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 = yf.download('TCS.NS',
start='2020-01-01',
end='2021-08-19',
progress=False)
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=(12, 8))
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc618ca3d90>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7fc61745ced0>],
dtype=object)How to describe TCS stock price in Python?
tcs.describe()
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))
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 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)
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 the 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 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(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
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
For answers to these questions see other blogs
How to investigate stock stylized facts of asset returns in Python?
What is technical analysis?
The Technical analysis is a way to read stock prices open, high, low, and close with volume.
How to create a candlestick chart in Python?
How to apply time series modeling in stocks?
How to apply time series using Facebook's prophet to get stock value?
How to test for stationarity in time series in stocks with Python?
How to correct for stationarity in time series with Python?
How to use modeling time series with exponential smoothing method of stocks in Python?
How do use modeling time series with the ARIMA class model to get stocks in Python?
How to get forecasting using the ARIMA class model to get stock prices in Python?
How to get multi-factor models to get stock price?
How to implement the CAPM model to get the stock price in python?
How to implement the Fama-French three-factor model on a portfolio analysis in Python?
How to implement four and five-factor models in Python?
What is volatility?
How to use model volatility with the GARCH model in Python?
How to explain stock return volatility with ARCH models in Python?
How to implement a CCC-GARCH model for multivariate volatility forecasting in Python?
How to forecast the conditional covariance matrix using DCC-GARCH to get the stock price in Python?
What is Monte Carlo Simulation in Finance?
How to simulate stock price dynamics using Geometric Brownian Motion in python?
How to get stock price American options with least squares Monte Carlo in Python?
How to get stock price American Option?
How to estimate stock value at risk using Monte Carlo in Python?
How to evaluate the performance of a basic 1/n portfolio in Python?
How to find the efficient frontier using Monte Carlo simulations to value stocks in Python?
How to find the efficient frontier using optimization stocks price with scipy in Python?
How to find the efficient frontier using convex optimization stocks price with cvxpy in Python?
0 Comments