stock data analysis and visualization.py

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from copy import copy
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go


pd.set_option('display.max_columns', 20)
stocks_df = pd.read_csv('/home/kashindra/PycharmProjects/Financial Analysis/stock.csv')
# print(stocks_df.head)

stocks_df = stocks_df.sort_values(by = ['Date'])

print("Total number of stock : {}".format(len(stocks_df.columns[1:])))

for i in stocks_df.columns[1:]:
    print(i)

print(stocks_df.describe())

print(stocks_df.isnull().sum())

print(stocks_df.info())


def show_plot(df, fig_title):
    df.plot(x='Date', figsize=(15, 7), linewidth=3, title=fig_title)
    plt.grid()
    plt.show()

# show_plot(stocks_df, 'RAW STOCK PRICES(WITHOUT NORMALIZATION)')


def normalize(df):
    x = df.copy()
    for i in x.columns[1:]:
        x[i] = x[i]/x[i][0]
    return x

# print(normalize(stocks_df))

# show_plot(normalize(stocks_df), 'NORMALIZED STOCK PRICES')


def interactive_plot(df, title):
    fig = px.line(title=title)

    for i in df.columns[1:]:
        fig.add_scatter(x=df['Date'], y=df[i], name=i)

    fig.show()


# interactive_plot(stocks_df, 'Prices')

# interactive_plot(normalize(stocks_df), 'NORMALIZED PRICES')

# df = stocks_df['sp500']
# df_daily_return = df.copy()
#
# for i in range(1, len(df)):
#     df_daily_return[i] = ((df[i] - df[i-1])/df[i-1]) * 100
#
# df_daily_return[0] = 0
# print(df_daily_return)


def daily_return(df):
    df_daily_return = df.copy()
    for i in df.columns[1:]:
        df_daily_return[i][0] = 0
        for j in range(1, len(df)):
            df_daily_return[i][j] = ((df[i][j] - df[i][j-1]) * 100)
    return df_daily_return


stocks_daily_return = daily_return(stocks_df)
# print(stocks_daily_return)

# show_plot(stocks_daily_return, "STOCKS DAILY RETURNS")
# interactive_plot(stocks_daily_return, "STOCKS DAILY RETURNS")

cm = stocks_daily_return.drop(columns=['Date']).corr()
plt.figure(figsize=(10, 10))
sns.heatmap(cm, annot=True)
# plt.show()


print(stocks_daily_return.hist(figsize=(10, 10), bins=40));
# plt.show()

df_hist = stocks_daily_return.copy()
df_hist = df_hist.drop(columns = ['Date'])
data = []

for i in df_hist.columns:
    data.append(stocks_daily_return[i].values)

# fig = ff.create_distplot(data, df_hist.columns)
# fig.show()