MACD TrueLevel StrategyThis strategy uses the MACD indicator to determine buy and sell signals. In addition, the strategy employs the use of "TrueLevel Bands," which are essentially envelope bands that are calculated based on the linear regression and standard deviation of the price data over various lengths.
The TrueLevel Bands are calculated for 14 different lengths and are plotted on the chart as lines. The bands are filled with a specified color to make them more visible. The highest upper band and lowest lower band values are stored in variables for easy access.
The user can input the lengths for the TrueLevel Bands and adjust the multiplier for the standard deviation. They can also select the bands they want to use for entry and exit, and enable long and short positions.
The entry conditions for a long position are either a crossover of the MACD line over the signal line or a crossover of the price over the selected entry lower band. The entry conditions for a short position are either a crossunder of the MACD line under the signal line or a crossunder of the price under the selected exit upper band.
The exit conditions for both long and short positions are not specified in the code and are left to the user to define.
Overall, the strategy aims to capture trends by entering long or short positions based on the MACD and TrueLevel Bands, and exiting those positions when the trend reverses.
Regressions
RSI TrueLevel StrategyThis strategy is a momentum-based strategy that uses the Relative Strength Index (RSI) indicator and a TrueLevel envelope to generate trade signals.
The strategy uses user-defined input parameters to calculate TrueLevel envelopes for 14 different lengths. The TrueLevel envelope is a volatility-based technical indicator that consists of upper and lower bands. The upper band is calculated by adding a multiple of the standard deviation to a linear regression line of the price data, while the lower band is calculated by subtracting a multiple of the standard deviation from the same regression line.
The strategy generates long signals when the RSI crosses above the oversold level or when the price crosses above the selected lower band of the TrueLevel envelope. It generates short signals when the RSI crosses below the overbought level or when the price crosses below the selected upper band of the TrueLevel envelope.
The strategy allows for long and short trades and sets the trade size as a percentage of the account equity. The colors of the bands and fills are also customizable through user-defined input parameters.
In this strategy, the 12th TrueLevel band was chosen due to its ability to capture significant price movements while still providing a reasonable level of noise reduction. The strategy utilizes a total of 14 TrueLevel bands, each with varying lengths. The 12th band, with a length of 2646, strikes a balance between sensitivity to market changes and reducing false signals, making it a suitable choice for this strategy.
RSI Parameters:
In this strategy, the RSI overbought and oversold levels are set at 65 and 40, respectively. These values were chosen to filter out more noise in the market and focus on stronger trends. Traditional RSI overbought and oversold levels are set at 70 and 30, respectively. By raising the oversold level and lowering the overbought level, the strategy aims to identify more significant trend reversals and potential trade opportunities.
Of course, the parameters can be adjusted to suit individual preferences.
Cyclic RegressionCyclic Regression is a new concept that uses Digital Signal Processing (DSP) to determine the regression of past and future cycles. This is a unique method of regression which has the ability to forecast into the future.
There are several ways to use this tool.
Firstly, it follows similar rules to moving averages and can be used to filter entries. Long entries should be considered when price action is above the line or the line direction is upwards. The opposite is applied for shorts, a downward direction or price action is below.
The regression line is also a strong SR (Support and Resistance) or trend line so traders can expect big moves when this line is broken or a pullback is made after the break.
Each new direction of regression signifies a new cycle so traders can plan for a possible big move when reaching the end of the line.
The Settings are not your typical length or lookback options:
The main modifier is the "Response" input, with this the frequency response for the signal processing can be adjusted. By default it is set at 5000 but this can be boosted to something like 10000 to tune it to bigger cycles.
The other modifiers include sensitivity which will fine tune the response, this can be use with in conjunction with threshold option which adjusts the threshold of the useable response.
There is also the ability to add an external sources to the signal using the source input box. This allows traders to include other sources of data such as volume or RSI.
TrueLevel BandsWhat are TrueLevel Bands ?
TrueLevel Bands is a powerful trading indicator that employs linear regression and standard deviation to create dynamic, envelope-style bands around the price action of a financial instrument. These bands are designed to help traders identify potential support and resistance levels, trend direction, and volatility.
The TrueLevel Bands indicator consists of multiple envelope bands, each constructed using different timeframes or lengths, and a multiple (mult) factor. The multiple factor determines the width of the bands by adjusting the number of standard deviations from the linear regression line.
Key Features of TrueLevel Bands
1. Multi-Timeframe Analysis: Unlike traditional moving average-based indicators, TrueLevel Bands allow traders to incorporate multiple timeframes into their analysis. This helps traders capture both short-term and long-term market dynamics, offering a more comprehensive understanding of price behavior.
2. Customization: The TrueLevel Bands indicator offers a high level of customization, allowing traders to adjust the lengths and multiple factors to suit their trading style and preferences. This flexibility enables traders to fine-tune the indicator to work optimally with various instruments and market conditions.
3. Adaptive Volatility: By incorporating standard deviation, TrueLevel Bands can automatically adjust to changing market volatility. This feature enables the bands to expand during periods of high volatility and contract during periods of low volatility, providing traders with a more accurate representation of market dynamics.
4. Dynamic Support and Resistance Levels: TrueLevel Bands can help traders identify dynamic support and resistance levels, as the bands adjust in real-time according to price action. This can be particularly useful for traders looking to enter or exit positions based on support and resistance levels.
Why TrueLevel Bands are Different from Classic Moving Averages
TrueLevel Bands differ from conventional moving averages in several ways:
1. Linear Regression: While moving averages are based on simple arithmetic means, TrueLevel Bands use linear regression to determine the centerline. This offers a more accurate representation of the trend and helps traders better assess potential entry and exit points.
