Mulai

Cari skrip untuk "KELTNER"

TA-Money Flow-Version4Updated for TV-Pine V4 This is the MACD of a stochastic OBV movement indicator and now the MACD of the Squeeze Momentum Indicator. It is good (right) to work with both price and volume...it is also good to utilize the most popular indicator ever in TV (Lazybear). I've included highlighting based on price divergence, yellow is divergence of either OBV or SQZ, red is both divergence, and then I've also built in the "squeeze on - blue" highlighting to show follow through of divergence. It works great on any time frame, but you need to have volume data. Not sure where I originally got this (stoch-OBV, somewhere off Tradingview several years ago, thanks to the person who shared), Squeeze is Lazybear, links below. Enjoy. Version 4: Updated OBV equation because TV-Pine V3 broke in V4 Included MACD of Squeeze for histogram Included "squeeze on" highlighting TA-Money-Flow-Version3 TA-Money-Flow-Version2 Squeeze-Momentum-Indicator-LazyBear
Indikator Pine Script®
oleh kocurekc
2
Hull Moving Average + Bollinger BandsThis study make use of Hull Moving Average and Bollinger Bands. The crosses give signal about HMA and BB crossovers, they are a bit lagging, if you stare well you will spot them a little earlier. It look like a good idea to buy and sell when HMA is near or on the outside of the outer bands. By default the Bollinger Bands uses Simple Moving Average with 21 periodes, and Hull Moving Average use 9 periodes. You can alter the settings in the format dialog. Please use as pleased, and if you do something clever with it I'll be happy to know :D
Indikator Pine Script®
oleh Skrymten
4
DepthHouse - Moving Average ChannelsThe indicator Moving Average Channels was created for experimental purposes due to the parabolic moves BTC has made in the recent past. How it works: The basis, or center line, is a standard moving average that is set by the user. The bands are then a customizable percentage of the basis. Which based on the settings, could serve as possible support and resistance. DepthHouse – Moving Average Channels has been published for you all to see and try for yourselves. Maybe this indicator has uses elsewhere? If you find something feel free to post it in the comments below! If you like this indicator, please drop a like or comment! They are very much appreciated! Be sure to go to my profile and check out my other indicators!
Indikator Pine Script®
oleh oh92
5
[RichG] Easy MTF Strategy v1.1This is a second attempt at an easy to understand multiple time frame strategy. This one uses ATR for exits. If the position is long, and the price closes below the ATR multiplier, it triggers a close. If the position is short, and the price closes above the ATR/multiplier, it triggers a close. This generates a lot of little trades but is useful because it uses multiple time frames along with cutting losses when the ATR disagrees.
Strategi Pine Script®
oleh RichGTastyTrader
7
Euler-Lagrange Bands [AGP] Ver.1.0Euler-Lagrange Bands : A Modern Approach to Volatility and Trend Analysis indicator is an innovative technical analysis tool that uses a Kalman Filter to create dynamic and price-sensitive volatility bands. Unlike traditional Bollinger Bands or Keltner Channels, which rely on moving averages, this approach applies advanced engineering and mathematical principles to intelligently smooth price data. This reduces market noise and provides a clearer view of an asset's boundaries and its fair value. Key Features Dynamic and Smoothed Bands: The upper and lower bands adapt in real time to market volatility, offering a fluid and precise channel for identifying overbought and oversold zones. "Fair Value" Midpoint: The indicator calculates and displays a midpoint that serves as a "fair value" reference. This is crucial for assessing whether the current price is over- or undervalued. Multidimensional Analysis: It integrates with RSI and volume analysis to provide a comprehensive market view. The floating RSI and volume labels change color, offering quick and effective visual alerts. Clear Visual Signals: The indicator draws shapes on the chart to mark band crosses, potential reversals, and trend confirmations. Additionally, the candlestick color changes to indicate whether the price is above or below the midpoint. Real-Time Information Panel: A table in the top corner displays the exact values of the bands and the midpoint, giving you all the crucial information at a glance without cluttering the chart. The table's cell colors also change to alert you to price crosses. Logic and Adjustments The elb_process_noise and elb_measurement_noise values are fixed in this code to optimize band performance. The only parameter the user can change from the indicator's settings in TradingView is length_periods, which allows for adjusting the bands' lookback period without altering the algorithm's sophisticated filtering logic. The default values have been selected to provide an optimal balance between the indicator's stability and responsiveness, aiming to avoid false signals and ensure accurate market tracking. How to Use It This indicator is ideal for traders looking for a more sophisticated alternative to conventional volatility bands. It can be used to: Identify an asset's price range. Detect potential reversals when the price reaches or crosses the bands. Confirm trend strength with volume and RSI analysis. Make decisions based on the price's relationship to its "Fair Value." Disclaimer WARNING: This indicator is provided for educational and technical analysis purposes only. It does not constitute, and should not be interpreted as, financial advice. The buying and selling of financial instruments involve significant risk, and losses may exceed deposits. The past performance of any indicator or strategy is not a guarantee of future results. Users must conduct their own research, exercise due diligence, and consider their personal financial situation before making any investment decisions. The code's creator is not responsible for any losses or damages that may arise from the use of this indicator.
