Hello and welcome! Today, we will explore Applying Technical Indicators to Identify Trends using Tesla's ($TSLA) stock data. You will revisit how to calculate Simple Moving Averages (SMAs), and learn how to identify trend signals like Golden Cross and Death Cross, and visualize these trends using pandas and matplotlib.

Lesson Goal: To understand and implement technical indicators (SMA) and identify trend signals (Golden Cross and Death Cross) in financial data using Python and Pandas.

Lesson Plan:

1. Loading and Preparing Tesla Stock Data
2. Calculating Simple Moving Averages (SMAs)
3. Identifying Golden Cross and Death Cross
4. Visualizing the Results

As a reminder, we will use historical prices of Tesla stock for our analysis. Let's load the dataset and prepare it:

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1import pandas as pd
2import matplotlib.pyplot as plt
3from datasets import load_dataset
4
5# Load the Tesla dataset
6dataset = load_dataset('codesignal/tsla-historic-prices')
7tesla_df = pd.DataFrame(dataset['train'])
8
9# Convert 'Date' column to datetime format and set it as the index
10tesla_df['Date'] = pd.to_datetime(tesla_df['Date'])
11tesla_df.set_index('Date', inplace=True)

Explanation:

• Import Libraries: We import pandas for data manipulation, matplotlib.pyplot for plotting, and load_dataset to fetch our Tesla stock data.
• Load Dataset: We use load_dataset to fetch the dataset and convert it to a DataFrame.
• Convert Date Column: The Date column is converted to datetime format for easier manipulation.
• Set Index: We set the Date column as the index to efficiently perform time series operations.

A Simple Moving Average (SMA) smooths out price data over a pre-defined time period to identify trends. Now, let's calculate the SMAs:

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1# Calculate 50-day and 200-day SMAs
2tesla_df['SMA_50'] = tesla_df['Close'].rolling(window=50).mean()
3tesla_df['SMA_200'] = tesla_df['Close'].rolling(window=200).mean()

Explanation:

• SMA_50: We calculate the 50-day SMA by using the rolling method with a window of 50 days on the 'Close' price and then applying the mean function.
• SMA_200: Similarly, we calculate the 200-day SMA by using a rolling window of 200 days.

In trading, golden and death crosses are defined as follows:

• Golden Cross: Occurs when a short-term SMA (50-day) crosses above a long-term SMA (200-day), indicating a potential upward trend.
• Death Cross: Occurs when a short-term SMA (50-day) crosses below a long-term SMA (200-day), indicating a potential downward trend.

Now, let's create signals for these crossover points:

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1# Identifying the "Golden Cross" and "Death Cross"
2tesla_df['Signal'] = 0  # Default value
3tesla_df.loc[tesla_df['SMA_50'] > tesla_df['SMA_200'], 'Signal'] = 1
4tesla_df.loc[tesla_df['SMA_50'] < tesla_df['SMA_200'], 'Signal'] = -1
5
6# Creating a column to mark crossover points
7tesla_df['Crossover'] = tesla_df['Signal'].diff()

Explanation:

• Signal Column: We create a 'Signal' column to store initial values as 0.
• Golden Cross Signal: When the 50-day SMA crosses above the 200-day SMA, we set the 'Signal' to 1.
• Death Cross Signal: When the 50-day SMA crosses below the 200-day SMA, we set the 'Signal' to -1.
• Crossover Column: We create a 'Crossover' column to store the points where the signal changes (differs from the previous day), indicating a crossover event.

We will use a smaller date range for better visualization. In this example, let's visualize the $TSLA data for the year 2018.

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1# Using a smaller date range for better visualization
2tesla_df_small = tesla_df.loc['2018']

Next, we will plot the Close price, 50-day SMA, and 200-day SMA.

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1# Plot with Golden Cross and Death Cross
2fig, ax = plt.subplots(figsize=(12, 6))
3tesla_df_small[['Close', 'SMA_50', 'SMA_200']].plot(ax=ax, title="TSLA with Golden Cross and Death Cross (2018)")

• Smaller Date Range: By focusing on a specific year ('2018'), we can more clearly see the trends and crossover points.
• Plot Data: We plot the 'Close' prices along with the 50-day and 200-day SMAs to visualize the trends.

Now, to highlight the crossover points, we loop through the 'Crossover' column in the DataFrame. We use green dots for the Golden Cross and red dots for the Death Cross.

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1# Highlighting Golden Cross and Death Cross points
2crosses = tesla_df_small[tesla_df_small['Crossover'] != 0]
3for idx, row in crosses.iterrows():
4    if row['Crossover'] == 2:
5        plt.plot(idx, row['SMA_50'], 'go', markersize=10, label='Golden Cross' if 'Golden Cross' not in [text.get_text() for text in ax.get_legend().get_texts()] else "")
6    elif row['Crossover'] == -2:
7        plt.plot(idx, row['SMA_50'], 'ro', markersize=10, label='Death Cross' if 'Death Cross' not in [text.get_text() for text in ax.get_legend().get_texts()] else "")
8
9plt.legend()
10plt.show()

• Select Crossover Points: We identify points where the 'Crossover' column is not zero, indicating a Golden Cross or Death Cross.
• Plot Crossovers: We loop through these points, plotting green dots for Golden Cross events and red dots for Death Cross events.
• For Loop Explanation:
  • Crosses Variable: We filter tesla_df_small to get rows where the 'Crossover' column is not zero.
  • Loop Through Crosses: For each index (idx) and row in the crosses DataFrame:
    • Check for Golden Cross: If the 'Crossover' value is 2, plot a green dot at the index on the 50-day SMA curve.
    • Check for Death Cross: If the 'Crossover' value is -2, plot a red dot at the index on the 50-day SMA curve.

You have now learned how to apply and visualize technical indicators such as SMAs to identify trends in Tesla stock data. This lesson covered importing and preparing financial data, calculating SMAs, identifying trend signals, and visualizing the results. Practice exercises will reinforce these essential skills, allowing you to analyze and make informed trading decisions. Understanding stock movements and improving your trading strategies by leveraging Python and Pandas is crucial for success in the financial market.

Ready to take your skills to the next level? Let's dive into the practice exercises!