python assignments

Question 1

Imports basics

Answer 1

import yfinance as yf
import datetime as dt
import pandas as pd
import numpy as np
start_date = dt.datetime(2015, 1, 2)
end_date = dt.datetime(2020, 4, 30)

Question 2

work out %

work out sma

Answer 2

df_MSFT["range"] = (df_MSFT["High"] - df_MSFT["Close"]) / df_MSFT["Close"]
df_MSFT["range"] = df_MSFT["range"].rolling(window=25).mean()

Question 3

work out on going returns

Answer 3

df_MSFT[‘cc_returns’].cumsum()

Question 4

how to add data sheets portrait to new df

Answer 4

df = pd.concat( [x, y, z], axis=1 )

Question 5

How to change column names

Answer 5

df.columns = [‘GLD’, ‘GDX’, ‘USO’]

Question 6

Change index to datetime

Answer 6

df.index = pd.to_datetime(df.index)

Question 7

Work out percentages for df, then cumulative growth

Answer 7

df_pct = (df.pct_change()+1).cumprod()

Question 8

plot df

Answer 8

df_pct.plot(figsize=(16,8))

plt. ylabel(“Percentage Change”)
plt. show()

Question 9

work with module that allows estimation by ordinary least square

Answer 9

from statsmodels.api import OLS

Question 10

Fit the model OLS to the first 90 rows,

Answer 10

model = OLS(df[“GLD”].iloc[:90], df[[“USO”,”GDX”]].iloc[:90])
model = model.fit()
print(‘The hedge ratio for GDX and USO are’)
model.params

Question 11

placing long position

Answer 11

df[‘long_entry’] = df.spread < df.lower_band
df[‘positions_long’] = np.nan
df.loc[df.long_entry, ‘positions_long’] = 1

Question 12

date for end and start

Answer 12

end_date = dt.date(2020, 4, 30)
start_date = dt.date(2015, 1, 2)

Question 13

Download using yfinance

Answer 13

import yfinance as yf

df = yf.download(“MSFT”, start=start_date, end=end_date)

Question 14

Work out return

Answer 14

df[“return”] = np.log(df[“Adg Close”] /df[“Adg Close”].shift(1))

Question 15

Find the day name

Answer 15

data[‘day’] = data.index.day_name()

Question 16

use a where to build up condition

Answer 16

data[‘signal’] = np.where((data[‘condition1’] == 1)
& (data[‘condition2’] == 1)
& (data[‘condition3’] == 1),
1, 0)

Question 17

How to use lambda on list

Answer 17

list ( map (lambda x, y: x + y, list_1, list_2 ) )

Question 18

What is the 20 rolling average mean

Answer 18

df2[‘20_day_sma’] = df2[‘Adj Close’].rolling(window=20, center=False).mean()

Question 19

Working out factorials

Answer 19

for i in range(1, num+1):

fact = fact * i