Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer
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1. Purpose of the Indicator
The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders:
• Save time by avoiding manual cross-referencing of disparate signals.
• Reduce decision-making errors that can arise from juggling multiple indicators.
• Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position.
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2. Why a Trader Should Use It
• Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually.
• Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant.
• Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction.
• Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases.
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3. Why These Specific Indicators?
This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions:
1. Trend Strength:
• ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish).
• Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend.
• Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness.
2. Momentum:
• Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals.
• Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum.
• MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum.
3. Price Action:
• Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score.
• Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish.
• Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation.
4. Market Activity:
• Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility.
• Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm.
• Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings.
• Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction.
By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone.
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4. What Makes This Indicator Stand Out
• Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity.
• Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized.
• Stability Mechanisms:
• Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes.
• Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw.
• Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2.
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5. Recommended Timeframes
• Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws.
• Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well.
• Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals.
Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.”
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6. How Scoring Works
A. Individual Indicator Scores
Each sub-indicator is assigned one of three discrete scores:
• +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound).
• 0 if it is neutral (e.g., RSI between upper and lower bounds).
• –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound).
Examples of individual score assignments:
• ADX/DMI:
• +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend)
• –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend)
• 0 if ADX < adxThreshold (trend strength below threshold)
• RSI:
• +1 if RSI > RSI_upperBound
• –1 if RSI < RSI_lowerBound
• 0 otherwise
• ATR (as part of Market Activity):
• +1 if ATR > (ATR_MA + stdev(ATR))
• –1 if ATR < (ATR_MA – stdev(ATR))
• 0 otherwise
Each of the four main categories shares this same +1/0/–1 logic across their sub-components.
B. Category Scores
Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows:
• Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score)
• Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score)
• Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score)
• Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score)
Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw.
C. Market Activity State and Dynamic Weight Adjustments
Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action:
1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1).
2. Bucket into High, Medium, or Low Activity:
• High if raw Score ≥ 2 (volatile market).
• Low if raw Score ≤ –2 (calm market).
• Medium otherwise.
3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label.
4. Set Category Weights:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default).
D. Calculating the Net Score
5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %).
6. Determine Current Weights based on the Market Activity State (High/Medium/Low).
7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight).
8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong).
9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise.
10. Apply Hysteresis to the Final Zone:
• If the smoothed netScore ≥ +2, the bar is classified as “Bullish.”
• If the smoothed netScore ≤ –2, the bar is classified as “Bearish.”
• Otherwise, it is “Sideways.”
• To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on).
E. Thresholds for Zone Classification
• BULLISH: netScore ≥ +2
• BEARISH: netScore ≤ –2
• SIDEWAYS: –2 < netScore < +2
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7. Role of Volatility (Market Activity State) in Scoring
Volatility acts as a dynamic switch that shifts which category carries the most influence:
1. High Activity (Volatile):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1.
• The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %.
• Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns.
2. Low Activity (Calm):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1.
• The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %.
• Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move.
3. Medium Activity (Balanced):
• Raw Score between –1 and +1 from the four volatility metrics.
• Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability.
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8. Scoring Example (Hypothetical Scenario)
• Symbol: Bitcoin on a 1-hour chart.
• Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity.
• Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Signals:
• ADX strong and +DI > –DI → +1
• Fast MA above Slow MA → +1
• Ichimoku Senkou A > Senkou B → +1
→ Trend Score = +3
• Momentum Signals:
• RSI above upper bound → +1
• MACD histogram positive → +1
• Stochastic %K within neutral zone → 0
→ Momentum Score = +2
• Price Action Signals:
• Highest High/Lowest Low check yields 0 (close not near extremes)
• Heikin-Ashi doji reading is neutral → 0
• Candle range slightly above upper bound but trend is strong, so → +1
→ Price Action Score = +1
• Compute Net Score (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 1 × 0.15 = 0.15
• Raw netScore = 1.50 + 0.70 + 0.15 = 2.35
• Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.”
Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled).
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9. Correlation Between Categories
The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations:
• Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory.
• Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias.
• Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing.
By dynamically recognizing these correlations and adjusting weights, the indicator ensures that:
• When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish.
• When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals.
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10. Market Activity State & Its Role (Detailed)
The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied:
1. Calculate Four Volatility Sub-Scores:
• BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0.
• ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0.
• KCW: Same logic as ATR but around the KCW mean.
• Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0.
2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4.
3. Assign Market Activity State:
• High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking).
• Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume).
• Medium Activity: Raw Score is between –1 and +1 inclusive.
4. Hysteresis for Stability:
• If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label.
• This prevents the Market Activity State from bouncing around when volatility is on the fence.
5. Set Category Weights Based on Activity State:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action.
7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.”
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11. Category Weights: Base vs. Dynamic
Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts:
• High Volatility Overrides:
• Trend jumps from its base (e.g., 40 %) to 50 %.
• Momentum jumps from its base (e.g., 30 %) to 35 %.
• Price Action is reduced to 15 %.
Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own.
• Low Volatility Overrides:
• Price Action leaps from its base (30 %) to 55 %.
• Trend falls to 25 %, Momentum falls to 20 %.
Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive.
Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases.
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12. Dominant Category Explained
Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely:
• Compute each category’s weighted contribution = (raw category score) × (current weight).
• Compare the absolute values of those three contributions.
• The category with the highest absolute value is flagged as Dominant for that bar.
Why It Matters:
• Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade.
• Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed.
• Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves.
By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways.
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13. Oscillator Plot: How to Read It
The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features:
1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range.
2. Horizontal Threshold Lines:
• +2 Line (Bullish threshold)
• 0 Line (Neutral midline)
• –2 Line (Bearish threshold)
3. Zone Coloring:
• Green Background (Bullish Zone): When netScore ≥ +2.
• Red Background (Bearish Zone): When netScore ≤ –2.
• Gray Background (Sideways Zone): When –2 < netScore < +2.
4. Dynamic Line Color:
• The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue.
Interpretation Tips:
• Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position.
• Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies.
• Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum.
• Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal.
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14. Comments and Their Necessity
Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples:
• “Strong bullish trend” or “Strong bearish trend” for ADX/DMI
• “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope
• “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI
• “MACD histogram positive” or “MACD histogram negative” for MACD Hist
• “Price near highs” or “Price near lows” for HH/LL checks
• “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring
• “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range
Additionally, the top-row comment for each category is:
• Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.”
• Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.”
• Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.”
• Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.”
Reasons for These Comments:
• Transparency: Shows exactly how each sub-indicator contributed to its category score.
• Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time.
• Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds.
In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment.
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15. Real-Life Example (Consolidated)
• Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart.
• Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on).
• Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Sub-Scores:
1. ADX = 25 (above threshold 20) with +DI > –DI → +1.
2. Fast MA (20-period) sits above Slow MA (50-period) → +1.
3. Ichimoku: Senkou A > Senkou B → +1.
→ Trend Score = +3.
• Momentum Sub-Scores:
4. RSI = 75 (above its moving average +1 stdev) → +1.
5. MACD histogram = +0.15 → +1.
6. Stochastic %K = 50 (mid-range) → 0.
→ Momentum Score = +2.
• Price Action Sub-Scores:
7. Price is not within 1 % of the 20-period high/low and slope = positive → 0.
8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1.
9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1.
→ Price Action Score = +2.
• Calculate netScore (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 2 × 0.15 = 0.30
• Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish.
• Oscillator & Dashboard Output:
• The oscillator line crosses above +2 and turns green.
• Dashboard displays:
• Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.”
• Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.”
• Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.”
• Market Activity State “High,” Comment = “Volatile Market.”
• Weights: Trend 50 %, Momentum 35 %, Price Action 15 %.
