Advanced MA and MACD PercentageIntroduction
The "Advanced MA and MACD Percentage" indicator is a powerful and innovative tool designed to help traders analyze financial markets with ease and precision. This indicator combines Moving Averages (MA) with the MACD indicator to assess the market’s overall trend and calculate the percentage of buy and sell signals based on current data.
Features
Multi-Timeframe Analysis:
Allows selecting your preferred timeframe for trend analysis, such as minute, hourly, daily, or weekly charts.
Support for Multiple Moving Average Types:
Offers the option to use either Simple Moving Average (SMA) or Exponential Moving Average (EMA), based on user preference.
Comprehensive MACD Analysis:
Analyzes the relationship between multiple moving averages (e.g., 20/50, 50/100) using MACD to provide deeper insights into market dynamics.
Calculation of Buy and Sell Percentages:
Computes the percentage of indicators signaling buy or sell conditions, providing a clear summary to assist trading decisions.
Intuitive Visual Interface:
Displays buy and sell percentages as two visible lines (green and red) on the chart.
Includes reference lines to clarify the range of percentages (100% to 0%).
How It Works
Moving Averages Calculation:
Calculates moving averages (20, 50, 100, 150, and 200) for the selected timeframe.
MACD Pair Analysis:
Computes the MACD to compare the performance between various moving average pairs, such as (20/50) and (50/100).
Identifying Buy and Sell Signals:
Counts the number of indicators signaling buy (price above MAs or positive MACD histogram).
Converts the count into percentages for both buy and sell signals.
Visual Representation:
Plots buy and sell percentages as clear lines (green for buy, red for sell).
Adds reference lines (100% and 0%) for easier interpretation.
How to Use the Indicator?
Settings:
Choose the type of moving average (SMA or EMA).
Select the timeframe that suits your strategy (e.g., 15 minutes, 1 hour, or daily).
Reading the Results:
If the buy percentage (green line) is above 50%, the overall trend is bullish (buy).
If the sell percentage (red line) is above 50%, the overall trend is bearish (sell).
Integrating Into Your Strategy:
Combine it with other indicators to confirm entry and exit signals.
Use it to quickly understand the market’s overall trend without needing complex manual analysis.
Benefits of the Indicator
Simplified Analysis: Provides a straightforward summary of the market's overall trend.
Adaptable to All Timeframes: Works perfectly on all timeframes.
Customizable: Allows users to adjust settings according to their needs.
Important Notes
This indicator does not provide direct buy or sell signals. Instead, it offers a summary of the market’s condition based on a combination of indicators.
It is recommended to use it alongside other technical analysis tools for precise trading signals.
Conclusion
The "Advanced MA and MACD Percentage" indicator is an ideal tool for traders who want to analyze the market using a combination of Moving Averages and MACD. It gives you a comprehensive overview of the overall trend, helping you make informed and quick trading decisions. Try it now and see the difference!
Trend Analysis
Alternative Price [OmegaTools]The Alternative Price script is a sophisticated and flexible indicator designed to redefine how traders visualize and interpret price data. By offering multiple unique charting modes, robust customization options, and advanced features, this tool provides a comprehensive alternative to traditional price charts. It is particularly useful for identifying market trends, detecting patterns, and simplifying complex data into actionable insights.
This script is highly versatile, allowing users to choose from five distinct charting modes: Candles, Line, Channel, Renko, and Bubbles. Each mode serves a unique purpose and presents price information in an innovative way. When using this script, it is strongly recommended to hide the platform’s default price candles or chart data. Doing so will eliminate redundancy and provide a clearer and more focused view of the alternative price visualization.
The Candles mode offers a traditional candlestick charting style but with added flexibility. Users can choose to enable smoothed opens or smoothed closes, which adjust the way the open and close prices are calculated. When smoothed opens are enabled, the opening price is computed as the average of the actual open price and the closing prices of the previous two bars. This creates a more gradual representation of price transitions, particularly useful in markets prone to sudden spikes or irregularities. Similarly, smoothed closes modify the closing price by averaging it with the previous close, the high-low midpoint, and an exponential moving average of the high-low-close mean. This technique filters out noise, making trends and price momentum easier to identify.
In the Line mode, the script displays a simple line chart that connects the smoothed closing prices. This mode is ideal for traders who prefer minimalism or need to focus on the overall trend without the distraction of individual bar details. The Channel mode builds upon this by plotting additional lines representing the highs and lows of each bar. The resulting visualization resembles a price corridor that helps identify support and resistance zones or price compression areas.
The Renko mode introduces a more advanced and noise-filtering method of visualizing price movements. Renko charts, constructed using the ATR (Average True Range) as a baseline, display blocks that represent a specific price range. The script dynamically calculates the size of these blocks based on ATR, with separate thresholds for upward and downward movements. This makes Renko mode particularly effective for identifying sustained trends while ignoring minor price fluctuations. Additionally, the open and close values of Renko blocks can be smoothed to further refine the visualization.
The Bubbles mode represents price activity using circles or bubbles whose size corresponds to relative volume. This mode provides a quick and intuitive way to assess market participation at different price levels. Larger bubbles indicate higher trading volumes, while smaller bubbles highlight periods of lower activity. This visualization is particularly valuable in understanding the relationship between price movements and market liquidity.
The coloring of candles and other chart elements is a core feature of this script. Users can select between two color modes: Normal and Volume. In Normal mode, bullish candles are displayed in the user-defined bullish color, while bearish candles use the bearish color. Neutral elements, such as midpoints or undecided price movements, are shaded with a neutral color. In Volume mode, the candle colors are dynamically adjusted based on trading volume. A gradient color scale is applied, where the intensity of the bullish or bearish colors reflects the volume for that particular bar. This feature allows traders to visually identify periods of heightened activity and associate them with specific price movements.
Engulfing patterns, a popular technical analysis tool, are automatically detected and marked on the chart when the corresponding setting is enabled. The script identifies long engulfing patterns, where the current bar's range completely encompasses the previous bar’s range and indicates a potential bullish reversal. Similarly, short engulfing patterns are identified where the current bar fully engulfs the previous bar in the opposite direction, suggesting a bearish reversal. These patterns are visually highlighted with circular markers to draw the trader’s attention.
Each feature and mode is highly customizable. The colors for bullish, bearish, and neutral movements can be personalized, and the thresholds for patterns or smoothing can be fine-tuned to match specific trading strategies. The script's ability to toggle between various modes makes it adaptable to different market conditions and analysis preferences.
In summary, the Alternative Price script is a comprehensive tool that redefines the way traders view price charts. By offering multiple visualization modes, customizable features, and advanced detection algorithms, it provides a powerful way to uncover market trends, volume relationships, and significant patterns. The recommendation to hide default chart elements ensures that the focus remains on this innovative tool, enhancing its usability and clarity. This script empowers traders to gain deeper insights into market behavior and make informed trading decisions, all while maintaining a clean and visually appealing chart layout.
Keep in mind that some of the modes of this indicator might not reflect the actual closing price of the underlying asset, before opening a trade, check carefully the actual price!
DonAlt - Smart Money Toolkit [BigBeluga]DonAlt - Smart Money Toolkit is inspired by the analytical insights of popular crypto influencer DonAlt.
This advanced toolkit integrates smart money concepts with key technical analysis elements to enhance your trading decisions.
🔵 KEY FEATURES:
SUPPORT AND RESISTANCE LEVELS Automatically identifies critical market turning points with significant volume. Levels turn green when the price is above them and red when below, providing a visual cue for key market thresholds.
ORDER BLOCKS: Highlights significant price zones preceding major price movements.
- If the move is down , it searches for the last bullish candle and plots a block from its body.
- If the move is up , it searches for the last bearish candle and creates a block from its body.
These blocks help identify areas of institutional interest and potential reversals.
TRENDLINES: Automatically plots trendlines to identify breakout zones or price accumulation areas.
• Bullish trendlines accumulation form when the current low is higher than the previous low.
• Bearish trendlines accumulation emerge when the current high is lower than the previous high.
• Bullish trendlines Breakout form when the price break above it.
• Bearish trendlines Breakout form when the price break below it.
Volatility Integration: The levels incorporate normalized volatility to ensure only significant zones are highlighted, filtering noise and emphasizing meaningful data.
🔵 WHEN TO USE:
This toolkit is ideal for traders seeking to align with "smart money" strategies by identifying key areas of institutional activity, strong support and resistance zones, and potential breakout setups.
🔵 CUSTOMIZATION:
Toggle the visibility of levels, order blocks, or trendlines to match your trading style and focus.
