📈 Exploring the Nifty-50 dataset 📉

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) #plotly import plotly.express as px import chart_studio import chart_studio.plotly as py import plotly.graph_objects as go import cufflinks cufflinks.go_offline() cufflinks.set_config_file(world_readable=True, theme='pearl') #datetime from datetime import datetime

nifty_50 = pd.read_csv('/work/Nifty Indices Dataset/NIFTY 50.csv',parse_dates=["Date"]) nifty_50.head()

nifty_50.info()

nifty_50.isnull().sum()

nifty_50.fillna(method='ffill',inplace=True)

fig = go.Figure() fig.add_trace(go.Scatter( x=nifty_50['Date'], y=nifty_50['High'], name='High Price', line=dict(color='blue'), opacity=0.8)) fig.add_trace(go.Scatter( x=nifty_50['Date'], y=nifty_50['Low'], name='Low Price', line=dict(color='orange'), opacity=0.8)) fig.update_layout(title_text='NIFTY 50 High vs Close Trend',plot_bgcolor='rgb(250, 242, 242)',yaxis_title='Value') fig.show(renderer="colab")

fig = go.Figure() fig.add_trace(go.Scatter( x=nifty_50['Date'], y=nifty_50['Close'], name='Closing Price', line=dict(color='blue'), opacity=0.8)) fig.update_layout(title_text='NIFTY 50 Closing Price',plot_bgcolor='rgb(250, 242, 242)',yaxis_title='Value') fig.show(renderer="colab")

fig = go.Figure() fig.add_trace(go.Scatter( x=nifty_50['Date'], y=nifty_50['P/E'], name='P/E Ratio', line=dict(color='green'), opacity=0.8)) fig.add_trace(go.Scatter( x=nifty_50['Date'], y=nifty_50['P/B'], name='P/B Ratio', line=dict(color='orange'), opacity=0.8)) fig.update_layout(title_text='P/E vs P/B Ratio',plot_bgcolor='rgb(250, 242, 242)',yaxis_title='Value') fig.show(renderer="colab")

nifty_50_2019 = nifty_50[nifty_50['Date'] >= '2019-01-01'] nifty_50_2019.head()

df=nifty_50_2019 fig = go.Figure(data=[go.Candlestick(x=df['Date'], open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'])]) fig.show(renderer="colab")

fig = px.line(nifty_50_2019, x='Date', y='Close', title='Time Series with Range Slider and Selectors') fig.update_xaxes( rangeslider_visible=False, rangeselector=dict( buttons=list([ dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all") ]) ) ) fig.update_layout(plot_bgcolor='rgb(250, 242, 242)', title='NIFTY_50 : Major single day falls -2019 onwards', yaxis_title='NIFTY 50 Stock', shapes = [dict(x0='2020-03-23', x1='2020-03-23', y0=0, y1=1, xref='x', yref='paper', line_width=2,opacity=0.3,line_color='red',editable=False), dict(x0='2019-09-3', x1='2019-09-3', y0=0, y1=1, xref='x', yref='paper',line_width=3,opacity=0.3,line_color='red'), dict(x0='2020-02-1', x1='2020-02-1', y0=0, y1=1, xref='x', yref='paper',line_width=3,opacity=0.3,line_color='red'), dict(x0='2020-03-12', x1='2020-03-12', y0=0, y1=1, xref='x', yref='paper',line_width=3,opacity=0.3,line_color='red')], annotations=[dict(x='2020-03-23', y=0.5, xref='x', yref='paper', showarrow=False, xanchor='left', text='Lockdown Phase-1 announced'), dict(x='2019-09-3', y=0.05, xref='x', yref='paper', showarrow=False, xanchor='left', text='Multiple PSU Bank Merger Announcements'), dict(x='2020-02-1', y=0.5, xref='x', yref='paper', showarrow=False, xanchor='right', text='Union Budget,coronavirus pandemic'), dict(x='2020-03-12', y=0.3, xref='x', yref='paper', showarrow=False, xanchor='right', text='Coronavirus declared Pandemic by WHO')] ) fig.show(renderer="colab")

