Essential Data for Backtesting in Algo Trading: A Simple Guide

In the fast-paced world of algo trading, data is everything. Whether you're an experienced trader or new to the field, the quality and type of data can make or break your algo trading journey. With algorithms executing trades at lightning speed, every decision must be based on accurate and reliable information. While building the "perfect" trading strategy is key, the data driving these strategies is just as crucial.

Just like a chef needs the freshest ingredients to create a perfect dish, your trading algorithm requires high-quality data to deliver accurate and profitable results. In this article, we'll explore the essential data you need for backtesting in algo trading.

But First, What is Backtesting?

Backtesting is a process that allows traders to evaluate the performance of their trading strategies using historical market data. By doing this exercise, you'll get invaluable insights and opportunities to refine your trading strategies.

Let’s consider a real-life example to understand backtesting better. Imagine you’ve created a trading strategy designed to capitalise on short-term price movements in the Nifty 50 index. Backtesting allows you to test your trading strategy with historical Nifty 50 data before risking capital in live markets. By using price data from the past 5-10 years, you can see how your strategy would have performed across different market conditions—bullish, bearish, or highly volatile periods.

It’s like turning back the clock to see how your trading strategy would have performed during a specific timeframe. So you can refine it before applying it in real trading!

Read: Why Should You Backtest Algo Trading Strategies?

Important Data You Would Need for Backtesting Algo Trading Strategies:

1. Historical Price Data

The cornerstone of any backtesting process is historical price data. This data captures the prices of stocks, derivatives (futures & options contracts), currencies, commodities, or other financial assets/instruments at different points in time. Think of historical data like a time machine. It allows your algorithm to trade in the past, giving you a sneak peek into how it might perform in the future.

What does historical price data include?

• Open price: The price at which an asset starts trading when the market opens in a session/specific period (eg, 1 minute, 5 minutes, 15 minutes, etc).
• Close price: The last price at which an asset trades during a session/specific period (eg, 1 minute, 5 minutes, 15 minutes, etc).
• High and low prices: The highest and lowest prices that an asset reaches during a session/specific period (eg, 1 minute, 5 minutes, 15 minutes, etc).

The National Stock Exchange (NSE) offers free historical index data for indices like Nifty 50, Bank Nifty, and FIN Nifty. Traders can download this data in CSV format for specific timeframes, providing them with the foundational data needed to test their trading strategies.

Data vendor platforms like TrueData, Global Datafeeds, and Accelpix offer market data services through monthly subscriptions. These platforms can provide traders with additional insights, such as intraday data (price movements within a single trading session) and more detailed financial statistics.

2. Volume Data

While price data is vital, understanding how much trading occurred at different prices is equally critical. This is where volume data comes in.

Volume data refers to the number of shares, contracts, or units traded during a particular period. It helps in assessing market liquidity and interest. These two key components ensure that your trades can be executed smoothly and avoid significant slippage or market impact.

[A slippage occurs when the price at which your order is executed does not match the price at which it was requested.]

For example, if you’re testing a momentum-based trading strategy, understanding volume data is essential. In markets with high trading volume, your strategy might quickly capture strong price trends. However, in low-volume markets, price movements can be less reliable, and your strategy may struggle to identify clear trends, leading to false signals or missed opportunities.

Use Technical Indicators

Technical indicators are mathematical tools or calculations derived from a financial asset's (stock, index, etc.) historical price and volume data. It is used to predict market trends or volatility. You can incorporate technical analysis and indicators into your algo trading system to make it more objective and rule-based. Popular indicators include:

• Moving Averages (MA): A moving average is the average of the closing prices of a security/asset (index, stock, F&O, etc.) over a specified period. It is an indicator that helps traders determine the trend in the market and identify key levels of support and resistance.
• Supertrend: A supertrend is a simple line used to indicate the market trend. This is one of the most used trend-following indicators in algo trading. It can also act as support or resistance.
• Average Directional Index (ADX): Traders use the ADX indicator to identify the strength of a trend, making it a valuable tool for avoiding sideways markets and improving trading decisions. During analysis, we can adjust the indicator settings based on time frames and market conditions to maximise its full potential.

By including these indicators in your backtesting process, your algorithm can better simulate real-world trading conditions. It’s easy to implement technical indicators using various programming languages (like Python or C++) and algo trading platforms.

Account for Transaction Costs and Slippage

When you're backtesting, it’s easy to get excited about hypothetical profits. However, to make your simulations more realistic, it’s essential to factor in transaction costs and slippage.

• Transaction costs: These are the fees your broker charges for every trade, such as brokerage charges, taxes, etc.
• Slippage: This is the difference between the expected price of a trade and the price at which the trade is executed. Slippage often occurs during periods of high volatility or low liquidity when prices move quickly.

Incorporating transaction costs and slippage into your backtesting framework provides more realistic outcomes, helping you avoid over-optimistic results.

Conclusion

The importance of data quality in backtesting cannot be emphasised enough. Using poor-quality data can lead to inaccurate assumptions, causing your trading strategy to fail when applied in real-world conditions. To avoid costly mistakes, always ensure your data sources are trustworthy, and take the time to double-check the accuracy of the data you're using.

Here are a few key factors to consider when evaluating data quality:

• Missing Data: Gaps in price or volume data can skew your backtest results, leading to unreliable performance estimates.
• Incorrect Timestamps: Properly timestamped data is crucial to ensure that trades and market events are sequenced accurately.

Backtesting in algo trading is a powerful tool that can provide insights into the viability of a trading strategy. However, it's only as good as the data you feed it. From historical price and volume data to transaction costs, and slippage every piece of information plays a critical role in ensuring the accuracy of your simulations. So, gather your data, eliminate biases, and ensure high-quality inputs to build a trading algorithm that stands a higher chance of success in live markets.

Also read: How to Source Market Data for Algo Trading?