The Flippening

The Flippening

By CPix | Everything Crypto | 7 Jul 2020


In 2017 there was a lot of hype around an event known as “The Flippening”. All this event referred to at the time was whether Ethereum’s Market Cap (Ether Price * Circulating Supply) would become larger than Bitcoin’s Market Cap (Bitcoin Price * Circulating Supply). Ethereum came fairly close a time or two but never managed to accomplish the feat. An important nuance to point out is the difference in circulating supply and inflation rate of both Ethereum and Bitcoin. Ethereum now has a circulating supply of ~111,000,000 Ether and an inflation rate of 4.49% whereas Bitcoin has a circulating supply of ~18,400,000 Bitcoin and an inflation rate of 1.8%. So for Ethereum to surpass Bitcoin in the next couple years it would not require a higher price per coin but rather an Ether price of about 1/6 of Bitcoin’s price. For example, if Bitcoin is $10,000 per coin, Ether would need to be $1,667 per coin to achieve a higher Market Cap. Below is a graph representing the Market Caps for both Bitcoin and Ethereum.

 

 

The 2017 comparison was entirely based on price per coin, which in my opinion is not an indicator but rather the result. For the Ethereum blockchain to be fairly valued more than the Bitcoin blockchain it needs to provide more value. This comes down to several metrics such as daily transaction fees, transaction volume, node counts, exchange trade volume, block capacity, google search history (interest), and many others. I recently came across a great website to track some of these metrics called BlockchainCenter. There you can track how Ethereum stacks up against Bitcoin regarding important metrics. These are the stats that will determine whether or not the Market Cap of Ethereum should be valued more than the Market Cap of Bitcoin. I’m not proposing this will be a perfect indicator but this could be a powerful tool to use when developing a value proposition for both networks.

The Flippening isn’t all that important in and of itself but rather a trend to keep an eye on as Bitcoin and Ethereum evolve over the coming years. While numbers and on-chain statistics can be gamed (spam transactions, number of nodes hosted by centralized solutions, overvalued token prices) a clear use case for public blockchains is developing rapidly and it’s hard to see that trend stopping any time soon.

Market Update (Monday 8:30 AM EST)

Percent Change (Rounded) Based on Last Monday Open (8:30 AM EST)

Bitcoin- $9,253 (+2%)

Ether- $235 (+6%)

Gold- $1,785 (+1%)

DJI Average- 25,996.08 (+3%)

NYSE Composite Index- 12,181.29 (+4%)

NASDAQ Composite Index- 10,360.40 (+6%)

S&P500 Index- 3,155.29 (+6%)

New Developments

  1. Dfinity creates a TikTok-style app that doesn’t snoop on user data, Decrypt

  2. Blockchain fantasy sports game Sorare reports $350,000 in June sales, Messari

  3. SBI takes $30 million stake in crypto trader B2C2, The Block

  4. Ethereum scaling solution SKALE Network launches Mainnet, Messari

  5. USDC Market Cap exceeds $1 billion for first time, Centre Blog

Industry Insights

Blockchain Activity

Mintbase (Create your own NFT store):

Mintbase allows anyone to create a NFT (non-fungible token) store. NFTs are a digital representation of unique value, no NFT is like another hence non-fungible. On Ethereum NFTs are made possible because of the ERC-721 standard which differs from the traditional Ethereum token ERC-20 (fungible) token standard.

Python Activity

Deep Learning and Neural Nets:

# import libraries and read in data

from pathlib import Path

import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

X = pd.read_csv(Path("../Resources/features.csv"), header=None)
X.head()

y = pd.read_csv(Path("../Resources/target.csv"))
y.head()

# organize data into train/test splits then scale the data

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

X_scaler = StandardScaler()
X_scaler.fit(X_train)

X_train_scaled = X_scaler.transform(X_train)
X_test_scaled = X_scaler.transform(X_test)

# encode data labels

enc = OneHotEncoder()
enc.fit(y_train)

encoded_y_train = enc.transform(y_train).toarray()
encoded_y_test = enc.transform(y_test).toarray()
encoded_y_train[0]

# build deep neural network

# Create a sequential model

model = Sequential()

# Add the first layer where the input dimensions are the 561 columns of the training data

model.add(Dense(100, activation='relu', input_dim=X_train_scaled.shape[1]))

# The output layer has 12 columns that are one-hot encoded

y_train.activity.value_counts()
number_outputs = 12

# Add output layer using 12 output nodes

model.add(Dense(number_outputs, activation="softmax"))

# Compile the model using categorical_crossentropy for the loss function, the adam optimizer and add accuracy to the training metrics

model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])

# Print the model summary

model.summary()

# fit the model

model.fit(
    X_train_scaled,
    encoded_y_train,
    epochs=30,
    shuffle=True,
    verbose=2
)

# evaluate the model

model_loss, model_accuracy = model.evaluate(X_test_scaled, encoded_y_test, verbose=2)
print(f"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}")

# Make predictions

predicted = model.predict(X_test_scaled)
predicted = enc.inverse_transform(predicted).flatten().tolist()
results = pd.DataFrame({
    "Actual": y_test.activity.values,
    "Predicted": predicted
})
results.head(10)

# Print the Classification Report

from sklearn.metrics import classification_report
print(classification_report(results.Actual, results.Predicted))

Earn Opportunity

The biggest misconception about cryptocurrency is that you need to buy some. This couldn’t be further from the truth. I can say first hand earning cryptocurrency is far more rewarding and doesn’t require a cash investment. There are so many places you can go to earn cryptocurrency. As a writer you can head over to Publish0x or SteamIt, if you know how to write code the Gitcoin community is great, and if you’re good at technical and fundamental analysis there are plenty of companies you can reach through Twitter. Whenever I talk to people about cryptocurrency for the first time I try to help them earn some before buying any, in my opinion it creates a better sense of what crypto can be used for. Bottom line, whatever your talent is, find someone who will pay you in cryptocurrency to do it.


CPix
CPix

Goal is simple. Speed up mass adoption!


Everything Crypto
Everything Crypto

In this blog I cover major public blockchain developments, cryptocurrency shifting from speculation to utility, and personal opinions as to how the space will develop going forward.

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