Uniswap V3 Order Book

Here we utilize UniswapPy to simulate an order book in Uniswap V3
Medium Article: How to Simulate a Uniswap V3 Order Book in Python

[1]:

from defipy import *
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

user_nm = MockAddress().apply()
eth_amount = 100
tkn_amount = 1000

fee = UniV3Utils.FeeAmount.MEDIUM
tick_spacing = UniV3Utils.TICK_SPACINGS[fee]

Simulate prices

Simulate prices using a geometric brownian motion process

[2]:

n_steps = 1000
start_price = eth_amount/tkn_amount
mu = 0.1; sigma = 0.5
n_paths = 1

b = BrownianModel(start_price)
p_arr = b.gen_gbms(mu, sigma, n_steps, n_paths)
exp_p_arr = np.median(p_arr, axis = 1)

accounts = MockAddress().apply(50)

Setup pool

[3]:

eth = ERC20("ETH", "0x09")
tkn = ERC20("TKN", "0x111")

exchg_data = UniswapExchangeData(tkn0 = eth, tkn1 = tkn, symbol="LP",
                                   address="0x011", version = 'V3',
                                   tick_spacing = tick_spacing,
                                   fee = fee)

factory = UniswapFactory("ETH pool factory", "0x2")
lp = factory.deploy(exchg_data)

lwr_tick = UniV3Helper().get_price_tick(lp, -1, 10, 1000)
upr_tick = UniV3Helper().get_price_tick(lp, 1, 10, 1000)

Join().apply(lp, user_nm, eth_amount, tkn_amount, lwr_tick, upr_tick)
lp.summary()

Exchange ETH-TKN (LP)
Real Reserves:   ETH = 96.70469739529014, TKN = 1000.0
Gross Liquidity: 6440.3320664241655

Simulate liquidity pool

[4]:

arb = CorrectReserves(lp, x0 = 1/exp_p_arr[0])
p_intervals = [500, 800, 1000, 1200, 1500, 1700, 2000]
lp_prices = [lp.get_price(tkn)]
lp_liquidity = [lp.total_supply]
lp_swaps = []; lp_net_deposits = [];

for k in range(1, n_steps):
    p = 1/exp_p_arr[k]
    arb.apply(p, lwr_tick, upr_tick)

    select_tkn = EventSelectionModel().bi_select(0.5)
    rnd_add_amt = TokenDeltaModel(25).delta()
    rnd_swap_amt = TokenDeltaModel(15).delta()
    user_add = random.choice(accounts)
    user_swap = random.choice(accounts)

    p_interval = random.choice(p_intervals)
    lwr_tick = UniV3Helper().get_price_tick(lp, -1, lp.get_price(eth), p_interval)
    upr_tick = UniV3Helper().get_price_tick(lp, 1, lp.get_price(eth), p_interval)

    if(select_tkn == 0):
        AddLiquidity().apply(lp, eth, user_add, rnd_add_amt, lwr_tick, upr_tick)
        out = Swap().apply(lp, eth, user_swap, rnd_swap_amt)
    else:
        AddLiquidity().apply(lp, tkn, user_add, p*rnd_add_amt, lwr_tick, upr_tick)
        out = Swap().apply(lp, tkn, user_swap,  p*rnd_swap_amt)

    lp_prices.append(lp.get_price(tkn))
    lp_liquidity.append(lp.total_supply)
    lp_swaps.append(rnd_swap_amt)
    lp_net_deposits.append(rnd_add_amt)

lp.summary()

Exchange ETH-TKN (LP)
Real Reserves:   ETH = 614127.7691542099, TKN = 17821512.609132715
Gross Liquidity: 35165267.83386152

Construct order book

[5]:

liquidity = {}
df_liq = pd.DataFrame(columns=['tick', 'price', 'liquidity'])
for k, pos in enumerate(lp.ticks):
    price = UniV3Helper().tick_to_price(pos)
    liq = lp.ticks[pos].liquidityGross/10**18
    df_liq.loc[k] = [pos,price,liq]

center_pos = UniV3Helper().price_to_tick(lp.get_price(eth))
price = lp.get_price(tkn)
df_liq.loc[k+1] = [center_pos,price,0]

df_liq.sort_values(by=['price'], inplace=True)
df_liq.reset_index(drop=True, inplace=True)

side_arr = []
for tick in df_liq['tick'].values:
    if (tick > center_pos):
        side_arr.append('asks')
    elif (tick < center_pos):
        side_arr.append('bids')
    else:
        side_arr.append('center')
df_liq['side'] = side_arr
idx = df_liq.index[df_liq['side'] == 'center']
df_liq.drop(idx[0], inplace=True)

