margot 0.2.8.1

simple to use, batteries included, tools for quantitative trading.

margot is a framework for quanting with pydata.

Margot currently includes three components; these can be used together, or independently:

• margot.data

• margot.backtest

• margot.live

Margot Data

The first component is margot.data.

Margot Data manages data collection, cleaning and assemblance into a well organised Pandas Dataframe using a clean, declarative API inspired by Django ORM.

Columns

Data retreived from external sources, such as “closing_price” or “volume”, we call Columns.

e.g. to get closing_price from AlphaVantage:

adj_close = av.Column(function='historical_daily_adjusted', column='adjusted_close')

Features

Columns can be augmented by derived time-series, such as “returns” or “SMA20”, which we call, Features.

e.g.

simple_returns = feature.SimpleReturns(column='adjusted_close')
log_returns = feature.LogReturns(column='adjusted_close')
sma20 = feature.SimpleMovingAverage(column='adjusted_close', window=20)

Margot Data includes many common financial Features, and it’s very easy to add more.

Symbols

Often, you want to make a dataframe combining a number of these columns and features. Margot Data makes this very easy. e.g.

class MyEquity(Symbol):

    adjusted_close = av.Column(function='historical_daily_adjusted', column='adjusted_close')
    log_returns = feature.LogReturns(column='adjusted_close')
    realised_vol = feature.RealisedVolatility(column='log_returns', window=30)
    upper_band = feature.UpperBollingerBand(column='adjusted_close', window=20, width=2.0)
    sma20 = feature.SimpleMovingAverage(column='adjusted_close', window=20)
    lower_band = feature.LowerBollingerBand(column='adjusted_close', window=20, width=2.0)

spy = MyEquity(symbol='SPY)

Ensembles

class MyEnsemble(Ensemble):
    spy = Equity(symbol='SPY')
    iwm = Equity(symbol='IWM')
    spy_iwm_ratio = Ratio(numerator=spy.adjusted_close, denominator=iwm.adjusted_close, label='spy_iwm_ratio')

my_df = MyEnsemble().to_pandas()

Margot backtest

The second major component, margot.backtest isn’t yet included in these releases.

Margot backtest provides a base class to inherit where you define your trading algorithm, and an implementation of a walk-forward backetesting algorithm that produced backtests of your algorithm using margot.data. Results of the backtest can be analysed with pyfolio.

Margot live

Margot live allows you to trade live using the exact same algorithm you backtested using margot.backtest.

Status

This is still an early stage software project, and should not be used for live trading.

Getting Started

pip install margot

Documentation

in progress - for examples see the notebooks.

Contributing

Feel free to make a pull request; but please feel even free-er to chat about your idea first via issues.

The general idea is to keep things simple. This is intended to be long-running operational software; it must be easy to maintain, and easy to understand.

Dependencies are kept to a minimum. Generally if there’s a way to do something in the standard library (or numpy / Pandas), let’s do it that way rather than seeking the convenience of another library.

Resources

If you come across this, I suggest you checkout http://robotwealth.com. Kris and James taught me everything I know about trading. They’re like 5th Dan blackbelts at quantitative finance. You should try one of their bootcamps.

License

This version of this software may only be used under the terms set out in the License.