Ultimate Momentum IndicatorThis is an indicator I've been playing with for a while, based on my previous MACD w/ RSI Warning indicator. This one takes it a step further, including information from MACD, RSI, ADX, and Parabolic SAR. These four indicators are represented in this indicator as follows:
MACD: The histogram itself is a normal MACD histogram. Nothing strange about it, and you can adjust the settings for it just as you would a normal MACD.
RSI: Any time the RSI is outside of normal ranges (which can be adjusted in the settings), the bar on the histogram will turn amber to warn you. The actual RSI value is also shown in a label to the left side of the indicator.
ADX: Crosses are drawn along the 0 line to indicate ADX. Blue means the ADX is below the trending level (adjustable in the settings), and orange means it is above that level. Darker colors indicate the ADX has gone up since the previous bar, while lighter colors indicate it has gone down. The actual ADX value is also shown in the label to the left side of the indicator.
Parabolic SAR: At the outside point of each bar in the histogram, a colored dot is drawn. If the dot is green, the Parabolic SAR (settings adjustable) is currently below the closing price. If the dot is red, the SAR is above the closing price.
I must stress that this indicator is not a replacement for any one of the indicators it includes, as it's really only pulling small bits of information from each. The point of this indicator is to give a cohesive picture of momentum at a quick glance. I encourage you to continue to use the normal versions of whichever of the basic indicators you already use, especially if those indicators are a key part of your strategy. This indicator is designed purely as a way to get a bird's eye view of the momentum.
Pretty much every normally adjustable value can be adjusted in the settings for each of the base indicators. You can also set:
The RSI warning levels (30 and 70 by default)
The ADX Crossover, i.e. the point at which you consider the ADX value to indicate a strong trend (25 by default)
The offset for the label which shows the actual RSI & ADX values (109 by default, which happens to line up with my chart layout--yours will almost certainly need to be different to look clean)
All of the colors, naturally
As always, I am open to suggestions on how I might make the indicator look cleaner, or even other indicators I might try to include in the data this indicator produces. My choice of indicators to base this one from is entirely based on the ones I use and know, but I'm sure there are other great indicators that may improve this combination indicator even more!
Wyszukaj w skryptach "histogram"
Bitcoin Relative Macro StrengthBTC Relative Macro Strength
Overview
The BTC Relative Macro Strength indicator measures Bitcoin's price strength relative to the global macro environment. By tracking deviations from the macro trend, it identifies potentially overvalued and undervalued market phases.
The global macro trend is derived by multiplying the ISM PMI (a widely-used proxy for the business cycle) by a simplified measure of global liquidity.
Calculations
Global Liquidity = Fed Balance Sheet − Reverse Repo − Treasury General Account + U.S. M2 + China M2
Global Macro Trend = ISM PMI × Global Liquidity
Understanding the Global Macro Trend
The global macro trend plot combines the ebb and flow of global liquidity with the cyclical patterns of the business cycle. The resulting composite exhibits strong directional correlation with Bitcoin—or more precisely, Bitcoin appears to move in lockstep with liquidity conditions and business cycle phases.
This relationship has strengthened notably since COVID, likely because Bitcoin's growing market capitalization has increased its exposure to macro forces.
The takeaway is that Bitcoin is acutely sensitive to growth in the money supply (it trends with liquidity expansion) and oscillates with the phases of the business cycle.
Indicator Components
📊 Histogram: BTC/Macro Change
Displays the rolling percentage change of Bitcoin's price relative to the global macro trend.
High values: Bitcoin is outpacing macro conditions (potentially overvalued)
Low values: Bitcoin is underperforming macro conditions (potentially undervalued)
Color scheme:
🟢 Green = Positive deviation
🔴 Red = Negative deviation
📈 Macro Slope Line
Plots the scaled percentage change of the global macro trend itself.
Color scheme:
🔵 Teal = BULLISH (slope positive and rising)
⚪ Gray = NEUTRAL (slope and trend disagree)
🟣 Pink = BEARISH (slope negative and falling)
FieldDescription
BTC/Macro Change : Percentage change of Bitcoin's price vs. the Global Macro Trend (default: 21-bar average)
Macro Trend : Composite assessment combining slope direction and trend momentum. Reads BULLISH when both align upward, BEARISH when both align downward, NEUTRAL when they disagree
Macro Slope : The global macro trend's average slope expressed as a percentage
BTC Valuation : Relative valuation category based on BTC/Macro deviation (Extreme Premium → Extreme Discount)
BTC Price : Current Bitcoin price
How to Use
This indicator is primarily useful for identifying market phases where Bitcoin's price has diverged from the global macro trend.
Identify extremes : Look for periods when the histogram reaches elevated positive or negative levels
Assess valuation : Use the BTC Valuation reading to gauge relative over/undervaluation
Confirm with trend : Check whether macro conditions support or contradict the current price level
Mean reversion : Consider that significant deviations from trend historically tend to revert
Note: This indicator identifies relative valuation based on macro conditions—it does not predict price direction or timing.
Settings
Lookback Period - 21 bars - Number of bars for calculating rolling averages
Macro Slope Scale - 3.0 - Multiplier for macro slope line visibility
Average Candle Body (24h Rolling)This indicator calculates the average size of candle bodies (|Close – Open|) over the last 24 hours, regardless of your current chart timeframe.
Unlike ATR or ADR, which measure total range (High – Low) or day-to-day volatility, this tool focuses purely on the real body size of candles — a more accurate representation of in-session price momentum and liquidity activity.
🔍 How it works
The script automatically determines how many candles represent the last 24 hours based on your current timeframe (e.g. 288 candles on a 5-minute chart).
It then computes a Simple Moving Average (SMA) of the absolute candle body size across that rolling 24-hour window.
Optionally, the script also plots the current candle body size as a grey histogram for quick comparison.
⚙️ Use cases
Gauge intraday volatility based on average body movement rather than wicks.
Build dynamic stop-loss models (e.g., Stop = 1.2 × AverageBodySize).
Detect periods of compression or expansion in price action.
Filter or confirm setups (e.g., only trade when candle bodies exceed their 24 h average).
📈 Displayed elements
Orange line: average candle body size (rolling 24 hours)
Grey histogram: current candle body size for each bar
Works automatically across all timeframes and assets (crypto, forex, indices, etc.)
💡 Pro tip
This indicator pairs exceptionally well with:
EMA-based momentum systems (e.g. EMA 8/21 crosses)
Session-based reversal or sweep strategies (Asia-London transitions)
VWAP or liquidity-based frameworks where candle compression matters
📘 How to Interpret
When the orange line (24h average candle body) is rising, it indicates that average body sizes are expanding — signaling increasing intraday momentum and participation. This often aligns with periods of higher volatility, stronger trends, or major session opens (London/New York).
When the orange line is falling, it shows contracting body sizes, meaning the market is entering consolidation, reduced volatility, or indecision. Such periods often precede major breakouts or reversals.
Use this reading to:
Avoid false breakouts during low-body periods.
Tighten or widen stops based on real-time market compression or expansion.
Confirm reversals: a shrinking average body after a strong impulse can signal momentum exhaustion.
Buy-or-Sell-WiPIndicator Features:
> Simple red/green histogram to indicate go long/buy or go short/sell
> Recommended to use with my other indicator: 5/15-Min-ORB-Trend-Finder-WiP
Strategy:
> Use with 1-min chart with 5-min High/Low or 5-min chart with 15-min High/Low
> After a breakout, wait for confirmation before placing a trade, which is:
- Two confirming candles (green for long/buy, red for short/sell)
and
- Buy-or-Sell-WiP histogram: green for long/buy, red for short/sell
Reversal Probability Meter PRO [optimized for Xau/Usd m5]🎯 Reversal Probability Meter PRO
A powerful multi-factor reversal probability detector that calculates the likelihood of bullish or bearish reversals using RSI, EMA bias, ATR spikes, candle patterns, volume spikes, and higher timeframe (HTF) trend alignment.
🧩 MAIN FEATURES
1. Reversal Probability (Bullish & Bearish)
Displays two key metrics:
Bull % — probability of bullish reversal
Bear % — probability of bearish reversal
These are computed using RSI, EMAs, ATR, demand/supply zones, candle confirmations, and volume spikes.
