margot 0.4

An algorithmic trading framework for PyData.

An algorithmic trading framework for pydata.

Margot is a library of components that may be used together or separately. The first major component is now availble for public preview. It should be considered early-beta.

• margot.data

Margot Data

Margot data makes it easy to create neat and tidy dataframes.

Margot manages data collection, caching, cleaning, time-series feature generation and Pandas Dataframe organisaiton and management using a clean, declarative API. If you’ve ever used Django you’ll find this approach similar to the Django ORM.

Columns

The heart of a time-series dataframe is the original data. Margot can retreive time series data from external sources (currently AlphaVantage). To add a time series such as “closing_price” or “volume”, we declare a Column.

e.g. to get closing_price and volume from AlphaVantage:

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

daily_volume = av.Column(function='historical_daily_adjusted',
                         column='volume')

Features

Columns are useful, but we usually want to derive new time series from them, such as “log_returns” or “SMA20”. Margot does this for you; we’ve called these derived time-series, Features.

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

Features can be piled on top of one another. For example, to create a time series of realised volatility based on log_returns with a lookback of 30 trading days, simply add the following feature:

realised_vol = feature.RealisedVolatility(column='log_returns', window=30)

Margot includes many common financial Features, and we’ll be adding more soon. It’s also very easy to add your own.

Symbols

Often, you want to make a dataframe combining a number of columns and features. Margot makes this very easy by providing the Symbol class 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

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()

The above code creates a Pandas DataFrame of both equities, and an additional feature that calculates a time-series of the ratio of their respective adjusted close prices.

Margot’s other parts

not yet released.

Margot also provides a simple framework for writing and backtesting trading signal generation algorithms using margot.data.

Results from margot’s trading algorithms 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.