in market structure Impulses Reversals Liquidity expansions These PT bars are drawn as small circles above the candle. If labels are enabled, the counter value (n) is also shown. 2️⃣ Vortex 3/6/9 Digital-Root Timing Every bar also has a digital root, calculated from the counter: If n → digitalRoot(n) = 3, 6, or 9, the bar is considered a Vortex bar. These moments often align with: Swing highs / swing lows Micro shifts Mini-reversals Minor liquidity grabs When a Prime-Time bar is also a 3/6/9 bar → high-probability timing. These bars are highlighted in green by default. 3️⃣ Filters & Display You can customize: Anchor time → when counting begins Reset daily → restart counter each new trading day Show only 3/6/9 → hides normal PT hits Label offset → distance above the candle Color themes This makes the indicator usable on: 1Min 5Min 15Min 1H Any timeframe you want 4️⃣ How To Apply It in Trading Use it as a time confluence tool, not a signal generator. ✔ Best ways to use: Look for MSS, sweeps, OB retests, FVG reactions when they occur on or near a Prime-Time or 3/6/9 bar Expect volatility increases after PT bars Use 3/6/9 hits to anticipate internal turning points Combine with: Session High/Low Killzones (London, NYO, PM) Purge Protocol MMXM Execution ✔ Example: If price sweeps a level and prints a 3/6/9 vortex bar inside a PT window → you have a very strong timing alignment for reversal. 5️⃣ Simple Summary Feature Meaning Prime-Time Hit (PT) Major time window where price often shifts 3/6/9 Vortex Bar Micro-timing for internal swings PT + 3/6/9 together High-probability timing for entries Reset Daily Perfect for intraday models like NYO & London Anchor Time Defines the entire cycle structure
Wskaźnik Pine Script®
od TIMEANDPRICEPR
THF Ultimate AIO Scalper & Trend PRO This is a comprehensive "All-In-One" trading suite designed to identify high-probability setups by combining **Trend Following**, **Price Action (FVG)**, and **Ichimoku Cloud** systems. The indicator is designed to be "Ready-to-Trade" out of the box, with all major confluence filters active by default. It helps traders avoid false signals by ensuring that momentum, trend, and support/resistance levels are in alignment. ### 🛠️ Key Features & Components: **1. Trend & Scalp Engine:** * **Scalp Signals:** Fast EMA crossovers (7/21) for quick entries. * **Trend Filter:** Signals are filtered by a long-term SMA (200) to ensure you are trading with the dominant trend. * **Golden/Death Cross:** Automatically highlights major trend shifts (SMA 50 crossing SMA 200). **2. Price Action (Fair Value Gaps):** * **FVG Detection:** Highlights unmitigated Bullish and Bearish imbalance zones. These act as high-probability targets or re-entry zones. * **Dashboard:** A built-in panel tracks the number of active vs. mitigated gaps. * **Mitigation Lines:** Automatically draws lines when price tests an FVG level. **3. Ichimoku Cloud Overlay:** * Displays the full Ichimoku system (Tenkan, Kijun, and Kumo Cloud) to identify dynamic support/resistance and trend strength. * **Usage:** Perfect for confirming breakout signals when price is above/below the Cloud. **4. Momentum & Volume:** * **Volume Coloring:** Bars are colored based on relative volume strength. * **RSI & MACD:** Integrated buy/sell signals to spot overbought/oversold conditions instantly. ### 🎯 How to Trade (Confluence Strategy): The power of this script lies in **Confluence** (multiple indicators agreeing): * **Buy Setup:** 1. Price is above the **Ichimoku Cloud** and **SMA 200**. 2. Wait for a **"SCALP BUY"** signal or **"Trend BUY"** label. 3. Confirm that price is reacting to a **Bullish FVG** (Green Box). 4. **RSI/MACD** should show bullish momentum. * **Sell Setup:** 1. Price is below the **Ichimoku Cloud** and **SMA 200**. 2. Wait for a **"SCALP SELL"** signal. 3. Confirm rejection from a **Bearish FVG** (Red Box). --- **CREDITS & ATTRIBUTION:** * **Fair Value Gap Logic:** This script utilizes the open-source FVG calculation method originally developed by **LuxAlgo**. We have integrated this logic with our custom trend system to provide a complete trading view. * **Trend Logic:** Custom compilation of Moving Average crossovers and Ichimoku standard calculations. *Disclaimer: This tool is for educational purposes only. Always manage your risk.*
Wskaźnik Pine Script®
od THF84
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise. █ OVERVIEW We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning). █ Detrend Fluctuation Analysis Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent. The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:  • Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.  • According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).  • Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned. █ Calculation DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series. Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.) We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression. We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale. As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement. The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window. Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements. The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases. If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation). If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation. If the slope = 0.5, It means the series follows a random walk. █ FEATURES  • Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.  • When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)  • “show filter” option applies a smoothing moving average to smooth the exponent.  • Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2. • For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues. The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot. When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use. █ How to Use Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.  • In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.  • When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.  • Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa. However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant. So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency. The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right. An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future. However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal. The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead. Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4). Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Wskaźnik Pine Script®
od Motgench
Weekly False Breakdown ScannerWeekly False Breakdown Scanner Weekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown ScannerWeekly False Breakdown Scanner
Wskaźnik Pine Script®
od muhdhafiz99
Fast Autocorrelation Estimator█ Overview: The Fast ACF and PACF Estimation indicator efficiently calculates the autocorrelation function (ACF) and partial autocorrelation function (PACF) using an online implementation. It helps traders identify patterns and relationships in financial time series data, enabling them to optimize their trading strategies and make better-informed decisions in the markets. █ Concepts: Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. This indicator displays autocorrelation based on lag number. The autocorrelation is not displayed based over time on the x-axis. It's based on the lag number which ranges from 1 to 30. The calculations can be done with "Log Returns", "Absolute Log Returns" or "Original Source" (the price of the asset displayed on the chart). When calculating autocorrelation, the resulting value will range from +1 to -1, in line with the traditional correlation statistic. An autocorrelation of +1 represents a perfect correlation (an increase seen in one time series leads to a proportionate increase in the other time series). An autocorrelation of -1, on the other hand, represents a perfect inverse correlation (an increase seen in one time series results in a proportionate decrease in the other time series). Lag number indicates which historical data point is autocorrelated. For example, if lag 3 shows significant autocorrelation, it means current data is influenced by the data three bars ago. The Fast Online Estimation of ACF and PACF Indicator is a powerful tool for analyzing the linear relationship between a time series and its lagged values in TradingView. The indicator implements an online estimation of the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) up to 30 lags, providing a real-time assessment of the underlying dependencies in your time series data. The Autocorrelation Function (ACF) measures the linear relationship between a time series and its lagged values, capturing both direct and indirect dependencies. The Partial Autocorrelation Function (PACF) isolates the direct dependency between the time series and a specific lag while removing the effect of any indirect dependencies. This distinction is crucial in understanding the underlying relationships in time series data and making more informed decisions based on those relationships. For example, let's consider a time series with three variables: A, B, and C. Suppose that A has a direct relationship with B, B has a direct relationship with C, but A and C do not have a direct relationship. The ACF between A and C will capture the indirect relationship between them through B, while the PACF will show no significant relationship between A and C, as it accounts for the indirect dependency through B. Meaning that when ACF is significant at for lag 5, the dependency detected could be caused by an observation that came in between, and PACF accounts for that. This indicator leverages the Fast Moments algorithm to efficiently calculate autocorrelations, making it ideal for analyzing large datasets or real-time data streams. By using the Fast Moments algorithm, the indicator can quickly update ACF and PACF values as new data points arrive, reducing the computational load and ensuring timely analysis. The PACF is derived from the ACF using the Durbin-Levinson algorithm, which helps in isolating the direct dependency between a time series and its lagged values, excluding the influence of other intermediate lags. █ How to Use the Indicator: Interpreting autocorrelation values can provide valuable insights into the market behavior and potential trading strategies. When applying autocorrelation to log returns, and a specific lag shows a high positive autocorrelation, it suggests that the time series tends to move in the same direction over that lag period. In this case, a trader might consider using a momentum-based strategy to capitalize on the continuation of the current trend. On the other hand, if a specific lag shows a high negative autocorrelation, it indicates that the time series tends to reverse its direction over that lag period. In this situation, a trader might consider using a mean-reversion strategy to take advantage of the expected reversal in the market. ACF of log returns: Absolute returns are often used to as a measure of volatility. There is usually significant positive autocorrelation in absolute returns. We will often see an exponential decay of autocorrelation in volatility. This means that current volatility is dependent on historical volatility and the effect slowly dies off as the lag increases. This effect shows the property of "volatility clustering". Which means large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes. ACF of absolute log returns: Autocorrelation in price is always significantly positive and has an exponential decay. This predictably positive and relatively large value makes the autocorrelation of price (not returns) generally less useful. ACF of price: █ Significance: The significance of a correlation metric tells us whether we should pay attention to it. In this script, we use 95% confidence interval bands that adjust to the size of the sample. If the observed correlation at a specific lag falls within the confidence interval, we consider it not significant and the data to be random or IID (identically and independently distributed). This means that we can't confidently say that the correlation reflects a real relationship, rather than just random chance. However, if the correlation is outside of the confidence interval, we can state with 95% confidence that there is an association between the lagged values. In other words, the correlation is likely to reflect a meaningful relationship between the variables, rather than a coincidence. A significant difference in either ACF or PACF can provide insights into the underlying structure of the time series data and suggest potential strategies for traders. By understanding these complex patterns, traders can better tailor their strategies to capitalize on the observed dependencies in the data, which can lead to improved decision-making in the financial markets. Significant ACF but not significant PACF: This might indicate the presence of a moving average (MA) component in the time series. A moving average component is a pattern where the current value of the time series is influenced by a weighted average of past values. In this case, the ACF would show significant correlations over several lags, while the PACF would show significance only at the first few lags and then quickly decay. Significant PACF but not significant ACF: This might indicate the presence of an autoregressive (AR) component in the time series. An autoregressive component is a pattern where the current value of the time series is influenced by a linear combination of past values at specific lags. Often we find both significant ACF and PACF, in that scenario simply and AR or MA model might not be sufficient and a more complex model such as ARMA or ARIMA can be used. █ Features: Source selection: User can choose either 'Log Returns' , 'Absolute Returns' or 'Original Source' for the input data. Autocorrelation Selection: User can choose either 'ACF' or 'PACF' for the plot selection. Plot Selection: User can choose either 'Autocorrelarrogram' or 'Historical Autocorrelation' for plotting the historical autocorrelation at a specified lag. Max Lag: User can select the maximum number of lags to plot. Precision: User can set the number of decimal points to display in the plot.
Wskaźnik Pine Script®
od Motgench
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility. In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution. Note: Normal distribution is just an assumption, it's not the real distribution of return Settings: "Estimation Period Selection" is for selecting the period we want to construct the prediction interval. For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours. For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead. There are also other source selections based on high low. Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar. Note: It's probably better to use "probability cone" for visual presentation when time > 1 Volatility Models : Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps. about 8 x efficient Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster. YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance. Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator. Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters. Standard deviations: Standard Deviation One shows the estimated range where the closing price will be about 68% of the time. Standard Deviation two shows the estimated range where the closing price will be about 95% of the time. Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time. Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability. Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel. People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1. When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation. Note: The significance of autocorrelation can be checked on an ACF indicator. ACF The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds. Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag. Note: The range here after smooth no long represent the probability Panel positions can be adjusted in the settings. X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left. Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down. Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands. Warnings: People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails. Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption). Expected move Occurrence Test The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
Wskaźnik Pine Script®
od Motgench