始めましょう

"donchian"に関するスクリプトを検索

SST Table NewData Table for Update GTT Orders in Stocks.... Updated GTT Order details for SST , also added Relative Strength of Stock compared with NIFTY Index for 55 Days (3 Months) period. RS line plotted on chart… taken reference of close price of day candle… just a bigger picture of actual RS indicator plotted on chart itself... RS line in red color means below zero line (negative) and green color means above zero line (positive). Added code for checking RS value on chart for any previous day.
Pine Script® インジケーター
atulkharadiの投稿
10
Trade Helper [Trading Nerd]Position Size Calculator / Lot Size Calculator Disclaimer: I do my best to avoid wrong calculations and bugs. I provide this indicator without warranties of any kind. You bear all risks associated with the use of this indicator. Inputs: Market: Adds a name tag to the Table to keep track of the trades. Entry Price: The entry Price of the Position. Entry Time: The entry Time/Candle of the Position. If Stop Loss Type is 'ATR' or 'HH/LL' the Value for this is calculated by this Candle. Stop Loss Type: Changes the Stop Loss Type. Direction: Define if the trade direction is 'Long' or 'Short'. Has no effect on Stop Loss Type 'Custom'. For this you can just set the Stop Loss below/above the Entry Price . ATR Multiplier: Multiplies the ATR Value by this number. Has only an effect on Stop Loss Type 'ATR'. HH/LL Lookback Length: Lookback length for determine Highest High/Lowest Low value. Has only an effect on Stop Loss Type 'HH/LL'. Custom SL Price: The Stop Loss Price if the Stop Loss Type is set to 'Custom'. Risk Reward Ratio: The Risk is multiplied by this number to determine the Take Profit Price. Balance: Balance Amount and Currency Contract Size: The Position Size is divided by this number. E.G. in Forex one Lot is 100.000 Contracts. Change this Value depending on your Broker and Market. Risk in %: Percent that is risked of the Balance for one Trade.
Pine Script® インジケーター
Trading_Nerdの投稿
3
HiLo Extension This Strategy is finding high and low breaks of the day and enter into the trader based on RSI value and time value 1) This strategy is created for Indian Index like Nifty, Bank Nifty and so... 2) Trades are initiate only after 10:15 AM and before 3:10PM 3) High and Low of the day break will be check during the above time frame 4) RSI value will be check (RSI 50) 5) and trade will be initiate 6) Stop loss set as vwma 20... Note: This Script will work fine in Index future chart not index spot chart... This is just my idea only... Please back test yourselve, before using it.. Your comments are welcome!
Pine Script® ストラテジー
Saravanan_Ragavanの投稿
2
2HLA very simple, almost naive strategy, in which you buy on the lowest of the two previous candles and sell at the highest of the two previous candles. You can configure these highest and lowest lenght, in some assets two is too small of a number to make profit. You can also configure to exit the position after X, and I found that 7 (which is a week of working days) is a good number for that. This is strategy is intended to be used as a swing trade. Your capital needs to be high enough so that it can pay the operaitonal costs, and reach it's target with a reasonable profit. Since this is a volatility based strategy, assets that are more liquid won't work properly.
Pine Script® ストラテジー
EduardoMattjeの投稿
7
[STRATEGY][UL]Price Divergence Strategy v1.0Created by Request: This is a trend trading strategy that uses Price Divergence detection signals that are confirmed by the "Murrey's Math Oscillator" (Donchanin Channel based). Strategy Code Based on: Price Divergence Detector V2 by RicardoSantos UCS_Murrey's Math Oscillator by Ucsgears Strategy Risk Management Based on: Strategy Code Example by JayRogers Information on Divergence Trading: - www.babypips.com *** USE AT YOUR OWN RISK ***
Pine Script® ストラテジー
JustUncleLの投稿
40
LP Sweep / Reclaim & Breakout Grading: Long-onlySignals 1) LP Sweep & Reclaim (mean-reversion entry) Compute LP bounds from prior-bar window extremes: lpLL_prev = lowest low of the last N bars (offset 1). lpHH_prev = highest high of the last N bars (offset 1). Sweep long trigger: current low dips below lpLL_prev and closes back above it. Real-time quality grading (A/B/C) for sweep: Trend filter & slope via EMA(88). BOS bonus: close > last confirmed swing high. Body size vs ATR, location above a long EMA, headroom to swing high (penalty if too close), and multi-sweep count bonus. Sum → score → grade A/B/C; A or B required for sweep entry. 2) Trend Breakout (momentum entry) Core trigger: close > previous Donchian high (length boLen) + ATR buffer. Optional filter: close must be above the default EMA. Breakout grading (A/B/C) in real time combining: Trend up (price > EMA and EMA rising), Body/ATR, Gap above breakout level (in ATR), Volume vs MA, Upper-wick penalty, Position-in-Score: headroom to last swing high (penalty if too near) + EMA slope bonus. Sum → score → A or B required if grading enabled.
