TCS stock price target 2023

How to get TCS stock data from Yahoo Finance in Python?

I'm sorry for any inconvenience, but I don't have access to real-time data or the ability to predict future stock prices, including those of specific companies like TCS (Tata Consultancy Services Limited). Stock prices are influenced by a wide range of factors, including the company's financial performance, market conditions, economic trends, and geopolitical events.

To obtain a stock price target for TCS in 2023, you may want to consider the following steps:

Consult Financial Analysts: Financial analysts and investment research firms often provide price targets and recommendations for individual stocks. These reports can be found on various financial news websites and investment platforms.

Company's Financials: Analyze TCS's financial statements, earnings reports, and guidance provided by the company. Pay attention to key financial metrics such as revenue growth, earnings per share (EPS), and operating margins.

Market Trends: Consider broader market trends and conditions that may impact TCS and the IT services sector. These could include changes in technology trends, global economic conditions, and the competitive landscape.

Analyst Consensus: Look for consensus estimates from multiple financial analysts and institutions. These estimates can provide a range of price targets based on different methodologies.

Risk Assessment: Assess the potential risks and uncertainties that could affect TCS's stock price in 2023. Consider factors such as regulatory changes, geopolitical tensions, and company-specific risks.

Technical Analysis: Some investors use technical analysis to analyze stock price trends and identify potential price targets based on historical price patterns and chart analysis.

Long-Term Prospects: Consider TCS's long-term growth prospects, including its expansion into new markets, investments in technology, and strategies for retaining and attracting clients.

Diversification: Ensure that any investment decisions align with your overall investment goals and risk tolerance. Diversify your portfolio to manage risk effectively.

It's important to note that stock price predictions and targets are subject to uncertainty, and there are no guarantees that a particular target will be achieved. Investors should conduct thorough research and consider their own investment objectives and risk tolerance before making investment decisions. Additionally, seeking advice from a financial advisor or conducting an in-depth financial analysis can be helpful when making investment decisions.
import pandas as pd
import yfinance as yf
from statsmodels.tsa.seasonal import seasonal_decompose
tcs ='TCS.NS',

What are alternative sources to get financial data?

There are a number of alternative sources like Quandal, Intinio, and Google.

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 describe TCS stock price in Python?


How to convert TCS Stock prices into logs 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 stock frequency in Python?

TCS stock price converted 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 the 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 analyze 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 do get identify 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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