The purpose of this article is to provide an in-depth look at the methodologies I have followed when choosing tokens to perform dilligence on throughout my experience as a Venture Analyst at Decasonic. This will be helpful to develop a more in-depth rationale around best practices for vetting and researching liquid tokens, as well as allowing me to have direct examples of tokens I would personally invest in and why.

A two step process is proposed to perform the liquid token diligence, consisting of (1) Screening and (2) Token Memo Creation.

The rationale for choosing liquid tokens is presented below:

1. Filter the database into separate dataframes where each dataframe represents the portfolio for the most prominent institutional investors on the web3 space:

   1. Coinbase Ventures: 16 Unicorns

   2. Digital Currency Group: 15 Unicorns

   3. Paradigm: 12 Unicorns

2. Merge dataframes and eliminate the datapoints that have less than two of these firms invested in them

3. Filter by market cap for protocols < $1Bn

4. Choose the top two tokens based on Total Addresses holding the token

First we call the necessary libraries for this exercise, define the API Key to access the CoinMarketCap API and instantiate the API:

import pandas as pd
import numpy as np
from coinmarketcapapi import CoinMarketCapAPI, CoinMarketCapAPIError

YOUR_API_KEY = '29c276dd-e1ef-4c35-8c4c-a1894767dc75'
cmc = CoinMarketCapAPI(YOUR_API_KEY)
r = cmc.cryptocurrency_categories(start=1, limit=500)

We proceed to define the necessary functions to retrieve category ID data, retrieve coin data and compile coin data into pandas dataframes:

#Get ID by Category Name:
def get_id(string, df):
    cat_names = []
    cat_id = []
    for i in range(0,len(df.data)):
        cat_names.append(df.data[i]['name'])
        cat_id.append(df.data[i]['id'])

    catdf = pd.DataFrame()
    catdf['Name'] = cat_names
    catdf['ID'] = cat_id
    return catdf.loc[catdf['Name']== string]['ID']
#Get category data function
def get_data(string, r):
    return cmc.cryptocurrency_category(id=get_id(string, r), start=1, limit=1000)

#Dataframe creation function
def compiler(r):
    names = []
    symbols = []
    mkt_cap = []
    
    for i in range(0,len(r.data['coins'])):
        names.append(r.data['coins'][i]['name'])
        symbols.append(r.data['coins'][i]['symbol'])
        mkt_cap.append(r.data['coins'][i]['quote']['USD']['fully_diluted_market_cap'])
    df = pd.DataFrame()
    df['Name'] = names
    df['Symbol'] = symbols
    df['FDM Cap'] = mkt_cap
    return df

We now look at the top three Institutional Investors in the web3 space as classified by the number of unicorn investments. According to The Block Research, we have:

1. Coinbase Ventures: 16 Unicorns

2. Digital Currency Group: 15 Unicorns

3. Paradigm: 12 Unicorns

Although the fact that these firms have a position on a particular token does not guarantee success, these firms have comprehensive dilligence procedures that have led to their leading performance in institutional web3 investment. We are then looking for tokens that have at least two of these firms as investors. This rule of thumb allows us to quickly vet thousands of tokens to a couple dozen.

coinbase_df = compiler(get_data('Coinbase Ventures Portfolio', r)) 
dcg_df = compiler(get_data('DCG Portfolio', r))
paradigm_df = compiler(get_data('Paradigm Portfolio', r)) 

#We concatenate the frames
frames = [coinbase_df, dcg_df, paradigm_df]
result = pd.concat(frames,ignore_index=True)
#We remove duplicated results
d_result = result[result.duplicated(subset=['Name'], keep=False)]
u_result = d_result[d_result.duplicated(subset=['Name'])]
U = u_result.drop_duplicates(subset=['Name', 'Symbol', 'FDM Cap'], keep='first')
result = U.drop(index=[62, 63])
#Filter the top protocols with Fully Discounted Market Cap below $1Bn
mer = pd.Series(result['FDM Cap'])
final = result.where(mer<1000000000).dropna().sort_values(by=['FDM Cap'], ascending=False)

Finally, these tokens are filtered in a descending order with respect to Total Addresses, we look at the most popular tokens:

final['Total Addresses'] = [104183, 4530, 87231, 194713, 8574]
final.sort_values(by=['Total Addresses'], ascending=False)

Based on the number of total addresses registered for each protocol, we can then make the official decision to develop writeups for the Compound ($COMP) and The Graph ($GRT) Protocols.

• Protocol Description / Executive Summary

• Product Market Fit

  1. Value Proposition: What is the utility of this protocol

  2. Target User

  3. Product Design

  4. Traction to Date

     1. Daily Active users

     2. User growth rates

• Market

  1. Projected Market Size (2030)

  2. Trends driving growth

• Token Design

  1. Total Supply and Circulating Supply

  2. Burn Mechanism

  3. Mint Mechanism

  4. Staking Mechanism

  5. Gini Index

• Governance

  1. Advisory Board

  2. C-suite

  3. Dev Team Efficiency: Number of Commits / Number of Contributors

• Investment Opportunity

  1. Proposed Investment

  2. Pros

I hope this framework is a helpful tool for nascent retail crypto investors looking to (1) Find good tokens to invest in and (2) analyze those tokens in a comprehensive way. In future publications, I will proceed to evaluate The Graph and Compound, and create token memos following the framework presented above!

Thank you for reading,

A

1. Subscribe for more

2. Follow me on Twitter

3. Connect with me on Linkedin

4. Reach out with any questions/feedback!

5. Read the disclaimer below

The Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained on this site constitutes a solicitation, recommendation, endorsement, or offer by me or any third party service provider to buy or sell any securities or other financial instruments in this or in in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction.

