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© 2018 Stable Unit

The Stable Unit token contract is designed to maintain a decentralized currency with low price volatility and low risk to its store of value.

As its means of achieving this, the contract uses internal Bitcoin collateral to dampen price movements: when the price increases above the pegged price, the contract issues new tokens to arbitrageurs willing to buy them for a price nearer the peg and, inversely, when the price decreases below the peg, the contract alleviates this downward trend by buying them back for a price closer to the peg. This simple mechanism is parameterized by the contract ‘spread’ parameter, its buy back price below the dollar and the sell price above the dollar, which changes in time to minimize the contract risk and price volatility.

We use statistical methods to evaluate the strength of this mechanism along two axis, (1) reserve risk (2) the token volatility. As a result, we show an empirical relationship between these two axis, and give a justification for how they could be adjusted to increase or decrease the risk to the token’s stability and long term store of value.

Abstractly, the Stable Unit contract is a machine which captures the demand for a stablecoin and transforms it into a reserve capable of maintaining a non-volatile price. This is similar in nature to the model of an insurance company which transforms the demand for risk reduction into a reserve capable of intervening when those risks occur.

The Stable Unit contract captures the demand for a stablecoin by acting as a market maker around a steady value. During times of price volatility, the arbitration around the peg captures the energy produced by fluctuating demand – always selling high, and buying low – converting it into reserves which elevate the contract value and allow the token contract to expand supply further.

This system is parametrized by a ‘spread’ parameter determining the lowest bid and highest ask offered by the contract to individuals attempting to buy or to sell Stable Units.

Selection of this value will be achieved by a decentralized voting mechanism carried out by system shareholders who profit from the stability the liquidity of the stable token. We predict that when the network is small for instance, and demand varies widely, the foundation will wish to hold the system risk low, ensuring that supply increases are small and the contract reserve is protected. During this period, we trade liquidity for risk aversion.

Inversely, when the market for Stable Units has grown, demand shocks of sufficient magnitude to hurt the reserve become much less likely. The foundation will offer a tighter spread around the dollar, thereby increasing the contract risk but allowing for greater liquidity.

For this reason, it is highly important that the foundation can accurately understand the mechanics of the spread parameter. We employ a number of statistical simulations in an attempt to understand it fully.

We employ Monte Carlo methods to statistically approximate the outcome distribution of the contract under varying parameter choices. The Monte Carlo simulation is driven by two non-correlated random processes with volatility parameter drawn from Bitcoin’s historical prices. We define two metrics of interest:

**Reserve Risk:** The likelihood that the contract reserve ratio drops below an undesirable threshold. Instance, ‘Given a chosen spread and our assumptions about the behaviour of the Bitcoin and exterior Stable Unit price distributions, there is a 98% chance that the contract reserve ratio will remain above above 0.5 within a years time.’

**Token Volatility:** The mean and variance of the Stable Unit price around the peg. Instance, ‘Given chosen spread and our assumptions about the behaviour of the Bitcoin and exterior Stable Unit price distributions, the real Stable Unit price will remain fixed with mean $0.99 and variance 0.1 around the peg.’

We simulate the behaviour of the Stable Unit contract using two non-correlated Geometric Brownian Motions of 1) Bitcoin and 2) Stable Unit price. In both instances we use a standard GBM model with a gaussian Wiener process. We evaluate the price difference between each contract step using the following step rule: = μdt + σ dW

These two motions interact with the contract according to the following rules:

- When the change in Stable Unit demand is positive, this reflects a net positive demand for Stable Units at the lowest ask price offered by the contract. The contract issues new tokens and deposits bitcoin into the reserve.
- When the change in Stable Unit demand is negative, this reflects a net negative demand for Stable Units at the highest bid price offered by the contract. The contract burns Stable Units and redeems them for Bitcoin held in reserve.

We run 1000 trials with each trial containing 1000 steps. The time delta is set to 1.0 to reflect that each step represents the outcome of a single day. The trials therefore run over a period approximately equal to 3 years. We fix the Bitcoin price volatility at 0.01 which is derived from the historical price movements over the previous 3 years to best fit outcomes. The bitcoin price drift is set to 0 reflecting zero knowledge about its price direction. The Stable Unit demand motion is set equally to the Bitcoin price with 0.01 volatility and 0.0 drift term. We perform no price rebasing.

