Finance Project

Question 1

start = datetime.datetime(2006, 1, 1)
end = datetime.datetime(2016, 1, 1)

Answer 1

Sukuriama pradzios data ir pabaigos

Question 2

from pandas_datareader import data, wb
import pandas as pd
import numpy as np
import datetime
%matplotlib inline

Answer 2

Reikalingos bibliotekos

Question 3

BAC = web.DataReader(“BAC”, ‘yahoo’, start, end)

Answer 3

Istraukiame dataframe is web

Question 4

bank_stocks = pd.concat([BAC, C, GS, JPM, MS, WFC],axis=1,keys=tickers)

Answer 4

Sujungiami duomenys

Question 5

bank_stocks.xs(key=’Close’,axis=1,level=’Stock Info’).max()

Answer 5

Isfiltruojama didziausia akcijos kaina

Question 6

for tick in tickers:
returns[tick+’ Return’] = bank_stocks[tick][‘Close’].pct_change()
returns.head()

Question 7

.xs

Answer 7

Naudojama to grab information from specific columns

Question 8

import seaborn as sns
sns.pairplot(returns[1:])

Answer 8

Sukuriamas pairplot

Question 9

returns.idxmin()

Answer 9

Isfiltruoja kai stock turejo didziausia drop

Question 10

returns.idxmax()

Answer 10

Isfiltruoja kai stock turedejo didziausia gain

Question 11

returns = pd.DataFrame()

Answer 11

Sukuriame empty data frame

Question 12

returns.ix[‘2015-01-01’:’2015-12-31’].std()

Answer 12

Grazina tu dienu standart deviation

Question 13

sns.distplot(returns.ix[‘2015-01-01’:’2015-12-31’][‘MS Return’],color=’green’,bins=100)

Answer 13

Sukuria distplot

Question 14

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(‘whitegrid’)
%matplotlib inline

Optional Plotly Method Imports
import plotly
import cufflinks as cf
cf.go_offline()

Answer 14

Importuojamas vizualizaciju bibliotekos

Question 15

for tick in tickers:
bank_stocks[tick][‘Close’].plot(figsize=(12,4),label=tick)
plt.legend()

Answer 15

Sukuria stocku kainu diagrama