Welcome to Quantopian! The Getting Started Tutorial will guide you through researching and developing a quantitative trading strategy on Quantopian. It covers many of the basics of Quantopian's API, and is designed for those who are new to the platform. All you need to get started on this tutorial is to have some basic Python programming skills.
A trading algorithm is a computer program that defines a set of rules for buying and selling assets. Most trading algorithms make decisions based on mathematical or statistical models that are derived from research conducted on historical data.
The first step to writing a trading algorithm is to find an economic or statistical relationship on which we can base our strategy. To do this, we can use Quantopian's Research environment to access and analyze historical datasets available in the platform. Research is a Jupyter Notebook environment that allows us to run Python code in units called 'cells'.
For example, the following code plots the daily closing price for Apple Inc. (AAPL), along with its 20 and 50 day moving averages:
In [1]:
# Research environment functions
from quantopian.research import prices, symbols
# Pandas library: https://pandas.pydata.org/
import pandas as pd
# Query historical pricing data for AAPL
aapl_close = prices(
assets=symbols('AAPL'),
start='2013-01-01',
end='2016-01-01',
)
# Compute 20 and 50 day moving averages on
# AAPL's pricing data
aapl_sma20 = aapl_close.rolling(20).mean()
aapl_sma50 = aapl_close.rolling(50).mean()
# Combine results into a pandas DataFrame and plot
pd.DataFrame({
'AAPL': aapl_close,
'SMA20': aapl_sma20,
'SMA50': aapl_sma50
}).plot(
title='AAPL Close Price / SMA Crossover'
);
In the next lesson we will use Research to explore Quantopian's datasets. Then, we will define our trading strategy and test whether it can effectively predict returns based on historical data. Finally, we will use our findings to develop and test a trading algorithm in the Interactive Development Environment (IDE).