Annualizer: New Indicator + CPI AnalysisThis indicator calculates the annualized month-over-month percent change of a cumulative index and plots it alongside the year-over-year percent change for comparison. It was developed for the purpose of analyzing the inflation rate of CPI indexes such as “CPIAUCSL.” It can also be used on M2 money supply and pretty much any cumulative index. It will not produce useful outputs on percent change indexes such as “USCCPI” because it performs percent change calculations which are already applied to those indexes.
This indicator takes data from the monthly chart, regardless of how often the data is reported or what the timeframe of the current chart is. Doing so allows it to work on all timeframes while displaying only monthly data outputs but limits it from recognizing data which might be released more often than once per month. This limitation should be suitable for macroeconomic data such as CPI and M2 money supply which are usually analyzed on a month-to-month basis.
If the ticker symbol is "M2SL" which is M2 money supply, annualized percent change is plotted in green, otherwise, it’s plotted in blue.
CPI analysis:
Upon deploying this indicator, it was observed that the year-over-year (YoY) inflation rate (red) is a lagging indicator of the annualized month-over-month (MoM) inflation rate (blue) and that it appears to almost be a moving average of it. A moving average plot was temporarily added for comparison to the YoY and it was found that the difference between the two plots is negligible and that for the purposes of high-level analysis of inflation, the two plots can be considered to be no different from one another. Below is a screenshot for demonstration. Notice how closely the white 12-month SMA of the annualized rate tracks the YoY rate.
For other indexes which may see more dramatic changes month-over-month such as M2 money supply, the difference between the two signals becomes more pronounced but they are still comparable. The conclusion is that the YoY inflation rate can be considered to be a 12-month simple moving average of the annualized MoM rate.
12-month SMA:
It’s easy to see and stands to reason that if the annualized MoM inflation rate (blue) remains where it has been for the previous 2 months YoY inflation (red) will begin falling and eventually reach similar levels due to its moving-average-like behavior. This will bring us back to the 2% YoY inflation target of the Fed within no more than 10 months. There may be a perception that deflation is required to bring prices back down to the purple channel of CPI to make prices pre-Covid "normal" again. We were headed in that direction in July with a slightly negative MoM CPI read. What may have freaked investors out about the August report (most recent as of this writing) is that the inflation rate, rather than continuing into negative deflationary territory, has bounced back into positive territory.
M2 money supply isn’t an integral part of this analysis, but it helps demonstrate the indicator. It can be observed that CPI growth lags M2 money supply growth which seems to have leveled off.
I’m not a macroeconomist so I’m probably missing some things, but I do not see a lagging indicator such as YoY inflation being at 8.25% while annualized MoM inflation is at 1.42% as something to freak out about as investors have seemingly done. I’m a stock market bear as of last week, but I do not feel this CPI analysis strongly supports a bearish thesis, nor is it bullish. Next month’s annualized MoM % change may begin to sway me one way or the other depending on what this chart looks like when it’s updated.
Cerca negli script per "跨境通12月4日地天板"
GT 5.1 Strategy═════════════════════════════════════════════════════════════════════════
█ OVERVIEW
People often look an indicator in their technical analysis to enter a position. We may also need to look at the signals of one or more indicators to verify the signals given by some indicators. In this context, I developed a strategy to test whether it really works by choosing some of the indicators that capture trend changes with the same characteristics. Also, since the subject is to catch the trend change, I thought it would be right to include an indicator using the heikin ashi logic. By averaging and smoothing the market noise, Heiken Ashi makes it easier to detect the direction of the trend helps to see possible reversal points on the chart. However, it should be noted that Heiken Ashi is a lagging indicator.
I picked 5 different indicators (but their purpose are similar) and combined them to produce buy and sell signals based on your choice(not repaint). First of all let's get some information about our indicators. So you will understand me why i picked these indicators and what is the meaning of their signals.
1 — Coral Trend Indicator by LazyBear
Coral Trend Indicator is a linear combination of moving averages, all obtained by a triple or higher order exponential smoothing. The indicator comes with a trend indication which is based on the normalized slope of the plot. the usage of this indicator is simple. When the color of the line is green that means the market is in uptrend. But when the color is red that means the market is in downtrend.
As you see the original indicator it is simple to find is it in uptrend or downtrend.
So i added a code to find when the color of the line change. When it turns green to red my script giving sell signals, when it turns red to green it gives buy signals.
I hide the candles to show you more clearly what is happening when you choose only Coral Strategy. But sometimes it is not enough only using itself. Even if green dots turn to red it continues in uptrend. So we need a to look another indicator to approve our signal.
2 — SSL channel by ErwinBeckers
Known as the SSL , the Semaphore Signal Level channel is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. This indicator creates a band by calculating the high and low values according to the determined period. Simply if you decide 10 as period, it calculates a 10-period moving average on the latest 10 highs. Calculate a 10-period moving average on the latest 10 lows. If the price falls below the low band, the downtrend begins, if the price closes above the high band, the uptrend begins. Lets look the original form of indicator and learn how it using.
If the red line is below and the green band is above, it means that we are in uptrend, and if it is on the opposite side, it means that we are in downtrend. Therefore, it would be logical to enter a position where the trend has changed. So i added a code to find when the crossover has occured.
As you see in my strategy, it gives you signals when the trend has changed. But sometimes it is not enough only using this indicator itself. So lets look 2 indicator together in one chart.
Look circle SSL is saying it is in downtrend but Coral is saying it has entered in uptrend. if we just look to coral signal it can misleads us. So it can be better to look another indicator for validating our signals.
3 — Heikin Ashi RSI Oscillator by JayRogers
The Heikin-Ashi technique is used by technical traders to identify a given trend more easily. Heikin-Ashi has a smoother look because it is essentially taking an average of the movement. There is a tendency with Heikin-Ashi for the candles to stay red during a downtrend and green during an uptrend, whereas normal candlesticks alternate color even if the price is moving dominantly in one direction. This indicator actually recalculates the RSI indicator with the logic of heikin ashi. Due to smoothing, the bars are formed with a slight lag, reflecting the trend rather than the exact price movement. So lets look the original version to understand more clearly. If red bars turn to green bars it means uptrend may begin, if green bars turn to red it means downtrend may begin.
As you see HARSI giving lots of signal some of them is really good but some of them are not very well. Because it gives so much signals Now i will change time period and lets look same chart again.
Now results are better because of heikin ashi's logic. it is not suitable for day traders, it gives more accurate result when using the time period is longer. But it can be useful to use this indicator in short time periods using with other indicators. So you may catch the trend changes more accurately.
4 — MACD DEMA by ToFFF
This indicator uses a double EMA and MACD algorithm to analyze the direction of the trend. Though it might seem a tough task to manage the trades with the help of MACD DEMA once you know how the proper way to interpret the signal lines, it will be an easy task.
This indicator also smoothens the signal lines with the time series algorithm which eventually makes the higher time frame important. So, expecting better results in the lower time frame can result in big losses as the data reading from the MACD DEMA will not be accurate. In order to understand the function of this indicator, you have to know the functions of the EMA also.
