href="https://www.wiley.com/en-us/Finding+Alphas%3A+A+Quantitative+Approach+to+Building+Trading+Strategies%2C+2nd+Edition-p-9781119571216">Finding Alphas: A Quantitative Approach to Building Trading Strategies, 2nd Edition, Edited by I. Tulchinsky, Wiley, New York, 2019.7 19 A. Jackson, From Preprints to E-prints: The Rise of Electronic Preprint Servers in Mathematics, Notices of the AMS, 49, 2002.
Chapter 1 Market Data
1.1 Tick and bar data
Perhaps the most useful predictor of future asset prices are past prices, trading volumes, and related exchange-originated data commonly referred to as technical, or price-volume data. Market data comes from quotes and trades. The most comprehensive view of the equity market includes exchange-specific limit order book by issue, which is built from limit orders forming buy and sell queues at different price depths, market orders, and their crossing (trades) per exchange rules such as price/time priority. In addition to the full depth of book tick stream, there are simplified datafeeds such as Level 2 (low-depth order book levels and trades), Level 1 (best bid and offer and trades), minute bars (cumulative quote and trade activity per discrete time intervals), and daily summary data (open, close, high, low, volume, etc).
Depth of book data is primarily used by high frequency trading (HFT) strategies and execution algos provided by brokers and other firms, although one can argue that a suitable analysis of the order book could detect the presence of a big directional trader affecting a longer-term price movement. Most non-HFT quant traders utilize either daily or bar data—market data recorded with certain granularity such as every 5 minutes—for research and real-time data for production execution.1
Major financial information companies such as Thompson Reuters and Bloomberg offer market data at different levels of granularity, both historical and in real time. A quant strategy needs the history for research and simulation (Chapter 7) and real time for production trading. Historical simulation is never exactly the same as production trading but can, and must, be reasonably close to the modeled reality, lest research code have a lookahead bug, that is, violate the causality principle by using “future-in-the-past” data. As discussed in Chapter 2, highly competitive and efficient financial markets keep the predictability of future price movements at a very low level. As a result even a subtle lookahead (Sec. 2.1.1) in a quant trading simulator can be picked up by a sensitive machine learning (ML) algorithm to generate a spurious forecast looking great in simulation but never working in production.
1.2 Corporate actions and adjustment factor
Compute the products:
1
2
From a quant interview
Equities as an asset class are subject to occasional corporate actions (“cax”) including dividends, splits, spin-offs, mergers, capital restructuring, and multi-way cax. Maintaining an accurate historical cax database is a challenge in itself. Failure to do so to a good approximation results in wrong asset returns and real-time performance not matching simulation (Sec. 7.1). For alpha research purposes it is generally sufficient to approximate each cax with two numbers, dividend
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1 1 Sometimes even real-time trading is done on bar data. The author has observed peculiar periodic pnl fluctuations of his medium-frequency US equity portfolio. The regular 30-minute price spikes indicated a repetitive portfolio rebalancing by a significant market participant whose trades were correlated with the author's positions.
Security return for day
To account for corporate actions, the prices are adjusted, that is, multiplied by an adjustment factor
(1.2)
The adjustment factor
Another way is forward adjustment, in which scheme
