The Barking Rat LiteMomentum & FVG Reversion Strategy
The Barking Rat Lite is a disciplined, short-term mean-reversion strategy that combines RSI momentum filtering, EMA bands, and Fair Value Gap (FVG) detection to identify short-term reversal points. Designed for practical use on volatile markets, it focuses on precise entries and ATR-based take profit management to balance opportunity and risk.
Core Concept
This strategy seeks potential reversals when short-term price action shows exhaustion outside an EMA band, confirmed by momentum and FVG signals:
EMA Bands:
Parameters used: A 20-period EMA (fast) and 100-period EMA (slow).
Why chosen:
- The 20 EMA is sensitive to short-term moves and reflects immediate momentum.
- The 100 EMA provides a slower, structural anchor.
When price trades outside both bands, it often signals overextension relative to both short-term and medium-term trends.
Application in strategy:
- Long entries are only considered when price dips below both EMAs, identifying potential undervaluation.
- Short entries are only considered when price rises above both EMAs, identifying potential overvaluation.
This dual-band filter avoids counter-trend signals that would occur if only a single EMA was used, making entries more selective..
Fair Value Gap Detection (FVG):
Parameters used: The script checks for dislocations using a 12-bar lookback (i.e. comparing current highs/lows with values 12 candles back).
Why chosen:
- A 12-bar displacement highlights significant inefficiencies in price structure while filtering out micro-gaps that appear every few bars in high-volatility markets.
- By aligning FVG signals with candle direction (bullish = close > open, bearish = close < open), the strategy avoids random gaps and instead targets ones that suggest exhaustion.
Application in strategy:
- Bullish FVGs form when earlier lows sit above current highs, hinting at downward over-extension.
- Bearish FVGs form when earlier highs sit below current lows, hinting at upward over-extension.
This gives the strategy a structural filter beyond simple oscillators, ensuring signals have price-dislocation context.
RSI Momentum Filter:
Parameters used: 14-period RSI with thresholds of 80 (overbought) and 20 (oversold).
Why chosen:
- RSI(14) is a widely recognized momentum measure that balances responsiveness with stability.
- The thresholds are intentionally extreme (80/20 vs. the more common 70/30), so the strategy only engages at genuine exhaustion points rather than frequent minor corrections.
Application in strategy:
- Longs trigger when RSI < 20, suggesting oversold exhaustion.
- Shorts trigger when RSI > 80, suggesting overbought exhaustion.
This ensures entries are not just technically valid but also backed by momentum extremes, raising conviction.
ATR-Based Take Profit:
Parameters used: 14-period ATR, with a default multiplier of 4.
Why chosen:
- ATR(14) reflects the prevailing volatility environment without reacting too much to outliers.
- A multiplier of 4 is a pragmatic compromise: wide enough to let trades breathe in volatile conditions, but tight enough to enforce disciplined exits before mean reversion fades.
Application in strategy:
- At entry, a fixed target is set = Entry Price ± (ATR × 4).
- This target scales automatically with volatility: narrower in calm periods, wider in explosive markets.
By avoiding discretionary exits, the system maintains rule-based discipline.
Visual Signals on Chart
Blue “▲” below candle: Potential long entry
Orange/Yellow “▼” above candle: Potential short entry
Green “✔️”: Trade closed at ATR take profit
Blue (20 EMA) & Orange (100 EMA) lines: Dynamic channel reference
⚙️Strategy report properties
Position size: 25% equity per trade
Initial capital: 10,000.00 USDT
Pyramiding: 10 entries per direction
Slippage: 2 ticks
Commission: 0.055% per side
Backtest timeframe: 1-minute
Backtest instrument: HYPEUSDT
Backtesting range: Jul 28, 2025 — Aug 17, 2025
Note on Sample Size:
You’ll notice the report displays fewer than the ideal 100 trades in the strategy report above. This is intentional. The goal of the script is to isolate high-quality, short-term reversal opportunities while filtering out low-conviction setups. This means that the Barking Rat Lite strategy is very selective, filtering out over 90% of market noise. The brief timeframe shown in the strategy report here illustrates its filtering logic over a short window — not its full capabilities. As a result, even on lower timeframes like the 1-minute chart, signals are deliberately sparse — each one must pass all criteria before triggering.
For a larger dataset:
Once the strategy is applied to your chart, users are encouraged to expand the lookback range or apply the strategy to other volatile pairs to view a full sample.
💡Why 25% Equity Per Trade?
While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why:
Backtests using this sizing show manageable drawdowns even under volatile periods.
The strategy generates a sizeable number of trades, reducing reliance on a single outcome.
Combined with conservative filters, the 25% setting offers a balance between aggression and control.
Users are strongly encouraged to customize this to suit their risk profile.
What makes Barking Rat Lite valuable
Combines multiple layers of confirmation: EMA bands + FVG + RSI
Adaptive to volatility: ATR-based exits scale with market conditions
Clear, actionable visuals: Easy to monitor and manage trades
Cerca negli script per "band"
Bitcoin Logarithmic Growth Curve 2025 Z-Score"The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements.
The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity.
Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences.
The Bitcoin logarithmic growth curve has the following function:
y = 10^(a * log10(x) - b)
In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart.
How is it made (You can skip this section if you’re not a fan of math):
To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows:
113, 18.55
240, 1004.42
451, 19128.27
655, 65502.47
The same process was applied to the bear market low values:
103, 2.48
267, 211.03
471, 3192.87
676, 16255.15
Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values.
For the cycle peak (x,y) values:
2.053, 1.268
2.380, 3.002
2.654, 4.282
2.816, 4.816
And for the bear market low (x,y) values:
2.013, 0.394
2.427, 2.324
2.673, 3.504
2.830, 4.211
Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary).
Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship.
The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value.
Below are the results:
snapshot
snapshot
It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression.
These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063).
The final bull cycle function is:
y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324)
The final bear cycle function is:
y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063)
The main Criticisms of growth curve models:
The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak.
This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band.
Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post:
This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns."
Now with Z-Score calculation for easy and constant valuation classification of Bitcoin according to this metric.
Created for TRW
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations.
To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market.
HOIW IT WORKS
At its core, this indicator fuses two ideas:
The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature.
A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity.
