ESSENTIAL FACTORS IN BACKTESTING

23 בספט׳

Testing trading strategies is essential for assessing their effectiveness based on historical market data. It allows traders to gain insights into how a strategy would perform under real market conditions, identify necessary adjustments, and understand various influencing factors. For instance, it can reveal how news releases impact trading outcomes or how a gradual increase in position volume can help recover losses. In this post, we will explore the most important criteria for effective testing.


Criteria for Testing Strategies on Historical Data


📍 1. Reliability and Quality of Historical Data

Accurate and comprehensive historical market data is crucial for effective testing. Any errors or omissions in the quotes can significantly skew the results. For instance, if there is a missing entry for a day that experienced a spike, the absence of this data could misrepresent the strategy's performance. This missing information might conceal a stop-loss trade that closed at a loss, ultimately distorting the yield curve and leading to misleading conclusions.

What to Consider:

🔹 Data Quality: It’s essential to use verified data sources that minimize errors and lag. Ideally, obtain quotes directly from your broker. If you are testing your strategy on third-party platforms, consider using data from TradingView.

🔹 Depth of History: The data should encompass a significant time period that includes various market conditions, such as trends, consolidations, and different volatility levels. For scalping strategies, a minimum of one year of data is recommended, while long-term strategies should be tested on data spanning more than three years.

🔹 Tick Accuracy: For high-frequency trading and scalping, having tick-level data is crucial. Conversely, for medium-term strategies, candlestick closing data may suffice.

The price history is sourced from your broker and may vary from the histories provided by other brokers due to differing liquidity providers. However, there should not be substantial discrepancies or noticeable gaps in the data.


📍 2. Reality of Order Execution

When testing a trading strategy using historical data, it's important to recognize that real trading differs significantly from backtesting:

🔹 Variable Spread: The spread can fluctuate based on market volatility, the time of day, or significant news events. It's essential to account for changing spreads during testing rather than relying on fixed values in your settings.

🔹 Slippage: Orders may be executed at prices different from the expected level, particularly in volatile markets or when liquidity is low.

🔹 Execution Delay: A delay may occur between the submission of an order and its actual execution, especially in fast-moving markets.

The key issue is that in a strategy tester, orders are executed instantly, whereas real trading involves slippage, server delays, and other factors. This can lead to discrepancies of several points. Therefore, it's advisable to establish a percentage deviation parameter to estimate the potential difference between the test results and real trading outcomes, leaning towards the conservative side. Alternatively, you can test the strategy under various spread conditions and analyze how performance metrics and the equity curve are affected by changes in the spread.


📍 3. Accounting for Commissions and Costs

Even a strategy that demonstrates positive results in a backtesting environment can become unprofitable once real trading costs are factored in:

🔹 Broker Commissions: It is crucial to consider the fixed or variable commissions charged by the broker for each trade. Some strategy testers allow you to integrate commission parameters; if that's not the case, you should manually subtract these costs from the profit for each full lot traded.

🔹 Spread: The spread can widen during periods of low liquidity, which can significantly impact profitability. Some testers include spread parameters, but others may not.

🔹 Swaps: Swap rates can vary drastically between brokers, and it’s important to remember that they can be substantial, particularly when held overnight due to rollover rates.

The core issue lies in the limitations of the testing software. If your tester does not account for floating spreads and swaps, it may be worth exploring alternative options.


📍 4. Optimization and Over-Optimization of the Strategy

While strategy testing is essential, it can lead to over-optimization, also known as data fitting. A strategy that appears perfect for historical data might not perform effectively in real market conditions.

To mitigate this risk, consider the following:

🔹 Avoid Deep Ad Hoc Parameter Fitting: Over-optimizing too many parameters for historical data can significantly reduce a strategy's robustness in live trading environments. A strategy that is fine-tuned solely for past performance may fail when faced with the unpredictability of future market conditions.

🔹 Use Forward Testing: This approach involves testing a strategy on one timeframe and then applying it to a different timeframe. This method can help prevent over-optimization by assessing the strategy's adaptability across various market conditions.