📊 Interpretation:
Bull % > 70% → Buying pressure building up
Bull % > 85% → Strong bullish reversal confirmed
Bear % > 70% → Selling pressure building up
Bear % > 85% → Strong bearish reversal confirmed
2. Alert Probability Threshold
Adjustable via alertThreshold (default = 85%).
Alerts trigger only when probability ≥ threshold, and confirmed by zone + volume spike + candle pattern.
🔔 Alerts Available:
✅ Bullish Smart Reversal
🔻 Bearish Smart Reversal
To activate: Right-click chart → “Add alert” → choose the alert condition from the indicator.
3. Demand / Supply Zone Detection
The script determines the price position within the last zoneLook (default 30) bars:
🟢 DEMAND → Lower 35% of range (potential bounce zone)
🔴 SUPPLY → Upper 35% of range (potential rejection zone)
⚪ MID → Neutral area
📘 Purpose: Validates reversals based on context:
Bullish only valid in Demand zones
Bearish only valid in Supply zones
4. Higher Timeframe (HTF) Trend Alignment
Reads EMA bias from a higher timeframe (default = 15m) for trend confirmation.
Reversals against HTF trend are automatically weighted down prevents false countertrend signals.
📈 Example:
M5 chart under M15 downtrend → Bullish probability is reduced.
5. Candle Confirmation Patterns
Two key price action confirmations:
Bullish: Engulfing or Pin Bar
Bearish: Engulfing or Pin Bar
A valid reversal requires both a candle confirmation and a volume spike.
6. Volume & ATR Spike Filters
Volume Spike: volume > SMA(20) × 1.3
ATR Spike: ATR > SMA(ATR, 50) × volMult
🎯 Ensures that only strong market moves with real energy are considered valid reversals.
7. Reversal Momentum Histogram
A color-gradient oscillator showing the momentum difference:
Green = bullish dominance
Red = bearish dominance
Flat near 0 = neutral
Controlled by showOscillator toggle.
8. Smart Info Panel
A compact dashboard displayed on the top-right with 4 rows:
Row Info Description
1 Bull % Bullish reversal probability
2 Bear % Bearish reversal probability
3 Zone Market context (DEMAND / SUPPLY / MID)
4 Signal Strength Current signal intensity (probability %)
Dynamic Colors:
90% → Bright (strong signal)
75–90% → Yellow/Orange (medium)
<75% → Gray (weak)
9. Sensitivity Mode
Fine-tunes indicator reactivity:
🟥 Aggressive: Detects reversals early (more signals, less accurate)
🟨 Normal: Balanced, default mode
🟩 Conservative: Filters only strongest reversals (fewer but more reliable)
10. Custom Color Options
Customize bullish and bearish colors via bullBaseColor and bearBaseColor inputs for your preferred chart theme.
⚙️ HOW TO USE
Add to Chart
→ Paste the script into Pine Editor → “Add to chart”.
Select Timeframe
→ Best for M5–M30 (scalping/intraday).
→ H1–H4 for swing trading.
Monitor the Info Panel:
Bull % ≥ 85% + Zone = Demand → Strong bullish reversal signal
Bear % ≥ 85% + Zone = Supply → Strong bearish reversal signal
Watch the Histogram:
Rising green bars = bullish momentum gaining
Deep red bars = bearish momentum gaining
Enable Alerts:
Right-click chart → “Add alert”
Choose Bullish Smart Reversal or Bearish Smart Reversal
🧠 TRADING TIPS
Use Conservative mode for noisy lower timeframes (M5–M15).
Use Aggressive mode for higher timeframes (H1–H4).
Combine with manual support/resistance or zone boxes for precision entries. Personally i use Order Block.
Best reversal setups occur when all align:
Bull % > 85%
Zone = DEMAND
Volume spike present
Candle = Bullish engulfing
HTF trend supportive
Buyer vs Seller ControlBuyer vs Seller Control Analysis
Technical indicator measuring market participation through candlestick wick analysis
Overview:
This indicator analyzes the relationship between closing prices and candlestick wicks to measure buying and selling pressure. It calculates two key metrics and displays their moving averages to help identify market sentiment shifts.
Calculation Method:
The indicator measures two distinct values for each candle:
Buyer Control Value: Distance from candle low to closing price (close - low)
Seller Control Value: Distance from candle high to closing price (high - close)
Both values are then smoothed using a Simple Moving Average (default period: 20) to reduce noise and show clearer trends.
Visual Components:
Lime Line: 20-period SMA of buyer control values
Fuchsia Line: 20-period SMA of seller control values
Area Fill: Colored region between the two lines
Histogram: Difference between buyer and seller control SMAs
Zero Reference Line: Horizontal line at zero level
Information Table: Current numerical values (optional display)
Interpretation:
When the lime line (buyer control) is above the fuchsia line (seller control), it indicates that recent candles have been closing closer to their highs than to their lows on average.
When the fuchsia line is above the lime line, recent candles have been closing closer to their lows than to their highs on average.
Fill Color Logic:
Lime (green) fill appears when buyer control SMA > seller control SMA
Fuchsia (red) fill appears when seller control SMA > buyer control SMA
Fill transparency adjusts based on the magnitude of difference between the two SMAs
Stronger differences result in more opaque fills
Settings:
Moving Average Period: Adjustable from 1-200 periods (default: 20)
Show Info Table: Toggle to display/hide the numerical values table
Technical Notes:
The indicator works on any timeframe
Values are displayed in the same units as the underlying asset's price
The histogram shows the mathematical difference between the two SMA lines
Transparency calculation uses a 50-period lookback for dynamic scaling
This indicator provides a quantitative approach to analyzing candlestick patterns by focusing on where prices close relative to their intraday ranges.
Fed Funds Rate-of-ChangeFed Funds Rate-of-Change
What it does:
This indicator pulls the Effective Federal Funds Rate (FRED:FEDFUNDS, monthly) and measures how quickly it’s changing over a user-defined lookback. It offers stabilized change metrics that avoid the “near-zero blow-up” you see with naive % ROC. The plot turns red only when the signal is below the lower threshold and heading down (i.e., value < –threshold and slope < 0).
This indicator is meant to be useful in monitoring fast cuts on the part of the FED - a signal that has preceded recession or market pullbacks in times prior.
Change modes: Percentage, log and delta.
Percent ROC (ε floor): 100 * (now - prev) / max(prev, ε)
Log change (ε): 100 * (ln(now + ε) - ln(prev + ε))
Delta (bps): (now - prev) * 100 (basis points; avoids percentage math)
Tip: For “least drama,” use Delta (bps). For relative change without explosions near zero, use Log change (ε).
Key inputs:
Lookback (months): ROC window in calendar months (because source is monthly).
Change Metric: one of the three options above.
ε (percentage points): small constant (e.g., 0.25 pp) used by Percent ROC (ε) and Log change (ε) to stabilize near-zero values.
EMA Smoothing length: light smoothing of the computed series.
Clip |value| at: optional hard cap to tame outliers (0 = off).
Threshold % / Threshold bps: lower/upper threshold band; unit adapts to the selected metric.
Plot as histogram: optional histogram view.
Coloring / signal logic
Red: value is below the lower threshold (–threshold) and the series is falling on the current bar.
How to use:
Add to any chart (timeframe doesn’t matter; data is monthly under the hood).
Pick a Change Metric and set Lookback (e.g., 3–6 months).
Choose a reasonable threshold:
Percent/Log: try 10–20%
Delta (bps): try 50–100 bps
Optionally smooth (EMA 3–6) and/or clip extreme spikes.
Interpretation
Sustained red often marks periods of accelerating downside in the Fed Funds change metric (e.g., policy easing momentum when using bps).
Neutral (gray) provides context without implying direction bias.
Notes & limitations
Source is monthly FRED series; values update on monthly closes and are stable (no intrabar repainting of the monthly series).
Threshold units switch automatically with the metric (%, %, or bps).
Smoothing/clip are convenience tools; adjust conservatively to avoid masking important shifts.
Top and Bottom Probability
The top and bottom probability oscillator is an educational indicator that estimates the probability of a local top or bottom using four ingredients:
price extension since the last RSI overbought/oversold,
time since that OB/OS event,
RSI divergence strength,
Directional Momentum Velocity (DMV) — a normalized, signed trend velocity.
It plots RSI, two probability histograms (Top %, Bottom %), and an optional 0–100 velocity gauge.
