Applying Technical Indicators to Identify Trends

Lesson Overview

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: Loading and Preparing Tesla Stock Data Calculating Simple Moving Averages (SMAs) Identifying Golden Cross and Death Cross Visualizing the Results

Loading and Preparing Tesla Stock Data

As a reminder, we will use historical prices of Tesla stock for our analysis. Let's load the dataset and prepare it: Python import pandas as pd import matplotlib.pyplot as plt from datasets import load_dataset # Load the Tesla dataset dataset = load_dataset('codesignal/tsla-historic-prices') tesla_df = pd.DataFrame(dataset['train']) # Convert 'Date' column to datetime format and set it as the index tesla_df['Date'] = pd.to_datetime(tesla_df['Date']) tesla_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.

Calculating Simple Moving Averages (SMAs)

A Simple Moving Average (SMA) smooths out price data over a pre-defined time period to identify trends. Now, let's calculate the SMAs: Python # Calculate 50-day and 200-day SMAs tesla_df['SMA_50'] = tesla_df['Close'].rolling(window=50).mean() tesla_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.

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Visualizing the Results

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

Python

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

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

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

Python

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

# Plot with Golden Cross and Death Cross
fig, ax = plt.subplots(figsize=(12, 6))
tesla_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.

Python

# Highlighting Golden Cross and Death Cross points
crosses = tesla_df_small[tesla_df_small['Crossover'] != 0]
for idx, row in crosses.iterrows():
    if row['Crossover'] == 2:
        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 "")
    elif row['Crossover'] == -2:
        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 "")

plt.legend()
plt.show()

# Highlighting Golden Cross and Death Cross points
crosses = tesla_df_small[tesla_df_small['Crossover'] != 0]
for idx, row in crosses.iterrows():
    if row['Crossover'] == 2:
        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 "")
    elif row['Crossover'] == -2:
        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 "")

plt.legend()
plt.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.

Applying Technical Indicators to Identify Trends

Lesson Overview

Loading and Preparing Tesla Stock Data

Calculating Simple Moving Averages (SMAs)

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Identifying Golden Cross and Death Cross

Visualizing the Results

Lesson Summary