Chervolinos Ultrafast RMTA MACDDescription of a classic MACD:
MACD, short for moving average convergence/divergence, is a trading indicator used in technical analysis of stock prices, created by Gerald Appel in the late 1970s. It is designed to reveal changes in the strength, direction, momentum, and duration of a trend in a stock's price. The MACD indicator (or "oscillator") is a collection of three time series calculated from historical price data, most often the closing price. These three series are: the MACD series proper, the "signal" or "average" series, and the "divergence" series which is the difference between the two. The MACD series is the difference between a "fast" (short period) exponential moving average (EMA), and a "slow" (longer period) EMA of the price series. The average series is an EMA of the MACD series itself. The MACD indicator thus depends on three time parameters, namely the time constants of the three EMAs. The notation "MACD" usually denotes the indicator where the MACD series is the difference of EMAs with characteristic times a and b, and the average series is an EMA of the MACD series with characteristic time c. These parameters are usually measured in days. The most commonly used values are 12, 26, and 9 days, that is, MACD. As true with most of the technical indicators, MACD also finds its period settings from the old days when technical analysis used to be mainly based on the daily charts. The reason was the lack of the modern trading platforms which show the changing prices every moment. As the working week used to be 6-days, the period settings of represent 2 weeks, 1 month and one and a half week. Now when the trading weeks have only 5 days, possibilities of changing the period settings cannot be overruled. However, it is always better to stick to the period settings which are used by the majority of traders as the buying and selling decisions based on the standard settings further push the prices in that direction.
Description of the new Ultrafast RMTA MACD:
Ultrafast RMTA MACD, short for moving average convergence/divergence, is a trading indicator used in technical analysis of stock prices, created by Chervolino. It is designed to reveal changes in the strength,
direction, momentum, and duration of a trend in a stock's price. The RMTA MACD indicator (or "oscillator") is a collection of three time series calculated from historical price data, from the closing price.
The RMTA MACD based on the THE RECURSIVE MOVING TRENDLINE SYSTEM technical.traders.com
and is series is the difference between a "fast" (short period) Recursive Moving Trend Average, and a "slow" (longer period) Recursive Moving Trend Average of the price series. The average series is an EMA of the MACD series itself.
The result is a non laging indicator, depends on the settings.
special thanks to
everget
LonesomeTheBlue
스크립트에서 "跨境通12月4日地天板"에 대해 찾기
T3 PPO [Loxx]T3 PPO is a percentage price oscillator indicator using T3 moving average. This indicator is used to spot reversals. Dark red is upward price exhaustion, dark green is downward price exhaustion.
What is Percentage Price Oscillator (PPO)?
The percentage price oscillator (PPO) is a technical momentum indicator that shows the relationship between two moving averages in percentage terms. The moving averages are a 26-period and 12-period exponential moving average (EMA).
The PPO is used to compare asset performance and volatility, spot divergence that could lead to price reversals, generate trade signals, and help confirm trend direction.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
MACD frontSide backSide + TTM Squeeze by bangkokskaterDark Mode is enabled by default for black theme
disable Dark Mode for white theme
MACD frontSide backSide
===================
an elegant, much better way to use MACD
for trend following momentum ( aka momo) style
MACD with default settings of 12/26 smoothing of 9
✔️ but without histogram
✔️ only has MACD and signal "lines"
green = frontSide momentum impulse
take longs only
red = backSide momentum impulse
take shorts only
black area = exit (once green or red is no longer showing)
or keep holding till next bigger TP
PS: credits to Warrior Trading Ross Cameron for this idea
youtu.be
TTM Squeeze
===================
white dots = incoming pump / dump (monitor for entry)
PS: credits to John Carter's TTM Squeeze & Greeny for PineScript adaptation
[HA] Heikin-Ashi Shadow Candles// For overlaying Heikin Ashi candles over basic charts, or for use in it's own panel as an oscillator.
// Enjoy the visual cues of HA candles, without giving up price action awareness.
// Good for learning and comparison.
// Aug 11 2022
Release Notes: * Bugfix: Candle color was based on classic direction not HA direction (did not update cover photo).
// Aug 12 2022
Release Notes: * Implemented true oscillator mode.
Provided as separate plot (styles tab) or mode switch option (Inputs tab). TV gets spazzy with "styles tab" "default hidden" plots, and will reset them if any variables are modified that affect them (i.e. wick color override). Mode switch should be sufficient for both users.
// Aug 21 2022
Republished because of typo in indicator name prevented search.
Ichimoku Cloud with MACD (By Coinrule)The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction. It does this by taking multiple averages and plotting them on a chart. It also uses these figures to compute a “cloud” that attempts to forecast where the price may find support or resistance in the future.
The Ichimoku Cloud was developed by Goichi Hosoda, a Japanese journalist, and published in the late 1960s. It provides more data points than the standard candlestick chart. While it seems complicated at first glance, those familiar with how to read the charts often find it easy to understand with well-defined trading signals.
The Ichimoku Cloud is composed of five lines or calculations, two of which comprise a cloud where the difference between the two lines is shaded in.
The lines include a nine-period average, a 26-period average, an average of those two averages, a 52-period average, and a lagging closing price line.
The cloud is a key part of the indicator. When the price is below the cloud, the trend is down. When the price is above the cloud, the trend is up.
The above trend signals are strengthened if the cloud is moving in the same direction as the price. For example, during an uptrend, the top of the cloud is moving up, or during a downtrend, the bottom of the cloud is moving down.
The MACD is a trend following momentum indicator and provides identification of short-term trend direction. In this variation it utilises the 12-period as the fast and 26-period as the slow length EMAs, with signal smoothing set at 9.
This strategy combines the Ichimoku Cloud with the MACD indicator to better enter trades.
Long/Short orders are placed when three basic signals are triggered.
Long Position:
Tenkan-Sen is above the Kijun-Sen
Chikou-Span is above the close of 26 bars ago
Close is above the Kumo Cloud
MACD line crosses over the signal line
Short Position:
Tenkan-Sen is below the Kijun-Sen
Chikou-Span is below the close of 26 bars ago
Close is below the Kumo Cloud
MACD line crosses under the signal line
The script is backtested from 1 June 2022 and provides good returns.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
This script also works well on MATIC (1h timeframe), AVA (45m timeframe), and BTC (30m timeframe).