// Step 1: Adaptive length via RSI
rsi = ta.rsi(src, rsiLen)
rsi_scale = 1 - rsi / 100
len = math.round(minLen + (maxLen - minLen) * rsi_scale)
pine_ema(src, length) =>
alpha = 2 / (length + 1)
sum = 0.0
sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum )
sum
// Step 2: T3 with adaptive length
e1 = pine_ema(src, len)
e2 = pine_ema(e1, len)
e3 = pine_ema(e2, len)
e4 = pine_ema(e3, len)
e5 = pine_ema(e4, len)
e6 = pine_ema(e5, len)
c1 = -v * v * v
c2 = 3 * v * v + 3 * v * v * v
c3 = -6 * v * v - 3 * v - 3 * v * v * v
c4 = 1 + 3 * v + v * v * v + 3 * v * v
t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
The result: an evolving trend line that adapts to market tempo in real-time.
KEY FEATURES
⯁ RSI-Based Adaptive Smoothing
The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI.
When RSI is low → the T3 shortens, tracking reversals faster.
When RSI is high → the T3 stretches, filtering out noise during euphoria phases.
Displayed length is shown in a floating table, colored on a gradient between min/max values.
⯁ T3 Calculation (Tim Tillson Method)
The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) .
Formula:
T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA.
⯁ Visual Trend Direction & Transitions
The T3 line changes color dynamically:
Color Up (default: blue) → bullish curvature
Color Down (default: orange) → bearish curvature
Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction.
Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries.
⯁ Adaptive Volatility Bands
Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100).
Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3.
Shaded band zones help frame breakout setups or mean-reversion zones.
⯁ Dynamic Info Table
A live stats panel shows:
Current adaptive length
Maximum smoothing (▲ MaxLen)
Minimum smoothing (▼ MinLen)
All values update in real time and are color-coded to match trend direction.
HOW TO USE
Use T3 crossovers to detect trend transitions, especially during periods of volatility compression.
Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts.
The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower.
CONCLUSION
RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format.
Low Volatility Breakout Detector)This indicator is designed to visually identify potential breakouts from consolidation during periods of low volatility. It is based on classic Bollinger Bands and relative volume. Its primary purpose is not to generate buy or sell signals but to assist in spotting moments when the market exits a stagnation phase.
Arrows appear only when the price breaks above the upper or below the lower Bollinger Band, the band width is below a specified threshold (expressed in percentage), and volume is above its moving average multiplied by a chosen multiplier (default is 1). This combination may indicate the start of a new impulse following a period of low activity.
The chart background during low volatility is colored based on volume strength—the lower the volume during stagnation, the less transparent the background. This helps quickly spot unusual market behavior under seemingly calm conditions. The background opacity is dynamically scaled relative to the range of volumes over a selected period, which can be set manually (default is 50 bars).
The indicator works best in classic horizontal consolidations, where price moves within a narrow range and volatility and volume clearly decline. It is not intended to detect breakouts from formations such as triangles or wedges, which may not always exhibit low volatility relative to Bollinger Bands.
Settings allow you to adjust:
Bollinger Band length and multiplier,
Volatility threshold (in %),
Background and arrow colors,
Volume moving average length and multiplier,
Bar range used for background opacity scaling.
Note: For reliable results, it’s advisable to tailor the volatility threshold and volume/background ranges to the specific market and timeframe, as different instruments have distinct dynamics. If you want the background color to closely match the color of breakout arrows, you should set the same volume analysis period as the volume moving average length.
Additional note: To achieve a cleaner chart and focus solely on breakout signals, you can disable the background and Bollinger Bands display in the settings. This will leave only the breakout arrows visible on the chart, providing a clearer and more readable market picture.
Consolidation Range with Signals (Zeiierman)█ Overview
Consolidation Range with Signals (Zeiierman) is a precision tool for identifying and trading market consolidation zones, where price contracts into tight ranges before significant movement. It provides dynamic range detection using either ADX-based trend strength or volatility compression metrics, and offers built-in take profit and stop loss signals based on breakout dynamics.
Whether you trade breakouts, range reversals, or trend continuation setups, this indicator visualizes the balance between supply and demand with clearly defined mid-bands, breakout zones, and momentum-sensitive TP/SL placements.
█ How It Works
⚪ Multi-Method Range Detection
ADX Mode
Uses the Average Directional Index (ADX) to detect low-trend-strength environments. When ADX is below your selected threshold, price is considered to be in consolidation.
Volatility Mode
This mode detects consolidation by identifying periods of volatility compression. It evaluates whether the following metrics are simultaneously below their respective historical rolling averages:
Standard Deviation
Variance
Average True Range (ATR)
⚪ Dynamic Range Band System
Once a range is confirmed, the system builds a dynamic band structure using a volatility-based filter and price-jump logic:
Middle Line (Trend Filter): Reacts to price imbalance using adaptive jump logic.
Upper & Lower Bands: Calculated by expanding from the middle line using a configurable multiplier.
This creates a clean, visual box that reflects current consolidation conditions and adapts as price fluctuates within or escapes the zone.
⚪ SL/TP Signal Engine
On detection of a breakout from the range, the indicator generates up to 3 Take Profit levels and one Stop Loss, based on the breakout direction:
All TP/SL levels are calculated using the filtered base range and multipliers.
Cooldown logic ensures signals are not spammed bar-to-bar.
Entries are visualized with colored lines and labeled levels.
This feature is ideal for traders who want automated risk and reward reference points for range breakout plays.
█ How to Use
⚪ Breakout Traders
Use the SL/TP signals when the price breaks above or below the range bands, especially after extended sideways movement. You can customize how far TP1, TP2, and TP3 sit from the entry using your own risk/reward profile.
⚪ Mean Reversion Traders
Use the bands to locate high-probability reversion zones. These serve as reference zones for scalping or fade entries within stable consolidation phases.
█ Settings
Range Detection Method – Choose between ADX or Volatility compression to define range criteria.
Range Period – Determines how many bars are used to compute trend/volatility.
Range Multiplier – Scales the width of the consolidation zone.
SL/TP System – Optional levels that project TP1/TP2/TP3 and SL from the base price using multipliers.
Cooldown – Prevents repeated SL/TP signals from triggering too frequently.
