Strategy

class src.boatwright.Strategy.Strategy(symbol, strategy_id=None)

Strategies systematically place and manage orders with the broker. This parent class imposes the methods necessary for the rest of package to run backtests and live execution instances.

Parameters:

• symbol (str) – symbol being to be traded by the strategy, e.g. “AAPL” or “BTC”

• strategy_id – optional strategy ID, defaults to the class name

Variables:

broker – upon the initialization of a Backtest or LiveExecution instance a Broker is attributed to the strategy which it can place orders to and query

abstract calc_prerequisite_data_length()

Returns:

length of dataframe required to generate all signals in calculate_signals()

Return type:

int

abstract calculate_signals(data)

takes a dataframe and returns an updated dataframe with additional columns for any technical inidcator signals required for the strategy

Parameters:

data (DataFrame) – pandas dataframe with columns [datetime, open, high, low, close, volume]

Returns:

(pd.DataFrame) dataframe with additional columns of technical indicators

plot_info()

This optional method is called by Visualization.plot_backtest() for instruction on how to plot signals. :return: (dict) dictionary of information specifying how technical indicators should be plotted.

Note

See Examples.Strategy and Examples.Backtest for an example of how the dict should be structured

abstract step(row)

actions to taken on each ‘bar’ of data in a Backtest or LiveExecution instance. e.g. check buy/sell triggers and create or check orders accordingly.

Parameters:

row – one row of the pd.Dataframe calculated by calculate_signals()

Returns:

None

Note

logic should only be applied to the passed row, and not self.dataframe. avoid accessing other rows such as self.df.iloc[i-10]; rather in ‘calculate signals’ calculate another pd.Series of the delayed data