Reliance stock price forcasting 2023

How to get reliance data from Yahoo Finance in Python?

Forecasting the stock price of Reliance Industries Limited (Reliance) or any other stock involves predicting its future price movements based on historical data, technical analysis, fundamental analysis, and sometimes machine learning models. Forecasting the stock price of Reliance Industries Limited, like any other publicly traded company, involves a combination of fundamental and technical analysis, market sentiment, and external factors.

 Here's a general overview of how one might approach forecasting the stock price of Reliance:

Fundamental Analysis: This involves examining the financial health and performance of Reliance Industries. Key factors include:

a. Earnings Reports: Review quarterly and annual financial reports to assess revenue growth, profit margins, and earnings per share (EPS).

b. Balance Sheet: Analyzing the company's assets, liabilities, and equity to gauge its financial stability and leverage.

c. Cash Flow: Evaluating the company's cash flow statements to understand its ability to generate cash and fund operations and investments.

d. Dividends: Consideration of dividend history and the company's dividend policy, which can impact investor sentiment.

e. Competitive Position: Assessing how Reliance Industries compares to competitors in its industry, including market share, product diversification, and growth prospects.

f. Industry Trends: Understanding trends and dynamics in the industries in which Reliance operates, such as telecommunications, petrochemicals, and retail.

Technical Analysis: Technical analysts use historical stock price charts, patterns, and technical indicators to predict future price movements. Common tools include moving averages, Relative Strength Index (RSI), and Bollinger Bands.

Market Sentiment: Sentiment analysis involves assessing how investors perceive Reliance Industries and its stock. News, social media, and analyst reports can provide insight into market sentiment.

External Factors: Factors beyond Reliance's control can influence its stock price. These include changes in government regulations, economic conditions, global commodity prices (since Reliance has interests in petrochemicals), and geopolitical events.

Management and Strategy: Evaluating the leadership of Reliance Industries, including the vision and strategies of its top executives, can provide insights into the company's future prospects.

Macroeconomic Trends: Broader economic indicators, such as GDP growth, inflation rates, and interest rates, can affect the overall stock market and, by extension, Reliance's stock.

Analyst Forecasts: Analysts from financial institutions often provide forecasts and recommendations for Reliance Industries' stock. These can be valuable inputs into the forecasting process.

Machine Learning and AI: Some investors and analysts use advanced techniques, including machine learning and artificial intelligence, to model and predict stock price movements based on historical data and various factors.

It's essential to remember that stock price forecasting is inherently uncertain, and past performance is not necessarily indicative of future results. Therefore, investors should use forecasts as one of many tools to inform their investment decisions and consider diversifying their portfolios to manage risk.

Additionally, consulting with financial professionals, conducting thorough research, and staying informed about the latest developments related to Reliance Industries and its industry can help in making more informed investment decisions.

Here are steps you can follow to create a basic stock price forecast for Reliance in Python:

Import Libraries:

Start by importing the necessary Python libraries for data analysis, visualization, and modeling:

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import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import yfinance as yf

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

Data Retrieval:

Use the yfinance library or another financial data source to fetch historical price data for Reliance:

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reliance = yf.download('RELIANCE.NS', start='2020-01-01', end='2021-12-31')

Data Preprocessing:

Preprocess the data by selecting the relevant features (usually 'Adj Close' price), and create a target variable by shifting the price to represent future values. You can also add additional features if needed.

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reliance['Next_Close'] = reliance['Adj Close'].shift(-1)  # Shift the price for prediction

Data Visualization:

Visualize the historical price data to get an idea of Reliance's price trends:

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plt.figure(figsize=(12, 6))

plt.plot(reliance.index, reliance['Adj Close'], label='Reliance Stock Price')

plt.title('Reliance Stock Price')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.show()

Feature Selection:

Select the features you want to use for forecasting. In this basic example, you can use 'Adj Close' as the only feature.

