def compute_rollovers(context, data):
next_open = context.trading_calendar.next_open(data.current_dt)
days = (next_open.date() - data.current_dt.date()).days
if days > 1:
days = days + 1
positions = get_positions(context)
rates_3m = short_rates.current(context, context.ccy_universe)
carry = 0
for asset, row in positions.iterrows():
rates_differential = (rates_3m[asset.quote_ccy] -
rates_3m[asset.base_ccy]) / 100.0
rates_spread = 2 * context.params['rollover_spread'] / 100.0
carry_effective_notional = row['amount'] * row[
'last_sale_price'] * row['last_fx_value']
carry_cost = carry_effective_notional * rates_differential
carry_spread_cost = abs(carry_effective_notional) * rates_spread
carry = carry + carry_cost - carry_spread_cost
# we assume ACT/360 convention for all currencies
daily_carry = (carry * days) / 360.0
context.perf_tracker.cumulative_performance.handle_cash_payment(
daily_carry)
context.perf_tracker.todays_performance.handle_cash_payment(daily_carry)
def signal_function_carry(context, data):
rates_3m = short_rates.current(context,context.ccy_universe)
rates_differentials = {}
for pair in context.universe:
rates_differentials[pair] = rates_3m[pair.quote_ccy] - rates_3m[ pair.base_ccy]
sorted_universe = sorted(rates_differentials.items(), key=lambda x: x[1])
n_pos = context.params['positions']
short_pos = [(k[0],-0.5/n_pos) for k in sorted_universe[:n_pos]]
zero_pos = [(k[0],0.0) for k in sorted_universe[n_pos:-n_pos]]
long_pos = [(k[0],0.5/n_pos) for k in sorted_universe[-n_pos:]]
context.weights = dict(short_pos+zero_pos+long_pos)