Rudy's BB with MartingaleMy first strategy script that uses Bollinger Bands and Martingale to increase contract size after negative profit.
Search in scripts for "bands"
[MAD] FibchannelsThis is an indicator that gives you bands around the Fibonacci levels High/Low of the asset.
There are 3 time frames available so you can use the hourly, daily and weekly at the same time.
You can change the bands via selectable inputs to suit your own preferences.
you can as example combine with RSI or MACD to find a entry
Weighted Standard Deviation BandsLinearly weighted standard deviations over linearly weighted mean.
The rationale of the study can be deduced from my latest publications where I go deeper into explaining the benefits of linear weighting, but in short, I can remind that by using linear weighting we are able to increase the information gain by communicating the sequential nature of time series to the calculations via linear weighting.
Note, that multiplier parameters can take both negative and positive values resulting in ability to have, for example, 1st and 6th weighted standard deviations higher than the weighted mean.
Despite the modification of the classic standard deviation formula, I assume that mathematical qualities of standard deviation will hold due to the fact we can alternately weight the window itself, and then apply the classic standard deviation over the weighted window. In both cases, the results will be the same.
Aight that was too formal, but your short strangles should be happy
Here is it, for you
RSI_OTT - TP/SLWhile creating this strategy, I was inspired by "ott(by Anıl Özekşi)" and "RSI + Bollinger Double Strategy(by ChartArt)".
Basically, the strategy uses ott bands instead of bollinger bands in the "RSI+Bollinger Double Strategy".
User can select take profit, stop loss, position direction(long, short or both) and the other ott parameters via interface.
[RS]Bollinger Bands Breakout Candles V0EXPERIMENTAL: a experiment using bollingers and directional momentum, Breakout detector.
Specter Trend Cloud [ChartPrime]⯁ OVERVIEW
Specter Trend Cloud is a flexible moving-average–based trend tool that builds a colored “cloud” around market direction and highlights key retest opportunities. Using two adaptive MAs (short vs. long), offset by ATR for volatility adjustment, it shades the background with a gradient cloud that switches color on trend flips. When price pulls back to retest the short MA during an active trend, the script plots diamond markers and extends dotted levels from that retest price. If price later breaks through that level, the extension is terminated—giving traders a clean visual of valid vs. invalid retests.
⯁ KEY FEATURES
Multi-MA Core Engine:
Choose from SMA, EMA, SMMA (RMA), WMA, or VWMA as the base. The indicator tracks both a short-term MA (Length) and a longer twin (2 × Length).
Volatility-Adjusted Offset:
Both MAs are shifted by ATR(200) depending on trend direction—pulling them down in uptrends, up in downtrends—so the cloud reflects realistic breathing room instead of razor-thin bands.
Gradient Trend Cloud:
Between the two shifted MAs, the script fills a shaded region:
• Aqua cloud = bullish trend
• Orange cloud = bearish trend
Gradient intensity increases toward the active edge, providing a visual sense of strength.
Trend Flip Logic:
A flip occurs whenever the short MA crosses above or below the long MA. The cloud instantly changes color and begins tracking the new regime.
Retest Detection:
During an ongoing trend (no flip), if price retests the short MA within a 5-bar “cooldown,” the tool:
• Marks the retest with diamond shapes below/above the bar.
• Draws a dotted horizontal line from the retest price, extending into the future.
Automatic Level Termination:
If price later closes through that dotted level, the line disappears—keeping only active, respected retest levels on your chart.
⯁ HOW IT WORKS (UNDER THE HOOD)
MA Calculations:
ma1 = MA(src, Length), ma2 = MA(src, 2 × Length).
Trend = ma1 > ma2 (bull) or ma1 < ma2 (bear).
ATR shift offsets both ma1 and ma2 by ±ATR depending on trend.
Cloud Fill:
Plots ma1 and ma2 (invisible for long MA). Uses fill() with semi-transparent aqua/orange gradient between the two.
Retest Logic:
• Bullish retest: ta.crossover(low, ma1) while trend = bull.
• Bearish retest: ta.crossunder(high, ma1) while trend = bear.
Only valid if at least 5 bars have passed since last retest.
When triggered, it stores bar index and price, draws diamonds, and extends a dotted line.
Level Clearing:
If current high > retest upper line (bearish case) or low < retest lower line (bullish case), that line is deleted (stops extending).
⯁ USAGE
Use the cloud color as the higher-level trend bias (aqua = long, orange = short).
Look for diamonds + dotted lines as pullback/retest zones where trend continuation may launch.
If a retest level holds and price rebounds, it strengthens confidence in the trend.
If a retest level is broken, treat it as a warning of weakening trend or possible reversal.
Experiment with MA Type (SMA vs. EMA, etc.) to align sensitivity with your asset or timeframe.
Adjust Length for faster flips on low timeframes or smoother signals on higher ones.
⯁ CONCLUSION
Specter Trend Cloud combines trend detection, volatility-adjusted shading, and retest visualization into a single tool. The gradient cloud provides instant clarity on direction, while diamonds and dotted retest levels give you tactical entry/retest zones that self-clean when invalidated. It’s a versatile trend-following and confirmation layer, adaptable across multiple assets and styles.
EMA Envelope + SMA + Purple DotThis indicator combines three tools into one:
📈 EMA Envelope with wedge and range contraction signals to highlight volatility squeezes.
