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TokenSPICE: EVM Agent-Based Token Simulator

Pytest Unit Testing MyPy Static Type Checking

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TokenSPICE simulates tokenized ecosystems via an agent-based approach, with EVM in-the-loop.

It can help in Token Engineering flows, to design, tune, and verify tokenized ecosystems. It's young but promising. We welcome you to contribute! 👋

  • TokenSPICE simulates by simply running a loop. At each iteration, each agent in the netlist takes a step. That's it! Simple is good.
  • A netlist wires up a collection of agents to interact in a given way. Each agent is a class. It has an Ethereum wallet, and does work to earn money. Agents may be written in pure Python, or with an EVM-based backend.
  • One models a system by writing a netlist and tracking metrics (KPIs). One can write their own netlists and agents to simulate whatever they like. The netlists directory has examples.

Contents

🏗 Initial Setup

Prerequisites

  • Linux/MacOS
  • Python 3.8.5+
  • solc 0.8.0+ [Instructions]
  • ganache. To install: npm install ganache-cli --global
  • nvm 16.13.2, not nvm 17. To install: nvm install 16.13.2; nvm use 16.13.2. [Details]

Install TokenSPICE

Open a new terminal and:

#clone repo
git clone https://github.com/tokenspice/tokenspice
cd tokenspice

#create a virtual environment
python3 -m venv venv

#activate env
source venv/bin/activate

#install dependencies
pip install -r requirements.txt

#install brownie packages (you can ignore FileExistsErrors)
./brownie-install.sh

TokenSPICE CLI

tsp is the command-line interface for TokenSPICE. From the same terminal:

#add pwd to bash path
export PATH=$PATH:.

#see tsp help
tsp

Compile the contracts

NOTE: if you have a directory named contracts from before, which is side-by-side with your tokenspice directory, you'll get issues. To avoid this, rename or move that contracts directory.

From the same terminal:

#install 3rd party libs, then call "brownie compile" in sol057/ and sol080/
tsp compile

TokenSPICE sees smart contracts as classes. How:

  • When it starts, it calls brownie.project.load('./sol057', name="MyProject") to load the ABIs in ./sol057/build/. Similar for sol080.
  • That's enough info to treat each contract in sol057/contracts/ as a class. Then, call deploy() on it to create a new object.

🏄 Running, Debugging

Testing

From terminal:

#run single test. It uses brownie, which auto-starts Ganache local blockchain node.
pytest sol057/contracts/simpletoken/test/test_Simpletoken.py::test_transfer

#run all of a directory's tests
pytest sol057/contracts/simpletoken/test

#run all unit tests
pytest

#run static type-checking. By default, uses config mypy.ini. Note: pytest does dynamic type-checking.
mypy ./

#run linting on code style
pylint *

#auto-fix some pylint complaints
black ./

Go here for details on linting / style.

Simulating with TokenSPICE

From terminal:

#run simulation, sending results to 'outdir_csv' (clear dir first, to be sure)
rm -rf outdir_csv; tsp run netlists/scheduler/netlist.py outdir_csv

You'll see an output like:

Arguments: NETLIST=netlists/...
Launching 'ganache-cli --accounts 10 --hardfork ...
mnemonic: 'sausage bunker giant drum ...
INFO:master:Begin.
INFO:master:SimStrategy={OCEAN_funded=5.0, duration_seconds=157680000, ...}
INFO:master:Tick=0 (0.0 h, 0.0 d, 0.0 mo, 0.0 y); timestamp=1642844072; OCEAN_vested=0, ...
INFO:master:Tick=3 (2160.0 h, 90.0 d, 3.0 mo, 0.2 y); timestamp=1650620073; OCEAN_vested=0.0, ...
INFO:master:Tick=6 (4320.0 h, 180.0 d, 6.0 mo, 0.5 y); timestamp=1658396073; OCEAN_vested=0.0, ...
INFO:master:Tick=9 (6480.0 h, 270.0 d, 9.0 mo, 0.7 y); timestamp=1666172074; OCEAN_vested=0.0, ...
INFO:master:Tick=12 (8640.0 h, 360.0 d, 12.0 mo, 1.0 y); timestamp=1673948074; OCEAN_vested=0.0, ...
INFO:master:Tick=15 (10800.0 h, 450.0 d, 15.0 mo, 1.2 y); timestamp=1681724074; OCEAN_vested=0.232876 ...

Now, let's view the results visually. In the same terminal:

#create output plots in 'outdir_png' (clear dir first, to be sure)
rm -rf outdir_png; tsp plot netlists/scheduler/netlist.py outdir_csv outdir_png

#view plots
eog outdir_png

To see the blockchain txs apart from the other logs: open a new terminal and:

#activate env't
cd tokenspice
source venv/bin/activate

#run ganache
export PATH=$PATH:.
tsp ganache

Now, from your original terminal:

#run the sim. It will auto-connect to ganache
rm -rf outdir_csv; tsp run netlists/scheduler/netlist.py outdir_csv

For longer runs (eg wsloop), we can log to a file while watching the console in real-time:

#run the sim in the background, logging to out.txt
rm -rf outdir_csv; tsp run netlists/wsloop/netlist.py outdir_csv > out.txt 2>&1 &

#monitor in real-time
tail -f out.txt

To kill a sim in the background:

#find the background process
ps ax |grep "tsp run"

#example result:
#223429 pts/4    Rl     0:02 python ./tsp run netlists/wsloop/netlist.py outdir_csv

#to kill it:
kill 223429

Debugging from Brownie Console

Brownie console is a Python console, with some extra Brownie goodness, so that we can interactively play with Solidity contracts as Python classes, and deployed Solidity contracts as Python objects.