• Dominant Category: Trend (because 1.50 > 0.70 > 0.30).
• Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0.
• Final Zone = “BULLISH.”
• The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long.
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Disclaimer
This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should:
• Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital.
• Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis.
• Understand that no indicator is foolproof; always be prepared for unexpected market moves.
Goodluck
-BullByte!
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Komut dosyalarını "weekly" için ara
Volume Point of Control with Fib Based Profile🍀Description:
This indicator is a comprehensive volume profile analysis tool designed to identify key price levels based on trading activity within user-defined timeframes. It plots the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL), along with dynamically calculated Fibonacci levels derived from the developing period's range. It offers extensive customization for both historical and developing levels.
🍀Core Features:
Volume Profiling (POC, VAH, VAL):
Calculates and plots the POC (price level with the highest volume), VAH, and VAL for a selected timeframe (e.g., Daily, Weekly).
The Value Area percentage is configurable. 70% is common on normal volume profiles, but this script allows you to configure multiple % levels via the fib levels. I recommend using 2 versions of this indicator on a chart, one has Value Area at 1 (100% - high and low of lookback) and the second is a specified VA area (i.e. 70%) like in the chart snapshot above. See examples at the bottom.
Historical Levels:
Plots POC, VAH, and VAL from previous completed periods.
Optionally displays only "Unbroken" levels – historical levels that price has not yet revisited, which can act as stronger magnets or resistance/support.
The user can manage the number of historical lines displayed to prevent chart clutter.
Developing Levels:
Shows the POC, VAH, and VAL as they form in real-time during the current, incomplete period. This provides insight into intraday/intra-period value migration.
Dynamic Fibonacci Levels:
Calculates and plots Fibonacci retracement/extension levels based dynamically on the range between the developing POC and the developing VAH/VAL.
Offers 8 configurable % levels above and below POC that can be toggled on/off.
Visual Customization:
Extensive options for colors, line styles, and widths for all plotted levels.
Optional gradient fill for the Value Area that visualizes current price distance from POC - option to invert the colors as well.
Labels for developing levels and Fibonacci levels for easy identification.
🍀Characteristics:
Volume-Driven: Levels are derived from actual trading volume, reflecting areas of high participation and price agreement/disagreement.
Timeframe Specific: The results are entirely dependent on the chosen profile timeframe.
Dynamic & Static Elements: Developing levels and Fibs update live, while historical levels remain fixed once their period closes.
Lagging (Historical) & Potentially Leading: Historical levels are based on the past, but are often respected by future price action. Developing levels show current dynamics.
🍀How to Use It:
Identifying Support & Resistance: Historical and developing POCs, VAHs, and VALs are often key areas where price may react. Unbroken levels are particularly noteworthy.
Market Context & Sentiment: Trading above the POC suggests bullish strength/acceptance of higher prices, while trading below suggests bearishness/acceptance of lower prices.
Entry/Exit Zones: Interactions with these levels (rejections, breakouts, tests) can provide potential entry or exit signals, especially when confirming with other analysis methods.
Dynamic Targets: The Fibonacci levels calculated from the developing POC-VA range offer potential intraday/intra-period price targets or areas of interest.
Understanding Value Migration: Observing the movement of the developing POC/VAH/VAL throughout the period reveals where value is currently being established.
🍀Potential Drawbacks:
Input Sensitivity: The choice of timeframe, Value Area percentage, and volume resolution heavily influences the generated levels. Experimentation is needed for optimal settings per instrument/market. (I've found that Range Charts can provide very accurate volume levels on TV since the time element is removed. This helps to refine the accuracy of price levels with high volume.)
Volume Data Dependency: Requires accurate volume data. May be less reliable on instruments with sparse or questionable volume reporting.
Chart Clutter: Enabling all features simultaneously can make the chart busy. Utilize the line management inputs and toggle features as needed.
Not a Standalone Strategy: This indicator provides context and key levels. It should be used alongside other technical analysis tools and price action reading for robust decision-making.
Developing Level Fluctuation: Developing POC/VA/Fib levels can shift considerably, especially early in a new period, before settling down as more volume accumulates and time passes.
🍀Recommendations/Examples:
I recommend have this indicator on your chart twice, one has the VA set at 1 (100%) and has the fib levels plotted. The second has the VA set to 0.7 (70%) to highlight the defined VA.
Here is an example with 3 on a chart. VA of 100%, VA of 80%, and VA of 20%
Options Volatility Strategy Analyzer [TradeDots]The Options Volatility Strategy Analyzer is a specialized tool designed to help traders assess market conditions through a detailed examination of historical volatility, market benchmarks, and percentile-based thresholds. By integrating multiple volatility metrics (including VIX and VIX9D) with color-coded regime detection, the script provides users with clear, actionable insights for selecting appropriate options strategies.
📝 HOW IT WORKS
1. Historical Volatility & Percentile Calculations
Annualized Historical Volatility (HV): The script automatically computes the asset’s historical volatility using log returns over a user-defined period. It then annualizes these values based on the chart’s timeframe, helping you understand the asset’s typical volatility profile.
Dynamic Percentile Ranks: To gauge where the current volatility level stands relative to past behavior, historical volatility values are compared against short, medium, and long lookback periods. Tracking these percentile ranks allows you to quickly see if volatility is high or low compared to historical norms.
2. Multi-Market Benchmark Comparison
VIX and VIX9D Integration: The script tracks market volatility through the VIX and VIX9D indices, comparing them to the asset’s historical volatility. This reveals whether the asset’s volatility is outpacing, lagging, or remaining in sync with broader market volatility conditions.
Market Context Analysis: A built-in term-structure check can detect market stress or relative calm by measuring how VIX compares to shorter-dated volatility (VIX9D). This helps you decide if the present environment is risk-prone or relatively stable.
3. Volatility Regime Detection
Color-Coded Background: The analyzer assigns a volatility regime (e.g., “High Asset Vol,” “Low Asset Vol,” “Outpacing Market,” etc.) based on current historical volatility percentile levels and asset vs. market ratios. A color-coded background highlights the regime, enabling traders to quickly interpret the market’s mood.
Alerts on Regime Changes & Spikes: Automated alerts warn you about any significant expansions or contractions in volatility, allowing you to react swiftly in changing conditions.
4. Strategy Forecast Table
Real-Time Strategy Suggestions: At the close of each bar, an on-chart table generates suggested options strategies (e.g., selling premium in high volatility or buying premium in low volatility). These suggestions provide a quick summary of potential tactics suited to the current regime.
Contextual Market Data: The table also displays key statistics, such as VIX levels, asset historical volatility percentile, or ratio comparisons, helping you confirm whether volatility conditions warrant more conservative or more aggressive strategies.
🛠️ HOW TO USE
1. Select Your Timeframe: The script supports multiple timeframes. For short-term trading, intraday charts often reveal faster shifts in volatility. For swing or position trading, daily or weekly charts may be more stable and produce fewer false signals.
2. Check the Volatility Regime: Observe the background color and on-chart labels to identify the current regime (e.g., “HIGH ASSET VOL,” “LOW VOL + LAGGING,” etc.).
3. Review the Forecast Table: The table suggests strategy ideas (e.g., iron condors, long straddles, ratio spreads) depending on whether volatility is elevated, subdued, or spiking. Use these as a starting point for designing trades that match your risk tolerance.
4. Combine with Additional Analysis: For optimal results, confirm signals with your broader trading plan, technical tools (moving averages, price action), and fundamental research. This script is most effective when viewed as one component in a comprehensive decision-making process.
❗️LIMITATIONS
Directional Neutrality: This indicator analyzes volatility environments but does not predict price direction (up/down). Traders must combine with directional analysis for complete strategy selection.