The exponential moving average tends to give more priority to the recent price changes. So, expecting better results when the volatility is very high is a very risky approach to trade the market. Moreover, the MACD has some lagging issues compared to the EMA, so it is super important to use a trading method that focuses on the higher time frame only. What does MACD 12 26 Close 9 mean? When the DEMA-9 crosses above the MACD(12,26), this is considered a bearish signal. It means the trend in the stock – its magnitude and/or momentum – is starting to shift course. When the MACD(12,26) crosses above the DEMA-9, this is considered a bullish signal. Lets see this indicator on Chart.
When the blue line crossover red line it is good time to buy. As you see from the chart i put arrows where the crossover are appeared.
When the red line crossover blue line it is good time to sell or exit from position.
5 — WaveTrend Oscillator by LazyBear
This is a technical indicator that creates high and low bands between two values. It then creates a trend indicator that draws waves with highs and lows within these boundaries. WaveTrend is a widely used indicator for finding direction of an asset.
Calculation period: number of candles used to calculate WaveTrend, defaults to 10. Averaging period: number of candles used to average WaveTrend, defaults to 21.
As you see in chart when the lines crossover occured my strategy gives buy or sell signals.
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█ HOW TO USE
I hope you understand how the indicators I mentioned above work and what they are used for. Now, I will explain in detail how to use the strategy I have created.
When you enter the settings section, you will see 5 types of indicators. If you want to use the signals of the indicators, simply tick the box next to the indicators. Also, under each option there is an area where you can set the "lookback". This setting is a field that will make the signals overlap when you select more than one option. If you are going to trade with only one option, you should make sure that this field is 0. Otherwise, it may continue to generate as many signals as you choose.
Lets see in chart for easy understanding.
As you see chart, if i chose only HARSI with lookback 0 (HARSI and CORAL should be 1 minumum because of algorithm-we looking 1 bar before, others 0 because we are looking crossovers), it will give signals only when harsı bar's color changed. But when i changed Lookback as 7 it will be like this in chart.
Now i will choose 2 indicator with settings of their lookback 0.
As you see it will give signals when both of them occurs same time. But HARSI is an indicator giving very early signal so we can enter position 5-6 bars after the first bar color change. So i will change HARSI Lookback settings as 7. Lets look what happens when we use lookback option.
So it wil be useful to change lookback settings to find best signals in each time period and in each symbol. But it shouldnt be too high. Because you can be late to catch trend's starting.
this is an image of MACD and WAVE trend used and lookback option are both 6.
Now lets see an example with 3 options are chosen with lookback option 11-1-5
Now lets talk about indicators settings. After strategy options you will see each indicators settings, you can change their settings as you desired. So each indicators signal will be changed according to your adjustment.
I left strategy options with default settings. You can change it manually as if you want.
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█ LIMITATIONS: Don't rely on non-standard charts results. For example Heikin Ashi is a technical analysis method used with the traditional candlestick chart.Heikin Ashi vs. Candlestick Chart: The decisive visual difference between Heikin Ashi and the traditional chart is that Heikin Ashi flattens the traditional candlestick chart using a modified formula.
The primary advantage of Heikin Ashi is that it makes the chart more reader-friendly and helps users identify and analyze trends .
Because Heikin Ashi provides averaged price information rather than real-time price and reacts slowly to volatility — not suitable for scalpers and high-frequency traders. I added HARSI indicator as a supportive signal because it is useful with using CORAL and SSL channel indicators. If you change your candle types to Heikin Ashi , your profit will change in good way but dont rely on it.
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█ THANKS:
Special thanks to authors of the scripts that i used.
@LazyBear and @ErwinBeckers and @JayRogers and @ToFFF
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█ DISCLAIMER
Any trade decisions you make are entirely your own responsibility.
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter [Loxx]STD/C-Filtered, N-Order Power-of-Cosine FIR Filter is a Discrete-Time, FIR Digital Filter that uses Power-of-Cosine Family of FIR filters. This is an N-order algorithm that turns the following indicator from a static max 16 orders to a N orders, but limited to 50 in code. You can change the top end value if you with to higher orders than 50, but the signal is likely too noisy at that level. This indicator also includes a clutter and standard deviation filter.
See the static order version of this indicator here:
STD/C-Filtered, Power-of-Cosine FIR Filter
Amplitudes for STD/C-Filtered, N-Order Power-of-Cosine FIR Filter:
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
What is Power-of-Sine Digital FIR Filter?
Also called Cos^alpha Window Family. In this family of windows, changing the value of the parameter alpha generates different windows.
f(n) = math.cos(alpha) * (math.pi * n / N) , 0 ≤ |n| ≤ N/2
where alpha takes on integer values and N is a even number
General expanded form:
alpha0 - alpha1 * math.cos(2 * math.pi * n / N)
+ alpha2 * math.cos(4 * math.pi * n / N)
- alpha3 * math.cos(4 * math.pi * n / N)
+ alpha4 * math.cos(6 * math.pi * n / N)
- ...
Special Cases for alpha:
alpha = 0: Rectangular window, this is also just the SMA (not included here)
alpha = 1: MLT sine window (not included here)
alpha = 2: Hann window (raised cosine = cos^2)
alpha = 4: Alternative Blackman (maximized roll-off rate)
This indicator contains a binomial expansion algorithm to handle N orders of a cosine power series. You can read about how this is done here: The Binomial Theorem
What is Pascal's Triangle and how was it used here?
In mathematics, Pascal's triangle is a triangular array of the binomial coefficients that arises in probability theory, combinatorics, and algebra. In much of the Western world, it is named after the French mathematician Blaise Pascal, although other mathematicians studied it centuries before him in India, Persia, China, Germany, and Italy.
The rows of Pascal's triangle are conventionally enumerated starting with row n = 0 at the top (the 0th row). The entries in each row are numbered from the left beginning with k=0 and are usually staggered relative to the numbers in the adjacent rows. The triangle may be constructed in the following manner: In row 0 (the topmost row), there is a unique nonzero entry 1. Each entry of each subsequent row is constructed by adding the number above and to the left with the number above and to the right, treating blank entries as 0. For example, the initial number in the first (or any other) row is 1 (the sum of 0 and 1), whereas the numbers 1 and 3 in the third row are added to produce the number 4 in the fourth row.
