Linear Cross Trading StrategyLinear Cross Trading Strategy
The Linear Cross trading strategy is a technical analysis strategy that uses linear regression to predict the future price of a stock. The strategy is based on the following principles:
The price of a stock tends to follow a linear trend over time.
The slope of the linear trend can be used to predict the future price of the stock.
The strategy enters a long position when the predicted price crosses above the current price, and exits the position when the predicted price crosses below the current price.
The Linear Cross trading strategy is implemented in the TradingView Pine script below. The script first calculates the linear regression of the stock price over a specified period of time. The script then plots the predicted price and the current price on the chart. The script also defines two signals:
Long signal: The long signal is triggered when the predicted price crosses above the current price.
Short signal: The short signal is triggered when the predicted price crosses below the current price.
The script enters a long position when the long signal is triggered and exits the position when the short signal is triggered.
Here is a more detailed explanation of the steps involved in the Linear Cross trading strategy:
Calculate the linear regression of the stock price over a specified period of time.
Plot the predicted price and the current price on the chart.
Define two signals: the long signal and the short signal.
Enter a long position when the long signal is triggered.
Exit the long position when the short signal is triggered.
The Linear Cross trading strategy is a simple and effective way to trade stocks. However, it is important to note that no trading strategy is guaranteed to be profitable. It is always important to do your own research and backtest the strategy before using it to trade real money.
Here are some additional things to keep in mind when using the Linear Cross trading strategy:
The length of the linear regression period is a key parameter that affects the performance of the strategy. A longer period will smooth out the noise in the price data, but it will also make the strategy less responsive to changes in the price.
The strategy is more likely to generate profitable trades when the stock price is trending. However, the strategy can also generate profitable trades in ranging markets.
The strategy is not immune to losses. It is important to use risk management techniques to protect your capital when using the strategy.
I hope this blog post helps you understand the Linear Cross trading strategy better. Booost and share with your friend, if you like.
Linear
AI Moving Average (Expo)█ Overview
The AI Moving Average indicator is a trading tool that uses an AI-based K-nearest neighbors (KNN) algorithm to analyze and interpret patterns in price data. It combines the logic of a traditional moving average with artificial intelligence, creating an adaptive and robust indicator that can identify strong trends and key market levels.
█ How It Works
The algorithm collects data points and applies a KNN-weighted approach to classify price movement as either bullish or bearish. For each data point, the algorithm checks if the price is above or below the calculated moving average. If the price is above the moving average, it's labeled as bullish (1), and if it's below, it's labeled as bearish (0). The K-Nearest Neighbors (KNN) is an instance-based learning algorithm used in classification and regression tasks. It works on a principle of voting, where a new data point is classified based on the majority label of its 'k' nearest neighbors.
The algorithm's use of a KNN-weighted approach adds a layer of intelligence to the traditional moving average analysis. By considering not just the price relative to a moving average but also taking into account the relationships and similarities between different data points, it offers a nuanced and robust classification of price movements.
This combination of data collection, labeling, and KNN-weighted classification turns the AI Moving Average (Expo) Indicator into a dynamic tool that can adapt to changing market conditions, making it suitable for various trading strategies and market environments.
█ How to Use
Dynamic Trend Recognition
The color-coded moving average line helps traders quickly identify market trends. Green represents bullish, red for bearish, and blue for neutrality.
Trend Strength
By adjusting certain settings within the AI Moving Average (Expo) Indicator, such as using a higher 'k' value and increasing the number of data points, traders can gain real-time insights into strong trends. A higher 'k' value makes the prediction model more resilient to noise, emphasizing pronounced trends, while more data points provide a comprehensive view of the market direction. Together, these adjustments enable the indicator to display only robust trends on the chart, allowing traders to focus exclusively on significant market movements and strong trends.
Key SR Levels
Traders can utilize the indicator to identify key support and resistance levels that are derived from the prevailing trend movement. The derived support and resistance levels are not just based on historical data but are dynamically adjusted with the current trend, making them highly responsive to market changes.