Late or Missed Signals: Since all calculations require a bar to close, sharp intrabar volatility moves may not appear in real-time.
False Positives in Choppy Markets: Rapid changes in percentile ranks or VIX movements can generate conflicting or premature regime shifts.
Data Sensitivity: Accuracy depends on the availability and stability of volatility data. Significant gaps or unusual market conditions may skew results.
Market Correlation Assumptions: The system assumes assets generally correlate with S&P 500 volatility patterns. May be less effective for:
Small-cap stocks with unique volatility drivers
International stocks with different market dynamics
Sector-specific events disconnected from broad market
Cryptocurrency-related assets with independent volatility patterns
RISK DISCLAIMER
Options trading involves substantial risk and is not suitable for all investors. Options strategies can result in significant losses, including the total loss of premium paid. The complexity of options strategies requires thorough understanding of the risks involved.
This indicator provides volatility analysis for educational and informational purposes only and should not be considered as investment advice. Past volatility patterns do not guarantee future performance. Market conditions can change rapidly, and volatility regimes may shift without warning.
No trading system can guarantee profits, and all trading involves the risk of loss. The indicator's regime classifications and strategy suggestions should be used as part of a comprehensive trading plan that includes proper risk management, directional analysis, and consideration of broader market conditions.
Last Week's APM & Daily % Move(Corrected)Last Week's Average Price Movement + Daily Percentage Move (based on NY time)
This indicator accurately displays last week's Average Pip Movement (APM) consistently across all timeframes and tracks the true daily percentage move relative to that APM in a clear table in the top-right corner.
Key Features:
-Consistent Last Week's APM: Calculates the average pip movement from Monday to Friday of the previous trading week (based on daily wick-to-wick ranges, divided by 5). This APM value is now stable and the same across all chart timeframes.
-Accurate Live Daily % Move: Tracks the maximum percentage the price has moved (either up or down) since the 5 PM New York time daily open, compared to last week's APM. The percentage holds the maximum value reached during the day and resets at the next 5 PM NY open.
-NY Time Alignment: All time-based calculations are aligned with the New York time zone
Pip Adjustment: Automatically adjusts for JPY pairs.
⚠️ Important: For the intended display and relevance of the daily percentage move, this indicator is best used on timeframes 4-hour and under. On Daily and Weekly timeframes, the APM display will show a message indicating this.
We hope this indicator enhances your trading analysis.
Directional Strength IndexThis indicator is designed to detect the dominant market direction and quantify its strength by aggregating signals across six key timeframes: 1H, 4H, 1D, 3D, 1W, and 1M.
At its core, it uses a SMEMA 'the Simple Moving Average of an EMA' as the main trend reference. This hybrid smoothing method was chosen for its balance: the EMA ensures responsiveness to recent price moves, while the SMA dampens short-term volatility. This makes the SMEMA more stable than a raw EMA and more reactive than a simple SMA, especially in noisy or volatile environments.
For each timeframe, a score between -10 and +10 is calculated. This score reflects:
- the distance of the price from the SMEMA, using ATR as a dynamic threshold
- the number of price deviations above or below the SMEMA
- the slope of the SMEMA, which adjusts the score based on momentum
These six timeframe scores are then combined into a single Global Score, using weighted averages. Three weighting profiles are available depending on your trading horizon:
- Long Term: emphasizes weekly and monthly data
- Swing Trading: gives balanced importance to all timeframes
- Short Term: prioritizes 1H and 4H action
This multi-timeframe aggregation makes the indicator adaptable to different styles while maintaining a consistent logic.
The result is displayed in a table on the chart, showing:
- the trend direction per timeframe (up, down or neutral)
- the strength score per timeframe
- the overall trend direction and strength based on the selected profile
Optional deviation bands based on ATR multiples are also plotted to provide visual context for overextensions relative to the SMEMA.
This indicator is non-repainting and built for objective, trend-based decision making.
Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment
## 1. Introduction
The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016).
The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms.
## 2. Theoretical Framework
### 2.1 Market Efficiency and Information Integration
The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009).
### 2.2 Multi-Factor Asset Pricing Theory
Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986).
## 3. Methodology
### 3.1 Data Sources and Quality Framework
The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds:
Supply Indicators:
- US Oil Production (ECONOMICS:USCOP)
- US Oil Rigs Count (ECONOMICS:USCOR)
- API Crude Runs (ECONOMICS:USACR)
Inventory Indicators:
- US Crude Stock Changes (ECONOMICS:USCOSC)
- Cushing Stocks (ECONOMICS:USCCOS)
- API Crude Stocks (ECONOMICS:USCSC)
- API Gasoline Stocks (ECONOMICS:USGS)
- API Distillate Stocks (ECONOMICS:USDS)
Demand Indicators:
- Refinery Crude Runs (ECONOMICS:USRCR)
- Gasoline Production (ECONOMICS:USGPRO)
- Distillate Production (ECONOMICS:USDFP)
- Industrial Production Index (FRED:INDPRO)
Trade Indicators:
- US Crude Imports (ECONOMICS:USCOI)
- US Oil Exports (ECONOMICS:USOE)
- API Crude Imports (ECONOMICS:USCI)
- Dollar Index (TVC:DXY)
Sentiment Indicators:
- Oil Volatility Index (CBOE:OVX)
### 3.2 Data Quality Monitoring System
Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring:
Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators
Where validity is determined by:
- Non-null data availability
- Positive value validation
- Temporal consistency checks
### 3.3 Statistical Normalization Framework
#### 3.3.1 Z-Score Normalization
The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability:
Z_i,t = (X_i,t - μ_i) / σ_i
Where:
- X_i,t = Raw value of indicator i at time t
- μ_i = Sample mean of indicator i
- σ_i = Sample standard deviation of indicator i
Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977).
#### 3.3.2 Percentile Rank Transformation
For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999):
Percentile_Rank = (Number of values < current_value) / Total_observations × 100
### 3.4 Exponential Smoothing Framework
Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter:
S_t = α × X_t + (1-α) × S_{t-1}
Where α = 2/(N+1) and N represents the smoothing period.
### 3.5 Dynamic Threshold Optimization
The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992):
Dynamic_Threshold = μ ± (k × σ)
Where k is the threshold multiplier adjusted for market volatility regime.
### 3.6 Composite Score Calculation
The fundamental score integrates component scores through weighted averaging:
Fundamental_Score = Σ(w_i × Score_i × Quality_i)
Where:
- w_i = Normalized component weight
- Score_i = Component fundamental score
- Quality_i = Data quality adjustment factor
## 4. Implementation Architecture
### 4.1 Adaptive Parameter Framework
The model incorporates regime-specific adjustments based on market volatility:
Volatility_Regime = σ_price / μ_price × 100
High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors.
### 4.2 Data Synchronization Protocol
Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators.
### 4.3 Quality-Adjusted Scoring
Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal:
Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor)
This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise.
## 5. Usage Guidelines and Best Practices
### 5.1 Configuration Recommendations
For Short-term Analysis (1-4 weeks):
- Lookback Period: 26 weeks
- Smoothing Length: 3-5 periods
- Confidence Period: 13 weeks
- Increase inventory and sentiment weights
For Medium-term Analysis (1-3 months):
- Lookback Period: 52 weeks
- Smoothing Length: 5-8 periods
- Confidence Period: 26 weeks
- Balanced component weights
For Long-term Analysis (3+ months):
- Lookback Period: 104 weeks
- Smoothing Length: 8-12 periods
- Confidence Period: 52 weeks
- Increase supply and demand weights
### 5.2 Signal Interpretation Framework
Bullish Signals (Score > 70):
- Fundamental conditions favor price appreciation
- Consider long positions or reduced short exposure
- Monitor for trend confirmation across multiple timeframes
Bearish Signals (Score < 30):
- Fundamental conditions suggest price weakness
- Consider short positions or reduced long exposure
- Evaluate downside protection strategies
Neutral Range (30-70):
- Mixed fundamental environment
- Favor range-bound or volatility strategies
- Wait for clearer directional signals
### 5.3 Risk Management Considerations
1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution.