ADX Threshold & Smoothing – Adjusts sensitivity of trend strength detection.
StdDev / Variance / ATR Multipliers – Fine-tune compression detection logic.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
IU Mean Reversion SystemDESCRIPTION
The IU Mean Reversion System is a dynamic mean reversion-based trading framework designed to identify optimal reversal zones using a smoothed mean and a volatility-adjusted band. This system captures price extremes by combining exponential and running moving averages with the Average True Range (ATR), effectively identifying overextended price action that is likely to revert back to its mean. It provides precise long and short entries with corresponding exit conditions, making it ideal for range-bound markets or phases of low volatility.
USER INPUTS :
Mean Length – Controls the smoothness of the mean; default is 9.
ATR Length – Defines the lookback period for ATR-based band calculation; default is 100.
Multiplier – Determines how wide the upper and lower bands are from the mean; default is 3.
LONG CONDITION :
A long entry is triggered when the closing price crosses above the lower band, indicating a potential upward mean reversion.
A position is taken only if there is no active long position already.
SHORT CONDITION :
A short entry is triggered when the closing price crosses below the upper band, signaling a potential downward mean reversion.
A position is taken only if there is no active short position already.
LONG EXIT :
A long position exits when the high price crosses above the mean, implying that price has reverted back to its average and may no longer offer favorable long risk-reward.
SHORT EXIT :
A short position exits when the low price crosses below the mean, indicating the mean reversion has occurred and the downside opportunity has likely played out.
WHY IT IS UNIQUE:
Uses a double smoothing approach (EMA + RMA) to define a stable mean, reducing noise and false signals.
Adapts dynamically to volatility using ATR-based bands, allowing it to handle different market conditions effectively.
Implements a state-aware entry system using persistent variables, avoiding redundant entries and improving clarity.
The logic is clear, concise, and modular, making it easy to modify or integrate with other systems.
HOW USER CAN BENEFIT FROM IT :
Traders can easily identify reversion opportunities in sideways or mean-reverting environments.
Entry and exit points are visually labeled on the chart, aiding in clarity and trade review.
Helps maintain discipline and consistency by using a rule-based framework instead of subjective judgment.
Can be combined with other trend filters, momentum indicators, or higher time frame context for enhanced results.
Moving Volume-Weighted Avg Price, % Channel, BBsThis script includes:
- Moving Volume-Weighted Average Price line.
- User-defined % band above and below, very useful for "breakout" signals, and mentally adjusting to the magnitude of price swings when viewing an automatic scale on the price axis.
- Volume-Weighted Bollinger Bands, which are more sensitive to volume.
More detail:
- This is like TV's basic VWAP in concept, except the major flaw in that is that it has reset periods that you can't override, and the volume is cumulative until the next hard reset. The 'reset' is OK for securities trading, that resets every day anyway. But not for crypto - and not if/when securities trading goes 24/7. Also, the denominator accumulating over the entire period is also *not* OK, because then what is shown means something different as the day progresses - which kind of makes it useless. In other words, it starts out very sensitive to volume, and gets progressively more numb to it as they day progresses, and starts flattening out.
- This fixes both problems, by using a user-definable moving window for the average. Essentially combining SMA with volume-weighting.
- You may also find an invaluable trading aid, in the % bands above and below.
- What can optionally be shown is standard deviation bands, aka Bollinger bands. The advantage over regular BB is that it's volume-weighted. Since it is already calculated on a moving average, the period for the standard deviation has been shortened by default, and the magnitude increased, to better approximate regular Bollinger Bands - but it's still more responsive to volume.
Q Momentum FlowQ Momentum Flow
A hybrid trend engine combining breakout-driven momentum shifts with adaptive volatility bands. Designed for traders who want clear entries, intelligent exits, and a balance between reactivity and noise control.
🔧 Core Features
1. Momentum Shift Detection
• Uses dynamic breakout levels (ATR-based) to identify impulse-driven price shifts.
• Filters weak moves by enforcing a cooldown period and direction alternation.
2. Adaptive Trend Framework
• Trend direction is derived from a dual-EMA anchor with dynamic volatility bands.
• Sensitivity automatically adjusts based on smoothed price deviation.
3. Entry & Exit System
• Buy and sell arrows appear on valid momentum + trend alignment.
• Exit markers signal early trend weakening before full reversal.
• Arrows and labels are visually separated to reduce chart clutter.
4. Alerts (Fully Integrated)
• Buy and Sell alerts on valid entry triggers.
• Separate alerts for early exits based on weakening trend conditions.
• Compatible with automation or notification setups.
⚙️ Configurable Inputs
• Trend Length — Controls how fast the adaptive bands react.
• Smoothing — Smooths volatility for more stable band generation.
• Sensitivity — Adjusts band width and breakout tolerance.
• Visual Settings — Customize background color, arrow styles, and label size.
• Exit Logic — Built-in reversal detection to signal when trend weakens.
📈 How to Use
• Follow Buy/Sell arrows for directional entries.
• Stay in trade until either:
— Opposite signal appears, or
— “Exit” label triggers based on adaptive trend weakening.
• Use background and bar colors for regime clarity.
Candle Trend PowerThe Candle Trend Power is a custom technical indicator designed for advanced trend analysis and entry signal generation. It combines multiple smoothing methods, candle transformations, and volatility bands to visually and analytically enhance your trading decisions.
🔧 Main Features:
📉 Custom Candle Types
It transforms standard OHLC candles into one of several advanced types:
Normal Candles, Heikin-Ashi, Linear Regression, Rational Quadratic (via kernel filtering), McGinley Dynamic Candles
These transformations help traders better see trend continuations and reversals by smoothing out market noise.
🧮 Smoothing Method for Candle Data
Each OHLC value can be optionally smoothed using:
EMA, SMA, SMMA (RMA), WMA, VWMA, HMA, Mode (Statistical mode) Or no smoothing at all.
This flexibility is useful for customizing to different market conditions.
📊 Volatility Bands
Volatility-based upper and lower bands are calculated using:
Band = price ± (price% + ATR * multiplier)
They help identify overbought/oversold zones and potential reversal points.
📍 Candle Color Logic
Each candle is colored:
Cyan (#00ffff) if it's bullish and stronger than the previous candle
Red (#fd0000) if it's bearish and weaker
Alternating bar index coloring improves visual clarity.
