Black FridayDo 80% ZNIŻKI

Wskaźniki i strategie

RSI UpDown [DivineTrade]This indicator displays the RSI values across multiple timeframes in real time. It provides a compact panel showing RSI readings for 1W, 1D, 4H, 1H, 15M, 5M and 1M, updating continuously as new price data arrives. Each value is color-coded based on market conditions: strong overbought levels, moderate overbought zones, neutral ranges and oversold areas. This allows traders to quickly assess multi-timeframe momentum and identify alignment or divergence across different market horizons.
Wskaźnik Pine Script®
od markdn1678
MA均线全仓回测//@version=5 strategy("39日MA均线全仓交易策略 (2017-2025)", overlay=true, initial_capital=1000, commission_type=strategy.commission.percent, commission_value=0.1, pyramiding=0) // 策略参数 startDate = input.time(timestamp("2017-08-01T00:00:00"), title="回测开始日期") endDate = input.time(timestamp("2025-11-11T23:59:59"), title="回测结束日期") maLength = input.int(39, title="MA周期", minval=1) useSMA = input.bool(true, title="使用SMA(简单移动平均)") // 计算移动平均线 maValue = useSMA ? ta.sma(close, maLength) : ta.ema(close, maLength) // 绘制MA线 plot(maValue, color=color.new(color.blue, 0), linewidth=2, title="39日MA") // 确定是否在回测时间范围内 inDateRange = time >= startDate and time <= endDate // 交易逻辑 - 全仓交易 if inDateRange // 生成交易信号：价格上穿MA时全仓买入，下穿MA时全仓卖出 longCondition = ta.crossover(close, maValue) shortCondition = ta.crossunder(close, maValue) // 执行交易 - 全仓操作 if longCondition strategy.entry("全仓买入", strategy.long, qty=strategy.equity/close) if shortCondition strategy.close("全仓买入", comment="全仓卖出") // 在回测时间范围外显示背景色 bgcolor(inDateRange ? color.new(color.green, 95) : na, title="回测期间背景") // 添加性能统计和资金曲线 var float totalReturn = 0.0 if barstate.islast and inDateRange totalReturn := ((strategy.netprofit + 1000) / 1000 - 1) * 100 label.new(bar_index, high, text="回测结果: 初始资金: 1000 USDT 最终权益: " + str.tostring(strategy.netprofit + 1000, "#.##") + " USDT 总收益率: " + str.tostring(totalReturn, "#.##") + "% 总交易次数: " + str.tostring(strategy.closedtrades) + "次", style=label.style_label_down, color=color.new(color.blue, 80), textcolor=color.white, size=size.normal) // 绘制权益曲线 plot(strategy.equity, title="资金曲线", color=color.new(color.purple, 0), linewidth=1, trackprice=true)
Strategia Pine Script®
od stars888
CVD [able0.1]# CVD Overlay iOS Style - Complete User Guide ## 📖 Table of Contents 1. (#what-is-cvd) 2. (#installation-guide) 3. (#understanding-the-display) 4. (#reading-the-info-table) 5. (#settings--customization) 6. (#trading-strategies) 7. (#common-mistakes-to-avoid) --- ## 🎯 What is CVD? **CVD (Cumulative Volume Delta)** tracks the **difference between buying and selling pressure** over time. ### Simple Explanation: - **Positive CVD** (Orange) = More buying than selling = Bulls winning - **Negative CVD** (Gray) = More selling than buying = Bears winning - **Rising CVD** = Increasing buying pressure = Potential uptrend - **Falling CVD** = Increasing selling pressure = Potential downtrend ### Why It Matters: CVD helps you see **who's really in control** of the market - not just price movement, but actual buying/selling volume. --- ## 🚀 Installation Guide ### Step 1: Open Pine Editor 1. Go to TradingView 2. Click the **"Pine Editor"** tab at the bottom of the screen 3. Click **"New"** or open an existing script ### Step 2: Copy & Paste the Code 1. Select all existing code (Ctrl+A / Cmd+A) 2. Delete it 3. Copy the entire CVD iOS Style code 4. Paste it into Pine Editor ### Step 3: Add to Chart 1. Click **"Save"** button (or Ctrl+S / Cmd+S) 2. Click **"Add to Chart"** button 3. The indicator will appear on your chart! ### Step 4: Initial Setup - The indicator appears as an **overlay** on your price chart - You'll see an **orange/gray line** following price - An **info table** appears in the top-right corner --- ## 📊 Understanding the Display ### Main Chart Elements: #### 1. **CVD Line** (Orange/Gray) - **Orange Line** = Positive CVD (buying pressure) - **Gray Line** = Negative CVD (selling pressure) - This line moves with your price chart but shows volume delta #### 2. **CVD Zone** (Shaded Area) - Light shaded box around the CVD line - Shows the "range" of CVD movement - Helps visualize CVD boundaries #### 3. **Center Line** (Dotted) - Gray dotted line in the middle of the zone - Represents the "neutral" point - CVD crossing this = shift in market control #### 4. **Reference Asset Line** (Light Gray) - Shows Bitcoin (BTC) price movement for comparison - Helps you see if your asset moves with or against BTC - Can be changed to any asset you want #### 5. **CVD Label** - Shows current CVD value - Positioned above/below zone to avoid overlap - Updates in real-time #### 6. **Reset Background** (Very Light Gray) - Appears when CVD resets - Indicates a new calculation period --- ## 📋 Reading the Info Table The info table (top-right) shows **8 key metrics**: ### Row 1: **Header** ``` ╔═ CVD able ═╗ | 15m | ████████ | able ``` - **CVD able** = Indicator name + creator - **15m** = Current timeframe - **████████** = Visual decoration - **able** = Creator signature ### Row 2: **CVD Value** ``` CVD▲ | 7.39K | ████████ | █ █ █ ``` - **CVD▲** = CVD with trend arrow - ▲ = CVD increasing - ▼ = CVD decreasing - ► = CVD unchanged - **7.39K** = Actual CVD number - **Progress Bar** = Visual strength (darker = stronger) - **Vertical Bars** = Height shows intensity ### Row 3: **Delta** ``` ◆DELTA | -1.274K | ████░░░░ | ░ ░ ``` - **Delta** = Volume change THIS BAR ONLY - **Negative** = More selling this bar - **Positive** = More buying this bar - Shows **immediate** pressure (not cumulative) ### Row 4: **UP Volume** ``` UP↑ | -1.263K | ████████ | █ █ █ ``` - Total **buying volume** this bar - Higher = Stronger buying pressure - Green/Orange vertical bars = Bullish strength ### Row 5: **DOWN Volume** ``` DN↓ | 2.643K | ████████ | ░ ░ ░ ``` - Total **selling volume** this bar - Higher = Stronger selling pressure - Gray vertical bars = Bearish strength ### Row 6-7: **Reference Asset** (if enabled) ``` ══ REF ══ | ══════ | ████████ | █ █ PRICE▲ | 4130.300 | ████████ | █ █ ``` - **REF** = Reference asset header - **PRICE▲** = Reference price with trend - Shows if BTC (or chosen asset) is rising/falling - Compare with your chart to see correlation ### Row 8: **Market Status** ``` ◄STATUS► | NEUT | ████░░░░ | ▒ ▒ ``` - **BULL** = CVD positive + Delta positive = Strong buying - **BEAR** = CVD negative + Delta negative = Strong selling - **NEUT** = Mixed signals = Wait for clarity **Status Colors:** - **Orange background** = Bullish (good for long) - **Gray background** = Bearish (good for short) - **White background** = Neutral (no clear signal) --- ## ⚙️ Settings & Customization ### Main Settings (⚙️) #### **CVD Reset** - **None** = CVD never resets (from beginning of data) - **On Higher Timeframe** = Resets when HTF candle closes - 15m chart → Resets hourly - 1h chart → Resets daily - Recommended for most traders - **On Session Start** = Resets at market open - **On Visible Chart** = Resets from leftmost visible bar #### **Precision** - **Low (Fast)** = Uses 1m data, faster but less accurate - **Medium** = Uses 5m data, balanced (recommended) - **High** = Uses 15m data, most accurate but slower #### **Cumulative** - ✅ On = CVD accumulates over time (recommended) - ❌ Off = Shows only current bar delta #### **Show Labels** - ✅ On = Shows CVD value label on chart - ❌ Off = Cleaner chart, no label #### **Show Info Table** - ✅ On = Shows info table (recommended for beginners) - ❌ Off = Hide table for minimalist view --- ### 🎨 iOS Style Colors You can customize **every color** to match your chart theme: #### **Primary Colors** - **Primary (Orange)** = Main bullish color (#FF9500) - **Secondary (Gray)** = Main bearish color (#8E8E93) - **Background** = Table background (#FFFFFF) - **Text** = Text color (#1C1C1E) #### **Bullish/Bearish** - **Bullish (Orange)** = Positive CVD color - **Bearish (Gray)** = Negative CVD color - **Opacity** = Zone transparency (0-100%) - **Show Zone** = Enable/disable shaded area #### **Table Colors** (📋) - **Header Background** = Top row background - **Header Text** = Top row text color - **Cell Background** = Data cells background - **Cell Text** = Data cells text color - **Border** = Table border color - **Accent Background** = Special rows background - **Alert Background** = Warning/status background --- ### 📊 Reference Asset Settings #### **Enable** - ✅ On = Shows reference asset line - ❌ Off = Hide reference asset #### **Symbol** - Default: `BINANCE:BTCUSDT` - Can change to any asset: - `BINANCE:ETHUSDT` (Ethereum) - `SPX` (S&P 500) - `DXY` (US Dollar Index) - Any ticker symbol #### **Color & Width** - Customize line appearance - Width: 1-4 (thickness) --- ## 💡 Trading Strategies ### Strategy 1: CVD Divergence (Beginner-Friendly) **What to Look For:** - Price making **higher highs** but CVD making **lower highs** = Bearish divergence - Price making **lower lows** but CVD making **higher lows** = Bullish divergence **How to Trade:** 1. Wait for divergence to form 2. Look for confirmation (price reversal, candlestick pattern) 3. Enter trade in divergence direction 4. Stop loss beyond recent high/low **Example:** ``` Price: /\ /\ /\ (higher highs) CVD: /\ / \/ (lower highs) = Bearish signal ``` ### Strategy 2: CVD Trend Following (Intermediate) **What to Look For:** - **Strongly rising CVD** + **rising price** = Strong uptrend - **Strongly falling CVD** + **falling price** = Strong downtrend **How to Trade:** 1. Wait for CVD and price moving in same direction 2. Enter on pullbacks to support/resistance 3. Stay in trade while CVD trend continues 4. Exit