2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure.
3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness.
4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes.
### 5.4 Alert System Optimization
Configure alerts conservatively to avoid false signals:
- Set alert threshold at 75+ for high-confidence signals
- Enable data quality warnings to maintain system integrity
- Use trend reversal alerts for early regime change detection
## 6. Model Validation and Performance Metrics
### 6.1 Statistical Validation
The model's statistical robustness is ensured through:
- Out-of-sample testing protocols
- Rolling window validation
- Bootstrap confidence intervals
- Regime-specific performance analysis
### 6.2 Economic Validation
Fundamental accuracy is validated against:
- Energy Information Administration (EIA) official reports
- International Energy Agency (IEA) market assessments
- Commercial inventory data verification
## 7. Limitations and Considerations
### 7.1 Model Limitations
1. Data Dependency: Model performance is contingent on data availability and quality from external sources.
2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability.
3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation.
4. Regime Shifts: Structural market changes may require model recalibration.
### 7.2 Market Environment Considerations
The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals.
## References
Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill.
Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403.
Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417.
Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069.
Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons.
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360.
Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58.
Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
BK AK-Scope🔭 Introducing BK AK-Scope — Target Locked. Signal Acquired. 🔭
After building five precision weapons for traders, I’m proud to unveil the sixth.
BK AK-Scope — the eye of the arsenal.
This is not just an indicator. It’s an intelligence system for volatility, signal clarity, and rate-of-change dynamics — forged for elite vision in any market terrain.
🧠 Why “Scope”? And Why “AK”?
Every shooter knows: you can’t hit what you can’t see.
The Scope brings range, clarity, and target distinction. It filters motion from noise. Purpose from panic.
“AK” continues to honor the man who trained my sight — my mentor, A.K.
His discipline taught me to wait for alignment. To move with reason, not emotion.
His vision lives in every code line here.
🔬 What Is BK AK-Scope?
A Triple-Tier TSI Correlation Engine, fused with adaptive opacity logic, a volatility scoring system, and real-time signal clarity. It’s momentum dissected — by speed, depth, and rate of change.
Built to serve traders who:
Need visual hierarchy between fast, mid, and slow TSI responses.
Want adaptive fills that pulse with volatility — not static zones.
Require a volatility scoring overlay that reads the battlefield in real time.
⚙️ Core Systems: How BK AK-Scope Works
✅ Fast/Mid/Slow TSI →
Three layers of correlation: like scopes with zoom levels.
You track micro moves, mid swings, and macro flow simultaneously.
✅ Rate-of-Change Adaptive Opacity →
Momentum fills fade or flash based on speed — giving you movement density at a glance.
Bull vs. Bear zones adapt to strength. You feel the market’s pulse.
✅ Volatility Score Intelligence →
Custom algorithm measuring:
Range expansion
Rate-of-change differentials
ATR dynamics
Standard deviation pressure
All combined into a score from 0–100 with live icons:
🔥 = Extreme Heat (70+)
🧊 = Cold Zone (<30)
⚠️ = ROC Warning
• = Neutral drift
✅ Auto-Detect Volatility Modes →
Scalp = <15min
Swing = intraday/hourly
Macro = daily/weekly
Or override manually with total control.
🎯 How To Use BK AK-Scope
🔹 Trend Continuation → When all three TSI layers align in direction + volatility score climbs, ride with the trend.
🔹 Early Reversals → Opposing TSI + rapid opacity change + volatility shift = sniper reversal zone.
🔹 Consolidation Filter → Neutral fills + score < 30 = stay out, wait for signal surge.
🔹 Signal Confluence → Pair with:
• Gann fans or angles
• Fib time/price clusters
• Elliott Wave structure
• Harmonics or divergence
To isolate entry perfection.
🛡️ Why This Indicator Changes the Game
It's not just momentum. It’s TSI with depth hierarchy.
It’s not just color. It’s real-time strength visualization.
It’s not just volatility. It’s rate-weighted market intelligence.
This is market optics for the advanced trader — built for vision, clarity, and discipline.
🙏 Final Thoughts
🔹 In honor of A.K., my mentor. The man who taught me to see what others miss.
🔹 Inspired by the power of vision — because execution without clarity is chaos.
🔹 Powered by faith — because Gd alone gives sight beyond the visible.
“He gives sight to the blind and wisdom to the humble.” — Psalms 146
Every tool I build is a prayer in code — that it helps someone trade with clarity, integrity, and precision.
⚡ Zoom In. Focus Deep. Trade Clean.
BK AK-Scope — Lock on the target. See what others don’t.
🔫 Clarity is power. 🔫
Gd bless. 🙏
Volume-Enhanced Candlestick Patterns 1
Overview
Scans for four major candlestick reversal patterns:
Harami
Engulfing
Morning/Evening Star
Piercing Line/Dark Cloud Cover
Underlying logic assumes that, at a turning point, the dominant side (bulls or bears) often delivers a “final” push—either a last surge of buying or selling—before the reversal truly takes hold.
Pattern Toggles
Each individual pattern can be turned on or off in the inputs.
Enable only the patterns you want to monitor to reduce chart clutter and speed up performance.
Volume Filter Toggle
On: Requires volume-based exhaustion or climax to confirm each pattern.
Off: Relies purely on price-action candlestick logic (no volume checks).
Grouped Labels & Confluence
When one or more patterns trigger on the same bar close, a single label is drawn:
Grouping multiple confirmed patterns on one bar increases confluence and signal strength.
Climax Volume × Multiplier
Adjusting this input affects signal frequency and conviction:
Higher multiplier → fewer signals but with stronger volume confirmation
Lower multiplier → more signals, each with a looser volume requirement
Alerts
Built-in alert condition for each individual pattern (bullish/bearish Harami, Engulfing, Star, Piercing, Dark Cloud Cover), so you can receive real-time notifications whenever a confirmation occurs.
Follow for Weekly Scripts
If you find this helpful, please hit Follow and 🚀button —I release a new scripts every week.
Disclaimer
Not Financial Advice. This script is for educational and research purposes only.
Use as Part of a Larger System. It should not be used in isolation; combine it with your own risk management rules, additional indicators, and broader market analysis.
No Guarantees. Candlestick patterns and volume filters can improve signal quality, but they do not guarantee profitable trades. Always perform your own due diligence before entering any position.
Institutional Volume Footprint ProOVERVIEW
The Institutional Volume Footprint Pro is a comprehensive volume analysis indicator designed to identify institutional trading activity and significant volume patterns. Based on the proven Pocket Pivot Volume methodology by Chris Kacher and Gil Morales, this indicator has been enhanced with multiple additional volume analysis techniques to provide traders with a complete picture of smart money movements.