Rsi/W%R/Stoch/Mfi: HTF overlay mini-plotsOverlay mini-plots for various indicators. Shows current timeframe; and option to plot 2x higher timeframes (i.e. 15min and 60min on the 5min chart above).
The idea is to de-clutter chart when you just want real-time snippets for an indicator.
Useful for gauging overbought/oversold, across timeframes, at a glance.
~~Indicators~~
~RSI: Relative strength index
~W%R: Williams percent range
~Stochastic
~MFI: Money flow index
~~Inputs~~
~indicator length (NB default is set to 12, NOT the standard 14)
~choose 2x HTFs, show/hide HTF plots
~choose number of bars to show (current timeframe only; HTF plots show only 6 bars)
~horizontal position: offset (bars); shift plots right or left. Can be negative
~vertical position: top/middle/bottom
~other formatting options (color, line thickness, show/hide labels, 70/30 lines, 80/20 lines)
~~tips~~
~should be relatively easy to add further indicators, so long as they are 0-100 based; by editing lines 9 and 11
~change the vertical compression of the plots by playing around with the numbers (+100, -400, etc) in lines 24 and 25
10-Year Bond Yields (Interest Rate Differential)With this little script, I have attempted to incorporate fundamental data (in this case, 10-year bond yields) into technical analysis . When pairing two currencies, the one with a higher bond interest rate usually appreciates when the interest rate differential widens, or, to use a simple example: in a currency pair A vs. B, with A showing a higher bond yield than B, a widening interest rate gap is likely to help A and create a buying opportunity (shown as a blue square at the bottom of the chart), while the opposite is true when the gap tightens (sell signal, red square).
While long-term investors know about and make use of the importance of bond yield fluctuations, most short-term traders tend to dismiss the idea of using fundamental data, mostly for lack of quantifiability and limited impact in an intraday environment. After extensive backtesting on daily and intraday charts (6-12 hours), however, I realized this indicator still managed to produce useful results (less useful than on monthly and yearly charts, to be fair, but still useful enough), especially when paired with simple price-driven indicators, such as Heikin Ashi or linear regression .
My personal (and thus subjective) thoughts: worth a try. Buy and sell signals frequently contradicted both more popular indicators and my gut feeling and managed to take out losing trades that I had considered trades with a high winning probability. In other words, when the market lures traders into seemingly promising trading decisions, this indicator might give you an early warning, especially when you manage to adjust period and continuity parameters to your trading strategy.
Currency pairs used in this script are all possible combinations of the eight majors. Each security has been assigned a name ("inst01" to "inst08" in the code) and a broker; if you make changes to the code, be sure not to mess with currency and broker names as this would render the entire script useless. Good luck trading, and feel free to suggest improvements!
TR_HighLow_LibLibrary "TR_HighLow_Lib"
TODO: add library description here
ShowLabel(_Text, _X, _Y, _Style, _Size, _Yloc, _Color)
TODO: Function to display labels
Parameters:
_Text : TODO: text (series string) Label text.
_X : TODO: x (series int) Bar index.
_Y : TODO: y (series int/float) Price of the label position.
_Style : TODO: style (series string) Label style.
_Size : TODO: size (series string) Label size.
_Yloc : TODO: yloc (series string) Possible values are yloc.price, yloc.abovebar, yloc.belowbar.
_Color : TODO: color (series color) Color of the label border and arrow
Returns: TODO: No return values
GetColor(_Index)
TODO: Function to take out 12 colors in order
Parameters:
_Index : TODO: color number.
Returns: TODO: color code
Tbl_position(_Pos)
TODO: Table display position function
Parameters:
_Pos : TODO: position.
Returns: TODO: Table position
DeleteLine()
TODO: Delete Line
Parameters:
: TODO: No parameter
Returns: TODO: No return value
DeleteLabel()
TODO: Delete Label
Parameters:
: TODO: No parameter
Returns: TODO: No return value
ZigZag(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _Histories, _Provisional_PHiLo, _Provisional_IHiLo, _Color1, _Width1, _Color2, _Width2, _ShowLabel, _ShowHighLowBar, _HighLowBarWidth, _HighLow_LabelSize)
TODO: Draw a zig-zag line.
Parameters:
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_a_FHiLo : TODO: High-Low flag array sequence 1:High 2:Low
_a_DHiLo : TODO: High-Low Price Differential Array
_Histories : TODO: Array size (High-Low length)
_Provisional_PHiLo : TODO: Provisional High-Low Price
_Provisional_IHiLo : TODO: Provisional High-Low INDEX
_Color1 : TODO: Normal High-Low color
_Width1 : TODO: Normal High-Low width
_Color2 : TODO: Provisional High-Low color
_Width2 : TODO: Provisional High-Low width
_ShowLabel : TODO: Label display flag True: Displayed False: Not displayed
_ShowHighLowBar : TODO: High-Low bar display flag True:Show False:Hide
_HighLowBarWidth : TODO: High-Low bar width
_HighLow_LabelSize : TODO: Label Size
Returns: TODO: No return value
TrendLine(_a_PHiLo, _a_IHiLo, _Histories, _MultiLine, _StartWidth, _EndWidth, _IncreWidth, _StartTrans, _EndTrans, _IncreTrans, _ColorMode, _Color1_1, _Color1_2, _Color2_1, _Color2_2, _Top_High, _Top_Low, _Bottom_High, _Bottom_Low)
TODO: Draw a Trend Line
Parameters:
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_Histories : TODO: Array size (High-Low length)
_MultiLine : TODO: Draw a multiple Line.