📈 Trend Momentum Labels
The script includes a trend strength estimation using a smoothed RSI:
If the candle is bullish, it shows a BUY label with the overbought offset.
If bearish, it shows a SELL label with the oversold offset.
These labels are dynamic and placed next to the bar.
📍 Signal Markers
It also plots triangles when the price crosses the volatility bands:
Triangle up for potential long
Triangle down for potential short
✅ Use Case Summary
This script is mainly used for:
Visual trend confirmation with enhanced candles
Volatility-based entry signals
RSI-based trend momentum suggestions
Integrating different smoothing & transformation methods to fine-tune your strategy
It’s a flexible tool for both manual traders and automated system developers who want clear, adaptive signals across different market conditions.
💡 What's Different
🔄 Candle Type Transformations
⚙️ Custom Candle Smoothing
📉 Candle's Multi-level Volatility Bands
🔺 Dynamic Entry Signals (Buy/Sell Labels)
❗Important Note:
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
VWAP StrategyVWAP and volatility filters for structured intraday trades.
How the Strategy Works
1. VWAP Anchored to Session
VWAP is calculated from the start of each trading day.
Standard deviations are used to create bands above/below the VWAP.
2. Entry Triggers: Al Brooks H1/H2 and L1/L2
H1/H2 (Long Entry): Opens below 2nd lower deviation, closes above it.
L1/L2 (Short Entry): Opens above 2nd upper deviation, closes below it.
3. Volatility Filter (ATR)
Skips trades when deviation bands are too tight (< 3 ATRs).
4. Stop Loss
Based on the signal bar’s high/low ± stop buffer.
Longs: signalBarLow - stopBuffer
Shorts: signalBarHigh + stopBuffer
5. Take Profit / Exit Target
Exit logic is customizable per side:
VWAP, Deviation Band, or None
6. Safety Exit
Exits early if X consecutive bars go against the trade.
Longs: X red bars
Shorts: X green bars
Explanation of Strategy Inputs
- Stop Buffer: Distance from signal bar for stop-loss.
- Long/Short Exit Rule: VWAP, Deviation Band, or None
- Long/Short Target Deviation: Standard deviation for target exit.
- Enable Safety Exit: Toggle emergency exit.
- Opposing Bars: Number of opposing candles before safety exit.
- Allow Long/Short Trades: Enable or disable entry side.
- Show VWAP/Entry Bands: Toggle visual aids.
- Highlight Low Vol Zones: Orange shading for low volatility skips.
Tuning Tips
- Stop buffer: Use 1–5 points.
- Target deviation: Start with VWAP. In strong trends use 2nd deviation and turn off the counter-trend entry.
- Safety exit: 3 bars recommended.
- Disable short/long side to focus on one type of reversal.
Backtest Setup Suggestions
- initial_capital = 2000
- default_qty_value = 1 (fixed contracts or percent-of-equity)
VIX Implied MovesKey Features:
Three Timeframe Bands:
Daily: Blue bands showing ±1σ expected move
Weekly: Green bands showing ±1σ expected move
30-Day: Red bands showing ±1σ expected move
Calculation Methodology:
Uses VIX's annualized volatility converted to specific timeframes using square root of time rule
Trading day convention (252 days/year)
Band width = Price × (VIX/100) ÷ √(number of periods)
Visual Features:
Colored semi-transparent backgrounds between bands
Progressive line thickness (thinner for shorter timeframes)
Real-time updates as VIX and ES prices change
Example Calculation (VIX=20, ES=5000):
Daily move = 5000 × (20/100)/√252 ≈ ±63 points
Weekly move = 5000 × (20/100)/√50 ≈ ±141 points
Monthly move = 5000 × (20/100)/√21 ≈ ±218 points
This indicator helps visualize expected price ranges based on current volatility conditions, with wider bands indicating higher market uncertainty. The probabilistic ranges represent 68% confidence levels (1 standard deviation) derived from options pricing.
Channels by SmanovIndicator Description
“Channels by Smanov” is a multi-channel indicator that plots dynamic support and resistance zones around a moving average line. It is composed of two main parts:
FL 1 (Flexible Channels):
A Simple Moving Average (SMA) serves as the Basis.
Upper and lower bands are calculated by adding and subtracting an ATR-based buffer from the Basis.
User-defined inputs (such as Half Length, ATR Period, and ATR Multiplier) allow for flexibility in adapting the channel width to different market conditions.
FL 2 (Fixed Channels):
Eight additional bands expand on the same SMA + ATR logic but use fixed ATR multipliers (ranging from 2.2 up to 5.0).
These extra lines can help you gauge more distant levels of potential support or resistance.
By combining an SMA (to smooth price data) with ATR (to gauge volatility), this indicator highlights areas where price may be “stretched” relative to recent volatility. Traders often use channel-based indicators to identify potential “overbought” or “oversold” conditions, as well as to spot trend continuations or reversals.
How to Use / Trading Strategy
Trend Identification (Basis Line):
The middle line (the SMA) can be used as a trend filter:
If price consistently stays above the basis, it suggests an uptrend.
If price consistently stays below the basis, it suggests a downtrend.
Reversal Opportunities (Outer Bands):
When price moves into or beyond the upper bands, it may signal overbought conditions, creating potential short (or profit-taking) opportunities.
Conversely, when price dips into or beyond the lower bands, it may signal oversold conditions, which some traders use for initiating or adding to long positions.
Breakout or Continuation Signals:
In a strong trend, price may “ride” along the outer channels.
A clear break above/below a channel that previously acted as resistance/support could hint at trend continuation.
Failure to break these levels could suggest a potential reversal or consolidation phase.
Stop-Loss Placement:
Traders often place stops just outside a relevant band. For example, if you go long on a dip near a lower band, you might place your stop slightly below that band, relying on the ATR-based buffer to reflect normal volatility.
Multiple Timeframe Analysis:
Consider confirming signals on a higher timeframe (e.g., 4-hour or daily) while taking entries on a lower timeframe.
Channels on higher timeframes can act as stronger support or resistance, offering additional confluence.
