Dashboard Objectives

The dashboard will look at the new lockups being created on Velodrome Finance.
It will attempt to derive insights into the lock-up periods preferred by the users.
It will also delve Pool Voting stats and behavours.

Author:

Twitter: @lelaughingman
Flipside Bounty Hunter: TheLaughingMan
Discord: TheLaughingMan#3062

QUICK! Lock it up!!! Lock it up!!

Taking a glance at Chart 1 to the right, we can spot 4 distinguishable lines across the scatter plot.
These lines are roughly at the following levels:
- 0-1 Weeks
- 4-5 Weeks = ~ 1 Month
- 48-51 Weeks = ~1 year
- 200 Weeks = ~ 4 years

These levels seem to be of interest…🧐

Proceeding further with Charts 2 & 3….

The figures on these charts also corroborate the keen interest around the aforementioned levels
1 year and lesser periods have mediocre interest…
The higher extremes (3 and more years) periods have the highest participation.
The middle ground periods (1-3 years) have the least interest…

Now that we know what the spans are, let’s hop on to the next logical question! …

Y deez specific duration spans though? 🤔🤔

The primary factor for choosing these spans is likely the basic crypto-enjoyer instinct: Numba Go Up

The longer the span chosen, the better the lockup reward rate as follows:

1 VELO locked for 4 years = 1.00 veVELO

1 VELO locked for 3 years = 0.75 veVELO

1 VELO locked for 2 years = 0.50 veVELO

1 VELO locked for 1 years = 0.25 veVELO

BUT WAIT! THERE’S MORE! ↗️↗️↗️

Human Psychology / Velodrome User Interface

The interface kind of makes it appear that the only valid/best choices for a user are the four radio-button options readily provided! That and the text below the buttons explaining the rates further would tell the User’s mind to concentrate/decide among only these 4 options 🤯
However, as the image (the bottom-right of this panel) shows if you click either the calendar icon, or manually type in the values, you can pick a ‘custom’ lockup period!
The custom inputs can also be seen on-chain 🕵️

veVELO Pool Voting

We take a glance at the Voting favorites/preferences the platform’s most users have opted to vote for so far.
The analysis can be viewed in Streamlit - here

Analysis Issues/Difficulty

There was a roadblock limiting the usual analysis via velocity/snowflake.

The voting data:

pool_addresses/token_ids

voting_weights/power

was present in a dynamic hex string as follows:

# MethodID: 0x7ac09bf7
# [0]:  0000000000000000000000000000000000000000000000000000000000001077
# [1]:  0000000000000000000000000000000000000000000000000000000000000060
# [2]:  00000000000000000000000000000000000000000000000000000000000000a0
# [3]:  0000000000000000000000000000000000000000000000000000000000000001  -&gt; pool count N
# [4]:  00000000000000000000000079c912fef520be002c2b6e57ec4324e260f38e50  -&gt; N pools
# [5]:  0000000000000000000000000000000000000000000000000000000000000001  -&gt; weight count N
# [6]:  0000000000000000000000000000000000000000000000000000000000002710  -&gt; N weights

Given the number of pools and their corresponding weights could vary from transaction to transaction, stronger programmability was needed! Hence, python/streamlit to the rescue!

Basic Algorithm:

Pulling all vote transactions with a simple SQL query:

    SELECT 
        block_timestamp,
        tx_hash,
        from_address,
        input_data
        
    from optimism.core.fact_transactions
    WHERE
        input_data LIKE '0x7ac09bf7%'
        AND to_address = '0x09236cff45047dbee6b921e00704bed6d6b8cf7e'

Once we have the unrefined input_data, Python takes over

output = []
    for row in data['results']:
        # remove
        long_hex = row[3].split('0x7ac09bf7')[1]
        
        # break up string into 64 length groups
        hex_rows = wrap(long_hex, 64)

        token_id = int(hex_rows[0], 16)
        pools = []
        weights = []

        #get each address from row 5 onward based on count, fix the address string
        for i in range(0, int(hex_rows[3], 16)):
            pools.append('0x'+ hex_rows[4 + i][24:])
        #get each weight 
        for i in range(0, int(hex_rows[3 + len(pools) + 1], 16)):
            weights.append(int(hex_rows[3 + len(pools) + 2 +i], 16))

Lastly, let’s look at the Top lockers in terms of most tokens locked

flipside