April 2019

In this post I will be looking at a few things all combined into one script – you ‘ll see what I mean in a moment…

Being a blog about Python for finance, and having an admitted leaning towards scripting, backtesting and optimising systematic strategies I thought I would look at all three at the same time…along with the concept of “multithreading” to help speed things up.

So the script we are going to create (2 scripts in fact – one operating in a multi-threaded capacity and the other single threaded) will carry out the following steps:

1. Write the code to carry out the simulated backtest of a simple moving average strategy.

2. Run brute-force optimisation on the strategy inputs (i.e. the two moving average window periods). The Sharpe Ratio will be recorded for each run, and then the data relating to the maximum achieved Sharpe with be extracted and analysed.

3. For each optimisation run, the return and volatilty parameters of that particular backtest will then be passed to a function that runs Monte Carlo analysis and produces a distribution of possible outcomes for that particular set of inputs (I realise its a little bit of overkill to run Monte Carlo analysis on the results of each and every optimisation run, however my main goal here is to display how to multi-thread a process and the benefits that can be had in terms of code run time rather than actually analyse all the output data).

If you want to follow along with the post, the stock price data that I am using can be downloaded by clicking on the below:

It is daily price data for Ford (F.N) from the middle of 1972 onward. Once read in to a Pandas DataFrame and displayed, it should look like this:

Let’s deal first with the code to run the steps in a single threaded manner. First we import the necessary modules:

import numpy as np
import pandas as pd
import itertools
import time

Next we quickly define a helper function to calculate annualised Sharpe Ratio for a backtest returns output:

#function to calculate Sharpe Ratio - Risk free rate element excluded for simplicity
def annualised_sharpe(returns, N=252):
    return np.sqrt(N) * (returns.mean() / returns.std())

We then define our moving average strategy function as shown below. It takes 3 arguments, “data”, “short_ma” and “long_ma” – these should be pretty self explanatory. “data” is just the pricing data that will be passed to test the strategy over, and the other two are just the two moving average window period lengths.