def experiment(results_csv_file: str,
auction_function: Callable,
auction_name: str,
recipe: tuple,
stocks_prices: list = None,
stock_names: list = None):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param results_csv_file: the experiment result file.
:param auction_function: the function for executing the auction under consideration.
:param auction_name: title of the experiment, for printouts.
:param recipe: can be any vector of ones, e.g. (1,1,1), for our trade-reduction mechanism, or any vector of positive integers for our ascending-auction mechanism.
:param stocks_prices: list of prices for each stock and each agent.
:param stock_names: list of stocks names which prices are belongs, for naming only.
"""
if stocks_prices is None:
(stocks_prices, stock_names) = getStocksPrices(recipe)
results_table = TeeTable(TABLE_COLUMNS, results_csv_file)
recipe_str = ":".join(map(str, recipe))
for i in range(len(stocks_prices)):
market = Market([
AgentCategory("agent", category) for category in stocks_prices[i]
])
num_of_possible_ps = min([
len(stocks_prices[i][j]) / recipe[j]
for j in range(len(stocks_prices[i]))
])
(optimal_trade, _) = market.optimal_trade(recipe)
auction_trade = auction_function(market, recipe)
optimal_count = optimal_trade.num_of_deals()
auction_count = auction_trade.num_of_deals()
if (auction_trade.num_of_deals() > optimal_trade.num_of_deals()):
print(
"Warning!!! the number of deals in action is greater than optimal!"
)
print("Optimal num of deals: ", optimal_trade.num_of_deals())
print("Auction num of deals: ", auction_trade.num_of_deals())
optimal_gft = optimal_trade.gain_from_trade()
auction_gft = auction_trade.gain_from_trade(including_auctioneer=True)
auction_market_gft = auction_trade.gain_from_trade(
including_auctioneer=False)
results_table.add(
OrderedDict((
("stock_name", stock_names[i]),
("auction_name", auction_name),
("recipe", recipe_str),
("num_possible_trades", round(num_of_possible_ps)),
("optimal_count", round(optimal_count, 2)),
("auction_count", round(auction_count, 2)),
("count_ratio", 0 if optimal_count == 0 else int(
(auction_count / optimal_count) * 100000) / 1000),
("optimal_gft", round(optimal_gft, 2)),
("auction_gft", round(auction_gft, 2)),
("auction_gft_ratio", 0 if optimal_gft == 0 else round(
auction_gft / optimal_gft * 100, 3)),
("auction_market_gft", round(auction_market_gft, 2)),
("market_gft_ratio", 0 if optimal_gft == 0 else round(
auction_market_gft / optimal_gft * 100, 3)),
)))
results_table.done()
def start_testing(args):
## instantiate exchanges, update candles, and run strategy
profits = defaultdict(dict)
for x in args["exchanges"]:
new_exchange = ccxtExchange(x)
portfolio = [Market(m, new_exchange) for m in args["markets"][x]]
new_exchange.markets = {
mkt_obj.symbol: mkt_obj
for mkt_obj in portfolio
}
candles_magnitude = new_exchange.get_ccxt_candles_magnitude_format(
args["candles_m_size"], args["candles_m_unit"])
## run real strategy
strat_profits = run_model(args["strategy"], x, portfolio,
candles_magnitude, args)
## run benchmark strategy (holding)
hold_profits = run_model("hold", x, portfolio, candles_magnitude, args)
## run study stats (it does not return anything)
run_model("study_stats", x, portfolio, candles_magnitude, args)
exchange_profits = {mkt: {args["strategy"]: strat_prf, "hold": hold_profits[mkt]} \
for mkt, strat_prf in \
zip(strat_profits.keys(), strat_profits.values())}
profits[x] = exchange_profits
return profits
def check_110_101(buyers: List[float], sellersA: List[float], sellersB: List[float],
expected_num_of_deals: int, expected_prices: List[float]):
market = Market([
AgentCategory("buyer", buyers),
AgentCategory("sellerA", sellersA),
AgentCategory("sellerB", sellersB),
])
# ps_recipes = [[1, 1, 0], [1, 0, 1]]
ps_recipe_struct = [0, [1, None, 2, None]]
self._check_market(market, ps_recipe_struct, expected_num_of_deals, expected_prices)
def check_1_1(buyers: List[float], sellers: List[float],
expected_num_of_deals: int,
expected_prices: List[float]):
market = Market([
AgentCategory("buyer", buyers),
AgentCategory("seller", sellers),
])
ps_recipe = [1, 1]
self._check_market(market, ps_recipe, expected_num_of_deals,
expected_prices)
def check_1100_1011(buyers: List[float], sellers: List[float], producers: List[float], movers: List[float],
expected_num_of_deals: int, expected_prices: List[float]):
market = Market([
AgentCategory("buyer", buyers),
AgentCategory("seller", sellers),
AgentCategory("producer", producers),
AgentCategory("mover", movers),
])
# ps_recipes = [[1, 1, 0, 0], [1, 0, 1, 1]]
ps_recipe_struct = [0, [1, None, 2, [3, None]]]
self._check_market(market, ps_recipe_struct, expected_num_of_deals, expected_prices)
def check_11100_10011(buyers: List[float], sellers1: List[float], sellers2: List[float], producers1: List[float], producers2: List[float],
expected_num_of_deals: int, expected_prices: List[float]):
market = Market([
AgentCategory("buyer", buyers),
AgentCategory("seller1", sellers1),
AgentCategory("seller2", sellers2),
AgentCategory("producer1", producers1),
AgentCategory("producer2", producers2),
])
ps_recipe_struct = [0, [1, [2, None], 3, [4, None]]]
self._check_market(market, ps_recipe_struct, expected_num_of_deals, expected_prices)
def test_trader_pipeline():
hitbtc2 = ExchangeTrader("hitbtc2")
eth_btc_market = Market("ETH/BTC", hitbtc2)
try:
hitbtc2.buy(eth_btc_market, 0.001, 0.00059498)
time.sleep(0.1)
hitbtc2.sell(eth_btc_market, 0.001, 0.1)
time.sleep(0.1)
except:
raise
time.sleep(1)
for order in hitbtc2.orders_alive:
print(order.status())
time.sleep(0.1)
print(order.status())
time.sleep(0.1)
print(order.status())
time.sleep(0.1)
assert (order.status() == 'open')
time.sleep(1)
num_orders = len(hitbtc2.orders_alive)
print hitbtc2.orders_history
for order in hitbtc2.orders_history:
order.cancel()
time.sleep(0.1)
num_orders -= 1
print hitbtc2.orders_alive
assert (len(hitbtc2.orders_alive) == num_orders)
time.sleep(1)
for order in hitbtc2.orders_history:
assert (order.status() == 'canceled')
def experiment(results_csv_file: str,
auction_functions: list,
auction_names: str,
recipe: tuple,
iterations: int,
nums_of_agents: list = None,
stocks_prices: list = None,
stock_names: list = None):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param results_csv_file: the experiment result file.
:param auction_functions: list of functions for executing the auction under consideration.
:param auction_names: titles of the experiment, for printouts.
:param recipe: can be any vector of ones, e.g. (1,1,1), for our trade-reduction mechanism, or any vector of positive integers for our ascending-auction mechanism.
:param nums_of_agents: list of n(s) for number of possible trades to make the calculations.
:param stocks_prices: list of prices for each stock and each agent.
:param stock_names: list of stocks names which prices are belongs, for naming only.
"""
TABLE_COLUMNS = [
"stockname", "recipe", "numpossibletrades", "optimalcount",
"optimalcountwithgftzero", "optimalgft"
]
AUCTION_COLUMNS = ["auctioncount", "countratio", "gft", "gftratio"]
print(recipe)
if stocks_prices is None:
(stocks_prices, stock_names) = getStocksPricesShuffled()
column_names = TABLE_COLUMNS
column_names += [
auction_name + column for auction_name in auction_names
for column in AUCTION_COLUMNS
]
results_table = TeeTable(column_names, results_csv_file)
recipe_str = ":".join(map(str, recipe))
recipe_sum = sum(recipe)
if nums_of_agents is None:
nums_of_agents = [10000000]
average_total_results = {}
for num_of_agents_per_category in nums_of_agents:
average_total_results[str(num_of_agents_per_category)] = []
for i in range(len(stocks_prices)):
total_results = {}
for num_of_agents_per_category in nums_of_agents:
total_results[str(num_of_agents_per_category)] = []
stock_prices = stocks_prices[i]
last_iteration = False
for num_of_agents_per_category in nums_of_agents:
for iteration in range(iterations):
num_of_possible_ps = min(num_of_agents_per_category,
int(len(stock_prices) / recipe_sum))
if last_iteration and num_of_possible_ps < num_of_agents_per_category:
break
if num_of_possible_ps < num_of_agents_per_category:
if last_iteration:
break
last_iteration = True
categories = []
buyer_agent_count = recipe[0]
index = 0
for category in recipe:
next_index = index + num_of_possible_ps * category
price_value_multiple = -1 * buyer_agent_count if index > 0 else recipe_sum - buyer_agent_count
categories.append(
AgentCategory("agent", [
int(price * price_value_multiple)
for price in stock_prices[index:next_index]
]))
index = next_index
market = Market(categories)
(optimal_trade,
_) = market.optimal_trade(ps_recipe=list(recipe),
max_iterations=10000000,
include_zero_gft_ps=False)
optimal_count = optimal_trade.num_of_deals()
optimal_gft = optimal_trade.gain_from_trade()
(optimal_trade_with_gft_zero,
_) = market.optimal_trade(ps_recipe=list(recipe),
max_iterations=10000000)
optimal_count_with_gft_zero = optimal_trade_with_gft_zero.num_of_deals(
)
results = [
("stockname", stock_names[i]), ("recipe", recipe_str),
("numpossibletrades", round(num_of_possible_ps)),
("optimalcount", optimal_count),
("optimalcountwithgftzero", optimal_count_with_gft_zero),
("optimalgft", optimal_gft)
]
for auction_index in range(len(auction_functions)):
if 'mcafee' in auction_names[auction_index]:
results.append((auction_name + "auctioncount", 0))
results.append((auction_name + "countratio", 0))
results.append((auction_name + "gft", 0))
results.append((auction_name + "gftratio", 0))
auction_trade = auction_functions[auction_index](market,
recipe)
auction_count = auction_trade.num_of_deals()
# for j in range(len(stocks_prices[i])):
# print(sorted(stocks_prices[i][j][:num_of_possible_ps*recipe[j]]))
if (auction_trade.num_of_deals() >
optimal_trade_with_gft_zero.num_of_deals()):
# print(sorted(stocks_prices[i][0][:num_of_possible_ps*recipe[0]]))
# print(sorted(stocks_prices[i][1][:num_of_possible_ps*recipe[1]]))
print(
"Warning!!! the number of deals in action is greater than optimal!"
