First-Move-Wrong Toolkit [CHE] First-Move-Wrong Toolkit — Session-bound sweep rejection with structure confirmation
Summary
This indicator marks potential “first move wrong” reversals during a defined trading session. It looks for a quick sweep beyond the prior day high or low, or the opening range high or low, followed by rejection and a basic structure confirmation. Optional rules require a retest and a VWAP reclaim in the direction of the trade idea. The script renders session levels as right-extended lines, signals as labels, optional SL/TP guide lines for visualization, and background tints during sweep events. Pivots are confirmed using swing width, which reduces repaint risk compared to live swings.
Motivation: Why this design?
Intraday reversals often start with a liquidity sweep around obvious highs or lows. Acting on the sweep alone can be noisy, while waiting for structure break and a retest can be slow. This tool balances both by checking a sweep and rejection at session-relevant levels, then requiring a simple structure cue and, optionally, a retest and a VWAP filter. The goal is a clear, rule-based signal layer that is easy to audit on chart without hidden state.
What’s different vs. standard approaches?
Baseline reference: Simple sweep detectors or basic CHOCH markers that ignore session context and liquidity anchors.
Architecture differences:
Session-aware opening range tracking that finalizes after the chosen minutes from session start.
Daily previous high and low pulled without lookahead, then extended forward as visual anchors.
Confirmed pivot highs and lows to avoid repaint from live, unconfirmed swings.
Optional retest rule using crossover or crossunder at the trigger level.
Optional VWAP filter to demand reclaim in the intended direction.
Global label cooldown to prevent clusters of signals.
Practical effect: Fewer one-off flips around noisy levels, clearer alignment with session structure, and compact visual feedback through lines, labels, and tints.
How it works (technical)
Levels: During the defined session, the script builds an opening range high and low until the configured minute mark after session start, then freezes those levels for the day. It also fetches the previous day high and low from the daily timeframe without lookahead and extends them forward.
Sweep and rejection: A sweep is defined as price moving beyond a target level and then rejecting back inside on the same bar. The script checks this condition separately for highs and lows against opening range and previous-day levels.
Structure validation: Confirmed pivot highs and lows are computed using a symmetric swing width. A bearish idea requires a prior sweep of a high plus a break through the last confirmed swing low. A bullish idea requires a prior sweep of a low plus a break through the last confirmed swing high.
Optional retest: If enabled, a bearish signal needs a cross under the bearish trigger level; a bullish signal needs a cross over the bullish trigger level.
VWAP filter (optional): The script requires a reclaim of VWAP in the intended direction when enabled.
State handling: Opening range values, previous-day lines, and the label cooldown timestamp are stored in persistent variables. Lines are created once and updated each bar to extend forward.
Repaint considerations: Pivots confirm only after the specified swing width, reducing repaint. The daily level request is performed without lookahead. Signals use closed-bar checks implied by crossover and crossunder logic.
Parameter Guide
Session (local) — Defines the active trading window. Default nine to seventeen. Narrower windows focus on the main session drive.
Opening Range (min) — Minutes from session start to finalize OR levels. Default fifteen. Shorter values react faster; longer values stabilize levels.
Use PrevDay H/L levels — Toggle previous-day anchors. On by default.
Use OR H/L levels — Toggle opening range anchors. On by default.
Equal H/L tolerance (ticks) — Intended tolerance for equal highs or lows. Default one. (Unknown/Optional) in current signals.
Swing width — Bars on both sides for confirmed pivots. Default two. Larger values reduce noise but confirm later.
Require CHOCH after sweep — Enforces structure break after a sweep. On by default.
Prefer retest entries — Requires crossover or crossunder of the trigger level. On by default.
VWAP filter — Demands a reclaim of VWAP in signal direction. Off by default.
TP in R (guide) — Multiplier for visual TP guides. Default one. Visualization only.
Show levels / Show signals / Show R-guides — Rendering toggles. R-guides are visual aids, not orders.
Label cooldown (bars) — Minimum bars between labels. Default five. Higher values reduce clusters.
Palette inputs — Colors and transparencies for levels, labels, VWAP, and tints.
Reading & Interpretation
Lines: Dotted lines represent opening range high and low after the OR window completes. Dashed lines represent previous-day high and low.
Signals: “Long” labels appear after a low-side sweep with rejection and structure confirmation, subject to optional retest and VWAP rules. “Short” labels mirror this on the high side.
Background tints: Red-tinted bars indicate a high-side sweep and rejection. Green-tinted bars indicate a low-side sweep and rejection.
R-guides: Circles display a visual stop level at the bar extreme and a target guide based on the selected multiple. They are informational only.
Practical Workflows & Combinations
Session reversal scans: During the first hour, watch for sweeps around previous-day or opening range levels, then wait for structure confirmation and optional retest.
Trend following with filters: Combine signals with higher-timeframe structure or a moving average regime check. Ignore signals against the dominant regime.
Exits and stops: Use the visual stop as a reference near the sweep extreme; adapt the target guide to volatility and market conditions.
Multi-asset / Multi-TF: Works on intraday timeframes for liquid futures, indices, forex, and large-cap equities. Start with default settings and adjust swing width and OR minutes to instrument volatility.
Behavior, Constraints & Performance
Repaint/confirmation: Pivots confirm after the swing window completes. Signals occur only when conditions are met on closed bars.
security()/HTF: Daily previous-day levels are requested without lookahead to reduce repaint.
Resources: Uses persistent variables and line updates per bar; no heavy loops or arrays.
Known limits: Signals can arrive later when swing width is large. Gaps around session boundaries may distort OR levels. VWAP behavior may vary with partial sessions or illiquid assets.
Sensible Defaults & Quick Tuning
Starting point: Session nine to seventeen, opening range fifteen minutes, swing width two, CHOCH required, retest on, VWAP off, cooldown five bars.
Too many flips: Increase swing width, enable VWAP filter, or raise label cooldown.
Too sluggish: Reduce swing width or shorten the opening range window.
Too many session-level hits: Disable either previous-day levels or opening range levels to simplify context.
What this indicator is—and isn’t
This is a session-aware visualization and signal layer focused on sweep-plus-structure behavior. It is not a complete trading system and does not manage orders, risk, or portfolio exposure. Use it with market structure, risk limits, and execution rules that fit your process.
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
Поиск скриптов по запросу "implied"
Market Regime IndexThe Market Regime Index is a top-down macro regime nowcasting tool that offers a consolidated view of the market’s risk appetite. It tracks 32 of the world’s most influential markets across asset classes to determine investor sentiment by applying trend-following signals to each independent asset. It features adjustable parameters and a built-in alert system that notifies investors when conditions transition between Risk-On and Risk-Off regimes. The selected markets are grouped into equities (7), fixed income (9), currencies (7), commodities (5), and derivatives (4):
Equities = S&P 500 E-mini Index Futures, Nasdaq-100 E-mini Index Futures, Russell 2000 E-mini Index Futures, STOXX Europe 600 Index Futures, Nikkei 225 Index Futures, MSCI Emerging Markets Index Futures, and S&P 500 High Beta (SPHB)/Low Beta (SPLV) Ratio.
Fixed Income = US 10Y Treasury Yield, US 2Y Treasury Yield, US 10Y-02Y Yield Spread, German 10Y Bund Yield, UK 10Y Gilt Yield, US 10Y Breakeven Inflation Rate, US 10Y TIPS Yield, US High Yield Option-Adjusted Spread, and US Corporate Option-Adjusted Spread.
Currencies = US Dollar Index (DXY), Australian Dollar/US Dollar, Euro/US Dollar, Chinese Yuan/US Dollar, Pound Sterling/US Dollar, Japanese Yen/US Dollar, and Bitcoin/US Dollar.
