Empiece

Educational

Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages. The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value. DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic: REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative. EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value. EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value. P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value. EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative. FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion. ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns. ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage. YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%. The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index. Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate) Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate) WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that TradingView’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures. In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value.
Indicador Pine Script®
por QuantitativeAlpha
1
Seasonality Heatmap [QuantAlgo]🟢 Overview The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns. 🟢 How It Works 1. Monthly Heatmap How % Return is Calculated: The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month: (Current Month Price - Previous Month Price) / Previous Month Price × 100 Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns What Averages Mean: The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.) This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed What Months Up % Mean: Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month) Calculated as: (Number of Months with Positive Returns / Total Months) × 100 Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months Example: If October shows "64%", then 64% of all historical Octobers had positive returns What Consistency Score Means: A 1-10 rating that measures how predictable and stable a month's returns have been Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year Medium scores (5-7, gray): Moderate consistency with some variability Low scores (1-4, red): High variability with unpredictable behavior across different years Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation What Best Means: Shows the highest percentage return achieved for that specific month, along with the year it occurred Reveals the maximum observed upside and identifies outlier years with exceptional performance Useful for understanding the range of possible outcomes beyond the average Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain What Worst Means: Shows the most negative percentage return for that specific month, along with the year it occurred Reveals maximum observed downside and helps understand the range of historical outcomes Important for risk assessment even in months with positive averages Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss 2. Day-of-Week Heatmap How % Return is Calculated: Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection) Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.) Each cell shows the average performance for that specific day-month combination across all historical data Formula: (Current Day Price - Previous Day Close) / Previous Day Close × 100 What Averages Mean: The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday Identifies broad weekly patterns across the entire dataset Calculated by summing all daily returns for that weekday across all months and dividing by total observations Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset What Days Up % Mean: Shows the percentage of historical occurrences where that weekday had a positive return Calculated as: (Number of Positive Days / Total Days Observed) × 100 Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns What Consistency Score Means: A 1-10 rating measuring how stable that weekday's performance has been across different months Based on the coefficient of variation of daily returns for that weekday across all 12 months High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month Medium scores (5-7, gray): Moderate consistency with some month-to-month variation Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year What Best Means: Shows which calendar month had the strongest average performance for that specific weekday Identifies favorable day-month combinations based on historical data Format shows the month abbreviation and the average return achieved Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset What Worst Means: Shows which calendar month had the weakest average performance for that specific weekday Identifies historically challenging day-month combinations Useful for understanding which month-weekday pairings have shown weaker performance Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset 3. Optimal Timing Table/Summary Table → Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower Based on the observation that buying during historically weaker months may position for subsequent recovery Shows the month name, its average return, and color-coded performance Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period → Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher Based on historical strength patterns in that month Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains → 2nd Best Month to BUY: The second-lowest performing month based on average returns Provides an alternative timing option based on historical patterns Offers flexibility for staged entries or when the primary month doesn't align with strategy Example: Identifies the next-most favorable historical buying period → 2nd Best Month to SELL: The second-highest performing month based on average returns Provides an alternative exit timing based on historical data Useful for staged profit-taking or multiple exit opportunities Identifies the secondary historical strength period Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days). 🟢 Examples Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year. Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent. Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
Indicador Pine Script®
por QuantAlgo
2