)
print("Optimal num of deals: ",
optimal_trade.num_of_deals())
print("Auction num of deals: ",
auction_trade.num_of_deals())
print("Auction name: ", auction_names[auction_index])
gft = auction_trade.gain_from_trade(
including_auctioneer=False)
auction_name = auction_names[auction_index]
results.append((auction_name + "auctioncount",
auction_trade.num_of_deals()))
results.append(
(auction_name + "countratio",
0 if optimal_count_with_gft_zero == 0 else
(auction_count / optimal_count_with_gft_zero) * 100))
results.append((auction_name + "gft", gft))
results.append(
(auction_name + "gftratio",
0 if optimal_gft == 0 else gft / optimal_gft * 100))
#results_table.add(OrderedDict(results))
if len(total_results[str(num_of_agents_per_category)]) == 0:
total_results[str(
num_of_agents_per_category)] = results[0:len(results)]
else:
sum_result = total_results[str(num_of_agents_per_category)]
for index in range(len(results)):
if index > 2:
sum_result[index] = (results[index][0],
sum_result[index][1] +
results[index][1])
for num_of_agents_per_category in nums_of_agents:
results = total_results[str(num_of_agents_per_category)]
if len(results) == 0:
continue
for index in range(len(results)):
if index > 2:
if 'ratio' in results[index][0]:
results[index] = (results[index][0],
results[index][1] / iterations)
else:
results[index] = (results[index][0],
results[index][1] / iterations)
#elif index == 0:
# results[index] = (results[index][0], 'Average')
results_table.add(OrderedDict(results))
if len(average_total_results[str(
num_of_agents_per_category)]) == 0:
average_total_results[str(
num_of_agents_per_category)] = results[0:len(results)]
else:
sum_result = average_total_results[str(
num_of_agents_per_category)]
for index in range(len(results)):
if index > 2:
sum_result[index] = (sum_result[index][0],
sum_result[index][1] +
results[index][1])
for num_of_agents_per_category in nums_of_agents:
results = average_total_results[str(num_of_agents_per_category)]
if len(results) == 0:
continue
for index in range(len(results)):
if index > 2:
if 'ratio' in results[index][0]:
results[index] = (
results[index][0],
int(results[index][1] / len(stocks_prices) * 1000) /
1000)
else:
results[index] = (results[index][0],
round(
results[index][1] /
len(stocks_prices), 1))
elif index == 0:
results[index] = (results[index][0], 'Average')
results_table.add(OrderedDict(results))
results_table.done()
#!python3
"""
Demonstration of a multiple-clock strongly-budget-balanced ascending auction
for a multi-lateral market with one buyer per two sellers (recipe: 1,2)
Author: Erel Segal-Halevi
Since: 2019-08
"""
from markets import Market
from agents import AgentCategory
import ascending_auction_protocol, prices
from ascending_auction_protocol import budget_balanced_ascending_auction
import logging
ascending_auction_protocol.logger.setLevel(logging.INFO)
prices.logger.setLevel(logging.INFO)
print("\n\n###### EXAMPLE OF PS with GFT=0")
market = Market([
AgentCategory("buyer", [1, 1, 1, 1, 1]),
AgentCategory("seller", [-1, -1, -1, -1, -1]),
])
print(budget_balanced_ascending_auction(market, [1, 1]))
# market = Market([
# AgentCategory("C0", [400, 300, 200, 100]),
# AgentCategory("C1", [-1, -11]),
# AgentCategory("C2", [-2, -22]),
# AgentCategory("C3", [-3, -33]),
# AgentCategory("C4", [-4, -44]),
# AgentCategory("C5", [-5, -55]),
# AgentCategory("C6", [-6, -66]),
# ])
market = Market([
AgentCategory("C0", [400, 300, 200, 100]),
AgentCategory("C1", [-1, -11]),
AgentCategory("C2", [-2]),
AgentCategory("C3", [-3]),
AgentCategory("C4", [-4, -44]),
AgentCategory("C5", [-5]),
AgentCategory("C6", [-6]),
])
recipes_4paths = [0, [1, [2, None, 3, None], 4, [5, None, 6, None]]]
# The recipes are:
# 1,1,1,0,0,0,0 [C0, C1, C2]
# 1,1,0,1,0,0,0 [C0, C1, C3]
# 1,0,0,0,1,1,0 [C0, C4, C5]
# 1,0,0,0,1,0,1 [C0, C4, C6]
print(budget_balanced_ascending_auction(market, recipes_4paths))
def experiment(results_csv_file: str, recipe: list, value_ranges: list,
nums_of_agents: list, num_of_iterations: int,
agent_counts: list, agent_values: list,
recipe_tree_agent_counts: list, num_recipes: int):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param results_csv_file: the experiment result file.
:param auction_functions: list of functions for executing the auction under consideration.
:param auction_names: titles of the experiment, for printouts.
:param recipe: can be any vector of ones, e.g. (1,1,1), for our trade-reduction mechanism, or any vector of positive integers for our ascending-auction mechanism.
:param nums_of_agents: list of n(s) for number of possible trades to make the calculations.
:param stocks_prices: list of prices for each stock and each agent.
:param stock_names: list of stocks names which prices are belongs, for naming only.
"""
TABLE_COLUMNS = [
"iterations", "recipe", "numofagents", "optimalcount", "optimalkmin",
"optimalkmax", "gftformula", "auctioncount", "auctionkmin",
"auctionkmax", "countratio", "optimalgft", "auctiongft", "gftratio"
]
print('recipe:', recipe, 'size:', num_recipes)
GFT_ROUND = 1 - 1
K_ROUND = 2 - 1
RATIO_ROUND = 3 - 1
results_table = TeeTable(TABLE_COLUMNS, results_csv_file)
recipe_str = str(recipe).replace(',', '-')
is_binary = len(set(recipe_tree_agent_counts)) == 1
for i in range(len(nums_of_agents)):
now = datetime.now()
sum_optimal_count = sum_auction_count = sum_optimal_kmin = sum_optimal_kmax = 0 # count the number of deals done in the optimal vs. the actual auction.
sum_optimal_gft = sum_auction_total_gft = sum_auction_kmin = sum_auction_kmax = 0
for iteration in range(num_of_iterations):
#if iteration % 10000 == 0:
# print('iteration:', iteration)
agents = []
for category in range(len(recipe_tree_agent_counts)):
sign = 0 if category == 0 else 1
agents.append(
AgentCategory.uniformly_random(
"agent",
int(nums_of_agents[i] * agent_counts[category]),
value_ranges[sign][0] * agent_values[category],
value_ranges[sign][1] * agent_values[category]))
#agents.append(AgentCategory.uniformly_random("agent", nums_of_agents[i], value_ranges[sign][0], value_ranges[sign][1]))
market = Market(agents)
#print(market)
#print(agents)
recipe_tree = RecipeTree(market.categories, recipe,
recipe_tree_agent_counts)
optimal_trade, optimal_count, optimal_gft, kmin, kmax, categories_optimal_counters = recipe_tree.optimal_trade_with_counters(
)
#print('counters:' + str(categories_counters))
#print('optimal trade:', optimal_trade, optimal_count, optimal_gft)
auction_trade = budget_balanced_ascending_auction(
market, recipe, recipe_tree_agent_counts)
auction_count = auction_trade.num_of_deals()
auction_kmin = auction_trade.min_num_of_deals()
auction_kmax = auction_trade.max_num_of_deals()
path_counters = auction_trade.path_counters
gft = auction_trade.gain_from_trade()
#print('Compare:', categories_optimal_counters, path_counters)
for counter in categories_optimal_counters.keys():
if categories_optimal_counters[
counter] > path_counters[counter] + 1 and False:
print(market)
print('Compare:', categories_optimal_counters,
path_counters)
print('Warning counters', str(counter),
'are not in same size!',
categories_optimal_counters[counter], '!=',
path_counters[counter])
#for i in range(len(path_counters)):
# print('Compare:', categories_optimal_counters, path_counters)
#if optimal_count > 0 and gft < optimal_gft * (kmin - 1)/(kmin + 2):
#the auction count is less more than 1 than the optimal count.
# print('Warning GFT!!!', 'optimal_count:', optimal_count, 'auction_count:', auction_count,
# 'num_of_possible_ps:', nums_of_agents[i], 'optimal_gft:', optimal_gft, 'gft:', gft, 'lower bound:', optimal_gft * (1 - 1/optimal_count))
# if nums_of_agents[i] < 20:
# print(market.categories)
sum_optimal_count += optimal_count
sum_auction_count += auction_count
sum_optimal_kmin += kmin
sum_optimal_kmax += kmax
sum_auction_kmin += auction_kmin
sum_auction_kmax += auction_kmax
sum_optimal_gft += optimal_gft
sum_auction_total_gft += gft
#if auction_count < optimal_count - 2:
#the auction count is less more than 1 than the optimal count.
#print('Warning!!!', 'optimal_count:', optimal_count, 'auction_count:', auction_count, 'num_of_possible_ps:', nums_of_agents[i])
#if nums_of_agents[i] < 10:
# print(market.categories)
# print("Num of times {} attains the maximum GFT: {} / {} = {:.2f}%".format(title, count_optimal_gft, num_of_iterations, count_optimal_gft * 100 / num_of_iterations))
# print("GFT of {}: {:.2f} / {:.2f} = {:.2f}%".format(title, sum_auction_gft, sum_optimal_gft, 0 if sum_optimal_gft==0 else sum_auction_gft * 100 / sum_optimal_gft))
kmin_mean = sum_optimal_kmin / num_of_iterations
if is_binary:
gft_formula = int_round(
(kmin_mean - 1) / kmin_mean * 100 if kmin_mean - 1 > 0 else 0,
RATIO_ROUND)
else:
gft_formula = int_round(
(kmin_mean - num_recipes) / (kmin_mean + num_recipes) *
100 if kmin_mean - num_recipes > 0 else 0, RATIO_ROUND)
results_table.add(
OrderedDict([
("iterations", num_of_iterations),
("recipe", recipe_str),
("numofagents", nums_of_agents[i]),
("optimalcount",
int_round(sum_optimal_count / num_of_iterations, K_ROUND)),
("optimalkmin", int_round(kmin_mean, K_ROUND)),
("optimalkmax",
int_round(sum_optimal_kmax / num_of_iterations, K_ROUND)),
("gftformula", gft_formula),
("auctioncount",
int_round(sum_auction_count / num_of_iterations, K_ROUND)),
("auctionkmin",
int_round(sum_auction_kmin / num_of_iterations, K_ROUND)),
("auctionkmax",
int_round(sum_auction_kmax / num_of_iterations, K_ROUND)),
("countratio",
int_round(
0 if sum_optimal_count == 0 else
(sum_auction_count / sum_optimal_count) * 100,
RATIO_ROUND)),
("optimalgft",
int_round(sum_optimal_gft / num_of_iterations, GFT_ROUND)),
("auctiongft",
int_round(sum_auction_total_gft / num_of_iterations,
GFT_ROUND)),
("gftratio", '0.00' if sum_optimal_gft == 0 else int_round(
sum_auction_total_gft / sum_optimal_gft *
100, RATIO_ROUND)),
]))
print('took', (datetime.now() - now).seconds)
results_table.done()
def budget_balanced_ascending_auction(
market: Market, ps_recipes: List[List]) -> TradeWithMultipleRecipes:
"""
Calculate the trade and prices using generalized-ascending-auction.
Allows multiple recipes, but they must all be binary, and must all start with 1. E.g.:
[ [1,1,0,0], [1,0,1,1] ]
I.e., there is 1 buyer category and n-1 seller categories.
Each buyer may wish to buy a different combination of products.
:param market: contains a list of k categories, each containing several agents.
:param ps_recipes: a list of lists of integers, one integer per category.
Each integer i represents the number of agents of category i
that should be in each procurement-set.
:return: Trade object, representing the trade and prices.
>>> # ONE BUYER, ONE SELLER
>>> recipe_11 = [[1,1]]
>>>
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0, 8.0], seller: [-4.0]]
No trade
>>> logger.setLevel(logging.WARNING)
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0], seller: [-3.0, -4.0]]
seller: [-3.0]: all 1 agents trade and pay -4.0
buyer: [9.0]: all 1 agents trade and pay 4.0
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0, 8.0], seller: [-3.0, -4.0]]
seller: [-3.0, -4.0]: random 1 out of 2 agents trade and pay -8.0
buyer: [9.0]: all 1 agents trade and pay 8.0
"""
num_recipes = len(ps_recipes)
if num_recipes < 1:
raise ValueError("Empty list of recipes")
num_categories = market.num_categories
for i, ps_recipe in enumerate(ps_recipes):
if len(ps_recipe) != num_categories:
raise ValueError(
"There are {} categories but {} elements in PS recipe #{}".
format(num_categories, len(ps_recipe), i))
if any((r != 1 and r != 0) for r in ps_recipe):
raise ValueError(
"Currently, the multi-recipe protocol supports only recipes of zeros and ones; {} was given"
.format(ps_recipe))
logger.info("\n#### Multi-Recipe Budget-Balanced Ascending Auction\n")
logger.info(market)
logger.info("Procurement-set recipes: {}".format(ps_recipes))
map_category_index_to_recipe_indices, common_category_indices, map_recipe_index_to_unique_category_indices = \
_analyze_recipes(num_categories, ps_recipes)
# Calculating the optimal trade with multiple recipes is left for future work.
# optimal_trade = market.optimal_trade(ps_recipe)[0]
# logger.info("For comparison, the optimal trade is: %s\n", optimal_trade)
remaining_market = market.clone()
prices = SimultaneousAscendingPriceVectors(ps_recipes, -MAX_VALUE)
# Functions for calculating the number of potential PS that can be supported by a category:
# Currently we assume a recipe of ones, so the number of potential PS is simply the category size:
fractional_potential_ps = lambda category_index: remaining_market.categories[
category_index].size()
integral_potential_ps = lambda category_index: remaining_market.categories[
category_index].size()
while True:
# find a category with a largest number of potential PS, and increase its price
largest_common_category_index = max(common_category_indices, key=fractional_potential_ps) \
if len(common_category_indices)>0 \
else None
largest_common_category_size = fractional_potential_ps(largest_common_category_index) \
if len(common_category_indices) > 0 \
else 0
logger.info("Largest common category is %d and its size is %d",
largest_common_category_index,
largest_common_category_size)
map_recipe_index_to_largest_unique_category_index = [
max(unique_category_indices, key=fractional_potential_ps)
for unique_category_indices in
map_recipe_index_to_unique_category_indices
]
if len(map_recipe_index_to_largest_unique_category_index) == 0:
raise ValueError("No unique categories")
unique_categories_size = sum([
fractional_potential_ps(largest_unique_category_index)
for largest_unique_category_index in
map_recipe_index_to_largest_unique_category_index
])
logger.info(
"Largest unique categories are %s and their total size is %d",
map_recipe_index_to_largest_unique_category_index,
unique_categories_size)
if unique_categories_size == 0:
logger.info("\nThe unique categories %s became empty - no trade!",
map_recipe_index_to_largest_unique_category_index)
logger.info(" Final price-per-unit vector: %s", prices)
logger.info(remaining_market)
return TradeWithMultipleRecipes(
remaining_market.categories, ps_recipes,
prices.map_category_index_to_price())
if largest_common_category_size >= unique_categories_size:
logger.info(
"Raising price of the largest common category (%d) in all recipes",
largest_common_category_index)
main_category_index = largest_common_category_index
main_category = remaining_market.categories[main_category_index]
logger.info("%s before: %d agents remain", main_category.name,
main_category.size())
increases = [
(main_category_index, main_category.lowest_agent_value(),
main_category.name)
] * num_recipes
else: # largest_common_category_size < unique_categories_size
logger.info(
"Raising price of the largest unique categories in each recipe: %s",
map_recipe_index_to_largest_unique_category_index)
increases = []
for recipe_index, main_category_index in enumerate(
map_recipe_index_to_largest_unique_category_index):
main_category = remaining_market.categories[
main_category_index]
logger.info("%s before: %d agents remain", main_category.name,
main_category.size())
if main_category.size() == 0:
logger.info(
"\nThe %s category became empty - no trade in recipe %d",
main_category.name, recipe_index)
del ps_recipes[recipe_index]
if len(ps_recipes) > 0:
return budget_balanced_ascending_auction(
market, ps_recipes)
else:
logger.info("\nNo recipes left - no trade!")
logger.info(" Final price-per-unit vector: %s",
prices)
logger.info(remaining_market)
return TradeWithMultipleRecipes(
remaining_market.categories, ps_recipes,
map_category_index_to_price)
increases.append(
(main_category_index, main_category.lowest_agent_value(),
main_category.name))
if len(increases) == 0:
raise ValueError("No increases!")
logger.info("Planned increases: %s", increases)
prices.increase_prices(increases)
map_category_index_to_price = prices.map_category_index_to_price()
if prices.status == PriceStatus.STOPPED_AT_ZERO_SUM:
logger.info("\nPrice crossed zero.")
logger.info(" Final price-per-unit vector: %s",
map_category_index_to_price)
logger.info(remaining_market)
return TradeWithMultipleRecipes(remaining_market.categories,
ps_recipes,
map_category_index_to_price)
for category_index in range(num_categories):
category = remaining_market.categories[category_index]
if map_category_index_to_price[category_index] is not None \
and category.size()>0 \
and category.lowest_agent_value() <= map_category_index_to_price[category_index]:
category.remove_lowest_agent()
logger.info(
"{} after: {} agents remain, {} PS supported".format(
category.name, category.size(),
integral_potential_ps(category_index)))
for a bilateral market with buyers and sellers.
Since: 2019-08
Author: Erel Segal-Halevi
"""
from markets import Market
from agents import AgentCategory
import mcafee_protocol
from mcafee_protocol import mcafee_trade_reduction
import logging
mcafee_protocol.logger.setLevel(logging.INFO)
recipe = [1, 1]
print("\n\n### Example without trade reduction")
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("seller", [-1, -4, -5, -8, -11]),
])
print(mcafee_trade_reduction(market, recipe))
print("\n\n### Example with trade reduction")
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("seller", [-1, -4, -5, -8, -13]),
])
print(mcafee_trade_reduction(market, recipe))
def experiment(results_csv_file: str, recipe: list, agent_counts:list, agent_values:list,
nums_of_agents:list = None, num_of_iterations:int = 1, stocks_prices=None, stock_names=None):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param results_csv_file: the experiment result file.
:param auction_functions: list of functions for executing the auction under consideration.
:param auction_names: titles of the experiment, for printouts.
:param recipe: can be any vector of ones, e.g. (1,1,1), for our trade-reduction mechanism, or any vector of positive integers for our ascending-auction mechanism.
:param nums_of_agents: list of n(s) for number of possible trades to make the calculations.
:param stocks_prices: list of prices for each stock and each agent.
:param stock_names: list of stocks names which prices are belongs, for naming only.
"""
TABLE_COLUMNS = ["stockname", "recipe", "numpossibletrades",
"optimalkmin", "optimalkmax","gftformula",
"optimalcount", "optimalgft", "auctioncount", "countratio", "gft", "gftratio"]
print('recipe:', recipe)
results_table = TeeTable(TABLE_COLUMNS, results_csv_file)
recipe_str = str(recipe).replace(',', '->')
if stocks_prices is None:
(stocks_prices, stock_names) = getStocksTreePrices(recipe, agent_counts, agent_values)
if nums_of_agents is None:
nums_of_agents = [10000000]
total_results = {}
for num_of_agents_per_category in nums_of_agents:
total_results[str(num_of_agents_per_category)] = []
for i in range(len(stock_names)):
for _ in range(num_of_iterations):
last_iteration = False
for j in range(len(stocks_prices[i])):
random.shuffle(stocks_prices[i][j])
for num_of_agents_per_category in nums_of_agents:
num_of_possible_ps = min(num_of_agents_per_category, len(stocks_prices[i][0]))
if last_iteration is True and num_of_possible_ps < num_of_agents_per_category:
break
if num_of_possible_ps < num_of_agents_per_category:
if last_iteration is True:
break
else:
last_iteration = True
market = Market([AgentCategory("agent", stocks_prices[i][j]) for j in range(len(stocks_prices[i]))])
else:
market = Market([AgentCategory("agent", stocks_prices[i][j][0:int(num_of_possible_ps*agent_counts[j])]) for j in range(len(stocks_prices[i]))])
if num_of_agents_per_category == 6 and _ == 0:
print(stock_names[i], market.categories)
recipe_tree = RecipeTree(market.categories, recipe)
optimal_trade, optimal_count, optimal_gft, kmin, kmax = recipe_tree.optimal_trade_with_counters()
#print('optimal trade:', optimal_trade, optimal_count, optimal_gft)
auction_trade = budget_balanced_ascending_auction(market, recipe)
auction_count = auction_trade.num_of_deals()
gft = auction_trade.gain_from_trade()
#if auction_count < optimal_count - 1:
# #the auction count is less more than 1 than the optimal count.
# print('Warning!!!', 'optimal_count:', optimal_count, 'auction_count:', auction_count, 'num_of_possible_ps:', num_of_possible_ps)
# if num_of_possible_ps < 10:
# print(market.categories)
#if optimal_count > 0 and gft < optimal_gft * (1 - 1/optimal_count):
# #the auction count is less more than 1 than the optimal count.
# print('Warning GFT!!!', 'optimal_count:', optimal_count, 'auction_count:', auction_count,
# 'num_of_possible_ps:', num_of_possible_ps, 'optimal_gft:', optimal_gft, 'gft:', gft)
# if num_of_possible_ps < 20:
# print(market.categories)
if optimal_count < auction_count :
#the auction count is less more than 1 than the optimal count.
print('Warning count!!!', 'optimal_count:', optimal_count, 'auction_count:', auction_count,
'num_of_possible_ps:', num_of_possible_ps, 'optimal_gft:', optimal_gft, 'gft:', gft)
if num_of_possible_ps < 20:
print(market.categories)
results = [("stockname", stock_names[i]),
("recipe", recipe_str),
("numpossibletrades", num_of_possible_ps),
("optimalcount", optimal_count),
("optimalkmin", kmin),
("optimalkmax", kmax),
("optimalgft", optimal_gft),
("auctioncount", auction_count),
("countratio", 0 if optimal_count==0 else auction_count / optimal_count*100),
("gft", gft),
("gftratio", 0 if optimal_gft==0 else gft / optimal_gft*100)
]
#results_table.add(OrderedDict(results))
if len(total_results[str(num_of_agents_per_category)]) == 0:
total_results[str(num_of_agents_per_category)] = results[0:len(results)]
else:
sum_result = total_results[str(num_of_agents_per_category)]
for index in range(len(results)):
if index > 2:
sum_result[index] = (results[index][0], sum_result[index][1] + results[index][1])
for num_of_agents_per_category in nums_of_agents:
results = total_results[str(num_of_agents_per_category)]
kmin = 0
for index in range(len(results)):
if index > 2:
#if 'ratio' in results[index][0]:
# results[index] = (results[index][0], results[index][1]/len(stock_names))
#elif 'count' in results[index][0]:
# results[index] = (results[index][0], results[index][1]/len(stock_names))
#else:
results[index] = (results[index][0], results[index][1]/len(stock_names)/num_of_iterations)
elif index == 0:
results[index] = (results[index][0], 'Average')
if results[index][0] == 'optimalkmin':
kmin = results[index][1]
results.append(('gftformula', 0 if kmin <= 1 else (1-1/kmin)*100))
results_table.add(OrderedDict(results))
results_table.done()
-13.75, -13.7, -13.15, -12.9, -12.9, -12.65
]
#
# buyers = removeSeconds(buyers, reduce_number)
# sellers = removeSeconds(sellers, reduce_number)
# mediators = removeSeconds(mediators, reduce_number)
# mediatorsB = removeSeconds(mediatorsB, reduce_number)
#
# random.shuffle(buyers)
# random.shuffle(sellers)
# random.shuffle(mediators)
# random.shuffle(mediatorsB)
market = Market([
AgentCategory("buyer", buyers),
AgentCategory("seller", sellers),
AgentCategory("mediator", mediators),
AgentCategory("mediatorB", mediatorsB),
])
without_gft0 = budget_balanced_trade_reduction(market, recipe, False)
with_gft0 = budget_balanced_trade_reduction(market, recipe, True)
print(without_gft0)
print(with_gft0)
without_count = without_gft0.num_of_deals()
with_count = with_gft0.num_of_deals()
without_gft = without_gft0.gain_from_trade()
with_gft = with_gft0.gain_from_trade()
print('Compare: Without:', without_gft, "With:", with_gft)
print('Compare: Without:', without_count, "With:", with_count)
break
def experiment(results_csv_file: str, auction_function: Callable,
auction_name: str, recipe: tuple, value_ranges: list,
nums_of_agents: list, num_of_iterations: int):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param auction_function: the function for executing the auction under consideration.