_StartWidth : TODO: Line width start value
_EndWidth : TODO: Line width end value
_IncreWidth : TODO: Line width increment value
_StartTrans : TODO: Transparent rate start value
_EndTrans : TODO: Transparent rate finally
_IncreTrans : TODO: Transparent rate increase value
_ColorMode : TODO: 0:Nomal 1:Gradation
_Color1_1 : TODO: Gradation Color 1_1
_Color1_2 : TODO: Gradation Color 1_2
_Color2_1 : TODO: Gradation Color 2_1
_Color2_2 : TODO: Gradation Color 2_2
_Top_High : TODO: _Top_High Value for Gradation
_Top_Low : TODO: _Top_Low Value for Gradation
_Bottom_High : TODO: _Bottom_High Value for Gradation
_Bottom_Low : TODO: _Bottom_Low Value for Gradation
Returns: TODO: No return value
Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index, _FrontMargin, _BackMargin)
TODO: Draw a Fibonacci line
Parameters:
_a_Fibonacci : TODO: Fibonacci Percentage Array
_a_PHiLo : TODO: High-Low price array
_Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)
_Index : TODO: Where to draw the Fibonacci line
_FrontMargin : TODO: Fibonacci line front-margin
_BackMargin : TODO: Fibonacci line back-margin
Returns: TODO: No return value
Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index1, _FrontMargin1, _BackMargin1, _Transparent1, _Index2, _FrontMargin2, _BackMargin2, _Transparent2)
TODO: Draw a Fibonacci line
Parameters:
_a_Fibonacci : TODO: Fibonacci Percentage Array
_a_PHiLo : TODO: High-Low price array
_Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)
_Index1 : TODO: Where to draw the Fibonacci line 1
_FrontMargin1 : TODO: Fibonacci line front-margin 1
_BackMargin1 : TODO: Fibonacci line back-margin 1
_Transparent1 : TODO: Transparent rate 1
_Index2 : TODO: Where to draw the Fibonacci line 2
_FrontMargin2 : TODO: Fibonacci line front-margin 2
_BackMargin2 : TODO: Fibonacci line back-margin 2
_Transparent2 : TODO: Transparent rate 2
Returns: TODO: No return value
High_Low_Judgment(_Length, _Extension, _Difference)
TODO: Judges High-Low
Parameters:
_Length : TODO: High-Low Confirmation Length
_Extension : TODO: Length of extension when the difference did not open
_Difference : TODO: Difference size
Returns: TODO: _HiLo=High-Low flag 0:Neither high nor low、1:High、2:Low、3:High-Low
_PHi=high price、_PLo=low price、_IHi=High Price Index、_ILo=Low Price Index、
_Cnt=count、_ECnt=Extension count、
_DiffHi=Difference from Start(High)、_DiffLo=Difference from Start(Low)、
_StartHi=Start value(High)、_StartLo=Start value(Low)
High_Low_Data_AddedAndUpdated(_HiLo, _Histories, _PHi, _PLo, _IHi, _ILo, _DiffHi, _DiffLo, _a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo)
TODO: Adds and updates High-Low related arrays from given parameters
Parameters:
_HiLo : TODO: High-Low flag
_Histories : TODO: Array size (High-Low length)
_PHi : TODO: Price Hi
_PLo : TODO: Price Lo
_IHi : TODO: Index Hi
_ILo : TODO: Index Lo
_DiffHi : TODO: Difference in High
_DiffLo : TODO: Difference in Low
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_a_FHiLo : TODO: High-Low flag array 1:High 2:Low
_a_DHiLo : TODO: High-Low Price Differential Array
Returns: TODO: _PHiLo price array、_IHiLo indexed array、_FHiLo flag array、_DHiLo price-matching array、
Provisional_PHiLo Provisional price、Provisional_IHiLo 暫定インデックス
High_Low(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _a_Fibonacci, _Length, _Extension, _Difference, _Histories, _ShowZigZag, _ZigZagColor1, _ZigZagWidth1, _ZigZagColor2, _ZigZagWidth2, _ShowZigZagLabel, _ShowHighLowBar, _ShowTrendLine, _TrendMultiLine, _TrendStartWidth, _TrendEndWidth, _TrendIncreWidth, _TrendStartTrans, _TrendEndTrans, _TrendIncreTrans, _TrendColorMode, _TrendColor1_1, _TrendColor1_2, _TrendColor2_1, _TrendColor2_2, _ShowFibonacci1, _FibIndex1, _FibFrontMargin1, _FibBackMargin1, _FibTransparent1, _ShowFibonacci2, _FibIndex2, _FibFrontMargin2, _FibBackMargin2, _FibTransparent2, _ShowInfoTable1, _TablePosition1, _ShowInfoTable2, _TablePosition2)
TODO: Draw the contents of the High-Low array.
Parameters:
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_a_FHiLo : TODO: High-Low flag sequence 1:High 2:Low
_a_DHiLo : TODO: High-Low Price Differential Array
_a_Fibonacci : TODO: Fibonacci Gnar Matching
_Length : TODO: Length of confirmation
_Extension : TODO: Extension Length of extension when the difference did not open
_Difference : TODO: Difference size
_Histories : TODO: High-Low Length
_ShowZigZag : TODO: ZigZag Display
_ZigZagColor1 : TODO: Colors of ZigZag1
_ZigZagWidth1 : TODO: Width of ZigZag1
_ZigZagColor2 : TODO: Colors of ZigZag2
_ZigZagWidth2 : TODO: Width of ZigZag2
_ShowZigZagLabel : TODO: ZigZagLabel Display
_ShowHighLowBar : TODO: High-Low Bar Display
_ShowTrendLine : TODO: Trend Line Display
_TrendMultiLine : TODO: Trend Multi Line Display
_TrendStartWidth : TODO: Line width start value
_TrendEndWidth : TODO: Line width end value
_TrendIncreWidth : TODO: Line width increment value
_TrendStartTrans : TODO: Starting transmittance value
_TrendEndTrans : TODO: Transmittance End Value
_TrendIncreTrans : TODO: Increased transmittance value
_TrendColorMode : TODO: color mode
_TrendColor1_1 : TODO: Trend Color 1_1
_TrendColor1_2 : TODO: Trend Color 1_2
_TrendColor2_1 : TODO: Trend Color 2_1
_TrendColor2_2 : TODO: Trend Color 2_2
_ShowFibonacci1 : TODO: Fibonacci1 Display
_FibIndex1 : TODO: Fibonacci1 Index No.
_FibFrontMargin1 : TODO: Fibonacci1 Front margin
_FibBackMargin1 : TODO: Fibonacci1 Back Margin
_FibTransparent1 : TODO: Fibonacci1 Transmittance
_ShowFibonacci2 : TODO: Fibonacci2 Display
_FibIndex2 : TODO: Fibonacci2 Index No.
_FibFrontMargin2 : TODO: Fibonacci2 Front margin
_FibBackMargin2 : TODO: Fibonacci2 Back Margin
_FibTransparent2 : TODO: Fibonacci2 Transmittance
_ShowInfoTable1 : TODO: InfoTable1 Display
_TablePosition1 : TODO: InfoTable1 position
_ShowInfoTable2 : TODO: InfoTable2 Display
_TablePosition2 : TODO: InfoTable2 position
Returns: TODO: 無し
TR_HighLowLibrary "TR_HighLow"
TODO: add library description here
ShowLabel(_Text, _X, _Y, _Style, _Size, _Yloc, _Color)
TODO: Function to display labels
Parameters:
_Text : TODO: text (series string) Label text.