2. Envelope Style Bands: Unlike moving averages, which are single lines, TrueLevel Bands form envelope-style bands around the price action. This provides traders with a visual representation of potential support and resistance levels, trend direction, and volatility.
3. Multi-Timeframe Analysis: Classic moving averages typically focus on a single timeframe. In contrast, TrueLevel Bands incorporate multiple timeframes, enabling traders to capture a broader understanding of market dynamics.
4. Adaptive Volatility: Traditional moving averages do not account for changing market volatility, whereas TrueLevel Bands automatically adjust to volatility shifts through the use of standard deviation.
The TrueLevel Bands indicator is a powerful, versatile tool that offers traders a unique approach to technical analysis. With its ability to adapt to changing market conditions, provide multi-timeframe analysis, and dynamic support and resistance levels, TrueLevel Bands can serve as an invaluable asset to both novice and experienced traders looking to gain an edge in the markets.
Regression Envelope MTFThe Regression Envelope MTF indicator is a technical analysis tool that uses linear regression to identify potential price reversal points in the market. The indicator plots a linear regression line based on the selected price source over a specified length, and adds and subtracts a multiple of the standard deviation to create upper and lower bands around the line.
One advantage of using linear regression over the traditional envelope indicator is that it takes into account the slope of the trend, rather than assuming that the trend is linear. This means that the bands will adapt to the slope of the trend, which can provide more accurate signals in trending markets.
Another advantage of using linear regression over a simple moving average (SMA) is that it is less sensitive to outliers. SMAs can be heavily influenced by extreme values in the data, which can result in false signals. Linear regression, on the other hand, is more robust to outliers, which can lead to more reliable signals.
Overall, the Regression Envelope MTF indicator can be a useful tool for traders and investors looking to identify potential price reversal points and generate trading signals. However, it should be used in conjunction with other technical analysis tools and with proper risk management strategies in place.
Investments/swing trading strategy for different assetsStop worrying about catching the lowest price, it's almost impossible!: with this trend-following strategy and protection from bearish phases, you will know how to enter the market properly to obtain benefits in the long term.
Backtesting context: 1899-11-01 to 2023-02-16 of SPX by Tvc. Commissions: 0.05% for each entry, 0.05% for each exit. Risk per trade: 2.5% of the total account
For this strategy, 5 indicators are used:
One Ema of 200 periods
Atr Stop loss indicator from Gatherio
Squeeze momentum indicator from LazyBear
Moving average convergence/divergence or Macd
Relative strength index or Rsi
Trade conditions:
There are three type of entries, one of them depends if we want to trade against a bearish trend or not.
---If we keep Against trend option deactivated, the rules for two type of entries are:---
First type of entry:
With the next rules, we will be able to entry in a pull back situation:
Squeeze momentum is under 0 line (red)
Close is above 200 Ema and close is higher than the past close
Histogram from macd is under 0 line and is higher than the past one
Once these rules are met, we enter into a buy position. Stop loss will be determined by atr stop loss (white point) and break even(blue point) by a risk/reward ratio of 1:1.
For closing this position: Squeeze momentum crosses over 0 and, until squeeze momentum crosses under 0, we close the position. Otherwise, we would have closed the position due to break even or stop loss.
Second type of entry:
With the next rules, we will not lose a possible bullish movement:
Close is above 200 Ema
Squeeze momentum crosses under 0 line
Once these rules are met, we enter into a buy position. Stop loss will be determined by atr stop loss (white point) and break even(blue point) by a risk/reward ratio of 1:1.
Like in the past type of entry, for closing this position: Squeeze momentum crosses over 0 and, until squeeze momentum crosses under 0, we close the position. Otherwise, we would have closed the position due to break even or stop loss.
---If we keep Against trend option activated, the rules are the same as the ones above, but with one more type of entry. This is more useful in weekly timeframes, but could also be used in daily time frame:---
Third type of entry:
Close is under 200 Ema
Squeeze momentum crosses under 0 line
Once these rules are met, we enter into a buy position. Stop loss will be determined by atr stop loss (white point) and break even(blue point) by a risk/reward ratio of 1:1.
Like in the past type of entries, for closing this position: Squeeze momentum crosses over 0 and, until squeeze momentum crosses under 0, we close the position. Otherwise, we would have closed the position due to break even or stop loss.
Risk management
For calculating the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a buy signal at price of 4,000 usd. The stop loss price from atr stop loss is 3,900. You calculate the distance in percent between 4,000 and 3,900. In this case, that distance would be of 2.50%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(2,5%) = 1000usd. It means, you have to use 1000 usd for risking 2.5% of your account.
We will use this risk management for applying compound interest.
In settings, with position amount calculator, you can enter the amount in usd of your account and the amount in percentage for risking per trade of the account. You will see this value in green color in the upper left corner that shows the amount in usd to use for risking the specific percentage of your account.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, break evens, positions, signals, indicators, etc.
You will find the settings for risk management at the end of the script if you want to change something. But rebember, do not change values from indicators, the idea is to not over optimize the strategy.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
If you activate break even using rsi, when rsi crosses under overbought zone break even will be activated. This can work in some assets.
---Important: In risk managment you can find an option called "Use leverage ?", activate this if you want to backtest using leverage, which means that in case of not having enough money for risking the % determined by you of your account using your initial capital, you will use leverage for using the enough amount for risking that % of your acount in a buy position. Otherwise, the amount will be limited by your initial/current capital---
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
Do not forget to change commissions and other parameters related with back testing results!