Colors of the Bull and Bear key features
Extend trendline
RSI to Price RatioThe RSI to Price Ratio is a technical indicator designed to provide traders with a unique perspective by analyzing the relationship between the Relative Strength Index (RSI) and the underlying asset's price. Unlike traditional RSI, which is viewed on a scale from 0 to 100, this indicator normalizes the RSI by dividing it by the price, resulting in a dynamic ratio that adjusts to price movements. The histogram format makes it easy to visualize fluctuations, with distinct color coding for overbought (red), oversold (green), and neutral (blue) conditions.
This indicator excels in helping traders identify potential reversal zones and trend continuation signals. Overbought and oversold levels are dynamically adjusted using the price source, making the indicator more adaptive to market conditions. Additionally, the ability to plot these OB/OS thresholds as lines on the histogram ensures traders can quickly assess whether the market is overstretched in either direction. By combining RSI’s momentum analysis with price normalization, this tool is particularly suited for traders who value precision and nuanced insights into market behavior. It can be used as a standalone indicator or in conjunction with other tools to refine entry and exit strategies.
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
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Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
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I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
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"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
Buy Low Sell High Composite Upgraded V6 [kristian6ncqq]NOTICE: This script is an upgraded and enhanced version of the original "Buy Low Sell High Composite" indicator by (published in 2017).
The original script provided a composite indicator combining multiple technical analysis metrics such as RSI, MACD, and MFI.
Why I Republished This Script
I found the original indicator to be exceptionally useful for identifying optimal accumulation zones for stocks or assets when prices are low (red area) and potential profit-taking zones when prices are high (green area).
To ensure it remains accessible and functional for modern trading strategies, I have updated and enhanced the original version with additional features and flexibility.
Intended Use
This indicator is designed for traders and investors looking to:
Accumulate stocks or assets when the price is in the low (red) zone.
Take profits or reduce positions when the price is in the high (green) zone.
The composite score provides a clear visualization of multiple technical indicators combined into a single actionable signal.
Enhancements in This Version
Updated to Pine Script v6 (from version 3).
Added input parameters for key settings (e.g., RSI length, MACD parameters, smoothing).
Introduced Chande Momentum Oscillator (CMO) and directional ADX for improved trend detection.
Implemented slope-based trend coloring for outer edges to highlight significant changes in trend direction.
Enhanced visualizations with customizable thresholds and smoothing for improved usability.
Credits
Original script: "Buy Low Sell High Composite" by , 2017.
URL to the original script: Buy Low Sell High Composite.
This script is designed to build upon the strengths of the original while adding flexibility and new features to meet the needs of modern traders.
Donchian Trend Ribbon (Gradient)Donchian Trend Ribbon (Gradient) Indicator
The Donchian Trend Ribbon (Gradient) uses Donchian Channels to visualize trend direction, strength, and market phases. Columns with varying colors and intensity help traders quickly assess trends.
Key Components:
Green Columns (Bullish):
Appear when price is above the upper Donchian Channel boundary.
Bright green in the top zone (25-50): Strong bullish trend.
Darker green in the lower zone (0-25): Weak/moderate bullish trend.
A full-height bright green column indicates a very strong upward move.
Red Columns (Bearish):
Appear when price is below the lower Donchian Channel boundary.
Bright red in the top zone (25-50): Strong bearish trend.
Darker red in the lower zone (0-25): Weak/moderate bearish trend.
A full-height bright red column indicates a very strong downward move.
Black Columns (Neutral):
Indicate no trend or market consolidation.
Signal to wait for trend emergence.
Expanding Steps:
Steps expanding downward from the upper edge (50) suggest diminishing momentum.
Steps expanding upward from the lower edge (0) indicate growing trend strength.
Methods of Use:
Identify Trends: Green (buy) or red (sell) columns in the top zone (25-50) signal strong trends.
Assess Strength: Bright colors = strong trends, darker colors = weaker trends. Full-height bright columns indicate very strong moves.
Neutral Phases: Black columns suggest waiting for a trend.
Example Strategy:
Buy when green columns appear in the 25-50 range with bright intensity.
Sell when red columns appear in the 25-50 range with bright intensity.
Exit positions if columns turn black or darker-colored.
Historical High/Lows Statistical Analysis(More Timeframe interval options coming in the future)
Indicator Description
The Hourly and Weekly High/Low (H/L) Analysis indicator provides a powerful tool for tracking the most frequent high and low points during different periods, specifically on an hourly basis and a weekly basis, broken down by the days of the week (DOTW). This indicator is particularly useful for traders seeking to understand historical behavior and patterns of high/low occurrences across both hourly intervals and weekly days, helping them make more informed decisions based on historical data.
With its customizable options, this indicator is versatile and applicable to a variety of trading strategies, ranging from intraday to swing trading. It is designed to meet the needs of both novice and experienced traders.
Key Features
Hourly High/Low Analysis:
Tracks and displays the frequency of hourly high and low occurrences across a user-defined date range.
Enables traders to identify which hours of the day are historically more likely to set highs or lows, offering valuable insights into intraday price action.
Customizable options for:
Hourly session start and end times.
22-hour session support for futures traders.
Hourly label formatting (e.g., 12-hour or 24-hour format).
Table position, size, and design flexibility.
Weekly High/Low Analysis by Day of the Week (DOTW):
Captures weekly high and low occurrences for each day of the week.
Allows traders to evaluate which days are most likely to produce highs or lows during the week, providing insights into weekly price movement tendencies.
Displays the aggregated counts of highs and lows for each day in a clean, customizable table format.
Options for hiding specific days (e.g., weekends) and customizing table appearance.
User-Friendly Table Display:
Both hourly and weekly data are displayed in separate tables, ensuring clarity and non-interference.
Tables can be positioned on the chart according to user preferences and are designed to be visually appealing yet highly informative.
Customizable Date Range:
Users can specify a start and end date for the analysis, allowing them to focus on specific periods of interest.
Possible Uses
Intraday Traders (Hourly Analysis):
Analyze hourly price action to determine which hours are more likely to produce highs or lows.
Identify intraday trading opportunities during statistically significant time intervals.
Use hourly insights to time entries and exits more effectively.
Swing Traders (Weekly DOTW Analysis):
Evaluate weekly price patterns by identifying which days of the week are more likely to set highs or lows.
Plan trades around days that historically exhibit strong movements or price reversals.
Futures and Forex Traders:
Use the 22-hour session feature to exclude the CME break or other session-specific gaps from analysis.
Combine hourly and DOTW insights to optimize strategies for continuous markets.
Data-Driven Trading Strategies:
Use historical high/low data to test and refine trading strategies.
Quantify market tendencies and evaluate whether observed patterns align with your strategy's assumptions.
How the Indicator Works
Hourly H/L Analysis:
The indicator calculates the highest and lowest prices for each hour in the specified date range.
Each hourly high and low occurrence is recorded and aggregated into a table, with counts displayed for all 24 hours.
Users can toggle the visibility of empty cells (hours with no high/low occurrences) and adjust the table's design to suit their preferences.
Supports both 12-hour (AM/PM) and 24-hour formats.
Weekly H/L DOTW Analysis:
The indicator tracks the highest and lowest prices for each day of the week during the user-specified date range.
Highs and lows are identified for the entire week, and the specific days when they occur are recorded.
Counts for each day are aggregated and displayed in a table, with a "Totals" column summarizing the overall occurrences.
The analysis resets weekly, ensuring accurate tracking of high/low days.
Code Breakdown:
Data Aggregation:
The script uses arrays to store counts of high/low occurrences for both hourly and weekly intervals.
Daily data is fetched using the request.security() function, ensuring consistent results regardless of the chart's timeframe.
Weekly Reset Mechanism:
Weekly high/low values are reset at the start of a new week (Monday) to ensure accurate weekly tracking.
A processing flag ensures that weekly data is counted only once at the end of the week (Sunday).
Table Visualization:
Tables are created using the table.new() function, with customizable styles and positions.
Header rows, data rows, and totals are dynamically populated based on the aggregated data.
User Inputs:
Customization options include text colors, background colors, table positioning, label formatting, and date ranges.
Code Explanation
The script is structured into two main sections:
Hourly H/L Analysis:
This section captures and aggregates high/low occurrences for each hour of the day.
The logic is session-aware, allowing users to define custom session times (e.g., 22-hour futures sessions).
Data is displayed in a clean table format with hourly labels.
Weekly H/L DOTW Analysis:
This section tracks weekly highs and lows by day of the week.
Highs and lows are identified for each week, and counts are updated only once per week to prevent duplication.
A user-friendly table displays the counts for each day of the week, along with totals.
Both sections are completely independent of each other to avoid interference. This ensures that enabling or disabling one section does not impact the functionality of the other.
