Data has always been an essential driver of trading, and traders have long made efforts to gain an advantage by having access to superior information. These efforts date back at least to the rumors that the House Rothschild benefited handsomely from bond purchases upon advance news about the British victory at Waterloo carried by pigeons across the channel.
Today, investments in faster data access take the shape of the Go West consortium of leading high-frequency trading (HFT) firms that connects the Chicago Mercantile Exchange (CME) with Tokyo. The round-trip latency between the CME and the BATS exchange in New York has dropped to close to the theoretical limit of eight milliseconds as traders compete to exploit arbitrage opportunities.
Traditionally, investment strategies mostly relied on publicly available data, with limited efforts to create or acquire private datasets. In the case of equities, fundamental strategies used financial models built on reported financials, possibly combined with industry or macro data. Strategies motivated by technical analysis extract signals from market data, such as prices and volumes.
Machine learning (ML) algorithms can exploit market and fundamental data more efficiently, in particular when combined with alternative data, which is the topic of the next chapter. We will address several techniques that focus on market and fundamental data in later chapters, such as classic and modern time-series techniques, including recurrent neural networks (RNNs).
This chapter introduces market and fundamental data sources and the environment in which they are created. Familiarity with various types of orders and the trading infrastructure matters because they affect backtest simulations of a trading strategy. We also illustrate how to use Python to access and work with trading and financial statement data.
In particular, this chapter will cover the following topics:
- How market microstructure shapes market data
- How to reconstruct the order book from tick data using Nasdaq ITCH
- How to summarize tick data using various types of bars
- How to work with eXtensible Business Reporting Language (XBRL)-encoded electronic filings
- How to parse and combine market and fundamental data to create a P/E series
- How to access various market and fundamental data sources using Python