def traniner(epoo=5):
# initialize number of epochs
epochs = epoo
for epo in range(epochs):
# create loss placeholder
iterm_loss = 0
# iterate through the data
for img, labels in traindownloader:
img, labels = img.to(device), labels.to(device)
# clear the gradients
optimin.zero_grad()
# compute the forward pass
output = model(img)
# compute loss
loss = criterion(output, labels)
# store the loss
iterm_loss += loss.item()
# compute backward pass
loss.backward()
# update weights
optimin.step()
else:
print2(f"Epoch {epo+1}/{epochs}..>..>.> " +
f"Training loss:{iterm_loss/len(traindownloader):.4f}")
def strcbreak(data, breakpoint):
import statsmodels.api as sm
from scipy import stats as st
mr = data
before = mr.loc[:breakpoint]
after = mr.loc[breakpoint:]
# print2("#"*20, before.tail())
kmr = np.ones([mr.shape[0]])
kkb = np.ones([before.shape[0]])
kka = np.ones([after.shape[0]])
mr_intercept = sm.add_constant(kmr)
before_with_intercept = sm.add_constant(kkb)
after_with_intercept = sm.add_constant(kka)
# # Fit OLS regressions to each tota; period
# result = sm.OLS(mr, mr_intercept).fit()
# # # Retrieve the sum-of-squared residuals
# ssr_total = result.ssr
# # Fit OLS regressions to each sub-period
# r_b = sm.OLS(before, before_with_intercept).fit()
# r_a = sm.OLS(after, after_with_intercept).fit()
# # # Retrieve the sum-of-squared residuals
# ssr_before = r_b.ssr
# ssr_after = r_a.ssr
# Fit OLS regressions to each total period
result = sm.RLM(mr, mr_intercept, M=sm.robust.norms.HuberT()).fit()
# Retrieve the sum-of-squared residuals
ssr_total = np.sqrt(np.power(result.resid, 2).sum())
# Fit OLS regressions to each sub-period
r_b = sm.RLM(before, before_with_intercept, M=sm.robust.norms.HuberT()).fit()
r_a = sm.RLM(after, after_with_intercept, M=sm.robust.norms.HuberT()).fit()
# Get sum-of-squared residuals for both regressions
ssr_before = np.sqrt(np.power(r_b.resid, 2).sum())
ssr_after = np.sqrt(np.power(r_a.resid, 2).sum())
# Compute and display the Chow test statistic
d_f = 1
df2 = 2*d_f
numerator = ((ssr_total - (ssr_before + ssr_after)) / d_f)
denominator = ((ssr_before + ssr_after) / (mr.shape[0]/2 - df2))
print("Chow test statistic: ", numerator / denominator)
f = st.f.ppf(q=1-0.01, dfn=d_f, dfd=(mr.shape[0]/2 - df2))
print2(f"F Critical point: {f}")
def return_var(prices, probb):
r = [np.dot(np.array(i[0]), np.array(i[1])) for i in zip(prices, probb)]
t = [
np.sqrt(
np.dot(np.array(i[2]),
np.subtract(np.array(i[0]), np.array(i[1]))**2))
for i in zip(prices, r, probb)
]
print2(r, t)
return r, t
def check__nulls(df):
"""
Test and report number of NAs in each column of the input data frame
:param df: pandas.DataFrame
:return: None
"""
for col in df.columns:
_nans = np.sum(df[col].isnull())
if _nans > 0:
print(f'{_nans} NaNs in column {col}')
print2(f'New shape of {get__name(df)}: {df.shape}')
def getload_decade(start=1920, end=1929, extension='prn'):
"specify the starting year of the decade eg. 1900, 2010, 2009"
webaddress = f'https://www.nyse.com/publicdocs/nyse/data/Daily_Share_Volume_{start}-{end}.{extension}'
try:
link = requests.get(webaddress)
print2(link.status_code)
if link.status_code == 404:
raise
else:
if extension == "prn":
data = pd.read_csv(webaddress,
sep=' ',
parse_dates=['Date'],
engine='python').iloc[2:, 0:2]
print2(data.head(), data.columns)
data.loc[:, " Stock U.S Gov't"] = pd.to_numeric(
data.loc[:, " Stock U.S Gov't"], errors='coerce')
data.Date = pd.to_datetime(data.Date,
format='%Y%m%d',