RSI VWAP v1 [JopAlgo]RSI VWAP v1.1 made stronger by volume-aware! We know there's nothing new and the original RSI already does an excellent job. We're just working on small, practical improvements – here's our take: The same basic idea, clearer display, and a single, specially developed rolling line: a VWAP of the RSI that incorporates volume (participation) into the calculation. Do you prefer the pure classic? You can still use Wilder or Cutler engines – but the star here is the VW-RSI + rolling line. This RSI also offers the possibility of illustrating a possible POC (Point of Control - or the HAL or VAL) level. However, the indicator does NOT plot any of these levels itself. We have included an illustration in the chart for this! We hope this version makes your decision-making easier. What you’ll see The RSI line with a 50 midline and optional bands: either static 70/30 or adaptive μ±k·σ of the Rolling Line. One smoothing concept only: the Rolling Line (light blue) = VWAP of RSI. Shadow shading between RSI and the Rolling Line (green when RSI > line, red when RSI < line). A lighter tint only on the parts of that shadow that sit above the upper band or below the lower band (quick overbought/oversold context). Simple divergence lines drawn from RSI pivots (green for regular bullish, red for regular bearish). No labels, no buy/sell text—kept deliberately clean. What’s new, and why it helps VW-RSI engine (default): RSI can be computed from volume-weighted up/down moves, so momentum reflects how much traded when price moved—not just the direction. Rolling Line (VWAP of RSI) with pure VWAP adaptation: Low volume: blends toward a faster VWAP so early, thin starts aren’t missed. Volume spikes: blends toward a slower VWAP so a single heavy bar doesn’t whip the curve. You can reveal the Base Rolling (pre-adaptation) line to see exactly how much adaptation is happening. Adaptive bands (optional): Instead of fixed 70/30, use mean ± k·stdev of the Rolling Line over a lookback. Levels breathe with the market—useful in strong trends where static bounds stay pinned. Minimal, readable panel: One smoothing, one story. The shadow tells you who’s in control; the lighter highlight shows stretch beyond your lines. How to read it (fast) Bias: RSI above 50 (and a rising Rolling Line) → bullish bias; below 50 → bearish bias. Trigger: RSI crossing the Rolling Line with the bias (e.g., above 50 and crossing up). Stretch: Near/above the upper band, avoid chasing; near/below the lower band, avoid panic—prefer a cross back through the line. Divergence lines: Use as context, not as standalone signals. They often help you wait for the next cross or avoid late entries into exhaustion. Settings that actually matter RSI Engine: VW-RSI (default), Wilder, or Cutler. Rolling Line Length: the VWAP length on RSI (higher = calmer, lower = earlier). Adaptive behavior (pure VWAP): Speed-up on Low Volume → blends toward fast VWAP (factor of your length). Dampen Spikes (volume z-score) → blends toward slow VWAP. Fast/Slow Factors → how far those fast/slow variants sit from the base length. Bands: choose Static 70/30 or Adaptive μ±k·σ (set the lookback and k). Visuals: show/hide Base Rolling (ref), main shadow, and highlight beyond bands. Signal gating: optional “ignore first bars” per day/session if you dislike open noise. Starter presets Scalp (1–5m): RSI 9–12, Rolling 12–18, FastFactor ~0.5, SlowFactor ~2.0, Adaptive on. Intraday (15m–1H): RSI 10–14, Rolling 18–26, Bands k = 1.0–1.4. Swing (4H–1D): RSI 14–20, Rolling 26–40, Bands k = 1.2–1.8, Adaptive on. Where it shines (and limits) Best: liquid markets where volume structure matters (majors, indices, large caps). Works elsewhere: even with imperfect volume, the shadow + bands remain useful. Limits: very thin/illiquid assets reduce the benefit of volume-weighting—lengthen settings if needed. Attribution & License Based on the concept and baseline implementation of the “Relative Strength Index” by TradingView (Pine v6 built-in). Released as Open-source (MPL-2.0). Please keep the license header and attribution intact. Disclaimer For educational purposes only; not financial advice. Markets carry risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
Indicador Pine Script®
por JopAlgo
4
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF) By: DskyzInvestments What this is LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity. Intended use Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice. Why this is original and useful Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows. Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay). Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass). One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard. Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible. Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic). Mathematical framework (full transparency) 1) Returns, volatility, and speed‑of‑market Log return: rₜ = ln(closeₜ / closeₜ₋₁) Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv | Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε) 2) Trend velocity and Lorentz gamma (time dilation) Trend velocity: v = |close − close | / (vel_len × ATR) Relative speed: v_rel = v / c Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10) Interpretation: γ > 1 compresses market time → use shorter effective windows. 3) Adaptive temporal scale Adaptive length: L = base_len / γ^power (bounded for safety) Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio 4) Lorentzian smoothing and Harmonic Flow Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ Z‑deviation: z_h = (close − lw_h)/ATR Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long) 5) Flow kinematics Velocity: HFL_vel = HFL − HFL Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL 6) Squeeze and temporal compression Bollinger width vs Keltner width using L Squeeze: BB_width < KC_width × squeeze_mult Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time 7) Entropy (regime complexity) Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime. 8) Memory bank and Lorentzian k‑NN Feature vector (5D): Outcomes stored: forward returns at H5, H13, H34 Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights Age fading: weight_age = mem_fade^age_bars Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ Assurance: mem_assurance = Σ sᵢ (confidence mass) Normalization: mem_bias normalized by ATR and clamped into band Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation. 