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Indicatori e strategie

BUY Sell Signal (Kewme)//@version=6 indicator("EMA Cross RR Box (1:4 TP Green / SL Red)", overlay=true, max_lines_count=500, max_boxes_count=500) // ===== INPUTS ===== emaFastLen = input.int(9, "Fast EMA") emaSlowLen = input.int(15, "Slow EMA") atrLen = input.int(14, "ATR Length") slMult = input.float(1.0, "SL ATR Multiplier") rr = input.float(4.0, "Risk Reward (1:4)") // 🔥 1:4 RR // ===== EMA ===== emaFast = ta.ema(close, emaFastLen) emaSlow = ta.ema(close, emaSlowLen) plot(emaFast, color=color.green, title="EMA Fast") plot(emaSlow, color=color.red, title="EMA Slow") // ===== ATR ===== atr = ta.atr(atrLen) // ===== EMA CROSS ===== buySignal = ta.crossover(emaFast, emaSlow) sellSignal = ta.crossunder(emaFast, emaSlow) // ===== VARIABLES ===== var box tpBox = na var box slBox = na var line tpLine = na var line slLine = na // ===== BUY ===== if buySignal if not na(tpBox) box.delete(tpBox) if not na(slBox) box.delete(slBox) if not na(tpLine) line.delete(tpLine) if not na(slLine) line.delete(slLine) entry = close sl = entry - atr * slMult tp = entry + atr * slMult * rr // ✅ 1:4 TP // TP ZONE (GREEN) tpBox := box.new( left=bar_index, top=tp, right=bar_index + 20, bottom=entry, bgcolor=color.new(color.green, 80), border_color=color.green ) // SL ZONE (RED) slBox := box.new( left=bar_index, top=entry, right=bar_index + 20, bottom=sl, bgcolor=color.new(color.red, 80), border_color=color.red ) tpLine := line.new(bar_index, tp, bar_index + 20, tp, color=color.green, width=2) slLine := line.new(bar_index, sl, bar_index + 20, sl, color=color.red, width=2) label.new(bar_index, low, "BUY", style=label.style_label_up, color=color.green, textcolor=color.white) // ===== SELL ===== if sellSignal if not na(tpBox) box.delete(tpBox) if not na(slBox) box.delete(slBox) if not na(tpLine) line.delete(tpLine) if not na(slLine) line.delete(slLine) entry = close sl = entry + atr * slMult tp = entry - atr * slMult * rr // ✅ 1:4 TP // TP ZONE (GREEN) tpBox := box.new( left=bar_index, top=entry, right=bar_index + 20, bottom=tp, bgcolor=color.new(color.green, 80), border_color=color.green ) // SL ZONE (RED) slBox := box.new( left=bar_index, top=sl, right=bar_index + 20, bottom=entry, bgcolor=color.new(color.red, 80), border_color=color.red ) tpLine := line.new(bar_index, tp, bar_index + 20, tp, color=color.green, width=2) slLine := line.new(bar_index, sl, bar_index + 20, sl, color=color.red, width=2) label.new(bar_index, high, "SELL", style=label.style_label_down, color=color.red, textcolor=color.white)
Indicatore Pine Script®
di arup19901
TWR of Bill WilliamsThis indicator was taken from the book “Trading Chaos Pt 1” by Bill Williams. TWR contains 3 Moving Averages Ripple - MA with 5 bars length Wave - MA with 13 bars length Tide - MA with 34 bars length According to Bill Williams, you should take only a long position if the Ripple(5 bars length) is higher than Wave(13) and Tide(34). Also, you should take only a short position, if the Ripple (the fastest MA) is lower than Wave MA and Tide MA(slowest MA). This indicator is also used if you want to fill in the Profitunity Trading Partner table.
