mechaFIL 1.4

Mechanistic model for the Filecoin Economy

Filecoin Mechanistic Twin

Mechanistic model for the Filecoin Economy. You can use this model to forecast all the components underlying Filecoin's circulating supply (i.e., minting, vesting, locking and burning), based on a set of parameters that encode storage provider behavior. The model uses the following assumptions:

• Forecasting is done daily. This means that each forecasting step corresponds to a day and the forecasted metrics correspond to the value we expect to see at the end of that day.
• The model uses the current sector states (i.e. known schedule expirations) and it estimates future onboardings and future renewals.
• The daily power onboarded is provided as a tunable parameter.
• The sector renewal rates are provided as a tunable parameter.
• Sector duration is a constant provided as a tunable parameter.
• Filecoin Plus sectors have the same renewal rates and sector durations as other sectors.
• The model uses the current pledge metrics (i.e. known scheduled expiration in pledge) to measure known active sectors, and it estimates pledge metrics coming from future onboardings and renewals using the same assumptions as the ones used to model storage power.
• The model ignores storage deal collateral when computing the total locked FIL.

To learn more about how the model is designed, check the specifications linked at the end of this readme.

Prerequisites

numpy>=1.23.1
pandas>=1.4.3
requests>=2.28.1
matplotlib>=3.5.2
seaborn>=0.11.2

Installing

This package is available on PyPI and thus can be directly installed with pip:

pip install mechaFIL

Alternatively, this package can be installed from source by cloning this repository and installing it manually with the command:

python setup.py install

Usage

There is a high-level function that can be used to run a forecast/simulation of circulating supply and its components. First you need to import the relevant packages:

import mechafil
import datetime

Now, you need to define some parameters:

# Starting date for the simulation
start_date = datetime.date(2021, 3, 15)
# Current date
current_date = datetime.date(2022, 11, 1) 
# Number of days to run the simulation (after current_rate)
forecast_length = 365
# Renewal rate of all sectors (percentage of raw-byte that will renew)
renewal_rate= 0.6
# Raw-byte power (in PiB) that is onboarded every day
rb_onboard_power = 12.0
# Percentage of raw-byte power onboarded that contains FIL+ deals
fil_plus_rate = 0.098
# Sector duration of newly onboarding sectors
duration = 360
# Method of computing QAP
qap_method = 'tunable'

A few important notes regarding the inputs:

• Due to data availability, the start date cannot be earlier than 2021-03-15.
• The current date needs to be at least 2 days after the start date.
• The parameters renewal_rate, rb_onboard_power and fil_plus_rate can be a single number or a vector of numbers. If they are a number, the model assumes that number as a constant throughout the simulation. If a vector is provided, then the vector needs to have the same size as the simulation length. The vector option gives the user the most flexibility since they can apply different constants throughout the simulation.
• The optional parameter qap_method determines how network QAP will be computed. Two approaches are provided in the library, which we term basic and tunable. Setting this value to tunable will enable QAP to be computed with tunable sector duration multipliers, but note that this is an approximation. The other method is basic which does not support sector duration multipliers. See here for more details.

Now, you can call the simulation function and collect the data in a DataFrame:

cil_df = mechafil.run_simple_sim(start_date,
    current_date,
    forecast_length,
    renewal_rate,
    rb_onboard_power,
    fil_plus_rate,
    duration,
    qap_method='tunable')

cil_df.head()

You can also run part of the simulation separately. To see more examples, check the available notebooks.

References

• Power model spec
• Locking model spec