This simple model consists of a buyer, a supplier, and a market.
The buyer represents a group of customers whose willingness to pay for a single unit of the good is captured by a vector of prices wta. You can initiate the buyer with a set_quantity function which randomly assigns the willingness to pay according to your specifications. You may ask for these willingness to pay quantities with a getbid function.
The supplier is similiar, but instead the supplier is willing to be paid to sell a unit of technology. The supplier for instance may have non-zero variable costs that make them unwilling to produce the good unless they receive a specified price. Similarly the supplier has a get_ask function which returns a list of desired prices.
The willingness to pay or sell are set randomly using uniform random distributions. The resultant lists of bids are effectively a demand curve. Likewise the list of asks is effectively a supply curve. A more complex determination of bids and asks is possible, for instance using time of year to vary the quantities being demanded.
The market assumes the presence of an auctioneer which will create a book, which seeks to match the bids and the asks as much as possible. If the auctioneer is neutral, then it is incentive compatible for the buyer and the supplier to truthfully announce their bids and asks. The auctioneer will find a single price which clears as much of the market as possible. Clearing the market means that as many willing swaps happens as possible. You may ask the market object at what price the market clears with the get_clearing_price function. You may also ask the market how many units were exchanged with the get_units_cleared function.
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import random as rnd
import pandas as pd
import numpy as np
import time
import datetime
In [2]:
# measure how long it takes to run the script
startit = time.time()
dtstartit = datetime.datetime.now()
class Seller():
def __init__(self, name):
self.name = name
self.wta = []
self.prod = 2000
self.lb_price = 0
self.ub_price = 10
# the supplier has n quantities that they can sell
# they may be willing to sell this quantity anywhere from a lower price of l
# to a higher price of u
def set_quantity(self):
n = self.prod
l = self.lb_price
u = self.ub_price
for i in range(n):
p = rnd.uniform(l, u)
self.wta.append(p)
def get_name(self):
return self.name
def get_asks(self):
return self.wta
def clear_wta(self):
self.wta = []
class Buyer():
def __init__(self, name):
self.name = name
self.wtp = []
self.step = 0
self.base_demand = 0
self.max_demand = 0
self.lb_price = 0
self.ub_price = 10
# the supplier has n quantities that they can buy
# they may be willing to sell this quantity anywhere from a lower price of l
# to a higher price of u
def set_quantity(self):
n = int(self.consumption(self.step))
l = self.lb_price
u = self.ub_price
for i in range(n):
p = rnd.uniform(l, u)
self.wtp.append(p)
def get_name(self):
return self.name
# return list of willingness to pay
def get_bids(self):
return self.wtp
def clear_wtp(self):
self.wtp = []
def consumption(self, x):
# make it initialise to seller
b = self.base_demand
m = self.max_demand
y = b + m * (.5 * (1 + np.cos((x/6)*np.pi)))
return(y)
class Book():
def __init__(self):
self.ledger = pd.DataFrame(columns = ("role","name","price","cleared"))
def set_asks(self,seller_list):
# ask each seller their name
# ask each seller their willingness
# for each willingness append the data frame
for seller in seller_list:
seller_name = seller.get_name()
seller_price = seller.get_asks()
for price in seller_price:
self.ledger=self.ledger.append({"role":"seller","name":seller_name,"price":price,"cleared":"in process"},
ignore_index=True)
def set_bids(self,buyer_list):
# ask each seller their name
# ask each seller their willingness
# for each willingness append the data frame
for buyer in buyer_list:
buyer_name = buyer.get_name()
buyer_price = buyer.get_bids()
for price in buyer_price:
self.ledger=self.ledger.append({"role":"buyer","name":buyer_name,"price":price,"cleared":"in process"},
ignore_index=True)
def update_ledger(self,ledger):
self.ledger = ledger
def get_ledger(self):
return self.ledger
class Market():
def __init__(self):
self.count = 0
self.last_price = ''
self.book = Book()
self.b = []
self.s = []
self.ledger = ''
def add_buyer(self,buyer):
