margot.data¶

In our journey with Margot, we begin with data.

To the quantitative trader, data is the link to the observable universe upon which we overlaycour ideas, to seek to uncover statistical or seasonality effects, mispricings or premium for carrying risk.

Margot data aims to provide a framework within which we can encapsulate this complexity, making the act of defining trading algorithms as declarative and as simple as possible.

class margot.data.MargotDataFrame(env: dict = {})¶

A MargotDataFrame brings together symbols, columns, features and ratios.

Example:

class Equity(Symbol):
    adj_close = av.Column(function='historical_daily_adjusted',
                          time_series='adjusted_close')
    log_returns = finance.LogReturns(column='adj_close')
    realised_vol = finance.RealisedVolatility(column='log_returns',
                                              window=30)

class ExampleDF(MargotDataFrame):
    spy = Equity(symbol='SPY', trading_calendar='NYSE')
    vtwo = Equity(symbol='VTWO', trading_calendar='NYSE')
    spy_russ_ratio = Ratio(numerator=spy.adj_close,
                           denominator=vtwo.adj_close,
                           label='spy_russ')

mydf = ExampleDF()

Parameters: env (dict) – optional env dictionary as an alternative to sysenv variables.

to_pandas(periods: int = None, dropna=True) → pandas.core.frame.DataFrame¶

Return a pandas Dataframe representing this MargotDataFrame.

Parameters

periods (int, optional) – only return the tail n periods.

Returns

a Pandas dataframe representing all data from: the MargotDataFrame

Return type

pd.DataFrame

refresh()¶: Refresh all Symbols in this DataFrame.

property start_date¶

First Timestamp of the time-series index.

Returns: a pandas timestamp.
Return type: Timestamp

property end_date¶

Last Timestamp value of the time-series index.

Returns: a pandas timestamp.
Return type: Timestamp

property index¶

Return the time-series index.

Returns: a pandas timeseries index.
Return type: pd.Index

simulate(when)¶

Create a dataframe simulating a datetime in history.

Used for backtesting to simplify the writing of trading algorithms. After simulating a historical datetime, it is not possible to go back to the future.

Parameters: when (tz_aware datetime or pd.Timestamp) – when to go back to.

class margot.data.Symbol(symbol: str, trading_calendar: str, env: dict = {})¶

A Symbol, represents a securitised tradable asset.

A symbol can contain columns and features as members.

Usage example:

class Equity(Symbol):
    adj_close = av.Column(function='historical_daily_adjusted',
                        time_series='adjusted_close')
    log_returns = finance.LogReturns(column='adj_close')
    realised_vol = finance.RealisedVolatility(column='log_returns',
                                            window=30)

spy = Equity(symbol='SPY', trading_calendar='NYSE')

Parameters

symbol (str) – the code for this symbol
trading_calendar (str) – ISO Code market identifier uses https://github.com/quantopian/trading_calendars/blob/master/README.md
env (dict) – Optional - pass in env values rather than use sysenv.

refresh()¶: Refresh all columns in this Symbol.

simulate(when=None)¶

Make the Symbol simulate a datetime in history.

Used for backtesting to simplify the writing of trading algorithms.

Parameters: when (tz_aware datetime or pd.Timestamp) – when to go back to.

class margot.data.Ratio(numerator, denominator, label, **kwargs)¶

Ratio of two series.

Ratio = numerator / denominator of two time-series.

Example:

my_ratio = Ratio(numerator=symbol_1.adj_close,
                 denominator=symbol_2.adj_close,
                 label='s1_s2_ratio')

Parameters

numerator (str) – the series to numerate the ratio
denominator (str) – the series to denominate the ratio
label (str) – give it a name for your dataframe. e.g. current_ratio

property latest¶: Return the latest value in this series

margot.data¶

Subpackages¶