Indikator Pine Script®
oleh Andromedae
1
Composite Sentiment Indicator (SPY/QQQ/SOXX + VixFix)# Multi-Index Composite Sentiment Indicator A comprehensive sentiment indicator that works across SPY, QQQ, SOXX, and custom symbols. Combines volatility, options flow, macro factors, technicals, and seasonality into a single z-score composite. ## What It Does Takes multiple market sentiment inputs (VIX, put/call ratios, breadth, yields, etc.) and smooshes them into one normalized line. When the composite is high = markets getting spooked. When it's low = markets getting complacent. ## Key Features - **Multi-Index Support**: Automatically adapts for SPY (uses VIX), QQQ (uses VXN), SOXX (uses VixFix), or custom symbols - **VixFix Integration**: Larry Williams' VixFix for indices without dedicated VIX measures - **Signal MA**: Choose from SMA/EMA/WMA/HMA/TEMA/DEMA with color coding (red above MA = risk-on, green below = risk-off) - **September Focus**: Built-in seasonality weighting for September weakness patterns - **Comprehensive Components**: Volatility, options sentiment, macro factors, technicals, and sector-specific metrics ## How to Use **Basic Setup:** 1. Pick your index (SPY/QQQ/SOXX) 2. Choose signal MA type and length (EMA 21 is a good start) 3. Watch for extreme readings and MA crossovers **Color Signals:** - Red composite = above signal MA = bearish sentiment - Green composite = below signal MA = bullish sentiment - Extreme high readings (red background) = potential tops - Extreme low readings (green background) = potential bottoms **For Different Indices:** - **QQQ**: Uses NASDAQ VIX (VXN) when available, falls back to VixFix - **SOXX**: Includes semiconductor cycle indicators, uses VixFix for volatility - **Custom**: Adapts automatically, relies on VixFix and general market metrics ## Components Included **Volatility**: VIX/VXN/VixFix, term structure, historical vol **Options**: Put/call ratios, SKEW index **Macro**: DXY, 10Y yields, yield curve, TIPS spreads **Technical**: RSI deviation, momentum **Seasonality**: September effects, quad witching, month-end patterns **Breadth**: S&P 500 and NASDAQ breadth measures ## Pro Tips - Works well on Daily Timeframe - September gets extra weight automatically - watch for August setup signals - Keltner envelope breaks often mark sentiment exhaustion points - Use alerts for extreme readings and MA crossovers Works best when you understand that sentiment extremes often mark turning points, not continuation signals. High readings don't mean "keep shorting" - they mean "start looking for reversal setups." ## Settings Worth Tweaking - Signal MA type/length for your timeframe - Component weights based on what matters for your index - Envelope multipliers for your risk tolerance - VixFix parameters if default doesn't fit your symbol's volatility The table shows all current component readings so you can see what's driving the signal. Good for context and debugging weird readings.
Indikator Pine Script®
oleh ky_yule1010
Mean-Reversion Indicator_V2_SamleeOverview This is the second version of my mean reversion indicator. It combines a moving average with adaptive standard deviation bands to detect when the price deviates significantly from its mean. The script provides automatic entry/exit signals, real-time PnL tracking, and shaded trade zones to make mean reversion trading more intuitive. Core Logic Mean benchmark: Simple Moving Average (MA). Volatility bands: Standard deviation of the spread (close − MA) defines upper and lower bands. Trading rules: Price breaks below the lower band → Enter Long Price breaks above the upper band → Enter Short Price reverts to MA → Exit position What’s different vs. classic Bollinger/Keltner Bandwidth is based on the standard deviation of the price–MA spread, not raw closing prices. Entry signals use previous-bar confirmation to reduce intrabar noise. Exit rule is a mean-touch condition, rather than fixed profit/loss targets. Enhanced visualization: A shaded box dynamically shows the distance between entry and current/exit price, making it easy to see profit/loss zones over the holding period. Instant PnL labels display current position side (Long/Short/Flat) and live profit/loss in both pips and %. Entry and exit points are clearly marked on the chart with labels and exact prices. These visualization tools go beyond what most indicators provide, giving traders a clearer, more practical view of trade evolution. Key Features Automatic detection of position status (Long / Short / Flat). Chart labels for entries (“Entry”) and exits (“Exit”). Real-time floating PnL calculation in both pips and %. Info panel (top-right) showing entry price, current price, position side, and PnL. Dynamic shading between entry and current/exit price to visualize profit/loss zones. Usage Notes & Risk Mean reversion may underperform in strong trending markets; parameters (len_ma, len_std, mult) should be validated per instrument and timeframe. Works best on relatively stable, mean-reverting pairs (e.g., AUDNZD). Risk management is essential: use independent stop-loss rules (e.g., limit risk to 1–2% of equity per trade). This script is provided for educational purposes only and is not financial advice.
