Reliance stock price forcasting 2023

How to get reliance data from yahoo finance in Python?

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

progress =False)

What is alternative sources to get financial data?

There are number of alternative sources like Quandal,intrinio,google others.

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 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 columns

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 return?

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 holt's smoothing model?

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