#!/usr/bin/env python # Import required modules for this CRT import numpy as np import pandas as pd import matplotlib.pyplot as plt 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()
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 def show_digits(i=0): """ Show some of the digits """ im = np.reshape(mnist.train.images[i], (28, 28)) plt.imshow(im, cmap='viridis') plt.title('The digits')
# starttime = '1997-12-31' # endtime = '2018-10-22' # etfs_data = pdr.get_data_yahoo(etf_symbols, starttime, endtime) # etfs_data.dropna(axis=1, inplace=True) # print2(etfs_data.head(), etfs_data.shape) # data = yf.download(etf_symbols) # data.dropna(axis=1, inplace=True) # print2(data.head()) # tt = robin_stocks.stocks.get_stock_historicals(etf_symbols) # print(tt) with changepath(pathway): data = pd.read_csv('StyleIndexes.csv') data.set_index(pd.to_datetime(data.Date), inplace=True) data.drop(columns=['Date'], inplace=True) data.plot() plt.show() data = data.pct_change().dropna() # data = data.iloc[::-1].iloc[-1000:, :] # data = data.iloc[:1000,:].iloc[::-1,:] # print2(data.head(), data.shape) data2 = data.add(1).cumprod() print2(data2.head(), data.shape) data3 = data2.loc["2016-12-31":"2000-01-01", :] print2(data3.head())