9) Blended score and breakout proxy Blend factor: α_mem = 0.45 + 0.15 × (γ − 1) Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm) Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings. Inputs — every control, purpose, mechanics, and tuning 🔮 Lorentz Core Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing. Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow. Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier. Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c. Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4. Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings. Max Kernel Span: Upper bound on smoothing loop (quality vs CPU). 🎼 Harmonic Flow Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias. Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable. 📈 Signals Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated. v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only. Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return. HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias. HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze. Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL. Assurance: Confidence mass from neighbors; higher → more reliable memory bias. Squeeze: ON/RELEASE/OFF from BB
Indicador Pine Script®
por DskyzInvestments
1
Volume v4 (Dollar Value) by Koenigsegg📊 Volume v3 (Dollar Value) by Koenigsegg 🎯 Purpose: Volume v3 (Dollar Value) by Koenigsegg transforms traditional raw-unit volume into dollar-denominated volume, revealing how much money actually flows through each candle. Instead of measuring how many coins or contracts were traded, this version calculates the total traded value = volume × average price (hlc3), allowing traders to visually assess capital intensity and market participation within each move. ⚙️ Core Features - Converts raw volume into USD-based traded value for each candle. - Color-coded bars show bullish (green/teal) vs. bearish (red) activity. - Built-in SMA and SMMA overlays highlight sustained shifts in value flow. - Designed for visual clarity to support momentum, exhaustion, and divergence studies. 📖 How to Read It Rising Dollar Volume — indicates growing market participation and strong capital flow, often aligning with impulsive waves in trend direction. Falling Dollar Volume — signals waning interest or reduced participation, potentially hinting at correction or exhaustion phases. Comparing Legs — when price makes new highs/lows but dollar volume weakens, it can reveal divergences between price movement and actual capital commitment. SMA / SMMA Lines — use them to identify longer-term accumulation or depletion of market activity, separating short bursts from sustained inflows or outflows. The goal is to visualize the strength of market moves in terms of capital energy, not just tick activity. This distinction helps traders interpret whether a trend is being driven by genuine money flow or low-liquidity drift. ⚠️ Disclaimer This script is provided for research and educational purposes only. It does not constitute financial advice, investment recommendations, or trading signals. Always conduct your own analysis and manage your own risk when trading live markets. The author accepts no liability for financial losses incurred from use of this tool. 🧠 Credits Developed and published by Koenigsegg. Written in Pine Script® v6, fully compliant with TradingView’s House Rules for Pine Scripts. Licensed under the Mozilla Public License 2.0.
Indicador Pine Script®
por CryptoPandaTA
Directional Indicator Crossovers v1[JopAlgo]Directional Indicator Crossovers v1 — the classic DMI, made clearer and easier to act on We'd like to introduce you to a more relaxed, streamlined version of DI. While it may not seem like it at first glance, we've taken the D+/D- method as a starting point and developed our own version of this indicator: two lines, a smooth green/red field indicating who's in control, and clear crossover alerts for a flip. We deliberately chose the step line representation because it closely matches the candlestick patterns on the chart. Designed to help you react faster—without clutter. What you’ll see +DI (green) and −DI (red) using classic Wilder smoothing. A soft control zone between the lines: green when +DI dominates, red when −DI dominates. Crossover alerts (no labels, no background flooding)—just the turning points. Why this helps Instant bias: the shaded field tells you who’s in control without reading values. Cleaner execution: minimal visuals keep focus on the handoff (+DI↔−DI) and your price levels. Actionable by design: built-in alerts fire right at the flip to route into your workflow. How to read it Bias: Green zone → buyers lead. Red zone → sellers lead. Trigger: Consider entries on the DI crossover that aligns with your higher-timeframe context (trend, S/R, OB). Patience in chop: If flips are frequent in tight ranges, wait for sustained zone dominance or confirm on a higher TF. Exit/flip: Opposite crossover or a clear loss of dominance. Settings that matter DI Length (default 14): Higher = calmer, fewer flips. Lower = faster, more signals. Visuals: Keep the control zone on for quick reads; hide crossover marks if you prefer pure lines. Alerts: Enable bullish and bearish DI cross alerts; connect to notifications or webhooks as needed. Starter presets Intraday (15m–1H): DI Length 12–14 for quicker handoffs. Swing (4H–1D): DI Length 14–20 for cleaner signals. Choppy assets: Nudge length higher to dampen noise. Where it shines (and limits) Best: Liquid markets (crypto majors, indices, large caps) where handoffs matter. Works elsewhere: Still useful on slower pairs; extend length for stability. Limit: Frequent flips in low-range sessions—pair with HTF bias or structure. Alerts included Bullish DI Crossover: +DI crosses above −DI. Bearish DI Crossover: −DI crosses above +DI. Attribution & License Built on the Directional Movement Index concept by J. Welles Wilder Jr. (1978). Independent Pine v6 implementation (not derived from TradingView’s built-in source). Released as Open Source (MPL-2.0)—please keep the license header intact. Disclaimer For educational purposes only; not financial advice. Trading involves risk. Test first, use clear levels, and manage risk. This project is independent and not affiliated with or endorsed by TradingView.
