Algorithmic Trading Backtesting

• Backtesting provides a historical simulation of a trading strategy
• Backtesting aims is to determine if a strategy works and how robust it is.
• Backtesting offers no guarantee of future profitability and can be misleading due to overfitting.
• Systematic backtesting is essential for algorithmic trading.

Workflow in Algorithmic Trading

• Idea → Backtesting → Paper Trading → Live Trading
• If backtesting or paper trading fails, return to the "Idea" stage

Backtesting Environment

• Historical data, trading strategy, backtesting engine and performance analysis are all crucial
• Historical data includes prices, volumes, tick data, fundamental data, and alternative data
• Trading strategy incorporates signals, positions, and trading rules
• Backtesting engine simulates trades based on historical data and trading strategy to assess profitability and risk
• Performance analysis comprises returns, risk-adjusted returns, maximum drawdowns, Sharpe ratio, etc.

Data Quality

• High-quality historical data is vital for algorithmic trading, quantitative finance, and backtesting
• Relevant data aspects: instruments traded, time horizon covered, and data frequency
• Data should be complete and clean
• High-quality data can be costly to acquire

Improved Workflow

• Data → Idea → Backtesting → Paper Trading → Live Trading

Vectorized Backtesting

• Vectorized backtesting offers more compact code, faster execution, and easier parallelization than event-based backtesting
• Vectorized backtesting relies on "Vectorized Data" using ndarray objects, "Vectorized Operations" from Numpy, and "Vectorized Logic" from Numpy
• Avoid "for loops" unless absolutely necessary

Random Strategy Example

• Randomly go long one share, short one share, or stay neutral
• Uses np.random.choice to generate random trades and translates trades into positions (+1, 0, -1)
• Positions are multiplied by the log returns of the instrument
• Assumes no trade costs or fees

Basic Statistics

• Relative measures and comparison are needed rather than absolute performance
• Key statistics include mean return, volatility, Sharpe ratio, and maximum drawdown
• Packages such as NumPy, SciPy, statsmodels, scikit-learn, and pandas are used in combination to provide these statistics

Example: Basic Statistics

• Annualized mean return is calculated by multiplying the mean return by 252
• Annualized volatility is calculated by multiplying the standard deviation by the square root of 252
• Basic Sharpe ratio is calculated by dividing the mean return by the standard deviation

Drawdowns

• Drawdown is the loss from peak to valley and maximum drawdown is the largest such loss
• Maximum drawdown is an important risk measure that shows potential for ruin and has psychological relevance
• pandas enables relatively simple implementation

Drawdown Duration

• Drawdown duration is an important risk measure that indicates how long it takes to recover
• The frequency of data is important

Further Considerations

• Transaction costs, market impact, taxation, regulations, data availability, survivorship bias, and parameter optimization should all be considered
• Parameter optimization can lead to overfitting/data mining

Python Packages For Backtesting

• Zipline, Backtrader, QuantStats, pyfolio, FinQuant, bt, and QuantConnect Lean are open-source packages in Python
• Some focus on specific aspects (e.g., risk analysis), while others are more comprehensive

Learn about backtesting, a historical simulation for trading strategies. Backtesting determines a strategy's efficacy, but doesn't guarantee future profits and can be misleading due to overfitting. Systematic backtesting is essential for algorithmic trading.