The purpose of this article is to provide an in-depth look at the methodologies I have followed when choosing tokens to perform dilligence on throughout my experience as a Venture Analyst at Decasonic. This will be helpful to develop a more in-depth rationale around best practices for vetting and researching liquid tokens, as well as allowing me to have direct examples of tokens I would personally invest in and why.

A two step process is proposed to perform the liquid token diligence, consisting of (1) Screening and (2) Token Memo Creation.

The rationale for choosing liquid tokens is presented below:

1. Filter the database into separate dataframes where each dataframe represents the portfolio for the most prominent institutional investors on the web3 space:

   1. Coinbase Ventures: 16 Unicorns

   2. Digital Currency Group: 15 Unicorns

   3. Paradigm: 12 Unicorns

2. Merge dataframes and eliminate the datapoints that have less than two of these firms invested in them

3. Filter by market cap for protocols < $1Bn

4. Choose the top two tokens based on Total Addresses holding the token

First we call the necessary libraries for this exercise, define the API Key to access the CoinMarketCap API and instantiate the API:

import pandas as pd
import numpy as np
from coinmarketcapapi import CoinMarketCapAPI, CoinMarketCapAPIError

YOUR_API_KEY = '29c276dd-e1ef-4c35-8c4c-a1894767dc75'
cmc = CoinMarketCapAPI(YOUR_API_KEY)
r = cmc.cryptocurrency_categories(start=1, limit=500)

We proceed to define the necessary functions to retrieve category ID data, retrieve coin data and compile coin data into pandas dataframes:

#Get ID by Category Name:
def get_id(string, df):
    cat_names = []
    cat_id = []
    for i in range(0,len(df.data)):
        cat_names.append(df.data[i]['name'])
        cat_id.append(df.data[i]['id'])

    catdf = pd.DataFrame()
    catdf['Name'] = cat_names
    catdf['ID'] = cat_id
    return catdf.loc[catdf['Name']== string]['ID']
#Get category data function
def get_data(string, r):
    return cmc.cryptocurrency_category(id=get_id(string, r), start=1, limit=1000)

#Dataframe creation function
def compiler(r):
    names = []
    symbols = []
    mkt_cap = []
    
    for i in range(0,len(r.data['coins'])):
        names.append(r.data['coins'][i]['name'])
        symbols.append(r.data['coins'][i]['symbol'])
        mkt_cap.append(r.data['coins'][i]['quote']['USD']['fully_diluted_market_cap'])
    df = pd.DataFrame()
    df['Name'] = names
    df['Symbol'] = symbols
    df['FDM Cap'] = mkt_cap
    return df

We now look at the top three Institutional Investors in the web3 space as classified by the number of unicorn investments. According to The Block Research, we have:

1. Coinbase Ventures: 16 Unicorns

2. Digital Currency Group: 15 Unicorns

3. Paradigm: 12 Unicorns

Although the fact that these firms have a position on a particular token does not guarantee success, these firms have comprehensive dilligence procedures that have led to their leading performance in institutional web3 investment. We are then looking for tokens that have at least two of these firms as investors. This rule of thumb allows us to quickly vet thousands of tokens to a couple dozen.

coinbase_df = compiler(get_data('Coinbase Ventures Portfolio', r)) 
dcg_df = compiler(get_data('DCG Portfolio', r))
paradigm_df = compiler(get_data('Paradigm Portfolio', r)) 

#We concatenate the frames
frames = [coinbase_df, dcg_df, paradigm_df]
result = pd.concat(frames,ignore_index=True)
#We remove duplicated results
d_result = result[result.duplicated(subset=['Name'], keep=False)]
u_result = d_result[d_result.duplicated(subset=['Name'])]
U = u_result.drop_duplicates(subset=['Name', 'Symbol', 'FDM Cap'], keep='first')
result = U.drop(index=[62, 63])
#Filter the top protocols with Fully Discounted Market Cap below $1Bn
mer = pd.Series(result['FDM Cap'])
final = result.where(mer<1000000000).dropna().sort_values(by=['FDM Cap'], ascending=False)

Finally, these tokens are filtered in a descending order with respect to Total Addresses, we look at the most popular tokens:

final['Total Addresses'] = [104183, 4530, 87231, 194713, 8574]
final.sort_values(by=['Total Addresses'], ascending=False)

Based on the number of total addresses registered for each protocol, we can then make the official decision to develop writeups for the Compound ($COMP) and The Graph ($GRT) Protocols.

• Protocol Description / Executive Summary

• Product Market Fit

  1. Value Proposition: What is the utility of this protocol

  2. Target User

  3. Product Design

  4. Traction to Date

     1. Daily Active users

     2. User growth rates

• Market

  1. Projected Market Size (2030)

  2. Trends driving growth

• Token Design

  1. Total Supply and Circulating Supply

  2. Burn Mechanism

  3. Mint Mechanism

  4. Staking Mechanism

  5. Gini Index

• Governance

  1. Advisory Board

  2. C-suite

  3. Dev Team Efficiency: Number of Commits / Number of Contributors

• Investment Opportunity

  1. Proposed Investment

  2. Pros

I hope this framework is a helpful tool for nascent retail crypto investors looking to (1) Find good tokens to invest in and (2) analyze those tokens in a comprehensive way. In future publications, I will proceed to evaluate The Graph and Compound, and create token memos following the framework presented above!

Thank you for reading,

A

1. Subscribe for more

2. Follow me on Twitter

3. Connect with me on Linkedin

4. Reach out with any questions/feedback!

5. Read the disclaimer below

The Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained on this site constitutes a solicitation, recommendation, endorsement, or offer by me or any third party service provider to buy or sell any securities or other financial instruments in this or in in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction.