Skip to main content

Simple moving average trading strategy using Python

Hi All,

I am presenting simple boiler point code that can quickly be applied to test technical indicator strategies using Python. The code:

1. downloads daily stock data from google,
2. calculates the short and long moving averages
3. generates the trading signals
4. calculates the daily returns
5. runs the moving average strategy and calculates the cumulative return
6. plots cumulative return of our simple strategy

Here is the code ... enjoy trying it out and extend it as required:

import numpy as np
import pandas_datareader as datar
import datetime
import matplotlib.pyplot as plt

date_start = datetime.datetime(2017,1,1)
date_end = datetime.datetime(2017,6,30)
data = datar.get_data_google('AAPL', date_start, date_end)

short_ma = 5long_ma = 20
data['short_ma'] = data['Close'].rolling(short_ma).mean()
data['long_ma'] = data['Close'].rolling(long_ma).mean()
data['masig'] = data['short_ma'] - data['long_ma']

data['signal'] = np.where(data['masig'] > 0, 1, 0)
data['signal'] = np.where(data['masig'] < 0, -1, data['signal'])
data['returns'] = np.log(data['Close']) - np.log(data['Close']).shift(1)
data['strategy'] = data['returns'] * data['signal'].shift(1)

data['strategy'].cumsum().plot(title='Cumulative Return')


Popular posts from this blog

Interfacing C# .Net and R - Integrating the best of both worlds

In specific software areas like in quantitative finance or else in other mathematical domains, data centric programming typically requires a good balance between three requirements - (1) a solid platform with rich mathematical/statistical functionality (2) having an easy to use, contemporary, programming environment which permits easy and flexible front end code development and (3) an easy to use interface between the two environments.
In this artcile I am going to explain how such a balance can be attained by using two of the best products in their specific worlds - using the rich R library as the mathematical/statistical component but then interfacing with C# for the front end application design. As an interfacing option I banked on using R (D)COM which provides an easy to use interfacing method which keeps you away from spending hours identifying interfacing problems.
The software required for this tutorial is the following: 1. R software (download from here) 2. R (D)COM Interface (dow…

Interfacing C# .Net and R - Integrating the best of both worlds (Part 3) - Trader Desk Example

In this third and final part of the series (Part 1, Part 2), I am going to continue shaping the examples in the previous posts by quickly building a small application - a simple Trading Desk application. In the example I will use the same C# and R interface method together with a specific Quantitative Financial Modelling R library called Quantmod.
Example Objective In the example the objective is to build a C# Web Form which acts as the main application controller that captures various user option parameters with the main functions being:
1. To automatically kick off an R routine to download data from Yahoo Finance. 2. To select specific dates of interest 3. To add or udpate different charts 4. To add different chart indicators
5. To calculate Period Return Statistics
Most of these functions are provided through the set of R functions exposed by the Quantmod R library.
Additions under the hood
Below I am showing some of the salient additions done to the code over and above the code in the pr…

Interfacing C# .Net and R - Integrating the best of both worlds (Part 2)

This post is a continuation from the previous post (Part I) focusing on interfacing C# with R using the R (D)COM. In this post I am going to enhance my previous exercise by creating a Facade .Net Class which facilitates access to specific functions in R.
Creating the R Facade Class
Creating a Facade Class (or a set of .Net classes) which acts as a .Net wrapper to R functions greatly facilitate the use of R functions and their integration within the .Net programming environment. Below I am showing an excerpt from the class RFacade that I have created in this example.
using System;

using System.Collections.Generic;
using System.Linq;
using System.Text;
using StatConnectorCommonLib;
using System.Runtime.InteropServices;

namespace R

class RFacade : IDisposable

     private StatConnector rconn;
     private bool disposed = false;

     public RFacade()
          rconn = new STATCONNECTORSRVLib.StatConnector();

     public void D…