Some assets and timeframes where the strategy has also worked:
BTCUSD : 4H, 1D, W
SPX (US500) : 4H, 1D, W
GOLD : 1D, W
SILVER : 1D, W
ETHUSD : 4H, 1D
DXY : 1D
AAPL : 4H, 1D, W
AMZN : 4H, 1D, W
META : 4H, 1D, W
(and others stocks)
BANKNIFTY : 4H, 1D, W
DAX : 1D, W
RUT : 1D, W
HSI : 1D, W
NI225 : 1D, W
USDCOP : 1D, W
MTF RSI Channel Smoothed (Linear Regression)The "MTF RSI Channel Smoothed (Linear Regression)" indicator is a momentum oscillator that displays smoothed RSI bands across multiple timeframes.
What sets this indicator apart is that it uses linear regression to smooth the RSI bands. Linear regression is a statistical technique that helps filter out the noise in the data, resulting in smoother and more precise RSI bands. This provides traders with a more advanced and reliable tool for monitoring the RSI bands across multiple timeframes.
One of the primary advantages of using linear regression to smooth the RSI bands is that it provides a more accurate and reliable way to identify overbought and oversold levels of the asset. The linear regression method smooths out the data by reducing the impact of temporary price fluctuations, allowing traders to see the underlying trend more clearly. This helps traders to make more informed trading decisions by identifying key levels of support and resistance, and spotting potential entry and exit points.
Another advantage of the "Multi-Timeframe Smoothed RSI Bands" indicator is that it allows traders to adjust the length of the smoothing period using the "Smooth Period" parameter. This flexibility gives traders the ability to fine-tune the indicator to their specific trading style and preferences, resulting in a more personalized and effective trading tool.
Overall, the "Multi-Timeframe Smoothed RSI Bands" indicator is a powerful tool for traders looking to monitor the RSI bands across multiple timeframes. By using linear regression to smooth the data, this indicator provides traders with a more accurate and reliable way to identify potential trading opportunities, and make more informed trading decisions.
Donchian Channel Smoothed (Linear Regression)The script is an implementation of the Donchian Channel Smoothed indicator using linear regression to smooth the data. The indicator plots three curves: the middle curve, which represents the average of the upper and lower curves, and the upper and lower curves, which are the standard Donchian channels.
The smoothing is done using linear regression on the highest and lowest of the given period. This helps filter out the noise in the data and provides a smoother curve that can help traders identify trends and key levels of support and resistance. The advantages of using linear regression for smoothing are reduced data volatility, better identification of long-term trends, and improved ability to identify support and resistance levels.
Using this indicator, traders can identify potential entry and exit points in a trend, as well as key support and resistance levels. Donchian channels are also useful for measuring asset volatility and determining trading range boundaries.
In summary, using linear regression to smooth the data in the Donchian Channel Smoothed indicator presents significant advantages for traders, such as reduced data volatility and better identification of long-term trends. This allows traders to more easily identify support and resistance levels and make more informed trading decisions.
Trend forecasting by c00l75----------- ITALIANO -----------
Questo codice è uno script di previsione del trend creato solo a scopo didattico. Utilizza una media mobile esponenziale (EMA) e una media mobile di Hull (HMA) per calcolare il trend attuale e prevedere il trend futuro. Il codice utilizza anche una regressione lineare per calcolare il trend attuale e un fattore di smorzamento per regolare l’effetto della regressione lineare sulla previsione del trend. Infine il codice disegna due linee tratteggiate per mostrare la previsione del trend per i periodi futuri specificati dall’utente. Se ti piace l'idea mettimi un boost e lascia un commento!
----------- ENGLISH -----------
This code is a trend forecasting script created for educational purposes only. It uses an exponential moving average (EMA) and a Hull moving average (HMA) to calculate the current trend and forecast the future trend. The code also uses a linear regression to calculate the current trend and a damping factor to adjust the effect of the linear regression on the trend prediction. Finally, the code draws two dashed lines to show the trend prediction for future periods specified by the user. If you like the idea please put a boost and leave a comment!
GKD-C Polynomial-Regression-Fitted Filter [Loxx]Giga Kaleidoscope GKD-C Polynomial-Regression-Fitted Filter is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Polynomial-Regression-Fitted Filter
Polynomial regression is a powerful tool in the field of data analysis, used to model the relationship between a dependent variable and one or more independent variables. In the case of a moving average, the aim is to smooth out fluctuations in time series data and reveal underlying trends. The following provides a thorough analysis of a polynomial regression function that calculates a moving average, delving into the intricacies of the code and explaining the steps involved in the process.
Function Overview
The polynomialRegressionMA(src, deg, len) function takes three input parameters: src, deg, and len. The src parameter represents the source data or time series, deg is the degree of the polynomial regression, and len is the length of the moving average window. Throughout the following description, we will discuss the various components of this function, explaining the role of each part in the overall process.
polynomialRegressionMA(src, deg, len)=>
float sumout = src
AX = matrix.new(12, 12, 0.)
float BX = array.new(12, 0.)
float ZX = array.new(12, 0.)
float Pow = array.new(12, 0.)
int Row = array.new(12, 0)
float CX = array.new(12, 0.)
for k = 1 to len
float YK = nz(src )
int XK = k
int Prod = 1
for j = 1 to deg + 1
array.set(BX, j, array.get(BX, j) + YK * Prod)
Prod *= XK
array.set(Pow, 0, len)
for k = 1 to len
int XK = k
int Prod = k
for j = 1 to 2 * deg
array.set(Pow, j, array.get(Pow, j) + Prod)
Prod *= XK
for j = 1 to deg + 1
for l = 1 to deg + 1
matrix.set(AX, j, l, array.get(Pow, j + l - 2))
for j = 1 to deg + 1
array.set(Row, j, j)
for i = 1 to deg
for k = i + 1 to deg + 1
if math.abs(matrix.get(AX, array.get(Row, k), i)) >
math.abs(matrix.get(AX, array.get(Row, i), i))
temp = array.get(Row, i)
array.set(Row, i, array.get(Row, k))
array.set(Row, k, temp)
for k = i + 1 to deg + 1
if matrix.get(AX, array.get(Row, i), i) != 0
matrix.set(AX, array.get(Row, k), i,
matrix.get(AX, array.get(Row, k), i) /
matrix.get(AX, array.get(Row, i), i))
for l = i + 1 to deg + 1
matrix.set(AX, array.get(Row, k), l,
matrix.get(AX, array.get(Row, k), l) -
matrix.get(AX, array.get(Row, k), i) *
matrix.get(AX, array.get(Row, i), l))
array.set(ZX, 1, array.get(BX, array.get(Row, 1)))
for k = 2 to deg + 1
float sum = 0.
