for day in days:
actual_stuff =
for tau in taus:
for fund in funds:
fund.update_expations(for all assets)
fund.portfolio_optimization
fund.get_demand
cleared, interm_price, exch_rate = marketmechanism.determine_intermediate_prices()
intermediate_stuff =
if cleared:
update_balance_sheets
break
In [ ]:
def model(param):
initialisation
for days in num_sweeps:
while not clearing
for fund in environment.funds:
[intermediate] default_probablities, = fund.expectation(returns, rho, V, m, phi, epsilon, theta, phi^p, current_prices)
exp_prices,
exp_exchange,
exp_returns
var_cov_matrix
weights = fund.optimal_portfolio