Indicador Pine Script®
por JopAlgo
3
India VIX Based Nifty/BankNifty Range Calculator (Auto Fetch)VIX-Based Expected Daily Range (Auto Volatility Forecast) Created by: Harshiv Symposium 📖 Purpose This indicator automatically fetches the India VIX value and calculates the expected daily price range for major Indian indices such as Nifty and BankNifty. It helps traders understand how much the market is likely to move today based on current volatility conditions. Designed for educational and analytical awareness, not for signals or profit-making systems. ⚙️ Core Logic Expected Daily Move (Range) = (India VIX × Current Index Price) ÷ Multiplier - Multiplier for Nifty: 1000 - Multiplier for BankNifty: 700 This calculation projects the 1-standard-deviation (≈ 68% probability) and 2-standard-deviation (≈ 95% probability) movement zones for the day. 📊 Example If India VIX = 15 and Nifty = 25,000: Expected Move ≈ (15 × 25,000) ÷ 1000 = 375 points Hence, - 68% Range: 24,625 – 25,375 - 95% Range: 24,250 – 25,750 This gives traders a realistic idea of daily volatility boundaries. 🧭 Key Features ✅ Auto-Fetch India VIX No need for manual input — automatically pulls live data from NSE:INDIAVIX. ✅ Dynamic Range Visualization Plots upper/lower boundaries for 1σ and 2σ probability zones with shaded expected-move area. ✅ Dashboard Panel Displays: - Current VIX - Expected Move (in points and %) - Upper and Lower Ranges ✅ Smart Alerts Alerts when price crosses upper or lower volatility range — potential breakout signal. 🎯 How It Helps Intraday Traders: Know the likely daily movement (e.g., ±220 pts on Nifty) and plan realistic targets or stops. Options Traders: Quickly assess whether it’s a seller-friendly (low VIX, small range) or buyer-friendly (high VIX, large range) session. Risk Managers: Use volatility context for stop-loss width and position sizing. Breakout Traders: If price breaks beyond the 2σ range → indicates potential volatility expansion. 💡 Interpretation Guide Condition Market Behavior Strategy Insight VIX ↓ ( < 14 ) Calm / Range-bound Option Selling Edge VIX ↑ ( > 20 ) Volatile Sessions Option Buying Edge Price within Range Stable Market Mean Reversion Setups Price breaks Range Volatility Expansion Breakout Trades ⚠️ Disclaimer This indicator is for educational and awareness purposes only. It does not generate buy/sell signals or guarantee returns. Always apply your own analysis and risk management.
Indicador Pine Script®
por harshivsymposium
Event Marking [zidaniee]This is not a technical analysis indicator, but a visual tool designed to mark important global events using vertical lines on your chart. By placing a single marker at the exact time an event occurred, you can compare how different assets reacted to that global event — before, during, and after it happened. In the example provided, the marking corresponds to the moment when U.S. President Donald Trump announced a 100% tariff on goods from China, which was immediately reflected in market reactions worldwide. The indicator includes full customization features for: • Event label text • Label size and position • Line color, style, and width Enjoy
Indicador Pine Script®
por zidaniee
1
EMA+SuperThis comprehensive indicator combines multiple powerful trend-following tools into a single chart overlay, designed for traders seeking clear entry and exit signals with market context. Features: Exponential Moving Averages (EMAs): Five EMAs (9, 21, 50, 100, 200) plotted for multi-timeframe trend analysis and dynamic support/resistance. Supertrend: Classic volatility-based trend indicator highlighting bullish and bearish phases with dynamic colored bands. NovaWave Cloud: Custom trend cloud created using fast and slow EMAs plus a signal moving average for visualizing market momentum shifts. Displaced Moving Averages (20, 50, 200 DMA): Simple moving averages with optional displacement to assess lagged trend confirmation and cyclical ranges. Buy/Sell Signal Labels: Automated labels show “BUY” when the 9 EMA crosses above the 21 EMA, and “SELL” when the 9 EMA crosses below the 21 EMA, providing timely entry/exit cues. Intended Use: Perfect for swing and position traders, this indicator combines trend confirmation and actionable signals to help identify sustained price moves in various markets. It works well on multiple timeframes, offering a clear visual framework for market direction and trading decisions. How to Use: Look for BUY labels for potential long entry opportunities when momentum shifts bullish. Look for SELL labels as potential exit or short signals when a bearish momentum crossover occurs. Use the overlaying EMAs, Supertrend, and cloud as additional confirmation for trend strength and timing. This all-in-one tool is ideal for traders who want a unified view of trend dynamics combined with simple, clear signals without needing multiple separate indicators. Feel free to modify or expand based on your style. Let me know if you want a shorter summary or technical details added!