We use two GBMs (Geometric Brownian Motion) as input to a mock Stable Unit contract as a means of estimating the likelyhood of contract outcomes.

Install and import python libraries.

```
!pip install numpy
!pip install pandas
!pip install matplotlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
import pandas as pd
```

```
class Params:
"""Params hold a set of hyper parameters relevant to a simulation experiment
Attributes:
total_steps (int): The total number of steps per trial.
btc_price_drift (float): BTC Monte Carlo GBM price drift.
btc_price_volatility (float): BTC Monte Carlo GBM price volatility.
su_price_drift (float): SU Monte Carlo GBM price drift.
su_price_volatility (float): SU Monte Carlo std price volatility.
initial_reserve_ratio (float): The ratio initial between Bitcoin and
Stable units.
initial_btc_reserve (float): Initial Bitcoin reserve size.
initial_btc_price (float): Initial Bitcoin price per unit.
lowest_ask (float): The contract spread above 1 dollar.
highest_bid (float): The contract spread bellow 1 dollar.
"""
def __init__(self):
self.total_steps = FLAGS_total_steps
self.total_trials = FLAGS_total_trials
self.delta_t = FLAGS_delta_t
self.btc_price_drift = FLAGS_btc_price_drift
self.btc_price_volatility = FLAGS_btc_price_volatility
self.su_price_drift = FLAGS_su_price_drift
self.su_price_volatility = FLAGS_su_price_volatility
self.initial_reserve_ratio = FLAGS_initial_reserve_ratio
self.initial_btc_reserve = FLAGS_initial_btc_reserve
self.initial_btc_price = FLAGS_initial_btc_price
self.lowest_ask = FLAGS_lowest_ask
self.highest_bid = FLAGS_highest_bid
self.print_step = FLAGS_print_step
def __str__(self):
return (' total steps: ' + "%0.2f" % self.total_steps + '\n' + \
' total trials: ' + "%0.2f" % self.total_trials + '\n' + \
' delta t: ' + "%0.4f" % self.delta_t + '\n' + \
' btc price drift: ' + "%0.4f" % self.btc_price_drift + '\n' + \
' btc price volatility: ' + "%0.4f" % self.btc_price_volatility + '\n' + \
' su price drift: ' + "%0.4f" % self.su_price_drift + '\n' + \
' su price volatility: ' + "%0.4f" % self.su_price_volatility + '\n' + \
' initial reserve ratio: '+"%0.4f" % self.initial_reserve_ratio + '\n' + \
' initial btc reserve: ' + "%0.4f" % self.initial_btc_reserve + '\n' + \
' initial btc price: ' + "%0.4f" % self.initial_btc_price + '\n' + \
' lowest ask: ' + "%0.3f" % self.lowest_ask + '\n' + \
' highest bid: ' + "%0.3f" % self.highest_bid + '\n' + \
' print step: ' + str(self.print_step))
class State:
"""State holds the current and historical values associated with a trial.
Attributes:
steps (list[int]): Step number index.
time_days (list[int]): Days since trial start.
su_price (list[int]): Days since trial start.
btc_reserve (list[float]): Total BTC in reserve at each step.
btc_prices (list[float]): USD per unit BTC per step.
btc_reserve_value (list[float]): US dollar value of BTC reserve.
reserve_ratio (list[float]): BTC reserve value over SU.
su_cumulative_demand (list[float]): Cumulative density of Stable unit
demand at each step.
su_circulation (list[float]): Total SU in circulation per step.
"""
def __init__(self, params):
""" Args:
params (Class): object carrying hyperparams for experiment.
"""
self.steps = [0]
self.time_days = [0]
self.su_price = [1.0]
self.btc_reserve = [params.initial_btc_reserve]
self.btc_prices = [params.initial_btc_price]
self.btc_reserve_value = \
[params.initial_btc_reserve * params.initial_btc_price]
self.reserve_ratio = [params.initial_reserve_ratio]
self.su_cumulative_demand = [0.0]
self.su_circulation = [params.initial_btc_reserve * \
params.initial_btc_price * 1 / params.initial_reserve_ratio]
def __str__(self):
return 'step ' + "%0.0f" % self.steps[-1] +\
' time_days ' + "%0.0f" % self.time_days[-1] +\
' su_price ' + "%0.2f" % self.su_price[-1] + \
' su_total ' + "%0.2f" % self.su_circulation[-1] + \
' btc_total ' + "%0.4f" % self.btc_reserve[-1] + \
' btc_price ' + "%0.4f" % self.btc_prices[-1] + \
' btc_value ' + "%0.4f" % self.btc_reserve_value[-1] + \
' reserve_ratio ' + "%0.4f" % self.reserve_ratio[-1]
```

Build plotting functions for realizing the individual outcomes from a set of trials.

```
def plot_bitcoin_price(results):
""" Plots the Bitcoin price trajectory from each trial..
Args:
results (list(Class)): List of state objects from each trial.
"""
f, (ax1, ax2) = plt.subplots(1, 2)
final_btc_price = []
for state in results:
if len(results) == 1:
final_btc_price.extend(state.btc_prices)
else:
final_btc_price.append(state.btc_prices[-1])
ax1.plot(state.time_days, state.btc_prices)
final_btc_price = np.asarray(final_btc_price)
ax1.set_title('Bitcoin Price (BTC/USD)')
ax1.set_xlabel('time (days)', fontsize=14)
ax1.set_ylabel('price (USD)', fontsize=14)
n, bins, patches = ax2.hist(final_btc_price, bins=int(math.sqrt(len(final_btc_price))), facecolor='green', alpha=0.5, orientation='horizontal')
ax2.axhline(final_btc_price.mean(), color='r', linestyle='dashed', label='mean=' + "%.2f" % final_btc_price.mean(), linewidth=2)
ax2.axhline(np.median(final_btc_price), color='b', linestyle='dashed', label='median=' + "%.2f" % np.median(final_btc_price), linewidth=2)
ax2.set_xlabel('N Outcomes (/' + str(len(final_btc_price)) +')', fontsize=14)
ax2.get_yaxis().set_visible(False)
ax2.legend()
plt.show()
def plot_su_price(results):
""" Plots the Contract Reserve Ratio trajectory from each trial.
Args:
results (list(Class)): List of state objects from each trial.
"""
f, (ax1, ax2) = plt.subplots(1, 2)
final_su_price = []
for state in results:
if len(results) == 1:
final_su_price.extend(state.su_price)
else:
final_su_price.append(state.su_price[-1])
ax1.plot(state.time_days, state.su_price)
final_su_price = np.asarray(final_su_price)
ax1.set_title('SU Price (USD)')
ax1.set_xlabel('time (days)', fontsize=14)
ax1.set_ylabel('SU Price (%)', fontsize=14)
ax1.set_ylim(0.8, 1.2)
n, bins, patches = ax2.hist(final_su_price, bins=int(math.sqrt(len(final_su_price))), facecolor='green', alpha=0.5, orientation='horizontal')
ax2.axhline(final_su_price.mean(), color='r', linestyle='dashed', label='mean=' + "%.2f" % final_su_price.mean(), linewidth=2)
ax2.axhline(np.median(final_su_price), color='b', linestyle='dashed', label='median=' + "%.2f" % np.median(final_su_price), linewidth=2)
ax2.set_xlabel('N Outcomes (/' + str(len(final_su_price)) +')', fontsize=14)
ax2.get_yaxis().set_visible(False)
ax2.set_ylim(0.8, 1.2)
ax2.legend()
plt.show()
def plot_reserve_ratio(results):
""" Plots the Contract Reserve Ratio trajectory from each trial.
Args:
results (list(Class)): List of state objects from each trial.
"""
f, (ax1, ax2) = plt.subplots(1, 2)
final_reserve_ratio = []
for state in results:
if len(results) == 1:
final_reserve_ratio.extend(state.reserve_ratio)
else:
final_reserve_ratio.append(state.reserve_ratio[-1])
ax1.plot(state.time_days, state.reserve_ratio)
final_reserve_ratio = np.asarray(final_reserve_ratio)
ax1.set_title('Reserve Ratio (Reserve Value/Market Cap)')
ax1.set_xlabel('time (days)', fontsize=14)
ax1.set_ylabel('Reserve Ratio (%)', fontsize=14)
n, bins, patches = ax2.hist(final_reserve_ratio, bins=int(math.sqrt(len(final_reserve_ratio))), facecolor='green', alpha=0.5, orientation='horizontal')
ax2.axhline(final_reserve_ratio.mean(), color='r', linestyle='dashed', label='mean=' + "%.2f" % final_reserve_ratio.mean(), linewidth=2)
ax2.axhline(np.median(final_reserve_ratio), color='b', linestyle='dashed', label='median=' + "%.2f" % np.median(final_reserve_ratio), linewidth=2)
ax2.set_xlabel('N Outcomes (/' + str(len(final_reserve_ratio)) +')', fontsize=14)
ax2.get_yaxis().set_visible(False)
ax2.legend()
plt.show()
def plot_circulation(results):
""" Plots the Stable Unit circulation from each trial.
Args:
results (list(Class)): List of state objects from each trial.
"""
f, (ax1, ax2) = plt.subplots(1, 2)
final_su_circulation = []
for state in results:
if len(results) == 1:
final_su_circulation.extend(state.su_circulation)
else:
final_su_circulation.append(state.su_circulation[-1])
ax1.plot(state.time_days, state.su_circulation)
final_su_circulation = np.asarray(final_su_circulation)
ax1.set_title('Stable Unit Circulation (SU)')
ax1.set_xlabel('time (days)', fontsize=14)
ax1.set_ylabel('Circulation (SU)', fontsize=14)
n, bins, patches = ax2.hist(final_su_circulation, bins=int(math.sqrt(len(final_su_circulation))), facecolor='green', alpha=0.5, orientation='horizontal')
ax2.axhline(final_su_circulation.mean(), color='r', linestyle='dashed', label='mean=' + "%.2f" % final_su_circulation.mean(), linewidth=2)
ax2.axhline(np.median(final_su_circulation), color='b', linestyle='dashed', label='median=' + "%.2f" % np.median(final_su_circulation), linewidth=2)
ax2.set_xlabel('N Outcomes (/' + str(len(final_su_circulation)) +')', fontsize=14)
ax2.get_yaxis().set_visible(False)
ax2.legend()
plt.show()
```

