Quantitative Portfolio Management. Michael Isichenko

Quantitative Portfolio Management - Michael Isichenko


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target="_blank" rel="nofollow" href="#ulink_2de29d26-c57c-545f-99bd-2a53fa872be6">15 J.-P. Bouchaud, J. Bonart, J. Donier, M. Gould, Trades, Quotes and Prices. Financial Markets Under the Microscope, Cambridge University Press, 2018.

      4 16 Z. Kakushadze, J.A. Serur, 151 Trading Strategies, Available at SSRN: https://ssrn.com/abstract=3247865, 2018.

      5 17 Finding Alphas: A Quantitative Approach to Building Trading Strategies, 2nd Edition, Edited by I. Tulchinsky, Wiley, New York, 2019.

      6 18 https://arxiv.org.

      7 19 A. Jackson, From Preprints to E-prints: The Rise of Electronic Preprint Servers in Mathematics, Notices of the AMS, 49, 2002.

      

      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).

      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.

      

      Compute the products:

      1 

      2 

      From a quant interview

and split
.2 The dividend can be an actual dividend paid by the issue in the universe currency such as US dollar (USD) or the current total value of any foreign currency dividend or stock spin-off.

      _____________

      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

is defined as the relative change in the closing price
from previous day
to current day
:

so (1.1) give a correct return on investment after the adjustment. In general, a multi-day return from day
to day
equals

      (1.2)

      The adjustment factor

is used only in a ratio across days and is therefore defined up to constant normalizing coefficient. There are two ways of price adjustment: backward and forward. The backward adjustment used, for example, in the Bloomberg terminal is normalized so today's adjustment factor equals one and changes by cax events going back in time. On a new day, all values Скачать книгу