:param auction_name: title of the experiment, for printouts.
:param nums_of_agents: a list of the numbers of agents with which to run the experiment.
:param value_ranges: for each category, a pair (min_value,max_value). The value for each agent in this category is selected uniformly at random between min_value and max_value.
:param num_of_iterations: how many times to repeat the experiment for each num of agents.
"""
results_table = TeeTable(TABLE_COLUMNS, results_csv_file)
recipe_str = ":".join(map(str, recipe))
num_of_categories = len(recipe)
for num_of_agents_per_category in nums_of_agents:
sum_optimal_count = sum_auction_count = 0 # count the number of deals done in the optimal vs. the actual auction.
sum_optimal_gft = sum_auction_total_gft = sum_auction_market_gft = 0
for _ in range(num_of_iterations):
market = Market([
AgentCategory.uniformly_random(
"agent", num_of_agents_per_category * recipe[category],
value_ranges[category][0], value_ranges[category][1])
for category in range(num_of_categories)
])
(optimal_trade, _) = market.optimal_trade(recipe)
auction_trade = auction_function(market, recipe)
sum_optimal_count += optimal_trade.num_of_deals()
sum_auction_count += auction_trade.num_of_deals()
sum_optimal_gft += optimal_trade.gain_from_trade()
sum_auction_total_gft += auction_trade.gain_from_trade(
including_auctioneer=True)
sum_auction_market_gft += auction_trade.gain_from_trade(
including_auctioneer=False)
# print("Num of times {} attains the maximum GFT: {} / {} = {:.2f}%".format(title, count_optimal_gft, num_of_iterations, count_optimal_gft * 100 / num_of_iterations))
# print("GFT of {}: {:.2f} / {:.2f} = {:.2f}%".format(title, sum_auction_gft, sum_optimal_gft, 0 if sum_optimal_gft==0 else sum_auction_gft * 100 / sum_optimal_gft))
results_table.add(
OrderedDict((
("iterations", num_of_iterations),
("auction_name", auction_name),
("recipe", recipe_str),
("num_of_agents", num_of_agents_per_category),
("mean_optimal_count",
round(sum_optimal_count / num_of_iterations, 2)),
("mean_auction_count",
round(sum_auction_count / num_of_iterations, 2)),
("count_ratio", 0 if sum_optimal_count == 0 else int(
(sum_auction_count / sum_optimal_count) * 10000) / 100),
("mean_optimal_gft",
round(sum_optimal_gft / num_of_iterations, 2)),
("mean_auction_total_gft",
round(sum_auction_total_gft / num_of_iterations, 2)),
("total_gft_ratio", 0 if sum_optimal_gft == 0 else round(
sum_auction_total_gft / sum_optimal_gft * 100, 2)),
("mean_auction_market_gft",
round(sum_auction_market_gft / num_of_iterations, 2)),
("market_gft_ratio", 0 if sum_optimal_gft == 0 else round(
sum_auction_market_gft / sum_optimal_gft * 100, 2)),
)))
results_table.done()
from agents import AgentCategory
from old import ascending_auction_multirecipe_protocol
from old.ascending_auction_multirecipe_protocol import budget_balanced_ascending_auction
import prices
import logging
ascending_auction_multirecipe_protocol.logger.setLevel(logging.INFO)
prices.logger.setLevel(logging.INFO)
print(
"\n\n###### TEST MULTI RECIPE AUCTION WITH TWO EQUIVALENT RECIPES: [1,0,1] [1,1,0]"
)
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("sellerA", [-1, -3, -4, -5, -8, -10]),
AgentCategory("sellerB", [-2, -7, -11]),
])
# print(budget_balanced_ascending_auction(market, [[1, 1, 0], [1, 0, 1]]))
print(
"\n\n###### TEST MULTI RECIPE AUCTION WITH TWO DIFFERENT RECIPES: [1,1,0,0] [1,0,1,1]"
)
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("seller", [-1, -3, -4, -5, -8, -10]),
AgentCategory("producerA", [-1, -3, -5]),
AgentCategory("producerB", [-1, -4, -6]),
])
def mcafee_trade_reduction(market: Market,
ps_recipe: list,
price_heuristic=True):
"""
Calculate the trade and prices using generalized-trade-reduction.
:param market: contains a list of k categories, each containing several agents.
:param ps_recipe: a list of integers, one integer per category.
Each integer i represents the number of agents of category i
that should be in each procurement-set.
:param price_heuristic: whether to use the heuristic of setting the price to (s_{k+1)+b_{k+1})/2.
Default is true, as in the original paper.
:return: Trade object, representing the trade and prices.
>>> # ONE BUYER, ONE SELLER
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.])])
>>> print(market); print(mcafee_trade_reduction(market, [1,1]))
Traders: [buyer: [9.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.])])
>>> print(market); print(mcafee_trade_reduction(market, [1,1]))
Traders: [buyer: [9.0, 8.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("seller", [-4.]), AgentCategory("buyer", [9.,8.])])
>>> print(market); print(mcafee_trade_reduction(market, [1,1]))
Traders: [seller: [-4.0], buyer: [9.0, 8.0]]
No trade
>>> market = Market([AgentCategory("seller", [-4.,-3.]), AgentCategory("buyer", [9.])])
>>> print(market); print(mcafee_trade_reduction(market, [1,1]))
Traders: [seller: [-3.0, -4.0], buyer: [9.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(mcafee_trade_reduction(market, [1,1]))
Traders: [buyer: [9.0], seller: [-3.0, -4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(mcafee_trade_reduction(market, [1,1]))
Traders: [buyer: [9.0, 8.0], seller: [-3.0, -4.0]]
buyer: [9.0]: all 1 agents trade and pay 8.0
seller: [-3.0]: all 1 agents trade and pay -4.0
>>> market = Market([AgentCategory("seller", [-4.,-3.]), AgentCategory("buyer", [9.,8.])])
>>> print(mcafee_trade_reduction(market, [1,1]))
seller: [-3.0]: all 1 agents trade and pay -4.0
buyer: [9.0]: all 1 agents trade and pay 8.0
>>> market = Market([AgentCategory("seller", [-8.,-3.]), AgentCategory("buyer", [9.,4.])])
>>> print(mcafee_trade_reduction(market, [1,1]))
seller: [-3.0]: all 1 agents trade and pay -6.0
buyer: [9.0]: all 1 agents trade and pay 6.0
#
# >>>
# >>> # ONE BUYER, ONE SELLER, ONE MEDIATOR
# >>> market = Market([AgentCategory("seller", [-4.,-3.]), AgentCategory("buyer", [9.,8.]), AgentCategory("mediator", [-1.,-2.])])
# >>> print(market); print(mcafee_trade_reduction(market, [1,1,1]))
# Traders: [seller: [-3.0, -4.0], buyer: [9.0, 8.0], mediator: [-1.0, -2.0]]
# seller: [-3.0]: all 1 agents trade and pay -4.0
# buyer: [9.0]: all 1 agents trade and pay 8.0
# mediator: [-1.0, -2.0]: random 1 out of 2 agents trade and pay -4.0
#
# >>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("mediator", [-1.,-2.]), AgentCategory("seller", [-4.,-3.,-10.])])
# >>> print(market); print(mcafee_trade_reduction(market, [1,1,1]))
# Traders: [buyer: [9.0, 8.0], mediator: [-1.0, -2.0], seller: [-3.0, -4.0, -10.0]]
# buyer: [9.0]: all 1 agents trade and pay 8.0
# mediator: [-1.0]: all 1 agents trade and pay -2.0
# seller: [-3.0, -4.0]: random 1 out of 2 agents trade and pay -6.0
#
# >>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("mediator", [-1.,-2.]), AgentCategory("seller", [-4.,-3.,-5.])])
# >>> print(market); print(mcafee_trade_reduction(market, [1,1,1]))
# Traders: [buyer: [9.0, 8.0], mediator: [-1.0, -2.0], seller: [-3.0, -4.0, -5.0]]
# buyer: [9.0]: all 1 agents trade and pay 8.0
# mediator: [-1.0, -2.0]: random 1 out of 2 agents trade and pay -3.0
# seller: [-3.0, -4.0]: random 1 out of 2 agents trade and pay -5.0
#
# >>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("mediator", [-1.,-2.]), AgentCategory("seller", [-4.,-3.,-2.])])
# >>> print(market); print(mcafee_trade_reduction(market, [1,1,1]))
# Traders: [buyer: [9.0, 8.0], mediator: [-1.0, -2.0], seller: [-2.0, -3.0, -4.0]]
# buyer: [9.0]: all 1 agents trade and pay 8.0
# mediator: [-1.0, -2.0]: random 1 out of 2 agents trade and pay -4.0
# seller: [-2.0, -3.0]: random 1 out of 2 agents trade and pay -4.0
#
# >>> market = Market([AgentCategory("buyer", [9.,8.,7.]), AgentCategory("mediator", [-1.,-2.,-3.]), AgentCategory("seller", [-4.,-3.,-2.])])
# >>> print(market); print(mcafee_trade_reduction(market, [1,1,1]))
# Traders: [buyer: [9.0, 8.0, 7.0], mediator: [-1.0, -2.0, -3.0], seller: [-2.0, -3.0, -4.0]]
# buyer: [9.0, 8.0]: all 2 agents trade and pay 7.0
# mediator: [-1.0, -2.0]: all 2 agents trade and pay -3.0
# seller: [-2.0, -3.0]: all 2 agents trade and pay -4.0
#
# >>> market = Market([AgentCategory("buyer", [9.,8.,4.]), AgentCategory("mediator", [-1.,-2.,-3.]), AgentCategory("seller", [-4.,-3.,-2.])])