Indicatore Pine Script®
di vildstein
ORB | Feng FuturesThe ORB | Feng Futures indicator automatically detects the Opening Range Breakout (ORB) for each trading session, plotting the High, Low, and Midline in real time. This tool is built for futures traders who rely on ORB structure to confirm trends, identify breakout zones, and recognize reversal areas early in the session. Features: • Auto-calculated ORB High, Low, and Midline • Multi-timezone session support (NY, Chicago, London, Tokyo, etc.) • Customize ORB time range and time window for display • Real-time updating lines that freeze at session close • Optional labels with customizable size, color, and offset • Save and view multiple previous ORB sessions • Full color customization for all levels • Automatically hides on higher timeframes (Daily+) to reduce clutter • Works on ES, NQ, and all intraday futures charts • Works on stocks, crypto, forex, and other tradeable assets where ORB is applicable Disclaimer: This indicator is for educational purposes only and does not constitute financial advice. Trading futures involves significant risk and may not be suitable for all investors. Always do your own research and use proper risk management.
Indicatore Pine Script®
di ericfengg
Risk Size Calculator - Indices/Metals This indicator is a universal position sizing tool that automatically calculates how many contracts or units to trade based on your defined dollar risk and stop size, while intelligently adapting to the asset you’re trading. Key Features Works on any asset: indices, metals, futures, stocks, crypto, etc. Auto stop interpretation: Metals (GC, MGC, SI, SIL, etc.) → Ticks Everything else → Points Single stop input (no switching between points/ticks manually) Auto preset stops per asset class (optional) Uses TradingView’s native contract data (pointvalue, mintick) for accuracy Clean, readable top-right panel with: Risk ($) Stop (Points or Ticks, auto-labeled) Contracts / Units Actual Risk ($) Optional manual $-per-point override for edge cases or custom instruments Designed for fast execution with zero mental math and minimal chart disruption.
Indicatore Pine Script®
di marcusblairbrand
Live PDH/PDL Dashboard - Exact Time Fix saleem shaikh//@version=5 indicator("Live PDH/PDL Dashboard - Exact Time Fix", overlay=true) // --- 1. Stocks ki List --- s1 = "NSE:RELIANCE", s2 = "NSE:HDFCBANK", s3 = "NSE:ICICIBANK" s4 = "NSE:INFY", s5 = "NSE:TCS", s6 = "NSE:SBIN" s7 = "NSE:BHARTIARTL", s8 = "NSE:AXISBANK", s9 = "NSE:ITC", s10 = "NSE:KOTAKBANK" // --- 2. Function: Har stock ke andar jaakar breakout time check karna --- get_data(ticker) => // Kal ka High/Low (Daily timeframe se) pdh_val = request.security(ticker, "D", high , lookahead=barmerge.lookahead_on) pdl_val = request.security(ticker, "D", low , lookahead=barmerge.lookahead_on) // Aaj ka breakout check karna (Current timeframe par) curr_close = close is_pdh_break = curr_close > pdh_val is_pdl_break = curr_close < pdl_val // Breakout kab hua uska time pakadna (ta.valuewhen use karke) var float break_t = na if (is_pdh_break or is_pdl_break) and na(break_t) // Sirf pehla breakout time capture karega break_t := time // --- 3. Sabhi stocks ka Data fetch karna --- = request.security(s1, timeframe.period, get_data(s1)) = request.security(s2, timeframe.period, get_data(s2)) = request.security(s3, timeframe.period, get_data(s3)) = request.security(s4, timeframe.period, get_data(s4)) = request.security(s5, timeframe.period, get_data(s5)) = request.security(s6, timeframe.period, get_data(s6)) = request.security(s7, timeframe.period, get_data(s7)) = request.security(s8, timeframe.period, get_data(s8)) = request.security(s9, timeframe.period, get_data(s9)) = request.security(s10, timeframe.period, get_data(s10)) // --- 4. Table UI Setup --- var tbl = table.new(position.top_right, 3, 11, bgcolor=color.rgb(33, 37, 41), border_width=1, border_color=color.gray) // Row update karne ka logic updateRow(row, name, price, hi, lo, breakT) => table.cell(tbl, 0, row, name, text_color=color.white, text_size=size.small) string timeDisplay = na(breakT) ? "-" : str.format("{0,time,HH:mm}", breakT) if price > hi table.cell(tbl, 1, row, "PDH BREAK", bgcolor=color.new(color.green, 20), text_color=color.white, text_size=size.small) table.cell(tbl, 2, row, timeDisplay, text_color=color.white, text_size=size.small) else if price < lo table.cell(tbl, 1, row, "PDL BREAK", bgcolor=color.new(color.red, 20), text_color=color.white, text_size=size.small) table.cell(tbl, 2, row, timeDisplay, text_color=color.white, text_size=size.small) else table.cell(tbl, 1, row, "Normal", text_color=color.gray, text_size=size.small) table.cell(tbl, 2, row, "-", text_color=color.gray, text_size=size.small) // --- 5. Table Draw Karna --- if barstate.islast table.cell(tbl, 0, 0, "Stock", text_color=color.white, bgcolor=color.gray) table.cell(tbl, 1, 0, "Signal", text_color=color.white, bgcolor=color.gray) table.cell(tbl, 2, 0, "Time", text_color=color.white, bgcolor=color.gray) updateRow(1, "RELIANCE", c1, h1, l1, t1) updateRow(2, "HDFC BANK", c2, h2, l2, t2) updateRow(3, "ICICI BANK", c3, h3, l3, t3) updateRow(4, "INFY", c4, h4, l4, t4) updateRow(5, "TCS", c5, h5, l5, t5) updateRow(6, "SBI", c6, h6, l6, t6) updateRow(7, "BHARTI", c7, h7, l7, t7) updateRow(8, "AXIS", c8, h8, l8, t8) updateRow(9, "ITC", c9, h9, l9, t9) updateRow(10, "KOTAK", c10, h10, l10, t10)