errors="coerce")
data.columns = ['Date', 'Volume']
print2(f"Successfully downloaded {start}-{end}")
return data
else:
data = pd.read_csv(webaddress)
data.iloc[:,
0] = data.iloc[:,
0].apply(lambda x: str(x).strip(' '))
data = data.iloc[:, 0].str.split(' ', 1, expand=True)
data.columns = ['Date', 'Volume']
data.loc[:, "Volume"] = pd.to_numeric(data.loc[:, "Volume"],
errors='coerce')
data.Date = pd.to_datetime(data.Date,
format='%Y%m%d',
errors="coerce")
print2(f"Successfully downloaded {start}-{end}")
return data
except:
print2("There was an issue with the download \n\
You may need a different date range or file extension.\n\
Check out https://www.nyse.com/data/transactions-statistics-data-library"
)
def load_data(start=1920, end=1929, extension="prn"):
# get the path
path = os.path.join(path33, "Data",
f"Daily_Share_Volume_{start}-{end}.{extension}")
# path = os.path.join(path33, f"Daily_Share_Volume_{start}-{end}.{extension}")
if extension == "prn":
data = pd.read_csv(path,
sep=' ',
parse_dates=['Date'],
engine='python').iloc[2:, 0:2]
print2(data.head(), data.columns)
data.loc[:, " Stock U.S Gov't"] = pd.to_numeric(
data.loc[:, " Stock U.S Gov't"], errors='coerce')
data.Date = pd.to_datetime(data.Date, format='%Y%m%d', errors="coerce")
data.columns = ['Date', 'Volume']
print2(f"Successfully downloaded {start}-{end}")
return data
else:
data = pd.read_csv(path)
data.iloc[:, 0] = data.iloc[:, 0].apply(lambda x: str(x).strip(' '))
data = data.iloc[:, 0].str.split(' ', 1, expand=True)
data.columns = ['Date', 'Volume']
data.loc[:, "Volume"] = pd.to_numeric(data.loc[:, "Volume"],
errors='coerce')
data.Date = pd.to_datetime(data.Date, format='%Y%m%d', errors="coerce")
print2(f"Successfully downloaded {start}-{end}")
return data
self.name = name
self.id = id
self.gender = gender
def __eq__(self, other):
return self.name == other.name and self.id == other.id\
and type(self) == type(other)
# return self.name == other.name and self.id == other.id\
# and isinstance(other, Patients)
class Staff:
def __init__(self, name, id, gender):
self.name = name
self.id = id
self.gender = gender
def __eq__(self, other):
return self.name == other.name and self.id == other.id\
and isinstance(other, Staff)
patient1 = Patients("Charles", 459234, "Male")
patient2 = Patients("Charles", 876323, "Male")
patient3 = Patients("Marylene", 459234, "Female")
patient4 = Patients("Charles", 459234, "Male")
patient5 = Staff("Charles", 459234, "Male")
print2(patient1 == patient2, patient3 == patient1, patient1 == patient4)
print2("$" * 20)
print2(patient1 == patient5, patient5 == patient1)
from sklearn.cluster import KMeans
from printdescribe import print2
nn = np.array([-8.0, -3.0, 0.0, 6.5, 9.0, 45.5]).reshape(-1, 1)
cc = np.array([-1.5, -1.0, -0.5, 1.5, 2.0, 2.5]).reshape(-1, 1)
gm = GaussianMixture(n_components=2, covariance_type='full')
gmm = gm.fit(nn)
pred = gmm.fit_predict(nn)
logprob = gmm.score_samples(nn)
responsibilities = gmm.predict_proba(nn)
pdf = np.exp(logprob)
pdf_individual = responsibilities * pdf[:, np.newaxis]
print2(nn, logprob, responsibilities, pdf, pdf_individual)
print2(np.round(gmm.weights_, 2), np.round(gmm.means_, 2),
np.round(gmm.covariances_, 2))
print2(np.round(gmm.precisions_, 2), np.round(gmm.precisions_cholesky_, 2))
print2(np.round(pred, 2))
print2(gmm.score(nn))
print("######################################################")
print("This is for [-1.5,-1.0,-0.5,1.5,2.0,2.5] data")
gcc = gm.fit(cc)
pred = gcc.fit_predict(cc)