for l = 1 to k - 1
sum += matrix.get(AX, array.get(Row, k), l) * array.get(ZX, l)
array.set(ZX, k, array.get(BX, array.get(Row, k)) - sum)
if matrix.get(AX, array.get(Row, deg + 1), deg + 1) != 0.
array.set(CX, deg + 1, array.get(ZX, deg + 1) / matrix.get(AX, array.get(Row, deg + 1), deg + 1))
for k = deg to 1
float sum = 0.
for l = k + 1 to deg + 1
sum += matrix.get(AX, array.get(Row, k), l) * array.get(CX, l)
array.set(CX, k, (array.get(ZX, k) - sum) / matrix.get(AX, array.get(Row, k), k))
sumout := array.get(CX, deg + 1)
for k = deg to 1
sumout := array.get(CX, k) + sumout * len
sumout
Variable Initialization
At the beginning of the function, several arrays and matrices are initialized: sumout, AX, BX, ZX, Pow, Row, and CX. These variables are used to store intermediate results and perform the necessary calculations.
sumout: This variable will store the final moving average result.
AX: A matrix that stores the coefficients of the system of linear equations representing the polynomial regression.
BX: An array that holds the values required for calculating the moving average.
ZX: An array used for storing intermediate results during the Gaussian elimination process.
Pow: An array containing the powers of the independent variable.
Row: An array that keeps track of the row order in the AX matrix.
CX: An array that stores the calculated coefficients of the polynomial regression.
Calculating the BX Array
The function begins by iterating through the length of the moving average window and the degree of the polynomial regression. The purpose of these nested loops is to calculate the values for the BX array. The outer loop iterates from 1 to len, while the inner loop iterates from 1 to deg + 1.
During each iteration, the YK variable is assigned the non-zero value of the source data at the index (len - k), and the XK variable is assigned the current value of k. The Prod variable is initialized with the value 1, and the inner loop calculates the product of YK and Prod. The value of Prod is then updated by multiplying it with XK.
After completing the inner loop, the BX array is updated by adding the product of YK and Prod to its current value at index j. This process continues until both loops are completed, and the BX array contains the necessary values for further calculations.
Calculating the Pow Array
Next, the function initializes the Pow array by setting its 0th element to the length of the moving average window. The Pow array will store the powers of the independent variable. The function then iterates through the length of the moving average window (from 1 to len) and calculates the values of the Pow array based on the polynomial degree.
During each iteration, the XK variable is assigned the current value of k, and the Prod variable is assigned the value of k. The loop then iterates from 1 to 2 * deg, updating the Pow array by adding the current value of Prod to the array element at index j. The value of Prod is updated by multiplying it with XK. Once the loop is complete, the Pow array contains the necessary values for initializing the AX matrix.
Initializing the AX Matrix
Following the calculation of the Pow array, the function initializes the AX matrix using the values from the Pow array. The AX matrix is a square matrix with dimensions (deg + 1) x (deg + 1) and is used to store the coefficients of the polynomial regression.
The function iterates through two nested loops, with the outer loop iterating from 1 to deg + 1 and the inner loop iterating from 1 to deg + 1 as well. During each iteration, the AX matrix is updated by setting the element at position (j, l) to the corresponding value from the Pow array at index (j + l - 2). This process continues until both loops are completed, and the AX matrix is fully populated with the necessary values.
Initializing the Row Array
The next part of the function initializes the Row array, which will be used later to keep track of the row order in the AX matrix. The function iterates through a loop that assigns each element of the array to its index (1 to deg + 1).
Gaussian Elimination
The function employs Gaussian elimination to solve the system of linear equations represented by the AX matrix. Gaussian elimination is an algorithm used to solve linear systems by transforming the system into a triangular matrix using a series of row operations, such as swapping rows, multiplying rows by constants, and adding or subtracting rows.
The function iterates through the deg elements, performing several nested loops that compare, swap, divide, and subtract the matrix elements accordingly. The outer loop iterates from 1 to deg, and the first inner loop iterates from i + 1 to deg + 1. This loop compares the absolute values of the matrix elements and swaps the rows when necessary. The process of comparing and swapping rows ensures that the matrix is in the proper format for Gaussian elimination.
The second inner loop iterates from i + 1 to deg + 1 and is responsible for dividing the matrix elements. If the matrix element at the position (array.get(Row, i), i) is not equal to 0, the matrix element at the position (array.get(Row, k), i) is divided by the matrix element at the position (array.get(Row, i), i).
The third inner loop iterates from i + 1 to deg + 1 and subtracts the matrix elements accordingly. This subtraction process eliminates the coefficients below the main diagonal, effectively transforming the AX matrix into an upper triangular matrix.
Back-substitution and Calculating the CX Array
The function proceeds to perform back-substitution to find the solution to the linear system. The ZX array is filled with the results from the BX array and the Row array. Then, the back-substitution process begins, and the CX array is filled with the calculated coefficients for the polynomial regression.