Indicatore Pine Script®
di hekagamers
Global Sovereign Spread MonitorIn the summer of 2011, the yield on Italian government bonds rose dramatically while German Bund yields fell to historic lows. This divergence, measured as the BTP-Bund spread, reached nearly 550 basis points in November of that year, signaling what would become the most severe test of the European monetary union since its inception. Portfolio managers who monitored this spread had days, sometimes weeks, of advance warning before equity markets crashed. Those who ignored it suffered significant losses. The Global Sovereign Spread Monitor is built on a simple but powerful observation that has been validated repeatedly in academic literature: sovereign bond spreads contain forward-looking information about systemic risk that is not fully reflected in equity prices (Longstaff et al., 2011). When investors demand higher yields to hold peripheral government debt relative to safe-haven bonds, they are expressing a view about credit risk, liquidity conditions, and the probability of systemic stress. This information, when properly analyzed, provides actionable signals for traders across all asset classes. The Science of Sovereign Spreads The academic study of government bond yield differentials began in earnest following the creation of the European Monetary Union. Codogno, Favero and Missale (2003) published what remains one of the foundational papers in this field, examining why yields on government bonds within a currency union should differ at all. Their analysis, published in Economic Policy, identified two primary drivers: credit risk and liquidity. Countries with higher debt-to-GDP ratios and weaker fiscal positions commanded higher yields, but importantly, these spreads widened dramatically during periods of market stress even when fundamentals had not changed significantly. This observation led to a crucial insight that Favero, Pagano and von Thadden (2010) explored in depth in the Journal of Financial and Quantitative Analysis. They found that liquidity effects can amplify credit risk during stress periods, creating a feedback loop where rising spreads reduce liquidity, which in turn pushes spreads even higher. This dynamic explains why sovereign spreads often move in non-linear fashion, remaining stable for extended periods before suddenly widening rapidly. Longstaff, Pan, Pedersen and Singleton (2011) extended this research in their American Economic Review paper by examining the relationship between sovereign credit default swap spreads and bond spreads across multiple countries. Their key finding was that a significant portion of sovereign credit risk is driven by global factors rather than country-specific fundamentals. This means that when spreads widen in Italy, it often reflects broader risk aversion that will eventually affect other asset classes including equities and corporate bonds. The practical implication of this research is clear: sovereign spreads function as a leading indicator for systemic risk. Aizenman, Hutchison and Jinjarak (2013) confirmed this in their analysis of European sovereign debt default probabilities, finding that spread movements preceded rating downgrades and provided earlier warning signals than traditional fundamental analysis. How the Indicator Works The Global Sovereign Spread Monitor translates these academic findings into a systematic framework for monitoring credit conditions. The indicator calculates yield differentials between peripheral government bonds and German Bunds, which serve as the benchmark safe-haven asset in European markets. Italian ten-year yields minus German ten-year yields produce the BTP-Bund spread, the single most important metric for Eurozone stress. Spanish yields minus German yields produce the Bonos-Bund spread, providing a secondary confirmation signal. The transatlantic US-Bund spread captures divergence between the two major safe-haven markets. Raw spreads are converted to Z-scores, which measure how many standard deviations the current spread is from its historical average over the lookback period. This normalization is