Disclaimer
This indicator is provided for educational purposes and does not guarantee specific results. Trading involves risk, and individual traders are responsible for managing their own risk and capital. Always conduct thorough analysis and use appropriate risk management (e.g., stop-losses) when entering any market positions.
Enjoy using Channels by Smanov! Your feedback and personal insights can further refine the indicator’s settings for your preferred trading style. Good luck and trade responsibly!
This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0.
© Smanov_I
Adaptive Fourier Transform Supertrend [QuantAlgo]Discover a brand new way to analyze trend with Adaptive Fourier Transform Supertrend by QuantAlgo , an innovative technical indicator that combines the power of Fourier analysis with dynamic Supertrend methodology. In essence, it utilizes the frequency domain mathematics and the adaptive volatility control technique to transform complex wave patterns into clear and high probability signals—ideal for both sophisticated traders seeking mathematical precision and investors who appreciate robust trend confirmation!
🟢 Core Architecture
At its core, this indicator employs an adaptive Fourier Transform framework with dynamic volatility-controlled Supertrend bands. It utilizes multiple harmonic components that let you fine-tune how market frequencies influence trend detection. By combining wave analysis with adaptive volatility bands, the indicator creates a sophisticated yet clear framework for trend identification that dynamically adjusts to changing market conditions.
🟢 Technical Foundation
The indicator builds on three innovative components:
Fourier Wave Analysis: Decomposes price action into primary and harmonic components for precise trend detection
Adaptive Volatility Control: Dynamically adjusts Supertrend bands using combined ATR and standard deviation
Harmonic Integration: Merges multiple frequency components with decreasing weights for comprehensive trend analysis
🟢 Key Features & Signals
The Adaptive Fourier Transform Supertrend transforms complex wave calculations into clear visual signals with:
Dynamic trend bands that adapt to market volatility
Sophisticated cloud-fill visualization system
Strategic L/S markers at key trend reversals
Customizable bar coloring based on trend direction
Comprehensive alert system for trend shifts
🟢 Practical Usage Tips
Here's how you can get the most out of the Adaptive Fourier Transform Supertrend :
1/ Setup:
Add the indicator to your favorites, then apply it to your chart ⭐️
Start with close price as your base source
Use standard Fourier period (14) for balanced wave detection
Begin with default harmonic weight (0.5) for balanced sensitivity
Start with standard Supertrend multiplier (2.0) for reliable band width
2/ Signal Interpretation:
Monitor trend band crossovers for potential signals
Watch for convergence of price with Fourier trend
Use L/S markers for trade entry points
Monitor bar colors for trend confirmation
Configure alerts for significant trend reversals
🟢 Pro Tips
Fine-tune Fourier parameters for optimal sensitivity:
→ Lower Base Period (8-12) for more reactive analysis
→ Higher Base Period (15-30) to filter out noise
→ Adjust Harmonic Weight (0.3-0.7) to control shorter trend influence
Customize Supertrend settings:
→ Lower multiplier (1.5-2.0) for tighter bands
→ Higher multiplier (2.0-3.0) for wider bands
→ Adjust ATR length based on market volatility
Strategy Enhancement:
→ Compare signals across multiple timeframes
→ Combine with volume analysis
→ Use with support/resistance levels
→ Integrate with other momentum indicators
Time Appliconic Macro | ForTF5m (Fixed)The Time Appliconic Macro (TAMcr) is a custom-built trading indicator designed for the 5-minute time frame (TF5m), providing traders with clear Buy and Sell signals based on precise technical conditions and specific time windows.
Key Features:
Dynamic Moving Average (MA):
The indicator utilizes a Simple Moving Average (SMA) to identify price trends.
Adjustable length for user customization.
Custom STARC Bands:
Upper and lower bands are calculated using the SMA and the Average True Range (ATR).
Includes a user-defined multiplier to adjust the band width for flexibility across different market conditions.
RSI Integration:
Signals are filtered using the Relative Strength Index (RSI), ensuring they align with overbought/oversold conditions.
Time-Based Signal Filtering:
Signals are generated only during specific time windows, allowing traders to focus on high-activity periods or times of personal preference.
Supports multiple custom time ranges with automatic adjustments for UTC-4 or UTC-5 offsets.
Clear Signal Visualization:
Buy Signals: Triggered when the price is below the lower band, RSI indicates oversold conditions, and the time is within the defined range.
Sell Signals: Triggered when the price is above the upper band, RSI indicates overbought conditions, and the time is within the defined range.
Signals are marked directly on the chart for easy identification.
Customizability:
Adjustable parameters for the Moving Average length, ATR length, and ATR multiplier.
Time zone selection and defined trading windows provide a tailored experience for global users.
Who is this Indicator For?
This indicator is perfect for intraday traders who operate in the 5-minute time frame and value clear, filtered signals based on price action, volatility, and momentum indicators. The time window functionality is ideal for traders focusing on specific market sessions or personal schedules.
How to Use:
Adjust the MA and ATR parameters to match your trading style or market conditions.
Set the desired time zone and time ranges to align with your preferred trading hours.
Monitor the chart for Buy (green) and Sell (red) signals, and use them as a guide for entering or exiting trades.
E9 Bollinger RangeThe E9 Bollinger Range is a technical trading tool that leverages Bollinger Bands to track volatility and price deviations, along with additional trend filtering via EMAs.
The script visually enhances price action with a combination of trend-filtering EMAs, bar colouring for trend direction, signals to indicate potential buy and sell points based on price extension and engulfing patterns.
Here’s a breakdown of its key components:
Bollinger Bands: The strategy plots multiple Bollinger Band deviations to create different price levels. The furthest deviation bands act as warning signs for traders when price extends significantly, signaling potential overbought or oversold conditions.
Bar Colouring: Visual bar colouring is applied to clearly indicate trend direction: green bars for an uptrend and red bars for a downtrend.
EMA Filtering: Two EMAs (50 and 200) are used to help filter out false signals, giving traders a better sense of the underlying trend.
This combination of signals, visual elements, and trend filtering provides traders with a systematic approach to identifying price deviations and taking advantage of market corrections.
Brief History of Bollinger Bands
Bollinger Bands were developed by John Bollinger in the early 1980s as a tool to measure price volatility in financial markets. The bands consist of a moving average (typically 20 periods) with upper and lower bands placed two standard deviations away. These bands expand and contract based on market volatility, offering traders a visual representation of price extremes and potential reversal zones.