fig = px.line(nifty_50_2019, x='Date', y='Close', title='Time Series with Range Slider and Selectors') fig.update_xaxes( rangeslider_visible=False, rangeselector=dict( buttons=list([ dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all") ]) ) ) fig.update_layout(plot_bgcolor='rgb(250, 242, 242)', title='NIFTY_50 : Major single day gains -2019 onwards', yaxis_title='NIFTY 50 Stock', shapes = [dict(x0='2019-05-20', x1='2019-05-20', y0=0, y1=1, xref='x', yref='paper', line_width=2,opacity=0.3,line_color='green',editable=False), dict(x0='2020-05-23', x1='2020-05-23', y0=0, y1=1, xref='x', yref='paper',line_width=3,opacity=0.3,line_color='green'), dict(x0='2019-09-20', x1='2019-09-20', y0=0, y1=1, xref='x', yref='paper',line_width=3,opacity=0.3,line_color='green'), dict(x0='2020-04-07', x1='2020-04-07', y0=0, y1=1, xref='x', yref='paper',line_width=3,opacity=0.3,line_color='green')], annotations=[dict(x='2019-05-20', y=0.54, xref='x', yref='paper', showarrow=False, xanchor='right', text='Exit-Polls predict majority'), dict(x='2019-05-20', y=0.5, xref='x', yref='paper', showarrow=False, xanchor='right', text='for BJP government'), dict(x='2019-09-3', y=0.08, xref='x', yref='paper', showarrow=False, xanchor='left', text='2019 General Elections'), dict(x='2019-09-3', y=0.05, xref='x', yref='paper', showarrow=False, xanchor='left', text='results announced'), dict(x='2019-09-20', y=0.54, xref='x', yref='paper', showarrow=False, xanchor='left', text='cut in the corporate tax rate announced'), dict(x='2020-04-07', y=0.3, xref='x', yref='paper', showarrow=False, xanchor='right', text='Italy Coronavirus Nos went down')] ) fig.show(renderer="colab")

nifty_auto = pd.read_csv('/work/Nifty Indices Dataset/NIFTY AUTO.csv',parse_dates=["Date"]) nifty_bank = pd.read_csv('/work/Nifty Indices Dataset/NIFTY BANK.csv',parse_dates=["Date"]) nifty_fmcg = pd.read_csv('/work/Nifty Indices Dataset/NIFTY FMCG.csv',parse_dates=["Date"]) nifty_IT = pd.read_csv('/work/Nifty Indices Dataset/NIFTY IT.csv',parse_dates=["Date"]) nifty_metal = pd.read_csv('/work/Nifty Indices Dataset/NIFTY METAL.csv',parse_dates=["Date"]) nifty_pharma = pd.read_csv('/work/Nifty Indices Dataset/NIFTY PHARMA.csv',parse_dates=["Date"]) #Fill in missing values nifty_auto.fillna(method='ffill',inplace=True) nifty_bank.fillna(method='ffill',inplace=True) nifty_fmcg.fillna(method='ffill',inplace=True) nifty_IT.fillna(method='ffill',inplace=True) nifty_metal.fillna(method='ffill',inplace=True) nifty_pharma.fillna(method='ffill',inplace=True)

nifty_auto_2019 = nifty_auto[nifty_auto['Date'] > '2019-12-31'] nifty_bank_2019 = nifty_bank[nifty_bank['Date'] > '2019-12-31'] nifty_fmcg_2019 = nifty_fmcg[nifty_fmcg['Date'] > '2019-12-31'] nifty_IT_2019 = nifty_IT[nifty_IT['Date'] > '2019-12-31'] nifty_metal_2019 = nifty_metal[nifty_metal['Date'] > '2019-12-31'] nifty_pharma_2019 = nifty_pharma[nifty_pharma['Date'] > '2019-12-31'] d = {'NIFTY Auto index': nifty_auto_2019['Close'].values, 'NIFTY Bank index': nifty_bank_2019['Close'].values, 'NIFTY FMCG index': nifty_fmcg_2019['Close'].values, 'NIFTY IT index': nifty_IT_2019['Close'].values, 'NIFTY Metal index': nifty_metal_2019['Close'].values, 'NIFTY Pharma index': nifty_pharma_2019['Close'].values, }

df = pd.DataFrame(data=d) df.index=nifty_auto_2019['Date'] df.head()

fig = df.iplot(asFigure=True,kind='box') fig.show(renderer="colab")

fig = df.iplot(asFigure=True, subplots=True, subplot_titles=True, legend=False) fig.show(renderer="colab")

fig = df.iplot(asFigure=True, hline=[2,4], vline=['2020-03-23']) fig.show(renderer="colab")

fig = df.iplot(asFigure=True, vspan={'x0':'2020-03-23','x1':'2020-04-14', 'color':'rgba(30,30,30,0.3)','color':'teal','fill':True,'opacity':.4}) fig.show(renderer="colab")

fig = df.iplot(asFigure=True, vspan={'x0':'2020-04-15','x1':'2020-05-03', 'color':'rgba(30,30,30,0.3)','color':'red','fill':True,'opacity':.4}) fig.show(renderer="colab")

df_a=df['NIFTY Pharma index'] max_val=df_a.max() min_val=df_a.min() max_date=df_a[df_a==max_val].index[0].strftime('%Y-%m-%d') min_date=df_a[df_a==min_val].index[0].strftime('%Y-%m-%d') shape1=dict(kind='line',x0=max_date,y0=max_val,x1=min_date,y1=min_val,color='blue',width=2) shape2=dict(kind='rect',x0=max_date,x1=min_date,fill=True,color='gray',opacity=.3) fig = df_a.iplot(asFigure=True,shapes=[shape1,shape2]) fig.show(renderer="colab")