█ Settings
k (Neighbors): Number of neighbors in the KNN algorithm. Increasing 'k' makes predictions more resilient to noise but may decrease sensitivity to local variations.
n (DataPoints): Number of data points considered in AI analysis. This affects how the AI interprets patterns in the price data.
maType (Select MA): Type of moving average applied. Options allow for different smoothing techniques to emphasize or dampen aspects of price movement.
length: Length of the moving average. A greater length creates a smoother curve but might lag recent price changes.
dataToClassify: Source data for classifying price as bullish or bearish. It can be adjusted to consider different aspects of price information
dataForMovingAverage: Source data for calculating the moving average. Different selections may emphasize different aspects of price movement.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Extrapolated Previous Trend [LuxAlgo]The Extrapolated Previous Trend indicator extrapolates the estimated linear trend of the prices within a previous interval to the current interval. Intervals can be user-defined.
🔶 USAGE
Returned lines can be used to provide a forecast of trends, assuming trends are persistent in sign and slope.
Using them as support/resistance can also be an effecting usage in case the trend in a new interval does not follow the characteristic of the trend in the previous interval.
The indicator includes a dashboard showing the degree of persistence between segmented trends for uptrends and downtrends. A higher value is indicative of more persistent trend signs.
A lower value could hint at an anti-persistent behavior, with uptrends over an interval often being followed by a down-trend and vice versa. We can invert candle colors to determine future trend direction in this case.
🔶 DETAILS
This indicator can be thought of as a segmented linear model ( a(n)t + b(n) ), where n is the specific interval index. Unlike a regular segmented linear regression model, this indicator is not subject to lookahead bias, coefficients of the model are obtained on previous intervals.
The quality of the fit of the model is dependent on the variability of its coefficients a(n) and b(n) . Coefficients being less subject to change over time are more indicative of trend persistence.
🔶 SETTINGS
Timeframe: Determine the frequency at which new trends are estimated.
HK Percentile Interpolation One
This script is designed to execute a trading strategy based on Heikin Ashi candlesticks, moving averages, and percentile levels.
Please note that you should keep your original chart in normal candlestick mode and not switch it to Heikin Ashi mode. The script itself calculates Heikin Ashi values from regular candlesticks. If your chart is already in Heikin Ashi mode, the script would be calculating Heikin Ashi values based on Heikin Ashi values, which would produce incorrect results.
The strategy begins trading from a start date that you can specify by modifying the `startDate` parameter. The format of the date is "YYYY MM DD". So, for example, to start the strategy from January 1, 2022, you would set `startDate = timestamp("2022 01 01")`.
The script uses Heikin Ashi candlesticks, which are plotted in the chart. This approach can be useful for spotting trends and reversals more easily than with regular candlestick charts. This is particularly useful when backtesting in TradingView's "Rewind" mode, as you can see how the Heikin Ashi candles behaved at each step of the strategy.
Buy and sell signals are generated based on two factors:
1. The crossing over or under of the Heikin Ashi close price and the 75th percentile price level.
2. The Heikin Ashi close price being above certain moving averages.
You have the flexibility to adjust several parameters in the script, including:
1. The stop loss and trailing stop percentages (`stopLossPercentage` and `trailStopPercentage`). These parameters allow the strategy to exit trades if the price moves against you by a certain percentage.
2. The lookback period (`lookback`) used to calculate percentile levels. This determines the range of past bars used in the percentile calculation.
3. The lengths of the two moving averages (`yellowLine_length` and `purplLine_length`). These determine how sensitive the moving averages are to recent price changes.
4. The minimum holding period (`holdPeriod`). This sets the minimum number of bars that a trade must be kept open before it can be closed.
Please adjust these parameters according to your trading preferences and risk tolerance. Happy trading!
Strongest TrendlineUnleashing the Power of Trendlines with the "Strongest Trendline" Indicator.
Trendlines are an invaluable tool in technical analysis, providing traders with insights into price movements and market trends. The "Strongest Trendline" indicator offers a powerful approach to identifying robust trendlines based on various parameters and technical analysis metrics.
When using the "Strongest Trendline" indicator, it is recommended to utilize a logarithmic scale . This scale accurately represents percentage changes in price, allowing for a more comprehensive visualization of trends. Logarithmic scales highlight the proportional relationship between prices, ensuring that both large and small price movements are given due consideration.