when CVD trend breaks **Signals:** - CVD ▲▲▲ + Price ↑ = Go LONG - CVD ▼▼▼ + Price ↓ = Go SHORT ### Strategy 3: CVD + Reference Asset (Advanced) **What to Look For:** - Your asset **rising** but BTC (reference) **falling** = Relative strength - Your asset **falling** but BTC (reference) **rising** = Relative weakness **How to Trade:** 1. Compare CVD movement with BTC 2. If your CVD rises faster than BTC = Buy signal 3. If your CVD falls faster than BTC = Sell signal 4. Use for **pair trading** or **asset selection** ### Strategy 4: Volume Delta Confirmation **What to Look For:** - **Large positive Delta** = Strong buying this bar - **Large negative Delta** = Strong selling this bar **How to Trade:** 1. Price breaks resistance + Large positive Delta = Confirmed breakout 2. Price breaks support + Large negative Delta = Confirmed breakdown 3. Use Delta to **confirm** price moves, not predict them **Rules:** - Delta > 2x average = Very strong pressure - Delta near zero at key level = Weak move, likely false breakout --- ## 🎓 Reading Real Scenarios ### Scenario 1: Strong Buying Pressure ``` Table Shows: CVD▲ | 12.5K | ████████ | ████ (CVD rising) ◆DELTA | +2.8K | ████████ | ▲ (Positive delta) UP↑ | 3.1K | ████████ | ████ (High buy volume) DN↓ | 0.3K | ██░░░░░░ | ░ (Low sell volume) ◄STATUS► | BULL | ████████ | ████ (Orange background) ``` **Interpretation:** Strong buying, good for LONG trades ### Scenario 2: Distribution (Hidden Selling) ``` Table Shows: CVD► | 8.2K | ████░░░░ | ▒▒ (CVD flat) ◆DELTA | -1.5K | ████████ | ▼ (Negative delta) UP↑ | 0.8K | ███░░░░░ | ░ (Low buy volume) DN↓ | 2.3K | ████████ | ████ (High sell volume) ◄STATUS► | BEAR | ████████ | ░░░░ (Gray background) ``` **Interpretation:** Price may look stable, but selling increasing = Prepare for drop ### Scenario 3: Neutral/Choppy Market ``` Table Shows: CVD► | 5.1K | ████░░░░ | ▒ (CVD sideways) ◆DELTA | +0.2K | ██░░░░░░ | ─ (Small delta) UP↑ | 1.2K | ████░░░░ | ▒ (Medium buy) DN↓ | 1.0K | ████░░░░ | ▒ (Medium sell) ◄STATUS► | NEUT | ████░░░░ | ▒▒ (White background) ``` **Interpretation:** No clear direction = Stay out or reduce position size --- ## ⚠️ Common Mistakes to Avoid ### Mistake 1: Trading on CVD Alone - ❌ **Wrong:** "CVD is rising, I'll buy immediately" - ✅ **Right:** "CVD is rising, let me check price structure, support/resistance, and wait for confirmation" ### Mistake 2: Ignoring Delta - ❌ **Wrong:** Looking only at cumulative CVD - ✅ **Right:** Watch both CVD (trend) and Delta (momentum) - Delta shows **immediate** pressure changes ### Mistake 3: Wrong Timeframe - ❌ **Wrong:** Using 1m chart with High Precision (too slow) - ✅ **Right:** Match precision to timeframe: - 1m-5m → Low Precision - 15m-1h → Medium Precision - 4h+ → High Precision ### Mistake 4: Not Using Reset - ❌ **Wrong:** Using "None" reset for intraday trading - ✅ **Right:** Use "On Higher Timeframe" to see fresh CVD each session ### Mistake 5: Overtrading Neutral Status - ❌ **Wrong:** Forcing trades when STATUS = NEUT - ✅ **Right:** Only trade clear BULL or BEAR status ### Mistake 6: Ignoring Reference Asset - ❌ **Wrong:** Trading altcoin without checking BTC - ✅ **Right:** Always check if BTC CVD agrees with your asset --- ## 🔥 Pro Tips ### Tip 1: Multi-Timeframe Analysis - Check CVD on **3 timeframes**: - Lower TF (15m) = Entry timing - Current TF (1h) = Trade direction - Higher TF (4h) = Overall trend ### Tip 2: Volume Confirmation - Big price move + Small Delta = **Weak move** (likely reversal) - Small price move + Big Delta = **Strong accumulation** (continuation) ### Tip 3: CVD Reset Zones - Pay attention to **reset backgrounds** (light gray) - Often marks **session starts** = High volatility periods ### Tip 4: Divergence + Status - Bearish divergence + STATUS = BEAR = **Strongest short signal** - Bullish divergence + STATUS = BULL = **Strongest long signal** ### Tip 5: Color Psychology - **Orange** (Bullish) is **warm** = Buying energy - **Gray** (Bearish) is **cool** = Selling pressure - Train your eye to read colors instantly ### Tip 6: Table as Quick Scan - Glance at table without reading numbers: - **All orange** = Bullish - **All gray** = Bearish - **Mixed** = Wait --- ## 📱 Quick Reference Card | Signal | CVD | Delta | Status | Action | |--------|-----|-------|--------|--------| | **Strong Buy** | ▲▲ High | ++ Positive | BULL | Long Entry | | **Strong Sell** | ▼▼ Low | -- Negative | BEAR | Short Entry | | **Divergence Buy** | ▲ Rising | Price ▼ | → BULL | Long Setup | | **Divergence Sell** | ▼ Falling | Price ▲ | → BEAR | Short Setup | | **Neutral** | → Flat | ~0 Near Zero | NEUT | Stay Out | | **Accumulation** | → Flat | ++ Positive | NEUT→BULL | Watch for Breakout | | **Distribution** | → Flat | -- Negative | NEUT→BEAR | Watch for Breakdown | --- ## 🆘 Troubleshooting ### Issue: "Indicator not showing" - **Solution:** Make sure overlay=true in code, re-add to chart ### Issue: "Table overlaps with price" - **Solution:** Change table position in code or use TradingView's "Move" feature ### Issue: "CVD line too far from price" - **Solution:** This is normal! CVD is volume-based, not price-based. Focus on CVD direction, not position ### Issue: "Too many lines on chart" - **Solution:** Disable "Show Zone" and "Show Labels" in settings for cleaner view ### Issue: "Calculations too slow" - **Solution:** Change Precision to "Low (Fast)" or use higher timeframe ### Issue: "Reference asset not showing" - **Solution:** Check if "Enable" is ON and symbol is valid (e.g., BINANCE:BTCUSDT) --- ## 🎬 Getting Started Checklist - Install indicator on TradingView - Set precision to "Medium" - Set reset to "On Higher Timeframe" - Enable info table - Add reference asset (BTC) - Practice reading the table on demo account - Test on different timeframes (15m, 1h, 4h) - Compare CVD with your current strategy - Paper trade for 1 week before going live - Keep a trading journal of CVD signals --- ## 📚 Summary **CVD shows WHO is winning: Buyers or Sellers** **Key Points:** 1. **Orange/Rising CVD** = Buying pressure = Bullish 2. **Gray/Falling CVD** = Selling pressure = Bearish 3. **Delta** = Immediate momentum THIS BAR 4. **Status** = Overall market condition 5. **Always confirm** with price action & other indicators **Remember:** - CVD is a **tool**, not a crystal ball - Use with proper risk management - Practice makes perfect - Stay disciplined! --- **Created by: able** **Version:** iOS Style v1.0 **Contact:** For questions, refer to TradingView community Happy Trading! 🚀📈
Wskaźnik Pine Script®
od krittaphat6
CoreTFRSIMD CoreTFRSIMD library — Reusable TFRSI core for consistent momentum inputs across scripts The library provides a reusable exported function such as calcTfrsi(src, len, signalLen) so you can compute TFRSI in your own indicators or strategies, e.g. tfrsi = CoreTFRSIMD.calcTfrsi(close, 6, 2) Summary CoreTFRSIMD is a Pine Script v6 library that implements a TFRSI-style oscillator core and exposes it as a reusable exported function. It is designed for authors who want the same TFRSI calculation across multiple indicators or strategies without duplicating logic. The library includes a simple demo plot and band styling so you can visually sanity-check the output. No higher-timeframe sampling is used, and there are no loops or arrays, so runtime cost is minimal for typical chart usage. Motivation: Why this design? When you reuse an oscillator across different tools, small implementation differences create inconsistent signals and hard-to-debug results. This library isolates the signal path into one exported function so that every dependent script consumes the exact same oscillator output. The design combines filtering, normalization, and a final smoothing pass to produce a stable, RSI-like readout intended for momentum and regime context. What’s different vs. standard approaches? Baseline: Traditional RSI computed directly from gains and losses with standard smoothing. Architecture differences: A high-pass stage to attenuate slower components before the main smoothing. A multi-pole smoothing stage implemented with persistent state to reduce noise. A running peak-tracker style normalization that adapts to changing signal amplitude. A final signal smoothing layer using a simple moving average. Practical effect: The oscillator output tends to be less dominated by raw volatility spikes and more consistent across changing conditions. The normalization step helps keep the output in an RSI-like reading space without relying on fixed scaling. How it works (technical) 1. Input source: The exported function accepts a source series and two integer parameters controlling responsiveness and final smoothing. 2. High-pass stage: A recursive filter is applied to the source to emphasize shorter-term movement. This stage uses persistent storage so it can reference prior internal states across bars. 3. Smoothing stage: The filtered stream is passed through a SuperSmoother-like recursive smoother derived from the chosen length. This again uses persistent state and prior values for continuity. 4. Adaptive normalization: The absolute magnitude of the smoothed stream is compared to a slowly decaying reference level. If the current magnitude exceeds the reference, the reference is updated. This acts like a “peak hold with decay” so the oscillator scales relative to recent conditions. 5. Oscillator mapping: The normalized value is mapped into an RSI-like reading range. 6. Signal smoothing: A simple moving average is applied over the requested signal length to reduce bar-to-bar chatter. 