Pine Script® ストラテジー
Shihyuuuの投稿
Turtle Trading System (Not Financial Advise)I am testing this script, not financial advise, but this is how it works, i hope; How the Script Works The script is a TradingView strategy that backtests and automates the Turtle rules. It uses two systems, a short-term S1 (20-day) and a long-term S2 (55-day), which you can select to test. The strategy follows these core principles: Trend Identification: The script uses Donchian Channels to identify breakouts. A new trend is signaled when the price breaks above the highest high or below the lowest low of a specified period. Position Sizing: The script automatically calculates the size of each trade unit to ensure you risk a consistent percentage of your capital on every trade. This is based on the Average True Range (ATR), which measures volatility. Pyramiding: When a trade moves in a profitable direction, the script will add more units at specific intervals to increase your position and ride the trend. This is how the Turtles made their big profits. Win/Loss Condition: A crucial rule is implemented for System 1 (S1): you only enter a trade if the previous S1 trade was a loss. If the last S1 trade was a win, you must wait for a signal from System 2 (S2). How to Enter and Exit a Long Position The script will automatically enter a long position when the price breaks above the highest high of the last 20 days (S1) or 55 days (S2), depending on which system you've selected. Stop-Loss (SL): Your stop-loss is automatically placed at a distance of 2 * N (ATR) below the entry price. . This is a crucial risk management rule that limits your potential loss. The stop-loss is also plotted visually on the chart. Exit Long: The script will close the long position automatically under two conditions: Reversal: The price drops and closes below the lowest low of the last 10 days (S1) or 20 days (S2). Stop-Loss Hit: The price hits your initial stop-loss, limiting your loss. How to Enter and Exit a Short Position Enter Short: The script will automatically enter a short position when the price breaks below the lowest low of the last 20 days (S1) or 55 days (S2). Stop-Loss (SL): Your stop-loss is automatically placed at a distance of 2 * N (ATR) above the entry price. . This prevents large losses if the price moves against you. Exit Short: The script will close the short position automatically under two conditions: Reversal: The price rallies and closes above the highest high of the last 10 days (S1) or 20 days (S2). Stop-Loss Hit: The price hits your initial stop-loss, limiting your loss.
Pine Script® ストラテジー
hushedDiamond97573の投稿
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
Pine Script® インジケーター
Mr_Rakunの投稿
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.
Pine Script® インジケーター
Fractalystの投稿
5
Breakout TrendTiltFolio Breakout Trend indicator The Breakout Trend indicator is designed to help traders clearly visualize trend direction by combining two complementary techniques: moving averages and Donchian-style breakout logic. Rather than relying on just one type of signal, this indicator merges short-term and long-term moving averages with breakout levels based on recent highs and lows. The moving averages define the broader trend regime, while the breakout logic pinpoints moments when price confirms directional momentum. This layered approach filters out many false signals while still capturing high-conviction moves. Yes, these are lagging indicators by design — and that’s the point. Instead of predicting every wiggle, the Breakout Trend waits for confirmation, offering higher signal quality and fewer whipsaws. When the price breaks above a recent high and sits above the long-term moving average, the trend is more likely to persist. That’s when this indicator shines. While it performs best on higher timeframes (daily/weekly), it's also adaptable to shorter timeframes for intraday traders who value clean, systematic trend signals. For early signal detection, we recommend pairing this with TiltFolio’s Buying/Selling Proxy, which anticipates pressure buildups—albeit with more noise. It's easy to read and built for real-world trading discipline.