# >>> print(market); print(mcafee_trade_reduction(market, [1,1,1]))
# Traders: [buyer: [9.0, 8.0, 4.0], mediator: [-1.0, -2.0, -3.0], seller: [-2.0, -3.0, -4.0]]
# buyer: [9.0, 8.0]: all 2 agents trade and pay 7.0
# mediator: [-1.0, -2.0]: all 2 agents trade and pay -3.0
# seller: [-2.0, -3.0]: all 2 agents trade and pay -4.0
"""
if len(ps_recipe) != market.num_categories:
raise ValueError(
"There are {} categories but {} elements in the PS recipe".format(
market.num_categories, len(ps_recipe)))
if any(r != 1 for r in ps_recipe):
raise ValueError(
"Currently, the trade-reduction protocol supports only recipes of ones; {} was given"
.format(ps_recipe))
logger.info("\n#### McAfee Trade Reduction\n")
logger.info(market)
(optimal_trade, remaining_market) = market.optimal_trade(ps_recipe)
for category in remaining_market.categories:
if len(category) == 0:
category.append(-MAX_VALUE)
logger.info("Optimal trade, by increasing GFT: {}".format(optimal_trade))
first_negative_ps = remaining_market.get_highest_agents(ps_recipe)
if price_heuristic:
price_candidate = sum([abs(x) for x in first_negative_ps
]) / len(first_negative_ps)
logger.info("First negative PS: {}, candidate price: {}".format(
first_negative_ps, price_candidate))
actual_traders = market.empty_agent_categories()
if optimal_trade.num_of_deals() > 0:
last_positive_ps = optimal_trade.procurement_sets[0]
if price_heuristic and is_price_good_for_ps(price_candidate,
last_positive_ps):
# All optimal traders trade in the candidate price - no reduction
prices = [
price_candidate * (-1 if last_positive_ps[i] < 0 else +1)
for i in range(market.num_categories)
]
else:
# Trade reduction
del optimal_trade.procurement_sets[0]
prices = last_positive_ps
for ps in optimal_trade.procurement_sets:
for i in range(market.num_categories):
if ps[i] is not None:
actual_traders[i].append(ps[i])
else:
prices = [0 for i in range(market.num_categories)]
logger.info("\n")
return TradeWithSinglePrice(actual_traders, ps_recipe, prices)
"""
Demonstration of a multiple-clock strongly-budget-balanced ascending auction
for a multi-lateral market with two buyers per three sellers (recipe: 2,3)
Author: Erel Segal-Halevi
Since: 2019-08
"""
import sys, os
sys.path.insert(0, os.path.abspath('..'))
from markets import Market
from agents import AgentCategory
import ascending_auction_protocol
from ascending_auction_protocol import budget_balanced_ascending_auction
import logging
ascending_auction_protocol.logger.setLevel(logging.INFO)
market = Market([
AgentCategory("buyer", [20., 18., 16., 9., 2., 1.]),
AgentCategory("seller", [-2., -4., -6., -8., -10., -12., -14.]),
])
print(budget_balanced_ascending_auction(market, [2, 3]))
# Here, k=1, and the final trade involves 2 buyers and 5 sellers
# (so 3 sellers should be selected at random).
print(budget_balanced_ascending_auction(market, [3, 2]))
# Here, k=1, and the final trade involves 5 buyers and 3 sellers
# (so 3 buyers and 2 sellers should be selected at random).
def experiment(results_csv_file: str,
auction_functions: list,
auction_names: str,
recipe: tuple,
nums_of_agents=None,
stocks_prices: list = None,
stock_names: list = None,
num_of_iterations=1000,
run_with_stock_prices=True,
report_diff=False):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param results_csv_file: the experiment result file.
:param auction_functions: list of functions for executing the auction under consideration.
:param auction_names: titles of the experiment, for printouts.
:param recipe: can be any vector of ones, e.g. (1,1,1), for our trade-reduction mechanism, or any vector of positive integers for our ascending-auction mechanism.
:param stocks_prices: list of prices for each stock and each agent.
:param stock_names: list of stocks names which prices are belongs, for naming only.
"""
TABLE_COLUMNS = [
"iterations", "stockname", "recipe", "numpossibletrades",
"optimalcount", "gftratioformula", "optimalcountwithgftzero",
"optimalgft", "optimalgftwithgftzero"
]
AUCTION_COLUMNS = [
"count", "countratio", "totalgft", "totalgftratio",
"withoutgftzerocountratio", "withoutgftzerototalgft",
"withoutgftzerototalgftratio", "marketgft", "marketgftratio"
]
if path.exists(results_csv_file):
print('The file', results_csv_file, 'already exists, skipping')
return
else:
print('Running for the file', results_csv_file)
if stocks_prices is None:
(stocks_prices, stock_names) = getStocksPricesShuffled()
column_names = TABLE_COLUMNS
column_names += [
auction_name + column for auction_name in auction_names
for column in AUCTION_COLUMNS
]
results_table = TeeTable(column_names, results_csv_file)
recipe_str = ":".join(map(str, recipe))
recipe_sum = sum(recipe)
recipe_sum_for_buyer = (recipe_sum - recipe[0]) / recipe[0]
if nums_of_agents is None:
nums_of_agents = [10000000]
#print(nums_of_agents)
total_results = {}
for num_of_agents_per_category in nums_of_agents:
total_results[str(num_of_agents_per_category)] = []
#print(total_results)
for i in range(len(stocks_prices)):
stock_prices = stocks_prices[i]
for num_of_possible_ps in nums_of_agents:
for iteration in range(num_of_iterations):
categories = []
if run_with_stock_prices:
while len(stock_prices) < num_of_possible_ps * recipe_sum:
stock_prices = stock_prices + stock_prices
random.shuffle(stock_prices)
index = 0
for category in recipe:
next_index = index + num_of_possible_ps * category
price_sign = recipe_sum_for_buyer if index == 0 else -1
#price_value_multiple = -1 * buyer_agent_count if index > 0 else recipe_sum - buyer_agent_count
categories.append(
AgentCategory("agent", [
int(price * price_sign)
for price in stock_prices[index:next_index]
]))
index = next_index
else: #prices from random.
for index in range(len(recipe)):
#for category in recipe:
min_value = -100000 if index > 0 else recipe_sum_for_buyer
max_value = -1 if index > 0 else 100000 * recipe_sum_for_buyer
categories.append(
AgentCategory.uniformly_random(
"agent", num_of_possible_ps * recipe[index],
min_value, max_value))
market = Market(categories)
(optimal_trade,
_) = market.optimal_trade(ps_recipe=list(recipe),
max_iterations=10000000,
include_zero_gft_ps=False)
optimal_count = optimal_trade.num_of_deals()
optimal_gft = optimal_trade.gain_from_trade()
(optimal_trade_with_gft_zero,
_) = market.optimal_trade(ps_recipe=list(recipe),
max_iterations=10000000)
optimal_count_with_gft_zero = optimal_trade_with_gft_zero.num_of_deals(
)
optimal_gft_with_gft_zero = optimal_trade_with_gft_zero.gain_from_trade(
)
results = [
("iterations", num_of_iterations),
("stockname", stock_names[i]), ("recipe", recipe_str),
("numpossibletrades", int(num_of_possible_ps)),
("optimalcount", optimal_count),
("gftratioformula", (optimal_count - 1) * 100 /
(optimal_count if min(recipe) == max(recipe)
and recipe[0] == 1 else optimal_count + 1)
if optimal_count > 1 else 0),
("optimalcountwithgftzero", optimal_count_with_gft_zero),
("optimalgft", optimal_gft),
("optimalgftwithgftzero", optimal_gft_with_gft_zero)
]
for auction_index in range(len(auction_functions)):
auction_trade = auction_functions[auction_index](market,
recipe)
count = auction_trade.num_of_deals()
total_gft = auction_trade.gain_from_trade(
including_auctioneer=True)
market_gft = auction_trade.gain_from_trade(
including_auctioneer=False)
auction_name = auction_names[auction_index]
results.append(
(auction_name + "count", auction_trade.num_of_deals()))
results.append(
(auction_name + "countratio",
0 if optimal_count == 0 else
(count / optimal_count_with_gft_zero) * 100))
results.append((auction_name + "totalgft", total_gft))
results.append((auction_name + "totalgftratio",
0 if optimal_gft == 0 else total_gft /
optimal_gft_with_gft_zero * 100))
results.append((auction_name + "marketgft", market_gft))
results.append((auction_name + "marketgftratio",
0 if optimal_gft == 0 else market_gft /
optimal_gft_with_gft_zero * 100))
results.append((auction_name + "withoutgftzerocountratio",
0 if optimal_count == 0 else
(count / optimal_count) * 100))
results.append(
(auction_name + "withoutgftzerototalgft", total_gft))
results.append(
(auction_name + "withoutgftzerototalgftratio",
0 if optimal_gft == 0 else total_gft / optimal_gft *
100))
#We check which auction did better and print the market and their results.
if report_diff:
gft_to_compare = -1
k_to_compare = -1
gft_found = False
k_found = False
for (label, value) in results:
if 'SBB' in label:
if gft_found is False and label.endswith(
'totalgft'):
if gft_to_compare < 0:
gft_to_compare = value
elif gft_to_compare != value:
with open('diff_in_sbbs_gft.txt',
'a') as f:
f.write(
'There is diff in gft between two auctions: '
+ str(gft_to_compare) + ' ' +
str(value) + '\n')
f.write(str(results) + '\n')
if num_of_possible_ps < 10:
f.write(str(market) + '\n')
gft_found = True
elif k_found is False and label.endswith('count'):
if k_to_compare < 0:
k_to_compare = value
elif k_to_compare != value:
with open('diff_in_sbbs_k.txt', 'a') as f:
f.write(
'There is diff in gft between two auctions: '
+ str(k_to_compare) + ' ' +
str(value) + '\n')
f.write(str(results) + '\n')
if num_of_possible_ps < 10:
f.write(str(market) + '\n')
k_found = True
compare_sbbs = True
if compare_sbbs:
gft_to_compare = -1
k_to_compare = -1
gft_found = False
k_found = False
for (label, value) in results:
if 'SBB' in label:
if gft_found is False and label.endswith(
'totalgft'):
if gft_to_compare < 0:
gft_to_compare = value
elif gft_to_compare > value:
with open('diff_in_sbbs_gft.txt',
'a') as f:
f.write(
'There is diff in gft between two auctions: '
+ str(gft_to_compare) + ' ' +
str(value) + '\n')
f.write(str(results) + '\n')
if num_of_possible_ps < 10:
f.write(str(market) + '\n')
gft_found = True
elif k_found is False and label.endswith('count'):
if k_to_compare < 0:
k_to_compare = value
elif k_to_compare > value:
with open('diff_in_sbbs_k.txt', 'a') as f:
f.write(
'There is diff in gft between two auctions: '
+ str(k_to_compare) + ' ' +
str(value) + '\n')
f.write(str(results) + '\n')
if num_of_possible_ps < 10:
f.write(str(market) + '\n')
k_found = True
#results_table.add(OrderedDict(results))
#print(results)
if len(total_results[str(num_of_possible_ps)]) == 0:
total_results[str(
num_of_possible_ps)] = results[0:len(results)]
else:
sum_result = total_results[str(num_of_possible_ps)]
for index in range(len(results)):
if index > 3:
sum_result[index] = (results[index][0],
sum_result[index][1] +
results[index][1])
#print(total_results)
print(stock_names[i], end=',')
#break
print()
division_number = num_of_iterations * len(stocks_prices)
#division_number = num_of_iterations
for num_of_possible_ps in nums_of_agents:
results = total_results[str(num_of_possible_ps)]
for index in range(len(results)):
if 'gftratio' in results[index][0]:
results[index] = (results[index][0],
padding_zeroes(
results[index][1] / division_number, 3))
elif index > 3:
results[index] = (results[index][0],
padding_zeroes(
results[index][1] / division_number, 2))
elif index == 1:
results[index] = (results[index][0], 'Average')
#print(results)
results_table.add(OrderedDict(results))
results_table.done()
# AgentCategory("sellerB", [-3, -4]),
# ])
# print(budget_balanced_ascending_auction_twolevels(market, [1, 1 ,1]))
# print("\n\n###### TWO NON-BINARY RECIPES: [1,2,0], [1,0,2]")
# market = Market([
# AgentCategory("buyer", [19,18,17,16,15,14,13]),
# AgentCategory("sellerA", [-1, -2]),
# AgentCategory("sellerB", [-3, -4]),
# ])
# print(budget_balanced_ascending_auction_twolevels(market, [1, 2, 2]))
print("\n\n###### COUNTER-EXAMPLE FOR CEILING CHILDREN")
num_of_seller_categories = 8
market = Market(
[AgentCategory("buyer", [101] * 10 * (num_of_seller_categories + 1))] +
[AgentCategory("producer", [-50] * 20)] +
[AgentCategory("seller", [-100] + [-2] * 21)] * num_of_seller_categories)
# print(budget_balanced_ascending_auction_twolevels(market, [1, 2] + [2] * num_of_seller_categories))
print("\n\n###### DVIR'S EXAMPLE BINARY")
num_of_seller_categories = 8
market = Market([AgentCategory("buyer", [100] * num_of_seller_categories)] + [
AgentCategory("seller", [-80 + i]) for i in range(num_of_seller_categories)
])
print(
budget_balanced_ascending_auction_twolevels(
market, [1] + [1] * num_of_seller_categories))
ascending_auction_recipetree_twolevels_protocol.logger.setLevel(logging.INFO)
prices.logger.setLevel(logging.INFO)
print("\n\n###### DVIR'S EXAMPLE")
from markets import Market
if __name__ == '__main__':
m = Market()
hd = m.full_historic_data()
hd['next_price'] = hd['price'].shift(-1) #target
hd['increase'] = (hd['next_price'] - hd['price']
) / hd['price'] > 0.01 #increase at least by 1%
features = [
'date', 'volume24', 'marketCap', 'availableSupplyNumber', 'price',
'next_price', 'increase'
]
hd[features].to_csv('~/Data/crypto/ETH.csv', index=False)
print "done"
# market = Market([
# AgentCategory("buyer", [20, 19, 18, 17, 16, 15]),
# AgentCategory("seller", [-1, -2, -5, -6, -9, -10]),
# AgentCategory("producerA", [-4, -8]),
# AgentCategory("producerB", [-3, -7]),
# ])
# print(budget_balanced_ascending_auction(market, recipes_1100_1011))
import sys, os; sys.path.insert(0, os.path.abspath('..'))