Indikator Pine Script®
oleh samleetianzhe
2
Multi-Band Trend LineThis Pine Script creates a versatile technical indicator called "Multi-Band Trend Line" that builds upon the concept of the popular "Follow Line Indicator" by Dreadblitz. While the original Follow Line Indicator uses simple trend detection to place a line at High or Low levels, this enhanced version combines multiple band-based trading strategies with dynamic trend line generation. The indicator supports five different band types and provides more sophisticated buy/sell signals based on price breakouts from various technical analysis bands. Key Features Multi-Band Support The indicator supports five different band types: - Bollinger Bands: Uses standard deviation to create bands around a moving average - Keltner Channels: Uses ATR (Average True Range) to create bands around a moving average - Donchian Channels: Uses the highest high and lowest low over a specified period - Moving Average Envelopes: Creates bands as a percentage above and below a moving average - ATR Bands: Uses ATR multiplier to create bands around a moving average Dynamic Trend Line Generation (Enhanced Follow Line Concept) - Similar to the Follow Line Indicator, the trend line is placed at High or Low levels based on trend direction - Key Enhancement: Instead of simple trend detection, this version uses band breakouts to trigger trend changes - When price breaks above the upper band (bullish signal), the trend line is set to the low (optionally adjusted with ATR) - similar to Follow Line's low placement - When price breaks below the lower band (bearish signal), the trend line is set to the high (optionally adjusted with ATR) - similar to Follow Line's high placement - The trend line acts as dynamic support/resistance, following the price action more precisely than the original Follow Line ATR Filter (Follow Line Enhancement) - Like the original Follow Line Indicator, an ATR filter can be selected to place the line at a more distance level than the normal mode settled at candles Highs/Lows - When enabled, it adds/subtracts ATR value to provide more conservative trend line placement - Helps reduce false signals in volatile markets - This feature maintains the core philosophy of the Follow Line while adding more precision through band-based triggers Signal Generation - Buy Signal: Generated when trend changes from bearish to bullish (trend line starts rising) - Sell Signal: Generated when trend changes from bullish to bearish (trend line starts falling) - Signals are displayed as labels on the chart Visual Elements - Upper and lower bands are plotted in gray - Trend line changes color based on direction (green for bullish, red for bearish) - Background color changes based on trend direction - Buy/sell signals are marked with labeled shapes How It Works Band Calculation: Based on the selected band type, upper and lower boundaries are calculated Signal Detection: When price closes above the upper band or below the lower band, a breakout signal is generated Trend Line Update: The trend line is updated based on the breakout direction and previous trend line value Trend Direction: Determined by comparing current trend line with the previous value Alert Generation: Buy/sell conditions trigger alerts and visual signals Use Cases Enhanced trend following strategies: More precise than basic Follow Line due to band-based triggers Breakout trading: Multiple band types provide various breakout opportunities Dynamic support/resistance identification: Combines Follow Line concept with band analysis Multi-timeframe analysis with different band types: Choose the most suitable band for your timeframe Reduced false signals: Band confirmation provides better entry/exit points compared to simple trend following
Indikator Pine Script®
oleh Mr_Rakun
4
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain? This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions. How does a Markov Chain work, and how should I read this spheres-and-arrows diagram? Think of three weather modes: Sunny, Rainy, Cloudy. Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow). The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy). Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step. Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny. Now relabel the spheres to markets: Bull, Bear, Neutral. Spheres: market regimes (uptrend, downtrend, range). Self‑loop: tendency for the current regime to continue on the next bar. Arrows: the most common next regime if a switch happens. How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear). What states and transitions are shown? The three market states visualized are: Bullish (Bull): Upward or strong-market regime. Bearish (Bear): Downward or weak-market regime. Neutral: Sideways or range-bound regime. Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull). How does the regime detection system work? You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.). Values are statistically normalized (Z-scored) over a configurable lookback period. The normalized outputs are classified into Bull, Bear, or Neutral zones. If using three indicators, their regime signals are averaged and smoothed for robustness. How are transition probabilities calculated? On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions. The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear). Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time. What is unique about the visualization? 3D animated spheres represent each regime and change visually when active. Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows. Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed. All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format. Can I use custom indicators for regime classification? Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement. What does the “Lookback Period” control? Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt. How is this different from a Hidden Markov Model (HMM)? It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust. How is this Markov Chain different from a Hidden Markov Model (HMM)? Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable. Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training. A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data. Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune. HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data. How does the indicator avoid “repainting” or look-ahead bias? All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation. Are there practical tuning tips? Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability. Use custom indicators if your asset has unique regime drivers. Watch for rapid changes in transition probabilities as early warning of a possible regime shift. Who is this indicator for? Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models. Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting: The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear). Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions. Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies. It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes. The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting. Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis. What's the mathematical foundation behind this? The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM). How to use this indicator programmatically? The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading. Step-by-Step Integration with Pine Script (input.source) Add the Markov Chain indicator to your chart. This must be done first, since your custom script will "pull" the bias signal from the indicator's plot. In your strategy, create an input using input.source() Example: //@version=5 strategy("MC Bias Strategy Example") mcBias = input.source(close, "MC Bias Source") After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot). Use the bias in your trading logic Example (long only on Bull, flat otherwise): if mcBias == 1 strategy.entry("Long", strategy.long) else strategy.close("Long") For more advanced workflows, combine mcBias with additional filters or trailing stops. How does this work behind-the-scenes? TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source). The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.). This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code. Integrating 45+ Indicators with Your Markov Chain — How & Why The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots. // Enhanced Custom Indicators Export - 45 Technical Indicators // Comprehensive technical analysis suite for advanced market regime detection //@version=6 indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500) // |----- Input