John Bollinger’s work revolutionized technical analysis by incorporating volatility into trend detection. His bands remain widely used across markets, including stocks, commodities, and cryptocurrencies. With the ability to highlight overbought and oversold conditions, Bollinger Bands have become a staple in many trading strategies.
Follow LineFollow Line is a common MT4 FX indicator based on trend following.
The main idea behind the calculation is volatility:
-Indicator Line increases as price goes above Bollinger Bands but with 1 standard deviation.
-Likewise when price moves below the lower Bollinger Band with 1 Standard deviation, Follow -Line decreases down.
-As you can imagine, indicator stays as a flat line when price moves between the bands.
There are two critical settings about the indicator:
1- Bollinger Bands Deviation is set to 1 as default but if you want to have early signals you have to decrease that amount. Also you'd better increase that to have flat values on sideways market conditions for not getting chopped by the early but false signals.
2- ATR Filter is activated in default settings and the indicator follows the trend with a distance from Highs and Lows considering ATR (default length 5) values. If you turn off the ATR filter, the indicator line only takes into account the Highs and the Lows. Indicator will get more agile but the risk of choppy signals can be taken that time. I personally advise you to increase the Bollinger Band Deviation from 1 to between 1.5-2 to stabilize the fake signals when ATR filter is turned off.
Signals can be shown on the graph:
BUY: when Follow Line changes direction from red to blue.(which means Price is above Bollinger Upper Band with 1 standard deviation)
SELL: when Follow Line changes direction from blue to red. (which means Price moves below Bollinger Lower Band with 1 standard deviation)
Finally, some of you may know there are also several Follow Line indicators on TradingView but unfortunately they don't show the same exact values (close but not same) comparing with original version of MT4 and the Metastock version that I've coded recently. So, I shared this stuff to have the exact same values on graphs on all platforms.
Adaptive VWAP [QuantAlgo]Introducing the Adaptive VWAP by QuantAlgo 📈🧬
Enhance your trading and investing strategies with the Adaptive VWAP , a versatile tool designed to provide dynamic insights into market trends and price behavior. This indicator offers a flexible approach to VWAP calculations by allowing users to adapt it based on lookback periods or fixed timeframes, making it suitable for a wide range of market conditions.
🌟 Key Features:
🛠 Customizable VWAP Settings: Choose between an adaptive VWAP that adjusts based on a rolling lookback period, or switch to a fixed timeframe (e.g., daily, weekly, monthly) for a more structured approach. Adjust the VWAP to suit your trading or investing style.
💫 Dynamic Bands and ATR Filter: Configurable deviation bands with multipliers allow you to visualize price movement around VWAP, while an ATR-based noise filter helps reduce false signals during periods of market fluctuation.
🎨 Trend Visualization: Color-coded trend identification helps you easily spot uptrends and downtrends based on VWAP positioning. The indicator fills the areas between the bands for clearer visual representation of price volatility and trend strength.
🔔 Custom Alerts: Set up alerts for when price crosses above or below the VWAP, signaling potential uptrend or downtrend opportunities. Stay informed without needing to monitor the charts constantly.
✍️ How to Use:
✅ Add the Indicator: Add the Adaptive VWAP to your favourites and apply to your chart. Choose between adaptive or timeframe-based VWAP calculation, adjust the lookback period, and configure the deviation bands to your preferred settings.
👀 Monitor Bands and Trends: Watch for price interaction with the VWAP and its deviation bands. The color-coded signals and band fills help identify potential trend shifts or price extremes.
🔔 Set Alerts: Configure alerts for uptrend and downtrend signals based on price crossing the VWAP, so you’re always informed of significant market movements.
⚙️ How It Works:
The Adaptive VWAP adjusts its calculation based on the user’s chosen configuration, allowing for a flexible approach to market analysis. The adaptive setting uses a rolling lookback period to continuously adjust the VWAP, while the fixed timeframe option anchors VWAP to key timeframes like daily, weekly, or monthly periods. This flexibility enables traders and investors to use the tool in various market environments.
Deviation bands, calculated with customizable multipliers, provide a clear visual of how far the price has moved from the VWAP, helping you gauge potential overbought or oversold conditions. To reduce false signals, an ATR-based filter can be applied, ensuring that only significant price movements trigger trend confirmations.
The tool also includes a fast exponential smoothing function for the VWAP, helping smooth out price fluctuations without sacrificing responsiveness. Trend confirmation is reinforced by the number of bars that price stays above or below the VWAP, ensuring a more consistent trend identification process.
Disclaimer:
The Adaptive VWAP is designed to enhance your market analysis but should not be relied upon as the sole basis for trading or investing decisions. Always combine it with other analytical tools and practices. No statements or signals from this indicator constitute financial advice. Past performance is not indicative of future results.
IMI and MFI CombinedFor a strategy using the combined IMI (Intraday Momentum Index), MFI (Money Flow Index), and Bollinger Bands on a 1-minute chart of Bank NIFTY (Bank Nifty Index), here's how you can interpret the indicators and define a sell signal strategy:
Strategy Explanation:
IMI (Intraday Momentum Index):
IMI measures the ratio of upward price changes to downward price changes over a specified period, indicating momentum.
In the script, IMI is plotted with a range from 0 to 100. Levels above 75 are considered overbought, and levels below 25 are oversold.
Strategy Condition: A sell signal can be considered when IMI is above 75, indicating a potentially overbought market condition.
MFI (Money Flow Index):
MFI measures the strength of money flowing in and out of a security, using price and volume.
In the script, MFI is plotted with levels at 80 (overbought) and 20 (oversold).
Strategy Condition: A sell signal can be considered when MFI is above 80, suggesting an overbought condition in the market.
Bollinger Bands:
Bollinger Bands consist of a middle band (SMA) and upper/lower bands representing volatility levels around the price.
In the script, Bollinger Bands are plotted with a length of 20 and a standard deviation multiplier of 2.
Strategy Condition: While not explicitly used for generating sell signals in this script, Bollinger Bands can help confirm price volatility and potential reversals when combined with other indicators.