logprob = gcc.score_samples(cc)
responsibilities = gcc.predict_proba(cc)
pdf = np.exp(logprob)
pdf_individual = responsibilities * pdf[:, np.newaxis]
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
from printdescribe import print2, describe2, changepath
# import excel sheets
path = r"D:\Wqu_FinEngr\Portfolio Theory and Asset Pricing\GroupWork"
with changepath(path):
data = pd.read_excel("GWP_PTAP_Data_2010.10.08.xlsx",
skiprows=1,
nrows=13,
sheet_name='10 SPDRs and S&P 500',
index_col=0)
describe2(data)
print2(data)
df_return = data.pct_change().dropna()
print2(df_return)
# df_activeReturn = df_return.sub(df_return.iloc[:,-1], axis=0).drop(['SP_500'], axis=1)
df_activeReturn = df_return.sub(df_return['S&P 500'], axis=0).drop(['S&P 500'],
axis=1)
print2(df_activeReturn)
tracking_error = df_activeReturn.std()
mate_ = np.sqrt((df_activeReturn**2).sum() / df_activeReturn.shape[0])
print2(tracking_error, mate_)
# for col in df_return.columns[:-1]:
# plt.figure(figsize=[10, 8])
# My two other ETFs are
# 1. Vanguard S$P500 ETF (VOO)
# 2. iShares Core S&P500 ETF (IVV)
etfs_tickers = ["IVV", "SPY", "VOO", "^GSPC"]
# using 2 years of data from January 01, 2018 to December 31, 2019
starttime = datetime.datetime(2018, 1, 1)
endtime = datetime.datetime(2019, 12, 31)
# get only the closing prices
etfs = pdr.get_data_yahoo(etfs_tickers, starttime, endtime)['Close']
etfs.columns = ["iShares", "SPDR", "Vanguard", "S&P500"]
# print out dataset head
print2(etfs.head())
# compute simple reurns
etfs_return = etfs.pct_change().dropna()
# etfs_return.fillna(0, inplace=True)
returns2 = round(etfs_return * 100, 3)
print2(etfs_return, returns2)
# compute active returns
eft_index = etfs_return["S&P500"]
ppp = returns2.sub(returns2.iloc[:, -1], axis=0).drop(['S&P500'], axis=1)
ppp2 = returns2.sub(returns2['S&P500'], axis=0).drop(['S&P500'], axis=1)
etfs_activeR = etfs_return.sub([eft_index, eft_index, eft_index, eft_index],
axis='columns')
etfs_activeR.drop("S&P500", axis=1, inplace=True)
import torch
from printdescribe import print2, describe2, changepath
from pytorchFunctions import sigmoid_activation
# set the seed
torch.manual_seed(90)
# create features
features = torch.randn((1, 10))
# define sizes of layers
input_size = features.shape[1]
n_hiddenlayers = 4
n_output = 1
# create weights
feature_weights = torch.randn((input_size, n_hiddenlayers))
hiddenlayer_weights = torch.randn((n_hiddenlayers, n_output))
# create biases
feature_bias = torch.randn((1, n_hiddenlayers))
hiddenlayer_bias = torch.randn((1, n_output))
# y = f2(f1(xW1)W2)
hiddenlayer_output = sigmoid_activation(
torch.mm(features, feature_weights) + feature_bias)
y = sigmoid_activation(
torch.mm(hiddenlayer_output, hiddenlayer_weights) + hiddenlayer_bias)
print2(y)
pp_labels = ["JPMorgan Chase", "Goldman Sachs", "BofA Securities", "Morgan Stanley", "Citigroup", "Credit Suisse"] starttime = datetime.datetime(2000, 1, 1) endtime = datetime.datetime(2019, 10, 1) # get only the closing prices assets = pdr.get_data_yahoo(stocklist, starttime, endtime)['Close'] # initialize the weights weights = [0.2, 0.15, 0.2, 0.15, 0.2, 0.1] # compute the simple returns returns = assets.pct_change().dropna() # visualse the data print2(assets.head(), returns.head()) describe2(assets, returns) # compute portfolio returns portfolioReturn = returns.dot(weights) # compute portfolio value for $1 investment portfolioValue = (1 + portfolioReturn).cumprod() print2(portReturn, meanDailyReturns, portfolioValue) # Calculate individual mean returns meanDailyReturns = returns.mean() # Define weights for the portfolio weights = np.array([0.2, 0.2, 0.2, 0.1, 0.15, 0.15])