Rows of Pascal's Triangle
0 Order: 1
1 Order: 1 1
2 Order: 1 2 1
3 Order: 1 3 3 1
4 Order: 1 4 6 4 1
5 Order: 1 5 10 10 5 1
6 Order: 1 6 15 20 15 6 1
7 Order: 1 7 21 35 35 21 7 1
8 Order: 1 8 28 56 70 56 28 8 1
9 Order: 1 9 36 34 84 126 126 84 36 9 1
10 Order: 1 10 45 120 210 252 210 120 45 10 1
11 Order: 1 11 55 165 330 462 462 330 165 55 11 1
12 Order: 1 12 66 220 495 792 924 792 495 220 66 12 1
13 Order: 1 13 78 286 715 1287 1716 1716 1287 715 286 78 13 1
For a 12th order Power-of-Cosine FIR Filter
1. We take the coefficients from the Left side of the 12th row
1 13 78 286 715 1287 1716 1716 1287 715 286 78 13 1
2. We slice those in half to
1 13 78 286 715 1287 1716
3. We reverse the array
1716 1287 715 286 78 13 1
This is our array of alphas: alpha1, alpha2, ... alphaN
4. We then pull alpha one from the previous order, order 11, the middle value
11 Order: 1 11 55 165 330 462 462 330 165 55 11 1
The middle value is 462, this value becomes our alpha0 in the calculation
5. We apply these alphas to the cosine calculations
example: + alpha4 * math.cos(6 * math.pi * n / N)
6. We then divide by the sum of the alphas to derive our final coefficient weighting kernel
**This is only useful for orders that are EVEN, if you use odd ordering, the following are the coefficient outputs and these aren't useful since they cancel each other out and result in a value of zero. See below for an odd numbered oder and compare with the amplitude of the graphic posted above of the even order amplitude:
What is a Standard Deviation Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Variety N-Tuple Moving Averages w/ Variety Stepping [Loxx]Variety N-Tuple Moving Averages w/ Variety Stepping is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 2 different moving average types. For example, using "50" as the depth will give you Quinquagintuple Moving Average. If you'd like to find the name of the moving average type you create with the depth input with this indicator, you can find a list of tuples here: Tuples extrapolated
Due to the coding required to adapt a moving average to fit into this indicator, additional moving average types will be added as they are created to fit into this unique use case. Since this is a work in process, there will be many future updates of this indicator. For now, you can choose from either EMA or RMA.
This indicator is also considered one of the top 10 forex indicators. See details here: forex-station.com
Additionally, this indicator is a computationally faster, more streamlined version of the following indicators with the addition of 6 stepping functions and 6 different bands/channels types.
STD-Stepped, Variety N-Tuple Moving Averages
STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Last but not least, a big shoutout to @lejmer for his help in formulating a looping solution for this streamlined version. this indicator is speedy even at 50 orders deep. You can find his scripts here: www.tradingview.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(depth) / (factorial(depth - k) * factorial(k); where depth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the calculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
In this new streamlined version, these MA calculations are packed into an array inside loop so Pine doesn't have to keep all possible series information in memory. This is handled with the following code:
temp = array.get(workarr, k + 1) + alpha * (array.get(workarr, k) - array.get(workarr, k + 1))
array.set(workarr, k + 1, temp)
After we pack the array, we apply the coefficients to derive the NTMA:
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Bands/Channels
See the information above for how bands/channels are calculated. After the one of the above deviations is calculated, the channels are calculated as output +/- deviation * multiplier
Signals
Green is uptrend, red is downtrend, yellow "L" signal is Long, fuchsia "S" signal is short.
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
6 bands/channels types
6 stepping types
Related indicators
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping
STD-Stepped Fast Cosine Transform Moving Average
ATR-Stepped PDF MA
STD-Stepped, Variety N-Tuple Moving Averages [Loxx]STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Variety N-Tuple Moving Averages is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 5 different moving average types including T3. A list of tuples can be found here if you'd like to name the order of the moving average by depth: Tuples extrapolated
STD-Stepped, You'll notice that this is a lot of code and could normally be packed into a single loop in order to extract the N-tuple MA, however due to Pine Script limitations and processing paradigm this is not possible ... yet.
If you choose the EMA option and select a depth of 2, this is the classic DEMA ; EMA with a depth of 3 is the classic TEMA , and so on and so forth this is to help you understand how this indicator works. This version of NTMA is restricted to a maximum depth of 30 or less. Normally this indicator would include 50 depths but I've cut this down to 30 to reduce indicator load time. In the future, I'll create an updated NTMA that allows for more depth levels.
This is considered one of the top ten indicators in forex. You can read more about it here: forex-station.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(nemadepth) / (factorial(nemadepth - k) * factorial(k); where nemadepth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the caculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
Standard deviation stepping
Variety N-Tuple Moving Averages [Loxx]Variety N-Tuple Moving Averages is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 5 different moving average types including T3. A list of tuples can be found here if you'd like to name the order of the moving average by depth: Tuples extrapolated
You'll notice that this is a lot of code and could normally be packed into a single loop in order to extract the N-tuple MA, however due to Pine Script limitations and processing paradigm this is not possible ... yet.
If you choose the EMA option and select a depth of 2, this is the classic DEMA; EMA with a depth of 3 is the classic TEMA, and so on and so forth this is to help you understand how this indicator works. This version of NTMA is restricted to a maximum depth of 30 or less. Normally this indicator would include 50 depths but I've cut this down to 30 to reduce indicator load time. In the future, I'll create an updated NTMA that allows for more depth levels.
This is considered one of the top ten indicators in forex. You can read more about it here: forex-station.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(nemadepth) / (factorial(nemadepth - k) * factorial(k); where nemadepth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA, the caculation is as follows
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
ema3 = ta.ema(ema2, length)
ema4 = ta.ema(ema3, length)
ema5 = ta.ema(ema4, length)
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Close v Open Moving Averages Strategy (Variable) [divonn1994]This is a simple moving average based strategy that works well with a few different coin pairings. It takes the moving average 'opening' price and plots it, then takes the moving average 'closing' price and plots it, and then decides to enter a 'long' position or exit it based on whether the two lines have crossed each other. The reasoning is that it 'enters' a position when the average closing price is increasing. This could indicate upwards momentum in prices in the future. It then exits the position when the average closing price is decreasing. This could indicate downwards momentum in prices in the future. This is only speculative, though, but sometimes it can be a very good indicator/strategy to predict future action.
What I've found is that there are a lot of coins that respond very well when the appropriate combination of: 1) type of moving average is chosen (EMA, SMA, RMA, WMA or VWMA) & 2) number of previous bars averaged (typically 10 - 250 bars) are chosen.
Depending on the coin.. each combination of MA and Number of Bars averaged can have completely different levels of success.
Example of Usage:
An example would be that the VWMA works well for BTCUSD (BitStamp), but it has different successfulness based on the time frame. For the 12 hour bar timeframe, with the 66 bar average with the VWMA I found the most success. The next best successful combo I've found is for the 1 Day bar timeframe with the 35 bar average with the VWMA.. They both have a moving average that records about a month, but each have a different successfulness. Below are a few pair combos I think are noticeable because of the net profit, but there are also have a lot of potential coins with different combos:
It's interesting to see the strategy tester change as you change the settings. The below pairs are just some of the most interesting examples I've found, but there might be other combos I haven't even tried on different coin pairs..
Some strategy settings:
BTCUSD (BitStamp) 12 Hr Timeframe : 66 bars, VWMA=> 10,387x net profit
BTCUSD (BitStamp) 1 Day Timeframe : 35 bars, VWMA=> 7,805x net profit
BNBUSD (Binance) 12 Hr Timeframe : 27 bars, VWMA => 15,484x net profit
ETHUSD (BitStamp) 16 Hr Timeframe : 60 bars, SMA => 5,498x net profit
XRPUSD (BitStamp) 16 Hr Timeframe : 33 bars, SMA => 10,178x net profit
I only chose these coin/combos because of their insane net profit factors. There are far more coins with lower net profits but more reliable trade histories.
Also, usually when I want to see which of these strategies might work for a coin pairing I will check between the different Moving Average types, for example the EMA or the SMA, then I also check between the moving average lengths (the number of bars calculated) to see which is most profitable over time.
Features:
-You can choose your preferred moving average: SMA, EMA, WMA, RMA & VWMA.
-You can also adjust the previous number of calculated bars for each moving average.