🔵 SMA with optional smoothing (SMA/EMA/WMA/SMMA/VWMA) and optional Bollinger Bands.
🟣 Purple Dot “PowerBars” that mark strong momentum bars when price ROC (%) and volume exceed user-defined thresholds.
It also includes:
Background highlighting of contraction zones (bullish/bearish/neutral colors).
A summary table showing PowerBar count and return (%) over custom lookback periods.
Flexible display settings (table position, dark/light theme, highlight toggle).
Designed for traders who want to track momentum bursts, volatility contraction, and trend strength all in one tool.
ATR-limited Donchian ChannelThe ATR-limited Donchian Channel is a modified version of the classic Donchian Channel that adapts more quickly to changing market conditions.
While a traditional Donchian Channel is based only on the highest high and lowest low over a given lookback period, this version introduces an ATR-based constraint that prevents the channel lines from extending too far away from price. This makes the channel more responsive and reduces lag compared to the standard Donchian Channel.
How it works
The upper band is based on the highest high of the last N candles, but it cannot exceed a maximum distance of ATR × Factor above the current median price (midpoint of high and low).
The lower band is based on the lowest low of the last N candles, but it cannot drop more than ATR × Factor below the median price.
If the Donchian Channel would normally extend further than this ATR-limited boundary, the line is capped and marked in blue .
Otherwise, the upper band is drawn in red and the lower band in green .
A middle line is also plotted as the average of the modified upper and lower bands.
An optional offset allows you to shift the channel backward or forward in time for easier visual alignment.
Why use this version?
Faster reaction: By constraining the channel with ATR, the indicator adapts quicker to volatility changes and avoids long periods of overextended levels.
Noise control: ATR filtering prevents extreme spikes or outlier highs/lows from stretching the channel unnecessarily.
Visual clarity: Color-coding highlights when ATR filtering is active, making it easy to distinguish capped vs. natural Donchian levels.
Typical use cases
Trend-following breakout systems, but with volatility-aware limits.
Identifying dynamic support and resistance zones that adjust to market conditions.
Filtering false breakouts by monitoring when the Donchian channel is capped by ATR.
✅ This indicator is designed for traders who want the structure of a Donchian Channel but with an adaptive, volatility-sensitive adjustment that makes it react faster and more reliably than the classic version.
Turtle Trading System IndicatorKey Features & Components
Donchian Channels
The core of the indicator is the Donchian Channel, represented by the upper and lower blue bands.
Upper Channel: The highest price over a user-defined period.
Lower Channel: The lowest price over the same period.
Middle Line: The midpoint between the upper and lower channels.
These channels are used to identify potential breakouts, which form the basis for trade entries.
Trading Signals
The script automatically generates clear, non-repainting signals for potential trades:
Long Entry (Green ▲): A green upward-facing triangle appears below the candle when the closing price breaks above the upper Donchian channel, signaling the start of a potential uptrend.
Short Entry (Red ▼): A red downward-facing triangle appears above the candle when the closing price breaks below the lower Donchian channel, signaling the start of a potential downtrend.
Long Exit (Green X): A green cross appears above the candle when the price crosses below the middle line, suggesting the uptrend is weakening.
Short Exit (Orange X): An orange cross appears below the candle when the price crosses above the middle line, suggesting the downtrend is losing momentum.
Integrated Risk Management
A crucial element of the Turtle strategy is disciplined risk management, which is built into this indicator.
Volatility-Based Position Sizing
You can enable position sizing that adapts to market volatility using the Average True Range (ATR). When an entry signal occurs, a label appears showing a calculated position size unit. The formula aims to normalize risk, meaning you would trade smaller sizes in volatile markets and larger sizes in calmer markets. The formula used is:
Volatility Unit=
100
Risk %
×
4×ATR
Close Price
Dynamic Stop Loss
Upon a long or short entry, a stop-loss level is plotted on the chart as red circles. This level is calculated based on the ATR, automatically adjusting to the market's current volatility to provide a data-driven exit point for managing losses. It is calculated as:
Long Stop: Close Price - 1.8 * ATR
Short Stop: Close Price + 1.8 * ATR
On-Chart Information Panel
A convenient table is displayed in the bottom-right corner of the chart, showing the current ATR value and the calculated Position Size unit for quick and easy reference.
Customizable Settings
You can tailor the indicator to your specific strategy and risk tolerance:
Donchian Channel Period: Sets the lookback period for the channels. The default is 20. Shorter periods will be more sensitive and generate more signals.
ATR Period: Sets the lookback period for the Average True Range calculation, affecting both position size and stop-loss levels. The default is 14.
Risk Percentage: The percentage of equity you wish to risk per trade. This directly influences the position size calculation.
Use Volatility Position Sizing: A simple checkbox to turn the ATR-based position sizing on or off.
SD Median NUPL-Z🧠 Overview
SD Median NUPL-Z is a trend-following indicator that leverages a normalized version of Bitcoin’s Net Unrealized Profit/Loss (NUPL) metric, filtered through a median-based volatility band. Unlike traditional NUPL which is often used to spot extremes, this indicator is designed to identify sustained directional trends — entering only when both on-chain momentum and price structure align.
🧩 Key Features
Z-Scored NUPL Trend Engine: Normalizes NUPL using rolling mean and standard deviation to create a smoothed trend signal.