from statsmodels.tsa.vector_ar.vecm import coint_johansen
import scipy.stats
from printdescribe import print2
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 10,8
plt.rcParams["axes.facecolor"] = "0.92"
show = plt.show
# Obtaining Stock Data of Microsoft and Benchmark Data
start_date = '2013-01-01'
end_date = '2019-12-31'
assets = ['MSFT', 'FDN', 'JPM', 'XLF']
datasets = dr.DataReader(assets, data_source='yahoo', start=start_date, end=end_date)
print2(datasets['Adj Close'].head())
# matplotlib.rcParams['figure.figsize'] = [15, 7]
plt.plot(datasets['Adj Close'])
plt.ylabel('Price')
plt.legend(assets)
plt.grid()
show()
# Obtaining the mean and standard devivation of the Assests
means = datasets['Adj Close'].mean()
stddevs = datasets['Adj Close'].std()
print2(means, stddevs)
parse_dates=True,
index_col="Date")
# datasets = pd.read_csv("assets.csv", compression='gzip', index_col=0)
# datasets, dataset2 = pd.read_csv(["assets.csv","assets2.csv"])
dataset2.rename(columns={"Adj Close": "SPX"}, inplace=True)
dataset3.drop(columns=["^GSPC"], inplace=True)
# # df.set_index('Date', inplace=True)
# print2(dataset2.head())
# datasets.reindex(dataset2.index)
alldata = pd.concat([dataset3, dataset2], axis=1)
data2 = alldata.copy()
data2 = data2.loc[:"2013-12-20", :]
print2(data2.iloc[:, :5].tail(), data2.shape)
# tt = "https://dumbstockapi.com/stock?format=tickers-only&exchange=NYSE"
# pp = pd.read_json(tt)
# pp = list(pp.values.ravel())
# download data and view
# data2 = dr.DataReader(pp, data_source='yahoo', start=start_date)['Adj Close']
# print2(f"Asset Adjusted Closing Pices shape: {data2.shape}", data2.iloc[:,:10].head())
# drop columns with NaN
data2.dropna(axis=1)
print(data2.iloc[:, :5].head())
# clean the datasets, remove NaN smartly
# Get a summary view of NaNs
oo = data2.isnull().sum()
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 10, 8
plt.rcParams["axes.facecolor"] = "0.92"
np.random.seed(42)
# create matrix
A = A = np.linspace(1, 9, 9).reshape(-1, 3)
B = np.arange(10, 26).reshape(-1, 4)
# PLU decomposition; used for square matrix
P, L, U = lu(A)
Pb, Lb, Ub = lu(B)
# print results
print2(P, L, U)
print2(Pb, Lb, Ub)
# recombine the triangular factor matrices
A_ = P @ L @ U
B_ = Pb @ Lb @ Ub
# print results
print2(A_, B_)
#########################################################################################
#########################################################################################
# QR decomposition for all matrix
# create matrix
A2 = np.linspace(1, 35, 35).reshape(-1, 5)
B2 = np.arange(10, 34).reshape(-1, 4)
#!/usr/bin/env python
import torch
from printdescribe import print2, describe2, changepath
from pytorchFunctions import sigmoid_activation
# set the seed
torch.manual_seed(90)
# generate features vector
features = torch.randn((1, 10))
# generate weights
weights = torch.randn_like(features)
# generate bias
bias = torch.randn((1, 1))
# computer the prediction
prob = sigmoid_activation(torch.sum(features * weights) + bias)
# also can do sigmoid_activation((features * weights).sum() + bias)
prob2 = sigmoid_activation(torch.mm(features, weights.view(-1, 1)) + bias)
if __name__ == "__main__":
print2(prob, prob2)
## y = f2(f1(xW1)W2)
"~/.pytch/MNIST_data/",
download=True,
train=True,
transform=transformer,
)
