SignalPlusis in the Cryptocurrencies Industry

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03/28 Crypto Market Update Let's face it: the crypto market's been about as exciting as watching paint dry lately. But then, in walks Litecoin, the rebellious teenager of the world, defying the odds and skyrocketing to a two-week high of around $99. Now, this 9.25% jump in 24 hours has us scratching our heads – what's going on here? crypto Sleeping Giants Awake (and Maybe Cause a Little Trouble First) Here's the first plot twist: a bunch of dormant Litecoin tokens worth over $60 million decided to wake up from their slumber and rejoin the party on March 5th. It was the biggest daily jump for these sleepyheads on record. Now, this reunion wasn't exactly smooth sailing. The market initially freaked out, causing a 19% price drop. But hey, maybe it was just a case of first-day jitters? Because guess what? The market quickly absorbed these revived tokens, leading to a 37.25% rebound. This whole scenario feels eerily similar to what happened before Litecoin's 2017 bull run, where a similar awakening of dormant tokens paved the way for a massive price surge. So, is history repeating itself? Buckle up, folks, it's getting interesting. Coinbase Futures: The Shiny New Toy That Might Lure the Big Guys Adding fuel to the fire is a rumor that Coinbase will launch cash-settled Litecoin futures contracts in April. Now, this might sound like financial jargon overload, but here's the gist: it could attract a whole new crowd – the big institutional investors. Remember what happened when CME launched Bitcoin futures in 2017? The market went nuts! Could this be the secret sauce for Litecoin's future success? We don't have a crystal ball (yet!), but it's definitely something to keep an eye on. Strong Foundations: Not Just About Price But it's not all about price jumps. Litecoin's network itself seems to be in tip-top shape. The hash rate, which basically measures how secure the network is, is hitting record highs. This suggests that miners, the folks who keep the whole thing running, are feeling pretty confident. Maybe it's because of the upcoming Bitcoin halving event (where the rewards for mining Bitcoin get cut in half). This often leads to a price increase for Bitcoin, and sometimes, its ripple effect reaches other cryptocurrencies like Litecoin. So, even if you're not a techie, understand this: strong network = happy miners = potentially good news for Litecoin. The Chart Whisperers and Their Predictions Now, for those who like to analyze charts and lines more than we do, here's some good news. Litecoin's recent price action suggests it might have broken free from a long downtrend. In financial speak, that's a bullish signal. Plus, there's strong support from traders (think of it like having a bunch of people cheering for Litecoin) and a positive indicator called the RSI. The bottom line? The charts seem to be whispering that Litecoin might keep climbing, potentially reaching around $127.25 by April. But hey, remember, the crypto market can be as unpredictable as a teenager's mood. There's always a chance it could retreat and test support levels at $75.50. Oh, and Don't Forget About the Rest of the Crypto Zoo While Litecoin steals the show, there's other drama unfolding in the crypto world: A judge said "no way" to Coinbase's attempt to ditch a lawsuit from the SEC. This legal battle could have a major impact on crypto exchanges and regulations. Coinbase vs. SEC: Rumors are swirling about a potential mega-merger between some big names in AI – SingularityNet, , and Ocean Protocol. This could create a $7.5 billion giant in the AI-powered blockchain space. Talk about a power couple! AI Token Merger? Fetch.ai The NFT game Munchables got hacked, resulting in a massive loss of ETH. This serves as a stark reminder that security in the NFT space is crucial. NFT Security Breach: The Final Word: Don't Be a Blind Follower Remember, the crypto market is exciting, but it's also risky. Don't just blindly follow the latest trends. Do your own research, understand the risks, and maybe consult with someone who isn't as easily swayed by shiny objects (like, ahem, some of us). This article is just a glimpse into the ever-changing world of crypto, but hopefully, it keeps you entertained (and maybe a little informed) along the way.

Crypto Market Update: Litecoin Soars Like a Rocket While Cryptoland Sleeps 

Introduction Time series data is a key component in a wide range of domains, including finance and meteorology. Effective feature engineering is critical in preparing time series data for machine learning models. In this article, we delve into advanced techniques for time series feature engineering, such as Fourier transform, wavelet transformation, derivatives, and autocorrelation. These techniques assist in uncovering hidden structures and trends, capturing both time and frequency information, and measuring the linear relationship between data points at varying lags. Techniques Fourier Transform Fourier transform is a mathematical technique that decomposes a time series signal into its frequency components. It's based on the principle that any signal can be broken down into a series of sinusoidal waves with varying amplitudes, frequencies, and phases. By capturing the periodic patterns in the data, the Fourier transform can help identify hidden structures and trends that might be useful for prediction. The Fourier Transform can be categorized into two types: the continuous Fourier Transform (CFT) for continuous signals and the discrete Fourier Transform (DFT) for discrete signals. Fast Fourier Transform (FFT) is an efficient algorithm for computing the DFT of a sequence. The power spectral density, obtained from the squared magnitudes of the Fourier transform, can be used as a feature in machine learning models to improve their performance, and here is a simple usage example for FFT: import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.fft import fft, ifft


time_series = np.random.random(200)

