FX Carry Strategy

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Environment

import sigtech.framework as sig

import datetime as dtm
import pandas as pd
import numpy as np
import seaborn as sns

sns.set(rc = {'figure.figsize': (18, 6)})
env = sig.init(env_date=dtm.date(2024, 2, 22), log_level = 'ERROR');

Define the investment universe

ASSETS = ['AUD', 'CAD', 'CHF', 'EUR', 'GBP', 'JPY', 'NOK', 'NZD', 'SEK']

START_DATE = dtm.date(2010, 1, 4)

fx_cross=env.fx_cross
def ccy_mapping(ccy):
    if fx_cross.is_flipped('USD', ccy):
        return f'USD{ccy} CURNCY'
    else:
        return f'{ccy}USD CURNCY'

CCY_MAPPING = {
    ccy: ccy_mapping(ccy) for ccy in ASSETS
}

spot_rates = {
    ccy: sig.obj.get(ticker).history()
    for ccy, ticker in CCY_MAPPING.items()
}

def create_rolling_fx_forwards(
    asset: str,
    start_date: dtm.date,
    fwd_tenor: str = "3M_IMM",
    base_ccy: str = "USD",
) -> str:
    '''Creates RollingFXForwardStrategy objects and returns the name of the object.'''

    rfxfs = sig.RollingFXForwardStrategy(
        # Long currency
        long_currency = asset,

        # Short currency
        currency = base_ccy,

        # Tenor of forward
        forward_tenor = fwd_tenor,

        # Start date of strategy
        start_date = start_date,

        # Name of strategy
        ticker = f'{asset} FWD'
    )

    return rfxfs.name

fwd_universe = {
    asset: create_rolling_fx_forwards(
        asset=asset,
        start_date=START_DATE,
    ) for asset in ASSETS
}
fwd_universe

fwd_histories = pd.concat({asset: sig.obj.get(fwd_universe[asset]).history() for asset in ASSETS}, axis=1)
fwd_histories.plot();

Create the strategy

def generate_fwd_rates(spot_dates, under: str, over: str, fx_market, forward_tenor: str = '3M'):
    maturities = [fx_market.fx_forward_date(over, under, date, forward_tenor) for date in spot_dates]
    return fx_market.calculate_forward_rate_batch(over, under, spot_dates, maturities)

fx_market = sig.FXMarket.instance()
fwd_rates = {
    ccy: pd.Series(generate_fwd_rates(
        spot_rates[ccy].index,
        over=CCY_MAPPING[ccy][:3],
        under=CCY_MAPPING[ccy][3:6],
        fx_market=fx_market
    ), index=spot_rates[ccy].index)
    for ccy, ticker in CCY_MAPPING.items()
}

signals_carry = pd.DataFrame(
    {ccy: np.sign(fwd_rates[ccy]-spot_rates[ccy]) for ccy in ASSETS},
    columns=ASSETS
)
signals_carry.tail()

signal_obj = sig.signal_library.from_ts(signals_carry)

Construct the portfolio

fx_carry_strat = sig.SignalStrategy(
    # Start date of strategy
    start_date=START_DATE,

    # Base currency for strategy
    currency='USD',

    # Reference to signal object
    signal_name=signal_obj.name,

    # Rebalance frequency of the strategy
    rebalance_frequency='3M',

    # Allocation function used between constituents
    # in strategy
    allocation_function=sig.signal_library.allocation.equally_weighted,

    # Mapping constituent in signals to the relevant
    # RollingFutureStrategy objects
    instrument_mapping=fwd_universe,

    # Name of strategy
    ticker=f'FX Carry'
)

Generate the performance report

sig.PerformanceReport(fx_carry_strat.history()).report()