KEY FEATURES
1. Pocket Pivot Volume (PPV) Detection
- Identifies bullish volume patterns where current volume exceeds the highest down-day volume of the past 10 days
- Blue volume bars with "PPV" labels mark potential institutional accumulation
- Customizable lookback period (5-20 days)
2. Pivot Negative Volume (PNV) Detection
- Spots bearish volume patterns where selling volume exceeds recent up-day volumes
- Orange bars with "PNV" labels indicate potential institutional distribution
- Early warning system for trend reversals
3. Advanced Institutional Patterns
- Accumulation Detection (Aqua): High volume with narrow price range - classic stealth accumulation
- Churning/Distribution (Yellow): Heavy volume with minimal price progress - potential topping pattern
- Volume Dry-up (Purple): Extremely low volume periods that often precede significant moves
- Volume Climax (Fuchsia): Extreme volume spikes signaling potential exhaustion
4. Real-time Analytics Dashboard
- Relative Volume: Current volume compared to 10-day average
- Volume vs MA: Multiple of current volume to selected moving average
- Price Range Analysis: Narrow/Normal/Wide range classification
5. Accumulation/Distribution Trend
- Background coloring shows overall money flow direction
- Green tint: Net accumulation phase
- Red tint: Net distribution phase
HOW TO USE
Entry Signals:
- PPV (Blue): Consider long positions when price breaks above resistance with PPV confirmation
- Accumulation (Aqua): Watch for breakouts following multiple accumulation days
- Volume Dry-up (Purple): Prepare for potential explosive moves
Exit/Warning Signals:
- PNV (Orange): Consider taking profits or tightening stops
- Churning (Yellow): Distribution may be occurring despite stable prices
- Volume Climax (Fuchsia): Potential reversal point - extreme caution advised
CUSTOMIZATION OPTIONS
Analysis Parameters:
- PPV Lookback Period (5-20 days)
- Volume MA Length & Type (SMA/EMA/WMA)
- Relative Volume Threshold
- Climax Volume Multiplier
Visual Controls:
- Toggle Info Table display
- Enable/disable individual label types (PPV, PNV, ACC)
- Show/hide volume moving averages
- Control A/D trend background
- Customize threshold lines
BUILT-IN ALERTS
- Pocket Pivot Volume detected
- Pivot Negative Volume detected
- Institutional Accumulation pattern
- Volume Climax warning
- Volume Dry-up alert
PRO TIPS
1. Combine with Price Action: Volume confirms price - look for PPV at breakouts and PNV at breakdowns
2. Multiple Timeframes: Check daily and weekly charts for confluence
3. Relative Volume Matters: Patterns are stronger when relative volume > 1.5x
4. Watch for Divergences: Price up with decreasing volume = weakness
COLOR LEGEND
- Blue: Pocket Pivot Volume (Bullish)
- Orange: Pivot Negative Volume (Bearish)
- Aqua: Institutional Accumulation
- Yellow: Churning/Distribution
- Purple: Volume Dry-up
- Fuchsia: Volume Climax
- Green: Above-average up volume
- Red: Above-average down volume
- Gray: Below-average volume
EDUCATIONAL BACKGROUND
This indicator implements concepts from:
- "Trade Like an O'Neil Disciple" by Gil Morales & Chris Kacher
- William O'Neil's volume analysis principles
- Richard Wyckoff's accumulation/distribution methodology
Happy Trading! May the volume be with you!
1A Monthly P&L Table - Using Library1A Monthly P&L Table: Track Your Performance Month-by-Month
Overview:
The 1A Monthly P&L Table is a straightforward yet powerful indicator designed to give you an immediate overview of your asset's (or strategy's) percentage performance on a monthly basis. Displayed conveniently in the bottom-right corner of your chart, this tool helps you quickly assess historical gains and losses, making it easier to analyze trends in performance over time.
Key Features:
Monthly Performance at a Glance: Clearly see the percentage change for each past month.
Cumulative P&L: A running total of the displayed monthly P&L is provided, giving you a quick sum of performance over the selected period.
Customizable Display:
Months to Display: Choose how many past months you want to see in the table (from 1 to 60 months).
Text Size: Adjust the text size (Tiny, Small, Normal, Large, Huge) to fit your viewing preferences.
Text Color: Customize the color of the text for better visibility against your chart background.
Intraday & Daily Compatibility: The table is optimized to display on daily and intraday timeframes, ensuring it's relevant for various trading styles. (Note: For very long-term analysis on weekly/monthly charts, you might consider other tools, as this focuses on granular monthly P&L.)
How It Works:
The indicator calculates the percentage change from the close of the previous month to the close of the current month. For the very first month displayed, it calculates the P&L from the opening price of the chart's first bar to the close of that month. This data is then neatly organized into a table, updated on the last bar of the day or session.
Ideal For:
Traders and investors who want a quick, visual summary of monthly performance.
Analyzing seasonal trends or consistent periods of profitability/drawdown.
Supplementing backtesting results with a clear month-by-month breakdown.
Settings:
Text Color: Changes the color of all text within the table.
Text Size: Controls the font size of the table content.
Months to Display: Determines the number of recent months included in the table.
Range Progress TrackerRANGE PROGRESS TRACKER(RPT)
PURPOSE
This indicator helps traders visually and statistically understand how much of the typical price range (measured by ATR) has already been covered in the current period (Daily, Weekly, or Monthly). It includes key features to assist in trend exhaustion analysis, reversal spotting, and smart alerting.
CORE LOGIC
The indicator calculates the current range of the selected time frame (e.g., Daily), which is:
Current Range = High - Low
This is then compared to the ATR (Average True Range) of the same time frame, which represents the average price movement range over a defined period (default is 14).
The comparison is expressed as a percentage, calculated with this formula:
Range % = (Current Range / ATR) × 100
This percentage shows how much of the “average expected move” has already occurred.
WHY IT MATTERS
When the current range approaches or exceeds 100% of ATR, it means the price has already moved as much as it typically does in a full session.
This indicates a lower probability of continuing the trend with a new high or low, especially when the price is already near the session's high or low.
This setup can signal:
A possible consolidation phase
A reversal in trend
The market entering a corrective phase
SMART ALERTS
The indicator can alert you when:
A new high is made after the range percentage exceeds your set threshold.
A new low is made after the range percentage exceeds your set threshold.
You can adjust the Range % Alert Threshold in the settings to tailor it to your trading style.
EWMA & EWVar + EWStd Expansion with MTF_V.5EWMA & EWVar + EWStd Expansion with MTF_V.5
This indicator combines adaptive trend smoothing (EWMA), variance estimation (EWVar) and dynamic volatility “bursts” (EWStd Expansion) with optional higher-timeframe confirmation. It’s designed both for visual chart analysis and for automated alerts on regime changes.
Key Features
EWMA (Exponential Smoothing):
• Computes an exponential moving average with either a custom α or a length-derived α = 2/(N+1).
• Option to recalculate only every N bars (reduces CPU load).
EWVar & EWStd (Variance & Standard Deviation):
• Exponentially weighted variance tracks recent price dispersion.
• EWStd (σ) is computed alongside the EWMA.
• Z-score (deviation in σ units) shows how far price has diverged from trend.
Multi-Timeframe Filter (MTF):
• Optionally require the same trend direction on a chosen higher timeframe (e.g. Daily, Weekly, H4).
• Real-time lookahead available (may repaint).
Gradient Around EWMA:
• A multi-layer “glow” zone of ±1σ, broken into up to 10 steps.
• Color interpolates between “upper” and “lower” shades for bullish, bearish and neutral regimes.
Instantaneous Trendline (ITL):
• Ultra-fast trend filter with slope-based coloring.
• Highlights micro-trends and short-lived accelerations.
Cross-Over Signals (ITL ↔ EWMA):
• Up/down triangles plotted when the ITL crosses the main EWMA.
EWStd Expansion (Volatility Bursts):
• Automatically detects σ expansions (σ growth above a set % threshold).
• Price filter: only when price moves beyond EWMA ± (multiplier·σ).
• Optional higher-timeframe confirmation.