Indicador Pine Script®
por All_in_Traders
38
[ZP] Fixed v6 testDISCLAIMER: This indicator in Pine V6 as my first ever Tradingview indicator, has been developed for my personal trading analysis, consolidating various powerful indicators that I frequently use. A number of the embedded indicators within this tool are the creations of esteemed Pine Script developers from the TradingView community. In recognition of their contributions, the names of these developers will be prominently displayed alongside the respective indicator names. My selection of these indicators is rooted in my own experience and reflects those that have proven most effective for me. Please note that the past performance of any trading system or methodology is not necessarily indicative of future results. Always conduct your own research and due diligence before using any indicator or tool. =========================================================================== Introducing the ultimate all-in-one DIY strategy builder indicator, With over 30+ famous indicators (some with custom configuration/settings) indicators included, you now have the power to mix and match to create your own custom strategy for shorter time or longer time frames depending on your trading style. Say goodbye to cluttered charts and manual/visual confirmation of multiple indicators and hello to endless possibilities with this indicator. Available indicators that you can choose to build your strategy, are coded to seamlessly print the BUY and SELL signal upon confirmation of all selected indicators: EMA Filter 2 EMA Cross 3 EMA Cross Range Filter (Guikroth) SuperTrend Ichimoku Cloud SuperIchi (LuxAlgo) B-Xtrender (QuantTherapy) Bull Bear Power Trend (Dreadblitz) VWAP BB Oscillator (Veryfid) Trend Meter (Lij_MC) Chandelier Exit (Everget) CCI Awesome Oscillator DMI ( Adx ) Parabolic SAR Waddah Attar Explosion (Shayankm) Volatility Oscillator (Veryfid) Damiani Volatility ( DV ) (RichardoSantos) Stochastic RSI MACD SSL Channel (ErwinBeckers) Schaff Trend Cycle ( STC ) (LazyBear) Chaikin Money Flow Volume Wolfpack Id (Darrellfischer1) QQE Mod (Mihkhel00) Hull Suite (Insilico) Vortex Indicator
Indicador Pine Script®
por Justas123
8
TSM + ADX Trend PowerLogic Behind This Indicator This indicator combines two momentum/trend tools to identify strong, reliable trends in price movement: 1. TSM (Time Series Momentum) What it does: Measures the difference between the current price and a smoothed average of past prices. Formula: EMA(close - EMA(close, 14), 14) Logic: If TSM > 0 → Price is above its recent average = upward momentum If TSM < 0 → Price is below its recent average = downward momentum 2. ADX (Average Directional Index) What it does: Measures trend strength (not direction). Logic: ADX > 25 → Strong trend (either up or down) ADX < 25 → Weak or no trend (choppy/sideways market) Combined Logic (TSM + ADX) The indicator only signals a trend when both conditions are met: Condition Meaning Uptrend TSM > 0 AND ADX > 25 → Strong upward momentum Downtrend TSM < 0 AND ADX > 25 → Strong downward momentum No signal ADX < 25 → Trend is too weak to trust What It Aims to Detect Strong, sustained trends (not just noise or small moves) Filters out weak/choppy markets where momentum indicators often give false signals Entry/exit points: Green background = Strong uptrend (consider buying/holding) Red background = Strong downtrend (consider selling/shorting) No color = Weak trend (stay out or wait)
Indicador Pine Script®
por Zorgs