📊 Moving Averages: Python Implementation

 📊 Python Implementation of Moving Averages for Traders: Code, Charts & Strategy Backtests

As promised, here’s a full implementation of the Moving Averages (MAs) guide — this time, it’s all in Python, with charts, backtests, and the code walkthrough you’ve been waiting for.

🔥 Why This Matters for Traders

Moving averages (MAs) are one of the most powerful and widely used technical indicators. From algorithmic traders to manual market watchers, understanding how to implement them in real-time trading strategies is essential.

In this section, I’ve converted the theories and strategies covered in the Ultimate Guide to Moving Averages into a Python-based model that you can apply immediately. Whether you’re automating trades or building your own quantitative models, this code will help you start working with MAs in a sophisticated, professional way.

📈 What You’ll Get

1. Step-by-Step Code: I’ll break down each line and explain how the logic works for moving averages and strategy implementation.

2. Trading Strategies: See how you can use MAs to create winning setups in live markets, with a backtest showing you the real potential.

3. Interactive Charts: Plot buy/sell signals, crossovers, and volatility analysis using MAs to help visualize your strategy in action.

4. Full Backtesting: Test strategies over real historical data to gauge the effectiveness of different MAs before live trading.

Step 1: Import Required Libraries

[1]:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

Step 2: Create a Sample Stock Price Dataset

[36]:

# Generate a sample dataset

dates = pd.date_range(start="2024-01-01", periods=100, freq="D")

prices = np.cumsum(np.random.randn(100) * 2 + 0.5) + 100 # Simulated stock prices

# Create a DataFrame

df = pd.DataFrame({"Date": dates, "Close": prices})

df.set_index("Date", inplace=True)

# Display the first few rows

print(df.head())

                 Close
Date                  
2024-01-01  100.415735
2024-01-02  101.423358
2024-01-03   99.384640
2024-01-04   95.836909
2024-01-05   97.628219

Step 3: Plot Stock Prices

[39]:

# Plot stock prices

plt.figure(figsize=(12, 6))

plt.plot(df.index, df["Close"], label="Stock Price", color="blue")

plt.xlabel("Date")

plt.ylabel("Closing Price")

plt.title("Stock Price Over Time")

plt.legend()

plt.grid()

plt.show()

Step 4: Calculate the SMA

[40]:

# Calculate 10-day Simple Moving Average (SMA)

df["SMA_10"] = df["Close"].rolling(window=10).mean()

# Display the first few rows with SMA

print(df.head(15)) # Show more rows to see SMA values

                 Close     SMA_10
Date                             
2024-01-01  100.415735        NaN
2024-01-02  101.423358        NaN
2024-01-03   99.384640        NaN
2024-01-04   95.836909        NaN
2024-01-05   97.628219        NaN
2024-01-06   98.139414        NaN
2024-01-07   98.099508        NaN
2024-01-08   97.842509        NaN
2024-01-09   99.749659        NaN
2024-01-10  101.005388  98.952534
2024-01-11  100.644324  98.975393
2024-01-12   99.282688  98.761326
2024-01-13   99.759187  98.798780
2024-01-14   98.665213  99.081611
2024-01-15  101.923012  99.511090

Step 5: Plot Stock Price with SMA

[41]:

# Plot stock price and 10-day SMA

plt.figure(figsize=(12, 6))

plt.plot(df.index, df["Close"], label="Stock Price", color="blue", alpha=0.6)

plt.plot(df.index, df["SMA_10"], label="10-day SMA", color="red", linewidth=2)

plt.xlabel("Date")

plt.ylabel("Price")

plt.title("Stock Price and 10-day SMA")

plt.legend()

plt.grid()

plt.show()

Step 6: Calculate the Exponential Moving Average (EMA)

[42]:

# Calculate 10-day Exponential Moving Average (EMA)

df["EMA_10"] = df["Close"].ewm(span=10, adjust=False).mean()

# Display the first few rows with EMA

print(df.head(15))

                 Close     SMA_10      EMA_10
Date                                         
2024-01-01  100.415735        NaN  100.415735
2024-01-02  101.423358        NaN  100.598939
2024-01-03   99.384640        NaN  100.378158
2024-01-04   95.836909        NaN   99.552476
2024-01-05   97.628219        NaN   99.202611
2024-01-06   98.139414        NaN   99.009303
2024-01-07   98.099508        NaN   98.843885
2024-01-08   97.842509        NaN   98.661817
2024-01-09   99.749659        NaN   98.859606
2024-01-10  101.005388  98.952534   99.249748
2024-01-11  100.644324  98.975393   99.503308
2024-01-12   99.282688  98.761326   99.463195
2024-01-13   99.759187  98.798780   99.517012
2024-01-14   98.665213  99.081611   99.362139
2024-01-15  101.923012  99.511090   99.827752

Step 7: Plot Stock Price, SMA, and EMA

Plot stock price, SMA, and EMA

plt.figure(figsize=(12, 6))

plt.plot(df.index, df["Close"], label="Stock Price", color="blue", alpha=0.6)

plt.plot(df.index, df["SMA_10"], label="10-day SMA", color="red", linewidth=2)

plt.plot(df.index, df["EMA_10"], label="10-day EMA", color="green", linewidth=2)

plt.xlabel("Date")

plt.ylabel("Price")

plt.title("Stock Price with 10-day SMA & 10-day EMA")

plt.legend()

plt.grid()

plt.show()

Step 8: Define a Trading Strategy

[54]:

# Step 11.1: Compute Total Return

total_return = df["Cumulative Return"].iloc[-1] / initial_capital - 1

print(f"Total Strategy Return: {total_return:.2%}")

Total Strategy Return: 17.65%

[55]:

# Step 11.2: Compute Sharpe Ratio

import numpy as np

# Risk-free rate assumption (approximate India savings rate)

risk_free_rate = 0.05

# Calculate daily excess returns

excess_return = df["Strategy Return"] - (risk_free_rate / 252)

# Compute annualized Sharpe Ratio

sharpe_ratio = np.sqrt(252) * excess_return.mean() / excess_return.std()

print(f"Sharpe Ratio: {sharpe_ratio:.2f}")

Sharpe Ratio: 1.56

[56]:

# Step 11.3: Compute Maximum Drawdown

# Compute rolling maximum portfolio value

df["Cumulative Max"] = df["Cumulative Return"].cummax()

# Compute drawdown (drop from peak)

df["Drawdown"] = df["Cumulative Return"] / df["Cumulative Max"] - 1

# Find the worst drawdown

max_drawdown = df["Drawdown"].min()

print(f"Maximum Drawdown: {max_drawdown:.2%}")

Maximum Drawdown: -6.97%

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