print("\n\n###### TEST MULTI RECIPE AUCTION - 4 PATHS")
market = Market([
AgentCategory("C0", [400, 300, 200, 100]),
AgentCategory("C1", [-1, -11]),
AgentCategory("C2", [-2, -22]),
AgentCategory("C3", [-3, -33]),
AgentCategory("C4", [-4, -44]),
AgentCategory("C5", [-5, -55]),
AgentCategory("C6", [-6, -66]),
])
recipes_4paths = [0, [1, [2, None, 3, None], 4, [5, None, 6, None]]]
# The recipes are:
# 1,1,1,0,0,0,0 [C0, C1, C2]
# 1,1,0,1,0,0,0 [C0, C1, C3]
# 1,0,0,0,1,1,0 [C0, C4, C5]
# 1,0,0,0,1,0,1 [C0, C4, C6]
print(budget_balanced_ascending_auction(market, recipes_4paths))
buyers = randomArray(2, 20, 10)
sellers = randomArray(-10, -1, 20)
#
# buyers = [135.9, 136.7999, 143.5499, 135.9, 136.7999, 143.5499, 144.0]
# sellers = [-135.9, -136.7999, -143.5499, -18.95, -18.9, -17.95, -17.9, -17.7999, -17.7999, -17.2999, -17.0, -16.95, -16.7999, -15.15, -15.0, -15.0, -14.9, -14.2, -14.2, -14.1, -13.95]
#
# random.shuffle(buyers)
# random.shuffle(sellers)
#
# buyers = removeSeconds(buyers, reduce_number)
# sellers = removeSeconds(sellers, reduce_number)
market = Market([
AgentCategory("buyer", buyers),
AgentCategory("seller", sellers),
])
without_gft0 = budget_balanced_trade_reduction(market, recipe, False)
with_gft0 = budget_balanced_trade_reduction(market, recipe, True)
print(without_gft0)
print(with_gft0)
without_count = without_gft0.num_of_deals()
with_count = with_gft0.num_of_deals()
without_gft = without_gft0.gain_from_trade()
with_gft = with_gft0.gain_from_trade()
print('Compare: Without:', without_gft, "With:", with_gft)
print('Compare: Without:', without_count, "With:", with_count)
if without_count != with_count and with_gft != without_gft:
print("Reached end")
Since: 2019-11
"""
import sys, os
sys.path.insert(0, os.path.abspath('..'))
from markets import Market
from agents import AgentCategory
import ascending_auction_protocol
from ascending_auction_protocol import budget_balanced_ascending_auction
import logging
ascending_auction_protocol.logger.setLevel(logging.INFO)
print("\n\n###### RUNNING EXAMPLE 3 FROM THE PAPER FOR TYPE (2,2,3)")
market = Market([
AgentCategory("buyer", [17, 16, 15, 14, 13, 12, 10, 6]),
AgentCategory("mediator", [-3, -4, -5, -6, -7, -8, -9, -10]),
AgentCategory("seller", [-1, -2, -3, -4, -5, -6, -7, -8]),
])
# print(budget_balanced_ascending_auction(market, [2,2,3]))
print("\n\n###### OTHER EXAMPLES FOR (2,2,3)")
market = Market([
AgentCategory("buyer", [17, 16, 15, 14, 13, 12, 10, 6]),
AgentCategory("mediator", [-3, -4, -5, -6, -7, -8, -9, -10]),
AgentCategory("seller", [-1, -2, -3, -4, -5, -6, -7, -8]),
])
print(budget_balanced_ascending_auction(market, [2, 2, 3]))
def budget_balanced_ascending_auction(
market: Market,
ps_recipe_struct: List[Any],
agent_counts: List[int] = None) -> TradeWithMultipleRecipes:
"""
Calculate the trade and prices using generalized-ascending-auction.
Allows multiple recipes, but they must be represented by a *recipe tree*.
:param market: contains a list of k categories, each containing several agents.
:param ps_recipe_struct: a nested list of integers. Each integer represents a category-index.
The nested list represents a tree, where each path from root to leaf represents a recipe.
For example: [0, [1, None]] is a single recipe with categories {0,1}.
[0, [1, None, 2, None]] is two recipes with categories {0,1} and {0,2}.
:return: Trade object, representing the trade and prices.
>>> logger.setLevel(logging.DEBUG)
>>> # ONE BUYER, ONE SELLER
>>> recipe_11 = [0, [1, None]]
>>> agent_counts = [1, 2, 1, 2]
>>> recipe_1100_1011 = [0, [1, None, 2, [3, None]]]
>>>
>>> market = Market([AgentCategory("buyer", [101,101,101,101,101]), AgentCategory("seller1", [-1,-90]), AgentCategory("seller2", [-1,-90]), AgentCategory("seller", [-50, -50, -50, -50, -50, -50])])
>>> RecipeTree(market.categories, [0, [1, None, 2, None, 3, None]], [1,2,2,2]).optimal_trade_with_counters()
([([101, -1, -90], {'counter': 1, 'index': 1}), ([101, -1, -90], {'counter': 1, 'index': 2}), ([101, -50, -50], {'counter': 3, 'index': 3}), ([101, -50, -50], {'counter': 3, 'index': 3}), ([101, -50, -50], {'counter': 3, 'index': 3})], 5, 23, 1, 3, {'1': 1, '2': 1, '3': 3})
>>> print(market); print(budget_balanced_ascending_auction(market, [0, [1, None, 2, None, 3, None]], [1,2,2,2]))
Traders: [buyer: [101, 101, 101, 101, 101], seller1: [-1, -90], seller2: [-1, -90], seller: [-50, -50, -50, -50, -50, -50]]
seller1: 0 potential deals, price=-50.5
seller2: 0 potential deals, price=-50.5
seller: 3 potential deals, price=-50.5
buyer: all 3 remaining traders selected, price=101.0
(seller1 + seller2 + seller): all 3 remaining deals selected
3 deals overall
>>> market = Market([AgentCategory("buyer", [101,101,101]), AgentCategory("seller1", [-1,-90]), AgentCategory("seller2", [-1,-90]), AgentCategory("seller", [-50, -50, -50, -50, -50, -50])])
>>> RecipeTree(market.categories, [0, [1, None, 2, None, 3, None]], [1,2,2,2]).optimal_trade_with_counters()
([([101, -1, -90], {'counter': 1, 'index': 1}), ([101, -1, -90], {'counter': 1, 'index': 2}), ([101, -50, -50], {'counter': 1, 'index': 3})], 3, 21, 1, 1, {'1': 1, '2': 1, '3': 1})
>>> print(market); print(budget_balanced_ascending_auction(market, [0, [1, None, 2, None, 3, None]], [1,2,2,2]))
Traders: [buyer: [101, 101, 101], seller1: [-1, -90], seller2: [-1, -90], seller: [-50, -50, -50, -50, -50, -50]]
seller1: 0 potential deals, price=-50.0
seller2: 0 potential deals, price=-50.0
seller: 2 potential deals, price=-50.0
buyer: 2 out of 3 remaining traders selected, price=100.0
(seller1 + seller2 + seller): all 2 remaining deals selected
2 deals overall
>>> market = Market([AgentCategory("buyer", [27.,21., 17.,11., 3.]), AgentCategory("seller", [-4.0, -5.0, -11.0]), AgentCategory("A", [-2.0, -3.0, -11.0]), AgentCategory("B", [-1.0, -2.0, -8.0])])
>>> RecipeTree(market.categories, recipe_1100_1011, agent_counts).optimal_trade_with_counters()
([([27.0, -2.0, -1.0, -2.0], {'counter': 1, 'index': 3}), ([21.0, -4.0, -5.0], {'counter': 1, 'index': 1})], 2, 34.0, 1, 1, {'3': 1, '1': 1})
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_1100_1011, agent_counts))
Traders: [buyer: [27.0, 21.0, 17.0, 11.0, 3.0], seller: [-4.0, -5.0, -11.0], A: [-2.0, -3.0, -11.0], B: [-1.0, -2.0, -8.0]]
seller: 1 potential deals, price=-8.5
B: 1 potential deals, price=-7.0
A: all 1 remaining traders selected, price=-3.0
(B): all 1 remaining deals selected
buyer: all 2 remaining traders selected, price=17.0
(seller + A): all 2 remaining deals selected
2 deals overall
>>> market = Market([AgentCategory("buyer", [17.,11.]), AgentCategory("seller", [-5.0]), AgentCategory("A", [-3.0]), AgentCategory("B", [-2.0])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_1100_1011))
Traders: [buyer: [17.0, 11.0], seller: [-5.0], A: [-3.0], B: [-2.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0, 8.0], seller: [-4.0]]
seller: 1 potential deals, price=-8.0
buyer: all 1 remaining traders selected, price=8.0
(seller): all 1 remaining deals selected
1 deals overall
>>> logger.setLevel(logging.WARNING)
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0], seller: [-3.0, -4.0]]
No trade
>>> logger.setLevel(logging.WARNING)
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0, 8.0], seller: [-3.0, -4.0]]
seller: 1 potential deals, price=-4.0
buyer: 1 out of 2 remaining traders selected, price=4.0
(seller): all 1 remaining deals selected
1 deals overall
"""
logger.info("\n#### Multi-Recipe Budget-Balanced Ascending Auction\n")
logger.info(market)
logger.info("Procurement-set recipe struct: {}".format(ps_recipe_struct))
logger.info("Procurement-set recipe agent counts: {}".format(agent_counts))
remaining_market = market.clone()
recipe_tree = RecipeTree(remaining_market.categories, ps_recipe_struct,
agent_counts)
logger.info("Tree of recipes: {}".format(recipe_tree.paths_to_leaf()))
ps_recipes = recipe_tree.recipes()
logger.info("Procurement-set recipes: {}".format(ps_recipes))
optimal_trade, optimal_count, optimal_GFT = recipe_tree.optimal_trade()
logger.info("For comparison, the optimal trade has k=%d, GFT=%f: %s\n",
optimal_count, optimal_GFT, optimal_trade)
# optimal_trade = market.optimal_trade(ps_recipe)[0]
#### STOPPED HERE
prices = SimultaneousAscendingPriceVectors(ps_recipes, -MAX_VALUE,
agent_counts)
while True:
largest_category_size, combined_category_size, indices_of_prices_to_increase = recipe_tree.largest_categories(
indices=True)
logger.info("\n")
logger.info(remaining_market)
logger.info(
"Largest category indices are %s. Largest category size = %d, combined category size = %d",
indices_of_prices_to_increase, largest_category_size,
combined_category_size)
if combined_category_size == 0:
logger.info("\nCombined category size is 0 - no trade!")
