Periodical Trend [BigBeluga]The Periodical Trend indicator is designed to provide a detailed analysis of market trends and volatility. It utilizes a combination of Moving Averages and volatility measures to plot trend line, highlight potential trend reversals, and indicate mean reversion opportunities. The indicator offers customizable display options, allowing traders to adjust for sensitivity, volatility bands, and price deviation visibility.
🔵 KEY FEATURES
● Periodical Trend Analysis
Uses (high + volatility) or (low - volatility) as the foundation for trend analysis with a set period.
// Condition to update the AVG array based on the selected mode
if mode == "Normal"
? bar_index == 122
: bar_index % period == 0
AVG.push(close) // Add the close price to the AVG array
// Update AVG array based on the period and price comparison
if bar_index % period == 0
if close > AVG.last() // If the current close is greater than the last stored value in AVG
AVG.push(low - vlt) // Add the low price minus volatility to the array
if close < AVG.last() // If the current close is lower than the last stored value in AVG
AVG.push(high + vlt) // Add the high price plus volatility to the array
Provides adjustable sensitivity modes ("Normal" and "Sensitive") for different market conditions.
Trend direction is visualized with dynamic color coding based on the relationship between the trend line and price.
● Volatility Bands
Displays upper and lower volatility bands derived from a moving average of price volatility (high-low).
The bands help identify potential breakout zones, overbought, or oversold conditions.
Users can toggle the visibility of the bands to suit their trading style.
● Mean Reversion Signals
Detects mean reversion opportunities when price deviates significantly from the trend line.
Includes both regular and strong mean reversion signals, marked directly on the chart.
Signals are based on oscillator crossovers, offering potential entry and exit points.
● Price Deviation Oscillator
Plots an oscillator that measures the deviation of price from the average trend line.
The oscillator is normalized using standard deviation, highlighting extreme price deviations.
Traders can choose to display the oscillator for in-depth analysis of price behavior relative to the trend.
● Dynamic Trend Coloring
The indicator colors the background on the direction of the trend.
Green indicates bullish trends, while blue indicates bearish trends.
The trend colors adapt dynamically to market conditions, providing clear visual cues for traders.
🔵 HOW TO USE
● Trend Analysis
The trend line represents the current market direction. A green trend line suggests a bullish trend, while a blue trend line indicates a bearish trend.
Use the trend line in conjunction with volatility bands to confirm potential breakouts or areas of consolidation.
● Volatility Bands
Volatility bands offer insight into potential overbought or oversold conditions.
Price exceeding these bands can signal a strong trend continuation or a possible reversal.
● Mean Reversion Strategies
Look for mean reversion signals (regular and strong) when price shows signs of reverting to the trend line after significant deviation.
Regular signals are represented by small dots, while strong signals are represented by larger circles.
These signals can be used as entry or exit points, depending on the market context.
● Price Deviation Analysis
The oscillator provides a detailed view of price deviations from the trend line.
A positive oscillator value indicates that the price is above the trend, while a negative value suggests it is below.
Use the oscillator to identify potential overbought or oversold conditions within the trend.
🔵 USER INPUTS
● Period
Defines the length of the period used for calculating the trend line. A higher period smooths out the trend, while a shorter period makes the trend line more sensitive to price changes.
● Mode
Choose between "Normal" and "Sensitive" modes for trend detection. The "Sensitive" mode responds more quickly to price changes, while the "Normal" mode offers smoother trend lines.
● Volatility Bands
Toggle the display of upper and lower volatility bands. These bands help identify potential areas of price exhaustion or continuation.
● Price Deviation
Toggle the display of the price deviation oscillator. This oscillator shows the deviation of the current price from the trend line and highlights extreme conditions.
● Mean Reversion Signals
Toggle the display of mean reversion signals. These signals highlight potential reversal points when the price deviates significantly from the trend.
● Strong Mean Reversion Signals
Toggle the display of stronger mean reversion signals, which occur at more extreme deviations from the trend.
● Width
Adjust the thickness of the trend line for better visibility on the chart.
🔵 CONCLUSION
The Periodical Trend indicator combines trend analysis, volatility bands, and mean reversion signals to provide traders with a comprehensive tool for market analysis. By offering customizable display options and dynamic trend coloring, this indicator can adapt to different trading styles and market conditions. Whether you are a trend follower or a mean reversion trader, the Periodical Trend indicator helps identify key market opportunities and potential reversals.
For optimal results, it is recommended to use this indicator alongside other technical analysis tools and within the context of a well-structured trading strategy.
Trend
Multi Adaptive Moving Average (MAMA)The Multi Adaptive Moving Average (MAMA) indicator is an advanced tool for technical analysis, designed to provide traders with a detailed understanding of market trends and potential future price movements. This indicator utilizes multiple Simple Moving Averages (SMAs) and forecasting techniques to enhance decision-making processes.
Simple Moving Averages (SMAs):
Short MA (20-period): This moving average is highly responsive to price changes, making it ideal for capturing short-term trends. It helps traders identify quick market shifts and potential entry or exit points.
Mid MA (50-period): This average strikes a balance between short- and long-term trends, offering insights into the market's intermediate direction. It aids in confirming the sustainability of short-term trends.
Long MA (100-period): By smoothing out price data over a longer period, this moving average is useful for identifying long-term trends and filtering out short-term volatility.
Very Long MA (200-period): Often considered a critical indicator for determining the overall market trend, this average helps confirm the direction and strength of long-term movements.
Forecasting:
Flat Forecast: This approach assumes that prices will remain constant in the near future, which is particularly useful in markets trading sideways without a clear trend direction.
Linear Regression Forecast: This method uses historical data to project future price movements, offering a dynamic forecast based on existing trends. It helps traders anticipate potential price changes and plan their strategies accordingly.
Advantages:
Comprehensive Trend Analysis: By incorporating four different SMAs, the indicator provides a layered view of market trends across various timeframes. This enables traders to identify potential trend reversals and continuations with greater accuracy.
Predictive Insights: The forecasting feature offers traders a forward-looking perspective, enabling them to anticipate market movements and adjust their trading strategies proactively. This can be especially advantageous in volatile markets.
Customization: The MAMA indicator is highly customizable, allowing traders to adjust parameters such as the source of price data and the inclusion of the current unclosed candle. This flexibility ensures that the indicator can be tailored to fit different trading styles and market conditions.
Visual Clarity: The use of distinct colors for each SMA and their forecasts enhances visual interpretation, making it easier for traders to quickly assess market conditions and make informed decisions. The inclusion of a legend further aids in distinguishing between the different moving averages and their respective forecasts.
How to Use:
Trend Confirmation: Use the alignment of the SMAs to confirm market trends. For example, when the Short MA crosses above the Mid and Long MAs, it may indicate a bullish trend, while the opposite could suggest a bearish trend.
Entry and Exit Points: Look for crossovers between the SMAs as potential signals for entering or exiting trades. The forecasts can help in timing these decisions by providing an expectation of future price movements.
Risk Management: Utilize the Very Long MA to set stop-loss and take-profit levels, as it reflects the long-term trend and can help in avoiding trades against the prevailing market direction.
The MAMA indicator is intended to support technical analysis and should not be used as the sole basis for making trading decisions. Financial markets are inherently uncertain, and past performance does not guarantee future results. Traders should use this tool in conjunction with other analytical methods and consider their risk tolerance and investment objectives. It is advisable to conduct thorough research and consult with a financial advisor before making significant trading decisions. Always be aware of the risks involved in trading and invest only what you can afford to lose.
Dow Theory based Strategy (Markttechnik)What makes this script unique?
calculates two trends at the same time: a big one for the overall strong trend - and a small one to trigger a trade after a small correction within the big trend
only if both trends (the small and the big trend) are in an uptrend, a buy signal is created: this prevents a buy signal from being generated in a falling market just because an upward movement begins in a small trend
the exit strategy can be configured very flexibly and individually: use the last low as stop loss and automatically switch to a trialing stop loss as soon as the take profit is reached (instead of finishing the trade)
the take profit strategy can also be configured - e.g. use the last high, a fixed percentage or a combination of it
plots each trade in detail on the chart - e.g. inner candles or the exact progression of the stop loss over the entire duration of the trade to allow you to analyze each trade precisely
What does the script do and how?
In this strategy an intact upward trend is characterized by higher highs and lower lows only if the big trend and the small trend are in an upward trend at the same time.
The following describes how the script calculates a buy signal. Every step is drawn to the chart immediately - see example chart above:
1. the stock rises in the big trend - i.e. in a longer time frame
2. a correction takes place (the share price falls) - but does not create a new low
3. the stock rises again in the big trend and creates a new high
From now on, the big trend is in an intact upward trend (until it falls below its last low).
This is drawn to the chart as 3 bold green zigzag lines.
But we do not buy right now! Instead, we want to wait for a correction in the big trend and for the start of a small upward trend.
4. a correction takes place (not below the low from 2.)
Now, the script also starts to calculate the small trend:
5. the stock rises in the small trend - i.e. in a shorter time frame
6. a small correction takes place (not below the low from 4.)
7. the stock rises above the high from 5.: a new high in the shorter time frame
Now, both trends are in an intact upward trend.
A buy signal is created and both the minor and major trend are colored green on the chart.
Now, the trade is active and:
the stop loss is calculated and drawn for each candle
the take profit is calculated and drawn to the chart
as soon as the price reaches the take profit or the stop loss, the trade is closed
Features and functionalities
Uptrend : An intact upward trend is characterized by higher highs and lower lows. Uptrends are shown in green on the chart.
