def bid_stack(state, isodate):
state = state.upper()
participant_meta = participant_service.participant_metadata
if state != "ALL":
participants_in_state = [p for p in participant_meta if participant_meta[p]['state'] == state ]
else:
participants_in_state = [p for p in participant_meta]
date = pendulum.from_timestamp(int(isodate))
try :
stack = BidStack.objects(trading_period = date).get()
bids = {}
for name in stack.getParticipants():
if name in participants_in_state:
bid = stack.getBid(name)
bid_dict = bid_to_dict(bid)
bid_dict['meta'] = participant_meta[name]
bids[name] = bid_dict
print(date, stack)
return jsonify(bids)
except DoesNotExist:
print("Couldn't find stack")
return jsonify({'message':'None found.'})
def generate(start_date, end_date):
counter = 0
date = start_date
# for date in dates:
while date < end_date:
# if counter > 131:
print(date)
try:
if BidStack.objects(trading_period=date).count() == 0:
s = BidStack(date)
s.save()
else:
print("Already exists")
except mongoengine.errors.NotUniqueError:
print("Bidstack already exists for ", date)
# counter += 1
date = date.add(minutes=30)
def process_bidstacks():
print("Processing Bidstacks")
request = BidStack.objects().fields(trading_period=1, id=1)
print('Retrieved Bidstacks')
PRICE_MIN = -300
PRICE_MAX = -10
i = 0
timeseries = {}
relevant_bidders = []
for bidstack in request:
# Get the trading period label.
dt = pendulum.instance(bidstack.trading_period)
if (dt.hour == 12 and dt.minute == 0 and dt.day % 5 == 0):
bidstack = BidStack.objects.get(id=bidstack.id)
print("Processing Bidstack", dt)
participants = bidstack.getParticipants()
for participant in participants:
# Get some relevant data about the participant.
# tech_type = participant_meta[participant]['technology_type_primary'] if participant in participant_meta else None
# fuel_source_descriptor = participant_meta[participant]['fuel_source_descriptor'] if participant in participant_meta else None
# fuel_source_primary = participant_meta[participant]['fuel_source_primary'] if participant in participant_meta else None
# Look at the participant's bids - see if any fall within the range we are interested in.
# print(participant)
bid = bidstack.getBid(participant)
for band in range(1, 9):
price = bid.get_price(band)
volume = bid.get_volume(band)
if volume > 0 and price >= PRICE_MIN and price <= PRICE_MAX:
# If we haven't seen this participant before, add them to the list.
if participant not in relevant_bidders:
relevant_bidders.append(participant)
# Add the data point to the timeseries
timeseries[
dt] = {} if not dt in timeseries else timeseries[dt]
timeseries[dt][participant] = price
# print(participant, band, bid.get_price(band), bid.get_volume(band), tech_type, fuel_source_descriptor, fuel_source_primary)
# In future runs, could use these to narrow down. Leave it for the moment.
# print(tech_type, fuel_source_descriptor, fuel_source_primary)
# break
# if i == 5:
# break
# i += 1
print("Finished Processing Bidstack")
return timeseries, relevant_bidders
from application.model.participants import ParticipantService
from application.model.demand import Demand
from application.model.price import Price
participant_service = ParticipantService()
import pendulum
from application import config
from application.model.bidstack import BidStack, DoesNotExist, bid_to_dict, bid_to_list
dates = BidStack.objects().distinct('trading_period')
dates = [
date for date in dates if (pendulum.instance(date).minute == 0
or pendulum.instance(date).minute == 30)
]
states = ['NSW', 'QLD', 'VIC', 'QLD', 'TAS']
participants = {}
for state in states:
participants[state] = [
p for p in participant_service.participant_metadata
if participant_service.participant_metadata[p]['state'] == state
]
for state in states:
for date in dates:
if not Price.objects(date_time=date, region=state, price_type='BASIC'):
print('Adding', state, date)
# Basic settlement.
rounded_time = pendulum.instance(date, tz=config.TZ)
if rounded_time.minute > 30:
rounded_time = rounded_time.start_of('hour').add(hours=1)
def bid_stack_dates():
dates = BidStack.objects().distinct('trading_period')
return jsonify([pendulum.instance(date).timestamp() for date in dates])
def process_bidstacks(start_date, end_date, timeseries={}):
""" Derives competition indicators from bids submitted by generators. """
print("Processing Bidstacks")
# Get bidstacks for every time period.
query = BidStack.objects(trading_period__gte=start_date,
trading_period__lte=end_date).fields(
trading_period=1, id=1)
print('Retrieved Bidstacks')
i = 0
for bidstack in query:
# Get the trading period label.
dt = pendulum.instance(bidstack.trading_period)
# if(dt.hour == 12 and dt.minute == 0 and dt.day %5 == 0):
# Filter based on hour so we don't process tonnes of them
if (dt.hour in RESEARCH_HOURS and dt.minute == 0):
timeseries[dt] = {} if not dt in timeseries else timeseries[dt]
print("Bid Analysis", dt)
# Grab the bids and order in economic dispatch order.
bidstack = BidStack.objects.get(id=bidstack.id)