-I made the background color Green when you're currently in a long position and Red when not. I made it so you can see when you'd be actively in a trade or not. The Red and Green background colors can be toggled on/off in order to see other indicators more clearly overlayed in the chart, or if you prefer a cleaner look on your charts.
-I also have a plot of the Open moving average and Close moving average together. The Opening moving average is Purple, the Closing moving average is White. White on top is a sign of a potential upswing and purple on top is a sign of a potential downswing. I've made this also able to be toggled on/off.
Please, comment interesting pairs below that you've found for everyone :) thank you!
I will post more pairs with my favorite settings as well. I'll also be considering the quality of the trades.. for example: net profit, total trades, percent profitable, profit factor, trade window and max drawdown.
*if anyone can figure out how to change the date range, I woul really appreciate the help. It confuses me -_- *
DatasetWeatherTokyoMeanAirTemperatureLibrary "DatasetWeatherTokyoMeanAirTemperature"
Provides a data set of the monthly mean air temperature (°C) for the city of Tokyo in Japan.
this was just for fun, no financial implications in this.
reference:
www.data.jma.go.jp
TOKYO WMO Station ID:47662 Lat 35o41.5'N Lon 139o45.0'E
year_()
the years of the data set.
Returns: array : year values.
january()
the january values of the dataset
Returns: array\ : data values for january.
february()
the february values of the dataset
Returns: array\ : data values for february.
march()
the march values of the dataset
Returns: array\ : data values for march.
april()
the april values of the dataset
Returns: array\ : data values for april.
may()
the may values of the dataset
Returns: array\ : data values for may.
june()
the june values of the dataset
Returns: array\ : data values for june.
july()
the july values of the dataset
Returns: array\ : data values for july.
august()
the august values of the dataset
Returns: array\ : data values for august.
september()
the september values of the dataset
Returns: array\ : data values for september.
october()
the october values of the dataset
Returns: array\ : data values for october.
november()
the november values of the dataset
Returns: array\ : data values for november.
december()
the december values of the dataset
Returns: array\ : data values for december.
annual()
the annual values of the dataset
Returns: array\ : data values for annual.
select_month(idx)
get the temperature values for a specific month.
Parameters:
idx : int, month index (1 -> 12 | any other value returns annual average values).
Returns: array\ : data values for selected month.
select_value(year_, month_)
get the temperature value of a specified year and month.
Parameters:
year_ : int, year value.
month_ : int, month index (1 -> 12 | any other value returns annual average values).
Returns: float : value of specified year and month.
diff_to_median(month_)
the difference of the month air temperature (ºC) to the median of the sample.
Parameters:
month_ : int, month index (1 -> 12 | any other value returns annual average values).
Returns: float : difference of current month to median in (Cº)
5EMA + VP IGHola Divinis
En una villa nació, fue deseo de Dios
Crecer y sobrevivir a la humilde expresión
Enfrentar la adversidad
Con afán de ganarse a cada paso la vida
En un potrero forjó una zurda inmortal
Con experiencia, sedienta ambición de llegar
De cebollita, soñaba jugar un Mundial
Y consagrarse en Primera
Tal vez jugando pudiera a su familia ayudar
En una villa nació, fue deseo de Dios
Crecer y sobrevivir a la humilde expresión
Enfrentar la adversidad
Con afán de ganarse a cada paso la vida
En un potrero forjó una zurda inmortal
Con experiencia, sedienta ambición de llegar
De cebollita, soñaba jugar un Mundial
Y consagrarse en Primera
Tal vez jugando pudiera a su familia ayudar
A poco que debutó (Maradó, Maradó)
La 12 fue quien coreó (Maradó, Maradó)
Su sueño tenía una estrella
Llena de gol y gambetas
Y todo el pueblo cantó (Maradó, Maradó)
Nació la mano de Dios (Maradó, Maradó)
Llenó alegría en el pueblo
Regó de gloria este suelo
Carga una cruz en los hombros por ser el mejor
Por no venderse jamás, al poder enfrentó
Curiosa debilidad, si Jesús tropezó
¿Por qué él no habría de hacerlo?
La fama le presentó una blanca mujer
De misterioso sabor y prohibido placer
Que lo hizo adicto al deseo de usarla otra vez
Involucrando su vida
Y es un partido que un día el Diego está por ganar
A poco que debutó (Maradó, Maradó)
La 12 fue quien coreó (Maradó, Maradó)
Su sueño tenía una estrella
Llena de gol y gambetas
Y todo el pueblo cantó (Maradó, Maradó)
Nació la mano de Dios (Maradó, Maradó)
Llenó alegría en el pueblo
Llenó de gloria este suelo
Olé, olé, olé, olé
¡Diego, Diego!
Olé, olé, olé, olé
¡Diego, Diego!
Olé, olé, olé, olé
¡Diego, Diego!
Olé, olé, olé, olé
¡Diego, Diego!
Y todo el pueblo cantó (Maradó, Maradó)
La 12 fue quien coreó (Maradó, Maradó)
Su sueño tenía una estrella
Llena de gol y gambetas
Y todo el pueblo cantó (Maradó, Maradó)
Nació la mano de Dios (Maradó, Maradó)
Llenó alegría en el pueblo
Regó de gloria este suelo
Regó de gloria este suelo
Regó de gloria
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Time█ OVERVIEW
This library is a Pine Script™ programmer’s tool containing a variety of time related functions to calculate or measure time, or format time into string variables.
█ CONCEPTS
`formattedTime()`, `formattedDate()` and `formattedDay()`
Pine Script™, like many other programming languages, uses timestamps in UNIX format, expressed as the number of milliseconds elapsed since 00:00:00 UTC, 1 January 1970. These three functions convert a UNIX timestamp to a formatted string for human consumption.
These are examples of ways you can call the functions, and the ensuing results:
CODE RESULT
formattedTime(timenow) >>> "00:40:35"
formattedTime(timenow, "short") >>> "12:40 AM"
formattedTime(timenow, "full") >>> "12:40:35 AM UTC"
formattedTime(1000 * 60 * 60 * 3.5, "HH:mm") >>> "03:30"
formattedDate(timenow, "short") >>> "4/30/22"
formattedDate(timenow, "medium") >>> "Apr 30, 2022"
formattedDate(timenow, "full") >>> "Saturday, April 30, 2022"
formattedDay(timenow, "E") >>> "Sat"
formattedDay(timenow, "dd.MM.yy") >>> "30.04.22"
formattedDay(timenow, "yyyy.MM.dd G 'at' hh:mm:ss z") >>> "2022.04.30 AD at 12:40:35 UTC"
These functions use str.format() and some of the special formatting codes it allows for. Pine Script™ documentation does not yet contain complete specifications on these codes, but in the meantime you can find some information in the The Java™ Tutorials and in Java documentation of its MessageFormat class . Note that str.format() implements only a subset of the MessageFormat features in Java.
`secondsSince()`
The introduction of varip variables in Pine Script™ has made it possible to track the time for which a condition is true when a script is executing on a realtime bar. One obvious use case that comes to mind is to enable trades to exit only when the exit condition has been true for a period of time, whether that period is shorter that the chart's timeframe, or spans across multiple realtime bars.
For more information on this function and varip please see our Using `varip` variables publication.
