TCS stock price target 2023

How to get  tcs stock data from yahoo finance in Python?

import pandas as pd

import yfinance as yf

from statsmodels.tsa.seasonal import seasonal_decompose

tcs = yf.download('TCS.NS',

 start='2020-01-01',

 end='2021-08-19',

 progress=False)

What is alternative sources to get financial data?

There are number of alternative sources like Quandal,intrinio,google others.

How to plot chart Close price and Volume in python?

tcs[['Close', 'Volume']].plot(subplots=True, style='b',figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc618ca3d90>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc61745ced0>],
      dtype=object)

How to discribe TCS stock price in python?

tcs.describe()

Mean standard deviation and max

How to convert TCS Stock prices  into  log and simple returns in python?

tcs['simple_rtn'] = tcs.Close.pct_change()

tcs['log_rtn'] = np.log(tcs.Close/tcs.Close.shift(1))

tcs['log_rtn'].tail()

Date 2021-08-12 0.002255 2021-08-13 0.032768 2021-08-16 0.002754 2021-08-17 0.022802 2021-08-18 0.002109 Name: log_rtn, dtype: float64

How to plot tcs log return in python?

tcs['log_rtn'].plot(subplots=True, style='b',

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc61736b590>],
      dtype=object)

How to change stocks frequency in python?

TCS stock price convert daily to monthly

df = tcs.loc[:, ['Adj Close']]

df = df.resample('M').last()

df.rename(columns={'Adj Close': 'price'}, inplace=True)

WINDOW_SIZE = 12

df['rolling_mean'] = df.price.rolling(window=WINDOW_SIZE).mean()

df['rolling_std'] = df.price.rolling(window=WINDOW_SIZE).std()

df.plot(title='tcs Price')

Plot Tcs price with trend

<matplotlib.axes._subplots.AxesSubplot at 0x7fc6172a89d0>

How to visualize TCS stock in 2022 with time series financial data in python?

df_future = model_prophet.make_future_dataframe(periods=365)

df_pred = model_prophet.predict(df_future)

model_prophet.plot(df_pred)

model_prophet.plot_components(df_pred)

How to predict vs actual Tcs stock price in 2021?

predicted versus actual tcs prices in 2021

selected_columns = ['ds', 'yhat_lower', 'yhat_upper', 'yhat']

df_pred = df_pred.loc[:, selected_columns].reset_index(drop=True)

df_test = df_test.merge(df_pred, on=['ds'], how='left')

df_test.ds = pd.to_datetime(df_test.ds)

df_test.set_index('ds', inplace=True)

fig, ax = plt.subplots(1, 1)

ax = sns.lineplot(data=df_test[['y', 'yhat_lower', 'yhat_upper',

 'yhat']])

fig, ax = plt.subplots(1, 1)

ax = sns.lineplot(data=df_test[['y', 'yhat_lower', 'yhat_upper',

 'yhat']])

ax.fill_between(df_test.index,

 df_test.yhat_lower,

 df_test.yhat_upper,

 alpha=0.3)

ax.set(title='Tcs Price - actual vs. predicted',

 xlabel='Date',

 ylabel='TCS Price ')

[Text(0, 0.5, 'TCS Price '),
 Text(0.5, 0, 'Date'),
 Text(0.5, 1.0, 'Tcs Price - actual vs. predicted')]

How to analyse stock p value and critical value in python?

def adf_test(x):

 indices = ['Test Statistic', 'p-value',

 '# of Lags Used', '# of Observations Used']

 adf_test = adfuller(x, autolag='AIC')

 results = pd.Series(adf_test[0:4], index=indices)

 for key, value in adf_test[4].items():

  results[f'Critical Value ({key})'] = value

 return results

adf_test(df.price)

Test Statistic -0.392382 p-value 0.911363 # of Lags Used 2.000000 # of Observations Used 17.000000 Critical Value (1%) -3.889266 dtype: float64

How to get identifying  stocks kpss in python?

def kpss_test(x, h0_type='c'):

 indices = ['Test Statistic', 'p-value', '# of Lags']

 kpss_test = kpss(x, regression=h0_type)

 results = pd.Series(kpss_test[0:3], index=indices)

 for key, value in kpss_test[3].items():

  results[f'Critical Value ({key})'] = value

 return results

kpss_test(df.price)

The behavior of using lags=None will change in the next release. Currently lags=None is the same as lags='legacy', and so a sample-size lag length is used. After the next release, the default will change to be the same as lags='auto' which uses an automatic lag length selection method. To silence this warning, either use 'auto' or 'legacy'

Test Statistic           0.360211
p-value                  0.094306
# of Lags                9.000000
Critical Value (10%)     0.347000
Critical Value (5%)      0.463000
Critical Value (2.5%)    0.574000
Critical Value (1%)      0.739000
dtype: float64

import statsmodels.tsa.api as smt
plot_acf(df.price)
plot_pacf(df.price)

FOR TCS UPDATE

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