Infosys VS Tcs (EDA)!

Meta Data:¶

  • Date: Represents the current date of trade.
  • Open: Represents the price at which the financial security opens in the market when trading begins.
  • High: Represents the highest price that a stock trades in that day.
  • Low: Represents the lowest price that a stock trades in that day.
  • Close: Represents a reference to the end of a trading session in the financial markets when the markets close for the day.
  • Adj Close: Adjusted closing price refers to the price of the stock after paying off the dividends.
  • Volume: Represents the total number of shares that have been bought or sold during the trading day.

Report Contains:¶

  1. Import Library
  2. TCS data
    2.1 (TCS) Dataset information
    2.2 (TCS) Number of null values in dataset
    2.3 (TCS) Dataset statistical information
  3. INFOSYS data
    3.1 (INFY) Dataset information
    3.2 (INFY) Number of null values in dataset
    3.3 (INFY) Dataset statistical information
  4. Comparison Analysis and EDA
    4.1 Calculate stocks Return (in %)
    4.2 Price Comparison both of the company
    4.3 Return rate Comparison both of the company
    4.5 Volume Comparsion for both stocks
    4.6 Check fluctuation on daily basis for both stock
    4.7 Last 50 days trend of both stocks side by side

⚫ Import Library ¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import mplfinance as mpl
# pip install --upgrade mplfinance

📙 TCS data ¶

In [2]:
tcs = pd.read_csv('TCS.NS.csv', index_col='Date', parse_dates=True)
tcs.head(10)
Out[2]:
Open High Low Close Adj Close Volume
Date
2017-11-02 1304.250000 1337.000000 1303.375000 1313.074951 1199.349854 1468422
2017-11-03 1316.199951 1318.349976 1301.400024 1310.050049 1196.586670 666886
2017-11-06 1310.000000 1335.900024 1305.875000 1332.775024 1217.343750 1377000
2017-11-07 1335.000000 1384.000000 1333.025024 1355.025024 1237.666748 2696892
2017-11-08 1361.500000 1379.500000 1359.125000 1367.500000 1249.061035 1402252
2017-11-09 1375.000000 1375.000000 1359.650024 1368.199951 1249.700439 1152432
2017-11-10 1361.099976 1363.000000 1346.675049 1354.375000 1237.072998 1356566
2017-11-13 1351.025024 1388.699951 1351.025024 1379.474976 1259.998901 1496996
2017-11-14 1378.275024 1378.275024 1353.750000 1357.300049 1239.744385 1657252
2017-11-15 1353.000000 1377.500000 1350.675049 1352.650024 1235.497314 6670834

.

⚫ (TCS) Dataset information

In [3]:
tcs.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1235 entries, 2017-11-02 to 2022-11-01
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Open       1235 non-null   float64
 1   High       1235 non-null   float64
 2   Low        1235 non-null   float64
 3   Close      1235 non-null   float64
 4   Adj Close  1235 non-null   float64
 5   Volume     1235 non-null   int64  
dtypes: float64(5), int64(1)
memory usage: 67.5 KB

.

⚫ (TCS) Number of null values in dataset ¶

In [4]:
tcs.isnull().sum()
Out[4]:
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64

.

⚫ (TCS) Dataset statistical information ¶

In [5]:
tcs.describe()
Out[5]:
Open High Low Close Adj Close Volume
count 1235.000000 1235.000000 1235.000000 1235.000000 1235.000000 1.235000e+03
mean 2517.772529 2543.625319 2489.836221 2516.272792 2428.493448 3.127995e+06
std 734.088662 738.030266 727.998626 732.955559 766.403293 3.075381e+06
min 1268.500000 1292.224976 1247.175049 1272.800049 1162.562744 1.445300e+05
25% 1984.000000 2002.000000 1962.525024 1983.099976 1847.940429 1.928873e+06
50% 2217.850098 2244.500000 2194.199951 2215.600098 2107.700684 2.611493e+06
75% 3198.474976 3224.924926 3162.075074 3195.474976 3150.350463 3.573045e+06
max 4033.949951 4043.000000 3980.000000 4019.149902 3964.502686 8.806715e+07

.