Build GBM movement functions which return increments in price and demand. When called in sequence these functions produce the random walks which are used as input to the contract.

```
def bitcoin_price_delta(state, params):
""" Calculates a random Bitcoin price step using GBM with params.
Args:
state (Class): Object containing the contract's current state.
params (Class): Object containing the contract hyperparameters.
"""
return state.btc_prices[-1] * \
(params.btc_price_drift * params.delta_t + \
params.btc_price_volatility * math.sqrt(params.delta_t) * \
np.random.standard_normal())
def stable_unit_demand_delta(state, params):
""" Calculates a random Stable Unit demand step using GBM with params.
Args:
state (Class): Object containing the contract's current state.
params (Class): Object containing the contract hyperparameters.
"""
return state.su_circulation[-1] * \
(params.su_demand_drift * params.delta_t + \
params.su_demand_volatility * math.sqrt(params.delta_t) * \
np.random.standard_normal())
def stable_unit_price_delta(state, params):
""" Calculates a random Stable Unit price step using GBM with params.
Args:
state (Class): Object containing the contract's current state.
params (Class): Object containing the contract hyperparameters.
"""
return state.su_price[-1] * \
(params.su_price_drift * params.delta_t + \
params.su_price_volatility * math.sqrt(params.delta_t) * \
np.random.standard_normal())
```