# simple_bids, srmc_bids, lrmc_bids = settle(bidstack)
# print("Got bid stacks.")
# Grab demand data.
demand_req = Demand.objects(date_time=dt)
regional_demand = {d.region: d.demand for d in demand_req}
total_demand = int(
float(
sum([
regional_demand[region] for region in regional_demand
])))
# Grab price data
price_req = Price.objects(date_time=dt, price_type='AEMO_SPOT')
regional_prices = {p.region: p.price for p in price_req}
# print(regional_prices)
weighted_average_price = float(
sum([
regional_prices[p] * regional_demand[p]
for p in regional_prices
])) / float(total_demand)
# print(weighted_average_price)
# Get a dict of all the residual supply indices, augmented with max network flows.
network_residual_supply_indices = get_network_extended_residual_supply_indices(
bidstack, regional_demand)
timeseries[dt]['weighted_average_price'] = weighted_average_price
timeseries[dt]['demand_ALL'] = total_demand
timeseries[dt]['datetime'] = dt
timeseries[dt]['price'] = regional_prices
for key in regional_demand:
timeseries[dt]['demand_' + key] = regional_demand[key]
for key in regional_prices:
timeseries[dt]['price_' + key] = regional_prices[key]
for state in STATES:
# Get a dict of all the bid-based market shares for this time period
generator_bid_shares = get_generator_bid_market_shares(
bidstack, state)
# Get a dict of all the residual supply indices for this time period
residual_supply_indices = get_residual_supply_indices(
bidstack, total_demand, state)
# Get a dict of all the pivotal supplier indices for this time period.
pivotal_supplier_indices = get_pivotal_supplier_indices(
bidstack, total_demand, state)
if state != "ALL":
# Get a dict of all firm weighted offers
firm_weighted_offer_prices = get_firm_volume_weighted_offer_price(
bidstack, state)
for firm in firm_weighted_offer_prices:
timeseries[dt][
firm.lower() + '_weighted_offer_price_' +
state] = firm_weighted_offer_prices[firm]
# Calculate average NERSI for all firms in the state.
for firm in network_residual_supply_indices[state]:
timeseries[dt][
firm.lower() + '_nersi_' +
state] = network_residual_supply_indices[state][
firm]
timeseries[dt]['average_nersi_' + state] = float(
sum([
network_residual_supply_indices[state][firm]
for firm in network_residual_supply_indices[state]
])) / float(len(
network_residual_supply_indices[state]))
timeseries[dt]['minimum_nersi_' + state] = min([
network_residual_supply_indices[state][firm]
for firm in network_residual_supply_indices[state]
])
# Record results.
timeseries[dt]['hhi_bids_' +
state] = get_hhi(generator_bid_shares)
timeseries[dt]['entropy_bids_' +
state] = get_entropy(generator_bid_shares)
timeseries[dt]['four_firm_concentration_ratio_bids_' +
state] = get_four_firm_concentration_ratio(
generator_bid_shares)
timeseries[dt]['average_rsi_' + state] = float(
sum([
residual_supply_indices[firm]
for firm in residual_supply_indices
])) / float(len([f for f in residual_supply_indices]))
timeseries[dt]['minimum_rsi_' + state] = min([
residual_supply_indices[firm]
for firm in residual_supply_indices
])
timeseries[dt]['sum_psi_' + state] = sum([
pivotal_supplier_indices[firm]
for firm in pivotal_supplier_indices
])
for firm in residual_supply_indices:
timeseries[dt][firm.lower() + '_rsi_' +
state] = residual_supply_indices[firm]
for firm in pivotal_supplier_indices:
timeseries[dt][firm.lower() + '_psi_' +
state] = pivotal_supplier_indices[firm]
print("Finished Processing Bidstack")
return timeseries
def process_bidstacks(start_date, end_date):
print("Processing Bidstacks")
request = BidStack.objects(trading_period__gte=start_date,
trading_period__lte=end_date).fields(
trading_period=1, id=1)
print('Retrieved Bidstacks')
i = 0
timeseries = {}
for bidstack in request:
# Get the trading period label.
dt = pendulum.instance(bidstack.trading_period)
# if(dt.hour == 12 and dt.minute == 0 and dt.day %5 == 0):
if (dt.hour in [0, 6, 12, 18, 24] and dt.minute == 0):
print(dt)
# Grab the bids and order in economic dispatch order.
bidstack = BidStack.objects.get(id=bidstack.id)
simple_bids, srmc_bids, lrmc_bids = settle(bidstack)
# print("Got bid stacks.")
# Grab demand data.
demand_req = Demand.objects(date_time=dt)
regional_demand = {d.region: d.demand for d in demand_req}
total_demand = int(
float(
sum([
regional_demand[region] for region in regional_demand
])))
# Grab price data
price_req = Price.objects(date_time=dt, price_type='AEMO_SPOT')
regional_prices = {p.region: p.price for p in price_req}
weighted_average_price = float(
sum([
regional_prices[p] * regional_demand[p]
for p in regional_prices
])) / float(total_demand)
# Grab the representative strings.
bids_string = get_representative_string(simple_bids, total_demand)
srmc_string = get_representative_string(srmc_bids, total_demand)
lrmc_string = get_representative_string(lrmc_bids, total_demand)
# print("Got Representative Strings")
metrics = {
'srmc_string_comp':
compare_representative_strings(bids_string, srmc_string),
'lrmc_string_comp':
compare_representative_strings(bids_string, lrmc_string),
'srmc_fraction_MWh_different':
get_fraction_MWh_different(bids_string, srmc_string,
total_demand),
'lrmc_fraction_MWh_different':
get_fraction_MWh_different(bids_string, lrmc_string,
total_demand),
'datetime':
dt,
'price':
regional_prices,
'weighted_average_price':
weighted_average_price,
'total_demand':
total_demand,
'regional_demand':
regional_demand,
'strike': {
'simple': get_strike_price(simple_bids, total_demand),
'srmc': get_strike_price(srmc_bids, total_demand),
'lrmc': get_strike_price(lrmc_bids, total_demand),
}
}
timeseries[dt] = metrics
print("Finished Processing Bidstack")
return timeseries