def run():
if (CheckVar1.get() == 0 and CheckVar2.get() == 0 and CheckVar3.get() == 0
and CheckVar4.get() == 0):
s_1 = '您还没选择任何组合策略'
else:
s1 = "认购" if CheckVar1.get() == 1 else ""
s2 = "认沽" if CheckVar2.get() == 1 else ""
s3 = "买入" if CheckVar3.get() == 1 else ""
s4 = "卖出" if CheckVar4.get() == 1 else ""
s_1 = "第一个合约：%s %s %s %s 合约" % (s1, s2, s3, s4)
lb2.config(text=s_1)
exe_price1 = float(inp1.get())
cost_price1 = float(inp2.get())
if (CheckVar5.get() == 0 and CheckVar6.get() == 0 and CheckVar7.get() == 0
and CheckVar8.get() == 0):
s_2 = '您还没选择任何组合策略'
else:
s5 = "认购" if CheckVar5.get() == 1 else ""
s6 = "认沽" if CheckVar6.get() == 1 else ""
s7 = "买入" if CheckVar7.get() == 1 else ""
s8 = "卖出" if CheckVar8.get() == 1 else ""
s_2 = "第二个合约：%s %s %s %s 合约" % (s5, s6, s7, s8)
lb3.config(text=s_2)
exe_price2 = float(inp3.get())
cost_price2 = float(inp4.get())
#运行画图函数
is_call1 = CheckVar1.get() if CheckVar1.get() == 1 else 0
is_long1 = CheckVar3.get() if CheckVar3.get() == 1 else 0
is_call2 = CheckVar5.get() if CheckVar5.get() == 1 else 0
is_long2 = CheckVar7.get() if CheckVar7.get() == 1 else 0
rev = Revenue()
print(exe_price1, cost_price1, is_call1, is_long1, exe_price2, cost_price2,
is_call2, is_long2)
rev.calculate(exe_price1, cost_price1, is_call1, is_long1, exe_price2,
cost_price2, is_call2, is_long2)
def serialize(cls, ref):
operationList = []
revenueList = []
balanceList = []
for operation in ref.operation:
operationList.append(Operation.serialize(operation))
for revenue in ref.revenue:
revenueList.append(Revenue.serialize(revenue))
for balance in ref.balance:
balanceList.append(Balance.serialize(balance))
# TODO: use constants for the names of all JSon fields
#return json.dumps(attributes)
return {
#"id": ref.id, "name": ref.name, "type": ref.type, "bank": ref.bank, "operations": ref.operation, "balance": ref.balance, "revenue": ref.revenue
"name": ref.name, "type": ref.type, "bank": ref.bank, "operations": operationList, "revenue": revenueList, "balance": balanceList
}
def check_area_ytp(pickle_name):
data = load(pickle_name)
n_years = 15
house_cost = data.loc[0].value
appreciation = 3.0
n_months = n_years * 12
dates = pd.date_range(data.loc[0].date, periods=n_months, freq="M")
rent = Revenue("rent", "monthly payment", 0.011 * house_cost)
# costs
utilities = Cost("utilities", "hvac", 150)
insurance = Cost("insurance", "full coverage", 100)
property_tax = Cost("insurance", "full coverage",
(house_cost * 0.019) / 12)
mortgage = Mortgage(house_cost, 25000, dates, 3.0)
# revenue
revenues = [rent]
costs = [utilities, insurance, property_tax]
house = House(revenues, costs, mortgage, dates, appreciation)
# house.summary()
house.graph_net_profit()
portfolio = Portfolio([house])
ytp = portfolio.get_years_to_positive(house)
print("years till positive", ytp)
exit(0)
return ytp
def addRevenue(self, newRevenue):
self.revenue.append(Revenue.unserialize(newRevenue))
self.revenue = sorted(self.revenue, reverse=True)
import matplotlib.pyplot as plt import seaborn as sns from revenue import Revenue revenue = Revenue().clean() are_old = revenue["Age"] > 60 plt.hist(revenue[are_old]["Age"], bins=50) plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from revenue import Revenue
revenue = Revenue().clean()
revenue.info()
revenue.describe()
for c in revenue.columns:
if revenue[c].dtype == np.dtype("O"):
print(c, revenue[c].unique())
plt.figure()
sns.pairplot(revenue)
plt.tight_layout()
plt.savefig("pairplot.png")
#PURPOSE: answer question 1
from revenue import Revenue
revenue = Revenue().clean()
UNIT = 1e6 #euros
print(revenue.groupby("Model")["Price"].sum().sort_values(ascending=False)/UNIT)
#PURPOSE: answer question 3
from revenue import Revenue
import matplotlib.pyplot as plt
import seaborn as sns
revenue = Revenue().clean()
median_all = revenue.groupby(["Sex", "is_weekend"]).median()[["Age", "Incomes"]]
is_clio = revenue["Model"] == "Clio"
is_yellow = revenue["Color"] == "yellow"
yellow_clio = revenue[is_clio & is_yellow]
yellow_clio = yellow_clio.drop(["Model", "Color", "Price"], axis=1)
median_yellow_clio = yellow_clio.groupby(["Sex", "is_weekend"]).median()
# age and incomes
comparison = median_all.merge(median_yellow_clio,
left_index=True,
right_index=True,
suffixes=["_all", "_yc"])
comparison["age_diff"] = comparison["Age_yc"] - comparison["Age_all"]
comparison["incomes_diff"] = comparison["Incomes_yc"] - comparison["Incomes_all"]
comparison = comparison.drop(["Age_all", "Incomes_all", "Age_yc", "Incomes_yc"], axis=1)
# sex and is_weekend