From terminal:

#brownie needs a directory with ./contracts/. Go to one.
cd sol057/

#start console
brownie console

In brownie console:

>>> st = Simpletoken.deploy("DT1", "Simpletoken 1", 18, Wei('100 ether'), {'from': accounts[0]})
Transaction sent: 0x9d20d3239d5c8b8a029f037fe573c343efd9361efd4d99307e0f5be7499367ab
  Gas price: 0.0 gwei   Gas limit: 6721975
  Simpletoken.constructor confirmed - Block: 1   Gas used: 601010 (8.94%)
  Simpletoken deployed at: 0x3194cBDC3dbcd3E11a07892e7bA5c3394048Cc87

>>> st.symbol()
'DT1'

>>> st.balanceOf(accounts[0])/1e18

>>> dir(st)
[abi, address, allowance, approve, balance, balanceOf, bytecode, decimals, decode_input, get_method, get_method_object, info, name, selectors, signatures, symbol, topics, totalSupply, transfer, transferFrom, tx]

🦑 Agents and Netlists

Agents Basics

Agents are defined at agents/. Agents are in a separate directory than netlists, to facilitate reuse across many netlists.

All agents are written in Python. Some may include EVM behavior (more on this later).

Each Agent has an AgentWallet, which holds a Web3Wallet. The Web3Wallet holds a private key and creates transactions (txs).

Netlists Basics

The netlist defines what you simulate, and how.

Netlists are defined at netlists/. You can reuse existing netlists or create your own.

What A Netlist Definition Must Hold

TokenSPICE expects a netlist module (in a netlist.py file) that defines these specific classes and functions:

  • SimStrategy class: simulation run parameters
  • KPIs class and netlist_createLogData() function: what metrics to log during the run
  • netlist_plotInstructions() function: how to plot the metrics after the run
  • SimState class: system-level structure & parameters, i.e. how agents are instantiated and connected. It imports agents defined in agents/*Agent.py. Some agents use EVM. You can add and edit Agents to suit your needs.

How to Implement Netlists

There are two practical ways to specify SimStrategy, KPIs, and so on for netlist.py:

  1. For simple netlists. Have just one file (netlist.py) to hold all the code for each class and method given above. This is appropriate for simple netlists, like simplegrant (just Python) and simplepool (Python+EVM).

  2. For complex netlists. Have one or more separate files for each class and method given above, such as netlists/NETLISTX/SimStrategy.py. Then, import them all into netlist.py file to unify their scope to a single module (netlist). This allows for arbitrary levels of netlist complexity. The wsloop netlist is a good example. It models the Web3 Sustainability Loop, which is inspired by the Amazon flywheel and used by Ocean, Boson and others as their system-level token design.

Agent.takeStep() method

The class SimState defines which agents are used. Some agents even spawn other agents. Each agent object is stored in the SimState.agents object, a dict with some added querying abilities. Key SimState methods to access this object are addAgent(agent), getAgent(name:str), allAgents(), and numAgents(). SimStateBase has details.

Every iteration of the engine make a call to each agent's takeStep() method. The implementation of GrantGivingAgent.takeStep() is shown below. Lines 26–33 determine whether it should disburse funds on this tick. Lines 35–37 do the disbursal if appropriate. There are no real constraints on how an agent's takeStep() is implemented. This which gives great TokenSPICE flexibility in agent-based simulation. For example, it can loop in EVM, like we show later.

Netlist Examples

Here are some existing netlists.

  • simplegrant - granter plus receiver, that's all. No EVM.
  • simplepool - publisher that periodically creates new pools. EVM.
  • scheduler - scheduled vesting from a wallet. EVM.
  • wsloop - Web3 Sustainability Loop. No EVM.
  • oceanv3 - Ocean Market V3 - initial design. EVM.
  • oceanv4 - Ocean Market V4 - solves rug pulls. EVM.

To learn more about how TokenSPICE netlists are structured, we refer you to the simplegrant (pure Python) and simplepool (Python+EVM) netlists, which each have more thorough explainers.

🐡 Backlog

Larger things we'd like to see:

  • Higher-level tools that use TokenSPICE, including design entry, verification, design space exploration, and more.
  • Improvements to TokenSPICE itself in the form of faster simulation speed, improved UX, and more.

See this board: https://github.com/orgs/tokenspice/projects/1/views/1

🐋 Benefits of EVM Agent Simulation

TokenSPICE and other EVM agent simulators have these benefits:

  • Faster and less error prone, because the model = the Solidity code. Don’t have to port any existing Solidity code into Python, just wrap it. Don’t have to write lower-fidelity equations.
  • Enables rapid iterations of writing Solidity code -> simulating -> changing Solidity code -> simulating.
  • Super high fidelity simulations, since it uses the actual code itself. Enables modeling of design, random and worst-case variables.
  • Mental model is general enough to extend to Vyper, LLL, and direct EVM bytecode. Can extend to non-EVM blockchain, and multi-chain scenarios.
  • Opportunity for real-time analysis / optimization / etc against live chains: grab the latest chain’s snapshot into ganache, run a local analysis / optimization etc for a few seconds or minutes, then do transaction(s) on the live chain. This can lead to trading systems, failure monitoring, more.

🦈 Resources

Here are further resources.

History:

Art:

Other links:

🏛 License

The license is MIT. Details