crvUSDsim is a tool simulating Curve Stablecoin.
- Simulate interactions with crvUSD pools in Python
- Analyze the effects of parameter changes on pool performance
- Develop custom simulation tools for parameter optimization
- Simulate the anti-risk ability of the protocol in extreme cases
- install with poetry
poetry install- run
Hello World
>>> poetry run python -m crvusdsim
[INFO][11:29:54][crvusdsim.pipelines.simple]-92751: Simulating mode: rate
[INFO][11:29:57][curvesim.price_data.sources]-92751: Fetching CoinGecko price data...
[INFO][11:30:08][curvesim.price_data.sources]-92751: Fetching CoinGecko price data...
[INFO][11:30:08][curvesim.price_data.sources]-92751: Fetching CoinGecko price data...
[INFO][11:30:08][curvesim.price_data.sources]-92751: Fetching CoinGecko price data...
[INFO][11:30:09][curvesim.price_data.sources]-92751: Fetching CoinGecko price data...
[INFO][11:30:16][crvusdsim.templates.Strategy]-92883: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.15}
[INFO][11:30:16][crvusdsim.templates.Strategy]-92880: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.1}
[INFO][11:30:16][crvusdsim.templates.Strategy]-92877: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.05}
Elapsed time: 28.6576099395752Check out the full documentation at https://crvusdsim.readthedocs.io/. We recommend starting with the "Quickstart" guide.
The autosim() function simulates existing Curve Stablecoin Market with range of parameters (such like rate0, A, loan_discount). The function fetches pool properties (e.g., current pool size) and 2 months of price/volume data, runs multiple simulations in parallel, and returns a results object that can be introspected or generate charts.
crvUSDsim supported by the Convex Community Subgraphs/crvusd can be simulated directly by inputting the LLAMMA pool's address or Collateral's Symbol (e.g. "wstETH").
Pythonic interaction with Curve Stablecoin market objects (including LLAMMA pool, Controller, Aggregator, PegKeepers, etc.)::
>>> import crvusdsim
>>> (pool, controller, collateral_token, stablecoin, aggregator, price_oracle, stableswap_pools, peg_keepers, policy, factory)
>>> = crvusdsim.pool.get(market_name, bands_data="controller")
>>> pool.name
'Curve.fi Stablecoin wstETH'
>>> pool.coin_names
['wstETH', 'crvUSD']
>>> pool.A
100
>>> sum(pool.bands_x.values())
0
>>> sum(pool.bands_y.values())
40106052164494685140992
>>> len(pool.user_shares)
392
>>> dx = 10**18
>>> pool.trade(0, 1, dx) # dx, dy, fees
(1000000000000000000, 445225238462727, 6000000000000000)
>>> controller.loan_discount
90000000000000000
>>> controller.liquidation_discount
60000000000000000
>>> len(controller.loan)
392
>>> user0 = controller.loans[1] # user address
>>> loan0 = controller.loan[user0] # :class:Loan
>>> (loan0.initial_debt, loan0.initial_collateral, loan0.rate_mul, loan0.timestamp)
(9779961749290509154648064, 6785745612366175797248, 1000000000000000000, 1700712599)Rate simulations to see results of varying rate0 parameters in MonetaryPolicy::
>>> import crvusdsim
>>> res = crvusdsim.autosim(pool="wstETH", sim_mode="rate", rate0=[0.05, 0.075, 0.10, 0.125, 0.15])
[INFO][10:02:42][crvusdsim.pipelines.simple]-84886: Simulating mode: rate
[INFO][10:02:50][curvesim.price_data.sources]-84886: Fetching CoinGecko price data...
[INFO][10:03:51][curvesim.price_data.sources]-84886: Fetching CoinGecko price data...
[INFO][10:03:52][curvesim.price_data.sources]-84886: Fetching CoinGecko price data...
[INFO][10:05:44][curvesim.price_data.sources]-84886: Fetching CoinGecko price data...
[INFO][10:07:22][curvesim.price_data.sources]-84886: Fetching CoinGecko price data...