How to read it
RSI & Levels: Standard RSI with OB/OS lines (70/30 by default).
Prob Top (%): Red histogram, 0–100. Higher values suggest increasing risk of a local top after an RSI overbought anchor.
Prob Bottom (%): Green histogram, 0–100. Higher values suggest increasing chance of a local bottom after an RSI oversold anchor.
Velocity (0–100): Optional line. Above 50 = positive/upward DMV; below 50 = negative/downward DMV. DMV pushes Top risk when trending down and Bottom chance when trending up.
These are composite, scale-free scores, not certainties or trade signals.
What the probabilities consider
Price Delta: How far price has moved beyond the last OB (for tops) or below the last OS (for bottoms). More extension → higher probability.
Time Since OB/OS: Longer time since the anchor → higher probability (until capped by the “Time Normalization (bars)” input).
Oscillator Divergence: RSI pulling away from its last OB/OS reading in the opposite direction implies weakening momentum and increases probability.
Directional Momentum Velocity (DMV):
Computes a regression slope of hlc3 vs. bar index, normalized by ATR, then squashed with tanh.
Downward DMV boosts Top probability; upward DMV boosts Bottom probability.
Toggle the velocity plot and adjust its sensitivity with Velocity Lookback, ATR Length, and Velocity Gain.
All four terms are blended with user-set weights. If Normalize Weights is ON, weights are rescaled to sum to 1.
Inputs (most useful)
RSI Length / OB / OS: Core RSI setup.
Time Normalization (bars): Sets how quickly the “time since OB/OS” term ramps from 0→1.
Weights:
Price Delta, Time Since OB/OS, Osc Divergence, Directional Velocity.
Turn Normalize Weights ON to keep the blend consistent when you experiment.
Settings:
Velocity Lookback: Window for slope estimation (shorter = more reactive).
ATR Length: Normalizes slope so symbols/timeframes are comparable.
Velocity Gain: Steepens or softens the tanh curve (higher = punchier extremes).
Show Velocity (0–100): Toggles the DMV display.
Tip: If you prefer momentum measured on RSI rather than price, in the DMV block replace hlc3 with rsi (concept stays identical).
Practical tips
Use Top/Bottom % as context, not triggers. Combine with structure (S/R), trend filters, and risk management.
On strong trends, expect the opposite probability (e.g., Top % during an uptrend) to stay suppressed longer.
Calibrate weights: e.g., raise Osc Divergence on mean-reversion symbols; raise Velocity in trending markets.
For lower noise: lengthen Velocity Lookback and ATR Length, or reduce Velocity Gain.
Adaptive Convergence Divergence### Adaptive Convergence Divergence (ACD)
By Gurjit Singh
The Adaptive Convergence Divergence (ACD) reimagines the classic MACD by replacing fixed moving averages with adaptive moving averages. Instead of a static smoothing factor, it dynamically adjusts sensitivity based on price momentum, relative strength, volatility, fractal roughness, or volume pressure. This makes the oscillator more responsive in trending markets while filtering noise in choppy ranges.
#### 📌 Key Features
1. Dual Adaptive Structure: The oscillator uses two adaptive moving averages to form its convergence-divergence line, with EMA/RMA as signal line:
* Primary Adaptive (MA): Fast line, reacts quickly to changes.
* Following Adaptive (FAMA): Slow line, with half-alpha smoothing for confirmation.
2. Adaptive MA Types
* ACMO: Adaptive CMO (momentum)
* ARSI: Adaptive RSI (relative strength)
* FRMA: Fractal Roughness (volatility + fractal dimension)
* VOLA: Volume adaptive (volume pressure)
3. PPO Option: Switch between classic MACD or Percentage Price Oscillator (PPO) style calculation.
4. Signal Smoothing: Choose between EMA or Wilder’s RMA.
5. Visuals: Colored oscillator, signal line, histogram with adaptive transparency.
6. Alerts: Bullish/Bearish crossovers built-in.
#### 🔑 How to Use
1. Add to chart: Works on any timeframe and asset.
2. Choose MA Type: Experiment with ACMO, ARSI, FRMA, or VOLA depending on market regime.
3. Crossovers:
* Bullish (🐂): Oscillator crosses above signal → potential long entry.
* Bearish (🐻): Oscillator crosses below signal → potential short entry.
4. Histogram: expansion = strengthening trend; contraction = weakening trend.
5. Divergences:
* Bullish (hidden strength): Price pushes lower, but ACD turns higher = potential upward reversal.
* Bearish (hidden weakness): Price pushes higher, but ACD turns lower = potential downward reversal.
6. Customize: Adjust lengths, smoothing type, and PPO/MACD mode to match your style.
7. Set Alerts:
* Enable Bullish or Bearish crossover alerts to catch momentum shifts in real time.
#### 💡 Tips
* PPO mode normalizes values across assets, useful for cross-asset analysis.
* Wilder’s smoothing is gentler than EMA, reducing whipsaws in sideways conditions.
* Adaptive smoothing helps reduce false divergence signals by filtering noise in choppy ranges.
EMA Range OscillatorEMA Range Oscillator (ERO) - User Guide
Overview
The EMA Range Oscillator (ERO) is a technical indicator that measures the distance between two Exponential Moving Averages (EMAs) and the distance between price and EMA. It normalizes these distances into a 0-100 range, helping traders identify trend strength, market momentum, and potential reversal points.
Components
Main Line
Green Line: EMA20 > EMA50 (Uptrend)
Red Line: EMA20 < EMA50 (Downtrend)
Histogram
White Histogram: Price distance from EMA20
Key Levels
Upper Level (80): High divergence zone
Middle Level (50): Neutral zone
Lower Level (20): Low divergence zone
Parameters
ParameterDefaultDescriptionFast EMA20Short-term EMA periodSlow EMA50Long-term EMA periodNormalization Period100Lookback period for scalingUpper80Upper threshold levelLower20Lower threshold level
How to Read the Indicator
High Values (Above 80)
Strong trend in progress
EMAs are widely separated
High momentum
Potential overbought/oversold conditions
Watch for possible trend exhaustion
Low Values (Below 20)
Consolidation phase
EMAs are close together
Low volatility
Potential breakout setup
Range-bound market conditions
Middle Zone (20-80)
Normal market conditions
Moderate trend strength
Balanced momentum
Look for directional clues from color changes
DYNAMIC TRADING DASHBOARDStudy Material for the "Dynamic Trading Dashboard"
This Dynamic Trading Dashboard is designed as an educational tool within the TradingView environment. It compiles commonly used market indicators and analytical methods into one visual interface so that traders and learners can see relationships between indicators and price action. Understanding these indicators, step by step, can help traders develop discipline, improve technical analysis skills, and build strategies. Below is a detailed explanation of each module.
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1. Price and Daily Reference Points
The dashboard displays the current price, along with percentage change compared to the day’s opening price. It also highlights whether the price is moving upward or downward using directional symbols. Alongside, it tracks daily high, low, open, and daily range.
For traders, daily levels provide valuable reference points. The daily high and low are considered intraday support and resistance, while the median price of the day often acts as a pivot level for mean reversion traders. Monitoring these helps learners see how price oscillates within daily ranges.
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2. VWAP (Volume Weighted Average Price)
VWAP is calculated as a cumulative average price weighted by volume. The dashboard compares the current price with VWAP, showing whether the market is trading above or below it.
For traders, VWAP is often a guide for institutional order flow. Price trading above VWAP suggests bullish sentiment, while trading below VWAP indicates bearish sentiment. Learners can use VWAP as a training tool to recognize trend-following vs. mean reversion setups.
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3. Volume Analysis
The system distinguishes between buy volume (when the closing price is higher than the open) and sell volume (when the closing price is lower than the open). A progress bar highlights the ratio of buying vs. selling activity in percentage.
This is useful because volume confirms price action. For instance, if prices rise but sell volume dominates, it can signal weakness. New traders learning with this tool should focus on how volume often precedes price reversals and trends.
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4. RSI (Relative Strength Index)
RSI is a momentum oscillator that measures price strength on a scale from 0 to 100. The dashboard classifies RSI readings into overbought (>70), oversold (<30), or neutral zones and adds visual progress bars.
RSI helps learners understand momentum shifts. During training, one should notice how trending markets can keep RSI extended for longer periods (not immediate reversal signals), while range-bound markets react more sharply to RSI extremes. It is an excellent tool for practicing trend vs. range identification.