The function iterates from 1 to deg + 1 to update the ZX array. During each iteration, a sum variable is initialized to 0, and an inner loop iterates from 1 to k - 1. Inside this loop, the sum variable is incremented by the product of the AX matrix element at the position (array.get(Row, k), l) and the ZX array element at index l. After the inner loop, the ZX array is updated by subtracting the sum from the BX array element at the index array.get(Row, k).
Once the ZX array is updated, the function checks if the AX matrix element at the position (array.get(Row, deg + 1), deg + 1) is not equal to 0. If this condition is met, the CX array element at the index (deg + 1) is updated by dividing the ZX array element at the index (deg + 1) by the AX matrix element at the position (array.get(Row, deg + 1), deg + 1).
The function then iterates from deg to 1 in reverse order to update the CX array. A sum variable is initialized to 0, and an inner loop iterates from k + 1 to deg + 1. Inside this loop, the sum variable is incremented by the product of the AX matrix element at the position (array.get(Row, k), l) and the CX array element at index l. After the inner loop, the CX array element at index k is updated by dividing the difference between the ZX array element at index k and the sum by the AX matrix element at the position (array.get(Row, k), k). Once this process is completed, the CX array contains the calculated coefficients of the polynomial regression.
Calculating the Moving Average
The final step of the function is to calculate the moving average using the coefficients stored in the CX array. To do this, the function iterates through the degree of the polynomial regression in reverse order, starting with the highest degree and ending with the lowest. The result is stored in the sumout variable.
The loop iterates from deg to 1. During each iteration, the sumout variable is updated by adding the CX array element at index k to the product of the sumout variable and the length of the moving average window (len). This process continues until the loop is complete, and the sumout variable contains the final moving average value.
Returning the Moving Average
The function concludes by returning the sumout variable, which represents the moving average value at the current data point. The polyout variable is assigned the result of the polynomialRegressionMA(src, dgr, flen) function, and the sig variable is assigned the first element of the polyout array, indicating that the moving average value at the current data point is stored in the sig variable.
Conclusion
The provided code is a comprehensive implementation of a polynomial regression function that calculates the moving average of a given time series data set (src) using a specified polynomial degree (deg) and a specified moving average window length (len). The function employs Gaussian elimination and back-substitution techniques to solve the system of linear equations and find the coefficients for the polynomial regression. These coefficients are then used to compute the moving average.
In conclusion, we dissected the code of a polynomial regression function that creates a moving average, explaining each component's role in the overall process. The function demonstrates the power of polynomial regression in smoothing out fluctuations in time series data and revealing underlying trends, making it a valuable tool in the field of data analysis.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Polynomial-Regression-Fitted Filter as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
RSI and Stochastic Probability Based Price Target IndicatorHello,
Releasing this beta indicator. It is somewhat experimental but I have had some good success with it so I figured I would share it!
What is it?
This is an indicator that combines RSI and Stochastics with probability levels.
How it works?
This works by applying a regression based analysis on both Stochastics and RSI to attempt to predict a likely close price of the stock.
It also assess the normal distribution range the stock is trading in. With this information it does the following:
2 lines are plotted:
Yellow line: This is the stochastic line. This represents the smoothed version of the stochastic price prediction of the most likely close price.
White Line: This is the RSI line. It represents the smoothed version of the RSI price prediction of the most likely close price.
When the Yellow Line (Stochastic Line) crosses over the White Line (the RSI line), this is a bearish indication. It will signal a bearish cross (red arrow) to signal that some selling or pullback may follow.
IF this bearish cross happens while the stock is trading in a low probability upper zone (anything 13% or less), it will trigger a label to print with a pullback price. The pullback price is the "regression to the mean" assumption price. Its the current mean at the time of the bearish cross.
The inverse is true if it is a bullish cross. If the stock has a bullish cross and is trading in a low probability bearish range, it will print the price target for a regression back to the upward mean.
Additional information:
The indicator also provides a data table. This data table provides you with the current probability range (i.e. whether the stock is trading in the 68% probability zone or the outer 13, 2.1 or 0.1 probability zones), as well as the overall probability of a move up or down.
It also provides the next bull and bear targets. These are calculated based on the next probability zone located immediately above and below the current trading zone of the stock.
Smoothing vs Non-smoothed data:
For those who like to assess RSI and Stochastic for divergences, there is an option in the indicator to un-smooth the stochastic and RSI lines. Doing so looks like this:
Un-smoothing the RSI and stochastic will not affect the analysis or price targets. However it does add some noise to the chart and makes it slightly difficult to check for crosses. But whatever your preference is you can use.
Cross Indicators :
A bearish cross (stochastic crosses above RSI line) is signalled with a red arrow down shape.
A bullish cross (RSI crosses above stochastic line) is signalled with a green arrow up shape.
Labels vs Arrows:
The arrows are lax in their signalling. They will signal at any cross. Thus you are inclined to get false signals.
The labels are programmed to only trigger on high probability setups.
Please keep this in mind when using the indicator!
Warning and disclaimer:
As with all indicators, no indicator is 100% perfect.
This will not replace the need for solid analysis, risk management and planning.
This is also kind of beta in its approach. As such, there are no real rules on how it should be or can be applied rigorously. Thus, its important to exercise caution and not rely on this alone. Do your due diligence before using or applying this indicator to your trading regimen.
As it is kind of different, I am interested in hearing your feedback and experience using it. Let me know your feedback, experiences and suggestions below.
Also, because it does have a lot of moving parts, I have done a tutorial video on its use linked below:
Thanks for checking it out, safe trades everyone and take care!
Trading pirate 2Trading Pirate Regression Candles. This turns regular candle sticks into regression candles. Colors are Gray and Red.
Crypto Performance Index1. The Crypto Performance Index (CPI) estimates the price appreciation of a crypto asset relative to the overall crypto market performance. The indicator is calculated using a Sharpe Ratio principle enhanced with time-domain normalization and cumulative parametrization.