Customization Options
For Hourly Analysis:
Toggle hourly table visibility.
Choose session start and end times.
Select hourly label format (12-hour or 24-hour).
Customize table appearance (colors, position, text size).
For Weekly DOTW Analysis:
Toggle DOTW table visibility.
Choose which days to include (e.g., hide weekends).
Customize table appearance (colors, position, text size).
Select values format (percentages or occurrences).
Conclusion
The Hourly and Weekly H/L Analysis indicator is a versatile tool designed to empower traders with data-driven insights into intraday and weekly market tendencies. Its highly customizable design ensures compatibility with various trading styles and instruments, making it an essential addition to any trader's toolkit.
With its focus on accuracy, clarity, and customization, this indicator adheres to TradingView's guidelines, ensuring a robust and valuable user experience.
CauchyTrend [InvestorUnknown]The CauchyTrend is an experimental tool that leverages a Cauchy-weighted moving average combined with a modified Supertrend calculation. This unique approach provides traders with insight into trend direction, while also offering an optional ATR-based range analysis to understand how often the market closes within, above, or below a defined volatility band.
Core Concepts
Cauchy Distribution and Gamma Parameter
The Cauchy distribution is a probability distribution known for its heavy tails and lack of a defined mean or variance. It is characterized by two parameters: a location parameter (x0, often 0 in our usage) and a scale parameter (γ, "gamma").
Gamma (γ): Determines the "width" or scale of the distribution. Smaller gamma values produce a distribution more concentrated near the center, giving more weight to recent data points, while larger gamma values spread the weight more evenly across the sample.
In this indicator, gamma influences how much emphasis is placed on values closer to the current price versus those further away in time. This makes the resulting weighted average either more reactive or smoother, depending on gamma’s value.
// Cauchy PDF formula used for weighting:
// f(x; γ) = (1/(π*γ)) *
f_cauchyPDF(offset, gamma) =>
numerator = gamma * gamma
denominator = (offset * offset) + (gamma * gamma)
pdf = (1 / (math.pi * gamma)) * (numerator / denominator)
pdf
A chart showing different Cauchy PDFs with various gamma values, illustrating how gamma affects the weight distribution.
Cauchy-Weighted Moving Average (CWMA)
Using the Cauchy PDF, we calculate normalized weights to create a custom Weighted Moving Average. Each bar in the lookback period receives a weight according to the Cauchy PDF. The result is a Cauchy Weighted Average (cwm_avg) that differs from typical moving averages, potentially offering unique sensitivity to price movements.
// Summation of weighted prices using Cauchy distribution weights
cwm_avg = 0.0
for i = 0 to length - 1
w_norm = array.get(weights, i) / sum_w
cwm_avg += array.get(values, i) * w_norm
Supertrend with a Cauchy Twist
The indicator integrates a modified Supertrend calculation using the cwm_avg as its reference point. The Supertrend logic typically sets upper and lower bands based on volatility (ATR), and flips direction when price crosses these bands.
In this case, the Cauchy-based average replaces the usual baseline, aiming to capture trend direction via a different weighting mechanism.
When price closes above the upper band, the trend is considered bullish; closing below the lower band signals a bearish trend.
ATR Stats Range (Optional)
Beyond the fundamental trend detection, the indicator optionally computes ATR-based stats to understand price distribution relative to a volatility corridor centered on the cwm_avg line:
Volatility Range:
Defined as cwm_avg ± (ATR * atr_mult), this range creates upper and lower bands. Turning on atr_stats computes how often the daily close falls: Within the range, Above the upper ATR boundary, Below the lower ATR boundary, Within the range but above cwm_avg, Within the range but below cwm_avg
These statistics can help traders gauge how the market behaves relative to this volatility envelope and possibly identify if the market tends to revert to the mean or break out more often.
Backtesting and Performance Metrics
The code is integrated with a backtesting library that allows users to assess strategy performance historically:
Equity Curve Calculation: Compares CauchyTrend-based signals against the underlying asset.
Performance Metrics Table: Once enabled, displays key metrics such as mean returns, Sharpe Ratio, Sortino Ratio, and more, comparing the strategy to a simple Buy & Hold approach.
Alerts and Notifications
The indicator provides Alerts for key events:
Long Alert: Triggered when the trend flips bullish.
Short Alert: Triggered when the trend flips bearish.
Customization and Calibration
Important: The default parameters are not optimized for any specific instrument or time frame. Traders should:
Adjust the length and gamma parameters to influence how sharply or broadly the cwm_avg reacts to price changes.
Tune the atr_len and atr_mult for the Supertrend logic to better match the asset’s volatility characteristics.
Experiment with atr_stats on/off to see if that additional volatility distribution information provides helpful insights.
Traders may find certain sets of parameters that align better with their preferred trading style, risk tolerance, or asset volatility profile.
Disclaimer: This indicator is for educational and informational purposes only. Past performance in backtesting does not guarantee future results. Always perform due diligence, and consider consulting a qualified financial advisor before trading.
faiz MACDMACD: Moving Average Convergence Divergence
The Moving Average Convergence Divergence (MACD) is a popular momentum indicator used in technical analysis to gauge the strength, direction, and potential reversal points of a trend in a financial asset's price movement. Developed by Gerald Appel in the late 1970s, MACD is particularly favored by traders for its ability to capture both trend-following and momentum aspects of price behavior.
Components of the MACD
The MACD is derived from two exponential moving averages (EMAs) of a security's price:
MACD Line: This is the difference between the 12-day and 26-day EMAs. The shorter 12-day EMA reacts more quickly to price changes, while the 26-day EMA smooths out price fluctuations, offering a longer-term perspective.
Formula: MACD Line = 12-day EMA - 26-day EMA
Signal Line: This is the 1-day EMA of the MACD Line itself. The signal line is used to generate buy and sell signals when it crosses the MACD line.
Formula: Signal Line = 1-day EMA of the MACD Line
MACD Histogram: The histogram represents the difference between the MACD Line and the Signal Line. It is displayed as bars that oscillate above and below a zero line, helping to visualize the convergence or divergence between the two lines.
Formula: Histogram = MACD Line - Signal Line
Interpretation of MACD
The MACD indicator is used to identify potential buy and sell signals based on the following observations:
MACD Line and Signal Line Crossovers:
Bullish Crossover: A buy signal occurs when the MACD Line crosses above the Signal Line. This suggests that the momentum is shifting in favor of the bulls, indicating a potential upward price movement.
Bearish Crossover: A sell signal occurs when the MACD Line crosses below the Signal Line. This suggests a bearish trend may be emerging, signaling a potential downward movement.
Divergence:
Bullish Divergence: Occurs when the price of the asset is making new lows, but the MACD is forming higher lows. This suggests that the downward momentum is weakening and a potential reversal to the upside may be imminent.
Bearish Divergence: Occurs when the price is making new highs, but the MACD is forming lower highs. This suggests that the upward momentum is weakening and a reversal to the downside may occur.
Only use it in timeframe m1, and solely use for XAUUSD pair.
Advisable to use it as a confirmation with other indicator such as
BBMA, SMC, SUPPORT RESISTANCE, SUPPLY AND DEMAND.
how to use :
MA 5 Crossing above MA9, will generate BUY signals
MA 5 Crossing below MA9, will generate SELL signals
Trade at your own SKILLS.
I dont mind people using this script for free.
All I want is just prayer for me and my family success.
Thank You and Have a nice and pleasant day :-)
Ichimoku by FarmerBTCLegal Disclaimer
This strategy, "Ichimoku by FarmerBTC," is provided for educational and informational purposes only. It does not constitute financial advice and should not be relied upon as such. Trading and investing involve substantial risk, including the potential for losing more than your initial investment. Past performance is not indicative of future results. Always consult with a qualified financial advisor before making trading or investment decisions. The author of this strategy is not responsible for any financial losses incurred through its use.
Overview
The "Ichimoku by FarmerBTC" strategy is a trend-following system built on the Ichimoku Cloud indicator, enhanced with volume analysis and a high-timeframe Simple Moving Average (HTF SMA) condition. It is designed to identify long-only trade opportunities and performs optimally on higher timeframes, such as the daily chart or above.
Core Components
1. Ichimoku Cloud
The Ichimoku Cloud is a comprehensive trend-following indicator that helps identify the overall market direction and momentum. It consists of:
Conversion Line (Tenkan-Sen): Measures short-term momentum.
Base Line (Kijun-Sen): Filters medium-term trends.
Leading Span A: The average of the Conversion and Base Lines, forming one cloud boundary.
