Backtesting a Simple Trading Strategy Using pandas-datareader

In the world of finance and trading, backtesting a strategy helps determine its potential effectiveness by simulating how it would have performed with historical data. One popular tool used for backtesting is Python, thanks to its robust libraries geared toward data analysis and handling time series data.

In this article, we'll explore how to backtest a simple trading strategy using pandas-datareader, a popular library to extract historical market data. This will help us gain insights into strategy performance without risking capital in actual markets.

Setting Up the Environment

Before we dive into the code, ensure your environment is properly set up. You should have Python installed, along with key libraries such as pandas and pandas-datareader. You can install these dependencies using pip:

pip install pandas pandas-datareader

Fetching Historical Data

We’ll start by fetching historical stock price data. For this example, we'll use data for Apple's stock (AAPL) from Yahoo Finance. First, we import the necessary libraries:

import pandas as pd
import pandas_datareader.data as web
from datetime import datetime

Next, define the timeframe for your data. Let’s assume we want data from the start of 2020 to the end of 2022.

start = datetime(2020, 1, 1)
end = datetime(2022, 12, 31)

Using this time range, we can extract the data:

df = web.DataReader('AAPL', 'yahoo', start, end)

Make sure to inspect the first few rows of your DataFrame with:

print(df.head())

Implementing a Simple Trading Strategy

We’ll implement a simple moving average crossover strategy. This involves using two moving averages:

• Short-term average (e.g., 20 days)
• Long-term average (e.g., 50 days)

The strategy signals a buy when the short-term moving average crosses above the long-term moving average, and a sell when it crosses below.

# Calculate moving averages
short_window = 20
long_window = 50

# Add the moving averages to the DataFrame
df['Short_MAvg'] = df['Close'].rolling(window=short_window).mean()
df['Long_MAvg'] = df['Close'].rolling(window=long_window).mean()

Generating Trade Signals

Now, we'll add trade signals to the DataFrame based on our strategy:

# Initialize a `Signal` column with 0
# Signal 1: Buy, Signal 0: Hold/Sell

# Initialize the signal column
signals = pd.DataFrame(index=df.index)
signals['Signal'] = 0

# Signal to buy when short MA crosses above long MA
signals['Signal'][short_window:] = np.where(df['Short_MAvg'][short_window:] > df['Long_MAvg'][short_window:], 1.0, 0.0)

# Generate trading orders
df['Position'] = signals['Signal'].diff()

In the above code, we’ve initialized the Signal column with zeros. It switches to 1 indicating a buy whenever the short moving average exceeds the long term moving average.

Evaluating Strategy Performance

Lastly, let's evaluate our trading strategy's potential performance. We can calculate and visualize portfolio returns based on generated buy/sell signals:

# Calculate the returns of the simple strategy
df['Strategy_Returns'] = (df['Close'].pct_change()) * df['Position'].shift(1)

# Calculate cumulative returns
cumulative_strategy_return = (df['Strategy_Returns'] + 1).cumprod()

# Plot cumulative returns
cumulative_strategy_return.plot()
plt.title('Cumulative Strategy Return')
plt.show()

The cumulative strategy return chart provides insights into the performance over the period you've analyzed. With more complex strategies, you'd need to account for trading costs, slippage, and additional factors.

Conclusion

Backtesting gives you a glimpse into how a given trading strategy might perform based on historical data. However, past performance isn't always indicative of future results. Ensure ample research and adjustment for real-world contexts before live deploying any strategy. The methodology explored with pandas-datareader is replicable and extendable to other strategies with more complexity or risk adjustment.