One of the notable advantages of logarithmic scales is their ability to balance price movements on a chart. This prevents larger price changes from dominating the visual representation, providing a more balanced perspective on the overall trend. Logarithmic scales are particularly useful when analyzing assets with significant price fluctuations.
In some cases, traders may need to scroll back on the chart to view the trendlines generated by the "Strongest Trendline" indicator. By scrolling back, traders ensure they have a sufficient historical context to accurately assess the strength and reliability of the trendline. This comprehensive analysis allows for the identification of trendline patterns and correlations between historical price movements and current market conditions.
The "Strongest Trendline" indicator calculates trendlines based on historical data, requiring an adequate number of data points to identify the strongest trend. By scrolling back and considering historical patterns, traders can make more informed trading decisions and identify potential entry or exit points.
When using the "Strongest Trendline" indicator, a higher Pearson's R value signifies a stronger trendline. The closer the Pearson's R value is to 1, the more reliable and robust the trendline is considered to be.
In conclusion, the "Strongest Trendline" indicator offers traders a robust method for identifying trendlines with significant predictive power. By utilizing a logarithmic scale and considering historical data, traders can unleash the full potential of this indicator and gain valuable insights into price trends. Trendlines, when used in conjunction with other technical analysis tools, can help traders make more informed decisions in the dynamic world of financial markets.
MultiMovesCombines 3 different moving averages together with the linear regression. The moving averages are the HMA, EMA, and SMA. The script makes use of two different lengths to allow the end user to utilize common crossovers in order to determine entry into a trade. The edge of each "cloud" is where each of the moving averages actually are. The bar color is the average of the shorter length combined moving averages.
-The Hull Moving Average (HMA), developed by Alan Hull, is an extremely fast and smooth moving average. In fact, the HMA almost eliminates lag altogether and manages to improve smoothing at the same time. A longer period HMA may be used to identify trend.
-The exponential moving average (EMA) is a technical chart indicator that tracks the price of an investment (like a stock or commodity) over time. The EMA is a type of weighted moving average (WMA) that gives more weighting or importance to recent price data.
-A simple moving average (SMA) is an arithmetic moving average calculated by adding recent prices and then dividing that figure by the number of time periods in the calculation average.
-The Linear Regression Indicator plots the ending value of a Linear Regression Line for a specified number of bars; showing, statistically, where the price is expected to be. Instead of plotting an average of past price action, it is plotting where a Linear Regression Line would expect the price to be, making the Linear Regression Indicator more responsive than a moving average.
The lighter colors = default 50 MA
The darker colors = default 200 MA
Advanced Trend Channel Detection (Log Scale)The Advanced Trend Channel Detection (Log Scale) indicator is designed to identify the strongest trend channels using logarithmic scaling. It does this by calculating the highest Pearson's R value among all length inputs and then determining which length input to use for the selected slope, average, and intercept. The script then draws the upper and lower deviation lines on the chart based on the selected slope, average, and intercept, and optionally displays the Pearson's R value.
To use this indicator, you will need to switch to logarithmic scale. There are several advantages to using logarithmic scale over regular scale. Firstly, logarithmic scale provides a better visualization of data that spans multiple orders of magnitude by compressing large ranges of values into a smaller space. Secondly, logarithmic scale can help to minimize the impact of outliers, making it easier to identify patterns and trends in the data. Finally, logarithmic scale is often utilized in scientific contexts as it can reveal relationships between variables that may not be visible on a linear scale.
If the trend channel does not appear on the chart, it may be necessary to scroll back to view historical data. The indicator uses past price data to calculate the trend channel, so if there is not enough historical data visible on the chart, the indicator may not be able to identify the trend channel. In this case, the user should adjust the chart's timeframe or zoom out to view more historical data. Additionally, the indicator may need to be recalibrated if there is a significant shift in market conditions or if the selected length input is no longer appropriate.
Deming Linear Regression [wbburgin]Deming regression is a type of linear regression used to model the relationship between two variables when there is variability in both variables. Deming regression provides a solution by simultaneously accounting for the variability in both the independent and dependent variables, resulting in a more accurate estimation of the underlying relationship. In the hard-science fields, where measurements are critically important to judging the conclusions drawn from data, Deming regression can be used to account for measurement error.