Leading Span B: The midpoint of the highest high and lowest low over a longer period, forming the other cloud boundary.
Key Ichimoku Rules Applied:
The strategy identifies bullish trends when:
The price is above the cloud.
The cloud is bullish (Leading Span A > Leading Span B).
2. High-Timeframe Simple Moving Average (HTF SMA)
This condition ensures alignment with the broader trend:
Default SMA Length: 13 periods.
Default Timeframe: 1 day.
HTF SMA Rule:
Trades are allowed only when the price is above the HTF SMA, ensuring alignment with the larger trend.
3. Volume Analysis
The strategy uses volume to validate trade setups:
Volume MA: A 20-period moving average of volume is calculated.
Trades are allowed only when the current volume is at least 1.5x the Volume MA, indicating strong market participation.
Entry and Exit Rules
Entry Condition (Long Only):
Price above the Ichimoku Cloud: Confirms a bullish trend.
Bullish Cloud: Leading Span A > Leading Span B indicates upward momentum.
Price above the HTF SMA: Ensures alignment with the broader trend.
Volume exceeds threshold: Confirms strong market participation.
Exit Condition:
The strategy exits the position when the price moves below the Ichimoku Cloud, signaling a potential trend reversal.
Best Timeframes
This strategy is optimized for daily (1D) or higher timeframes (e.g., weekly 1W). Using it on lower timeframes may produce false signals due to increased noise in price and volume data.
Default Settings
Ichimoku Settings:
Conversion Line Period: 10
Base Line Period: 30
Lagging Span Period: 53
Displacement: 26
HTF SMA Settings:
SMA Length: 13
Timeframe: 1 Day
Volume Settings:
Volume MA Length: 20
Volume Multiplier: 1.5x
Visualization
Ichimoku Cloud:
Dynamic cloud coloring (green for bullish, red for bearish) helps identify the current trend.
HTF SMA:
A purple line overlays the chart, providing a clear representation of the high-timeframe trend.
Volume Panel:
An optional panel displays volume (blue histogram) and the Volume Moving Average (orange line) to analyze market participation.
Advantages of This Strategy
High Accuracy on Higher Timeframes:
Filtering trades using the Ichimoku Cloud, HTF SMA, and volume ensures robust trend alignment, reducing false signals.
Volume Confirmation:
Incorporates volume as a validation metric to enter trades only during strong market participation.
Easy Customization:
Parameters like Ichimoku periods, SMA length, timeframe, and volume thresholds can be adjusted to suit different assets or trading styles.
Limitations
Not Suitable for Low Timeframes:
Lower timeframes can produce excessive noise, leading to false signals.
Long-Only:
The strategy is designed only for bullish markets and does not support short trades.
Lagging Nature of Indicators:
Both the Ichimoku Cloud and SMA are lagging indicators, meaning they react to past price movements.
Conclusion
The "Ichimoku by FarmerBTC" strategy is an excellent tool for trend-following on daily or higher timeframes. Its combination of Ichimoku Cloud, high-timeframe SMA, and volume ensures a robust framework for identifying high-probability long trades in trending markets. However, users are advised to test the strategy thoroughly and manage their risk appropriately. Always consult with a financial professional before making trading decisions.
Multi-Timeframe Stochastic Alert [tradeviZion]# Multi-Timeframe Stochastic Alert : Complete User Guide
## 1. Introduction
### What is the Multi-Timeframe Stochastic Alert?
The Multi-Timeframe Stochastic Alert is an advanced technical analysis tool that helps traders identify potential trading opportunities by analyzing momentum across multiple timeframes. It combines the power of the stochastic oscillator with multi-timeframe analysis to provide more reliable trading signals.
### Key Features and Benefits
- Simultaneous analysis of 6 different timeframes
- Advanced alert system with customizable conditions
- Real-time visual feedback with color-coded signals
- Comprehensive data table with instant market insights
- Motivational trading messages for psychological support
- Flexible theme support for comfortable viewing
### How it Can Help Your Trading
- Identify stronger trends by confirming momentum across multiple timeframes
- Reduce false signals through multi-timeframe confirmation
- Stay informed of market changes with customizable alerts
- Make more informed decisions with comprehensive market data
- Maintain trading discipline with clear visual signals
## 2. Understanding the Display
### The Stochastic Chart
The main chart displays three key components:
1. ** K-Line (Fast) **: The primary stochastic line (default color: green)
2. ** D-Line (Slow) **: The signal line (default color: red)
3. ** Reference Lines **:
- Overbought Level (80): Upper dashed line
- Middle Line (50): Center dashed line
- Oversold Level (20): Lower dashed line
### The Information Table
The table provides a comprehensive view of stochastic readings across all timeframes. Here's what each column means:
#### Column Explanations:
1. ** Timeframe **
- Shows the time period for each row
- Example: "5" = 5 minutes, "15" = 15 minutes, etc.
2. ** K Value **
- The fast stochastic line value (0-100)
- Higher values indicate stronger upward momentum
- Lower values indicate stronger downward momentum
3. ** D Value **
- The slow stochastic line value (0-100)
- Helps confirm momentum direction
- Crossovers with K-line can signal potential trades
4. ** Status **
- Shows current momentum with symbols:
- ▲ = Increasing (bullish)
- ▼ = Decreasing (bearish)
- Color matches the trend direction
5. ** Trend **
- Shows the current market condition:
- "Overbought" (above 80)
- "Bullish" (above 50)
- "Bearish" (below 50)
- "Oversold" (below 20)
#### Row Explanations:
1. ** Title Row **
- Shows "🎯 Multi-Timeframe Stochastic"
- Indicates the indicator is active
2. ** Header Row **
- Contains column titles
- Dark blue background for easy reading
3. ** Timeframe Rows **
- Six rows showing different timeframe analyses
- Each row updates independently
- Color-coded for easy trend identification
4. **Message Row**
- Shows rotating motivational messages
- Updates every 5 bars
- Helps maintain trading discipline
### Visual Indicators and Colors
- ** Green Background **: Indicates bullish conditions
- ** Red Background **: Indicates bearish conditions
- ** Color Intensity **: Shows strength of the signal
- ** Background Highlights **: Appear when alert conditions are met
## 3. Core Settings Groups
### Stochastic Settings
These settings control the core calculation of the stochastic oscillator.
1. ** Length (Default: 14) **
- What it does: Determines the lookback period for calculations
- Higher values (e.g., 21): More stable, fewer signals
- Lower values (e.g., 8): More sensitive, more signals
- Recommended:
* Day Trading: 8-14
* Swing Trading: 14-21
* Position Trading: 21-30
2. ** Smooth K (Default: 3) **
- What it does: Smooths the main stochastic line
- Higher values: Smoother line, fewer false signals
- Lower values: More responsive, but more noise
- Recommended:
* Day Trading: 2-3
* Swing Trading: 3-5
* Position Trading: 5-7
3. ** Smooth D (Default: 3) **
- What it does: Smooths the signal line
- Works in conjunction with Smooth K
- Usually kept equal to or slightly higher than Smooth K
- Recommended: Keep same as Smooth K for consistency
4. ** Source (Default: Close) **
- What it does: Determines price data for calculations
- Options: Close, Open, High, Low, HL2, HLC3, OHLC4
- Recommended: Stick with Close for most reliable signals
### Timeframe Settings
Controls the multiple timeframes analyzed by the indicator.
