rpopplewell / curvesim

Simulates Curve Finance pools

CurveSim

CurveSim simulates Curve finance pools with optimal arbitrageurs trading against them. It's primary use is to determine optimal amplitude (A) and fee parameters given historical price and volume feeds.

Dependencies:

Python3, scipy, numpy, pandas, Web3, matplotlib, requests

Basic Use: Autosim

The autosim() function simulates existing Curve pools with a range of A and/or fee parameters. The function fetches pool properties (e.g., current pool size) and 2 months of price/volume data, runs multiple simulations in parallel, and saves results plots to the "pools" directory.

Any pool with an entry in poolDF_cg.csv (if using CoinGecko data) or poolDF_nomics.csv (if using Nomics or local data) can be simulated by passing its name as an argument to autosim(). Additional entries can easily be added to these CSVs (see Adding Pools).

Example:

For example, to simulate 3pool with the default configuration, simply run:

import CurveSim
res = CurveSim.autosim('3pool')

Results:

Plots of the results will be saved to the "pools/poolname" (e.g., pools/3pool) directory. The output dictionary, "res", contains pandas dataframes for all of the data plotted in the figures:

• ar: annualized returns
• bal: balance parameter over time, bal=1 when in perfect balance, and bal=0 when all holdings are in 1 coin
• pool_value: time series of pool's value (based on virtual price)
• depth: time series of price depth, averaged across pool's coins
• volume: time series of pool volume
• log_returns: log returns over time
• err: time series of absolute price errors, (dy-fee)/dx - p, summed accros coin pairs
• x: time series of pool holdings
• p: time series of pool precisions (incl. basepool virtual price and/or RAI redemption price)

Customizing Simulation Parameters

By default, pools are simulated using:

• parameters prescribed in poolDF_*.csv
• on-chain or Curve Subgraph data about the pool
• a broad range of A (16 values, 64 to 11,585) and fee (5 values, 0.02 to 0.06%) values
• CoinGecko price/volume data
• 4 parallel threads

However, all of these can be altered using optional keyword arguments.

Custom A and/or Fee Ranges

Custom A and fee ranges can be specified using the "A" and "fee" arguments. Inputs must be lists or numpy arrays containing lists:

import CurveSim
import numpy as np

#Specify A values:
res = CurveSim.autosim('3pool', A=np.linspace(1000,20000,20))

#Specify fees (0.04% and 0.05%):
res = CurveSim.autosim('3pool', fee=[.0003, .0004])

#Specify custom A range and 0.03% fee
#Note that single fee must still be a list
res = CurveSim.autosim('3pool', A=np.linspace(1000,20000,20), fee=[.0003])

Additionally, a small number of A/fee values (2 each) can be set for testing purposes:

res = CurveSim.autosim('3pool', test=True)

Overriding Simulation Parameters

The following parameters are automatically specified by autosim(), but can be overridden with keyword arguments:

• D: total deposit size; default: fetched from on-chain data
• vol_mult: multiplied by market volume to produce trade size limits; default: computed from Curve Subraph data (see Volume Limits for details)
• feemul: fee multiplier used in dynamic fee pools; default: specified in poolDF_*.csv

import CurveSim

#Simulate 3pool assuming total deposit of $10B, fee = 0.03%
res = CurveSim.autosim('3pool', D=10000000000, fee=[.0003])

#For metapools, specifying D effects the deposit in the metapool, but not the basepool
#Simulate BUSDv2 metapool assuming total deposit of $1B, fee = 0.03%
res = CurveSim.autosim('busdv2', D=1000000000, fee=[.0003])

#Simulate 3pool, limiting volume to 75% of market volume, fee = 0.03% 
#Note: it is not reccomended to adjust this parameter, try vol_mode instead (see below)
res = CurveSim.autosim('3pool', vol_mult=.75, fee=[.0003])

#Simulate hypothetical 3pool with dynamic fee like AAVE pool, fee = 0.03% 
res = CurveSim.autosim('3pool', feemul=2*10**10, fee=[.0003])

Volume Limits

To approximate realistic market conditions, the "vol_mult" argument is used to limit trade volume at each timepoint in the simulation. By default (vol_mode=1), vol_mult is simply the expected proportion of market volume that goes through the Curve pool (e.g., monthly pool volume / monthly price feed volume). At each timepoint, vol_mult is multiplied by the each coin-pair's market volume to produce a volume limit for each pair, thereby appropriately scaling trade volume while retaining natural volume dynamics. For metapools, vol_mult is computed seperately for the base pool and meta-pool.

In some cases, it may be helpful to limit trade volume differently. In particular, for new coins with little volume for some (but not all) pairs included in the pool, more robust simulations can be achieved by assuming equal volumes across trading pairs. We therefore provide three volume-limiting modes (vol_mode):

• vol_mode = 1: limits trade volumes proportionally to market volume for each pair (default)
• vol_mode = 2: limits trade volumes equally across pairs
• vol_mode = 3: mode 2 for trades with meta-pool asset, mode 1 for basepool-only trades

We reccomend using the default vol_mode 1 in most cases. However, if that returns noisy/uninterpretable results, it may be worth trying mode 2 (for normal pools) or mode 3 (for meta-pools).

Data Sources

The "src" argument can be used to choose between 3 different data sources:

• src = "cg": CoinGecko API (free); default
• src = "nomics": Nomics API (paid); must input API key in nomics.py
• src = "local": local data stored in the "data" folder

Because CoinGecko and Nomics use different naming conventions for each coin, we provide poolDF_cg.csv and poolDF_nomics.csv specifying coin names for each source. The "local" option uses poolDF_nomics.

Note on CoinGecko vs. Nomics Data

While Nomics provides 30-minute-interval data for each specific coin-pair, CoinGecko provides prices per coin in 1-hour intervals. Each coin's price is computed relative to all its trading pairs and converted to a quote currency (e.g., USD), with volume summed across all trading pairs. Therefore, market volume taken from CoinGecko is often much higher than one can expect for a specific coin-pair. This issue is largely ameloriated by our volume limiting approach, with CoinGecko results typically mirroring Nomics results qualitatively, but it should be noted that CoinGecko data may be less reliable than Nomics data for certain simulations.

For comparison, compare 3pool_cg and 3pool_nomics results in the pools/demo direectory.

Technical Parameters

Additionally, one can specify:

• ncpu: number of CPUs to use for parallel processing (default: 4)
• trunc: a pair of indicies to truncate price/volume data to [trunc[0]:trunc[1]]

Adding Pools

We have provided information for many (but not all) pools in the poolDF_*.csv files. To add additional pools, simply add a line to the relevant CSV with the following attributes seperated by semi-colons:

• name: name to reference the pool by
• address: address on ETH mainnet
• coins: list of coin IDs used for fetching price/volume data; differs for Coingecko and Nomics!
• precmul: list of multipliers to bring coin precision up to 10**18; "PRECISION_MUL" in pool contract
• basepool: if metapool, name of basepool
• tokentype: list specifying if any token is cToken or yToken; only used to calculate initial holdings
• feemul: fee multiplier for dynamic fee pools; "offpeg_fee_multiplier" in pool contract

Simulating Hypothetical Pools

If a pool does not already exist, the easiest way to simulate it is to copy a poolDF_*.csv line for a similar pool (e.g., if metapool, copy busdv2 or usdn, etc.), replace the coin IDs ("coins") with the coins of interest, update precmul to the appropriate values, and then specify D and volmult when running autosim().

Alternatively, you can directly input the relevant pool values into the psim() function. Instructions for this will be added soon.

Forthcoming

• support for additional blockchains
• auto-populating poolDF_*.csv from Curve pool registry