Data Splitting:

Split the dataset into training and testing sets. The training set will be used to train the model, and the testing set will be used to evaluate its performance:

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X = reliance[['Adj Close']]

y = reliance['Next_Close']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Model Building:

Build a simple linear regression model to forecast future prices based on historical prices:

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model = LinearRegression()

model.fit(X_train, y_train)

Model Evaluation:

Evaluate the model's performance on the testing data:

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y_pred = model.predict(X_test)

Visualization of Predictions:

Visualize the model's predictions compared to the actual prices:

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plt.figure(figsize=(12, 6))

plt.plot(reliance.index[-len(y_test):], y_test, label='Actual Prices', color='blue')

plt.plot(reliance.index[-len(y_test):], y_pred, label='Predicted Prices', color='red')

plt.title('Reliance Stock Price Prediction')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.show()

Forecasting:

Use the trained model to make forecasts for future prices based on new data. You can predict the next day's price or multiple days into the future.

Please note that this is a basic example of stock price forecasting using linear regression. More complex models and additional features may be required for accurate forecasting. Additionally, stock price forecasting is inherently uncertain, and predictions should be used cautiously for investment decisions.

df= yf.download('RELIANCE.NS',start='2012-01-01',end='2021-09-04',

progress =False)

What are alternative sources to get financial data?

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

df.reset_index(drop=False, inplace=True)

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

df=DataFrame(df)

df

	index	ds	Open	High	Low	Close	y	Volume
0	0	2012-01-02	345.128540	351.542725	340.348846	349.957764	323.036774	8679938
1	1	2012-01-03	352.780975	360.037201	351.839905	358.922760	331.312103	9455771
2	2	2012-01-04	360.284851	362.043182	353.325836	354.712677	327.425934	8557084
3	3	2012-01-05	354.143066	359.071350	343.791199	346.465851	319.813477	13364666
4	4	2012-01-06	345.252350	358.600830	345.054230	355.406097	328.065948	9495456
...	...	...	...	...	...	...	...	...
2377	2377	2021-08-30	2250.000000	2275.850098	2236.800049	2270.250000	2270.250000	6473487
2378	2378	2021-08-31	2276.899902	2283.750000	2242.250000	2258.149902	2258.149902	12223037
2379	2379	2021-09-01	2273.000000	2292.899902	2263.000000	2267.100098	2267.100098	5143640
2380	2380	2021-09-02	2255.000000	2307.800049	2255.000000	2294.399902	2294.399902	4595048
2381	2381	2021-09-03	2310.000000	2395.000000	2302.500000	2388.500000	2388.500000	14151629

2382 rows × 8 columns

train_indices = df.ds.apply(lambda x: x.year) < 2021

df_train = df.loc[train_indices].dropna()

df_test = df.loc[~train_indices].reset_index(drop=True)

model_prophet = Prophet(seasonality_mode='additive')

model_prophet.add_seasonality(name='monthly', period=30.5,

 fourier_order=5)

How to plot the close price of 2022 reliance?

model_prophet.fit(df_train)

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)

df_yah= yf.download('RELIANCE.NS',start='2012-01-01',end='2021-09-04',

progress =False)

import pandas as pd

import numpy as np

from pandas import Series, DataFrame

data=DataFrame(df_yah)

data

Open	High	Low	Close	Adj Close	Volume
Date
2012-01-02	345.128540	351.542725	340.348846	349.957764	323.036774	8679938
2012-01-03	352.780975	360.037201	351.839905	358.922760	331.312103	9455771
2012-01-04	360.284851	362.043182	353.325836	354.712677	327.425934	8557084
2012-01-05	354.143066	359.071350	343.791199	346.465851	319.813477	13364666
2012-01-06	345.252350	358.600830	345.054230	355.406097	328.065948	9495456
...	...	...	...	...	...	...
2021-08-30	2250.000000	2275.850098	2236.800049	2270.250000	2270.250000	6473487
2021-08-31	2276.899902	2283.750000	2242.250000	2258.149902	2258.149902	12223037
2021-09-01	2273.000000	2292.899902	2263.000000	2267.100098	2267.100098	5143640
2021-09-02	2255.000000	2307.800049	2255.000000	2294.399902	2294.399902	4595048
2021-09-03	2310.000000	2395.000000	2302.500000	2388.500000	2388.500000	14151629