Parameters -----| // momentum_group = "Momentum Indicators" trend_group = "Trend Indicators" volume_group = "Volume Indicators" volatility_group = "Volatility Indicators" oscillator_group = "Oscillator Indicators" display_group = "Display Settings" // Common lengths length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group) length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group) length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group) // Display options show_table = input.bool(true, "Show Values Table", group=display_group) table_size = input.string("Small", "Table Size", options= , group=display_group) // |----- MOMENTUM INDICATORS (15 indicators) -----| // // 1. RSI (Relative Strength Index) rsi_14 = ta.rsi(close, length_14) rsi_centered = rsi_14 - 50 // 2. Stochastic Oscillator stoch_k = ta.stoch(close, high, low, length_14) stoch_d = ta.sma(stoch_k, 3) stoch_centered = stoch_k - 50 // 3. Williams %R williams_r = ta.stoch(close, high, low, length_14) - 100 // 4. MACD (Moving Average Convergence Divergence) = ta.macd(close, 12, 26, 9) // 5. Momentum (Rate of Change) momentum = ta.mom(close, length_14) momentum_pct = (momentum / close ) * 100 // 6. Rate of Change (ROC) roc = ta.roc(close, length_14) // 7. Commodity Channel Index (CCI) cci = ta.cci(close, length_20) // 8. Money Flow Index (MFI) mfi = ta.mfi(close, length_14) mfi_centered = mfi - 50 // 9. Awesome Oscillator (AO) ao = ta.sma(hl2, 5) - ta.sma(hl2, 34) // 10. Accelerator Oscillator (AC) ac = ao - ta.sma(ao, 5) // 11. Chande Momentum Oscillator (CMO) cmo = ta.cmo(close, length_14) // 12. Detrended Price Oscillator (DPO) dpo = close - ta.sma(close, length_20) // 13. Price Oscillator (PPO) ppo = ta.sma(close, 12) - ta.sma(close, 26) ppo_pct = (ppo / ta.sma(close, 26)) * 100 // 14. TRIX trix_ema1 = ta.ema(close, length_14) trix_ema2 = ta.ema(trix_ema1, length_14) trix_ema3 = ta.ema(trix_ema2, length_14) trix = ta.roc(trix_ema3, 1) * 10000 // 15. Klinger Oscillator klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55) // 16. Fisher Transform fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25 fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2)) // 17. Stochastic RSI stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14) stoch_rsi_centered = stoch_rsi - 50 // 18. Relative Vigor Index (RVI) rvi_num = ta.swma(close - open) rvi_den = ta.swma(high - low) rvi = rvi_den != 0 ? rvi_num / rvi_den : 0 // 19. Balance of Power (BOP) bop = (close - open) / (high - low) // |----- TREND INDICATORS (10 indicators) -----| // // 20. Simple Moving Average Momentum sma_20 = ta.sma(close, length_20) sma_momentum = ((close - sma_20) / sma_20) * 100 // 21. Exponential Moving Average Momentum ema_20 = ta.ema(close, length_20) ema_momentum = ((close - ema_20) / ema_20) * 100 // 22. Parabolic SAR sar = ta.sar(0.02, 0.02, 0.2) sar_trend = close > sar ? 1 : -1 // 23. Linear Regression Slope lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1) // 24. Moving Average Convergence (MAC) mac = ta.sma(close, 10) - ta.sma(close, 30) // 25. Trend Intensity Index (TII) tii_sum = 0.0 for i = 1 to length_20 tii_sum += close > close ? 1 : 0 tii = (tii_sum / length_20) * 100 // 26. Ichimoku Cloud Components ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2 ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2 ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1 // 27. MESA Adaptive Moving Average (MAMA) mama_alpha = 2.0 / (length_20 + 1) mama = ta.ema(close, length_20) mama_momentum = ((close - mama) / mama) * 100 // 28. Zero Lag Exponential Moving Average (ZLEMA) zlema_lag = math.round((length_20 - 1) / 2) zlema_data = close + (close - close ) zlema = ta.ema(zlema_data, length_20) zlema_momentum = ((close - zlema) / zlema) * 100 // |----- VOLUME INDICATORS (6 indicators) -----| // // 29. On-Balance Volume (OBV) obv = ta.obv // 30. Volume Rate of Change (VROC) vroc = ta.roc(volume, length_14) // 31. Price Volume Trend (PVT) pvt = ta.pvt // 32. Negative Volume Index (NVI) nvi = 0.0 nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi // 33. Positive Volume Index (PVI) pvi = 0.0 pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi // 34. Volume Oscillator vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10) // 35. Ease of Movement (EOM) eom_distance = high - low eom_box_height = volume / 1000000 eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0 eom_sma = ta.sma(eom, length_14) // 36. Force Index force_index = volume * (close - close ) force_index_sma = ta.sma(force_index, length_14) // |----- VOLATILITY INDICATORS (10 indicators) -----| // // 37. Average True Range (ATR) atr = ta.atr(length_14) atr_pct = (atr / close) * 100 // 38. Bollinger Bands Position bb_basis = ta.sma(close, length_20) bb_dev = 2.0 * ta.stdev(close, length_20) bb_upper = bb_basis + bb_dev bb_lower = bb_basis - bb_dev bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0 bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0 // 39. Keltner Channels Position kc_basis = ta.ema(close, length_20) kc_range = ta.ema(ta.tr, length_20) kc_upper = kc_basis + (2.0 * kc_range) kc_lower = kc_basis - (2.0 * kc_range) kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0 // 40. Donchian Channels Position dc_upper = ta.highest(high, length_20) dc_lower = ta.lowest(low, length_20) dc_basis = (dc_upper + dc_lower) / 2 dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0 // 41. Standard Deviation std_dev = ta.stdev(close, length_20) std_dev_pct = (std_dev / close) * 100 // 42. Relative Volatility Index (RVI) rvi_up = ta.stdev(close > close ? close : 0, length_14) rvi_down = ta.stdev(close < close ? close : 0, length_14) rvi_total = rvi_up + rvi_down rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50 // 43. Historical Volatility hv_returns = math.log(close / close ) hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100 // 44. Garman-Klass Volatility gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open) gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100 // 45. Parkinson Volatility park_vol = math.log(high/low) * math.log(high/low) parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100 // 46. Rogers-Satchell Volatility rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open) rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100 // |----- OSCILLATOR INDICATORS (5 indicators) -----| // // 47. Elder Ray Index elder_bull = high - ta.ema(close, 13) elder_bear = low - ta.ema(close, 13) elder_power = elder_bull + elder_bear // 48. Schaff Trend Cycle (STC) stc_macd = ta.ema(close, 23) - ta.ema(close, 50) stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10) stc_d = ta.ema(stc_k, 3) stc = ta.stoch(stc_d, stc_d, stc_d, 10) // 49. Coppock Curve coppock_roc1 = ta.roc(close, 14) coppock_roc2 = ta.roc(close, 11) coppock = ta.wma(coppock_roc1 + coppock_roc2, 10) // 50. Know Sure Thing (KST) kst_roc1 = ta.roc(close, 10) kst_roc2 = ta.roc(close, 15) kst_roc3 = ta.roc(close, 20) kst_roc4 = ta.roc(close, 30) kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15) // 51. Percentage Price Oscillator (PPO) ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100 ppo_signal = ta.ema(ppo_line, 9) ppo_histogram = ppo_line - ppo_signal // |----- PLOT MAIN INDICATORS -----| // // Plot key momentum indicators plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1) plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1) plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1) plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1) plot(cci, title="05_CCI", color=color.green, linewidth=1) // Plot trend indicators plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1) plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1) plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1) plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1) plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1) // Plot volatility indicators plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1) plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1) plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1) plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1) plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1) // Plot volume indicators plot(vroc, title="16_VROC", color=color.blue, linewidth=1) plot(eom_sma, title="17_EOM", color=color.red, linewidth=1) plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1) plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1) plot(obv, title="20_OBV", color=color.purple, linewidth=1) // Plot additional oscillators plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1) plot(cmo, title="22_CMO", color=color.maroon, linewidth=1) plot(dpo, title="23_DPO", color=color.teal, linewidth=1) plot(trix, title="24_TRIX", color=color.lime, linewidth=1) plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1) // Plot