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5. MACD (Moving Average Convergence Divergence)
The MACD indicator involves a fast EMA, slow EMA, and signal line, with focus on crossovers. The dashboard shows whether a “bullish cross” (MACD above signal line) or “bearish cross” (MACD below signal line) has occurred.
MACD teaches traders to identify trend momentum shifts and divergence. During practice, traders can explore how MACD signals align with VWAP trends or RSI levels, which helps in building a structured multi-indicator analysis.
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6. Stochastic Oscillator
This indicator compares the current close relative to a range of highs and lows over a period. Displayed values oscillate between 0 and 100, marking zones of overbought (>80) and oversold (<20).
Stochastics are useful for students of trading to recognize short-term momentum changes. Unlike RSI, it reacts faster to price volatility, so false signals are common. Part of the training exercise can be to observe how stochastic “flips” can align with volume surges or daily range endpoints.
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7. Trend & Momentum Classification
The dashboard adds simple labels for trend (uptrend, downtrend, neutral) based on RSI thresholds. Additionally, it provides quick momentum classification (“bullish hold”, “bearish hold”, or neutral).
This is beneficial for beginners as it introduces structured thinking: differentiating long-term market bias (trend) from short-term directional momentum. By combining both, traders can practice filtering signals instead of trading randomly.
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8. Accumulation / Distribution Bias
Based on RSI levels, the script generates simplified tags such as “Accumulate Long”, “Accumulate Short”, or “Wait”.
This is purely an interpretive guide, helping learners think in terms of accumulation phases (when markets are low) and distribution phases (when markets are high). It reinforces the concept that trading is not only directional but also involves timing.
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9. Overall Market Status and Score
Finally, the dashboard compiles multiple indicators (VWAP position, RSI, MACD, Stochastics, and price vs. median levels) into a Market Score expressed as a percentage. It also labels the market as Overbought, Oversold, or Normal.
This scoring system isn’t a recommendation but a learning framework. Students can analyze how combining different indicators improves decision-making. The key training focus here is confluence: not depending on one indicator but observing when several conditions align.
Extended Study Material with Formulas
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1. Daily Reference Levels (High, Low, Open, Median, Range)
• Day High (H): Maximum price of the session.
DayHigh=max(Hightoday)DayHigh=max(Hightoday)
• Day Low (L): Minimum price of the session.
DayLow=min(Lowtoday)DayLow=min(Lowtoday)
• Day Open (O): Opening price of the session.
DayOpen=OpentodayDayOpen=Opentoday
• Day Range:
Range=DayHigh−DayLowRange=DayHigh−DayLow
• Median: Mid-point between high and low.
Median=DayHigh+DayLow2Median=2DayHigh+DayLow
These act as intraday guideposts for seeing how far the price has stretched from its key reference levels.
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2. VWAP (Volume Weighted Average Price)
VWAP considers both price and volume for a weighted average:
VWAPt=∑i=1t(Pricei×Volumei)∑i=1tVolumeiVWAPt=∑i=1tVolumei∑i=1t(Pricei×Volumei)
Here, Price_i can be the average price (High + Low + Close) ÷ 3, also known as hlc3.
• Interpretation: Price above VWAP = bullish bias; Price below = bearish bias.
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3. Volume Buy/Sell Analysis
The dashboard splits total volume into buy volume and sell volume based on candle type.
• Buy Volume:
BuyVol=Volumeif Close > Open, else 0BuyVol=Volumeif Close > Open, else 0
• Sell Volume:
SellVol=Volumeif Close < Open, else 0SellVol=Volumeif Close < Open, else 0
• Buy Ratio (%):
VolumeRatio=BuyVolBuyVol+SellVol×100VolumeRatio=BuyVol+SellVolBuyVol×100
This helps traders gauge who is in control during a session—buyers or sellers.
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4. RSI (Relative Strength Index)
RSI measures strength of momentum by comparing gains vs. losses.
Step 1: Compute average gains (AG) and losses (AL).
AG=Average of Upward Closes over N periodsAG=Average of Upward Closes over N periodsAL=Average of Downward Closes over N periodsAL=Average of Downward Closes over N periods
Step 2: Calculate relative strength (RS).
RS=AGALRS=ALAG
Step 3: RSI formula.
RSI=100−1001+RSRSI=100−1+RS100
• Used to detect overbought (>70), oversold (<30), or neutral momentum zones.
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5. MACD (Moving Average Convergence Divergence)
• Fast EMA:
EMAfast=EMA(Close,length=fast)EMAfast=EMA(Close,length=fast)
• Slow EMA:
EMAslow=EMA(Close,length=slow)EMAslow=EMA(Close,length=slow)
• MACD Line:
MACD=EMAfast−EMAslowMACD=EMAfast−EMAslow
• Signal Line:
Signal=EMA(MACD,length=signal)Signal=EMA(MACD,length=signal)
• Histogram:
Histogram=MACD−SignalHistogram=MACD−Signal
Crossovers between MACD and Signal are used in studying bullish/bearish phases.
________________________________________
6. Stochastic Oscillator
Stochastic compares the current close against a range of highs and lows.
%K=Close−LowestLowHighestHigh−LowestLow×100%K=HighestHigh−LowestLowClose−LowestLow×100
Where LowestLow and HighestHigh are the lowest and highest values over N periods.
The %D line is a smooth version of %K (using a moving average).
%D=SMA(%K,smooth)%D=SMA(%K,smooth)
• Values above 80 = overbought; below 20 = oversold.
________________________________________
7. Trend and Momentum Classification
This dashboard generates simplified trend/momentum logic using RSI.
• Trend:
• RSI < 40 → Downtrend
• RSI > 60 → Uptrend
• In Between → Neutral
• Momentum Bias:
• RSI > 70 → Bullish Hold
• RSI < 30 → Bearish Hold
• Otherwise Neutral
This is not predictive, only a classification framework for educational use.
________________________________________
8. Accumulation/Distribution Bias
Based on extreme RSI values:
• RSI < 25 → Accumulate Long Bias
• RSI > 80 → Accumulate Short Bias
• Else → Wait/No Action
This helps learners understand the idea of accumulation at lows (strength building) and distribution at highs (profit booking).
________________________________________
9. Overall Market Status and Score
The tool adds up 5 bullish conditions:
1. Price above VWAP
2. RSI > 50
3. MACD > Signal
4. Stochastic > 50
5. Price above Daily Median
BullishScore=ConditionsMet5×100BullishScore=5ConditionsMet×100
Then it categorizes the market:
• RSI > 70 or Stoch > 80 → Overbought
• RSI < 30 or Stoch < 20 → Oversold
• Else → Normal
This encourages learners to think in terms of probabilistic conditions instead of single-indicator signals.
________________________________________
⚠️ Warning:
• Trading financial markets involves substantial risk.
• You can lose more money than you invest.
• Past performance of indicators does not guarantee future results.
• This script must not be copied, resold, or republished without authorization from aiTrendview.
By using this material or the code, you agree to take full responsibility for your trading decisions and acknowledge that this is not financial advice.
________________________________________
⚠️ Disclaimer and Warning (From aiTrendview)
This Dynamic Trading Dashboard is created strictly for educational and research purposes on the TradingView platform. It does not provide financial advice, buy/sell recommendations, or guaranteed returns. Any use of this tool in live trading is completely at the user’s own risk. Markets are inherently risky; losses can exceed initial investment.
The intellectual property of this script and its methodology belongs to aiTrendview. Unauthorized reproduction, modification, or redistribution of this code is strictly prohibited. By using this study material or the script, you acknowledge personal responsibility for any trading outcomes. Always consult professional financial advisors before making investment decisions.
Stochastic MACDStochastic MACD Indicator: Quick Guide
This Pine Script indicator, "Stochastic MACD" (SMACD), blends MACD and Stochastic Oscillator principles to help you spot momentum shifts and potential reversals.
What it Shows:
SMACD Line: Tracks momentum.
Signal Line: Averages the SMACD line, creating crossovers.
Histogram: Visualizes momentum strength, changing color with direction.
Overbought/Oversold Levels: (Default 10 and -10) Help identify stretched market conditions. Adjustable in settings.