7. Demo rendering: The library script plots the oscillator, draws horizontal guide levels, and applies background plus gradient fills for overbought and oversold regions. Parameter Guide Parameter — Effect — Default — Trade-offs/Tips src — Input series used by the oscillator — close in demo — Use close for general momentum, or a derived series if you want to emphasize a specific behavior. len — Controls the responsiveness of internal filtering and smoothing — six in demo — Smaller values react faster but can increase short-term noise; larger values smooth more but can lag turns. signalLen — Controls the final smoothing of the mapped oscillator — two in demo — Smaller values preserve detail but can flicker; larger values reduce flicker but can delay transitions. Reading & Interpretation The plot is an oscillator intended to be read similarly to an RSI-style momentum gauge. The demo includes three reference levels: upper at one hundred, mid at fifty, and lower at zero. The fills visually emphasize zones above the midline and below the midline. Treat these as context, not as standalone entries. If the oscillator appears unusually compressed or unusually jumpy, the normalization reference may be adapting to an abrupt change in amplitude. That is expected behavior for adaptive normalization. Practical Workflows & Combinations Trend following: Use structure first, then confirm with oscillator behavior around the midline. Prefer signals aligned with higher-high higher-low or lower-low lower-high context from price. Exits/Stops: Use oscillator loss of momentum as a caution flag rather than an automatic exit trigger. In strong trends, consider keeping risk rules price-based and use the oscillator mainly to avoid adding into exhaustion. Multi-asset/Multi-timeframe: Start with the demo defaults when you want a responsive oscillator. If an asset is noisier, increase the main length or the signal smoothing length to reduce false flips. Behavior, Constraints & Performance Repaint/confirmation: No higher-timeframe sampling is used. Output updates on the live bar like any normal series. There is no explicit closed-bar gating in the library. security or HTF: Not used, so there is no HTF synchronization risk. Resources: No loops, no arrays, no large history buffers. Persistent variables are used for filter state. Known limits: Like any filtered oscillator, sharp gaps and extreme one-bar events can produce transient distortions. The adaptive normalization can also make early bars unstable until enough history has accumulated. Sensible Defaults & Quick Tuning Starting values: length six, signal smoothing two. Too many flips: Increase signal smoothing length, or increase the main length. Too sluggish: Reduce the main length, or reduce signal smoothing length. Choppy around midline: Increase signal smoothing length slightly and rely more on price structure filters. What this indicator is—and isn’t This library is a reusable signal component and visualization aid. It is not a complete trading system, not predictive, and not a substitute for market structure, execution rules, and risk controls. Use it as a momentum and regime context layer, and validate behavior per asset and timeframe before relying on it. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino
Biblioteka Pine Script®
od chervolino
SPX AbuBasel Scalping PRO – Stable//@version=5 indicator("SPX AbuBasel Scalping PRO – Stable", overlay=true, precision=2) // ==== Inputs ==== lenRSI = input.int(7, "RSI Length") tp1ATR = input.float(0.35, "TP1 ATR Factor", step=0.05) tp2ATR = input.float(0.70, "TP2 ATR Factor", step=0.05) tp3ATR = input.float(1.00, "TP3 ATR Factor", step=0.05) slATR = input.float(0.45, "SL ATR Factor", step=0.05) useVol = input.bool(true, "Use Volume Filter") // ==== Indicators ==== rsi = ta.rsi(close, lenRSI) vwap = ta.vwap atr = ta.atr(14) // Bollinger Bands basis = ta.sma(close, 20) dev = 2.0 * ta.stdev(close, 20) upper = basis + dev lower = basis - dev // Volume filter volOK = volume > ta.sma(volume, 20) // Divergence bullDiv = low < low and rsi > rsi bearDiv = high > high and rsi < rsi // Reversal candles bullCandle = close > open and close > high bearCandle = close < open and close < low // VWAP slope trendUp = vwap > vwap trendDown = vwap < vwap // ==== Entry Conditions ==== buySig = bullDiv and bullCandle and close > vwap and trendUp and (not useVol or volOK) and close < lower sellSig = bearDiv and bearCandle and close < vwap and trendDown and (not useVol or volOK) and close > upper // ==== Targets ==== tp1 = buySig ? close + atr * tp1ATR : sellSig ? close - atr * tp1ATR : na tp2 = buySig ? close + atr * tp2ATR : sellSig ? close - atr * tp2ATR : na tp3 = buySig ? close + atr * tp3ATR : sellSig ? close - atr * tp3ATR : na sl = buySig ? close - atr * slATR : sellSig ? close + atr * slATR : na // ==== Plot Signals ==== plotshape(buySig, title="BUY", style=shape.labelup, color=color.green, text="BUY", size=size.small) plotshape(sellSig, title="SELL", style=shape.labeldown, color=color.red, text="SELL", size=size.small) // ==== Draw Levels ==== if buySig or sellSig line.new(bar_index, tp1, bar_index + 1, tp1, extend=extend.right, color=color.new(color.green, 0)) line.new(bar_index, tp2, bar_index + 1, tp2, extend=extend.right, color=color.new(color.lime, 0)) line.new(bar_index, tp3, bar_index + 1, tp3, extend=extend.right, color=color.new(color.green, 40)) line.new(bar_index, sl, bar_index + 1, sl, extend=extend.right, color=color.new(color.red, 0)) // ==== Alerts ==== alertcondition(buySig, "SPX BUY", "AbuBasel PRO: BUY Signal") alertcondition(sellSig, "SPX SELL", "AbuBasel PRO: SELL Signal")
Wskaźnik Pine Script®
od abobasel5544
QuantAgentSignalsLibLibrary "QuantAgentSignalsLib" decision(rsi_len, rsi_upper, rsi_lower, macd_fast, macd_slow, macd_signal, roc_len, willr_len, htf_gate, htf_rsi)   Parameters:      rsi_len (simple int)      rsi_upper (int)      rsi_lower (int)      macd_fast (simple int)      macd_slow (simple int)      macd_signal (simple int)      roc_len (int)      willr_len (int)      htf_gate (bool)      htf_rsi (float) is_long(rsi_len, rsi_upper, rsi_lower, macd_fast, macd_slow, macd_signal, roc_len, willr_len, htf_gate, htf_rsi)   Parameters:      rsi_len (simple int)      rsi_upper (int)      rsi_lower (int)      macd_fast (simple int)      macd_slow (simple int)      macd_signal (simple int)      roc_len (int)      willr_len (int)      htf_gate (bool)      htf_rsi (float) is_short(rsi_len, rsi_upper, rsi_lower, macd_fast, macd_slow, macd_signal, roc_len, willr_len, htf_gate, htf_rsi)   Parameters:      rsi_len (simple int)      rsi_upper (int)      rsi_lower (int)      macd_fast (simple int)      macd_slow (simple int)      macd_signal (simple int)      roc_len (int)      willr_len (int)      htf_gate (bool)      htf_rsi (float) signal(rsi_len, rsi_upper, rsi_lower, macd_fast, macd_slow, macd_signal, roc_len, willr_len, htf_gate, htf_rsi)   Parameters:      rsi_len (simple int)      rsi_upper (int)      rsi_lower (int)      macd_fast (simple int)      macd_slow (simple int)      macd_signal (simple int)      roc_len (int)      willr_len (int)      htf_gate (bool)      htf_rsi (float)
Biblioteka Pine Script®
od rithsilanew2020
X VFI (LB) w absorptiona variation of the On-Balance Volume (OBV) introduced by Markos Katsanos and further refined by LazyBear, is a robust volume-based momentum oscillator designed to measure the strength and direction of money flow. It utilizes advanced filtering mechanisms to enhance signal quality for active trading environments. This version has added an absorption feature. Core Functionality and Enhancements Filtered Volume Flow: The VFI is calculated using the Typical Price (HLC/3) and incorporates filters for Volatility (coef) and Excessive Volume (vcoef). This ensures the indicator responds only to price changes supported by sustained, relevant volume, filtering out market noise and anomalous spikes. Zero-Line Bias: VFI values above zero indicate net accumulation (bullish flow), while values below zero indicate net distribution (bearish flow). Signal Line Timing (vfima): The Exponential Moving Average (EMA) of the VFI acts as the Signal Line. Crossovers between the VFI (fast line) and the Signal Line are primary triggers for trade entries and exits. Absorption/Distribution Signals This customized version introduces unique features to visually isolate periods where underlying volume conviction contradicts immediate price action—the most powerful setups for reversals and strong continuations. Absorption/Distribution Highlighting: The histogram's color is dynamically changed to highlight hidden buying or selling pressure: (Absorption Signal): Indicates strong positive VFI momentum occurring on a bearish (down) candle. This signals aggressive buying absorption of supply, where large traders are accumulating positions despite brief selling pressure, often preceding a sharp upward move. (Distribution Signal): Indicates strong negative VFI momentum occurring on a bullish (up) candle. This signals aggressive selling distribution into demand, where large traders are offloading positions into brief rallies, often preceding a sharp downward move. Volume-Filtered Conviction: The visual intensity (transparency) of the signal color is adjusted based on a Volume Filter (minVolFilter). Darker, solid colors denote high-conviction signals supported by above-average volume, while transparent colors indicate lower-conviction signals. Histogram Magnification: The magnification input allows users to visually increase the height of the histogram bars (e.g., 2x). This enhances the immediate visual recognition of momentum acceleration or deceleration.