1. ** Main Timeframes (TF1-TF6) **
- TF1 (Default: 10): Shortest timeframe for quick signals
- TF2 (Default: 15): Short-term trend confirmation
- TF3 (Default: 30): Medium-term trend analysis
- TF4 (Default: 30): Additional medium-term confirmation
- TF5 (Default: 60): Longer-term trend analysis
- TF6 (Default: 240): Major trend confirmation
Recommended Combinations:
* Scalping: 1, 3, 5, 15, 30, 60
* Day Trading: 5, 15, 30, 60, 240, D
* Swing Trading: 15, 60, 240, D, W, M
2. ** Wait for Bar Close (Default: true) **
- What it does: Controls when calculations update
- True: More reliable but slightly delayed signals
- False: Faster signals but may change before bar closes
- Recommended: Keep True for more reliable signals
### Alert Settings
#### Main Alert Settings
1. ** Enable Alerts (Default: true) **
- Master switch for all alert notifications
- Toggle this off when you don't want any alerts
- Useful during testing or when you want to focus on visual signals only
2. ** Alert Condition (Options) **
- "Above Middle": Bullish momentum alerts only
- "Below Middle": Bearish momentum alerts only
- "Both": Alerts for both directions
- Recommended:
* Trending Markets: Choose direction matching the trend
* Ranging Markets: Use "Both" to catch reversals
* New Traders: Start with "Both" until you develop a specific strategy
3. ** Alert Frequency **
- "Once Per Bar": Immediate alerts during the bar
- "Once Per Bar Close": Alerts only after bar closes
- Recommended:
* Day Trading: "Once Per Bar" for quick reactions
* Swing Trading: "Once Per Bar Close" for confirmed signals
* Beginners: "Once Per Bar Close" to reduce false signals
#### Timeframe Check Settings
1. ** First Check (TF1) **
- Purpose: Confirms basic trend direction
- Alert Triggers When:
* For Bullish: Stochastic is above middle line (50)
* For Bearish: Stochastic is below middle line (50)
* For Both: Triggers in either direction based on position relative to middle line
- Settings:
* Enable/Disable: Turn first check on/off
* Timeframe: Default 5 minutes
- Best Used For:
* Quick trend confirmation
* Entry timing
* Scalping setups
2. ** Second Check (TF2) **
- Purpose: Confirms both position and momentum
- Alert Triggers When:
* For Bullish: Stochastic is above middle line AND both K&D lines are increasing
* For Bearish: Stochastic is below middle line AND both K&D lines are decreasing
* For Both: Triggers based on position and direction matching current condition
- Settings:
* Enable/Disable: Turn second check on/off
* Timeframe: Default 15 minutes
- Best Used For:
* Trend strength confirmation
* Avoiding false breakouts
* Day trading setups
3. ** Third Check (TF3) **
- Purpose: Confirms overall momentum direction
- Alert Triggers When:
* For Bullish: Both K&D lines are increasing (momentum confirmation)
* For Bearish: Both K&D lines are decreasing (momentum confirmation)
* For Both: Triggers based on matching momentum direction
- Settings:
* Enable/Disable: Turn third check on/off
* Timeframe: Default 30 minutes
- Best Used For:
* Major trend confirmation
* Swing trading setups
* Avoiding trades against the main trend
Note: All three conditions must be met simultaneously for the alert to trigger. This multi-timeframe confirmation helps reduce false signals and provides stronger trade setups.
#### Alert Combinations Examples
1. ** Conservative Setup **
- Enable all three checks
- Use "Once Per Bar Close"
- Timeframe Selection Example:
* First Check: 15 minutes
* Second Check: 1 hour (60 minutes)
* Third Check: 4 hours (240 minutes)
- Wider gaps between timeframes reduce noise and false signals
- Best for: Swing trading, beginners
2. ** Aggressive Setup **
- Enable first two checks only
- Use "Once Per Bar"
- Timeframe Selection Example:
* First Check: 5 minutes
* Second Check: 15 minutes
- Closer timeframes for quicker signals
- Best for: Day trading, experienced traders
3. ** Balanced Setup **
- Enable all checks
- Use "Once Per Bar"
- Timeframe Selection Example:
* First Check: 5 minutes
* Second Check: 15 minutes
* Third Check: 1 hour (60 minutes)
- Balanced spacing between timeframes
- Best for: All-around trading
### Visual Settings
#### Alert Visual Settings
1. ** Show Background Color (Default: true) **
- What it does: Highlights chart background when alerts trigger
- Benefits:
* Makes signals more visible
* Helps spot opportunities quickly
* Provides visual confirmation of alerts
- When to disable:
* If using multiple indicators
* When preferring a cleaner chart
* During manual backtesting
2. ** Background Transparency (Default: 90) **
- Range: 0 (solid) to 100 (invisible)
- Recommended Settings:
* Clean Charts: 90-95
* Multiple Indicators: 85-90
* Single Indicator: 80-85
- Tip: Adjust based on your chart's overall visibility
3. ** Background Colors **
- Bullish Background:
* Default: Green
* Indicates upward momentum
* Customizable to match your theme
- Bearish Background:
* Default: Red
* Indicates downward momentum
* Customizable to match your theme
#### Level Settings
1. ** Oversold Level (Default: 20) **
- Traditional Setting: 20
- Adjustable Range: 0-100
- Usage:
* Lower values (e.g., 10): More conservative
* Higher values (e.g., 30): More aggressive
- Trading Applications:
* Potential bullish reversal zone
* Support level in uptrends
* Entry point for long positions
2. ** Overbought Level (Default: 80) **
- Traditional Setting: 80
- Adjustable Range: 0-100
- Usage:
* Lower values (e.g., 70): More aggressive
* Higher values (e.g., 90): More conservative
- Trading Applications:
* Potential bearish reversal zone
* Resistance level in downtrends
* Exit point for long positions
3. ** Middle Line (Default: 50) **
- Purpose: Trend direction separator
- Applications:
* Above 50: Bullish territory
* Below 50: Bearish territory
* Crossing 50: Potential trend change
- Trading Uses:
* Trend confirmation
* Entry/exit trigger
* Risk management level
#### Color Settings
1. ** Bullish Color (Default: Green) **
- Used for:
* K-Line (Main stochastic line)
* Status symbols when trending up
* Trend labels for bullish conditions
- Customization:
* Choose colors that stand out
* Match your trading platform theme
* Consider color blindness accessibility
2. ** Bearish Color (Default: Red) **
- Used for:
* D-Line (Signal line)
* Status symbols when trending down
* Trend labels for bearish conditions
- Customization:
* Choose contrasting colors
* Ensure visibility on your chart
* Consider monitor settings
3. ** Neutral Color (Default: Gray) **
- Used for:
* Middle line (50 level)
- Customization:
* Should be less prominent
* Easy on the eyes
* Good background contrast
### Theme Settings
1. **Color Theme Options**
- Dark Theme (Default):
* Dark background with white text
* Optimized for dark chart backgrounds
* Reduces eye strain in low light
- Light Theme:
* Light background with black text
* Better visibility in bright conditions
- Custom Theme:
* Use your own color preferences
2. ** Available Theme Colors **
- Table Background
- Table Text
- Table Headers
Note: The theme affects only the table display colors. The stochastic lines and alert backgrounds use their own color settings.
### Table Settings
#### Position and Size
1. ** Table Position **
- Options:
* Top Right (Default)
* Middle Right
* Bottom Right
* Top Left
* Middle Left
* Bottom Left
- Considerations:
* Chart space utilization
* Personal preference
* Multiple monitor setups
2. ** Text Sizes **
- Title Size Options:
* Tiny: Minimal space usage
* Small: Compact but readable
* Normal (Default): Standard visibility
* Large: Enhanced readability
* Huge: Maximum visibility
- Data Size Options:
* Recommended: One size smaller than title
* Adjust based on screen resolution
* Consider viewing distance
3. ** Empowering Messages **
- Purpose:
* Maintain trading discipline
* Provide psychological support
* Remind of best practices
- Rotation:
* Changes every 5 bars
* Categories include:
- Market Wisdom
- Strategy & Discipline
- Mindset & Growth
- Technical Mastery
- Market Philosophy
## 4. Setting Up for Different Trading Styles
### Day Trading Setup
1. **Timeframes**
- Primary: 5, 15, 30 minutes
- Secondary: 1H, 4H
- Alert Settings: "Once Per Bar"
2. ** Stochastic Settings **
- Length: 8-14
- Smooth K/D: 2-3
- Alert Condition: Match market trend
3. ** Visual Settings **
- Background: Enabled
- Transparency: 85-90
- Theme: Based on trading hours
### Swing Trading Setup
1. ** Timeframes **
- Primary: 1H, 4H, Daily
- Secondary: Weekly
- Alert Settings: "Once Per Bar Close"
2. ** Stochastic Settings **
- Length: 14-21
- Smooth K/D: 3-5
- Alert Condition: "Both"
3. ** Visual Settings **
- Background: Optional
- Transparency: 90-95
- Theme: Personal preference
### Position Trading Setup
1. ** Timeframes **
- Primary: Daily, Weekly
- Secondary: Monthly
- Alert Settings: "Once Per Bar Close"
2. ** Stochastic Settings **
- Length: 21-30
- Smooth K/D: 5-7
- Alert Condition: "Both"
3. ** Visual Settings **
- Background: Disabled
- Focus on table data
- Theme: High contrast
## 5. Troubleshooting Guide
### Common Issues and Solutions
1. ** Too Many Alerts **
- Cause: Settings too sensitive
- Solutions:
* Increase timeframe intervals
* Use "Once Per Bar Close"
* Enable fewer timeframe checks
* Adjust stochastic length higher
2. ** Missed Signals **
- Cause: Settings too conservative
- Solutions:
* Decrease timeframe intervals
* Use "Once Per Bar"
* Enable more timeframe checks
* Adjust stochastic length lower
3. ** False Signals **
- Cause: Insufficient confirmation
- Solutions:
* Enable all three timeframe checks
* Use larger timeframe gaps
* Wait for bar close
* Confirm with price action
4. ** Visual Clarity Issues **
- Cause: Poor contrast or overlap
- Solutions:
* Adjust transparency
* Change theme settings
* Reposition table
* Modify color scheme
### Best Practices
1. ** Getting Started **
- Start with default settings
- Use "Both" alert condition
- Enable all timeframe checks
- Wait for bar close
- Monitor for a few days
2. ** Fine-Tuning **
- Adjust one setting at a time
- Document changes and results
- Test in different market conditions
- Find your optimal timeframe combination
- Balance sensitivity with reliability
3. ** Risk Management **
- Don't trade against major trends
- Confirm signals with price action
- Use appropriate position sizing
- Set clear stop losses
- Follow your trading plan
4. ** Regular Maintenance **
- Review settings weekly
- Adjust for market conditions
- Update color scheme for visibility
- Clean up chart regularly
- Maintain trading journal
## 6. Tips for Success
1. ** Entry Strategies **
- Wait for all timeframes to align
- Confirm with price action
- Use proper position sizing
- Consider market conditions
2. ** Exit Strategies **
- Trail stops using indicator levels
- Take partial profits at targets
- Honor your stop losses
- Don't fight the trend
3. ** Psychology **
- Stay disciplined with settings
- Don't override system signals
- Keep emotions in check
- Learn from each trade
4. ** Continuous Improvement **
- Record your trades
- Review performance regularly
- Adjust settings gradually
- Stay educated on markets
Auto-Support v 0.3The "Auto-Support v 0.3" indicator is designed to automatically detect and plot multiple levels of support and resistance on a chart. It aims to help traders identify key price levels where the market tends to reverse or consolidate. Here’s a breakdown of its functionality and goals:
Objective:
The primary objective of the Auto-Support v 0.3 indicator is to provide traders with a clear, visual representation of support and resistance levels. These levels are determined based on a predefined sensitivity parameter, which adjusts how tightly or loosely the indicator reacts to recent price movements. The indicator can be applied to any chart to assist in identifying potential entry and exit points for trades, enhancing technical analysis by displaying these important price zones.
Description:
Support and Resistance Calculation:
The indicator calculates multiple levels of support and resistance using the highest and lowest prices over a defined period. The "sensitivity" parameter, which ranges from 1 to 10, determines how sensitive the calculation is to recent price changes. A higher value increases the number of bars used to calculate these levels, making the levels more stable but less responsive to short-term price movements.
Visual Representation:
The support levels are drawn in green with a customizable transparency setting, while resistance levels are displayed in red with similar transparency controls. This visual representation helps traders identify these levels on the chart and see the strength or weakness of the support/resistance zones depending on the transparency setting.
Multiple Levels:
The indicator plots 10 distinct levels of support and resistance (from 1 to 10), which can offer a more granular view of price action. Traders can use these levels to assess potential breakout or breakdown points.
Customization:
Sensitivity: The sensitivity input allows traders to adjust how aggressively the indicator reacts to recent price data. This ensures flexibility, enabling the indicator to be tailored to different trading styles and market conditions.
Transparency: The transparency input adjusts the visual opacity of the support and resistance lines, making it easier to overlay the indicator without obscuring other chart elements.
Key Goals:
Dynamic Support/Resistance Identification: Automatically detect and display relevant support and resistance levels based on price history, removing the need for manual chart analysis.
Customizable Sensitivity: Offer a flexible method to adjust how the indicator identifies key levels, allowing it to fit different market conditions.
Clear Visualization: Provide easy-to-read support and resistance levels with customizable colors and transparencies, enhancing visual clarity and decision-making.
Multiple Levels: Display up to 10 levels of support and resistance, allowing traders to consider both short-term and longer-term price action when making trading decisions.
By using this indicator, traders can more effectively identify key price zones where price may reverse, consolidate, or break out, providing a solid foundation for developing trading strategies.
Linear Regression Intensity [AlgoAlpha]Introducing the Linear Regression Intensity indicator by AlgoAlpha, a sophisticated tool designed to measure and visualize the strength of market trends using linear regression analysis. This indicator not only identifies bullish and bearish trends with precision but also quantifies their intensity, providing traders with deeper insights into market dynamics. Whether you’re a novice trader seeking clearer trend signals or an experienced analyst looking for nuanced trend strength indicators, Linear Regression Intensity offers the clarity and detail you need to make informed trading decisions.
Key Features:
📊 Comprehensive Trend Analysis: Utilizes linear regression over customizable periods to assess and quantify trend strength.
🎨 Customizable Appearance: Choose your preferred colors for bullish and bearish trends to align with your trading style.
🔧 Flexible Parameters: Adjust the lookback period, range tolerance, and regression length to tailor the indicator to your specific strategy.
📉 Dynamic Bar Coloring: Instantly visualize trend states with color-coded bars—green for bullish, red for bearish, and gray for neutral.
🏷️ Intensity Labels: Displays dynamic labels that represent the intensity of the current trend, helping you gauge market momentum at a glance.
🔔 Alert Conditions: Set up alerts for strong bullish or bearish trends and trend neutrality to stay ahead of market movements without constant monitoring.
Quick Guide to Using Linear Regression Intensity:
🛠 Add the Indicator: Simply add Linear Regression Intensity to your TradingView chart from your favorites. Customize the settings such as lookback period, range tolerance, and regression length to fit your trading approach.
📈 Market Analysis: Observe the color-coded bars to quickly identify the current trend state. Use the intensity labels to understand the strength behind each trend, allowing for more strategic entry and exit points.
🔔 Set Up Alerts: Enable alerts for when strong bullish or bearish trends are detected or when the trend reaches a neutral zone. This ensures you never miss critical market movements, even when you’re away from the chart.
How It Works:
The Linear Regression Intensity indicator leverages linear regression to calculate the underlying trend of a selected price source over a specified length. By analyzing the consistency of the regression values within a defined lookback period, it determines the trend’s intensity based on a percentage tolerance. The indicator aggregates pairwise comparisons of regression values to assess whether the trend is predominantly upward or downward, assigning a state of bullish, bearish, or neutral accordingly. This state is then visually represented through dynamic bar colors and intensity labels, offering a clear and immediate understanding of market conditions. Additionally, the inclusion of Average True Range (ATR) ensures that the intensity visualization accounts for market volatility, providing a more robust and reliable trend assessment. With customizable settings and alert conditions, Linear Regression Intensity empowers traders to fine-tune their strategies and respond swiftly to evolving market trends.
Elevate your trading strategy with Linear Regression Intensity and gain unparalleled insights into market trends! 🌟📊
Kalman Filter Oscillator v4The Kalman Filter Oscillator v4 is an advanced tool designed to help traders and investors identify trends more effectively while reducing the impact of market noise. As the latest iteration in its development, this version integrates improvements that make it more adaptive and precise, catering to the challenges of today’s financial markets.
This indicator operates on the principle of the Kalman filter, a well-regarded mathematical approach used for estimating the state of a dynamic system. By filtering out random fluctuations, it smooths price data to provide clearer insights into underlying trends. Unlike traditional methods such as moving averages, which often lag and can miss rapid shifts, the Kalman Filter Oscillator is reactive in real time, making it particularly suited for dynamic markets.
Version v4 builds on earlier versions by offering a refined combination of short-term and long-term trend analysis. Through adjustable parameters, traders can balance sensitivity to immediate price changes with a broader perspective of the market direction. Additionally, the oscillator incorporates a unique feature that tracks a price’s position relative to its recent highs and lows, which enhances its ability to pinpoint potential turning points or key market conditions.
The indicator’s value lies in its adaptability and practicality. Traders can use it to confirm trends, identify overbought or oversold conditions, or smooth out erratic price movements, reducing the likelihood of false signals. By presenting information in a clear and actionable format, it allows users to make better-informed decisions with greater confidence.
As of late 2024, the Kalman Filter Oscillator v4 represents a sophisticated yet user-friendly advancement in trend analysis. While not a one-size-fits-all solution, it serves as a valuable component in a trader’s toolkit, complementing other strategies and enhancing overall market understanding.
HTF Candles Overlay [Trendoscope®]🎲 HTF Candles Overlay is a simple indicator where you can overlay higher timeframe candles on current timeframe chart.
Most of the code is encapsulated in the library HTFCandlesLib . After publishing the library as open source, many people requested to convert that into an indicator. Based on this, we decided to publish this small code for the use of community.