essential because absolute spread levels vary over time with interest rate cycles and structural changes in sovereign debt markets. A spread of 150 basis points might have been concerning in 2007 but entirely normal in 2023 following the European debt crisis and subsequent ECB interventions. The composite index combines these individual Z-scores using weights that reflect the relative importance of each spread for global risk assessment. Italy receives the highest weight because it represents the third-largest sovereign bond market globally and any Italian debt crisis would have systemic implications for the entire Eurozone. Spain provides confirmation of peripheral stress, while the US-Bund spread captures flight-to-quality dynamics between the two primary safe-haven markets. Regime classification transforms the continuous Z-score into discrete states that correspond to different market environments. The Stress regime indicates that spreads have widened to levels historically associated with crisis periods. The Elevated regime signals rising risk aversion that warrants increased attention. Normal conditions represent typical spread behavior, while the Calm regime may actually signal complacency and potential mean-reversion opportunities. Retail Trader Applications For individual traders without access to institutional research teams, the Global Sovereign Spread Monitor provides a window into the macro environment that typically remains opaque. The most immediate application is risk management for equity positions. Consider a trader holding a diversified portfolio of European stocks. When the composite Z-score rises above 1.0 and enters the Elevated regime, historical data suggests an increased probability of equity market drawdowns in the coming days to weeks. This does not mean the trader must immediately liquidate all positions, but it does suggest reducing position sizes, tightening stop-losses, or adding hedges such as put options or inverse ETFs. The BTP-Bund spread specifically provides actionable information for anyone trading EUR/USD or European equity indices. Research by De Grauwe and Ji (2013) demonstrated that sovereign spreads and currency movements are closely linked during stress periods. When the BTP-Bund spread widens sharply, the Euro typically weakens against the Dollar as investors question the sustainability of the monetary union. A retail forex trader can use the indicator to time entries into EUR/USD short positions or to exit long positions before spread-driven selloffs occur. The regime classification system simplifies decision-making for traders who cannot constantly monitor multiple data feeds. When the dashboard displays Stress, it is time to adopt a defensive posture regardless of what individual stock charts might suggest. When it displays Calm, the trader knows that risk appetite is elevated across institutional markets, which typically supports equity prices but also means that any negative catalyst could trigger a sharp reversal. Mean-reversion signals provide opportunities for more active traders. When spreads reach extreme levels in either direction, they tend to revert toward their historical average. A Z-score above 2.0 that begins declining suggests professional investors are starting to buy peripheral debt again, which historically precedes broader risk-on behavior. A Z-score below minus 1.0 that starts rising may indicate that complacency is ending and risk-off positioning is beginning. The key for retail traders is to use the indicator as a filter rather than a primary signal generator. If technical analysis suggests a long entry in European stocks, check the sovereign spread regime first. If spreads are elevated or rising, the technical setup becomes higher risk. If spreads are stable or compressing, the technical signal has a higher probability of success. Professional Applications Institutional investors use sovereign spread analysis in more sophisticated ways that go beyond simple risk filtering. Systematic macro funds incorporate spread data into quantitative models that generate trading signals across multiple asset classes simultaneously. Portfolio managers at large asset allocators use sovereign spreads to make strategic allocation decisions. When the composite Z-score trends higher over several weeks, they reduce exposure to peripheral European equities and bonds while increasing allocations to German Bunds, US Treasuries, and other safe-haven assets. This rotation often happens before explicit risk-off signals appear in equity markets, giving these investors a performance advantage. Fixed income specialists at banks and hedge funds use sovereign spreads for relative value trades. When the BTP-Bund spread widens to historically elevated