# Perform Fast Fourier Transform (FFT)
fft_values = fft(time_series)

# Get the magnitude and frequencies
fft_magnitude = np.abs(fft_values)
frequencies = np.fft.fftfreq(len(time_series))

# Plot the frequency spectrum
plt.plot(frequencies, fft_magnitude)
plt.xlabel('Frequency')
plt.ylabel('Magnitude')
plt.title('Frequency Spectrum')
plt.show()

# Filter out the low magnitude frequencies
threshold = 7
fft_values_filtered = fft_values.copy()
fft_values_filtered[fft_magnitude < threshold] = 0

# Perform the inverse Fast Fourier Transform (IFFT)
filtered_time_series = ifft(fft_values_filtered)

# Plot the original and filtered time series
plt.plot(time_series, label='Original')
plt.plot(filtered_time_series.real, label='Filtered')
plt.xlabel('Time')
plt.ylabel('Value')
plt.title('Time Series Forecasting')
plt.legend()
plt.show() Wavelet Transformation Wavelet transformation is a mathematical technique used to decompose a signal into different frequency components. It is particularly useful in time series analysis because it allows us to capture both time and frequency information in the data. Unlike a Fourier decomposition which always uses complex exponential (sine and cosine) basis functions, a wavelet decomposition uses a time-localized oscillatory function as the analyzing or mother wavelet. The most common wavelet transformation is the Continuous Wavelet Transform (CWT). CWT is defined as: The wavelet function can be chosen based on the type of data and the desired features. A popular choice is the Morlet wavelet, a product of a complex exponential and a Gaussian function, defined as: Scipy example to compute the CWT using the Morlet wavelet: from scipy import signal
import matplotlib.pyplot as plt

t = np.linspace(-1, 1, 200, endpoint=False)
sig = np.cos(2 * np.pi * 7 * t) + signal.gausspulse(t - 0.4, fc=2)
widths = np.arange(1, 31)

cwtmatr = signal.cwt(sig, signal.morlet2, widths)

real, imag = np.real(cwtmatr), np.imag(cwtmatr)

fig, axes = plt.subplots(ncols=2, figsize=(20, 5))
axes[0].imshow(
    real,
    extent=[-1, 1, 1, 31],
    cmap="PRGn",
    aspect="auto",
    vmax=abs(real).max(),
    vmin=-abs(real).max(),
)
axes[1].imshow(
    imag,
    extent=[-1, 1, 1, 31],
    cmap="PRGn",
    aspect="auto",
    vmax=abs(imag).max(),
    vmin=-abs(imag).max(),
) Derivatives Derivatives can be used to describe the rate of change in time series data. For example, the first derivative represents the velocity, while the second derivative represents acceleration. By incorporating these features, we can better capture the dynamics of the time series data. The first-order derivative represents the instantaneous rate of change of a variable with respect to time. It can help identify the direction and magnitude of the trend in the data. As it’s shown on the slide, it can be used as a marker for detecting changes in the stock or market behavior regimes. The second-order derivative represents the rate of change of the first-order derivative. It can help identify acceleration or deceleration in the trend and detect points of inflection. Also, here it needs to be mentioned, Seasonal derivatives and Cross-derivative: help to capture the seasonal variations in the data and pairwise variables influencing. To compute the derivatives, we can use the finite difference method: It’s the simplest method to compute the first derivative with and use as a feature: pandas time_series = pd.Series(data, index=dates)

# Calculate the first-order derivative
time_series_diff = time_series.diff().dropna()

# Combine the original time series and the first-order derivative
data = pd.concat([time_series.shift(1), time_series_diff], axis=1).dropna()
data.columns = ['y_t', 'first_derivative'] Here's an example of using the first-order derivative as an indicator for regime changes. When the derivative changes its sign, it signifies a change in the regime: Autocorrelation and Partial Autocorrelation Autocorrelation, also known as the autocovariance function or serial correlation, measures the linear relationship between the time series data points at different lags. Partial autocorrelation, also known as the partial autocovariance function, on the other hand, measures the correlation between two data points at different lags after removing the effect of other data points at smaller lags. Code example for autocorrelation and partial autocorrelation: from statsmodels.tsa.stattools 
import acf, pacf 
data = np.array([...])  
# Input time series data 
lags = 10 
autocorr = acf(data, nlags=lags) 
partial_autocorr = pacf(data, nlags=lags)

f, ax = plt.subplots(nrows=2, ncols=1, figsize=(width, 2*height))
plot_acf(data,lags=lag_acf, ax=ax[0])
plot_pacf(data,lags=lag_pacf, ax=ax[1], method='ols')
plt.tight_layout()
plt.show() Conclusion Advanced feature engineering techniques for time series data can significantly improve the performance of machine learning models. Fourier transform, wavelet transformation, derivatives, and autocorrelation each contribute unique insights into the underlying structure and trends of temporal data. By leveraging these techniques, analysts can create more accurate and efficient forecasting models, ultimately leading to better decision-making in various domains.

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