M.
Vasanth
Besant Technology, Indiranagar
### STOCK PREDICTION
## The change in price of the stock overtime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
plt.style.use("fivethirtyeight")
%matplotlib inline
df=pd.read_csv(r"C:\Users\Vasanth\Documents\HistoricalQuotes.csv")
df
import yfinance as yf
## For time stamps
from datetime import datetime
## The tech stocks we'll use for this analysis
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']
# Set up End and Start times for data grab
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']
end = datetime.now()
start = datetime(end.year - 1, end.month, end.day)
for stock in tech_list:
globals()[stock] = yf.download(stock, start, end)
company_list = [AAPL, GOOG, MSFT, AMZN]
company_name = ["APPLE", "GOOGLE", "MICROSOFT", "AMAZON"]
for company, com_name in zip(company_list, company_name):
company["company_name"] = com_name
df = pd.concat(company_list, axis=0)
df.tail(10)
AAPL.describe()
AAPL.info()
## view of the closing price
plt.figure(figsize=(15, 10))
plt.subplots_adjust(top=1.25, bottom=1.2)
for i, company in enumerate(company_list, 1):
plt.subplot(2, 2, i)
company['Adj Close'].plot()
plt.ylabel('Adj Close')
plt.xlabel(None)
plt.title(f"Closing Price of {tech_list[i - 1]}")
plt.tight_layout()
## 2) What was the moving average of the various stocks?
ma_day = [10, 20, 50]
for ma in ma_day:
for company in company_list:
column_name = f"MA for {ma} days"
company[column_name] = company['Adj Close'].rolling(ma).mean()
fig, axes = plt.subplots(nrows=2, ncols=2)
fig.set_figheight(10)
fig.set_figwidth(15)
AAPL[['Adj Close', 'MA for 10 days', 'MA for 20 days', 'MA for 50 days']].plot(ax=axes[0,0])
axes[0,0].set_title('APPLE')
GOOG[['Adj Close', 'MA for 10 days', 'MA for 20 days', 'MA for 50 days']].plot(ax=axes[0,1])
axes[0,1].set_title('GOOGLE')
MSFT[['Adj Close', 'MA for 10 days', 'MA for 20 days', 'MA for 50 days']].plot(ax=axes[1,0])
axes[1,0].set_title('MICROSOFT')
AMZN[['Adj Close', 'MA for 10 days', 'MA for 20 days', 'MA for 50 days']].plot(ax=axes[1,1])
axes[1,1].set_title('AMAZON')
fig.tight_layout()
###6. Predicting the closing price stock price of APPLE inc:
# Get the stock quote
df
from sklearn.model_selection import train_test_split
##train and test the data
data = df.filter(['Close'])
# Convert the dataframe to a numpy array
dataset = data.values
# Get the number of rows to train the model on
training_data_len = int(np.ceil( len(dataset) * .95 ))
training_data_len
# Create the training data set
train_data = scaled_data[0:int(training_data_len), :]
# Split the data into x_train and y_train data sets
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i-60:i, 0])
y_train.append(train_data[i, 0])
if i<= 61:
print(x_train)
print(y_train)
print()
# Convert the x_train and y_train to numpy arrays
# x_train.shape
x_train, y_train = np.array(x_train), np.array(y_train)
## Reshape the data
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