Price Structure Filter: Implements a median-based price band to avoid false entries during short-term volatility.
Custom Thresholds: User-defined thresholds determine when the trend signal is strong enough to justify a long or short directional bias.
Directional Candle Coloring: Reinforces current trend regime visually with aqua (bullish) and red (bearish) plots and candles.
Optimized for BTC: Uses Bitcoin’s Market Cap and Realized Cap to construct the NUPL input.
🔍 How It Works
On-Chain Core: NUPL is calculated as the percentage of unrealized profit in the market: (Market Cap - Realized Cap) / Market Cap * 100.
Z-Score Transformation: The raw NUPL value is normalized using a rolling average and standard deviation over a set window (default 134 days), producing the NUPL-Z series.
Median-Based Price Filter: A rolling 50th percentile (median) of price is used alongside its own standard deviation to create upper and lower bounds.
These bounds define a "volatility corridor" around price; the trend signal is only acted upon if price confirms by staying outside these bands.
Signal Logic:
A Long signal is triggered when NUPL-Z rises above the long threshold and price is not below the lower band.
A Short signal is triggered when NUPL-Z falls below the short threshold.
State Variable (CD): Tracks the current market regime, used to control plotting and color changes.
🔁 Use Cases & Applications
Momentum-Based Trend Following: Helps traders align with directional moves backed by both on-chain sentiment and supportive price structure.
Filtered Entry Timing: Reduces premature or noise-based entries by requiring price confirmation before committing to NUPL-based signals.
Best Suited for BTC: This tool is designed specifically around Bitcoin’s on-chain metrics and is not intended for altcoins or low-volume assets.
✅ Conclusion
SD Median NUPL-Z repurposes a traditionally cyclical valuation tool into a modern trend-following signal by combining statistical normalization with dynamic price structure filtering. It offers a more robust way to participate in high-conviction directional trends, reducing the likelihood of entering during short-lived counter moves.
⚠️ Disclaimer
The content provided by this indicator is for educational and informational purposes only. Nothing herein constitutes financial or investment advice. Trading and investing involve risk, including the potential loss of capital. Always backtest and apply risk management suited to your strategy.
OBV by readCrypto
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OBV is used as a leading indicator to predict stock price movements by measuring changes in trading volume.
Reflecting the cumulative value of trading volume,
- When the price rises, if the trading volume increases, OBV rises,
- When the price falls, if the trading volume decreases, OBV falls.
Therefore, the movement of the OBV indicator must be checked along with the price movement, and it has the disadvantage of being unreliable for coins (tokens) with low trading volume.
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(First interpretation method)
By adding a signal line for the OBV indicator,
- If the OBV indicator moves above the signal line, it is likely to show an upward trend,
- If the OBV indicator moves beyond the signal line, it is likely to show a downward trend.
This interpretation method is difficult to use in actual trading strategies because the OBV indicator often moves up and down repeatedly based on the signal line.
Therefore, it is recommended to use this interpretation method as reference when analyzing charts.
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(Second interpretation method)
Draw support and resistance lines based on the high and low points of the OBV indicator
- If the OBV indicator breaks through the previous high point, it is likely to show an upward trend,
- If the OBV indicator breaks through the previous low point, it is likely to show a downward trend.
This interpretation method is a bit more reliable than the first interpretation method, but it has the disadvantage of having to consider support and resistance lines separately based on the high and low points.
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To compensate for this, a High line for the high point and a Low line for the low point were added.
- If the OBV indicator shows an upward breakout of each line (Low, HL2, High), the price is likely to rise,
- If the OBV indicator shows a downward breakout of each line (Low, HL2, High), the price is likely to fall.
-
Also, the Low and High lines can be interpreted like Bollinger Bands.
That is, if the Low and High lines show a contraction, the price is likely to move sideways, and if they show an expansion, the price is likely to show a trend.
Therefore, if the High line breaks upward in a contracted state,
- It is likely to show an upward trend,
- If the Low line breaks downward, it is likely to show a downward trend.
In an expanded state, you should focus on finding the point to realize profits rather than conducting new transactions.
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It is not easy to interpret the change in actual transaction volume and use it to create a trading strategy.
In particular, it is more difficult in the coin market where multiple exchanges are linked to show movements for one coin (token).
Therefore, the coin market is actively conducting transactions without referring to trading volume at all by following trends.
However, I think that if you interpret the change in trading volume and use it to find a trading point, it can help you find a more accurate trading point.
In that sense, I think that an indicator that adds the High and Low lines of the OBV indicator can be used as meaningful reference material.
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OBV는 거래량의 변화를 측정하여 주가 움직임을 예측하는 선행 지표로 활용됩니다.
거래량의 누적값을 반영하여
- 가격이 상승할 때 거래량이 증가면 OBV가 상승하고,
- 가격이 하락할 때 거래량이 줄면 OBV가 하락하게 됩니다.
따라서, 가격의 움직임과 함께 OBV 지표의 움직임을 확인하여야 하고 거래량이 적은 코인(토큰)에서는 신뢰성이 떨어지는 단점도 가지고 있습니다.