The beginning of an uptrend is numbered 1, each subsequent high is numbered 2, and each low is numbered 3.
Downtrend: An intact downtrend is characterized by lower highs and lower lows. Downtrends are displayed in red on the chart.
Note that our indicator does not show the numbering of the points of the downtrend.
Trendless phases: If there is no intact trend, we are in a trendless phase. Trendless phases are shown in blue on the chart.
This occurs after an uptrend, when a lower low or a lower high is formed. Or after a downtrend, when a higher low or a higher high is formed.
Buy signals
A buy signal is generated as soon as a new upward trend has been formed or a new high has been established in an intact upward trend.
But even before a buy signal is generated, this strategy anticipates a possible emerging trend and draws the next possible trading opportunity to the chart.
In addition to the (not yet reached) buy price, the risk-reward ratio, the StopLoss and the TakeProfit price is shown.
With this information, you can already enter a StopBuy order, which is thus triggered directly with the then created buy signal.
You can configure, if a buy signal shall be created while the big trend is an uptrend, a downtrend and/or trendless.
Exit strategy
With this strategy, you have multiple possibilities to close your position. All of them can be configured within the settings. In general, you can combine a take profit strategy with a stop loss strategy.
The take profit price will be calculated once for each trade. It will be drawn to the chart for active trade.
Depending on your configuration, this can be the last high (which is often a resistance level), a fixed percentage added to the buy price or the maximum of both.
You can also configure that a trailing stop loss is used as soon as the take profit price is reached once.
The stop loss gets recalculated with each candle and is displayed and plotted for each active and finished trade. With this, you can easily check how the stop loss changed during your trades.
The stop loss can be configured flexibly:
Use the classic "trailing stop loss" that follows the price from below.
Set the stop loss to the last low and tighten it every time the small trend marks a new local low.
Confiure that the stop loss is tightened as soon as the break even is reached. Nothing is more annoying than a trade turning from a win to a loss.
Ignore inside candles (see description below) and relax the stop loss to use the outside candle for its calculation.
Inner candles
Inner candles are created when the candle body is within the maximum values of a previous candle (the outer candle). There can be any number of consecutive inner candles. As soon as you have activated the "Check inner candles" setting, all consecutive inner candles will be highlighted in yellow on the chart.
Prices during an inner candle scenario might be irrelevant for trading and can be interpreted as fluctuations within the outside candle. For this reason, the trailing stop loss should not be aligned with inner candles. Therefore, as soon as an inner candle occurs, the stop loss is reset and the low at the time of the outside candle is used as the calculation for the trailing stop loss. This will all be plotted for you on the chart.
Display of the trades:
All active and closed trades of the last 5 years are displayed in the chart with buy signal, sell, stop loss history, inside candles and statistics.
Backtesting:
The strategy can be simulated for each stock over the period of the last 5 years. Each individual trade is recorded and can be traced and analyzed in the chart including stop loss history. Detailed evaluations and statistics are available to evaluate the performance of the strategy.
Additional Statistics
This strategy immediately displays a statistic table to the chart area giving you an overview of its performance over the last years for the given chart.
This includes:
The total win/loss in $ and %
The win/loss per year in %
The active investment time in days and % (e.g. invested 10 of 100 trading days -> 10%)
The total win/loss in %, extrapolated to 100% equity usage: Only with this value can strategies really be compared. Because you are not invested between the trades and could invest in other stocks during this time. This value indicates how much profit you would have made if you had been invested 100% of the time - or to put it another way - if you had been invested 100% of the time in stocks with exactly the same performance. Let's say you had only one trade in the last 5 years that lasted, say, only one month and made 5% profit. This would be significantly better than a strategy with which you were invested for, say, 5 years and made 10% profit.
The total profit/loss per year in %, extrapolated to 100% equity usage
Notifications (alerts):
Get alerted before a new buy signal emerges to create an order if necessary and not miss a trade. You can also be notified when the stop loss needs to be adjusted. The notification can be done in different ways, e.g. by Mail, PopUp or App-Notification. This saves them the annoying, time-consuming and error-prone "click through" all the charts.
Settings: Display Settings
With these settings, you have the possibility to:
Show the small or the big trend as a background color
Configure if the numbers (1-2-3-2-3) shall be shown at all or only for the small, the big trend or both
Settings: Trend calculation - fine tuning
Drawing trend lines on a chart is not an exact science. Some highs and lows are not very clear or significant. And so it will always happen that 2 different people would draw different trendlines for the same chart. Unfortunately, there is no exact "right" or "wrong" here.
With the options under "Trend Calculation - Fine Tuning" you have the possibility to influence the drawing in of trends and to adapt it to your personal taste.
Small Trend, Big Trend : With these settings you can influence how significant a high or low has to be to recognize them as an independent high or low. The larger the values, the more significant a high or low must be to be recognized as such.
High and low recognition : With this setting you can influence when two adjacent, almost identical highs or lows should be recognized as independent highs or lows. The higher the value, the more different "similar" highs or lows must be in order to be recognized as such.
Which default settings were selected and why
Show Trades: true - its often useful to see all recent trades in the chart
Time Frame: 1 day - most common time frame (except for day traders)
Take Profit: combined 10% - the last high is taken as take profit because the trend often changes there, but only if there is at least 10% profit to ensure we do not risk money for a tiny profit
Stop Loss: combined - the last low is used as stop loss because the trend would break there and switch to a trailing stop loss as soon as our take profit is reached to let our profits run without risking them anymore
Stop Loss distance: 3% - we are giving the price 3% air (below the last low) to avoid being stopped out due to a short price drop
Trailing Stop Loss: 2% - we have to give the stop loss some room to avoid being stopped out prematurely; this is a value that is well balanced between a certain downside distance and the profit-taking ratio
Set Stop Loss to break even: true, 2% - once we reached the break even, it is a common practice to not risk our money anymore, the value is set to the same value as the trailing stop loss
Trade Filter: Uptrend - we only start trades if the big trend is an uptrend in the expectation that it will continue after a small correction
Display settings: those will not influence the trades, feel free to change them to your needs
Trend calculation - Fine Tuning: 1/1,5/0,05; influences the internal calculation for highs and lows and how significant they need to be to be considered a new high or low; the default values will provide you nicely calculated trends in the daily time frame; if there are too many or too few lows and highs according to your taste, feel free to play around and immediately see the result drawn to the chart; read the manual for a detailed description of this values
Note that you can (and should) configure the general trading properties like your initial capital, order size, slippage and commission.
Displacement [QuantVue]Displacement refers to a significant and forceful price movement that indicates a potential shift in market sentiment or trend. Displacement is characterized by a strong push in price action, often seen after a period of consolidation or within a trending market. It is a key concept used to identify the strength of a move and to confirm the direction of the market.
The "Displacement" indicator does this by focusing on identifying strong, directional price movements by combining candlestick analysis with volatility (ATR).
Displacement often appears as a group of candles that are all positioned in the same direction, these candles typically have large bodies and short wicks.
How the indicator works:
Body Size Requirement: Ensures that only candles with a significant body size (relative to their total range) are considered, helping to identify strong market moves.
Consecutive Candle Analysis: Identifies shifts in market sentiment by requiring a series of consecutive bullish or bearish candles to confirm a potential change in trend.
ATR-Based Analysis: Uses the Average True Range (ATR) to gauge market volatility and filter out minor price fluctuations, focusing on substantial movements.
Once all of the requirements are met a triangle is plotted above or below the bar.
Trend Signals with TP & SL [UAlgo] StrategyThe "Trend Signals with TP & SL Strategy" is a trading strategy designed to capture trend continuation signals while incorporating sophisticated risk management techniques. This strategy is tailored for traders who wish to capitalize on trending market conditions with precise entry and exit points, automatically calculating Take Profit (TP) and Stop Loss (SL) levels based on either Average True Range (ATR) or percentage values. The strategy aims to enhance trade management by preventing multiple simultaneous positions and dynamically adapting to changing market conditions.
This strategy is highly configurable, allowing traders to adjust sensitivity, the ATR calculation method, and the cloud moving average length. Additionally, the strategy can display buy and sell signals directly on the chart, along with visual representation of entry points, stop losses, and take profits. It also features a cloud-based trend analysis using a MACD-driven color fill that indicates the strength and direction of the trend.
🔶 Key Features
Configurable Trend Continuation Signals:
Source Selection: The strategy uses the midpoint of the high-low range as the default source, but it is adjustable.
Sensitivity: The sensitivity of the trend signals can be adjusted using a multiplier, ranging from 0.5 to 5.
ATR Calculation: The strategy allows users to choose between two ATR calculation methods for better adaptability to different market conditions.
Cloud Moving Average: Traders can adjust the cloud moving average length, which is used in conjunction with MACD to provide a visual trend indication.
Take Profit & Stop Loss Management:
ATR-Based or Percent-Based: The strategy offers flexibility in setting TP and SL levels, allowing traders to choose between ATR-based multipliers or fixed percentage values.
Dynamic Adjustment: TP and SL levels are dynamically adjusted according to the selected method, ensuring trades are managed based on real-time market conditions.
Prevention of Multiple Positions:
Single Position Control: To reduce risk and enhance strategy reliability, the strategy includes an option to prevent multiple positions from being opened simultaneously.
Visual Trade Indicators:
Buy/Sell Signals: Clearly displays buy and sell signals on the chart for easy interpretation.