Visual Signals (Triangles):
Red Down Arrow (Overbought Signal): Appears when both SMACD and Signal lines are above the Overbought level (default 10) AND SMACD crosses the Signal line upwards. This suggests strong overbought conditions and a potential reversal down.
Green Up Arrow (Oversold Signal): Appears when both SMACD and Signal lines are below the Oversold level (default -10) AND SMACD crosses the Signal line upwards. This suggests potential buying opportunities from oversold conditions and a possible reversal up.
How to Use It:
Confirm Trends: Use the histogram and line directions.
Spot Reversals: Look for the red and green triangles for quick alerts.
Combine: Always use with other analysis like price action or support/resistance.
Important: This is an analytical tool, not financial advice. Trading involves risk.
Footprint-Style Order Flow by Kalibea📊 Indicator: "Footprint-Style Order Flow by Kalibea"
Simplified Order Flow Analysis for TradingView
This indicator was created by Kalibea to bring you the power of Order Flow analysis in a clear, practical way—without technical complexity and fully compatible with TradingView.
While TradingView doesn’t support traditional footprint charts, this tool simulates institutional market reading using a smart calculation of estimated volume delta, helping you make more informed trading decisions.
🔍 What does this indicator do?
Estimated Delta: Calculates the difference between buying and selling pressure per candle, based on price movement and volume.
Smart Visual Signals:
🔼 Green Triangle: Potential buy entry (buyer dominance).
🔽 Red Triangle: Potential sell entry (seller dominance).
Delta Histogram: Displays whether each candle was driven more by buyers or sellers.
Live Labels: Shows real-time delta values above each candle for quick interpretation.
🧠 How does it help your trading?
Detects real-time market imbalances (who's in control: buyers or sellers).
Improves entry and exit timing, especially on lower timeframes.
Helps you confirm other strategies such as supply/demand zones, support/resistance, or candlestick patterns.
Provides an institutional-style reading simplified for use within TradingView.
⚙️ Fully Customizable to Your Style
Adjust the delta sensitivity to suit any market: Forex, Crypto, Indices, and more.
Turn on/off visual signals and histogram as needed.
🔑 Recommended by Kalibea for:
✅ Intraday traders and scalpers
✅ Traders looking to take the next step into institutional-style analysis
✅ Those seeking precise entries without overcomplicating their charts
💬 “Order Flow is the market’s internal voice. This indicator helps you hear it—no expensive footprint software required.”
— Kalibea
Stochastic Z-Score [AlgoAlpha]🟠 OVERVIEW
This indicator is a custom-built oscillator called the Stochastic Z-Score , which blends a volatility-normalized Z-Score with stochastic principles and smooths it using a Hull Moving Average (HMA). It transforms raw price deviations into a normalized momentum structure, then processes that through a stochastic function to better identify extreme moves. A secondary long-term momentum component is also included using an ALMA smoother. The result is a responsive oscillator that reacts to sharp imbalances while remaining stable in sideways conditions. Colored histograms, dynamic oscillator bands, and reversal labels help users visually assess shifts in momentum and identify potential turning points.
🟠 CONCEPTS
The Z-Score is calculated by comparing price to its mean and dividing by its standard deviation—this normalizes movement and highlights how far current price has stretched from typical values. This Z-Score is then passed through a stochastic function, which further refines the signal into a bounded range for easier interpretation. To reduce noise, a Hull Moving Average is applied. A separate long-term trend filter based on the ALMA of the Z-Score helps determine broader context, filtering out short-term traps. Zones are mapped with thresholds at ±2 and ±2.5 to distinguish regular momentum from extreme exhaustion. The tool is built to adapt across timeframes and assets.
🟠 FEATURES
Z-Score histogram with gradient color to visualize deviation intensity (optional toggle).
Primary oscillator line (smoothed stochastic Z-Score) with adaptive coloring based on momentum direction.
Dynamic bands at ±2 and ±2.5 to represent regular vs extreme momentum zones.
Long-term momentum line (ALMA) with contextual coloring to separate trend phases.
Automatic reversal markers when short-term crosses occur at extremes with supporting long-term momentum.
Built-in alerts for oscillator direction changes, zero-line crosses, overbought/oversold entries, and trend confirmation.
🟠 USAGE
Use this script to track momentum shifts and identify potential reversal areas. When the oscillator is rising and crosses above the previous value—especially from deeply negative zones (below -2)—and the ALMA is also above zero, this suggests bullish reversal conditions. The opposite holds for bearish setups. Reversal labels ("▲" and "▼") appear only when both short- and long-term conditions align. The ±2 and ±2.5 thresholds act as momentum warning zones; values inside are typical trends, while those beyond suggest exhaustion or extremes. Adjust the length input to match the asset’s volatility. Enable the histogram to explore underlying raw Z-Score movements. Alerts can be configured to notify key changes in momentum or zone entries.
Volume-Time Imbalance (VTI)Volume-Time Imbalance (VTI) – Indicator Description
This indicator measures the imbalance between traded volume and the time elapsed between bars to identify unusual spikes in volume per second (volume per unit of time). Its purpose is to highlight volume movements that may indicate moments of strong interest, acceleration, or reversal in the market.
How it works:
It calculates the traded volume divided by the time (in seconds) elapsed since the previous bar — thus obtaining the volume per second.
An EMA (exponential moving average) of this volume per second is calculated to smooth the data.
The VTI value is the ratio between the current volume per second and this moving average, showing if the current volume is above what is expected for that pace.
The higher the VTI, the greater the imbalance between volume and time, indicating possible bursts of activity.
Settings:
VTI Moving Average Length: The period of the moving average used to smooth the volume per second (default is 20).
Alert Thresholds: Alert levels to identify moderate and high imbalances (defaults are 1.5 and 2.0).
Show VTI Histogram: Displays the VTI histogram in the indicator window.
Color Background: Colors the indicator background based on the strength of the imbalance (orange for moderate, red for high).
Show Alert Arrows: Shows arrows below the chart when a strong volume spike occurs (high alert).
Interpretation:
VTI values above the moderate level (1.5) indicate an unusual increase in volume relative to time.
Values above the high level (2.0) signal strong spikes that may anticipate significant moves or trend changes.
Use the colors and arrows as visual confirmations to quickly identify these moments.
Enhanced Stock Ticker with 50MA vs 200MADescription
The Enhanced Stock Ticker with 50MA vs 200MA is a versatile Pine Script indicator designed to visualize the relative position of a stock's price within its short-term and long-term price ranges, providing actionable bullish and bearish signals. By calculating normalized indices based on user-defined lookback periods (defaulting to 50 and 200 bars), this indicator helps traders identify potential reversals or trend continuations. It offers the flexibility to plot signals either on the main price chart or in a separate lower pane, leveraging Pine Script v6's force_overlay functionality for seamless integration. The indicator also includes a customizable ticker table, visual fills, and alert conditions for automated trading setups.
Key Features
Dual Lookback Indices: Computes short-term (default: 50 bars) and long-term (default: 200 bars) indices, normalizing the closing price relative to the high/low range over the specified periods.
Flexible Signal Plotting: Users can toggle between plotting crossover signals (triangles) on the main price chart (location.abovebar/belowbar) or in the lower pane (location.top/bottom) using the Plot Signals on Main Chart option.
Crossover Signals: Generates bullish (Golden Cross) and bearish (Death Cross) signals when the short or long index crosses above 5 or below 95, respectively.
Visual Enhancements:
Plots short-term (blue) and long-term (white) indices in a separate pane with customizable lookback periods.
Includes horizontal reference lines at 0, 20, 50, 80, and 100, with green and red fills to highlight overbought/oversold zones.
Dynamic fill between indices (green when short > long, red when long > short) for quick trend visualization.
Displays a ticker and legend table in the top-right corner, showing the symbol and lookback periods.
Alert Conditions: Supports alerts for bullish and bearish crossovers on both short and long indices, enabling integration with TradingView's alert system.
Technical Innovation: Utilizes Pine Script v6's force_overlay parameter to plot signals on the main chart from a non-overlay indicator, combining the benefits of a separate pane and chart-based signals in a single script.
Technical Details
Calculation Logic:
Uses confirmed bars (barstate.isconfirmed) to calculate indices, ensuring reliability by avoiding real-time bar fluctuations.
