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Grid Trading in High-Frequency Environments

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Objectives

  • Data Handling: Efficiently process large volumes of tick data using multiprocessing techniques.
  • Latency Analysis: Generate and incorporate realistic latency data into the trading simulation.
  • Strategy Implementation: Develop and implement a grid trading strategy tailored for high-frequency environments.
  • Backtesting: Assess the strategy's performance using historical tick data.
  • Analysis and Visualization: Present the results through detailed analysis and graphical representations.

Technologies and Tools

  • Programming Language: Python
  • Libraries and Frameworks:
    • numpy: Numerical computations.
    • numba: Just-In-Time (JIT) compilation for performance optimization.
    • polars: High-performance DataFrame library.
    • matplotlib: Data visualization.
    • hftbacktest: Custom library for high-frequency backtesting.

Why Hadoop or Spark Isn’t Fit for This?

Hadoop and Spark are not suitable for this project due to the following reasons:

  1. Granularity and Latency: HFT relies on tick-by-tick data with nanosecond-level timestamps, which Hadoop and Spark cannot efficiently handle.
  2. Real-time Performance: Generating latency data requires real-time capabilities, which distributed systems like Hadoop and Spark cannot offer efficiently.
  3. Specialized Computations: Tools like Numba and Python multiprocessing provide better performance for HFT computations than Hadoop or Spark.
  4. Iterative and Adaptive Processing: HFT strategies involve continuous adjustments, which in-memory processing with Python handles more efficiently than Hadoop or Spark.

Why Use Polars?

Polars offers high-performance features ideal for this project, including:

  • Columnar Data Storage: Efficient for analytical workloads and SIMD optimizations.
  • Lazy Execution Engine: Defers computations for optimized query plans.
  • Parallelism: Enables multi-threaded operations for scalability.
  • Memory Efficiency: Leverages Rust's memory safety for efficient management.

Architecture

Data Collection and Preprocessing

  • Source: Binance Futures tick-level data.
  • Method:
    • Used hftbacktest to fetch data.
    • Stored 30 days of BTC-USDT and ETH-USDT tick data in .gz format.
_ = binancefutures.convert(
    input_filename=filepath,
    output_filename=output_filepath,
    combined_stream=True
)
  • Outcome: High-resolution tick data for preprocessing.

Data Conversion (GZ to NPZ)

  • Rationale: .npz format is more efficient for in-memory operations.
  • Process: Converted .gz files to .npz using multiprocessing.
for file in gz_files:
    output_file = file.replace(".gz", ".npz")
    hftbacktest.convert(input_filename=file, output_filename=output_file)
  • Result: Structured arrays ready for analysis.

Market Depth Snapshot Creation

  • Necessity: Ensures continuity for daily trading simulations.
  • Methodology: Generated EOD snapshots using create_last_snapshot.
_ = create_last_snapshot(
    ['usdm/btcusdt_20240808.npz'],
    tick_size=0.1,
    lot_size=0.001,
    output_snapshot_filename='usdm/btcusdt_20240808_eod.npz'
)

Grid Trading Strategy Implementation

Overview

Grid trading involves placing buy and sell orders at predefined intervals around a reference price, capitalizing on market volatility.

Strategy Parameters

  • Half Spread: Distance from the mid-price for initial orders.
  • Grid Interval: Spacing between successive orders.
  • Skew: Adjustment based on the current position.
  • Order Quantity: Maintained at a notional value of $100.

Implementation

@njit
def grid_trading(hbt, recorder, half_spread, grid_interval, skew, order_qty):
    # Function implementation
    pass

half_spread = 0.023% of mid_price
grid_interval = 0.086% of mid_price
skew = 0.0004% of mid-price
  • Used Numba JIT compilation for speed.
  • Dynamically managed orders and recorded performance metrics.

Visualization of Equity Curve

from matplotlib import pyplot as plt

plt.plot(net_equity_df['timestamp'], net_equity_df['cum_ret'])
plt.ylabel('Cumulative Returns (%)')
plt.grid()
plt.show()

Results and Analysis

Performance Metrics

  • Cumulative Returns: Total return over the backtesting period.
  • Sharpe Ratio: Risk-adjusted return efficiency.

Key Insights

  • Profitability: Consistent cumulative returns.
  • Risk Management: Favorable Sharpe Ratio.
  • Scalability: Efficient multiprocessing and data handling.

Conclusion and Future Work

Conclusion

  • Demonstrated end-to-end HFT backtesting, including:
    • Data acquisition and preprocessing.
    • Latency modeling.
    • Grid strategy execution and analysis.

Future Work

  • Parametric Optimization: Leverage machine learning for refining parameters.
  • Expanded Asset Coverage: Apply to more cryptocurrency pairs or asset classes.
  • Real-Time Adaptation: Integrate dynamic adjustments based on real-time data.
  • Deeper Risk Analytics: Explore tail-risk behavior and extreme volatility scenarios.

References

  • Binance API Documentation
  • NumPy, Numba, Polars, Matplotlib Documentation
  • HFTBacktest Library