Sell Signal Criteria:
IMI Sell Signal: Look for instances where IMI rises above 75. This indicates that the recent upward price momentum may be reaching an unsustainable level, potentially signaling a reversal or a pullback in prices.
MFI Sell Signal: Look for MFI rising above 80. This suggests that the market has experienced strong buying pressure, possibly leading to an overbought condition where a price correction or reversal might occur.
Implementation Considerations:
Confirmation: Consider waiting for both IMI and MFI to confirm the overbought condition simultaneously before entering a sell trade. This can increase the reliability of the signal.
Risk Management: Use stop-loss orders to manage risk in case the market moves against the anticipated direction after the sell signal is triggered.
Timeframe: This strategy is tailored for a 1-minute chart, meaning signals should be interpreted and acted upon quickly due to the rapid nature of price movements in intraday trading.
By combining these indicators and interpreting their signals, you can develop a systematic approach to identifying potential sell opportunities in the Bank NIFTY index on a 1-minute timeframe. Adjustments to indicator parameters and additional technical analysis may further refine the strategy based on your trading preferences and risk tolerance.
Trend Gaussian Channels [DeltaAlgo]This Script Introduces The Use Of The Gaussian Channel Concepts
This indicator consists of three lines: a central line that represents the moving average, and an upper and lower band that represent the volatility of the price movements.
The Gaussian channels is a concept consists of an upper & lower bands along with the basis; the mid band. The use of the Gaussian bands are simple, as described below.👇
Use Case:
There are many ways the Gaussian channel indicator can be used!
1. Look for the price to touch or cross the upper/lower bands of the Gaussian Channel Indicator. This indicates that the price has reached an high level of volatility. a reversal or correction may be imminent.
2. Wait for confirmation of the potential reversal or correction. This can be in the form of a bearish or bullish candlestick pattern, or a signal from another technical indicator.
a. For this reason I have implemented some signals that indicate trend shifts & candle colors to clearly display the switching between a bullish sentiment or bearish.
3. Enter a trade in the direction of the reversal or correction. For example, if the price touches the upper band and a bearish candlestick pattern occurs or if you get a bearish signal, enter a short trade. If the price touches the lower band and indicates bullish candlestick pattern or bullish signal, enter a long trade.
Sometimes this band can act as a support & resistance, THIS is not always the case as it is not meant to be used as support & resistance.
Greedy DCA█ OVERVIEW
Detect price crashes in volatile conditions. This is an indicator for a greedy dollar cost average (DCA) strategy. That is, for people who want to repeatedly buy an asset over time when its price is crashing.
█ CONCEPTS
Price crashes are indicated if the price falls below one or more of the 4 lower Bollinger Bands which are calculated with increasing multipliers for the standard deviation.
In these conditions, the price is far below the average. Therefore they are considered good buying opportunities.
No buy signals are emitted if the Bollinger Bands are tight, i.e. if the bandwidth (upper -lower band) is below the value of the moving average multiplied with a threshold factor. This ensures that signals are only emitted if the conditions are highly volatile.
The Bollinger Bands are calculated based on the daily candles, irrespective the chart time frame. This allows to check the strategy on lower time frames
Ahsan Tufail Precise MA Crossover Filter for Reliable SignalsIntroduction:
In the ever-evolving world of Forex trading, strategies that provide a competitive edge are highly sought after. The Moving Average (MA) crossover technique is a popular long-term approach, but its vulnerability to false signals can lead to potential losses. To overcome this challenge, we introduce a game-changing MA crossover filter designed to weed out false signals and unlock the full potential of this strategy. In this article, we delve into the mechanics of this filter, providing a comprehensive analysis of its components and how it enhances the accuracy of buy and sell signals.
The Power of the MA Crossover Filter:
The essence of our MA crossover filter lies in the integration of a specialized indicator that operates on a scale of 0 to 100. This ingenious indicator dynamically measures the distance between the middle Bollinger band and either the upper or lower Bollinger band. By analyzing the values of the last 504 candlesticks, it maps the range from 50 to 100 for the largest and smallest distances between the middle and upper Bollinger bands. Similarly, for values ranging from 0 to 50, it measures the distance between the middle and lower Bollinger bands.
Unveiling the Signal Execution Process:
The brilliance of this filter is revealed in its meticulous execution of buy and sell signals, which significantly reduces false crossovers. Let's explore the process step-by-step:
Buy Signal Precision:
To initiate a buy signal, the price must be positioned above the 200-period Simple Moving Average (SMA).
The filter validates the crossover by checking the indicator's value, ensuring it falls below the threshold of 25.
Sell Signal Accuracy:
For a sell signal, the price must be below the 200-period Simple Moving Average (SMA).
The filter confirms the crossover by verifying the indicator's value, which should exceed the threshold of 75.
This selective approach ensures that only high-confidence crossovers are considered, maximizing the potential for profitable trades.
Fine-Tuning the Filter for Optimal Performance:
While the MA crossover filter exhibits its prowess in GBPUSD and EURUSD currency pairs, it may require adjustments for other pairs. Currency pairs possess unique characteristics, and adapting the filter to specific behavior is crucial for its success.
To fine-tune the filter for alternative currency pairs, traders should conduct rigorous backtesting and analyze historical price data. By experimenting with indicator threshold values, traders can calibrate the filter to accurately match the dynamics of the target currency pair. This iterative process allows for customization, ultimately resulting in a finely-tuned filter that aligns with the unique behavior of the selected market.
Conclusion:
The MA crossover filter represents a paradigm shift in long-term Forex trading strategies. By intelligently filtering false signals, this precision tool unleashes the true potential of the MA crossover technique, elevating its profitability and enhancing overall trading performance. While no strategy guarantees absolute success, incorporating this filter empowers traders with a heightened level of confidence in their buy and sell signals. Embracing the power of this innovative filter can be a transformative step towards mastering Forex profits and staying ahead in the dynamic world of currency trading.
FibonRSI / ErkOziHello,
This software is a technical analysis script written in the TradingView Pine language. The script creates a trading indicator based on Fibonacci retracement levels and the RSI indicator, providing information about price movements and asset volatility by using Bollinger Bands.
There are many different scripts in the market that draw RSI and Fibonacci retracement levels. However, this script was originally designed by me and shared publicly on TradingView.