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(첫번째 해석 방법)
OBV 지표에 대한 Signal선을 추가하여
- OBV 지표가 Signal선 이상에서 이동하게 되면 상승세를 보일 가능성이 높고,
- OBV 지표가 Signal선 이항에서 이동하게 되면 하락세를 보일 가능성이 높습니다.
이러한 해석 방법은 Signal선을 기준으로 OBV 지표가 반복적으로 위아래로 움직임을 보이는 경우가 많기 때문에 실제 거래 전략에 활용되기가 어려운 면이 있습니다.
따라서, 이러한 해설 방법은 차트 분석을 할 때 참고 자료로 활용하는 것이 좋습니다.
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(두번째 해석 방법)
OBV 지표의 고점과 저점을 기준하여 지지와 저항선을 그려
- OBV 지표가 이전 고점을 상향 돌파하면 상승세를 보일 가능성이 높고,
- OBV 지표가 이전 저점을 하향 돌파하면 하락세를 보일 가능성이 높습니다.
이러한 해석 방법은 첫번째 해석 방법보다 좀 더 신뢰성이 있는 방법이지만, 고점과 저점을 기준으로 지지와 저항선을 나누어 생각해야 하는 단점이 있습니다.
-
이를 보완하고자 고점에 대한 High선과 저점에 대한 Low선을 추가하였습니다.
- OBV 지표가 각 선(Low, HL2, High)을 상향 돌파하는 모습을 보이면 가격이 상승할 가능성이 높고,
- OBV 지표가 각 선(Low, HL2, High)을 하향 돌파하는 모습을 보이면 가격이 하락할 가능성이 높습니다.
-
또한, Low선과 High선을 볼린저밴드와 같이 해석할 수 있습니다.
즉, Low선과 High선이 수축하는 모습을 보이면 가격은 횡보할 가능성이 높고, 확장하는 모습을 보이면 가격은 추세를 나타낼 가능성이 높습니다.
따라서, 수축한 상태에서
- High선을 상향 돌파하게 되면 상승세를 나타낼 가능성이 높고,
- Low선을 하향 돌파하게 되면 하락세를 나타낼 가능성이 높습니다.
확장된 상태에서는 신규 거래를 진행하기 보다 수익 실현할 시점을 찾는데 집중해야 합니다.
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실제 거래량의 변화를 해석하여 거래 전략을 만드는데 활용하기가 쉽지 않습니다.
특히, 하나의 코인(토큰)에 대해서 여러 개의 거래소가 연동되어 움직임을 나타내는 코인 시장에서는 더욱 어려움이 있습니다.
따라서, 코인 시장은 추세 추종으로 아예 거래량을 참고하지 않고 거래를 진행하는 방법이 활성화되어 있기도 합니다.
하지만, 거래량의 변화를 해석하여 거래 시점을 찾는데 활용한다면 보다 정확한 거래 시점을 찾는데 도움을 받을 수 있다고 생각합니다.
그러한 의미에서 OBV 지표의 High선과 Low선을 추가한 지표가 의미 있는 참고 자료로 활용될 수 있다고 생각합니다.
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Keltner Channel StrategyOverview
The Keltner Channel Strategy is a powerful trend-following and mean-reversion system that leverages the Keltner Channels, EMA crossovers, and ATR-based stop-losses to optimize trade entries and exits. This strategy has proven to be highly effective, particularly when applied to Gold (XAUUSD) and other commodities with strong trend characteristics.
📈 How It Works
This strategy incorporates two trading approaches: 1️⃣ Keltner Channel Reversal Trades – Identifies overbought and oversold conditions when price touches the outer bands.
2️⃣ Trend Following Trades – Uses the 9 EMA & 21 EMA crossover, with confirmation from the 50 EMA, to enter trades in the direction of the trend.
🔍 Entry & Exit Criteria
📊 Keltner Channel Entries (Reversal Strategy)
✅ Long Entry: When the price crosses below the lower Keltner Band (potential reversal).
✅ Short Entry: When the price crosses above the upper Keltner Band (potential reversal).
⏳ Exit Conditions:
Long positions close when price crosses back above the mid-band (EMA-based).
Short positions close when price crosses back below the mid-band (EMA-based).
📈 Trend Following Entries (Momentum Strategy)
✅ Long Entry: When the 9 EMA crosses above the 21 EMA, and price is above the 50 EMA (bullish momentum).
✅ Short Entry: When the 9 EMA crosses below the 21 EMA, and price is below the 50 EMA (bearish momentum).
⏳ Exit Conditions:
Long positions close when the 9 EMA crosses back below the 21 EMA.
Short positions close when the 9 EMA crosses back above the 21 EMA.
📌 Risk Management & Profit Targeting
ATR-based Stop-Losses:
Long trades: Stop set at 1.5x ATR below entry price.
Short trades: Stop set at 1.5x ATR above entry price.
Take-Profit Levels:
Long trades: Profit target 2x ATR above entry price.
Short trades: Profit target 2x ATR below entry price.
🚀 Why Use This Strategy?
✅ Works exceptionally well on Gold (XAUUSD) due to high volatility.
✅ Combines reversal & trend strategies for improved adaptability.
✅ Uses ATR-based risk management for dynamic position sizing.
✅ Fully automated alerts for trade entries and exits.
🔔 Alerts
This script includes automated TradingView alerts for:
🔹 Keltner Band touches (Reversal signals).
🔹 EMA crossovers (Momentum trades).
