COMPOUND FINANCE: The Yield Farmers Edition

COMPOUND FINANCE: The Yield Farmers Edition

By CPix | Everything Crypto | 22 Jun 2020

Mike McDonald recently wrote in a Medium article, “Liquidity Bootstrapping Pools (LBPs): a smart pool template that allows teams to release a project token while at the same time building deep liquidity.”

While other Ethereum protocols like Uniswap DEX, Synthetix Network, and dydx have previously proven the LBP concept, Compound has executed on the LBP model flawlessly. I believe this to be true because they over-delivered on a protocol with clear product-market fit, didn’t over-hype the governance token before launch, and aligned incentives for several groups of market participants. Compound’s native governance token (COMP) increased in value from an initial listing price of $16 to now over $300 on various exchanges, also receiving support from large exchanges like Coinbase and FTX. The token appreciation is astounding, probably not even sustainable, but as we enter the next cycle of blockchain adoption, the price of a token should reflect its actual utility within an application/protocol. In this case, the Compound protocol has incentivized a 10x increase in assets earning interest from ~$100m a week ago to now almost $900m across 9 markets. The largest markets include ETH, USDT, USDC, BAT. Total assets locked in the protocol could be seen as a good indicator on the success and health of the network but doesn’t necessarily tell the whole story. First off, when lenders are offered 10%-25% APR it isn’t tough to imagine an increase in lender participation. Second, the protocol has created unique opportunities for almost anyone to lend out a digital asset, use that asset as collateral to borrow against, then earn COMP rewards from both lending and borrowing simultaneously. Lastly, as more participants use Compound and other lending/borrowing platforms there are vast amounts of liquidity for those who really understand the market to use to their advantage. I expect this to increase the amount of volatility within the crypto markets, especially Ethereum and ERC-20 tokens, presenting great opportunities for those who develop a well-constructed strategy and execute.

Market Update (Monday 8:30 AM EST)

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

Bitcoin- $9,483 (+.005%)

Ether- $238 (+1%)

Gold- $1,754 (+1%)

DJI Average- 25,865.08 (-2%)

NYSE Composite Index- 11,963.30 (-2%)

NASDAQ Composite Index - 10,042.10 (+.1%)

S&P500 Index- 3,094.42 (-.1%)

New Developments

  1. Compound Finance launches COMP token on UniswapV2, The Block

    • Compound is a decentralized lending/borrowing app built on Ethereum.

    • Compound did not raise money through an ICO, rather built a user base of over 25,000 unique addresses and ~$400,000,000 earning interest in the app.

    • COMP has a Total Supply of 10,000,000: 2,880 COMP are given everyday to users who lend and borrow in the platform. This will last for 4+ years.

    • is the main source of liquidity for buyers which uses a bonding curve model to price and attract liquidity for assets.

    • Distribution of the COMP token has given full control of the decision-making process to community of COMP holders (hence governance token).

    • Compound is now one of the most valuable DeFi applications/protocols along with MakerDAO.

    • The move away from ICOs that will fund a project to creating a project then using token-economics to reward both users and team members.

    • COMP is one of the first projects to leverage Uniswap and DeFi, this is only the beginning. I expect Uniswap to create a governance token as well.

    Compound Tutorial (Mobile Using Argent App):
    DeFi_Dad.jpgDeFi Dad - FIND ME ON GITCOIN GRANTS ROUND 6! @DeFi_Dad If you’re an @argentHQ wallet user, you can lend on @compoundfinance and pay zero txn fees, which qualifies you to earn COMP. 📺See how I did some lending in less than 30 secs. If you’d like to learn more about how COMP distribution works, watch my video on @BanklessHQ YouTube. nobhnl2dvcz3e3euudfu


    🏦If you are subscribed to @BanklessHQ Market Mondays, you just received a timely tutorial on How to Earn $COMP tokens with @compoundfinance by lending or borrowing! 📺Below, I also cover how to delegate your COMP voting rights. Lmk if you have questions!