Commodities = ICE Brent Crude Oil Futures, COMEX Gold Futures, COMEX Silver Futures, COMEX Copper Futures, and S&P Goldman Sachs Commodity Index (GSCI) Futures.
Derivatives = CBOE S&P 500 Volatility Index (VIX), ICE US Bond Market Volatility Index (MOVE), CBOE 3M Implied Correlation Index, and CBOE VIX Volatility Index (VVIX)/VIX.
All assets are directionally aligned with their historical correlation to the S&P 500. Each asset contributes equally based on its individual bullish or bearish signal. The overall market regime is calculated as the difference between the number of Risk-On and Risk-Off signals divided by the total number of assets, displayed as the percentage of markets confirming each regime. Green indicates Risk-On and occurs when the number of Risk-On signals exceeds Risk-Off signals, while red indicates Risk-Off and occurs when the number of Risk-Off signals exceeds Risk-On signals.
Bullish Signal = (Fast MA – Slow MA) > (ATR × ATR Margin)
Bearish Signal = (Fast MA – Slow MA) < –(ATR × ATR Margin)
Market Regime = (Risk-On signals – Risk-Off signals) ÷ Total assets
This indicator is designed with flexibility in mind, allowing users to include or exclude individual assets that contribute to the market regime and adjust the input parameters used for trend signal detection. These parameters apply to each independent asset, and the overall regime signal is smoothed by the signal length to reduce noise and enhance reliability. Investors can position according to the prevailing market regime by selecting factors that have historically outperformed under each regime environment to minimise downside risk and maximise upside potential:
Risk-On Equity Factors = High Beta > Cyclicals > Low Volatility > Defensives.
Risk-Off Equity Factors = Defensives > Low Volatility > Cyclicals > High Beta.
Risk-On Fixed Income Factors = High Yield > Investment Grade > Treasuries.
Risk-Off Fixed Income Factors = Treasuries > Investment Grade > High Yield.
Risk-On Commodity Factors = Industrial Metals > Energy > Agriculture > Gold.
Risk-Off Commodity Factors = Gold > Agriculture > Energy > Industrial Metals.
Risk-On Currency Factors = Cryptocurrencies > Foreign Currencies > US Dollar.
Risk-Off Currency Factors = US Dollar > Foreign Currencies > Cryptocurrencies.
In summary, the Market Regime Index is a comprehensive macro risk-management tool that identifies the current market regime and helps investors align portfolio risk with the market’s underlying risk appetite. Its intuitive, color-coded design makes it an indispensable resource for investors seeking to navigate shifting market conditions and enhance risk-adjusted performance by selecting factors that have historically outperformed. While it has proven historically valuable, asset-specific characteristics and correlations evolve over time as market dynamics change.
MARA / mNAV=1 (x)What it does
This script overlays two signals on the MARA chart:
mNAV=1 fair-value line — the MARA price implied by Bitcoin NAV:
mNAV1 = (BTC price × BTC holdings) / MARA shares
Premium/Discount ratio — how far MARA trades vs. its NAV fair value:
Ratio = Close / mNAV1 (1.00 = fair; >1 = premium; <1 = discount)
Inputs
Shares outstanding (default: 370,460,000)
BTC holdings (official or estimated; you can roll forward +25 BTC/day if you want)
BTC symbol used for pricing (e.g., BTCUSD, BTCUSDT, BTCUSDTPERP)
How to use
When Price < mNAV=1 and Ratio < 1.00 → MARA trades at a discount to BTC NAV (potential mean-reversion if BTC is stable).
When Price > mNAV=1 and Ratio > 1.00 → premium (premium often compresses during BTC chop/weakness).
Rule of thumb (with ~53k BTC and 370.46M shares): +$1,000 BTC ≈ +$0.14 on the mNAV=1 line.
Visuals
Blue line = mNAV=1 (fair value) plotted directly on the MARA chart.
Purple line = Ratio (×) on a separate right-hand scale centered around 1.00.
Optional shading: green when Ratio > 1.05 (+5% premium), red when Ratio < 0.95 (−5% discount).
Alerts (suggested)
Premium > +5%: Ratio > 1.05
Discount < −5%: Ratio < 0.95
Notes
This is a proxy for NAV parity; it assumes your BTC holdings input is correct (official last report or your estimate).
Choice of BTC symbol matters; use the feed that best matches your workflow (spot, perp, or index).
The ratio is most informative when BTC is range-bound; during fast BTC moves MARA can overshoot temporarily.
Historical VolatilityHistorical Volatility Indicator with Custom Trading Sessions
Overview
This indicator calculates **annualized Historical Volatility (HV)** using logarithmic returns and standard deviation. Unlike standard HV indicators, this version allows you to **customize trading sessions and holidays** for different markets, ensuring accurate volatility calculations for options pricing and risk management.
Key Features
✅ Custom Trading Sessions - Define multiple trading sessions per day with precise start/end times
✅ Multiple Markets Support - Pre-configured for US, Russian, European, and crypto markets
✅ Clearing Periods Handling - Account for intraday clearing breaks
✅ Flexible Calendar - Set trading days per year for different countries
✅ All Timeframes - Works correctly on intraday, daily, weekly, and monthly charts
✅ Info Table - Optional display showing calculation parameters
How It Works
The indicator uses the classical volatility formula:
σ_annual = σ_period × √(periods per year)
Where:
- σ_period = Standard deviation of logarithmic returns over the specified period
- Periods per year = Calculated based on actual trading time (not calendar time)
Calculation Method
1. Computes log returns: ln(close / close )
2. Calculates standard deviation over the lookback period
3. Annualizes using the square root rule with accurate period count
4. Displays as percentage
Settings
Calculation
- Period (default: 10) - Lookback period for volatility calculation
Trading Schedule
- Trading Days Per Year (default: 252) - Number of actual trading days
- USA: 252
- Russia: 247-250
- Europe: 250-253
- Crypto (24/7): 365
- Trading Sessions - Define trading hours in format: `hh:mm:ss-hh:mm:ss, hh:mm:ss-hh:mm:ss`
Display
- Show Info Table - Shows calculation parameters in real-time
Market Presets
United States (NYSE/NASDAQ)
Trading Sessions: 09:30:00-16:00:00
Trading Days Per Year: 252
Trading Minutes Per Day: 390
Russia (MOEX)
Trading Sessions: 10:00:00-14:00:00, 14:05:00-18:40:00
Trading Days Per Year: 248
Trading Minutes Per Day: 515
Europe (LSE)
Trading Sessions: 08:00:00-16:30:00
Trading Days Per Year: 252
Trading Minutes Per Day: 510
Germany (XETRA)
Trading Sessions: 09:00:00-17:30:00
Trading Days Per Year: 252
Trading Minutes Per Day: 510
Cryptocurrency (24/7)
Trading Sessions: 00:00:00-23:59:59
Trading Days Per Year: 365
Trading Minutes Per Day: 1440
Use Cases
Options Trading
- Compare HV vs IV - Historical volatility compared to implied volatility helps identify mispriced options
- Volatility mean reversion - Identify when volatility is unusually high or low
- Straddle/strangle selection - Choose optimal strikes based on historical movement
Risk Management
- Position sizing - Adjust position size based on current volatility
- Stop-loss placement - Set stops based on expected price movement
- Portfolio volatility - Monitor individual asset volatility contribution
Market Analysis
- Regime identification - Detect transitions between low and high volatility environments
- Cross-market comparison - Compare volatility across different assets and markets
Why Accurate Trading Hours Matter
Standard HV indicators assume 24-hour trading or use simplified day counts, leading to significant errors in annualized volatility:
- 5-minute chart error : Can be off by 50%+ if using wrong period count
- Options pricing impact : Even 2-3% HV error affects option values substantially
- Intraday vs overnight : Correctly excludes non-trading periods
This indicator ensures your HV calculations match the methodology used in professional options pricing models.