Build the `do_step`

function. This function evaluates the next contract state given a current state and simulation parameters.

```
def do_step(params, state):
""" Performs a single Monte Carlo Step simulating the state transition of the contract.
Args:
params (Class): Object containing the contract hyperparameters.
state (Class): Object containing the contract's current state.
"""
# BTC price change for this step.
btc_price_delta = bitcoin_price_delta(state, params)
# Next BTC price.
btc_price = state.btc_prices[-1] + btc_price_delta
# SU price change for this step.
su_price_delta = stable_unit_price_delta(state, params)
# Next SU price.
su_price = state.su_price[-1] + su_price_delta
# When prices rise or drop beyond the highest bid and lowest ask prices,
# arbitragers trigger the contract through it's buy and sell functions.
if su_price > params.lowest_ask:
# Here, arbitragers sell Stable Units at the contract to equalize the price,
# this expands the Stable Unit circulation.
# Expansion estimated using the quantity theory of money. (i.e. price = supply/demand)
su_circulation_delta = int(state.su_circulation[-1] * (su_price - params.lowest_ask))
# The price reverts to the lowest ask during this process.
su_price = params.lowest_ask
# SUs minted from the smart contract are made in exchange for Bitcoin.
# The Bitcoin reserve is credited with the new sale of stable units.
btc_reserve_delta = su_circulation_delta * (1 / btc_price) * params.lowest_ask
elif su_price < params.highest_bid:
# Here, arbitragers buy Stable Units at the contract and sell them at exchanges to equalize price,
# this contracts the Stable Unit circulation.
# Contraction estimated using the quantity theory of money. (i.e. price = supply/demand)
su_circulation_delta = int(state.su_circulation[-1] * (su_price - params.highest_bid))
# The price reverts to the highest bid during this process.
su_price = params.highest_bid
# SUs are being sold to the contract in exchange for Bitcoin.
# The contracts reserve is depleted as it buys back stable units.
btc_reserve_delta = su_circulation_delta * (1 / btc_price) * params.highest_bid
else:
su_circulation_delta = 0
su_price = su_price
btc_reserve_delta = 0
# Calculate additional parameters of next state.
su_circulation = state.su_circulation[-1] + su_circulation_delta
btc_reserve = state.btc_reserve[-1] + btc_reserve_delta
btc_reserve_value = btc_reserve * btc_price
reserve_ratio = btc_reserve_value / su_circulation
# Update the state object.
state.su_price.append(su_price)
state.su_circulation.append(su_circulation)
state.btc_prices.append(btc_price)
state.btc_reserve.append(btc_reserve)
state.btc_reserve_value.append(btc_reserve_value)
state.reserve_ratio.append(reserve_ratio)
state.steps.append(state.steps[-1] + 1)
state.time_days.append(state.time_days[-1] + params.delta_t)
if params.print_step:
print( 'step', "%0.0f" % state.steps[-1], \
'time_days', "%0.0f" % state.time_days[-1], \
'su_total', "%0.2f" % su_circulation, \
'su_delta', "%0.2f" % su_price_delta, \
'su_price', "%0.2f" % su_price, \
'su_circulation_delta',"%0.4f" % su_circulation_delta, \
'su_rebase_delta', "%0.2f" % su_rebase_delta, \
'btc_total', "%0.4f" % btc_reserve, \
'btc_price', "%0.4f" % btc_price, \
'btc_price_delta', "%0.4f" % btc_price_delta, \
'btc_delta', "%0.4f" % btc_reserve_delta, \
'btc_value', "%0.4f" % btc_reserve_value, \
'reserve_ratio', "%0.4f" % reserve_ratio)
return state
```

Functions for running and stopping experiments.

```
def should_end_trial(params, state):
if (state.btc_reserve[-1] < 0):
return True
elif (state.su_circulation[-1] < 0):
return True
elif (state.steps[-1] >= params.total_steps):
return True
else:
return False
def run_trial(params):
state = State(params)
while not should_end_trial(params, state):
state = do_step(params, state)
return state
def run_experiment(params):
results = []
for trial in range(0, params.total_trials):
results.append(run_trial(params))
return results
```

Estimation of the Bitcoin and SU drift and volatility parameters based on historical returns.

```
btc_url = 'bitcoin_prices.csv'
usdt_url = 'tether_prices.csv'
btc_data = pd.read_csv(btc_url)
usdt_data = pd.read_csv(usdt_url)
btc_returns = np.log(btc_data['Open']).diff().as_matrix()[1:]
usdt_returns = np.log(usdt_data['Open']).diff().as_matrix()[1:]
# Estimate SU GBM parameters from Tether historical data.
btc_price_volatility = np.std(btc_returns)
btc_price_drift = np.mean(btc_returns) +0.5*np.std(btc_returns)*np.std(btc_returns)
print ('btc_price_volatility: ', btc_price_volatility)
print ('btc_price_drift: ', btc_price_drift)
# Estimate SU GBM parameters from Tether historical data.
su_price_volatility = np.std(usdt_returns)
su_price_drift = np.mean(usdt_returns) +0.5*np.std(usdt_returns)*np.std(usdt_returns)
print ('su_price_volatility: ', su_price_volatility)
print ('su_price_drift: ', su_price_drift)
# Hide warnings. NoOp.
import warnings
warnings.filterwarnings('ignore')
```

```
btc_price_volatility: 0.03911116416625796
btc_price_drift: -0.0021224304994087334
su_price_volatility: 0.006227870327911209
su_price_drift: 1.9393184410638417e-05
```