`timeFrom( )`
When plotting lines , boxes , and labels one often needs to calculate an offset for past or future end points relative to the time a condition or point occurs in history. Using xloc.bar_index is often the easiest solution, but some situations require the use of xloc.bar_time . We introduce `timeFrom()` to assist in calculating time-based offsets. The function calculates a timestamp using a negative (into the past) or positive (into the future) offset from the current bar's starting or closing time, or from the current time of day. The offset can be expressed in units of chart timeframe, or in seconds, minutes, hours, days, months or years. This function was ported from our Time Offset Calculation Framework .
`formattedNoOfPeriods()` and `secondsToTfString()`
Our final two offerings aim to confront two remaining issues:
How much time is represented in a given timestamp?
How can I produce a "simple string" timeframe usable with request.security() from a timeframe expressed in seconds?
`formattedNoOfPeriods()` converts a time value in ms to a quantity of time units. This is useful for calculating a difference in time between 2 points and converting to a desired number of units of time. If no unit is supplied, the function automatically chooses a unit based on a predetermined time step.
`secondsToTfString()` converts an input time in seconds to a target timeframe string in timeframe.period string format. This is useful for implementing stepped timeframes relative to the chart time, or calculating multiples of a given chart timeframe. Results from this function are in simple form, which means they are useable as `timeframe` arguments in functions like request.security() .
█ NOTES
Although the example code is commented in detail, the size of the library justifies some further explanation as many concepts are demonstrated. Key points are as follows:
• Pivot points are used to draw lines from. `timeFrom( )` calculates the length of the lines in the specified unit of time.
By default the script uses 20 units of the charts timeframe. Example: a 1hr chart has arrows 20 hours in length.
• At the point of the arrows `formattedNoOfPeriods()` calculates the line length in the specified unit of time from the input menu.
If “Use Input Time” is disabled, a unit of time is automatically assigned.
• At each pivot point a label with a formatted date or time is placed with one of the three formatting helper functions to display the time or date the pivot occurred.
• A label on the last bar showcases `secondsSince()` . The label goes through three stages of detection for a timed alert.
If the difference between the high and the open in ticks exceeds the input value, a timer starts and will turn the label red once the input time is exceeded to simulate a time-delayed alert.
• In the bottom right of the screen `secondsToTfString()` posts the chart timeframe in a table. This can be multiplied from the input menu.
Look first. Then leap.
█ FUNCTIONS
formattedTime(timeInMs, format)
Converts a UNIX timestamp (in milliseconds) to a formatted time string.
Parameters:
timeInMs : (series float) Timestamp to be formatted.
format : (series string) Format for the time. Optional. The default value is "HH:mm:ss".
Returns: (string) A string containing the formatted time.
formattedDate(timeInMs, format)
Converts a UNIX timestamp (in milliseconds) to a formatted date string.
Parameters:
timeInMs : (series float) Timestamp to be formatted.
format : (series string) Format for the date. Optional. The default value is "yyyy-MM-dd".
Returns: (string) A string containing the formatted date.
formattedDay(timeInMs, format)
Converts a UNIX timestamp (in milliseconds) to the name of the day of the week.
Parameters:
timeInMs : (series float) Timestamp to be formatted.
format : (series string) Format for the day of the week. Optional. The default value is "EEEE" (complete day name).
Returns: (string) A string containing the day of the week.
secondsSince(cond, resetCond)
The duration in milliseconds that a condition has been true.
Parameters:
cond : (series bool) Condition to time.
resetCond : (series bool) When `true`, the duration resets.
Returns: The duration in seconds for which `cond` is continuously true.
timeFrom(from, qty, units)
Calculates a +/- time offset in variable units from the current bar's time or from the current time.
Parameters:
from : (series string) Starting time from where the offset is calculated: "bar" to start from the bar's starting time, "close" to start from the bar's closing time, "now" to start from the current time.
qty : (series int) The +/- qty of units of offset required. A "series float" can be used but it will be cast to a "series int".
units : (series string) String containing one of the seven allowed time units: "chart" (chart's timeframe), "seconds", "minutes", "hours", "days", "months", "years".
Returns: (int) The resultant time offset `from` the `qty` of time in the specified `units`.
formattedNoOfPeriods(ms, unit)
Converts a time value in ms to a quantity of time units.
Parameters:
ms : (series int) Value of time to be formatted.
unit : (series string) The target unit of time measurement. Options are "seconds", "minutes", "hours", "days", "weeks", "months". If not used one will be automatically assigned.
Returns: (string) A formatted string from the number of `ms` in the specified `unit` of time measurement
secondsToTfString(tfInSeconds, mult)
Convert an input time in seconds to target string TF in `timeframe.period` string format.
Parameters:
tfInSeconds : (simple int) a timeframe in seconds to convert to a string.
mult : (simple float) Multiple of `tfInSeconds` to be calculated. Optional. 1 (no multiplier) is default.
Returns: (string) The `tfInSeconds` in `timeframe.period` format usable with `request.security()`.
Momentum ScoreMomentum is the tendency of assets that have gone up in price to continue going up in price - and for assets that have gone down in price to continue going down in price. The reasons behind it are not well understood by academics, but momentum is a property that exists across geographies and asset classes.
The Momentum Score is a system that scores companies based on their one year total returns, excluding the last month of returns. In other words, a momentum score for today will be based on the total returns of a stock from 12 months ago today to one month ago today.
Our Momentum Score 13612W has the following composition:
MS 13612W = 12 * Roc(1) + 4 * Roc(3) + 2 * Roc(6) + 1 * Roc(12)
with ROC = (p0/pt) - 1, where pt equals price p with a t-month lag
Median Convergence DivergenceIntroduction
The Median Convergence Divergence (MCD) is a derivative of the Moving Average Convergence Divergence (MACD). The difference is the change in the use of the measure of central tendency. In MACD, moving average (mean) is used, whereas, in MCD, the median is used instead. The purpose of using the median is to eliminate the outlying values, which would be calculated for a moving average. The outliers would affect the value of the moving average.
For example: 3, 5, 7, 8, 5, 4, 2, 1, 6, 21, 8. The data set average is 6.3, whereas the median value is 5. There is a difference of about 23% in the example. The reason is the outlying value '21' in the data set.
As the markets are volatile, outlying values can always emerge. A moving average will consider those values; on the other hand, the median will ignore. If the strategy calls for a tool to ignore the outliers, the Median Convergence Divergence would be a great centered oscillator.
The default values have changed to suit the current trading days in a week. When the MACD was introduced, there would be six trading days in a week. Therefore, it used 12 (2 weeks), 26(4 weeks), and 9 ( 1.5 weeks). But now that there are five trading days per week. The default values are adapted to them. Feel free to change them as per your wish.
Recommended Settings
The current settings are set to be used for the Daily Time Frame: 5 day period for the fast line, a 20 day period for the slow line, and a 10 day period for the signal line. (5 days represent a trading week, 10 days is two weeks, and 20 days is 4 weeks or a month)
For the weekly charts, use 4 week period for the fast line, 13 week period for the slow line, and 8 week period for the signal line. (4 weeks represent a month, 8 weeks is two months, and 13 weeks is 3 months or quarterly)
And for monthly charts, use 3 month period for the fast line, 12 month period for the slow line, and 6 month period for the signal line. (3 months is quarterly, 6 months is bi-yearly, and 12 month is yearly)
It'll be challenging to measure for intraday since there are many different timeframes within intraday. The settings mentioned above should also be customized as per the requirements of the trading strategy.