📙 INFOSYS data ¶

In [6]:
infy = pd.read_csv('INFY.NS.csv', index_col='Date', parse_dates=True)
infy.head(10)
Out[6]:
Open High Low Close Adj Close Volume
Date
2017-11-02 461.524994 463.000000 460.000000 461.450012 406.673004 4686138
2017-11-03 458.500000 464.950012 458.475006 463.325012 408.325439 3699086
2017-11-06 461.575012 467.500000 461.325012 464.350006 409.228668 5066534
2017-11-07 463.125000 480.000000 462.924988 477.750000 421.038086 13442820
2017-11-08 480.125000 480.924988 473.750000 476.149994 419.627991 5369994
2017-11-09 476.500000 481.500000 474.125000 477.250000 420.597412 4723232
2017-11-10 479.399994 482.174988 475.000000 481.024994 423.924316 6589428
2017-11-13 481.024994 484.700012 474.674988 475.975006 419.473785 5021216
2017-11-14 475.975006 477.424988 471.625000 474.549988 418.217926 5733710
2017-11-15 472.575012 476.750000 472.250000 475.899994 419.407684 7023516

.

⚫ (INFY) Dataset information ¶

In [7]:
infy.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1235 entries, 2017-11-02 to 2022-11-01
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Open       1235 non-null   float64
 1   High       1235 non-null   float64
 2   Low        1235 non-null   float64
 3   Close      1235 non-null   float64
 4   Adj Close  1235 non-null   float64
 5   Volume     1235 non-null   int64  
dtypes: float64(5), int64(1)
memory usage: 67.5 KB

.

⚫ (INFY) Number of null values in dataset ¶

In [8]:
infy.isnull().sum()
Out[8]:
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64

.

⚫ (INFY) Dataset statistical information ¶

In [9]:
infy.describe()
Out[9]:
Open High Low Close Adj Close Volume
count 1235.000000 1235.000000 1235.000000 1235.000000 1235.000000 1.235000e+03
mean 1040.153726 1051.354049 1029.087085 1040.137409 988.241594 8.488027e+06
std 433.301951 436.597649 429.126905 432.849813 443.829212 5.944440e+06
min 458.500000 463.000000 458.475006 461.450012 406.673004 4.834070e+05
25% 690.325012 699.000000 682.662476 691.174988 627.743866 5.362184e+06
50% 788.000000 797.000000 782.049988 789.549988 728.917236 7.016424e+06
75% 1453.500000 1467.799988 1437.725036 1451.824951 1430.209045 9.723727e+06
max 1938.550049 1953.900024 1930.500000 1939.500000 1898.427979 9.043211e+07

.

📉Comparison Analysis and EDA📈

⚫ Calculate stocks Return (in %) ¶

Return = (Close-Open)/Open¶

Return of daily basis for TCS¶

In [10]:
tcs['Return'] = (tcs.Close - tcs.Open)/tcs.Open
tcs.head()
Out[10]:
Open High Low Close Adj Close Volume Return
Date
2017-11-02 1304.250000 1337.000000 1303.375000 1313.074951 1199.349854 1468422 0.006766
2017-11-03 1316.199951 1318.349976 1301.400024 1310.050049 1196.586670 666886 -0.004672
2017-11-06 1310.000000 1335.900024 1305.875000 1332.775024 1217.343750 1377000 0.017386
2017-11-07 1335.000000 1384.000000 1333.025024 1355.025024 1237.666748 2696892 0.015000
2017-11-08 1361.500000 1379.500000 1359.125000 1367.500000 1249.061035 1402252 0.004407

Return of daily basis for INFOSYS¶

In [11]:
infy['Return'] = (infy.Close - infy.Open)/infy.Open
infy.head()
Out[11]:
Open High Low Close Adj Close Volume Return
Date
2017-11-02 461.524994 463.000000 460.000000 461.450012 406.673004 4686138 -0.000162
2017-11-03 458.500000 464.950012 458.475006 463.325012 408.325439 3699086 0.010523
2017-11-06 461.575012 467.500000 461.325012 464.350006 409.228668 5066534 0.006012
2017-11-07 463.125000 480.000000 462.924988 477.750000 421.038086 13442820 0.031579
2017-11-08 480.125000 480.924988 473.750000 476.149994 419.627991 5369994 -0.008279

.