more momentum indicators plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1) plot(ac, title="27_AC", color=color.aqua, linewidth=1) plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1) plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1) plot(klinger, title="30_Klinger", color=color.gray, linewidth=1) // Plot trend continuation plot(tii, title="31_TII", color=color.blue, linewidth=1) plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1) plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1) plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1) plot(bop, title="35_BOP", color=color.purple, linewidth=1) // Plot volume continuation plot(nvi, title="36_NVI", color=color.navy, linewidth=1) plot(pvi, title="37_PVI", color=color.maroon, linewidth=1) plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1) plot(roc, title="39_ROC", color=color.lime, linewidth=1) plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1) // Plot volatility continuation plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1) plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1) plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1) plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1) plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1) // Plot final oscillators plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1) plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1) plot(stc, title="48_STC", color=color.green, linewidth=1) plot(coppock, title="49_Coppock", color=color.orange, linewidth=1) plot(kst, title="50_KST", color=color.purple, linewidth=1) // Plot final indicators plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1) plot(pvt, title="52_PVT", color=color.maroon, linewidth=1) // |----- Reference Lines -----| // hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1) hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) // |----- Enhanced Information Table -----| // if show_table and barstate.islast table_position = position.top_right table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray) // Headers table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) // Key Momentum Indicators table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size) table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size) table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size) table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size) table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size) table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size) table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size) // Key Trend Indicators table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90)) table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size) table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size) table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size) table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size) // Key Volatility Indicators table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90)) table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size) // Key Volume Indicators table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90)) table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size) // Key Oscillators table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size) // Summary Statistics table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70)) table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size) regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL" table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size) This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator. How to use this script with the Markov Chain for research and backtesting: Add the Enhanced Indicator Export to your chart. Every calculated indicator is available as an individual data stream. Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option. In the Markov Chain indicator’s settings, turn ON the custom indicator mode. For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name. This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering. Backtest regime logic directly. Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns. The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration. Export the regime bias for programmatic use. As described above, use input.source() in your Pine Script strategy or system and link the bias output. You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes. With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you. Optimize your signal generation by backtesting across a universe of meaningful indicator blends. Enhance risk management with objective, real-time regime boundaries. Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes. Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic. Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem. References & Further Reading Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182. Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384. Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg. Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544. Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance. Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764. Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC. On Quantitative Finance and Markov Models: Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press. Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press. TradingView Pine Script Documentation: www.tradingview.com TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org What makes this indicator original and unique? - On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm. - Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script. - Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time. - Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window. - Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior. Integrate your own signals (using input.source()) - In settings, choose the Source . This is powered by input.source() . - Feed it price, an indicator you compute inside the script, or a custom composite series. - The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time. Credits: Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community. Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization. Disclaimer This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Indikator Pine Script®
oleh Fractalyst
5
Linh Index Trend & Exhaustion SuitePurpose: One overlay to judge trend, reversal risk, overextension, and volatility squeezes on indexes (built for VNINDEX/VN30, works on any symbol & timeframe). What it shows Trend state: Bull / Bear / Transition via 20/50/200 EMAs + slope check. Overextension heatmap: Background paints when price is stretched vs the 20-EMA by ATR or % (you set the thresholds). Squeeze detection: Squeeze ON (yellow dot): Bollinger Bands (20,2) inside Keltner Channels (20,1.5). Squeeze OFF + Release: White dot; script confirms direction only when close > BB upper (up) or close < BB lower (down). 52-week context: Distance to 52-week high/low (%). Higher-TF alignment: Optional weekly trend reading shown on the label while you’re on the daily. Anchored VWAP(s): Two optional AVWAPs from dates you choose (e.g., YTD open, last big gap/earnings). Plots & labels EMAs 20/50/200 (toggle on/off). Optional BB & KC bands for diagnostics. AVWAP #1 / #2 (optional). Status label with: Trend, EMAs, Dist to 20-EMA (%, ATR), 52-week distances, HTF state. Built-in alerts (set “Once per bar close”) EMA10 ↔ EMA20 cross (early momentum shift) EMA20 ↔ EMA50 cross (trend confirmation/negation) Price ↔ EMA200 cross (long-term regime) Squeeze Release UP / DOWN (BB breakout after squeeze) Overextension Cool-off UP / DN (stretched vs 20-EMA + momentum rolling) Near 52-week High (within your % threshold) How to use (playbook) Map regime: Prefer trades when Daily = Bull and HTF (Weekly) = Bull (shown on label). Hunt expansion: Yellow → White dot and close beyond BB = fresh move. Avoid chasing stretch: If background is painted (overextended vs 20-EMA), wait for a pullback or intraday base. Locations matter: 52-week proximity + HTF Bull improves breakout quality. Anchors: Add AVWAP from YTD open or last major gap to frame support/resistance. Suggested settings Overextension: ATR = 2.0, % = 4.0 to start; tune per index volatility. Squeeze bands: BB(20,2) & KC(20,1.5) default are balanced; tighten KC (1.3) for more signals, widen (1.8) for fewer/higher quality. Timeframes: Daily for signals, Weekly for bias. Optional 65-min for entries.