🔹 Stop-loss & Take-profit activations.
📊 Ideal Markets & Timeframes
Best for: Gold (XAUUSD), NASDAQ (NQ), Crude Oil (CL), and trending assets.
Recommended Timeframes: 15m, 1H, 4H, Daily.
⚡️ How to Use
1️⃣ Add this script to your TradingView chart.
2️⃣ Select a 15m, 1H, or 4H timeframe for optimal results.
3️⃣ Enable alerts to receive trade notifications in real time.
4️⃣ Backtest and tweak ATR settings to fit your trading style.
🚀 Optimize your Gold trading with this Keltner Channel Strategy! Let me know how it performs for you. 💰📊
FVG LevelsFVG Levels Indicator Description
The FVG Levels indicator dynamically identifies and displays key price zones that may represent fair value gaps and order block areas, helping traders to visually pinpoint potential support and resistance levels directly on the chart.
Key Features
Order Block Identification:
The indicator detects bullish and bearish order blocks by analyzing specific candle patterns. For bullish zones, it checks if a candle two bars ago was bullish (close greater than open) coupled with a subsequent gap condition. Similarly, bearish zones are identified when bearish candle conditions are met with an appropriate gap.
Dynamic Zone Calculation:
It computes critical levels such as the highest highs, lowest lows, highest lows, and lowest highs over a series of recent bars. These levels define the boundaries of potential buy and sell zones and adjust dynamically as new price data comes in.
Visual Representation:
Buy zones are plotted in lime and sell zones in yellow, with the indicator filling the areas between the high and low lines to create clear, shaded bands. This visual aid helps in quickly recognizing zones of potential price reaction.
Chart Overlay:
Designed to work as an overlay, the indicator integrates directly onto your price chart, allowing for seamless correlation between price action and identified zones.
How It Works
Bullish Zones:
When a bullish candle (with the candle's close above its open) is detected along with a significant gap, the indicator marks the upper and lower boundaries of the bullish order block. It further refines these levels by tracking the lowest low and highest high over recent bars to enhance the zone's definition.
Bearish Zones:
In a similar manner, the indicator calculates bearish order blocks by confirming bearish candle conditions and corresponding gap criteria. It then updates the bearish zone levels and computes the highest high and lowest low to establish clear sell zone boundaries.
Usage
Traders can use the FVG Levels indicator to:
Identify potential entry and exit points by observing where price may reverse or consolidate.
Recognize fair value gaps or imbalances that often act as magnet points for price action.
Enhance risk management by using the dynamically calculated zones to set stop-losses or take-profits.
Adaptive Linear Regression ChannelOverview
The Adaptive Linear Regression Channel Script is an advanced, multi-functional trading tool crafted to help traders pinpoint market trends, identify potential reversals, assess volatility, and establish dynamic levels for profit-taking and position exits. By incorporating key concepts such as linear regression , standard deviation , and other volatility measures like the ATR , the script offers a comprehensive view of market behavior beyond traditional deviation metrics.
This dynamic model continuously adapts to changing market conditions, adjusting in real-time to provide clear visualizations of trends, channels, and volatility levels. This adaptability makes the script invaluable for both trend-following and counter-trend strategies, giving traders the flexibility to respond effectively to different market environments.
Background
What is Linear Regression?
Definition : Linear regression is a statistical technique used to model the relationship between a dependent variable (target) and one or more independent variables (predictors).
In its simplest form (simple linear regression), the relationship between two variables is represented by a straight line (the regression line).
y = mx + b
where :
- y is the target variable (price)
- m is the slope
- x is the independent variable (time)
- b is the intercept
Slope of the Regression Line
Definition: The slope (m) measures the rate at which the dependent variable (y) changes as the independent variable (x) changes.
Interpretation:
- A positive slope indicates an uptrend.
- A negative slope indicates a downtrend.
Uses in Trading:
- Identifying the strength and direction of market trends.
- Assessing the momentum of price movements.
R-squared (Coefficient of Determination)
Definition: A measure of how well the regression line fits the data, ranging from 0 to 1.
Calculation :
R2 = 1− (SS tot/SS res)
where:
- SSres is the sum of squared residuals.
- SStot is the total sum of squares.
Interpretation:
- Higher R2 indicates a better fit, meaning the model explains a larger proportion of the variance in the data.
Uses in Trading:
- Higher R-squared values give traders confidence in trend-based signals.
- Low R-squared values may suggest that the market is more random or volatile.
Standard Deviation
Definition: Standard Deviation quantifies the dispersion of data points in a dataset relative to the mean. A low standard deviation indicates that data points tend to be close to the mean, while a high standard deviation indicates that the data points are spread out over a larger range of values.
Calculation
σ=√∑(xi−μ)2/N
Where
- σ is the standard deviation.
- ∑ is the summation symbol, indicating that the expression that follows should be summed over all data points.
- xi, this represents the i-th data point in the dataset.
- μ\mu, this represents the mean(average) of all the data points in the dataset.
- (xi−μ)2, this is the squared difference between each data point and the mean.
- N is the total number of data points in the dataset.
- **Interpretation**
- A higher standard deviation indicates greater volatility.
- Useful for identifying overbought/oversold conditions in markets.