Run a single trial using the volatility parameters derived from the previous step

```
"""
Run contract simulations to produce a set of random trials.
"""
# Simulation Paramters.
FLAGS_total_trials = 1
FLAGS_total_steps = 1000000
FLAGS_print_step = False
FLAGS_delta_t = 1.0 # One day to match dataset.
FLAGS_btc_price_drift = 0.0
FLAGS_btc_price_volatility = btc_price_volatility
FLAGS_su_price_drift = 0.0
FLAGS_su_price_volatility = su_price_volatility
FLAGS_initial_reserve_ratio = 1.0
FLAGS_initial_btc_reserve = 100
FLAGS_initial_btc_price = 8000
FLAGS_lowest_ask = 1.01
FLAGS_highest_bid = 0.99
params = Params()
print ("Run experiment with params... \n" + params.__str__())
results = run_experiment(params)
print ("Done.")
print ("Plotting results...")
plot_reserve_ratio(results)
plot_circulation(results)
plot_bitcoin_price(results)
plot_su_price(results)
```

```
Run experiment with params...
total steps: 1000000.00
total trials: 1.00
delta t: 1.0000
btc price drift: 0.0000
btc price volatility: 0.0391
su price drift: 0.0000
su price volatility: 0.0062
initial reserve ratio: 1.0000
initial btc reserve: 100.0000
initial btc price: 8000.0000
lowest ask: 1.010
highest bid: 0.990
print step: False
Done.
Plotting results...
```

Run a multiple trials using the volatility parameters derived from the previous step

```
"""
Run contract simulations to produce a set of random trials.
"""
# Simulation Paramters.
FLAGS_total_trials = 1000
FLAGS_total_steps = 10000
FLAGS_print_step = False
FLAGS_delta_t = 1.0 # One day to match dataset.
FLAGS_btc_price_drift = 0.0
FLAGS_btc_price_volatility = btc_price_volatility
FLAGS_su_price_drift = 0.0
FLAGS_su_price_volatility = su_price_volatility
FLAGS_initial_reserve_ratio = 1.0
FLAGS_initial_btc_reserve = 100
FLAGS_initial_btc_price = 8000
FLAGS_lowest_ask = 1.01
FLAGS_highest_bid = 0.99
params = Params()
print ("Run experiment with params... \n" + params.__str__())
results = run_experiment(params)
print ("Done.")
print ("Plotting results...")
plot_reserve_ratio(results)
plot_circulation(results)
plot_bitcoin_price(results)
plot_su_price(results)
```

```
Run experiment with params...
total steps: 10000.00
total trials: 1000.00
delta t: 1.0000
btc price drift: 0.0000
btc price volatility: 0.0391
su price drift: 0.0000
su price volatility: 0.0062
initial reserve ratio: 1.0000
initial btc reserve: 100.0000
initial btc price: 8000.0000
lowest ask: 1.010
highest bid: 0.990
print step: False
Done.
Plotting results...
```

