What is Backtesting a Trading Strategy?

Backtesting: Validating Trading Strategies with Historical Data

Backtesting, a crucial component in formulating investment strategies, is an approach that employs historical market data to simulate past trading scenarios. The purpose of this process is to recreate past investment landscapes, comparing your trading ideas against actual market shifts, assessing the performance of your strategy under historical conditions with scientific rigor.

In the realm of financial investing, when investors or traders develop new trading models or approaches, they don't immediately apply them to real-time markets. Instead, they first run a "dry run" through backtesting. It acts like a time machine, transporting your trading logic through the fog of historical price fluctuations, revealing potential profits and risks the strategy might have incurred under different market situations that actually occurred.

By conducting such simulations based on historical data, backtesting furnishes compelling evidence to help you initially gauge whether a designed trading strategy has the potential for profitability. This, in turn, serves as a critical reference in deciding whether to implement it in live trading.

Selecting Trading Styles and Preparing for Backtesting

Before diving into backtesting, the first step is to define your trading approach and prepare accordingly. Traders generally fall into two categories: discretionary and systematic.

Discretionary traders rely on their personal experience and intuition, adapting to market fluctuations with flexible entry and exit strategies based on subjective assessments of real-time market conditions. While this style offers freedom and adaptability, it can lead to inconsistent backtesting results due to the lack of rigid criteria. However, discretionary traders should not disregard backtesting entirely; they can use simulation trading to evaluate their decision-making patterns historically, refining the process and minimizing emotional influences.

On the other hand, systematic traders are better suited for backtesting. They follow a clear and comprehensive set of trading rules, specifying when to buy or sell under specific conditions (e.g., "Buy when both A and B are met, sell when condition X occurs"). By eliminating emotional interference through logical and quantitative standards, these strategies offer more predictable profitability. As such, systematic approaches tend to yield more consistent and reliable backtesting outcomes, particularly in the realm of algorithmic trading that seeks precision and automation.

To ensure effective backtesting, both discretionary and systematic traders must have well-defined and rule-based strategies prior to testing. This is especially crucial for systematic traders, as an undefined strategy will result in unstable test results.

Traders can opt for automated backtesting software, which generates test reports upon inputting relevant data. Nevertheless, this article will focus on manual backtesting methods, which, despite being more labor-intensive, provide deeper understanding and learning opportunities at a lower cost.

Practical Guide: Conducting a Manual Backtest for Trading Strategies

When backtesting trading strategies, start by creating an exhaustive data template to track and analyze historical trade data. Whether using a pre-built template in Google Sheets or coding your own in Excel or Python, ensure the template captures all essential information fields, such as date, market, trade direction, entry price, stop loss and take profit levels, risk-to-reward ratio, and actual profits or losses.

Using Bitcoin as an example, we can design a backtesting approach based on a moving average crossover strategy. When the 50-day moving average crosses above the 200-day moving average, forming a golden cross, we assume a buy at the next daily closing price; conversely, when the 200-day moving average falls below the 50-day MA, forming a death cross, we set a sell at the subsequent daily close.

To validate the test, specify the applicable time frame (e.g., daily charts) and the historical period for backtesting (e.g., from early 2019). Consulting Bitcoin's historical price movements, identify corresponding trade signals according to the strategy rules and record them in the backtesting table:

- Buy @$5,400 (golden cross)

- Sell @$9,200 (death cross)

- Buy @$9,600 (another golden cross)

- Sell @$6,700 (second death cross)

- Buy @$9,000 (subsequent golden cross)

Visualizing these trades on a chart illustrates the strategy's performance. Calculating each trade's profit or loss reveals a gain of approximately $3,800 for the first trade and a loss of $2,900 for the second, resulting in a net realized profit of $900. As of a specific date (e.g., December 2020), unrealized profits amount to around $9,000, suggesting potential gains if the position is closed upon the next death cross.

During backtesting, pay attention to key metrics like trading frequency, win rate, risk-to-reward ratio per trade, and overall asset curve changes. By systematically simulating past trading scenarios, you can assess the strategy's adaptability and stability across different market conditions, providing valuable insights for real-world trading decisions.

Decoding Backtesting Results and Optimization Techniques

Backtesting analysis is a crucial step in assessing the effectiveness and robustness of trading strategies. From our simple backtest example, while the current realized profit and loss may not be impressive, it's essential to delve beneath the surface and examine the market conditions and strategy adaptability behind the results.

For instance, during the market crash triggered by the COVID-19 pandemic in March 2020, unprofitable trades highlight the strategy's performance under extreme scenarios. This suggests accounting for black swan events' impact on strategy stability and conducting further backtests with an expanded historical data range to determine if losses are outliers or indicative of inherent flaws.

Beyond specific profit and loss figures, backtesting results can reveal valuable insights:

1. Volatility measures: Maximum profit peaks and maximum drawdowns provide insight into a strategy's volatility across different market cycles and the psychological stress you might face.

2. Risk exposure: Calculate the required risk capital based on backtest results to allocate portfolio funds effectively and manage overall risk levels.

3. Annualized return: Comparing the average annual return of the strategy allows for benchmarking against other investment products or strategies, evaluating its attractiveness.

4. Win-loss ratio: Analyzing the proportion of profitable and losing trades, along with average profit and loss per trade, helps assess the strategy's profitability and win rate.

5. Average entry and exit prices: Tracking these averages gives insight into the effectiveness of the timing of your strategy's execution.

However, the true value lies not just in obtaining these metrics but also in leveraging them to enhance the strategy. Disciplined traders meticulously document various detailed parameters during backtesting, providing opportunities for learning and adjustment.

Lastly, when interpreting backtesting outcomes, beware of overfitting (the optimization trap), which occurs when a strategy is overly optimized using historical data, leading to poor real-world performance. To ensure strategy validity and forward-looking prowess, validate through forward testing (simulated trading) to confirm adaptability and consistent profitability under unseen market conditions.

Conclusion

In wrapping up, backtesting emerges as an indispensable tool for assessing investment strategies in the financial world. It simulates trading scenarios using historical data, enabling investors to validate and refine their trading logic. Whether one is a discretionary or systematic trader, backtesting allows for a deeper understanding of how strategies perform under various market conditions, thus fostering wiser investment choices.

Recent practices show that, amidst the rapidly evolving financial markets, regularly combining backtesting with forward testing can effectively prevent overfitting, enhancing the robustness and adaptability of trading strategies. Looking ahead, as artificial intelligence and big data technologies advance, backtesting methodologies will become more refined and automated, offering investors even more precise and efficient means for strategy validation.