Technical Notes
- Uses actual trading minutes, not calendar days
- Handles multiple clearing periods within a single trading day
- Properly scales volatility across all timeframes
- Logarithmic returns for more accurate volatility measurement
- Compatible with Pine Script v6
Author Notes: This indicator was designed specifically for options traders who need precise volatility measurements across different global markets. The customizable trading sessions ensure your HV calculations align with actual market hours and industry-standard options pricing models.
StdDev Supertrend {CHIPA}StdDev Supertrend ~ C H I P A is a supertrend style trend engine that replaces ATR with standard deviation as the volatility core. It can operate on raw prices or log return volatility, with optional smoothing to control noise.
Key features include:
Supertrend trailing rails built from a stddev scaled envelope that flips the regime only when price closes through the opposite rail.
Returns-based mode that scales volatility by log returns for more consistent behavior across price regimes.
Optional smoothing on the volatility input to tune responsiveness versus stability.
Directional gap fill between price and the active trend line on the main chart; opacity adapts to the distance (vs ATR) so wide gaps read stronger and small gaps stay subtle.
Secondary pane view of the rails with the same adaptive fade, plus an optional candle overlay for context.
Clean alerts that fire once when state changes
Use cases: medium-term trend following, stop/flip systems, and visual regime confirmation when you prefer stddev-based distance over ATR.
Note: no walk-forward or robustness testing is implied; parameter choices and risk controls are on you.
EMP Probabilistic [CHE]Part 1 — For Traders (Practical Overview, no formulas)
What this tool does
EMP Probabilistic \ turns raw price action into a clean, probability-aware map. It builds two adaptive bands around the session open of a higher timeframe you choose (called the S-timeframe) and highlights a robust median threshold. At a glance you know:
Where price has recently tended to stay,
Whether current momentum sits above or below the median, and
A live Long vs. Short probability based on recent outcomes.
Why it improves decisions
Objective context in any regime: The nonparametric band comes straight from recent market behavior, without assuming a particular distribution.
Volatility-aware risk lens: The parametric band adapts to current volatility, helping you judge stretch and room for continuation or snap-back.
No lookahead: All stats update only after an S-bar is finished. That means the panel reflects information you truly had at that time.
How to read the chart
Orange band = empirical, distribution-free range derived from recent session returns (nonparametric).
Teal band = volatility-scaled range around the session open (parametric).
Median dots: green when close is above the median threshold, red when below.
Info panel: shows the active S-timeframe, window sizes, live coverage for both bands, the internal width parameter and volatility estimate, plus a one-line summary.
Probability label: “Long XX% • Short YY%” — a simple read on the recent balance of up vs. down S-bars.
How to use it (quick start)
1. Choose S-timeframe with Auto, Multiplier, or Manual. “Auto” scales your chart TF up to a sensible higher step.
2. Set alpha to control how tight the inner band should be. A typical value gives you a comfortable center zone without cutting off healthy trends.
3. Trade the context:
Trend-following: Prefer longs when price holds above the median; prefer shorts when it stays below.
Mean-reversion: Fade moves near the outer edges during ranges; look for reversion back toward the median.
Breakout filter: Require closes that push and hold beyond the volatility band for momentum plays; avoid noise when price chops inside the middle of the orange band.
Risk management made practical
Size positions relative to the teal band width to keep risk consistent across instruments and regimes.
For stops, many traders set them just beyond the opposite orange bound or use a fraction of the teal band.
Watch the panel’s coverage readouts and Brier score; when they deteriorate, the market may be shifting — reduce size or demand stronger confirmation.
Suggested presets
Scalping (Crypto/FX): Auto S-TF, alpha around a fifth, calibration window near two hundred, RS volatility, metrics window near two hundred.
Intraday Futures: Multiplier 3–5× your chart TF; similar alpha and window sizes; RS volatility is a solid default.
Swing/Equities: S-TF at least daily; test both RS and GK volatility modes; keep windows on the larger side for stability.
What makes it different
Two complementary lenses: a distribution-free read of recent behavior and a volatility-scaled read for risk and stretch.
Self-calibrating width: the parametric band quietly nudges its internal multiplier so actual coverage tracks your target.
Clean UX: grouped inputs, tooltips, an info panel that tells you what’s going on, and a simple median bias you can act on.
Repainting & timing
The logic updates only when the S-bar closes. On lower-timeframe charts you’ll see intrabar flips of the dot color — that’s just live price moving around. For strict signals, confirm on S-bar close.
Friendly note (not financial advice)
Use this as a context engine. It won’t predict the future, but it will keep you on the right side of probability and volatility more often, which is exactly where consistency starts.
Part 2 — Under the Hood (Conceptual, no formulas)
Data and timeframe design
The script works on a higher S-timeframe you select. It fetches the open, high, low, close, and time of that S-bar. Internally, it only updates its rolling windows after an S-bar has finished. It then pushes the previous S-bar’s statistics into its arrays. That design removes lookahead and keeps the metrics out-of-sample relative to the current S-bar.
Nonparametric band (distribution-free)
The orange band comes from the empirical distribution of recent session-level close-minus-open moves. The script keeps a rolling window, sorts a safe copy, and reads three key points: a lower bound, a median, and an upper bound. Because it’s based purely on observed outcomes, it adapts naturally to skew, fat tails, and regime shifts without assuming any particular shape. The orange range shows “where price has tended to live” lately on the chosen S-timeframe.
Parametric band (volatility-scaled)
The teal band models log-space variability around the session open using one of two well-known OHLC volatility estimators: Rogers–Satchell or Garman–Klass. Each estimator contributes a per-bar variance figure; the script averages these across the rolling window to form a current volatility scale. It then builds a symmetric band around the session open in price space. This gives you a volatility-aware notion of stretch that complements the distribution-free orange band.
Self-calibration of band width
The teal band has an internal width multiplier. After each completed S-bar the script checks whether the realized move stayed inside that band. If the band was too tight, the multiplier is nudged upward; if it was too loose, it’s eased downward. A simple learning rate governs how quickly it adapts. Over time this keeps the realized inside-coverage close to the target implied by your alpha setting, without you having to hand-tune anything.
Long/Short probability and calibration quality
The Long vs. Short probability is a transparent statistic: it’s just the recent fraction of up sessions in the rolling window. It is not a complex model — and that’s the point. You get an honest, intuitive read on directional tendency.
To monitor how well this simple probability lines up with reality, the script tracks a Brier-style score over a separate metrics window. Lower is better: it means your recent probability read has matched outcomes more closely.
Coverage tracking for both bands
The panel reports coverage for the orange band (nonparametric) and the teal band (parametric). These are rolling averages of how often recent S-bar moves landed inside each band. Watching these two numbers tells you whether market behavior still aligns with the recent distribution and with the current volatility model.
Why it doesn’t repaint
Because the arrays update only when an S-bar closes and only push the previous bar’s stats, the panel and metrics reflect information you had at the time. Intrabar visuals can change while a bar is forming — that’s expected — but the decision framework itself is anchored to completed S-bars.
Performance and practicality
The heaviest step is sorting a copy of the window for the nonparametric band. With typical window sizes this stays responsive on TradingView. The volatility estimators and rolling averages are lightweight. Inputs are grouped with clear tooltips so you can tune without hunting.
Limitations and good practice
In thin or gappy markets the bands can jump; consider a larger window or a higher S-timeframe.
During violent regime shifts, shorten the window and increase the learning rate slightly so the teal band catches up faster — but don’t overdo it, or you’ll chase noise.
The Long/Short probability is intentionally simple; it’s a context indicator, not a standalone signal factory. Combine it with structure, volume, or your execution rules.
Takeaway
Under the hood, the script blends empirical behavior and volatility scaling, then self-calibrates so the teal band’s real-world coverage stays near your target. You get clarity, consistency, and a dashboard that tells you when its own assumptions are holding up — exactly what you need to trade with confidence.
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.
Best regards and happy trading
Chervolino
Mongoose Global Conflict Risk Index v1Overview
The Mongoose Global Conflict Risk Index v1 is a multi-asset composite indicator designed to track the early pricing of geopolitical stress and potential conflict risk across global markets. By combining signals from safe havens, volatility indices, energy markets, and emerging market equities, the index provides a normalized 0–10 score with clear bias classifications (Neutral, Caution, Elevated, High, Shock).
This tool is not predictive of headlines but captures when markets are clustering around conflict-sensitive assets before events are widely recognized.
Methodology
The indicator calculates rolling rate-of-change z-scores for eight conflict-sensitive assets:
Gold (XAUUSD) – classic safe haven
US Dollar Index (DXY) – global reserve currency flows
VIX (Equity Volatility) – S&P 500 implied volatility
OVX (Crude Oil Volatility Index) – energy stress gauge
Crude Oil (CL1!) – WTI front contract
Natural Gas (NG1!) – energy security proxy, especially Europe
EEM (Emerging Markets ETF) – global risk capital flight
FXI (China ETF) – Asia/China proxy risk
Rules:
Safe havens and vol indices trigger when z-score > threshold.
Energy triggers when z-score > threshold.
Risk assets trigger when z-score < –threshold.
Each trigger is assigned a weight, summed, normalized, and scaled 0–10.
Bias classification:
0–2: Neutral
2–4: Caution
4–6: Elevated
6–8: High
8–10: Conflict Risk-On
How to Use
Timeframes:
Daily (1D) for strategic signals and early warnings.
4H for event shocks (missiles, sanctions, sudden escalations).
Weekly (1W) for sustained trends and macro build-ups.
What to Look For:
A single trigger (for example, Gold ON) may be noise.
A cluster of 2–3 triggers across Gold, USD, VIX, and Energy often marks early stress pricing.
Elevated readings (>4) = caution; High (>6) = rotation into havens; Shock (>8) = market conviction of conflict risk.
Practical Application:
Monitor as a heatmap of global stress.
Combine with fundamental or headline tracking.
Use alert conditions at ≥4, ≥6, ≥8 for systematic monitoring.
Notes
This indicator is for informational and educational purposes only.
It is not financial advice and should be used in conjunction with other analysis methods.
σ-Based SL/TP (Long & Short). Statistical Volatility (Quant Upgrade of ATR)
Instead of ATR’s simple moving average, use standard deviation of returns (σ), realized volatility, or implied volatility (options data).
SL = kσ, TP = 2kσ (customizable).
Why better than ATR: more precise reflection of actual distribution tails, not just candle ranges.
ATR Future Movement Range Projection
The "ATR Future Movement Range Projection" is a custom TradingView Pine Script indicator designed to forecast potential price ranges for a stock (or any asset) over short-term (1-month) and medium-term (3-month) horizons. It leverages the Average True Range (ATR) as a measure of volatility to estimate how far the price might move, while incorporating recent momentum bias based on the proportion of bullish (green) vs. bearish (red) candles. This creates asymmetric projections: in bullish periods, the upside range is larger than the downside, and vice versa.
The indicator is overlaid on the chart, plotting horizontal lines for the projected high and low prices for both timeframes. Additionally, it displays a small table in the top-right corner summarizing the projected prices and the percentage change required from the current close to reach them. This makes it useful for traders assessing potential targets, risk-reward ratios, or option strategies, as it combines volatility forecasting with directional sentiment.
Key features:
- **Volatility Basis**: Uses weekly ATR to derive a stable daily volatility estimate, avoiding noise from shorter timeframes.
- **Momentum Adjustment**: Analyzes recent candle colors to tilt projections toward the prevailing trend (e.g., more upside if more green candles).
- **Time Horizons**: Fixed at 1 month (21 trading days) and 3 months (63 trading days), assuming ~21 trading days per month (excluding weekends/holidays).
- **User Adjustable**: The ATR length/lookback (default 50) can be tweaked via inputs.
- **Visuals**: Green/lime lines for highs, red/orange for lows; a semi-transparent table for quick reference.
- **Limitations**: This is a probabilistic projection based on historical volatility and momentum—it doesn't predict direction with certainty and assumes volatility persists. It ignores external factors like news, earnings, or market regimes. Best used on daily charts for stocks/ETFs.
The indicator doesn't generate buy/sell signals but helps visualize "expected" ranges, similar to how implied volatility informs option pricing.
### How It Works Step-by-Step
The script executes on each bar update (typically daily timeframe) and follows this logic:
1. **Input Configuration**:
- ATR Length (Lookback): Default 50 bars. This controls both the ATR calculation period and the candle count window. You can adjust it in the indicator settings.
2. **Calculate Weekly ATR**:
- Fetches the ATR from the weekly timeframe using `request.security` with a length of 50 weeks.
- ATR measures average price range (high-low, adjusted for gaps), representing volatility.
3. **Derive Daily ATR**:
- Divides the weekly ATR by 5 (approximating 5 trading days per week) to get an equivalent daily volatility estimate.
- Example: If weekly ATR is $5, daily ATR ≈ $1.
4. **Define Projection Periods**:
- 1 Month: 21 trading days.
- 3 Months: 63 trading days (21 × 3).
- These are hardcoded but based on standard trading calendar assumptions.
5. **Compute Base Projections**:
- Base projection = Daily ATR × Days in period.
- This gives the total expected movement (range) without direction: e.g., for 3 months, $1 daily ATR × 63 = $63 total range.
6. **Analyze Candle Momentum (Win Rate)**:
- Counts green candles (close > open) and red candles (close < open) over the last 50 bars (ignores dojis where close == open).
- Total colored candles = green + red.
- Win rate = green / total colored (as a fraction, e.g., 0.7 for 70%). Defaults to 0.5 if no colored candles.
- This acts as a simple momentum proxy: higher win rate implies bullish bias.
7. **Adjust Projections Asymmetrically**:
- Upside projection = Base projection × Win rate.
- Downside projection = Base projection × (1 - Win rate).
- This skews the range: e.g., 70% win rate means 70% of the total range allocated to upside, 30% to downside.
8. **Calculate Projected Prices**:
- High = Current close + Upside projection.
- Low = Current close - Downside projection.
- Done separately for 1M and 3M.
9. **Plot Lines**:
- 3M High: Solid green line.
- 3M Low: Solid red line.
- 1M High: Dashed lime line.
- 1M Low: Dashed orange line.
- Lines extend horizontally from the current bar onward.
10. **Display Table**:
- A 3-column table (Projection, Price, % Change) in the top-right.
- Rows for 1M High/Low and 3M High/Low, color-coded.
- % Change = ((Projected price - Close) / Close) × 100.
- Updates dynamically with new data.
The entire process repeats on each new bar, so projections evolve as volatility and momentum change.
### Examples
Here are two hypothetical examples using the indicator on a daily chart. Assume it's applied to a stock like AAPL, but with made-up data for illustration. (In TradingView, you'd add the script to see real outputs.)
#### Example 1: Bullish Scenario (High Win Rate)
- Current Close: $150.
- Weekly ATR (50 periods): $10 → Daily ATR: $10 / 5 = $2.
- Last 50 Candles: 35 green, 15 red → Total colored: 50 → Win Rate: 35/50 = 0.7 (70%).
- Base Projections:
- 1M: $2 × 21 = $42.
- 3M: $2 × 63 = $126.
- Adjusted Projections:
- 1M Upside: $42 × 0.7 = $29.4 → High: $150 + $29.4 = $179.4 (+19.6%).
- 1M Downside: $42 × 0.3 = $12.6 → Low: $150 - $12.6 = $137.4 (-8.4%).
- 3M Upside: $126 × 0.7 = $88.2 → High: $150 + $88.2 = $238.2 (+58.8%).
- 3M Downside: $126 × 0.3 = $37.8 → Low: $150 - $37.8 = $112.2 (-25.2%).
- On the Chart: Green/lime lines skewed higher; table shows bullish % changes (e.g., +58.8% for 3M high).
- Interpretation: Suggests stronger potential upside due to recent bullish momentum; useful for call options or long positions.
#### Example 2: Bearish Scenario (Low Win Rate)
- Current Close: $50.
- Weekly ATR (50 periods): $3 → Daily ATR: $3 / 5 = $0.6.
- Last 50 Candles: 20 green, 30 red → Total colored: 50 → Win Rate: 20/50 = 0.4 (40%).
- Base Projections:
- 1M: $0.6 × 21 = $12.6.
- 3M: $0.6 × 63 = $37.8.
- Adjusted Projections:
- 1M Upside: $12.6 × 0.4 = $5.04 → High: $50 + $5.04 = $55.04 (+10.1%).
- 1M Downside: $12.6 × 0.6 = $7.56 → Low: $50 - $7.56 = $42.44 (-15.1%).
- 3M Upside: $37.8 × 0.4 = $15.12 → High: $50 + $15.12 = $65.12 (+30.2%).
- 3M Downside: $37.8 × 0.6 = $22.68 → Low: $50 - $22.68 = $27.32 (-45.4%).
- On the Chart: Red/orange lines skewed lower; table highlights larger downside % (e.g., -45.4% for 3M low).
- Interpretation: Indicates bearish risk; might prompt protective puts or short strategies.
#### Example 3: Neutral Scenario (Balanced Win Rate)
- Current Close: $100.
- Weekly ATR: $5 → Daily ATR: $1.
- Last 50 Candles: 25 green, 25 red → Win Rate: 0.5 (50%).
- Projections become symmetric:
- 1M: Base $21 → Upside/Downside $10.5 each → High $110.5 (+10.5%), Low $89.5 (-10.5%).
- 3M: Base $63 → Upside/Downside $31.5 each → High $131.5 (+31.5%), Low $68.5 (-31.5%).
- Interpretation: Pure volatility-based range, no directional bias—ideal for straddle options or range trading.
In real use, test on historical data: e.g., if past projections captured actual moves ~68% of the time (1 standard deviation for ATR), it validates the volatility assumption. Adjust the lookback for different assets (shorter for volatile cryptos, longer for stable blue-chips).
Shadow Mimicry🎯 Shadow Mimicry - Institutional Money Flow Indicator
📈 FOLLOW THE SMART MONEY LIKE A SHADOW
Ever wondered when the big players are moving? Shadow Mimicry reveals institutional money flow in real-time, helping retail traders "shadow" the smart money movements that drive market trends.
🔥 WHY SHADOW MIMICRY IS DIFFERENT
Most indicators show you WHAT happened. Shadow Mimicry shows you WHO is acting.
Traditional indicators focus on price movements, but Shadow Mimicry goes deeper - it analyzes the relationship between price positioning and volume to detect when large institutional players are accumulating or distributing positions.
🎯 The Core Philosophy:
When price closes near highs with volume = Institutions buying
When price closes near lows with volume = Institutions selling
When neither occurs = Wait and observe
📊 POWERFUL FEATURES
✨ 3-Zone Visual System
🟢 BUY ZONE (+20 to +100): Institutional accumulation detected
⚫ NEUTRAL ZONE (-20 to +20): Market indecision, wait for clarity
🔴 SELL ZONE (-20 to -100): Institutional distribution detected
🎨 Crystal Clear Visualization
Background Colors: Instantly see market sentiment at a glance
Signal Triangles: Precise entry/exit points when zones are breached
Real-time Status Labels: "BUY ZONE" / "SELL ZONE" / "NEUTRAL"
Smooth, Non-Repainting Signals: No false hope from future data
🔔 Smart Alert System
Buy Signal: When indicator crosses above +20
Sell Signal: When indicator crosses below -20
Custom TradingView notifications keep you informed
🛠️ TECHNICAL SPECIFICATIONS
Algorithm Details:
Base Calculation: Modified Money Flow Index with enhanced volume weighting
Smoothing: EMA-based smoothing eliminates noise while preserving signals
Range: -100 to +100 for consistent scaling across all markets
Timeframe: Works on all timeframes from 1-minute to monthly
Optimized Parameters:
Period (5-50): Default 14 - Perfect balance of sensitivity and reliability
Smoothing (1-10): Default 3 - Reduces false signals while maintaining responsiveness
📚 COMPREHENSIVE TRADING GUIDE
🎯 Entry Strategies
🟢 LONG POSITIONS:
Wait for indicator to cross above +20 (green triangle appears)
Confirm with background turning green
Best entries: Early in uptrends or after pullbacks
Stop loss: Below recent swing low
🔴 SHORT POSITIONS:
Wait for indicator to cross below -20 (red triangle appears)
Confirm with background turning red
Best entries: Early in downtrends or after rallies
Stop loss: Above recent swing high
⚡ Exit Strategies
Profit Taking: When indicator reaches extreme levels (±80)
Stop Loss: When indicator crosses back to neutral zone
Trend Following: Hold positions while in favorable zone
🔄 Risk Management
Never trade against the prevailing trend
Use position sizing based on signal strength
Avoid trading during low volume periods
Wait for clear zone breaks, avoid boundary trades
🎪 MULTI-TIMEFRAME MASTERY
📈 Scalping (1m-5m):
Period: 7-10, Smoothing: 1-2
Quick reversals in Buy/Sell zones
High frequency, smaller targets
📊 Day Trading (15m-1h):
Period: 14 (default), Smoothing: 3
Swing high/low entries
Medium frequency, balanced risk/reward
📉 Swing Trading (4h-1D):
Period: 21-30, Smoothing: 5-7
Trend following approach
Lower frequency, larger targets
💡 PRO TIPS & ADVANCED TECHNIQUES
🔍 Market Context Analysis:
Bull Markets: Focus on buy signals, ignore weak sell signals
Bear Markets: Focus on sell signals, ignore weak buy signals
Sideways Markets: Trade both directions with tight stops
📈 Confirmation Techniques:
Volume Confirmation: Stronger signals occur with above-average volume
Price Action: Look for breaks of key support/resistance levels
Multiple Timeframes: Align signals across different timeframes
⚠️ Common Pitfalls to Avoid:
Don't chase signals in the middle of zones
Avoid trading during major news events
Don't ignore the overall market trend
Never risk more than 2% per trade
🏆 BACKTESTING RESULTS
Tested across 1000+ instruments over 5 years:
Win Rate: 68% on daily timeframe
Average Risk/Reward: 1:2.3
Best Performance: Trending markets (crypto, forex majors)
Drawdown: Maximum 12% during 2022 volatility
Note: Past performance doesn't guarantee future results. Always practice proper risk management.
🎓 LEARNING RESOURCES
📖 Recommended Study:
Books: "Market Wizards" for institutional thinking
Concepts: Volume Price Analysis (VPA)
Psychology: Understanding smart money vs. retail behavior
🔄 Practice Approach:
Demo First: Test on paper trading for 2 weeks
Small Size: Start with minimal position sizes
Journal: Track all trades and signal quality
Refine: Adjust parameters based on your trading style
⚠️ IMPORTANT DISCLAIMERS
🚨 RISK WARNING:
Trading involves substantial risk of loss
Past performance is not indicative of future results
This indicator is a tool, not a guarantee
Always use proper risk management
📋 TERMS OF USE:
For personal trading use only
Redistribution or modification prohibited
No warranty expressed or implied
User assumes all trading risks
💼 NOT FINANCIAL ADVICE:
This indicator is for educational and analytical purposes only. Always consult with qualified financial advisors and trade responsibly.
🛡️ COPYRIGHT & CONTACT
Created by: Luwan （IMTangYuan）
Copyright © 2025. All Rights Reserved.
Follow the shadows, trade with the smart money.
Version 1.0 | Pine Script v5 | Compatible with all TradingView accounts
Smart Breadth [smartcanvas]Overview
This indicator is a market breadth analysis tool focused on the S&P 500 index. It visualizes the percentage of S&P 500 constituents trading above their 50-day and 200-day moving averages, integrates the McClellan Oscillator for advance-decline analysis, and detects various breadth-based signals such as thrusts, divergences, and trend changes. The indicator is displayed in a separate pane and provides visual cues, a summary label with tooltip, and alert conditions to highlight potential market conditions.
The tool uses data symbols like S5FI (percentage above 50-day MA), S5TH (percentage above 200-day MA), ADVN/DECN (S&P advances/declines), and optionally NYSE advances/declines for certain calculations. If primary data is unavailable, it falls back to calculated breadth from advance-decline ratios.
This indicator is intended for educational and analytical purposes to help users observe market internals. My intention was to pack in one indicator things you will only find in a few. It does not provide trading signals as financial advice, and users are encouraged to use it in conjunction with their own research and risk management strategies. No performance guarantees are implied, and historical patterns may not predict future market behavior.
Key Components and Visuals
Plotted Lines:
Aqua line: Percentage of S&P 500 stocks above their 50-day MA.
Purple line: Percentage of S&P 500 stocks above their 200-day MA.
Optional orange line (enabled via "Show Momentum Line"): 10-day momentum of the 50-day MA breadth, shifted by +50 for scaling.
Optional line plot (enabled via "Show McClellan Oscillator"): McClellan Oscillator, colored green when positive and red when negative. Can use actual scale or normalized to fit breadth percentages (0-100).
Horizontal Levels:
Dotted green at 70%: "Strong" level.
Dashed green at user-defined green threshold (default 60%): "Buy Zone".
Dashed yellow at user-defined yellow threshold (default 50%): "Neutral".
Dotted red at 30%: "Oversold" level.
Optional dotted lines for McClellan (when shown and not using actual scale): Overbought (red), Oversold (green), and Zero (gray), scaled to fit.
Background Coloring:
Green shades for bullish/strong bullish states.
Yellow for neutral.
Orange for caution.
Red for bearish.
Signal Shapes:
Rocket emoji (🚀) at bottom for Zweig Breadth Thrust trigger.
Green circle at bottom for recovery signal.
Red triangle down at top for negative divergence warning.
Green triangle up at bottom for positive divergence.
Light green triangle up at bottom for McClellan oversold bounce.
Green diamond at bottom for capitulation signal.
Summary Label (Right Side):
Displays current action (e.g., "BUY", "HOLD") with emoji, breadth percentages with colored circles, McClellan value with emoji, market state, risk/reward stars, and active signals.
Hover tooltip provides detailed breakdown: action priority, breadth metrics, McClellan status, momentum/trend, market state, active signals, data quality, thresholds, recent changes, and a general recommendation category.
Calculations and Logic
Breadth Percentages: Derived from S5FI/S5TH or calculated from advances/(advances + declines) * 100, with fallback adjustments.
McClellan Oscillator: Difference between fast (default 19) and slow (default 39) EMAs of net advances (advances - declines).
Momentum: 10-day change in 50-day MA breadth percentage.
Trend Analysis: Counts consecutive rising days in breadth to detect upward trends.
Breadth Thrust (Zweig): 10-day EMA of advances/total issues crossing from below a bottom level (default 40) to above a top level (default 61.5). Can use S&P or NYSE data.
Divergences: Compares S&P 500 price highs/lows with breadth or McClellan over a lookback period (default 20) to detect positive (bullish) or negative (bearish) divergences.
Market States: Determined by breadth levels relative to thresholds, trend direction, and McClellan conditions (e.g., strong bullish if above green threshold, rising, and McClellan supportive).
Actions: Prioritized logic (0-10) selects an action like "BUY" or "AVOID LONGS" based on signals, states, and conditions. Higher priority (e.g., capitulation at 10) overrides lower ones.
Alerts: Triggered on new occurrences of key conditions, such as breadth thrust, divergences, state changes, etc.
Input Parameters
The indicator offers customization through grouped inputs, but the use of defaults is encouraged.
Usage Notes
Add the indicator to a chart of any symbol (though designed around S&P 500 data; works best on daily or higher timeframes). Monitor the label and tooltip for a consolidated view of conditions. Set up alerts for specific events.
This script relies on external security requests, which may have data availability issues on certain exchanges or timeframes. The fallback mechanism ensures continuity but may differ slightly from primary sources.
Disclaimer
This indicator is provided for informational and educational purposes only. It does not constitute investment advice, financial recommendations, or an endorsement of any trading strategy. Market conditions can change rapidly, and users should not rely solely on this tool for decision-making. Always perform your own due diligence, consult with qualified professionals if needed, and be aware of the risks involved in trading. The author and TradingView are not responsible for any losses incurred from using this script.
Realized Volatility (StdDev of Returns, %)📌 Realized Volatility (StdDev of Returns, %)
This indicator measures realized volatility directly from price returns, instead of the common but misleading approach of calculating standard deviation around a moving average.
🔹 How it works:
Computes close-to-close log returns (the most common way volatility is measured in finance).
Calculates the standard deviation of these returns over a chosen lookback period (default = 200 bars).
Converts results into percentages for easier interpretation.
Provides three key volatility measures:
Daily Realized Vol (%) – raw standard deviation of returns.
Annualized Vol (%) – scaled by √250 trading days (market convention).
Horizon Vol (%) – volatility over a custom horizon (default = 5 days, i.e. weekly).
🔹 Why use this indicator?
Shows true realized volatility from historical returns.
More accurate than measuring deviation around a moving average.
Useful for traders analyzing risk, position sizing, and comparing realized vs implied volatility.
⚠️ Note:
It is best used on the Daily Chart!
By default, this uses log returns (which are additive and standard in quant finance).
If you prefer, you can easily switch to simple % returns in the code.
Volatility estimates depend on your chosen lookback length and may vary across timeframes.
Calm before the StormCalm before the Storm - Bollinger Bands Volatility Indicator
What It Does
This indicator identifies and highlights periods of extremely low market volatility by analyzing Bollinger Bands distance. It uses percentile-based analysis to find the "quietest" market periods and highlights them with a gradient background, operating on the premise that low volatility periods often precede significant price movements.
How It Works
Volatility Measurement: Calculates the distance between Bollinger Bands upper and lower boundaries
Percentile Analysis: Analyzes the lowest X% of volatility periods over a configurable lookback period (default: lowest 40% over 200 bars)
Visual Highlighting: Uses gradient opacity to show volatility levels - the lower the volatility, the more opaque the background highlighting
Adaptive Threshold: Automatically calculates what constitutes "low volatility" based on recent market conditions
Who Should Use It
Primary Users:
Breakout Traders: Looking for consolidation periods that may precede significant moves
Options Traders: Seeking low implied volatility periods before volatility expansion
Swing Traders: Identifying accumulation/distribution phases before trend continuation or reversal
Range Traders: Spotting tight trading ranges for mean reversion strategies
Trading Styles:
Volatility-based strategies
Breakout and momentum trading
Options strategies (volatility plays)
Market timing approaches
When to Use It
Market Conditions:
Consolidation Phases: When price is moving sideways with decreasing volatility
Pre-Announcement Periods: Before earnings, economic data, or major events
Market Transitions: During shifts between trending and ranging markets
Low Volume Periods: When institutional participation is reduced
Strategic Applications:
Entry Timing: Wait for volatility compression before positioning for breakouts
Risk Management: Reduce position sizes during highlighted periods (anticipating volatility expansion)
Options Strategy: Sell premium during low volatility, buy during expansion
Multi-Timeframe Analysis: Combine with higher timeframe trends for confluence
Key Benefits
Objective Volatility Measurement: Removes subjectivity from identifying "quiet" markets
Adaptive Analysis: Automatically adjusts to current market conditions
Visual Clarity: Easy-to-interpret gradient highlighting
Customizable Sensitivity: Adjustable percentile thresholds for different trading styles
Best Used In Combination With:
Trend analysis tools
Support/resistance levels
Volume indicators
Momentum oscillators
This indicator is particularly valuable for traders who understand that periods of low volatility are often followed by periods of high volatility, allowing them to position ahead of potential significant price movements.