Strategy
The strategy application is the same as the MACD, i.e., Signal Line Crossovers, Zero Line Crossovers, and Divergence.
Signal Line Crossovers: When the MCD line crosses above the Signal line, it's a bullish crossover. When the MCD line crosses below the Signal line, it's a bearish crossover.
Zero Line Crossovers: It's a bullish crossover when the MCD line crosses above the Zero line. When the MCD line crosses below the Zero Line, it's a bearish crossover.
Divergence: When price shows a lower low, but MCD shows a higher low, it's a bullish divergence. When the price shows a higher high but MCD shows a lower high, it's a bearish divergence.
Using other indicators in conjunction with the Median Convergence Divergence is recommended to take entry and exit signals.
DAYOFWEEK performance1 -Objective
"What is the ''best'' day to trade .. Monday, Tuesday...."
This script aims to determine if there are different results depending on the day of the week.
The way it works is by dividing data by day of the week (Monday, Tuesday, Wednesday ... ) and perform calculations for each day of the week.
1 - Objective
2 - Features
3 - How to use (Examples)
4 - Inputs
5 - Limitations
6 - Notes
7 - Final Tooughs
2 - Features
AVG OPEN-CLOSE
Calculate de Percentage change from day open to close
Green % (O-C)
Percentage of days green (open to close)
Average Change
Absolute day change (O-C)
AVG PrevD. Close-Close
Percentage change from the previous day close to the day of the week close
(Example: Monday (C-C) = Friday Close to Monday close
Tuesday (C-C) = Monday C. to Tuesday C.
Green % (C1-C)
Percentage of days green (open to close)
AVG Volume
Day of the week Average Volume
Notes:
*Mon(Nº) - Nº = Number days is currently calculated
Example: Monday (12) calculation based on the last 12 Mondays. Note: Discrepancies in numbers example Monday (12) - Friday (11) depend on the initial/end date or the market was closed (Holidays).
3 - How to use (Examples)
For the following example, NASDAQ:AAPL from 1 Jan 21 to 1 Jul 21 the results are following.
The highest probability of a Close being higher than the Open is Monday with 52.17 % and the Lowest Tuesday with 38.46 %. Meaning that there's a higher chance (for NASDAQ:AAPL ) of closing at a higher value on Monday while the highest chance of closing is lower is Tuesday. With an average gain on Tuesday of 0.21%
Long - The best day to buy (long) at open (on average) is Monday with a 52.2% probability of closing higher
Short - The best day to sell (short) at open (on average) is Tuesday with a 38.5% probability of closing higher (better chance of closing lower)
Since the values change from ticker to ticker, there is a substantial change in the percentages and days of the week. For example let's compare the previous example ( NASDAQ:AAPL ) to NYSE:GM (same settings)
For the same period, there is a substantial difference where there is a 62.5% probability Friday to close higher than the open, while Tuesday there is only a 28% probability.
With an average gain of 0.59% on Friday and an average loss of -0.34%
Also, the size of the table (number of days ) depends if the ticker is traded or not on that day as an example COINBASE:BTCUSD
4 - Inputs
DATE RANGE
Initial Date - Date from which the script will start the calculation.
End Date - Date to which the script will calculate.
TABLE SETTINGS
Text Color - Color of the displayed text
Cell Color - Background color of table cells
Header Color - Color of the column and row names
Table Location - Change the position where the table is located.
Table Size - Changes text size and by consequence the size of the table
5 - LIMITATIONS
The code determines average values based on the stored data, therefore, the range (Initial data) is limited to the first bar time.
As a consequence the lower the timeframe the shorter the initial date can be and fewer weeks can be calculated. To warn about this limitation there's a warning text that appears in case the initial date exceeds the bar limit.
Example with initial date 1 Jan 2021 and end date 18 Jul 2021 in 5m and 10 m timeframe:
6 - Notes and Disclosers
The script can be moved around to a new pane if need. -> Object Tree > Right Click Script > Move To > New pane
The code has not been tested in higher subscriptions tiers that allow for more bars and as a consequence more data, but as far I can tell, it should work without problems and should be in fact better at lower timeframes since it allows more weeks.
The values displayed represent previous data and at no point is guaranteed future values
7 - Final Tooughs
This script was quite fun to work on since it analysis behavioral patterns (since from an abstract point a Tuesday is no different than a Thursday), but after analyzing multiple tickers there are some days that tend to close higher than the open.
PS: If you find any mistake ex: code/misspelling please comment.
Phoenix Ascending 2.201Hi Everyone!
It's time to make this indicator public to relieve myself of replying to requests for access. There has been an update to this indicator; in which a Stochastic RSI was added to this indicator. Please follow the directions to SETUP the indicator in the SETUP VIDEO provided below.
Phoenix Ascending 2.201 and Bollinger Bands Setup Video.
The following are BASIC rules for the Phoenix 2.201 Indicator. More advanced rules and the requirements for those rules can be found in my publications in my public profile. Unfortunately, I do not have organized videos created on how to use this indicator in full but will be available in the future.
IMPORTANT: The BASIC rules below are beneficial but these are NOT all the rules. More rules and requirements for those rules will be available in the future.
RULE NO. 1
We PREFER the Blue LSMA to be at 80% or higher for SAFE EXIT (SHORT) bets.
We PREFER the Blue LSMA to be at 20% or lower for SAFE ENTRY (LONG) bets.
Rule No. 2
ANY time the red line is approaching a green line that’s moving UPWARD,
Be prepared to make an ENTRY (LONG) when the red line is about to touch the green line that’s moving upward.
One can look at a lower time frame to get a better idea of how much longer you may have
To wait for the red line to touch the green line. In many cases, you may make ENTRY (LONG)
Just before the red line actually touches the green line that’s moving up in that higher time frame
You were initially using as your COMPASS. I currently have the 1-Month TF as a compass for EURUSD.
Rule No. 3
ANY time the red line is approaching a green line that’s moving DOWNWARD,
Be prepared to make an EXIT (SHORT) when the red line is about to touch the green line that’s moving downward.
One can look at a lower time frame to get a better idea of how much longer you may have
To wait for the red line to touch the green line. In many cases, you may make your EXIT (SHORT)
Just before the red line actually touches the green line that’s moving downward in that higher time frame
You were initially using as your COMPASS. I currently have the 1-Month TF as a compass for EURUSD.
Rule No. 4
The Green Line and/or Ghost Line can often help one determine when an upward or downward move in a particular time frame
Is nearly exhausted and about to reverse.
Example for Upside Exhaustion about to reverse to the Downside:
When the Green Line and/or Ghost line is at 80% level or higher, this is a good indicator to inform
Us the current upside move may be approaching exhaustion. You can look at a higher time frame to try to gain
More insight as to whether this will only be a brief dip down in the lower time frame IF the higher time frame you
Went to reveals there is a lot more room remaining for the Green and/or Ghost Lines to reach the 80% or higher level.