    June 17th 2020

    10 Retweets38 Likes
  2. UniswapV2 surpasses UniswapV1 in terms of liquidity, Messari

  3. Bitcoin mining manufacturer company eBang to IPO on Nasdaq, Messari

  4. ConsenSys spins up Staking Service for Ethereum 2.0, CoinDesk

  5. Coinbase open-sources Rosetta, a digital asset listing framework, CoinDesk

  6. KE International awards US $6 Billion construction contract for Akon City

    akon ?size=l KE International awarded US $6 Billion construction contract for Akon City, 🇸🇳 JUNE 15, 2020

Industry Insights

Blockchain Activity

OpenSea Marketplace:

OpenSea is a blockchain marketplace matching buyers and sellers of tokenized collectables such as Crypto Kitties or Gods Unchained playing cards. I kind of like to think of OpenSea as a digital version of a pawn shop or Ebay. The only difference is they never take custody of your items and take much smaller cuts of item sales. Another name for blockchain collectables are NFTs (Non-Fungible Tokens). NFTs are Ethereum assets built using the ERC-721 standard rather than the ERC-20 standard used to create tokens for ICOs back in 2017. The difference between the two is that each NFT is different from the next, whereas an ERC-20 token like DAI is exactly the same as every other DAI. Thus NFTs are perfect for in-game items and collectables because they can be provably unique and rare.

Last week I auctioned off several NFTs I have been holding for a couple years just to see if any of them would sell. I auctioned a Gods Unchained playing card, two landscape portraits, ENS domain (dogwizard.eth), and a couple War Riders vehicles. I’ll revisit this in a few months to breakdown any items sold or bids entered.

Python Activity

Deep Learning using LTSM RNN:

# import libraries and random seed for reproducibility

import numpy as np
import pandas as pd
import hvplot.pandas

from sklearn.preprocessing import MinMaxScaler

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

from numpy.random import seed
from tensorflow import random

# read in dataset .csv file (stored on my machine)

df = pd.read_csv('btc_sentiment.csv', index_col="date", infer_datetime_format=True, parse_dates=True)

df2 = pd.read_csv('btc_historic.csv', index_col="Date", infer_datetime_format=True, parse_dates=True)['Close']
df2 = df2.sort_index()

df = df.join(df2, how="inner")

# chunk data into rolling window and return a numpy array

def window_data(df, window, feature_col_number, target_col_number):
    X = []
    y = []
    for i in range(len(df) - window - 1):
        features = df.iloc[i:(i + window), feature_col_number]
        target = df.iloc[(i + window), target_col_number]
    return np.array(X), np.array(y).reshape(-1, 1)

# set window size (1 day) and set feature and target column, split 70% of data to train

window_size = 1
feature_column = 1
target_column = 1
X, y = window_data(df, window_size, feature_column, target_column)

split = int(0.7 * len(X))

X_train = X[: split]
X_test = X[split:]
y_train = y[: split]
y_test = y[split:]

# use minmaxscaler to scale feature sets

scaler = MinMaxScaler()
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
y_train = scaler.transform(y_train)
y_test = scaler.transform(y_test)

# reshape the features for the model

X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))

# build sequential model with 30 units, dropout fraction to reduce overfit and 3 layers

model = Sequential()
number_units = 30
dropout_fraction = 0.2

    input_shape=(X_train.shape[1], 1))

model.add(LSTM(units=number_units, return_sequences=True))



# compile the model

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

# train the model
    batch_size = 1,

# make predictions

predicted = model.predict(X_test) 

# reverse scale the dataset

predicted_prices = scaler.inverse_transform(predicted)
real_prices = scaler.inverse_transform(y_test.reshape(-1, 1))

# create dataframe with real and predicted prices

stocks = pd.DataFrame({
    "Real": real_prices.ravel(),
    "Predicted": predicted_prices.ravel()

# plot the data

stocks.head(100).hvplot(title = "Closing Price Data")


Earn Opportunity

Gitcoin is “A community for developers to collaborate and monetize their skills while working on Open Source projects through bounties.” Founded in 2017, Gitcoin continues to push development of open source code and attract developer talent to the blockchain/crypto space. I became familiar with Gitcoin by completing one of their famous Gitcoin Quests. Quests are a retro style quiz/game to test/teach anyone on various topics ranging from Ethereum to IPFS. More recently, I have followed the progress of Gitcoin Grants which uses quadratic funding bootstrap projects and individuals working on various software/marketing solutions. Quadratic Funding compounds the total value of donations given to a cause by matching unique donations from a pooled amount of capital. Anyone can apply to receive a Gitcoin Grant and with the help of social media can get the word out to just about anyone.


I'm a crypto enthusiast who fell down the rabbit hole during my senior year of college and now dedicate my time spreading the word through dApp tutorials, explainer videos, and blog posts. My goal is to speed up mass adoption!

Everything Crypto
Everything Crypto

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