🎯 Usage
The indicator is simple, it helps users visualise higher timeframe candles. We majorly use this for debugging or validating our implementations based on higher timeframe. Instead of switching back and forth to different timeframes, it helps us visualise higher timeframe candles on the same chart when we are validating the implementation that involves higher timeframe calculations.
🎯 Components
The indicator provides two types of displays
Candles - overlay candles built through lines and labels
Plot - close price of higher timeframe plotted on chart
🎯 Candles
The behaviour of the candles are similar to that of hollow candles. The color of the body and the border+wick demonstrates the movement of the candle.
Body color is lime if the HTF close is higher than HTF open. Body color is orange if the HTF close is lower than the HTF open.
Wick and border color is lime if HTF close price is higher than previous HTF close price. And they are orange if HTF close price is lower than the previous HTF close price
In most cases body color will be same as the wick color. In case of stocks and indices, it may happen that the open price is too far away from previous close price due to gaps. This can lead to close price being relatively in different direction when compared to open and previous close.
Wicks are not at the centre of the candle. Instead wicks are drawn on the current chart timeframe position where the current timeframe has reached the highest or lowest point within the given HTF candle
Candles also list OHLC price of HTF candle along with HTF bar index and the range of LTF bar index that the candle spawns
Here are some pictorial representations that can help understand better.
Here are the examples of candles with gaps where body and wick/border are in different directions (colours)
🎯 Indicator Settings
Simple settings allow users to select the timeframe, whether to display candles and plots and their specific colors.
🎯 Possible inconsistencies
The overlay can show inconsistent data in certain situations. Here are some of the scenarios where the indicator may not show consistent display of the data.
When the HTF data from request.security does not match that of combined LTF data . In such cases, HTF candles may not form inline with the current timeframe candles. This happens when there is a data issue of different OHLC data available in tradingview.
When using weekly candle as either chart timeframe or higher timeframe - end of week may not coincide with end of month or other timeframes. This can cause some inconsistencies in the visuals of the indicator.
When open and close time of either LTF or HTF falls under different day due to time zone used. - time is always the time on which the candle close. So, when we use time zone that causes the exchange day to open and close on different days, that can cause some inconsistencies in the candles being drawn.
ATR HEMA [SeerQuant]What is the ATR Holt Moving Average (HEMA)?
The ATR Holt Moving Average (HEMA) is an advanced smoothing technique that incorporates the Holt exponential smoothing method. Unlike traditional moving averages, HEMA uses two smoothing factors (alpha and gamma) to forecast both the current trend and the trend change rate. This dual-layer approach improves the responsiveness of the moving average to both stable trends and volatile price swings.
When paired with the Average True Range (ATR), the HEMA becomes even more powerful. The ATR acts as a volatility filter, defining a "neutral zone" where minor price fluctuations are ignored. This allows traders to focus on significant market movements while reducing the impact of noise.
⚙️ How the Code Works
The ATR Holt Moving Average (HEMA) combines trend smoothing with volatility filtering to provide traders with dynamic signals. Here's how it functions step by step:
User Inputs and Customization:
Traders can customize the lengths for HEMA's smoothing factors (alphaL and gammaL), the ATR calculation length, and the neutral zone multiplier (atrMult).
The src input allows users to choose the price source for calculations (e.g., hl2), while the col input offers various color themes (Default, Modern, Warm, Cool).
Holt Exponential Moving Average (HEMA) Calculation:
Alpha and Gamma Smoothing Factors:
alpha controls how much weight is given to the current price versus past prices.
gamma smooths the trend change rate, reducing noise. The HEMA formula combines the current price, the previous HEMA value, and a trend adjustment (via the b variable) to create a smooth yet responsive average. The b variable tracks the rate of change in the HEMA over time, further refining the trend detection.
ATR-Based Neutral Zone:
If the change in HEMA (hemaChange) falls within the neutral zone, it is considered insignificant, and the trend color remains unchanged.
Color and Signal Detection:
Bullish Trend: The color is set to bull when HEMA rises above the neutral zone.
Bearish Trend: The color is set to bear when HEMA falls below the neutral zone.
Neutral Zone: The color remains unchanged, signalling no significant trend.
🚀 Summary
This indicator enhances traditional moving averages by combining the Holt smoothing method with ATR-based volatility filtering. The HEMA adapts to market conditions, detecting trends and transitions while filtering out insignificant price changes. The result is a versatile tool for:
The ATR Holt Moving Average (HEMA) is ideal for traders seeking a balance between responsiveness and stability, offering precise signals in both trending and volatile markets.
📜 Disclaimer
The information provided by this script is for educational and informational purposes only and does not constitute financial, investment, or trading advice. Past performance of any trading system or indicator, including this one, is not indicative of future results. Trading and investing in financial markets involve risk, and it is possible to lose your entire investment.
Users are advised to perform their own due diligence and consult with a licensed financial advisor before making any trading or investment decisions. The creator of this script is not responsible for any trading or investment decisions made based on the use of this script.
This script complies with TradingView's guidelines and is provided as-is, without any guarantee of accuracy, reliability, or performance. Use at your own risk.
Volume Weighted Jurik Moving AverageThe Jurik Moving Average (JMA) is a smoothing indicator that is designed to improve upon traditional moving averages by reducing lag while enhancing responsiveness to price movements. It was created by Jurik Research and is often used to track trends with greater accuracy and minimal delay. The JMA is based on a combination of **exponential smoothing** and **phase adjustments**, making it more adaptable to varying market conditions compared to standard moving averages like SMA (Simple Moving Average) or EMA (Exponential Moving Average).
The core advantage of the JMA lies in its ability to adjust to price changes without excessively lagging, which is a common issue with traditional moving averages. It incorporates a **phase parameter** that can be adjusted to smooth out the signal further or make it more responsive to recent price action. This phase adjustment allows traders to fine-tune the JMA's sensitivity to the market, optimizing it for different timeframes and trading strategies.
How JMA Works and Benefits of Adding Volume Weight
The JMA works by applying a **smoothing process** to price data while allowing for adjustments through its phase and power parameters. These parameters help control the degree of smoothness and responsiveness. The result is a curve that follows price trends closely but with less lag than traditional moving averages.
Adding **volume weighting** to the JMA enhances its ability to reflect market activity more accurately. Just like the **Volume-Weighted Moving Average (VWMA)**, volume-weighting adjusts the moving average based on the strength of trading volume, meaning that price movements with higher volume will have a greater influence on the JMA. This can help traders identify trends that are supported by significant market participation, making the moving average more reliable.
The benefit of a volume-weighted JMA is that it responds more effectively to price movements that occur during periods of high trading volume, which are often considered more significant. This can help traders avoid false signals that may occur during low-volume periods when price changes may not reflect true market sentiment. By incorporating volume into the calculation, the JMA becomes more aligned with real market conditions, enhancing its effectiveness for trend identification and decision-making.
Combined Zero Lag EMA with Crosses | ASHGCombined Zero Lag EMA with Crosses
This indicator combines the power of Zero Lag Exponential Moving Averages (EMAs) with the widely used Golden Cross and Death Cross signals. It provides an efficient and precise trend-following tool for traders.
Key Features:
Short and Long Zero Lag EMAs: The indicator uses two Zero Lag EMAs with customizable periods (Short and Long). The short EMA is typically more responsive to price changes, while the long EMA smooths out price data, providing a broader trend perspective.
Golden Cross and Death Cross signals: The Golden Cross occurs when the short EMA crosses above the long EMA, indicating a potential bullish trend. The Death Cross occurs when the short EMA crosses below the long EMA, signaling a possible bearish trend.
Combined Zero Lag EMA: The average of the Short and Long Zero Lag EMAs gives a balanced view of the market's overall direction.
Plotting and Alerts: The indicator plots both the short and long Zero Lag EMAs, as well as the combined EMA, with visual cues for Golden and Death Crosses. Alerts can be set for when these crosses occur.
Use this indicator for clearer entry and exit points, helping you stay ahead of market movements.
This indicator is based on Kıvanç ÖZBİLGİÇ's "Zero Lag EMA v2" indicator.
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Birleştirilmiş Zero Lag EMA ve Cross (Kesişim) Sinyalleri
Bu gösterge, Zero Lag (Sıfır Gecikmeli) Üssel Hareketli Ortalamaların (EMA) gücünü, yaygın olarak kullanılan Golden Cross (Altın Kesişim) ve Death Cross (Ölüm Kesişimi) sinyalleriyle birleştirir. Yatırımcılar için verimli ve hassas bir trend takip aracıdır.
