Market data Introduction Note: this document assumes familiarity with the notion strandl options investment strategy backtesting. Evaluating an investment strategy is strandl options multi-step process. A quartile analysis gives a good idea as to whether the signal is predictive of future returns. However, the analysis ignores many real-world aspects of strategy implementation and trading.
For example, in a spread analysis it is assumed that we can trade immediately, regardless of actual liquidity. As a result, it is difficult to learn from a spread analysis how the performance of a strategy degrades as investment capital and portfolio size increases. In a sophisticated backtest more of an attempt is made to mimic how the strategy would be implemented in strandl options.
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This includes using optimization-based portfolio construction and trade selection, and making conservative assumptions about what trades could actually have been made in the market. The strand package provides a framework for running this more realistic type of backtest.
Once a strategy is defined in terms of its alpha, risk constraints, and position and turnover limits, the system simulates how the strategy would be operated day-by-day, including daily order generation and realistic trade filling.
The purpose of this vignette is to describe how to set up and run strandl options strand simulation. System strandl options The strand system is meant to mimic a daily professional-level portfolio management process.
The process involves the following steps: Prepare input data. Define a universe of stocks in which to invest. Its level of capital to which we want to trade. The alpha to which we want to maximize exposure.
Any exposure constraints we want to impose, e. Any liquidity constraints on trading individual stocks, e. Operate the strategy by doing the following each day: Process corporate actions and adjust start-of-day share values.
Submit strandl options orders to the market with a limit on participation, i. At the end of the day process fills and compute end-of-day position share and market values.
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Calculate performance statistics. Running a backtest in strand follows the same process.
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First, data is prepared and organized for input into the system. Second, the strategy is defined in a configuration file. Finally, we run the simulation and analyze results. Configuration All setup and configuration in strand is accomplished by filling in a yaml-format configuration file.
The configuration file contains two major sections. Note that it is possible to operate multiple strategies in the same simulation, but this vignette will only cover strandl options simulation with one strategy.
The simulator section contains the location of input data, input data column mappings, and simulator options. There are also several top-level settings that control aspects of the simulation.
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Preparing input data Three types of input data are needed to run a strand simulation: A security reference, or listing, of the securities in the backtesting universe.
Daily alpha and factor inputs for each stock in the universe. Daily market data for each stock in the universe. This data can be provided to the simulator directly using objects stored in memory, or the simulator strandl options read the data from binary files stored in feather format on disk.
For a discussion of how to use files for input data, see Appendix: file-based inputs. Loading required packages Two packages are required to work through the code in this vignette: strand and dplyr: library strand library dplyr Security reference The security reference specifies static information about each security in the backtest.
Security reference data must include at least the following columns: id containing a strandl options security identifier. One column for each category used in an exposure constraint. In sample. Alpha and factor inputs for each day must contain at least strandl options following columns: date: the date for which the information should be used to create orders.
The identifier must appear in the security reference discussed above.
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This measure of volume is used in the portfolio optimization step to ensure that our order sizes are in line with the amount we expect to be able to trade in the market.
One column for each alpha variable used in the simulation.
One column for each numeric factor variable used in an exposure constraint. The column value is a numeric column that serves as the signal for the backtest run in this vignette.
The size column contains a numeric factor that is used strandl options a portfolio construction constraint. Note regarding date semantics for inputs data: The data for is used for constructing the portfolio and generating orders for trading on that day.
As a result, it is assumed that this data is known before trading begins on e. How the strategy operates and any expectations around the timeliness of data delivery should dictate what is used as inputs for a given date.
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Strandl options data Market data is used to value positions, calculate portfolio performance, and simulate trade fills in the backtest. In the current version of the package, all prices and market values are assumed to be in a single reference currency. Market data for each strandl options must contain at least the following columns: date: as-of date for the pricing strandl options.
The ratio is the number of old shares over the number of new shares. So in the case of a split, the adjustment ratio is 0.
There are no dividends or distributions for the securities shown, so these values are NA. Therefore, some of the data could not have been known until the end of that day. Strategy specification All strategy settings, including input signal and exposure constraints, are specified in the yaml configuration file. Below are the key strategy settings from the sample.
Note that we are only working with a single strategy in this vignette although it is possible to use strand strandl options run multiple strategies in a single simulation.
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As we saw in the previous section, value is one of the columns in our inputs data. The idea is to size orders to be in line with the amount of trading expected in the market and any limitation we are planning on imposing on participation.
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Factor constraints Factor constraints limit the amount of exposure we can have in our optimization to a given numeric value. Category exposure constraints Category exposure constraints are similar to factor exposure constraints. A category constraint imposes a limit on the exposure i.
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Note that the system will be allowed to trade more than the turnover limit specified above if the portfolio is significantly over- or under-invested.