❕NEWS – According To Google Trend Data, Cryptocurrency Searches Hit A New High | Proxies123.com

Ever since Elon Musk criticize energy consumption of Bitcoin , the price has been falling drastically and this has made lots of people to search the word “Crypto Currency” Available data shows that the search by US residents on “Should I sell my bitcoin” in creased by 400%. It really not funny.

L1 trend filtering derivation

I’m working on a school project. I need to implement and understand the l1 trend filter. The paper on the algorithm can be found here :


I don’t understand how they got the dual function from the Lagrangian in section 5.2 . I’m guessing they applied somehow the conditions from section 5.1 but I don’t understand how.

2021 Hot Trend – Short Shorts for Men

You might see that new trend this summer with men wearing short shorts! The saying used to be that when the economy was good that women’s hemlines go up and now the new saying is when the price of Bitcoin goes up then up goes men’s hemlines LOL

So what do you think?

Hamburger menu icons – Recent trend of moving away from three full-width lines to having two full-width lines and a shortened line?

Some examples in various iOS apps:

  1. US BankUS Bank

  2. RentCafe enter image description here

  3. Uber enter image description here

The first two kinda reminds me of the icon for aligning text justified left.

I’m quite curious about the reason for this change. I couldn’t really find anything about it online. Could someone provide some insight and perhaps some online resources that talk about this?

Reversal Trend Following – Vertexfx Autotrader

The Reversal Trend following Autotrader is an always in the market trend following strategy. This is a 100 bar high low breakout strategy. Trend following systems uses different strategies like breakout, moving averages. Usually, trend-following systems are applied to a portfolio containing different asset classes. The aim is to capture some big trends and profit from them. The downside is it makes small losing trades many times and profits from big trends. It is important to use such systems in a diversified portfolio.
This system is run on daily charts. The system exits sell positions and open buy position when price break above 100 days high and exit buy position and open a sell position when price break below 100 days low. Positions are opened after a breakout is confirmed. For a buy, First bar close above last 100 day high, then next bar close above the high of the breakout bar. At the next bar open the Autotrader buys. Money management is an important factor in using trend following systems because they remain in drawdown for longer periods. Use this system on multiple symbols at the same time and divide a small part of the capital for a symbol. Never use full capital among all symbols. The portfolio might contain noncorrelated assets including index futures, bullion, currency pairs, etc.

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Why do most of the crypto prices follow the trend of bitcoin?

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fitting – Removing Irregular Transient Spike from Linear data trend

The x-y data that is being analyzed generates a linear relationship (y=mx+b) when in steady-state flow. NON-REGULAR transients are often introduced into the system. These transients prevent the fitting of a linear relationship to the raw data. I am looking for advice on how to improve the identification of these transients automatically. Using FindAnomalies appears to select the steady state data more often the transient data (see plot down further below) – particulary in the case of the Multinormal method.

Ideally, the following criteria must be met:

  • The analyst does not want to manually remove the transient data
  • If we can identify (therefore remove) the transient points, we can easily generate our own linear regression (or other procedure) for generating the linear relationship
  • The steady state condition is always a linear relationship with a negative slope
  • The analyst can adjust the tolerance, nothing more

As indicated, I am using the FindAnomalies function experimenting with the Method detect the transient spikes and then remove them using AnomalyPositions property.

A review of the plot below will shows the points which are being detected by FindAnomalies using both Multinormal and KernelDensityEstimation methods. Even with small tolerances, the function tends to identify on trend data as an anomaly.

Visualization of detected anomalies

The complete code for the demonstration is below. Any advice or suggestions is appreciated

 TR = FindAnomalies(data((All, 2)), AcceptanceThreshold -> ACT, 
   Method -> "Multinormal");
 TR2 = FindAnomalies(data((All, 2)), "AnomalyPositions", 
    AcceptanceThreshold -> ACT, Method -> "Multinormal") // Flatten;
 XR = FindAnomalies(data((All, 2)), AcceptanceThreshold -> ACT, 
   Method -> "KernelDensityEstimation");
 XR2 = FindAnomalies(data((All, 2)), "AnomalyPositions", 
    AcceptanceThreshold -> ACT, Method -> "KernelDensityEstimation") //
 AnomData = Table(
                data((TR2((t)), 1)), TR((t))
            }, {t, 1, Length(TR)}
 XAnomData = Table(
                data((XR2((t)), 1)), XR((t))
            }, {t, 1, Min(Length(XR), Length(XR2))}
 ListPlot({AnomData, XAnomData, data},
            PlotMarkers -> {{"(Alpha)", 11}, {"(Beta)", 10}, {"(Gamma)", 
            Joined -> {False, False, True},
            GridLines -> Automatic,
            PlotLegends -> {"Multinormal", "KernelDensityEstimation", 
    "Raw Data"}
 {{ACT, 0.2, "Tolerance"}, 0.01, 1, 0.01}

machine learning – Do you have any suggestions on how I should give the trend lines to ANN for predict the finance series?

I coded a program that automatically finds the trend lines.But I can’t decide how I should give the trend lines to ANN for predict the finance series? Trend Lines exist between certain dates and doesnt exist in all data. While giving the trend as a parameter to the ANN, there will be missing data on certain dates. I think this creates a problem. How can I overcome this problem?

The trend lines I have drawn are as follows:

social engineering – New trend? Recognizable and specific subject in Spam / malware mails

It seems that a new trend has emerged in the past few weeks. While users are (meanwhile) aware that an unexpected mail with subject “Your invoice” or “Your delivery” is suspicious, it seems that nowadays such attempts more often (in fact, according to my observations: massively) use specific subject lines that match some mail conversations of the past (e.g., “Re: Suggested changes to sales contract Samplestreet” when contract negotiations about estate in Samplestreet really were a thing the attacked recipient was involved with). Of course, such a subject line gives the recipient a false sense of trust and may make them open malicious attachments.

Apparently, some machine was infected where the malware harvested subject-recipient pairs (and as of now, I doubt that it happened here).

Apart from telling my users in general to be even more cautious than usual, what are suitable measures against this form of attack? (To begin with: Does it have a specific name that helps me find information about it? While being more specific than phishing, this is still less specific than spear phishing, I guess)

Practically the only thing that comes to my mind is to inform all external parties involved (in the Samplestreet deal, say) that they may have been infected. And also warn involved internal users that they may receive more such attacks in this matter in their department.

Need to Create a Twitter Trend

Hi there,

I need someone (preferably with a team) to generate a Twitter trend. It will be a regular gig, but only after you satisfy me with a small sample first (not free, I will pay you up).

So for the first gig, I want to trend something from 300 different twitter accounts. Just 1 single tweet from each account. Let me know your best offers.

If it turns out to be successful, you can expect 10x-20x orders regularly (2-3 times a week).

I prefer someone from India, so that I can make the payment directly through UPI.

Waiting for your response. Thanks.