[6]:

df_liq

[6]:

	tick	price	liquidity	side
1	17940.0	6.012919	50341.631295	bids
2	18120.0	6.122126	22053.081205	bids
3	18180.0	6.158967	16493.867865	bids
4	18300.0	6.233316	154124.547473	bids
5	18420.0	6.308563	1242.862215	bids
...	...	...	...	...
192	29640.0	19.372447	1834.850559	asks
193	29700.0	19.489025	454.070649	asks
194	29820.0	19.724291	106.278197	asks
195	29940.0	19.962396	205.903139	asks
196	30480.0	21.069945	13.774698	asks

196 rows × 4 columns

Review simulation output

[7]:

fig = plt.figure(figsize = (10, 5))

current_price = lp.get_price(tkn)
prices = 1/df_liq['price'].values
liquidity = df_liq['liquidity'].values

fig, (book_ax, price_ax, liq_ax) = plt.subplots(nrows=3, sharex=False, sharey=False, figsize=(12, 8))

book_ax.bar(prices, liquidity, color ='steelblue', width = 0.0005, label = 'liquidity', alpha=0.7)
book_ax.axvline(x=current_price, color = 'mediumvioletred', linewidth = 1, linestyle = 'dashdot', label = 'current price')
book_ax.set_xlabel("Price (USD)", size=10)
book_ax.set_ylabel("Liquidity", size=14)
book_ax.set_title("Uniswap V3: Liquidity distribution")
book_ax.legend()

x_val = np.arange(0,len(p_arr))
price_ax.plot(x_val[1:-1], p_arr[1:-1], color = 'r',linestyle = 'dashdot', label='market price')
price_ax.plot(x_val[1:-1], lp_prices[1:], color = 'b',linestyle = 'dashed', label='lp price')
price_ax.set_ylabel('Price (ETH/TKN)', size=14)
price_ax.set_xlabel('Time sample', size=10)
price_ax.legend()

liq_ax.plot(x_val[1:-1], lp_liquidity[1:], color = 'b',linestyle = 'dashed', label='lp liquidity')
liq_ax.set_ylabel('Liquidity', size=14)
liq_ax.set_xlabel('Time sample', size=10)
liq_ax.legend()
plt.tight_layout()

<Figure size 1000x500 with 0 Axes>

../_images/tutorials_order_book_12_1.png

[8]:

fig, ax = plt.subplots(1, 2, figsize=(12,5))

sns.distplot(lp_net_deposits, hist=True, kde=False, bins=int(30), color = 'darkblue',
             hist_kws={'edgecolor':'black'}, kde_kws={'linewidth': 2}, ax=ax[0])

ax[0].set_title('Histogram: Net Deposit Volume')
ax[0].set_xlabel('Volume ETH')
ax[0].set_ylabel('Frequency')

sns.distplot(lp_swaps, hist=True, kde=False, bins=int(30), color = 'darkblue',
             hist_kws={'edgecolor':'black'}, kde_kws={'linewidth': 2}, ax=ax[1])

ax[1].set_title('Histogram: Net Swap Volume')
ax[1].set_xlabel('Volume ETH')
ax[1].set_ylabel('Frequency')

[8]:

Text(0, 0.5, 'Frequency')

../_images/tutorials_order_book_13_1.png

Plot order book

[9]:

fig, ax = plt.subplots(figsize = (10, 5))
ax.set_title(f"ETH/TKN Order Book - Scatterplot")
sns.scatterplot(x="price", y="liquidity", hue="side",
                data=df_liq, ax=ax, palette=["green", "red"])
ax.set_xlabel("Price (TKN/ETH)", fontsize = 14)
ax.set_ylabel("Liquidity", fontsize = 14)

[9]:

Text(0, 0.5, 'Liquidity')

../_images/tutorials_order_book_15_1.png

[10]:

fig, ax = plt.subplots(figsize = (10, 5))
ax.set_title(f"ETH/TKN Order Book - Depth Chart")
sns.ecdfplot(x="price", weights="liquidity", stat="count",
             complementary=True, data=df_liq.query("side == 'bids'"),
             color="green", ax=ax)
sns.ecdfplot(x="price", weights="liquidity", stat="count",
             data=df_liq.query("side == 'asks'"), color="red",
             ax=ax)
ax.set_xlabel("Price (TKN/ETH)", fontsize = 14)
ax.set_ylabel("Liquidity", fontsize = 14)

[10]:

Text(0, 0.5, 'Liquidity')

../_images/tutorials_order_book_16_1.png