Pine Script® インジケーター
TiltFolioの投稿
6
True Range eXpansion🕯️ TRX — True Range eXpansion Clean Candle Bodies · Volatility Bands · Adaptive Range Envelope System Not your grandfather’s candles. Not your brokerage’s bands. ---------------------------------------------------- TRX begins with a simple concept: visualize the true range of every candle, without the noise of flickering wicks. From there, it grows into a fully adaptive price visualization framework. What started as a candle-only visualizer evolved into a modular, user-controlled price engine. From wickless candle clarity to dynamic volatility envelopes, TRX adapts to you. There are plenty of band and channel indicators out there — Bollinger, Keltner, Donchian, Envelope, the whole crew. But none of them are built on the true candle range, adaptive ATR shaping, and full user control like TRX. This isn’t just another indicator — it’s a new framework. Most bands and channels are based on close price and statistical deviation — useful, but limited. TRX uses the full true range of each candle as its foundation, then applies customizable smoothing and directional ATR scaling to form a dynamic, volatility-reactive envelope. The result? Bands that breathe with the market — not lag behind it. ---------------------------------------------------- 🔧 Core Features: 🕯️ True Range Candles — Each candle is plotted from low to high, body-only, colored by open/close. 📈 Adjustable High/Low Moving Averages — Select your smoothing style: SMA, EMA, WMA, RMA, or HMA. 🌬️ ATR-Based Expansion — Bands dynamically breathe based on market volatility. 🔀 Per-Band Multipliers — Fine-tune expansion individually for the upper and lower bands. ⚖️ Basis Line — Optional centerline between bands for structure tracking and equilibrium zones. 🎛️ Full Visual Control — Width, transparency, color, on/off toggles for each element. ---------------------------------------------------- 🧠 Default Use Case: With the included default settings, TRX behaves like an evolved Bollinger Band system — based on True Range candle structure, not just close price and standard deviation. ---------------------------------------------------- 🔄 How to Zero Out the Bands (for Minimalist Use): Want just candles? A clean MA? Single band? You got it. ➤ Use TRX like a clean moving average: • Set ATR Multiplier to 0 • Set both Band ATR Adjustments to 0 • Leave the Basis Line ON or OFF — your call ➤ Show only candles (no bands at all): • Turn off "Show High/Low MAs" • Turn off Basis Line ➤ Single-line ceiling or floor tracking: • Set one band’s Transparency to 100 • Use the remaining band as a price envelope or support/resistance guide ---------------------------------------------------- 🧬 Notes: TRX can be made: • Spiky or silky (via smoothing & ATR) • Wide or tight (via multipliers) • Subtle or aggressive (via color/transparency) • Clean as a compass or dirty as a chaos meter Built by accident. Tuned with intention. Released to the world as one of the most adaptable and expressive visual overlays ever made. Created by Sherlock_MacGyver
Pine Script® インジケーター
Sherlock_MacGyverの投稿
My S.T.A.C.K.📊 My S.T.A.C.K. (Simplified TA Combined Kit) All your favorite technical tools in one clean, customizable overlay. My S.T.A.C.K. is a power-packed indicator designed to streamline your chart by combining the most commonly used technical analysis tools into a single, space-saving script. Whether you're a trend trader, swing trader, or just looking to declutter your view — this kit gives you everything you need, nothing you don’t. 🔧 Features: 5 Customizable Moving Averages: Choose your type (SMA, EMA, WMA, etc.) and periods to match your strategy. Bollinger Bands: Visualize volatility and overbought/oversold zones with precision. Donchian Channels: Spot breakouts and trend reversals based on high/low ranges. ATR Bands: Adaptive support/resistance zones based on Average True Range. Clean Visualization: Toggle each element on or off, adjust colors, and focus only on what matters. ✅ Ideal For: - Traders who want multiple indicators in one place - Reducing indicator clutter on TradingView - Quick visual analysis without switching scripts
Pine Script® インジケーター
saplumbagaの投稿
[blackcat] L3 Ichimoku FusionCOMPREHENSIVE ANALYSIS OF THE L3 ICHIMOKU FUSION INDICATOR 🌐 Overview: The L3 Ichimoku Fusion is a sophisticated multi-layered technical analysis tool integrating classic Japanese market forecasting techniques with enhanced dynamic elements designed specifically for identifying potential turning points in financial instruments' pricing action. Key Purpose: To provide traders with an intuitive yet powerful framework combining established ichimoku principles while incorporating additional validation checkpoints derived from cross-timeframe convergence studies. THEORETICAL FOUNDATION EXPLAINED 🎓 Conceptual Background: : • Conversion & Base Lines tracking intermediate term averages • Lagging Span providing delayed feedback mechanism • Lead Spans projecting future equilibrium states : • Adaptive parameter scaling options • Automated labeling system for critical junctures • Real-time alert infrastructure enabling immediate response