Tradingview's default linear regression indicator (the ta.linreg() function) uses least squares linear regression, which is similar but different than Deming regression. In least squares regression, the regression function minimizes the sum of the squared vertical distances between the data points and the fitted line. This method assumes that the errors or variability are only present in the y-values (dependent variable), and that the x-values (independent variable) are measured without error.
In time series data used in trading, Deming regression can be more accurate than least squares regression because the ratio of the variances of the x and y variables is large. X is the bar index, which is an incrementally-increasing function that has little variance, while Y is the price data, which has extremely high variance when compared to the bar index. In such situations, least squares regression can be heavily influenced by outliers or extreme points in the data, whereas Deming regression is more resistant to such influence.
Additionally, if your x-axis uses variable widths - such as renko blocks or other types of non-linear widths - Deming regression might be more effective than least-squares linear regression because it accounts for the variability in your x-values as well. Additionally, if you are creating a machine-learning model that uses linear regression to filter or extrapolate data, this regression method may be more accurate than least squares.
In contrast to least squares regression, Deming regression takes into account the variability or errors in both the x- and y-values. It minimizes the sum of the squared perpendicular distances between the data points and the fitted line, accounting for both the x- and y-variability. This makes Deming regression more robust in both variables than least squares regression.
Chandelier Exit ZLSMA StrategyIntroducing a Powerful Trading Indicator: Chandelier Exit with ZLSMA
If you're a trader, you know the importance of having the right tools and indicators to make informed decisions. That's why we're excited to introduce a powerful new trading indicator that combines the Chandelier Exit and ZLSMA: two widely-used and effective indicators for technical analysis.
The Chandelier Exit (CE) is a popular trailing stop-loss indicator developed by Chuck LeBeau. It's designed to follow the price trend of a security and provide an exit signal when the price crosses below the CE line. The CE line is based on the Average True Range (ATR), which is a measure of volatility. This means that the CE line adjusts to the volatility of the security, making it a reliable indicator for trailing stop-losses.
The ZLEMA (Zero Lag Exponential Moving Average) is a type of exponential moving average that's designed to reduce lag and improve signal accuracy. The ZLSMA takes into account not only the current price but also past prices, using a weighted formula to calculate the moving average. This makes it a smoother indicator than traditional moving averages, and less prone to giving false signals.
When combined, the CE and ZLSMA create a powerful indicator that can help traders identify trend changes and make more informed trading decisions. The CE provides the trailing stop-loss signal, while the ZLSMA provides a smoother trend line to help identify potential entry and exit points.
In our indicator, the CE and ZLSMA are plotted together on the chart, making it easy to see both the trailing stop-loss and the trend line at the same time. The CE line is displayed as a dotted line, while the ZLSMA line is displayed as a solid line.
Using this indicator, traders can set their stop-loss levels based on the CE line, while also using the ZLSMA line to identify potential entry and exit points. The combination of these two indicators can help traders reduce their risk and improve their trading performance.
In conclusion, the Chandelier Exit with ZLSMA is a powerful trading indicator that combines two effective technical analysis tools. By using this indicator, traders can identify trend changes, set stop-loss levels, and make more informed trading decisions. Try it out for yourself and see how it can improve your trading performance.
Warning: The results in the backtest are from a repainting strategy. Don't take them seriously. You need to do a dry live test in order to test it for its useability.
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Here is a description of each input field in the provided source code:
length: An integer input used as the period for the ATR (Average True Range) calculation. Default value is 1.
mult: A float input used as a multiplier for the ATR value. Default value is 2.
showLabels: A boolean input that determines whether to display buy/sell labels on the chart. Default value is false.
isSignalLabelEnabled: A boolean input that determines whether to display signal labels on the chart. Default value is true.
useClose: A boolean input that determines whether to use the close price for extrema calculations. Default value is true.
zcolorchange: A boolean input that determines whether to enable rising/decreasing highlighting for the ZLSMA (Zero-Lag Exponential Moving Average) line. Default value is false.
zlsmaLength: An integer input used as the length for the ZLSMA calculation. Default value is 50.
offset: An integer input used as an offset for the ZLSMA calculation. Default value is 0.