_X : TODO: x (series int) Bar index.
_Y : TODO: y (series int/float) Price of the label position.
_Style : TODO: style (series string) Label style.
_Size : TODO: size (series string) Label size.
_Yloc : TODO: yloc (series string) Possible values are yloc.price, yloc.abovebar, yloc.belowbar.
_Color : TODO: color (series color) Color of the label border and arrow
Returns: TODO: No return values
GetColor(_Index)
TODO: Function to take out 12 colors in order
Parameters:
_Index : TODO: color number.
Returns: TODO: color code
Tbl_position(_Pos)
TODO: Table display position function
Parameters:
_Pos : TODO: position.
Returns: TODO: Table position
DeleteLine()
TODO: Delete Line
Parameters:
: TODO: No parameter
Returns: TODO: No return value
DeleteLabel()
TODO: Delete Label
Parameters:
: TODO: No parameter
Returns: TODO: No return value
ZigZag(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _Histories, _Provisional_PHiLo, _Provisional_IHiLo, _Color1, _Width1, _Color2, _Width2, _ShowLabel, _ShowHighLowBar, _HighLowBarWidth, _HighLow_LabelSize)
TODO: Draw a zig-zag line.
Parameters:
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_a_FHiLo : TODO: High-Low flag array sequence 1:High 2:Low
_a_DHiLo : TODO: High-Low Price Differential Array
_Histories : TODO: Array size (High-Low length)
_Provisional_PHiLo : TODO: Provisional High-Low Price
_Provisional_IHiLo : TODO: Provisional High-Low INDEX
_Color1 : TODO: Normal High-Low color
_Width1 : TODO: Normal High-Low width
_Color2 : TODO: Provisional High-Low color
_Width2 : TODO: Provisional High-Low width
_ShowLabel : TODO: Label display flag True: Displayed False: Not displayed
_ShowHighLowBar : TODO: High-Low bar display flag True:Show False:Hide
_HighLowBarWidth : TODO: High-Low bar width
_HighLow_LabelSize : TODO: Label Size
Returns: TODO: No return value
TrendLine(_a_PHiLo, _a_IHiLo, _Histories, _MultiLine, _StartWidth, _EndWidth, _IncreWidth, _StartTrans, _EndTrans, _IncreTrans, _ColorMode, _Color1_1, _Color1_2, _Color2_1, _Color2_2, _Top_High, _Top_Low, _Bottom_High, _Bottom_Low)
TODO: Draw a Trend Line
Parameters:
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_Histories : TODO: Array size (High-Low length)
_MultiLine : TODO: Draw a multiple Line.
_StartWidth : TODO: Line width start value
_EndWidth : TODO: Line width end value
_IncreWidth : TODO: Line width increment value
_StartTrans : TODO: Transparent rate start value
_EndTrans : TODO: Transparent rate finally
_IncreTrans : TODO: Transparent rate increase value
_ColorMode : TODO: 0:Nomal 1:Gradation
_Color1_1 : TODO: Gradation Color 1_1
_Color1_2 : TODO: Gradation Color 1_2
_Color2_1 : TODO: Gradation Color 2_1
_Color2_2 : TODO: Gradation Color 2_2
_Top_High : TODO: _Top_High Value for Gradation
_Top_Low : TODO: _Top_Low Value for Gradation
_Bottom_High : TODO: _Bottom_High Value for Gradation
_Bottom_Low : TODO: _Bottom_Low Value for Gradation
Returns: TODO: No return value
Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index, _FrontMargin, _BackMargin)
TODO: Draw a Fibonacci line
Parameters:
_a_Fibonacci : TODO: Fibonacci Percentage Array
_a_PHiLo : TODO: High-Low price array
_Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)
_Index : TODO: Where to draw the Fibonacci line
_FrontMargin : TODO: Fibonacci line front-margin
_BackMargin : TODO: Fibonacci line back-margin
Returns: TODO: No return value
Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index1, _FrontMargin1, _BackMargin1, _Transparent1, _Index2, _FrontMargin2, _BackMargin2, _Transparent2)
TODO: Draw a Fibonacci line
Parameters:
_a_Fibonacci : TODO: Fibonacci Percentage Array
_a_PHiLo : TODO: High-Low price array
_Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)
_Index1 : TODO: Where to draw the Fibonacci line 1
_FrontMargin1 : TODO: Fibonacci line front-margin 1
_BackMargin1 : TODO: Fibonacci line back-margin 1
_Transparent1 : TODO: Transparent rate 1
_Index2 : TODO: Where to draw the Fibonacci line 2
_FrontMargin2 : TODO: Fibonacci line front-margin 2
_BackMargin2 : TODO: Fibonacci line back-margin 2
_Transparent2 : TODO: Transparent rate 2
Returns: TODO: No return value
High_Low_Judgment(_Length, _Extension, _Difference)
TODO: Judges High-Low
Parameters:
_Length : TODO: High-Low Confirmation Length
_Extension : TODO: Length of extension when the difference did not open
_Difference : TODO: Difference size
Returns: TODO: _HiLo=High-Low flag 0:Neither high nor low、1:High、2:Low、3:High-Low
_PHi=high price、_PLo=low price、_IHi=High Price Index、_ILo=Low Price Index、
_Cnt=count、_ECnt=Extension count、
_DiffHi=Difference from Start(High)、_DiffLo=Difference from Start(Low)、
_StartHi=Start value(High)、_StartLo=Start value(Low)
High_Low_Data_AddedAndUpdated(_HiLo, _Histories, _PHi, _PLo, _IHi, _ILo, _DiffHi, _DiffLo, _a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo)
TODO: Adds and updates High-Low related arrays from given parameters
Parameters:
_HiLo : TODO: High-Low flag
_Histories : TODO: Array size (High-Low length)
_PHi : TODO: Price Hi
_PLo : TODO: Price Lo
_IHi : TODO: Index Hi
_ILo : TODO: Index Lo
_DiffHi : TODO: Difference in High
_DiffLo : TODO: Difference in Low
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_a_FHiLo : TODO: High-Low flag array 1:High 2:Low
_a_DHiLo : TODO: High-Low Price Differential Array
Returns: TODO: _PHiLo price array、_IHiLo indexed array、_FHiLo flag array、_DHiLo price-matching array、
Provisional_PHiLo Provisional price、Provisional_IHiLo 暫定インデックス
High_Low(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _a_Fibonacci, _Length, _Extension, _Difference, _Histories, _ShowZigZag, _ZigZagColor1, _ZigZagWidth1, _ZigZagColor2, _ZigZagWidth2, _ShowZigZagLabel, _ShowHighLowBar, _ShowTrendLine, _TrendMultiLine, _TrendStartWidth, _TrendEndWidth, _TrendIncreWidth, _TrendStartTrans, _TrendEndTrans, _TrendIncreTrans, _TrendColorMode, _TrendColor1_1, _TrendColor1_2, _TrendColor2_1, _TrendColor2_2, _ShowFibonacci1, _FibIndex1, _FibFrontMargin1, _FibBackMargin1, _FibTransparent1, _ShowFibonacci2, _FibIndex2, _FibFrontMargin2, _FibBackMargin2, _FibTransparent2, _ShowInfoTable1, _TablePosition1, _ShowInfoTable2, _TablePosition2)
TODO: Draw the contents of the High-Low array.