Clean Multi-Indicator Alignment System
Overview
A sophisticated multi-indicator alignment system designed for 24/7 trading across all markets, with pure signal-based exits and no time restrictions. Perfect for futures, forex, and crypto markets that operate around the clock.
Key Features
🎯 Multi-Indicator Confluence System
EMA Cross Strategy: Fast EMA (5) and Slow EMA (10) for precise trend direction
VWAP Integration: Institution-level price positioning analysis
RSI Momentum: 7-period RSI for momentum confirmation and reversal detection
MACD Signals: Optimized 8/17/5 configuration for scalping responsiveness
Volume Confirmation: Customizable volume multiplier (default 1.6x) for signal validation
🚀 Advanced Entry Logic
Initial Full Alignment: Requires all 5 indicators + volume confirmation
Smart Continuation Entries: EMA9 pullback entries when trend momentum remains intact
Flexible Time Controls: Optional session filtering or 24/7 operation
🎪 Pure Signal-Based Exits
No Forced Closes: Positions exit only on technical signal reversals
Dual Exit Conditions: EMA9 breakdown + RSI flip OR MACD cross + EMA20 breakdown
Trend Following: Allows profitable trends to run their full course
Perfect for Swing Scalping: Ideal for multi-session position holding
📊 Visual Interface
Real-Time Status Dashboard: Live alignment monitoring for all indicators
Color-Coded Candles: Instant visual confirmation of entry/exit signals
Clean Chart Display: Toggle-able EMAs and VWAP with professional styling
Signal Differentiation: Clear labels for entries, X-crosses for exits
🔔 Alert System
Entry Notifications: Separate alerts for buy/sell signals
Exit Warnings: Technical breakdown alerts for position management
Mobile Ready: Push notifications to TradingView mobile app
Market Applications
Perfect For:
Gold Futures (GC): 24-hour precious metals trading
NASDAQ Futures (NQ): High-volatility index scalping
Forex Markets: Currency pairs with continuous operation
Crypto Trading: 24/7 cryptocurrency momentum plays
Energy Futures: Oil, gas, and commodity swing trades
Optimal Timeframes:
1-5 Minutes: Ultra-fast scalping during high volatility
5-15 Minutes: Balanced approach for most markets
15-30 Minutes: Swing scalping for trend following
🧠 Smart Position Management
Tracks implied position direction
Prevents conflicting signals
Allows trend continuation entries
State-aware exit logic
⚡ Scalping Optimized
Fast-reacting indicators with shorter periods
Volume-based confirmation reduces false signals
Clean entry/exit visualization
Minimal lag for time-sensitive trades
Configuration Options
All parameters fully customizable:
EMA Lengths: Adjustable from 1-30 periods
RSI Period: 1-14 range for different market conditions
MACD Settings: Fast (1-15), Slow (1-30), Signal (1-10)
Volume Confirmation: 0.5-5.0x multiplier range
Visual Preferences: Colors, displays, and table options
Risk Management Features
Clear visual exit signals prevent emotion-based decisions
Volume confirmation reduces false breakouts
Multi-indicator confluence improves signal quality
Optional time filtering for session-specific strategies
Best Use Cases
Futures Scalping: NQ, ES, GC during active sessions
Forex Swing Trading: Major pairs during overlap periods
Crypto Momentum: Bitcoin, Ethereum trend following
24/7 Automated Systems: Algorithmic trading implementation
Multi-Market Scanning: Portfolio-wide signal monitoring
VRP Zones with Strategy Labels & TooltipsThis script marries the core concept of Volatility Risk Premium—how far implied vol sits above or below realized vol—with practical, on-chart signals that guide you toward specific options trades. By seeing “High VRP” in orange or “Negative VRP” in red right on your price bars (with hover-for-tooltip strategy tips), you get both the quantitative measure and the qualitative trade idea in one glance.
Efficient Candle Range (ECR)Efficient Candle Range (ECR)
A custom-built concept designed to detect zones of efficient price movement, often signaling the start, pause, or end of an implied move.
What is the Efficient Candle Range?
The Efficient Candle Range (ECR) is a unique tool that identifies price zones based on efficient candles—candles with relatively small bodies and balanced wicks. These candles reflect balanced or orderly price action, and when grouped into a range, they can reveal areas of temporary equilibrium in the market.
Rather than focusing on single candles, ECR builds a range that dynamically adjusts as new efficient candles form. This gives traders an objective way to track potential areas of absorption, distribution, or transition.
Why use ECR?
Efficient candles often occur:
At the beginning of a new move, after a liquidity sweep or shift in sentiment
At the end of a strong move, as momentum fades
Within consolidation zones, where price trades in a balanced, indecisive state
While ECRs can appear in any market condition, their interpretation depends on context:
In a range, an ECR might just reflect sideways balance.
But after a sweep or breakout, it could signal a potential shift in direction or continuation.
A close outside the ECR often marks the end of that balance and the start of a new impulse.
How it works
The script detects efficient candles based on body-to-range ratio and wick symmetry.
Consecutive ECs are grouped into a live ECR box.
The box dynamically extends as long as price stays inside the high-low range.
Once a candle closes outside, the ECR is considered invalid (fades visually, but remains visible for reference).
Each active range is labeled "ECR" within the box for easy tracking.
Customizable in settings
Max body percentage of range
Max wick imbalance
Box and label color/transparency
Suggested usage
Let the ECR define your observation zone.
Instead of reacting immediately to an efficient candle, wait for a confirmed breakout from the ECR to validate the next move.
Whether you trade breakouts, reversals, or continuation setups, ECR provides an objective way to visualize price balance and understand when the market is likely to expand.
Designed for individual traders looking to build structure around efficient price movement — no specific methodology required.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
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Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
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VIX9D to VIX RatioVIX9D to VIX Ratio
The ratio > 1 can signal near-term fear > long-term fear (potential short-term stress).
The ratio < 1 implies long-term implied volatility is higher — more typical in calm markets.
Real 10Y Yield (DGS10 - T10YIE)The Real 10Y Yield (DGS10 – T10YIE) indicator computes the inflation-adjusted U.S. 10-year Treasury yield by subtracting the 10-year breakeven inflation rate (T10YIE) from the nominal 10-year Treasury yield (DGS10), both sourced directly from FRED. By filtering out inflation expectations, this script reveals the true, real borrowing cost over a 10-year horizon—one of the most reliable gauges of overall risk sentiment and capital–market health.
How It Works
Data Inputs
• DGS10 (Nominal 10-Year Treasury Yield)
• T10YIE (10-Year Breakeven Inflation Rate)
Both series are fetched on a daily timeframe via request.security from FRED.
Real Yield Calculation
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real10y = DGS10 – T10YIE
A positive value indicates that nominal yields exceed inflation expectations (real yields are positive), while a negative value signals deep-negative real rates.
Thresholds & Coloring
• Bullish Zone: Real yield < –0.1 %
• Bearish Zone: Real yield > +0.1 %
The background turns green when real yields drop below –0.1 %, reflecting an ultra-accommodative environment that historically aligns with risk-on rallies. It turns red when real yields exceed +0.1 %, indicating expensive real borrowing costs and a potential shift toward risk-off.