Wskaźnik Pine Script®
od KinetiCapital
ORB + Fair Value Gaps (FVG/iFVG) Suite with Daily 50% MidlineA complete smart-money–focused price-action toolkit combining the New York Open Range Breakout (ORB), ICT-style Fair Value Gaps, Inverted FVGs, and a dynamic Daily 50% Midline. Designed for traders who want a clean, fast, and highly visual way to track liquidity, imbalances, and intraday directional bias. 📌 Key Features 1. NY Session ORB (09:30–09:45 New York Time) Automatically plots: ORB High ORB Low Labels for ORB high/low Optional 5-minute chart restriction Lines extend forward for easy reference Used to identify breakout conditions, liquidity sweeps, and directional bias into the morning session. 📌 2. ICT-Style Fair Value Gaps (FVGs) Full automated detection of bullish & bearish FVGs based on the classic 3-candle displacement structure: Bullish FVG: high < low Bearish FVG: low > high Each FVG is drawn as a box with: Custom colour Custom border style (solid, dashed, dotted) Automatic extension to the right until filled Optional size text showing the gap in points (font size/colour adjustable) Adjustable max lookback for performance 📌 3. Inverted FVGs (iFVGs) Once price fully fills an FVG, it automatically becomes an iFVG, shown with: Custom iFVG colour Custom border style Extension to the right Once price trades through the zone from the opposite side, the iFVG is considered “consumed” and: It stops extending And optionally auto-deletes based on user settings This makes it easy to track meaningful imbalances that turn into liquidity pockets. 📌 4. “Show Only After ORB” Filter Optionally hide all FVGs/iFVGs formed before the ORB completes. This is especially useful for intraday strategies focused on NY session structure only. 📌 5. Daily 50% Midline (OHLC Midpoint) A dynamic, always-updating midpoint of the current daily candle: Mid = (Daily High + Daily Low) / 2 Features: Custom colour Dashed styling Extends left and right as a horizontal ray Updates live as the daily candle forms Great for bias filters, mean reversion, and daily liquidity zones. 📌 6. Performance-Optimized (Fast!) Built with: Fully configurable max lookback Memory-efficient arrays Auto-cleaning of old FVG/iFVG objects Lightweight daily midline recalculation This allows extremely fast rendering even on 1-minute charts. 📌 7. Alerts Includes a clean alert condition: Price returned to a Fair Value Gap Works for both bullish and bearish FVG revisits. 🎯 Who This Indicator Is For This tool is ideal for traders who use: ICT / SMC concepts Liquidity-based trading ORB strategies Imbalance-driven price action Intraday or NY session-focused setups Futures, crypto, forex, and equities 🎁 Summary This indicator gives you: A clean ORB framework Automatic, dynamic FVG and iFVG analysis Real-time daily candle context Customizable visuals Powerful session filtering Efficient performance All in one clean, intuitive package built for real-time decision making.
Wskaźnik Pine Script®
od jiggggggerr
New Day Opening Gaps (1m 3:29-9:15)Precise Day Opening Gap IndicatorThis custom Pine Script indicator is designed for traders who rely on high-precision gap analysis, particularly for markets like the NSE/BSE.🎯 Core Functionality: Precision Gap IdentificationThis indicator calculates and highlights the exact price gap between the previous day's close and the current day's open. Unlike standard gap analysis that relies on higher timeframes, this script ensures accuracy by strictly using 1-minute data:Previous Close: Captured from the 1-minute candle closing at 3:29 PM (15:29).Current Open: Captured from the 1-minute candle opening at 9:15 AM (09:15).The resulting gap zone is plotted and automatically extends to the right, serving as a critical level for current price interaction.✨ Key Features1. Cross-Timeframe PersistenceThe gap markings are calculated based on the 1-minute chart but are displayed correctly and persist across all timeframes, including 5-minute, 15-minute, 1-hour, and even the Daily chart, ensuring consistency no matter how you analyze the price action.2. Controlled Historical ViewAvoid chart clutter with the "Number of Gaps to Show" input. Easily control how many historical day-opening gaps you want to display on your chart, allowing you to focus only on the most recent and relevant levels.3. Full CustomizationCustomize the look and feel to fit your charting style:Gap Zone: Adjust the color and opacity (transparency) of the gap box.Date Label: Toggle the date label display on/off and control its color, background, opacity, and size. The label is optimally placed at the top-right of the gap zone for clear visibility.🛠️ Recommended UseThis tool is perfect for intraday traders looking to:Identify immediate support and resistance zones based on overnight price action.Track where price action reacts to prior day gaps (filling or holding the gap).Maintain a clear visual reference of daily market openings.
Wskaźnik Pine Script®
od Aviiinash
Dynamic Equity Allocation Model//@version=6 indicator('Dynamic Equity Allocation Model', shorttitle = 'DEAM', overlay = false, precision = 1, scale = scale.right, max_bars_back = 500) // DYNAMIC EQUITY ALLOCATION MODEL // Quantitative framework for dynamic portfolio allocation between stocks and cash. // Analyzes five dimensions: market regime, risk metrics, valuation, sentiment, // and macro conditions to generate allocation recommendations (0-100% equity). // // Uses real-time data from TradingView including fundamentals (P/E, ROE, ERP), // volatility indicators (VIX), credit spreads, yield curves, and market structure. // INPUT PARAMETERS group1 = 'Model Configuration' model_type = input.string('Adaptive', 'Allocation Model Type', options = , group = group1, tooltip = 'Conservative: Slower to increase equity, Aggressive: Faster allocation changes, Adaptive: Dynamic based on regime') use_crisis_detection = input.bool(true, 'Enable Crisis Detection System', group = group1, tooltip = 'Automatic detection and response to crisis conditions') use_regime_model = input.bool(true, 'Use Market Regime Detection', group = group1, tooltip = 'Identify Bull/Bear/Crisis regimes for dynamic allocation') group2 = 'Portfolio Risk Management' target_portfolio_volatility = input.float(12.0, 'Target Portfolio Volatility (%)', minval = 3, maxval = 20, step = 0.5, group = group2, tooltip = 'Target portfolio volatility (Cash reduces volatility: 50% Equity = ~10% vol, 100% Equity = ~20% vol)') max_portfolio_drawdown = input.float(15.0, 'Maximum Portfolio Drawdown (%)', minval = 5, maxval = 35, step = 2.5, group = group2, tooltip = 'Maximum acceptable PORTFOLIO drawdown (not market drawdown - portfolio with cash has lower drawdown)') enable_portfolio_risk_scaling = input.bool(true, 'Enable Portfolio Risk Scaling', group = group2, tooltip = 'Scale allocation based on actual portfolio risk characteristics (recommended)') risk_lookback = input.int(252, 'Risk Calculation Period (Days)', minval = 60, maxval = 504, group = group2, tooltip = 'Period for calculating volatility and risk metrics') group3 = 'Component Weights (Total = 100%)' w_regime = input.float(35.0, 'Market Regime Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_risk = input.float(25.0, 'Risk Metrics Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_valuation = input.float(20.0, 'Valuation Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_sentiment = input.float(15.0, 'Sentiment Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_macro = input.float(5.0, 'Macro Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) group4 = 'Crisis Detection Thresholds' crisis_vix_threshold = input.float(40, 'Crisis VIX Level', minval = 30, maxval = 80, group = group4, tooltip = 'VIX level indicating crisis conditions (COVID peaked at 82)') crisis_drawdown_threshold = input.float(15, 'Crisis Drawdown Threshold (%)', minval = 10, maxval = 30, group = group4, tooltip = 'Market drawdown indicating crisis conditions') crisis_credit_spread = input.float(500, 'Crisis Credit Spread (bps)', minval = 300, maxval = 1000, group = group4, tooltip = 'High yield spread indicating crisis conditions') group5 = 'Display Settings' show_components = input.bool(false, 'Show Component Breakdown', group = group5, tooltip = 'Display individual component analysis lines') show_regime_background = input.bool(true, 'Show Dynamic Background', group = group5, tooltip = 'Color background based on allocation signals') show_reference_lines = input.bool(false, 'Show Reference Lines', group = group5, tooltip = 'Display allocation percentage reference lines') show_dashboard = input.bool(true, 'Show Analytics Dashboard', group = group5, tooltip = 'Display comprehensive analytics table') show_confidence_bands = input.bool(false, 'Show Confidence Bands', group = group5, tooltip = 'Display uncertainty quantification bands') smoothing_period = input.int(3, 'Smoothing Period', minval = 1, maxval = 10, group = group5, tooltip = 'Smoothing to reduce allocation noise') background_intensity = input.int(95, 'Background Intensity (%)', minval = 90, maxval = 99, group = group5, tooltip = 'Higher values = more transparent background') // Styling Options color_scheme = input.string('EdgeTools', 'Color Theme', options = , group = 'Appearance', tooltip = 'Professional color themes') use_dark_mode = input.bool(true, 'Optimize for Dark Theme', group = 'Appearance') main_line_width = input.int(3, 'Main Line Width', minval = 1, maxval = 5, group = 'Appearance') // DATA RETRIEVAL // Market Data sp500 = request.security('SPY', timeframe.period, close) sp500_high = request.security('SPY', timeframe.period, high) sp500_low = request.security('SPY', timeframe.period, low) sp500_volume = request.security('SPY', timeframe.period, volume) // Volatility Indicators vix = request.security('VIX', timeframe.period, close) vix9d = request.security('VIX9D', timeframe.period, close) vxn = request.security('VXN', timeframe.period, close) // Fixed Income and Credit us2y = request.security('US02Y', timeframe.period, close) us10y = request.security('US10Y', timeframe.period, close) us3m = request.security('US03MY', timeframe.period, close) hyg = request.security('HYG', timeframe.period, close) lqd = request.security('LQD', timeframe.period, close) tlt = request.security('TLT', timeframe.period, close) // Safe Haven Assets gold = request.security('GLD', timeframe.period, close) usd = request.security('DXY', timeframe.period, close) yen = request.security('JPYUSD', timeframe.period, close) // Financial data with fallback values get_financial_data(symbol, fin_id, period, fallback) => data = request.financial(symbol, fin_id, period, ignore_invalid_symbol = true) na(data) ? fallback : data // SPY fundamental metrics spy_earnings_per_share = get_financial_data('AMEX:SPY', 'EARNINGS_PER_SHARE_BASIC', 'TTM', 20.0) spy_operating_earnings_yield = get_financial_data('AMEX:SPY', 'OPERATING_EARNINGS_YIELD', 'FY', 4.5) spy_dividend_yield = get_financial_data('AMEX:SPY', 'DIVIDENDS_YIELD', 'FY', 1.8) spy_buyback_yield = get_financial_data('AMEX:SPY', 'BUYBACK_YIELD', 'FY', 2.0) spy_net_margin = get_financial_data('AMEX:SPY', 'NET_MARGIN', 'TTM', 12.0) spy_debt_to_equity = get_financial_data('AMEX:SPY', 'DEBT_TO_EQUITY', 'FY', 0.5) spy_return_on_equity = get_financial_data('AMEX:SPY', 'RETURN_ON_EQUITY', 'FY', 15.0) spy_free_cash_flow = get_financial_data('AMEX:SPY', 'FREE_CASH_FLOW', 'TTM', 100000000) spy_ebitda = get_financial_data('AMEX:SPY', 'EBITDA', 'TTM', 200000000) spy_pe_forward = get_financial_data('AMEX:SPY', 'PRICE_EARNINGS_FORWARD', 'FY', 18.0) spy_total_debt = get_financial_data('AMEX:SPY', 'TOTAL_DEBT', 'FY', 500000000) spy_total_equity = get_financial_data('AMEX:SPY', 'TOTAL_EQUITY', 'FY', 1000000000) spy_enterprise_value = get_financial_data('AMEX:SPY', 'ENTERPRISE_VALUE', 'FY', 30000000000) spy_revenue_growth = get_financial_data('AMEX:SPY', 'REVENUE_ONE_YEAR_GROWTH', 'TTM', 5.0) // Market Breadth Indicators nya = request.security('NYA', timeframe.period, close) rut = request.security('IWM', timeframe.period, close) // Sector Performance xlk = request.security('XLK', timeframe.period, close) xlu = request.security('XLU', timeframe.period, close) xlf = request.security('XLF', timeframe.period, close) // MARKET REGIME DETECTION // Calculate Market Trend sma_20 = ta.sma(sp500, 20) sma_50 = ta.sma(sp500, 50) sma_200 = ta.sma(sp500, 200) ema_10 = ta.ema(sp500, 10) // Market Structure Score trend_strength = 0.0 trend_strength := trend_strength + (sp500 > sma_20 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_50 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_200 ? 2 : -2) trend_strength := trend_strength + (sma_50 > sma_200 ? 2 : -2) // Volatility Regime returns = math.log(sp500 / sp500 ) realized_vol_20d = ta.stdev(returns, 20) * math.sqrt(252) * 100 realized_vol_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 ewma_vol = ta.ema(math.pow(returns, 2), 20) realized_vol = math.sqrt(ewma_vol * 252) * 100 vol_premium = vix - realized_vol // Drawdown Calculation running_max = ta.highest(sp500, risk_lookback) current_drawdown = (running_max - sp500) / running_max * 100 // Regime Score regime_score = 0.0 // Trend Component (40%) if trend_strength >= 4 regime_score := regime_score + 40 regime_score else if trend_strength >= 2 regime_score := regime_score + 30 regime_score else if trend_strength >= 0 regime_score := regime_score + 20 regime_score else if trend_strength >= -2 regime_score := regime_score + 10 regime_score else regime_score := regime_score + 0 regime_score // Volatility Component (30%) if vix < 15 regime_score := regime_score + 30 regime_score else if vix < 20 regime_score := regime_score + 25 regime_score else if vix < 25 regime_score := regime_score + 15 regime_score else if vix < 35 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Drawdown Component (30%) if current_drawdown < 3 regime_score := regime_score + 30 regime_score else if current_drawdown < 7 regime_score := regime_score + 20 regime_score else if current_drawdown < 12 regime_score := regime_score + 10 regime_score else if current_drawdown < 20 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Classify Regime market_regime = regime_score >= 80 ? 'Strong Bull' : regime_score >= 60 ? 'Bull Market' : regime_score >= 40 ? 'Neutral' : regime_score >= 20 ? 'Correction' : regime_score >= 10 ? 'Bear Market' : 'Crisis' // RISK-BASED ALLOCATION // Calculate Market Risk parkinson_hl = math.log(sp500_high / sp500_low) parkinson_vol = parkinson_hl / (2 * math.sqrt(math.log(2))) * math.sqrt(252) * 100 garman_klass_vol = math.sqrt((0.5 * math.pow(math.log(sp500_high / sp500_low), 2) - (2 * math.log(2) - 1) * math.pow(math.log(sp500 / sp500 ), 2)) * 252) * 100 market_volatility_20d = math.max(ta.stdev(returns, 20) * math.sqrt(252) * 100, parkinson_vol) market_volatility_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 market_drawdown = current_drawdown // Initialize risk allocation risk_allocation = 50.0 if enable_portfolio_risk_scaling // Volatility-based allocation vol_based_allocation = target_portfolio_volatility / math.max(market_volatility_20d, 5.0) * 100 vol_based_allocation := math.max(0, math.min(100, vol_based_allocation)) // Drawdown-based allocation dd_based_allocation = 100.0 if market_drawdown > 1.0 dd_based_allocation := max_portfolio_drawdown / market_drawdown * 100 dd_based_allocation := math.max(0, math.min(100, dd_based_allocation)) dd_based_allocation // Combine (conservative) risk_allocation := math.min(vol_based_allocation, dd_based_allocation) // Dynamic adjustment current_equity_estimate = 50.0 estimated_portfolio_vol = current_equity_estimate / 100 * market_volatility_20d estimated_portfolio_dd = current_equity_estimate / 100 * market_drawdown vol_utilization = estimated_portfolio_vol / target_portfolio_volatility dd_utilization = estimated_portfolio_dd / max_portfolio_drawdown risk_utilization = math.max(vol_utilization, dd_utilization) risk_adjustment_factor = 1.0 if risk_utilization > 1.0 risk_adjustment_factor := math.exp(-0.5 * (risk_utilization - 1.0)) risk_adjustment_factor := math.max(0.5, risk_adjustment_factor) risk_adjustment_factor else if risk_utilization < 0.9 risk_adjustment_factor := 1.0 + 0.2 * math.log(1.0 / risk_utilization) risk_adjustment_factor := math.min(1.3, risk_adjustment_factor) risk_adjustment_factor risk_allocation := risk_allocation * risk_adjustment_factor risk_allocation else vol_scalar = target_portfolio_volatility / math.max(market_volatility_20d, 10) vol_scalar := math.min(1.5, math.max(0.2, vol_scalar)) drawdown_penalty = 0.0 if current_drawdown > max_portfolio_drawdown drawdown_penalty := (current_drawdown - max_portfolio_drawdown) / max_portfolio_drawdown drawdown_penalty := math.min(1.0, drawdown_penalty) drawdown_penalty risk_allocation := 100 * vol_scalar * (1 - drawdown_penalty) risk_allocation risk_allocation := math.max(0, math.min(100, risk_allocation)) // VALUATION ANALYSIS // Valuation Metrics actual_pe_ratio = spy_earnings_per_share > 0 ? sp500 / spy_earnings_per_share : spy_pe_forward actual_earnings_yield = nz(spy_operating_earnings_yield, 0) > 0 ? spy_operating_earnings_yield : 100 / actual_pe_ratio total_shareholder_yield = spy_dividend_yield + spy_buyback_yield // Equity Risk Premium (multi-method calculation) method1_erp = actual_earnings_yield - us10y method2_erp = actual_earnings_yield + spy_buyback_yield - us10y payout_ratio = spy_dividend_yield > 0 and actual_earnings_yield > 0 ? spy_dividend_yield / actual_earnings_yield : 0.4 sustainable_growth = spy_return_on_equity * (1 - payout_ratio) / 100 method3_erp = spy_dividend_yield + sustainable_growth * 100 - us10y implied_growth = spy_revenue_growth * 0.7 method4_erp = total_shareholder_yield + implied_growth - us10y equity_risk_premium = method1_erp * 0.35 + method2_erp * 0.30 + method3_erp * 0.20 + method4_erp * 0.15 ev_ebitda_ratio = spy_enterprise_value > 0 and spy_ebitda > 0 ? spy_enterprise_value / spy_ebitda : 15.0 debt_equity_health = spy_debt_to_equity < 1.0 ? 1.2 : spy_debt_to_equity < 2.0 ? 1.0 : 0.8 // Valuation Score base_valuation_score = 50.0 if equity_risk_premium > 4 base_valuation_score := 95 base_valuation_score else if equity_risk_premium > 3 base_valuation_score := 85 base_valuation_score else if equity_risk_premium > 2 base_valuation_score := 70 base_valuation_score else if equity_risk_premium > 1 base_valuation_score := 55 base_valuation_score else if equity_risk_premium > 0 base_valuation_score := 40 base_valuation_score else if equity_risk_premium > -1 base_valuation_score := 25 base_valuation_score else base_valuation_score := 10 base_valuation_score growth_adjustment = spy_revenue_growth > 10 ? 10 : spy_revenue_growth > 5 ? 5 : 0 margin_adjustment = spy_net_margin > 15 ? 5 : spy_net_margin < 8 ? -5 : 0 roe_adjustment = spy_return_on_equity > 20 ? 5 : spy_return_on_equity < 10 ? -5 : 0 valuation_score = base_valuation_score + growth_adjustment + margin_adjustment + roe_adjustment valuation_score := math.max(0, math.min(100, valuation_score * debt_equity_health)) // SENTIMENT ANALYSIS // VIX Term Structure vix_term_structure = vix9d > 0 ? vix / vix9d : 1 backwardation = vix_term_structure > 1.05 steep_backwardation = vix_term_structure > 1.15 // Safe Haven Flows gold_momentum = ta.roc(gold, 20) dollar_momentum = ta.roc(usd, 20) yen_momentum = ta.roc(yen, 20) treasury_momentum = ta.roc(tlt, 20) safe_haven_flow = gold_momentum * 0.3 + treasury_momentum * 0.3 + dollar_momentum * 0.25 + yen_momentum * 0.15 // Advanced Sentiment Analysis vix_percentile = ta.percentrank(vix, 252) vix_zscore = (vix - ta.sma(vix, 252)) / ta.stdev(vix, 252) vix_momentum = ta.roc(vix, 5) vvix_proxy = ta.stdev(vix_momentum, 20) * math.sqrt(252) risk_reversal_proxy = (vix - realized_vol) / realized_vol // Sentiment Score base_sentiment = 50.0 vix_adjustment = 0.0 if vix_zscore < -1.5 vix_adjustment := 40 vix_adjustment else if vix_zscore < -0.5 vix_adjustment := 20 vix_adjustment else if vix_zscore < 0.5 vix_adjustment := 0 vix_adjustment else if vix_zscore < 1.5 vix_adjustment := -20 vix_adjustment else vix_adjustment := -40 vix_adjustment term_structure_adjustment = backwardation ? -15 : steep_backwardation ? -30 : 5 vvix_adjustment = vvix_proxy > 2.0 ? -10 : vvix_proxy < 1.0 ? 