BacktestExecutionModel

tradeexecutor.backtest.backtest_execution.BacktestExecutionModel Python class in Trading Strategy framework.

class BacktestExecutionModel

Bases: ExecutionModel

Simulate trades against historical data.

__init__(wallet, max_slippage, lp_fees=0.003, stop_loss_data_available=False)

Parameters:

• wallet (SimulatedWallet) –

• max_slippage (float) –

• lp_fees (float) –

Methods

__init__(wallet, max_slippage[, lp_fees, ...])
execute_trades(ts, state, trades, ...[, ...])	Execute the trades on a simulated environment.
get_routing_state_details()	Get needed details to establish a routing state.
initialize()	Set up the wallet
is_live_trading()
is_stop_loss_supported()	Do we support stop-loss/take profit functionality with this execution model?
preflight_check()	Check that we can start the trade executor
repair_unconfirmed_trades(state)	Repair unconfirmed trades.
simulate_trade(ts, state, idx, trade)	Set backtesting trade state from planned to executed.

__init__(wallet, max_slippage, lp_fees=0.003, stop_loss_data_available=False)

Parameters:

• wallet (SimulatedWallet) –

• max_slippage (float) –

• lp_fees (float) –

is_stop_loss_supported()

Do we support stop-loss/take profit functionality with this execution model?

• For backtesting we need to have data stream for candles used to calculate stop loss

• For production execution, we need to have special oracle data streams for checking real-time stop loss

preflight_check()

Check that we can start the trade executor

Raise:

AssertionError if something is a miss

initialize()

Set up the wallet

simulate_trade(ts, state, idx, trade)

Set backtesting trade state from planned to executed.

Currently, always executes trades “perfectly” i.e. no different slipppage that was planned, etc.

Poram ts:

Strategy cycle timestamp

Parameters:

• state (State) – Current backtesting state

• idx (int) – Index of the trade to be executed on this cycle

• trade (TradeExecution) – The actual trade

• ts (datetime) –

Returns:

Executed quantity and executed reserve amounts

Return type:

Tuple[Decimal, Decimal]

execute_trades(ts, state, trades, routing_model, routing_state, check_balances=False)

Execute the trades on a simulated environment.

Calculates price impact based on historical data and fills the expected historical trade output.

Parameters:

• check_balances – Raise an error if we run out of balance to perform buys in some point.

• ts (datetime) –

• state (State) –

• trades (List[TradeExecution]) –

• routing_model (BacktestRoutingModel) –

• routing_state (BacktestRoutingState) –

get_routing_state_details()

Get needed details to establish a routing state.

Return type:

dict

repair_unconfirmed_trades(state)

Repair unconfirmed trades.

Repair trades that failed to properly broadcast or confirm due to blockchain node issues.

Returns:

List of fixed trades

Parameters:

state (State) –

Return type:

List[TradeExecution]