capability PARAMETER CONFIGURATION GUIDE ⚙️ Input Parameters Explained In Detail: Regional Setting Selection:** → Oriental Configuration: Standardized approach emphasizing slower oscillation cycles → Occidental Variation: Optimized settings reducing lag characteristics typical of original methodology Multiplier Adjustment Functionality:** ↔ Allows fine-graining oscillator responsiveness without altering core relationship dynamics ↕ Enables adaptation to various instrument volatility profiles efficiently Displacement Value Control:** ↓ Controls lead/lag offset positioning relative to current prices ↑ Provides flexibility in adjusting visual representation alignment preferences DYNAMIC CALCULATION PROCESSES 💻 Algorithmic Foundation: : Utilizes highest/lowest extremes over specified lookback windows Produces more responsive conversions compared to simple MAs : → Confirms directional bias across multiple independent criteria ← Ensures higher probability outcomes reduce random noise influence : ♾ Creates persistent annotations documenting significant events 🔄 Handles complex state transitions maintaining historical record integrity VISUALIZATION COMPONENTS OVERVIEW 🎨 Display Architecture Details: : → Solid colored trendlines representing conversion/base relationships ↑ Fill effect overlay differentiating expansion/compression phases ↔ Offset spans positioned according to calculated displacement values : → Green shading indicates positive configuration scenarios ↘ Red filling highlights negative arrangement situations ⟳ Orange transition areas mark transitional periods requiring caution : ✔️ LE: Long Entry opportunity confirmed ❌ SE: Short Setup validated ☑ XL/XS: Position closure triggers active ✓ RL/RS: Potential re-entry chances emerging STRATEGIC APPLICATION FRAMEWORK 📋 Practical Deployment Guidelines: Initial Integration Phase: Select appropriate timeframe matching trading horizon preference Configure input parameters aligning with target asset behavior traits Test thoroughly under simulated conditions prior to live usage Active Monitoring Procedures: • Regular observation of cloud formation evolution • Tracking label placements against actual price movements • Noting pattern development leading up to signaled entry/exit moments Decision Making Process Flowchart: → Identify clear breakout/crossover events exceeding confirmation thresholds ← Evaluate contextual factors supporting/rejecting indicated direction ↑ Execute trades only after achieving required number of confirming inputs PERFORMANCE OPTIMIZATION TECHNIQUES 🚀 Refinement Strategies: Calibration Optimization Approach: → Start testing with default suggested configurations ↓ Gradually adjust individual components observing outcome changes ↑ Document findings systematically building personalized version profile Context Adaptability Methods: ➕ Add supplementary indicators enhancing overall reliability ➖ Remove unnecessary complexity layers if causing confusion ✨ Incorporate custom rules adapting to specific security behaviors Efficiency Improvement Tactics: 🔧 Streamline redundant processing routines where possible ♻️ Leverage shared data streams whenever feasible ⚡ Optimize refresh frequencies balancing update speed vs computational load RISK MITIGATION PROTOCOLS 🛡️ Safety Measures Implementation Guide: Position Sizing Principles: ∅ Never exceed preset maximum exposure limits defined by risk tolerance ± Scale positions proportionally per account size/market capitalization × Include slippage allowances within planning stages accounting for liquidity variations Validation Requirements Hierarchy: ☐ Verify signals meet minimum number of concurrent validations ⛔ Ignore isolated occurrences lacking adequate evidence backing ▶ Look for convergent evidence strengthening conviction level Emergency Response Planning: ↩ Establish predefined exit strategies including trailing stops mechanisms 🌀 Plan worst-case scenario responses ahead avoiding panic reactions ⇄ Maintain contingency plans addressing unexpected adverse developments USER EXPERIENCE ENHANCEMENT FEATURES 🌟 Additional Utility Functions: Alert System Infrastructure: → Automatic notifications delivered directly to user devices ↑ Message content customized explaining triggered condition specifics ↔ Timing optimization ensuring minimal missed opportunities due to latency issues Historical Review Capability: → Ability to analyze past performance retrospectively ↓ Assess effectiveness across varying market regimes objectively ↗ Generate statistics measuring success/failure rates quantitatively Community Collaboration Support: ↪ Share personal optimizations benefiting wider trader community ↔ Exchange experiences improving collective understanding base ✍️ Provide constructive feedback aiding ongoing refinement process CONCLUSION AND NEXT STEPS This comprehensive guide serves as your roadmap toward mastering the capabilities offered by the L3 Ichimoku Fusion indicator effectively. Success relies heavily on disciplined application combined with continuous learning and adjustment processes throughout implementation journey. Wishing you prosperous trading endeavors! 👋💰
Pine Script® インジケーター
blackcat1402の投稿