Parameters:
_a_PHiLo : TODO: High-Low price array
_a_IHiLo : TODO: High-Low INDEX array
_a_FHiLo : TODO: High-Low flag sequence 1:High 2:Low
_a_DHiLo : TODO: High-Low Price Differential Array
_a_Fibonacci : TODO: Fibonacci Gnar Matching
_Length : TODO: Length of confirmation
_Extension : TODO: Extension Length of extension when the difference did not open
_Difference : TODO: Difference size
_Histories : TODO: High-Low Length
_ShowZigZag : TODO: ZigZag Display
_ZigZagColor1 : TODO: Colors of ZigZag1
_ZigZagWidth1 : TODO: Width of ZigZag1
_ZigZagColor2 : TODO: Colors of ZigZag2
_ZigZagWidth2 : TODO: Width of ZigZag2
_ShowZigZagLabel : TODO: ZigZagLabel Display
_ShowHighLowBar : TODO: High-Low Bar Display
_ShowTrendLine : TODO: Trend Line Display
_TrendMultiLine : TODO: Trend Multi Line Display
_TrendStartWidth : TODO: Line width start value
_TrendEndWidth : TODO: Line width end value
_TrendIncreWidth : TODO: Line width increment value
_TrendStartTrans : TODO: Starting transmittance value
_TrendEndTrans : TODO: Transmittance End Value
_TrendIncreTrans : TODO: Increased transmittance value
_TrendColorMode : TODO: color mode
_TrendColor1_1 : TODO: Trend Color 1_1
_TrendColor1_2 : TODO: Trend Color 1_2
_TrendColor2_1 : TODO: Trend Color 2_1
_TrendColor2_2 : TODO: Trend Color 2_2
_ShowFibonacci1 : TODO: Fibonacci1 Display
_FibIndex1 : TODO: Fibonacci1 Index No.
_FibFrontMargin1 : TODO: Fibonacci1 Front margin
_FibBackMargin1 : TODO: Fibonacci1 Back Margin
_FibTransparent1 : TODO: Fibonacci1 Transmittance
_ShowFibonacci2 : TODO: Fibonacci2 Display
_FibIndex2 : TODO: Fibonacci2 Index No.
_FibFrontMargin2 : TODO: Fibonacci2 Front margin
_FibBackMargin2 : TODO: Fibonacci2 Back Margin
_FibTransparent2 : TODO: Fibonacci2 Transmittance
_ShowInfoTable1 : TODO: InfoTable1 Display
_TablePosition1 : TODO: InfoTable1 position
_ShowInfoTable2 : TODO: InfoTable2 Display
_TablePosition2 : TODO: InfoTable2 position
Returns: TODO: 無し
PPO w/ Discontinued Signal Lines [Loxx]PPO w/ Discontinued Signal Lines is a Percentage Price Oscillator with some upgrades. This indicator has 33 source types and 35+ moving average types as well as Discontinued Signal Lines and divergences. These additions reduce noise and increase hit rate.
What is the Price Percentage Oscillator?
The percentage price oscillator (PPO) is a technical momentum indicator that shows the relationship between two moving averages in percentage terms. The moving averages are a 26-period and 12-period exponential moving average (EMA).
The PPO is used to compare asset performance and volatility, spot divergence that could lead to price reversals, generate trade signals, and help confirm trend direction.
Included:
Bar coloring
3 signal variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Nyquist Moving Average (NMA) MACD [Loxx]Nyquist Moving Average (NMA) MACD is a MACD indicator using Nyquist Moving Average for its calculation.
What is the Nyquist Moving Average?
A moving average outlined originally developed by Dr . Manfred G. Dürschner in his paper "Gleitende Durchschnitte 3.0".
In signal processing theory, the application of a MA to itself can be seen as a Sampling procedure. The sampled signal is the MA (referred to as MA.) and the sampling signal is the MA as well (referred to as MA). If additional periodic cycles which are not included in the price series are to be avoided sampling must obey the Nyquist Criterion.
It can be concluded that the Moving Averages 3.0 on the basis of the Nyquist Criterion bring about a significant improvement compared with the Moving Averages 2.0 and 1.0. Additionally, the efficiency of the Moving Averages 3.0 can be proven in the result of a trading system with NWMA as basis.
What is the MACD?
Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence (MACD) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
Included
Bar coloring
2 types of signal output options
Alerts
Loxx's Expanded Source Types
Fisher Transform of MACD w/ Quantile Bands [Loxx]Fisher Transform of MACD w/ Quantile Bands is a Fisher Transform indicator with Quantile Bands that takes as it's source a MACD. The MACD has two different source inputs for fast and slow moving averages.
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
What is Quantile Bands?
