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 ='TCS.NS',

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=(128))
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc618ca3d90>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc61745ced0>],
tcs close and volume price

How to discribe TCS stock price in python?


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))

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',
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc61736b590>],
Tcslog price

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)
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>
Tcs mean and standard deviation

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)
TCSstockprice forecasting 2022
TCSstockprice monthly weekly

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(11)
ax = sns.lineplot(data=df_test[['y''yhat_lower''yhat_upper',
TCSpredicted vs actual price
fig, ax = plt.subplots(11)
ax = sns.lineplot(data=df_test[['y''yhat_lower''yhat_upper',
ax.set(title='Tcs Price - actual vs. predicted',
 ylabel='TCS Price ')
[Text(0, 0.5, 'TCS Price '),
 Text(0.5, 0, 'Date'),
 Text(0.5, 1.0, 'Tcs Price - actual vs. predicted')]
TCSpredicted price

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
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(xh0_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
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


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