TradeAnalyzer#

tradingstrategy.analysis.tradeanalyzer.TradeAnalyzer Python class in Trading Strategy framework.

class TradeAnalyzer[source]#

Bases: object

Analysis of trades in a portfolio.

__init__(asset_histories=<factory>)#

Parameters:: asset_histories (Dict[object, AssetTradeHistory]) –
Return type:: None

Methods

`__init__`([asset_histories])
`calculate_summary_statistics`(initial_cash, ...)	Calculate some statistics how our trades went.
`create_timeline`()	Create a timeline feed how we traded over a course of time.
`get_all_positions`()	Return open and closed positions over all traded assets.
`get_first_opened_at`()
`get_last_closed_at`()
`get_open_positions`()	Return open and closed positions over all traded assets.

Attributes

asset_histories

How a particular asset traded.

asset_histories: Dict[object, AssetTradeHistory]#: How a particular asset traded. Asset id -> Asset history mapping

get_all_positions()[source]#

Return open and closed positions over all traded assets.

Return type:: Iterable[Tuple[int, TradePosition]]

get_open_positions()[source]#

Return open and closed positions over all traded assets.

Return type:: Iterable[Tuple[int, TradePosition]]

calculate_summary_statistics(initial_cash, uninvested_cash, extra_return=0)[source]#

Calculate some statistics how our trades went.

Return type:: TradeSummary

create_timeline()[source]#

Create a timeline feed how we traded over a course of time.

Note: We assume each position has only one enter and exit event, not position increases over the lifetime.

Returns:: DataFrame with timestamp and timeline_event columns
Return type:: DataFrame

__init__(asset_histories=<factory>)#

Parameters:: asset_histories (Dict[object, AssetTradeHistory]) –
Return type:: None