self.b.append(buyer)
def add_seller(self,seller):
self.s.append(seller)
def set_book(self):
self.book.set_bids(self.b)
self.book.set_asks(self.s)
def get_ledger(self):
self.ledger = self.book.get_ledger()
return self.ledger
def get_bids(self):
# this is a data frame
ledger = self.book.get_ledger()
rows= ledger.loc[ledger['role'] == 'buyer']
# this is a series
prices=rows['price']
# this is a list
bids = prices.tolist()
return bids
def get_asks(self):
# this is a data frame
ledger = self.book.get_ledger()
rows = ledger.loc[ledger['role'] == 'seller']
# this is a series
prices=rows['price']
# this is a list
asks = prices.tolist()
return asks
# return the price at which the market clears
# this fails because there are more buyers then sellers
def get_clearing_price(self):
# buyer makes a bid starting with the buyer which wants it most
b = self.get_bids()
s = self.get_asks()
# highest to lowest
self.b=sorted(b, reverse=True)
# lowest to highest
self.s=sorted(s, reverse=False)
# find out whether there are more buyers or sellers
# then drop the excess buyers or sellers; they won't compete
n = len(b)
m = len(s)
# there are more sellers than buyers
# drop off the highest priced sellers
if (m > n):
s = s[0:n]
matcher = n
# There are more buyers than sellers
# drop off the lowest bidding buyers
else:
b = b[0:m]
matcher = m
# It's possible that not all items sold actually clear the market here
for i in range(matcher):
if (self.b[i] > self.s[i]):
self.count +=1
self.last_price = self.b[i]
return self.last_price
# TODO: Annotate the ledger
def annotate_ledger(self,clearing_price):
ledger = self.book.get_ledger()
for index, row in ledger.iterrows():
if (row['role'] == 'seller'):
if (row['price'] < clearing_price):
ledger.loc[index,'cleared'] = 'True'
else:
ledger.loc[index,'cleared'] = 'False'
else:
if (row['price'] > clearing_price):
ledger.loc[index,'cleared'] = 'True'
else:
ledger.loc[index,'cleared'] = 'False'
self.book.update_ledger(ledger)
def get_units_cleared(self):
return self.count
class Observer():
def __init__(self, x, y, z):
self.wta = []
#self.god_info = period
self.init_buyer = x
self.init_seller = y
self.maxrun = z
self.hist_book = []
self.buyer_dict = {}
self.seller_dict = {}
self.timetick = 0
def set_buyer(self, buyerinfo):
for name in buyerinfo:
self.buyer_dict[name] = Buyer('%s' % name)
self.buyer_dict[name].base_demand = buyerinfo[name]['b']
self.buyer_dict[name].max_demand = buyerinfo[name]['m']
def set_seller(self, seller_info):
for name in seller_info:
self.seller_dict[name] = Seller('%s' % name)
self.seller_dict[name].prod = seller_info[name][0]
def update_seller(self, seller_info):
for i in self.seller_dict:
self.seller_dict[i].clear_wta()
self.seller_dict[i].set_quantity()
def update_buyer(self):
for i in self.buyer_dict:
self.buyer_dict[i].step += 1
self.buyer_dict[i].clear_wtp()
self.buyer_dict[i].set_quantity()
def run_it(self):
first_run = True
#seller_info = {'natural gas' : (2000, 0, 10)}
if first_run:
#self.set_buyer(buyerinfo)
self.set_buyer(self.init_buyer)
self.set_seller(self.init_seller)
first_run=False
for period in range(self.maxrun):
self.timetick += 1
print('#######################################')
print(god_info[period][0])
# time the period
startit_period = time.time()
# time initialising
startit_init = time.time()
self.update_buyer()
self.update_seller(self.init_seller)
#create a book and submit bids
book = Book()
book.set_asks([sell for sell in self.seller_dict.values()])
book.set_bids([buy for buy in self.buyer_dict.values()])
ledger = book.get_ledger()
# create a market
gas_market = Market()
#add suplliers and buyers to this market
for supplier in self.seller_dict.values():
gas_market.add_seller(supplier)
for buyer in self.buyer_dict.values():
gas_market.add_buyer(buyer)
gas_market.set_book()
asks = gas_market.get_asks()
# time init stop
stopit_init = time.time() - startit_init
print('%s : init' % stopit_init)
# start clearing
startit_clearing = time.time()
clearing = gas_market.get_clearing_price()
gas_market.annotate_ledger(clearing)
new_ledger = gas_market.get_ledger()
# write time and save result
stopit_clearing = time.time() - startit_clearing
print('%s : clearing' % stopit_clearing)
# since this operation can take quite a while, print after every operation
period_time = time.time() - startit_period
print('%s : period time' % period_time)
