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Tech Stock Prices

This dataset consists of the daily stock prices and volume of ten different tech companies: Apple (AAPL), Amazon (AMZN), Alibaba (BABA), Salesforce (CRM), Facebook (FB), Alphabet (GOOG), Intel (INTC), Microsoft (MSFT), Nvidia (NVDA), and Tesla (TSLA).

There are ten CSV files in the data/ folder named with the stock symbol for each of the ten companies listed above. Looking for another company? You can download it from Yahoo Finance and upload it to your workspace.

Not sure where to begin? Scroll to the bottom to find challenges!

import pandas as pd
aapl = pd.read_csv("data/AAPL.csv")
print(aapl.shape)
aapl.head(5)

Data Dictionary

ColumnExplanation
DateDate of observation
OpenOpening price
HighHighest price during trading day
LowLowest price during trading day
CloseClose price
Adj CloseAdjusted close price adjusted for splits and dividend and/or capital gain distribution
VolumeNumber of shares traded during trading day

Source of dataset.

Don't know where to start?

Challenges are brief tasks designed to help you practice specific skills:

  • 🗺️ Explore: Which of the ten companies has the highest closing price based on the most recent data?
  • 📊 Visualize: Create a plot that visualizes the closing price at the end of each month for the 10 tech stocks.
  • 🔎 Analyze: Which of the ten companies have experienced the greatest percent increase in closing price over the course of their existence?

Scenarios are broader questions to help you develop an end-to-end project for your portfolio:

You have been hired as an analyst for a small investment firm. They currently specialize in commodities, focusing on coffee, cocoa, and sugar. However, they are now interested in expanding to technology companies. Your manager has asked you to explore the returns and volatilities of the ten stocks provided and contrast them with the three commodities they currently invest in.

They also want you to recommend how tech stocks could be integrated into a portfolio with the existing commodities they invest in to minimize risk while gaining exposure to the new market.

You will need to prepare a report that is accessible to a broad audience. It should outline your motivation, steps, findings, and conclusions.

Commodity prices can be found here.

You have been hired as an analyst for a small investment firm. They currently specialize in commodities, focusing on coffee, cocoa, and sugar. However, they are now interested in expanding to technology companies. Your manager has asked you to explore the returns and volatilities of the ten stocks provided and contrast them with the three commodities they currently invest in.

Analyzing Returns and Volatilities of Tech Stocks and Commodities

In this analysis, we will explore the returns and volatilities of ten technology stocks and compare them with the three commodities that the investment firm currently specializes in: coffee, cocoa, and sugar. This will help us understand how tech stocks can be integrated into the existing portfolio to minimize risk while gaining exposure to the new market.

Steps to Follow:

  1. Data Collection: Gather historical price data for the ten tech stocks and the three commodities.
  2. Data Preprocessing: Clean and organize the data for analysis.
  3. Calculate Returns: Compute the historical returns for both the stocks and commodities.
  4. Analyze Volatility: Measure the volatility of each asset to understand the risk involved.
  5. Portfolio Analysis: Explore how tech stocks can be integrated into the current portfolio to optimize returns and minimize risk.
  6. Report Findings: Summarize the analysis in a report that is accessible to a broad audience.
# Exploring the dataset 
print (aapl.columns)

Please, explain in detail this data so that I can better understand it

Understanding the AAPL Dataset

The dataset you are working with contains historical stock data for Apple Inc. (AAPL). Each row in the dataset represents a single trading day, and the columns provide various pieces of information about the stock's performance on that day. Here's a detailed explanation of each column:

  • Date: This column contains the date for each trading day. It is stored as an object type, which typically means it is in string format. You may want to convert this to a datetime object for easier manipulation and analysis.

  • Open: The opening price of the stock on that particular day. This is the price at which the stock first traded upon the opening of the stock market.

  • High: The highest price at which the stock traded during the trading day.

  • Low: The lowest price at which the stock traded during the trading day.

  • Close: The closing price of the stock on that day. This is the last price at which the stock traded before the market closed.

  • Adj Close: The adjusted closing price, which accounts for any corporate actions such as stock splits, dividends, and rights offerings that occurred before the next trading day. This is often used for calculating returns as it provides a more accurate reflection of the stock's value.

  • Volume: The total number of shares that were traded during the day. This can give insights into the stock's liquidity and investor interest on that particular day.

Understanding these columns is crucial for analyzing the stock's historical performance, calculating returns, and assessing volatility.

What are the possible analysis that can be done on such data

The dataset you have, which contains historical stock prices for Apple Inc. (AAPL), can be used for a variety of financial analyses. Here are some possible analyses you can perform:

  1. Descriptive Statistics:

    • Calculate basic statistics such as mean, median, standard deviation, and range for each of the columns (Open, High, Low, Close, Adj Close, Volume).

  2. Time Series Analysis:

    • Analyze trends over time by plotting the closing prices.
    • Decompose the time series to observe seasonal patterns, trends, and noise.

  3. Moving Averages:

    • Calculate moving averages (e.g., 50-day, 200-day) to smooth out price data and identify trends.

  4. Volatility Analysis:

    • Measure the volatility of the stock using standard deviation or other metrics.

  5. Correlation Analysis:

    • Analyze the correlation between different columns, such as Open and Close prices, or between AAPL and other stocks if data is available.

  6. Volume Analysis:

    • Study the trading volume to identify periods of high activity and potential price movements.

  7. Technical Indicators:

    • Calculate technical indicators such as RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and Bollinger Bands.

  8. Candlestick Patterns:

    • Identify common candlestick patterns that might indicate bullish or bearish trends.

  9. Predictive Modeling:

    • Use machine learning models to predict future stock prices based on historical data.

  10. Event Study:

    • Analyze the impact of specific events (e.g., earnings announcements) on stock prices.

Each of these analyses can provide insights into the stock's past performance and potential future movements. You can choose one or more analyses based on your specific interests or research questions.

Exploring the data

aapl = pd.read_csv("data/AAPL.csv")
print(aapl.head())

goog = pd.read_csv("data/GOOG.csv")
print(goog.head())

tesla = pd.read_csv("data/TSLA.csv")
print(tesla.head())
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