In statistics and the theory of probability, quantiles are cutpoints dividing the range of a probability distribution into contiguous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one less quantile than the number of groups created. Thus quartiles are the three cut points that will divide a dataset into four equal-size groups (cf. depicted example). Common quantiles have special names: for instance quartile, decile (creating 10 groups: see below for more). The groups created are termed halves, thirds, quarters, etc., though sometimes the terms for the quantile are used for the groups created, rather than for the cut points.
q-Quantiles are values that partition a finite set of values into q subsets of (nearly) equal sizes. There are q − 1 of the q-quantiles, one for each integer k satisfying 0 < k < q. In some cases the value of a quantile may not be uniquely determined, as can be the case for the median (2-quantile) of a uniform probability distribution on a set of even size. Quantiles can also be applied to continuous distributions, providing a way to generalize rank statistics to continuous variables. When the cumulative distribution function of a random variable is known, the q-quantiles are the application of the quantile function (the inverse function of the cumulative distribution function) to the values {1/q, 2/q, …, (q − 1)/q}.
What is MACD?
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA .
Included:
Zero-line and signal cross options for bar coloring, signals, and alerts
Alerts
Signals
Loxx's Expanded Source Types
35+ moving average types
Session High Low
This indicator shows Session High Low line and prices.
1: Session range is adjustable based on your timeframe. Nomore confusing timezone settings.
You can choose your timezone then set your Session start and end time.
Script will show you the following session high and low lines which is extendable until next session.
2: All historical lines and price levels are can be partially or fully hidden.
And line colors are adjustable so you can use suitable color on your chart.
Based on session you choose this script can be used as a session break strategy AKA (Asian session break, London session break strategy).
You can create your own trading Session and high lows.
Personally I monitor how price reacts on London session high lows during the NewYork trading session.
In this chart Session starts at 8am (London open) and closes at 12:30 (NewYork open). Script is showing high lows only in this session range.
Always double confirm with your trading style. It's not a Financial advice.
Inputs:
1: Hide history - Hides all historical lines and prices that means you can see only todays session.
2: Show price - Shows price level of session high lows. You can hide price level if you want to see only lines.
3: Session time - You can set your time range of session.
4: Start time - Session start time. You can see vertical line on your chart or you can hide line.
5: End time - Session end time. You can see vertical line on your chart or you can hide line.
6: Line extend time - End of the high low lines. You can draw line until the end of the session or you can draw short line.
7: All line and price colors are optional.
Thank you.
PA-Adaptive MACD w/ Variety Levels [Loxx]PA-Adaptive MACD w/ Variety Levels is a Phase Accumulation Adaptive MACD with both floating and quantile levels. This is tuned for Forex. You'll have to adjust the Phase Accumulation Cycle settings to work for crypto and stock markets.
What is MACD?
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA .
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included:
Zero-line and signal cross options for bar coloring, signals, and alerts
Alerts
Signals
Loxx's Expanded Source Types
4 moving average types
MACD-V Volatility NormalisationUsing MACD-V by Alex Spiroglou (CMT) Method
Calculation MACD-V = * 100
While
⚠️MACD-V >150 - Risk
📈MACD-V between 50 - 150 : Rallying or Retracing📈
〰️MACD-V between -50 - 50 : Ranging (Sideway) 〰️
↪️MACD-V between -150 - -50 : Rebounding or Reversing ↪️
⚠️MACD-V <150 - Risk ⚠️
5MSM VISHNU5MSM VISHNU Indicator for Trending Markets originally written by patrick1994.
It was originally based on the MACD 12-26 and the 50 bar EMA .
The macd hist is color coded with green as buy and sell as red.
I added an option to use a couple of lower lag ema's (See line 13 - ema_signal).
5MSM VISHNU with MACD Indicator for Trending Markets
Originally written by Trading Rush
Note that the user may choose lower lags to compute the MACD signals
added lower lag ema functions - see lines 21 to 30
added plot for the MACD signal 'hist' - computed in lines 36 to 41
The extra MACD line was added for clarity for the placement of the buy sell signals.
MACD DEMA by ToffMACD DEMA by Toff
converted to version 5
Changed Histogram formatting
Changed MACD plot to indicate macd direction change
//@version=5
//by ToFFF converted to version 5, changed histogram formating changed macd plot to show macd direction changed with lighter color
indicator('MACD DEMA', timeframe = "", timeframe_gaps=true)
sma = input(12,title='DEMA Short')
lma = input(26,title='DEMA Long')
tsp = input(9,title='Signal')
lines = input(true,title="Lines")
col_grow_above = input(#26A69A, "Above Grow", group="Histogram", inline="Above")
col_fall_above = input(#B2DFDB, "Fall", group="Histogram", inline="Above")
col_grow_below = input(#FFCDD2, "Below Grow", group="Histogram", inline="Below")
col_fall_below = input(#FF5252, "Fall", group="Histogram", inline="Below")
col_macd = input(#2962FF, "MACD Line ", group="Color Settings", inline="MACD")
col_signal = input(#FF6D00, "Signal Line ", group="Color Settings", inline="Signal")
col_macd_i = #0000FF
col_macd_d = #66FFFF
slowa = ta.ema(close,lma)
slowb = ta.ema(slowa,lma)
DEMAslow = ((2 * slowa) - slowb)
fasta = ta.ema(close,sma)
fastb = ta.ema(fasta,sma)
DEMAfast = ((2 * fasta) - fastb)
MACD = (DEMAfast - DEMAslow)
signala = ta.ema(MACD, tsp)
signalb = ta.ema(signala, tsp)
signal = ((2 * signala) - signalb)
hist = (MACD - signal)
//swap1 = MACDZeroLag>0?green:red
plot(hist,style=plot.style_columns, color=(hist>=0 ? (hist < hist ? col_grow_above : col_fall_above) : (hist < hist ? col_grow_below : col_fall_below)),title='HIST')
p1 = plot(lines?MACD:na,style = plot.style_line, color=(MACD < MACD) ? col_macd_i : col_macd_d , linewidth =3,title='MACD')
p2 = plot(lines?signal:na, color=col_signal, linewidth =2,title='Signal')
hline(0)
Inverse MACD + DMI Scalping with Volatility Stop (By Coinrule)This script is focused on shorting during downtrends and utilises two strength based indicators to provide confluence that the start of a short-term downtrend has occurred - catching the opportunity as soon as possible.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Alternatively, you can use this when trading contracts on futures markets where there is no need to already own the underlying asset prior to shorting it.
ENTRY
The trading system uses the Momentum Average Convergence Divergence (MACD) indicator and the Directional Movement Index (DMI) indicator to confirm when the best time is for selling. Combining these two indicators prevents trading during uptrends and reduces the likelihood of getting stuck in a market with low volatility.