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Ty for checking this out and good luck on your trading journey! Likes and comments are appreciated. 👍
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Credits to:
▪ @everget – Chandelier Exit (CE)
▪ @netweaver2022 – ZLSMA
Autoregressive Covariance Oscillator by TenozenWell to be honest I don't know what to name this indicator lol. But anyway, here is my another original work! Gonna give some background of why I create this indicator, it's all pretty much a coincidence when I'm learning about time series analysis.
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Well, the formula of Auto-covariance is:
E{(X(t)-(t) * (X(t-s)-(t-s))}= Y_s
But I don't multiply both values but rather subtract them:
E{(X(t)-(t) - (X(t-s)-(t-s))}= Y_s?
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For arm_vald, the equation is as follows:
arm_vald = val_mu + mu_plus_lsm + et
val_mu --> mean of time series
mu_plus_lsm --> val_mu + LSM
et --> error term
As you can see, val_mu^2. I did this so the oscillator is much smoother.
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After I get the value, I normalize them:
aco = Y_s? / arm_vald
So by this calculation, I get something like an oscillator!
(more details in the code)
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So how to use this indicator? It's so easy! If the value is above 0, we gonna expect a bullish response, if the value is below 0, we gonna expect a bearish response; that simple. Be aware that you should wait for the price to be closed before executing a trade.
Well, try it out! So far this is the most powerful indicator that I've created, hope it's useful. Ciao.
(more updates for the indicator if needed)
Linear Regression Volume ProfileLinear Regression Volume Profile plots the volume profile fixated on the linear regression of the lookback period rather than statically across y = 0. This helps identify potential support and resistance inside of the price channel.
Settings
Linear Regression
Linear Regression Source: the price source in which to sample when calculating the linear regression
Length: the number of bars to sample when calculating the linear regression
Deviation: the number of standard deviations away from the linear regression line to draw the upper and lower bounds
Linear Regression
Rows: the number of rows to divide the linear regression channel into when calculating the volume profile
Show Point of Control: toggle whether or not to plot the level with highest amount of volume
Usage
Similar to the traditional Linear Regression and Volume Profile this indicator is mainly to determine levels of support and resistance. One may interpret a level with high volume (i.e. point of control) to be a potential reversal point.
Details
This indicator first calculates the linear regression of the specified lookback period and, subsequently, the upper and lower bound of the linear regression channel. It then divides this channel by the specified number of rows and sums the volume that occurs in each row. The volume profile is scaled to the min and max volume.
Linear Regress on Price And VolumeLinear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the dependent variable and the independent variable(s) and attempts to fit a straight line that best describes the relationship.
In the context of predicting the price of a stock based on the volume, we can use linear regression to build a model that relates the price of the stock (dependent variable) to the volume (independent variable). The idea is to use lookback period to predict future prices based on the volume.
To build this indicator, we start by collecting data on the price of the stock and the volume over a selected of time or by default 21 days. We then plot the data on a scatter plot with the volume on the x-axis and the price on the y-axis. If there is a clear pattern in the data, we can fit a straight line to the data using a method called least squares regression. The line represents the best linear approximation of the relationship between the price and the volume.
Once we have the line, we can use it to make predictions. For example, if we observe a certain volume, we can use the line to estimate the corresponding price.
It's worth noting that linear regression assumes a linear relationship between the variables. In reality, the relationship between the price and the volume may be more complex, and other factors may also influence the price of the stock. Therefore, while linear regression can be a useful tool, it should be used in conjunction with other methods and should be interpreted with caution.
Premium Linear Regression - The Quant ScienceThis script calculates the average deviation of the source data from the linear regression. When used with the indicator, it can plot the data line and display various pieces of information, including the maximum average dispersion around the linear regression.
The code includes various user configurations, allowing for the specification of the start and end dates of the period for which to calculate linear regression, the length of the period to use for the calculation, and the data source to use.
The indicator is designed for multi-timeframe use and to facilitate analysis for traders who use regression models in their analysis. It displays a green linear regression line when the price is above the line and a red line when the price is below. The indicator also highlights areas of dispersion around the regression using circles, with bullish areas shown in green and bearish areas shown in red.