Short-term index: (close - lowest(low, lookback_short)) / (highest(high, lookback_short) - lowest(low, lookback_short)) * 100
Long-term index: (close - lowest(low, lookback_long)) / (highest(high, lookback_long) - lowest(low, lookback_long)) * 100
Signals are triggered using ta.crossover() and ta.crossunder() for indices crossing 5 (bullish) and 95 (bearish).
Signal Plotting:
Main chart signals use force_overlay=true with location.abovebar/belowbar for precise alignment with price bars.
Lower pane signals use location.top/bottom for visibility within the indicator pane.
Plotting is controlled by boolean conditions (e.g., bullishLong and plot_on_chart) to ensure compliance with Pine Script's global scope requirements.
Performance Considerations: Optimized for efficiency by calculating indices only on confirmed bars and using lightweight plotting functions.
How to Use
Add to Chart:
Copy the script into TradingView's Pine Editor and add it to your chart.
Configure Settings:
Short Lookback Period: Adjust the short-term lookback (default: 50 bars) to match your trading style (e.g., 20 for shorter-term analysis).
Long Lookback Period: Adjust the long-term lookback (default: 200 bars) for broader market context.
Plot Signals on Main Chart: Check this box to display signals on the price chart; uncheck to show signals in the lower pane.
Interpret Signals:
Golden Cross (Bullish): Green (long) or blue (short) triangles indicate the index crossing above 5, suggesting a potential buying opportunity.
Death Cross (Bearish): Red (long) or white (short) triangles indicate the index crossing below 95, signaling a potential selling opportunity.
Set Alerts:
Use TradingView's alert system to create notifications for the four alert conditions: Long Index Valley, Long Index Peak, Short Index Valley, and Short Index Peak.
Customize Visuals:
The ticker table displays the symbol and lookback periods in the top-right corner.
Adjust colors and styles via TradingView's settings if desired.
Example Use Cases
Swing Trading: Use the short-term index (e.g., 50 bars) to identify short-term reversals within a broader trend defined by the long-term index.
Trend Confirmation: Monitor the fill between indices to confirm whether the short-term trend aligns with the long-term trend.
Automated Trading: Leverage alert conditions to integrate with bots or manual trading strategies.
Notes
Testing: Always backtest the indicator on your chosen market and timeframe to validate its effectiveness.
Optional Histogram: The script includes a commented-out histogram for the index difference (index_short - index_long). Uncomment the plot(index_diff, ...) line to enable it.
Compatibility: Built for Pine Script v6 and tested on TradingView as of May 27, 2025.
Acknowledgments
This indicator was inspired by the need for a flexible tool that combines lower-pane analysis with main chart signals, made possible by Pine Script's force_overlay feature. Share your feedback or suggestions in the comments below, and happy trading!
Scalper's Fractal Cloud with RSI + VWAP + MACD (Fixed)Scalper’s Fractal Confluence Dashboard
1. Purpose of the Indicator
This TradingView indicator script provides a high-confluence setup for scalping and day trading. It blends momentum indicators (RSI, MACD), trend bias tools (EMA Cloud, VWAP), and structure (fractal swings, gap zones) to help confirm precise entries and exits.
2. Components of the Indicator
- EMA Cloud (50 & 200 EMA): Trend bias – green means bullish, red means bearish. Avoid longs under red cloud.
- VWAP: Institutional volume anchor. Ideal entries are pullbacks to VWAP in direction of trend.
- Gap Zones: Shows open-air zones (white space) where price can move fast. Used to anticipate momentum moves.
- ZigZag Swings: Marks structural pivots (highs/lows) – useful for stop placement and range anticipation.
- MACD Histogram: Shows bullish or bearish momentum via background color.
- RSI: Overbought (>70) or oversold (<30) warnings. Good for exits or countertrend reversion plays.
- EMA Spread Label: Quick view of momentum strength. Wide spread = strong trend.
3. Scalping Entry Checklist
Before entering a trade, confirm these conditions:
• • Bias: EMA cloud color supports trade direction
• • Price is above/below VWAP (confirming institutional flow)
• • MACD histogram matches direction (green for long, red for short)
• • RSI not at extreme (unless you’re fading trend)
• • If entering gap zone, expect fast move
• • Recent swing high/low nearby for target or stop
4. Risk & Sizing Guidelines
Risk 1–2% of account per trade. Place stop below recent swing low (for longs) or high (for shorts). Use fractional sizing near VWAP or white space zones for scalping reversals.
5. Daily Trade Journal Template
- Date:
- Ticker:
- Setup Type (VWAP pullback, Gap Break, EMA reversion):
- Entry Time:
- Bias (Green/Red Cloud):
- RSI Level / MACD Reading:
- Stop Loss:
- Target:
- Result (P/L):
- What I Did Well:
- What Needs Work:
Advanced Candlestick Pattern DetectorWhat Does This Indicator Do?
This indicator looks at the way price moves in the market using candlesticks (those red and green bars you see on charts). It tries to find special patterns like Bullish Engulfing, Hammer, Doji, and others. When one of these patterns shows up, the indicator checks a bunch of filters to decide if the pattern is strong enough to be a signal to buy or sell.
The Main Parts of the Indicator
1. Candlestick Pattern Detection
Bullish Engulfing:
Imagine you see a small down candle (red) and then a big up candle (green) that completely “covers” the red one. That’s a bullish engulfing pattern. It can signal that buyers are taking over.
Bearish Engulfing:
The opposite of bullish engulfing. A small up candle (green) is followed by a big down candle (red) that covers the previous candle. This suggests sellers might be in control.
Hammer & Shooting Star:
Hammer: A candle with a very short body and a long shadow at the bottom. It shows that buyers stepped in after a drop.
Shooting Star:
Similar to the hammer but with a long shadow on top. It can indicate that sellers are starting to push the price down.
Doji:
A candle with almost no body. This means the opening and closing prices are very close. It shows indecision in the market.
Harami Patterns (Bullish & Bearish):
These are two-candle patterns where the second candle is completely inside the body of the first candle. They signal that the previous trend might be about to change.
Morning Star & Evening Star:
These are three-candle patterns.
Morning Star:
Often seen at the bottom of a downtrend, it can signal a reversal to an uptrend.
Evening Star:
Seen at the top of an uptrend, it can signal that the price may soon go down.
2. Filters: Making the Signals Smarter
The indicator doesn’t just rely on patterns. It uses several “filters” to decide if a pattern is strong enough to trade on. Here’s what each filter does:
a. Adaptive Thresholds (ATR-Based)
What It Is:
The indicator uses something called ATR (Average True Range) to see how much the price is moving (volatility).
How It Works:
Instead of using fixed numbers to decide if a candle is a Hammer or a Doji, it adjusts these numbers based on current market activity.
User Settings:
Use Adaptive Thresholds: Turn this on to let the indicator adjust automatically.
Body Factor, Shadow Factor, Doji Factor: These numbers are multipliers that decide how small or big the body and shadows of the candle should be. You can change them if you want the indicator to be more or less sensitive.
b. Volume Filter
What It Is:
Volume shows how many trades are happening.
How It Works:
The filter checks if the current volume is higher than the average volume (multiplied by a set factor). This helps ensure that the signal isn’t coming from a very quiet market.
User Settings:
Use Volume Filter: Turn this on if you want to ignore signals when there’s not much trading.
Volume MA Period & Volume Multiplier: These settings determine what “normal” volume is and how much higher the current volume must be to count.
c. Multi-Timeframe Trend Filter
What It Is:
This filter looks at a bigger picture by using a moving average (MA) from a higher timeframe (for example, daily charts).
How It Works:
For a bullish (buy) signal, the indicator checks if the price is above this MA.
For a bearish (sell) signal, the price must be below the MA.
User Settings:
Use Multi-Timeframe Trend Filter: Enable or disable this filter.
Higher Timeframe for Trend: Choose which timeframe (like Daily) to use.
Trend MA Type (SMA or EMA) & Trend MA Period: Choose the type of moving average and how many candles to average.
d. Additional Trend Filters (ADX & RSI)
ADX Filter:
What It Is:
ADX stands for Average Directional Index. It measures how strong a trend is.
How It Works:
If the ADX is above a certain threshold, it means the trend is strong.
User Setting:
ADX Threshold: Set the minimum strength the trend should have.
RSI Filter:
What It Is:
RSI (Relative Strength Index) tells you if the price is overbought (too high) or oversold (too low).
