analysis.py

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
from nsepy import get_history
from datetime import datetime as dt 

plt.style.use("seaborn")

start_date = dt(2000,1,1)
end_date = dt.now()


# df = get_history("Nifty",start_date,end_date,index=True)

# print(df.tail())

# df.to_csv("Nifty50.csv")

def eichermot():


    df = pd.read_csv("./data/EICHERMOT.csv",parse_dates=True,index_col=0)



    df = df.resample('W').mean()
    # print(df.head())


    fig = plt.figure(figsize=(12,6))
    plt.plot(df.index,df['Close'])
    plt.xlabel("Year")
    plt.ylabel("Price")
    plt.title("Eichermotors stock price over the years")
    # plt.show()

    #Calculate a 50 day moving average
    df['50ma'] = df['Close'].rolling(window=50).mean()

    print(df.tail())

    fig = plt.figure(figsize=(12,6))
    ax1 = plt.subplot2grid((6,1),(0,0),rowspan=5,colspan=1)
    ax2 = plt.subplot2grid((6,1),(5,0),rowspan=1,colspan=1,sharex=ax1)

    ax1.plot(df.index,df['Close'])
    ax1.plot(df.index,df['50ma'])
    ax2.plot(df.index,df['Volume'])

    plt.show()


df = pd.read_csv("./Nifty50_combined.csv",parse_dates=True,index_col=0)

df = df["2010-01-01":"2019-12-31"]

print(df.head())



def get_insights():

    print("Summary Statistics of 50 stocks:")
    print(df.describe().T)

    #Plot average price of stocks

    summary = df.describe().T

    fig = plt.figure(figsize=(8,12))
    plt.barh(summary.index,summary['mean'])
    plt.title("Average price of Stocks")
    plt.ylabel("Stocks")
    plt.xlabel("Average price")
    # plt.show()

    #calculate simple returns or percentage returns of the stocks
    fig2 = plt.figure()
    df.pct_change().mean().plot(figsize=(10,6),kind='bar')
    plt.xlabel("Stocks")
    plt.ylabel("Percentage Change")
    plt.title("Simple returns")
    # plt.show()



    #calculate and plot the log returns 
    rets = np.log(df/df.shift(1))
    rets[rets.columns[30:45]].cumsum().apply(np.exp).plot(figsize=(10,6))
    plt.title("Log returns")
    # plt.show()



    # select some well performing stocks overtime

    print(df.columns)

    stocks = df[['SHREECEM','BAJAJFINSV','EICHERMOT','INDUSINDBK','HINDUNILVR']]


    ax = stocks.plot(figsize=(10,8),subplots=True,title="Well performing stocks over the past years")
    ax[2].set_ylabel("Stock Price")

    # plt.show()


    #Simple moving average stratergy
    #Rolling statistics 

    tick = "SHREECEM"

    EM = df[:][tick].reset_index()

    EM.set_index('Date',inplace=True)

    # print(EM.head())


    window = 30

    #calculate mean,median,max,std_dev for the selected stock data

    EM['min'] = EM[tick].rolling(window=window).min()
    EM['max'] = EM[tick].rolling(window=window).max()
    EM['mean'] = EM[tick].rolling(window=window).mean()
    EM['std'] = EM[tick].rolling(window=window).std()
    EM['median'] = EM[tick].rolling(window=window).median()

    ax = EM[['min','mean','max']].iloc[-750:-350].plot(figsize=(10,6), style=['g--','r--','g--'], lw=0.8)
    EM[tick].iloc[-750:-350].plot(ax=ax, lw=2)
    plt.xlabel("Date")
    plt.ylabel("Price")
    plt.title("30 days max min simple moving average")
    # plt.show()

    # moving average cross over

    EM['SMA1'] = EM[tick].rolling(window=52).mean()
    EM['SMA2'] = EM[tick].rolling(window=252).mean()

    EM.dropna(inplace=True)

    EM['positions'] = np.where(EM['SMA1']>EM['SMA2'],1,-1)




    ax = EM[[tick,'SMA1','SMA2','positions']].plot(figsize=(10,6), secondary_y='positions')
    ax.get_legend().set_bbox_to_anchor((0.25, 0.85))

    plt.title('SMA cross over stratergy')
    plt.show()


# Correlation 

def get_correlation():

    df_corr = df.corr()
    print(df_corr.head())

    corr_data = df_corr.values

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    heatmap1 = ax1.pcolor(corr_data, cmap=plt.cm.RdYlGn)
    fig.colorbar(heatmap1)
    ax1.set_xticks(np.arange(corr_data.shape[1]) + 0.5, minor=False)
    ax1.set_yticks(np.arange(corr_data.shape[0]) + 0.5, minor=False)
    ax1.invert_yaxis()
    ax1.xaxis.tick_top()
    column_labels = df_corr.columns
    row_labels = df_corr.index
    ax1.set_xticklabels(column_labels)
    ax1.set_yticklabels(row_labels)
    plt.xticks(rotation=90)
    heatmap1.set_clim(-1,1)
    plt.tight_layout()
    plt.title("Correlation plot")
    plt.show()


def correlated_stocks():
    data = df[["ICICIBANK","EICHERMOT"]]
    data["2021-01-01":'2021-01-25'].plot(secondary_y="EICHERMOT",figsize=(10,6))
    plt.title("ICICIvsEICHER")
    plt.show()



#Function call to get the summary statistics of the data and plots
get_insights()

#Function call to plot the correlation among the 50 stocks
get_correlation()

#Function call to visualize example correlated stocks
correlated_stocks()