Öne Çıkan Özellikler:
Kısa ve Uzun Zero Lag EMA: Gösterge, özelleştirilebilir periyotlarla iki Zero Lag EMA kullanır (Kısa ve Uzun). Kısa EMA, fiyat değişimlerine daha hızlı tepki verirken, uzun EMA fiyat verilerini düzleştirerek daha geniş bir trend perspektifi sunar.
Golden Cross ve Death Cross sinyalleri: Golden Cross, kısa EMA'nın uzun EMA'yı yukarı doğru kesmesiyle oluşur ve potansiyel bir yükseliş trendine işaret eder. Death Cross ise, kısa EMA'nın uzun EMA'yı aşağı doğru kesmesiyle oluşur ve düşüş trendi sinyali verir.
Birleştirilmiş Zero Lag EMA: Kısa ve uzun Zero Lag EMA'larının ortalaması, piyasanın genel yönünü dengeli bir şekilde gösterir.
Grafik ve Uyarılar: Gösterge, kısa ve uzun Zero Lag EMA'ları ile birleştirilmiş EMA'yı çizerek Golden Cross ve Death Cross sinyalleri için görsel uyarılar sağlar. Bu kesişimler gerçekleştiğinde alarm kurabilirsiniz.
Bu göstergeleri kullanarak, piyasa hareketlerinden önce net giriş ve çıkış noktaları belirleyebilir, böylece daha bilinçli kararlar alabilirsiniz.
Bu indikatör Kıvanç ÖZBİLGİÇ'in "Zero Lag EMA v2" indikatörünü temel alarak hazırlanmıştır.
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Volume Weighted TWAP (VW-TWAP)The Volume Weighted Time Weighted Average Price (VW-TWAP) is an indicator that combines the principles of price averaging with volume sensitivity. Unlike the traditional TWAP, which calculates a simple time-weighted average, VW-TWAP integrates volume into its computation, emphasizing price movements that occur during periods of higher trading activity. This makes it particularly effective for identifying realistic price levels influenced by significant market participation. It is computed by summing the volume-weighted prices over a specified period and dividing by the total volume, providing a more accurate reflection of the price participants value most.
The key benefits of VW-TWAP lie in its ability to guide both traders and investors with a data-driven perspective. By accounting for both time and volume, it highlights fair value zones where significant accumulation or distribution might occur. This can improve trade entries and exits by aligning decisions with zones of substantial market consensus. Furthermore, its adaptability to different timeframes enhances its utility in multi-timeframe analysis, making it suitable for intraday scalpers and long-term swing traders alike. The VW-TWAP's focus on volume sensitivity also minimizes noise from low-volume, erratic price movements, offering a clearer view of market dynamics.
Levy Flight Relative Strength Index [SeerQuant]Lévy Flight Relative Strength Index
A nuanced improvement on the classic RSI, the Lévy Flight RSI leverages the Lévy Flight model to calculate dynamic weighted gains and losses, offering improved responsiveness and smoothness in trend detection compared to the regular RSI. Ideal for traders seeking a balance between precision and adaptability, the Lévy Flight RSI is packed with customizable features and a sleek, modern aesthetic.
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🧠 What is Lévy Flight Modelling?
Lévy Flight modelling is a concept derived from probability theory and fractal mathematics, widely applied in fields such as finance and physics. In trading, Lévy Flights describe a random walk process characterized by small, frequent movements interspersed with larger, less frequent movements. This behaviour reflects real-world price dynamics, where markets often exhibit periods of relative calm followed by sharp, volatile movements. The Lévy Flight model introduces a weighting mechanism that amplifies extreme price changes while smoothing smaller ones, providing a more nuanced view of market trends.
In the context of the Lévy Flight RSI, this model enhances traditional RSI calculations by dynamically weighting price changes (gains and losses) based on their magnitude. This results in an RSI that is more responsive to significant price movements, making it ideal for detecting shifts in momentum and market direction.
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🌟 Key Features:
- Dynamic Lévy Flight Modelling: Adjust alpha (1 to 2) for responsive or smooth signals, making it perfect for varying market conditions.
- Custom RSI Smoothing: Choose from multiple moving average types, including TEMA, DEMA, HMA, ALMA, and more, to match your trading style.
- Visually Intuitive: Neon-inspired gradient colours and centered histogram provide instant insights into market conditions.
- Customizable Overbought/Oversold Levels: Clearly defined thresholds, with additional shaded regions for strength identification.
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⚙️ How the Code Works
The Lévy Flight RSI enhances the traditional RSI calculation by incorporating two primary elements:
Dynamic Weighting Using Lévy Flight:
The code calculates the price change (change) on each bar and applies a power function (alpha) to these changes. Gains are raised to the power of alpha (for positive price changes), and losses are similarly transformed (for negative price changes).
The parameter alpha (ranging from 1 to 2) determines the sensitivity of the weighting. Lower values emphasize responsiveness, while higher values smooth out signals.
Enhanced Moving Averages:
The weighted gains and losses are smoothed using a customizable moving average. Options include traditional averages like SMA and EMA, and more advanced ones like TEMA, HMA, and ALMA. These smoothed values are used to calculate the final RSI value.
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📈 Why Use Lévy Flight RSI?
This unique RSI indicator captures price momentum with enhanced sensitivity to market dynamics. Whether you’re trend-following, scalping, or identifying reversals, the Lévy Flight RSI provides robust insights to refine your trading decisions.
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🔧 Inputs:
RSI Settings: Control RSI length, calculation source, and smoothing type.
Lévy Flight Settings: Adjust alpha to tune the indicator's responsiveness.
Style Customization: Tailor the appearance with different colour themes and gradients.
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Relative Price Strength (RPS)Relative Price Strength (RPS) is a technical analysis indicator that measures the performance of a specific symbol relative to a benchmark or "Base Symbol".
It's essentially a ratio that compares the price of the specific symbol to the price of the benchmark.
Rising RPS: Indicates that the symbol is outperforming the benchmark.
Falling RPS: Suggests that the symbol is underperforming the benchmark.
RSP is smoothed over a period for better visualization.
Boltzmann Weighted Moving average ( BWMA )Overview:
Introducing the Boltzmann Weighted Moving Average (BWMA) – a novel approach that draws inspiration from statistical mechanics to emphasize recent market data more than older data. By applying an exponential decay governed by a “temperature” parameter, BWMA provides a unique perspective on price trends and enhances noise filtering. An EMA-based smoothing is then applied for an even cleaner, more stable signal.
Key Features:
Boltzmann Weighting: The BWMA assigns weights to each data point based on a Boltzmann-like formula, giving more influence to recent bars and reducing the impact of older ones. This creates a dynamic, adaptive moving average that can quickly respond to market changes.
Adaptive Temperature Control: Users can adjust the “Temperature” (T) parameter. A lower T puts a stronger emphasis on the most recent data, while a higher T makes the weight distribution more uniform across the chosen period.
EMA Smoothing: After computing the weighted average, an EMA is applied to smooth out short-term noise, resulting in a cleaner trend indication.
Color-Coded Trend Indicator: The BWMA line changes color depending on its slope, allowing traders to quickly identify bullish (green) or bearish (red) conditions at a glance.
Parameters:
Period: Defines the lookback window over which the Boltzmann weights are calculated.
Temperature (T): Controls the steepness of the weight decay. Lower T emphasizes recency, while higher T spreads weights more evenly.
Alpha (Energy Scale): Adjusts how quickly “Energy” (and thus weight decay) increases with older data points.
Smoothing Period: Determines the EMA length for reducing noise after weighting, providing a more stable signal.
How It Works:
The BWMA calculates a weighted average of recent prices, where the weight for each data point i is given by:
weight = math.exp(-energy / (k_B * T))
Energy_i: Increases as the data point is further back in time.
k_B: A scaling constant, set to 1 for simplicity.
T: "Temperature" parameter that controls how quickly the weights decay. A lower T emphasizes more recent data strongly, while a higher T spreads out the emphasis more evenly.
Visuals:
BWMA Line: Plotted as a smooth line that changes color based on trend direction.
Green: BWMA is rising (bullish trend).
Red: BWMA is falling (bearish trend).
Usage:
The BWMA can be used similarly to traditional moving averages but offers greater flexibility and adaptability:
Adjust T and Alpha: Fine-tune the weighting profile to match your trading style, whether you prefer rapid response to recent changes or a more balanced view.
Trend Confirmation: Use color changes to confirm bullish or bearish momentum.
Filtering Noise: The combination of Boltzmann weighting and EMA smoothing can help reduce the impact of sudden price spikes and yield clearer trend signals.
By blending the concepts of statistical mechanics with classic technical analysis techniques, the Boltzmann Weighted Moving Average provides traders with an innovative tool for revealing underlying market trends.