***The indicator uses RSI (Relative Strength Index) and Bollinger Bands (BB) as the basis for the FibonRSI strategy. RSI measures the strength of a price movement, and BB measures the volatility of an asset. The FibonRSI strategy is based on the idea that the Fibonacci ratios and RSI can be used to predict a asset's price retracement levels.
***The script allows for various parameters to be adjusted. Users can specify the price source type and adjust the periods for RSI and Bollinger Bands. The standard deviation number for Bollinger Bands can also be customized.
***The script calculates the current RSI indicator position and the basic, upper, and lower levels of Bollinger Bands. It then calculates and draws the Fibonacci retracement levels. The color of the RSI line is determined by the upper and lower distribution levels of Bollinger Bands. Additionally, the color of the Fibonacci retracement levels can also be customized by the user.
***This script can be used to determine potential buy and sell signals using Fibonacci retracement levels and RSI. For example, when the RSI is oversold and the price is close to a Fibonacci retracement level, it can be interpreted as a buying opportunity. Similarly, when the RSI is overbought and the price is close to a Fibonacci retracement level, it can be interpreted as a selling opportunity.
***The script takes input parameters such as the price source used for calculation, the period for the RSI indicator, the period for the Moving Average in Bollinger Bands, and the number of standard deviations used in Bollinger Bands.
***The script's conditions include elements such as calculating the current position of the RSI indicator, calculating the upper and lower Bollinger Bands, calculating the dispersion factor, and calculating Fibonacci levels.
***The parameters in the code can be adjusted for calculation, including the price type used, the RSI period, the Moving Average period for BB, and the standard deviation count for BB. After this, the current position of the RSI, Moving Average, and standard deviation for BB are calculated. After calculating the upper and lower BB, the levels above and below the average are calculated using a specific dispersion constant.
CONDITIONS FOR THE SCRIPT
current_rsi = ta.rsi(src, for_rsi) // Current position of the RSI indicator
basis = ta.ema(current_rsi, for_ma)
dev = for_mult * ta.stdev(current_rsi, for_ma)
upper = basis + dev
lower = basis - dev
dispersion = 1
disp_up = basis + (upper - lower) * dispersion
disp_down = basis - (upper - lower) * dispersion
// Fibonacci Levels
f100 = basis + (upper - lower) * 1.0
f78 = basis + (upper - lower) * 0.78
f65 = basis + (upper - lower) * 0.65
f50 = basis
f35 = basis - (upper - lower) * 0.65
f23 = basis - (upper - lower) * 0.78
f0 = basis - (upper - lower) * 1.0
***When calculating Fibonacci levels, the distance between the average of BB and the upper and lower BB is used. These levels are 0%, 23.6%, 35%, 50%, 65%, 78.6%, and 100%. Finally, the RSI line that changes color according to a specific RSI position, Fibonacci levels, and BB are visualized. Additionally, the levels of 70, 30, and 50 are also shown.
The script then sets the color of the RSI position according to the EMA and draws Bollinger Bands, RSI, Fibonacci levels, and the 70, 30, and 50 levels.
In conclusion, this script enables traders to analyze market trends and make informed decisions. It can also be customized to suit individual trading strategies.
This script analyzes the RSI indicator using Bollinger Bands and Fibonacci levels. The default settings are 14 periods for RSI, 233 periods and 2 standard deviations for BB. The MA period inside BB is selected as the BB period and is used when calculating Fibonacci levels.
***The reason for selecting these settings is to provide enough time for BB period to confirm a possible trend. Additionally, the MA period inside BB is matched with the BB period and used when calculating Fibonacci levels.
***Fibonacci levels are calculated from the distance between the upper and lower bands of BB and show how RSI movement is related to these levels. Better results can be achieved when RSI periods are set to Fibonacci numbers such as 21, 55, and 89. Therefore, the use of Fibonacci numbers is recommended when adjusting RSI periods. Fibonacci numbers are among the technical analysis tools that can capture the reflection of naturally occurring movements in the market. Therefore, the use of Fibonacci numbers often helps to better track fluctuations in the market.
Finally, the indicator also displays the 70 and 30 levels and the middle level (50) with Fibonacci levels drawn in circles. Changing these settings can help optimize the Fibonacci levels and further improve the indicator.
Thank you in advance for your suggestions and opinions......
Flying Dragon Trend IndicatorFlying Dragon Trend Indicator can be used to indicate the trend on all timeframes by finetuning the input settings.
The Flying Dragon Trend family includes both the strategy and the indicator, where the strategy supports of selecting the optimal set of inputs for the indicator in each scenario. Highly recommended to get familiar with the strategy first to get the best out of the indicator.
Flying Dragon Trend plots the trend bands into the ribbon, where the colours indicate the trend of each band. The plotting of the bands can be turned off in the input settings. Based on the user selectable Risk Level the trend pivot indicator is shown for the possible trend pivot when the price crosses the certain moving average line, or at the Lowest risk level all the bands have the same colour. The trend pivot indicator is not shown on the Lowest risk level, but the colour of the trend bands is the indicator instead .
The main idea is to combine two different moving averages to cross each other at the possible trend pivot point, but trying to avoid any short term bounces to affect the trend indication. The ingenuity resides in the combination of selected moving average types, lengths and especially the offsets. The trend bands give visual hint for the user while observing the price interaction with the bands, one could say that when "the Dragon swallows the candles the jaws wide open", then there is high possibility for the pivot. The leading moving average should be fast while the lagging moving average should be, well, lagging behind the leading one. There is Offset selections for each moving average, three for leading one and one for the lagging one, those are where the magic happens. After user has selected preferred moving average types and lengths, by tuning each offset the optimal sweet spot for each timeframe and equity will be found. The default values are good enough starting points for longer (4h and up) timeframes, but shorter timeframes (minutes to hours) require different combination of settings, some hints are provided in tooltips. Basically the slower the "leading" moving average (like HMA75 or HMA115) and quicker the "lagging" moving average (like SMA12 or SMA5) become, the better performance at the Lowest risk level on minute scales. This "reversed" approach at the minute scales is shown also as reversed colour for the "lagging" moving average trend band, which seems to make it work surprisingly well.
The Flying Dragon Trend does not necessarily work well on zig zag and range bounce scenarios without additional finetuning of the input settings to fit the current condition.