2. The CPI is based on the idea that the performance of an asset should be evaluated not only in terms of its absolute price movement, but also in terms of its risk-adjusted returns compared to the broader market. The Sharpe Ratio, which takes into account both the asset's return and its volatility, is a commonly used measure of risk-adjusted performance.
3. The CPI takes the Sharpe Ratio principle further by incorporating a time-domain normalization technique that adjusts for differences in volatility across different time periods. The cumulative parametrization ensures that the CPI considers the overall performance of the asset over a specified period of time.
4. To use the indicator, select a timeframe and set the standard deviation period (default is 20). The CPI line can be compared against various market benchmarks, including the total crypto market cap (white line), altcoins total market cap (blue line), low-cap altcoins (without ETH), and Bitcoin.
5. An upward slope of the CPI line indicates strong price performance of an asset, with a relatively high chance for the asset to continue growing faster than the market in the future. Conversely, a downward slope of the CPI line indicates weak price performance of an asset, with a relatively high chance for the asset to depreciate in price with respect to the rest of the market in the future.
6. Overall, the CPI provides a comprehensive measure of an asset's price performance, taking into account both its absolute return and its risk-adjusted return relative to the broader market. This makes it a valuable tool for investors looking to evaluate the performance of their crypto holdings and make informed decisions about buying, selling, or holding assets.
BTC Log High/Low ChartThis indicator calculates the logarithmic values of the high and low prices of BTC based on a mathematical formula and plots them on the chart. The code uses the current time and width of the chart to calculate the logarithmic values of the high and low prices. It defines functions to convert a timestamp to the number of days since January 1st, 2009.
You can use it with BTC Log High/Low:
Correlation AnalysisAs the name suggests, this indicator is a market correlation analysis tool.
It contains two main features:
- The Curve: represents the historic correlation coefficient between the current chart and the “Reference Market” input from the settings menu. It aims to give more depth to the current correlation values found in the second feature.
- The Screener: this second feature displays all correlation coefficient values between the (max) 20 markets inputs. You can use it to create several screeners for several market types (crypto, forex, metals, etc.) or even replicate your current portfolio of investments and gauge the correlation of its components.
Aside from these two previous features, you can visually plot the variation rate from one bar to another along with the covariance coefficient (both used in the correlation calculation). Finally, a simple “signal” moving average can be applied to the correlation coefficient .
I might add alerts to this script or even turn it into a strategy to do some backtesting. Do not hesitate to contact me or comment below if this is something you would be interested in or if you have any suggestions for improvement.
Enjoy!!
Capital Line PackThe Capital Line Pack ( CLP ) indicator is a technical analysis tool that is designed to help traders and investors identify potential buying and selling opportunities in financial markets by using, inter alia, kernel regression methodoliges. It is a standalone indicator that can be placed on top of price chart displaying the Base MA, Capital Line and standard deviation bands.
The Capital Line is calculated based on volatility, measured by a z-scores* of a selected price source and a moving average (Base MA). The Base MA serves as the foundation for the Capital Line calculation and plays a critical role in determining its behavior and responsiveness to price movements. By selecting different types of moving averages as the Base MA, traders can adjust the sensitivity of the Capital Line to changes in market conditions, which can impact the signals generated by the indicator. The Base MA can be set at the user's choice including: SMA, EMA, Volume Weighted Moving Average (VWMA), Kernel Regression MA, HEMA, DEMA, T3.
For example, if a trader selects a EMA as the Base MA, the Capital Line will respond more quickly to changes in price compared to a more smoothed moving average, like a Volume Weighted Moving Average (VWMA) or Kernel Regression MA. This means that the Capital Line will be more sensitive to short-term price fluctuations with a EMA as the Base MA, while a VWMA or Kernel Regression MA will be less reactive to short-term price movements and more focused on longer-term trends.
Therefore, the choice of Base MA can have a significant impact on the behavior of the Capital Line, and traders need to select the most appropriate Base MA that suits their trading strategy and risk management preferences.
*The z-scores are calculated by comparing the current price to the average price over a certain period of time, and then dividing the difference by the standard deviation of the prices over that same period of time.
The Bands are calculated by adding and subtracting a standard deviation from the Base MA.
Bands help identify the volatility of the market, and when the bands are narrow, it suggests that the market is in a range-bound or flat period.
Indicator incorporates trade signals (labels and alerts). The method by which signals are generated can be selected by the user from several options:
Cap line color switch: Turning blue when it rises and red when it starts to fall.
Cap Line crosses the Base MA: This can be useful when the Base MA is weighted, for example, by volume, and the Cap Line Bandwidth and Relative weighting are set to small values.
Price crosses the Base MA: This is a popular and widely-used method that can provide reliable signals during trending market conditions. However, it may generate false signals during range-bound or flat market conditions.
Crossing of secondary MAs which can be selected in the indicator settings: This method provides traders with more flexibility and control over the signals generated by the indicator, but it may also be more complex and require more advanced technical analysis skills.
One of the standout features of our indicator is the ability to choose from several different style themes:
Pro
Modern
and Stealth
The "Pro" and "Modern" themes offer a clean and visually appealing display, while the "Stealth" theme is perfect for traders who want to focus on the price action or other indicators. The "Stealth" theme shades all the elements of the indicator while still keeping them in the field of visibility, allowing traders to concentrate on the most important aspects of their charts.
In addition to its trade signals, alerts, labels, and customizable themes, the indicator also offers several trend highlighting options to help traders visually backtest their trades. These options include candle coloring, background coloring, and highlighting with a histogram.