How It Works:
For a buy signal, RSI should be low (under a set threshold).
For a sell signal, RSI should be high (above a set threshold).
User Settings:
RSI Buy Threshold & RSI Sell Threshold: These set the levels for buying or selling.
3. How the Final Signal Is Determined
For a signal (buy or sell) to be generated, the indicator first checks if one of the candlestick patterns is present. Then it goes through all these filters (trend, volume, ADX, RSI). Only if everything is in line will it show:
A BUY signal when all bullish conditions are met.
A SELL signal when all bearish conditions are met.
4. Visual Elements on the Chart
Trend MA Line:
A blue line is drawn on your chart showing the moving average from the higher timeframe (if you enable the trend filter). This helps you see the overall direction of the market.
Labels on the Chart:
When a signal is detected, you’ll see:
A BUY label below the candle (green).
A SELL label above the candle (red).
Background Colors:
The chart background might change slightly (green for bullish and red for bearish) to give you a quick visual cue.
Histogram:
At the bottom, there is a histogram that shows +1 for bullish signals, -1 for bearish signals, and 0 when there’s no clear signal.
5. Alerts
Alerts are built into the indicator so you can get a notification when a signal appears. The alert messages are fixed strings, meaning they always say something like “BUY signal on at price .” You can set up these alerts in TradingView to be notified via sound, email, or pop-up.
How to Use and Adjust the Filters
Deciding on Patterns:
You can choose which candlestick patterns you want to detect by toggling the options (e.g., Bullish Engulfing, Hammer, etc.).
Adjusting Adaptive Thresholds:
If you feel that the indicator is too sensitive (or not sensitive enough) during volatile times, adjust the Body Factor, Shadow Factor, and Doji Factor. These change how the indicator recognizes different candle shapes based on market movement.
Volume Filter Settings:
Use Volume Filter:
Turn this on if you want to ignore signals when there’s not enough trading activity.
Adjust the Volume MA Period and Volume Multiplier to change what “normal” volume is for your chart.
Multi-Timeframe Trend Filter Settings:
Choose a higher timeframe (like Daily) to see the bigger picture trend. Select the type of moving average (SMA or EMA) and its period. This filter ensures you only trade in the direction of the overall trend.
ADX & RSI Filters:
ADX:
Adjust the ADX Threshold if you want to change the minimum strength of the trend needed for a signal.
RSI:
Set the RSI Buy Threshold (for oversold conditions) and RSI Sell Threshold (for overbought conditions) to refine when a signal is valid.
Summary
This indicator is like having a smart assistant that not only looks for specific price patterns (candlesticks) but also checks if the overall market conditions are right using several filters. By combining:
Pattern Detection
Adaptive thresholds (based on ATR)
Volume Checks
Multi-Timeframe Trend Analysis
Additional Trend Strength and Overbought/Oversold Indicators (ADX & RSI)
...it helps you decide if it might be a good time to buy or sell. You can customize each part to fit your trading style, and with the built-in alerts, you can be notified when everything lines up.
Feel free to adjust the settings to see how each filter changes the signals on your chart. Experimenting with these will help you learn how the market behaves and how you can best use the indicator for your own strategy!
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
.
(If you are comfortable with statistics feel free to skip ahead to the next section)
.
-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
.
---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
[blackcat] L2 Kiosotto IndicatorOVERVIEW
The Kiosotto Indicator is a versatile technical analysis tool designed for forex trading but applicable to other financial markets. It excels in detecting market reversals and trends without repainting, ensuring consistent and reliable signals. The indicator has evolved over time, with different versions focusing on specific aspects of market analysis.
KEY FEATURES
Reversal Detection: Identifies potential market reversals, crucial for traders looking to capitalize on turning points.
Trend Detection: Earlier versions focused on detecting trends, useful for traders who prefer to follow the market direction.
Non-Repainting: Signals remain consistent on the chart, providing reliable and consistent signals.
Normalization: Later versions, such as Normalized Kiosotto and Kiosotto_2025, incorporate normalization to assess oversold and overbought conditions, enhancing interpretability.
VERSIONS AND EVOLUTION
Early Versions: Focused on trend detection, useful for following market direction.
2 in 1 Kiosotto: Emphasizes reversal detection and is considered an improvement by users.
Normalized Versions (e.g., Kiosotto_2025, Kiosotto_3_2025): Introduce normalization to assess oversold and overbought conditions, enhancing interpretability.
HOW TO USE THE KIOSOTTO INDICATOR
Understanding Signals:
Reversals: Look for the indicator's signals that suggest a potential reversal, indicated by color changes, line crossings, or other visual cues.
Trends: Earlier versions might show stronger trending signals, indicated by the direction or slope of the indicator's lines.
Normalization Interpretation (for normalized versions):
Oversold: When the indicator hits the lower boundary, it might indicate an oversold condition, suggesting a potential buy signal.
Overbought: Hitting the upper boundary could signal an overbought condition, suggesting a potential sell signal.
PINE SCRIPT IMPLEMENTATION
The provided Pine Script code is a version of the Kiosotto indicator. Here's a detailed explanation of the code:
//@version=5
indicator(" L2 Kiosotto Indicator", overlay=false)
//Pine version of Kiosotto 2015 v4 Alert ms-nrp
// Input parameters
dev_period = input.int(150, "Dev Period")
alerts_level = input.float(15, "Alerts Level")
tsbul = 0.0
tsber = 0.0
hpres = 0.0
lpres = 9999999.0
for i = 0 to dev_period - 1
rsi = ta.rsi(close , dev_period)
if high > hpres
hpres := high
tsbul := tsbul + rsi * close
if low < lpres
lpres := low
tsber := tsber + rsi * close
buffer1 = tsber != 0 ? tsbul / tsber : 0
buffer2 = tsbul != 0 ? tsber / tsbul : 0
// Plotting
plot(buffer1, color=color.aqua, linewidth=3, style=plot.style_histogram)
plot(buffer2, color=color.fuchsia, linewidth=3, style=plot.style_histogram)
hline(alerts_level, color=color.silver)
EXPLANATION OF THE CODE
Indicator Definition:
indicator(" L2 Kiosotto Indicator", overlay=false): Defines the indicator with the name " L2 Kiosotto Indicator" and specifies that it should not be overlaid on the price chart.
Input Parameters:
dev_period = input.int(150, "Dev Period"): Allows users to set the period for the deviation calculation.
alerts_level = input.float(15, "Alerts Level"): Allows users to set the level for alerts.
Initialization:
tsbul = 0.0: Initializes the tsbul variable to 0.0.
tsber = 0.0: Initializes the tsber variable to 0.0.
hpres = 0.0: Initializes the hpres variable to 0.0.
lpres = 9999999.0: Initializes the lpres variable to a very high value.
Loop for Calculation:
The for loop iterates over the last dev_period bars.
rsi = ta.rsi(close , dev_period): Calculates the RSI for the current bar.
if high > hpres: If the high price of the current bar is greater than hpres, update hpres and add the product of RSI and close price to tsbul.
if low < lpres: If the low price of the current bar is less than lpres, update lpres and add the product of RSI and close price to tsber.
Buffer Calculation:
buffer1 = tsber != 0 ? tsbul / tsber : 0: Calculates the first buffer as the ratio of tsbul to tsber if tsber is not zero.
buffer2 = tsbul != 0 ? tsber / tsbul : 0: Calculates the second buffer as the ratio of tsber to tsbul if tsbul is not zero.
Plotting:
plot(buffer1, color=color.aqua, linewidth=3, style=plot.style_histogram): Plots the first buffer as a histogram with an aqua color.
plot(buffer2, color=color.fuchsia, linewidth=3, style=plot.style_histogram): Plots the second buffer as a histogram with a fuchsia color.
hline(alerts_level, color=color.silver): Draws a horizontal line at the alerts_level with a silver color.
FUNCTIONALITY
The Kiosotto indicator calculates two buffers based on the RSI and price levels over a specified period. The buffers are plotted as histograms, and a horizontal line is drawn at the alerts level. The indicator helps traders identify potential reversals and trends by analyzing the relationship between the RSI and price levels.
ALGORITHMS
RSI Calculation:
The Relative Strength Index (RSI) measures the speed and change of price movements. It is calculated using the formula:
RSI=100− (1+RS) / 100
where RS is the ratio of the average gain to the average loss over the specified period.