Entry, SL, and TP Lines: Draws lines for entry price, stop loss, and take profit directly on the chart, helping traders to monitor trades visually.
Trend Cloud: A color-filled cloud based on MACD and the cloud moving average provides a visual cue of the trend’s direction and strength.
Performance Summary Table:
In-Chart Statistics: A table in the top right of the chart displays key performance metrics, including total trades, wins, losses, and win rate percentage, offering a quick overview of the strategy’s effectiveness.
🔶 Interpreting the Indicator
Trend Signals: The strategy identifies trend continuation signals based on price action relative to an ATR-based threshold. A buy signal is generated when the price crosses above a key level, indicating an uptrend. Conversely, a sell signal occurs when the price crosses below a level, signaling a downtrend.
Cloud Visualization: The cloud, derived from MACD and moving averages, changes color to reflect the current trend. A positive cloud in aqua suggests an uptrend, while a red cloud indicates a downtrend. The transparency of the cloud offers further nuance, with more solid colors denoting stronger trends.
Entry and Exit Management: Once a trend signal is generated, the strategy automatically sets TP and SL levels based on your chosen method (ATR or percentage). The stop loss and take profit lines will appear on the chart, showing where the strategy will exit the trade. If the price reaches either the SL or TP, the trade is closed, and the respective line is deleted from the chart.
Performance Metrics: The strategy’s performance is tracked in real-time with an in-chart table. This table provides essential information about the number of trades executed, the win/loss ratio, and the overall win rate. This information helps traders assess the strategy's effectiveness and make necessary adjustments.
This strategy is designed for those who seek to engage with trending markets, offering robust tools for entry, exit, and overall trade management. By understanding and leveraging these features, traders can potentially improve their trading outcomes and risk management.
🔷 Related Script
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Change in State of Delivery (CISD) [LuxAlgo]The Change In State Of Delivery (CISD) indicator detects and displays Change in State Of Delivery, a concept related to market structures.
Users can choose between two different CISD detection methods. Various filtering options are also included to filter out less significant CISDs.
🔶 USAGE
A Change in State of Delivery (CISD) is a concept closely related to market structures, where price breaks a level of interest, confirming trends and their continuations from the resulting breakouts.
Unlike more traditional market structures which rely on swing points, CISDs rely on a persistent sequence of candles, using the sequence extremes as breakout levels.
CISDs are detected as follows:
Bullish: The price closes above the opening price of the first candle in a sequence of bearish candles (or its own opening price if it's the only candle).
Bearish: The price closes below the opening price of the first candle in a sequence of bullish candles (or its own opening price if it's the only candle).
If a newly detected CISD aligns with the indicator's current established trend, this confirms a trend continuation (represented with a dashed line).
On the other hand, if a newly detected CISD is in the opposite direction to the detected trend it can confirm a trend reversal (represented with a solid line).
🔹 Liquidity Sweep Detection Method
Using Liquidity Sweeps to update CISD breakout levels allows us to obtain less frequent and more relevant levels that are less sensitive to noisy price variations.
Sweeps are obtained from detected Swing Points , with a higher Swing Length allowing us to obtain longer-term swing levels and potentially more detected sweeps from a specific level over time.
Note: The 'Swing Length' setting is only applicable on the Liquidity Sweep Detection Method and will only change the Liquidity levels.
A Liquidity Sweep is valid when the price reaches an important liquidity level , after which the price closes below/above this level.
Bullish scenario: The price goes below a previous unbroken Swing Low but closes above.
Bearish scenario: The price goes above a previous unbroken Swing High but closes below.
After a Liquidity Sweep has been detected, the last level of importance acts as support/resistance . Breaking this level in the other direction changes the state of delivery .
Users must keep observing the price and significant levels, as highlighted by the white rectangle in the above example.
🔹 CISD Filtering
Users can adjust the following two settings:
Minimum CISD Duration: The minimum length of the 'CISD' line
Maximum Swing Validity: The maximum length of the 'CISD' line; potential CISD lines that aren't broken are deleted when exceeding the limit.
The chart can get cluttered when the Minimum CISD Duration is low. Users could focus on a switch in trend (first solid line CISD ), where the following dashed CISD lines can be seen as extra opportunities/confirmations.
🔶 DETAIL
🔹 Using Different Timeframes
When an important liquidity level (Previous Swing high/low, FVG, etc.) is reached on the higher timeframe, the user can move to a lower timeframe to check whether there is a CISD .
Above example:
The high of the last candle breaches a liquidity level (previous Swing High). The opening price of the last candle acts as a trigger/confirmation level.
A confirmed CISD is seen in a lower timeframe, just after this Liquidity Sweep. This could be an early opportunity.
Later, a confirmed CISD on the higher timeframe is established.
🔶 SETTINGS
Detection Method: Classic or Liquidity Sweep
Swing Length: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Minimum CISD Duration: The minimum length of the CISD line
Maximum Swing Validity: The maximum length of the CISD line; potential CISD lines that aren't broken are deleted when exceeding the limit.
Radius Trend [ChartPrime]RADIUS TREND
⯁ OVERVIEW
The Radius Trend [ ChartPrime ] indicator is an innovative technical analysis tool designed to visualize market trends using a dynamic, radius-based approach. By incorporating adaptive bands that adjust based on price action and volatility, this indicator provides traders with a unique perspective on trend direction, strength, and potential reversal points.
The Radius Trend concept involves creating a dynamic trend line that adjusts its angle and position based on market movements, similar to a radius sweeping across a chart. This approach allows for a more fluid and adaptive trend analysis compared to traditional linear trend lines.
◆ KEY FEATURES
Dynamic Trend Band: Calculates and plots a main trend band that adapts to market conditions.
Radius-Based Adjustment: Uses a step-based radius approach to adjust the trend band angle.
// Apply step angle to trend lines
if bar_index % n == 0 and trend
multi1 := 0
multi2 += step
band += distance1 * multi2
if bar_index % n == 0 and not trend
multi1 += step
multi2 := 0
band -= distance1 * multi1
Volatility-Adjusted Calculations: Incorporates price range volatility for more accurate band placement.
Trend Direction Visualization: Provides clear color-coding to distinguish between uptrends and downtrends.
Flexible Parameters: Allows users to adjust the radius step and initial distance for customized analysis.
◆ USAGE
Trend Identification: Use the color and direction of the main band to determine the current market trend.
Trend Strength Analysis: Observe the angle and consistency of the band for insights into trend strength.
Reversal Detection: Watch for price crossing the main band or crossing a dashed band as a potential trend reversal signal.
Volatility Assessment: The distance between price and bands can provide insights into market volatility.
⯁ USER INPUTS
Radius Step: Controls the rate of angle adjustment for the trend band (default: 0.15, step: 0.001).
Start Points Distance: Sets the initial distance multiplier for band calculations (default: 2, step: 0.1).
The Radius Trend indicator offers traders a unique and dynamic approach to trend analysis. By combining radius-based trend adjustments with volatility-sensitive calculations, it provides a fluid representation of market trends. This indicator is particularly useful for traders looking to identify trend persistence, potential reversal points, and adaptive support/resistance levels across various market conditions and timeframes.
Gaussian Kernel Smoothing EMAGaussian Kernel Smoothing EMA
The Gaussian Kernel Smoothing EMA integrates the exponential moving average with kernel smoothing techniques to refine the trend tool. Kernel smoothing is a non-parametric technique used to estimate a smooth curve from a set of data points. It is particularly useful in reducing noise and capturing the underlying structure of data. The smoothed value at each point is calculated as a weighted average of neighboring points, with the weights determined by a kernel function.
The Gaussian kernel is a popular choice in kernel smoothing due to its properties of being smooth, symmetric, and having infinite support. This function gives higher weights to data points closer to the target point and lower weights to those further away, resulting in a smooth and continuous estimate. Since price isn't normally distributed a logarithmic transformation is performed to remove most of its skewness to be able to fit the Gaussian kernel.
This indicator also has a bandwidth, which in kernel smoothing controls the width of the window over which the smoothing is performed. It determines how much influence nearby data points have on the smoothed value. In this indicator, the bandwidth is dynamically adjusted based on the standard deviation of the log-transformed prices so that the smoothing adapts to the underlying variability and potential volatility.
Bandwidth Factor: The bandwidth factor in this indicator is used to adjust the degree of the smoothing applied to the MA. In kernel smoothing, Bandwidth controls the width of the window over which the smoothing is applied. It determines how many data points around a central point are considered when calculating a smooth value. A smaller bandwidth results in less smoothing, while a larger bandwidth smooths out more noise, leading to a broader, more general trend.
Gaussian Kernel Smoothing MomentumOverview:
The Gaussian Kernel Smoothing Momentum indicator analyzes and quantifies market momentum by applying statistical techniques to price and returns data. This indicator uses Gaussian kernel smoothing to filter noise and provide a more accurate representation of momentum. Additionally, it includes a option to evaluate the absolute score of the momentum to determine if the beginning of a "trend" is likely or if you can expect a "trend" to come to an end.
Kernels and Their Role In Time Series Analysis:
In statistical analysis, a kernel is a weighting function used to estimate the properties of a dataset. Kernels are particularly useful in non-parametric methods, where they serve to smooth data or estimate probability density functions without assuming a specific underlying distribution. The Gaussian kernel, one of the most commonly used, is characterized by its smooth, bell-shaped curve which provides a natural way to give more weight to data points closer to the target value and less weight to those further away.
