Choosing the Best Data Feeder for Algorithmic Trading Backtesting
Backtesting is a crucial step in developing and validating algorithmic trading strategies. The accuracy and reliability of the data used in the backtest directly impact the trustworthiness of the results. While there are many free data sources available, the quality and completeness of free data often fall short, leaving many traders and researchers seeking more reliable options.
The Limitations of Free Data for Backtesting
Free datasets, while convenient, often come with significant limitations. For instance, they may not be as accurate or complete as commercial datasets, especially when it comes to historical data. Free data can also suffer from survivorship bias, which can skew the results of backtested strategies. This bias occurs when only surviving firms or securities are included in the dataset, leading to an overestimation of performance.
Why You Need Quality Data for Backtesting
Quality data is essential for developing reliable trading strategies. Poor data can lead to incorrect conclusions and potentially disastrous outcomes when the strategy is deployed in live markets. While the cost of acquiring high-quality data can be a concern, the investment is often worth it for traders seeking to backtest thoroughly. Platforms like QuantConnect offer affordable yet high-quality data, making it easier for traders to conduct robust backtests.
High-Quality Data Options for Backtesting
Several companies provide data that is specifically designed for backtesting and quantitative analysis. One such provider is QuantConnect. QuantConnect’s LEAN trading engine allows users to design, test, and deploy trading strategies on a range of live and historical data sources. The platform offers a free tier that can be sufficient for initial backtesting, with the option to upgrade to a paid plan for more advanced features.
Intrinio: A Reliable and Affordable Data Source
Another highly regarded data provider is Intrinio. Intrinio offers a vast collection of historical and real-time data, making it an excellent choice for backtesting algorithmic trading strategies. Their datasets are particularly well-suited for AI and machine learning applications, including those for algorithmic trading. One of Intrinio’s key advantages is the inclusion of delisted securities in their datasets, which can help avoid survivorship bias. Intrinio primarily offers bulk data subscriptions, which are widely considered to be both reliable and affordable.
API Solutions for Real-Time and Historical Data
For real-time and historical data, the Quotient API from Intrinio is a powerful choice. This API provides access to live and historical end-of-day (EOD), intraday data at the 1-minute level. It covers a wide range of asset classes, including stocks, ETFs, futures, forex, and cryptocurrencies. The Quotient API also supports non-US stock exchanges in countries such as Canada, the UK, Australia, and Europe, making it a versatile tool for international trading.
Conclusion: Weighing the Costs of Quality Data
While free data sources can be tempting, they often fall short in terms of accuracy and completeness. To develop robust and reliable trading strategies, it is essential to invest in high-quality data from reputable providers. Platforms like QuantConnect and vendors like Intrinio offer cost-effective solutions that can significantly improve the quality of your backtests. By investing in quality data, you can ensure that your backtested strategies stand a better chance of success when deployed in live markets.