traindownloader = tch.utils.data.DataLoader(traindata,
batch_size=64,
shuffle=True)
# create an iterator to read the dataset
iterloader = iter(traindownloader)
img, labels = iterloader.next()
# if __name__ == "__main__":
print2(type(img), type(labels), img.shape, labels.shape)
# display the image
plt.imshow(img[1].numpy().squeeze(), cmap="Greys")
plt.show()
# Flatten the 2D images to 1D images
flat1d_img = img.view(img.shape[0], -1)
# create model parameters
input_size = flat1d_img.shape[1]
n_hiddenlayers = 256
n_output = 10
# create weights
feature_weights = tch.randn((input_size, n_hiddenlayers))
# timeout=30, session=None, interval='day',
# span='year').read().reset_index()
# dw = durbin_watson(pd.to_numeric(apple.close_price).pct_change().dropna().values)
# print2(f'DW_Statistics: {dw}')
# Define start and end dates
starttime = '2018-01-01'
endtime = '2019-01-01'
# Download apple stock prices
apple = pdr.get_data_yahoo('AAPL', starttime, endtime)
dw = durbin_watson(pd.to_numeric(apple.Close).pct_change().dropna().values)
# Compute durbin_watson statistics
print2(f'DW_Statistics: {dw}')
# Get Nasdaq tickers
tickers = pdr.nasdaq_trader.get_nasdaq_symbols(retry_count=3,
timeout=300,
pause=None)
etfs = tickers.loc[tickers.ETF == True, :]
symbols = etfs.sample(75).index.tolist()
print2(etfs.head(), etfs.shape, symbols)
# packet = pdr.robinhood.RobinhoodHistoricalReader(symbols, retry_count=3, pause=0.1,
# timeout=30, session=None, interval='day',
# span='year')
# data = packet.read().reset_index()
# pivot = data.loc[:['symbol', 'begins_at', 'close_price']].drop_duplicates(),pivot(
# index='begins_at', columns = 'symbol', values='close_price'
# download test dataset
testdata = datasets.FashionMNIST(
"~/.pytch/F_MNIST_data/",
download=True,
train=False,
transform=transformer,
)
testdownloader = tch.utils.data.DataLoader(testdata,
batch_size=64,
shuffle=True)
# view the images
img, label = next(iter(traindownloader))
helper.imshow(img[10, :])
print2(label[10])
plt.show()
# define new classifier class
class MyNeuroNetwork(nn.Module):
_inputs = 784
_neuron1 = 128
_neuron2 = 64
_neuron3 = 32
_output = 10
def __init__(self):
super().__init__()
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import seaborn as sns
from printdescribe import print2
genes = ["gene" + str(i) for i in range(1, 101)]
wt = ["wt" + str(i) for i in range(1, 7)]
ko = ["ko" + str(i) for i in range(1, 7)]
data = pd.DataFrame(columns=[*wt, *ko], index=genes)
print2(wt, ko)
data = pd.DataFrame(columns=[*wt, *ko], index=genes)
print2(data.head())
n = 2
for gene in data.index:
data.loc[gene, :"wt6"] = np.random.poisson(lam=np.random.randint(10, 100),
size=6)
np.random.seed(90 + n)
data.loc[gene, "ko1":] = np.random.poisson(lam=np.random.randint(10, 100),
size=6)
n += 5
scaled_data = preprocessing.scale(data.T)
scaled_data[:20]
def findIntersection(fun1, fun2, x0):
return fsolve(lambda x: fun1(x) - fun2(x), x0)
country_list = ['USA', 'GBR', 'MEX', 'CAN', 'ZAF', 'NGA']
startdate = '1970'
enddate = '2019'
crisis_year = pd.to_datetime('1987-01-01')
gdp = wb.download(indicator='NY.GDP.PCAP.KD', country=country_list,
start=pd.to_datetime(startdate, yearfirst=True),
end=pd.to_datetime(enddate, yearfirst=True))\
.reset_index().dropna().iloc[::-1, :]
print2(gdp.shape, gdp.head(), gdp.info(),
gdp.country.value_counts(dropna=False))
gdp2 = gdp.copy()
gdp2['year'] = pd.to_datetime(gdp2['year'])
gdp2.set_index('year', inplace=True)
gdp2.loc[:, "NY.GDP.PCAP.KD"] = gdp2.groupby('country')["NY.GDP.PCAP.KD"]\