Uses of Kernels in Time Series Analysis
Kernels play a significant role in time series analysis, especially in the context of smoothing and filtering. With kernel functions, you can reduce noise and extract the underlying systematic component or signal from the data. This process is essential for identifying long-term patterns in the data, which is often obscured by short-term fluctuations and random noise.
Kernel Smoothing
Kernel smoothing is a technique that applies a kernel function to a set of data points to create a smooth curve, effectively reducing the impact of random variations. In time series analysis, kernel smoothing helps to filter out short-term noise while retaining significant trends and "patterns". The Gaussian kernel, with its emphasis on nearby points, is particularly effective for this purpose, as it smooths the data in a way that highlights the underlying structure without overfitting to random fluctuations.
Additionally, kernels are used in non-parametric volatility estimation, option pricing models, and for detecting anomalies in financial data. Their flexibility and ability to handle complex, non-linear relationships make them well-suited for the often noisy data encountered in financial markets.
Momentum Component
The momentum component of the indicator is designed to quantify the directional movement of asset prices by applying the Gaussian kernel smoothing to the expected return of the price data. The data then has the variance stabilized and normalizes the distribution of price changes to be able to more efficiently analyze the momentum.
The Gaussian kernel smoothing function serves to filter out high-frequency noise, isolating the underlying systematic component of the momentum. This is achieved by weighting the data points based on their proximity to the current observation, with closer data points exerting a stronger influence. The resulting smoothed momentum provides a clearer of the directional bias in the market, devoid of short-term volatility.
Absolute Move Component
The absolute move component is a extension of the momentum analysis, focusing on the magnitude rather than the direction of the price movements. This component captures the absolute score of the smoothed momentum series, providing a measure of strength or intensity of the price movement, independent from its direction. The absolute move component also incorporates a Kalman filter to further smooth and refine the signal. The Kalman filter dynamically adjusts based on the observed variance in the data, to reduce the impact of outliers.
What to make of this indicator
The smoothed momentum line helps determine whether the market is experiencing upward and downward momentum. If the momentum line is above zero and rising, this suggest a positive expected returns. Conversely, if the momentum line is below zero and falling, it indicates negative expected returns.
You should also pay attention to changes in the slope of the momentum line and the moving average of the smoothed momentum(weighted with an optimal sampling size algorithm). A flattening or reversal of the slope may signal a potential shift in market direction. For example, if the momentum line and moving average transitions from rising to falling, it means that the expected return is going from positive to negative so you can see the "trend" as weakening or forming a trend of negative expected returns.
The absolute move component is designed to measure the intensity or strength of the current market movement. A low absolute move value, especially when they are negative or at the lower end of their band, indicates that the momentum and expected return is close to zero, which suggest that the market is experiencing minimal directional movement, which can be a sign of consolidation. High absolute values signal that the market is undergoing a significant price movement. When the absolute move is high and/or rising, it indicates that the movement of the momentum is strong, regardless of whether it is bullish or bearish.
If the absolute move reaches unusually high levels, it could indicate that the market is experiencing an exceptional price move, which might be unsustainable. Traders can anticipate potential reversals or profit taking targets. However, you should avoid trying to trade reversals as exceptionally high values in a time series do not guarantee an immediate reversal. This high values often occur during periods of strong trends or significant events, which can continue longer than expected, and you cant time when it will return to its mean. The mean-reverting nature of some statistical models can suggest a return to the mean, but this assumption can be misleading in financial markets, where trends can persist despite overextending conditions.
AutocorrelationWhat is Autocorrelation?
Autocorrelation is a mathematical concept used to measure the degree of similarity between a given time series and a lagged version of itself over successive time intervals. Mathematically, it is the correlation of a signal with a delayed copy of itself as a function of delay. In simpler terms, autocorrelation helps us understand whether and how past values in a time series are related to future values.
Autocorrelation in Finance:
In finance, the autocorrelation is a tool used to analyze the behavior of time series data, such as asset prices or returns. It can reveal "patterns", trends, or cycles within the data.
Price Autocorrelation: When applied to prices, autocorrelations can indicate whether an asset price tends to follow a trend. On price you will typically observe positive autocorrelation because price often exhibits a momentum effect where today's price is positively correlated with past prices. As a result, when prices are trending, they tend to continue in the same direction, creating a positive autocorrelation.
Returns Autocorrelation: Returns on the other hand, generally show less autocorrelation than prices. This is because returns represent the change in prices over time, and in efficient markets, returns are often modeled as a random walk, leading to low or no significant autocorrelation. However, under certain market conditions, you may observe positive or negative autocorrelation in returns. Positive Autocorrelation of returns indicates a trend effect, where past returns can predict future returns. Negative Returns Autocorrelation suggest mean reversion, where large positive returns are often followed by negative returns and vice versa.
Critical Value Analysis:
This indicator comes with critical values based on user-selected confidence levels (90%,95%,99%). It assesses whether the autocorrelation at a particular lag is statistically significant, which is crucial for distinguishing between random noise and meaningful events.
Trading Based on Autocorrelation:
While this indicator was not really developed to be directly used for trading, this indicator is was instead to raise awareness on why you should avoid strategies involving mean reversion on price.
Pace ProOverview
The Pace Pro indicator is a robust trend-following tool designed for versatile application across various timeframes and markets, including stocks, forex, futures and cryptocurrencies. It provides traders with "bull" and "bear" signals, take profit (TP) signals, and volume spike indications. This indicator aims to help traders identify potential trading opportunities through trends, reversals and price exhaustion.
Key Features
Bull and Bear Signals: Pace Pro generates green "bull" and red "bear" signals based on a trend strength score derived from an aggregation of components.
Take Profit (TP) Signals: The indicator plots black "TP" signals at areas of price exhaustion.
Volume Spike Indicators: The indicator colors candles to signify high volume spikes—light green for high bullish volume and light red for high bearish volume.
Price Clouds: The indicator includes three types of Bollinger Band clouds. These clouds help visualize exhaustion and volatility, providing traders with multiple perspectives on market dynamics.
How it works:
Trend Strength: This score is calculated using a proprietary formula that assesses the magnitude and direction of market movement with standard deviation and regression analysis. Standard deviation computes the average price over a specified period and then calculates the standard deviation of prices from this average. A linear regression is performed on the closing prices over a specified period. The slope of the regression line is used to identify the trend direction, and the standard deviation is used to assess trend stability and filter out noise, working together to clearly identify direction and robustness. Bull/Bear signals are produced based on trend strength reaching specific thresholds, configurable in the settings.
Overbought/Oversold Strength: This strength identifies price exhaustion using a unique formula that aggregates values from several indicators such as RVI, RSI and CCI. RVI captures price trends, RSI measures momentum, and CCI identifies price deviations from the mean, providing a comprehensive view of market conditions. Take profit signals are plotted at points of high price exhaustion, indicating optimal exit prices.
Volume Analysis: Volume spikes are identified and highlighted with colored candles using an ATR calculation that pinpoints outliers in volume. This is calculated using the math.abs function, identifying volume spikes in the last 14 bars. Volume spike candle size can be configured in settings to the user's liking.
Bollinger Band Clouds: The indicator employs Bollinger Band clouds based on WMA, VWMA, and EMA to provide a comprehensive view of market volatility and trend strength. WMA responds quickly to price changes, VWMA incorporates volume, and EMA smooths out data, offering a unique and adaptive perspective on market conditions. This combination is used to provide a unique perspective on market volatility, utilizing different moving averages. These clouds adapt to price fluctuations and offer visual cues to enhance trend analysis.
Utility
This tool provides traders with valuable information for trend-following and reversal strategies across different timeframes. It helps traders by:
-Generating "bull" and "bear" signals to indicate potential long, short and exit points. The precise calculation methods and statistical components used in deriving the trend strength score are designed to filter out market noise and provide a clear indication of prevailing market trends.
-Providing "TP" signals at areas of price exhaustion, areas where taking profit is optimal. These also serve as potential reversal points in the market as they incorporate reversion analysis techniques.
-Highlighting high volume spikes with colored candles to indicate significant market activity. These volatile candles can indicate a significant and rapid surge in price.
-Offering visual insights through Bollinger Band clouds, which help traders assess overbought and oversold conditions on a broad scale. These aid in visualizing potential reversals in the market.
Rationale and Benefits of Component Combination
The combination of trend strength, overbought/oversold strength, volume analysis, and Bollinger Band clouds provides a holistic approach to market analysis and allows users to use various techniques of trading analysis to make sound trading decisions. Each component serves a distinct purpose:
-Trend Strength identifies and confirms the direction and magnitude of market trends, offering clear bull and bear signals. A trend score is calculated to clearly identify where price is strongly trending and where it is quite weak. This customizable feature allows traders to configure this indicator to their liking by only plotting signals when the trend reaches a desired threshold.
-Overbought/Oversold Strength pinpoints areas of price exhaustion, providing crucial take profit and reversal conditions in the market. I combine RSI, RVI, and CCI to provide a more robust reversion score. My rationale for this is to leverage data from multiple indicators, to ensure a comprehensive assessment of price exhaustion rather than relying on a single source.
-Volume Analysis highlights significant market activity, giving traders insights into potential price movements. This feature is included to provide users with a visual representation of price pumps/dumps, that can aid in trading decisions in combination with entry and exit signals.