VHF Adaptive Linear Regression KAMAIntroduction
Heyo, in this indicator I decided to add VHF adaptivness, linear regression and smoothing to a KAMA in order to squeeze all out of it.
KAMA:
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
VHF:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Linear Regression Curve:
A line that best fits the prices specified over a user-defined time period.
This is very good to eliminate bad crosses of KAMA and the pric.
Usage
You can use this indicator on every timeframe I think. I mostly tested it on 1 min, 5 min and 15 min.
Signals
Enter Long -> crossover(close, kama) and crossover(kama, kama )
Enter Short -> crossunder(close, kama) and crossunder(kama, kama )
Thanks for checking this out!
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Credits to
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@LucF – Gradient
Dynamic Linear Regression Oscillator | AdulariDescription:
This dynamic linear regression oscillator visualizes the general price trend of specific ranges in the chart based on the linear regression calculation, it automatically determines these ranges with pivot detection. The central line of the indicator is the baseline of the linear regression itself. This is a good tool to use to determine when a price is unusually far away from its baseline. The lines above or below it are overbought and oversold zones. These zones are based on the high or low of the range, in combination with the set multipliers.
The overbought and oversold lines indicate support and resistance; when the prices stay outside these levels for a significant period of time, a reversal can be expected soon. When the oscillator's value crosses above the signal or smoothed line the trend may become bullish. When it crosses below, the trend may become bearish.
This indicator is quite special, as it first determines price ranges using pivot detection. It then uses the middle of the range to determine how far the current price is from the baseline. This value is then rescaled compared to a set amount of bars back, putting it into relevant proportions with the current price action.
How do I use it?
Never use this indicator as standalone trading signal, it should be used as confluence.
When the value crosses above the signal this indicates the current bearish trend is getting weak and may reverse upwards.
When the value crosses below the signal this indicates the current bullish trend is getting weak and may reverse downwards.
When the value is above the middle line this shows the bullish trend is strong.
When the value is below the middle line this shows the bearish trend is strong.
When the value crosses above the upper line this indicates the trend may reverse downwards.
When the value crosses below the lower line this indicates the trend may reverse upwards.
Features:
Oscillator value indicating how far the price has currently deviated from the middle of the range. Proportioned to data from a set amount of bars ago.
Signal value to indicate whether or not the price is abnormally far from the middle of the range.
Horizontal lines such as oversold, overbought and middle lines, indicating possible reversal zones.
Automatic range detection using pivots.
Built-in rescaling functionality to ensure values are proportionate with the latest data.
How does it work? (simplified)
1 — Calculate the middle of the range.
2 — Define whether the current price is above the middle of the range or below.
3 — If above the middle of the range, calculate the difference of the current high and the middle line. If below, calculate the difference of the current low and the middle line.
4 — Smooth the value using a set moving average type.
5 — Rescale the value to proportionate it with the latest data.
Three Linear Regression ChannelsPlot three linear regression channels using alexgrover 's Computing The Linear Regression Using The WMA And SMA indicator for the linear regression calculations.
Settings
Length : Number of inputs to be used
Source : Source input of the indicator
Midline Colour : The colour of the midline
Channel One, Two, and Three Multiplicative Factor : Multiplication factor for the RMSE, determine the distance between the upper and lower level
Channel One, Two, and Three Colour : The channel's lines colour
Usage
For usage details, please refer to alexgrover 's Computing The Linear Regression Using The WMA And SMA indicator.
Multi-Optimized Linear Regression ChannelA take on alexgrover 's Optimized Linear Regression Channel script which allows users to apply multiple linear regression channel with unique multiplicative factors.
Multiplicative Factors
Adjust the amount of channels and multiplicative factors of existing or additional channels using the "Mults" input.
An input of "1" creates a single linear regression channel with the multiplicative factor of one.
An input of "4" creates a single linear regression channel with the multiplicative factor of four.
An input of "1,4" creates two linear regression channels with multiplicative factors of one and four.
An input of "1,2,3" creates three linear regression channels with multiplicative factors of one, two, and three.
DB Change Forecast ProDB Change Forecast Pro
What does the indicator do?