Labels & Alerts:
• Text labels and circular markers on bars where a volatility burst occurs.
• Built-in alertcondition calls for both bullish and bearish expansions.
How to Use
Visual Analysis:
• The gradient around EWMA shows the width of the volatility channel expanding or contracting.
• ITL color changes instantly highlight short-term impulses.
• EWMA line color switches (bullish/bearish/neutral) indicate trend state.
Spotting Volatility Breakouts:
• “EWStd Expansion” labels and circles signal the onset of strong moves when σ spikes.
• Useful for entering at the start of new impulses.
Automated Alerts:
• Set alerts on the built-in conditions “Bullish EWStd Expansion Alert” or “Bearish EWStd Expansion Alert” to receive a popup or mobile push when a burst occurs.
This compact tool unifies trend, volatility and multi-timeframe analysis into a single indicator—ideal for traders who want to see trend direction, current dispersion, and timely volatility burst signals all at once.
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
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. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125.
Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Multi-Session ORBThe Multi-Session ORB Indicator is a customizable Pine Script (version 6) tool designed for TradingView to plot Opening Range Breakout (ORB) levels across four major trading sessions: Sydney, Tokyo, London, and New York. It allows traders to define specific ORB durations and session times in Central Daylight Time (CDT), making it adaptable to various trading strategies.
Key Features:
1. Customizable ORB Duration: Users can set the ORB duration (default: 15 minutes) via the inputMax parameter, determining the time window for calculating the high and low of each session’s opening range.
2. Flexible Session Times: The indicator supports user-defined session and ORB times for:
◦ Sydney: Default ORB (17:00–17:15 CDT), Session (17:00–01:00 CDT)
◦ Tokyo: Default ORB (19:00–19:15 CDT), Session (19:00–04:00 CDT)
◦ London: Default ORB (02:00–02:15 CDT), Session (02:00–11:00 CDT)
◦ New York: Default ORB (08:30–08:45 CDT), Session (08:30–16:00 CDT)
3. Session-Specific ORB Levels: For each session, the indicator calculates and tracks the high and low prices during the specified ORB period. These levels are updated dynamically if new highs or lows occur within the ORB timeframe.
4. Visual Representation:
◦ ORB high and low lines are plotted only during their respective session times, ensuring clarity.
◦ Each session’s lines are color-coded for easy identification:
▪ Sydney: Light Yellow (high), Dark Yellow (low)
▪ Tokyo: Light Pink (high), Dark Pink (low)
▪ London: Light Blue (high), Dark Blue (low)
▪ New York: Light Purple (high), Dark Purple (low)
◦ Lines are drawn with a linewidth of 2 and disappear when the session ends or if the timeframe is not intraday (or exceeds the ORB duration).
5. Intraday Compatibility: The indicator is optimized for intraday timeframes (e.g., 1-minute to 15-minute charts) and only displays when the chart’s timeframe multiplier is less than or equal to the ORB duration.
How It Works:
• Session Detection: The script uses the time() function to check if the current bar falls within the user-defined ORB or session time windows, accounting for all days of the week.
• ORB Logic: At the start of each session’s ORB period, the script initializes the high and low based on the first bar’s prices. It then updates these levels if subsequent bars within the ORB period exceed the current high or fall below the current low.
• Plotting: ORB levels are plotted as horizontal lines during the respective session, with visibility controlled to avoid clutter outside session times or on incompatible timeframes.
Use Case:
Traders can use this indicator to identify key breakout levels for each trading session, facilitating strategies based on price action around the opening range. The flexibility to adjust ORB and session times makes it suitable for various markets (e.g., forex, stocks, or futures) and time zones.
Limitations:
• The indicator is designed for intraday timeframes and may not display on higher timeframes (e.g., daily or weekly) or if the timeframe multiplier exceeds the ORB duration.
• Time inputs are in CDT, requiring users to adjust for their local timezone or market requirements.
• If you need to use this for GC/CL/SPY/QQQ you have to adjust the times by one hour.
This indicator is ideal for traders focusing on session-based breakout strategies, offering clear visualization and customization for global market sessions.
Bitcoin Open Interest [SAKANE]Bitcoin Open Interest
— Unveiling the True Flow of Capital
PurposeVisualize and compare Bitcoin open interest (OI) from CME and Binance, the leading derivatives exchanges, in a single intuitive chart, providing traders with clear insights into crypto market capital dynamics.
Background & MotivationIn the 24/7 crypto market, price movements alone reveal only part of the story. Open interest (OI)—the total outstanding futures contracts—offers critical clues to the market’s next move. Yet, accessing and interpreting OI data is challenging:
CME Constraints: Commitment of Traders (COT) reports are weekly, and standalone BTC1! or BTC2! OI is noisy due to contract rollovers, obscuring true OI changes.
Existing Tool Limitations: Most OI indicators are fixed to either USD or BTC, limiting flexible analysis.
This indicator overcomes these hurdles, enabling seamless comparison of CME and Binance OI to track the market’s “capital center of gravity” in real time.
Key Features
Synthetic CME OI: Combines BTC1! and BTC2! to deliver high-accuracy OI, eliminating rollover noise.
Multi-Timeframe Analysis: Displays daily CME OI as pseudo-candlestick (OHLC) on any timeframe (e.g., 4H), allowing intuitive capital flow tracking across timeframes.
CME/Binance One-Click Toggle: Instantly compare institutional-driven CME and retail-driven Binance OI.
USD/BTC Flexibility: Switch between BTC (real demand) and USD (margin) perspectives for OI analysis.
Robust Design: Concise, global-scope code ensures stability and adaptability to TradingView updates.
Insights & Use Cases
Holistic Market Sentiment: Analyze capital flows by region and exchange for a multidimensional view.
Signal Detection: E.g., a sharp drop in CME OI during a sell-off may signal institutional withdrawal.
Retail Trends: A surge in Binance OI suggests retail-driven inflows.
Event-Driven Insights: E.g., during a hypothetical April 2025 “Trump Tariff Shock,” instantly identify which exchange drives capital shifts.
Unique ValueUnlike price-centric indicators, this tool focuses on capital flow (OI). It’s the only indicator offering one-click multi-timeframe and multi-exchange OI comparison, empowering traders to uncover the market’s “true intent” and gain a strategic edge.
ConclusionBitcoin Open Interest makes the market’s hidden capital movements accessible to all. By capturing market dynamics and pinpointing the “leading forces” during events, it sets a new standard for traders seeking a revolutionary perspective.
HTF High/Low Targets This script plots the previous Highs and Lows of the 1HR, 4HR, Daily, and Weekly timeframes.
Each level is color-coded, extends across the chart, and includes labels to help you spot key areas of past support and resistance.
Use this tool to:
- Confirm intraday price reactions at HTF zones
- Identify high-probability reversal or breakout areas
- Get notified with built-in alerts when price crosses a level
You can toggle each timeframe level on/off in the settings panel.
Great for:
- Day traders and scalpers who trade off 1-minute or 5-minute charts
-Swing traders looking for confluence with HTF zones
- Anyone using a multi-timeframe analysis approach
Created by @mychaellesliemedia.
Supertrend with Volume Filter AlertSupertrend with Volume Filter Alert - Indicator Overview
What is the Supertrend Indicator?
The Supertrend indicator is a popular trend-following tool used by traders to identify the direction of the market and potential entry/exit points. It is based on the Average True Range (ATR), which measures volatility, and plots a line on the chart that acts as a dynamic support or resistance level. When the price is above the Supertrend line, it signals an uptrend (bullish), and when the price is below, it indicates a downtrend (bearish). The indicator is particularly effective in trending markets but can generate false signals during choppy or sideways conditions.
