def test_initialize(self):
database = Database()
number_cruncher = NumberCruncher(database.snapshots)
self.assertTrue(len(number_cruncher.snapshots) > 0)
from Classes.Database import Database
from Classes.HTMLWriter import HTMLWriter
from Classes.DataCollector import DataCollector
from Classes.NumberCruncher import NumberCruncher
data_collector = DataCollector()
data_collector.collect_data()
data_collector.export_content()
database = Database()
number_cruncher = NumberCruncher(database.snapshots)
number_cruncher.write_bias_graph_to_csv("bias_graph.csv")
html_writer = HTMLWriter()
html_writer.write("predictit-forecasting.html")
print("MainDriver run complete!")
def test_averages(self):
database = Database("data_logs_tests/test_2")
number_cruncher = NumberCruncher(database.snapshots)
graph = number_cruncher.graph
average_bias = graph.points[1].average_bias
self.assertTrue(0.01 >= average_bias >= -0.01)
def test_resolve_to_yes(self):
database = Database("data_logs_tests/test_1")
number_cruncher = NumberCruncher(database.snapshots)
graph = number_cruncher.graph
self.assertTrue(graph.points[1].average_bias == -0.5)
def test_graph_has_points(self):
database = Database()
number_cruncher = NumberCruncher(database.snapshots)
graph = number_cruncher.graph
points = graph.points
self.assertTrue(len(points) > 0)
from datetime import timedelta
from Classes.ExperimentTwoDataObject import ExperimentTwoDataObject
from Classes.Database import Database
from Classes.NumberCruncher import NumberCruncher
from Classes.ExperimentOneDataObject import ExperimentOneDataObject
import matplotlib.pyplot as plt
database = Database()
number_cruncher = NumberCruncher(database.snapshots)
closed_markets_dict = number_cruncher.get_markets_that_closed_on_end_date()
threshold = 0.89
viable_contracts_close = []
agg_profits = []
num_num_contracts = []
agg_percent_profits = []
lookback = 60
for day in range(0, lookback):
agg_profit = 0.0
agg_current_price = 0.0
agg_close_price = 0.0
num_contracts = 0
for closed_market_id, closed_market_date in closed_markets_dict.items():
proceed = True
lookback_date = closed_market_date - timedelta(days=day)
try:
closed_market_pre_close = number_cruncher.market_lookup(
closed_market_id, lookback_date)
except KeyError:
proceed = False
from Classes.Database import Database
from Classes.NumberCruncher import NumberCruncher
from Classes.ExperimentOneDataObject import ExperimentOneDataObject
import matplotlib.pyplot as plt
database = Database()
number_cruncher = NumberCruncher(database.snapshots)
closed_markets = number_cruncher.get_markets_that_closed_on_end_date()
# Declare experiment outputs and parameters
percent_profit = []
num_num_data_points = []
days_to_check = 60
for day in range(0, days_to_check):
data_objects = [
ExperimentOneDataObject(x, y, day, database)
for x, y in closed_markets.items()
]
agg_prices_close = 0.0
agg_prices_pre_close = 0.0
num_data_points = 0
for data_object in data_objects:
if data_object.price_pre_close is not None and data_object.price_close is not None:
agg_prices_pre_close += data_object.price_pre_close
agg_prices_close += data_object.price_close
num_data_points += 1
percent_profit.append(agg_prices_pre_close / agg_prices_close)