Indikator Pine Script®
oleh EmmaMia1618
S/R Clouds Overview The S/R Clouds Indicator is a sophisticated TradingView tool designed to visualize support and resistance levels through dynamic cloud formations. Built on the principles of Keltner Channels, it employs a central moving average enveloped by volatility-based bands to highlight potential price reversal zones. This indicator enhances chart analysis with customizable aesthetics and practical alerts, making it suitable for traders across various strategies and timeframes. Key Features Dynamic Bands: Calculates upper and lower bands using a configurable moving average (SMA or EMA) offset by multiples of the average true range (derived from high-low ranges), capturing volatility deviations for precise S/R identification. Cloud Visualization: Renders semi-transparent clouds between primary and extended bands, providing a clear, layered view of support (lower) and resistance (upper) areas. Trend Detection: Incorporates a trend state logic based on price position relative to bands and moving average direction, aiding in bullish/bearish market assessments. Customization Options: Select from multiple color themes (e.g., Neon, Grayscale) or use custom colors for bands. Enable glow effects for enhanced visual depth and adjust opacity for chart clarity. Volatility Insights: Monitors band width to detect squeezes (low volatility) and expansions (high volatility), signaling potential breakouts. Alerts System: Triggers notifications for price crossings of bands, trend changes, and other key events to support timely decision-making. How It Works At its core, the indicator centers on a user-defined period moving average. Volatility is measured via an exponential moving average of the high-low range, multiplied by adjustable factors to form the bands. This setup creates adaptive clouds that expand/contract with market volatility, offering a more responsive alternative to static S/R lines. The result is a clean, professional overlay that integrates seamlessly with other technical tools. This high-quality indicator prioritizes usability and visual appeal, ensuring traders can focus on analysis without distraction.
Indikator Pine Script®
oleh AlchimistOfCrypto
1
Universal Valuation[public code]Universal valuation indicator for all assets. Consists of 12 different indicators which are z-scored and averaged out. > Volatility bands via Keltner Channels with a NWMA > Confluence when price > vol.bands and valuation is high/low. The confluence is marked with red arrows when above the upper third band(green when below the lower on the downside), and 50% transparency when between 2/3 band(green when below the lower 2/3 bands on the downside.) > Can be used separately of course. > Can be used as valuation of indicators, when possible. (eg. Global Liquidity index valuation) Code is a mess a bit, but parts can be extracted and a new strategy/indicator can be made. *Big probs to the creator of this indicator . Inspired by him. I want to make it possible for people to extrapolate and create their own indicators/strategies. And of course, so I can do the same.
Indikator Pine Script®
oleh boyanoff_B13
1
Multi-Session Levels + EMA Crosses + TP Calculator (GBP/USD)# Multi-Session Levels + EMA Crosses + TP Calculator ## 📋 Description **Advanced trading indicator combining multi-session analysis, EMA cross validation, and automated Take Profit calculations for Forex markets.** This comprehensive tool integrates session-based level analysis with validated EMA crossovers and intelligent TP calculations, designed specifically for serious traders who need precise entry signals with calculated exit strategies. ## 🎯 Key Features ### 📊 **Multi-Session Analysis** - **Asian Session (6PM-1AM Mexico)**: Generates key support/resistance levels - **London Session (1AM-6AM Mexico)**: Analyzes manipulation patterns - **New York Session (8AM-4PM Mexico)**: Dynamic levels with trend confirmation - **AMD Setup Detection**: Combines all sessions for high-probability setups ### 📈 **Advanced EMA System** - **4 EMAs**: 8, 13, 21, and 55 periods with visual display - **Validated Crossovers**: EMA 8 vs EMA 13 with multiple confirmations - **Smart Filtering**: Only shows signals during optimal trading hours (6AM-12PM Mexico) ### ✅ **Triple Validation System** - **MACD Confirmation**: Histogram strength + signal line position + momentum direction - **RSI Filter**: Overbought/oversold levels with moving average confirmation - **Squeeze Momentum**: Bollinger Bands vs Keltner Channels compression detection ### 💰 **Intelligent TP Calculator** - **ADR-Based Targets**: Uses Average Daily Range for realistic profit expectations - **ATR Multipliers**: Conservative (1.5x), Aggressive (2.5x), Very Aggressive (3.5x) - **Session-Aware**: Considers already-traveled distance in NY session - **Real-Time Table**: Live pip calculations for all TP levels - **Visual Levels**: Automatic TP lines drawn on chart with color coding ### 🚨 **Smart Alert System** - **Validated Signals Only**: Alerts trigger only when ALL confirmations align - **TP Integration**: Alerts include suggested take profit levels - **Non-Validated Tracking**: Shows basic crosses that don't meet full criteria ## 📐 **Technical Calculations** ### **ADR (Average Daily Range)** - 20-period average of daily high-low ranges - Converted to pips for easy interpretation - Used for percentage-based TP targets (50%, 75%, 100% of ADR) ### **ATR (Average True Range)** - 14-period ATR from H1 timeframe (configurable) - Accounts for gaps and volatility - Base for multiplier-based TP levels ### **Session Tracking** - Real-time monitoring of NY session range - Calculates remaining potential movement - Optimizes TP placement based on session progress ## 🎨 **Visual Elements** ### **Chart Levels** - **Orange Lines**: Asian and London session levels - **White/Green/Red Lines**: NY session levels (color changes with trend direction) - **TP Lines**: Color-coded take profit levels with different styles ### **EMA Display** - **Blue**: EMA 8 (fastest) - **Green**: EMA 13 (signal line) - **Yellow**: EMA 21 (trend filter) - **Red**: EMA 55 (major trend) ### **Signal Shapes** - **Bright Triangles**: Fully validated signals - **Faded Triangles**: Non-validated basic crosses - **Size Variation**: Signal strength indication ## 📊 **Information Table** Real-time display showing: - **TP Levels**: All calculated take profit targets in pips - **Session Data**: NY range already traveled vs average - **Volatility Metrics**: Current ATR and ADR values - **Clean Design**: Easy-to-read format with color coding ## ⚙️ **Customization Options** ### **Session Times** - Fully configurable session times - Mexico City timezone support - Enable/disable individual session analysis ### **Validation Controls** - Toggle MACD, RSI, Squeeze validation independently - Adjust RSI overbought/oversold levels - Customize MACD and Squeeze parameters ### **Display Options** - Show/hide EMAs, crosses, TP levels, table - Customize TP calculation periods (ADR, ATR) - Choose ATR timeframe for calculations ## 🎯 **Ideal For** - **Forex Day Traders**: Especially USD pairs during NY session - **Session-Based Strategies**: Traders who respect market sessions - **Risk Management Focus**: Those who need calculated exit strategies - **Multi-Timeframe Analysis**: Traders using H1-H4 charts ## 📈 **Best Practices** 1. **Use during high-volume sessions** (London-NY overlap) 2. **Wait for full validation** before entering trades 3. **Consider session context** when setting TPs 4. **Combine with proper risk management** (1-2% per trade) 5. **Backtest thoroughly** before live trading ## ⚠️ **Important Notes** - **Signals work best** during trending market conditions - **AMD setups** provide highest probability entries - **TP levels are suggestions** - adjust based on market context - **Always use stop losses** (not included in this indicator) - **Designed for Forex markets** - may need adjustment for other instruments --- *This indicator combines proven technical analysis concepts with modern session-based trading approaches, providing both entry timing and exit planning in one comprehensive tool.*
Indikator Pine Script®
oleh ecasvar
Volatility Squeeze – Blue Zone (classic) Volatility Squeeze – Blue Zone Highlights periods when volatility contracts by showing a blue band between the Bollinger Bands (BB) whenever they fall inside the Keltner Channel (KC). Blue zone = squeeze: BB upper & lower are inside KC – market coiling. Automatic breakout alert: optional alert fires on the first bar after the squeeze releases. Fully adjustable: BB/KC length, BB σ, KC ATR multiplier, zone colour & opacity, border on/off. Clean overlay: zone hugs price bar-by-bar and disappears only when the squeeze ends, so past squeezes remain visible for context. Use it to spot low-volatility setups, then watch for momentum or volume confirmations when the squeeze breaks.
Indikator Pine Script®
oleh awax1978
Ultimate ATR Extreme DetectorUltimate ATR Extreme Detector​ Professional Volatility Analysis Tool for Strategic Trading ​Discover Market Turning Points with Precision​ Key Features ​Smart Extremum Detection: Identifies when ATR reaches its highest or lowest point in your specified lookback period ​Quad Visual Alert System: ▲ Green bottom triangles for low volatility signals ▼ Red top triangles for high volatility signals Background color highlighting for instant state recognition Status panel showing current volatility extremes ​Dual Alert Modes: TradingView native alerts ("ATR Low/High Signal") Visual chart alerts with period details (e.g., "Alert: ATR Low (50 bars)") ​4 Calculation Methods: RMA (Wilder's), SMA, EMA, and WMA ​Fully Customizable: Adjustable ATR period (default: 14) Variable lookback window (default: 50) Toggle features on/off via intuitive input settings ​How It Works​ The indicator scans volatility extremes using proprietary logic: Calculates True Range using selected method (RMA/SMA/EMA/WMA) Compares current ATR value against historical data Flags critical moments when: Volatility contracts to N-period lows (prepare for breakouts) Volatility expands to N-period highs (watch for trend exhaustion) ​Strategic Applications​ markdown 复制 | SIGNAL | MARKET CONDITION | TRADING IMPLICATION | |------------------|-----------------------|--------------------------------| | Low Volatility | Contraction/Consolidation | Anticipate breakout moves | | High Volatility | Expansion/Climax | Prepare for reversals or pauses | ​Position Sizing: Use ATR values to determine optimal stop distances ​Entry Timing: Combine with price action at key support/resistance ​Risk Management: Adjust stops dynamically based on volatility regime ​Optimization Guide​ Day Trading: Short lookback (20-30 periods) Swing Trading: Medium lookback (50-100 periods) Position Trading: Long lookback (100-200 periods) Volatility Analysis: Compare multiple timeframes simultaneously ​Professional Setup Recommendations​ Combine with: ​Breakout Confirmation: Volume spikes, chart patterns ​Reversal Signals: RSI divergence, candlestick reversals ​Volatility Filters: Bollinger Band contraction, Keltner Channel breakout ​Compatibility: Works flawlessly across FX, stocks, crypto, and commodities on all timeframes. ​Why Traders Choose This Indicator​ "Transforms complex volatility analysis into clear, actionable visual cues – the essential tool for breakout traders and risk managers alike." ​Install Now to: Spot consolidation before big moves Identify exhaustion at trend extremes Automate volatility-based position sizing Receive instant alerts at critical volatility turns Master market rhythms with professional-grade volatility intelligence!