How This Script Works
The "Supertrend with Volume Filter Alert" enhances the classic Supertrend indicator by adding a customizable volume filter to improve signal reliability.
Here's how it functions:
Supertrend Calculation:The Supertrend is calculated using the ATR over a user-defined period (default: 55) and a multiplier (default: 1.85). These parameters control the sensitivity of the indicator:A higher ATR period smooths out volatility, making the indicator less reactive to short-term price fluctuations.The multiplier determines the distance of the Supertrend line from the price, affecting how quickly it responds to trend changes.The script plots the Supertrend line in cyan for uptrends and red for downtrends, making it easy to visualize the market direction.
Volume Filter:A key feature of this script is the volume filter, which helps filter out false signals in choppy markets. The filter compares the current volume to the average volume over a lookback period (default: 20) and only triggers signals if the volume exceeds the average by a specified multiplier (default: 2.0).This ensures that trend changes are accompanied by significant market participation, increasing the likelihood of a genuine trend shift.
Signals and Alerts:
Buy signals (cyan triangle below the bar) are generated when the price crosses above the Supertrend line (indicating an uptrend) and the volume condition is met.Sell signals (red triangle above the bar) are generated when the price crosses below the Supertrend line (indicating a downtrend) and the volume condition is met.Alerts are set up for both buy and sell signals, notifying traders only when the volume filter confirms the trend change.
Customizable Settings for Multiple Markets
The default settings in this script (ATR Period: 55, ATR Multiplier: 1.85, Volume Lookback Period: 20, Volume Multiplier: 2.0) were carefully chosen to provide a balance of sensitivity and reliability across various markets, including stocks, indices (like the S&P 500), forex, and cryptocurrencies.
Here's why these settings work well:
ATR Period (55): A longer ATR period smooths out volatility, making the indicator less prone to whipsaws in volatile markets like crypto or forex, while still being responsive enough for trending markets like indices.
ATR Multiplier (1.85): This multiplier strikes a balance between capturing early trend changes and avoiding noise. A smaller multiplier would make the indicator too sensitive, while a larger one might miss early opportunities.
Volume Lookback Period (20): A 20-bar lookback for volume averaging provides a robust baseline for identifying significant volume spikes, adaptable to both short-term (e.g., daily charts) and longer-term (e.g., weekly charts) timeframes.
Volume Multiplier (2.0): Requiring volume to be at least 2x the average ensures that only high-conviction moves trigger signals, which is crucial for markets with varying liquidity levels.
These parameters are fully customizable, allowing traders to adjust the indicator to their specific market, timeframe, or trading style. For example, you might reduce the ATR period for faster-moving markets or increase the volume multiplier for more conservative signal filtering.
How the Volume Filter Reduces Bad Trades in Choppy Markets
One of the main drawbacks of the Supertrend indicator is its tendency to generate false signals during choppy or ranging markets, where price fluctuates without a clear trend. The volume filter in this script addresses this issue by ensuring that trend changes are backed by significant market activity:
In choppy markets, price movements often lack strong volume, leading to false breakouts or reversals. By requiring volume to be a multiple (default: 2x) of the average volume over the lookback period, the script filters out these low-volume, low-conviction moves.This reduces the likelihood of taking bad trades during sideways markets, as only trend changes with strong volume confirmation will trigger signals. For example, on a daily chart of the S&P 500, a buy signal will only fire if the price crosses above the Supertrend line and the volume on that day is at least twice the 20-day average, indicating genuine buying pressure.
Usage Tips
Markets and Timeframes: This indicator is versatile and can be used on various assets (stocks, indices, forex, crypto) and timeframes (1-minute, 1-hour, daily, etc.). Adjust the settings based on the market's volatility and your trading strategy.
Combine with Other Indicators: While the volume filter improves reliability, consider using additional indicators like RSI or MACD to confirm trends, especially in ranging markets.
Backtesting: Test the indicator on historical data for your chosen market to optimize the settings and ensure they align with your trading goals.
Alerts: Set up alerts for buy and sell signals to stay informed of high-probability trend changes without constantly monitoring the chart.
ConclusionThe "Supertrend with Volume Filter Alert" is a powerful tool for trend-following traders, combining the simplicity of the Supertrend indicator with a volume-based filter to enhance signal accuracy. Its customizable settings make it adaptable to multiple markets, while the volume filter helps reduce false signals in choppy conditions, allowing traders to focus on high-probability trades. Whether you're trading stocks, indices, forex, or crypto, this indicator can help you identify trends with greater confidence.
Anchored VWAP by Time (Math by Thomas)📄 Description
This tool lets you plot an Anchored Volume Weighted Average Price (VWAP) starting from any specific date and time you choose. Unlike standard VWAPs that reset daily or weekly, this version gives you full control to track institutional pricing zones from precise anchor points—such as key swing highs/lows, market open, or news-driven candles.
It’s especially useful for price action and Smart Money Concepts (SMC) traders who track liquidity, fair value gaps (FVGs), and institutional zones.
🇮🇳 For NSE India Traders
You can anchor VWAP to Indian market open (e.g., 9:15 AM IST) or major events like RBI policy, earnings, or breakout candles.
The time input uses UTC by default, so for Indian Standard Time (IST), remember:
9:15 AM IST = 3:45 AM UTC
3:30 PM IST = 10:00 AM UTC
⚙️ How to Use
Add the indicator to your chart.
Open the settings panel.
Under “Anchor Start Time”, choose the date & time to begin the VWAP.
Use UTC format (adjust from IST if needed).
Customize the line color and thickness to suit your chart style.
The VWAP will begin plotting from that time forward.
🔎 Best Use Cases
Track VWAP from intraday range breakouts
Anchor from swing highs/lows to identify mean reversion zones
Combine with your FVGs, Order Blocks, or CHoCHs
Monitor VWAP reactions during key macro events or expiry days
🔧 Clean Design
No labels are used, keeping your chart clean.
Works on all timeframes (1min to Daily).
Designed for serious intraday & positional traders.
Candle Range DetectorCandle Range Detector
// Pine Script v6
// Detects candle-based ranges, mitigations, and sweeps with advanced logic
Overview
This indicator automatically detects price ranges based on candle containment, then tracks when those ranges are mitigated (broken) and when a sweep occurs. It is designed for traders who want to identify liquidity events and range breaks with precision.
How It Works
- Range Detection: A range is formed when a candle is fully contained within the previous candle (its high is lower and its low is higher). This marks a potential area of price balance or liquidity.
- Mitigation: A range is considered mitigated when price closes beyond its extension levels (configurable by normal or Fibonacci logic). This signals that the range has been invalidated or "taken out" by price action.
- Sweep Detection: After mitigation, the script watches for a sweep event: a candle that both trades through the range extreme and closes decisively beyond the log-mid of the candle itself. This is a strong sign of a liquidity grab or stop run.
- Alerts & Visuals: You can enable alerts and on-chart labels for sweeps. Only the most recent mitigated range can be swept, and each range can only be swept once.
- Timeframe Sensitivity: On weekly or monthly charts, a candle can both mitigate and sweep a range on the same bar. On lower timeframes, only one event can occur per bar.
Why It Works
- Candle containment is a robust way to identify natural price ranges and liquidity pools, as it reflects where price is consolidating or being absorbed.
- Mitigation marks the moment when a range is no longer defended, often leading to new directional moves.
- Sweeps are powerful signals of stop hunts or liquidity grabs, especially when confirmed by a close beyond the log-mid of the candle, indicating strong intent.
Visual Explanation
Tip: Use this tool to spot high-probability reversal or continuation zones, and to get alerted to key liquidity events in real time.