10 : 0 sentiment_score = base_sentiment + vix_adjustment + term_structure_adjustment + vvix_adjustment sentiment_score := math.max(0, math.min(100, sentiment_score)) // MACRO ANALYSIS // Yield Curve yield_spread_2_10 = us10y - us2y yield_spread_3m_10 = us10y - us3m // Credit Conditions hyg_return = ta.roc(hyg, 20) lqd_return = ta.roc(lqd, 20) tlt_return = ta.roc(tlt, 20) hyg_duration = 4.0 lqd_duration = 8.0 tlt_duration = 17.0 hyg_log_returns = math.log(hyg / hyg ) lqd_log_returns = math.log(lqd / lqd ) hyg_volatility = ta.stdev(hyg_log_returns, 20) * math.sqrt(252) lqd_volatility = ta.stdev(lqd_log_returns, 20) * math.sqrt(252) hyg_yield_proxy = -math.log(hyg / hyg ) * 100 lqd_yield_proxy = -math.log(lqd / lqd ) * 100 tlt_yield = us10y hyg_spread = (hyg_yield_proxy - tlt_yield) * 100 lqd_spread = (lqd_yield_proxy - tlt_yield) * 100 hyg_distance = (hyg - ta.lowest(hyg, 252)) / (ta.highest(hyg, 252) - ta.lowest(hyg, 252)) lqd_distance = (lqd - ta.lowest(lqd, 252)) / (ta.highest(lqd, 252) - ta.lowest(lqd, 252)) default_risk_proxy = 2.0 - (hyg_distance + lqd_distance) credit_spread = hyg_spread * 0.5 + (hyg_volatility - lqd_volatility) * 1000 * 0.3 + default_risk_proxy * 200 * 0.2 credit_spread := math.max(50, credit_spread) credit_market_health = hyg_return > lqd_return ? 1 : -1 flight_to_quality = tlt_return > (hyg_return + lqd_return) / 2 // Macro Score macro_score = 50.0 yield_curve_score = 0 if yield_spread_2_10 > 1.5 and yield_spread_3m_10 > 2 yield_curve_score := 40 yield_curve_score else if yield_spread_2_10 > 0.5 and yield_spread_3m_10 > 1 yield_curve_score := 30 yield_curve_score else if yield_spread_2_10 > 0 and yield_spread_3m_10 > 0 yield_curve_score := 20 yield_curve_score else if yield_spread_2_10 < 0 or yield_spread_3m_10 < 0 yield_curve_score := 10 yield_curve_score else yield_curve_score := 5 yield_curve_score credit_conditions_score = 0 if credit_spread < 200 and not flight_to_quality credit_conditions_score := 30 credit_conditions_score else if credit_spread < 400 and credit_market_health > 0 credit_conditions_score := 20 credit_conditions_score else if credit_spread < 600 credit_conditions_score := 15 credit_conditions_score else if credit_spread < 1000 credit_conditions_score := 10 credit_conditions_score else credit_conditions_score := 0 credit_conditions_score financial_stability_score = 0 if spy_debt_to_equity < 0.5 and spy_return_on_equity > 15 financial_stability_score := 20 financial_stability_score else if spy_debt_to_equity < 1.0 and spy_return_on_equity > 10 financial_stability_score := 15 financial_stability_score else if spy_debt_to_equity < 1.5 financial_stability_score := 10 financial_stability_score else financial_stability_score := 5 financial_stability_score macro_score := yield_curve_score + credit_conditions_score + financial_stability_score macro_score := math.max(0, math.min(100, macro_score)) // CRISIS DETECTION crisis_indicators = 0 if vix > crisis_vix_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if vix > 60 crisis_indicators := crisis_indicators + 2 crisis_indicators if current_drawdown > crisis_drawdown_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if current_drawdown > 25 crisis_indicators := crisis_indicators + 1 crisis_indicators if credit_spread > crisis_credit_spread crisis_indicators := crisis_indicators + 1 crisis_indicators sp500_roc_5 = ta.roc(sp500, 5) tlt_roc_5 = ta.roc(tlt, 5) if sp500_roc_5 < -10 and tlt_roc_5 < -5 crisis_indicators := crisis_indicators + 2 crisis_indicators volume_spike = sp500_volume > ta.sma(sp500_volume, 20) * 2 sp500_roc_1 = ta.roc(sp500, 1) if volume_spike and sp500_roc_1 < -3 crisis_indicators := crisis_indicators + 1 crisis_indicators is_crisis = crisis_indicators >= 3 is_severe_crisis = crisis_indicators >= 5 // FINAL ALLOCATION CALCULATION // Convert regime to base allocation regime_allocation = market_regime == 'Strong Bull' ? 100 : market_regime == 'Bull Market' ? 80 : market_regime == 'Neutral' ? 60 : market_regime == 'Correction' ? 40 : market_regime == 'Bear Market' ? 20 : 0 // Normalize weights total_weight = w_regime + w_risk + w_valuation + w_sentiment + w_macro w_regime_norm = w_regime / total_weight w_risk_norm = w_risk / total_weight w_valuation_norm = w_valuation / total_weight w_sentiment_norm = w_sentiment / total_weight w_macro_norm = w_macro / total_weight // Calculate Weighted Allocation weighted_allocation = regime_allocation * w_regime_norm + risk_allocation * w_risk_norm + valuation_score * w_valuation_norm + sentiment_score * w_sentiment_norm + macro_score * w_macro_norm // Apply Crisis Override if use_crisis_detection if is_severe_crisis weighted_allocation := math.min(weighted_allocation, 10) weighted_allocation else if is_crisis weighted_allocation := math.min(weighted_allocation, 25) weighted_allocation // Model Type Adjustment model_adjustment = 0.0 if model_type == 'Conservative' model_adjustment := -10 model_adjustment else if model_type == 'Aggressive' model_adjustment := 10 model_adjustment else if model_type == 'Adaptive' recent_return = (sp500 - sp500 ) / sp500 * 100 if recent_return > 5 model_adjustment := 5 model_adjustment else if recent_return < -5 model_adjustment := -5 model_adjustment // Apply adjustment and bounds final_allocation = weighted_allocation + model_adjustment final_allocation := math.max(0, math.min(100, final_allocation)) // Smooth allocation smoothed_allocation = ta.sma(final_allocation, smoothing_period) // Calculate portfolio risk metrics (only for internal alerts) actual_portfolio_volatility = smoothed_allocation / 100 * market_volatility_20d actual_portfolio_drawdown = smoothed_allocation / 100 * current_drawdown // VISUALIZATION // Color definitions var color primary_color = #2196F3 var color bullish_color = #4CAF50 var color bearish_color = #FF5252 var color neutral_color = #808080 var color text_color = color.white var color bg_color = #000000 var color table_bg_color = #1E1E1E var color header_bg_color = #2D2D2D switch color_scheme // Apply color scheme 'Gold' => primary_color := use_dark_mode ? #FFD700 : #DAA520 bullish_color := use_dark_mode ? #FFA500 : #FF8C00 bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #C0C0C0 : #808080 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A00 : #FFFEF0 header_bg_color := use_dark_mode ? #2D2600 : #F5F5DC header_bg_color 'EdgeTools' => primary_color := use_dark_mode ? #4682B4 : #1E90FF bullish_color := use_dark_mode ? #4CAF50 : #388E3C bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #708090 : #696969 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0F1419 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A3A : #E6F3FF header_bg_color 'Behavioral' => primary_color := #808080 bullish_color := #00FF00 bearish_color := #8B0000 neutral_color := #FFBF00 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A1A : #F8F8F8 header_bg_color := use_dark_mode ? #2D2D2D : #E8E8E8 header_bg_color 'Quant' => primary_color := #808080 bullish_color := #FFA500 bearish_color := #8B0000 neutral_color := #4682B4 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0D0D0D : #FAFAFA header_bg_color := use_dark_mode ? #1A1A1A : #F0F0F0 header_bg_color 'Ocean' => primary_color := use_dark_mode ? #20B2AA : #008B8B bullish_color := use_dark_mode ? #00CED1 : #4682B4 bearish_color := use_dark_mode ? #FF4500 : #B22222 neutral_color := use_dark_mode ? #87CEEB : #2F4F4F text_color := use_dark_mode ? #F0F8FF : #191970 bg_color := use_dark_mode ? #001F3F : #F0F8FF table_bg_color := use_dark_mode ? #001A2E : #E6F7FF header_bg_color := use_dark_mode ? #002A47 : #CCF2FF header_bg_color 'Fire' => primary_color := use_dark_mode ? #FF6347 : #DC143C bullish_color := use_dark_mode ? #FFD700 : #FF8C00 bearish_color := use_dark_mode ? #8B0000 : #800000 neutral_color := use_dark_mode ? #FFA500 : #CD853F text_color := use_dark_mode ? #FFFAF0 : #2F1B14 bg_color := use_dark_mode ? #2F1B14 : #FFFAF0 table_bg_color := use_dark_mode ? #261611 : #FFF8F0 header_bg_color := use_dark_mode ? #3D241A : #FFE4CC header_bg_color 'Matrix' => primary_color := use_dark_mode ? #00FF41 : #006400 bullish_color := use_dark_mode ? #39FF14 : #228B22 bearish_color := use_dark_mode ? #FF073A : #8B0000 neutral_color := use_dark_mode ? #00FFFF : #008B8B text_color := use_dark_mode ? #C0FF8C : #003300 bg_color := use_dark_mode ? #0D1B0D : #F0FFF0 table_bg_color := use_dark_mode ? #0A1A0A : #E8FFF0 header_bg_color := use_dark_mode ? #112B11 : #CCFFCC header_bg_color 'Arctic' => primary_color := use_dark_mode ? #87CEFA : #4169E1 bullish_color := use_dark_mode ? #00BFFF : #0000CD bearish_color := use_dark_mode ? #FF1493 : #8B008B neutral_color := use_dark_mode ? #B0E0E6 : #483D8B text_color := use_dark_mode ? #F8F8FF : #191970 bg_color := use_dark_mode ? #191970 : #F8F8FF table_bg_color := use_dark_mode ? #141B47 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A5C : #E0F0FF header_bg_color // Transparency settings bg_transparency = use_dark_mode ? 85 : 92 zone_transparency = use_dark_mode ? 90 : 95 band_transparency = use_dark_mode ? 70 : 85 table_transparency = use_dark_mode ? 