How does data granularity impact the results of a backtested trading strategy in the Indian markets, especially when considering intraday versus daily data? Can you highlight some pitfalls and best practices?
Ah, data granularity is a pivotal aspect of backtesting. The granularity refers to the time resolution of your data, whether it’s monthly, daily, hourly, or down to the millisecond. Both intraday (e.g., 5-minute or 1-hour bars) and daily data have their uses, challenges, and implications.
1. Impact of Granularity:
- Intraday Data:
- Pros: Can capture intraday volatility, allowing for strategies that profit from short-term movements.
- Cons: Often comes with higher transaction costs due to increased trading frequency and might entail more slippage (difference between expected and actual execution price).
- Daily Data:
- Pros: Suitable for longer-term strategies with less frequent trading, usually resulting in lower transaction costs.
- Cons: Cannot capture intraday price movements and might miss out on intraday profit opportunities.
2. Pitfalls & Challenges:
- Overfitting: Higher granularity can sometimes lead to overfitting where a strategy performs exceptionally well on historical data but falters in real-time trading.
- Data Size: Intraday data can be significantly larger, making it essential to have a good storage and processing solution.
- Market Microstructure Effects: With intraday data, you need to be wary of market microstructure issues like bid-ask bounce, liquidity, and order book depth.
3. Best Practices:
- Consistency is Key: Ensure that whatever granularity you choose, your data is consistent. For instance, if using 5-minute bars, make sure they cover the entire trading session without gaps.
- Adjust for Corporate Actions: Corporate actions like dividends, splits, and bonus can distort price data. Ensure your data is adjusted to account for these, especially when backtesting on daily data.
- Slippage: Particularly with intraday strategies, it’s crucial to account for slippage in your backtest. This is because high-frequency strategies are more susceptible to discrepancies between backtest and live trading due to execution delays.
4. Example:
Consider a strategy that targets the opening range breakout on Nifty50:
- Using daily data, you’d take the high and low of the first hour and trade on a breakout for the day’s remainder. The strategy might generate 1 signal/day.
- Using 5-minute data, you can target smaller range breakouts, trading multiple times a day. Here, the same strategy might generate 5-10 signals/day.
Experimenting (Hypothetical figures):
| Metrics | Daily Data | Intraday (5-min) Data |
|---|---|---|
| Total Trades in a Year | 250 | 1800 |
| Average Profit per Trade | ₹500 | ₹50 |
| Gross Profit | ₹1,25,000 | ₹90,000 |
| Transaction Costs | ₹12,500 | ₹45,000 |
| Net Profit | ₹1,12,500 | ₹45,000 |
From the table, we see that even though intraday data gives more trades and more gross profit, the transaction costs eat into the profits significantly, making the daily data strategy more profitable in this case.
Note: The choice of data granularity should align with your trading goals, strategy logic, and risk appetite. While it’s tempting to dive into high-frequency intraday data, one must be aware of the challenges that come with it.