The candle coloring feature allows traders to customize the color of the candlesticks on their chart based on the direction of the trend. For example, bullish candles could be colored in teal, while bearish candles could be colored purple etc. This can make it easier for traders to identify trend movements and backtest their strategy.
The background coloring feature works similarly to the candle coloring feature, but it applies a color to the background of the chart rather than the candlesticks. This can be a useful way to highlight trends on the chart without obscuring the price action.
The histogram highlighting feature displays a histogram on the chart to show the difference between the upper and lower bands. This can be a useful way to visualize the strength of the trend and backtest trades based on the histogram readings.
NB! Remember, it is important to have a solid trading plan in place and to properly manage risk when trading.
Some traders may, depending upon customized settings, use the Capital Line as a capital risk management feature in trading. Our Capital Line indicator can be a useful tool, but it should not be the only factor considered when making trade decisions.
Lorentzian ML [Sublime Traders]Lorentzian ML
Context: The whole idea of this indicator is to use the Lorentzian Classifier (a popular machine learning model suited for analyzing data in a time series) , add some oscillators and filter them with volume averages in order to get precise swing move indications.
The Lorentzian ML indicator uses the Lorenzian Classifier (LDC) algorithm that takes into account the Commodity Channel Index (CCI) and Relative Strength Index (RSI) signals as raw material to provide buy and sell signals. The indicator is accompanied by take profit , stop loss and entry lines based on the Average True Range (ATR).
Features:
1. Lorentzian Classifier:
Uses the difference between the current and previous values of CCI and RSI to generate buy and sell signals.
The classifier threshold can be adjusted using the input parameter.
2. ATR-based Take Profit Line:
A horizontal take profit line is plotted when buy or sell signals occur.
The line is based on the ATR value and a user-defined multiplier.
3. VMA filtering
Using the simple switches: Scalper, Swing or Holder , the users can easily filter the frequency of the signals in addition to the lookback and threshold filters. This will affect the used VMA lines that use data gathered from multiple timeframes.
Visual Representation:
The indicator plots green candles for buy signals and red candles for sell signals.
Buy and sell labels are displayed on the chart to mark the points where signals occur.
The ATR-based take profit line is displayed in a user-defined color and line width.
Visual representation of the VMA lines : Red - bearish , Blue - uncertain , Green - bullish
Changes and features to come
Fix "holder" switch on sell side that sometimes bugs the whole chart.
Add more intuitive filtering methods.
Add two more oscillators to the Lorentzian pool.
Create switches for Lorentzian source.
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.
Leveraged Share Conversion IndicatorHello everyone,
Releasing my leveraged share conversion indicator.
I noticed that the option traders have all the fun and resources but the share traders don't really have many resources in terms of adjusting or profits on leveraged and inverse shares. So, I decided to change that this this indicator!
What it does:
In a nut shell, the calculator converts one share to the price of another through the use of a regression based analysis.
There are multiple pre-stored libraries available in the indicator, including IWM, SPY, BTC and QQQ.
However, if the ticker you want to convert is not in one of the pre-defined libraries, you can select "Use Alternative Ticker" and indicate the stock you wish to convert.
Using Libraries:
If the conversion you want is available in one of the libraries, simply select the conversion you would like. For example, if you want to convert SPY to SPXU, select that conversion. The indicator will then launch up the conversion results which it will display in a dashboard to the right and will also display the plotted conversion on a chart (see imagine below:
In the dashboard, the indicator will show you:
a) The conversion result: This is the most likely price based on the analysis
b) The standard error: This is the degree of error within the conversion. This is the basis of the upper and lower bands. In statistics, we can add and subtract the standard error from the likely result to get the "Upper" and "Lower" Confidence levels of assessment. This is just a fancy way of saying the range in which our predicted result will fall. So, for example, in the image above it shows you the price of SPXU is assessed to be around 16$ based on SPY's price. The standard error range is 15-17. This means that, the majority of the time, based on this SPY close price, SPXU should fall between 15-17$ with the most likely result being the 16$ range.
Why is there error?
Because leveraged shares have an inherent decay in them. The degree of decay can be captured utilizing the standard error. So at any given time, the small changes in price fluctuations caused by the fact that the share is leveraged can be assessed and displayed using standard error measurements.
c) The current correlation: This is important! Because if the stocks are not strongly correlated, it tells you there is a problem. In general, a perfect correlation is 1 or -1 (perfectly negative correlation or inverse correlation) and a bad correlation is anything under 0.5 or -0.5. So, for an INVERSE leveraged share, you would expect the correlation to read a negative value. Ideally -1. Because the inverse share is doing the opposite of the underlying (if the underlying goes up, the inverse goes down and vice versa). For a non-inverse leveraged share, the correlation should read a positive value. As the underlying goes up, so too does the leveraged.
Manual Conversion using Library:
If you are using a pre-defined library but want to convert a manual close price, simply select "Enable manual conversion" at the bottom of the settings and then type in the manual close price. If you are converting SPY to SPXU, type in the manual close price of SPY to get the result in SPXU and vice versa.
Using an Alternative Ticker:
If the ticker you want is not available in a pre-defined library (i.e. UDOW, BOIL, APPU, TSLL, etc.), simply select "Use Alternative Ticker" in the settings menu. When you select this, make sure your chart is set to the dominant chart. The "Dominant chart" is the chart of the underlying. So, if you want TSLA to TSLL, be sure you have the TSLA chart open and then set your Alternative Ticker to TSLL or TSLQ.
The process of using an Alternative Ticker remains the same. If you wish to enter a manual close price, simply select "Enable Manual Conversion".
Special Considerations:
The indicator uses 1 hour candles. Thus, please leave your dominant chart set on the 1 hour time frame to avoid confusing the indicator.
