How To Use Python For Stocks Trading

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The Moving Average (MA) is a widely used tool in technical analysis, valued by traders for its ability to streamline price data over a given timeframe.

The main function of the MA is to eliminate random price volatility, enabling traders to concentrate on the broader trend direction.

• Uptrend: When the price remains steadily above the moving average, it often indicates a bullish market or an upward trend.

Downtrend: Conversely, when the price stays consistently below the moving average, it generally suggests a bearish market or a downward trend.

We'll use Tesla's stock data to illustrate these concepts.import yfinance as yf
import mplfinance as mpf

df = yf.download('TSLA', start='2023-01-01', end='2024-01-01')
df.columns = df.columns.get_level_values(0)
mpf.plot(df, type='candle', style='charles', title='TSLA Candlestick Chart', volume=True)

Simple Moving Average (SMA)

It is the unweighted average of prices over a specific number of periods.

import matplotlib.pyplot as plt

# Calculate the Simple Moving Averages (SMA) for different periods
df['SMA_20'] = df['Close'].rolling(window=20).mean()
df['SMA_50'] = df['Close'].rolling(window=50).mean()
df['SMA_100'] = df['Close'].rolling(window=100).mean()

# Plot the closing price and multiple SMAs using mplfinance
plt.figure(figsize=(12, 8))

# Plot closing price
plt.plot(df.index, df['Close'], label='Close Price', alpha=0.5)

# Plot 20-day, 50-day, and 100-day SMAs
plt.plot(df.index, df['SMA_20'], label='20-day SMA', alpha=0.8, color='orange')
plt.plot(df.index, df['SMA_50'], label='50-day SMA', alpha=0.8, color='green')
plt.plot(df.index, df['SMA_100'], label='100-day SMA', alpha=0.8, color='red')

# Customize the plot
plt.title('Tesla Stock Price with Multiple SMAs (20, 50, 100 days)')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()

# Display the plot
plt.show()

Exponential Moving Average (EMA)

It gives more weight to recent prices, making it more responsive to price changes compared to the SMA.

# Calculate the Exponential Moving Averages (EMA) for different periods
df['EMA_20'] = df['Close'].ewm(span=20, adjust=False).mean()
df['EMA_50'] = df['Close'].ewm(span=50, adjust=False).mean()
df['EMA_100'] = df['Close'].ewm(span=100, adjust=False).mean()

# Plot the closing price and multiple EMAs using matplotlib
plt.figure(figsize=(12, 8))

# Plot closing price
plt.plot(df.index, df['Close'], label='Close Price', alpha=0.5)

# Plot 20-day, 50-day, and 100-day EMAs
plt.plot(df.index, df['EMA_20'], label='20-day EMA', alpha=0.8, color='orange')
plt.plot(df.index, df['EMA_50'], label='50-day EMA', alpha=0.8, color='green')
plt.plot(df.index, df['EMA_100'], label='100-day EMA', alpha=0.8, color='red')

# Customize the plot
plt.title('Tesla Stock Price with Multiple EMAs (20, 50, 100 days)')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()

# Display the plot
plt.show()

Signals

• Bullish Signal: If the price is above the moving average, it suggests a bullish trend.

• Bearish Signal: If the price is below the moving average, it suggests a bearish trend.

Crossover Strategy

• Golden Cross: A bullish signal occurs when a short-term MA (e.g., 20-day) crosses above a long-term MA (e.g., 50-day).

• Death Cross: A bearish signal occurs when a short-term MA crosses below a long-term MA.

# Calculate the 20-day and 50-day Simple Moving Averages (SMA)
df['SMA_20'] = df['Close'].rolling(window=20).mean()
df['SMA_50'] = df['Close'].rolling(window=50).mean()

# Identify Golden Cross and Death Cross
df['Golden_Cross'] = (df['SMA_20'] > df['SMA_50']) & (df['SMA_20'].shift(1) <= df['SMA_50'].shift(1))
df['Death_Cross'] = (df['SMA_20'] < df['SMA_50']) & (df['SMA_20'].shift(1) >= df['SMA_50'].shift(1))

# Plot the closing price, 20-day SMA, and 50-day SMA
plt.figure(figsize=(12, 8))

# Plot closing price
plt.plot(df.index, df['Close'], label='Close Price', alpha=0.5)

# Plot the SMAs
plt.plot(df.index, df['SMA_20'], label='20-day SMA', color='orange', alpha=0.8)
plt.plot(df.index, df['SMA_50'], label='50-day SMA', color='blue', alpha=0.8)

# Mark Golden Cross (bullish) points
plt.plot(df.index[df['Golden_Cross']], df['SMA_20'][df['Golden_Cross']], '^', markersize=10, color='green', lw=0, label='Golden Cross')

# Mark Death Cross (bearish) points
plt.plot(df.index[df['Death_Cross']], df['SMA_20'][df['Death_Cross']], 'v', markersize=10, color='red', lw=0, label='Death Cross')

# Customize the plot
plt.title('Tesla Stock Price with Golden and Death Crosses (20-day and 50-day SMA)')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()

# Display the plot
plt.show()

Support & Resistance

• Support: In an uptrend, the moving average can act as a floor, preventing the price from falling.

• Resistance: In a downtrend, the moving average can act as a ceiling, preventing the price from rising.

Limitations

• Lagging Indicator: Moving averages are lagging indicators, meaning they are based on past prices and may not respond quickly to sudden market changes.

• Whipsaws: In volatile markets, moving averages can generate false signals or “whipsaws” (frequent crossing of price and the MA without a clear trend).

Common Time Perios

• Short-term: 5, 10, 20 periods — used for short-term trends and signals.

• Medium-term: 50 periods — popular for observing intermediate trends.

• Long-term: 100, 200 periods — used for long-term trend analysis.

The insights and visual tools shared in this blog post, including the application of moving averages to identify support, resistance, and trading signals (such as the Golden Cross and Death Cross), are intended solely for educational purposes. Markets like stocks and cryptocurrencies are highly volatile and subject to rapid, unpredictable changes. Past performance using these techniques does not ensure future success. Always conduct in-depth research, consider seeking advice from a financial professional, and fully understand the risks before making trading decisions. Apply these strategies responsibly and with care.