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❓ASK – Scam Signals From HYIP services. | Proxies-free

This services are common nowadays. Three years ago this services were not common this days. I lost lot of crypto coins this services. But not too much to make me sad. I figure out some patterns about this services. I decided to share with you and define together more accurately those signals. My list;

1] High yield.
For example double your money in 24 hours or 3x 48 hours even 10x 96 hours. This yields only possible with gambling. I can’t imagine what they are possible. On the contrary, some services may try to gain trust with small yields. I mean it is a good signal, but too much obvious. By that reason we can be manipulated with this signal. So, We should consider all signals together.

2] Reference Bonuses
Opposite to advertise a service, offering bonus who bring new users is more cheap. And clicking an ads and an offer from friend is so different things. But like %50 invest bonus is a good signal to us. Every service must be gain money and keeping live. This ratios is not make sense. Beside if referral system doesn’t require mail approve it is a good signal. Because I can use VPN or something and gain fake mining rate for example. I think these systems should give bonus when referral invest.

3] Contract Duration
Everyone is not rich. We have average money, so we are trying make money with HYIP. 6 months, 1 year etc. contracts is holding our money so long. If contract is long we should service has a good foundation and good signals.

4] Support
I called support but it covers them all.
– Live support
– E-mail account which already exist and answers mails.
– Telegram or other groups.
– Phone to call.

5] Foundation
I am software developer. I can design and code same system in one day. It is so easy and there is already coded system can buy. You don’t have to be a programmer. We can ask questions;
– Is this a registered company?
– Address and phone is valid?
– When is company founded?

6] Business
As a business, it has to gain money and hopefully pay to us some part of. Business idea is so important. We should ask how this service working, gaining and paying. Is a reasonable idea. This is important signal for me.

7] Gambling
If a service has gambling feature that service wins long term all the time. Gambling systems are designed and calculated to win, always. We assume gambling feature is totally legit. Pure legit. When Gambling is in it, some people wins and others lost. This feature is important signal for service to live.

8] Others
There are many signals I haven’t noticed of course. Please contribute this and tell your signals and what signal related each other. I will update this post sometimes. Most important thing is this signals sometimes are show to opposite. Because people is manipulates each other trust.

Excuse my English mistakes, I am not native as you may notice :]

algorithm – Peak detection for damped signals in python

I have a signal where I would like to implement a peak detection algorithm where it only collects peaks that follow exponential decay behaviour and stop at a certain height. Here is an example of a signal that I am dealing with

Exponential decay signal

I would like the detection to implement similar to the question in this link,

Peak Detection in Real-Time Python

I know with signals, I might face with noise within the signal. I would like it the algorithm to also be able to compare the neighbouring peaks and select the highest peak. I have looked into the following links:

Also, looked into some of the following GitHub links:

I found the code where it helped me to collect peaks above a certain threshold. The code can be found below:

def peakdet(v, delta, x=None):
    """
    Converted from MATLAB script at http://billauer.co.il/peakdet.html

    Returns two arrays

    function (maxtab, mintab)=peakdet(v, delta, x)
    %PEAKDET Detect peaks in a vector
    %        (MAXTAB, MINTAB) = PEAKDET(V, DELTA) finds the local
    %        maxima and minima ("peaks") in the vector V.
    %        MAXTAB and MINTAB consists of two columns. Column 1
    %        contains indices in V, and column 2 the found values.
    %
    %        With (MAXTAB, MINTAB) = PEAKDET(V, DELTA, X) the indices
    %        in MAXTAB and MINTAB are replaced with the corresponding
    %        X-values.
    %
    %        A point is considered a maximum peak if it has the maximal
    %        value, and was preceded (to the left) by a value lower by
    %        DELTA.

    % Eli Billauer, 3.4.05 (Explicitly not copyrighted).
    % This function is released to the public domain; Any use is allowed.

    """
    maxtab = ()
    mintab = ()

    if x is None:
        x = arange(len(v))

    v = asarray(v)

    if len(v) != len(x):
        sys.exit('Input vectors v and x must have same length')

    if not isscalar(delta):
        sys.exit('Input argument delta must be a scalar')

    if delta <= 0:
        sys.exit('Input argument delta must be positive')

    mn, mx = Inf, -Inf
    mnpos, mxpos = NaN, NaN

    lookformax = True

    for i in arange(len(v)):
        this = v(i)
        if this > mx:
            mx = this
            mxpos = x(i)
        if this < mn:
            mn = this
            mnpos = x(i)

        if lookformax:
            if this < mx - delta:
                maxtab.append((mxpos, mx))
                mn = this
                mnpos = x(i)
                lookformax = False
        else:
            if this > mn + delta:
                mintab.append((mnpos, mn))
                mx = this
                mxpos = x(i)
                lookformax = True
    return array(maxtab), array(mintab)

Where would I need to modify to get what I need and if any other algorithms can perform better peak detection with exponential decay?