[INFO][10:07:32][crvusdsim.templates.Strategy]-84936: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.05}
[INFO][10:07:32][crvusdsim.templates.Strategy]-84937: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.125}
[INFO][10:07:32][crvusdsim.templates.Strategy]-84935: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.075}
[INFO][10:07:32][crvusdsim.templates.Strategy]-84934: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.1}
[INFO][10:07:33][crvusdsim.templates.Strategy]-84938: [Curve.fi Stablecoin wstETH] Simulating with {'rate0': 0.15}Arbitrage simulations to see results of varying fee and amplification (A) parameters in LLAMMA pool:
>>> res = crvusdsim.autosim(pool="wstETH", sim_mode="pool", A=[50, 60, 80, 100])
[INFO][14:57:58][crvusdsim.pipelines.simple]-82656: Simulating mode: pool
[INFO][14:58:00][curvesim.price_data.sources]-82656: Fetching CoinGecko price data...
[INFO][14:58:05][crvusdsim.templates.Strategy]-82729: [Curve.fi Stablecoin wstETH] Simulating with {'A': 50}
[INFO][14:58:05][crvusdsim.templates.Strategy]-82730: [Curve.fi Stablecoin wstETH] Simulating with {'A': 100}
[INFO][14:58:05][crvusdsim.templates.Strategy]-82731: [Curve.fi Stablecoin wstETH] Simulating with {'A': 60}
[INFO][14:58:05][crvusdsim.templates.Strategy]-82732: [Curve.fi Stablecoin wstETH] Simulating with {'A': 80}Arbitrage simulations to see results of varying loan_discount and liquidation_discount parameters in Controller::
>>> loan_discounts = [int(d * 10**18) for d in [0.09, 0.10, 0.11, 0.12]]
>>> res = crvusdsim.autosim(pool="wstETH", sim_mode="controller", loan_discount=loan_discounts, liquidation_discount=[int(0.06 * 10**18)])
[INFO][17:01:13][crvusdsim.pipelines.simple]-91016: Simulating mode: controller
[INFO][17:01:15][curvesim.price_data.sources]-91016: Fetching CoinGecko price data...
[INFO][17:02:56][crvusdsim.templates.Strategy]-91050: [Curve.fi Stablecoin wstETH] Simulating with {'loan_discount': 100000000000000000, 'liquidation_discount': 60000000000000000}
[INFO][17:02:56][crvusdsim.templates.Strategy]-91052: [Curve.fi Stablecoin wstETH] Simulating with {'loan_discount': 110000000000000000, 'liquidation_discount': 60000000000000000}
[INFO][17:02:56][crvusdsim.templates.Strategy]-91049: [Curve.fi Stablecoin wstETH] Simulating with {'loan_discount': 90000000000000000, 'liquidation_discount': 60000000000000000}
[INFO][17:02:56][crvusdsim.templates.Strategy]-91053: [Curve.fi Stablecoin wstETH] Simulating with {'loan_discount': 120000000000000000, 'liquidation_discount': 60000000000000000}Arbitrage simulations to see results of varying N of user's position::
>>> res = crvusdsim.autosim(pool="wstETH", sim_mode="N", N=[4, 6, 8, 10, 20, 40, 50])
[INFO][17:17:50][crvusdsim.pipelines.simple]-91016: Simulating mode: N
[INFO][17:17:53][curvesim.price_data.sources]-91016: Fetching CoinGecko price data...
[INFO][17:17:59][crvusdsim.templates.Strategy]-91351: [Curve.fi Stablecoin wstETH] Simulating with {'N': 8}
[INFO][17:18:01][crvusdsim.templates.Strategy]-91354: [Curve.fi Stablecoin wstETH] Simulating with {'N': 40}
[INFO][17:18:01][crvusdsim.templates.Strategy]-91349: [Curve.fi Stablecoin wstETH] Simulating with {'N': 4}
[INFO][17:18:01][crvusdsim.templates.Strategy]-91355: [Curve.fi Stablecoin wstETH] Simulating with {'N': 50}
[INFO][17:18:01][crvusdsim.templates.Strategy]-91352: [Curve.fi Stablecoin wstETH] Simulating with {'N': 10}
[INFO][17:18:01][crvusdsim.templates.Strategy]-91353: [Curve.fi Stablecoin wstETH] Simulating with {'N': 20}
[INFO][17:18:01][crvusdsim.templates.Strategy]-91350: [Curve.fi Stablecoin wstETH] Simulating with {'N': 6}The simulation returns a SimResults object that can plot simulation metrics or return them as DataFrames.
