Backtesting

Ever wondered which strategies truly worked in the past and potentially in the future? Often, we rely on personal opinions, but our judgment can be biased. Our brains tend to emphasize positive results and downplay negatives, making us think certain strategies are better than they really are, like relying on oversold/overbought levels in technical indicators. In this article, we will be exploring back-testing principles, performance measurement, and the coding language for tests. Trading is challenging, and creating effective strategies requires reliable tests to prove their long-term success.

Steps in BackTesting

1. Data Retrieval and Cleaning:

The first phase of a backtesting framework involves acquiring historical market data, including price movements and trading volumes. Subsequently, a meticulous data-cleaning process is undertaken to ensure accuracy and consistency. This step is crucial in eliminating discrepancies and preparing the dataset for meaningful analysis.

Python is increasingly becoming the preferred language for creating and testing strategies due to its speed, versatility, and user-friendly nature. This language facilitates various operations crucial for backtesting. Key tasks in this context include:

• Importing Libraries: For the majority of tasks, three essential libraries are utilized: numpy and pandas. Numpy is instrumental in manipulating arrays and performing mathematical operations efficiently, especially in handling time series data. Pandas, similar to numpy but with distinctions, excels in reading, slicing data frames, and working with Excel files. To import these libraries in Python, the ‘import’ statement is used, often with the standard shortcuts ‘np’ for numpy and ‘pd’ for pandas.
• Importing Data: Datasets, typically sourced from reputable online platforms in spreadsheet formats like Excel, need to be imported into the Python environment, for instance, Spyder. Using the pandas library, data can be easily read and transformed into a data frame, ready for analysis. Common file types, such as comma-delimited and Excel files, are effortlessly handled.
• Most Commonly Used Pandas Commands:
  • pd.read_excel('filename.xlsx'): Reads an Excel file and converts it into a data frame.
  • pd.read_csv('filename.csv'): Reads a CSV file and creates a data frame.
  • data.head(): Displays the first few rows of the data frame.
  • data.info(): Provides information about the data, including data types and missing values.
  • data.describe(): Generates descriptive statistics for numerical columns.
  • data['column_name']: Accesses a specific column in the data frame.
  • data.drop('column_name', axis=1): Deletes a column.
  • data.drop(index=0): Deletes a row by index.
  • data['new_column'] = values: Adds a new column.
• Adding and Deleting Rows and Columns: When working with tables, having an array structure is often preferred. The numpy library offers functions for array manipulation:
  • To add a column: data['new_column'] = values
  • To delete a column:data = data.drop('column_name', axis=1)
  • To add a row: data = data.append(new_row, ignore_index=True)
  • To delete a row: data = data.drop(index=row_index, axis=0)
  Most Commonly Used Numpy Commands:
  • np.array(data): Converts a data frame to a numpy array.
  • np.concatenate((array1, array2), axis=0): Concatenates arrays along a specified axis.
  • np.delete(array, index, axis): Deletes elements along a specified axis.
  • np.insert(array, index, values, axis): Inserts values along a specified axis.

2. Strategy Definition:

In this phase, the trader defines the rules and conditions that constitute the trading strategy. This involves setting criteria for buy and sell decisions, often based on technical indicators, moving averages, or other quantifiable metrics. Clarity in strategy definition is key to the subsequent stages of the backtesting framework.

3. Backtesting Execution:

The heart of the backtesting framework lies in the execution of the defined strategy on historical data. This step simulates how the strategy would have performed in the past under varying market conditions. It serves as a crucial evaluation of the strategy’s historical effectiveness.

Trading rules involve straightforward decisions, either going long or short based on signals, and maintaining the position for a predefined duration. This is achieved by employing a binary system, where long positions are assigned a value of 1, and short positions are assigned a value of -1. Consider the example below:

# Define binary values for trading positions
long_position = 1
short_position = -1

# Implement trading rules based on signals
if signal_condition:
    # Execute long position
    execute_trade(long_position)
else:
    # Execute short position
    execute_trade(short_position)

4. Performance Analysis:

Following the backtest, a comprehensive performance analysis is conducted. Key metrics such as returns, risk-adjusted returns, and drawdowns are scrutinized to assess the overall efficacy of the trading strategy. This analysis provides valuable insights into the strategy’s strengths and weaknesses.

In assessing the performance of a trading strategy, various metrics are employed to provide a comprehensive overview of its effectiveness and potential for profitability. These metrics collectively paint a nuanced picture of the strategy’s risk management, accuracy, and overall financial success. Let’s delve into each parameter and elucidate their significance:

• Realized Risk-Reward (RR):
  • Realized Risk-Reward represents the actual ratio of profit to loss achieved in executed trades.
  • Importance: This metric is pivotal in evaluating the strategy’s ability to manage risk effectively while capitalizing on profitable opportunities. It provides insight into the strategy’s risk-reward balance and its actual performance in live trading conditions.
• Hit Ratio:
  • Hit Ratio, expressed as a percentage, indicates the proportion of successful trades out of the total number of trades.
  • A high hit ratio suggests a greater accuracy in predicting market movements, reflecting the strategy’s overall success rate. Monitoring the hit ratio is crucial for understanding the reliability of the strategy’s signals and its potential for consistent profitability.
• Expectancy:
  • Expectancy is a measure of the average expected profit or loss per trade.
  • By considering both the hit ratio and realized risk-reward, expectancy provides a comprehensive assessment of the strategy’s expected profitability. It serves as a key metric for gauging the strategy’s potential long-term viability and attractiveness to investors.
• Net Profit:
  • Net Profit represents the total profit generated by the trading strategy.
  • This straightforward metric measures the overall financial success of the strategy. Net profit provides a clear indication of the strategy’s ability to generate returns, encompassing both winning and losing trades and is a fundamental gauge of its profitability.
• Minimum:
  • Minimum indicates the smallest individual profit or loss among all executed trades.
  • Understanding the minimum value is essential for assessing the worst-case scenario for a single trade in terms of profitability. It highlights the strategy’s resilience and ability to navigate adverse market conditions.
• Maximum:
  • The maximum represents the largest individual profit achieved in any single trade.
  • This metric showcases the best-case scenario for a single trade in terms of profitability. Monitoring the maximum value provides insights into the strategy’s capacity to capture significant profit opportunities when market conditions are favorable.
• Trades:
  • The total number of trades executed by the strategy.
  • This fundamental metric serves as a basis for evaluating the strategy’s activity and frequency. The total number of trades provides context for assessing the strategy’s level of engagement and its overall trading behavior.

5. Optimization and Out-of-Sample Testing :

Optimization, an optional but often beneficial phase, involves fine-tuning the strategy based on insights from the initial backtest. Parameters and rules may be adjusted to enhance performance. Additionally, traders may choose to conduct out-of-sample testing, evaluating the strategy’s performance on new, unseen data to ensure its robustness.

6. Implementation:

Successful strategies from the backtesting phase can be deployed for real-time trading. This transition from simulation to live trading involves using actual capital to execute the defined strategy in the dynamic market environment.

7. Monitoring and Maintenance:

The final stage involves continuous monitoring and maintenance of the implemented strategy. Regular oversight is essential to adapt the strategy to evolving market conditions, making necessary adjustments to ensure its continued effectiveness over time.

In essence, a backtesting framework comprises these interconnected stages, each playing a vital role in the development, evaluation, and implementation of effective trading strategies.