Indikator Pine Script®
oleh Dooogiig
Pivot Squeeze IndicatorThe Pivot Squeeze Indicator is an oscillator that identifies when markets are "squeezed" between recent pivot highs and lows, then signals when they're ready to make their next big move. How it Works The indicator calculates the percentage distance between the current price and the most recent pivot high vs. pivot low. When this distance gets compressed (small), the market is "squeezed" and building energy. When it expands rapidly, you get your breakout signal. The indicator adapts to current market volatility using four different modes: - ATR-Based - Bollinger Bands - Keltner Channels - Fixed % What to Look For 🟠 Orange Background = Squeeze Zone Market is compressed between recent pivots Low volatility, building pressure 🟢 Green Breakout = Bullish Signal Price breaking out above recent highs Momentum shifting upward Time to look for long opportunities 🔴 Red Breakout = Bearish Signal Price breaking down below recent lows Momentum shifting downward Time to look for short opportunities Using Histogram Colors: Green bars = Bullish territory (closer to recent highs) Red bars = Bearish territory (closer to recent lows) Orange bars = Squeeze conditions (compressed between pivots) Using MA Line: When Histogram bars cross below or above MA Line in opposite direction, it might be good time to exit. Default Settings: ATR-based thresholds with 14-period lookback - works great out of the box, but feel free to experiment with the different threshold modes to find what works best for your trading style! Recommended to use with other indicators to confirm signals
Indikator Pine Script®
oleh ky_yule1010
RSI-Adaptive T3 + Squeeze Momentum Strategy✅ Strategy Guide: RSI-Adaptive T3 + Squeeze Momentum Strategy 📌 Overview The RSI-Adaptive T3 + Squeeze Momentum Strategy is a dynamic trend-following strategy based on an RSI-responsive T3 moving average and Squeeze Momentum detection . It adapts in real-time to market volatility to enhance entry precision and optimize risk. ⚠️ This strategy is provided for educational and research purposes only. Past performance does not guarantee future results. 🎯 Strategy Objectives The main objective of this strategy is to catch the early phase of a trend and generate consistent entry signals. Designed to be intuitive and accessible for traders from beginner to advanced levels. ✨ Key Features RSI-Responsive T3: T3 length dynamically adjusts according to RSI values for adaptive trend detection Squeeze Momentum: Combines Bollinger Bands and Keltner Channels to identify trend buildup phases Visual Triggers: Entry signals are generated from T3 crossovers and momentum strength after squeeze release 📊 Trading Rules Long Entry: When T3 crosses upward, momentum is positive, and the squeeze has just been released. Short Entry: When T3 crosses downward, momentum is negative, and the squeeze has just been released. Exit (Reversal): When the opposite condition to the entry is triggered, the position is reversed. 💰 Risk Management Parameters Pair & Timeframe: BTC/USD (30-minute chart) Capital (simulated): $30,00 Order size: `$100` per trade (realistic, low-risk sizing) Commission: 0.02% Slippage: 2 pips Risk per Trade: 5% Number of Trades (backtest period): 181 📊 Performance Overview Symbol: BTC/USD Timeframe: 30-minute chart Date Range: January 1, 2024 – July 3, 2025 Win Rate: 47.8% Profit Factor: 2.01 Net Profit: 173.16 (units not specified) Max Drawdown: 5.77% or 24.91 (0.79%) ⚙️ Indicator Parameters Indicator Name: RSI-Adaptive T3 + Squeeze Momentum RSI Length: 14 T3 Min Length: 5 T3 Max Length: 50 T3 Volume Factor: 0.7 BB Length: 27 (Multiplier: 2.0) KC Length: 20 (Multiplier: 1.5, TrueRange enabled) 🖼 Visual Support T3 slope direction, squeeze status, and momentum bars are visually plotted on the chart, providing high clarity for quick trend analysis and execution. 🔧 Strategy Improvements & Uniqueness Inspired by the RSI Adaptive T3 by ChartPrime and Squeeze Momentum Indicator by LazyBear , this strategy fuses both into a hybrid trend-reversal and momentum breakout detection system . Compared to traditional trend-following methods, it excels at capturing early trend signals with greater sensitivity . ✅ Summary The RSI-Adaptive T3 + Squeeze Momentum Strategy combines momentum detection with volatility-responsive risk management. With a strong balance between visual clarity and practicality, it serves as a powerful tool for traders seeking high repeatability. ⚠️ This strategy is based on historical data and does not guarantee future profits. Always use appropriate risk management when applying it.
Strategi Pine Script®
oleh PakunFX