def pair_trading_strategy(context,data):
"""
function to define Pairs Trading strategy logic.
"""
try:
# Get the historic data for the stocks pair
prices = data.history( assets = [context.x, context.y],
fields = "price",
bar_count = context.lookback,
frequency = "1d"
)
except:
return
# Take log of the prices
prices = np.log(prices)
# Store the price data in y and x
y = prices[context.y]
x = prices[context.x]
# Calculate the hedge ratio and z_score
_, context.hedge_ratio, resids = hedge_ratio(y, x)
context.z_score = z_score(resids, lookback=context.z_window)
# Compute the trading signal
context.signal = trading_signal(context, data)
# Place the order to trade the pair
place_order(context)
def pair_trading_strategy(context, data):
"""
function to define Pairs Trading strategy logic.
"""
if context.trading_hours == False:
return
try:
# Get the historic data for the stocks pair
prices = data.history(assets=[context.x, context.y],
fields="close",
nbars=context.lookback,
frequency="1m")
except:
return
# drop nan values
prices = prices.dropna()
if len(prices) < 5:
print(f'too few data points for z-score:{len(prices)}.')
return
# Take log of the prices
prices = np.log(prices)
# Store the price data in y and x
y = prices[context.y]
x = prices[context.x]
# Calculate the hedge ratio and z_score
_, context.hedge_ratio, resids = hedge_ratio(y, x)
context.z_score = z_score(resids, lookback=context.z_window)
# Compute the trading signal
context.signal = trading_signal(context, data)
# Place the order to trade the pair
place_order(context)