How to collect sbi historical price?
Analyzing and forecasting the stock price of the State Bank of India (SBI) involves examining historical price data, conducting data analysis, and applying forecasting techniques. Here's a step-by-step guide on how to perform SBI stock price analysis and forecasting using Python:
Import Libraries:
Start by importing the necessary Python libraries for data manipulation, analysis, and visualization. Commonly used libraries include pandas, numpy, matplotlib, yfinance for fetching historical data, and libraries for time series forecasting, such as stats models or Prophet:
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf
from statsmodels.tsa.holtwinters import ExponentialSmoothing
Data Retrieval:
Use the yfinance library or other financial data sources to fetch historical price data for SBI's stock. Specify the start and end dates for the data you want to analyze:
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sbi = yf.download('SBIN.BO', start='2020-01-01', end='2021-12-31')
Data Exploration:
Explore the fetched data to understand its structure and contents. Use functions like head(), tail(), describe(), and info() to inspect the dataset:
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print(sbi.head())
Data Visualization:
Create visualizations to analyze the historical performance of SBI's stock. Common visualizations include line charts to visualize price movements:
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plt.figure(figsize=(12, 6))
plt.plot(sbi['Adj Close'], label='SBI Stock Price')
plt.title('SBI Stock Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()
Time Series Analysis:
Conduct time series analysis to understand trends, seasonality, and potential stationarity in the data. Use tools like autocorrelation and partial autocorrelation plots to identify the order of differencing for stationarity.
Data Preprocessing:
Prepare the data for forecasting by splitting it into training and testing sets, handling missing values, and applying any necessary transformations.
Forecasting Model Selection:
Choose an appropriate forecasting model. Popular models for time series forecasting include ARIMA (AutoRegressive Integrated Moving Average), Exponential Smoothing, and Prophet.
For example, to apply Exponential Smoothing:
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model = ExponentialSmoothing(sbi['Adj Close'], trend='add', seasonal='add', seasonal_periods=12)
results = model.fit()
forecast = results.forecast(steps=12) # Forecast for the next 12 months
Model Evaluation:
Evaluate the forecasting model's performance using appropriate metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Compare the forecasted values to the actual values in the testing set.
Visualization of Forecast:
Visualize the forecasted stock prices alongside the actual prices to assess the model's accuracy.
Regular Updates and Monitoring:
Keep your forecasting model up to date with the latest data and monitor its performance over time. Adjust the model as needed to improve accuracy.
Risk Management and Decision Making:
Based on your analysis and forecasting, formulate investment strategies, set risk management parameters, and make informed investment decisions regarding SBI's stock.
Please note that forecasting stock prices is inherently uncertain, and predictions should be used cautiously for investment decisions. The choice of forecasting model and parameters may require tuning, and additional factors such as market news, economic indicators, and company-specific events should also be considered when making investment decisions.
!pip install yfinance
How to install sbi data from yahoo in python?
import yfinance as yf
import numpy as np
import os
import random
import torch
import torch.optim as optim
import torch.nn as nn
from torch.utils.data import (Dataset, TensorDataset,
DataLoader, Subset)
from collections import OrderedDict
from sklearn.metrics import mean_squared_error
import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose
df = yf.download('SBIN.NS',
start='2010-01-01',
end='2021-09-08',
adjusted=True,
progress=False)
df.tail()
df.describe()
df = df.loc[:, ['Adj Close']]
df.rename(columns={'Adj Close': 'price'}, inplace=True)
df = df.resample('M').last()
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='State Bank Stock Price')
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