Functions for computing the cumulative probability of the reserve dropping bellow a threshold.

```
"""
The reserve risk is the likelyhood that the contract's reserve ratio drops bellow a specified bound.
"""
def plot_reserve_risk(results):
""" Plots the estimate risk that the reserve will be depleted at each lower bound.
Args:
results (list(Class)): List of state objects from each trial.
"""
fig = plt.figure()
ax = plt.subplot(111)
plt.title('Reserve Risk')
plt.xlabel('time (days)', fontsize=14)
plt.ylabel('Cumulative (Pr)', fontsize=14)
for bound in np.linspace(0.0, 1.0, 10):
rr_at_bound = reserve_risk_at_bound(results, bound)
plt.plot(rr_at_bound, label='Bound:' + "%.2f" % bound + ' Risk:' + "%.2f" % rr_at_bound[-1])
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
def plot_success_likelihood(results):
""" Plots the estimate success likelihood that the reserve will be increase above each upper bound.
Args:
results (list(Class)): List of state objects from each trial.
"""
fig = plt.figure()
ax = plt.subplot(111)
plt.title('Dividend likelihood')
plt.xlabel('time (days)', fontsize=14)
plt.ylabel('Cumulative (Pr)', fontsize=14)
for bound in np.linspace(1.0, 10.0, 10):
rr_at_bound = success_likelihood_at_bound(results, bound)
plt.plot(rr_at_bound, label='Bound:' + "%.2f" % bound + ' Risk:' + "%.2f" % rr_at_bound[-1])
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
def success_likelihood_at_bound(results, bound):
""" Calculates the percentage of trials where the contract reserve ratio jumps above the bound.
Args:
results (list(Class)): List of state objects from each trial.
bound (float): The reserve lower bound.
Returns:
cumulative_risk (list(float)): The percentage of trials who have passed the lower bound
starting from t0 -> tn.
"""
cumulative_failed = [0]*len(results)
remaining = range(len(results))
for step_idx in range(len(results)):
next_remaining = []
for idx in remaining:
if results[idx].steps[-1] >= step_idx and results[idx].reserve_ratio[step_idx] > bound:
cumulative_failed[step_idx] += 1
else:
next_remaining.append(idx)
remaining = next_remaining
for idx in range(1, len(results)):
cumulative_failed[idx] += cumulative_failed[idx-1]
for idx in range(1, len(results)):
cumulative_failed[idx] /= len(results)
return cumulative_failed
def reserve_risk_at_bound(results, bound):
""" Calculates the percentage of trials where the contract reserve ratio drops bellow the bound.
Args:
results (list(Class)): List of state objects from each trial.
bound (float): The reserve lower bound.
Returns:
cumulative_risk (list(float)): The percentage of trials who have passed the lower bound
starting from t0 -> tn.
"""
cumulative_failed = [0]*len(results)
remaining = range(len(results))
for step_idx in range(len(results)):
next_remaining = []
for idx in remaining:
if results[idx].steps[-1] >= step_idx and results[idx].reserve_ratio[step_idx] < bound:
cumulative_failed[step_idx] += 1
else:
next_remaining.append(idx)
remaining = next_remaining
for idx in range(1, len(results)):
cumulative_failed[idx] += cumulative_failed[idx-1]
for idx in range(1, len(results)):
cumulative_failed[idx] /= len(results)
return cumulative_failed
```

We calculate the cumulative reserve risk at different lower bounds. (i.e the probability that the reserve ratio will drop below a bound within 3 years time)

```
len(results)
plot_reserve_risk(results)
plot_success_likelihood(results)
```

```
# Simulation Paramters.
FLAGS_total_trials = 200
FLAGS_total_steps = 1000
bound = 0.8
risk_by_spread = []
for spread in np.linspace(0.0, 0.5, 20):
FLAGS_lowest_ask = 1.0 + spread
FLAGS_highest_bid = 1.0 - spread
params = Params()
mc_states = run_experiment(params)
risk_estimate = reserve_risk_at_bound(mc_states, bound)[-1]
risk_by_spread.append((spread, risk_estimate))
```

```
def plot_reserve_risk_by_spread(risk_by_spread, bound):
fig = plt.figure()
ax = plt.subplot(111)
spread, risk = zip(*risk_by_spread)
plt.title('Reserve Risk by spread, at bound = ' + str(bound))
plt.xlabel('spread (USD)', fontsize=14)
plt.ylabel('Risk to reserve (Pr)', fontsize=14)
plt.plot(spread, risk)
plt.show()
```

```
plot_reserve_risk_by_spread(risk_by_spread, 0.8)
```

Using the above simplified contract and by deriving best effort estimations for the price drift and volatility parameters for Bitcoin and tether using historical prices, we estimate risk bounds for the contract reserve. These cumulative probabilities at different bounds reflect the likelihood that the contract reaches this state within 3 years time. We see that the likelihood of the contract dropping below a 10 percent bound is 22%.

Therefore, the contract reserve without any additional mechanisms is able to provide necessary liquidity by its own with a 78% probability which increases during the period. To further increase it close to 100% - it’s necessary to use an initial reserve funding or additional stabilization mechanism such as multi-layer model proposed in the whitepaper.