Sample code: Implementing moving average crossover strategy using IBridgePy

March 18, 2018

The moving average crossover strategy is a popular algorithmic trading approach. This moving average crossover strategy analyzes when fast and slow moving averages intersect to generate trading signals with Interactive Brokers and Python.

Understanding Moving Average Crossover Strategy

Moving average crossover strategy is one of the popular strategies that lots of traders have paid attention to. The details of the strategy is described at Wikipedia. https://en.wikipedia.org/wiki/Moving_average_crossover

In a short summary of moving average crossover strategy, the trend of the security is going up when the fast moving average line cross over the slow moving average line from the lower area and it is a signal about long positions.

In the following example, the code calculates the moving average of 5 (fast moving average line) and 15 (slow moving average line) at 15:59:00 US Eastern time, 1 min before the market closes, on every trading day. It places an order of SPY, ETF tracking S&P 500, when the fast moving average line starts to cross over the slow moving average line and exits the positions when crossover happens in the opposite direction.

The code can be backtested at Quantopian.com because IBridgePy can run most of the strategies posted at Quantopian without any changes.

If you need any help to live trader your codes from Quantopian, please contact with us at IBridgePy@gmail.com

Rent-a-Coder

# -*- coding: utf-8 -*-
'''
There is a risk of loss in stocks, futures, forex and options trading. Please
trade with capital you can afford to lose. Past performance is not necessarily 
indicative of future results. Nothing in this computer program/code is intended
to be a recommendation to buy or sell any stocks or futures or options or any 
tradable securities. 
All information and computer programs provided is for education and 
entertainment purpose only; accuracy and thoroughness cannot be guaranteed. 
Readers/users are solely responsible for how they use the information and for 
their results.

If you have any questions, please send email to IBridgePy@gmail.com
'''

# This code can be backtested at Quantopian
import pandas as pd
def initialize(context):
    context.security=symbol('SPY') # Define a security, SP500 ETF
    
    # schedule_function is an IBridgePy function, also supported by Quantopian
    # date_rules.every_day : the dailyFunc will be run on every business day
    # time_rules.market_close(minutes=1) : the time to run dailyFunc is 
    # 1 mintue before U.S. market close (15:59:00 US/Eastern time)
    schedule_function(dailyFunc, date_rule=date_rules.every_day(),
                      time_rule=time_rules.market_close(minutes=1))

    
def dailyFunc(context, data):
    # dailyFunc is scheduled by schedule_function
    # It will run at 15:59:00 US/Eastern time on every business day
    hist = data.history(context.security, 'close', 20, '1d')
    
    # Calculate 5 days and 15 days moving average
    MA5 = hist.rolling(window=5).mean()[-1]
    MA15 = hist.rolling(window=15).mean()[-1]
    current_price = hist[-1]
    
    # Get current position
    current_position = context.portfolio.positions[context.security].amount
    
    print('current_position=', current_position)
    print('current_price=', current_price, 'MA5=', MA5, 'MA15=', MA15)
    
    # Buy signal: MA5 crosses above MA15
    if MA5 > MA15 and current_position == 0:
        order_target_percent(context.security, 1.0)
        print('Buy', context.security)
    
    # Sell signal: MA5 crosses below MA15
    elif MA5 < MA15 and current_position > 0:
        order_target_percent(context.security, 0.0)
        print('Sell', context.security)

Implementation Tips

When implementing technical indicators, consider using longer lookback periods during volatile markets. Always backtest your parameters thoroughly before live deployment.

For more trading strategies and examples, check our tutorials or visit our documentation.