Futures - IMF Commodity Data

This template uses commodity data from the International Monetary Fund for creating an algorithm on futures contracts.

You can clone and edit this example there (tab Examples).

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The International Monetary Fund (IMF) publishes a range of time series data on IMF lending, exchange rates and other economic and financial indicators.

In this template we show how to to use commodity data for developing a trading algorithm.

Need help? Check the Documentation and find solutions/report problems in the Forum section.

More help with Jupyter? Check the official Jupyter page.

Documentation on the IMF data can be found here.

Once you are done, click on Submit to the contest and take part to our competitions.

API reference:

• data: check how to work with data;

• backtesting: read how to run the simulation and check the results.

In this template we use the optimizer function described in:

• optimization: read more on our article.

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%%javascript
window.IPython && (IPython.OutputArea.prototype._should_scroll = function(lines) { return false; })
// disable widget scrolling

In [ ]:

import xarray as xr
import numpy as np
import pandas as pd

import qnt.backtester as qnbt
import qnt.data as qndata

In [ ]:

# commodity listing
commodity_list = qndata.imf_load_commodity_list()
pd.DataFrame(commodity_list)

In [ ]:

def load_data(period):
    # load Futures Gold and Gold data:
    futures   = qndata.futures_load_data(assets=['F_GC'], tail=period, dims=('time','field','asset'))
    commodity = qndata.imf_load_commodity_data(assets=['PGOLD'], tail=period).isel(asset=0)
    return dict(commodity=commodity, futures=futures), futures.time.values


def window(data, max_date: np.datetime64, lookback_period: int):
    # build sliding window for rolling evaluation:
    min_date = max_date - np.timedelta64(lookback_period, 'D')
    return dict(
        futures   = data['futures'].sel(time=slice(min_date, max_date)),
        commodity = data['commodity'].sel(time=slice(min_date, max_date))
    )


def strategy(data):
    # strategy uses both Futures Gold and Gold data:
    close = data['futures'].sel(field='close')
    commodity = data['commodity']
    if commodity.isel(time=-1) > commodity.isel(time=-2) and close.isel(time=-1) > close.isel(time=-20):
        return xr.ones_like(close.isel(time=-1))
    else:
        return xr.zeros_like(close.isel(time=-1))


weights = qnbt.backtest(
    competition_type='futures',
    load_data=load_data,
    window=window,
    lookback_period=365,
    start_date='2006-01-01',
    strategy=strategy,
    analyze=True,
    build_plots=True
)