Stock Price Prediction Using Time Series Analysis

import pandas as pd import numpy as np

df=pd.read_csv('/content/BAJFINANCE.csv') df.head()

df.set_index('Date',inplace=True)

df['VWAP'].plot()

#Feature Engineering Almost every time series problem will have some external features or some internal feature engineering to help the model. Let's add some basic features like lag values of available numeric features that are widely used for time series problems. Since we need to predict the price of the stock for a day, we cannot use the feature values of the same day since they will be unavailable at actual inference time. We need to use statistics like mean, standard deviation of their lagged values. We will use two sets of lagged values, one looking back 3 days and another looking back 7 days as a proxy for last week and last month metrics.

df.shape

df.isna().sum()

df.dropna(inplace=True)

df.isna().sum()

df.shape

data=df.copy()

data.dtypes

data.columns

lag_features=['High','Low','Volume','Turnover','Trades'] window1=3 window2=7

for feature in lag_features: data[feature+'rolling_mean_3']=data[feature].rolling(window=window1).mean() data[feature+'rolling_mean_7']=data[feature].rolling(window=window2).mean()

for feature in lag_features: data[feature+'rolling_std_3']=data[feature].rolling(window=window1).std() data[feature+'rolling_std_7']=data[feature].rolling(window=window2).std()

data.head()

data.columns

data.shape

data.isna().sum()

data.dropna(inplace=True)

data.columns

ind_features=['Highrolling_mean_3', 'Highrolling_mean_7', 'Lowrolling_mean_3', 'Lowrolling_mean_7', 'Volumerolling_mean_3', 'Volumerolling_mean_7', 'Turnoverrolling_mean_3', 'Turnoverrolling_mean_7', 'Tradesrolling_mean_3', 'Tradesrolling_mean_7', 'Highrolling_std_3', 'Highrolling_std_7', 'Lowrolling_std_3', 'Lowrolling_std_7', 'Volumerolling_std_3', 'Volumerolling_std_7', 'Turnoverrolling_std_3', 'Turnoverrolling_std_7', 'Tradesrolling_std_3', 'Tradesrolling_std_7']

training_data=data[0:1800] test_data=data[1800:]

training_data

!pip install pmdarima

from pmdarima import auto_arima

import warnings warnings.filterwarnings('ignore')

model=auto_arima(y=training_data['VWAP'],exogenous=training_data[ind_features],trace=True)

Performing stepwise search to minimize aic
 ARIMA(2,0,2)(0,0,0)[0] intercept   : AIC=20931.529, Time=5.67 sec
 ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=20925.219, Time=3.07 sec
 ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=20926.342, Time=3.16 sec
 ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=20926.314, Time=4.23 sec
 ARIMA(0,0,0)(0,0,0)[0]             : AIC=32616.913, Time=2.89 sec
 ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=20929.229, Time=4.50 sec

Best model:  ARIMA(0,0,0)(0,0,0)[0] intercept
Total fit time: 23.566 seconds

model.fit(training_data['VWAP'],training_data[ind_features])

forecast=model.predict(n_periods=len(test_data), exogenous=test_data[ind_features])

test_data['Forecast_ARIMA']=forecast

test_data[['VWAP','Forecast_ARIMA']].plot(figsize=(14,7))

from sklearn.metrics import mean_absolute_error, mean_squared_error

np.sqrt(mean_squared_error(test_data['VWAP'],test_data['Forecast_ARIMA']))

mean_absolute_error(test_data['VWAP'],test_data['Forecast_ARIMA'])