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. WARNING: THIS IS A LEARNING EXAMPLE ONLY. DO NOT TRY TO TRADE SOMETHING THIS SIMPLE. https://www.quantopian.com/workshops https://www.quantopian.com/lectures 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 =  constraints.append(opt.MaxGrossExposure(MAX_GROSS_EXPOSURE)) algo.order_optimal_portfolio( objective=objective, constraints=constraints, )
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