margot.signals¶
The margot.signals module contains classes to help you construct trading algorithms using the MargotDataFrame
-
class
margot.signals.BaseAlgo(env: dict = {}, calendar='XNYS')¶ A base class to inherit when implementing your trading algorithm.
You should at least implement signal() which is the output of a trading algorithm.
- Parameters
env (dict) – a dictionary of environment variables, e.g. API keys. Overrides anything provided in sys env.
- Raises
ValueError – the attribute, ‘data’ must be a reference to a MargotDataFrame.
NotImplementedError – If your subclass does not implement signal(), you will receive a NotImplementedError.
-
signal() → list¶ Return a list of Position objects for a given datetime.
-
simulate(when: datetime.datetime)¶ Simulate a signal from a point in time.
- Parameters
when (datetime) – when to go back to
- Returns
time-series of Positions.
- Return type
pd.DataFrame
-
run(when: datetime.datetime = None)¶ Call to run this algo at a point in time (when).
We store the results of fetch_data() so that we can backtest without hitting the data provider more than once.
- Returns
a list of Position objects for a given datetime
-
class
margot.signals.Position(symbol: str, weight: float)¶ Represents a Position with a symbol and a weight.
- Parameters
symbol (str) – The identifier of the symbol. e.g. ‘SPY’.
weight (float) – A value between -1.0 and +1.0 representing the weight of this symbol in the position list.