margot.data¶

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

An 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(when: datetime.datetime = None) → pandas.core.frame.DataFrame¶

Return a pandas Dataframe representing this MargotDataFrame.

Parameters

when (datetime, optional) – slice to only show data that was available at when. That is, the EOD from the previous day. Defaults to None.

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)¶

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¶