Example for Downside Exhaustion about to reverse to the Upside:
When the Green Line and/or Ghost line is at 20% level or lower, this is a good indicator to inform
Us the current downside move may be approaching exhaustion. You can look at a higher time frame to try to gain
More insight as to whether this will only be a brief dip up in the lower time frame IF the higher time frame you
Went to reveals there is a lot more room remaining for the Green and/or Ghost Lines to reach the 20% or lower level.
Rule No. 5
The same rules you see in Rule No. 4 also apply to the Stochastic RSI. Keep in mind I changed the colors of the
Stochastic RSI to the following: Red default changed to Purple and Blue changed changed to Black to avoid confusing
Them with the lines in Godmode.
When the Stochastic RSI is at 80% or higher level, we need to be on guard for a reversal to the downside.
When the Stochastic RSI is at 20% or lower level, we need to be on guard for a reversal to the upside.
EXTREMELY IMPORTANT to apply these rules in GROUPS OF TIME FRAMES.
"TYPES" OF TIME FRAME GROUP TRADING SIGNALS
Scalping Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Short Term Group as a compass and Scalping Group for confirmation and more precise entry/exit.
Scalping Group: 6min. 12min. 23min & 45min.
Short Term Group: 90min. 3hr. 6hr. & 12hr.
Short Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. NearTerm Group as a compass and Short Term Group for confirmation and more precise entry/exit.
Short Term Group: 90min. 3hr. 6hr. & 12hr.
Near Term Group: 24hr. 2-Day, 3-Day & 4-Day
Near Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Mid Term Group as a compass and Near Term Group for confirmation and more precise entry/exit.
Near Term Group: 24hr. 2-Day, 3-Day & 4-Day
Mid Term Group: 3-Day, 6-Day, 9-Day & 12-Day
Mid Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Long Term Group as a compass and Mid Term Group for confirmation and more precise entry/exit.
Mid Term Group: 3-Day, 6-Day, 9-Day & 12-Day
Long Term Group: 1-Week, 2-Week, 3-Week & 4-Week
Long Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Macro Term Group as a compass and Long Term Group for confirmation and more precise entry/exit.
Long Term Group: 1-Week, 2-Week, 3-Week & 4-Week
Macro Term Group: 1-Month, 2-Month, 3-Month & 4-Month
Macro Term Group Signals: Signals provided for this group involve analyzing the following two groups of time frames. Macro Term Group as a compass and Long Term Group for confirmation and more precise entry/exit.
Macro Term Group: 1-Month, 2-Month, 3-Month & 4-Month
Super Macro Group: 3-Month , 6-Month, 12-Month & 24-Month
Reverse MACD IndicatorIntroducing the reverse MACD Indicator.
This is my Pinescript implementation of the reverse MACD indicator.
Much respect to Mr Johnny Dough the original creator of this idea.
Feel free to reuse this script, drop me a note below if you find this useful.
Investopedia defines the MACD as 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.
MACD triggers technical signals when it crosses above (to buy) or below (to sell) its signal line.
The speed of crossovers is also taken as a signal of a market is overbought or oversold.
MACD helps investors understand whether the bullish or bearish movement in the price is strengthening or weakening.
The MACD has a positive value (shown as the red line on the price chart ) whenever the 12-period EMA ( indicated by the blue line on the price chart) is above the 26-period EMA (the red line in the price chart) and a negative value when the 12-period EMA is below the 26-period EMA .
The more distant the MACD is above or below its baseline indicates that the distance between the two EMAs is growing.
The baseline here is the white line.
The Reverse function of the MACD provides value by letting the user know the specific price needed to expect a MACD cross over in the opposite direction.
This function can be used to designate risk parameters for a potential trade if using the MACD as their source of edge, letting the user know exactly where and how much their risk is for a potential trade which can be used to design an effective trading plan.
Percentage Volume Oscillator (PVO)The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume. The PVO measures the difference between two volume-based moving averages as a percentage of the larger moving average. As with MACD and the Percentage Price Oscillator (PPO), it is shown with a signal line, a histogram and a centerline. The PVO is positive when the shorter volume EMA is above the longer volume EMA and negative when the shorter volume EMA is below. This indicator can be used to define the ups and downs for volume, which can then be used to confirm or refute other signals. Typically, a breakout or support break is validated when the PVO is rising or positive.
Generally speaking, volume is above average when the PVO is positive and below average when the PVO is negative. A negative and rising PVO indicates that volume levels are increasing. A positive and falling PVO indicates that volume levels are decreasing. Chartists can use this information to confirm or refute movements on the price chart.
Even though the PVO is based on a momentum oscillator formula, it is important to remember that moving averages lag. A 12-day EMA include 12 days of volume data, with newer data weighted more heavily. A 26-day EMA lags even more because it contains 26 days of data. This means that the PVO(12,26,9) can sometimes be out of sync with price action.
The Percentage Volume Oscillator (PVO) is a momentum indicator applied to volume. This oscillator can be quite choppy due to the fact that volume doesn't trend. Bullish and bearish divergences are not well suited for the PVO. Instead, chartists would be better off looking for signs of increasing volume with a move into positive territory and signs of decreasing volume with a move into negative territory. Increasing volume can validate a support or resistance break. Similarly, a surge or significant support break on low volume may be less robust. As with all technical indicators, it is important to use the Percentage Volume Oscillator (PVO) in conjunction with other aspects of technical analysis, such as chart patterns and momentum oscillators.
ETF / Stocks / Crypto - DCA Strategy v1Simple "benchmark" strategy for ETFs, Stocks and Crypto! Super-easy to implement for beginners, a DCA (dollar-cost-averaging) strategy means that you buy a fixed amount of an ETF / Stock / Crypto every several months. For instance, to DCA the S&P 500 (SPY), you could purchase $10,000 USD every 12 months, irrespective of the market price. Assuming the macro-economic conditions of the underlying country remain favourable, DCA strategies will result in capital gains over a period of many years, e.g. 10 years. DCA is the safest strategy that beginners can employ to make money in the markets, and all other types of strategies should be "benchmarked" against DCA; if your strategy cannot outperform DCA, then your strategy is useless.
Recommended Chart Settings:
Asset Class: ETF / Stocks / Crypto
Time Frame: H1 (Hourly) / D1 (Daily) / W1 (Weekly) / M1 (Monthly)
Necessary ETF Macro Conditions:
1. Country must have healthy demographics, good ratio of young > old
2. Country population must be increasing
3. Country must be experiencing price-inflation
Necessary Stock Conditions:
1. Growing revenue
2. Growing net income
3. Consistent net margins
4. Higher gross/net profit margin compared to its peers in the industry
5. Growing share holders equity
6. Current ratios > 1
7. Debt to equity ratio (compare to peers)
8. Debt servicing ratio < 30%
9. Wide economic moat
10. Products and services used daily, and will stay relevant for at least 1 decade
Necessary Crypto Conditions:
1. Honest founders
2. Competent technical co-founders
3. Fair or non-existent pre-mine
4. Solid marketing and PR
5. Legitimate use-cases / adoption
Default Robot Settings:
Contribution (USD): $10,000
Frequency (Months): 12
*Robot buys $10,000 worth of ETF, Stock, Crypto, regardless of the market price, every 12 months since its founding time.*
*Equity curve can be seen from the bottom panel*
Risk Warning:
This strategy is low-risk, however it assumes you have a long time horizon of at least 5 to 10 years. The longer your holding-period, the better your returns. The only thing the user has to keep-in-mind are the macro-economic conditions as stated above. If unsure, please stick to ETFs rather than buying individual stocks or cryptocurrencies.