levels but fundamentals have not deteriorated proportionally, they may go long Italian government bonds and short German Bunds, betting on mean reversion. These trades require careful risk management because spreads can widen further before reversing, but when properly sized they offer attractive risk-adjusted returns. Risk managers at financial institutions use sovereign spread monitoring as an input to Value-at-Risk models and stress testing frameworks. Elevated spreads indicate higher correlation among risk assets, which means diversification benefits are reduced precisely when they are needed most. This information feeds into position sizing decisions across the entire trading book. Currency traders at proprietary trading firms incorporate sovereign spreads into their EUR/USD and EUR/CHF models. The relationship between the BTP-Bund spread and EUR weakness is well-documented in academic literature and provides a systematic edge when combined with other factors such as interest rate differentials and positioning data. Central bank watchers use sovereign spreads to anticipate policy responses. The European Central Bank has demonstrated repeatedly that it will intervene when spreads reach levels that threaten financial stability, most notably through the Outright Monetary Transactions program announced in 2012 and the Transmission Protection Instrument introduced in 2022. Understanding spread dynamics helps investors anticipate these interventions and position accordingly. Interpreting the Dashboard The statistics panel provides real-time information that supports both quick assessments and deeper analysis. The composite Z-score is the primary metric, representing the weighted average of all spread Z-scores. Values above zero indicate spreads are wider than their historical average, while values below zero indicate compression. The magnitude matters: a reading of 0.5 suggests modestly elevated stress, while 2.0 or higher indicates conditions similar to historical crisis periods. The regime classification translates the Z-score into actionable categories. Stress should trigger immediate review of risk exposure and consideration of hedges. Elevated warrants increased vigilance and potentially reduced position sizes. Normal indicates no immediate concerns from sovereign markets. Calm suggests risk appetite may be elevated, which supports risk assets but also creates potential for sharp reversals if sentiment changes. The percentile ranking provides historical context by showing where the current Z-score falls within its distribution over the lookback period. A reading of 90 percent means spreads are wider than they have been 90 percent of the time over the past year, which is significant even if the absolute Z-score is not extreme. This metric helps identify when spreads are creeping higher before they reach official stress thresholds. Momentum indicates whether spreads are widening or compressing. Rising momentum during elevated spread conditions is particularly concerning because it suggests stress is accelerating. Falling momentum during stress suggests the worst may be past and mean reversion could be beginning. Individual spread readings allow traders to identify which component is driving the composite signal. If the BTP-Bund spread is elevated but Bonos-Bund remains normal, the stress may be Italy-specific rather than systemic. If all spreads are widening together, the signal reflects broader flight-to-quality that affects all risk assets. The bias indicator provides a simple summary for traders who need quick guidance. Risk-Off means spreads indicate defensive positioning is appropriate. Risk-On means spread conditions support risk-taking. Neutral means spreads provide no clear directional signal. Limitations and Risk Factors No indicator provides perfect signals, and sovereign spread analysis has specific limitations that users must understand. The European Central Bank has demonstrated its willingness to intervene in sovereign bond markets when spreads threaten financial stability. The Transmission Protection Instrument announced in 2022 specifically targets situations where spreads widen beyond levels justified by fundamentals. This creates a floor under peripheral bond prices and means that extremely elevated spreads may not persist as long as historical patterns would suggest. Political events can cause sudden spread movements that are impossible to anticipate. Elections, government formation crises, and policy announcements can move spreads by 50 basis points or more in a single session. The indicator will reflect these moves but cannot predict them. Liquidity conditions in sovereign bond markets can temporarily distort spread readings, particularly around quarter-end and