self.hist_book.append([god_info[period][0], clearing])
f = open('hist_book.csv', 'a')
for item in self.hist_book:
f.write('%s,%s\n' % (item[0], item[1]))
f.close()
In [3]:
# is dictionary really suited? Sorted vs unsorted
# god_info is the the input for the model
# This is a example
'''
god_info = {
'jan': {'home': (100, 0, 10), 'industry': (50, 0, 10), 'cat': (75, 0, 10)},
'feb': {'home': (90, 0, 10), 'industry': (40, 0, 10), 'cat': (60, 0, 10)},
'march': {'home': (100, 0, 10), 'industry': (50, 0, 10), 'cat': (75, 0, 10)}
}
#'''
#read montly consumption data of 2010 into a dataframe
df = pd.read_csv('2010cbstestrun.csv', header=0, index_col=0)
df = df.transpose()
#make an array of for the buyers from the csv in the following format
#buyerinfo = [time ['home', (100, 0, 10)], ['industry', (50, 0, 10)], ['cat', (75, 0, 10)]]
god_info = [[x, [['elec',(y,0,10)],['indu',(z,0,10)],['home',(u,0,10)]]]
for x,y,z,u in zip(df.index,df['elec'],df['indu'],df['home'])]
#plot the 2010 monthly consumption data
df.plot();
df
Out[3]:
In [4]:
# make initialization dictionary
init_buyer = {'elec':{'b':400, 'm' : 673}, 'indu':{'b':400, 'm':1171}, 'home':{'b': 603, 'm': 3615}}
init_seller = {'natural gas' : (2000, 0, 10)}
#print(init_buyer)
# make a history book to record every timestep
hist_book = []
In [5]:
# create observer and run the model
#obser1 = Observer(god_info,12)
# first data about buyers then sellers and then model ticks
obser1 = Observer(init_buyer, init_seller, 12)
obser1.run_it()
#get the info from the observer
hist_book = obser1.hist_book
In [6]:
#pd.DataFrame.head(new_ledger)
df_hb = pd.DataFrame(hist_book)
df_hb = df_hb.set_index(0)
df_hb.index.name = 'month'
df_hb.rename(columns={1: 'price'}, inplace=True)
The market can also be formulated as a very simple linear program or linear complementarity problem. It is clearer and easier to implement this market clearing mechanism with agents. One merit of the agent-based approach is that we don't need linear or linearizeable supply and demand function.
The auctioneer is effectively following a very simple linear program subject to constraints on units sold. The auctioneer is, in the primal model, maximizing the consumer utility received by customers, with respect to the price being paid, subject to a fixed supply curve. On the dual side the auctioneer is minimizing the cost of production for the supplier, with respect to quantity sold, subject to a fixed demand curve. It is the presumed neutrality of the auctioneer which justifies the honest statement of supply and demand.
An alternative formulation is a linear complementarity problem. Here the presence of an optimal space of trades ensures that there is a Pareto optimal front of possible trades. The perfect opposition of interests in dividing the consumer and producer surplus means that this is a zero sum game. Furthermore the solution to this zero-sum game maximizes societal welfare and is therefore the Hicks optimal solution.
A possible addition of this model would be to have a weekly varying demand of customers, for instance caused by the use of natural gas as a heating agent. This would require the bids and asks to be time varying, and for the market to be run over successive time periods. A second addition would be to create transport costs, or enable intermediate goods to be produced. This would need a more elaborate market operator. Another possible addition would be to add a profit maximizing broker. This may require adding belief, fictitious play, or message passing.
The object-orientation of the models will probably need to be further rationalized. Right now the market requires very particular ordering of calls to function correctly.
In [7]:
# timeit
stopit = time.time()
dtstopit = datetime.datetime.now()
print('it took us %s seconds to get to this conclusion' % (stopit-startit))
print('in another notation (h:m:s) %s'% (dtstopit - dtstartit))
In [8]:
# print the run results
df_hb
df_hb.plot()
plt.ylabel('€ / unit');
In [9]:
# print the time of last run
print('last run of this notebook:')
time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime())
Out[9]:
In [10]:
# demand aproximation of the home owners
x = np.arange(12)
y = 603 + 3615*(.5 * (1 + np.cos((x/6)*np.pi)))
plt.plot(x,y)
def consumption(x):
y = 603 + 3615*(.5 * (1 + np.cos((x/6)*np.pi)))
return(y)
# print([consumption(i) for i in np.arange(12)])
In [ ]: