margot 0.3.5

An algorithmic trading framework for PyData.

An algorithmic trading framework for pydata.

Margot is a library of two parts that may be used together or separately:

• margot.data

• margot.backtest

Margot Data

The first component is margot.data.

Margot manages data collection, cleaning and assemblance of time series into a nicely organised Pandas Dataframe using a clean, declarative API. If you’ve ever used Django you’ll find this approach very similar to the Django ORM.

Columns

Margot can retreive time series data from external sources, like AlphaVantage. To add a time series such as “closing_price” or “volume”, we declare a Column.

e.g. to get closing_price from AlphaVantage:

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

Features

Columns are useful, but we usually want to derived another time series from them, such as “returns” or “SMA20”. Margot does this for you; we call them Features.

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 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')

MargotDataFrames

In systematic trading, you usually you want to look at more than one symbol. That’s where ensembles come in. MargotDataFrame really brings power to margot.data.

class MyEnsemble(MargotDataFrame):
    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

margot.backtest isn’t yet included in this release.

Margot backtest provides a base class to inherit to define your trading algorithm, an implementation of a walk-forward backetesting algorithm that produced backtests of your algorithm using margot.data.

Results from margot backtest can be analysed with pyfolio.

Getting Started

pip install margot

Next you need to make sure you have a couple of environment variables set:

export ALPHAVANTAGE_API_KEY=YOUR_API_KEY
export DATA_CACHE=PATH_TO_FOLDER_TO_STORE_HDF5_FILES

Once you’ve done that, try running the code in the notebook.

Status

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

Documentation

in progress - for examples see the notebook.

Contributing

Feel free to make a pull request or chat about your idea first using issues.

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 add another library.

License

Margot is licensed for use under Apache 2.0. For details see the License.