The MACD is a trend following momentum indicator and provides identification of short-term trend direction. In this variation it utilises the 12-period as the fast and 26-period as the slow length EMAs, with signal smoothing set at 9.
The DMI indicates what way price is trending and compares prior lows and highs with two lines drawn between each - the positive directional movement line (+DI) and the negative directional movement line (-DI). The trend can be interpreted by comparing the two lines and what line is greater. When the negative DMI is greater than the positive DMI, there are more chances that the asset is trading in a sustained downtrend, and vice versa.
The system will enter trades when two conditions are met:
1) The MACD histogram turns bearish.
2) When the negative DMI is greater than the positive DMI.
EXIT
The strategy comes with a fixed take profit combined with a volatility stop, which acts as a trailing stop to adapt to the trend's strength. Depending on your long-term confidence in the asset, you can edit the fixed take profit to be more conservative or aggressive.
The position is closed when:
Take-Profit Exit: +8% price decrease from entry price.
OR
Stop-Loss Exit: Price crosses above the volatility stop.
In general, this approach suits medium to long term strategies. The backtesting for this strategy begins on 1 April 2022 to 18 July 2022 in order to demonstrate its results in a bear market. Back testing it further from the beginning of 2022 onwards further also produces good returns.
Pairs that produce very strong results include SOLUSDT on the 45m timeframe, MATICUSDT on the 2h timeframe, and AVAUSDT on the 1h timeframe. Generally, the back testing suggests that it works best on the 45m/1h timeframe across most pairs.
A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Pre Market \ Opening Range High LowGreen vertical lines are showing pre market open and then the opening range as the first hour of market NYSE market open
Pre market high and low are blue lines | intraday opening range high low are in white
Trades are taken in the current direction above | below range breaks with the direction of price action using the moving averages
Price breaking through moving averages and a range is the optimal trade to enter - exit at next range for target - stop loss below the lower | higher moving average depending on short or long
A break above or below the intraday high or low and pre market high or low can give massive profits trailing your stop loss as price runs
Using MA 5 and 12 to filter out entries and exits above or below the ranges short or long is also another strategy to implement
BEST TIME FRAME TO USE IS 5 MINUTE
Goertzel Cycle Period [Loxx]Goertzel Cycle Period is an indicator that uses Goertzel algorithm to extract the cycle period of ticker's price input to then be injected into advanced, adaptive indicators and technical analysis algorithms.
The following information is extracted from: "MESA vs Goertzel-DFT, 2003 by Dennis Meyers"
Background
MESA which stands for Maximum Entropy Spectral Analysis is a widely used mathematical technique designed to find the frequencies present in data. MESA was developed by J.P Burg for his Ph.D dissertation at Stanford University in 1975. The use of the MESA technique for stocks has been written about in many articles and has been popularized as a trading technique by John Ehlers.
The Fourier Transform is a mathematical technique named after the famed French mathematician Jean Baptiste Joseph Fourier 1768-1830. In its digital form, namely the discrete-time Fourier Transform (DFT) series, is a widely used mathematical technique to find the frequencies of discrete time sampled data. The use of the DFT has been written about in many articles in this magazine (see references section).
Today, both MESA and DFT are widely used in science and engineering in digital signal processing. The application of MESA and Fourier mathematical techniques are prevalent in our everyday life from everything from television to cell phones to wireless internet to satellite communications.
MESA Advantages & Disadvantage
MESA is a mathematical technique that calculates the frequencies of a time series from the autoregressive coefficients of the time series. We have all heard of regression. The simplest regression is the straight line regression of price against time where price(t) = a+b*t and where a and b are calculated such that the square of the distance between price and the best fit straight line is minimized (also called least squares fitting). With autoregression we attempt to predict tomorrows price by a linear combination of M past prices.
One of the major advantages of MESA is that the frequency examined is not constrained to multiples of 1/N (1/N is equal to the DFT frequency spacing and N is equal to the number of sample points). For instance with the DFT and N data points we can only look a frequencies of 1/N, 2/N, Ö.., 0.5. With MESA we can examine any frequency band within that range and any frequency spacing between i/N and (i+1)/N . For example, if we had 100 bars of price data, we might be interested in looking for all cycles between 3 bars per cycle and 30 bars/ cycle only and with a frequency spacing of 0.5 bars/cycle. DFT would examine all bars per cycle of between 2 and 50 with a frequency spacing constrained to 1/100.
Another of the major advantages of MESA is that the dominant spectral (frequency) peaks of the price series, if they exist, can be identified with fewer samples than the DFT technique. For instance if we had a 10 bar price period and a high signal to noise ratio we could accurately identify this period with 40 data samples using the MESA technique. This same resolution might take 128 samples for the DFT. One major disadvantage of the MESA technique is that with low signal to noise ratios, that is below 6db (signal amplitude/noise amplitude < 2), the ability of MESA to find the dominant frequency peaks is severely diminished.(see Kay, Ref 10, p 437). With noisy price series this disadvantage can become a real problem. Another disadvantage of MESA is that when the dominant frequencies are found another procedure has to be used to get the amplitude and phases of these found frequencies. This two stage process can make MESA much slower than the DFT and FFT . The FFT stands for Fast Fourier Transform. The Fast Fourier Transform(FFT) is a computationally efficient algorithm which is a designed to rapidly evaluate the DFT. We will show in examples below the comparisons between the DFT & MESA using constructed signals with various noise levels.
DFT Advantages and Disadvantages.
The mathematical technique called the DFT takes a discrete time series(price) of N equally spaced samples and transforms or converts this time series through a mathematical operation into set of N complex numbers defined in what is called the frequency domain. Why would we what to do that? Well it turns out that we can do all kinds of neat analysis tricks in the frequency domain which are just to hard to do, computationally wise, with the original price series in the time domain. If we make the assumption that the price series we are examining is made up of signals of various frequencies plus noise, than in the frequency domain we can easily filter out the frequencies we have no interest in and minimize the noise in the data. We could then transform the resultant back into the time domain and produce a filtered price series that hopefully would be easier to trade. The advantages of the DFT and itís fast computation algorithm the FFT, are that it is extremely fast in calculating the frequencies of the input price series. In addition it can determine frequency peaks for very noisy price series even when the signal amplitude is less than the noise amplitude. One of the disadvantages of the FFT is that straight line, parabolic trends and edge effects in the price series can distort the frequency spectrum. In addition, end effects in the price series can distort the frequency spectrum. Another disadvantage of the FFT is that it needs a lot more data than MESA for spectral resolution. However this disadvantage has largely been nullified by the speed of today's computers.