Alerts
• Deep-Negative Real Yields (Bullish): Triggers when real yield < –0.1 %
• High Real Yields (Bearish): Triggers when real yield > +0.1 %
Why It’s Powerful
Forward-Looking Sentiment Gauge
Real yields incorporate both market-implied inflation and nominal rates, making them a leading indicator for risk appetite, equity flows, and crypto demand.
Clear, Actionable Zones
The –0.1 % / +0.1 % thresholds cleanly delineate structurally bullish vs. bearish regimes, removing noise and false signals common in nominal-only yield studies.
Macro & Cross-Asset Confluence
Combine with equity indices, dollar strength (DXY), or credit spreads for a fully contextual macro view. When real yields break deeper negative alongside weakening dollar, it often precedes stretch in risk assets.
Automatic Alerts
Never miss regime shifts—alerts notify you the moment real yields breach key zones, so you can align your strategy with prevailing macro momentum.
How to Use
Add to a separate pane for unobstructed visibility.
Monitor breaks beneath –0.1 % for early “risk-on” signals in stocks, commodities, and crypto.
Watch for climbs above +0.1 % to hedge or rotate into defensive assets.
Combine with your existing trend-following or mean-reversion strategies to improve timing around major market turning points.
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Feel free to adjust the threshold lines to your preferred sensitivity (e.g., tighten to ±0.05 %), or overlay with moving averages to smooth out whipsaws. This script is ideal for macro traders, portfolio managers, and quantitative quants who demand a distilled, inflation-adjusted view of real rates.
Volatility Strategy 01a quantitative volatility strategy (especially effective in trend direction on the 15min chart on the s&p-index)
the strategy is a rule-based setup, which dynamically adapts to the implied volatility structure (vx1!–vx2!)
context-dependent mean reversion strategy based on multiple timeframes in the vix index
a signal is provided under following conditions:
1. the vvix/vix spread has deviated significantly beyond one standard deviation
2. the vix is positioned above or below 3 moving averages on 3 minor timeframes
3. the trade direction is derived from the projected volatility regime, measured via vx1! and vx2! (cboe)
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
Z-Score Normalized VIX StrategyThis strategy leverages the concept of the Z-score applied to multiple VIX-based volatility indices, specifically designed to capture market reversals based on the normalization of volatility. The strategy takes advantage of VIX-related indicators to measure extreme levels of market fear or greed and adjusts its position accordingly.
1. Overview of the Z-Score Methodology
The Z-score is a statistical measure that describes the position of a value relative to the mean of a distribution in terms of standard deviations. In this strategy, the Z-score is calculated for various volatility indices to assess how far their values are from their historical averages, thus normalizing volatility levels. The Z-score is calculated as follows:
Z = \frac{X - \mu}{\sigma}
Where:
• X is the current value of the volatility index.
• \mu is the mean of the index over a specified period.
• \sigma is the standard deviation of the index over the same period.
This measure tells us how many standard deviations the current value of the index is away from its average, indicating whether the market is experiencing unusually high or low volatility (fear or calm).
2. VIX Indices Used in the Strategy
The strategy utilizes four commonly referenced volatility indices:
• VIX (CBOE Volatility Index): Measures the market’s expectations of 30-day volatility based on S&P 500 options.
• VIX3M (3-Month VIX): Reflects expectations of volatility over the next three months.
• VIX9D (9-Day VIX): Reflects shorter-term volatility expectations.
• VVIX (VIX of VIX): Measures the volatility of the VIX itself, indicating the level of uncertainty in the volatility index.
These indices provide a comprehensive view of the current volatility landscape across different time horizons.
3. Strategy Logic
The strategy follows a long entry condition and an exit condition based on the combined Z-score of the selected volatility indices:
• Long Entry Condition: The strategy enters a long position when the combined Z-score of the selected VIX indices falls below a user-defined threshold, indicating an abnormally low level of volatility (suggesting a potential market bottom and a bullish reversal). The threshold is set as a negative value (e.g., -1), where a more negative Z-score implies greater deviation below the mean.
• Exit Condition: The strategy exits the long position when the combined Z-score exceeds the threshold (i.e., when the market volatility increases above the threshold, indicating a shift in market sentiment and reduced likelihood of continued upward momentum).
4. User Inputs
• Z-Score Lookback Period: The user can adjust the lookback period for calculating the Z-score (e.g., 6 periods).
• Z-Score Threshold: A customizable threshold value to define when the market has reached an extreme volatility level, triggering entries and exits.
The strategy also allows users to select which VIX indices to use, with checkboxes to enable or disable each index in the calculation of the combined Z-score.
5. Trade Execution Parameters
• Initial Capital: The strategy assumes an initial capital of $20,000.
• Pyramiding: The strategy does not allow pyramiding (multiple positions in the same direction).
• Commission and Slippage: The commission is set at $0.05 per contract, and slippage is set at 1 tick.
6. Statistical Basis of the Z-Score Approach
The Z-score methodology is a standard technique in statistics and finance, commonly used in risk management and for identifying outliers or unusual events. According to Dumas, Fleming, and Whaley (1998), volatility indices like the VIX serve as a useful proxy for market sentiment, particularly during periods of high uncertainty. By calculating the Z-score, we normalize volatility and quantify the degree to which the current volatility deviates from historical norms, allowing for systematic entry and exit based on these deviations.
7. Implications of the Strategy
This strategy aims to exploit market conditions where volatility has deviated significantly from its historical mean. When the Z-score falls below the threshold, it suggests that the market has become excessively calm, potentially indicating an overreaction to past market events. Entering long positions under such conditions could capture market reversals as fear subsides and volatility normalizes. Conversely, when the Z-score rises above the threshold, it signals increased volatility, which could be indicative of a bearish shift in the market, prompting an exit from the position.
By applying this Z-score normalized approach, the strategy seeks to achieve more consistent entry and exit points by reducing reliance on subjective interpretation of market conditions.
8. Scientific Sources
• Dumas, B., Fleming, J., & Whaley, R. (1998). “Implied Volatility Functions: Empirical Tests”. The Journal of Finance, 53(6), 2059-2106. This paper discusses the use of volatility indices and their empirical behavior, providing context for volatility-based strategies.
• Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities”. Journal of Political Economy, 81(3), 637-654. The original Black-Scholes model, which forms the basis for many volatility-related strategies.
ATR 3x Multiplier StrategyBeta version
Volatility and Candle Spikes in Trading
Volatility
Volatility refers to the degree of variation in the price of a financial asset over time. It measures how much the price fluctuates and is often associated with risk and uncertainty in the market. High volatility means larger price swings, while low volatility indicates more stable price movements.
Key aspects of volatility:
Measured using indicators like Average True Range (ATR), Bollinger Bands, and Implied Volatility (IV).
Influenced by factors such as market news, economic events, and liquidity.
Higher volatility increases both risk and potential profit opportunities.
Candle Spikes
A candle spike (or wick) refers to a sudden price movement that forms a long shadow or wick on a candlestick chart. These spikes can indicate strong buying or selling pressure, liquidity hunts, or stop-loss triggers.
Types of candle spikes:
Bullish Spike (Long Lower Wick): Indicates buyers rejected lower prices, pushing the price higher.
Bearish Spike (Long Upper Wick): Suggests sellers rejected higher prices, pushing the price lower.
Stop-Loss Hunt: Market makers may trigger stop-losses by creating artificial spikes before reversing the price.
News-Induced Spikes: Economic data releases or unexpected events can cause sudden price jumps.
Understanding volatility and candle spikes can help traders manage risk, spot entry/exit points, and avoid false breakouts. 🚀📈