Key Features
Dynamic Linear Regression Channels:
The script automatically generates adaptive regression channels that expand or contract based on the current market volatility. This real-time adjustment ensures that traders are always working with the most relevant data, making it easier to spot key support and resistance levels.
The channel width itself serves as an indicator of market volatility, expanding during periods of heightened uncertainty and contracting during more stable phases. Additionally, the channel width is trained on previous channel widths , allowing the script to adapt and provide a more accurate view of volatility trends of the asset. Traders can also customize the script to train on less historical data , enabling a more recent view of volatility , which is particularly useful in fast-moving or changing markets.
Dynamic Profits and Stops:
What is it?
Dynamic profit levels allow traders to adjust take-profit targets based on real-time market conditions. Unlike static levels, which remain fixed regardless of market changes, these adaptive levels leverage past volatility data to create more flexible profit-taking strategies.
How does it work?
The script determines these levels using previously stored deviation values. These deviations are categorized into quantiles (like Q1, Q2, Q3, etc.) to classify current market conditions. As new deviation data is recorded, the profit levels are adjusted dynamically to reflect changes in market volatility. This approach helps to refine profit targets, especially when using regression channels with standard deviation rather than traditional ATR bands.
Why is it valuable?
By utilizing adaptive profit levels, traders can optimize their exits based on the current volatility landscape. For instance, when volatility increases, the dynamic levels expand, allowing trades to capture larger price movements. Conversely, during low volatility, profit targets tighten to lock in gains sooner, reducing exposure to market reversals. This flexibility is especially beneficial when combined with adaptive regression channels that respond to changes in standard deviation.
Slope-Based Trend Analysis:
One of the core elements of this script is the slope of the regression line , which helps define the direction and strength of the trend. Positive slopes indicate bullish momentum, while negative slopes suggest bearish conditions. The slope's steepness gives traders insight into the market's momentum, allowing them to adjust their strategies based on the strength of the trend.
Additionally, the script uses the slope to create a color gradient , which visually represents the intensity of the market's momentum. The gradient peaks at one color to show the maximum bullish momentum experienced in the past, while another color represents the maximum bearish momentum experienced in the past. This color-coded visualization makes it easier for traders to quickly assess the market's strength and direction at a glance.
Volatility Heatmap:
The integrated heatmap provides an intuitive, color-coded visualization of market volatility. The heatmap highlights areas where price action is expanding or contracting, giving traders a clear view of where volatility is rising or falling. By mapping out deviations from the regression line, the heatmap makes it easier to spot periods of high volatility that could lead to major market moves or potential reversals.
Deviation Concepts:
The script tracks price deviations from the regression line when a new range is formed, providing valuable insights when the price significantly deviates from the expected trend. These deviations are key in identifying potential breakout points or trend shifts .
This helps traders understand when the market is overextended or when a pullback may be imminent, allowing them to make more informed trading decisions.
Adaptive Model Properties:
Unlike static indicators, this script adapts over time . As the market changes, it stores historical data related to channel widths , slope dynamics , and volatility levels , adjusting its analysis accordingly to stay relevant to current market conditions.
Traders have the ability to train the model on all available data or specify a set number of bars to focus on more recent market activity. This flexibility allows for more tailored analysis , ensuring that traders can work with data that best fits their trading style and time horizon.
This continuous learning approach ensures that traders always have the most up-to-date insight into the market's structure.
Table
The table displays key metrics in real time to provide deeper insights into market behavior:
1. Deviation & Slope : Shows the current deviation if set to standard deviation or atr if set to atr(values used to calculated the channel widths) and the trend slope, helping to gauge market volatility and trend direction.
2. Rate of Change : For both deviation/atr and slope, the table also calculates the rate of change of their rates—essentially capturing the acceleration or deceleration of trends and volatility. This helps identify shifts in market momentum early.
3. R-squared : Indicates the strength and reliability of the trend fit. A higher value means the regression line better explains the price movements.
4. Quantiles : Uses historical deviation data to categorize current market conditions into quartiles (e.g., Q1, Q2, Q3). This helps classify the market's current volatility level, allowing traders to adjust strategies dynamically.
By combining these metrics, the table offers a comprehensive, real-time snapshot of market conditions, enabling more informed and adaptive trading decisions.
Settings
Here’s a breakdown of the script's settings for easy reference:
Linear Regression Settings
Show Dynamic Levels :Toggle to display dynamic profit levels on the chart.
Deviation Type :Select the method for calculating deviation—options include ATR (Average True Range) or Standard Deviation.
Timeframe :Sets the specific timeframe for the regression analysis (default is the chart’s timeframe).
Period :Defines the number of bars used for calculating the regression line (e.g., 50 bars).
Deviation Multiplier :Multiplier used to adjust the width of the deviation channel around the regression line.
Rate of Change :Sets the period for calculating the rate of change of the slope (used for momentum analysis).
Max Bars Back :Limits the number of historical bars to analyze (0 means all available data).
Slope Lookback :Number of bars used to calculate the slope gradient for trend detection.
Slope Gradient Display :Toggle to enable gradient coloring based on slope direction.
Slope Gradient Colors :Set colors for positive and negative slopes, respectively.
Slope Fill :Adjusts the transparency of the slope gradient fill.
Volatility Gradient Display :Toggle to enable gradient coloring based on volatility levels.
Volatility Gradient Colors :Set colors for low and high volatility, respectively.