-Bollinger Band Clouds offer a visual representation of market volatility and trend strength, enhancing the overall analytical framework. Bands were calculated using a mixture of WMA, VWMA, and EMA to diversify data and to bring variety to its display. This can enhance its use as it does not use a single data source and relies on multiple.
Uniqueness:
This indicator stands out due to its innovative integration of standard deviation and regression analysis, offering traders a unique and comprehensive market analysis tool. By combining standard deviation to measure volatility and filter out noise with regression analysis to identify trend direction and strength, it provides insightful trend signals that help traders make informed decisions. This indicator's versatility is enhanced by its customizable settings, allowing traders to adapt it to their specific needs and trading styles with the trend sensitivity setting. Combining RSI, RVI, and CCI for reversion and exit points is unique as it integrates multiple perspectives on price momentum and volatility, providing a more comprehensive assessment of price exhaustion than using any single indicator. Combining WMA, EMA, and VWMA as bands is beneficial and unique as it blends different averaging methods to offer a more nuanced and adaptive view of market volatility and trend strength.
By integrating these components, it delivers a multifaceted tool that addresses various aspects of market analysis, making it a valuable asset for traders seeking to improve their decision-making process.
Disclaimer
Trading involves substantial risk and is not suitable for every investor. This indicator is designed to assist in decision-making but does not guarantee profits or prevent losses. Always conduct your own research and consider seeking advice from a financial professional.
TradeMate - Trend TamerTradeMate Trend Tamer
The TradeMate Trend Tamer is designed to help traders identify potential trend reversals and navigate periods of high market volatility. This tool combines a custom EMA-based oscillator with a volatility detection mechanism, providing traders with actionable signals that are easy to interpret and apply.
🔶 Originality and Utility
The TradeMate Trend Tamer is not just a mashup of indicators but a well-integrated system that enhances the reliability of trend detection. The core of this indicator is a custom EMA calculation that identifies trend shifts based on price momentum and directional changes. This EMA is further enhanced by a volatility detection system that colors bars yellow during periods of high volatility, indicating potential market reversals.
The indicator is particularly useful for traders who are looking for clear and straightforward signals to identify buying and selling opportunities, especially in volatile markets where traditional indicators might produce false signals. By combining trend arrows with volatility signals, the TradeMate Trend Tamer helps traders confirm the strength of a signal and avoid getting caught in market noise.
🔶 Description and Underlying Logic
The TradeMate Trend Tamer uses a custom EMA calculation that smooths price movements to detect significant shifts in momentum. This EMA is plotted on the chart and is complemented by arrows indicating potential buy or sell signals:
Upward Arrows: These appear when the EMA indicates an upward momentum shift, suggesting a potential buying opportunity.
Downward Arrows: These indicate a downward momentum shift, signaling a potential selling opportunity.
The volatility detection mechanism works by analyzing the ATR (Average True Range) over a specified lookback period. The indicator identifies extreme volatility zones where the ATR exceeds a certain threshold, coloring the bars yellow to visually alert traders. This helps traders identify when the market is more likely to reverse, making the combination of trend arrows and volatility signals a powerful tool for decision-making.
🔶 Using the TradeMate Trend Tamer
Traders should use the trend arrows as an initial signal and confirm it with the yellow-colored volatility bars. For example:
High Volatility with Upward Arrow: Indicates a strong buy signal as the market is likely to reverse upwards.
High Volatility with Downward Arrow: Indicates a strong sell signal, suggesting a potential downward reversal.
By following these signals, traders can enhance their entry and exit strategies, especially in markets prone to sudden moves.
Polynomial Regression Keltner Channel [ChartPrime]Polynomial Regression Keltner Channel
⯁ OVERVIEW
The Polynomial Regression Keltner Channel [ ChartPrime ] indicator is an advanced technical analysis tool that combines polynomial regression with dynamic Keltner Channels. This indicator provides traders with a sophisticated method for trend analysis, volatility assessment, and identifying potential overbought and oversold conditions.
◆ KEY FEATURES
Polynomial Regression: Uses polynomial regression for trend analysis and channel basis calculation.
Dynamic Keltner Channels: Implements Keltner Channels with adaptive volatility-based bands.
Overbought/Oversold Detection: Provides visual cues for potential overbought and oversold market conditions.
Trend Identification: Offers clear trend direction signals and change indicators.
Multiple Band Levels: Displays four levels of upper and lower bands for detailed market structure analysis.
Customizable Visualization: Allows toggling of additional indicator lines and signals for enhanced chart analysis.
◆ FUNCTIONALITY DETAILS
⬥ Polynomial Regression Calculation:
Implements a custom polynomial regression function for trend analysis.
Serves as the basis for the Keltner Channel, providing a smoothed centerline.
//@function Calculates polynomial regression
//@param src (series float) Source price series
//@param length (int) Lookback period
//@returns (float) Polynomial regression value for the current bar
polynomial_regression(src, length) =>
sumX = 0.0
sumY = 0.0
sumXY = 0.0
sumX2 = 0.0
sumX3 = 0.0
sumX4 = 0.0
sumX2Y = 0.0
n = float(length)
for i = 0 to n - 1
x = float(i)
y = src
sumX += x
sumY += y
sumXY += x * y
sumX2 += x * x
sumX3 += x * x * x
sumX4 += x * x * x * x
sumX2Y += x * x * y
slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX)
intercept = (sumY - slope * sumX) / n
n - 1 * slope + intercept
⬥ Dynamic Keltner Channel Bands:
Calculates ATR-based volatility for dynamic band width adjustment.
Uses a base multiplier and adaptive volatility factor for flexible band calculation.
Generates four levels of upper and lower bands for detailed market structure analysis.
atr = ta.atr(length)
atr_sma = ta.sma(atr, 10)
// Calculate Keltner Channel Bands
dynamicMultiplier = (1 + (atr / atr_sma)) * baseATRMultiplier
volatility_basis = (1 + (atr / atr_sma)) * dynamicMultiplier * atr
⬥ Overbought/Oversold Indicator line and Trend Line:
Calculates an OB/OS value based on the price position relative to the innermost bands.
Provides visual representation through color gradients and optional signal markers.
Determines trend direction based on the polynomial regression line movement.
Generates signals for trend changes, overbought/oversold conditions, and band crossovers.
◆ USAGE
Trend Analysis: Use the color and direction of the basis line to identify overall trend direction.
Volatility Assessment: The width and expansion/contraction of the bands indicate market volatility.
Support/Resistance Levels: Multiple band levels can serve as potential support and resistance areas.
Overbought/Oversold Trading: Utilize OB/OS signals for potential reversal or pullback trades.
Breakout Detection: Monitor price crossovers of the outermost bands for potential breakout trades.
⯁ USER INPUTS
Length: Sets the lookback period for calculations (default: 100).
Source: Defines the price data used for calculations (default: HLC3).
Base ATR Multiplier: Adjusts the base width of the Keltner Channels (default: 0.1).
Indicator Lines: Toggle to show additional indicator lines and signals (default: false).
⯁ TECHNICAL NOTES
Implements a custom polynomial regression function for efficient trend calculation.
Uses dynamic ATR-based volatility adjustment for adaptive channel width.
Employs color gradients and opacity levels for intuitive visual representation of market conditions.
Utilizes Pine Script's plotchar function for efficient rendering of signals and heatmaps.
The Polynomial Regression Keltner Channel indicator offers traders a sophisticated tool for trend analysis, volatility assessment, and trade signal generation. By combining polynomial regression with dynamic Keltner Channels, it provides a comprehensive view of market structure and potential trading opportunities. The indicator's adaptability to different market conditions and its customizable nature make it suitable for various trading styles and timeframes.
Bias Finder [UAlgo]The "Bias Finder " indicator is a tool designed to help traders identify market bias and trends effectively. This indicator leverages smoothed Heikin Ashi candles and oscillators to provide a clear visual representation of market trends and potential reversals. By utilizing higher timeframes and smoothing techniques, the indicator aims to filter out market noise and offer a more reliable signal for trading decisions.
🔶 Key Features
Heikin Ashi Candles: The indicator uses Heikin Ashi candles, a special type of candlestick that incorporates information from the previous candle to potentially provide smoother visuals and highlight potential trend direction.
Oscillator: The indicator calculates an oscillator based on the difference between the smoothed opening and closing prices of a higher timeframe. This oscillator helps visualize the strength of the bias.
Light Teal: Strong bullish trend.
Dark Teal: Weakening bullish trend.
Light Red: Strong bearish trend.
Dark Red: Weakening bearish trend.
Standard Deviation: The indicator can optionally display upper and lower standard deviation bands based on the Heikin Ashi high and low prices. These bands can help identify potential breakout areas.
Oscillator Period: Adjust the sensitivity of the oscillator.
Higher Timeframe: Select a timeframe for the Heikin Ashi candles and oscillator calculations (must be equal to or greater than the chart's timeframe).
Display Options: Choose whether to display Heikin Ashi candles, market bias fill, standard deviation bands, and HA candle colors based on the bias.
Alerts: Enable/disable specific alerts and customize their messages.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
LOWESS (Locally Weighted Scatterplot Smoothing) [ChartPrime]LOWESS (Locally Weighted Scatterplot Smoothing)
⯁ OVERVIEW
The LOWESS (Locally Weighted Scatterplot Smoothing) [ ChartPrime ] indicator is an advanced technical analysis tool that combines LOWESS smoothing with a Modified Adaptive Gaussian Moving Average. This indicator provides traders with a sophisticated method for trend analysis, pivot point identification, and breakout detection.