The lookback period of the manual conversion is 10, 1 hour candles. As such, the results should not be used to make longer term predictions (i.e. anything over 6 months is pushing the capabilities of a manual conversion but fair game for the pre-defined library conversions which use more longer-term data).
You can technically use the indicator to make assessments between 2 separate equities. For example, the relationship between QQQ and ARKK, SPY and DIA, IWM and SPY, etc. If there is a good enough correlation, you can use it to make predictions of the opposing ticker. For example, if DIA goes to 340, what would SPY likely do? And vice versa.
As always, I have prepared a tutorial and getting started video for your reference:
As always, let me know your questions and requests/recommendations for the indicator below. This indicator is my final reference indicator in my 3 part reference indicator release. I will be going back over the feedback to make improvements based on the suggestions I have received. So please feel free to leave any suggestions here and I will take them into consideration for improvement!
Thank you for checking this out and as always, safe trades!
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.
Triple Quadratic Regression - Supplementary UnderlayThis indicator is supplementary to our Triple Quadratic Regression overlaid indicator (which includes three step lines - a fast (fuchsia), a medium (yellow), and a slow (blue) quadratic regression line to help the user obtain a clearer picture of current trends).
Quadratic regression is better suited to determining (and predicting) trend than linear regression ; y = ax^2 + bx + c is better to use than a simple y = ax + b. Calculating the regression involves five summation equations that utilize the bar index (x1), the price source (defaulted to ohlc4), the desired lengths, and the square of x1. Determining the coefficient values requires an additional step that factors in the simple moving average of the source, bar index, and the squared bar index.
Instead of overlaying the three quadratic regression lines themselves, this underlaid indicator is used to show the normalized (-1 to +1) values of ax^2 and bx. The color of the lines and histogram match the associated lines on our overlaid indicator. Here, the solid fuchsia line is the fast QR's normalized ax^2 value, the solid yellow line is the mid QR's normalized ax^2 value, and the solid blue line is the slow QR's normalized ax^2 value. The histograms reflect the normalized bx values. In addition to these, the momentum of the ax^2 values was calculated and represented as a dotted line of the same colors.
Bar color is influenced by the values of ax^2 and bx of the fast and medium length regressions. If ax^2 and bx for both the fast and medium lengths are above 0, the bar color is green. If they are both under 0, the bar color is red. Otherwise, bars are colored gray.
When combined with our overlaid Triple Quadratic Regression indicator and the Triple Quadratic Regression Macro Score strategy (part of the LeafAlgo Premium Macro Strategies) to gather all of the information possible, your chart should look like this:
Triple Quadratic Regression (w/ Normalized Value Table)This indicator draws three step lines - a fast (fuchsia), a medium (yellow), and a slow (blue) quadratic regression line to help the user obtain a clearer picture of current trends. Quadratic regression is better suited to determining (and predicting) trend than linear regression; y = ax^2 + bx + c is better to use than a simple y = ax + b. Calculating the regression involves five summation equations that utilize the bar index (x1), the price source (defaulted to ohlc4), the desired lengths, and the square of x1. Determining the coefficient values requires an additional step that factors in the simple moving average of the source, bar index, and the squared bar index.
In addition to the plotted lines, a change in bar color and a table were added. The bar color is influenced by the values of ax^2 and bx of the fast and medium length regressions. If ax^2 and bx for both the fast and medium lengths are above 0, the bar color is green. If they are both under 0, the bar color is red. Otherwise, bars are colored gray. In the table, located at the bottom of the chart (but can be moved), the ax^2 and bx values for each regression length are shown. The option to view normalized (scale of -1 to +1) values or the standard values is included in the indicator settings menu. By default, the normalized values are shown.
Distance from the High/Low priceThis indicator shows how far the price is from the Top and Bottom over a set period of time.
The basic purpose of this indicator is to quickly compare how many symbols have risen over a certain period of time.
For example,
For example, let's say I want to see what the maximum increase is from the December, and how much it's currently down from there.
Then, let's set the "Length" to approximately 1500 and check it from December 18th.
So now you can see that bitcoin is up to about 44%, and it's down 6.9% from its peak.
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For the second example, let's say I want to see what the maximum increase in ALPHA is and how far it is currently from that maximum.
So, as you can see in the chart above, the maximum increase over the period was about 120%, and now it's down by 22.8%.
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In addition, if you check 'Retracement' in the indicator setting, you can see the ratio of the currently located returns based on Top and Bottom.
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이 지표는, 특정 기간동안 여러개의 symbol들이 얼마만큼의 상승을 했는지 빠르게 비교하기 위해 만들었습니다.
위에 첨부한 사진을 기준으로 말씀드리겠습니다.
2022년 12월 말부터 올라온 상승의 최대폭이 얼마인지, 그리고 그 최대 상승으로부터 현재 얼마나 떨어졌는지를 확인하고 싶은 상황이라고 하겠습니다.
그렇다면 'Length'를 대략 1500으로 설정하여 12월 18일부터 확인해보겠습니다.
그러면 비트코인은 최대 약 44%만큼 상승하였고, 현재 최고점으로부터 6.9% 떨어진 상황이라는 것을 확인할 수 있습니다.
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두 번째 예시로, ALPHA의 최대 상승폭이 얼마인지, 그리고 그 최댓값으로부터 현재 얼마만큼 떨어져 있는 상황인지를 확인하고 싶다고 가정해보겠습니다.
그렇다면 위의 차트에서 보이는 바와 같이, 해당 기간동안 최대 상승폭이 약 120%였고, 현재 그 최댓값으로부터 22.8%정도 하락한 상황이라는 것을 확인할 수 있습니다.
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번외로, 지표 설정에서 'Retracement'를 체크하시면, Top과 Bottom을 기준으로 현재 위치한 되돌림의 비율을 확인할 수 있습니다.
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.
