Backtesting a Mean Reversion Strategy with yfinance

Backtesting a trading strategy is a crucial step before deploying it in real financial markets. Among various strategies, the mean reversion strategy is premised on the idea that prices will move back to their historical averages. In this article, we will walk through the process of backtesting a mean reversion strategy using Python's yfinance library to fetch historical financial data.

Understanding Mean Reversion

Mean reversion is a financial theory suggesting that asset prices and historical returns will revert to their long-term mean or average level over time. This strategy assumes that if a price is currently higher than its historical average, it will eventually decline, and if it's lower, it will eventually rise.

Prerequisites

To backtest a mean reversion strategy, you'll need the following components:

• Python 3.x environment
• An installed yfinance library
• Understanding of basic Python programming and Pandas for data manipulation

Setting Up the Environment

First, ensure you have yfinance installed. You can do this via pip:

pip install yfinance

Fetching Historical Data with yfinance

Use the yfinance library to download stock price data. For this example, we’ll use Tesla's stock data.

import yfinance as yf
import pandas as pd

ticker = 'TSLA'
data = yf.download(ticker, start='2020-01-01', end='2023-01-01')
print(data.head())

This script fetches Tesla’s stock data from January 1, 2020, to January 1, 2023.

Implementing the Mean Reversion Strategy

Now let's implement a basic version of the mean reversion strategy. We will calculate the moving average and place buy/sell signals accordingly.

# Calculate the 20-day moving average
window = 20
data['MA20'] = data['Close'].rolling(window=window).mean()

# Create buy/sell signals
signals = pd.DataFrame(index=data.index)
signals['Price'] = data['Close']
signals['Signal'] = 0.0
signals['Signal'][window:] = np.where(
    data['Close'][window:] < data['MA20'][window:], 1.0, 
    (np.where(data['Close'][window:] > data['MA20'][window:], -1.0, 0.0))
)
print(signals)

Please ensure you import necessary libraries like numpy before executing this snippet.

Backtesting the Strategy

To assess the effectiveness of our strategy, let’s backtest it to see its hypothetical performance.

# Calculate returns
signals['Returns'] = data['Close'].pct_change()

# Calculate strategy returns
signals['Strategy Returns'] = signals['Signal'].shift(1) * signals['Returns']

# Plot the strategy performance
(1 + signals['Strategy Returns']).cumprod().plot()
(1 + signals['Returns']).cumprod().plot()

Analyzing the Results

The cumulative product plot will show you how your mean reversion strategy would have performed compared to a simple buy-and-hold strategy. A strategy is considered potentially viable if its total growth outperforms the market return. However, always remember backtesting does not account for future unknown risks and real trading environments where execution lag and slippage can eat into returns.

Improving the Strategy

While this straightforward mean reversion strategy can provide insights, traders often implement more sophisticated models, add trailing stop-losses, incorporate indicators like RSI, or blend multiple strategies to suit different market conditions. Experiment and use statistical metrics such as Sharpe Ratio, Maximum Drawdown, or CAGR to quantify the performance of your enhanced strategies.

Conclusion

Backtesting trading strategies with yfinance is a valuable tool for traders looking to understand potential performance before risking actual capital. By iterating over simplistic versions, like this mean reversion example, you can develop insights, optimize parameters and evolve into more complex algorithms potentially yielding better returns.