⚫ Price Comparison both of the company ¶

In [12]:
plt.figure(figsize=(16,5), dpi=400)
plt.title('Price Comparison', size=20)
sns.lineplot(data=tcs, x=tcs.index, y='Close', label='TCS')
sns.lineplot(data=infy, x=infy.index, y='Close', label='INFOSYS')
plt.show()

Conclusion: TCS stocks are all time on hike than INFOSYS stocks.¶

.

⚫ Return rate Comparison both of the company ¶

In [13]:
tcs_return = []
x = 0
for i in tcs.Return:
    x += i
    tcs_return.append(x)
In [14]:
infy_return = []
x = 0
for i in infy.Return:
    x += i
    infy_return.append(x)
In [15]:
plt.figure(figsize=(16,5), dpi=400)
plt.title('Return Comparison(in %)', size=20)
sns.lineplot(data=tcs, x=tcs.index, y=tcs_return, label='TCS')
sns.lineplot(data=infy, x=infy.index, y=infy_return, label='INFOSYS')
plt.show()

Conclusion: TCS stocks return rate is less than INFOSYS stocks.¶

.

⚫ Volume Comparsion for both stocks ¶

In [16]:
plt.figure(figsize=(16,5), dpi=400)
plt.title('Volume Comparsion', size=20)
sns.lineplot(data=tcs, x=tcs.index, y='Volume', label='TCS')
sns.lineplot(data=infy, x=infy.index, y='Volume', label='INFOSYS')
plt.show()

Conclusion: Availability of INFOSYS stocks is more than TCS stocks.¶

.

⚫ Check fluctuation on daily basis for both stock ¶

Fluctuation = difference of High and Low¶

Fluctuation of daily basis for TCS¶

In [17]:
tcs['Fluctuation'] = tcs.High - tcs.Low
tcs.head()
Out[17]:
Open High Low Close Adj Close Volume Return Fluctuation
Date
2017-11-02 1304.250000 1337.000000 1303.375000 1313.074951 1199.349854 1468422 0.006766 33.625000
2017-11-03 1316.199951 1318.349976 1301.400024 1310.050049 1196.586670 666886 -0.004672 16.949952
2017-11-06 1310.000000 1335.900024 1305.875000 1332.775024 1217.343750 1377000 0.017386 30.025024
2017-11-07 1335.000000 1384.000000 1333.025024 1355.025024 1237.666748 2696892 0.015000 50.974976
2017-11-08 1361.500000 1379.500000 1359.125000 1367.500000 1249.061035 1402252 0.004407 20.375000

Fluctuation of daily basis for INFOSYS¶

In [18]:
infy['Fluctuation'] = infy.High - infy.Low
infy.head()
Out[18]:
Open High Low Close Adj Close Volume Return Fluctuation
Date
2017-11-02 461.524994 463.000000 460.000000 461.450012 406.673004 4686138 -0.000162 3.000000
2017-11-03 458.500000 464.950012 458.475006 463.325012 408.325439 3699086 0.010523 6.475006
2017-11-06 461.575012 467.500000 461.325012 464.350006 409.228668 5066534 0.006012 6.174988
2017-11-07 463.125000 480.000000 462.924988 477.750000 421.038086 13442820 0.031579 17.075012
2017-11-08 480.125000 480.924988 473.750000 476.149994 419.627991 5369994 -0.008279 7.174988

Comparison of Fluctuation¶

In [19]:
plt.figure(figsize=(16,5), dpi=400)
plt.title('Fluctuation Comparsion', size=20)
sns.lineplot(data=tcs, x=tcs.index, y='Fluctuation', label='TCS')
sns.lineplot(data=infy, x=infy.index, y='Fluctuation', label='INFOSYS', alpha=0.5)
plt.show()

Conclusion: Fluctuation rate of TCS stocks is higher than INFOSYS stocks.¶

.

⚫ Last 50 days trend of both stocks side by side ¶

Last 50 days data of TCS stocks.¶

In [20]:
data_tcs = tcs[['Open','High','Low','Close']].tail(50)

Last 50 days data of INFOSYS stocks.¶

In [21]:
data_infy = infy[['Open','High','Low','Close']].tail(50)

Explaination of Candlestick chart!

Side by Side stocks Comparison¶

In [22]:
mpl.plot(data_tcs, type='candle', mav=(3,6,9), style='yahoo', title='TCS')
mpl.plot(data_infy, type='candle', mav=(3,6,9), style='yahoo', title='INFOSYS')