The DB Change Forecast Pro is a unique indicator that uses price change on HLC3 to detect buy and sell periods along with plotting a linear regression price channel with oversold and undersold zones. It also has a linear regression change forecast mode to optionally project market direction.
Change is calculated by taking a two-bar change of HLC3 and dividing that by the price or, optionally, a fixed divisor.
A fast-moving change cloud is then calculated and displayed as the "regular version" plot (shown in light gray). When the cloud bottom is above low, a buy zone is detected. When the cloud top is below the high, a sell zone is detected.
The linear regression price channel is calculated similarly but using a much slower change rate. The linear regression price channel shows reasonable high, low and HLC3 ranges. At the bar's opening, the channel will be more compact and come fairly accurate about 1/4 into the bar timeframe.
The change forecasted price is projected on the right side of the current bar to indicate the current timeframe direction. Please note this forecasting feature is shown in orange when it's early in the timeframe and gray when the timeframe is more likely to produce an accurate direction forecast for the upcoming bar.
You can use these projected dashed lines to see possible market movements for the Current bar and possible market direction for the next bar. Kindly note these projects change; they should be used to understand possible extreme highs/lows for the current bar or market direction.
The indicator includes an optional change forecast projection feature hidden by default. It will project the market forecast channel with an offset of 1. The forecast is defaulted to an offset of 1 to show market direction. However, you can modify to zero the offset to show the current bar forecast and forecast history.
How should this indicator be used?
First, very important,
1. Settings > Set Symbol to Desired
2. Settings > Set High Timeframe to "Chart"
3. Settings > Ensure "Use price as divisor" is checked.
It's recommended to use this indicator in higher timeframes. Buy and sell signals are displayed in real-time. However, waiting until 1/4 to 1/2 into the current bar is recommended before taking action, and change can happen.
The buy/sell signals (zones) provide recommendations on playing a long vs. a short. When in a buy sone, only play longs. When in a sell zone, only play shorts.
Then use the linear regression price channel oversold and undersold zones to optionally open and close positions within the buy/sell zones.
For example, consider opening a long in a buy zone when the linear regression price channel shows undersold. Then consider closing the long when the price moves into the linear regression oversold or higher. Then repeat as long as it's in the buy zone. Then vice versa for sell zones and shorting.
At basic design, buy in the buy zone, sell or short in the sell zone. If you are up for higher trading frequencies, use the linear regression price channel as described in the example above.
Please note, as, with all indicators, you may need to adjust to fit the indicator to your symbol and desired timeframe.
This is only an example of use. Please use this indicator as your own risk and after doing your due diligence.
Does the indicator include any alerts?
Yes,
"DB CFHLC3: Signal BUY" - Is triggered when a buy signal is fired.
"DB CFHLC3: Signal SELL" - Is triggered when a sell signal is fired.
"DB CFHLC3: Zone BUY" - Is triggered when a buy zone is detected.
"DB CFHLC3: Zeon SELL" - Is triggered when a sell zone is detected.
"DB CFHLC3: Oversold SELL" - Is triggered when the price exceeds the oversold level.
"DB CFHLC3: Undersold BUY" - Is triggered when the price goes below the undersold level.
Any other tips?
Once you have configured the indicator for your symbol and chart timeframe. Meaning the plots are displayed over the price. Check out larger timeframes such as W, 2W, 3W, 4W, M, and 4M. It works wonderfully for showing market lows and highs for long-term investing too!
Another, tip is to combine it with your favorite indicator, such as TTM Squeeze or MACD for confirmation purposes. You may be surprised how fast the indicator shows market direction changes on higher timeframes.
You can just as easily use a high timeframe such as D, 2D, or 3D for day trading due to how the linear price channel works.
Why am I not selling this indicator?
I would like to bless the TradingView community, and I enjoy publishing custom indicators.
If you enjoy this indicator, please consider leaving a thumbs up or a comment for others to know about your experience or recommendations.
Enjoy!
Leavitt Convolution [CC]The Leavitt Convolution indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to my Leavitt Projection script and I forgot to mention that both of these indicators are actually predictive moving averages. The Leavitt Convolution indicator doubles down on this idea by creating a prediction of the Leavitt Projection which is another prediction for the next bar. Obviously this means that it isn't always correct in its predictions but it does a very good job at predicting big trend changes before they happen. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.