logger.info(" Final price-per-unit vector: %s", prices)
logger.info(remaining_market)
return TradeWithMultipleRecipes(
remaining_market.categories, recipe_tree,
prices.map_category_index_to_price(), agent_counts)
increases = []
for category_index in indices_of_prices_to_increase:
category = remaining_market.categories[category_index]
target_price = category.lowest_agent_value(
) if category.size() > 0 else MAX_VALUE
increases.append((category_index, target_price, category.name))
logger.info("Planned price-increases: %s", increases)
prices.increase_prices(increases)
map_category_index_to_price = prices.map_category_index_to_price()
if prices.status == PriceStatus.STOPPED_AT_ZERO_SUM:
logger.info("\nPrice crossed zero.")
logger.info(" Final price-per-unit vector: %s",
map_category_index_to_price)
logger.info(remaining_market)
return TradeWithMultipleRecipes(remaining_market.categories,
recipe_tree,
map_category_index_to_price,
agent_counts)
remove_lowest_agent(market, remaining_market,
map_category_index_to_price,
indices_of_prices_to_increase)
def budget_balanced_ascending_auction(
market: Market,
ps_recipe_struct: List[Any]) -> TradeWithMultipleRecipes:
"""
Calculate the trade and prices using generalized-ascending-auction.
Allows multiple recipes, but they must be represented by a *recipe tree*.
:param market: contains a list of k categories, each containing several agents.
:param ps_recipe_struct: a nested list of integers. Each integer represents a category-index.
The nested list represents a tree, where each path from root to leaf represents a recipe.
For example: [0, [1, None]] is a single recipe with categories {0,1}.
[0, [1, None, 2, None]] is two recipes with categories {0,1} and {0,2}.
:return: Trade object, representing the trade and prices.
>>> logger.setLevel(logging.WARNING)
>>> # ONE BUYER, ONE SELLER
>>> recipe_11 = [0, [1, None]]
>>>
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0, 8.0], seller: [-4.0]]
seller: 1 potential deals, price=-8.0
buyer: all 1 traders selected, price=8.0
seller: all 1 traders selected
1 deals overall
>>> logger.setLevel(logging.WARNING)
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0], seller: [-3.0, -4.0]]
No trade
>>> logger.setLevel(logging.WARNING)
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, recipe_11))
Traders: [buyer: [9.0, 8.0], seller: [-3.0, -4.0]]
seller: 1 potential deals, price=-4.0
buyer: 1 out of 2 traders selected, price=4.0
seller: all 1 traders selected
1 deals overall
"""
logger.info("\n#### Multi-Recipe Budget-Balanced Ascending Auction\n")
logger.info(market)
logger.info("Procurement-set recipe struct: {}".format(ps_recipe_struct))
remaining_market = market.clone()
recipe_tree = RecipeTree(remaining_market.categories, ps_recipe_struct)
logger.info("Tree of recipes: {}".format(recipe_tree.paths_to_leaf()))
ps_recipes = recipe_tree.recipes()
logger.info("Procurement-set recipes: {}".format(ps_recipes))
optimal_trade, optimal_count, optimal_GFT = recipe_tree.optimal_trade()
logger.info("For comparison, the optimal trade has k=%d, GFT=%f: %s\n",
optimal_count, optimal_GFT, optimal_trade)
# optimal_trade = market.optimal_trade(ps_recipe)[0]
#### STOPPED HERE
prices = SimultaneousAscendingPriceVectors(ps_recipes, -MAX_VALUE)
while True:
largest_category_size, combined_category_size, indices_of_prices_to_increase = recipe_tree.largest_categories(
indices=True)
logger.info("\n")
logger.info(remaining_market)
logger.info(
"Largest category indices are %s. Largest category size = %d, combined category size = %d",
indices_of_prices_to_increase, largest_category_size,
combined_category_size)
if combined_category_size == 0:
logger.info("\nCombined category size is 0 - no trade!")
logger.info(" Final price-per-unit vector: %s", prices)
logger.info(remaining_market)
return TradeWithMultipleRecipes(
remaining_market.categories, recipe_tree,
prices.map_category_index_to_price())
increases = []
for category_index in indices_of_prices_to_increase:
category = remaining_market.categories[category_index]
target_price = category.lowest_agent_value(
) if category.size() > 0 else MAX_VALUE
increases.append((category_index, target_price, category.name))
logger.info("Planned price-increases: %s", increases)
prices.increase_prices(increases)
map_category_index_to_price = prices.map_category_index_to_price()
if prices.status == PriceStatus.STOPPED_AT_ZERO_SUM:
logger.info("\nPrice crossed zero.")
logger.info(" Final price-per-unit vector: %s",
map_category_index_to_price)
logger.info(remaining_market)
return TradeWithMultipleRecipes(remaining_market.categories,
recipe_tree,
map_category_index_to_price)
for category_index in range(market.num_categories):
category = remaining_market.categories[category_index]
if map_category_index_to_price[category_index] is not None \
and category.size()>0 \
and category.lowest_agent_value() <= map_category_index_to_price[category_index]:
category.remove_lowest_agent()
logger.info("{} after: {} agents remain".format(
category.name, category.size()))
"""
from markets import Market
from agents import AgentCategory
import ascending_auction_protocol
from ascending_auction_protocol import budget_balanced_ascending_auction
import logging
ascending_auction_protocol.logger.setLevel(logging.INFO)
ps_recipe = [1,1,1]
print("\n\n###### RUNNING EXAMPLE FROM THE PAPER FOR TYPE (1,1,1)")
# Price stops between buyers, k=3:
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("seller", [-1, -4, -5, -8, -11]),
AgentCategory("mediator", [-1, -3, -4, -7, -10])])
print(budget_balanced_ascending_auction(market, ps_recipe))
print("\n\n###### SIMILAR EXAMPLE, WHERE PRICE STOPS BETWEEN SELLERS:")
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("seller", [-1, -4, -5, -8, -11]),
AgentCategory("mediator", [-1, -3, -6, -7, -10])])
print(budget_balanced_ascending_auction(market, ps_recipe))
print("\n\n###### SIMILAR EXAMPLE, WHERE PRICE STOPS BETWEEN MEDIATORS:")
market = Market([
AgentCategory("buyer", [17, 14, 13, 9, 6]),
AgentCategory("seller", [-1, -4, -6.5, -8, -11]),
def experiment(results_csv_file: str, recipe: list, value_ranges: list,
nums_of_agents: list, num_of_iterations: int,
agent_counts: list, agent_values: list,
agent_counts_integer: list):
"""
Run an experiment similar to McAfee (1992) experiment on the given auction.
:param results_csv_file: the experiment result file.
:param auction_functions: list of functions for executing the auction under consideration.
:param auction_names: titles of the experiment, for printouts.
:param recipe: can be any vector of ones, e.g. (1,1,1), for our trade-reduction mechanism, or any vector of positive integers for our ascending-auction mechanism.
:param nums_of_agents: list of n(s) for number of possible trades to make the calculations.
:param stocks_prices: list of prices for each stock and each agent.
:param stock_names: list of stocks names which prices are belongs, for naming only.
"""
TABLE_COLUMNS = [
"iterations", "recipe", "numofagents", "optimalcount", "optimalkmin",
"optimalkmax", "gftformula", "auctioncount", "auctionkmin",
"auctionkmax", "countratio", "optimalgft", "auctiongft", "gftratio"
]
GFT_ROUND = 1
K_ROUND = 2
RATIO_ROUND = 3
print('recipe:', recipe)
results_table = TeeTable(TABLE_COLUMNS, results_csv_file)
results_table_integer = TeeTable(TABLE_COLUMNS,
results_csv_file[0:-4] + '_integer.csv')
recipe_str = str(recipe).replace(',', '-')
category_size_list = get_agents_analyze(recipe)
for i in range(len(nums_of_agents)):
sum_optimal_count = sum_auction_count = sum_optimal_kmin = sum_optimal_kmax = 0 # count the number of deals done in the optimal vs. the actual auction.
sum_optimal_gft = sum_auction_total_gft = sum_auction_kmin = sum_auction_kmax = 0
sum_optimal_count_integer = sum_auction_count_integer = sum_optimal_kmin_integer = sum_optimal_kmax_integer = 0 # count the number of deals done in the optimal vs. the actual auction.
sum_optimal_gft_integer = sum_auction_total_gft_integer = sum_auction_kmin_integer = sum_auction_kmax_integer = 0
for iteration in range(num_of_iterations):
if iteration % 10000 == 0:
print('iteration:', iteration)
agents = []
agents_integer = []
for category in range(len(category_size_list)):
sign = 0 if category == 0 else 1
agent_category = AgentCategory.uniformly_random(
"agent", int(nums_of_agents[i] * agent_counts[category]),
value_ranges[sign][0] * agent_values[category],
value_ranges[sign][1] * agent_values[category])
agents.append(agent_category)
agents_integer.append(
AgentCategory(
agent_category.name,
agent_category.values + agent_category.values))
#agents.append(AgentCategory.uniformly_random("agent", nums_of_agents[i], value_ranges[sign][0], value_ranges[sign][1]))
market = Market(agents)
market_integer = Market(agents_integer)
#print(agents)
recipe_tree = RecipeTree(market.categories, recipe)
recipe_tree_integer = RecipeTree(market_integer.categories, recipe,
agent_counts_integer)
optimal_trade, optimal_count, optimal_gft, kmin, kmax = recipe_tree.optimal_trade_with_counters(
)
optimal_trade_integer, optimal_count_integer, optimal_gft_integer, kmin_integer, kmax_integer = recipe_tree_integer.optimal_trade_with_counters(
)
#print('optimal trade:', optimal_trade, optimal_count, optimal_gft)
auction_trade = budget_balanced_ascending_auction(market, recipe)
auction_trade_integer = budget_balanced_ascending_auction(
market_integer, recipe, agent_counts_integer)
auction_count = auction_trade.num_of_deals()
auction_count_integer = auction_trade_integer.num_of_deals()
auction_kmin = auction_trade.min_num_of_deals()
auction_kmin_integer = auction_trade_integer.min_num_of_deals()
auction_kmax = auction_trade.max_num_of_deals()
auction_kmax_integer = auction_trade_integer.max_num_of_deals()
gft = auction_trade.gain_from_trade()
gft_integer = auction_trade_integer.gain_from_trade()
#if optimal_count > 0 and gft < optimal_gft * (1 - 1/optimal_count):
#the auction count is less more than 1 than the optimal count.