year-end when banks adjust their balance sheets. These technical factors can cause spread widening or compression that does not reflect fundamental credit risk. The relationship between sovereign spreads and other asset classes is not constant over time. During some periods, spread movements lead equity moves by several days. During others, both markets move simultaneously. The indicator provides valuable information about credit conditions, but users should not expect mechanical relationships between spread signals and subsequent price moves in other markets. Conclusion The Global Sovereign Spread Monitor represents a systematic application of academic research on sovereign credit risk to practical trading decisions. The indicator monitors yield differentials between peripheral and safe-haven government bonds, normalizes these spreads using statistical methods, and classifies market conditions into regimes that correspond to different risk environments. For retail traders, the indicator provides risk management information that was previously available only to institutional investors with access to Bloomberg terminals and dedicated research teams. By checking the sovereign spread regime before executing trades, individual investors can avoid taking excessive risk during periods of elevated credit stress. For professional investors, the indicator offers a standardized framework for monitoring sovereign credit conditions that can be integrated into broader macro models and risk management systems. The real-time calculation of Z-scores, regime classifications, and component spreads provides the inputs needed for systematic trading strategies. The academic foundation is robust, built on peer-reviewed research published in top finance and economics journals over the past two decades. The practical applications have been validated through multiple market cycles including the European debt crisis of 2011-2012, the COVID-19 shock of 2020, and the rate normalization stress of 2022. Sovereign spreads will continue to provide valuable forward-looking information about systemic risk for as long as credit conditions vary across countries and investors respond rationally to changes in default probabilities. The Global Sovereign Spread Monitor makes this information accessible and actionable for traders at all levels of sophistication. References Aizenman, J., Hutchison, M. and Jinjarak, Y. (2013) What is the Risk of European Sovereign Debt Defaults? Fiscal Space, CDS Spreads and Market Pricing of Risk. Journal of International Money and Finance, 34, pp. 37-59. Codogno, L., Favero, C. and Missale, A. (2003) Yield Spreads on EMU Government Bonds. Economic Policy, 18(37), pp. 503-532. De Grauwe, P. and Ji, Y. (2013) Self-Fulfilling Crises in the Eurozone: An Empirical Test. Journal of International Money and Finance, 34, pp. 15-36. Favero, C., Pagano, M. and von Thadden, E.L. (2010) How Does Liquidity Affect Government Bond Yields? Journal of Financial and Quantitative Analysis, 45(1), pp. 107-134. Longstaff, F.A., Pan, J., Pedersen, L.H. and Singleton, K.J. (2011) How Sovereign Is Sovereign Credit Risk? American Economic Review, 101(6), pp. 2191-2212. Manganelli, S. and Wolswijk, G. (2009) What Drives Spreads in the Euro Area Government Bond Market? Economic Policy, 24(58), pp. 191-240. Arghyrou, M.G. and Kontonikas, A. (2012) The EMU Sovereign-Debt Crisis: Fundamentals, Expectations and Contagion. Journal of International Financial Markets, Institutions and Money, 22(4), pp. 658-677.
Indicatore Pine Script®
di EdgeTools
Sesion Operativa - Codigo InstitucionalThis indicator is designed for institutional and precision traders who need to visualize market liquidity and key session operating ranges without visual clutter. Unlike standard session indicators, this tool focuses on clarity and the projection of key levels (Highs and Lows) to identify potential future reaction zones. Key Features: 4 Customizable Sessions: Pre-configured with key institutional times (Pre-NY, NY Open, London, and Asia). Each session is fully adjustable in time, color, and style. Minimalist Labeling: Displays the session name and operating range (in pips/points) in a clean, direct format (e.g., NY - 45), removing decimals and unnecessary text to keep the chart clean. Range Projections: Option to project the Highs and Lows of each session forward (N candles) to use them as dynamic support or resistance levels. Opening Highlight (NYSE): Special feature to highlight candle colors during specific high-volatility times (default 09:30 - 09:35 UTC-5), perfect for identifying manipulation or liquidity injections at the stock market open. Adjustable Time Zone: Default setting is UTC-5 (New York), but fully adaptable to any user time zone.
Indicatore Pine Script®
di sebaztiansaenzX
Discipline Sleeping TimeThe Sleeping Time indicator highlights a predefined time window on the chart that represents your sleeping hours. This will help doing backtest easily by filtering out unrealistic result of trades while we are still sleeping. During the selected period: - The chart background is softly shaded to visually mark your sleep window - The first candle of the range is labeled “Sleep” - The last candle of the range is labeled “Wake Up” You can also use it for other purpose. This makes it easy to: - Visually avoid trading during sleep hours - Identify when a trading session should be inactive - Maintain discipline and consistency across different markets and timezones Key Features: - Custom Time Range Define your sleeping hours using a start and end time. - UTC Offset Selector Adjust the time window using a UTC offset dropdown (−10 to +13), so the indicator aligns correctly with your local time. - Clear Visual Markers Background shading during sleep hours - Start label: Sleep - End label: Wake Up - Customizable Labels Change label text, size, and style to suit your chart layout. Best Use Case Use this indicator to lock in rest time, avoid emotional trades, and respect personal trading boundaries. Because good trades start with good sleep 😴
Indicatore Pine Script®
di LightNCI
Strategy H4-H1-M15 Triple Screen + TableMaster of Multi-Timeframe Trading: "Triple Screen" Strategy "▲▼ & BUY/SELL M15 Tags" — H1 Ready signals warn the trader in advance that a reversal is brewing on the medium timeframe. Settings: Stochastic Settings: Oscillator length and smoothing adjustment. Overbought/Oversold: Overbought/oversold level settings (default 80/20). SL Offset: Buffer in ticks/pips for setting stop-loss beyond extremes. Usage Instructions: Long: Background painted light green (H4 Trend UP + H1 Stoch Low), wait for green "BUY M15" tag. Short: Background painted light red (H4 Trend DOWN + H1 Stoch High), wait for red "SELL M15" tag. Entry → SL → TP = PROFIT Short Description (for preview): Comprehensive "Triple Screen" strategy based on MACD (H4) and Stochastic (H1, M15). Features trend monitoring panel and precise entry signals with automatic Stop Loss calculation. Technical Notes (for developers): Hardcoded Timeframes: "240" (H4) and "60" (H1) are hardcoded. For universal use on other timeframe combinations (D1-H4-H1), make these input.timeframe variables. Repainting: request.security may cause repainting on historical bars (current bar is honest). Standard practice for multi-timeframe TradingView indicators. Alerts: Built-in alert support for one-click trading convenience.
Indicatore Pine Script®
di Alex8181999
1
Pro Minimalist ATR (Black)The script I provided is a tool that automatically calculates and displays volatility "zones" around the average price. Here is the plain English explanation of what it is doing and why: 1. The Anchor: 20 DMA (The "Fair Value") The script starts by calculating the 20-Day Moving Average (20 DMA). What it represents: Think of this as the "fair price" or the "center of gravity" for the market over the last month. In the script: It looks at the closing price of the last 20 candles, adds them up, and divides by 20. This is your baseline. 2. The Ruler: ATR (The "Volatility") Next, it measures the Average True Range (ATR) over the last 14 days. What it represents: This measures the "energy" or "noise" of the market. If candles are huge, the ATR is high. If candles are tiny, the ATR is low. Why we use it: Using a fixed number (like $50) doesn't work because stocks move differently. ATR adapts to the current market mood. 3. The Zones: +1, +2, -1, -2 The script then takes that "center" (20 DMA) and adds/subtracts the "ruler" (ATR) to create four distinct levels: +1 ATR: This is the "Upper Normal" limit. Price hanging here is bullish but normal. +2 ATR: This is the "Extreme" limit. Statistically, price rarely stays above this line for long without snapping back. This is often an overbought signal. -1 ATR: This is the "Lower Normal" limit. -2 ATR: This is the "Extreme" discount. If price hits this, it is statistically stretched far below its average. 4. The Visuals: "Clean" Labeling Finally, the script focuses on presentation: No Lines: It specifically avoids drawing lines all over your history to keep your chart clean. Dynamic Labels: It creates text labels only on the very last bar (the current moment). It constantly deletes the old label and draws a new one as the price moves, so it looks like the text is "floating" next to the current price. Axis Marking: It forces marks onto the right-hand price scale (display=display.price_scale) so you can see the exact price levels (e.g., 154.20) without having to guess.
Indicatore Pine Script®
di GoyalKapil354