SpotTrade

tradeexecutor.analysis.trade_analyser.SpotTrade Python class in Trading Strategy framework.

class SpotTrade

Bases: object

Track spot trades to construct position performance.

For sells, quantity is negative.

__init__(trade_id, pair_id, timestamp, price, executed_price, quantity, commission, slippage, trade_type=None, state_details=None, lp_fees_paid=None, bad_data_issues=False)

Parameters:

• trade_id (int) –

• pair_id (int) –

• timestamp (Timestamp) –

• price (float) –

• executed_price (float) –

• quantity (float) –

• commission (float) –

• slippage (float) –

• trade_type (Optional[TradeType]) –

• state_details (Optional[Dict]) –

• lp_fees_paid (Optional[Union[list[float], float]]) –

• bad_data_issues (bool) –

Return type:

None

Methods

__init__(trade_id, pair_id, timestamp, ...)
is_buy()
is_sell()

Attributes

bad_data_issues	Set for legacy trades with legacy data.
lp_fees_paid	LP fees paid, currency convereted to the USD.
state_details	Internal state dump of the algorithm when this trade was made.
trade_type	Any hints applied for this trade why it was performed
value
trade_id	Internal running counter to uniquely label all trades in trade analysis
pair_id	Trading pair for this trade
timestamp	When this trade was made, the backtes simulation thick
price	Asset mid-price
executed_price
quantity	How much we bought the asset.
commission	How much fees we paid to the exchange
slippage	How much we lost against the midprice due to the slippage

trade_id: int

Internal running counter to uniquely label all trades in trade analysis

pair_id: int

Trading pair for this trade

timestamp: Timestamp

When this trade was made, the backtes simulation thick

price: float

Asset mid-price

quantity: float

How much we bought the asset. Negative value for sells.

commission: float

How much fees we paid to the exchange

slippage: float

How much we lost against the midprice due to the slippage

trade_type: Optional[TradeType] = None

Any hints applied for this trade why it was performed

state_details: Optional[Dict] = None

Internal state dump of the algorithm when this trade was made. This is mostly useful when doing the trade analysis try to understand why some trades were made. It also allows you to reconstruct the portfolio state over the time.

lp_fees_paid: Optional[Union[list[float], float]] = None

LP fees paid, currency convereted to the USD.

The value is read back from the realised trade. LP fee is usually % of the trade. For Uniswap style exchanges fees are always taken from amount in token and directly passed to the LPs as the part of the swap, these is no separate fee information.

bad_data_issues: bool = False

Set for legacy trades with legacy data.

Mark that calculations on this trade might be incorrect

__init__(trade_id, pair_id, timestamp, price, executed_price, quantity, commission, slippage, trade_type=None, state_details=None, lp_fees_paid=None, bad_data_issues=False)

Parameters:

• trade_id (int) –

• pair_id (int) –

• timestamp (Timestamp) –

• price (float) –

• executed_price (float) –

• quantity (float) –

• commission (float) –

• slippage (float) –

• trade_type (Optional[TradeType]) –

• state_details (Optional[Dict]) –

• lp_fees_paid (Optional[Union[list[float], float]]) –

• bad_data_issues (bool) –

Return type:

None