Volatility Fill :Adjusts the transparency of the volatility gradient fill.
Table Settings
Show Table :Toggle to display the metrics table on the chart.
Table Position :Choose where to position the table (e.g., top-right, middle-center, etc.).
Font Size :Set the size of the text in the table. Options include Tiny, Small, Normal, Large, and Huge.
Original Keltner with Support And ResistanceThis indicator is based on the original Keltner Channels using typical price and calculating the 10 period average of high - low
Typical price = (high + low + close)/3
In this case, I've taken Typical price as (open + high + low + close)/4 on the advice of John Bollinger from his book Bollinger on Bollinger Bands.
Buy Line = 10 Period Typical Price Average + 10 Period Average of (High - Low)
Sell Line = 10 Period Typical Price Average - 10 Period Average of (High - Low)
This is the basis for the indicator. I've added the highest of the Buy Line and lowest of the Sell Line for the same period which acts as Support and Resistance.
If price is trending below the Lowest of Sell Line, take only sell trades and the Lowest Line acts as resistance.
If price is trending above the Highest of Buy Line, take only buy trades and the Highest Line acts as support.
Bullish Candlestick Patterns With Filters [TradeDots]The "Bullish Candlestick Patterns With Filters" is a trading indicator that identifies 6 core bullish candlestick patterns. This is further enhanced by applying channel indicator as filters, designed to further increase the accuracy of the recognized patterns.
6 CANDLESTICK PATTERNS
Hammer
Inverted Hammer
Bullish Engulfing
The Piercing Line
The Morning Star
The 3 White Soldiers
SIGNAL FILTERING
The indicator incorporates with 2 primary methodologies aimed at filtering out lower accuracy signals.
Firstly, it comes with a "Lowest period" parameter that examines whether the trough of the bullish candlestick configuration signifies the lowest point within a specified retrospective bar length. The longer the period, the higher the probability that the price will rebound.
Secondly, the channel indicators, the Keltner Channels or Bollinger Bands. This indicator examines whether the lowest point of the bullish candlestick pattern breaches the lower band, indicating an oversold signal. Users have the flexibility to modify the length and band multiplier, enabling them to custom-tune signal sensitivity.
Without Filtering:
With Filtering
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Mean and Standard Deviation Lines Description:
Calculates the mean and standard deviation of close-to-close price differences over a specified period, providing insights into price volatility and potential breakouts.
Manually calculates mean and standard deviation for a deeper understanding of statistical concepts.
Plots the mean line, upper bound (mean + standard deviation), and lower bound (mean - standard deviation) to visualize price behavior relative to these levels.
Highlights bars that cross the upper or lower bounds with green (above) or red (below) triangles for easy identification of potential breakouts or breakdowns.
Customizable period input allows for analysis of short-term or long-term volatility patterns.
Probability Interpretations based on Standard Deviation:
50% probability: mean or expected value
68% probability: Values within 1 standard deviation of the mean (mean ± stdev) represent roughly 68% of the data in a normal distribution. This implies that around 68% of closing prices in the past period fell within this range.
95% probability: Expanding to 2 standard deviations (mean ± 2*stdev) captures approximately 95% of the data. So, in theory, there's a 95% chance that future closing prices will fall within this wider range.
99.7% probability: Going further to 3 standard deviations (mean ± 3*stdev) encompasses nearly 99.7% of the data. However, these extreme values become less likely as you move further away from the mean.
Key Features:
Uses manual calculations for mean and standard deviation, providing a hands-on approach.
Excludes the current bar's close price from calculations for more accurate analysis of past data.
Ensures valid index usage for robust calculation logic.
Employs unbiased standard deviation calculation for better statistical validity.
Offers clear visual representation of mean and volatility bands.
Considerations:
Manual calculations might have a slight performance impact compared to built-in functions.
Not a perfect normal distribution: Financial markets often deviate from a perfect normal distribution. This means probability interpretations based on standard deviation shouldn't be taken as absolute truths.
Non-stationarity: Market conditions and price behavior can change over time, impacting the validity of past data as a future predictor.
Other factors: Many other factors influence price movements beyond just the mean and standard deviation.
Always consider other technical and fundamental factors when making trading decisions.
Potential Use Cases:
Identifying periods of high or low volatility.
Discovering potential breakout or breakdown opportunities.
Comparing volatility across different timeframes.
Complementing other technical indicators for confirmation.
Understanding statistical concepts for financial analysis.
McGinley Dynamic x Donchian ChannelsThis indicator combines the McGinley Dynamic and Donchian Channels by taking the lowest and highest values over a set length (defaulted to 14) then applying the McGinley Dynamic math to these values. The upper range is denoted by a green line while the lower range is denoted by a red line. Additionally, standard deviations of 1, 2, and 3 have been put in place using the upper and lower values as the basis for the deviations as opposed to the baseline average of the upper and lower bands. These deviations are plotted as lime and orange colors. These channels can be used to determine when the price is gaining or losing momentum based on the distance between the channels. Otherwise, the channels can be used to determine potential overbought and oversold levels.
Bollinger Pair TradeNYSE:MA-1.6*NYSE:V
Revision: 1
Author: @ozdemirtrading
Revision 2 Considerations :
- Simplify and clean up plotting
Disclaimer: This strategy is currently working on the 5M chart. Change the length input to accommodate your needs.