◆ KEY FEATURES
LOWESS Smoothing: Implements Locally Weighted Scatterplot Smoothing for trend analysis.
Modified Adaptive Gaussian Moving Average: Incorporates a volatility-adapted Gaussian MA for enhanced trend detection.
Pivot Point Identification: Detects and visualizes significant pivot highs and lows.
Breakout Detection: Tracks and optionally displays the count of consecutive breakouts.
Gaussian Scatterplot: Offers a unique visualization of price movements using randomly colored points.
Customizable Parameters: Allows users to adjust calculation length, pivot detection, and visualization options.
◆ FUNCTIONALITY DETAILS
⬥ LOWESS Calculation:
Utilizes a weighted local regression to smooth price data.
Adapts to local trends, reducing noise while preserving important price movements.
⬥ Modified Adaptive Gaussian Moving Average:
Combines Gaussian weighting with volatility adaptation using ATR and standard deviation.
Smooths the Gaussian MA using LOWESS for enhanced trend visualization.
⬥ Pivot Point Detection and Visualization:
Identifies pivot highs and lows using customizable left and right bar counts.
Draws lines and labels to mark broke pivot points on the chart.
⬥ Breakout Tracking:
Monitors price crossovers of pivot lines to detect breakouts.
Optionally displays and updates the count of consecutive breakouts.
◆ USAGE
Trend Analysis: Use the color and direction of the smoothed Gaussian MA line to identify overall trend direction.
Breakout Trading: Monitor breakouts from pivot levels and their persistence using the breakout count feature.
Volatility Assessment: The spread of the Gaussian scatterplot can provide insights into market volatility.
⯁ USER INPUTS
Length: Sets the lookback period for LOWESS and Gaussian MA calculations (default: 30).
Pivot Length: Determines the number of bars to the left for pivot calculation (default: 5).
Count Breaks: Toggle to show the count of consecutive breakouts (default: false).
Gaussian Scatterplot: Toggle to display the Gaussian MA as a scatterplot (default: true).
⯁ TECHNICAL NOTES
Implements a custom LOWESS function for efficient local regression smoothing.
Uses a modified Gaussian MA calculation that adapts to market volatility.
Employs Pine Script's line and label drawing capabilities for clear pivot point visualization.
Utilizes random color generation for the Gaussian scatterplot to enhance visual distinction between different time periods.
The LOWESS (Locally Weighted Scatterplot Smoothing) indicator offers traders a sophisticated tool for trend analysis and breakout detection. By combining advanced smoothing techniques with pivot point analysis, it provides a comprehensive view of market dynamics. The indicator's adaptability to different market conditions and its customizable nature make it suitable for various trading styles and timeframes.
Brooks Always In [KintsugiTrading]Brooks Always In
Overview:
The "Brooks Always In Indicator" by KintsugiTrading is a tool designed for traders who follow price action methodologies inspired by Al Brooks. This indicator identifies key bar patterns and breakouts, plots an Exponential Moving Average (EMA), and highlights consecutive bullish and bearish bars. It is intended to assist traders in making informed decisions based on price action dynamics.
Features:
Consecutive Bar Patterns:
Identifies and highlights consecutive bullish and bearish bars.
Differentiates between bars that are above/below the EMA and those that are not.
Customizable EMA:
Option to display an Exponential Moving Average (EMA) with user-defined length and offset.
The EMA can be smoothed using various methods such as SMA, EMA, SMMA (RMA), WMA, and VWMA.
Breakout Patterns:
Recognizes bullish and bearish breakout bars and outside bars.
Tracks inside bars and prior bar conditions to better understand the market context.
Customizable Display:
Users can display or hide the EMA, consecutive bar patterns, and consecutive bars relative to the moving average.
How to Use:
Customize Settings:
First, I like to navigate to the top right corner of the chart (bolt icon), and change both the bull and bear body color to match the background (white/black) - this helps the user visualize the indicator far better.
Next, Toggle to display EMA, consecutive bar patterns, and consecutive bars relative to the moving average using the provided input options.
Adjust the EMA length, source, and offset as per your trading strategy.
Select the smoothing method and length for the EMA if desired.
Analyze Key Patterns:
Observe the highlighted bars on the chart to identify consecutive bullish and bearish patterns.
Use the plotted EMA to gauge the general trend and analyze the relationship between price bars and the moving average.
Informed Decision Making:
Utilize the identified bar patterns and breakouts to make informed trading decisions, such as identifying potential entry and exit points based on price action dynamics.
Good luck with your trading!
Trend LinesThis script, titled "Trend Lines," is designed to detect and plot significant trend lines on a TradingView chart, based on pivot points. It highlights both uptrend and downtrend lines using different colors and allows customization of line styles, including color and thickness. Here's a breakdown of how the script works:
Inputs
Left Bars (lb) and Right Bars (rb): These inputs determine the number of bars to the left and right of a pivot point used to identify significant highs and lows.
Show Pivot Points: A boolean input to display markers at detected pivot points on the chart.
Show Old Line as Dashed: A boolean input to display older trend lines as dashed for visual distinction.
Uptrend Line Color (ucolor) and Downtrend Line Color (dcolor): Color inputs to customize the appearance of uptrend and downtrend lines.
Uptrend Line Thickness (uthickness) and Downtrend Line Thickness (dthickness): Inputs to adjust the thickness of the trend lines.
Calculations
Pivot Highs and Lows: The script calculates potential pivot highs and lows by looking at lb bars to the left and rb bars to the right. If a bar's high is the highest (or low is the lowest) within this window, it is considered a pivot point.
Trend Lines: The script connects the most recent and previous pivot highs to form downtrend lines, and the most recent and previous pivot lows to form uptrend lines. These lines are drawn with the specified color and thickness.
Angles: The angle of each trend line is calculated to determine whether the trend is strengthening or weakening. If the trend changes significantly, the line's extension is adjusted accordingly.
Plotting
Pivot Point Markers: If Show Pivot Points is enabled, markers labeled "H" for highs and "L" for lows are plotted at the pivot points.
Trend Lines: The script draws lines between pivot points, coloring them according to the trend direction (uptrend or downtrend). If Show Old Line as Dashed is enabled, the script sets older lines to a dashed style to indicate they are no longer the most recent trend lines.
This script is useful for traders who want to visually identify key support and resistance levels based on historical price action, helping them to make more informed trading decisions. The customization options allow traders to tailor the appearance of the trend lines to suit their personal preferences or charting style.
OrderBlock Trend (CISD)OrderBlock Trend (CISD) Indicator
Overview:
The "OrderBlock Trend (CISD)" AKA: change in state of delivery by ICT inner circle trader this indicator is designed to help traders identify and visualize market trends based on higher timeframe candle behavior. This script leverages the concept of order blocks, which are price levels where significant buying or selling activity has occurred, to signal potential trend reversals or continuations. By analyzing bullish and bearish order blocks on a higher timeframe, the indicator provides visual cues and statistical insights into the market's current trend dynamics.
Key Features:
Higher Timeframe Analysis: The indicator uses a higher timeframe (e.g., Daily) to assess the trend direction based on the open and close prices of candles. This approach helps in identifying more significant and reliable trend changes, filtering out noise from lower timeframes.
Bullish and Bearish Order Blocks: The script detects the first bullish or bearish candle on the selected higher timeframe and uses these candles as reference points (order blocks) to determine the trend direction. A bullish trend is indicated when the current price is above the last bearish order block's open price, and a bearish trend is indicated when the price is below the last bullish order block's open price.
Visual Trend Indication: The indicator visually represents the trend using background colors and plot shapes:
A green background and a square shape above the bars indicate a bullish trend.
A red background and a square shape above the bars indicate a bearish trend.
Candle Count and Statistics: The script keeps track of the number of up and down candles during bullish and bearish trends, providing percentages of up and down candles in each trend. This data is displayed in a table, giving traders a quick overview of market sentiment during each trend phase.
User Customization: The higher timeframe can be adjusted according to the trader's preference, allowing flexibility in trend analysis based on different time horizons.
Concepts and Calculations:
The "OrderBlock Trend (CISD)" indicator is based on the concept of order blocks, a key area where institutional traders are believed to place large orders, creating significant support or resistance levels. By identifying these blocks on a higher timeframe, the indicator aims to highlight potential trend reversals or continuations. The use of higher timeframe data helps filter out minor fluctuations and focus on more meaningful price movements.
The candle count and percentage calculations provide additional context, allowing traders to understand the proportion of bullish or bearish candles within each trend. This information can be useful for assessing the strength and consistency of a trend.
How to Use:
Select the Higher Timeframe: Choose the higher timeframe (e.g., Daily) that best suits your trading strategy. The default setting is "D" (Daily), but it can be adjusted to other timeframes as needed.
Interpret the Trend Signals:
A green background indicates a bullish trend, while a red background indicates a bearish trend. The corresponding square shapes above the bars reinforce these signals.
Use the information on the proportion of up and down candles during each trend to gauge the trend's strength and consistency.
Trading Decisions: The indicator can be used in conjunction with other technical analysis tools and indicators to make informed trading decisions. It is particularly useful for identifying trend reversals and potential entry or exit points based on the behavior of higher timeframe order blocks.
Customization and Optimization: Experiment with different higher timeframes and settings to optimize the indicator for your specific trading style and preferences.
Conclusion:
The "OrderBlock Trend (CISD)" indicator offers a comprehensive approach to trend analysis, combining the power of higher timeframe order blocks with clear visual cues and statistical insights. By understanding the underlying concepts and utilizing the provided features, traders can enhance their trend detection and decision-making processes in the markets.
