Setting Up Backtesting Scenarios for High-Frequency and Low-Latency Trading

Setting Up a Scenario for Backtesting

Backtesting is essential in quantitative finance, allowing traders to evaluate strategies against historical data to assess performance and risk. A robust backtesting framework simulates realistic market conditions and ensures unbiased results. This document outlines the process of setting up backtesting scenarios within the ExSan framework.

Step 1: Configuring the Backtesting Scenario

Begin by defining the parameters of the backtesting scenario, including asset selection, historical data time horizon, and specific features or metrics for evaluation. Once defined, stream the data to ExSan, the core analytical engine for backtesting. A critical task here is data clustering.

Data Clustering

ExSan uses advanced clustering algorithms to group data points based on statistical properties. These clusters help construct the covariance matrix, essential in portfolio optimization and risk management. Due to the asynchronous nature of market data, only clusters with overlapping time periods can estimate the covariance matrix. Ensuring temporal alignment is crucial for accurate covariance estimates and reliable backtesting results.

Step 2: Introducing Scenario Randomization

To avoid biases from static data arrangements and test model resilience across varied configurations, apply randomization in each simulation iteration.

Stock ID File Scramble

In each backtesting iteration, randomly permute the stock ID file. This scrambling reorders stock IDs, creating a unique scenario for every simulation. Introducing randomness diversifies testing conditions, enhancing backtesting robustness. By simulating various potential scenarios, this approach evaluates strategy performance across different market conditions and reduces overfitting risk.

Step 3: Streaming Data and Constructing the Covariance Matrix

After setting the randomized scenario, stream historical data corresponding to the new configuration to ExSan. The primary goal here is computing the covariance matrix, which captures relationships between asset returns. Although this setup doesn't account for timing and lag associated with real-time market conditions, simulate streaming timing by using a probability distribution to model time-based volatility, mimicking real-time data dynamics.

The covariance matrix is fundamental in Modern Portfolio Theory (MPT), guiding asset allocation to optimize returns while minimizing risk. Accurate estimation requires careful alignment of input data, especially given the asynchronous nature of financial time series. By leveraging clusters of temporally aligned data, the framework ensures statistically sound covariance estimates reflective of realistic market dynamics.

Implementing this setup varies the sequence of stocks and their corresponding starting indexed closing values with each simulation, effectively replicating the dynamic and stochastic nature of real-world market data streams.

The Process Itself

Data Acquisition

• Data Source: Closing values for approximately 1,000 stocks were sourced from Yahoo Finance.
• Data Storage: The downloaded data was stored in individual .txt files for efficient management.
• Stock ID Storage: A separate .txt file stored the unique identifiers of each stock.

Randomization

To mimic real-world market volatility, the following strategy was employed:

• I Stock ID File Scramble: For each simulation, generate a new scenario by scrambling the stock ID file in a randomized order. This ensures every simulation introduces unique conditions, enhancing the robustness of backtesting results.

Pseudo-code that outlines the steps for generating a scrambled database file from stock data.

1. Define the scrambled database file path: dbScrambledFile = ExSan_Data_path + "stockScrambled.txt"
2. Open the scrambled database file for writing: open fto with dbScrambledFile in write mode
3. Loop through all stock data entries:
   1. Write the scrambled line to the file: write fileLine[randIntVector[i]] to fto
   2. Check if it is not the last line:
      • If i != stockCounterDB - 1, write a newline to the file.
      • Otherwise, break the loop.
4. Close the scrambled database file: close fto
5. Update the dbFile pointer to point to the scrambled file: dbFile = dbScrambledFile


• II shifting file pointers randomly:Each simulation accesses a data file, and the file pointer is randomly shifted from the beginning. As a result, the combined effect described in point I consistently produces a new scenario. This approach ensures that every simulation introduces unique conditions.

  Pseudo-Code this representation explains the logic of opening database files and shifting file pointers randomly.

  1. Initialize a variable to store the random shift value: shftStock = 0
  2. Loop through all available stock IDs:
     1. Construct the file name by concatenating: fileID[i] = core + stockID[i] + ".txt"
     2. Attempt to open the file:
        • If the file cannot be opened:
          • Display an error message: Error!
          • Exit the program.
     3. Generate a random shift value using the generator: shftStock = generate_random_shift(generator)
     4. Shift the file pointer by reading and discarding:
        • While shftStock > 0:
          • Read and discard one line from the file.
          • Decrement shftStock.

ExSan Backtesting Suite: Outcomes and Observations

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