Buffer Calculation:
The buffers are calculated as the ratio of the sum of RSI multiplied by the close price for high and low price conditions. This helps in identifying the balance between buying and selling pressure.
Signal Generation:
The indicator generates signals based on the values of the buffers and the alerts level. Traders can use these signals to make informed trading decisions, such as entering or exiting trades based on potential reversals or trends.
APPLICATION SCENARIOS
Reversal Trading: Traders can use the Kiosotto indicator to identify potential reversals by looking for significant changes in the buffer values or crossings of the alerts level.
Trend Following: The indicator can also be used to follow trends by analyzing the direction and slope of the buffer lines.
Oversold/Overbought Conditions: For normalized versions, traders can use the indicator to identify oversold and overbought conditions, which can provide buy or sell signals.
THANKS
Special thanks to the TradingView community and the original developers for their contributions and support in creating and refining the Kiosotto Indicator.
LRSI-TTM Squeeze - AynetThis Pine Script code creates an indicator called LRSI-TTM Squeeze , which combines two key concepts to analyze momentum, squeeze conditions, and price movements in the market:
Laguerre RSI (LaRSI): A modified version of RSI used to identify trend reversals in price movements.
TTM Squeeze: Identifies market compressions (low volatility) and potential breakouts from these squeezes.
Functionality and Workflow of the Code
1. Laguerre RSI (LaRSI)
Purpose:
Provides a smoother and less noisy version of RSI to track price movements.
Calculation:
The script uses a filtering coefficient (alpha) to process price data through four levels (L0, L1, L2, L3).
Movement differences between these levels calculate buying pressure (cu) and selling pressure (cd).
The ratio of these pressures forms the Laguerre RSI:
bash
Kodu kopyala
LaRSI = cu / (cu + cd)
The LaRSI value indicates:
Below 20: Oversold condition (potential buy signal).
Above 80: Overbought condition (potential sell signal).
2. TTM Squeeze
Purpose:
Analyzes the relationship between Bollinger Bands (BB) and Keltner Channels (KC) to determine whether the market is compressed (low volatility) or expanded (high volatility).
Calculation:
Bollinger Bands:
Calculated based on the moving average (SMA) of the price, with an upper and lower band.
Keltner Channels:
Created using the Average True Range (ATR) to calculate an upper and lower band.
Squeeze States:
Squeeze On: BB is within KC.
Squeeze Off: BB is outside KC.
Other States (No Squeeze): Neither of the above applies.
3. Momentum Calculation
Momentum is computed using the linear regression of the difference between the price and its SMA. This helps anticipate the direction and strength of price movements when the squeeze ends.
Visuals on the Chart
Laguerre RSI Line:
An RSI indicator scaled to 0-100 is plotted.
The line's color changes based on its movement:
Green line: RSI is rising.
Red line: RSI is falling.
Key levels:
20 level: Oversold condition (buy signal can be triggered).
80 level: Overbought condition (sell signal can be triggered).
Momentum Histogram:
Displays momentum as histogram bars with colors based on its direction and strength:
Lime (light green): Positive momentum increasing.
Green: Positive momentum decreasing.
Red: Negative momentum decreasing.
Maroon (dark red): Negative momentum increasing.
Squeeze Status Indicator:
A marker is plotted on the zero line to indicate the squeeze state:
Yellow: Squeeze On (compression active).
Blue: Squeeze Off (compression ended, movement expected).
Gray: No Squeeze.
Information Table
A table is displayed in the top-right corner of the chart, showing closing prices for different timeframes (e.g., 1 minute, 5 minutes, 1 hour, etc.). Each timeframe is color-coded.
Alerts
LaRSI Alerts:
Crosses above 20: Exiting oversold condition (buy signal).
Crosses below 80: Exiting overbought condition (sell signal).
Squeeze Alerts:
When the squeeze ends: Indicates a potential price move.
When the squeeze starts: Indicates volatility is decreasing.
Summary
This indicator is a powerful tool for determining market trends, momentum, and squeeze conditions. It helps users identify periods when the market is likely to move or remain stagnant, providing alerts based on these analyses to support trading strategies.
Ultimate Machine Learning MACD (Deep Learning Edition)This script is a "Deep Learning MACD" indicator that combines traditional MACD calculations with advanced machine learning techniques, including recursive feedback, adaptive learning rates, Monte Carlo simulations, and volatility-based adjustments. Here’s a breakdown of its key components:
Inputs
Lookback: The length of historical data (1000 by default) used for learning and volatility measurement.
Momentum and Volatility Weighting: Adjusts how much momentum and volatility contribute to the learning process (momentum weight: 1.2, volatility weight: 1.5).
MACD Lengths: Defines the range for MACD fast and slow lengths, starting at minimum of 1 and max of 1000.
Learning Rate: Defines how much the model learns from its predictions (very small learning rate by default).
Adaptive Learning: Enables dynamic learning rates based on market volatility.
Memory Factor: A feedback factor that determines how much weight past performance has in the current model.
Simulations: The number of Monte Carlo simulations used for probabilistic modeling.
Price Change: Calculated as the difference between the current and previous close.
Momentum: Measured using a lookback period (1000 bars by default).
Volatility: Standard deviation of closing prices.
ATR: Average true range over 14 periods for measuring market volatility.
Custom EMA Calculation
Implements an exponential moving average (EMA) formula from scratch using a recursive calculation with a smoothing factor.
Dynamic Learning Rate
Adjusts the learning rate based on market volatility. When volatility is high, the learning rate increases, and when volatility is low, it decreases. This makes the model more responsive during volatile markets and more stable during calm periods.
Error Calculation and Adjustment
Error Calculation: Measures the difference between the predicted value (via Monte Carlo simulations) and the true MACD value.
Adjust MACD Length: Uses the error to adjust the fast and slow MACD lengths dynamically, so the system can learn from market conditions.
Probabilistic Monte Carlo Simulation
Runs multiple simulations (200 by default) to generate probabilistic predictions. It uses random values weighted by momentum and volatility to simulate various market scenarios, enhancing
prediction accuracy.
MACD Calculation (Learning-Enhanced)
A custom MACD function that calculates:
Fast EMA and Slow EMA for MACD line.
Signal Line: An EMA of the MACD line.
Histogram: The difference between the MACD and signal lines.
Adaptive MACD Calculation
Adjusts the fast and slow MACD lengths based on the error from the Monte Carlo prediction.
Calculates the adaptive MACD, signal, and histogram using dynamically adjusted lengths.
Recursive Memory Feedback
Stores previous MACD values in an array (macdMemory) and averages them to create a feedback loop. This adds a "memory" to the system, allowing it to learn from past behaviors and refine future predictions.
Volatility-Based Reinforcement
Introduces a volatility reinforcement factor that influences the signal based on market conditions. It adds volatility awareness to the feedback system, making the system more reactive during high volatility periods.
Smoothed MACD
After all the adjustments, the MACD line is further smoothed based on the current market volatility, resulting in a final smoothed MACD.
Key Features
Monte Carlo Simulation: Runs multiple simulations to enhance predictions based on randomness and market behavior.
Adaptive Learning: Dynamic adjustments of learning rates and MACD lengths based on market conditions.
Recursive Feedback: Uses past data as feedback to refine the system’s predictions over time.
Volatility Awareness: Integrates market volatility into the system, making the MACD more responsive to market fluctuations.
This combination of traditional MACD with machine learning creates an adaptive indicator capable of learning from past behaviors and adjusting its sensitivity based on changing market conditions.
MB - Currency Strength ROCCurrency Strength ROC Enhanced is a technical indicator designed to measure and visualize the relative strength of different currencies in the foreign exchange market. Using a Rate of Change (ROC) approach and moving averages, this indicator provides valuable insights into the dynamics of currency strengths.
Key Features:
Relative Strength Measurement:
Calculates the strength of each currency relative to others, allowing you to identify which currencies are appreciating or depreciating.
Strength Histogram:
Presents normalized strength in a histogram format, making it easy to quickly see areas of positive (green) and negative (red) strength
Moving Averages:
Includes moving averages of normalized strength and trend, providing a clear view of the overall direction of strength over time.
Overbought and Oversold Zones:
Highlights critical levels of strength through horizontal lines, allowing traders to identify potential trend reversals.