MACD StrategyThis script sends buy and sell signals as alerts to 3Commas (online software with trading bots in cryptocurreny)
It's based on 2 indicators:
- MACD
- 12 EMA and 26 EMA
When the 12 EMA and 26 EMA crossover, the MACD line crosses above 0. The goal here is to look for buy signals when the MACD and Signal are below 0, the histogram is positive, and there was or will be a 12 EMA and 26 EMA crossover.
I struggle with the following:
- There are multiple ways to use this as a crossover signal. I want to calculate the win rate of every posibility.
- What should be my take profit and my stoploss?
I think a 2:1 R/R,and a 60% win rate would make a great strategy! I could use some advice.
PowerX Strategy Bar Coloring [OFFICIAL VERSION]This script colors the bars according to the PowerX Strategy by Markus Heitkoetter:
The PowerX Strategy uses 3 indicators:
- RSI (7)
- Stochastics (14, 3, 3)
- MACD (12, 26 , 9)
The bars are colored GREEN if...
1.) The RSI (7) is above 50 AND
2.) The Stochastic (14, 3, 3) is above 50 AND
3.) The MACD (12, 26, 9) is above its Moving Average, i.e. MACD Histogram is positive.
The bars are colored RED if...
1.) The RSI (7) is below 50 AND
2.) The Stochastic (14, 3, 3) is below 50 AND
3.) The MACD (12, 26, 9) is below its Moving Average, i.e. MACD Histogram is negative.
If only 2 of these 3 conditions are met, then the bars are black (default color)
We highly recommend plotting the indicators mentioned above on your chart, too, so that you can see when bars are getting close to being "RED" or "GREEN", e.g. RSI is getting close to the 50 line.
Price Action and 3 EMAs Momentum plus Sessions FilterThis indicator plots on the chart the parameters and signals of the Price Action and 3 EMAs Momentum plus Sessions Filter Algorithmic Strategy. The strategy trades based on time-series (absolute) and relative momentum of price close, highs, lows and 3 EMAs.
I am still learning PS and therefore I have only been able to write the indicator up to the Signal generation. I plan to expand the indicator to Entry Signals as well as the full Strategy.
The strategy works best on EURUSD in the 15 minutes TF during London and New York sessions with 1 to 1 TP and SL of 30 pips with lots resulting in 3% risk of the account per trade. I have already written the full strategy in another language and platform and back tested it for ten years and it was profitable for 7 of the 10 years with average profit of 15% p.a which can be easily increased by increasing risk per trade. I have been trading it live in that platform for over two years and it is profitable.
Contributions from experienced PS coders in completing the Indicator as well as writing the Strategy and back testing it on Trading View will be appreciated.
STRATEGY AND INDICATOR PARAMETERS
Three periods of 12, 48 and 96 in the 15 min TF which are equivalent to 3, 12 and 24 hours i.e (15 min * period / 60 min) are the foundational inputs for all the parameters of the PA & 3 EMAs Momentum + SF Algo Strategy and its Indicator.
3 EMAs momentum parameters and conditions
• FastEMA = ema of 12 periods
• MedEMA = ema of 48 periods
• SlowEMA = ema of 96 periods
• All the EMAs analyse price close for up to 96 (15 min periods) equivalent to 24 hours
• There’s Upward EMA momentum if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA
• There’s Downward EMA momentum if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA
PA momentum parameters and conditions
• HH = Highest High of 48 periods from 1st closed bar before current bar
• LL = Lowest Low of 48 periods from 1st closed bar from current bar
• Previous HH = Highest High of 84 periods from 12th closed bar before current bar
• Previous LL = Lowest Low of 84 periods from 12th closed bar before current bar
• All the HH & LL and prevHH & prevLL are within the 96 periods from the 1st closed bar before current bar and therefore indicative of momentum during the past 24 hours
• There’s Upward PA momentum if price close > HH and HH > prevHH and LL > prevLL
• There’s Downward PA momentum if price close < LL and LL < prevLL and HH < prevHH
Signal conditions and Status (BuySignal, SellSignal or Neutral)
• The strategy generates Buy or Sell Signals if both 3 EMAs and PA momentum conditions are met for each direction and these occur during the London and New York sessions
• BuySignal if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA and price close > HH and HH > prevHH and LL > prevLL and timeinrange (LDN&NY) else Neutral
• SellSignal if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA and price close < LL and LL < prevLL and HH < prevHH and timeinrange (LDN&NY) else Neutral
Entry conditions and Status (EnterBuy, EnterSell or Neutral)(NOT CODED YET)
• ENTRY IS NOT AT THE SIGNAL BAR but at the current bar tick price retracement to FastEMA after the signal
• EnterBuy if current bar tick price <= FastEMA and current bar tick price > prevHH at the time of the Buy Signal
• EnterSell if current bar tick price >= FastEMA and current bar tick price > prevLL at the time of the Sell Signal
Smart labelling - Candlestick FunctionOftentimes a single look at the candlestick configuration happens to be enough to understand what is going on. The chandlestick function is an experiment in smart labelling that produces candles for various time frames, not only for the fixed 1m, 3m , 5m, 15m, etc. ones, and helps in decision-making when eye-balling the chart. This function generates up to 12 last candlesticks , which is generally more than enough.
Mind that since this is an experiment, the function does not cover all possible combinations. In some time frames the produced candles overlap. This is a todo item for those who are unterested. For instance, the current version covers the following TFs:
Chart - TF in the script
1m - 1-20,24,30,32
3m - 1-10
5m - 1-4,6,9,12,18,36
15m - 1-4,6,12
Tested chart TFs: 1m, 3m ,5m,15m. Tested securities: BTCUSD , EURUSD
[astropark] Power Tools Overlay//******************************************************************************
// Power Tools Overlay
// Inner Version 1.2.1 13/12/2018
// Developer: iDelphi
// Developer: astropark (Ichimoku Cloud), SMA EMA & Cross tools
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// 21/11/2018 Added EMA SMA WMA
// 21/11/2018 Added SMA-EMA EMA-WMA WMA-SMA (Thanks to mariobros1 for the idea of the Simultaneous MA)
// 21/11/2018 Added Bollinger Bands
// 21/11/2018 Added Ichimoku Cloud (Thanks to astropark for all the code of the Ichimoku Cloud)
// 23/11/2018 Show all the indicator as default
// 23/11/2018 Added a cross when single Moving Averages crossing (Thanks to astropark for the idea)
// 24/11/2018 Descriptions Fix
// 24/11/2018 Added Option to enable/disable all Moving Averages
// 10/12/2018 Added EMAs and Crosses
// 13/12/2018 indicator number fixes
//******************************************************************************
[Delphi] Power Tools OscillatorsFEATURES
- RSI
- Stochastic
//******************************************************************************
// Power Tools Oscillators
// Inner Version 1.0 04/12/2018
// Developer: iDelphi
//------------------------------------------------------------------------------
// 04/12/2018 Added RSI
// 04/12/2018 Added Stochastic
//******************************************************************************
