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.

In [1]:

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

In [2]:

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

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

In [3]:

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

100% (39563 of 39563) |##################| Elapsed Time: 0:00:00 Time:  0:00:00

Out[3]:

	id	COMMODITY_CODE	COMMODITY_NAME	COMMODITY_TOPIC	COMMODITY_FULL_NAME	COMMODITY_SDMX_CODE	COMMODITY_SDMX_NAME	COMMODITY_DEFINITION	COMMODITY_SHORT_NAME	COMMODITY_SOURCE_CODE
0	LMICS	LMICS	Low and Middle Income Commodity Index (World B...	Real Sector	Primary Commodity Prices, Low and Middle Incom...	LMICS	Primary Commodity Prices, Low and Middle Incom...	World Bank Non-Energy Commodities Price Index ...	Low and Middle Income Commodity Index (World B...	LMICS
1	PALUM	PALUM	Aluminum	Real Sector	Primary Commodity Prices, Aluminum	PALUM	Primary Commodity Prices, Aluminum	Aluminum, 99.5% minimum purity, LME spot price...	Aluminum	PALUM
2	PAPPLE	PAPPLE	Non-Citrus Fruit, Apple	Real Sector	Primary Commodity Prices, Non-Citrus Fruit, Apple	PAPPLE	Primary Commodity Prices, Non-Citrus Fruit, Apple	Monthly average consumer prices in metropolita...	Non-Citrus Fruit, Apple	PAPPLE
3	PBANSOP	PBANSOP	Bananas	Real Sector	Primary Commodity Prices, Bananas	PBANSOP	Primary Commodity Prices, Bananas	Bananas, Central American and Ecuador, FOB U.S...	Bananas	PBANSOP
4	PBARL	PBARL	Barley	Real Sector	Primary Commodity Prices, Barley	PBARL	Primary Commodity Prices, Barley	Barley, Canadian no.1 Western Barley, spot pri...	Barley	PBARL
...	...	...	...	...	...	...	...	...	...	...
77	PVANPENT	PVANPENT	Vanadium	Real Sector	Primary Commodity Prices, Vanadium	PVANPENT	Primary Commodity Prices, Vanadium	Vanadium pentoxide, CIF North West Europe, USD...	Vanadium	PVANPENT
78	PWHEAMT	PWHEAMT	Wheat	Real Sector	Primary Commodity Prices, Wheat	PWHEAMT	Primary Commodity Prices, Wheat	Wheat, No.1 Hard Red Winter, ordinary protein,...	Wheat	PWHEAMT
79	PWOOLC	PWOOLC	Wool, Coarse	Real Sector	Primary Commodity Prices, Wool, Coarse	PWOOLC	Primary Commodity Prices, Wool, Coarse	Wool, coarse, 23 micron, Australian Wool Excha...	Wool, Coarse	PWOOLC
80	PWOOLF	PWOOLF	Wool, Fine	Real Sector	Primary Commodity Prices, Wool, Fine	PWOOLF	Primary Commodity Prices, Wool, Fine	Wool, fine, 19 micron, Australian Wool Exchang...	Wool, Fine	PWOOLF
81	PZINC	PZINC	Zinc	Real Sector	Primary Commodity Prices, Zinc	PZINC	Primary Commodity Prices, Zinc	Zinc, high grade 98% pure, US$ per metric ton	Zinc	PZINC

82 rows × 10 columns

In [4]:

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
)

Run last pass...
Load data...

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100% (272368 of 272368) |################| Elapsed Time: 0:00:00 Time:  0:00:00

Run strategy...

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-4-c9e98e7f379f> in <module>
     33     strategy=strategy,
     34     analyze=True,
---> 35     build_plots=True
     36 )

/usr/local/lib/python3.7/site-packages/qnt/backtester.py in backtest(competition_type, strategy, load_data, lookback_period, test_period, start_date, end_date, window, step, analyze, build_plots, collect_all_states)
    289     if is_submitted() and args_count > 1:
    290         state = qnstate.read()
--> 291     result = strategy_wrap(data, state)
    292     result, state = unpack_result(result)
    293 

/usr/local/lib/python3.7/site-packages/qnt/backtester.py in <lambda>(d, s)
    268 
    269     args_count = len(inspect.getfullargspec(strategy).args)
--> 270     strategy_wrap = (lambda d, s: strategy(d)) if args_count < 2 else strategy
    271 
    272     # ---

<ipython-input-4-c9e98e7f379f> in strategy(data)
     19     close = data['futures'].sel(field='close')
     20     commodity = data['commodity']
---> 21     if commodity.isel(time=-1) > commodity.isel(time=-2) and close.isel(time=-1) > close.isel(time=-20):
     22         return xr.ones_like(close.isel(time=-1))
     23     else:

/usr/local/lib/python3.7/site-packages/xarray/core/dataarray.py in isel(self, indexers, drop, missing_dims, **indexers_kwargs)
   1206         # lists, or zero or one-dimensional np.ndarray's
   1207 
-> 1208         variable = self._variable.isel(indexers, missing_dims=missing_dims)
   1209 
   1210         coords = {}

/usr/local/lib/python3.7/site-packages/xarray/core/variable.py in isel(self, indexers, missing_dims, **indexers_kwargs)
   1191 
   1192         key = tuple(indexers.get(dim, slice(None)) for dim in self.dims)
-> 1193         return self[key]
   1194 
   1195     def squeeze(self, dim=None):

/usr/local/lib/python3.7/site-packages/xarray/core/variable.py in __getitem__(self, key)
    784         """
    785         dims, indexer, new_order = self._broadcast_indexes(key)
--> 786         data = as_indexable(self._data)[indexer]
    787         if new_order:
    788             data = np.moveaxis(data, range(len(new_order)), new_order)

/usr/local/lib/python3.7/site-packages/xarray/core/indexing.py in __getitem__(self, key)
   1158     def __getitem__(self, key):
   1159         array, key = self._indexing_array_and_key(key)
-> 1160         return array[key]
   1161 
   1162     def __setitem__(self, key, value):

IndexError: index -1 is out of bounds for axis 0 with size 0