Disclaimer:
This indicator is intended for educational purposes and should be used in conjunction with other analysis methods. Always perform your own research and risk management before making trading decisions.
Some known bugs when you switch to lower timeframe while using daily timeframe data it didn't use the daily candle close to establish the trend change but your current time frame If some of you know how to fix it that would be great if you help me to I would try my best to fix this in the future :) credit to ChatGPT 4o
Multi-Regression StrategyIntroducing the "Multi-Regression Strategy" (MRS) , an advanced technical analysis tool designed to provide flexible and robust market analysis across various financial instruments.
This strategy offers users the ability to select from multiple regression techniques and risk management measures, allowing for customized analysis tailored to specific market conditions and trading styles.
Core Components:
Regression Techniques:
Users can choose one of three regression methods:
1 - Linear Regression: Provides a straightforward trend line, suitable for steady markets.
2 - Ridge Regression: Offers a more stable trend estimation in volatile markets by introducing a regularization parameter (lambda).
3 - LOESS (Locally Estimated Scatterplot Smoothing): Adapts to non-linear trends, useful for complex market behaviors.
Each regression method calculates a trend line that serves as the basis for trading decisions.
Risk Management Measures:
The strategy includes nine different volatility and trend strength measures. Users select one to define the trading bands:
1 - ATR (Average True Range)
2 - Standard Deviation
3 - Bollinger Bands Width
4 - Keltner Channel Width
5 - Chaikin Volatility
6 - Historical Volatility
7 - Ulcer Index
8 - ATRP (ATR Percentage)
9 - KAMA Efficiency Ratio
The chosen measure determines the width of the bands around the regression line, adapting to market volatility.
How It Works:
Regression Calculation:
The selected regression method (Linear, Ridge, or LOESS) calculates the main trend line.
For Ridge Regression, users can adjust the lambda parameter for regularization.
LOESS allows customization of the point span, adaptiveness, and exponent for local weighting.
Risk Band Calculation:
The chosen risk measure is calculated and normalized.
A user-defined risk multiplier is applied to adjust the sensitivity.
Upper and lower bounds are created around the regression line based on this risk measure.
Trading Signals:
Long entries are triggered when the price crosses above the regression line.
Short entries occur when the price crosses below the regression line.
Optional stop-loss and take-profit mechanisms use the calculated risk bands.
Customization and Flexibility:
Users can switch between regression methods to adapt to different market trends (linear, regularized, or non-linear).
The choice of risk measure allows adaptation to various market volatility conditions.
Adjustable parameters (e.g., regression length, risk multiplier) enable fine-tuning of the strategy.
Unique Aspects:
Comprehensive Regression Options:
Unlike many indicators that rely on a single regression method, MRS offers three distinct techniques, each suitable for different market conditions.
Diverse Risk Measures: The strategy incorporates a wide range of volatility and trend strength measures, going beyond traditional indicators to provide a more nuanced view of market dynamics.
Unified Framework:
By combining advanced regression techniques with various risk measures, MRS offers a cohesive approach to trend identification and risk management.
Adaptability:
The strategy can be easily adjusted to suit different trading styles, timeframes, and market conditions through its various input options.
How to Use:
Select a regression method based on your analysis of the current market trend (linear, need for regularization, or non-linear).
Choose a risk measure that aligns with your trading style and the market's current volatility characteristics.
Adjust the length parameter to match your preferred timeframe for analysis.
Fine-tune the risk multiplier to set the desired sensitivity of the trading bands.
Optionally enable stop-loss and take-profit mechanisms using the calculated risk bands.
Monitor the regression line for potential trend changes and the risk bands for entry/exit signals.
By offering this level of customization within a unified framework, the Multi-Regression Strategy provides traders with a powerful tool for market analysis and trading decision support. It combines the robustness of regression analysis with the adaptability of various risk measures, allowing for a more comprehensive and flexible approach to technical trading.
Momentum Trend [MT]The Momentum Trend indicator is an innovative technical analysis tool designed to capture and visualize momentum trends in financial markets. This advanced indicator goes beyond traditional momentum measures, offering a unique perspective on price action and trend strength.
Core Functionality:
Trend Momentum Index (TMI) Calculation:
At the heart of this indicator is the Trend Momentum Index (TMI), a proprietary algorithm that combines moving averages with price action analysis to gauge momentum. The TMI is calculated using a user-defined source, length, and moving average type.
Dynamic Trend Visualization:
The indicator uses a color-coded column plot to represent the TMI values, providing an intuitive visual representation of trend strength and direction. The colors change based on specific conditions, offering instant insights into the current market state.
Adaptive Momentum Analysis:
The TMI adapts to changing market conditions by comparing current values to historical ones, allowing for a more nuanced understanding of momentum shifts.
Key Inputs and Their Significance:
TMI Source:
Allows users to select the price data for TMI calculations. The default is the closing price, but users can choose alternative sources for different analytical perspectives.
TMI Length:
Defines the lookback period for the TMI calculation. The default of 8 provides a balance between responsiveness and stability, but users can adjust this to suit their trading style.
Moving Average Type:
Users can select from various moving average types (SMA, EMA, SMMA, WMA, VWMA) for the base calculation, allowing for customization based on trading preferences.
What Makes It Unique:
Comprehensive Momentum Analysis:
The TMI combines elements of trend following and momentum, providing a more holistic view of market dynamics than traditional momentum indicators.
Multi-Faceted Trend Identification:
The color-coding system doesn't just show bullish or bearish trends, but also identifies accelerating and decelerating momentum in both directions.
Flexible Moving Average Integration:
The ability to choose different moving average types allows traders to fine-tune the indicator's responsiveness and smoothness.
Visual Clarity:
The column-style plot with color changes offers clear, at-a-glance insights into trend strength and direction.
Momentum Comparison Logic:
The indicator incorporates logic to compare current momentum changes with recent historical changes, providing context for the current market state.
The Momentum Trend indicator represents a sophisticated approach to momentum and trend analysis. By combining moving averages, price action, and comparative momentum logic, it offers traders a powerful tool for identifying potential trend continuations, reversals, and momentum shifts.
This indicator is particularly valuable for traders looking to:
- Identify the start of new trends
- Spot potential trend reversals
- Gauge the strength of ongoing trends
- Time entries and exits based on momentum shifts
Crab Harmonic Pattern [TradingFinder] Harmonic Chart patterns🔵 Introduction
The Crab pattern is recognized as a reversal pattern in technical analysis, utilizing Fibonacci numbers and percentages for chart analysis. This pattern can predict suitable price reversal areas on charts using Fibonacci ratios.
The structure of the Crab pattern can manifest in both bullish and bearish forms on the chart. By analyzing this structure, traders can identify points where the price direction changes, which are essential for making informed trading decisions.
The pattern's structure is visually represented on charts as shown below. To gain a deeper understanding of the Crab pattern's functionality, it is beneficial to become familiar with its various harmonic forms.
🟣 Types of Crab Patterns
The Crab pattern is categorized into two types based on its structure: bullish and bearish. The bullish Crab is denoted by the letter M, while the bearish Crab is indicated by the letter W in technical analysis.
Typically, a bullish Crab pattern signals a potential price increase, whereas a bearish Crab pattern suggests a potential price decrease on the chart.
The direction of price movement depends significantly on the price's position within the chart. By identifying whether the pattern is bullish or bearish, traders can determine the likely direction of the price reversal.
Bullish Crab :
Bearish Crab :
🔵 How to Use
When trading using the Crab pattern, crucial parameters include the end time of the correction and the point at which the chart reaches its peak. Generally, the best time to buy is when the chart nears the end of its correction, and the best time to sell is when it approaches the peak price.
As we discussed, the end of the price correction and the time to reach the peak are measured using Fibonacci ratios. By analyzing these levels, traders can estimate the end of the correction in the chart waves and select a buying position for their stock or asset upon reaching that ratio.
🟣 Bullish Crab Pattern
In this pattern, the stock price is expected to rise at the pattern's completion, transitioning into an upward trend. The bullish Crab pattern usually begins with an upward trend, followed by a price correction, after which the stock resumes its upward movement.
If a deeper correction occurs, the price will change direction at some point on the chart and rise again towards its target price. Price corrections play a critical role in this pattern, as it aims to identify entry and exit points using Fibonacci ratios, allowing traders to make purchases at the end of the corrections.
When the price movement lines are connected on the chart, the bullish Crab pattern resembles the letter M.
🟣 Bearish Crab Pattern
In this pattern, the stock price is expected to decline at the pattern's completion, leading to a strong downward trend. The bearish Crab pattern typically starts with a price correction in a downward trend and, after several fluctuations, reaches a peak where the direction changes downward, resulting in a significant price drop.
This pattern uses Fibonacci ratios to identify points where the price movement is likely to change direction, enabling traders to exit their positions at the chart's peak. When the price movement lines are connected on the chart, the bearish Crab pattern resembles the letter W.
🔵 Setting
🟣 Logical Setting
ZigZag Pivot Period : You can adjust the period so that the harmonic patterns are adjusted according to the pivot period you want. This factor is the most important parameter in pattern recognition.
Show Valid Format : If this parameter is on "On" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "Off" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
Show Formation Last Pivot Confirm : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the latest pattern seeing and a sharp reduction in reward to risk.
Period of Formation Last Pivot : Using this parameter you can determine that the last pivot is based on Pivot period.