#Plot results using Altair
res.plot()
#Save plot results as results.html
res.plot(save_as="results.html")sim_mode="rate"
sim_mode="rate"
>>> res.summary()
metric annualized_rate users_debt crvusd_price agg_price
stat mean mean mean mean
0 0.044408 1.580274e+06 1.002537 1.002775
1 0.066533 1.583135e+06 1.002537 1.002775
2 0.088607 1.585936e+06 1.002537 1.002775
3 0.110631 1.588681e+06 1.002537 1.002775
4 0.132608 1.591372e+06 1.002537 1.002775
>>> res.summary(full=True)
rate0 annualized_rate mean users_debt mean crvusd_price mean agg_price mean
0 0.050 0.044408 1.580274e+06 1.002537 1.002775
1 0.075 0.066533 1.583135e+06 1.002537 1.002775
2 0.100 0.088607 1.585936e+06 1.002537 1.002775
3 0.125 0.110631 1.588681e+06 1.002537 1.002775
4 0.150 0.132608 1.591372e+06 1.002537 1.002775
>>> res.data()
run timestamp annualized_rate users_debt crvusd_price agg_price
0 0 2023-10-03 23:30:00+00:00 0.046259 1.574365e+06 1.001229 1.001890
1 0 2023-10-03 23:38:34+00:00 0.046259 1.574366e+06 1.001229 1.001890
2 0 2023-10-03 23:47:08+00:00 0.046259 1.574367e+06 1.001229 1.001890
3 0 2023-10-03 23:55:42+00:00 0.046259 1.574368e+06 1.001229 1.001890
4 0 2023-10-04 00:04:17+00:00 0.046259 1.574369e+06 1.001229 1.001890
... ... ... ... ... ... ...
51240 4 2023-12-03 22:55:42+00:00 0.123962 1.607443e+06 1.003847 1.003959
51241 4 2023-12-03 23:04:17+00:00 0.123962 1.607447e+06 1.003847 1.003959
51242 4 2023-12-03 23:12:51+00:00 0.123962 1.607450e+06 1.003847 1.003959
51243 4 2023-12-03 23:21:25+00:00 0.123962 1.607453e+06 1.003847 1.003959
51244 4 2023-12-03 23:30:00+00:00 0.124045 1.607456e+06 1.003847 1.003946
51245 rows × 6 columns
>>> res.data(full=True)
rate0 run timestamp annualized_rate users_debt crvusd_price agg_price
0 0.05 0 2023-10-03 23:30:00+00:00 0.046259 1.574365e+06 1.001229 1.001890
1 0.05 0 2023-10-03 23:38:34+00:00 0.046259 1.574366e+06 1.001229 1.001890
2 0.05 0 2023-10-03 23:47:08+00:00 0.046259 1.574367e+06 1.001229 1.001890
3 0.05 0 2023-10-03 23:55:42+00:00 0.046259 1.574368e+06 1.001229 1.001890
4 0.05 0 2023-10-04 00:04:17+00:00 0.046259 1.574369e+06 1.001229 1.001890
... ... ... ... ... ... ... ...
51240 0.15 4 2023-12-03 22:55:42+00:00 0.123962 1.607443e+06 1.003847 1.003959
51241 0.15 4 2023-12-03 23:04:17+00:00 0.123962 1.607447e+06 1.003847 1.003959
51242 0.15 4 2023-12-03 23:12:51+00:00 0.123962 1.607450e+06 1.003847 1.003959
51243 0.15 4 2023-12-03 23:21:25+00:00 0.123962 1.607453e+06 1.003847 1.003959
51244 0.15 4 2023-12-03 23:30:00+00:00 0.124045 1.607456e+06 1.003847 1.003946
51245 rows × 7 columnsIf you are having issues, please let us know. You can reach us via the following
GitHub: crvUSDsim issues https://github.com/0xreviews/crvusdsim/issues
Portions of the codebase are authorized derivatives of code owned by Curve.fi (Swiss Stake GmbH). These are the Vyper snippets used for testing and the Python code derived from them (crvusdsim/pool/crvusd); there are copyright notices placed appropriately. The rest of the codebase has an MIT license.