Goertzel algorithm attempts to resolve these problems...
What is the Goertzel algorithm?
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT). It is useful in certain practical applications, such as recognition of dual-tone multi-frequency signaling (DTMF) tones produced by the push buttons of the keypad of a traditional analog telephone. The algorithm was first described by Gerald Goertzel in 1958.
Like the DFT, the Goertzel algorithm analyses one selectable frequency component from a discrete signal. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. For covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms, but for computing a small number of selected frequency components, it is more numerically efficient. The simple structure of the Goertzel algorithm makes it well suited to small processors and embedded applications.
The main calculation in the Goertzel algorithm has the form of a digital filter, and for this reason the algorithm is often called a Goertzel filter
Where is Goertzel algorithm used?
This package contains the advanced mathematical technique called the Goertzel algorithm for discrete Fourier transforms. This mathematical technique is currently used in today's space-age satellite and communication applications and is applied here to stock and futures trading.
While the mathematical technique called the Goertzel algorithm is unknown to many, this algorithm is used everyday without even knowing it. When you press a cell phone button have you ever wondered how the telephone company knows what button tone you pushed? The answer is the Goertzel algorithm. This algorithm is built into tiny integrated circuits and immediately detects which of the 12 button tones(frequencies) you pushed.
Future Additions:
Bartels test for cycle significance, testing output cycles for utility
Hodrick Prescott Detrending, smoothing
Zero-Lag Regression Detrending, smoothing
High-pass or Double WMA filtering of source input price data
References:
1. Burg, J. P., ëMaximum Entropy Spectral Analysisî, Ph.D. dissertation, Stanford University, Stanford, CA. May 1975.
2. Kay, Steven M., ìModern Spectral Estimationî, Prentice Hall, 1988
3. Marple, Lawrence S. Jr., ìDigital Spectral Analysis With Applicationsî, Prentice Hall, 1987
4. Press, William H., et al, ìNumerical Receipts in C++: the Art of Scientific Computingî,
Cambridge Press, 2002.
5. Oppenheim, A, Schafer, R. and Buck, J., ìDiscrete Time Signal Processingî, Prentice Hall,
1996, pp663-634
6. Proakis, J. and Manolakis, D. ìDigital Signal Processing-Principles, Algorithms and
Applicationsî, Prentice Hall, 1996., pp480-481
7. Goertzel, G., ìAn Algorithm for he evaluation of finite trigonometric seriesî American Math
Month, Vol 65, 1958 pp34-35.
BT Leading Candle IndicatorThe oscillator display consists of 3 lines (K, D and J - hence the name of the display) and 2 levels. K and D are the same lines you see when using the stochastic oscillator. The J line in turn represents the deviation of the D value from the K value. The convergence of these lines indicates new trading opportunities. Just like the Stochastic Oscillator, oversold and overbought levels correspond to the times when the trend is likely to reverse.
Function
BT Leading KDJ Candle Indicator use candles to indicate KD relationship. E.g. yellow candles for bull (K>=D) and fuchsia candles for bear (K=D and fuchsia for K KDJ K value
d --> KDJ D value
buysig --> KD buy signal in green triangle
selsig --> KD sell signal in red triangle
leadingline --> colorful leading line for KDJ
Pros and Cons
Pros:
1. Candle height can indicates the strength of trend and different colors are used for indicating KD relationship
2. a leading line is added as aux method to confirm KDJ signal
Cons:
1. It may satruate for extreme conditions of long and short as described in the chart, which is inherent KDJ shortcoming.
2. Not accurate for long and short entries and need filtering out noise and fake signal.
Remarks
More direct to observe and confirm trend with the leading line.
Read me
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Trading view is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Trading view community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or man hours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many
Streamer WatermarkThis unique indicator doesn’t help you trade but it makes your charts look super clean and professional in images and live streams! This indicator works by displaying two tables. The first table has day of the week, date, and free form text. The second table has ticker symbol and timeframe of the current chart.
Everything about the tables and the cells is completely controllable by the user! Here is a breakdown of how customizable the user can make this indicator:
Table:
Toggle each table to be displayed on or off
Move each table into 9 different locations around the chart
Move each table separately
Table background color and transparency
Table border color and transparency
Table border width
Table frame width
Cells:
Each cell can be individually toggled on or off (the table will resize dynamically)
Cell text color and transparency
Text size with 6 different options
Date format with 12 different formats
Input Text:
Text
Emoji
Text & emojis
ASCII characters
Symbols
Anything that can by copied and pasted
Any combination of the above
Notes
Use text size “Auto” if viewing the same chart on desktop and on smart phone (Auto makes the text scale based upon screen size)
Gallery
Disclaimer
Please read the TradingView House Rules carefully before using this indicator to add text, symbols, characters, or anything else to your charts and posting on TradingView Ideas or Scripts. This indicator and the author are not responsible for users not reading, fully understanding, and abiding by TradingView’s House Rules. Please watermark responsibly.
Jurik Composite Fractal Behavior (CFB) on EMA [Loxx]Jurik Composite Fractal Behavior (CFB) on EMA is an exponential moving average with adaptive price trend duration inputs. This purpose of this indicator is to introduce the formulas for the calculation Composite Fractal Behavior. As you can see from the chart above, price reacts wildly to shifts in volatility--smoothing out substantially while riding a volatility wave and cutting sharp corners when volatility drops. Notice the chop zone on BTC around August 2021, this was a time of extremely low relative volatility.
This indicator uses three previous indicators from my public scripts. These are:
JCFBaux Volatility
Jurik Filter
Jurik Volty
The CFB is also related to the following indicator
Jurik Velocity ("smoother moment")
Now let's dive in...
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Modifications and improvements
1. Jurik's original calculation for CFB only allowed for depth lengths of 24, 48, 96, and 192. For theoretical purposes, this indicator allows for up to 20 different depth inputs to sample volatility. These depth lengths are
2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536
Including these additional length inputs is arguable useless, but they are are included for completeness of the algorithm.
2. The result of the CFB calculation is forced to be an integer greater than or equal to 1.
3. The result of the CFB calculation is double filtered using an advanced, (and adaptive itself) filtering algorithm called the Jurik Filter. This filter and accompanying internal algorithm are discussed above.