I tried to work with the second link and used it with what I need, find the code I used below:

Function:

def thresholding_algo(y, lag, threshold, influence):
    signals = np.zeros(len(y))
    filteredY = np.array(y)
    avgFilter = (0)*len(y)
    stdFilter = (0)*len(y)
    avgFilter(lag - 1) = np.mean(y(0:lag))
    stdFilter(lag - 1) = np.std(y(0:lag), ddof=1)
    for i in range(lag, len(y)):
        if abs(y(i) - avgFilter(i-1)) > threshold * stdFilter (i-1):
            if y(i) > avgFilter(i-1):
                signals(i) = 1
            else:
                signals(i) = -1

            filteredY(i) = influence * y(i) + (1 - influence) * filteredY(i-1)
            avgFilter(i) = np.mean(filteredY((i+1-lag):(i+1)))
            stdFilter(i) = np.std(filteredY((i+1-lag):(i+1)), ddof=1)
        else:
            signals(i) = 0
            filteredY(i) = y(i)
            avgFilter(i) = np.mean(filteredY((i+1-lag):(i+1)))
            stdFilter(i) = np.std(filteredY((i+1-lag):(i+1)), ddof=1)

    return dict(signals = np.asarray(signals), avgFilter = np.asarray(avgFilter), stdFilter = np.asarray(stdFilter))

Code:

        # Settings: lag = 30, threshold = 5, influence = 0
        lag = 30
        threshold = 5
        influence = 1
        
        # Run algo with settings from above
        result = thresholding_algo(process_y, lag=lag, threshold=threshold, influence=influence)
        
        # Plot result
        pylab.subplot(211)
        pylab.plot(np.arange(1, len(process_y)+1), process_y)
        pylab.plot(np.arange(1, len(process_y)+1), result("avgFilter"), color="cyan", lw=2)
        pylab.plot(np.arange(1, len(process_y)+1), result("avgFilter") + threshold * result("stdFilter"), color="green", lw=2)
        pylab.plot(np.arange(1, len(process_y)+1), result("avgFilter") - threshold * result("stdFilter"), color="red", lw=2)
        
        pylab.subplot(212)
        pylab.step(np.arange(1, len(process_y)+1), result("signals"), color="blue", lw=2)
        pylab.ylim(-1.5, 1.5)
        
        S_max_peaks = np.array(result("avgFilter") + threshold * result("stdFilter"), dtype=np.int32)
        S_min_peaks = np.array(result("avgFilter") - threshold * result("stdFilter"), dtype=np.int32)

        # Forming arrays to get the peaks to get the y and x axes for signal:
        S_x_max_peaks, S_y_max_peaks = x_time(S_max_peaks), process_y(S_max_peaks)
        S_x_min_peaks, S_y_min_peaks = x_time(S_min_peaks), process_y(S_min_peaks)

Where process_y is the signal. The result is shown below,

Signal with peaks

Results from thresholding_algo algorithm

What did I do wrong here?

I managed to create custom function but it only prints out the highest peaks, it does not collect peaks that lies within assigned conditions. The code can be found below:

def custom_peakdetection(y_axis, data_ahead, peak_height, x_axis=None):
    """
    keyword arguments:
    y_axis -- A list containg the signal over which to find peaks
    x_axis -- A x-axis whose values correspond to the 'y_axis' list and is used in the return to specify the postion of the peaks. If omitted the index of the y_axis is used. (default: None)
    data_ahead -- (optional) distance to look ahead and previous from a peak candidate to determine if it is the actual peak
    peak_height -- (optional) this specifies a minimum height of the peak
    
    return -- two lists (maxtab, mintab) containing the positive and negative peaks respectively. Each cell of the lists contains a tupple of:
    (position, peak_value) to get the average peak value do 'np.mean(maxtab, 0)(1)' on the results
    """
    
    maxtab = ()
    mintab = ()

    if x_axis is None:
        x = arange(len(y_axis))
    else:
        x = asarray(x_axis)

    y = asarray(y_axis)

    if len(y) != len(x):
        sys.exit('Input vectors y and x must have same length')

    if not isscalar(peak_height):
        sys.exit('Input argument peak_height must be a scalar')

    # if peak_height <= 0:
    #     sys.exit('Input argument peak_height must be positive')

    # maxima and minima candidates are temporarily stored in mx and mn respectively:
    mn, mx = np.Inf, -np.Inf
    mnpos, mxpos = NaN, NaN
    
    # Obtaining the maximum and minimum peaks of the signal:
    key_list = list(x)
    value_list = list(y)
    signal_dict = dict(zip(key_list, value_list))
    signal_full_dict = defaultdict(list)
    for key, value in chain(signal_dict.items()):
        signal_full_dict(key).append(value)
    max_peak = max(signal_full_dict.items(), key = lambda x: x(1))(1)
    min_peak = min(signal_full_dict.items(), key = lambda x: x(1))(1)
    mxpkpos = max(signal_full_dict.items(), key = lambda x: x(1))(0)
    mnpkpos = min(signal_full_dict.items(), key = lambda x: x(1))(0)
    maxtab.append((mxpkpos, max_peak))
    mintab.append((mnpkpos, min_peak))
    
    for i in arange(1, len(y)):
        this = y(i)
        prev = y(i - data_ahead)            
        ahead = y(i:i + data_ahead)
        ahead = np.asscalar(ahead)
        # if (this > mx) & (this < prev) & (this > ahead) & (this < max_peak) & (this > peak_height):
        if (this > mx) & (this < prev) & (this > ahead) & (this > peak_height):
            mx = this
            mxpos = x(i)
            maxtab.append((mxpos, mx))
        # if (this < mn) & (this > prev) & (this < ahead) & (this < min_peak) & (this < peak_height):
        if (this < mn) & (this > prev) & (this < ahead) & (this < peak_height):
            mn = this
            mnpos = x(i)
            mintab.append((mnpos, mn))
    return (maxtab, mintab)
    # return array(maxtab), array(mintab)

obtain the phase between two signals

I would like to calculate the phase between two signals like

f= Table[Sin[x-0.8],{x,-100,100,0.1}]
g= Table[Sin[x],{x,-100,100,0.1}]

For doing that I use the ArcCos[Correlation[f, g]], that instruction gives the phase between two functions but is independent of the sign of the phase. There is some way to consider the sign of the phase?

Thanks