🟣 Genaral Setting
Show : Enter "On" to display the template and "Off" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Alert Setting
Alert : On / Off
Message Frequency : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone : The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
TrendMaster ProTrendMaster Pro: A Comprehensive Trend Analysis Tool for Long-Term Investors
TrendMaster Pro is an advanced technical indicator designed to provide long-term investors with a robust and comprehensive analysis of market trends. This sophisticated tool operates exclusively on daily timeframes, making it ideal for those focused on long-term investment strategies. By combining multiple analytical approaches, TrendMaster Pro offers investors a powerful means to assess trend quality and make informed decisions.
Automatic Trend Detection
At the heart of TrendMaster Pro lies its ability to automatically identify the most statistically significant trend. The indicator analyzes various timeframes ranging from 1000 to 5000 days, selecting the one that exhibits the highest correlation. This feature ensures that investors are always working with the most relevant trend data, eliminating the subjectivity often associated with manual trend identification.
The trend detection algorithm employs a regression analysis approach, evaluating approximately 80,000 different trend alternatives each day. Each potential trend is assigned a score based on criteria such as trend density, deviation from regression, and the number of price points near the trend's floor and ceiling. The trend with the highest score is then selected and displayed on the chart.
Comprehensive Scoring System
TrendMaster Pro employs a multi-faceted scoring system that evaluates four key aspects of a trend, providing a holistic view of its quality and potential. Each aspect is scored on a scale of 0 to 10, with the overall trend quality score being a weighted average of these individual scores.
1. Length Score
The Length Score measures the duration of the detected trend. Longer trends receive higher scores, reflecting increased reliability and significance. This score is calculated by normalizing the auto-selected period (which ranges from 1000 to 5000 days) to a scale of 5 to 10.
For example, if the auto-selected period is 3000 days, it would receive a score of around 7.5. This emphasizes the importance of long-term trends in investment decision-making, as they tend to be more stable and indicative of underlying market forces.
2. Strength Score
The Strength Score utilizes Pearson's Correlation Coefficient to assess trend strength. This statistical measure gauges the linear relationship between price and trend projection. A value closer to 1 indicates a strong positive correlation, reinforcing confidence in the trend direction based on historical price movements.
The indicator translates the Pearson's Correlation Coefficient into a score from 0 to 10. For instance, a correlation coefficient of 0.95 might translate to a Strength Score of 8, indicating a strong and reliable trend.
3. Performance Score
The Performance Score compares the asset's Compound Annual Growth Rate (CAGR) to a chosen benchmark, typically a major index like the S&P 500. This score provides insight into how well the asset is performing relative to the broader market.
The CAGR is calculated using the formula: CAGR = (Ending Value / Beginning Value)^(1/n) - 1, where n is the number of years. The Performance Score is then determined by comparing this CAGR to the benchmark's CAGR over the same period. A higher score indicates outperformance relative to the benchmark.
4. Level Score
The Level Score evaluates the current price position within the trend channel. Lower prices within the channel receive higher scores, suggesting potential value or buying opportunities. This score helps identify possible entry points based on historical trend behavior.
For example, if the current price is near the lower boundary of the trend channel, it might receive a Level Score of 9, indicating a potentially attractive entry point.
Visual Representation
TrendMaster Pro provides a clear visual representation of the detected trend by displaying a regression channel on the chart. This channel consists of three lines: a middle line representing the main trend, and upper and lower lines representing standard deviations from the main trend.
The channel offers a quick visual reference for support and resistance levels, helping investors identify potential entry and exit points. The color and style of these lines can be customized to suit individual preferences.
Detailed Information Table
A comprehensive table presents all scores and relevant data, allowing for quick and easy interpretation of the trend analysis. This table includes:
The auto-selected trend length
The Pearson's Correlation Coefficient
The asset's CAGR and the benchmark's CAGR
Individual scores for Length, Strength, Performance, and Level
The overall Trend Quality Score
This table provides investors with a clear, at-a-glance summary of the trend's key characteristics and quality.
Practical Application
To use TrendMaster Pro effectively, investors should consider the following:
Focus on the overall Trend Quality Score as a primary indicator of trend strength and reliability.
Use the Length Score to gauge the trend's longevity and potential stability.
Pay attention to the Strength Score to assess how well the price action aligns with the identified trend.
Utilize the Performance Score to compare the asset's performance against the broader market.
Consider the Level Score when timing entries, looking for opportunities when prices are relatively low within the trend channel.
Use the visual trend channel as a guide for potential support and resistance levels.
Limitations and Considerations
While TrendMaster Pro offers powerful insights, it's important to remember that no indicator can predict future market movements with certainty. The tool should be used in conjunction with fundamental analysis and other market information.
Additionally, as the indicator is designed for daily charts and long-term analysis, it may not be suitable for short-term trading strategies. Users should also be aware that past performance does not guarantee future results, even with strong trend indications.
Conclusion
TrendMaster Pro represents a significant advancement in trend analysis for long-term investors. By combining automatic trend detection, comprehensive scoring, and benchmark comparison, it offers a powerful tool for those seeking to make informed, data-driven investment decisions. Its ability to objectively assess trend quality across multiple dimensions provides investors with a valuable edge in navigating complex market conditions.
For investors looking to deepen their understanding of market trends and enhance their long-term investment strategies, TrendMaster Pro offers a sophisticated yet accessible solution. As with any investment tool, users are encouraged to thoroughly familiarize themselves with its features and interpret its outputs in the context of their overall investment approach.
CofG Oscillator w/ Added Normalizations/TransformationsThis indicator is a unique study in normalization/transformation techniques, which are applied to the CG (center of gravity) Oscillator, a popular oscillator made by John Ehlers.
The idea to transform the data from this oscillator originated from observing the original indicator, which exhibited numerous whips. Curious about the potential outcomes, I began experimenting with various normalization/transformation methods and discovered a plethora of interesting results.
The indicator offers 10 different types of normalization/transformation, each with its own set of benefits and drawbacks. My personal favorites are the Quantile Transformation , which converts the dataset into one that is mostly normally distributed, and the Z-Score , which I have found tends to provide better signaling than the original indicator.
I've also included the option of showing the mean, median, and mode of the data over the period specified by the transformation period. Using this will allow you to gather additional insights into how these transformations effect the distribution of the data series.
I've also included some notes on what each transformation does, how it is useful, where it fails, and what I've found to be the best inputs for it (though I'd encourage you to play around with it yourself).
Types of Normalization/Transformation:
1. Z-Score
Overview: Standardizes the data by subtracting the mean and dividing by the standard deviation.
Benefits: Centers the data around 0 with a standard deviation of 1, reducing the impact of outliers.
Disadvantages: Works best on data that is normally distributed
Notes: Best used with a mid-longer transformation period.
2. Min-Max
Overview: Scales the data to fit within a specified range, typically 0 to 1.
Benefits: Simple and fast to compute, preserves the relationships among data points.
Disadvantages: Sensitive to outliers, which can skew the normalization.
Notes: Best used with mid-longer transformation period.
3. Decimal Scaling
Overview: Normalizes data by moving the decimal point of values.
Benefits: Simple and straightforward, useful for data with varying scales.
Disadvantages: Not commonly used, less intuitive, less advantageous.
Notes: Best used with a mid-longer transformation period.
4. Mean Normalization
Overview: Subtracts the mean and divides by the range (max - min).
Benefits: Centers data around 0, making it easier to compare different datasets.
Disadvantages: Can be affected by outliers, which influence the range.
Notes: Best used with a mid-longer transformation period.
5. Log Transformation
Overview: Applies the logarithm function to compress the data range.
Benefits: Reduces skewness, making the data more normally distributed.
Disadvantages: Only applicable to positive data, breaks on zero and negative values.
Notes: Works with varied transformation period.
6. Max Abs Scaler
Overview: Scales each feature by its maximum absolute value.
Benefits: Retains sparsity and is robust to large outliers.
Disadvantages: Only shifts data to the range , which might not always be desirable.
Notes: Best used with a mid-longer transformation period.
7. Robust Scaler
Overview: Uses the median and the interquartile range for scaling.
Benefits: Robust to outliers, does not shift data as much as other methods.
Disadvantages: May not perform well with small datasets.
Notes: Best used with a longer transformation period.
8. Feature Scaling to Unit Norm
Overview: Scales data such that the norm (magnitude) of each feature is 1.
Benefits: Useful for models that rely on the magnitude of feature vectors.
Disadvantages: Sensitive to outliers, which can disproportionately affect the norm. Not normally used in this context, though it provides some interesting transformations.
Notes: Best used with a shorter transformation period.
9. Logistic Function
Overview: Applies the logistic function to squash data into the range .
Benefits: Smoothly compresses extreme values, handling skewed distributions well.
Disadvantages: May not preserve the relative distances between data points as effectively.
Notes: Best used with a shorter transformation period. This feature is actually two layered, we first put it through the mean normalization to ensure that it's generally centered around 0.
10. Quantile Transformation
Overview: Maps data to a uniform or normal distribution using quantiles.
Benefits: Makes data follow a specified distribution, useful for non-linear scaling.
Disadvantages: Can distort relationships between features, computationally expensive.
Notes: Best used with a very long transformation period.
Conclusion
Feel free to explore these normalization/transformation techniques to see how they impact the performance of the CG Oscillator. Each method offers unique insights and benefits, making this study a valuable tool for traders, especially those with a passion for data analysis.