.apply(lambda x: pd.Series(x).interpolate())
gdp2.groupby(['country'])['NY.GDP.PCAP.KD'].plot()
plt.axvline(crisis_year, color="black")
plt.legend()
plt.show()
print2(gdp2.info())
gdp3 = gdp.copy()
import sys
import matplotlib.pyplot as plt
import numpy as np
import shutil
# import tensorflow as tf
from ipywidgets import interact
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from printdescribe import print2, changepath
from datetime import datetime
print2(" ")
path22 = r"D:\PythonDataScience"
sys.path.insert(0, path22)
import input_data
path2 = r"D:\Wqu_FinEngr\Machine Learning in Finance\CourseMaterials\Module5\WQU_MLiF_Module5_Notebooks\ML M5 Notebooks (updated)"
with changepath(path2):
print2(os.getcwd())
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
m, n = mnist.train.images.shape
number_to_show = 100
warnings.filterwarnings('ignore')
# instantiate start date
start_date = '2013-01-01'
# end_date = '2020-02-29'
# Download daily Amazon stock Adjusted close prices and indexes
assets = ['AMZN', "^GSPC", "^DJI", "^IXIC", "^RUT", "CL=F"]
datasets = dr.DataReader(assets, data_source='yahoo',
start=start_date)["Adj Close"]
datasets.tail()
# Name of the columns
col = ["Amazon", "Sp500", "Dow20", "Nasdaq", "R2000", "Crude20"]
datasets.columns = col
print2(datasets.head())
datasets.iloc[:, ~datasets.columns.isin(["Dow20", "Nasdaq"])].plot(figsize=(10,
5))
data = datasets.copy()
data['close'] = data["Amazon"]
# compute moving averages
fast_window = 20
slow_window = 50
data['fast_mavg'] = data['close'].rolling(window=fast_window,
min_periods=fast_window,
center=False).mean()
data['slow_mavg'] = data['close'].rolling(window=slow_window,
min_periods=slow_window,
# %matplotlab inline
# %config InlineBackend.fig_format = "retina"
import numpy as np
import matplotlib.pyplot as plt
import helper
import torch as tch
from torch import nn, optim
import torch.nn.functional as F
from torchvision import datasets, transforms
from printdescribe import print2, describe2, changepath
plt.style.use("dark_background")
print2(tch.cuda.is_available())
# transformer to transform and normalize
transformer = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])
traindata = datasets.MNIST(
"~/.pytch/MNIST_data/",
download=True,
train=True,
transform=transformer,
)
traindownloader = tch.utils.data.DataLoader(traindata,
batch_size=64,
shuffle=True)
#!/usr/bin/env python
import numpy as np
import pandas as pd
from printdescribe import print2, describe2, changepath
S0 = [[120, 90], [140, 90], [130, 100]]
prob = [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]
def return_var(prices, probb):
r = [np.dot(np.array(i[0]), np.array(i[1])) for i in zip(prices, probb)]
t = [
np.sqrt(
np.dot(np.array(i[2]),
np.subtract(np.array(i[0]), np.array(i[1]))**2))
for i in zip(prices, r, probb)
]
print2(r, t)
return r, t
vars = return_var(S0, prob)
print2(S0, prob, vars)
# select the Average Value Weighted Returns -- Daily (1220 rows x 100 cols) portfolios100 = portfolios100[0] factors5 = factors5[0] # Checking for missing values porfolios dataset print(portfolios100[portfolios100.iloc[:,0] >98.0].sum().sum(), portfolios100.isnull().sum().sum()) # Checking for missing values in factors dataset print(factors5[portfolios100.iloc[:,0] >98.0].sum().sum(), factors5.isnull().sum().sum()) portfolios100.iloc[:, 90:100].head() print2(portfolios100.shape, factors5.head(), factors5.shape) # # 1a. Visually analyze the covariance between various factors and identify <br> the variance explained in principle components of these factors. # # ## 1b. Next, consider the ACF and PACF of the process and its square. # pd.melt(factors5.add(1).cumprod().reset_index(), id_vars=["Date"]).hvplot.line(x='Date', y='value', by='variable') factors_cov = factors5.cov() factors_cov plt.figure(figsize = [10, 6]) # Visualize the covariance matrix using a heatmap
# print(pv_fcfe)
comp1 = sum(pv_fcfe)
