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DeFiPy

Basic Uniswap Simulation

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pymc
import datetime
WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
from uniswappy import *
n_steps = 5000
start_price = 0.1
mu = 0.3; sigma = 0.5
n_paths = 25


b = BrownianModel(start_price)
p_arr = b.gen_gbms(mu, sigma, n_steps, n_paths)
exp_p_arr = np.median(p_arr, axis = 1)
t_deltas = np.linspace(0, 365, num=n_steps+1)
start = datetime.datetime(2023, 1, 1, 0, 0, 0)
date_times = np.array([start + datetime.timedelta(days=t_delta) for t_delta in t_deltas])


fig = plt.figure(figsize=(15, 7))
USD_ax = fig.gca()


pymc.gp.util.plot_gp_dist(
    ax=USD_ax,
    x=date_times,
    samples=p_arr.transpose(),
    palette='cool',
    plot_samples=False
)


USD_ax.plot(date_times, exp_p_arr, color = 'white',linestyle = 'dashed', label='SYSUSD')
USD_ax.set_title("GBM Simulated SYS prices (USD) ", fontsize=20)
USD_ax.set_ylabel("USD ($)", fontsize=16)
USD_ax.legend(fontsize=16, facecolor="lightgray", loc='upper left')
<matplotlib.legend.Legend at 0x1649c3dc0>
output
sys_arr = np.zeros((n_steps, n_paths), np.float64)
x_amt_arr = np.zeros((n_steps, n_paths), np.float64)
y_amt_arr = np.zeros((n_steps, n_paths), np.float64)


for k in range(n_paths):
    print(f'Trial run {k}')
    p_trial_arr = p_arr[:,k]


    tkn = ERC20('TKN', "0x09")
    dai = ERC20('DAI', "0x111")


    lp_sim = SimpleLPSimulation()
    lp_sim.init_amts(10000, p_trial_arr[0])
    lp_sim.create_lp(tkn, dai)
    lp_sim.run(p_trial_arr)


    sys_arr[:,k] = lp_sim.get_tkn_price_sim()
    x_amt_arr[:,k] = lp_sim.get_x_amt_sim()
    y_amt_arr[:,k] = lp_sim.get_y_amt_sim()
Trial run 0 Trial run 1 Trial run 2 Trial run 3 Trial run 4 Trial run 5 Trial run 6 Trial run 7 Trial run 8 Trial run 9 Trial run 10 Trial run 11 Trial run 12 Trial run 13 Trial run 14 Trial run 15 Trial run 16 Trial run 17 Trial run 18 Trial run 19 Trial run 20 Trial run 21 Trial run 22 Trial run 23 Trial run 24
fig, (P_ax, SYS_ax, DAI_ax) = plt.subplots(nrows=3, sharex=False, sharey=False, figsize=(15, 12))


pymc.gp.util.plot_gp_dist(
    ax=P_ax,
    x=date_times[1:],
    samples=sys_arr.transpose(),
    palette='cool',
    plot_samples=False
)
P_ax.plot(date_times[1:], np.mean(sys_arr, axis = 1), color = 'w', linewidth=3, label='Price')
P_ax.set_title("Price (SYS)", fontsize=20)
P_ax.legend(fontsize=16, facecolor="lightgray", loc='upper left')
P_ax.set_ylabel("Price (SYS/DAI)", fontsize=16)


pymc.gp.util.plot_gp_dist(
    ax=SYS_ax,
    x=date_times[1:],
    samples=x_amt_arr.transpose(),
    palette='cool',
    plot_samples=False
)
SYS_ax.plot(date_times[1:], np.mean(x_amt_arr, axis = 1), color = 'w', linewidth=3, label='SYS')
SYS_ax.set_title("Reserve Amount (SYS)", fontsize=20)
SYS_ax.legend(fontsize=16, facecolor="lightgray", loc='upper left')
SYS_ax.set_ylabel("SYS", fontsize=16)


pymc.gp.util.plot_gp_dist(
    ax=DAI_ax,
    x=date_times[1:],
    samples=y_amt_arr.transpose(),
    palette='cool',
    plot_samples=False
)
DAI_ax.plot(date_times[1:], np.mean(y_amt_arr, axis = 1), color = 'w', linewidth=3, label='DAI')
DAI_ax.set_title("Reserve Amount (DAI)", fontsize=20)
DAI_ax.legend(fontsize=16, facecolor="lightgray", loc='upper left')
DAI_ax.set_ylabel("DAI", fontsize=16)
Text(0, 0.5, 'DAI')
output