80 : 15 // Allocation color alloc_color = smoothed_allocation >= 80 ? bullish_color : smoothed_allocation >= 60 ? color.new(bullish_color, 30) : smoothed_allocation >= 40 ? primary_color : smoothed_allocation >= 20 ? color.new(bearish_color, 30) : bearish_color // Dynamic background var color dynamic_bg_color = na if show_regime_background if smoothed_allocation >= 70 dynamic_bg_color := color.new(bullish_color, background_intensity) dynamic_bg_color else if smoothed_allocation <= 30 dynamic_bg_color := color.new(bearish_color, background_intensity) dynamic_bg_color else if smoothed_allocation > 60 or smoothed_allocation < 40 dynamic_bg_color := color.new(primary_color, math.min(99, background_intensity + 2)) dynamic_bg_color bgcolor(dynamic_bg_color, title = 'Allocation Signal Background') // Plot main allocation line plot(smoothed_allocation, 'Equity Allocation %', color = alloc_color, linewidth = math.max(1, main_line_width)) // Reference lines (static colors for hline) hline_bullish_color = color_scheme == 'Gold' ? use_dark_mode ? #FFA500 : #FF8C00 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4CAF50 : #388E3C : color_scheme == 'Behavioral' ? #00FF00 : color_scheme == 'Quant' ? #FFA500 : color_scheme == 'Ocean' ? use_dark_mode ? #00CED1 : #4682B4 : color_scheme == 'Fire' ? use_dark_mode ? #FFD700 : #FF8C00 : color_scheme == 'Matrix' ? use_dark_mode ? #39FF14 : #228B22 : color_scheme == 'Arctic' ? use_dark_mode ? #00BFFF : #0000CD : #4CAF50 hline_bearish_color = color_scheme == 'Gold' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'EdgeTools' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'Behavioral' ? #8B0000 : color_scheme == 'Quant' ? #8B0000 : color_scheme == 'Ocean' ? use_dark_mode ? #FF4500 : #B22222 : color_scheme == 'Fire' ? use_dark_mode ? #8B0000 : #800000 : color_scheme == 'Matrix' ? use_dark_mode ? #FF073A : #8B0000 : color_scheme == 'Arctic' ? use_dark_mode ? #FF1493 : #8B008B : #FF5252 hline_primary_color = color_scheme == 'Gold' ? use_dark_mode ? #FFD700 : #DAA520 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4682B4 : #1E90FF : color_scheme == 'Behavioral' ? #808080 : color_scheme == 'Quant' ? #808080 : color_scheme == 'Ocean' ? use_dark_mode ? #20B2AA : #008B8B : color_scheme == 'Fire' ? use_dark_mode ? #FF6347 : #DC143C : color_scheme == 'Matrix' ? use_dark_mode ? #00FF41 : #006400 : color_scheme == 'Arctic' ? use_dark_mode ? #87CEFA : #4169E1 : #2196F3 hline(show_reference_lines ? 100 : na, '100% Equity', color = color.new(hline_bullish_color, 70), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 80 : na, '80% Equity', color = color.new(hline_bullish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 60 : na, '60% Equity', color = color.new(hline_bullish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(50, '50% Balanced', color = color.new(hline_primary_color, 50), linestyle = hline.style_solid, linewidth = 2) hline(show_reference_lines ? 40 : na, '40% Equity', color = color.new(hline_bearish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 20 : na, '20% Equity', color = color.new(hline_bearish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 0 : na, '0% Equity', color = color.new(hline_bearish_color, 70), linestyle = hline.style_dotted, linewidth = 1) // Component plots plot(show_components ? regime_allocation : na, 'Regime', color = color.new(#4ECDC4, 70), linewidth = 1) plot(show_components ? risk_allocation : na, 'Risk', color = color.new(#FF6B6B, 70), linewidth = 1) plot(show_components ? valuation_score : na, 'Valuation', color = color.new(#45B7D1, 70), linewidth = 1) plot(show_components ? sentiment_score : na, 'Sentiment', color = color.new(#FFD93D, 70), linewidth = 1) plot(show_components ? macro_score : na, 'Macro', color = color.new(#6BCF7F, 70), linewidth = 1) // Confidence bands upper_band = plot(show_confidence_bands ? math.min(100, smoothed_allocation + ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Upper Band') lower_band = plot(show_confidence_bands ? math.max(0, smoothed_allocation - ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Lower Band') fill(upper_band, lower_band, color = show_confidence_bands ? color.new(neutral_color, zone_transparency) : na, title = 'Uncertainty') // DASHBOARD if show_dashboard and barstate.islast var table dashboard = table.new(position.top_right, 2, 20, border_width = 1, bgcolor = color.new(table_bg_color, table_transparency)) table.clear(dashboard, 0, 0, 1, 19) // Header header_color = color.new(header_bg_color, 20) dashboard_text_color = text_color table.cell(dashboard, 0, 0, 'DEAM', text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) table.cell(dashboard, 1, 0, model_type, text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) // Core metrics table.cell(dashboard, 0, 1, 'Equity Allocation', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 1, str.tostring(smoothed_allocation, '##.#') + '%', text_color = alloc_color, text_size = size.small) table.cell(dashboard, 0, 2, 'Cash Allocation', text_color = dashboard_text_color, text_size = size.small) cash_color = 100 - smoothed_allocation > 70 ? bearish_color : primary_color table.cell(dashboard, 1, 2, str.tostring(100 - smoothed_allocation, '##.#') + '%', text_color = cash_color, text_size = size.small) // Signal signal_text = 'NEUTRAL' signal_color = primary_color if smoothed_allocation >= 70 signal_text := 'BULLISH' signal_color := bullish_color signal_color else if smoothed_allocation <= 30 signal_text := 'BEARISH' signal_color := bearish_color signal_color table.cell(dashboard, 0, 3, 'Signal', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 3, signal_text, text_color = signal_color, text_size = size.small) // Market Regime table.cell(dashboard, 0, 4, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_color_display = market_regime == 'Strong Bull' or market_regime == 'Bull Market' ? bullish_color : market_regime == 'Neutral' ? primary_color : market_regime == 'Crisis' ? bearish_color : bearish_color table.cell(dashboard, 1, 4, market_regime, text_color = regime_color_display, text_size = size.small) // VIX table.cell(dashboard, 0, 5, 'VIX Level', text_color = dashboard_text_color, text_size = size.small) vix_color_display = vix < 20 ? bullish_color : vix < 30 ? primary_color : bearish_color table.cell(dashboard, 1, 5, str.tostring(vix, '##.##'), text_color = vix_color_display, text_size = size.small) // Market Drawdown table.cell(dashboard, 0, 6, 'Market DD', text_color = dashboard_text_color, text_size = size.small) market_dd_color = current_drawdown < 5 ? bullish_color : current_drawdown < 10 ? primary_color : bearish_color table.cell(dashboard, 1, 6, '-' + str.tostring(current_drawdown, '##.#') + '%', text_color = market_dd_color, text_size = size.small) // Crisis Detection table.cell(dashboard, 0, 7, 'Crisis Detection', text_color = dashboard_text_color, text_size = size.small) crisis_text = is_severe_crisis ? 'SEVERE' : is_crisis ? 'CRISIS' : 'Normal' crisis_display_color = is_severe_crisis or is_crisis ? bearish_color : bullish_color table.cell(dashboard, 1, 7, crisis_text, text_color = crisis_display_color, text_size = size.small) // Real Data Section financial_bg = color.new(primary_color, 85) table.cell(dashboard, 0, 8, 'REAL DATA', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) table.cell(dashboard, 1, 8, 'Live Metrics', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) // P/E Ratio table.cell(dashboard, 0, 9, 'P/E Ratio', text_color = dashboard_text_color, text_size = size.small) pe_color = actual_pe_ratio < 18 ? bullish_color : actual_pe_ratio < 25 ? primary_color : bearish_color table.cell(dashboard, 1, 9, str.tostring(actual_pe_ratio, '##.#'), text_color = pe_color, text_size = size.small) // ERP table.cell(dashboard, 0, 10, 'ERP', text_color = dashboard_text_color, text_size = size.small) erp_color = equity_risk_premium > 2 ? bullish_color : equity_risk_premium > 0 ? primary_color : bearish_color table.cell(dashboard, 1, 10, str.tostring(equity_risk_premium, '##.##') + '%', text_color = erp_color, text_size = size.small) // ROE table.cell(dashboard, 0, 11, 'ROE', text_color = dashboard_text_color, text_size = size.small) roe_color = spy_return_on_equity > 20 ? bullish_color : spy_return_on_equity > 10 ? primary_color : bearish_color table.cell(dashboard, 1, 11, str.tostring(spy_return_on_equity, '##.#') + '%', text_color = roe_color, text_size = size.small) // D/E Ratio table.cell(dashboard, 0, 12, 'D/E Ratio', text_color = dashboard_text_color, text_size = size.small) de_color = spy_debt_to_equity < 0.5 ? bullish_color : spy_debt_to_equity < 1.0 ? primary_color : bearish_color table.cell(dashboard, 1, 12, str.tostring(spy_debt_to_equity, '##.##'), text_color = de_color, text_size = size.small) // Shareholder Yield table.cell(dashboard, 0, 13, 'Dividend+Buyback', text_color = dashboard_text_color, text_size = size.small) yield_color = total_shareholder_yield > 4 ? bullish_color : total_shareholder_yield > 2 ? primary_color : bearish_color table.cell(dashboard, 1, 13, str.tostring(total_shareholder_yield, '##.#') + '%', text_color = yield_color, text_size = size.small) // Component Scores component_bg = color.new(neutral_color, 80) table.cell(dashboard, 0, 14, 'Components', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 1, 14, 'Scores', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 0, 15, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_score_color = regime_allocation > 60 ? bullish_color : regime_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 15, str.tostring(regime_allocation, '##'), text_color = regime_score_color, text_size = size.small) table.cell(dashboard, 0, 16, 'Risk', text_color = dashboard_text_color, text_size = size.small) risk_score_color = risk_allocation > 60 ? bullish_color : risk_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 16, str.tostring(risk_allocation, '##'), text_color = risk_score_color, text_size = size.small) table.cell(dashboard, 0, 17, 'Valuation', text_color = dashboard_text_color, text_size = size.small) val_score_color = valuation_score > 60 ? bullish_color : valuation_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 17, str.tostring(valuation_score, '##'), text_color = val_score_color, text_size = size.small) table.cell(dashboard, 0, 18, 'Sentiment', text_color = dashboard_text_color, text_size = size.small) sent_score_color = sentiment_score > 60 ? bullish_color : sentiment_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 18, str.tostring(sentiment_score, '##'), text_color = sent_score_color, text_size = size.small) table.cell(dashboard, 0, 19, 'Macro', text_color = dashboard_text_color, text_size = size.small) macro_score_color = macro_score > 60 ? bullish_color : macro_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 19, str.tostring(macro_score, '##'), text_color = macro_score_color, text_size = size.small) // ALERTS // Major allocation changes alertcondition(smoothed_allocation >= 80 and smoothed_allocation < 80, 'High Equity Allocation', 'Equity allocation reached 80% - Bull market conditions') alertcondition(smoothed_allocation <= 20 and smoothed_allocation > 20, 'Low Equity Allocation', 'Equity allocation dropped to 20% - Defensive positioning') // Crisis alerts alertcondition(is_crisis and not is_crisis , 'CRISIS DETECTED', 'Crisis conditions detected - Reducing equity allocation') alertcondition(is_severe_crisis and not is_severe_crisis , 'SEVERE CRISIS', 'Severe crisis detected - Maximum defensive positioning') // Regime changes regime_changed = market_regime != market_regime alertcondition(regime_changed, 'Regime Change', 'Market regime has changed') // Risk management alerts risk_breach = enable_portfolio_risk_scaling and (actual_portfolio_volatility > target_portfolio_volatility * 1.2 or actual_portfolio_drawdown > max_portfolio_drawdown * 1.2) alertcondition(risk_breach, 'Risk Breach', 'Portfolio risk exceeds target parameters') // USAGE // The indicator displays a recommended equity allocation percentage (0-100%). // Example: 75% allocation = 75% stocks, 25% cash/bonds. // // The model combines market regime analysis (trend, volatility, drawdowns), // risk management (portfolio-level targeting), valuation metrics (P/E, ERP), // sentiment indicators (VIX term structure), and macro factors (yield curve, // credit spreads) into a single allocation signal. // // Crisis detection automatically reduces exposure when multiple warning signals // converge. Alerts available for major allocation shifts and regime changes. // // Designed for SPY/S&P 500 portfolio allocation. Adjust component weights and // risk parameters in settings to match your risk tolerance. View in Pine
Wskaźnik Pine Script®
od Alfred773
Pivot Points Standard + 9/20/50/200 EMA by NK//@version=6 indicator("Pivot Points Standard + 9/20/50/200 EMA", "Pivots+EMA", overlay=true, max_lines_count=500, max_labels_count=500) // --- EMA calculations and plots ema9 = ta.ema(close, 9) ema20 = ta.ema(close, 20) ema50 = ta.ema(close, 50) ema200 = ta.ema(close, 200) plot(ema9, color=color.green, linewidth=2, title="EMA 9") plot(ema20, color=color.red, linewidth=2, title="EMA 20") plot(ema50, color=color.new(color.blue, 0), linewidth=2, title="EMA 50") // dark blue plot(ema200, color=color.black, linewidth=2, title="EMA 200") // --- Pivots Inputs pivotTypeInput = input.string(title="Type", defval="Traditional", options= ) pivotAnchorInput = input.string(title="Pivots Timeframe", defval="Auto", options= ) maxHistoricalPivotsInput = input.int(title="Number of Pivots Back", defval=15, minval=1, maxval=200, display = display.data_window) isDailyBasedInput = input.bool(title="Use Daily-based Values", defval=true, display = display.data_window, tooltip="When this option is unchecked, Pivot Points will use intraday data while calculating on intraday charts. If Extended Hours are displayed on the chart, they will be taken into account during the pivot level calculation. If intraday OHLC values are different from daily-based values (normal for stocks), the pivot levels will also differ.") showLabelsInput = input.bool(title="Show Labels", defval=true, group="labels", display = display.data_window) showPricesInput = input.bool(title="Show Prices", defval=true, group="labels", display = display.data_window) positionLabelsInput = input.string("Left", "Labels Position", options= , group="labels", display = display.data_window, active = showLabelsInput or showPricesInput) linewidthInput = input.int(title="Line Width", defval=1, minval=1, maxval=100, group="levels", display = display.data_window) DEFAULT_COLOR = #FB8C00 showLevel2and3 = pivotTypeInput != "DM" showLevel4 = pivotTypeInput != "DM" and pivotTypeInput != "Fibonacci" showLevel5 = pivotTypeInput == "Traditional" or pivotTypeInput == "Camarilla" pColorInput = input.color(DEFAULT_COLOR, "P ", inline="P", group="levels", display = display.data_window) pShowInput = input.bool(true, "", inline="P", group="levels", display = display.data_window) s1ColorInput = input.color(DEFAULT_COLOR, "S1", inline="S1/R1" , group="levels", display = display.data_window) s1ShowInput = input.bool(true, "", inline="S1/R1", group="levels", display = display.data_window) r1ColorInput = input.color(DEFAULT_COLOR, " R1", inline="S1/R1", group="levels", display = display.data_window) r1ShowInput = input.bool(true, "", inline="S1/R1", group="levels", display = display.data_window) s2ColorInput = input.color(DEFAULT_COLOR, "S2", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) s2ShowInput = input.bool(true, "", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) r2ColorInput = input.color(DEFAULT_COLOR, " R2", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) r2ShowInput = input.bool(true, "", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) s3ColorInput = input.color(DEFAULT_COLOR, "S3", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) s3ShowInput = input.bool(true, "", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) r3ColorInput = input.color(DEFAULT_COLOR, " R3", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) r3ShowInput = input.bool(true, "", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) s4ColorInput = input.color(DEFAULT_COLOR, "S4", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) s4ShowInput = input.bool(true, "", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) r4ColorInput = input.color(DEFAULT_COLOR, " R4", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) r4ShowInput = input.bool(true, "", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) s5ColorInput = input.color(DEFAULT_COLOR, "S5", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) s5ShowInput = input.bool(true, "", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) r5ColorInput = input.color(DEFAULT_COLOR, " R5", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) r5ShowInput = input.bool(true, "", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) type graphicSettings string levelName color levelColor bool showLevel var graphicSettingsArray = array.from( graphicSettings.new(" P", pColorInput, pShowInput), graphicSettings.new("R1", r1ColorInput, r1ShowInput), graphicSettings.new("S1", s1ColorInput, s1ShowInput), graphicSettings.new("R2", r2ColorInput, r2ShowInput), graphicSettings.new("S2", s2ColorInput, s2ShowInput), graphicSettings.new("R3", r3ColorInput, r3ShowInput), graphicSettings.new("S3", s3ColorInput, s3ShowInput), graphicSettings.new("R4", r4ColorInput, r4ShowInput), graphicSettings.new("S4", s4ColorInput, s4ShowInput), graphicSettings.new("R5", r5ColorInput, r5ShowInput), graphicSettings.new("S5", s5ColorInput, s5ShowInput)) autoAnchor = switch timeframe.isintraday => timeframe.multiplier <= 15 ? "1D" : "1W" timeframe.isdaily => "1M" => "12M" pivotTimeframe = switch pivotAnchorInput "Auto" => autoAnchor "Daily" => "1D" "Weekly" => "1W" "Monthly" => "1M" "Quarterly" => "3M" => "12M" pivotYearMultiplier = switch pivotAnchorInput "Biyearly" => 2 "Triyearly" => 3 "Quinquennially" => 5 "Decennially" => 10 => 1 numOfPivotLevels = switch pivotTypeInput "Traditional" => 11 "Camarilla" => 11 "Woodie" => 9 "Classic" => 9 "Fibonacci" => 7 "DM" => 3 type pivotGraphic line pivotLine label pivotLabel method delete(pivotGraphic graphic) => graphic.pivotLine.delete() graphic.pivotLabel.delete() var drawnGraphics = matrix.new() localPivotTimeframeChange = timeframe.change(pivotTimeframe) and year % pivotYearMultiplier == 0 securityPivotTimeframeChange = timeframe.change(timeframe.period) and year % pivotYearMultiplier == 0 pivotTimeframeChangeCounter(condition) => var count = 0 if condition and bar_index > 0 count += 1 count localPivots = ta.pivot_point_levels(pivotTypeInput, localPivotTimeframeChange) securityPivotPointsArray = ta.pivot_point_levels(pivotTypeInput, securityPivotTimeframeChange) securityTimeframe = timeframe.isintraday ? "1D" : timeframe.period = request.security(syminfo.tickerid, pivotTimeframe, , lookahead = barmerge.lookahead_on) pivotPointsArray = isDailyBasedInput ? securityPivots : localPivots affixOldPivots(endTime) => if drawnGraphics.rows() > 0 lastGraphics = drawnGraphics.row(drawnGraphics.rows() - 1) for graphic in lastGraphics graphic.pivotLine.set_x2(endTime) if positionLabelsInput == "Right" graphic.pivotLabel.set_x(endTime) drawNewPivots(startTime) => newGraphics = array.new() for in pivotPointsArray levelSettings = graphicSettingsArray.get(index) if not na(coord) and levelSettings.showLevel lineEndTime = startTime + timeframe.in_seconds(pivotTimeframe) * 1000 * pivotYearMultiplier pivotLine = line.new(startTime, coord, lineEndTime, coord, xloc = xloc.bar_time, color=levelSettings.levelColor, width=linewidthInput) pivotLabel = label.new(x = positionLabelsInput == "Left" ? startTime : lineEndTime, y = coord, text = (showLabelsInput ? levelSettings.levelName + " " : "") + (showPricesInput ? "(" + str.tostring(coord, format.mintick) + ")" : ""), style = positionLabelsInput == "Left" ? label.style_label_right : label.style_label_left, textcolor = levelSettings.levelColor, color = #00000000, xloc=xloc.bar_time) newGraphics.push(pivotGraphic.new(pivotLine, pivotLabel)) drawnGraphics.add_row(array_id = newGraphics) if drawnGraphics.rows() > maxHistoricalPivotsInput oldGraphics = drawnGraphics.remove_row(0) for graphic in oldGraphics graphic.delete() localPivotDrawConditionStatic = not isDailyBasedInput and localPivotTimeframeChange securityPivotDrawConditionStatic = isDailyBasedInput and securityPivotCounter != securityPivotCounter var isMultiYearly = array.from("Biyearly", "Triyearly", "Quinquennially", "Decennially").includes(pivotAnchorInput) localPivotDrawConditionDeveloping = not isDailyBasedInput and time_close == time_close(pivotTimeframe) and not isMultiYearly securityPivotDrawConditionDeveloping = false if (securityPivotDrawConditionStatic or localPivotDrawConditionStatic) affixOldPivots(time) drawNewPivots(time) var FIRST_BAR_TIME = time if (barstate.islastconfirmedhistory and drawnGraphics.columns() == 0) if not na(securityPivots) and securityPivotCounter > 0 if isDailyBasedInput drawNewPivots(FIRST_BAR_TIME) else runtime.error("Not enough intraday data to calculate Pivot Points. Lower the Pivots Timeframe or turn on the 'Use Daily-based Values' option in the indicator settings.") else runtime.error("Not enough data to calculate Pivot Points. Lower the Pivots Timeframe in the indicator settings.")
Wskaźnik Pine Script®
od NagsenKhade