Expanded on documentation

This commit is contained in:
Frederic Fortier
2017-08-17 16:13:04 -04:00
parent f7db6471ca
commit d48373a75a
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@@ -183,3 +183,50 @@ The following sample algorithm uses the run_algorithm interface:
```
catalyst/examples/buy_and_hold_live.py
```
<h2>Portfolio Management</h2>
Zipline has a Portfolio class containing key metrics used by zipline
for, but not only, these reasons:
* Placing orders: When placing orders (e.g. order_target_percent),
zipline queries the portfolio to assess the size of current positions,
cash available, etc.
* Measuring performance: The portfolio contains attributes like
cost basis of each asset, p&l, etc. which zipline uses to compute all
of its performance criteria.
When backtesting, zipline automatically updates the Portfolio object
of its corresponding algorithm. When live trading, these updates should
be the responsibility of the exchange as it holds the truth for:
* Executed price of each order (including fees and slippage)
* Partial / failed orders
* Cash (i.e. base currency) available
* Cost basis of each position
If each exchange account had a one-to-one relationship with an
algorithm, portfolio metrics could be retrieved directly from the
exchange without persisting any data to the algorithm. However,
doing this would have at least the following drawbacks:
* It may not be reasonable to ask users to dedicate an
exchange account to a single algorithm. Exchanges are not easy
to partition.
* If an exchange account contains existing positions, the calculated
cost basis would correspond to all positions, not just those
initiated by the algorithm.
* It would not be possible impose trading limits on algorithms.
It follow that Portfolio metrics should be calculated using a strategic
combination of the exchange data and algorithm activity. While tracking
the activity of an algorithm works well in backtesting, it is more
challenging during live trading. A live algorithm might run over
several months. It might have to stop and start for many reasons.
This means that the platform should have the ability to persist
algorithm activity in order to be reliable.
In the interest of time, I will start by persisting algorithm
activity in memory. Data will be lost when the algorithm execution stops.
The intent it to offer a simple basis from which to implement data
persistence strategies in the future.