10 Monday's 1H Avg Range + 30-Day Daily RangeWhat This Script Does
This indicator is designed for traders who want to monitor volatility and range behavior at the start of the trading week . It focuses specifically on the first four 15-minute candles of each Monday and tracks their combined high-low range over time.
How It Works
Monday 1H Range Detection:
Each week, it automatically detects and highlights the first 4 candles of Monday on a 15-minute chart (1 hour total). It calculates the range between the highest high and lowest low of these candles.
10-Week Average of Monday 1H Ranges:
It stores and averages the last 10 such ranges, displaying this average in a table for weekly comparison.
30-Day Daily Range Average:
Separately, it calculates the average daily range (high – low) of the last 30 daily candles. This value helps put the Monday 1H range into broader context and can guide Stop Loss or TP planning.
Dynamic Labeling & Visual Highlights:
The script visually highlights the first 4 candles of Monday and places a label showing the pip range once the 4 candles have completed. It also updates a small table with the two averages described above.
How to Use It
Use it on the 15-minute timeframe to activate the Monday 1H logic.
Compare the current week’s Monday range to the 10-week average to see if volatility is increasing or decreasing.
Use the 30-day daily range to determine if the Monday opening movement is unusually large or small.
Consider adjusting trade entries, stops, or targets if the Monday range is disproportionately large compared to recent historical behavior.
What Makes It Original?
This is not a typical volatility indicator like ATR or standard deviation. Instead, it’s a purpose-built tool combining:
Time-specific behavior (first hour of the week),
Historical contextualization (10-week average tracking),
A dual-timeframe analysis (15-min + daily),
A user-friendly table and visual interface.
This script helps intraday or swing traders spot abnormal volatility early in the week and adjust their strategies accordingly—especially in fast-moving Forex or Index markets.
Ichimoku Cloud Breakout Only LongThis is a very simple trading strategy based exclusively on the Ichimoku Cloud. There are no additional indicators or complex rules involved. The key condition is that we only open long positions when the price is clearly above the cloud — indicating a bullish trend.
For optimal results, the recommended timeframes are 1D (daily) or 1W (weekly) charts. These higher timeframes help filter out market noise and provide more reliable trend signals.
We do not short the market under any circumstances. The focus is purely on riding upward momentum when the price breaks out or stays above the cloud.
This strategy works best when applied to growth stocks with strong upward trends and good fundamentals — such as Google (GOOGL), Tesla (TSLA), Apple (AAPL), or NVIDIA (NVDA).
Sunday OpenSunday Open – Weekly Open Levels
Description
The "Sunday Open" indicator automatically displays the market open levels for Sunday. These levels are often used by traders as potential support and resistance zones. The script requires no manual data input and works on any timeframe.
Functionality
- Displays the N most recent Sunday Open levels, including the current and previous ones.
- The color of the current level differs from the older ones, making it easier to identify on the chart.
- Lines can be customized by style, thickness, and label visibility.
- Alerts are triggered when the price crosses any Sunday Open level (either up or down).
User Settings
- Number of levels to display (1–100)
- Color for the current and past levels
- Line style: solid, dashed, dotted
Line thickness
- Display text labels on the chart
- Enable/disable alerts for price crossing
Useful For
- Quickly identifying key reference points at the beginning of the week
- Setting up trading scenarios around the open zone
- Receiving signals for breakouts or touches of the levels
Important !
All elements are created automatically—no manual drawing required.
If you want to create alerts for when the price crosses a Sunday Open level, you need to go to "Alerts" at the top of the screen and select "SO" in the "Condition" field.
Enhanced Cycle IndicatorEnhanced Cycle Indicator Guide
DISCLAIMER
"This PineScript indicator evolved from a foundational algorithm designed to visualize cycle-based center average differentials. The original concept has been significantly enhanced and optimized through collaborative refinement with AI, resulting in improved functionality, performance, and visualization capabilities while maintaining the core mathematical principles of the original design"
Overview
The Enhanced Cycle Indicator is designed to identify market cycles with minimal lag while ensuring the cycle lows and highs correspond closely with actual price bottoms and tops. This indicator transforms price data into observable cycles that help you identify when a market is likely to change direction.
Core Principles
Cycle Detection: Identifies natural market rhythms using multiple timeframes
Dynamic Adaptation: Adjusts to changing market conditions for consistent performance
Precise Signals: Provides clear entry and exit points aligned with actual market turns
Reduced Lag: Uses advanced calculations to minimize delay in cycle identification
How To Use
1. Main Cycle Interpretation
Green Histogram Bars: Bullish cycle phase (upward momentum)
Red Histogram Bars: Bearish cycle phase (downward momentum)
Cycle Extremes: When the histogram reaches extreme values (+80/-80), the market is likely approaching a turning point
Zero Line: Crossovers often indicate a shift in the underlying market direction
2. Trading Signals
Green Triangle Up (bottom of chart): Strong bullish signal - ideal for entries or covering shorts
Red Triangle Down (top of chart): Strong bearish signal - ideal for exits or short entries
Diamond Shapes: Indicate divergence between price and cycle - early warning of potential reversals
Small Circles: Minor cycle turning points - useful for fine-tuning entries/exits
3. Optimal Signal Conditions
Bullish Signals Work Best When:
The cycle is deeply oversold (below -60)
RSI is below 40 or turning up
Price is near a significant low
Multiple confirmation bars have occurred
Bearish Signals Work Best When:
The cycle is heavily overbought (above +60)
RSI is above 60 or turning down
Price is near a significant high
Multiple confirmation bars have occurred
4. Parameter Adjustments
For Shorter Timeframes: Reduce cycle periods and smoothing factor for faster response
For Daily/Weekly Charts: Increase cycle periods and smoothing for smoother signals
For Volatile Markets: Reduce cycle responsiveness to filter noise
For Trending Markets: Increase signal confirmation requirement to avoid false signals
Recommended Settings
Default (All-Purpose)
Main Cycle: 50
Half Cycle: 25
Quarter Cycle: 12
Smoothing Factor: 0.5
RSI Filter: Enabled
Signal Confirmation: 2 bars
Faster Response (Day Trading)
Main Cycle: 30
Half Cycle: 15
Quarter Cycle: 8
Smoothing Factor: 0.3
Cycle Responsiveness: 1.2
Signal Confirmation: 1 bar
Smoother Signals (Swing Trading)
Main Cycle: 80
Half Cycle: 40
Quarter Cycle: 20
Smoothing Factor: 0.7
Cycle Responsiveness: 0.8
Signal Confirmation: 3 bars
Advanced Features
Adaptive Period
When enabled, the indicator automatically adjusts cycle periods based on recent price volatility. This is particularly useful in markets that alternate between trending and ranging behaviors.
Momentum Filter
Enhances cycle signals by incorporating price momentum, making signals more responsive during strong trends and less prone to whipsaws during consolidations.
RSI Filter
Adds an additional confirmation layer using RSI, helping to filter out lower-quality signals and improve overall accuracy.
Divergence Detection
Identifies situations where price makes a new high/low but the cycle doesn't confirm, often preceding significant market reversals.
Best Practices
Use the indicator in conjunction with support/resistance levels
Look for signal clusters across multiple timeframes
Reduce position size when signals appear far from cycle extremes
Pay special attention to signals that coincide with divergences
Customize cycle periods to match the natural rhythm of your traded instrument
Troubleshooting
Too Many Signals: Increase signal confirmation bars or reduce cycle responsiveness
Missing Major Turns: Decrease smoothing factor or increase cycle responsiveness
Signals Too Late: Decrease cycle periods and smoothing factor
False Signals: Enable RSI filter and increase signal confirmation requirement