2382 rows × 6 columns

DataFrame(data, columns=['Close', 'Volume'])

Close	Volume
Date
2012-01-02	349.957764	8679938
2012-01-03	358.922760	9455771
2012-01-04	354.712677	8557084
2012-01-05	346.465851	13364666
2012-01-06	355.406097	9495456
...	...	...
2021-08-30	2270.250000	6473487
2021-08-31	2258.149902	12223037
2021-09-01	2267.100098	5143640
2021-09-02	2294.399902	4595048
2021-09-03	2388.500000	14151629

2382 rows × 2 columns

frame2=DataFrame(data,columns=['Close'])

frame2

Close
Date
2012-01-02	349.957764
2012-01-03	358.922760
2012-01-04	354.712677
2012-01-05	346.465851
2012-01-06	355.406097
...	...
2021-08-30	2270.250000
2021-08-31	2258.149902
2021-09-01	2267.100098
2021-09-02	2294.399902
2021-09-03	2388.500000

2382 rows × 1 column

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

init_notebook_mode()

goog = data.resample('M') \

 .last() \

 .rename(columns={'Adj Close': 'adj_close'}) \

 .adj_close

goog  # convert daily into monthly goog(RIL)

Date 2012-01-31 373.580566 2012-02-29 375.249329 2012-03-31 343.176544 2012-04-30 340.661957 2012-05-31 326.532379 ... 2021-05-31 2153.372803 2021-06-30 2110.649902 2021-07-31 2035.300049 2021-08-31 2258.149902 2021-09-30 2388.500000 Freq: M, Name: adj_close, Length: 117, dtype: float64

train_indices = goog.index.year < 2021

goog_train = goog[train_indices]

goog_test = goog[~train_indices]

test_length = len(goog_test)

goog.plot(title="Relian's Stock Price");

How to plot daily stock returns?

ses_1 = SimpleExpSmoothing(goog_train).fit(smoothing_level=0.2)

ses_forecast_1 = ses_1.forecast(test_length)

ses_2 = SimpleExpSmoothing(goog_train).fit(smoothing_level=0.5)

ses_forecast_2 = ses_2.forecast(test_length)

ses_3 = SimpleExpSmoothing(goog_train).fit()

alpha = ses_3.model.params['smoothing_level']

ses_forecast_3 = ses_3.forecast(test_length)

goog.plot(color="g",

 title='Simple Exponential Smoothing',

 label='Actual',

 legend=True)

ses_forecast_1.plot(color="r", legend=True,

 label=r'$\alpha=0.2$')

ses_1.fittedvalues.plot(color="r")

ses_forecast_2.plot(color="k", legend=True,

 label=r'$\alpha=0.5$')

ses_2.fittedvalues.plot(color="k")

ses_forecast_3.plot(color="b", legend=True,

 label=r'$\alpha={0:.4f}$'.format(alpha))

ses_3.fittedvalues.plot(color="b")

How to plot simple exponential smoothing daily stock return?

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Fit three variants of Holt's smoothing model and create forecasts:

Holt's model with linear trend

hs_1 = Holt(goog_train).fit()

hs_forecast_1 = hs_1.forecast(test_length)

hs_2 = Holt(goog_train, exponential=True).fit()  # Holt's model with exponential trend

hs_forecast_2 = hs_2.forecast(test_length)

hs_3 = Holt(goog_train, exponential=False,      # Holt's model with exponential trend and damping

 damped=True).fit(damping_slope=0.99)

hs_forecast_3 = hs_3.forecast(test_length)

goog.plot(color="g",

 title="Holt's Smoothing models",

 label='Actual',

 legend=True)

hs_1.fittedvalues.plot(color="r")

hs_forecast_1.plot(color="r", legend=True,

 label='Linear trend')

hs_2.fittedvalues.plot(color="k")

hs_forecast_2.plot(color="k", legend=True,

 label='Exponential trend')

hs_3.fittedvalues.plot(color="b")

hs_forecast_3.plot(color="b", legend=True,

 label='Exponential trend (damped)')

how to plot reliance on Holt's smoothing model?

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