# print('Warning GFT!!!', 'optimal_count:', optimal_count, 'auction_count:', auction_count,
# 'num_of_possible_ps:', nums_of_agents[i], 'optimal_gft:', optimal_gft, 'gft:', gft)
# if nums_of_agents[i] < 20:
# print(market.categories)
sum_optimal_count += optimal_count
sum_optimal_count_integer += optimal_count_integer
sum_auction_count += auction_count
sum_auction_count_integer += auction_count_integer
sum_optimal_kmin += kmin
sum_optimal_kmin_integer += kmin_integer
sum_optimal_kmax += kmax
sum_optimal_kmax_integer += kmax_integer
sum_auction_kmin += auction_kmin
sum_auction_kmin_integer += auction_kmin_integer
sum_auction_kmax += auction_kmax
sum_auction_kmax_integer += auction_kmax_integer
sum_optimal_gft += optimal_gft
sum_optimal_gft_integer += optimal_gft_integer
sum_auction_total_gft += gft
sum_auction_total_gft_integer += gft_integer
if auction_count < optimal_count - 2:
#the auction count is less more than 1 than the optimal count.
print('Warning!!!', 'optimal_count:', optimal_count,
'auction_count:', auction_count, 'num_of_possible_ps:',
nums_of_agents[i])
if nums_of_agents[i] < 10:
print(market.categories)
# print("Num of times {} attains the maximum GFT: {} / {} = {:.2f}%".format(title, count_optimal_gft, num_of_iterations, count_optimal_gft * 100 / num_of_iterations))
# print("GFT of {}: {:.2f} / {:.2f} = {:.2f}%".format(title, sum_auction_gft, sum_optimal_gft, 0 if sum_optimal_gft==0 else sum_auction_gft * 100 / sum_optimal_gft))
kmin_mean = sum_optimal_kmin / num_of_iterations
kmin_mean_integer = sum_optimal_kmin_integer / num_of_iterations
results_table.add(
OrderedDict([
("iterations", num_of_iterations),
("recipe", recipe_str),
("numofagents", nums_of_agents[i]),
("optimalcount",
int_round(sum_optimal_count / num_of_iterations, K_ROUND)),
("optimalkmin", int_round(kmin_mean, K_ROUND)),
("optimalkmax",
int_round(sum_optimal_kmax / num_of_iterations, K_ROUND)),
("gftformula",
int_round((1 - 1 / kmin_mean) * 100 if kmin_mean > 1 else 0,
RATIO_ROUND)),
("auctioncount",
int_round(sum_auction_count / num_of_iterations, K_ROUND)),
("auctionkmin",
int_round(sum_auction_kmin / num_of_iterations, K_ROUND)),
("auctionkmax",
int_round(sum_auction_kmax / num_of_iterations, K_ROUND)),
("countratio",
int_round(
0 if sum_optimal_count == 0 else
(sum_auction_count / sum_optimal_count) * 100,
RATIO_ROUND)),
("optimalgft",
int_round(sum_optimal_gft / num_of_iterations, GFT_ROUND)),
("auctiongft",
int_round(sum_auction_total_gft / num_of_iterations,
GFT_ROUND)),
("gftratio", '0.00' if sum_optimal_gft == 0 else int_round(
sum_auction_total_gft / sum_optimal_gft *
100, RATIO_ROUND)),
]))
results_table_integer.add(
OrderedDict([
("iterations", num_of_iterations),
("recipe", recipe_str),
("numofagents", nums_of_agents[i]),
("optimalcount",
int_round(sum_optimal_count_integer / num_of_iterations,
K_ROUND)),
("optimalkmin", int_round(kmin_mean_integer, K_ROUND)),
("optimalkmax",
int_round(sum_optimal_kmax_integer / num_of_iterations,
K_ROUND)),
("gftformula",
int_round((1 - 1 / kmin_mean_integer) *
100 if kmin_mean_integer > 1 else 0, RATIO_ROUND)),
("auctioncount",
int_round(sum_auction_count_integer / num_of_iterations,
K_ROUND)),
("auctionkmin",
int_round(sum_auction_kmin_integer / num_of_iterations,
K_ROUND)),
("auctionkmax",
int_round(sum_auction_kmax_integer / num_of_iterations,
K_ROUND)),
("countratio",
int_round(
0 if sum_optimal_count_integer == 0 else
(sum_auction_count_integer / sum_optimal_count_integer) *
100, RATIO_ROUND)),
("optimalgft",
int_round(sum_optimal_gft_integer / num_of_iterations,
GFT_ROUND)),
("auctiongft",
int_round(sum_auction_total_gft_integer / num_of_iterations,
GFT_ROUND)),
("gftratio",
'0.00' if sum_optimal_gft_integer == 0 else int_round(
sum_auction_total_gft_integer / sum_optimal_gft_integer *
100, RATIO_ROUND)),
]))
results_table.done()
def budget_balanced_ascending_auction(
market:Market, ps_recipes: list)->TradeWithMultipleRecipes:
"""
Calculate the trade and prices using generalized-ascending-auction.
Allows multiple recipes, but only of the following kind:
[ [1,0,0,x], [0,1,0,y], [0,0,1,z] ]
(i.e., there are n-1 buyer categories and 1 seller category.
One agent of category 1 buys x units; of category 2 buys y units; of category 3 buys z units; etc.)
:param market: contains a list of k categories, each containing several agents.
:param ps_recipes: a list of lists of integers, one integer per category.
Each integer i represents the number of agents of category i
that should be in each procurement-set.
:return: Trade object, representing the trade and prices.
>>> # ONE BUYER, ONE SELLER
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, [[1,1]]))
Traders: [buyer: [9.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.])])
>>> print(market); print(budget_balanced_ascending_auction(market, [[1,1]]))
Traders: [buyer: [9.0, 8.0], seller: [-4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, [[1,1]]))
Traders: [buyer: [9.0], seller: [-3.0, -4.0]]
seller: [-3.0]: all 1 agents trade and pay -4.0
buyer: [9.0]: all 1 agents trade and pay 4.0
>>> market = Market([AgentCategory("buyer", [9.,8.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, [[1,1]]))
Traders: [buyer: [9.0, 8.0], seller: [-3.0, -4.0]]
seller: [-3.0, -4.0]: random 1 out of 2 agents trade and pay -8.0
buyer: [9.0]: all 1 agents trade and pay 8.0
>>> # ONE BUYER, TWO SELLERS
>>> market = Market([AgentCategory("buyer", [9.]), AgentCategory("seller", [-4.,-3.])])
>>> print(market); print(budget_balanced_ascending_auction(market, [[1,2]]))
Traders: [buyer: [9.0], seller: [-3.0, -4.0]]
No trade
>>> market = Market([AgentCategory("buyer", [9., 8., 7., 6.]), AgentCategory("seller", [-6., -5., -4.,-3.,-2.,-1.])])
>>> print(market); print(budget_balanced_ascending_auction(market, [[1,2]]))
Traders: [buyer: [9.0, 8.0, 7.0, 6.0], seller: [-1.0, -2.0, -3.0, -4.0, -5.0, -6.0]]
seller: [-1.0, -2.0, -3.0, -4.0]: random 2 out of 4 agents trade and pay -4.0
buyer: [9.0]: all 1 agents trade and pay 8.0
"""
logger.info("\n#### Budget-Balanced Ascending Auction with Multiple Recipes - n-1 buyer categories\n")
logger.info(market)
logger.info("Procurement-set recipes: %s", ps_recipes)
map_buyer_category_to_seller_count = _convert_recipes_to_seller_counts(ps_recipes, market.num_categories)
logger.info("Map buyer category index to seller count: %s", map_buyer_category_to_seller_count)
# NOTE: Calculating the optimal trade cannot be done greedily -
# it requires solving a restricted instance of Knapsack.
# optimal_trade = market.optimal_trade(ps_recipe, max_iterations=max_iterations)[0]
# logger.info("For comparison, the optimal trade is: %s\n", optimal_trade)
remaining_market = market.clone()
buyer_categories = remaining_market.categories[:-1]
num_buyer_categories = market.num_categories-1
seller_category = remaining_market.categories[-1]
prices = AscendingPriceVector([1, 1], -MAX_VALUE)
buyer_price_index = 0
seller_price_index = 1
# prices[0] represents the price for all buyer-categories per single unit.
# prices[1] represents the price for all sellers.
try:
num_units_offered = len(seller_category)
num_units_demanded = sum([len(buyer_categories[i])*map_buyer_category_to_seller_count[i] for i in range(num_buyer_categories)])
target_unit_count = min(num_units_demanded, num_units_offered)
logger.info("%d units demanded by buyers, %d units offered by sellers, minimum is %d",
num_units_demanded, num_units_offered, target_unit_count)
while True:
logger.info("Prices: %s, Target unit count: %d", prices, target_unit_count)
price_index = buyer_price_index
while True:
num_units_demanded = sum([len(buyer_categories[i]) * map_buyer_category_to_seller_count[i] for i in range(num_buyer_categories)])
logger.info(" Buyers demand %d units", num_units_demanded)
if num_units_demanded == 0: raise EmptyCategoryException()
if num_units_demanded <= target_unit_count: break
map_buyer_category_to_lowest_value = [category.lowest_agent_value() for category in buyer_categories]
logger.debug(" map_buyer_category_to_lowest_value=%s", map_buyer_category_to_lowest_value)
map_buyer_category_to_lowest_value_per_unit = [value / count for value,count in zip(map_buyer_category_to_lowest_value,map_buyer_category_to_seller_count)]
logger.debug(" map_buyer_category_to_lowest_value_per_unit=%s", map_buyer_category_to_lowest_value_per_unit)
category_index_with_lowest_value_per_unit = min(range(num_buyer_categories), key=lambda i:map_buyer_category_to_lowest_value_per_unit[i])
category_with_lowest_value_per_unit = buyer_categories[category_index_with_lowest_value_per_unit]
lowest_value_per_unit = map_buyer_category_to_lowest_value_per_unit[category_index_with_lowest_value_per_unit]
logger.info(" lowest value per unit is %f, of category %d (%s)", lowest_value_per_unit, category_index_with_lowest_value_per_unit, category_with_lowest_value_per_unit.name)
prices.increase_price_up_to_balance(price_index, category_with_lowest_value_per_unit.lowest_agent_value()/map_buyer_category_to_seller_count[category_index_with_lowest_value_per_unit], category_with_lowest_value_per_unit.name)
category_with_lowest_value_per_unit.remove_lowest_agent()
category = seller_category
# logger.info("\n### Step 1a: balancing the sellers (%s)", category.name)
price_index = seller_price_index
while True:
num_units_offered = len(category)
logger.info(" Sellers offer %d units", num_units_offered)
if num_units_offered == 0: raise EmptyCategoryException()
if num_units_offered <= target_unit_count: break
prices.increase_price_up_to_balance(price_index, category.lowest_agent_value(), category.name)
category.remove_lowest_agent()
target_unit_count -= 1
except EmptyCategoryException:
logger.info("\nOne of the categories became empty. No trade!")
logger.info(" Final price-per-unit vector: %s", prices)
# Construct the final price-vector:
buyer_price_per_unit = prices[buyer_price_index]
seller_price_per_unit = prices[seller_price_index]
final_prices = \
[buyer_price_per_unit * unit_count for unit_count in map_buyer_category_to_seller_count] + \
[seller_price_per_unit]
logger.info(" %s", remaining_market)
return TradeWithMultipleRecipes(remaining_market.categories, map_buyer_category_to_seller_count, final_prices)