Regression Channel, Candles and Candlestick Patterns by MontyRegression Candles by ugurvu
Regression Channel by Tradingview
All Candlestick Patterns By Tradingview
This script was combined for a friend of mine who needed this.
This Script has regression candles by ugurvu, Regression channel and Candlestick patterns by tradingview.
The intention was to fuse these together so more information can be processed on the cost of a single indicator.
Leavitt Projection [CC]The Leavitt Projection indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very simple but is also the building block of many other indicators, so I'm starting with the publication of this one. Since this is the first in a series I will be publishing, keep in mind that the concepts introduced in this script will be the same across the entire series. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
I know many of you have wondered where I have been, and my personal life has become super hectic. I was recently hired full-time by TradingView, and my wife is pregnant with twins, and she is due in a few months. I will do my absolute best to get back to posting scripts regularly, but I will post a bunch today in the meantime to fulfill a special request from one of my loyal followers (@ashok1961).
RSI + MA, LinReg, ZZ (HH HL LH LL), Div, Ichi, MACD and TSI HistRelative Strength Index with Moving Average, Linear Regression, Zig Zag (Highs and Lows), Divergence, Ichimoku Cloud, Moving Average Convergence Divergence and True Strength Index Histogram
This script is based on zdmre's RSI script, I revamped a lot of things and added a few indicators from ParkF's RSI script.
Disable Labels in the Style tab and the histogram if you don't enlarge the indicator and it seems too small.
Look to buy in the oversold area and bounce of the support of the linear regression.
Look to sell in the overbought area and bounce of the resistance of the linear regression.
Look for retracement to the moving average or horizontal lines, and divergences for potential reversal.
RSI
The Relative Strength Index (RSI) is a well versed momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements.
Moving Average
Moving Average (MA) is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance.
Linear Regression
The Linear Regression indicator visualizes the general price trend of a specific part of the chart based on the Linear Regression calculation.
Zig Zag (Highs and Lows)
The Zig Zag indicator is used to identify price trends, and in doing so plots points on the chart to mark whenever prices reverse by a larger percentage point than a predetermined variable or marker.
Divergence
The divergence indicator warns traders and technical analysts of changes in a price trend, oftentimes that it is weakening or changing direction.
Ichimoku Cloud
The Ichimoku Cloud is a package of multiple technical indicators that signal support, resistance, market trend, and market momentum.
MACD and TSI Histogram
MACD can be used to identify aspects of a security's overall trend.
The True Strength Index indicator is a momentum oscillator designed to detect, confirm or visualize the strength of a trend.
Itakura-Saito Autoregressive Extrapolation of Price [Loxx]Itakura-Saito Autoregressive Extrapolation of Price is an indicator that uses an autoregressive analysis to predict future prices. This is a linear technique that was originally derived or speech analysis algorithms.
What is Itakura-Saito Autoregressive Analysis?
The technique of linear prediction has been available for speech analysis since the late 1960s (Itakura & Saito, 1973a, 1970; Atal & Hanauer, 1971), although the basic principles were established long before this by Wiener (1947). Linear predictive coding, which is also known as autoregressive analysis, is a time-series algorithm that has applications in many fields other than speech analysis (see, e.g., Chatfield, 1989).
Itakura and Saito developed a formulation for linear prediction analysis using a lattice form for the inverse filter. The Itakura–Saito distance (or Itakura–Saito divergence) is a measure of the difference between an original spectrum and an approximation of that spectrum. Although it is not a perceptual measure it is intended to reflect perceptual (dis)similarity. It was proposed by Fumitada Itakura and Shuzo Saito in the 1960s while they were with NTT. The distance is defined as: The Itakura–Saito distance is a Bregman divergence, but is not a true metric since it is not symmetric and it does not fulfil triangle inequality.
read more: Selected Methods for Improving Synthesis Speech Quality Using Linear Predictive Coding: System Description, Coefficient Smoothing and Streak
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Related Indicators (linear extrapolation of price)
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