# print(comp1)
comp2_ = comp2(round(FCFE[-1], 2), listt[2], listt[3], n, one)
# print(comp2_)
mp_ggm = comp1 + comp2_
return round(mp_ggm, 2)
Year = ["2008", "2009", "2010", "2011", "2012", "2013"]
FCFE_growth = [0.18, 0.18, 0.16, 0.12, 0.11, 0.06]
equity_discount_rate = 0.125
fcfe2007 = 2.0
g = 0.06
print(multi_ggm22(FCFE_growth, fcfe2007, g, equity_discount_rate))
initial = 5000
mmm = []
for i in range(40):
final = initial * 1.05
mmm.append(final)
final += 5000
initial = final
if i > 20:
initial -= 5000
print2(final, mmm, mmm[-1] / 5000)
classifier.means_init = np.array([X_train[y_train == i].mean(axis=0)
for i in range(n_classes)])
# define colors and markers
markers = ["*","o", "+"]
# colors = ["r", "y", "k"]
colors = ['navy', 'turquoise', 'darkorange']
col = ['r*','yo','k+']
labels = y_test
# Fit to data and predict using pipelined scaling, PCA.
gmm = make_pipeline(StandardScaler(), classifier)
gmm.fit(X_train)
pred_train = gmm.predict(X_train)
train_accuracy = np.mean(pred_train.ravel() == y_train.ravel()) * 100
print2(f"Train accuracy: {np.round(train_accuracy,2)}")
pred_test = gmm.predict(X_test)
test_accuracy = np.mean(pred_test.ravel() == y_test.ravel()) * 100
print2(f"Test accuracy: {np.round(test_accuracy,2)}")
for n, color in enumerate(colors):
# plot the original data
data = iris.data[iris.target == n]
plt.scatter(data[:, 0], data[:, 1], s=2.8, color=color,label=iris.target_names[n])
# plot the test data
data = X_test[y_test == n]
plt.plot(data[:, 0], data[:, 1], 'x', color=color)
#!/usr/bin/env python
import numpy as np
from printdescribe import print2, describe2, changepath
nlist = [0.06, 0.07, 0.08, 0.09, 0.10]
def forward_rate(spotList, tstar, t):
"""The forward_rate function calculates the forward rate e.g f(T*, T)
given yearly spot rates.
Inputs:
spotList (list) : yearly spot rates
tstar (int): time until initiation of the rate
t (int): the tiemto maturity from tstar
Output:
result (float): forward rate
"""
nume = pow(1 + spotList[t + tstar - 1], t + tstar)
denom = pow(1 + spotList[tstar - 1], tstar)
result = pow((nume / denom), 1 / t) - 1
return round(result * 100, 2)
print2(forward_rate(nlist, 1, 2))
import numpy as np
import pandas as pd
from printdescribe import print2
# download dataset
url2 = "https://archive.ics.uci.edu/ml/machine-learning-databases/00529/diabetes_data_upload.csv"
df = pd.read_csv(url2)
# expore dataset
print2(df.shape, df.dtypes, df.columns, df.info(), df.describe())
# Explore categorical features
for col in [x for x in df.select_dtypes(include=['object'])]:
print2(df[col].value_counts(ascending=True, dropna=False))
# Add nan to dataset
num = df.Age.max()
for col in [x for x in df.select_dtypes(include=['object'])]:
numm = num - 3
df[col] = np.where(df.Age > numm, np.nan, df[col])
num = numm
# view the new dataset
print2(df.isnull().sum())
print(df.info())
# explore the categorical datasets
for col in [x for x in df.select_dtypes(include=['object'])]:
print2(df[col].value_counts(ascending=True, dropna=False))
# drop nan row