The key takeaway is achieving balance. Attempting to utilize numerous indicators simultaneously and fine-tuning their values extensively can lead to problems. If you have adjusted parameters based on a selective set of trades (let’s say, a 15-trade segment), it’s likely that you will need to readjust for different segments.


📍 5. Testing Under Different Market Conditions

To ensure comprehensive evaluation, a trading strategy should be tested under various market conditions:

🔹 Trend and Range Markets: It’s essential to verify that the strategy performs effectively during periods of strong trending markets as well as in sideways (range-bound) movements. A robust strategy should be adaptable to both scenarios.

🔹 Volatility: The strategy should be assessed across different volatility levels. While it might perform well in low-volatility environments, it may struggle or become unprofitable during sharp market movements. Testing across diverse volatility conditions is crucial for understanding the strategy's resilience.

🔹 Macroeconomic Events: Significant economic factors, such as news releases and central bank decisions, can greatly impact market behavior. Therefore, it’s important to test the strategy over timeframes that include these critical events to gauge its performance in response to external shocks.

By thoroughly testing across these varying conditions, traders can better understand the strategy’s strengths and weaknesses, leading to more informed trading decisions.


📍 6. Stress Testing

Stress testing is a crucial process for assessing the sustainability and resilience of a trading strategy under adverse conditions. Consider the following factors during stress testing:

🔹 Falling Liquidity: Evaluate how the strategy performs during scenarios of sharply reduced market liquidity. Understanding its behavior in these situations is vital, as low liquidity can lead to wider spreads and slippage, affecting trade execution and overall performance.

🔹 Price Spikes: Assess the strategy's response to unexpected price changes, such as those triggered by significant news events (e.g., interest rate announcements). Observing how the strategy reacts to rapid market movements helps gauge its robustness in volatile conditions.

🔹 Crisis Events: Testing the strategy against data from historical financial crises can provide insight into its resilience during extreme market conditions. For instance, analyzing performance during the 2008/2020 financial crisis or the market disruptions caused by geopolitical events (such as the 2022 war) can reveal potential weaknesses and strengths.


📍 7. Analyzing Strategy Metrics

After conducting tests on your trading strategy, it is essential to analyze the results through key performance metrics. The following metrics provide valuable insights into the strategy's effectiveness and risk profile:

🔹 Maximum Drawdown: This measures the maximum peak-to-trough decline in funds during the testing period. A lower drawdown indicates a less risky strategy, as it shows how much the capital could potentially decrease before recovering.

🔹 Risk/Profit Ratio: This metric assesses the profit generated for every dollar at risk. A favorable risk/profit ratio indicates that the potential rewards justify the risks taken, making the strategy more appealing.

🔹 Percentage of Profitable Trades: While the sheer number of profitable trades is important, it's equally crucial to analyze the ratio of profitable trades to losing trades. A higher percentage signifies a consistently effective strategy, but it should also be evaluated in conjunction with other metrics.

🔹 Average Profit/Loss: This metric calculates the average profit of winning trades and the average loss of losing trades. Understanding these averages helps to contextualize the strategy’s overall performance and can guide adjustments to improve outcomes.


📍 Conclusion

There is no one-size-fits-all algorithm or set of criteria for testing trading systems. Each strategy has unique characteristics that require tailored evaluation methods. However, there are general recommendations that should be considered when assessing any type of trading strategy.

Gaining an understanding of parameter selection and optimization comes with experience. It is advisable to first gather this experience on a demo account by running a strategy that has been fine-tuned in a testing environment. However, it’s important to note that testing environments may not replicate real market conditions accurately—issues such as price delays and slippage can significantly affect trade execution in live markets.

Thus, when transitioning from a demo account to a real account, continuous monitoring is essential. Traders should keep track of statistical parameters and be vigilant for any deviations from the outcomes observed during testing. This oversight will help ensure better alignment with the strategy’s expected performance and provide an opportunity to make necessary adjustments in response to changing market conditions.

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