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Lecture 45

Example: Basic Pairs Trading Algorithm


This algorithm is a very simple educational example to go along with the Introduction to Pairs Trading Lecture. For a more advanced algorithm closer to something you could actually trade, please see later in the lecture series.

This is a basic pairs trading algorithm that uses the Optimize API.

For any questions, email [email protected]
import numpy as np
import pandas as pd
import quantopian.optimize as opt
import quantopian.algorithm as algo
MAX_GROSS_EXPOSURE = 1.0 # Set exposure constraint constant value for optimizer
def initialize(context):
    Called once at the start of the algorithm.
    schedule_function(check_pair_status, date_rules.every_day(), time_rules.market_close(minutes=60))
    context.stock1 = symbol('ABGB')
    context.stock2 = symbol('FSLR')
    context.stocks = [context.stock1, context.stock2]
    # Our threshold for trading on the z-score
    context.entry_threshold = 0.2
    context.exit_threshold = 0.1
    # Create a variable to store our target weights
    context.target_weights = pd.Series(index=context.stocks, data=0.0)
    # Moving average lengths
    context.long_ma_length = 30
    context.short_ma_length = 1
    # Flags to tell us if we're currently in a trade
    context.currently_long_the_spread = False
    context.currently_short_the_spread = False

def check_pair_status(context, data):
    # For notational convenience
    s1 = context.stock1
    s2 = context.stock2
    # Get pricing history
    prices = data.history([s1, s2], "price", context.long_ma_length, '1d')
    # Try debugging me here to see what the price
    # data structure looks like
    # To debug, click on the line number to the left of the
    # next command. Line numbers on blank lines or comments
    # won't work.
    short_prices = prices.iloc[-context.short_ma_length:]
    # Get the long mavg
    long_ma = np.mean(prices[s1] - prices[s2])
    # Get the std of the long window
    long_std = np.std(prices[s1] - prices[s2])
    # Get the short mavg
    short_ma = np.mean(short_prices[s1] - short_prices[s2])
    # Compute z-score
    if long_std > 0:
        zscore = (short_ma - long_ma)/long_std
        # Our two entry cases
        if zscore > context.entry_threshold and \
            not context.currently_short_the_spread:
            context.target_weights[s1] = -0.5 # short top
            context.target_weights[s2] = 0.5 # long bottom
            context.currently_short_the_spread = True
            context.currently_long_the_spread = False
        elif zscore < -context.entry_threshold and \
            not context.currently_long_the_spread:
            context.target_weights[s1] = 0.5 # long top
            context.target_weights[s2] = -0.5 # short bottom
            context.currently_short_the_spread = False
            context.currently_long_the_spread = True
        # Our exit case
        elif abs(zscore) < context.exit_threshold:
            context.target_weights[s1] = 0 # close out
            context.target_weights[s2] = 0 # close out
            context.currently_short_the_spread = False
            context.currently_long_the_spread = False
        record('zscore', zscore)
    # Call the optimizer
    allocate(context, data)
def allocate(context, data):    
    # Set objective to match target weights as closely as possible, given constraints
    objective = opt.TargetWeights(context.target_weights)
    # Define constraints
    constraints = []

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