For the backtesting of more than 3 months, you may need to upgrade your membership.
Description:
The general idea of the strategy is very straightforward: it takes positions according to the lower and upper Bollinger bands.
But I am mainly using this strategy for pair trading stocks. Do not forget that you will get better results if you trade with cointegrated pairs.
Bollinger band: Moving average & standard deviation are calculated based on 20 bars on the 1H chart (approx 240 bars on a 5m chart). X-day moving averages (20 days as default) are also used in the background in some of the exit strategy choices.
You can define position entry levels as the multipliers of standard deviation (for exp: mult2 as 2 * standard deviation).
There are 4 choices for the exit strategy:
SMA: Exit when touches simple moving average (SMA)
SKP: Skip SMA and do not stop if moving towards 20D SMA, and exit if it touches the other side of the band
SKPXDSMA: Skip SMA if moving towards 20D SMA, and exit if it touches 20D SMA
NoExit: Exit if it touches the upper & lower band only.
Options:
- Strategy hard stop: if trade loss reaches a point defined as a percent of the initial capital. Stop taking new positions. (not recommended for pair trade)
- Loss per trade: close position if the loss is at a defined level but keeps watching for new positions.
- Enable expected profit for trade (expected profit is calculated as the distance to SMA) (recommended for pair trade)
- Enable VIX threshold for the following options: (recommended for volatile periods)
- Stop trading if VIX for the previous day closes above the threshold
- Reverse active trade direction if VIX for the previous day is above the threshold
- Take reverse positions (assuming the Bollinger band is going to expand) for all trades
Backtesting:
Close positions after a defined interval: mark this if you want the close the final trade for backtesting purposes. Unmark it to get live signals.
Use custom interval: Backtest specific time periods.
Other Options:
- Use EMA: use an exponential moving average for the calculations instead of simple moving average
- Not against XDSMA: do not take a position against 20D SMA (if X is selected as 20) (recommended for pairs with a clear trend)
- Not in XDSMA 1 DEV: do not take a position in 20D SMA 1*standart deviation band (recommended if you need to decrease # of trades and increase profit for trade)
- Not in XDSMA 2 DEV: do not take a position in 20D SMA 2*standart deviation band
Session management:
- Not in session: Session start and end times can be defined here. If you do not want to trade in certain time intervals, mark that session.(helps to reduce slippage and get more realistic backtest results)
baguette by multigrainRationale
The rationale behind this indicator is that: when the price of an asset reaches an extreme, regardless of the trend, there is a (maybe not equal but) opposite reaction.
Settings
The default settings will not be the best for whatever timeframe you choose. I personally believe a longer than 'normal' JMA Length is best.
JMA Source: The source in which the Jurik Moving Average calculations are based off of.
JMA Length: Controls the length of the Jurik Moving Average.
JMA Phase: A lag controller of sorts. Increasing the phase increases overshoots but reduces lag, decreasing the phase decreases overshoots but increases lag.
ATR Length: The length in which an average true range value will be calculated with.
ATR Multiplier: This multiplier controls the 'width' of our envelope or our extreme bands.
Credits
@gorx1 for the improved and more accurate (?) Jurik Moving Average calculations.
@redktrader for the ATR envelope calculations.
ka66: Percent Stop ChannelOften used as a dynamic stop loss management tool, this indicator:
Takes a source series as input, e.g. a moving average, or close prices.
Draws configurable channels, some percentage above and below the source series (e.g. for long vs. short stop losses)
Since long vs. short trade profiles can be different, differing percentage inputs are allowed for the bands.
While in forex or futures we tend to use ATRs (see my other script: ATR Stop Channels), in stocks, a percentage is more the norm, it's still as dynamic as the source series, being a function of it, and may at times be simpler to reason about in terms of money.
An idea might be to set your stop loss at the point of entry where the band currently is (assuming you have observed and set a reasonable percentage).
Hull Keltner ChannelThis script is a Keltner Channel that uses a Hull Moving Average as source, instead of the 20-period EMA.
A hull band improves on lag and smoothness to Simple and Exponential Moving Averages.
And ATR based envelop is generated from this improved MA to form the Keltner Channel.
Hull on EHMA source with 180 periods loopback, coupled with a 200 period loopback for the Keltner Channel and 2 and 6 standard deviations, are my fav settings on Bitcoin, but feel free to try new settings.
Use it as you would use a normal Keltner Channel or Bollinger Bands.
SnakeBand█ Overview.
This indicator is based on a calculation method made using a ichimoku and Fibonacci.
There are two lines, the upper line is the upper limit and the lower line is the lower limit.
These upper and lower limits are drawn ahead of 26 candles, just like Ichimoku.
█ Role.
The characteristic of this indicator is that
When prices reach the upper limit, they usually hesitate or try to fall, and when they reach the lower limit, they usually rebound or hesitate.
In particular, it has an excellent effect on low-point purchases.
Of course, it is often not the case, so you have to observe the speed and movement of the decline carefully, and it can be more effective if applied with the Elliot wave or harmonic.
It can also be more effective if used with rsi or macd bowling bands.
█ Memo.
It applies to all four-hour bong, three-hour bong, one-bong, and main bong.
It is important to keep studying and observing. This can give you the ability to capture the upward transition after hitting the lower limit.
